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Sample records for metagenomic gene prediction

  1. Gene Prediction in Metagenomic Fragments with Deep Learning

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    Shao-Wu Zhang

    2017-01-01

    Full Text Available Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features and using deep stacking networks learning model, we present a novel method (called Meta-MFDL to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

  2. Combining gene prediction methods to improve metagenomic gene annotation

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    Rosen Gail L

    2011-01-01

    Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

  3. MOCAT: a metagenomics assembly and gene prediction toolkit.

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    Kultima, Jens Roat; Sunagawa, Shinichi; Li, Junhua; Chen, Weineng; Chen, Hua; Mende, Daniel R; Arumugam, Manimozhiyan; Pan, Qi; Liu, Binghang; Qin, Junjie; Wang, Jun; Bork, Peer

    2012-01-01

    MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

  4. MOCAT: a metagenomics assembly and gene prediction toolkit.

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    Jens Roat Kultima

    Full Text Available MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

  5. Gene prediction in metagenomic fragments: A large scale machine learning approach

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    Morgenstern Burkhard

    2008-04-01

    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  6. Benchmarking of gene prediction programs for metagenomic data.

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    Yok, Non; Rosen, Gail

    2010-01-01

    This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from one hundred species of diverse lineages. We defined four different types of fragments; two types come from the inter- and intra-coding regions and the other types are from the gene edges. Hoff et al. used only 12 species in their comparison; therefore, their sample is too small to represent an environmental sample. Also, no predecessors has separately examined fragments that contain gene edges as opposed to intra-coding regions. General observations in our results are that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show low annotation error in all of the programs if compared to the gene edge fragments. Overall, we found an upper-bound performance by combining all the methods.

  7. Meta4: a web application for sharing and annotating metagenomic gene predictions using web services.

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    Richardson, Emily J; Escalettes, Franck; Fotheringham, Ian; Wallace, Robert J; Watson, Mick

    2013-01-01

    Whole-genome shotgun metagenomics experiments produce DNA sequence data from entire ecosystems, and provide a huge amount of novel information. Gene discovery projects require up-to-date information about sequence homology and domain structure for millions of predicted proteins to be presented in a simple, easy-to-use system. There is a lack of simple, open, flexible tools that allow the rapid sharing of metagenomics datasets with collaborators in a format they can easily interrogate. We present Meta4, a flexible and extensible web application that can be used to share and annotate metagenomic gene predictions. Proteins and predicted domains are stored in a simple relational database, with a dynamic front-end which displays the results in an internet browser. Web services are used to provide up-to-date information about the proteins from homology searches against public databases. Information about Meta4 can be found on the project website, code is available on Github, a cloud image is available, and an example implementation can be seen at.

  8. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

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    Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

    2018-02-01

    Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

  9. Metagenomic Analyses Reveal That Energy Transfer Gene Abundances Can Predict the Syntrophic Potential of Environmental Microbial Communities

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    Lisa Oberding

    2016-01-01

    Full Text Available Hydrocarbon compounds can be biodegraded by anaerobic microorganisms to form methane through an energetically interdependent metabolic process known as syntrophy. The microorganisms that perform this process as well as the energy transfer mechanisms involved are difficult to study and thus are still poorly understood, especially on an environmental scale. Here, metagenomic data was analyzed for specific clusters of orthologous groups (COGs related to key energy transfer genes thus far identified in syntrophic bacteria, and principal component analysis was used in order to determine whether potentially syntrophic environments could be distinguished using these syntroph related COGs as opposed to universally present COGs. We found that COGs related to hydrogenase and formate dehydrogenase genes were able to distinguish known syntrophic consortia and environments with the potential for syntrophy from non-syntrophic environments, indicating that these COGs could be used as a tool to identify syntrophic hydrocarbon biodegrading environments using metagenomic data.

  10. Tentacle: distributed quantification of genes in metagenomes.

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    Boulund, Fredrik; Sjögren, Anders; Kristiansson, Erik

    2015-01-01

    In metagenomics, microbial communities are sequenced at increasingly high resolution, generating datasets with billions of DNA fragments. Novel methods that can efficiently process the growing volumes of sequence data are necessary for the accurate analysis and interpretation of existing and upcoming metagenomes. Here we present Tentacle, which is a novel framework that uses distributed computational resources for gene quantification in metagenomes. Tentacle is implemented using a dynamic master-worker approach in which DNA fragments are streamed via a network and processed in parallel on worker nodes. Tentacle is modular, extensible, and comes with support for six commonly used sequence aligners. It is easy to adapt Tentacle to different applications in metagenomics and easy to integrate into existing workflows. Evaluations show that Tentacle scales very well with increasing computing resources. We illustrate the versatility of Tentacle on three different use cases. Tentacle is written for Linux in Python 2.7 and is published as open source under the GNU General Public License (v3). Documentation, tutorials, installation instructions, and the source code are freely available online at: http://bioinformatics.math.chalmers.se/tentacle.

  11. A human gut microbial gene catalogue established by metagenomic sequencing

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    dos Santos, Marcelo Bertalan Quintanilha; Sicheritz-Pontén, Thomas; Nielsen, Henrik Bjørn

    2010-01-01

    To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence...

  12. Reconstruction of ribosomal RNA genes from metagenomic data.

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    Lu Fan

    Full Text Available Direct sequencing of environmental DNA (metagenomics has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.

  13. Metagenomic species profiling using universal phylogenetic marker genes

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    Sunagawa, Shinichi; Mende, Daniel R; Zeller, Georg

    2013-01-01

    To quantify known and unknown microorganisms at species-level resolution using shotgun sequencing data, we developed a method that establishes metagenomic operational taxonomic units (mOTUs) based on single-copy phylogenetic marker genes. Applied to 252 human fecal samples, the method revealed th...... that on average 43% of the species abundance and 58% of the richness cannot be captured by current reference genome-based methods. An implementation of the method is available at http://www.bork.embl.de/software/mOTU/.......To quantify known and unknown microorganisms at species-level resolution using shotgun sequencing data, we developed a method that establishes metagenomic operational taxonomic units (mOTUs) based on single-copy phylogenetic marker genes. Applied to 252 human fecal samples, the method revealed...

  14. Identification of nitrogen-fixing genes and gene clusters from metagenomic library of acid mine drainage.

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    Dai, Zhimin; Guo, Xue; Yin, Huaqun; Liang, Yili; Cong, Jing; Liu, Xueduan

    2014-01-01

    Biological nitrogen fixation is an essential function of acid mine drainage (AMD) microbial communities. However, most acidophiles in AMD environments are uncultured microorganisms and little is known about the diversity of nitrogen-fixing genes and structure of nif gene cluster in AMD microbial communities. In this study, we used metagenomic sequencing to isolate nif genes in the AMD microbial community from Dexing Copper Mine, China. Meanwhile, a metagenome microarray containing 7,776 large-insertion fosmids was constructed to screen novel nif gene clusters. Metagenomic analyses revealed that 742 sequences were identified as nif genes including structural subunit genes nifH, nifD, nifK and various additional genes. The AMD community is massively dominated by the genus Acidithiobacillus. However, the phylogenetic diversity of nitrogen-fixing microorganisms is much higher than previously thought in the AMD community. Furthermore, a 32.5-kb genomic sequence harboring nif, fix and associated genes was screened by metagenome microarray. Comparative genome analysis indicated that most nif genes in this cluster are most similar to those of Herbaspirillum seropedicae, but the organization of the nif gene cluster had significant differences from H. seropedicae. Sequence analysis and reverse transcription PCR also suggested that distinct transcription units of nif genes exist in this gene cluster. nifQ gene falls into the same transcription unit with fixABCX genes, which have not been reported in other diazotrophs before. All of these results indicated that more novel diazotrophs survive in the AMD community.

  15. Identification of nitrogen-fixing genes and gene clusters from metagenomic library of acid mine drainage.

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    Zhimin Dai

    Full Text Available Biological nitrogen fixation is an essential function of acid mine drainage (AMD microbial communities. However, most acidophiles in AMD environments are uncultured microorganisms and little is known about the diversity of nitrogen-fixing genes and structure of nif gene cluster in AMD microbial communities. In this study, we used metagenomic sequencing to isolate nif genes in the AMD microbial community from Dexing Copper Mine, China. Meanwhile, a metagenome microarray containing 7,776 large-insertion fosmids was constructed to screen novel nif gene clusters. Metagenomic analyses revealed that 742 sequences were identified as nif genes including structural subunit genes nifH, nifD, nifK and various additional genes. The AMD community is massively dominated by the genus Acidithiobacillus. However, the phylogenetic diversity of nitrogen-fixing microorganisms is much higher than previously thought in the AMD community. Furthermore, a 32.5-kb genomic sequence harboring nif, fix and associated genes was screened by metagenome microarray. Comparative genome analysis indicated that most nif genes in this cluster are most similar to those of Herbaspirillum seropedicae, but the organization of the nif gene cluster had significant differences from H. seropedicae. Sequence analysis and reverse transcription PCR also suggested that distinct transcription units of nif genes exist in this gene cluster. nifQ gene falls into the same transcription unit with fixABCX genes, which have not been reported in other diazotrophs before. All of these results indicated that more novel diazotrophs survive in the AMD community.

  16. Identification of Nitrogen-Fixing Genes and Gene Clusters from Metagenomic Library of Acid Mine Drainage

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    Yin, Huaqun; Liang, Yili; Cong, Jing; Liu, Xueduan

    2014-01-01

    Biological nitrogen fixation is an essential function of acid mine drainage (AMD) microbial communities. However, most acidophiles in AMD environments are uncultured microorganisms and little is known about the diversity of nitrogen-fixing genes and structure of nif gene cluster in AMD microbial communities. In this study, we used metagenomic sequencing to isolate nif genes in the AMD microbial community from Dexing Copper Mine, China. Meanwhile, a metagenome microarray containing 7,776 large-insertion fosmids was constructed to screen novel nif gene clusters. Metagenomic analyses revealed that 742 sequences were identified as nif genes including structural subunit genes nifH, nifD, nifK and various additional genes. The AMD community is massively dominated by the genus Acidithiobacillus. However, the phylogenetic diversity of nitrogen-fixing microorganisms is much higher than previously thought in the AMD community. Furthermore, a 32.5-kb genomic sequence harboring nif, fix and associated genes was screened by metagenome microarray. Comparative genome analysis indicated that most nif genes in this cluster are most similar to those of Herbaspirillum seropedicae, but the organization of the nif gene cluster had significant differences from H. seropedicae. Sequence analysis and reverse transcription PCR also suggested that distinct transcription units of nif genes exist in this gene cluster. nifQ gene falls into the same transcription unit with fixABCX genes, which have not been reported in other diazotrophs before. All of these results indicated that more novel diazotrophs survive in the AMD community. PMID:24498417

  17. Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

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    Olm, Matthew R.; Morowitz, Michael J.

    2018-01-01

    ABSTRACT Antibiotic resistance in pathogens is extensively studied, and yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leveraged genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We found that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants than C. difficile strains lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have higher replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism’s direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data to five principal components classified by boosted decision trees. Among the genes involved in predicting whether an organism increased in relative abundance after treatment are those that encode subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics treatment and predict how organisms in the gut microbiome will respond to antibiotic administration. IMPORTANCE The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to

  18. Evaluation of the Cow Rumen Metagenome: Assembly by Single Copy Gene Analysis and Single Cell Genome Assemblies (Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

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    Sczyrba, Alex

    2011-10-13

    DOE JGI's Alex Sczyrba on "Evaluation of the Cow Rumen Metagenome" and "Assembly by Single Copy Gene Analysis and Single Cell Genome Assemblies" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.

  19. Metagenomic Functional Potential Predicts Degradation Rates of a Model Organophosphorus Xenobiotic in Pesticide Contaminated Soils

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    Thomas C. Jeffries

    2018-02-01

    Full Text Available Chemical contamination of natural and agricultural habitats is an increasing global problem and a major threat to sustainability and human health. Organophosphorus (OP compounds are one major class of contaminant and can undergo microbial degradation, however, no studies have applied system-wide ecogenomic tools to investigate OP degradation or use metagenomics to understand the underlying mechanisms of biodegradation in situ and predict degradation potential. Thus, there is a lack of knowledge regarding the functional genes and genomic potential underpinning degradation and community responses to contamination. Here we address this knowledge gap by performing shotgun sequencing of community DNA from agricultural soils with a history of pesticide usage and profiling shifts in functional genes and microbial taxa abundance. Our results showed two distinct groups of soils defined by differing functional and taxonomic profiles. Degradation assays suggested that these groups corresponded to the organophosphorus degradation potential of soils, with the fastest degrading community being defined by increases in transport and nutrient cycling pathways and enzymes potentially involved in phosphorus metabolism. This was against a backdrop of taxonomic community shifts potentially related to contamination adaptation and reflecting the legacy of exposure. Overall our results highlight the value of using holistic system-wide metagenomic approaches as a tool to predict microbial degradation in the context of the ecology of contaminated habitats.

  20. MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data.

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    Gupta, Ankit; Kapil, Rohan; Dhakan, Darshan B; Sharma, Vineet K

    2014-01-01

    The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51-100 amino acids and Blind B: 30-50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100-150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.

  1. Abundance profiling of specific gene groups using precomputed gut metagenomes yields novel biological hypotheses.

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    Konstantin Yarygin

    Full Text Available The gut microbiota is essentially a multifunctional bioreactor within a human being. The exploration of its enormous metabolic potential provides insights into the mechanisms underlying microbial ecology and interactions with the host. The data obtained using "shotgun" metagenomics capture information about the whole spectrum of microbial functions. However, each new study presenting new sequencing data tends to extract only a little of the information concerning the metabolic potential and often omits specific functions. A meta-analysis of the available data with an emphasis on biomedically relevant gene groups can unveil new global trends in the gut microbiota. As a step toward the reuse of metagenomic data, we developed a method for the quantitative profiling of user-defined groups of genes in human gut metagenomes. This method is based on the quick analysis of a gene coverage matrix obtained by pre-mapping the metagenomic reads to a global gut microbial catalogue. The method was applied to profile the abundance of several gene groups related to antibiotic resistance, phages, biosynthesis clusters and carbohydrate degradation in 784 metagenomes from healthy populations worldwide and patients with inflammatory bowel diseases and obesity. We discovered country-wise functional specifics in gut resistome and virome compositions. The most distinct features of the disease microbiota were found for Crohn's disease, followed by ulcerative colitis and obesity. Profiling of the genes belonging to crAssphage showed that its abundance varied across the world populations and was not associated with clinical status. We demonstrated temporal resilience of crAssphage and the influence of the sample preparation protocol on its detected abundance. Our approach offers a convenient method to add value to accumulated "shotgun" metagenomic data by helping researchers state and assess novel biological hypotheses.

  2. Gene identification and protein classification in microbial metagenomic sequence data via incremental clustering

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    Li Weizhong

    2008-04-01

    Full Text Available Abstract Background The identification and study of proteins from metagenomic datasets can shed light on the roles and interactions of the source organisms in their communities. However, metagenomic datasets are characterized by the presence of organisms with varying GC composition, codon usage biases etc., and consequently gene identification is challenging. The vast amount of sequence data also requires faster protein family classification tools. Results We present a computational improvement to a sequence clustering approach that we developed previously to identify and classify protein coding genes in large microbial metagenomic datasets. The clustering approach can be used to identify protein coding genes in prokaryotes, viruses, and intron-less eukaryotes. The computational improvement is based on an incremental clustering method that does not require the expensive all-against-all compute that was required by the original approach, while still preserving the remote homology detection capabilities. We present evaluations of the clustering approach in protein-coding gene identification and classification, and also present the results of updating the protein clusters from our previous work with recent genomic and metagenomic sequences. The clustering results are available via CAMERA, (http://camera.calit2.net. Conclusion The clustering paradigm is shown to be a very useful tool in the analysis of microbial metagenomic data. The incremental clustering method is shown to be much faster than the original approach in identifying genes, grouping sequences into existing protein families, and also identifying novel families that have multiple members in a metagenomic dataset. These clusters provide a basis for further studies of protein families.

  3. Insights into resistome and stress responses genes in Bubalus bubalis rumen through metagenomic analysis.

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    Reddy, Bhaskar; Singh, Krishna M; Patel, Amrutlal K; Antony, Ancy; Panchasara, Harshad J; Joshi, Chaitanya G

    2014-10-01

    Buffalo rumen microbiota experience variety of diets and represents a huge reservoir of mobilome, resistome and stress responses. However, knowledge of metagenomic responses to such conditions is still rudimentary. We analyzed the metagenomes of buffalo rumen in the liquid and solid phase of the rumen biomaterial from river buffalo adapted to varying proportion of concentrate to green or dry roughages, using high-throughput sequencing to know the occurrence of antibiotics resistance genes, genetic exchange between bacterial population and environmental reservoirs. A total of 3914.94 MB data were generated from all three treatments group. The data were analysed with Metagenome rapid annotation system tools. At phyla level, Bacteroidetes were dominant in all the treatments followed by Firmicutes. Genes coding for functional responses to stress (oxidative stress and heat shock proteins) and resistome genes (resistance to antibiotics and toxic compounds, phages, transposable elements and pathogenicity islands) were prevalent in similar proportion in liquid and solid fraction of rumen metagenomes. The fluoroquinolone resistance, MDR efflux pumps and Methicillin resistance genes were broadly distributed across 11, 9, and 14 bacterial classes, respectively. Bacteria responsible for phages replication and prophages and phage packaging and rlt-like streptococcal phage genes were mostly assigned to phyla Bacteroides, Firmicutes and proteaobacteria. Also, more reads matching the sigma B genes were identified in the buffalo rumen. This study underscores the presence of diverse mechanisms of adaptation to different diet, antibiotics and other stresses in buffalo rumen, reflecting the proportional representation of major bacterial groups.

  4. A primer on metagenomics.

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    John C Wooley

    2010-02-01

    Full Text Available Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.

  5. High frequency of phylogenetically diverse reductive dehalogenase-homologous genes in deep subseafloor sedimentary metagenomes

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    Mikihiko eKawai

    2014-03-01

    Full Text Available Marine subsurface sediments on the Pacific margin harbor diverse microbial communities even at depths of several hundreds meters below the seafloor (mbsf or more. Previous PCR-based molecular analysis showed the presence of diverse reductive dehalogenase gene (rdhA homologs in marine subsurface sediment, suggesting that anaerobic respiration of organohalides is one of the possible energy-yielding pathways in the organic-rich sedimentary habitat. However, primer-independent molecular characterization of rdhA has remained to be demonstrated. Here, we studied the diversity and frequency of rdhA homologs by metagenomic analysis of five different depth horizons (0.8, 5.1, 18.6, 48.5 and 107.0 mbsf at Site C9001 off the Shimokita Peninsula of Japan. From all metagenomic pools, remarkably diverse rdhA-homologous sequences, some of which are affiliated with novel clusters, were observed with high frequency. As a comparison, we also examined frequency of dissimilatory sulfite reductase genes (dsrAB, key functional genes for microbial sulfate reduction. The dsrAB were also widely observed in the metagenomic pools whereas the frequency of dsrAB genes was generally smaller than that of rdhA-homologous genes. The phylogenetic composition of rdhA-homologous genes was similar among the five depth horizons. Our metagenomic data revealed that subseafloor rdhA homologs are more diverse than previously identified from PCR-based molecular studies. Spatial distribution of similar rdhA homologs across wide depositional ages indicates that the heterotrophic metabolic processes mediated by the genes can be ecologically important, functioning in the organic-rich subseafloor sedimentary biosphere.

  6. Exploration of noncoding sequences in metagenomes.

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    Fabián Tobar-Tosse

    Full Text Available Environment-dependent genomic features have been defined for different metagenomes, whose genes and their associated processes are related to specific environments. Identification of ORFs and their functional categories are the most common methods for association between functional and environmental features. However, this analysis based on finding ORFs misses noncoding sequences and, therefore, some metagenome regulatory or structural information could be discarded. In this work we analyzed 23 whole metagenomes, including coding and noncoding sequences using the following sequence patterns: (G+C content, Codon Usage (Cd, Trinucleotide Usage (Tn, and functional assignments for ORF prediction. Herein, we present evidence of a high proportion of noncoding sequences discarded in common similarity-based methods in metagenomics, and the kind of relevant information present in those. We found a high density of trinucleotide repeat sequences (TRS in noncoding sequences, with a regulatory and adaptive function for metagenome communities. We present associations between trinucleotide values and gene function, where metagenome clustering correlate with microorganism adaptations and kinds of metagenomes. We propose here that noncoding sequences have relevant information to describe metagenomes that could be considered in a whole metagenome analysis in order to improve their organization, classification protocols, and their relation with the environment.

  7. Comparison of normalization methods for the analysis of metagenomic gene abundance data.

    Science.gov (United States)

    Pereira, Mariana Buongermino; Wallroth, Mikael; Jonsson, Viktor; Kristiansson, Erik

    2018-04-20

    In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated. Here, we present a systematic evaluation of nine normalization methods for gene abundance data. The methods were evaluated through resampling of three comprehensive datasets, creating a realistic setting that preserved the unique characteristics of metagenomic data. Performance was measured in terms of the methods ability to identify differentially abundant genes (DAGs), correctly calculate unbiased p-values and control the false discovery rate (FDR). Our results showed that the choice of normalization method has a large impact on the end results. When the DAGs were asymmetrically present between the experimental conditions, many normalization methods had a reduced true positive rate (TPR) and a high false positive rate (FPR). The methods trimmed mean of M-values (TMM) and relative log expression (RLE) had the overall highest performance and are therefore recommended for the analysis of gene abundance data. For larger sample sizes, CSS also showed satisfactory performance. This study emphasizes the importance of selecting a suitable normalization methods in the analysis of data from shotgun metagenomics. Our results also demonstrate that improper methods may result in unacceptably high levels of false positives, which in turn may lead

  8. Bacterial Human Virulence Genes across Diverse Habitats As Assessed by In silico Analysis of Environmental Metagenomes

    DEFF Research Database (Denmark)

    Søborg, Ditte A; Hendriksen, Niels B; Kilian, Mogens

    2016-01-01

    of natural environments in the evolution of bacterial virulence. Twenty four bacterial virulence genes were analyzed in 46 diverse environmental metagenomic datasets, representing various soils, seawater, freshwater, marine sediments, hot springs, the deep-sea, hypersaline mats, microbialites, gutless worms......The occurrence and distribution of clinically relevant bacterial virulence genes across natural (non-human) environments is not well understood. We aimed to investigate the occurrence of homologs to bacterial human virulence genes in a variety of ecological niches to better understand the role...... in non-human environments point to an important ecological role of the genes for the activity and survival of environmental bacteria. Furthermore, the high degree of sequence conservation between several of the environmental and clinical genes suggests common ancestral origins....

  9. Metagenomic analysis of lysogeny in Tampa Bay: implications for prophage gene expression.

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    Lauren McDaniel

    Full Text Available Phage integrase genes often play a role in the establishment of lysogeny in temperate phage by catalyzing the integration of the phage into one of the host's replicons. To investigate temperate phage gene expression, an induced viral metagenome from Tampa Bay was sequenced by 454/Pyrosequencing. The sequencing yielded 294,068 reads with 6.6% identifiable. One hundred-three sequences had significant similarity to integrases by BLASTX analysis (e < or =0.001. Four sequences with strongest amino-acid level similarity to integrases were selected and real-time PCR primers and probes were designed. Initial testing with microbial fraction DNA from Tampa Bay revealed 1.9 x 10(7, and 1300 gene copies of Vibrio-like integrase and Oceanicola-like integrase L(-1 respectively. The other two integrases were not detected. The integrase assay was then tested on microbial fraction RNA extracted from 200 ml of Tampa Bay water sampled biweekly over a 12 month time series. Vibrio-like integrase gene expression was detected in three samples, with estimated copy numbers of 2.4-1280 L(-1. Clostridium-like integrase gene expression was detected in 6 samples, with estimated copy numbers of 37 to 265 L(-1. In all cases, detection of integrase gene expression corresponded to the occurrence of lysogeny as detected by prophage induction. Investigation of the environmental distribution of the two expressed integrases in the Global Ocean Survey Database found the Vibrio-like integrase was present in genome equivalents of 3.14% of microbial libraries and all four viral metagenomes. There were two similar genes in the library from British Columbia and one similar gene was detected in both the Gulf of Mexico and Sargasso Sea libraries. In contrast, in the Arctic library eleven similar genes were observed. The Clostridium-like integrase was less prevalent, being found in 0.58% of the microbial and none of the viral libraries. These results underscore the value of metagenomic data

  10. Pre- and post-weaning diet alters the faecal metagenome in the cat with differences vitamin and carbohydrate metabolism gene abundances

    Science.gov (United States)

    Young, Wayne; Moon, Christina D.; Thomas, David G.; Cave, Nick J.; Bermingham, Emma N.

    2016-01-01

    Dietary format, and its role in pet nutrition, is of interest to pet food manufacturers and pet owners alike. The aim of the present study was to investigate the effects of pre- and post-weaning diets (kibbled or canned) on the composition and function of faecal microbiota in the domestic cat by shotgun metagenomic sequencing and gene taxonomic and functional assignment using MG-RAST. Post-weaning diet had a dramatic effect on community composition; 147 of the 195 bacterial species identified had significantly different mean relative abundances between kittens fed kibbled and canned diets. The kittens fed kibbled diets had relatively higher abundances of Lactobacillus (>100-fold), Bifidobacterium (>100-fold), and Collinsella (>9-fold) than kittens fed canned diets. There were relatively few differences in the predicted microbiome functions associated with the pre-weaning diet. Post-weaning diet affected the abundance of functional gene groups. Genes involved in vitamin biosynthesis, metabolism, and transport, were significantly enriched in the metagenomes of kittens fed the canned diet. The impact of post-weaning diet on the metagenome in terms of vitamin biosynthesis functions suggests that modulation of the microbiome function through diet may be an important avenue for improving the nutrition of companion animals. PMID:27876765

  11. Identification and characterization of a novel fumarase gene by metagenome expression cloning from marine microorganisms

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    Tang Xian-Lai

    2010-11-01

    Full Text Available Abstract Background Fumarase catalyzes the reversible hydration of fumarate to L-malate and is a key enzyme in the tricarboxylic acid (TCA cycle and in amino acid metabolism. Fumarase is also used for the industrial production of L-malate from the substrate fumarate. Thermostable and high-activity fumarases from organisms that inhabit extreme environments may have great potential in industry, biotechnology, and basic research. The marine environment is highly complex and considered one of the main reservoirs of microbial diversity on the planet. However, most of the microorganisms are inaccessible in nature and are not easily cultivated in the laboratory. Metagenomic approaches provide a powerful tool to isolate and identify enzymes with novel biocatalytic activities for various biotechnological applications. Results A plasmid metagenomic library was constructed from uncultivated marine microorganisms within marine water samples. Through sequence-based screening of the DNA library, a gene encoding a novel fumarase (named FumF was isolated. Amino acid sequence analysis revealed that the FumF protein shared the greatest homology with Class II fumarate hydratases from Bacteroides sp. 2_1_33B and Parabacteroides distasonis ATCC 8503 (26% identical and 43% similar. The putative fumarase gene was subcloned into pETBlue-2 vector and expressed in E. coli BL21(DE3pLysS. The recombinant protein was purified to homogeneity. Functional characterization by high performance liquid chromatography confirmed that the recombinant FumF protein catalyzed the hydration of fumarate to form L-malate. The maximum activity for FumF protein occurred at pH 8.5 and 55°C in 5 mM Mg2+. The enzyme showed higher affinity and catalytic efficiency under optimal reaction conditions: Km= 0.48 mM, Vmax = 827 μM/min/mg, and kcat/Km = 1900 mM/s. Conclusions We isolated a novel fumarase gene, fumF, from a sequence-based screen of a plasmid metagenomic library from uncultivated

  12. Chitinase genes revealed and compared in bacterial isolates, DNA extracts and a metagenomic library from a phytopathogen suppressive soil

    Energy Technology Data Exchange (ETDEWEB)

    Hjort, K.; Bergstrom, M.; Adesina, M.F.; Jansson, J.K.; Smalla, K.; Sjoling, S.

    2009-09-01

    Soil that is suppressive to disease caused by fungal pathogens is an interesting source to target for novel chitinases that might be contributing towards disease suppression. In this study we screened for chitinase genes, in a phytopathogen-suppressive soil in three ways: (1) from a metagenomic library constructed from microbial cells extracted from soil, (2) from directly extracted DNA and (3) from bacterial isolates with antifungal and chitinase activities. Terminal-restriction fragment length polymorphism (T-RFLP) of chitinase genes revealed differences in amplified chitinase genes from the metagenomic library and the directly extracted DNA, but approximately 40% of the identified chitinase terminal-restriction fragments (TRFs) were found in both sources. All of the chitinase TRFs from the isolates were matched to TRFs in the directly extracted DNA and the metagenomic library. The most abundant chitinase TRF in the soil DNA and the metagenomic library corresponded to the TRF{sup 103} of the isolate, Streptomyces mutomycini and/or Streptomyces clavifer. There were good matches between T-RFLP profiles of chitinase gene fragments obtained from different sources of DNA. However, there were also differences in both the chitinase and the 16S rRNA gene T-RFLP patterns depending on the source of DNA, emphasizing the lack of complete coverage of the gene diversity by any of the approaches used.

  13. Functional Screening of Antibiotic Resistance Genes from a Representative Metagenomic Library of Food Fermenting Microbiota

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    Chiara Devirgiliis

    2014-01-01

    Full Text Available Lactic acid bacteria (LAB represent the predominant microbiota in fermented foods. Foodborne LAB have received increasing attention as potential reservoir of antibiotic resistance (AR determinants, which may be horizontally transferred to opportunistic pathogens. We have previously reported isolation of AR LAB from the raw ingredients of a fermented cheese, while AR genes could be detected in the final, marketed product only by PCR amplification, thus pointing at the need for more sensitive microbial isolation techniques. We turned therefore to construction of a metagenomic library containing microbial DNA extracted directly from the food matrix. To maximize yield and purity and to ensure that genomic complexity of the library was representative of the original bacterial population, we defined a suitable protocol for total DNA extraction from cheese which can also be applied to other lipid-rich foods. Functional library screening on different antibiotics allowed recovery of ampicillin and kanamycin resistant clones originating from Streptococcus salivarius subsp. thermophilus and Lactobacillus helveticus genomes. We report molecular characterization of the cloned inserts, which were fully sequenced and shown to confer AR phenotype to recipient bacteria. We also show that metagenomics can be applied to food microbiota to identify underrepresented species carrying specific genes of interest.

  14. Metagenomic analysis revealed highly diverse microbial arsenic metabolism genes in paddy soils with low-arsenic contents

    International Nuclear Information System (INIS)

    Xiao, Ke-Qing; Li, Li-Guan; Ma, Li-Ping; Zhang, Si-Yu; Bao, Peng; Zhang, Tong; Zhu, Yong-Guan

    2016-01-01

    Microbe-mediated arsenic (As) metabolism plays a critical role in global As cycle, and As metabolism involves different types of genes encoding proteins facilitating its biotransformation and transportation processes. Here, we used metagenomic analysis based on high-throughput sequencing and constructed As metabolism protein databases to analyze As metabolism genes in five paddy soils with low-As contents. The results showed that highly diverse As metabolism genes were present in these paddy soils, with varied abundances and distribution for different types and subtypes of these genes. Arsenate reduction genes (ars) dominated in all soil samples, and significant correlation existed between the abundance of arr (arsenate respiration), aio (arsenite oxidation), and arsM (arsenite methylation) genes, indicating the co-existence and close-relation of different As resistance systems of microbes in wetland environments similar to these paddy soils after long-term evolution. Among all soil parameters, pH was an important factor controlling the distribution of As metabolism gene in five paddy soils (p = 0.018). To the best of our knowledge, this is the first study using high-throughput sequencing and metagenomics approach in characterizing As metabolism genes in the five paddy soil, showing their great potential in As biotransformation, and therefore in mitigating arsenic risk to humans. - Highlights: • Use metagenomics to analyze As metabolism genes in paddy soils with low-As content. • These genes were ubiquitous, abundant, and associated with diverse microbes. • pH as an important factor controlling their distribution in paddy soil. • Imply combinational effect of evolution and selection on As metabolism genes. - Metagenomics was used to analyze As metabolism genes in paddy soils with low-As contents. These genes were ubiquitous, abundant, and associated with diverse microbes.

  15. Metagenomic analysis of antibiotic resistance genes in coastal industrial mariculture systems.

    Science.gov (United States)

    Wang, Jian-Hua; Lu, Jian; Zhang, Yu-Xuan; Wu, Jun; Luo, Yongming; Liu, Hao

    2018-04-01

    The overuse of antibiotics has posed a propagation of antibiotic resistance genes (ARGs) in aquaculture systems. This study firstly explored the ARGs profiles of the typical mariculture farms including conventional and recirculating systems using metagenomics approach. Fifty ARGs subtypes belonging to 21 ARGs types were identified, showing the wide-spectrum profiles of ARGs in the coastal industrial mariculture systems. ARGs with multiple antibiotics resistance have emerged in the mariculure systems. The co-occurrence pattern between ARGs and microbial taxa showed that Proteobacteria and Bacteroidetes were potential dominant hosts of ARGs in the industrial mariculture systems. Typical nitrifying bacteria such as Nitrospinae in mariculture systems also carried with some resistance genes. Relative abundance of ARGs in fish ponds and wastewater treatment units was relatively high. The investigation showed that industrial mariculture systems were important ARGs reservoirs in coastal area, indicating the critical role of recirculating systems in the terms of ARGs pollution control. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Metagenomic profiling of antibiotic resistance genes and mobile genetic elements in a tannery wastewater treatment plant.

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    Zhu Wang

    Full Text Available Antibiotics are often used to prevent sickness and improve production in animal agriculture, and the residues in animal bodies may enter tannery wastewater during leather production. This study aimed to use Illumina high-throughput sequencing to investigate the occurrence, diversity and abundance of antibiotic resistance genes (ARGs and mobile genetic elements (MGEs in aerobic and anaerobic sludge of a full-scale tannery wastewater treatment plant (WWTP. Metagenomic analysis showed that Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria dominated in the WWTP, but the relative abundance of archaea in anaerobic sludge was higher than in aerobic sludge. Sequencing reads from aerobic and anaerobic sludge revealed differences in the abundance of functional genes between both microbial communities. Genes coding for antibiotic resistance were identified in both communities. BLAST analysis against Antibiotic Resistance Genes Database (ARDB further revealed that aerobic and anaerobic sludge contained various ARGs with high abundance, among which sulfonamide resistance gene sul1 had the highest abundance, occupying over 20% of the total ARGs reads. Tetracycline resistance genes (tet were highly rich in the anaerobic sludge, among which tet33 had the highest abundance, but was absent in aerobic sludge. Over 70 types of insertion sequences were detected in each sludge sample, and class 1 integrase genes were prevalent in the WWTP. The results highlighted prevalence of ARGs and MGEs in tannery WWTPs, which may deserve more public health concerns.

  17. MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes.

    Science.gov (United States)

    Pericard, Pierre; Dufresne, Yoann; Couderc, Loïc; Blanquart, Samuel; Touzet, Hélène

    2018-02-15

    Advances in the sequencing of uncultured environmental samples, dubbed metagenomics, raise a growing need for accurate taxonomic assignment. Accurate identification of organisms present within a community is essential to understanding even the most elementary ecosystems. However, current high-throughput sequencing technologies generate short reads which partially cover full-length marker genes and this poses difficult bioinformatic challenges for taxonomy identification at high resolution. We designed MATAM, a software dedicated to the fast and accurate targeted assembly of short reads sequenced from a genomic marker of interest. The method implements a stepwise process based on construction and analysis of a read overlap graph. It is applied to the assembly of 16S rRNA markers and is validated on simulated, synthetic and genuine metagenomes. We show that MATAM outperforms other available methods in terms of low error rates and recovered fractions and is suitable to provide improved assemblies for precise taxonomic assignments. https://github.com/bonsai-team/matam. pierre.pericard@gmail.com or helene.touzet@univ-lille1.fr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  18. Metagenomic analysis of buffalo rumen microbiome: Effect of roughage diet on Dormancy and Sporulation genes.

    Science.gov (United States)

    Singh, K M; Reddy, B; Patel, A K; Panchasara, H; Parmar, N; Patel, A B; Shah, T M; Bhatt, V D; Joshi, C G

    2014-12-01

    Buffalo rumen microbiome experiences a variety of diet stress and represents reservoir of Dormancy and Sporulation genes. However, the information on genomic responses to such conditions is very limited. The Ion Torrent PGM next generation sequencing technology was used to characterize general microbial diversity and the repertoire of microbial genes present, including genes associated with Dormancy and Sporulation in Mehsani buffalo rumen metagenome. The research findings revealed the abundance of bacteria at the domain level and presence of Dormancy and Sporulation genes which were predominantly associated with the Clostridia and Bacilli taxa belonging to the phyla Firmicutes. Genes associated with Sporulation cluster and Sporulation orphans were increased from 50% to 100% roughage treatment, thereby promoting sporulation all along the treatments. The spore germination is observed to be the highest in the 75% roughage treatment both in the liquid and solid rumen fraction samples with respect to the decrease in the values of the genes associated with spore core dehydration, thereby facilitating spore core hydration which is necessary for spore germination.

  19. Metagenomes reveal microbial structures, functional potentials, and biofouling-related genes in a membrane bioreactor.

    Science.gov (United States)

    Ma, Jinxing; Wang, Zhiwei; Li, Huan; Park, Hee-Deung; Wu, Zhichao

    2016-06-01

    Metagenomic sequencing was used to investigate the microbial structures, functional potentials, and biofouling-related genes in a membrane bioreactor (MBR). The results showed that the microbial community in the MBR was highly diverse. Notably, function analysis of the dominant genera indicated that common genes from different phylotypes were identified for important functional potentials with the observation of variation of abundances of genes in a certain taxon (e.g., Dechloromonas). Despite maintaining similar metabolic functional potentials with a parallel full-scale conventional activated sludge (CAS) system due to treating the identical wastewater, the MBR had more abundant nitrification-related bacteria and coding genes of ammonia monooxygenase, which could well explain its excellent ammonia removal in the low-temperature period. Furthermore, according to quantification of the genes involved in exopolysaccharide and extracellular polymeric substance (EPS) protein metabolism, the MBR did not show a much different potential in producing EPS compared to the CAS system, and bacteria from the membrane biofilm had lower abundances of genes associated with EPS biosynthesis and transport compared to the activated sludge in the MBR.

  20. Mining for Nonribosomal Peptide Synthetase and Polyketide Synthase Genes Revealed a High Level of Diversity in the Sphagnum Bog Metagenome.

    Science.gov (United States)

    Müller, Christina A; Oberauner-Wappis, Lisa; Peyman, Armin; Amos, Gregory C A; Wellington, Elizabeth M H; Berg, Gabriele

    2015-08-01

    Sphagnum bog ecosystems are among the oldest vegetation forms harboring a specific microbial community and are known to produce an exceptionally wide variety of bioactive substances. Although the Sphagnum metagenome shows a rich secondary metabolism, the genes have not yet been explored. To analyze nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs), the diversity of NRPS and PKS genes in Sphagnum-associated metagenomes was investigated by in silico data mining and sequence-based screening (PCR amplification of 9,500 fosmid clones). The in silico Illumina-based metagenomic approach resulted in the identification of 279 NRPSs and 346 PKSs, as well as 40 PKS-NRPS hybrid gene sequences. The occurrence of NRPS sequences was strongly dominated by the members of the Protebacteria phylum, especially by species of the Burkholderia genus, while PKS sequences were mainly affiliated with Actinobacteria. Thirteen novel NRPS-related sequences were identified by PCR amplification screening, displaying amino acid identities of 48% to 91% to annotated sequences of members of the phyla Proteobacteria, Actinobacteria, and Cyanobacteria. Some of the identified metagenomic clones showed the closest similarity to peptide synthases from Burkholderia or Lysobacter, which are emerging bacterial sources of as-yet-undescribed bioactive metabolites. This report highlights the role of the extreme natural ecosystems as a promising source for detection of secondary compounds and enzymes, serving as a source for biotechnological applications. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  1. Phylogeny and phylogeography of functional genes shared among seven terrestrial subsurface metagenomes reveal N-cycling and microbial evolutionary relationships

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    Maggie CY Lau

    2014-10-01

    Full Text Available Comparative studies on community phylogenetics and phylogeography of microorganisms living in extreme environments are rare. Terrestrial subsurface habitats are valuable for studying microbial biogeographical patterns due to their isolation and the restricted dispersal mechanisms. Since the taxonomic identity of a microorganism does not always correspond well with its functional role in a particular community, the use of taxonomic assignments or patterns may give limited inference on how microbial functions are affected by historical, geographical and environmental factors. With seven metagenomic libraries generated from fracture water samples collected from five South African mines, this study was carried out to (1 screen for ubiquitous functions or pathways of biogeochemical cycling of CH4, S and N; (2 to characterize the biodiversity represented by the common functional genes; (3 to investigate the subsurface biogeography as revealed by this subset of genes; and (4 to explore the possibility of using metagenomic data for evolutionary study. The ubiquitous functional genes are NarV, NPD, PAP reductase, NifH, NifD, NifK, NifE and NifN genes. Although these 8 common functional genes were taxonomically and phylogenetically diverse and distinct from each other, the dissimilarity between samples did not correlate strongly with either geographical, environmental or residence time of the water. Por genes homologous to those of Thermodesulfovibrio yellowstonii detected in all metagenomes were deep lineages of Nitrospirae, suggesting that subsurface habitats have preserved ancestral genetic signatures that inform the study of the origin and evolution of prokaryotes.

  2. Metagenomic profiles of antibiotic resistance genes (ARGs) between human impacted estuary and deep ocean sediments.

    Science.gov (United States)

    Chen, Baowei; Yang, Ying; Liang, Ximei; Yu, Ke; Zhang, Tong; Li, Xiangdong

    2013-11-19

    Knowledge of the origins and dissemination of antibiotic resistance genes (ARGs) is essential for understanding modern resistomes in the environment. The mechanisms of the dissemination of ARGs can be revealed through comparative studies on the metagenomic profiling of ARGs between relatively pristine and human-impacted environments. The deep ocean bed of the South China Sea (SCS) is considered to be largely devoid of anthropogenic impacts, while the Pearl River Estuary (PRE) in south China has been highly impacted by intensive human activities. Commonly used antibiotics (sulfamethazine, norfloxacin, ofloxacin, tetracycline, and erythromycin) have been detected through chemical analysis in the PRE sediments, but not in the SCS sediments. In the relatively pristine SCS sediments, the most prevalent and abundant ARGs are those related to resistance to macrolides and polypeptides, with efflux pumps as the predominant mechanism. In the contaminated PRE sediments, the typical ARG profiles suggest a prevailing resistance to antibiotics commonly used in human health and animal farming (including sulfonamides, fluoroquinolones, and aminoglycosides), and higher diversity in both genotype and resistance mechanism than those in the SCS. In particular, antibiotic inactivation significantly contributed to the resistance to aminoglycosides, β-lactams, and macrolides observed in the PRE sediments. There was a significant correlation in the levels of abundance of ARGs and those of mobile genetic elements (including integrons and plasmids), which serve as carriers in the dissemination of ARGs in the aquatic environment. The metagenomic results from the current study support the view that ARGs naturally originate in pristine environments, while human activities accelerate the dissemination of ARGs so that microbes would be able to tolerate selective environmental stress in response to anthropogenic impacts.

  3. Metagenomic Profiling of Soil Microbes to Mine Salt Stress Tolerance Genes

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    Vasim Ahmed

    2018-02-01

    Full Text Available Osmotolerance is one of the critical factors for successful survival and colonization of microbes in saline environments. Nonetheless, information about these osmotolerance mechanisms is still inadequate. Exploration of the saline soil microbiome for its community structure and novel genetic elements is likely to provide information on the mechanisms involved in osmoadaptation. The present study explores the saline soil microbiome for its native structure and novel genetic elements involved in osmoadaptation. 16S rRNA gene sequence analysis has indicated the dominance of halophilic/halotolerant phylotypes affiliated to Proteobacteria, Actinobacteria, Gemmatimonadetes, Bacteroidetes, Firmicutes, and Acidobacteria. A functional metagenomics approach led to the identification of osmotolerant clones SSR1, SSR4, SSR6, SSR2 harboring BCAA_ABCtp, GSDH, STK_Pknb, and duf3445 genes. Furthermore, transposon mutagenesis, genetic, physiological and functional studies in close association has confirmed the role of these genes in osmotolerance. Enhancement in host osmotolerance possibly though the cytosolic accumulation of amino acids, reducing equivalents and osmolytes involving BCAA-ABCtp, GSDH, and STKc_PknB. Decoding of the genetic elements prevalent within these microbes can be exploited either as such for ameliorating soils or their genetically modified forms can assist crops to resist and survive in saline environment.

  4. Cloning and identification of novel hydrolase genes from a dairy cow rumen metagenomic library and characterization of a cellulase gene

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    Gong Xia

    2012-10-01

    Full Text Available Abstract Background Interest in cellulose degrading enzymes has increased in recent years due to the expansion of the cellulosic biofuel industry. The rumen is a highly adapted environment for the degradation of cellulose and a promising source of enzymes for industrial use. To identify cellulase enzymes that may be of such use we have undertaken a functional metagenomic screen to identify cellulase enzymes from the bacterial community in the rumen of a grass-hay fed dairy cow. Results Twenty five clones specifying cellulose activity were identified. Subcloning and sequence analysis of a subset of these hydrolase-positive clones identified 10 endoglucanase genes. Preliminary characterization of the encoded cellulases was carried out using crude extracts of each of the subclones. Zymogram analysis using carboxymethylcellulose as a substrate showed a single positive band for each subclone, confirming that only one functional cellulase gene was present in each. One cellulase gene, designated Cel14b22, was expressed at a high level in Escherichia coli and purified for further characterization. The purified recombinant enzyme showed optimal activity at pH 6.0 and 50°C. It was stable over a broad pH range, from pH 4.0 to 10.0. The activity was significantly enhanced by Mn2+ and dramatically reduced by Fe3+ or Cu2+. The enzyme hydrolyzed a wide range of beta-1,3-, and beta-1,4-linked polysaccharides, with varying activities. Activities toward microcrystalline cellulose and filter paper were relatively high, while the highest activity was toward Oat Gum. Conclusion The present study shows that a functional metagenomic approach can be used to isolate previously uncharacterized cellulases from the rumen environment.

  5. A new tetracycline efflux gene, tet(40), is located in tandem with tet(O/32/O) in a human gut firmicute bacterium and in metagenomic library clones.

    Science.gov (United States)

    Kazimierczak, Katarzyna A; Rincon, Marco T; Patterson, Andrea J; Martin, Jennifer C; Young, Pauline; Flint, Harry J; Scott, Karen P

    2008-11-01

    The bacterium Clostridium saccharolyticum K10, isolated from a fecal sample obtained from a healthy donor who had received long-term tetracycline therapy, was found to carry three tetracycline resistance genes: tet(W) and the mosaic tet(O/32/O), both conferring ribosome protection-type resistance, and a novel, closely linked efflux-type resistance gene designated tet(40). tet(40) encodes a predicted membrane-associated protein with 42% amino acid identity to tetA(P). Tetracycline did not accumulate in Escherichia coli cells expressing the Tet(40) efflux protein, and resistance to tetracycline was reduced when cells were incubated with an efflux pump inhibitor. E. coli cells carrying tet(40) had a 50% inhibitory concentration of tetracycline of 60 microg/ml. Analysis of a transconjugant from a mating between donor strain C. saccharolyticum K10 and the recipient human gut commensal bacterium Roseburia inulinivorans suggested that tet(O/32/O) and tet(40) were cotransferred on a mobile element. Sequence analysis of a 37-kb insert identified on the basis of tetracycline resistance from a metagenomic fosmid library again revealed a tandem arrangement of tet(O/32/O) and tet(40), flanked by regions with homology to parts of the VanG operon previously identified in Enterococcus faecalis. At least 10 of the metagenomic inserts that carried tet(O/32/O) also carried tet(40), suggesting that tet(40), although previously undetected, may be an abundant efflux gene.

  6. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance.

    Directory of Open Access Journals (Sweden)

    Rainer Roehe

    2016-02-01

    Full Text Available Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions. The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed. We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets. Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency. Sire progeny groups differed significantly in their methane emissions measured in respiration chambers. Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet, suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly. In the metagenomic analysis of rumen contents, we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively. These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks. Methanogenesis genes (e.g. mcrA and fmdB were associated with methane emissions, whilst host-microbiome cross talk genes (e.g. TSTA3 and FucI were associated with feed conversion efficiency. These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts. Generally, the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e

  7. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases.

    Science.gov (United States)

    Brulc, Jennifer M; Antonopoulos, Dionysios A; Miller, Margret E Berg; Wilson, Melissa K; Yannarell, Anthony C; Dinsdale, Elizabeth A; Edwards, Robert E; Frank, Edward D; Emerson, Joanne B; Wacklin, Pirjo; Coutinho, Pedro M; Henrissat, Bernard; Nelson, Karen E; White, Bryan A

    2009-02-10

    The complex microbiome of the rumen functions as an effective system for the conversion of plant cell wall biomass to microbial protein, short chain fatty acids, and gases. As such, it provides a unique genetic resource for plant cell wall degrading microbial enzymes that could be used in the production of biofuels. The rumen and gastrointestinal tract harbor a dense and complex microbiome. To gain a greater understanding of the ecology and metabolic potential of this microbiome, we used comparative metagenomics (phylotype analysis and SEED subsystems-based annotations) to examine randomly sampled pyrosequence data from 3 fiber-adherent microbiomes and 1 pooled liquid sample (a mixture of the liquid microbiome fractions from the same bovine rumens). Even though the 3 animals were fed the same diet, the community structure, predicted phylotype, and metabolic potentials in the rumen were markedly different with respect to nutrient utilization. A comparison of the glycoside hydrolase and cellulosome functional genes revealed that in the rumen microbiome, initial colonization of fiber appears to be by organisms possessing enzymes that attack the easily available side chains of complex plant polysaccharides and not the more recalcitrant main chains, especially cellulose. Furthermore, when compared with the termite hindgut microbiome, there are fundamental differences in the glycoside hydrolase content that appear to be diet driven for either the bovine rumen (forages and legumes) or the termite hindgut (wood).

  8. Molecular cloning of a novel bioH gene from an environmental metagenome encoding a carboxylesterase with exceptional tolerance to organic solvents

    DEFF Research Database (Denmark)

    Shi, Yuping; Pan, Yingjie; Li, Bailin

    2013-01-01

    with a strong potential in industrial applications. CONCLUSIONS: This study constituted the first investigation of a novel bioHx gene in a biotin biosynthetic gene cluster cloned from an environmental metagenome. The bioHx gene was successfully cloned, expressed and characterized. The results demonstrated...... that BioHx is a novel carboxylesterase, displaying distinct biochemical properties with strong application potential in industry. Our results also provided the evidence for the effectiveness of functional metagenomic approach for identifying novel bioH genes from complex ecosystem.......ABSTRACT: BACKGROUND: BioH is one of the key enzymes to produce the precursor pimeloyl-ACP to initiate biotin biosynthesis de novo in bacteria. To date, very few bioH genes have been characterized. In this study, we cloned and identified a novel bioH gene, bioHx, from an environmental metagenome...

  9. Identification of aminoglycoside and β-lactam resistance genes from within an infant gut functional metagenomic library.

    Directory of Open Access Journals (Sweden)

    Fiona Fouhy

    Full Text Available The infant gut microbiota develops rapidly during the first 2 years of life, acquiring microorganisms from diverse sources. During this time, significant opportunities exist for the infant to acquire antibiotic resistant bacteria, which can become established and constitute the infant gut resistome. With increased antibiotic resistance limiting our ability to treat bacterial infections, investigations into resistance reservoirs are highly pertinent. This study aimed to explore the nascent resistome in antibiotically-naïve infant gut microbiomes, using a combination of metagenomic approaches. Faecal samples from 22 six-month-old infants without previous antibiotic exposure were used to construct a pooled metagenomic library, which was functionally screened for ampicillin and gentamicin resistance. Our library of ∼220Mb contained 0.45 ampicillin resistant hits/Mb and 0.059 gentamicin resistant hits/Mb. PCR-based analysis of fosmid clones and uncloned metagenomic DNA, revealed a diverse and abundant aminoglycoside and β-lactam resistance reservoir within the infant gut, with resistance determinants exhibiting homology to those found in common gut inhabitants, including Escherichia coli, Enterococcus sp., and Clostridium difficile, as well as to genes from cryptic environmental bacteria. Notably, the genes identified differed from those revealed when a sequence-driven PCR-based screen of metagenomic DNA was employed. Carriage of these antibiotic resistance determinants conferred substantial, but varied (2-512x, increases in antibiotic resistance to their bacterial host. These data provide insights into the infant gut resistome, revealing the presence of a varied aminoglycoside and β-lactam resistance reservoir even in the absence of selective pressure, confirming the infant resistome establishes early in life, perhaps even at birth.

  10. Salt resistance genes revealed by functional metagenomics from brines and moderate-salinity rhizosphere within a hypersaline environment

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    Salvador eMirete

    2015-10-01

    Full Text Available Hypersaline environments are considered one of the most extreme habitats on earth and microorganisms have developed diverse molecular mechanisms of adaptation to withstand these conditions. The present study was aimed at identifying novel genes involved in salt resistance from the microbial communities of brines and the rhizosphere from the Es Trenc saltern (Mallorca, Spain. The microbial diversity assessed by pyrosequencing of 16S rRNA gene libraries revealed the presence of communities that are typical in such environments. Metagenomic libraries from brine and rhizosphere samples, were transferred to the osmosensitive strain Escherichia coli MKH13, and screened for salt resistance. As a result, eleven genes that conferred salt resistance were identified, some encoding for well known proteins previously related to osmoadaptation as a glycerol and a proton pump, whereas others encoded for proteins not previously related to this function in microorganisms as DNA/RNA helicases, an endonuclease III (Nth and hypothetical proteins of unknown function. Furthermore, four of the retrieved genes were cloned and expressed in Bacillus subtilis and they also exhibited salt resistance in this bacterium, broadening the spectrum of bacterial species where these genes can operate. This is the first report of salt resistance genes recovered from metagenomes of a hypersaline environment.

  11. Computational prediction of CRISPR cassettes in gut metagenome samples from Chinese type-2 diabetic patients and healthy controls.

    Science.gov (United States)

    Mangericao, Tatiana C; Peng, Zhanhao; Zhang, Xuegong

    2016-01-11

    CRISPR has been becoming a hot topic as a powerful technique for genome editing for human and other higher organisms. The original CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats coupled with CRISPR-associated proteins) is an important adaptive defence system for prokaryotes that provides resistance against invading elements such as viruses and plasmids. A CRISPR cassette contains short nucleotide sequences called spacers. These unique regions retain a history of the interactions between prokaryotes and their invaders in individual strains and ecosystems. One important ecosystem in the human body is the human gut, a rich habitat populated by a great diversity of microorganisms. Gut microbiomes are important for human physiology and health. Metagenome sequencing has been widely applied for studying the gut microbiomes. Most efforts in metagenome study has been focused on profiling taxa compositions and gene catalogues and identifying their associations with human health. Less attention has been paid to the analysis of the ecosystems of microbiomes themselves especially their CRISPR composition. We conducted a preliminary analysis of CRISPR sequences in a human gut metagenomic data set of Chinese individuals of type-2 diabetes patients and healthy controls. Applying an available CRISPR-identification algorithm, PILER-CR, we identified 3169 CRISPR cassettes in the data, from which we constructed a set of 1302 unique repeat sequences and 36,709 spacers. A more extensive analysis was made for the CRISPR repeats: these repeats were submitted to a more comprehensive clustering and classification using the web server tool CRISPRmap. All repeats were compared with known CRISPRs in the database CRISPRdb. A total of 784 repeats had matches in the database, and the remaining 518 repeats from our set are potentially novel ones. The computational analysis of CRISPR composition based contigs of metagenome sequencing data is feasible. It provides an efficient

  12. Metagenomic Analysis of Antibiotic Resistance Genes in Dairy Cow Feces following Therapeutic Administration of Third Generation Cephalosporin.

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    Lindsey Chambers

    Full Text Available Although dairy manure is widely applied to land, it is relatively understudied compared to other livestock as a potential source of antibiotic resistance genes (ARGs to the environment and ultimately to human pathogens. Ceftiofur, the most widely used antibiotic used in U.S. dairy cows, is a 3rd generation cephalosporin, a critically important class of antibiotics to human health. The objective of this study was to evaluate the effect of typical ceftiofur antibiotic treatment on the prevalence of ARGs in the fecal microbiome of dairy cows using a metagenomics approach. β-lactam ARGs were found to be elevated in feces from Holstein cows administered ceftiofur (n = 3 relative to control cows (n = 3. However, total numbers of ARGs across all classes were not measurably affected by ceftiofur treatment, likely because of dominance of unaffected tetracycline ARGs in the metagenomics libraries. Functional analysis via MG-RAST further revealed that ceftiofur treatment resulted in increases in gene sequences associated with "phages, prophages, transposable elements, and plasmids", suggesting that this treatment also enriched the ability to horizontally transfer ARGs. Additional functional shifts were noted with ceftiofur treatment (e.g., increase in genes associated with stress, chemotaxis, and resistance to toxic compounds; decrease in genes associated with metabolism of aromatic compounds and cell division and cell cycle, along with measureable taxonomic shifts (increase in Bacterioidia and decrease in Actinobacteria. This study demonstrates that ceftiofur has a broad, measureable and immediate effect on the cow fecal metagenome. Given the importance of 3rd generation cephalospirins to human medicine, their continued use in dairy cattle should be carefully considered and waste treatment strategies to slow ARG dissemination from dairy cattle manure should be explored.

  13. Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes.

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    Stephen Nayfach

    2015-11-01

    Full Text Available Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP. ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn's disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.

  14. Virulence-associated and antibiotic resistance genes of microbial populations in cattle feces analyzed using a metagenomic approach.

    Science.gov (United States)

    Durso, Lisa M; Harhay, Gregory P; Bono, James L; Smith, Timothy P L

    2011-02-01

    The bovine fecal microbiota impacts human food safety as well as animal health. Although the bacteria of cattle feces have been well characterized using culture-based and culture-independent methods, techniques have been lacking to correlate total community composition with community function. We used high throughput sequencing of total DNA extracted from fecal material to characterize general community composition and examine the repertoire of microbial genes present in beef cattle feces, including genes associated with antibiotic resistance and bacterial virulence. Results suggest that traditional 16S sequencing using "universal" primers to generate full-length sequence may under represent Acitinobacteria and Proteobacteria. Over eight percent (8.4%) of the sequences from our beef cattle fecal pool sample could be categorized as virulence genes, including a suite of genes associated with resistance to antibiotic and toxic compounds (RATC). This is a higher proportion of virulence genes found in Sargasso sea, chicken cecum, and cow rumen samples, but comparable to the proportion found in Antarctic marine derived lake, human fecal, and farm soil samples. The quantitative nature of metagenomic data, combined with the large number of RATC classes represented in samples from widely different habitats indicates that metagenomic data can be used to track relative amounts of antibiotic resistance genes in individual animals over time. Consequently, these data can be used to generate sample-specific and temporal antibiotic resistance gene profiles to facilitate an understanding of the ecology of the microbial communities in each habitat as well as the epidemiology of antibiotic resistant gene transport between and among habitats. Published by Elsevier B.V.

  15. Effect of temperature on removal of antibiotic resistance genes by anaerobic digestion of activated sludge revealed by metagenomic approach.

    Science.gov (United States)

    Zhang, Tong; Yang, Ying; Pruden, Amy

    2015-09-01

    As antibiotic resistance continues to spread globally, there is growing interest in the potential to limit the spread of antibiotic resistance genes (ARGs) from wastewater sources. In particular, operational conditions during sludge digestion may serve to discourage selection of resistant bacteria, reduce horizontal transfer of ARGs, and aid in hydrolysis of DNA. This study applied metagenomic analysis to examine the removal efficiency of ARGs through thermophilic and mesophilic anaerobic digestion using bench-scale reactors. Although the relative abundance of various ARGs shifted from influent to effluent sludge, there was no measureable change in the abundance of total ARGs or their diversity in either the thermophilic or mesophilic treatment. Among the 35 major ARG subtypes detected in feed sludge, substantial reductions (removal efficiency >90%) of 8 and 13 ARGs were achieved by thermophilic and mesophilic digestion, respectively. However, resistance genes of aadA, macB, and sul1 were enriched during the thermophilic anaerobic digestion, while resistance genes of erythromycin esterase type I, sul1, and tetM were enriched during the mesophilic anaerobic digestion. Efflux pump remained to be the major antibiotic resistance mechanism in sludge samples, but the portion of ARGs encoding resistance via target modification increased in the anaerobically digested sludge relative to the feed. Metagenomic analysis provided insight into the potential for anaerobic digestion to mitigate a broad array of ARGs.

  16. Identification of Carbohydrate Metabolism Genes in the Metagenome of a Marine Biofilm Community Shown to Be Dominated by Gammaproteobacteria and Bacteroidetes

    Directory of Open Access Journals (Sweden)

    Jennifer L. Edwards

    2010-10-01

    Full Text Available Polysaccharides are an important source of organic carbon in the marine environment and degradation of the insoluble and globally abundant cellulose is a major component of the marine carbon cycle. Although a number of species of cultured bacteria are known to degrade crystalline cellulose, little is known of the polysaccharide hydrolases expressed by cellulose-degrading microbial communities, particularly in the marine environment. Next generation 454 Pyrosequencing was applied to analyze the microbial community that colonizes and degrades insoluble polysaccharides in situ in the Irish Sea. The bioinformatics tool MG-RAST was used to examine the randomly sampled data for taxonomic markers and functional genes, and showed that the community was dominated by members of the Gammaproteobacteria and Bacteroidetes. Furthermore, the identification of 211 gene sequences matched to a custom-made database comprising the members of nine glycoside hydrolase families revealed an extensive repertoire of functional genes predicted to be involved in cellulose utilization. This demonstrates that the use of an in situ cellulose baiting method yielded a marine microbial metagenome considerably enriched in functional genes involved in polysaccharide degradation. The research reported here is the first designed to specifically address the bacterial communities that colonize and degrade cellulose in the marine environment and to evaluate the glycoside hydrolase (cellulase and chitinase gene repertoire of that community, in the absence of the biases associated with PCR-based molecular techniques.

  17. Metagenomic survey of methanesulfonic acid (MSA catabolic genes in an Atlantic Ocean surface water sample and in a partial enrichment

    Directory of Open Access Journals (Sweden)

    Ana C. Henriques

    2016-10-01

    Full Text Available Methanesulfonic acid (MSA is a relevant intermediate of the biogeochemical cycle of sulfur and environmental microorganisms assume an important role in the mineralization of this compound. Several methylotrophic bacterial strains able to grow on MSA have been isolated from soil or marine water and two conserved operons, msmABCD coding for MSA monooxygenase and msmEFGH coding for a transport system, have been repeatedly encountered in most of these strains. Homologous sequences have also been amplified directly from the environment or observed in marine metagenomic data, but these showed a base composition (G + C content very different from their counterparts from cultivated bacteria. The aim of this study was to understand which microorganisms within the coastal surface oceanic microflora responded to MSA as a nutrient and how the community evolved in the early phases of an enrichment by means of metagenome and gene-targeted amplicon sequencing. From the phylogenetic point of view, the community shifted significantly with the disappearance of all signals related to the Archaea, the Pelagibacteraceae and phylum SAR406, and the increase in methylotroph-harboring taxa, accompanied by other groups so far not known to comprise methylotrophs such as the Hyphomonadaceae. At the functional level, the abundance of several genes related to sulfur metabolism and methylotrophy increased during the enrichment and the allelic distribution of gene msmA diagnostic for MSA monooxygenase altered considerably. Even more dramatic was the disappearance of MSA import-related gene msmE, which suggests that alternative transporters must be present in the enriched community and illustrate the inadequacy of msmE as an ecofunctional marker for MSA degradation at sea.

  18. A Bioinformatician's Guide to Metagenomics

    Energy Technology Data Exchange (ETDEWEB)

    Kunin, Victor; Copeland, Alex; Lapidus, Alla; Mavromatis, Konstantinos; Hugenholtz, Philip

    2008-08-01

    As random shotgun metagenomic projects proliferate and become the dominant source of publicly available sequence data, procedures for best practices in their execution and analysis become increasingly important. Based on our experience at the Joint Genome Institute, we describe step-by-step the chain of decisions accompanying a metagenomic project from the viewpoint of a bioinformatician. We guide the reader through a standard workflow for a metagenomic project beginning with pre-sequencing considerations such as community composition and sequence data type that will greatly influence downstream analyses. We proceed with recommendations for sampling and data generation including sample and metadata collection, community profiling, construction of shotgun libraries and sequencing strategies. We then discuss the application of generic sequence processing steps (read preprocessing, assembly, and gene prediction and annotation) to metagenomic datasets by contrast to genome projects. Different types of data analyses particular to metagenomes are then presented including binning, dominant population analysis and gene-centric analysis. Finally data management systems and issues are presented and discussed. We hope that this review will assist bioinformaticians and biologists in making better-informed decisions on their journey during a metagenomic project.

  19. A novel feruloyl esterase from rumen microbial metagenome: Gene cloning and enzyme characterization in the release of mono- and diferulic acids

    Science.gov (United States)

    A feruloyl esterase (FAE) gene was isolated from a rumen microbial metagenome, cloned into E. coli, and expressed in active form. The enzyme (RuFae4) was classified as a Type D feruloyl esterase based on its action on synthetic substrates and ability to release diferulates. The RuFae4 alone releas...

  20. Culture-Independent Identification of Manganese-Oxidizing Genes from Deep-Sea Hydrothermal Vent Chemoautotrophic Ferromanganese Microbial Communities Using a Metagenomic Approach

    Science.gov (United States)

    Davis, R.; Tebo, B. M.

    2013-12-01

    Microbial activity has long been recognized as being important to the fate of manganese (Mn) in hydrothermal systems, yet we know very little about the organisms that catalyze Mn oxidation, the mechanisms by which Mn is oxidized or the physiological function that Mn oxidation serves in these hydrothermal systems. Hydrothermal vents with thick ferromanganese microbial mats and Mn oxide-coated rocks observed throughout the Pacific Ring of Fire are ideal models to study the mechanisms of microbial Mn oxidation, as well as primary productivity in these metal-cycling ecosystems. We sampled ferromanganese microbial mats from Vai Lili Vent Field (Tmax=43°C) located on the Eastern Lau Spreading Center and Mn oxide-encrusted rhyolytic pumice (4°C) from Niua South Seamount on the Tonga Volcanic Arc. Metagenomic libraries were constructed and assembled from these samples and key genes known to be involved in Mn oxidation and carbon fixation pathways were identified in the reconstructed genomes. The Vai Lili metagenome assembled to form 121,157 contiguous sequences (contigs) greater than 1000bp in length, with an N50 of 8,261bp and a total metagenome size of 593 Mbp. Contigs were binned using an emergent self-organizing map of tetranucleotide frequencies. Putative homologs of the multicopper Mn-oxidase MnxG were found in the metagenome that were related to both the Pseudomonas-like and Bacillus-like forms of the enzyme. The bins containing the Pseudomonas-like mnxG genes are most closely related to uncultured Deltaproteobacteria and Chloroflexi. The Deltaproteobacteria bin appears to be an obligate anaerobe with possible chemoautotrophic metabolisms, while the Chloroflexi appears to be a heterotrophic organism. The metagenome from the Mn-stained pumice was assembled into 122,092 contigs greater than 1000bp in length with an N50 of 7635 and a metagenome size of 385 Mbp. Both forms of mnxG genes are present in this metagenome as well as the genes encoding the putative Mn

  1. Microbiota composition, gene pool and its expression in Gir cattle (Bos indicus) rumen under different forage diets using metagenomic and metatranscriptomic approaches.

    Science.gov (United States)

    Pandit, Ramesh J; Hinsu, Ankit T; Patel, Shriram H; Jakhesara, Subhash J; Koringa, Prakash G; Bruno, Fosso; Psifidi, Androniki; Shah, S V; Joshi, Chaitanya G

    2018-03-09

    Zebu (Bos indicus) is a domestic cattle species originating from the Indian subcontinent and now widely domesticated on several continents. In this study, we were particularly interested in understanding the functionally active rumen microbiota of an important Zebu breed, the Gir, under different dietary regimes. Metagenomic and metatranscriptomic data were compared at various taxonomic levels to elucidate the differential microbial population and its functional dynamics in Gir cattle rumen under different roughage dietary regimes. Different proportions of roughage rather than the type of roughage (dry or green) modulated microbiome composition and the expression of its gene pool. Fibre degrading bacteria (i.e. Clostridium, Ruminococcus, Eubacterium, Butyrivibrio, Bacillus and Roseburia) were higher in the solid fraction of rumen (Pcomparison of metagenomic shotgun and metatranscriptomic sequencing appeared to be a much richer source of information compared to conventional metagenomic analysis. Copyright © 2018 Elsevier GmbH. All rights reserved.

  2. Stalking the fourth domain in metagenomic data: searching for, discovering, and interpreting novel, deep branches in marker gene phylogenetic trees.

    Directory of Open Access Journals (Sweden)

    Dongying Wu

    Full Text Available BACKGROUND: Most of our knowledge about the ancient evolutionary history of organisms has been derived from data associated with specific known organisms (i.e., organisms that we can study directly such as plants, metazoans, and culturable microbes. Recently, however, a new source of data for such studies has arrived: DNA sequence data generated directly from environmental samples. Such metagenomic data has enormous potential in a variety of areas including, as we argue here, in studies of very early events in the evolution of gene families and of species. METHODOLOGY/PRINCIPAL FINDINGS: We designed and implemented new methods for analyzing metagenomic data and used them to search the Global Ocean Sampling (GOS expedition data set for novel lineages in three gene families commonly used in phylogenetic studies of known and unknown organisms: small subunit rRNA and the recA and rpoB superfamilies. Though the methods available could not accurately identify very deeply branched ss-rRNAs (largely due to difficulties in making robust sequence alignments for novel rRNA fragments, our analysis revealed the existence of multiple novel branches in the recA and rpoB gene families. Analysis of available sequence data likely from the same genomes as these novel recA and rpoB homologs was then used to further characterize the possible organismal source of the novel sequences. CONCLUSIONS/SIGNIFICANCE: Of the novel recA and rpoB homologs identified in the metagenomic data, some likely come from uncharacterized viruses while others may represent ancient paralogs not yet seen in any cultured organism. A third possibility is that some come from novel cellular lineages that are only distantly related to any organisms for which sequence data is currently available. If there exist any major, but so-far-undiscovered, deeply branching lineages in the tree of life, we suggest that methods such as those described herein currently offer the best way to search for them.

  3. Metagenomes of complex microbial consortia derived from different soils as sources for novel genes conferring formation of carbonyls from short-chain polyols on Escherichia coli.

    Science.gov (United States)

    Knietsch, Anja; Waschkowitz, Tanja; Bowien, Susanne; Henne, Anke; Daniel, Rolf

    2003-01-01

    Metagenomic DNA libraries from three different soil samples (meadow, sugar beet field, cropland) were constructed. The three unamplified libraries comprised approximately 1267000 independent clones and harbored approximately 4.05 Gbp of environmental DNA. Approximately 300000 recombinant Escherichia coli strains of each library per test substrate were screened for the production of carbonyls from short-chain (C2 to C4) polyols such as 1,2-ethanediol, 2,3-butanediol, and a mixture of glycerol and 1,2-propanediol on indicator agar. Twenty-four positive E. COLI clones were obtained during the initial screen. Fifteen of them contained recombinant plasmids, designated pAK201-215, which conferred a stable carbonyl-forming phenotype on E. coli Sequencing revealed that the inserts of pAK201-215 encoded 26 complete and 14 incomplete predicted protein-encoding genes. Most of these genes were similar to genes with unknown functions from other microorganisms or unrelated to any other known gene. The further analysis was focused on the 7 plasmids (pAK204, pAK206, pAK208, and pAK210-213) recovered from the positive clones, which exhibited an NAD(H)-dependent alcohol oxidoreductase activity with polyols or the correlating carbonyls as substrates in crude extracts. Three genes (ORF6, ORF24, and ORF25) conferring this activity were identified during subcloning of the inserts of pAK204, pAK211, and pAK212. The sequences of the three deduced gene products revealed no significant similarities to known alcohol oxidoreductases, but contained putative glycine-rich regions, which are characteristic for binding of nicotinamide cofactors. Copyright 2003 S. Karger AG, Basel

  4. Metagenome Analysis of Protein Domain Collocation within Cellulase Genes of Goat Rumen Microbes

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    SooYeon Lim

    2013-08-01

    Full Text Available In this study, protein domains with cellulase activity in goat rumen microbes were investigated using metagenomic and bioinformatic analyses. After the complete genome of goat rumen microbes was obtained using a shotgun sequencing method, 217,892,109 pair reads were filtered, including only those with 70% identity, 100-bp matches, and thresholds below E−10 using METAIDBA. These filtered contigs were assembled and annotated using blastN against the NCBI nucleotide database. As a result, a microbial community structure with 1431 species was analyzed, among which Prevotella ruminicola 23 bacteria and Butyrivibrio proteoclasticus B316 were the dominant groups. In parallel, 201 sequences related with cellulase activities (EC.3.2.1.4 were obtained through blast searches using the enzyme.dat file provided by the NCBI database. After translating the nucleotide sequence into a protein sequence using Interproscan, 28 protein domains with cellulase activity were identified using the HMMER package with threshold E values below 10−5. Cellulase activity protein domain profiling showed that the major protein domains such as lipase GDSL, cellulase, and Glyco hydro 10 were present in bacterial species with strong cellulase activities. Furthermore, correlation plots clearly displayed the strong positive correlation between some protein domain groups, which was indicative of microbial adaption in the goat rumen based on feeding habits. This is the first metagenomic analysis of cellulase activity protein domains using bioinformatics from the goat rumen.

  5. Challenges and Opportunities of Airborne Metagenomics

    KAUST Repository

    Behzad, H.; Gojobori, Takashi; Mineta, K.

    2015-01-01

    microorganisms. Airborne metagenomic studies could also lead to discoveries of novel genes and metabolic pathways relevant to meteorological and industrial applications, environmental bioremediation, and biogeochemical cycles.

  6. Antibiotic Resistance Genes and Correlations with Microbial Community and Metal Resistance Genes in Full-Scale Biogas Reactors As Revealed by Metagenomic Analysis

    DEFF Research Database (Denmark)

    Luo, Gang; Li, Bing; Li, Li-Guan

    2017-01-01

    resistance genes (MRGs). The total abundance of ARGs in all the samples varied from 7 × 10-3 to 1.08 × 10-1 copy of ARG/copy of 16S-rRNA gene, and the samples obtained from thermophilic biogas reactors had a lower total abundance of ARGs, indicating the superiority of thermophilic anaerobic digestion......Digested residues from biogas plants are often used as biofertilizers for agricultural crops cultivation. The antibiotic resistance genes (ARGs) in digested residues pose a high risk to public health due to their potential spread to the disease-causing microorganisms and thus reduce...... the susceptibility of disease-causing microorganisms to antibiotics in medical treatment. A high-throughput sequencing (HTS)-based metagenomic approach was used in the present study to investigate the variations of ARGs in full-scale biogas reactors and the correlations of ARGs with microbial communities and metal...

  7. Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

    Energy Technology Data Exchange (ETDEWEB)

    Mavromatis, K [U.S. Department of Energy, Joint Genome Institute; Ivanova, N [U.S. Department of Energy, Joint Genome Institute; Barry, Kerrie [U.S. Department of Energy, Joint Genome Institute; Shapiro, Harris [U.S. Department of Energy, Joint Genome Institute; Goltsman, Eugene [U.S. Department of Energy, Joint Genome Institute; McHardy, Alice C. [IBM T. J. Watson Research Center; Rigoutsos, Isidore [IBM T. J. Watson Research Center; Salamov, Asaf [U.S. Department of Energy, Joint Genome Institute; Korzeniewski, Frank [U.S. Department of Energy, Joint Genome Institute; Land, Miriam L [ORNL; Lapidus, Alla L. [U.S. Department of Energy, Joint Genome Institute; Grigoriev, Igor [U.S. Department of Energy, Joint Genome Institute; Hugenholtz, Philip [U.S. Department of Energy, Joint Genome Institute; Kyrpides, Nikos C [U.S. Department of Energy, Joint Genome Institute

    2007-01-01

    Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene-finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity-based ( blast hit distribution) and two sequence composition-based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.

  8. Low Maternal Microbiota Sharing across Gut, Breast Milk and Vagina, as Revealed by 16S rRNA Gene and Reduced Metagenomic Sequencing

    Directory of Open Access Journals (Sweden)

    Ekaterina Avershina

    2018-05-01

    Full Text Available The maternal microbiota plays an important role in infant gut colonization. In this work we have investigated which bacterial species are shared across the breast milk, vaginal and stool microbiotas of 109 women shortly before and after giving birth using 16S rRNA gene sequencing and a novel reduced metagenomic sequencing (RMS approach in a subgroup of 16 women. All the species predicted by the 16S rRNA gene sequencing were also detected by RMS analysis and there was good correspondence between their relative abundances estimated by both approaches. Both approaches also demonstrate a low level of maternal microbiota sharing across the population and RMS analysis identified only two species common to most women and in all sample types (Bifidobacterium longum and Enterococcus faecalis. Breast milk was the only sample type that had significantly higher intra- than inter- individual similarity towards both vaginal and stool samples. We also searched our RMS dataset against an in silico generated reference database derived from bacterial isolates in the Human Microbiome Project. The use of this reference-based search enabled further separation of Bifidobacterium longum into Bifidobacterium longum ssp. longum and Bifidobacterium longum ssp. infantis. We also detected the Lactobacillus rhamnosus GG strain, which was used as a probiotic supplement by some women, demonstrating the potential of RMS approach for deeper taxonomic delineation and estimation.

  9. Metagenomic-based study of the phylogenetic and functional gene diversity in Galápagos land and marine iguanas.

    Science.gov (United States)

    Hong, Pei-Ying; Mao, Yuejian; Ortiz-Kofoed, Shannon; Shah, Rushabh; Cann, Isaac; Mackie, Roderick I

    2015-02-01

    In this study, a metagenome-based analysis of the fecal samples from the macrophytic algae-consuming marine iguana (MI; Amblyrhynchus cristatus) and terrestrial biomass-consuming land iguanas (LI; Conolophus spp.) was conducted. Phylogenetic affiliations of the fecal microbiome were more similar between both iguanas than to other mammalian herbivorous hosts. However, functional gene diversities in both MI and LI iguana hosts differed in relation to the diet, where the MI fecal microbiota had a functional diversity that clustered apart from the other terrestrial-biomass consuming reptilian and mammalian hosts. A further examination of the carbohydrate-degrading genes revealed that several of the prevalent glycosyl hydrolases (GH), glycosyl transferases (GT), carbohydrate binding modules (CBM), and carbohydrate esterases (CE) gene classes were conserved among all examined herbivorous hosts, reiterating the important roles these genes play in the breakdown and metabolism of herbivorous diets. Genes encoding some classes of carbohydrate-degrading families, including GH2, GH13, GT2, GT4, CBM50, CBM48, CE4, and CE11, as well as genes associated with sulfur metabolism and dehalogenation, were highly enriched or unique to the MI. In contrast, gene sequences that relate to archaeal methanogenesis were detected only in LI fecal microbiome, and genes coding for GH13, GH66, GT2, GT4, CBM50, CBM13, CE4, and CE8 carbohydrate active enzymes were highly abundant in the LI. Bacterial populations were enriched on various carbohydrates substrates (e.g., glucose, arabinose, xylose). The majority of the enriched bacterial populations belong to genera Clostridium spp. and Enterococcus spp. that likely accounted for the high prevalence of GH13 and GH2, as well as the GT families (e.g., GT2, GT4, GT28, GT35, and GT51) that were ubiquitously present in the fecal microbiota of all herbivorous hosts.

  10. Metagenomic-Based Study of the Phylogenetic and Functional Gene Diversity in Galápagos Land and Marine Iguanas

    KAUST Repository

    Hong, Pei-Ying

    2014-12-19

    In this study, a metagenome-based analysis of the fecal samples from the macrophytic algae-consuming marine iguana (MI; Amblyrhynchus cristatus) and terrestrial biomass-consuming land iguanas (LI; Conolophus spp.) was conducted. Phylogenetic affiliations of the fecal microbiome were more similar between both iguanas than to other mammalian herbivorous hosts. However, functional gene diversities in both MI and LI iguana hosts differed in relation to the diet, where the MI fecal microbiota had a functional diversity that clustered apart from the other terrestrial-biomass consuming reptilian and mammalian hosts. A further examination of the carbohydrate-degrading genes revealed that several of the prevalent glycosyl hydrolases (GH), glycosyl transferases (GT), carbohydrate binding modules (CBM), and carbohydrate esterases (CE) gene classes were conserved among all examined herbivorous hosts, reiterating the important roles these genes play in the breakdown and metabolism of herbivorous diets. Genes encoding some classes of carbohydrate-degrading families, including GH2, GH13, GT2, GT4, CBM50, CBM48, CE4, and CE11, as well as genes associated with sulfur metabolism and dehalogenation, were highly enriched or unique to the MI. In contrast, gene sequences that relate to archaeal methanogenesis were detected only in LI fecal microbiome, and genes coding for GH13, GH66, GT2, GT4, CBM50, CBM13, CE4, and CE8 carbohydrate active enzymes were highly abundant in the LI. Bacterial populations were enriched on various carbohydrates substrates (e.g., glucose, arabinose, xylose). The majority of the enriched bacterial populations belong to genera Clostridium spp. and Enterococcus spp. that likely accounted for the high prevalence of GH13 and GH2, as well as the GT families (e.g., GT2, GT4, GT28, GT35, and GT51) that were ubiquitously present in the fecal microbiota of all herbivorous hosts.

  11. Genetic variability of psychrotolerant Acidithiobacillus ferrivorans revealed by (meta)genomic analysis.

    Science.gov (United States)

    González, Carolina; Yanquepe, María; Cardenas, Juan Pablo; Valdes, Jorge; Quatrini, Raquel; Holmes, David S; Dopson, Mark

    2014-11-01

    Acidophilic microorganisms inhabit low pH environments such as acid mine drainage that is generated when sulfide minerals are exposed to air. The genome sequence of the psychrotolerant Acidithiobacillus ferrivorans SS3 was compared to a metagenome from a low temperature acidic stream dominated by an A. ferrivorans-like strain. Stretches of genomic DNA characterized by few matches to the metagenome, termed 'metagenomic islands', encoded genes associated with metal efflux and pH homeostasis. The metagenomic islands were enriched in mobile elements such as phage proteins, transposases, integrases and in one case, predicted to be flanked by truncated tRNAs. Cus gene clusters predicted to be involved in copper efflux and further Cus-like RND systems were predicted to be located in metagenomic islands and therefore, constitute part of the flexible gene complement of the species. Phylogenetic analysis of Cus clusters showed both lineage specificity within the Acidithiobacillus genus as well as niche specificity associated with an acidic environment. The metagenomic islands also contained a predicted copper efflux P-type ATPase system and a polyphosphate kinase potentially involved in polyphosphate mediated copper resistance. This study identifies genetic variability of low temperature acidophiles that likely reflects metal resistance selective pressures in the copper rich environment. Copyright © 2014 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  12. Fate of antibiotic and metal resistance genes during two-phase anaerobic digestion of residue sludge revealed by metagenomic approach.

    Science.gov (United States)

    Wu, Ying; Cui, Erping; Zuo, Yiru; Cheng, Weixiao; Chen, Hong

    2018-03-07

    The prevalence and persistence of antibiotic resistance genes in wastewater treatment plants (WWTPs) is of growing interest, and residual sludge is among the main sources for the release of antibiotic resistance genes (ARGs). Moreover, heavy metals concentrated in dense microbial communities of sludge could potentially favor co-selection of ARGs and metal resistance genes (MRGs). Residual sludge treatment is needed to limit the spread of resistance from WWTPs into the environment. This study aimed to explore the fate of ARGs and MRGs during thermophilic two-phase (acidogenic/methanogenic phase) anaerobic digestion by metagenomic analysis. The occurrence and abundance of mobile genetic elements were also determined based on the SEED database. Among the 27 major ARG subtypes detected in feed sludge, large reductions (> 50%) in 6 ARG subtypes were achieved by acidogenic phase (AP), while 63.0% of the ARG subtypes proliferated in the following methanogenic phase (MP). In contrast, a 2.8-fold increase in total MRG abundance was found in AP, while the total abundance during MP decreased to the same order of magnitude as in feed sludge. The distinct dynamics of ARGs and MRGs during the two-phase anaerobic digestion are noteworthy, and more specific treatments are required to limit their proliferation in the environment.

  13. A metagenome for lacustrine Cladophora (Cladophorales) reveals remarkable diversity of eukaryotic epibionts and genes relevant to materials cycling.

    Science.gov (United States)

    Graham, Linda E; Knack, Jennifer J; Graham, Melissa E; Graham, James M; Zulkifly, Shahrizim

    2015-06-01

    Periphyton dominated by the cellulose-rich filamentous green alga Cladophora forms conspicuous growths along rocky marine and freshwater shorelines worldwide, providing habitat for diverse epibionts. Bacterial epibionts have been inferred to display diverse functions of biogeochemical significance: N-fixation and other redox reactions, phosphorus accumulation, and organic degradation. Here, we report taxonomic diversity of eukaryotic and prokaryotic epibionts and diversity of genes associated with materials cycling in a Cladophora metagenome sampled from Lake Mendota, Dane Co., WI, USA, during the growing season of 2012. A total of 1,060 distinct 16S, 173 18S, and 351 28S rRNA operational taxonomic units, from which >220 genera or species of bacteria (~60), protists (~80), fungi (6), and microscopic metazoa (~80), were distinguished with the use of reference databases. We inferred the presence of several algal taxa generally associated with marine systems and detected Jaoa, a freshwater periphytic ulvophyte previously thought endemic to China. We identified six distinct nifH gene sequences marking nitrogen fixation, >25 bacterial and eukaryotic cellulases relevant to sedimentary C-cycling and technological applications, and genes encoding enzymes in aerobic and anaerobic pathways for vitamin B12 biosynthesis. These results emphasize the importance of Cladophora in providing habitat for microscopic metazoa, fungi, protists, and bacteria that are often inconspicuous, yet play important roles in ecosystem biogeochemistry. © 2015 Phycological Society of America.

  14. Identification of genes and pathways related to phenol degradation in metagenomic libraries from petroleum refinery wastewater.

    Directory of Open Access Journals (Sweden)

    Cynthia C Silva

    Full Text Available Two fosmid libraries, totaling 13,200 clones, were obtained from bioreactor sludge of petroleum refinery wastewater treatment system. The library screening based on PCR and biological activity assays revealed more than 400 positive clones for phenol degradation. From these, 100 clones were randomly selected for pyrosequencing in order to evaluate the genetic potential of the microorganisms present in wastewater treatment plant for biodegradation, focusing mainly on novel genes and pathways of phenol and aromatic compound degradation. The sequence analysis of selected clones yielded 129,635 reads at an estimated 17-fold coverage. The phylogenetic analysis showed Burkholderiales and Rhodocyclales as the most abundant orders among the selected fosmid clones. The MG-RAST analysis revealed a broad metabolic profile with important functions for wastewater treatment, including metabolism of aromatic compounds, nitrogen, sulphur and phosphorus. The predicted 2,276 proteins included phenol hydroxylases and cathecol 2,3- dioxygenases, involved in the catabolism of aromatic compounds, such as phenol, byphenol, benzoate and phenylpropanoid. The sequencing of one fosmid insert of 33 kb unraveled the gene that permitted the host, Escherichia coli EPI300, to grow in the presence of aromatic compounds. Additionally, the comparison of the whole fosmid sequence against bacterial genomes deposited in GenBank showed that about 90% of sequence showed no identity to known sequences of Proteobacteria deposited in the NCBI database. This study surveyed the functional potential of fosmid clones for aromatic compound degradation and contributed to our knowledge of the biodegradative capacity and pathways of microbial assemblages present in refinery wastewater treatment system.

  15. Characterization of Metagenomes in Urban Aquatic Compartments Reveals High Prevalence of Clinically Relevant Antibiotic Resistance Genes in Wastewaters

    Directory of Open Access Journals (Sweden)

    Charmaine Ng

    2017-11-01

    Full Text Available The dissemination of antimicrobial resistance (AMR is an escalating problem and a threat to public health. Comparative metagenomics was used to investigate the occurrence of antibiotic resistant genes (ARGs in wastewater and urban surface water environments in Singapore. Hospital and municipal wastewater (n = 6 were found to have higher diversity and average abundance of ARGs (303 ARG subtypes, 197,816 x/Gb compared to treated wastewater effluent (n = 2, 58 ARG subtypes, 2,692 x/Gb and surface water (n = 5, 35 subtypes, 7,985 x/Gb. A cluster analysis showed that the taxonomic composition of wastewaters was highly similar and had a bacterial community composition enriched in gut bacteria (Bacteroides, Faecalibacterium, Bifidobacterium, Blautia, Roseburia, Ruminococcus, the Enterobacteriaceae group (Klebsiella, Aeromonas, Enterobacter and opportunistic pathogens (Prevotella, Comamonas, Neisseria. Wastewater, treated effluents and surface waters had a shared resistome of 21 ARGs encoding multidrug resistant efflux pumps or resistance to aminoglycoside, macrolide-lincosamide-streptogramins (MLS, quinolones, sulfonamide, and tetracycline resistance which suggests that these genes are wide spread across different environments. Wastewater had a distinctively higher average abundance of clinically relevant, class A beta-lactamase resistant genes (i.e., blaKPC, blaCTX-M, blaSHV, blaTEM. The wastewaters from clinical isolation wards, in particular, had a exceedingly high levels of blaKPC-2 genes (142,200 x/Gb, encoding for carbapenem resistance. Assembled scaffolds (16 and 30 kbp from isolation ward wastewater samples indicated this gene was located on a Tn3-based transposon (Tn4401, a mobilization element found in Klebsiella pneumonia plasmids. In the longer scaffold, transposable elements were flanked by a toxin–antitoxin (TA system and other metal resistant genes that likely increase the persistence, fitness and propagation of the plasmid in the

  16. Metagenomic Analysis of the Microbiota from the Crop of an Invasive Snail Reveals a Rich Reservoir of Novel Genes

    Science.gov (United States)

    Cardoso, Alexander M.; Cavalcante, Janaína J. V.; Cantão, Maurício E.; Thompson, Claudia E.; Flatschart, Roberto B.; Glogauer, Arnaldo; Scapin, Sandra M. N.; Sade, Youssef B.; Beltrão, Paulo J. M. S. I.; Gerber, Alexandra L.; Martins, Orlando B.; Garcia, Eloi S.; de Souza, Wanderley; Vasconcelos, Ana Tereza R.

    2012-01-01

    The shortage of petroleum reserves and the increase in CO2 emissions have raised global concerns and highlighted the importance of adopting sustainable energy sources. Second-generation ethanol made from lignocellulosic materials is considered to be one of the most promising fuels for vehicles. The giant snail Achatina fulica is an agricultural pest whose biotechnological potential has been largely untested. Here, the composition of the microbial population within the crop of this invasive land snail, as well as key genes involved in various biochemical pathways, have been explored for the first time. In a high-throughput approach, 318 Mbp of 454-Titanium shotgun metagenomic sequencing data were obtained. The predominant bacterial phylum found was Proteobacteria, followed by Bacteroidetes and Firmicutes. Viruses, Fungi, and Archaea were present to lesser extents. The functional analysis reveals a variety of microbial genes that could assist the host in the degradation of recalcitrant lignocellulose, detoxification of xenobiotics, and synthesis of essential amino acids and vitamins, contributing to the adaptability and wide-ranging diet of this snail. More than 2,700 genes encoding glycoside hydrolase (GH) domains and carbohydrate-binding modules were detected. When we compared GH profiles, we found an abundance of sequences coding for oligosaccharide-degrading enzymes (36%), very similar to those from wallabies and giant pandas, as well as many novel cellulase and hemicellulase coding sequences, which points to this model as a remarkable potential source of enzymes for the biofuel industry. Furthermore, this work is a major step toward the understanding of the unique genetic profile of the land snail holobiont. PMID:23133637

  17. Metagenomic analysis of the microbiota from the crop of an invasive snail reveals a rich reservoir of novel genes.

    Directory of Open Access Journals (Sweden)

    Alexander M Cardoso

    Full Text Available The shortage of petroleum reserves and the increase in CO(2 emissions have raised global concerns and highlighted the importance of adopting sustainable energy sources. Second-generation ethanol made from lignocellulosic materials is considered to be one of the most promising fuels for vehicles. The giant snail Achatina fulica is an agricultural pest whose biotechnological potential has been largely untested. Here, the composition of the microbial population within the crop of this invasive land snail, as well as key genes involved in various biochemical pathways, have been explored for the first time. In a high-throughput approach, 318 Mbp of 454-Titanium shotgun metagenomic sequencing data were obtained. The predominant bacterial phylum found was Proteobacteria, followed by Bacteroidetes and Firmicutes. Viruses, Fungi, and Archaea were present to lesser extents. The functional analysis reveals a variety of microbial genes that could assist the host in the degradation of recalcitrant lignocellulose, detoxification of xenobiotics, and synthesis of essential amino acids and vitamins, contributing to the adaptability and wide-ranging diet of this snail. More than 2,700 genes encoding glycoside hydrolase (GH domains and carbohydrate-binding modules were detected. When we compared GH profiles, we found an abundance of sequences coding for oligosaccharide-degrading enzymes (36%, very similar to those from wallabies and giant pandas, as well as many novel cellulase and hemicellulase coding sequences, which points to this model as a remarkable potential source of enzymes for the biofuel industry. Furthermore, this work is a major step toward the understanding of the unique genetic profile of the land snail holobiont.

  18. Metagenomic studies of the Red Sea.

    Science.gov (United States)

    Behzad, Hayedeh; Ibarra, Martin Augusto; Mineta, Katsuhiko; Gojobori, Takashi

    2016-02-01

    Metagenomics has significantly advanced the field of marine microbial ecology, revealing the vast diversity of previously unknown microbial life forms in different marine niches. The tremendous amount of data generated has enabled identification of a large number of microbial genes (metagenomes), their community interactions, adaptation mechanisms, and their potential applications in pharmaceutical and biotechnology-based industries. Comparative metagenomics reveals that microbial diversity is a function of the local environment, meaning that unique or unusual environments typically harbor novel microbial species with unique genes and metabolic pathways. The Red Sea has an abundance of unique characteristics; however, its microbiota is one of the least studied among marine environments. The Red Sea harbors approximately 25 hot anoxic brine pools, plus a vibrant coral reef ecosystem. Physiochemical studies describe the Red Sea as an oligotrophic environment that contains one of the warmest and saltiest waters in the world with year-round high UV radiations. These characteristics are believed to have shaped the evolution of microbial communities in the Red Sea. Over-representation of genes involved in DNA repair, high-intensity light responses, and osmoregulation were found in the Red Sea metagenomic databases suggesting acquisition of specific environmental adaptation by the Red Sea microbiota. The Red Sea brine pools harbor a diverse range of halophilic and thermophilic bacterial and archaeal communities, which are potential sources of enzymes for pharmaceutical and biotechnology-based application. Understanding the mechanisms of these adaptations and their function within the larger ecosystem could also prove useful in light of predicted global warming scenarios where global ocean temperatures are expected to rise by 1-3°C in the next few decades. In this review, we provide an overview of the published metagenomic studies that were conducted in the Red Sea, and

  19. Metagenomic Analysis of Genes Encoding Nutrient Cycling Pathways in the Microbiota of Deep-Sea and Shallow-Water Sponges.

    Science.gov (United States)

    Li, Zhiyong; Wang, Yuezhu; Li, Jinlong; Liu, Fang; He, Liming; He, Ying; Wang, Shenyue

    2016-12-01

    Sponges host complex symbiotic communities, but to date, the whole picture of the metabolic potential of sponge microbiota remains unclear, particularly the difference between the shallow-water and deep-sea sponge holobionts. In this study, two completely different sponges, shallow-water sponge Theonella swinhoei from the South China Sea and deep-sea sponge Neamphius huxleyi from the Indian Ocean, were selected to compare their whole symbiotic communities and metabolic potential, particularly in element transformation. Phylogenetically diverse bacteria, archaea, fungi, and algae were detected in both shallow-water sponge T. swinhoei and deep-sea sponge N. huxleyi, and different microbial community structures were indicated between these two sponges. Metagenome-based gene abundance analysis indicated that, though the two sponge microbiota have similar core functions, they showed different potential strategies in detailed metabolic processes, e.g., in the transformation and utilization of carbon, nitrogen, phosphorus, and sulfur by corresponding microbial symbionts. This study provides insight into the putative metabolic potentials of the microbiota associated with the shallow-water and deep-sea sponges at the whole community level, extending our knowledge of the sponge microbiota's functions, the association of sponge- microbes, as well as the adaption of sponge microbiota to the marine environment.

  20. Metagenomic analysis of microbial communities and beyond

    DEFF Research Database (Denmark)

    Schreiber, Lars

    2014-01-01

    From small clone libraries to large next-generation sequencing datasets – the field of community genomics or metagenomics has developed tremendously within the last years. This chapter will summarize some of these developments and will also highlight pitfalls of current metagenomic analyses...... heterologous expression of metagenomic DNA fragments to discover novel metabolic functions. Lastly, the chapter will shortly discuss the meta-analysis of gene expression of microbial communities, more precisely metatranscriptomics and metaproteomics....

  1. Vertebrate gene predictions and the problem of large genes

    DEFF Research Database (Denmark)

    Wang, Jun; Li, ShengTing; Zhang, Yong

    2003-01-01

    To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistent...

  2. Beyond biodiversity: fish metagenomes.

    Directory of Open Access Journals (Sweden)

    Alba Ardura

    Full Text Available Biodiversity and intra-specific genetic diversity are interrelated and determine the potential of a community to survive and evolve. Both are considered together in Prokaryote communities treated as metagenomes or ensembles of functional variants beyond species limits.Many factors alter biodiversity in higher Eukaryote communities, and human exploitation can be one of the most important for some groups of plants and animals. For example, fisheries can modify both biodiversity and genetic diversity (intra specific. Intra-specific diversity can be drastically altered by overfishing. Intense fishing pressure on one stock may imply extinction of some genetic variants and subsequent loss of intra-specific diversity. The objective of this study was to apply a metagenome approach to fish communities and explore its value for rapid evaluation of biodiversity and genetic diversity at community level. Here we have applied the metagenome approach employing the barcoding target gene coi as a model sequence in catch from four very different fish assemblages exploited by fisheries: freshwater communities from the Amazon River and northern Spanish rivers, and marine communities from the Cantabric and Mediterranean seas.Treating all sequences obtained from each regional catch as a biological unit (exploited community we found that metagenomic diversity indices of the Amazonian catch sample here examined were lower than expected. Reduced diversity could be explained, at least partially, by overexploitation of the fish community that had been independently estimated by other methods.We propose using a metagenome approach for estimating diversity in Eukaryote communities and early evaluating genetic variation losses at multi-species level.

  3. Beyond biodiversity: fish metagenomes.

    Science.gov (United States)

    Ardura, Alba; Planes, Serge; Garcia-Vazquez, Eva

    2011-01-01

    Biodiversity and intra-specific genetic diversity are interrelated and determine the potential of a community to survive and evolve. Both are considered together in Prokaryote communities treated as metagenomes or ensembles of functional variants beyond species limits.Many factors alter biodiversity in higher Eukaryote communities, and human exploitation can be one of the most important for some groups of plants and animals. For example, fisheries can modify both biodiversity and genetic diversity (intra specific). Intra-specific diversity can be drastically altered by overfishing. Intense fishing pressure on one stock may imply extinction of some genetic variants and subsequent loss of intra-specific diversity. The objective of this study was to apply a metagenome approach to fish communities and explore its value for rapid evaluation of biodiversity and genetic diversity at community level. Here we have applied the metagenome approach employing the barcoding target gene coi as a model sequence in catch from four very different fish assemblages exploited by fisheries: freshwater communities from the Amazon River and northern Spanish rivers, and marine communities from the Cantabric and Mediterranean seas.Treating all sequences obtained from each regional catch as a biological unit (exploited community) we found that metagenomic diversity indices of the Amazonian catch sample here examined were lower than expected. Reduced diversity could be explained, at least partially, by overexploitation of the fish community that had been independently estimated by other methods.We propose using a metagenome approach for estimating diversity in Eukaryote communities and early evaluating genetic variation losses at multi-species level.

  4. Marine metagenomics as a source for bioprospecting

    KAUST Repository

    Kodzius, Rimantas

    2015-08-12

    This review summarizes usage of genome-editing technologies for metagenomic studies; these studies are used to retrieve and modify valuable microorganisms for production, particularly in marine metagenomics. Organisms may be cultivable or uncultivable. Metagenomics is providing especially valuable information for uncultivable samples. The novel genes, pathways and genomes can be deducted. Therefore, metagenomics, particularly genome engineering and system biology, allows for the enhancement of biological and chemical producers and the creation of novel bioresources. With natural resources rapidly depleting, genomics may be an effective way to efficiently produce quantities of known and novel foods, livestock feed, fuels, pharmaceuticals and fine or bulk chemicals.

  5. Assembling large, complex environmental metagenomes

    Energy Technology Data Exchange (ETDEWEB)

    Howe, A. C. [Michigan State Univ., East Lansing, MI (United States). Microbiology and Molecular Genetics, Plant Soil and Microbial Sciences; Jansson, J. [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division; Malfatti, S. A. [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Tringe, S. G. [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Tiedje, J. M. [Michigan State Univ., East Lansing, MI (United States). Microbiology and Molecular Genetics, Plant Soil and Microbial Sciences; Brown, C. T. [Michigan State Univ., East Lansing, MI (United States). Microbiology and Molecular Genetics, Computer Science and Engineering

    2012-12-28

    The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two pre-assembly filtering approaches, digital normalization and partitioning, to make large metagenome assemblies more computationaly tractable. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.

  6. Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

    DEFF Research Database (Denmark)

    Yamada, Takuji; Waller, Alison S.; Raes, Jeroen

    2012-01-01

    Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently a...... Systems Biology 8: 581; published online 8 May 2012; doi:10.1038/msb.2012.13...

  7. Plasmid metagenomics reveals multiple antibiotic resistance gene classes among the gut microbiomes of hospitalised patients

    DEFF Research Database (Denmark)

    Jitwasinkul, Tossawan; Suriyaphol, Prapat; Tangphatsornruang, Sithichoke

    2016-01-01

    Antibiotic resistance genes are rapidly spread between pathogens and the normal flora, with plasmids playing an important role in their circulation. This study aimed to investigate antibiotic resistance plasmids in the gut microbiome of hospitalised patients. Stool samples were collected from seven...... inpatients at Siriraj Hospital (Bangkok, Thailand) and were compared with a sample from a healthy volunteer. Plasmids from the gut microbiomes extracted from the stool samples were subjected to high-throughput DNA sequencing (GS Junior). Newbler-assembled DNA reads were categorised into known and unknown...... in the gut microbiome; however, it was difficult to link these to the antibiotic resistance genes identified. That the antibiotic resistance genes came from hospital and community environments is worrying....

  8. Metagenomics shows that low-energy anaerobic-aerobic treatment reactors reduce antibiotic resistance gene levels from domestic wastewater.

    Science.gov (United States)

    Christgen, Beate; Yang, Ying; Ahammad, S Z; Li, Bing; Rodriquez, D Catalina; Zhang, Tong; Graham, David W

    2015-02-17

    Effective domestic wastewater treatment is among our primary defenses against the dissemination of infectious waterborne disease. However, reducing the amount of energy used in treatment processes has become essential for the future. One low-energy treatment option is anaerobic-aerobic sequence (AAS) bioreactors, which use an anaerobic pretreatment step (e.g., anaerobic hybrid reactors) to reduce carbon levels, followed by some form of aerobic treatment. Although AAS is common in warm climates, it is not known how its compares to other treatment options relative to disease transmission, including its influence on antibiotic resistance (AR) in treated effluents. Here, we used metagenomic approaches to contrast the fate of antibiotic-resistant genes (ARG) in anaerobic, aerobic, and AAS bioreactors treating domestic wastewater. Five reactor configurations were monitored for 6 months, and treatment performance, energy use, and ARG abundance and diversity were compared in influents and effluents. AAS and aerobic reactors were superior to anaerobic units in reducing ARG-like sequence abundances, with effluent ARG levels of 29, 34, and 74 ppm (198 ppm influent), respectively. AAS and aerobic systems especially reduced aminoglycoside, tetracycline, and β-lactam ARG levels relative to anaerobic units, although 63 persistent ARG subtypes were detected in effluents from all systems (of 234 assessed). Sulfonamide and chloramphenicol ARG levels were largely unaffected by treatment, whereas a broad shift from target-specific ARGs to ARGs associated with multi-drug resistance was seen across influents and effluents. AAS reactors show promise for future applications because they can reduce more ARGs for less energy (32% less energy here), but all three treatment options have limitations and need further study.

  9. The Dark Side of the Mushroom Spring Microbial Mat: Life in the Shadow of Chlorophototrophs. II. Metabolic Functions of Abundant Community Members Predicted from Metagenomic Analyses.

    Science.gov (United States)

    Thiel, Vera; Hügler, Michael; Ward, David M; Bryant, Donald A

    2017-01-01

    Microbial mat communities in the effluent channels of Octopus and Mushroom Springs within the Lower Geyser Basin of Yellowstone National Park have been extensively characterized. Previous studies have focused on the chlorophototrophic organisms of the phyla Cyanobacteria and Chloroflexi . However, the diversity and metabolic functions of the other portion of the community in the microoxic/anoxic region of the mat are poorly understood. We recently described the diverse but extremely uneven microbial assemblage in the undermat of Mushroom Spring based on 16S rRNA amplicon sequences, which was dominated by Roseiflexus members, filamentous anoxygenic chlorophototrophs. In this study, we analyzed the orange-colored undermat portion of the community of Mushroom Spring mats in a genome-centric approach and discuss the metabolic potentials of the major members. Metagenome binning recovered partial genomes of all abundant community members, ranging in completeness from ~28 to 96%, and allowed affiliation of function with taxonomic identity even for representatives of novel and Candidate phyla. Less complete metagenomic bins correlated with high microdiversity. The undermat portion of the community was found to be a mixture of phototrophic and chemotrophic organisms, which use bicarbonate as well as organic carbon sources derived from different cell components and fermentation products. The presence of rhodopsin genes in many taxa strengthens the hypothesis that light energy is of major importance. Evidence for the usage of all four bacterial carbon fixation pathways was found in the metagenome. Nitrogen fixation appears to be limited to Synechococcus spp. in the upper mat layer and Thermodesulfovibrio sp. in the undermat, and nitrate/nitrite metabolism was limited. A closed sulfur cycle is indicated by biological sulfate reduction combined with the presence of genes for sulfide oxidation mainly in phototrophs. Finally, a variety of undermat microorganisms have genes for

  10. The Dark Side of the Mushroom Spring Microbial Mat: Life in the Shadow of Chlorophototrophs. II. Metabolic Functions of Abundant Community Members Predicted from Metagenomic Analyses

    Directory of Open Access Journals (Sweden)

    Vera Thiel

    2017-06-01

    Full Text Available Microbial mat communities in the effluent channels of Octopus and Mushroom Springs within the Lower Geyser Basin of Yellowstone National Park have been extensively characterized. Previous studies have focused on the chlorophototrophic organisms of the phyla Cyanobacteria and Chloroflexi. However, the diversity and metabolic functions of the other portion of the community in the microoxic/anoxic region of the mat are poorly understood. We recently described the diverse but extremely uneven microbial assemblage in the undermat of Mushroom Spring based on 16S rRNA amplicon sequences, which was dominated by Roseiflexus members, filamentous anoxygenic chlorophototrophs. In this study, we analyzed the orange-colored undermat portion of the community of Mushroom Spring mats in a genome-centric approach and discuss the metabolic potentials of the major members. Metagenome binning recovered partial genomes of all abundant community members, ranging in completeness from ~28 to 96%, and allowed affiliation of function with taxonomic identity even for representatives of novel and Candidate phyla. Less complete metagenomic bins correlated with high microdiversity. The undermat portion of the community was found to be a mixture of phototrophic and chemotrophic organisms, which use bicarbonate as well as organic carbon sources derived from different cell components and fermentation products. The presence of rhodopsin genes in many taxa strengthens the hypothesis that light energy is of major importance. Evidence for the usage of all four bacterial carbon fixation pathways was found in the metagenome. Nitrogen fixation appears to be limited to Synechococcus spp. in the upper mat layer and Thermodesulfovibrio sp. in the undermat, and nitrate/nitrite metabolism was limited. A closed sulfur cycle is indicated by biological sulfate reduction combined with the presence of genes for sulfide oxidation mainly in phototrophs. Finally, a variety of undermat

  11. Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

    Science.gov (United States)

    Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

    2018-02-23

    Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

  12. Current and future resources for functional metagenomics

    Directory of Open Access Journals (Sweden)

    Kathy Nguyen Lam

    2015-10-01

    Full Text Available Functional metagenomics is a powerful experimental approach for studying gene function, starting from the extracted DNA of mixed microbial populations. A functional approach relies on the construction and screening of metagenomic libraries – physical libraries that contain DNA cloned from environmental metagenomes. The information obtained from functional metagenomics can help in future annotations of gene function and serve as a complement to sequence-based metagenomics. In this Perspective, we begin by summarizing the technical challenges of constructing metagenomic libraries and emphasize their value as resources. We then discuss libraries constructed using the popular cloning vector, pCC1FOS, and highlight the strengths and shortcomings of this system, alongside possible strategies to maximize existing pCC1FOS-based libraries by screening in diverse hosts. Finally, we discuss the known bias of libraries constructed from human gut and marine water samples, present results that suggest bias may also occur for soil libraries, and consider factors that bias metagenomic libraries in general. We anticipate that discussion of current resources and limitations will advance tools and technologies for functional metagenomics research.

  13. Seasonal changes in the abundance of bacterial genes related to dimethylsulfoniopropionate catabolism in seawater from Ofunato Bay revealed by metagenomic analysis

    KAUST Repository

    Kudo, Toshiaki

    2018-04-26

    Ofunato Bay is located in the northeastern Pacific Ocean area of Japan, and it has the highest biodiversity of marine organisms in the world, primarily due to tidal influences from the cold Oyashio and warm Kuroshio currents. Our previous results from performing shotgun metagenomics indicated that Candidatus Pelagibacter ubique and Planktomarina temperata were the dominant bacteria (Reza et al., 2018a, 2018b). These bacteria are reportedly able to catabolize dimethylsulfoniopropionate (DMSP) produced from phytoplankton into dimethyl sulfide (DMS) or methanethiol (MeSH). This study was focused on seasonal changes in the abundances of bacterial genes (dddP, dmdA) related to DMSP catabolism in the seawater of Ofunato Bay by BLAST+ analysis using shotgun metagenomic datasets. We found seasonal changes among the Candidatus Pelagibacter ubique strains, including those of the HTCC1062 type and the Red Sea type. A good correlation was observed between the chlorophyll a concentrations and the abundances of the catabolic genes, suggesting that the bacteria directly interact with phytoplankton in the marine material cycle system and play important roles in producing DMS and MeSH from DMSP as signaling molecules for the possible formation of the scent of the tidewater or as fish attractants.

  14. Seasonal changes in the abundance of bacterial genes related to dimethylsulfoniopropionate catabolism in seawater from Ofunato Bay revealed by metagenomic analysis

    KAUST Repository

    Kudo, Toshiaki; Kobiyama, Atsushi; Rashid, Jonaira; Reza, Shaheed; Yamada, Yuichiro; Ikeda, Yuri; Ikeda, Daisuke; Mizusawa, Nanami; Ikeo, Kazuho; Sato, Shigeru; Ogata, Takehiko; Jimbo, Mitsuru; Kaga, Shinnosuke; Watanabe, Shiho; Naiki, Kimiaki; Kaga, Yoshimasa; Segawa, Satoshi; Mineta, Katsuhiko; Bajic, Vladimir B.; Gojobori, Takashi; Watabe, Shugo

    2018-01-01

    Ofunato Bay is located in the northeastern Pacific Ocean area of Japan, and it has the highest biodiversity of marine organisms in the world, primarily due to tidal influences from the cold Oyashio and warm Kuroshio currents. Our previous results from performing shotgun metagenomics indicated that Candidatus Pelagibacter ubique and Planktomarina temperata were the dominant bacteria (Reza et al., 2018a, 2018b). These bacteria are reportedly able to catabolize dimethylsulfoniopropionate (DMSP) produced from phytoplankton into dimethyl sulfide (DMS) or methanethiol (MeSH). This study was focused on seasonal changes in the abundances of bacterial genes (dddP, dmdA) related to DMSP catabolism in the seawater of Ofunato Bay by BLAST+ analysis using shotgun metagenomic datasets. We found seasonal changes among the Candidatus Pelagibacter ubique strains, including those of the HTCC1062 type and the Red Sea type. A good correlation was observed between the chlorophyll a concentrations and the abundances of the catabolic genes, suggesting that the bacteria directly interact with phytoplankton in the marine material cycle system and play important roles in producing DMS and MeSH from DMSP as signaling molecules for the possible formation of the scent of the tidewater or as fish attractants.

  15. Identification, characterization and metagenome analysis of oocyte-specific genes organized in clusters in the mouse genome

    Directory of Open Access Journals (Sweden)

    Vaiman Daniel

    2005-05-01

    Full Text Available Abstract Background Genes specifically expressed in the oocyte play key roles in oogenesis, ovarian folliculogenesis, fertilization and/or early embryonic development. In an attempt to identify novel oocyte-specific genes in the mouse, we have used an in silico subtraction methodology, and we have focused our attention on genes that are organized in genomic clusters. Results In the present work, five clusters have been studied: a cluster of thirteen genes characterized by an F-box domain localized on chromosome 9, a cluster of six genes related to T-cell leukaemia/lymphoma protein 1 (Tcl1 on chromosome 12, a cluster composed of a SPErm-associated glutamate (E-Rich (Speer protein expressed in the oocyte in the vicinity of four unknown genes specifically expressed in the testis on chromosome 14, a cluster composed of the oocyte secreted protein-1 (Oosp-1 gene and two Oosp-related genes on chromosome 19, all three being characterized by a partial N-terminal zona pellucida-like domain, and another small cluster of two genes on chromosome 19 as well, composed of a TWIK-Related spinal cord K+ channel encoding-gene, and an unknown gene predicted in silico to be testis-specific. The specificity of expression was confirmed by RT-PCR and in situ hybridization for eight and five of them, respectively. Finally, we showed by comparing all of the isolated and clustered oocyte-specific genes identified so far in the mouse genome, that the oocyte-specific clusters are significantly closer to telomeres than isolated oocyte-specific genes are. Conclusion We have studied five clusters of genes specifically expressed in female, some of them being also expressed in male germ-cells. Moreover, contrarily to non-clustered oocyte-specific genes, those that are organized in clusters tend to map near chromosome ends, suggesting that this specific near-telomere position of oocyte-clusters in rodents could constitute an evolutionary advantage. Understanding the biological

  16. Metagenomic approach reveals microbial diversity and predictive microbial metabolic pathways in Yucha, a traditional Li fermented food

    OpenAIRE

    Zhang, Jiachao; Wang, Xiaoru; Huo, Dongxue; Li, Wu; Hu, Qisong; Xu, Chuanbiao; Liu, Sixin; Li, Congfa

    2016-01-01

    Yucha is a typical traditional fermented food of the Li population in the Hainan province of China, and it is made up of cooked rice and fresh fish. In the present study, metagenomic approach and culture-dependent technology were applied to describe the diversity of microbiota and identify beneficial microbes in the Yucha. At the genus level, Lactobacillus was the most abundant genus (43.82% of the total reads), followed by Lactococcus, Enterococcus, Vibrio, Weissella, Pediococcus, Enterobact...

  17. A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds

    DEFF Research Database (Denmark)

    Munk, Patrick; Dalhoff Andersen, Vibe; de Knegt, Leonardo

    2016-01-01

    Objectives Reliable methods for monitoring antimicrobial resistance (AMR) in livestock and other reservoirs are essential to understand the trends, transmission and importance of agricultural resistance. Quantification of AMR is mostly done using culture-based techniques, but metagenomic read...... mapping shows promise for quantitative resistance monitoring. Methods We evaluated the ability of: (i) MIC determination for Escherichia coli; (ii) cfu counting of E. coli; (iii) cfu counting of aerobic bacteria; and (iv) metagenomic shotgun sequencing to predict expected tetracycline resistance based...... cultivation-based techniques in terms of predicting expected tetracycline resistance based on antimicrobial consumption. Our metagenomic approach had sufficient resolution to detect antimicrobial-induced changes to individual resistance gene abundances. Pen floor manure samples were found to represent rectal...

  18. Metagenomic applications in environmental monitoring and bioremediation.

    Science.gov (United States)

    Techtmann, Stephen M; Hazen, Terry C

    2016-10-01

    With the rapid advances in sequencing technology, the cost of sequencing has dramatically dropped and the scale of sequencing projects has increased accordingly. This has provided the opportunity for the routine use of sequencing techniques in the monitoring of environmental microbes. While metagenomic applications have been routinely applied to better understand the ecology and diversity of microbes, their use in environmental monitoring and bioremediation is increasingly common. In this review we seek to provide an overview of some of the metagenomic techniques used in environmental systems biology, addressing their application and limitation. We will also provide several recent examples of the application of metagenomics to bioremediation. We discuss examples where microbial communities have been used to predict the presence and extent of contamination, examples of how metagenomics can be used to characterize the process of natural attenuation by unculturable microbes, as well as examples detailing the use of metagenomics to understand the impact of biostimulation on microbial communities.

  19. OTU analysis using metagenomic shotgun sequencing data.

    Directory of Open Access Journals (Sweden)

    Xiaolin Hao

    Full Text Available Because of technological limitations, the primer and amplification biases in targeted sequencing of 16S rRNA genes have veiled the true microbial diversity underlying environmental samples. However, the protocol of metagenomic shotgun sequencing provides 16S rRNA gene fragment data with natural immunity against the biases raised during priming and thus the potential of uncovering the true structure of microbial community by giving more accurate predictions of operational taxonomic units (OTUs. Nonetheless, the lack of statistically rigorous comparison between 16S rRNA gene fragments and other data types makes it difficult to interpret previously reported results using 16S rRNA gene fragments. Therefore, in the present work, we established a standard analysis pipeline that would help confirm if the differences in the data are true or are just due to potential technical bias. This pipeline is built by using simulated data to find optimal mapping and OTU prediction methods. The comparison between simulated datasets revealed a relationship between 16S rRNA gene fragments and full-length 16S rRNA sequences that a 16S rRNA gene fragment having a length >150 bp provides the same accuracy as a full-length 16S rRNA sequence using our proposed pipeline, which could serve as a good starting point for experimental design and making the comparison between 16S rRNA gene fragment-based and targeted 16S rRNA sequencing-based surveys possible.

  20. Comparative metagenomics of the Red Sea

    KAUST Repository

    Mineta, Katsuhiko

    2016-01-26

    Metagenome produces a tremendous amount of data that comes from the organisms living in the environments. This big data enables us to examine not only microbial genes but also the community structure, interaction and adaptation mechanisms at the specific location and condition. The Red Sea has several unique characteristics such as high salinity, high temperature and low nutrition. These features must contribute to form the unique microbial community during the evolutionary process. Since 2014, we started monthly samplings of the metagenomes in the Red Sea under KAUST-CCF project. In collaboration with Kitasato University, we also collected the metagenome data from the ocean in Japan, which shows contrasting features to the Red Sea. Therefore, the comparative metagenomics of those data provides a comprehensive view of the Red Sea microbes, leading to identify key microbes, genes and networks related to those environmental differences.

  1. Antibiotic Resistome: Improving Detection and Quantification Accuracy for Comparative Metagenomics.

    Science.gov (United States)

    Elbehery, Ali H A; Aziz, Ramy K; Siam, Rania

    2016-04-01

    The unprecedented rise of life-threatening antibiotic resistance (AR), combined with the unparalleled advances in DNA sequencing of genomes and metagenomes, has pushed the need for in silico detection of the resistance potential of clinical and environmental metagenomic samples through the quantification of AR genes (i.e., genes conferring antibiotic resistance). Therefore, determining an optimal methodology to quantitatively and accurately assess AR genes in a given environment is pivotal. Here, we optimized and improved existing AR detection methodologies from metagenomic datasets to properly consider AR-generating mutations in antibiotic target genes. Through comparative metagenomic analysis of previously published AR gene abundance in three publicly available metagenomes, we illustrate how mutation-generated resistance genes are either falsely assigned or neglected, which alters the detection and quantitation of the antibiotic resistome. In addition, we inspected factors influencing the outcome of AR gene quantification using metagenome simulation experiments, and identified that genome size, AR gene length, total number of metagenomics reads and selected sequencing platforms had pronounced effects on the level of detected AR. In conclusion, our proposed improvements in the current methodologies for accurate AR detection and resistome assessment show reliable results when tested on real and simulated metagenomic datasets.

  2. Screening a novel Na+/H+ antiporter gene from a metagenomic library of halophiles colonizing in the Dagong Ancient Brine Well in China.

    Science.gov (United States)

    Xiang, Wenliang; Zhang, Jie; Li, Lin; Liang, Huazhong; Luo, Hai; Zhao, Jian; Yang, Zhirong; Sun, Qun

    2010-05-01

    Metagenomic DNA libraries constructed from the Dagong Ancient Brine Well were screened for genes with Na(+)/H(+) antiporter activity on the antiporter-deficient Escherichia coli KNabc strain. One clone with a stable Na(+)-resistant phenotype was obtained and its Na(+)/H(+) antiporter gene was sequenced and designated as m-nha. The deduced amino acid sequence of M-Nha protein consists of 523 residues with a calculated molecular weight of 58 147 Da and a pI of 5.50, which is homologous with NhaH from Halobacillus dabanensis D-8(T) (92%) and Halobacillus aidingensis AD-6(T) (86%), and with Nhe2 from Bacillus sp. NRRL B-14911 (64%). It had a hydropathy profile with 10 putative transmembrane domains and a long carboxyl terminal hydrophilic tail of 140 amino acid residues, similar to Nhap from Synechocystis sp. and Aphanothece halophytica, as well as NhaG from Bacillus subtilis. The m-nha gene in the antiporter-negative mutant E. coli KNabc conferred resistance to Na(+) and the ability to grow under alkaline conditions. The difference in amino acid sequence and the putative secondary structure suggested that the m-nha isolated from the Dagong Ancient Brine Well in this study was a novel Na(+)/H(+) antiporter gene.

  3. Distribution of triclosan-resistant genes in major pathogenic microorganisms revealed by metagenome and genome-wide analysis

    Science.gov (United States)

    Khan, Raees; Roy, Nazish; Choi, Kihyuck

    2018-01-01

    The substantial use of triclosan (TCS) has been aimed to kill pathogenic bacteria, but TCS resistance seems to be prevalent in microbial species and limited knowledge exists about TCS resistance determinants in a majority of pathogenic bacteria. We aimed to evaluate the distribution of TCS resistance determinants in major pathogenic bacteria (N = 231) and to assess the enrichment of potentially pathogenic genera in TCS contaminated environments. A TCS-resistant gene (TRG) database was constructed and experimentally validated to predict TCS resistance in major pathogenic bacteria. Genome-wide in silico analysis was performed to define the distribution of TCS-resistant determinants in major pathogens. Microbiome analysis of TCS contaminated soil samples was also performed to investigate the abundance of TCS-resistant pathogens. We experimentally confirmed that TCS resistance could be accurately predicted using genome-wide in silico analysis against TRG database. Predicted TCS resistant phenotypes were observed in all of the tested bacterial strains (N = 17), and heterologous expression of selected TCS resistant genes from those strains conferred expected levels of TCS resistance in an alternative host Escherichia coli. Moreover, genome-wide analysis revealed that potential TCS resistance determinants were abundant among the majority of human-associated pathogens (79%) and soil-borne plant pathogenic bacteria (98%). These included a variety of enoyl-acyl carrier protein reductase (ENRs) homologues, AcrB efflux pumps, and ENR substitutions. FabI ENR, which is the only known effective target for TCS, was either co-localized with other TCS resistance determinants or had TCS resistance-associated substitutions. Furthermore, microbiome analysis revealed that pathogenic genera with intrinsic TCS-resistant determinants exist in TCS contaminated environments. We conclude that TCS may not be as effective against the majority of bacterial pathogens as previously presumed

  4. Distribution of triclosan-resistant genes in major pathogenic microorganisms revealed by metagenome and genome-wide analysis.

    Directory of Open Access Journals (Sweden)

    Raees Khan

    Full Text Available The substantial use of triclosan (TCS has been aimed to kill pathogenic bacteria, but TCS resistance seems to be prevalent in microbial species and limited knowledge exists about TCS resistance determinants in a majority of pathogenic bacteria. We aimed to evaluate the distribution of TCS resistance determinants in major pathogenic bacteria (N = 231 and to assess the enrichment of potentially pathogenic genera in TCS contaminated environments. A TCS-resistant gene (TRG database was constructed and experimentally validated to predict TCS resistance in major pathogenic bacteria. Genome-wide in silico analysis was performed to define the distribution of TCS-resistant determinants in major pathogens. Microbiome analysis of TCS contaminated soil samples was also performed to investigate the abundance of TCS-resistant pathogens. We experimentally confirmed that TCS resistance could be accurately predicted using genome-wide in silico analysis against TRG database. Predicted TCS resistant phenotypes were observed in all of the tested bacterial strains (N = 17, and heterologous expression of selected TCS resistant genes from those strains conferred expected levels of TCS resistance in an alternative host Escherichia coli. Moreover, genome-wide analysis revealed that potential TCS resistance determinants were abundant among the majority of human-associated pathogens (79% and soil-borne plant pathogenic bacteria (98%. These included a variety of enoyl-acyl carrier protein reductase (ENRs homologues, AcrB efflux pumps, and ENR substitutions. FabI ENR, which is the only known effective target for TCS, was either co-localized with other TCS resistance determinants or had TCS resistance-associated substitutions. Furthermore, microbiome analysis revealed that pathogenic genera with intrinsic TCS-resistant determinants exist in TCS contaminated environments. We conclude that TCS may not be as effective against the majority of bacterial pathogens as previously

  5. [Mini review] metagenomic studies of the Red Sea

    KAUST Repository

    Behzad, Hayedeh; Ibarra, Martin Augusto; Mineta, Katsuhiko; Gojobori, Takashi

    2015-01-01

    Metagenomics has significantly advanced the field of marine microbial ecology, revealing the vast diversity of previously unknown microbial life forms in different marine niches. The tremendous amount of data generated has enabled identification of a large number of microbial genes (metagenomes), their community interactions, adaptation mechanisms, and their potential applications in pharmaceutical and biotechnology-based industries. Comparative metagenomics reveals that microbial diversity is a function of the local environment, meaning that unique or unusual environments typically harbor novel microbial species with unique genes and metabolic pathways. The Red Sea has an abundance of unique characteristics; however, its microbiota is one of the least studied amongst marine environments. The Red Sea harbors approximately 25 hot anoxic brine pools, plus a vibrant coral reef ecosystem. Physiochemical studies describe the Red Sea as an oligotrophic environment that contains one of the warmest and saltiest waters in the world with year-round high UV radiations. These characteristics are believed to have shaped the evolution of microbial communities in the Red Sea. Over-representation of genes involved in DNA repair, high-intensity light responses, and osmolyte C1 oxidation were found in the Red Sea metagenomic databases suggesting acquisition of specific environmental adaptation by the Red Sea microbiota. The Red Sea brine pools harbor a diverse range of halophilic and thermophilic bacterial and archaeal communities, which are potential sources of enzymes for pharmaceutical and biotechnology-based application. Understanding the mechanisms of these adaptations and their function within the larger ecosystem could also prove useful in light of predicted global warming scenarios where global ocean temperatures are expected to rise by 1–3 °C in the next few decades. In this review, we provide an overview of the published metagenomic studies that were conducted in the

  6. [Mini review] metagenomic studies of the Red Sea

    KAUST Repository

    Behzad, Hayedeh

    2015-10-23

    Metagenomics has significantly advanced the field of marine microbial ecology, revealing the vast diversity of previously unknown microbial life forms in different marine niches. The tremendous amount of data generated has enabled identification of a large number of microbial genes (metagenomes), their community interactions, adaptation mechanisms, and their potential applications in pharmaceutical and biotechnology-based industries. Comparative metagenomics reveals that microbial diversity is a function of the local environment, meaning that unique or unusual environments typically harbor novel microbial species with unique genes and metabolic pathways. The Red Sea has an abundance of unique characteristics; however, its microbiota is one of the least studied amongst marine environments. The Red Sea harbors approximately 25 hot anoxic brine pools, plus a vibrant coral reef ecosystem. Physiochemical studies describe the Red Sea as an oligotrophic environment that contains one of the warmest and saltiest waters in the world with year-round high UV radiations. These characteristics are believed to have shaped the evolution of microbial communities in the Red Sea. Over-representation of genes involved in DNA repair, high-intensity light responses, and osmolyte C1 oxidation were found in the Red Sea metagenomic databases suggesting acquisition of specific environmental adaptation by the Red Sea microbiota. The Red Sea brine pools harbor a diverse range of halophilic and thermophilic bacterial and archaeal communities, which are potential sources of enzymes for pharmaceutical and biotechnology-based application. Understanding the mechanisms of these adaptations and their function within the larger ecosystem could also prove useful in light of predicted global warming scenarios where global ocean temperatures are expected to rise by 1–3 °C in the next few decades. In this review, we provide an overview of the published metagenomic studies that were conducted in the

  7. Metagenomic of Actinomycetes Based on 16S rRNA and nifH Genes in Soil and Roots of Four Indonesian Rice Cultivars Using PCR-DGGE

    Directory of Open Access Journals (Sweden)

    Mahyarudin

    2015-07-01

    Full Text Available The research was conducted to study the metagenomic of actinomycetes based on 16S ribosomal RNA (rRNA and bacterial nifH genes in soil and roots of four rice cultivars. The denaturing gradient gel electrophoresis profile based on 16S rRNA gene showed that the diversity of actinomycetes in roots was higher than soil samples. The profile also showed that the diversity of actinomycetes was similar in four varieties of rice plant and three types of agroecosystem. The profile was partially sequenced and compared to GenBank database indicating their identity with closely related microbes. The blast results showed that 17 bands were closely related ranging from 93% to 100% of maximum identity with five genera of actinomycetes, which is Geodermatophilus, Actinokineospora, Actinoplanes, Streptomyces and Kocuria. Our study found that Streptomyces species in soil and roots of rice plants were more varied than other genera, with a dominance of Streptomyces alboniger and Streptomyces acidiscabies in almost all the samples. Bacterial community analyses based on nifH gene denaturing gradient gel electrophoresis showed that diversity of bacteria in soils which have nifH gene was higher than that in rice plant roots. The profile also showed that the diversity of those bacteria was similar in four varieties of rice plant and three types of agroecosystem. Five bands were closely related with nifH gene from uncultured bacterium clone J50, uncultured bacterium clone clod-38, and uncultured bacterium clone BG2.37 with maximum identity 99%, 98%, and 92%, respectively. The diversity analysis based on 16S rRNA gene differed from nifH gene and may not correlate with each other. The findings indicated the diversity of actinomycetes and several bacterial genomes analyzed here have an ability to fix nitrogen in soil and roots of rice plant.

  8. A catalog of the mouse gut metagenome

    DEFF Research Database (Denmark)

    Xiao, Liang; Feng, Qiang; Liang, Suisha

    2015-01-01

    laboratories and fed either a low-fat or high-fat diet. Similar to the human gut microbiome, >99% of the cataloged genes are bacterial. We identified 541 metagenomic species and defined a core set of 26 metagenomic species found in 95% of the mice. The mouse gut microbiome is functionally similar to its human......We established a catalog of the mouse gut metagenome comprising ∼2.6 million nonredundant genes by sequencing DNA from fecal samples of 184 mice. To secure high microbiome diversity, we used mouse strains of diverse genetic backgrounds, from different providers, kept in different housing...... counterpart, with 95.2% of its Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologous groups in common. However, only 4.0% of the mouse gut microbial genes were shared (95% identity, 90% coverage) with those of the human gut microbiome. This catalog provides a useful reference for future studies....

  9. An Experimental Metagenome Data Management and AnalysisSystem

    Energy Technology Data Exchange (ETDEWEB)

    Markowitz, Victor M.; Korzeniewski, Frank; Palaniappan, Krishna; Szeto, Ernest; Ivanova, Natalia N.; Kyrpides, Nikos C.; Hugenholtz, Philip

    2006-03-01

    The application of shotgun sequencing to environmental samples has revealed a new universe of microbial community genomes (metagenomes) involving previously uncultured organisms. Metagenome analysis, which is expected to provide a comprehensive picture of the gene functions and metabolic capacity of microbial community, needs to be conducted in the context of a comprehensive data management and analysis system. We present in this paper IMG/M, an experimental metagenome data management and analysis system that is based on the Integrated Microbial Genomes (IMG) system. IMG/M provides tools and viewers for analyzing both metagenomes and isolate genomes individually or in a comparative context.

  10. Metagenomic approach reveals microbial diversity and predictive microbial metabolic pathways in Yucha, a traditional Li fermented food.

    Science.gov (United States)

    Zhang, Jiachao; Wang, Xiaoru; Huo, Dongxue; Li, Wu; Hu, Qisong; Xu, Chuanbiao; Liu, Sixin; Li, Congfa

    2016-08-31

    Yucha is a typical traditional fermented food of the Li population in the Hainan province of China, and it is made up of cooked rice and fresh fish. In the present study, metagenomic approach and culture-dependent technology were applied to describe the diversity of microbiota and identify beneficial microbes in the Yucha. At the genus level, Lactobacillus was the most abundant genus (43.82% of the total reads), followed by Lactococcus, Enterococcus, Vibrio, Weissella, Pediococcus, Enterobacter, Salinivibrio, Acinetobacter, Macrococcus, Kluyvera and Clostridium; this result was confirmed by q-PCR. PCoA based on Weighted UniFrac distances showed an apparent clustering pattern for Yucha samples from different locations, and Lactobacillus sakei, Lactobacillus saniviri and Staphylococcus sciuri represented OTUs according to the major identified markers. At the microbial functional level, it was observed that there was an enrichment of metabolic functional features, including amino acid and carbohydrate metabolism, which implied that the microbial metabolism in the Yucha samples tended to be vigorous. Accordingly, we further investigated the correlation between the predominant microbes and metabolic functional features. Thirteen species of Lactobacillus (147 strains) were isolated, and Lactobacillus plantarum (60 isolates) and Lactobacillus pentosus (34 isolates) were isolated from every sample.

  11. Analysis and comparison of very large metagenomes with fast clustering and functional annotation

    Directory of Open Access Journals (Sweden)

    Li Weizhong

    2009-10-01

    Full Text Available Abstract Background The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand. Results The new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes". Conclusion RAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from http://tools.camera.calit2.net/camera/rammcap/.

  12. Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

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    Richard Danger

    2018-01-01

    Full Text Available Bronchiolitis obliterans syndrome (BOS, the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group, and 26 samples at or after BOS diagnosis (diagnosis group. An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group. We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1, T-cell leukemia/lymphoma protein 1A (TCL1A, and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01 and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS.

  13. Novel resistance functions uncovered using functional metagenomic investigations of resistance reservoirs

    Directory of Open Access Journals (Sweden)

    Erica C. Pehrsson

    2013-06-01

    Full Text Available Rates of infection with antibiotic-resistant bacteria have increased precipitously over the past several decades, with far-reaching healthcare and societal costs. Recent evidence has established a link between antibiotic resistance genes in human pathogens and those found in non-pathogenic, commensal, and environmental organisms, prompting deeper investigation of natural and human-associated reservoirs of antibiotic resistance. Functional metagenomic selections, in which shotgun-cloned DNA fragments are selected for their ability to confer survival to an indicator host, have been increasingly applied to the characterization of many antibiotic resistance reservoirs. These experiments have demonstrated that antibiotic resistance genes are highly diverse and widely distributed, many times bearing little to no similarity to known sequences. Through unbiased selections for survival to antibiotic exposure, functional metagenomics can improve annotations by reducing the discovery of false-positive resistance and by allowing for the identification of previously unrecognizable resistance genes. In this review, we summarize the novel resistance functions uncovered using functional metagenomic investigations of natural and human-impacted resistance reservoirs. Examples of novel antibiotic resistance genes include those highly divergent from known sequences, those for which sequence is entirely unable to predict resistance function, bifunctional resistance genes, and those with unconventional, atypical resistance mechanisms. Overcoming antibiotic resistance in the clinic will require a better understanding of existing resistance reservoirs and the dissemination networks that govern horizontal gene exchange, informing best practices to limit the spread of resistance-conferring genes to human pathogens.

  14. Mining of unexplored habitats for novel chitinases - chiA as a helper gene proxy in metagenomics

    DEFF Research Database (Denmark)

    Cretoiu, Mariana Silvia; Kielak, Anna Maria; Abu Al-Soud, Waleed

    2012-01-01

    encompassed (1) classical overall enzymatic assays, (2) chiA gene abundance measurement by qPCR, (3) chiA gene pyrosequencing, and (4) chiA gene-based PCR-DGGE was used. The chiA gene pyrosequencing is unprecedented, as it is the first massive parallel sequencing of this gene. The data obtained showed...... the existence across habitats of core bacterial communities responsible for chitin assimilation irrespective of ecosystem origin. Conversely, there were habitat-specific differences. In addition, a suite of sequences were obtained that are as yet unregistered in the chitinase database. In terms of chiA gene...

  15. Challenges and Opportunities of Airborne Metagenomics

    KAUST Repository

    Behzad, H.

    2015-05-06

    Recent metagenomic studies of environments, such as marine and soil, have significantly enhanced our understanding of the diverse microbial communities living in these habitats and their essential roles in sustaining vast ecosystems. The increase in the number of publications related to soil and marine metagenomics is in sharp contrast to those of air, yet airborne microbes are thought to have significant impacts on many aspects of our lives from their potential roles in atmospheric events such as cloud formation, precipitation, and atmospheric chemistry to their major impact on human health. In this review, we will discuss the current progress in airborne metagenomics, with a special focus on exploring the challenges and opportunities of undertaking such studies. The main challenges of conducting metagenomic studies of airborne microbes are as follows: 1) Low density of microorganisms in the air, 2) efficient retrieval of microorganisms from the air, 3) variability in airborne microbial community composition, 4) the lack of standardized protocols and methodologies, and 5) DNA sequencing and bioinformatics-related challenges. Overcoming these challenges could provide the groundwork for comprehensive analysis of airborne microbes and their potential impact on the atmosphere, global climate, and our health. Metagenomic studies offer a unique opportunity to examine viral and bacterial diversity in the air and monitor their spread locally or across the globe, including threats from pathogenic microorganisms. Airborne metagenomic studies could also lead to discoveries of novel genes and metabolic pathways relevant to meteorological and industrial applications, environmental bioremediation, and biogeochemical cycles.

  16. Comparative Metagenomics of Freshwater Microbial Communities

    International Nuclear Information System (INIS)

    Hemme, Chris; Deng, Ye; Tu, Qichao; Fields, Matthew; Gentry, Terry; Wu, Liyou; Tringe, Susannah; Watson, David; He, Zhili; Hazen, Terry; Tiedje, James; Rubin, Eddy; Zhou, Jizhong

    2010-01-01

    Previous analyses of a microbial metagenome from uranium and nitric-acid contaminated groundwater (FW106) showed significant environmental effects resulting from the rapid introduction of multiple contaminants. Effects include a massive loss of species and strain biodiversity, accumulation of toxin resistant genes in the metagenome and lateral transfer of toxin resistance genes between community members. To better understand these results in an ecological context, a second metagenome from a pristine groundwater system located along the same geological strike was sequenced and analyzed (FW301). It is hypothesized that FW301 approximates the ancestral FW106 community based on phylogenetic profiles and common geological parameters; however, even if is not the case, the datasets still permit comparisons between healthy and stressed groundwater ecosystems. Complex carbohydrate metabolism has been almost entirely lost in the stressed ecosystem. In contrast, the pristine system encodes a wide diversity of complex carbohydrate metabolism systems, suggesting that carbon turnover is very rapid and less leaky in the healthy groundwater system. FW301 encodes many (∼160+) carbon monoxide dehydrogenase genes while FW106 encodes none. This result suggests that the community is frequently exposed to oxygen from aerated rainwater percolating into the subsurface, with a resulting high rate of carbon metabolism and CO production. When oxygen levels fall, the CO then serves as a major carbon source for the community. FW301 appears to be capable of CO2 fixation via the reductive carboxylase (reverse TCA) cycle and possibly acetogenesis, activities; these activities are lacking in the heterotrophic FW106 system which relies exclusively on respiration of nitrate and/or oxygen for energy production. FW301 encodes a complete set of B12 biosynthesis pathway at high abundance suggesting the use of sodium gradients for energy production in the healthy groundwater community. Overall

  17. Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies

    KAUST Repository

    Wang, Yong

    2009-10-09

    Bacterial 16S ribosomal DNA (rDNA) amplicons have been widely used in the classification of uncultured bacteria inhabiting environmental niches. Primers targeting conservative regions of the rDNAs are used to generate amplicons of variant regions that are informative in taxonomic assignment. One problem is that the percentage coverage and application scope of the primers used in previous studies are largely unknown. In this study, conservative fragments of available rDNA sequences were first mined and then used to search for candidate primers within the fragments by measuring the coverage rate defined as the percentage of bacterial sequences containing the target. Thirty predicted primers with a high coverage rate (>90%) were identified, which were basically located in the same conservative regions as known primers in previous reports, whereas 30% of the known primers were associated with a coverage rate of <90%. The application scope of the primers was also examined by calculating the percentages of failed detections in bacterial phyla. Primers A519-539, E969- 983, E1063-1081, U515 and E517, are highly recommended because of their high coverage in almost all phyla. As expected, the three predominant phyla, Firmicutes, Gemmatimonadetes and Proteobacteria, are best covered by the predicted primers. The primers recommended in this report shall facilitate a comprehensive and reliable survey of bacterial diversity in metagenomic studies. © 2009 Wang, Qian.

  18. Predicting Hydrologic Function With Aquatic Gene Fragments

    Science.gov (United States)

    Good, S. P.; URycki, D. R.; Crump, B. C.

    2018-03-01

    Recent advances in microbiology techniques, such as genetic sequencing, allow for rapid and cost-effective collection of large quantities of genetic information carried within water samples. Here we posit that the unique composition of aquatic DNA material within a water sample contains relevant information about hydrologic function at multiple temporal scales. In this study, machine learning was used to develop discharge prediction models trained on the relative abundance of bacterial taxa classified into operational taxonomic units (OTUs) based on 16S rRNA gene sequences from six large arctic rivers. We term this approach "genohydrology," and show that OTU relative abundances can be used to predict river discharge at monthly and longer timescales. Based on a single DNA sample from each river, the average Nash-Sutcliffe efficiency (NSE) for predicted mean monthly discharge values throughout the year was 0.84, while the NSE for predicted discharge values across different return intervals was 0.67. These are considerable improvements over predictions based only on the area-scaled mean specific discharge of five similar rivers, which had average NSE values of 0.64 and -0.32 for seasonal and recurrence interval discharge values, respectively. The genohydrology approach demonstrates that genetic diversity within the aquatic microbiome is a large and underutilized data resource with benefits for prediction of hydrologic function.

  19. Characterization and detection of a widely distributed gene cluster that predicts anaerobic choline utilization by human gut bacteria.

    Science.gov (United States)

    Martínez-del Campo, Ana; Bodea, Smaranda; Hamer, Hilary A; Marks, Jonathan A; Haiser, Henry J; Turnbaugh, Peter J; Balskus, Emily P

    2015-04-14

    Elucidation of the molecular mechanisms underlying the human gut microbiota's effects on health and disease has been complicated by difficulties in linking metabolic functions associated with the gut community as a whole to individual microorganisms and activities. Anaerobic microbial choline metabolism, a disease-associated metabolic pathway, exemplifies this challenge, as the specific human gut microorganisms responsible for this transformation have not yet been clearly identified. In this study, we established the link between a bacterial gene cluster, the choline utilization (cut) cluster, and anaerobic choline metabolism in human gut isolates by combining transcriptional, biochemical, bioinformatic, and cultivation-based approaches. Quantitative reverse transcription-PCR analysis and in vitro biochemical characterization of two cut gene products linked the entire cluster to growth on choline and supported a model for this pathway. Analyses of sequenced bacterial genomes revealed that the cut cluster is present in many human gut bacteria, is predictive of choline utilization in sequenced isolates, and is widely but discontinuously distributed across multiple bacterial phyla. Given that bacterial phylogeny is a poor marker for choline utilization, we were prompted to develop a degenerate PCR-based method for detecting the key functional gene choline TMA-lyase (cutC) in genomic and metagenomic DNA. Using this tool, we found that new choline-metabolizing gut isolates universally possessed cutC. We also demonstrated that this gene is widespread in stool metagenomic data sets. Overall, this work represents a crucial step toward understanding anaerobic choline metabolism in the human gut microbiota and underscores the importance of examining this microbial community from a function-oriented perspective. Anaerobic choline utilization is a bacterial metabolic activity that occurs in the human gut and is linked to multiple diseases. While bacterial genes responsible for

  20. A novel esterase gene cloned from a metagenomic library from neritic sediments of the South China Sea

    Science.gov (United States)

    2011-01-01

    Background Marine microbes are a large and diverse group, which are exposed to a wide variety of pressure, temperature, salinity, nutrient availability and other environmental conditions. They provide a huge potential source of novel enzymes with unique properties that may be useful in industry and biotechnology. To explore the lipolytic genetic resources in the South China Sea, 23 sediment samples were collected in the depth South China Sea sediments assemblage in plasmid vector containing about 194 Mb of community DNA was prepared. Screening of a part of the unamplified library resulted in isolation of 15 unique lipolytic clones with the ability to hydrolyze tributyrin. A positive recombinant clone (pNLE1), containing a novel esterase (Est_p1), was successfully expressed in E. coli and purified. In a series of assays, Est_p1 displayed maximal activity at pH 8.57, 40°C, with ρ-Nitrophenyl butyrate (C4) as substrate. Compared to other metagenomic esterases, Est_p1 played a notable role in specificity for substrate C4 (kcat/Km value 11,500 S-1m M-1) and showed no inhibited by phenylmethylsulfonyl fluoride, suggested that the substrate binding pocket was suitable for substrate C4 and the serine active-site residue was buried at the bottom of substrate binding pocket which sheltered by a lid structure. Conclusions Esterase, which specificity towards short chain fatty acids, especially butanoic acid, is commercially available as potent flavoring tools. According the outstanding activity and specificity for substrate C4, Est_p1 has potential application in flavor industries requiring hydrolysis of short chain esters. PMID:22067554

  1. Predicting cellular growth from gene expression signatures.

    Directory of Open Access Journals (Sweden)

    Edoardo M Airoldi

    2009-01-01

    Full Text Available Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

  2. The Present and Future of Whole Genome Sequencing (WGS and Whole Metagenome Sequencing (WMS for Surveillance of Antimicrobial Resistant Microorganisms and Antimicrobial Resistance Genes across the Food Chain

    Directory of Open Access Journals (Sweden)

    Elena A. Oniciuc

    2018-05-01

    Full Text Available Antimicrobial resistance (AMR surveillance is a critical step within risk assessment schemes, as it is the basis for informing global strategies, monitoring the effectiveness of public health interventions, and detecting new trends and emerging threats linked to food. Surveillance of AMR is currently based on the isolation of indicator microorganisms and the phenotypic characterization of clinical, environmental and food strains isolated. However, this approach provides very limited information on the mechanisms driving AMR or on the presence or spread of AMR genes throughout the food chain. Whole-genome sequencing (WGS of bacterial pathogens has shown potential for epidemiological surveillance, outbreak detection, and infection control. In addition, whole metagenome sequencing (WMS allows for the culture-independent analysis of complex microbial communities, providing useful information on AMR genes occurrence. Both technologies can assist the tracking of AMR genes and mobile genetic elements, providing the necessary information for the implementation of quantitative risk assessments and allowing for the identification of hotspots and routes of transmission of AMR across the food chain. This review article summarizes the information currently available on the use of WGS and WMS for surveillance of AMR in foodborne pathogenic bacteria and food-related samples and discusses future needs that will have to be considered for the routine implementation of these next-generation sequencing methodologies with this aim. In particular, methodological constraints that impede the use at a global scale of these high-throughput sequencing (HTS technologies are identified, and the standardization of methods and protocols is suggested as a measure to upgrade HTS-based AMR surveillance schemes.

  3. Genomic Prediction of Gene Bank Wheat Landraces

    Directory of Open Access Journals (Sweden)

    José Crossa

    2016-07-01

    Full Text Available This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H for the highly heritable traits, days to heading (DTH, and days to maturity (DTM. Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E. Two alternative prediction strategies were studied: (1 random cross-validation of the data in 20% training (TRN and 80% testing (TST (TRN20-TST80 sets, and (2 two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm

  4. Metagenomics as a Tool for Enzyme Discovery: Hydrolytic Enzymes from Marine-Related Metagenomes.

    Science.gov (United States)

    Popovic, Ana; Tchigvintsev, Anatoly; Tran, Hai; Chernikova, Tatyana N; Golyshina, Olga V; Yakimov, Michail M; Golyshin, Peter N; Yakunin, Alexander F

    2015-01-01

    This chapter discusses metagenomics and its application for enzyme discovery, with a focus on hydrolytic enzymes from marine metagenomic libraries. With less than one percent of culturable microorganisms in the environment, metagenomics, or the collective study of community genetics, has opened up a rich pool of uncharacterized metabolic pathways, enzymes, and adaptations. This great untapped pool of genes provides the particularly exciting potential to mine for new biochemical activities or novel enzymes with activities tailored to peculiar sets of environmental conditions. Metagenomes also represent a huge reservoir of novel enzymes for applications in biocatalysis, biofuels, and bioremediation. Here we present the results of enzyme discovery for four enzyme activities, of particular industrial or environmental interest, including esterase/lipase, glycosyl hydrolase, protease and dehalogenase.

  5. Metagenomic identification of a novel salt tolerance gene from the human gut microbiome which encodes a membrane protein with homology to a brp/blh-family β-carotene 15,15'-monooxygenase.

    Directory of Open Access Journals (Sweden)

    Eamonn P Culligan

    Full Text Available The human gut microbiome consists of at least 3 million non-redundant genes, 150 times that of the core human genome. Herein, we report the identification and characterisation of a novel stress tolerance gene from the human gut metagenome. The locus, assigned brpA, encodes a membrane protein with homology to a brp/blh-family β-carotene monooxygenase. Cloning and heterologous expression of brpA in Escherichia coli confers a significant salt tolerance phenotype. Furthermore, when cultured in the presence of exogenous β-carotene, cell pellets adopt a red/orange pigmentation indicating the incorporation of carotenoids in the cell membrane.

  6. PCR-Based Analysis of ColE1 Plasmids in Clinical Isolates and Metagenomic Samples Reveals Their Importance as Gene Capture Platforms

    Directory of Open Access Journals (Sweden)

    Manuel Ares-Arroyo

    2018-03-01

    Full Text Available ColE1 plasmids are important vehicles for the spread of antibiotic resistance in the Enterobacteriaceae and Pasteurellaceae families of bacteria. Their monitoring is essential, as they harbor important resistant determinants in humans, animals and the environment. In this work, we have analyzed ColE1 replicons using bioinformatic and experimental approaches. First, we carried out a computational study examining the structure of different ColE1 plasmids deposited in databases. Bioinformatic analysis of these ColE1 replicons revealed a mosaic genetic structure consisting of a host-adapted conserved region responsible for the housekeeping functions of the plasmid, and a variable region encoding a wide variety of genes, including multiple antibiotic resistance determinants. From this exhaustive computational analysis we developed a new PCR-based technique, targeting a specific sequence in the conserved region, for the screening, capture and sequencing of these small plasmids, either specific for Enterobacteriaceae or specific for Pasteurellaceae. To validate this PCR-based system, we tested various collections of isolates from both bacterial families, finding that ColE1 replicons were not only highly prevalent in antibiotic-resistant isolates, but also present in susceptible bacteria. In Pasteurellaceae, ColE1 plasmids carried almost exclusively antibiotic resistance genes. In Enterobacteriaceae, these plasmids encoded a large range of traits, including not only antibiotic resistance determinants, but also a wide variety of genes, showing the huge genetic plasticity of these small replicons. Finally, we also used a metagenomic approach in order to validate this technique, performing this PCR system using total DNA extractions from fecal samples from poultry, turkeys, pigs and humans. Using Illumina sequencing of the PCR products we identified a great diversity of genes encoded by ColE1 replicons, including different antibiotic resistance

  7. Ecological transition predictably associated with gene degeneration.

    Science.gov (United States)

    Wessinger, Carolyn A; Rausher, Mark D

    2015-02-01

    Gene degeneration or loss can significantly contribute to phenotypic diversification, but may generate genetic constraints on future evolutionary trajectories, potentially restricting phenotypic reversal. Such constraints may manifest as directional evolutionary trends when parallel phenotypic shifts consistently involve gene degeneration or loss. Here, we demonstrate that widespread parallel evolution in Penstemon from blue to red flowers predictably involves the functional inactivation and degeneration of the enzyme flavonoid 3',5'-hydroxylase (F3'5'H), an anthocyanin pathway enzyme required for the production of blue floral pigments. Other types of genetic mutations do not consistently accompany this phenotypic shift. This pattern may be driven by the relatively large mutational target size of degenerative mutations to this locus and the apparent lack of associated pleiotropic effects. The consistent degeneration of F3'5'H may provide a mechanistic explanation for the observed asymmetry in the direction of flower color evolution in Penstemon: Blue to red transitions are common, but reverse transitions have not been observed. Although phenotypic shifts in this system are likely driven by natural selection, internal constraints may generate predictable genetic outcomes and may restrict future evolutionary trajectories. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. Coupled high-throughput functional screening and next generation sequencing for identification of plant polymer decomposing enzymes in metagenomic libraries

    Directory of Open Access Journals (Sweden)

    Mari eNyyssönen

    2013-09-01

    Full Text Available Recent advances in sequencing technologies generate new predictions and hypotheses about the functional roles of environmental microorganisms. Yet, until we can test these predictions at a scale that matches our ability to generate them, most of them will remain as hypotheses. Function-based mining of metagenomic libraries can provide direct linkages between genes, metabolic traits and microbial taxa and thus bridge this gap between sequence data generation and functional predictions. Here we developed high-throughput screening assays for function-based characterization of activities involved in plant polymer decomposition from environmental metagenomic libraries. The multiplexed assays use fluorogenic and chromogenic substrates, combine automated liquid handling and use a genetically modified expression host to enable simultaneous screening of 12,160 clones for 14 activities in a total of 170,240 reactions. Using this platform we identified 374 (0.26 % cellulose, hemicellulose, chitin, starch, phosphate and protein hydrolyzing clones from fosmid libraries prepared from decomposing leaf litter. Sequencing on the Illumina MiSeq platform, followed by assembly and gene prediction of a subset of 95 fosmid clones, identified a broad range of bacterial phyla, including Actinobacteria, Bacteroidetes, multiple Proteobacteria sub-phyla in addition to some Fungi. Carbohydrate-active enzyme genes from 20 different glycoside hydrolase families were detected. Using tetranucleotide frequency binning of fosmid sequences, multiple enzyme activities from distinct fosmids were linked, demonstrating how biochemically-confirmed functional traits in environmental metagenomes may be attributed to groups of specific organisms. Overall, our results demonstrate how functional screening of metagenomic libraries can be used to connect microbial functionality to community composition and, as a result, complement large-scale metagenomic sequencing efforts.

  9. A comparative analysis of soft computing techniques for gene prediction.

    Science.gov (United States)

    Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

    2013-07-01

    The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

  10. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

    2011-01-01

    The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome

  11. Metagenomic-Based Study of the Phylogenetic and Functional Gene Diversity in Galápagos Land and Marine Iguanas

    KAUST Repository

    Hong, Pei-Ying; Mao, Yuejian; Ortiz-Kofoed, Shannon; Shah, Rushabh S.; Cann, Isaac Ko O; Mackie, Roderick Ian

    2014-01-01

    affiliations of the fecal microbiome were more similar between both iguanas than to other mammalian herbivorous hosts. However, functional gene diversities in both MI and LI iguana hosts differed in relation to the diet, where the MI fecal microbiota had a

  12. Interactive metagenomic visualization in a Web browser

    Directory of Open Access Journals (Sweden)

    Phillippy Adam M

    2011-09-01

    Full Text Available Abstract Background A critical output of metagenomic studies is the estimation of abundances of taxonomical or functional groups. The inherent uncertainty in assignments to these groups makes it important to consider both their hierarchical contexts and their prediction confidence. The current tools for visualizing metagenomic data, however, omit or distort quantitative hierarchical relationships and lack the facility for displaying secondary variables. Results Here we present Krona, a new visualization tool that allows intuitive exploration of relative abundances and confidences within the complex hierarchies of metagenomic classifications. Krona combines a variant of radial, space-filling displays with parametric coloring and interactive polar-coordinate zooming. The HTML5 and JavaScript implementation enables fully interactive charts that can be explored with any modern Web browser, without the need for installed software or plug-ins. This Web-based architecture also allows each chart to be an independent document, making them easy to share via e-mail or post to a standard Web server. To illustrate Krona's utility, we describe its application to various metagenomic data sets and its compatibility with popular metagenomic analysis tools. Conclusions Krona is both a powerful metagenomic visualization tool and a demonstration of the potential of HTML5 for highly accessible bioinformatic visualizations. Its rich and interactive displays facilitate more informed interpretations of metagenomic analyses, while its implementation as a browser-based application makes it extremely portable and easily adopted into existing analysis packages. Both the Krona rendering code and conversion tools are freely available under a BSD open-source license, and available from: http://krona.sourceforge.net.

  13. Mining of Ruminant Microbial Phytase (RPHY1) from Metagenomic Data of Mehsani Buffalo Breed: Identification, Gene Cloning, and Characterization.

    Science.gov (United States)

    Mootapally, Chandra Shekar; Nathani, Neelam M; Patel, Amrutlal K; Jakhesara, Subhash J; Joshi, Chaitanya G

    2016-01-01

    Phytases have been widely used as animal feed supplements to increase the availability of digestible phosphorus, especially in monogastric animals fed cereal grains. The present study describes the identification of a full-length phytase gene of Prevotella species present in Mehsani buffalo rumen. The gene, designated as RPHY1, consists of 1,251 bp and is expressed into protein with 417 amino acids. A homology search of the deduced amino acid sequence of the RPHY1 phytase gene in a nonredundant protein database showed that it shares 92% similarity with the histidine acid phosphatase domain. Subsequently, the RPHY1 gene was expressed using a pET32a expression vector in Escherichia coli BL21 and purified using a His60 Ni-NTA gravity column. The mass of the purified RPHY1 was estimated to be approximately 63 kDa by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The optimal RPHY1 enzyme activity was observed at 55°C (pH 5) and exhibited good stability at 5°C and within the acidic pH range. Significant inhibition of RPHY1 activity was observed for Mg2+ and K+ metal ions, while Ca2+, Mn2+, and Na+ slightly inhibited enzyme activity. The RPHY1 phytase was susceptible to SDS, and it was highly stimulated in the presence of EDTA. Overall, the observed comparatively high enzyme activity levels and characteristics of the RPHY1 gene mined from rumen prove its promising candidature as a feed supplement enzyme in animal farming. © 2016 S. Karger AG, Basel.

  14. Metagenomics at Grass Roots

    Indian Academy of Sciences (India)

    CAMERA (Community Cyber-infrastructure for Advanced Mi- crobial Ecology .... Acidobacteria known to metabolize a variety of car- bon sources .... [7] J Nesme et al., Back to the future of soil metagenomics, Frontiers in Microbi- ology, Vol.7 ...

  15. Metagenomics at Grass Roots

    Indian Academy of Sciences (India)

    Metagenomics is a robust, interdisciplinary approach for studyingmicrobial community composition, function, and dynamics.It typically involves a core of molecular biology, microbiology,ecology, statistics, and computational biology. Excitingoutcomes anticipated from these studies include unravelingof complex interactions ...

  16. A metagenomic approach to decipher the indigenous microbial communities of arsenic contaminated groundwater of Assam

    Directory of Open Access Journals (Sweden)

    Saurav Das

    2017-06-01

    Full Text Available Metagenomic approach was used to understand the structural and functional diversity present in arsenic contaminated groundwater of the Ganges Brahmaputra Delta aquifer system. A metagene dataset (coded as TTGW1 of 89,171 sequences (totaling 125,449,864 base pairs with an average length of 1406 bps was annotated. About 74,478 sequences containing 101,948 predicted protein coding regions passed the quality control. Taxonomical classification revealed abundance of bacteria that accounted for 98.3% of the microbial population of the metagenome. Eukaryota had an abundance of 1.1% followed by archea that showed 0.4% abundance. In phylum based classification, Proteobacteria was dominant (62.6% followed by Bacteroidetes (11.7%, Planctomycetes (7.7%, Verrucomicrobia (5.6%, Actinobacteria (3.7% and Firmicutes (1.9%. The Clusters of Orthologous Groups (COGs analysis indicated that the protein regulating the metabolic functions constituted a high percentage (18,199 reads; 39.3% of the whole metagenome followed by the proteins regulating the cellular processes (22.3%. About 0.07% sequences of the whole metagenome were related to genes coding for arsenic resistant mechanisms. Nearly 50% sequences of these coded for the arsenate reductase enzyme (EC. 1.20.4.1, the dominant enzyme of ars operon. Proteins associated with iron acquisition and metabolism were coded by 2% of the metagenome as revealed through SEED analysis. Our study reveals the microbial diversity and provides an insight into the functional aspect of the genes that might play crucial role in arsenic geocycle in contaminated ground water of Assam.

  17. An algorithm to discover gene signatures with predictive potential

    Directory of Open Access Journals (Sweden)

    Hallett Robin M

    2010-09-01

    Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

  18. Metagenomic insights into lignocellulose-degrading genes through Illumina-based de novo sequencing of the microbiome in Vietnamese native goats' rumen

    NARCIS (Netherlands)

    Do, Thi Huyen; Le, Ngoc Giang; Dao, Trong Khoa; Nguyen, Thi Mai Phuong; Le, Tung Lam; Luu, Han Ly; Nguyen, Khanh Hoang Viet; Nguyen, Van Lam; Le, Lan Anh; Phung, Thu Nguyet; van Straalen, Nico M; Roelofs, Dick; Truong, Nam Hai

    2018-01-01

    The scarcity of enzymes having an optimal activity in lignocellulose deconstruction is an obstacle for industrial-scale conversion of cellulosic biomass into biofuels. With the aim of mining novel lignocellulolytic enzymes, a ~9 Gb metagenome of bacteria in Vietnamese native goats' rumen was

  19. Critical Assessment of Metagenome Interpretation

    DEFF Research Database (Denmark)

    Sczyrba, Alexander; Hofmann, Peter; Belmann, Peter

    2017-01-01

    Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchma...

  20. Metagenomic analysis of bacterial community composition and antibiotic resistance genes in a wastewater treatment plant and its receiving surface water.

    Science.gov (United States)

    Tang, Junying; Bu, Yuanqing; Zhang, Xu-Xiang; Huang, Kailong; He, Xiwei; Ye, Lin; Shan, Zhengjun; Ren, Hongqiang

    2016-10-01

    The presence of pathogenic bacteria and the dissemination of antibiotic resistance genes (ARGs) may pose big risks to the rivers that receive the effluent from municipal wastewater treatment plants (WWTPs). In this study, we investigated the changes of bacterial community and ARGs along treatment processes of one WWTP, and examined the effects of the effluent discharge on the bacterial community and ARGs in the receiving river. Pyrosequencing was applied to reveal bacterial community composition including potential bacterial pathogen, and Illumina high-throughput sequencing was used for profiling ARGs. The results showed that the WWTP had good removal efficiency on potential pathogenic bacteria (especially Arcobacter butzleri) and ARGs. Moreover, the bacterial communities of downstream and upstream of the river showed no significant difference. However, the increase in the abundance of potential pathogens and ARGs at effluent outfall was observed, indicating that WWTP effluent might contribute to the dissemination of potential pathogenic bacteria and ARGs in the receiving river. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Challenges and opportunities of airborne metagenomics.

    Science.gov (United States)

    Behzad, Hayedeh; Gojobori, Takashi; Mineta, Katsuhiko

    2015-05-06

    Recent metagenomic studies of environments, such as marine and soil, have significantly enhanced our understanding of the diverse microbial communities living in these habitats and their essential roles in sustaining vast ecosystems. The increase in the number of publications related to soil and marine metagenomics is in sharp contrast to those of air, yet airborne microbes are thought to have significant impacts on many aspects of our lives from their potential roles in atmospheric events such as cloud formation, precipitation, and atmospheric chemistry to their major impact on human health. In this review, we will discuss the current progress in airborne metagenomics, with a special focus on exploring the challenges and opportunities of undertaking such studies. The main challenges of conducting metagenomic studies of airborne microbes are as follows: 1) Low density of microorganisms in the air, 2) efficient retrieval of microorganisms from the air, 3) variability in airborne microbial community composition, 4) the lack of standardized protocols and methodologies, and 5) DNA sequencing and bioinformatics-related challenges. Overcoming these challenges could provide the groundwork for comprehensive analysis of airborne microbes and their potential impact on the atmosphere, global climate, and our health. Metagenomic studies offer a unique opportunity to examine viral and bacterial diversity in the air and monitor their spread locally or across the globe, including threats from pathogenic microorganisms. Airborne metagenomic studies could also lead to discoveries of novel genes and metabolic pathways relevant to meteorological and industrial applications, environmental bioremediation, and biogeochemical cycles. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  2. Exploring the Optimal Strategy to Predict Essential Genes in Microbes

    Directory of Open Access Journals (Sweden)

    Yao Lu

    2011-12-01

    Full Text Available Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.

  3. The binning of metagenomic contigs for microbial physiology of mixed cultures.

    Science.gov (United States)

    Strous, Marc; Kraft, Beate; Bisdorf, Regina; Tegetmeyer, Halina E

    2012-01-01

    So far, microbial physiology has dedicated itself mainly to pure cultures. In nature, cross feeding and competition are important aspects of microbial physiology and these can only be addressed by studying complete communities such as enrichment cultures. Metagenomic sequencing is a powerful tool to characterize such mixed cultures. In the analysis of metagenomic data, well established algorithms exist for the assembly of short reads into contigs and for the annotation of predicted genes. However, the binning of the assembled contigs or unassembled reads is still a major bottleneck and required to understand how the overall metabolism is partitioned over different community members. Binning consists of the clustering of contigs or reads that apparently originate from the same source population. In the present study eight metagenomic samples from the same habitat, a laboratory enrichment culture, were sequenced. Each sample contained 13-23 Mb of assembled contigs and up to eight abundant populations. Binning was attempted with existing methods but they were found to produce poor results, were slow, dependent on non-standard platforms or produced errors. A new binning procedure was developed based on multivariate statistics of tetranucleotide frequencies combined with the use of interpolated Markov models. Its performance was evaluated by comparison of the results between samples with BLAST and in comparison to existing algorithms for four publicly available metagenomes and one previously published artificial metagenome. The accuracy of the new approach was comparable or higher than existing methods. Further, it was up to a 100 times faster. It was implemented in Java Swing as a complete open source graphical binning application available for download and further development (http://sourceforge.net/projects/metawatt).

  4. The binning of metagenomic contigs for microbial physiology of mixed cultures

    Directory of Open Access Journals (Sweden)

    Marc eStrous

    2012-12-01

    Full Text Available So far, microbial physiology has dedicated itself mainly to pure cultures. In nature, cross feeding and competition are important aspects of microbial physiology and these can only be addressed by studying complete communities such as enrichment cultures. Metagenomic sequencing is a powerful tool to characterize such mixed cultures. In the analysis of metagenomic data, well established algorithms exist for the assembly of short reads into contigs and for the annotation of predicted genes. However, the binning of the assembled contigs or unassembled reads is still a major bottleneck and required to understand how the overall metabolism is partitioned over different community members. Binning consists of the clustering of contigs or reads that apparently originate from the same source population.In the present study eight metagenomic samples originating from the same habitat, a laboratory enrichment culture, were sequenced. Each sample contained 13-23 Mb of assembled contigs and up to eight abundant populations. Binning was attempted with existing methods but they were found to produce poor results, were slow, dependent on non-standard platforms or produced errors. A new binning procedure was developed based on multivariate statistics of tetranucleotide frequencies combined with the use of interpolated Markov models. Its performance was evaluated by comparison of the results between samples with BLAST and in comparison to exisiting algorithms for four publicly available metagenomes and one previously published artificial metagenome. The accuracy of the new approach was comparable or higher than existing methods. Further, it was up to a hunderd times faster. It was implemented in Java Swing as a complete open source graphical binning application available for download and further development (http://sourceforge.net/projects/metawatt.

  5. Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data

    DEFF Research Database (Denmark)

    Raes, Jeroen; Letunic, Ivica; Yamada, Takuji

    2011-01-01

    Using metagenomic 'parts lists' to infer global patterns on microbial ecology remains a significant challenge. To deduce important ecological indicators such as environmental adaptation, molecular trait dispersal, diversity variation and primary production from the gene pool of an ecosystem, we...... integrated 25 ocean metagenomes with geographical, meteorological and geophysicochemical data. We find that climatic factors (temperature, sunlight) are the major determinants of the biomolecular repertoire of each sample and the main limiting factor on functional trait dispersal (absence of biogeographic...... provincialism). Molecular functional richness and diversity show a distinct latitudinal gradient peaking at 20° N and correlate with primary production. The latter can also be predicted from the molecular functional composition of an environmental sample. Together, our results show that the functional community...

  6. High throughtput comparisons and profiling of metagenomes for industrially relevant enzymes

    KAUST Repository

    Alam, Intikhab

    2016-01-26

    More and more genomes and metagenomes are being sequenced since the advent of Next Generation Sequencing Technologies (NGS). Many metagenomic samples are collected from a variety of environments, each exhibiting a different environmental profile, e.g. temperature, environmental chemistry, etc… These metagenomes can be profiled to unearth enzymes relevant to several industries based on specific enzyme properties such as ability to work on extreme conditions, such as extreme temperatures, salinity, anaerobically, etc.. In this work, we present the DMAP platform comprising of a high-throughput metagenomic annotation pipeline and a data-warehouse for comparisons and profiling across large number of metagenomes. We developed two reference databases for profiling of important genes, one containing enzymes related to different industries and the other containing genes with potential bioactivity roles. In this presentation we describe an example analysis of a large number of publicly available metagenomic sample from TARA oceans study (Science 2015) that covers significant part of world oceans.

  7. Bracken: estimating species abundance in metagenomics data

    Directory of Open Access Journals (Sweden)

    Jennifer Lu

    2017-01-01

    Full Text Available Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.

  8. A retrospective metagenomics approach to studying Blastocystis.

    Science.gov (United States)

    Andersen, Lee O'Brien; Bonde, Ida; Nielsen, Henrik Bjørn; Stensvold, Christen Rune

    2015-07-01

    Blastocystis is a common single-celled intestinal parasitic genus, comprising several subtypes. Here, we screened data obtained by metagenomic analysis of faecal DNA for Blastocystis by searching for subtype-specific genes in coabundance gene groups, which are groups of genes that covary across a selection of 316 human faecal samples, hence representing genes originating from a single subtype. The 316 faecal samples were from 236 healthy individuals, 13 patients with Crohn's disease (CD) and 67 patients with ulcerative colitis (UC). The prevalence of Blastocystis was 20.3% in the healthy individuals and 14.9% in patients with UC. Meanwhile, Blastocystis was absent in patients with CD. Individuals with intestinal microbiota dominated by Bacteroides were much less prone to having Blastocystis-positive stool (Matthew's correlation coefficient = -0.25, P < 0.0001) than individuals with Ruminococcus- and Prevotella-driven enterotypes. This is the first study to investigate the relationship between Blastocystis and communities of gut bacteria using a metagenomics approach. The study serves as an example of how it is possible to retrospectively investigate microbial eukaryotic communities in the gut using metagenomic datasets targeting the bacterial component of the intestinal microbiome and the interplay between these microbial communities. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  9. A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

    Science.gov (United States)

    Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

    2015-01-01

    Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

  10. Gene prediction validation and functional analysis of redundant pathways

    DEFF Research Database (Denmark)

    Sønderkær, Mads

    2011-01-01

    have employed a large mRNA-seq data set to improve and validate ab initio predicted gene models. This direct experimental evidence also provides reliable determinations of UTR regions and polyadenylation sites, which are not easily predicted in plants. Furthermore, once an annotated genome sequence...... is available, gene expression by mRNA-Seq enables acquisition of a more complete overview of gene isoform usage in complex enzymatic pathways enabling the identification of key genes. Metabolism in potatoes This information is useful e.g. for crop improvement based on manipulation of agronomically important...

  11. A network approach to predict pathogenic genes for Fusarium graminearum.

    Science.gov (United States)

    Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

    2010-10-04

    Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which

  12. Soil metagenomics and tropical soil productivity

    OpenAIRE

    Garrett, Karen A.

    2009-01-01

    This presentation summarizes research in the soil metagenomics cross cutting research activity. Soil metagenomics studies soil microbial communities as contributors to soil health.C CCRA-4 (Soil Metagenomics)

  13. Metagenomic Analysis of the Gut Microbiome of the Common Black Slug Arion ater in Search of Novel Lignocellulose Degrading Enzymes

    Directory of Open Access Journals (Sweden)

    Ryan Joynson

    2017-11-01

    Full Text Available Some eukaryotes are able to gain access to well-protected carbon sources in plant biomass by exploiting microorganisms in the environment or harbored in their digestive system. One is the land pulmonate Arion ater, which takes advantage of a gut microbial consortium that can break down the widely available, but difficult to digest, carbohydrate polymers in lignocellulose, enabling them to digest a broad range of fresh and partially degraded plant material efficiently. This ability is considered one of the major factors that have enabled A. ater to become one of the most widespread plant pest species in Western Europe and North America. Using metagenomic techniques we have characterized the bacterial diversity and functional capability of the gut microbiome of this notorious agricultural pest. Analysis of gut metagenomic community sequences identified abundant populations of known lignocellulose-degrading bacteria, along with well-characterized bacterial plant pathogens. This also revealed a repertoire of more than 3,383 carbohydrate active enzymes (CAZymes including multiple enzymes associated with lignin degradation, demonstrating a microbial consortium capable of degradation of all components of lignocellulose. This would allow A. ater to make extensive use of plant biomass as a source of nutrients through exploitation of the enzymatic capabilities of the gut microbial consortia. From this metagenome assembly we also demonstrate the successful amplification of multiple predicted gene sequences from metagenomic DNA subjected to whole genome amplification and expression of functional proteins, facilitating the low cost acquisition and biochemical testing of the many thousands of novel genes identified in metagenomics studies. These findings demonstrate the importance of studying Gastropod microbial communities. Firstly, with respect to understanding links between feeding and evolutionary success and, secondly, as sources of novel enzymes with

  14. Metagenomic sequence of saline desert microbiota from wild ass sanctuary, Little Rann of Kutch, Gujarat, India.

    Science.gov (United States)

    Patel, Rajesh; Mevada, Vishal; Prajapati, Dhaval; Dudhagara, Pravin; Koringa, Prakash; Joshi, C G

    2015-03-01

    We report Metagenome from the saline desert soil sample of Little Rann of Kutch, Gujarat State, India. Metagenome consisted of 633,760 sequences with size 141,307,202 bp and 56% G + C content. Metagenome sequence data are available at EBI under EBI Metagenomics database with accession no. ERP005612. Community metagenomics revealed total 1802 species belonged to 43 different phyla with dominating Marinobacter (48.7%) and Halobacterium (4.6%) genus in bacterial and archaeal domain respectively. Remarkably, 18.2% sequences in a poorly characterized group and 4% gene for various stress responses along with versatile presence of commercial enzyme were evident in a functional metagenome analysis.

  15. Reranking candidate gene models with cross-species comparison for improved gene prediction

    Directory of Open Access Journals (Sweden)

    Pereira Fernando CN

    2008-10-01

    Full Text Available Abstract Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc. Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.

  16. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    KAUST Repository

    Hou, Siqing

    2018-05-21

    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.

  17. Semi-supervised prediction of gene regulatory networks using ...

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging ... two types of methods differ primarily based on whether ..... negligible, allowing us to draw the qualitative conclusions .... research will be conducted to develop additional biologically.

  18. The YNP metagenome project

    DEFF Research Database (Denmark)

    Inskeep, William P.; Jay, Zackary J.; Tringe, Susannah G.

    2013-01-01

    The Yellowstone geothermal complex contains over 10,000 diverse geothermal features that host numerous phylogenetically deeply rooted and poorly understood archaea, bacteria, and viruses. Microbial communities in high-temperature environments are generally less diverse than soil, marine, sediment......, and environmental variables. Twenty geochemically distinct geothermal ecosystems representing a broad spectrum of Yellowstone hot-spring environments were used for metagenomic and geochemical analysis and included approximately equal numbers of: (1) phototrophic mats, (2) “filamentous streamer” communities, and (3...

  19. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  20. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning.

    Science.gov (United States)

    He, Zhili; Zhang, Ping; Wu, Linwei; Rocha, Andrea M; Tu, Qichao; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D; Wu, Liyou; Yang, Yunfeng; Elias, Dwayne A; Watson, David B; Adams, Michael W W; Fields, Matthew W; Alm, Eric J; Hazen, Terry C; Adams, Paul D; Arkin, Adam P; Zhou, Jizhong

    2018-02-20

    Contamination from anthropogenic activities has significantly impacted Earth's biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly ( P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness/diversity decreased as uranium increased in groundwater, while specific key microbial guilds increased significantly as

  1. Construction and screening of marine metagenomic libraries.

    Science.gov (United States)

    Weiland, Nancy; Löscher, Carolin; Metzger, Rebekka; Schmitz, Ruth

    2010-01-01

    Marine microbial communities are highly diverse and have evolved during extended evolutionary processes of physiological adaptations under the influence of a variety of ecological conditions and selection pressures. They harbor an enormous diversity of microbes with still unknown and probably new physiological characteristics. Besides, the surfaces of marine multicellular organisms are typically covered by a consortium of epibiotic bacteria and act as barriers, where diverse interactions between microorganisms and hosts take place. Thus, microbial diversity in the water column of the oceans and the microbial consortia on marine tissues of multicellular organisms are rich sources for isolating novel bioactive compounds and genes. Here we describe the sampling, construction of large-insert metagenomic libraries from marine habitats and exemplarily one function based screen of metagenomic clones.

  2. FY11 Report on Metagenome Analysis using Pathogen Marker Libraries

    Energy Technology Data Exchange (ETDEWEB)

    Gardner, Shea N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Allen, Jonathan E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); McLoughlin, Kevin S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Slezak, Tom [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2011-06-02

    detection probability appears to be a function of both coverages. Multiple species could be detected simultaneously in a simulated low-coverage, complex metagenome, and the largest PML gave no false negative species and no false positive genera. The presence of multiple species was predicted in a complex metagenome from a human gut microbiome with 1.9 GB of short reads (75 nt); the species predicted were reasonable gut flora and no biothreat agents were detected, showing the feasibility of PML analysis of empirical complex metagenomes.

  3. Genome signature analysis of thermal virus metagenomes reveals Archaea and thermophilic signatures

    Directory of Open Access Journals (Sweden)

    Pride David T

    2008-09-01

    Full Text Available Abstract Background Metagenomic analysis provides a rich source of biological information for otherwise intractable viral communities. However, study of viral metagenomes has been hampered by its nearly complete reliance on BLAST algorithms for identification of DNA sequences. We sought to develop algorithms for examination of viral metagenomes to identify the origin of sequences independent of BLAST algorithms. We chose viral metagenomes obtained from two hot springs, Bear Paw and Octopus, in Yellowstone National Park, as they represent simple microbial populations where comparatively large contigs were obtained. Thermal spring metagenomes have high proportions of sequences without significant Genbank homology, which has hampered identification of viruses and their linkage with hosts. To analyze each metagenome, we developed a method to classify DNA fragments using genome signature-based phylogenetic classification (GSPC, where metagenomic fragments are compared to a database of oligonucleotide signatures for all previously sequenced Bacteria, Archaea, and viruses. Results From both Bear Paw and Octopus hot springs, each assembled contig had more similarity to other metagenome contigs than to any sequenced microbial genome based on GSPC analysis, suggesting a genome signature common to each of these extreme environments. While viral metagenomes from Bear Paw and Octopus share some similarity, the genome signatures from each locale are largely unique. GSPC using a microbial database predicts most of the Octopus metagenome has archaeal signatures, while bacterial signatures predominate in Bear Paw; a finding consistent with those of Genbank BLAST. When using a viral database, the majority of the Octopus metagenome is predicted to belong to archaeal virus Families Globuloviridae and Fuselloviridae, while none of the Bear Paw metagenome is predicted to belong to archaeal viruses. As expected, when microbial and viral databases are combined, each of

  4. Genome signature analysis of thermal virus metagenomes reveals Archaea and thermophilic signatures.

    Science.gov (United States)

    Pride, David T; Schoenfeld, Thomas

    2008-09-17

    Metagenomic analysis provides a rich source of biological information for otherwise intractable viral communities. However, study of viral metagenomes has been hampered by its nearly complete reliance on BLAST algorithms for identification of DNA sequences. We sought to develop algorithms for examination of viral metagenomes to identify the origin of sequences independent of BLAST algorithms. We chose viral metagenomes obtained from two hot springs, Bear Paw and Octopus, in Yellowstone National Park, as they represent simple microbial populations where comparatively large contigs were obtained. Thermal spring metagenomes have high proportions of sequences without significant Genbank homology, which has hampered identification of viruses and their linkage with hosts. To analyze each metagenome, we developed a method to classify DNA fragments using genome signature-based phylogenetic classification (GSPC), where metagenomic fragments are compared to a database of oligonucleotide signatures for all previously sequenced Bacteria, Archaea, and viruses. From both Bear Paw and Octopus hot springs, each assembled contig had more similarity to other metagenome contigs than to any sequenced microbial genome based on GSPC analysis, suggesting a genome signature common to each of these extreme environments. While viral metagenomes from Bear Paw and Octopus share some similarity, the genome signatures from each locale are largely unique. GSPC using a microbial database predicts most of the Octopus metagenome has archaeal signatures, while bacterial signatures predominate in Bear Paw; a finding consistent with those of Genbank BLAST. When using a viral database, the majority of the Octopus metagenome is predicted to belong to archaeal virus Families Globuloviridae and Fuselloviridae, while none of the Bear Paw metagenome is predicted to belong to archaeal viruses. As expected, when microbial and viral databases are combined, each of the Octopus and Bear Paw metagenomic contigs

  5. Exploring nucleo-cytoplasmic large DNA viruses in Tara Oceans microbial metagenomes.

    Science.gov (United States)

    Hingamp, Pascal; Grimsley, Nigel; Acinas, Silvia G; Clerissi, Camille; Subirana, Lucie; Poulain, Julie; Ferrera, Isabel; Sarmento, Hugo; Villar, Emilie; Lima-Mendez, Gipsi; Faust, Karoline; Sunagawa, Shinichi; Claverie, Jean-Michel; Moreau, Hervé; Desdevises, Yves; Bork, Peer; Raes, Jeroen; de Vargas, Colomban; Karsenti, Eric; Kandels-Lewis, Stefanie; Jaillon, Olivier; Not, Fabrice; Pesant, Stéphane; Wincker, Patrick; Ogata, Hiroyuki

    2013-09-01

    Nucleo-cytoplasmic large DNA viruses (NCLDVs) constitute a group of eukaryotic viruses that can have crucial ecological roles in the sea by accelerating the turnover of their unicellular hosts or by causing diseases in animals. To better characterize the diversity, abundance and biogeography of marine NCLDVs, we analyzed 17 metagenomes derived from microbial samples (0.2-1.6 μm size range) collected during the Tara Oceans Expedition. The sample set includes ecosystems under-represented in previous studies, such as the Arabian Sea oxygen minimum zone (OMZ) and Indian Ocean lagoons. By combining computationally derived relative abundance and direct prokaryote cell counts, the abundance of NCLDVs was found to be in the order of 10(4)-10(5) genomes ml(-1) for the samples from the photic zone and 10(2)-10(3) genomes ml(-1) for the OMZ. The Megaviridae and Phycodnaviridae dominated the NCLDV populations in the metagenomes, although most of the reads classified in these families showed large divergence from known viral genomes. Our taxon co-occurrence analysis revealed a potential association between viruses of the Megaviridae family and eukaryotes related to oomycetes. In support of this predicted association, we identified six cases of lateral gene transfer between Megaviridae and oomycetes. Our results suggest that marine NCLDVs probably outnumber eukaryotic organisms in the photic layer (per given water mass) and that metagenomic sequence analyses promise to shed new light on the biodiversity of marine viruses and their interactions with potential hosts.

  6. Predictability of Genetic Interactions from Functional Gene Modules

    Directory of Open Access Journals (Sweden)

    Jonathan H. Young

    2017-02-01

    Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

  7. Metagenomic analysis of permafrost microbial community response to thaw

    Energy Technology Data Exchange (ETDEWEB)

    Mackelprang, R.; Waldrop, M.P.; DeAngelis, K.M.; David, M.M.; Chavarria, K.L.; Blazewicz, S.J.; Rubin, E.M.; Jansson, J.K.

    2011-07-01

    We employed deep metagenomic sequencing to determine the impact of thaw on microbial phylogenetic and functional genes and related this data to measurements of methane emissions. Metagenomics, the direct sequencing of DNA from the environment, allows for the examination of whole biochemical pathways and associated processes, as opposed to individual pieces of the metabolic puzzle. Our metagenome analyses revealed that during transition from a frozen to a thawed state there were rapid shifts in many microbial, phylogenetic and functional gene abundances and pathways. After one week of incubation at 5°C, permafrost metagenomes converged to be more similar to each other than while they were frozen. We found that multiple genes involved in cycling of C and nitrogen shifted rapidly during thaw. We also constructed the first draft genome from a complex soil metagenome, which corresponded to a novel methanogen. Methane previously accumulated in permafrost was released during thaw and subsequently consumed by methanotrophic bacteria. Together these data point towards the importance of rapid cycling of methane and nitrogen in thawing permafrost.

  8. Databases of the marine metagenomics

    KAUST Repository

    Mineta, Katsuhiko

    2015-10-28

    The metagenomic data obtained from marine environments is significantly useful for understanding marine microbial communities. In comparison with the conventional amplicon-based approach of metagenomics, the recent shotgun sequencing-based approach has become a powerful tool that provides an efficient way of grasping a diversity of the entire microbial community at a sampling point in the sea. However, this approach accelerates accumulation of the metagenome data as well as increase of data complexity. Moreover, when metagenomic approach is used for monitoring a time change of marine environments at multiple locations of the seawater, accumulation of metagenomics data will become tremendous with an enormous speed. Because this kind of situation has started becoming of reality at many marine research institutions and stations all over the world, it looks obvious that the data management and analysis will be confronted by the so-called Big Data issues such as how the database can be constructed in an efficient way and how useful knowledge should be extracted from a vast amount of the data. In this review, we summarize the outline of all the major databases of marine metagenome that are currently publically available, noting that database exclusively on marine metagenome is none but the number of metagenome databases including marine metagenome data are six, unexpectedly still small. We also extend our explanation to the databases, as reference database we call, that will be useful for constructing a marine metagenome database as well as complementing important information with the database. Then, we would point out a number of challenges to be conquered in constructing the marine metagenome database.

  9. Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

    Science.gov (United States)

    Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

    2014-01-01

    Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

  10. The relative abundance of predicted genes associated with ammonia-oxidation, nitrate reduction, and biomass decomposition in mineral soil are altered by intensive timber harvest.

    Science.gov (United States)

    Mushinski, R. M.; Zhou, Y.; Gentry, T. J.; Boutton, T. W.

    2017-12-01

    Forest ecosystems in the southern United States are substantially altered by anthropogenic disturbances such as timber harvest and land conversion, with effects being observed in carbon and nutrient pools as well as biogeochemical processes. Furthermore, the desire to develop renewable energy sources in the form of biomass extraction from logging residues may result in alterations in soil community structure and function. While the impact of forest management on soil physicochemical properties of the region has been studied, its' long-term effect on soil bacterial community composition and metagenomic potential is relatively unknown, especially at deeper soil depths. This study investigates how intensive organic matter removal intensities associated with timber harvest influence decadal-scale alterations in bacterial community structure and functional potential in the upper 1-m of the soil profile, 18 years post-harvest in a Pinus taeda L. forest of eastern Texas. Amplicon sequencing of the 16S rRNA gene was used in conjunction with soil chemical analyses to evaluate treatment-induced differences in community composition and potential environmental drivers of associated change. Furthermore, functional potential was assessed by using amplicon data to make metagenomic predictions. Results indicate that increasing organic matter removal intensity leads to altered community composition and the relative abundance of dominant OTUs annotated to Burkholderia and Aciditerrimonas. The relative abundance of predicted genes associated with dissimilatory nitrate reduction and denitrification were highest in the most intensively harvested treatment while genes involved in nitrification were significantly lower in the most intensively harvested treatment. Furthermore, genes associated with glycosyltransferases were significantly reduced with increasing harvest intensity while polysaccharide lyases increased. These results imply that intensive organic matter removal may create

  11. Global discriminative learning for higher-accuracy computational gene prediction.

    Directory of Open Access Journals (Sweden)

    Axel Bernal

    2007-03-01

    Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

  12. Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Liying Yang

    2016-01-01

    Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

  13. A deep auto-encoder model for gene expression prediction.

    Science.gov (United States)

    Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

    2017-11-17

    Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

  14. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.

    Science.gov (United States)

    Nielsen, H Bjørn; Almeida, Mathieu; Juncker, Agnieszka Sierakowska; Rasmussen, Simon; Li, Junhua; Sunagawa, Shinichi; Plichta, Damian R; Gautier, Laurent; Pedersen, Anders G; Le Chatelier, Emmanuelle; Pelletier, Eric; Bonde, Ida; Nielsen, Trine; Manichanh, Chaysavanh; Arumugam, Manimozhiyan; Batto, Jean-Michel; Quintanilha Dos Santos, Marcelo B; Blom, Nikolaj; Borruel, Natalia; Burgdorf, Kristoffer S; Boumezbeur, Fouad; Casellas, Francesc; Doré, Joël; Dworzynski, Piotr; Guarner, Francisco; Hansen, Torben; Hildebrand, Falk; Kaas, Rolf S; Kennedy, Sean; Kristiansen, Karsten; Kultima, Jens Roat; Léonard, Pierre; Levenez, Florence; Lund, Ole; Moumen, Bouziane; Le Paslier, Denis; Pons, Nicolas; Pedersen, Oluf; Prifti, Edi; Qin, Junjie; Raes, Jeroen; Sørensen, Søren; Tap, Julien; Tims, Sebastian; Ussery, David W; Yamada, Takuji; Renault, Pierre; Sicheritz-Ponten, Thomas; Bork, Peer; Wang, Jun; Brunak, Søren; Ehrlich, S Dusko

    2014-08-01

    Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

  15. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

    Science.gov (United States)

    Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

    2004-03-05

    Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

  16. Functional Metagenomic Investigations of the Human Intestinal Microbiota

    DEFF Research Database (Denmark)

    Moore, Aimee M.; Munck, Christian; Sommer, Morten Otto Alexander

    2011-01-01

    The human intestinal microbiota encode multiple critical functions impacting human health, including metabolism of dietary substrate, prevention of pathogen invasion, immune system modulation, and provision of a reservoir of antibiotic resistance genes accessible to pathogens. The complexity...... microorganisms, but relatively recently applied to the study of the human commensal microbiota. Metagenomic functional screens characterize the functional capacity of a microbial community, independent of identity to known genes, by subjecting the metagenome to functional assays in a genetically tractable host....... Here we highlight recent work applying this technique to study the functional diversity of the intestinal microbiota, and discuss how an approach combining high-throughput sequencing, cultivation, and metagenomic functional screens can improve our understanding of interactions between this complex...

  17. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  18. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  19. Bioinformatic prediction and functional characterization of human KIAA0100 gene

    Directory of Open Access Journals (Sweden)

    He Cui

    2017-02-01

    Full Text Available Our previous study demonstrated that human KIAA0100 gene was a novel acute monocytic leukemia-associated antigen (MLAA gene. But the functional characterization of human KIAA0100 gene has remained unknown to date. Here, firstly, bioinformatic prediction of human KIAA0100 gene was carried out using online softwares; Secondly, Human KIAA0100 gene expression was downregulated by the clustered regularly interspaced short palindromic repeats (CRISPR/CRISPR-associated (Cas 9 system in U937 cells. Cell proliferation and apoptosis were next evaluated in KIAA0100-knockdown U937 cells. The bioinformatic prediction showed that human KIAA0100 gene was located on 17q11.2, and human KIAA0100 protein was located in the secretory pathway. Besides, human KIAA0100 protein contained a signalpeptide, a transmembrane region, three types of secondary structures (alpha helix, extended strand, and random coil , and four domains from mitochondrial protein 27 (FMP27. The observation on functional characterization of human KIAA0100 gene revealed that its downregulation inhibited cell proliferation, and promoted cell apoptosis in U937 cells. To summarize, these results suggest human KIAA0100 gene possibly comes within mitochondrial genome; moreover, it is a novel anti-apoptotic factor related to carcinogenesis or progression in acute monocytic leukemia, and may be a potential target for immunotherapy against acute monocytic leukemia.

  20. A network approach to predict pathogenic genes for Fusarium graminearum.

    Directory of Open Access Journals (Sweden)

    Xiaoping Liu

    Full Text Available Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB, which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other

  1. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes.

    Science.gov (United States)

    Fierer, Noah; Leff, Jonathan W; Adams, Byron J; Nielsen, Uffe N; Bates, Scott Thomas; Lauber, Christian L; Owens, Sarah; Gilbert, Jack A; Wall, Diana H; Caporaso, J Gregory

    2012-12-26

    For centuries ecologists have studied how the diversity and functional traits of plant and animal communities vary across biomes. In contrast, we have only just begun exploring similar questions for soil microbial communities despite soil microbes being the dominant engines of biogeochemical cycles and a major pool of living biomass in terrestrial ecosystems. We used metagenomic sequencing to compare the composition and functional attributes of 16 soil microbial communities collected from cold deserts, hot deserts, forests, grasslands, and tundra. Those communities found in plant-free cold desert soils typically had the lowest levels of functional diversity (diversity of protein-coding gene categories) and the lowest levels of phylogenetic and taxonomic diversity. Across all soils, functional beta diversity was strongly correlated with taxonomic and phylogenetic beta diversity; the desert microbial communities were clearly distinct from the nondesert communities regardless of the metric used. The desert communities had higher relative abundances of genes associated with osmoregulation and dormancy, but lower relative abundances of genes associated with nutrient cycling and the catabolism of plant-derived organic compounds. Antibiotic resistance genes were consistently threefold less abundant in the desert soils than in the nondesert soils, suggesting that abiotic conditions, not competitive interactions, are more important in shaping the desert microbial communities. As the most comprehensive survey of soil taxonomic, phylogenetic, and functional diversity to date, this study demonstrates that metagenomic approaches can be used to build a predictive understanding of how microbial diversity and function vary across terrestrial biomes.

  2. Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

    Energy Technology Data Exchange (ETDEWEB)

    Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie; Schwientek, Patrick; Tremblay, Julien; Schackwitz, Wendy; Martin, Joel; Pati, Amrita; Bushnell, Brian; Foster, Brian; Kang, Dongwan; Tringe, Susannah G.; Bertilsson, Stefan; Moran, Mary Ann; Shade, Ashley; Newton, Ryan J.; Stevens, Sarah; McMcahon, Katherine D.; Mamlstrom, Rex R.

    2014-05-12

    Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ecotype model? of diversification, but not previously observed in natural populations.

  3. Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

    Energy Technology Data Exchange (ETDEWEB)

    Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie; Schwientek, Patrick; Tremblay, Julien; Schackwitz, Wendy; Martin, Joel; Pati, Amrita; Bushnell, Brian; Foster, Brian; Kang, Dongwan; Tringe, Susannah G.; Bertilsson, Stefan; Moran, Mary Ann; Shade, Ashley; Newton, Ryan J.; Stevens, Sarah; McMahon, Katherine D.; Malmstrom, Rex R.

    2014-06-18

    Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ‘ecotype model’ of diversification, but not previously observed in natural populations.

  4. A Metagenomic and in Silico Functional Prediction of Gut Microbiota Profiles May Concur in Discovering New Cystic Fibrosis Patient-Targeted Probiotics.

    Science.gov (United States)

    Vernocchi, Pamela; Del Chierico, Federica; Quagliariello, Andrea; Ercolini, Danilo; Lucidi, Vincenzina; Putignani, Lorenza

    2017-12-09

    Cystic fibrosis (CF) is a life-limiting hereditary disorder that results in aberrant mucosa in the lungs and digestive tract, chronic respiratory infections, chronic inflammation, and the need for repeated antibiotic treatments. Probiotics have been demonstrated to improve the quality of life of CF patients. We investigated the distribution of gut microbiota (GM) bacteria to identify new potential probiotics for CF patients on the basis of GM patterns. Fecal samples of 28 CF patients and 31 healthy controls (HC) were collected and analyzed by 16S rRNA-based pyrosequencing analysis of GM, to produce CF-HC paired maps of the distribution of operational taxonomic units (OTUs), and by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) for Kyoto Encyclopedia of Genes and Genomes (KEGG) biomarker prediction. The maps were scanned to highlight the distribution of bacteria commonly claimed as probiotics, such as bifidobacteria and lactobacilli, and of butyrate-producing colon bacteria, such as Eubacterium spp. and Faecalibacterium prausnitzii. The analyses highlighted 24 OTUs eligible as putative probiotics. Eleven and nine species were prevalently associated with the GM of CF and HC subjects, respectively. Their KEGG prediction provided differential CF and HC pathways, indeed associated with health-promoting biochemical activities in the latter case. GM profiling and KEGG biomarkers concurred in the evaluation of nine bacterial species as novel putative probiotics that could be investigated for the nutritional management of CF patients.

  5. A Metagenomic and in Silico Functional Prediction of Gut Microbiota Profiles May Concur in Discovering New Cystic Fibrosis Patient-Targeted Probiotics

    Directory of Open Access Journals (Sweden)

    Pamela Vernocchi

    2017-12-01

    Full Text Available Cystic fibrosis (CF is a life-limiting hereditary disorder that results in aberrant mucosa in the lungs and digestive tract, chronic respiratory infections, chronic inflammation, and the need for repeated antibiotic treatments. Probiotics have been demonstrated to improve the quality of life of CF patients. We investigated the distribution of gut microbiota (GM bacteria to identify new potential probiotics for CF patients on the basis of GM patterns. Fecal samples of 28 CF patients and 31 healthy controls (HC were collected and analyzed by 16S rRNA-based pyrosequencing analysis of GM, to produce CF-HC paired maps of the distribution of operational taxonomic units (OTUs, and by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt for Kyoto Encyclopedia of Genes and Genomes (KEGG biomarker prediction. The maps were scanned to highlight the distribution of bacteria commonly claimed as probiotics, such as bifidobacteria and lactobacilli, and of butyrate-producing colon bacteria, such as Eubacterium spp. and Faecalibacterium prausnitzii. The analyses highlighted 24 OTUs eligible as putative probiotics. Eleven and nine species were prevalently associated with the GM of CF and HC subjects, respectively. Their KEGG prediction provided differential CF and HC pathways, indeed associated with health-promoting biochemical activities in the latter case. GM profiling and KEGG biomarkers concurred in the evaluation of nine bacterial species as novel putative probiotics that could be investigated for the nutritional management of CF patients.

  6. Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes.

    Directory of Open Access Journals (Sweden)

    Quan Li

    Full Text Available The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.

  7. Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Osamu Komori

    2013-01-01

    Full Text Available This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions. We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes. It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects. We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance. We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes. We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches. We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set. We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.

  8. A metagenomic snapshot of taxonomic and functional diversity in an alpine glacier cryoconite ecosystem

    International Nuclear Information System (INIS)

    Edwards, Arwyn; Pachebat, Justin A; Swain, Martin; Hegarty, Matt; Rassner, Sara M E; Hodson, Andrew J; Irvine-Fynn, Tristram D L; Sattler, Birgit

    2013-01-01

    Cryoconite is a microbe–mineral aggregate which darkens the ice surface of glaciers. Microbial process and marker gene PCR-dependent measurements reveal active and diverse cryoconite microbial communities on polar glaciers. Here, we provide the first report of a cryoconite metagenome and culture-independent study of alpine cryoconite microbial diversity. We assembled 1.2 Gbp of metagenomic DNA sequenced using an Illumina HiScanSQ from cryoconite holes across the ablation zone of Rotmoosferner in the Austrian Alps. The metagenome revealed a bacterially-dominated community, with Proteobacteria (62% of bacterial-assigned contigs) and Bacteroidetes (14%) considerably more abundant than Cyanobacteria (2.5%). Streptophyte DNA dominated the eukaryotic metagenome. Functional genes linked to N, Fe, S and P cycling illustrated an acquisitive trend and a nitrogen cycle based upon efficient ammonia recycling. A comparison of 32 metagenome datasets revealed a similarity in functional profiles between the cryoconite and metagenomes characterized from other cold microbe–mineral aggregates. Overall, the metagenomic snapshot reveals the cryoconite ecosystem of this alpine glacier as dependent on scavenging carbon and nutrients from allochthonous sources, in particular mosses transported by wind from ice-marginal habitats, consistent with net heterotrophy indicated by productivity measurements. A transition from singular snapshots of cryoconite metagenomes to comparative analyses is advocated. (letter)

  9. Metagenomic evidence for reciprocal particle exchange between the mainstem estuary and lateral bay sediments of the lower Columbia River

    Directory of Open Access Journals (Sweden)

    Mariya W Smith

    2015-10-01

    Full Text Available Lateral bays of the lower Columbia River estuary are areas of enhanced water retention that influence net ecosystem metabolism through activities of their diverse microbial communities. Metagenomic characterization of sediment microbiota from three disparate sites in two brackish lateral bays (Baker and Youngs produced approximately 100 Gbp of DNA sequence data analyzed subsequently for predicted SSU rRNA and peptide-coding genes. The metagenomes were dominated by Bacteria. A large component of Eukaryota was present in Youngs Bay samples, i.e. the inner bay sediment was enriched with the invasive New Zealand mudsnail, Potamopyrgus antipodarum, known for high ammonia production. The metagenome was also highly enriched with an archaeal ammonia oxidizer closely related to Nitrosoarchaeum limnia. Combined analysis of sequences and continuous, high-resolution time series of biogeochemical data from fixed and mobile platforms revealed the importance of large-scale reciprocal particle exchanges between the mainstem estuarine water column and lateral bay sediments. Deposition of marine diatom particles in sediments near Youngs Bay mouth was associated with a dramatic enrichment of Bacteroidetes (58% of total Bacteria and corresponding genes involved in phytoplankton polysaccharide degradation. The Baker Bay sediment metagenome contained abundant Archaea, including diverse methanogens, as well as functional genes for methylotrophy and taxonomic markers for syntrophic bacteria, suggesting that active methane cycling occurs at this location. Our previous work showed enrichments of similar anaerobic taxa in particulate matter of the mainstem estuarine water column. In total, our results identify the lateral bays as both sources and sinks of biogenic particles significantly impacting microbial community composition and biogeochemical activities in the estuary.

  10. The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

    Science.gov (United States)

    Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

    2014-06-01

    With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

  11. Metagenome Assembly at the DOE JGI (Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

    Energy Technology Data Exchange (ETDEWEB)

    Chain, Patrick

    2011-10-13

    Patrick Chain of DOE JGI at LANL, Co-Chair of the Metagenome-specific Assembly session, on Metagenome Assembly at the DOE JGIat the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.

  12. Inductive matrix completion for predicting gene-disease associations.

    Science.gov (United States)

    Natarajan, Nagarajan; Dhillon, Inderjit S

    2014-06-15

    Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has bigdata.ices.utexas.edu/project/gene-disease. © The Author 2014. Published by Oxford University Press.

  13. Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

    International Nuclear Information System (INIS)

    Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

    2000-01-01

    A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society

  14. Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

    Energy Technology Data Exchange (ETDEWEB)

    Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

    2000-09-18

    A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society.

  15. Prediction of epigenetically regulated genes in breast cancer cell lines

    Energy Technology Data Exchange (ETDEWEB)

    Loss, Leandro A; Sadanandam, Anguraj; Durinck, Steffen; Nautiyal, Shivani; Flaucher, Diane; Carlton, Victoria EH; Moorhead, Martin; Lu, Yontao; Gray, Joe W; Faham, Malek; Spellman, Paul; Parvin, Bahram

    2010-05-04

    panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

  16. Prediction of human protein function according to Gene Ontology categories

    DEFF Research Database (Denmark)

    Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

    2003-01-01

    developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...

  17. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

    Science.gov (United States)

    Noar, Roslyn D; Daub, Margaret E

    2016-01-01

    Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity) for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity) to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that they may encode

  18. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

    Directory of Open Access Journals (Sweden)

    Roslyn D Noar

    Full Text Available Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that

  19. A feruloyl esterase derived from a leachate metagenome library

    CSIR Research Space (South Africa)

    Rashamuse, K

    2012-01-01

    Full Text Available A feruloyl esterase encoding gene (designated fae6), derived from a leachate metagenomic library, was cloned and the nucleotide sequence of the insert DNA determined. Translational analysis revealed that fae6 consists of a 515 amino acid polypeptide...

  20. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. The role of gene-gene interaction in the prediction of criminal behavior.

    Science.gov (United States)

    Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

    2014-04-01

    A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. Single-Cell-Genomics-Facilitated Read Binning of Candidate Phylum EM19 Genomes from Geothermal Spring Metagenomes.

    Science.gov (United States)

    Becraft, Eric D; Dodsworth, Jeremy A; Murugapiran, Senthil K; Ohlsson, J Ingemar; Briggs, Brandon R; Kanbar, Jad; De Vlaminck, Iwijn; Quake, Stephen R; Dong, Hailiang; Hedlund, Brian P; Swingley, Wesley D

    2016-02-15

    The vast majority of microbial life remains uncatalogued due to the inability to cultivate these organisms in the laboratory. This "microbial dark matter" represents a substantial portion of the tree of life and of the populations that contribute to chemical cycling in many ecosystems. In this work, we leveraged an existing single-cell genomic data set representing the candidate bacterial phylum "Calescamantes" (EM19) to calibrate machine learning algorithms and define metagenomic bins directly from pyrosequencing reads derived from Great Boiling Spring in the U.S. Great Basin. Compared to other assembly-based methods, taxonomic binning with a read-based machine learning approach yielded final assemblies with the highest predicted genome completeness of any method tested. Read-first binning subsequently was used to extract Calescamantes bins from all metagenomes with abundant Calescamantes populations, including metagenomes from Octopus Spring and Bison Pool in Yellowstone National Park and Gongxiaoshe Spring in Yunnan Province, China. Metabolic reconstruction suggests that Calescamantes are heterotrophic, facultative anaerobes, which can utilize oxidized nitrogen sources as terminal electron acceptors for respiration in the absence of oxygen and use proteins as their primary carbon source. Despite their phylogenetic divergence, the geographically separate Calescamantes populations were highly similar in their predicted metabolic capabilities and core gene content, respiring O2, or oxidized nitrogen species for energy conservation in distant but chemically similar hot springs. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

  3. Comparative analysis of metagenomes of Italian top soil improvers

    International Nuclear Information System (INIS)

    Gigliucci, Federica; Brambilla, Gianfranco; Tozzoli, Rosangela; Michelacci, Valeria; Morabito, Stefano

    2017-01-01

    Biosolids originating from Municipal Waste Water Treatment Plants are proposed as top soil improvers (TSI) for their beneficial input of organic carbon on agriculture lands. Their use to amend soil is controversial, as it may lead to the presence of emerging hazards of anthropogenic or animal origin in the environment devoted to food production. In this study, we used a shotgun metagenomics sequencing as a tool to perform a characterization of the hazards related with the TSIs. The samples showed the presence of many virulence genes associated to different diarrheagenic E. coli pathotypes as well as of different antimicrobial resistance-associated genes. The genes conferring resistance to Fluoroquinolones was the most relevant class of antimicrobial resistance genes observed in all the samples tested. To a lesser extent traits associated with the resistance to Methicillin in Staphylococci and genes conferring resistance to Streptothricin, Fosfomycin and Vancomycin were also identified. The most represented metal resistance genes were cobalt-zinc-cadmium related, accounting for 15–50% of the sequence reads in the different metagenomes out of the total number of those mapping on the class of resistance to compounds determinants. Moreover the taxonomic analysis performed by comparing compost-based samples and biosolids derived from municipal sewage-sludges treatments divided the samples into separate populations, based on the microbiota composition. The results confirm that the metagenomics is efficient to detect genomic traits associated with pathogens and antimicrobial resistance in complex matrices and this approach can be efficiently used for the traceability of TSI samples using the microorganisms’ profiles as indicators of their origin. - Highlights: • Sludge- and green- based biosolids analysed by metagenomics. • Biosolids may introduce microbial hazards in the food chain. • Metagenomics enables tracking biosolids’ sources.

  4. Comparative analysis of metagenomes of Italian top soil improvers

    Energy Technology Data Exchange (ETDEWEB)

    Gigliucci, Federica, E-mail: Federica.gigliucci@libero.it [Department of Veterinary Public Health and Food Safety, Istituto Superiore di Sanità, Viale Regina Elena, 299 00161 Rome (Italy); Department of Sciences, University Roma,Tre, Viale Marconi, 446, 00146 Rome (Italy); Brambilla, Gianfranco; Tozzoli, Rosangela; Michelacci, Valeria; Morabito, Stefano [Department of Veterinary Public Health and Food Safety, Istituto Superiore di Sanità, Viale Regina Elena, 299 00161 Rome (Italy)

    2017-05-15

    Biosolids originating from Municipal Waste Water Treatment Plants are proposed as top soil improvers (TSI) for their beneficial input of organic carbon on agriculture lands. Their use to amend soil is controversial, as it may lead to the presence of emerging hazards of anthropogenic or animal origin in the environment devoted to food production. In this study, we used a shotgun metagenomics sequencing as a tool to perform a characterization of the hazards related with the TSIs. The samples showed the presence of many virulence genes associated to different diarrheagenic E. coli pathotypes as well as of different antimicrobial resistance-associated genes. The genes conferring resistance to Fluoroquinolones was the most relevant class of antimicrobial resistance genes observed in all the samples tested. To a lesser extent traits associated with the resistance to Methicillin in Staphylococci and genes conferring resistance to Streptothricin, Fosfomycin and Vancomycin were also identified. The most represented metal resistance genes were cobalt-zinc-cadmium related, accounting for 15–50% of the sequence reads in the different metagenomes out of the total number of those mapping on the class of resistance to compounds determinants. Moreover the taxonomic analysis performed by comparing compost-based samples and biosolids derived from municipal sewage-sludges treatments divided the samples into separate populations, based on the microbiota composition. The results confirm that the metagenomics is efficient to detect genomic traits associated with pathogens and antimicrobial resistance in complex matrices and this approach can be efficiently used for the traceability of TSI samples using the microorganisms’ profiles as indicators of their origin. - Highlights: • Sludge- and green- based biosolids analysed by metagenomics. • Biosolids may introduce microbial hazards in the food chain. • Metagenomics enables tracking biosolids’ sources.

  5. GOPET: A tool for automated predictions of Gene Ontology terms

    Directory of Open Access Journals (Sweden)

    Glatting Karl-Heinz

    2006-03-01

    Full Text Available Abstract Background Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. Description We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO. Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool. It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar Conclusion Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.

  6. Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

    Science.gov (United States)

    Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

    2017-10-03

    Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

  7. Mining the metagenome of activated biomass of an industrial wastewater treatment plant by a novel method.

    Science.gov (United States)

    Sharma, Nandita; Tanksale, Himgouri; Kapley, Atya; Purohit, Hemant J

    2012-12-01

    Metagenomic libraries herald the era of magnifying the microbial world, tapping into the vast metabolic potential of uncultivated microbes, and enhancing the rate of discovery of novel genes and pathways. In this paper, we describe a method that facilitates the extraction of metagenomic DNA from activated sludge of an industrial wastewater treatment plant and its use in mining the metagenome via library construction. The efficiency of this method was demonstrated by the large representation of the bacterial genome in the constructed metagenomic libraries and by the functional clones obtained. The BAC library represented 95.6 times the bacterial genome, while, the pUC library represented 41.7 times the bacterial genome. Twelve clones in the BAC library demonstrated lipolytic activity, while four clones demonstrated dioxygenase activity. Four clones in pUC library tested positive for cellulase activity. This method, using FTA cards, not only can be used for library construction, but can also store the metagenome at room temperature.

  8. MetaStorm: A Public Resource for Customizable Metagenomics Annotation.

    Science.gov (United States)

    Arango-Argoty, Gustavo; Singh, Gargi; Heath, Lenwood S; Pruden, Amy; Xiao, Weidong; Zhang, Liqing

    2016-01-01

    Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution.

  9. MetaStorm: A Public Resource for Customizable Metagenomics Annotation.

    Directory of Open Access Journals (Sweden)

    Gustavo Arango-Argoty

    Full Text Available Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/, which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution.

  10. MetaStorm: A Public Resource for Customizable Metagenomics Annotation

    Science.gov (United States)

    Arango-Argoty, Gustavo; Singh, Gargi; Heath, Lenwood S.; Pruden, Amy; Xiao, Weidong; Zhang, Liqing

    2016-01-01

    Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution. PMID:27632579

  11. Moleculo Long-Read Sequencing Facilitates Assembly and Genomic Binning from Complex Soil Metagenomes

    Energy Technology Data Exchange (ETDEWEB)

    White, Richard Allen; Bottos, Eric M.; Roy Chowdhury, Taniya; Zucker, Jeremy D.; Brislawn, Colin J.; Nicora, Carrie D.; Fansler, Sarah J.; Glaesemann, Kurt R.; Glass, Kevin; Jansson, Janet K.; Langille, Morgan

    2016-06-28

    ABSTRACT

    Soil metagenomics has been touted as the “grand challenge” for metagenomics, as the high microbial diversity and spatial heterogeneity of soils make them unamenable to current assembly platforms. Here, we aimed to improve soil metagenomic sequence assembly by applying the Moleculo synthetic long-read sequencing technology. In total, we obtained 267 Gbp of raw sequence data from a native prairie soil; these data included 109.7 Gbp of short-read data (~100 bp) from the Joint Genome Institute (JGI), an additional 87.7 Gbp of rapid-mode read data (~250 bp), plus 69.6 Gbp (>1.5 kbp) from Moleculo sequencing. The Moleculo data alone yielded over 5,600 reads of >10 kbp in length, and over 95% of the unassembled reads mapped to contigs of >1.5 kbp. Hybrid assembly of all data resulted in more than 10,000 contigs over 10 kbp in length. We mapped three replicate metatranscriptomes derived from the same parent soil to the Moleculo subassembly and found that 95% of the predicted genes, based on their assignments to Enzyme Commission (EC) numbers, were expressed. The Moleculo subassembly also enabled binning of >100 microbial genome bins. We obtained via direct binning the first complete genome, that of “CandidatusPseudomonas sp. strain JKJ-1” from a native soil metagenome. By mapping metatranscriptome sequence reads back to the bins, we found that several bins corresponding to low-relative-abundanceAcidobacteriawere highly transcriptionally active, whereas bins corresponding to high-relative-abundanceVerrucomicrobiawere not. These results demonstrate that Moleculo sequencing provides a significant advance for resolving complex soil microbial communities.

    IMPORTANCESoil microorganisms carry out key processes for life on our planet, including cycling of carbon and other nutrients and supporting growth of plants. However, there is poor molecular-level understanding of their

  12. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

    Directory of Open Access Journals (Sweden)

    Zhili He

    2018-02-01

    Full Text Available Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN, representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5 increased significantly (P < 0.05 as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.

  13. Functional Metagenomic Investigations of the Human Intestinal Microbiota

    Directory of Open Access Journals (Sweden)

    Aimee Marguerite Moore

    2011-10-01

    Full Text Available The human intestinal microbiota encode multiple critical functions impacting human health, including, metabolism of dietary substrate, prevention of pathogen invasion, immune system modulation, and provision of a reservoir of antibiotic resistance genes accessible to pathogens. The complexity of this microbial community, its recalcitrance to standard cultivation and the immense diversity of its encoded genes has necessitated the development of novel molecular, microbiological, and genomic tools. Functional metagenomics is one such culture-independent technique used for decades to study environmental microorganisms but relatively recently applied to the study of the human commensal microbiota. Metagenomic functional screens characterize the functional capacity of a microbial community independent of identity to known genes by subjecting the metagenome to functional assays in a genetically tractable host. Here we highlight recent work applying this technique to study the functional diversity of the intestinal microbiota, and discuss how an approach combining high-throughput sequencing, cultivation, and metagenomic functional screens can improve our understanding of interactions between this complex community and its human host.

  14. Metagenomic approaches to exploit the biotechnological potential of the microbial consortia of marine sponges.

    Science.gov (United States)

    Kennedy, Jonathan; Marchesi, Julian R; Dobson, Alan D W

    2007-05-01

    Natural products isolated from sponges are an important source of new biologically active compounds. However, the development of these compounds into drugs has been held back by the difficulties in achieving a sustainable supply of these often-complex molecules for pre-clinical and clinical development. Increasing evidence implicates microbial symbionts as the source of many of these biologically active compounds, but the vast majority of the sponge microbial community remain uncultured. Metagenomics offers a biotechnological solution to this supply problem. Metagenomes of sponge microbial communities have been shown to contain genes and gene clusters typical for the biosynthesis of biologically active natural products. Heterologous expression approaches have also led to the isolation of secondary metabolism gene clusters from uncultured microbial symbionts of marine invertebrates and from soil metagenomic libraries. Combining a metagenomic approach with heterologous expression holds much promise for the sustainable exploitation of the chemical diversity present in the sponge microbial community.

  15. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients.

    Science.gov (United States)

    Fierer, Noah; Lauber, Christian L; Ramirez, Kelly S; Zaneveld, Jesse; Bradford, Mark A; Knight, Rob

    2012-05-01

    Terrestrial ecosystems are receiving elevated inputs of nitrogen (N) from anthropogenic sources and understanding how these increases in N availability affect soil microbial communities is critical for predicting the associated effects on belowground ecosystems. We used a suite of approaches to analyze the structure and functional characteristics of soil microbial communities from replicated plots in two long-term N fertilization experiments located in contrasting systems. Pyrosequencing-based analyses of 16S rRNA genes revealed no significant effects of N fertilization on bacterial diversity, but significant effects on community composition at both sites; copiotrophic taxa (including members of the Proteobacteria and Bacteroidetes phyla) typically increased in relative abundance in the high N plots, with oligotrophic taxa (mainly Acidobacteria) exhibiting the opposite pattern. Consistent with the phylogenetic shifts under N fertilization, shotgun metagenomic sequencing revealed increases in the relative abundances of genes associated with DNA/RNA replication, electron transport and protein metabolism, increases that could be resolved even with the shallow shotgun metagenomic sequencing conducted here (average of 75 000 reads per sample). We also observed shifts in the catabolic capabilities of the communities across the N gradients that were significantly correlated with the phylogenetic and metagenomic responses, indicating possible linkages between the structure and functioning of soil microbial communities. Overall, our results suggest that N fertilization may, directly or indirectly, induce a shift in the predominant microbial life-history strategies, favoring a more active, copiotrophic microbial community, a pattern that parallels the often observed replacement of K-selected with r-selected plant species with elevated N.

  16. Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes.

    Science.gov (United States)

    Niu, Sheng-Yong; Yang, Jinyu; McDermaid, Adam; Zhao, Jing; Kang, Yu; Ma, Qin

    2017-05-08

    Metagenomic and metatranscriptomic sequencing approaches are more frequently being used to link microbiota to important diseases and ecological changes. Many analyses have been used to compare the taxonomic and functional profiles of microbiota across habitats or individuals. While a large portion of metagenomic analyses focus on species-level profiling, some studies use strain-level metagenomic analyses to investigate the relationship between specific strains and certain circumstances. Metatranscriptomic analysis provides another important insight into activities of genes by examining gene expression levels of microbiota. Hence, combining metagenomic and metatranscriptomic analyses will help understand the activity or enrichment of a given gene set, such as drug-resistant genes among microbiome samples. Here, we summarize existing bioinformatics tools of metagenomic and metatranscriptomic data analysis, the purpose of which is to assist researchers in deciding the appropriate tools for their microbiome studies. Additionally, we propose an Integrated Meta-Function mapping pipeline to incorporate various reference databases and accelerate functional gene mapping procedures for both metagenomic and metatranscriptomic analyses. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  17. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

    Science.gov (United States)

    Zhang, Ping; Wu, Linwei; Rocha, Andrea M.; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D.; Wu, Liyou; Watson, David B.; Adams, Michael W. W.; Alm, Eric J.; Adams, Paul D.; Arkin, Adam P.

    2018-01-01

    ABSTRACT Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. PMID:29463661

  18. New Bacterial Phytase through Metagenomic Prospection

    Directory of Open Access Journals (Sweden)

    Nathálya Farias

    2018-02-01

    Full Text Available Alkaline phytases from uncultured microorganisms, which hydrolyze phytate to less phosphorylated myo-inositols and inorganic phosphate, have great potential as additives in agricultural industry. The development of metagenomics has stemmed from the ineluctable evidence that as-yet-uncultured microorganisms represent the vast majority of organisms in most environments on earth. In this study, a gene encoding a phytase was cloned from red rice crop residues and castor bean cake using a metagenomics strategy. The amino acid identity between this gene and its closest published counterparts is lower than 60%. The phytase was named PhyRC001 and was biochemically characterized. This recombinant protein showed activity on sodium phytate, indicating that PhyRC001 is a hydrolase enzyme. The enzymatic activity was optimal at a pH of 7.0 and at a temperature of 35 °C. β-propeller phytases possess great potential as feed additives because they are the only type of phytase with high activity at neutral pH. Therefore, to explore and exploit the underlying mechanism for β-propeller phytase functions could be of great benefit to biotechnology.

  19. Variations in the post-weaning human gut metagenome profile as result of Bifidobacterium acquisition in the Western microbiome

    Directory of Open Access Journals (Sweden)

    Matteo Soverini

    2016-07-01

    Full Text Available Studies of the gut microbiome variation among human populations revealed the existence of robust compositional and functional layouts matching the three subsistence strategies that describe a trajectory of changes across our recent evolutionary history: hunting and gathering, rural agriculture, and urban post-industrialized agriculture. In particular, beside the overall reduction of ecosystem diversity, the gut microbiome of Western industrial populations is typically characterized by the loss of Treponema and the acquisition of Bifidobacterium as an abundant inhabitant of the post-weaning gut microbial ecosystem. In order to advance the hypothesis about the possible adaptive nature of this exchange, here we explore specific functional attributes that correspond to the mutually exclusive presence of Treponema and Bifidobacterium using publically available gut metagenomic data from Hadza hunter-gatherers and urban industrial Italians. According to our findings, Bifidobacterium provides the enteric ecosystem with a diverse panel of saccharolytic functions, well suited to the array of gluco- and galacto-based saccharides that abound in the Western diet. On the other hand, the metagenomic functions assigned to Treponema are more predictive of a capacity to incorporate complex polysaccharides, such as those found in unrefined plant foods, which are consistently incorporated in the Hadza diet. Finally, unlike Treponema, the Bifidobacterium metagenome functions include genes that permit the establishment of microbe-host immunological cross-talk, suggesting recent co-evolutionary events between the human immune system and Bifidobacterium that are adaptive in the context of agricultural subsistence and sedentary societies.

  20. Comparative metagenomics of the Red Sea

    KAUST Repository

    Mineta, Katsuhiko

    2016-01-01

    started monthly samplings of the metagenomes in the Red Sea under KAUST-CCF project. In collaboration with Kitasato University, we also collected the metagenome data from the ocean in Japan, which shows contrasting features to the Red Sea. Therefore

  1. Marine metagenomics as a source for bioprospecting

    KAUST Repository

    Kodzius, Rimantas; Gojobori, Takashi

    2015-01-01

    This review summarizes usage of genome-editing technologies for metagenomic studies; these studies are used to retrieve and modify valuable microorganisms for production, particularly in marine metagenomics. Organisms may be cultivable

  2. FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies.

    Science.gov (United States)

    Kim, Jiwoong; Kim, Min Soo; Koh, Andrew Y; Xie, Yang; Zhan, Xiaowei

    2016-10-10

    Given the lack of a complete and comprehensive library of microbial reference genomes, determining the functional profile of diverse microbial communities is challenging. The available functional analysis pipelines lack several key features: (i) an integrated alignment tool, (ii) operon-level analysis, and (iii) the ability to process large datasets. Here we introduce our open-sourced, stand-alone functional analysis pipeline for analyzing whole metagenomic and metatranscriptomic sequencing data, FMAP (Functional Mapping and Analysis Pipeline). FMAP performs alignment, gene family abundance calculations, and statistical analysis (three levels of analyses are provided: differentially-abundant genes, operons and pathways). The resulting output can be easily visualized with heatmaps and functional pathway diagrams. FMAP functional predictions are consistent with currently available functional analysis pipelines. FMAP is a comprehensive tool for providing functional analysis of metagenomic/metatranscriptomic sequencing data. With the added features of integrated alignment, operon-level analysis, and the ability to process large datasets, FMAP will be a valuable addition to the currently available functional analysis toolbox. We believe that this software will be of great value to the wider biology and bioinformatics communities.

  3. Predictive modelling of gene expression from transcriptional regulatory elements.

    Science.gov (United States)

    Budden, David M; Hurley, Daniel G; Crampin, Edmund J

    2015-07-01

    Predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and ϵ-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  4. In-depth resistome analysis by targeted metagenomics.

    Science.gov (United States)

    Lanza, Val F; Baquero, Fernando; Martínez, José Luís; Ramos-Ruíz, Ricardo; González-Zorn, Bruno; Andremont, Antoine; Sánchez-Valenzuela, Antonio; Ehrlich, Stanislav Dusko; Kennedy, Sean; Ruppé, Etienne; van Schaik, Willem; Willems, Rob J; de la Cruz, Fernando; Coque, Teresa M

    2018-01-15

    Antimicrobial resistance is a major global health challenge. Metagenomics allows analyzing the presence and dynamics of "resistomes" (the ensemble of genes encoding antimicrobial resistance in a given microbiome) in disparate microbial ecosystems. However, the low sensitivity and specificity of available metagenomic methods preclude the detection of minority populations (often present below their detection threshold) and/or the identification of allelic variants that differ in the resulting phenotype. Here, we describe a novel strategy that combines targeted metagenomics using last generation in-solution capture platforms, with novel bioinformatics tools to establish a standardized framework that allows both quantitative and qualitative analyses of resistomes. We developed ResCap, a targeted sequence capture platform based on SeqCapEZ (NimbleGene) technology, which includes probes for 8667 canonical resistance genes (7963 antibiotic resistance genes and 704 genes conferring resistance to metals or biocides), and 2517 relaxase genes (plasmid markers) and 78,600 genes homologous to the previous identified targets (47,806 for antibiotics and 30,794 for biocides or metals). Its performance was compared with metagenomic shotgun sequencing (MSS) for 17 fecal samples (9 humans, 8 swine). ResCap significantly improves MSS to detect "gene abundance" (from 2.0 to 83.2%) and "gene diversity" (26 versus 14.9 genes unequivocally detected per sample per million of reads; the number of reads unequivocally mapped increasing up to 300-fold by using ResCap), which were calculated using novel bioinformatic tools. ResCap also facilitated the analysis of novel genes potentially involved in the resistance to antibiotics, metals, biocides, or any combination thereof. ResCap, the first targeted sequence capture, specifically developed to analyze resistomes, greatly enhances the sensitivity and specificity of available metagenomic methods and offers the possibility to analyze genes

  5. Identification of rat genes by TWINSCAN gene prediction, RT-PCR, and direct sequencing

    DEFF Research Database (Denmark)

    Wu, Jia Qian; Shteynberg, David; Arumugam, Manimozhiyan

    2004-01-01

    an alternative approach: reverse transcription-polymerase chain reaction (RT-PCR) and direct sequencing based on dual-genome de novo predictions from TWINSCAN. We tested 444 TWINSCAN-predicted rat genes that showed significant homology to known human genes implicated in disease but that were partially...... in the single-intron experiment. Spliced sequences were amplified in 46 cases (34%). We conclude that this procedure for elucidating gene structures with native cDNA sequences is cost-effective and will become even more so as it is further optimized.......The publication of a draft sequence of a third mammalian genome--that of the rat--suggests a need to rethink genome annotation. New mammalian sequences will not receive the kind of labor-intensive annotation efforts that are currently being devoted to human. In this paper, we demonstrate...

  6. Web Resources for Metagenomics Studies

    Directory of Open Access Journals (Sweden)

    Pravin Dudhagara

    2015-10-01

    Full Text Available The development of next-generation sequencing (NGS platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.

  7. Effects of chlortetracycline and copper supplementation on the prevalence, distribution, and quantity of antimicrobial resistance genes in the fecal metagenome of weaned pigs.

    Science.gov (United States)

    Agga, Getahun E; Scott, H Morgan; Vinasco, Javier; Nagaraja, T G; Amachawadi, Raghavendra G; Bai, Jianfa; Norby, Bo; Renter, David G; Dritz, Steve S; Nelssen, Jim L; Tokach, Mike D

    2015-05-01

    Use of in-feed antibiotics such as chlortetracycline (CTC) in food animals is fiercely debated as a cause of antimicrobial resistance in human pathogens; as a result, alternatives to antibiotics such as heavy metals have been proposed. We used a total community DNA approach to experimentally investigate the effects of CTC and copper supplementation on the presence and quantity of antimicrobial resistance elements in the gut microbial ecology of pigs. Total community DNA was extracted from 569 fecal samples collected weekly over a 6-week period from groups of 5 pigs housed in 32 pens that were randomized to receive either control, CTC, copper, or copper plus CTC regimens. Qualitative and quantitative PCR were used to detect the presence of 14 tetracycline resistance (tet) genes and to quantify gene copies of tetA, tetB, blaCMY-2 (a 3rd generation cephalosporin resistance gene), and pcoD (a copper resistance gene), respectively. The detection of tetA and tetB decreased over the subsequent sampling periods, whereas the prevalence of tetC and tetP increased. CTC and copper plus CTC supplementation increased both the prevalence and gene copy numbers of tetA, while decreasing both the prevalence and gene copies of tetB. In summary, tet gene presence was initially very diverse in the gut bacterial community of weaned pigs; thereafter, copper and CTC supplementation differentially impacted the prevalence and quantity of the various tetracycline, ceftiofur and copper resistance genes resulting in a less diverse gene population. Published by Elsevier B.V.

  8. Dinucleotide controlled null models for comparative RNA gene prediction

    Directory of Open Access Journals (Sweden)

    Gesell Tanja

    2008-05-01

    Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

  9. Dinucleotide controlled null models for comparative RNA gene prediction.

    Science.gov (United States)

    Gesell, Tanja; Washietl, Stefan

    2008-05-27

    Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

  10. A Statistical Framework for the Functional Analysis of Metagenomes

    Energy Technology Data Exchange (ETDEWEB)

    Sharon, Itai; Pati, Amrita; Markowitz, Victor; Pinter, Ron Y.

    2008-10-01

    Metagenomic studies consider the genetic makeup of microbial communities as a whole, rather than their individual member organisms. The functional and metabolic potential of microbial communities can be analyzed by comparing the relative abundance of gene families in their collective genomic sequences (metagenome) under different conditions. Such comparisons require accurate estimation of gene family frequencies. They present a statistical framework for assessing these frequencies based on the Lander-Waterman theory developed originally for Whole Genome Shotgun (WGS) sequencing projects. They also provide a novel method for assessing the reliability of the estimations which can be used for removing seemingly unreliable measurements. They tested their method on a wide range of datasets, including simulated genomes and real WGS data from sequencing projects of whole genomes. Results suggest that their framework corrects inherent biases in accepted methods and provides a good approximation to the true statistics of gene families in WGS projects.

  11. Quantitative Field Testing Rotylenchulus reniformis DNA from Metagenomic Samples Isolated Directly from Soil

    Science.gov (United States)

    Showmaker, Kurt; Lawrence, Gary W.; Lu, Shien; Balbalian, Clarissa; Klink, Vincent P.

    2011-01-01

    A quantitative PCR procedure targeting the β-tubulin gene determined the number of Rotylenchulus reniformis Linford & Oliveira 1940 in metagenomic DNA samples isolated from soil. Of note, this outcome was in the presence of other soil-dwelling plant parasitic nematodes including its sister genus Helicotylenchus Steiner, 1945. The methodology provides a framework for molecular diagnostics of nematodes from metagenomic DNA isolated directly from soil. PMID:22194958

  12. MEGGASENSE - The Metagenome/Genome Annotated Sequence Natural Language Search Engine: A Platform for 
the Construction of Sequence Data Warehouses.

    Science.gov (United States)

    Gacesa, Ranko; Zucko, Jurica; Petursdottir, Solveig K; Gudmundsdottir, Elisabet Eik; Fridjonsson, Olafur H; Diminic, Janko; Long, Paul F; Cullum, John; Hranueli, Daslav; Hreggvidsson, Gudmundur O; Starcevic, Antonio

    2017-06-01

    The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya . The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel 'functional' assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.

  13. Metagenome Fragment Classification Using -Mer Frequency Profiles

    Directory of Open Access Journals (Sweden)

    Gail Rosen

    2008-01-01

    Full Text Available A vast amount of microbial sequencing data is being generated through large-scale projects in ecology, agriculture, and human health. Efficient high-throughput methods are needed to analyze the mass amounts of metagenomic data, all DNA present in an environmental sample. A major obstacle in metagenomics is the inability to obtain accuracy using technology that yields short reads. We construct the unique -mer frequency profiles of 635 microbial genomes publicly available as of February 2008. These profiles are used to train a naive Bayes classifier (NBC that can be used to identify the genome of any fragment. We show that our method is comparable to BLAST for small 25 bp fragments but does not have the ambiguity of BLAST's tied top scores. We demonstrate that this approach is scalable to identify any fragment from hundreds of genomes. It also performs quite well at the strain, species, and genera levels and achieves strain resolution despite classifying ubiquitous genomic fragments (gene and nongene regions. Cross-validation analysis demonstrates that species-accuracy achieves 90% for highly-represented species containing an average of 8 strains. We demonstrate that such a tool can be used on the Sargasso Sea dataset, and our analysis shows that NBC can be further enhanced.

  14. MALINA: a web service for visual analytics of human gut microbiota whole-genome metagenomic reads.

    Science.gov (United States)

    Tyakht, Alexander V; Popenko, Anna S; Belenikin, Maxim S; Altukhov, Ilya A; Pavlenko, Alexander V; Kostryukova, Elena S; Selezneva, Oksana V; Larin, Andrei K; Karpova, Irina Y; Alexeev, Dmitry G

    2012-12-07

    MALINA is a web service for bioinformatic analysis of whole-genome metagenomic data obtained from human gut microbiota sequencing. As input data, it accepts metagenomic reads of various sequencing technologies, including long reads (such as Sanger and 454 sequencing) and next-generation (including SOLiD and Illumina). It is the first metagenomic web service that is capable of processing SOLiD color-space reads, to authors' knowledge. The web service allows phylogenetic and functional profiling of metagenomic samples using coverage depth resulting from the alignment of the reads to the catalogue of reference sequences which are built into the pipeline and contain prevalent microbial genomes and genes of human gut microbiota. The obtained metagenomic composition vectors are processed by the statistical analysis and visualization module containing methods for clustering, dimension reduction and group comparison. Additionally, the MALINA database includes vectors of bacterial and functional composition for human gut microbiota samples from a large number of existing studies allowing their comparative analysis together with user samples, namely datasets from Russian Metagenome project, MetaHIT and Human Microbiome Project (downloaded from http://hmpdacc.org). MALINA is made freely available on the web at http://malina.metagenome.ru. The website is implemented in JavaScript (using Ext JS), Microsoft .NET Framework, MS SQL, Python, with all major browsers supported.

  15. An integrated metagenome and -proteome analysis of the microbial community residing in a biogas production plant.

    Science.gov (United States)

    Ortseifen, Vera; Stolze, Yvonne; Maus, Irena; Sczyrba, Alexander; Bremges, Andreas; Albaum, Stefan P; Jaenicke, Sebastian; Fracowiak, Jochen; Pühler, Alfred; Schlüter, Andreas

    2016-08-10

    To study the metaproteome of a biogas-producing microbial community, fermentation samples were taken from an agricultural biogas plant for microbial cell and protein extraction and corresponding metagenome analyses. Based on metagenome sequence data, taxonomic community profiling was performed to elucidate the composition of bacterial and archaeal sub-communities. The community's cytosolic metaproteome was represented in a 2D-PAGE approach. Metaproteome databases for protein identification were compiled based on the assembled metagenome sequence dataset for the biogas plant analyzed and non-corresponding biogas metagenomes. Protein identification results revealed that the corresponding biogas protein database facilitated the highest identification rate followed by other biogas-specific databases, whereas common public databases yielded insufficient identification rates. Proteins of the biogas microbiome identified as highly abundant were assigned to the pathways involved in methanogenesis, transport and carbon metabolism. Moreover, the integrated metagenome/-proteome approach enabled the examination of genetic-context information for genes encoding identified proteins by studying neighboring genes on the corresponding contig. Exemplarily, this approach led to the identification of a Methanoculleus sp. contig encoding 16 methanogenesis-related gene products, three of which were also detected as abundant proteins within the community's metaproteome. Thus, metagenome contigs provide additional information on the genetic environment of identified abundant proteins. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. New Hydrocarbon Degradation Pathways in the Microbial Metagenome from Brazilian Petroleum Reservoirs

    Science.gov (United States)

    Sierra-García, Isabel Natalia; Correa Alvarez, Javier; Pantaroto de Vasconcellos, Suzan; Pereira de Souza, Anete; dos Santos Neto, Eugenio Vaz; de Oliveira, Valéria Maia

    2014-01-01

    Current knowledge of the microbial diversity and metabolic pathways involved in hydrocarbon degradation in petroleum reservoirs is still limited, mostly due to the difficulty in recovering the complex community from such an extreme environment. Metagenomics is a valuable tool to investigate the genetic and functional diversity of previously uncultured microorganisms in natural environments. Using a function-driven metagenomic approach, we investigated the metabolic abilities of microbial communities in oil reservoirs. Here, we describe novel functional metabolic pathways involved in the biodegradation of aromatic compounds in a metagenomic library obtained from an oil reservoir. Although many of the deduced proteins shared homology with known enzymes of different well-described aerobic and anaerobic catabolic pathways, the metagenomic fragments did not contain the complete clusters known to be involved in hydrocarbon degradation. Instead, the metagenomic fragments comprised genes belonging to different pathways, showing novel gene arrangements. These results reinforce the potential of the metagenomic approach for the identification and elucidation of new genes and pathways in poorly studied environments and contribute to a broader perspective on the hydrocarbon degradation processes in petroleum reservoirs. PMID:24587220

  17. Composition and predicted functional ecology of mussel - associated bacteria in Indonesian marine lakes

    NARCIS (Netherlands)

    Cleary, D.F.R.; Becking, L.E.; Polonia, A.; Freitas, R.M.; Gomes, N.

    2015-01-01

    In the present study, we sampled bacterial communities associated with mussels inhabiting two distinct coastal marine ecosystems in Kalimantan, Indonesia, namely, marine lakes and coastal mangroves. We used 16S rRNA gene pyrosequencing and predicted metagenomic analysis to compare microbial

  18. Metaviz: interactive statistical and visual analysis of metagenomic data.

    Science.gov (United States)

    Wagner, Justin; Chelaru, Florin; Kancherla, Jayaram; Paulson, Joseph N; Zhang, Alexander; Felix, Victor; Mahurkar, Anup; Elmqvist, Niklas; Corrada Bravo, Héctor

    2018-04-06

    Large studies profiling microbial communities and their association with healthy or disease phenotypes are now commonplace. Processed data from many of these studies are publicly available but significant effort is required for users to effectively organize, explore and integrate it, limiting the utility of these rich data resources. Effective integrative and interactive visual and statistical tools to analyze many metagenomic samples can greatly increase the value of these data for researchers. We present Metaviz, a tool for interactive exploratory data analysis of annotated microbiome taxonomic community profiles derived from marker gene or whole metagenome shotgun sequencing. Metaviz is uniquely designed to address the challenge of browsing the hierarchical structure of metagenomic data features while rendering visualizations of data values that are dynamically updated in response to user navigation. We use Metaviz to provide the UMD Metagenome Browser web service, allowing users to browse and explore data for more than 7000 microbiomes from published studies. Users can also deploy Metaviz as a web service, or use it to analyze data through the metavizr package to interoperate with state-of-the-art analysis tools available through Bioconductor. Metaviz is free and open source with the code, documentation and tutorials publicly accessible.

  19. Comparative fecal metagenomics unveils unique functional capacity of the swine gut

    Directory of Open Access Journals (Sweden)

    Martinson John

    2011-05-01

    Full Text Available Abstract Background Uncovering the taxonomic composition and functional capacity within the swine gut microbial consortia is of great importance to animal physiology and health as well as to food and water safety due to the presence of human pathogens in pig feces. Nonetheless, limited information on the functional diversity of the swine gut microbiome is available. Results Analysis of 637, 722 pyrosequencing reads (130 megabases generated from Yorkshire pig fecal DNA extracts was performed to help better understand the microbial diversity and largely unknown functional capacity of the swine gut microbiome. Swine fecal metagenomic sequences were annotated using both MG-RAST and JGI IMG/M-ER pipelines. Taxonomic analysis of metagenomic reads indicated that swine fecal microbiomes were dominated by Firmicutes and Bacteroidetes phyla. At a finer phylogenetic resolution, Prevotella spp. dominated the swine fecal metagenome, while some genes associated with Treponema and Anareovibrio species were found to be exclusively within the pig fecal metagenomic sequences analyzed. Functional analysis revealed that carbohydrate metabolism was the most abundant SEED subsystem, representing 13% of the swine metagenome. Genes associated with stress, virulence, cell wall and cell capsule were also abundant. Virulence factors associated with antibiotic resistance genes with highest sequence homology to genes in Bacteroidetes, Clostridia, and Methanosarcina were numerous within the gene families unique to the swine fecal metagenomes. Other abundant proteins unique to the distal swine gut shared high sequence homology to putative carbohydrate membrane transporters. Conclusions The results from this metagenomic survey demonstrated the presence of genes associated with resistance to antibiotics and carbohydrate metabolism suggesting that the swine gut microbiome may be shaped by husbandry practices.

  20. Metagenome reveals potential microbial degradation of hydrocarbon coupled with sulfate reduction in an oil-immersed chimney from Guaymas Basin

    Directory of Open Access Journals (Sweden)

    Ying eHe

    2013-06-01

    Full Text Available Deep-sea hydrothermal vent chimneys contain a high diversity of microorganisms, yet the metabolic activity and the ecological functions of the microbial communities remain largely unexplored. In this study, a metagenomic approach was applied to characterize the metabolic potential in a Guaymas hydrothermal vent chimney and to conduct comparative genomic analysis among a variety of environments with sequenced metagenomes. Complete clustering of functional gene categories with a comparative metagenomic approach showed that this Guaymas chimney metagenome was clustered most closely with a chimney metagenome from Juan de Fuca. All chimney samples were enriched with genes involved in recombination and repair, chemotaxis and flagellar assembly, highlighting their roles in coping with the fluctuating extreme deep-sea environments. A high proportion of transposases was observed in all the metagenomes from deep-sea chimneys, supporting the previous hypothesis that horizontal gene transfer may be common in the deep-sea vent chimney biosphere. In the Guaymas chimney metagenome, thermophilic sulfate reducing microorganisms including bacteria and archaea were found predominant, and genes coding for the degradation of refractory organic compounds such as cellulose, lipid, pullullan, as well as a few hydrocarbons including toluene, ethylbenzene and o-xylene were identified. Therefore, this oil-immersed chimney supported a thermophilic microbial community capable of oxidizing a range of hydrocarbons that served as electron donors for sulphate reduction under anaerobic conditions.

  1. Metagenomic Analysis of Dairy Bacteriophages

    DEFF Research Database (Denmark)

    Muhammed, Musemma K.; Kot, Witold; Neve, Horst

    2017-01-01

    Despite their huge potential for characterizing the biodiversity of phages, metagenomic studies are currently not available for dairy bacteriophages, partly due to the lack of a standard procedure for phage extraction. We optimized an extraction method that allows to remove the bulk protein from...

  2. Vikodak--A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets.

    Directory of Open Access Journals (Sweden)

    Sunil Nagpal

    Full Text Available The overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (reconstructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile. In this study, we present Vikodak--a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.Validation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b functional resolution of distinct metagenomic environments, (c inferring patterns of functional interaction between resident microbes, and (d automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.With novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.A web implementation of Vikodak can be publicly accessed at: http://metagenomics

  3. Extremozymes from metagenome: Potential applications in food processing.

    Science.gov (United States)

    Khan, Mahejibin; Sathya, T A

    2017-06-12

    The long-established use of enzymes for food processing and product formulation has resulted in an increased enzyme market compounding to 7.0% annual growth rate. Advancements in molecular biology and recognition that enzymes with specific properties have application for industrial production of infant, baby and functional foods boosted research toward sourcing the genes of microorganisms for enzymes with distinctive properties. In this regard, functional metagenomics for extremozymes has gained attention on the premise that such enzymes can catalyze specific reactions. Hence, metagenomics that can isolate functional genes of unculturable extremophilic microorganisms has expanded attention as a promising tool. Developments in this field of research in relation to food sector are reviewed.

  4. Metagenomic analysis of soil and freshwater from zoo agricultural area with organic fertilization

    Science.gov (United States)

    Meneghine, Aylan K.; Nielsen, Shaun; Thomas, Torsten; Carareto Alves, Lucia Maria

    2017-01-01

    Microbial communities drive biogeochemical cycles in agricultural areas by decomposing organic materials and converting essential nutrients. Organic amendments improve soil quality by increasing the load of essential nutrients and enhancing the productivity. Additionally, fresh water used for irrigation can affect soil quality of agricultural soils, mainly due to the presence of microbial contaminants and pathogens. In this study, we investigated how microbial communities in irrigation water might contribute to the microbial diversity and function of soil. Whole-metagenomic sequencing approaches were used to investigate the taxonomic and the functional profiles of microbial communities present in fresh water used for irrigation, and in soil from a vegetable crop, which received fertilization with organic compost made from animal carcasses. The taxonomic analysis revealed that the most abundant genera were Polynucleobacter (~8% relative abundance) and Bacillus (~10%) in fresh water and soil from the vegetable crop, respectively. Low abundance (0.38%) of cyanobacterial groups were identified. Based on functional gene prediction, denitrification appears to be an important process in the soil community analysed here. Conversely, genes for nitrogen fixation were abundant in freshwater, indicating that the N-fixation plays a crucial role in this particular ecosystem. Moreover, pathogenicity islands, antibiotic resistance and potential virulence related genes were identified in both samples, but no toxigenic genes were detected. This study provides a better understanding of the community structure of an area under strong agricultural activity with regular irrigation and fertilization with an organic compost made from animal carcasses. Additionally, the use of a metagenomic approach to investigate fresh water quality proved to be a relevant method to evaluate its use in an agricultural ecosystem. PMID:29267397

  5. Metagenomic analysis of soil and freshwater from zoo agricultural area with organic fertilization.

    Directory of Open Access Journals (Sweden)

    Aylan K Meneghine

    Full Text Available Microbial communities drive biogeochemical cycles in agricultural areas by decomposing organic materials and converting essential nutrients. Organic amendments improve soil quality by increasing the load of essential nutrients and enhancing the productivity. Additionally, fresh water used for irrigation can affect soil quality of agricultural soils, mainly due to the presence of microbial contaminants and pathogens. In this study, we investigated how microbial communities in irrigation water might contribute to the microbial diversity and function of soil. Whole-metagenomic sequencing approaches were used to investigate the taxonomic and the functional profiles of microbial communities present in fresh water used for irrigation, and in soil from a vegetable crop, which received fertilization with organic compost made from animal carcasses. The taxonomic analysis revealed that the most abundant genera were Polynucleobacter (~8% relative abundance and Bacillus (~10% in fresh water and soil from the vegetable crop, respectively. Low abundance (0.38% of cyanobacterial groups were identified. Based on functional gene prediction, denitrification appears to be an important process in the soil community analysed here. Conversely, genes for nitrogen fixation were abundant in freshwater, indicating that the N-fixation plays a crucial role in this particular ecosystem. Moreover, pathogenicity islands, antibiotic resistance and potential virulence related genes were identified in both samples, but no toxigenic genes were detected. This study provides a better understanding of the community structure of an area under strong agricultural activity with regular irrigation and fertilization with an organic compost made from animal carcasses. Additionally, the use of a metagenomic approach to investigate fresh water quality proved to be a relevant method to evaluate its use in an agricultural ecosystem.

  6. Experimental Design and Bioinformatics Analysis for the Application of Metagenomics in Environmental Sciences and Biotechnology.

    Science.gov (United States)

    Ju, Feng; Zhang, Tong

    2015-11-03

    Recent advances in DNA sequencing technologies have prompted the widespread application of metagenomics for the investigation of novel bioresources (e.g., industrial enzymes and bioactive molecules) and unknown biohazards (e.g., pathogens and antibiotic resistance genes) in natural and engineered microbial systems across multiple disciplines. This review discusses the rigorous experimental design and sample preparation in the context of applying metagenomics in environmental sciences and biotechnology. Moreover, this review summarizes the principles, methodologies, and state-of-the-art bioinformatics procedures, tools and database resources for metagenomics applications and discusses two popular strategies (analysis of unassembled reads versus assembled contigs/draft genomes) for quantitative or qualitative insights of microbial community structure and functions. Overall, this review aims to facilitate more extensive application of metagenomics in the investigation of uncultured microorganisms, novel enzymes, microbe-environment interactions, and biohazards in biotechnological applications where microbial communities are engineered for bioenergy production, wastewater treatment, and bioremediation.

  7. Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

    International Nuclear Information System (INIS)

    Korkola, James E; Waldman, Frederic M; Blaveri, Ekaterina; DeVries, Sandy; Moore, Dan H II; Hwang, E Shelley; Chen, Yunn-Yi; Estep, Anne LH; Chew, Karen L; Jensen, Ronald H

    2007-01-01

    Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients

  8. Forest harvesting reduces the soil metagenomic potential for biomass decomposition.

    Science.gov (United States)

    Cardenas, Erick; Kranabetter, J M; Hope, Graeme; Maas, Kendra R; Hallam, Steven; Mohn, William W

    2015-11-01

    Soil is the key resource that must be managed to ensure sustainable forest productivity. Soil microbial communities mediate numerous essential ecosystem functions, and recent studies show that forest harvesting alters soil community composition. From a long-term soil productivity study site in a temperate coniferous forest in British Columbia, 21 forest soil shotgun metagenomes were generated, totaling 187 Gb. A method to analyze unassembled metagenome reads from the complex community was optimized and validated. The subsequent metagenome analysis revealed that, 12 years after forest harvesting, there were 16% and 8% reductions in relative abundances of biomass decomposition genes in the organic and mineral soil layers, respectively. Organic and mineral soil layers differed markedly in genetic potential for biomass degradation, with the organic layer having greater potential and being more strongly affected by harvesting. Gene families were disproportionately affected, and we identified 41 gene families consistently affected by harvesting, including families involved in lignin, cellulose, hemicellulose and pectin degradation. The results strongly suggest that harvesting profoundly altered below-ground cycling of carbon and other nutrients at this site, with potentially important consequences for forest regeneration. Thus, it is important to determine whether these changes foreshadow long-term changes in forest productivity or resilience and whether these changes are broadly characteristic of harvested forests.

  9. Metagenomic frameworks for monitoring antibiotic resistance in aquatic environments.

    Science.gov (United States)

    Port, Jesse A; Cullen, Alison C; Wallace, James C; Smith, Marissa N; Faustman, Elaine M

    2014-03-01

    High-throughput genomic technologies offer new approaches for environmental health monitoring, including metagenomic surveillance of antibiotic resistance determinants (ARDs). Although natural environments serve as reservoirs for antibiotic resistance genes that can be transferred to pathogenic and human commensal bacteria, monitoring of these determinants has been infrequent and incomplete. Furthermore, surveillance efforts have not been integrated into public health decision making. We used a metagenomic epidemiology-based approach to develop an ARD index that quantifies antibiotic resistance potential, and we analyzed this index for common modal patterns across environmental samples. We also explored how metagenomic data such as this index could be conceptually framed within an early risk management context. We analyzed 25 published data sets from shotgun pyrosequencing projects. The samples consisted of microbial community DNA collected from marine and freshwater environments across a gradient of human impact. We used principal component analysis to identify index patterns across samples. We observed significant differences in the overall index and index subcategory levels when comparing ecosystems more proximal versus distal to human impact. The selection of different sequence similarity thresholds strongly influenced the index measurements. Unique index subcategory modes distinguished the different metagenomes. Broad-scale screening of ARD potential using this index revealed utility for framing environmental health monitoring and surveillance. This approach holds promise as a screening tool for establishing baseline ARD levels that can be used to inform and prioritize decision making regarding management of ARD sources and human exposure routes. Port JA, Cullen AC, Wallace JC, Smith MN, Faustman EM. 2014. Metagenomic frameworks for monitoring antibiotic resistance in aquatic environments. Environ Health Perspect 122:222–228; http://dx.doi.org/10.1289/ehp

  10. Functional metagenomics to decipher food-microbe-host crosstalk.

    Science.gov (United States)

    Larraufie, Pierre; de Wouters, Tomas; Potocki-Veronese, Gabrielle; Blottière, Hervé M; Doré, Joël

    2015-02-01

    The recent developments of metagenomics permit an extremely high-resolution molecular scan of the intestinal microbiota giving new insights and opening perspectives for clinical applications. Beyond the unprecedented vision of the intestinal microbiota given by large-scale quantitative metagenomics studies, such as the EU MetaHIT project, functional metagenomics tools allow the exploration of fine interactions between food constituents, microbiota and host, leading to the identification of signals and intimate mechanisms of crosstalk, especially between bacteria and human cells. Cloning of large genome fragments, either from complex intestinal communities or from selected bacteria, allows the screening of these biological resources for bioactivity towards complex plant polymers or functional food such as prebiotics. This permitted identification of novel carbohydrate-active enzyme families involved in dietary fibre and host glycan breakdown, and highlighted unsuspected bacterial players at the top of the intestinal microbial food chain. Similarly, exposure of fractions from genomic and metagenomic clones onto human cells engineered with reporter systems to track modulation of immune response, cell proliferation or cell metabolism has allowed the identification of bioactive clones modulating key cell signalling pathways or the induction of specific genes. This opens the possibility to decipher mechanisms by which commensal bacteria or candidate probiotics can modulate the activity of cells in the intestinal epithelium or even in distal organs such as the liver, adipose tissue or the brain. Hence, in spite of our inability to culture many of the dominant microbes of the human intestine, functional metagenomics open a new window for the exploration of food-microbe-host crosstalk.

  11. Predictive gene testing for Huntington disease and other neurodegenerative disorders.

    Science.gov (United States)

    Wedderburn, S; Panegyres, P K; Andrew, S; Goldblatt, J; Liebeck, T; McGrath, F; Wiltshire, M; Pestell, C; Lee, J; Beilby, J

    2013-12-01

    Controversies exist around predictive testing (PT) programmes in neurodegenerative disorders. This study sets out to answer the following questions relating to Huntington disease (HD) and other neurodegenerative disorders: differences between these patients in their PT journeys, why and when individuals withdraw from PT, and decision-making processes regarding reproductive genetic testing. A case series analysis of patients having PT from the multidisciplinary Western Australian centre for PT over the past 20 years was performed using internationally recognised guidelines for predictive gene testing in neurodegenerative disorders. Of 740 at-risk patients, 518 applied for PT: 466 at risk of HD, 52 at risk of other neurodegenerative disorders - spinocerebellar ataxias, hereditary prion disease and familial Alzheimer disease. Thirteen percent withdrew from PT - 80.32% of withdrawals occurred during counselling stages. Major withdrawal reasons related to timing in the patients' lives or unknown as the patient did not disclose the reason. Thirty-eight HD individuals had reproductive genetic testing: 34 initiated prenatal testing (of which eight withdrew from the process) and four initiated pre-implantation genetic diagnosis. There was no recorded or other evidence of major psychological reactions or suicides during PT. People withdrew from PT in relation to life stages and reasons that are unknown. Our findings emphasise the importance of: (i) adherence to internationally recommended guidelines for PT; (ii) the role of the multidisciplinary team in risk minimisation; and (iii) patient selection. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.

  12. Oral Metagenomic Biomarkers in Rheumatoid Arthritis

    Science.gov (United States)

    2017-09-01

    individuals with rheumatoid arthritis (RA). The goal is to test the  hypothesis that oral microbiome and metagenomic analyses will allow  us  to identify new...biomarkers  that are  useful  for the diagnosis of early RA and/or biomarkers that help to predict the efficacy of  specific therapeutic interventions... RNA  microbiome analysis as well as whole genome shotgun sequencing.  Upon completion of these aims, any identified bacterial biomarkers may be

  13. Network-based prediction and knowledge mining of disease genes.

    Science.gov (United States)

    Carson, Matthew B; Lu, Hui

    2015-01-01

    In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network

  14. Compositional profile of α / β-hydrolase fold proteins in mangrove soil metagenomes: prevalence of epoxide hydrolases and haloalkane dehalogenases in oil-contaminated sites.

    Science.gov (United States)

    Jiménez, Diego Javier; Dini-Andreote, Francisco; Ottoni, Júlia Ronzella; de Oliveira, Valéria Maia; van Elsas, Jan Dirk; Andreote, Fernando Dini

    2015-05-01

    The occurrence of genes encoding biotechnologically relevant α/β-hydrolases in mangrove soil microbial communities was assessed using data obtained by whole-metagenome sequencing of four mangroves areas, denoted BrMgv01 to BrMgv04, in São Paulo, Brazil. The sequences (215 Mb in total) were filtered based on local amino acid alignments against the Lipase Engineering Database. In total, 5923 unassembled sequences were affiliated with 30 different α/β-hydrolase fold superfamilies. The most abundant predicted proteins encompassed cytosolic hydrolases (abH08; ∼ 23%), microsomal hydrolases (abH09; ∼ 12%) and Moraxella lipase-like proteins (abH04 and abH01; mangroves BrMgv01-02-03. This suggested selection and putative involvement in local degradation/detoxification of the pollutants. Seven sequences that were annotated as genes for putative epoxide hydrolases and five for putative haloalkane dehalogenases were found in a fosmid library generated from BrMgv02 DNA. The latter enzymes were predicted to belong to Actinobacteria, Deinococcus-Thermus, Planctomycetes and Proteobacteria. Our integrated approach thus identified 12 genes (complete and/or partial) that may encode hitherto undescribed enzymes. The low amino acid identity (< 60%) with already-described genes opens perspectives for both production in an expression host and genetic screening of metagenomes. © 2014 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

  15. Compositional profile of α/β-hydrolase fold proteins in mangrove soil metagenomes: prevalence of epoxide hydrolases and haloalkane dehalogenases in oil-contaminated sites

    Science.gov (United States)

    Jiménez, Diego Javier; Dini-Andreote, Francisco; Ottoni, Júlia Ronzella; de Oliveira, Valéria Maia; van Elsas, Jan Dirk; Andreote, Fernando Dini

    2015-01-01

    The occurrence of genes encoding biotechnologically relevant α/β-hydrolases in mangrove soil microbial communities was assessed using data obtained by whole-metagenome sequencing of four mangroves areas, denoted BrMgv01 to BrMgv04, in São Paulo, Brazil. The sequences (215 Mb in total) were filtered based on local amino acid alignments against the Lipase Engineering Database. In total, 5923 unassembled sequences were affiliated with 30 different α/β-hydrolase fold superfamilies. The most abundant predicted proteins encompassed cytosolic hydrolases (abH08; ∼ 23%), microsomal hydrolases (abH09; ∼ 12%) and Moraxella lipase-like proteins (abH04 and abH01; mangroves BrMgv01-02-03. This suggested selection and putative involvement in local degradation/detoxification of the pollutants. Seven sequences that were annotated as genes for putative epoxide hydrolases and five for putative haloalkane dehalogenases were found in a fosmid library generated from BrMgv02 DNA. The latter enzymes were predicted to belong to Actinobacteria, Deinococcus-Thermus, Planctomycetes and Proteobacteria. Our integrated approach thus identified 12 genes (complete and/or partial) that may encode hitherto undescribed enzymes. The low amino acid identity (< 60%) with already-described genes opens perspectives for both production in an expression host and genetic screening of metagenomes. PMID:25171437

  16. Metagenome Analyses of Corroded Concrete Wastewater Pipe Biofilms Reveals a Complex Microbial System

    Science.gov (United States)

    Analysis of whole-metagenome pyrosequencing data and 16S rRNA gene clone libraries was used to determine microbial composition and functional genes associated with biomass harvested from crown (top) and invert (bottom) sections of a corroded wastewater pipe. Taxonomic and functio...

  17. Metagenomics and the protein universe

    Science.gov (United States)

    Godzik, Adam

    2011-01-01

    Metagenomics sequencing projects have dramatically increased our knowledge of the protein universe and provided over one-half of currently known protein sequences; they have also introduced a much broader phylogenetic diversity into the protein databases. The full analysis of metagenomic datasets is only beginning, but it has already led to the discovery of thousands of new protein families, likely representing novel functions specific to given environments. At the same time, a deeper analysis of such novel families, including experimental structure determination of some representatives, suggests that most of them represent distant homologs of already characterized protein families, and thus most of the protein diversity present in the new environments are due to functional divergence of the known protein families rather than the emergence of new ones. PMID:21497084

  18. Genome and metagenome enabled analyses reveal new insight into the global biogeography and potential urea utilization in marine Thaumarchaeota.

    Science.gov (United States)

    Ahlgren, N.; Parada, A. E.; Fuhrman, J. A.

    2016-02-01

    Marine Thaumarchaea are an abundant, important group of marine microbial communities as they fix carbon, oxidize ammonium, and thus contribute to key N and C cycles in the oceans. From an enrichment culture, we have sequenced the complete genome of a new Thaumarchaeota strain, SPOT01. Analysis of this genome and other Thaumarchaeal genomes contributes new insight into its role in N cycling and clarifies the broader biogeography of marine Thaumarchaeal genera. Phylogenomics of Thaumarchaeota genomes reveal coherent separation into clusters roughly equivalent to the genus level, and SPOT01 represents a new genus of marine Thaumarchaea. Competitive fragment recruitment of globally distributed metagenomes from TARA, Ocean Sampling Day, and those generated from a station off California shows that the SPOT01 genus is often the most abundant genus, especially where total Thaumarchaea are most abundant in the overall community. The SPOT01 genome contains urease genes allowing it to use an alternative form of N. Genomic and metagenomic analysis also reveal that among planktonic genomes and populations, the urease genes in general are more frequently found in members of the SPOT01 genus and another genus dominant in deep waters, thus we predict these two genera contribute most significantly to urea utilization among marine Thaumarchaea. Recruitment also revealed broader biogeographic and ecological patterns of the putative genera. The SPOT01 genus was most abundant at colder temperatures (45 degrees). The genus containing Nitrosopumilus maritimus had the highest temperature range, and the genus containing Candidatus Nitrosopelagicus brevis was typically most abundant at intermediate temperatures and intermediate latitudes ( 35-45 degrees). Together these genome and metagenome enabled analyses provide significant new insight into the ecology and biogeochemical contributions of marine archaea.

  19. Integrative Workflows for Metagenomic Analysis

    Directory of Open Access Journals (Sweden)

    Efthymios eLadoukakis

    2014-11-01

    Full Text Available The rapid evolution of all sequencing technologies, described by the term Next Generation Sequencing (NGS, have revolutionized metagenomic analysis. They constitute a combination of high-throughput analytical protocols, coupled to delicate measuring techniques, in order to potentially discover, properly assemble and map allelic sequences to the correct genomes, achieving particularly high yields for only a fraction of the cost of traditional processes (i.e. Sanger. From a bioinformatic perspective, this boils down to many gigabytes of data being generated from each single sequencing experiment, rendering the management or even the storage, critical bottlenecks with respect to the overall analytical endeavor. The enormous complexity is even more aggravated by the versatility of the processing steps available, represented by the numerous bioinformatic tools that are essential, for each analytical task, in order to fully unveil the genetic content of a metagenomic dataset. These disparate tasks range from simple, nonetheless non-trivial, quality control of raw data to exceptionally complex protein annotation procedures, requesting a high level of expertise for their proper application or the neat implementation of the whole workflow. Furthermore, a bioinformatic analysis of such scale, requires grand computational resources, imposing as the sole realistic solution, the utilization of cloud computing infrastructures. In this review article we discuss different, integrative, bioinformatic solutions available, which address the aforementioned issues, by performing a critical assessment of the available automated pipelines for data management, quality control and annotation of metagenomic data, embracing various, major sequencing technologies and applications.

  20. Culture-independent discovery of natural products from soil metagenomes.

    Science.gov (United States)

    Katz, Micah; Hover, Bradley M; Brady, Sean F

    2016-03-01

    Bacterial natural products have proven to be invaluable starting points in the development of many currently used therapeutic agents. Unfortunately, traditional culture-based methods for natural product discovery have been deemphasized by pharmaceutical companies due in large part to high rediscovery rates. Culture-independent, or "metagenomic," methods, which rely on the heterologous expression of DNA extracted directly from environmental samples (eDNA), have the potential to provide access to metabolites encoded by a large fraction of the earth's microbial biosynthetic diversity. As soil is both ubiquitous and rich in bacterial diversity, it is an appealing starting point for culture-independent natural product discovery efforts. This review provides an overview of the history of soil metagenome-driven natural product discovery studies and elaborates on the recent development of new tools for sequence-based, high-throughput profiling of environmental samples used in discovering novel natural product biosynthetic gene clusters. We conclude with several examples of these new tools being employed to facilitate the recovery of novel secondary metabolite encoding gene clusters from soil metagenomes and the subsequent heterologous expression of these clusters to produce bioactive small molecules.

  1. Metagenome of a Versatile Chemolithoautotroph from Expanding Oceanic Dead Zones

    Energy Technology Data Exchange (ETDEWEB)

    Walsh, David A.; Zaikova, Elena; Howes, Charles L.; Song, Young; Wright, Jody; Tringe, Susannah G.; Tortell, Philippe D.; Hallam, Steven J.

    2009-07-15

    Oxygen minimum zones (OMZs), also known as oceanic"dead zones", are widespread oceanographic features currently expanding due to global warming and coastal eutrophication. Although inhospitable to metazoan life, OMZs support a thriving but cryptic microbiota whose combined metabolic activity is intimately connected to nutrient and trace gas cycling within the global ocean. Here we report time-resolved metagenomic analyses of a ubiquitous and abundant but uncultivated OMZ microbe (SUP05) closely related to chemoautotrophic gill symbionts of deep-sea clams and mussels. The SUP05 metagenome harbors a versatile repertoire of genes mediating autotrophic carbon assimilation, sulfur-oxidation and nitrate respiration responsive to a wide range of water column redox states. Thus, SUP05 plays integral roles in shaping nutrient and energy flow within oxygen-deficient oceanic waters via carbon sequestration, sulfide detoxification and biological nitrogen loss with important implications for marine productivity and atmospheric greenhouse control.

  2. A metagenomic framework for the study of airborne microbial communities.

    Science.gov (United States)

    Yooseph, Shibu; Andrews-Pfannkoch, Cynthia; Tenney, Aaron; McQuaid, Jeff; Williamson, Shannon; Thiagarajan, Mathangi; Brami, Daniel; Zeigler-Allen, Lisa; Hoffman, Jeff; Goll, Johannes B; Fadrosh, Douglas; Glass, John; Adams, Mark D; Friedman, Robert; Venter, J Craig

    2013-01-01

    Understanding the microbial content of the air has important scientific, health, and economic implications. While studies have primarily characterized the taxonomic content of air samples by sequencing the 16S or 18S ribosomal RNA gene, direct analysis of the genomic content of airborne microorganisms has not been possible due to the extremely low density of biological material in airborne environments. We developed sampling and amplification methods to enable adequate DNA recovery to allow metagenomic profiling of air samples collected from indoor and outdoor environments. Air samples were collected from a large urban building, a medical center, a house, and a pier. Analyses of metagenomic data generated from these samples reveal airborne communities with a high degree of diversity and different genera abundance profiles. The identities of many of the taxonomic groups and protein families also allows for the identification of the likely sources of the sampled airborne bacteria.

  3. Metagenomes provide valuable comparative information on soil microeukaryotes

    DEFF Research Database (Denmark)

    Jacquiod, Samuel Jehan Auguste; Stenbæk, Jonas; Santos, Susana

    2016-01-01

    has been identified. Our analyses suggest that publicly available metagenome data can provide valuable information on soil microeukaryotes for comparative purposes when handled appropriately, complementing the current view provided by ribosomal amplicon sequencing methods......., providing microbiologists with substantial amounts of accessible information. We took advantage of public metagenomes in order to investigate microeukaryote communities in a well characterized grassland soil. The data gathered allowed the evaluation of several factors impacting the community structure......, including the DNA extraction method, the database choice and also the annotation procedure. While most studies on soil microeukaryotes are based on sequencing of PCR-amplified taxonomic markers (18S rRNA genes, ITS regions), this work represents, to our knowledge, the first report based solely...

  4. Phylogenetic analysis of a spontaneous cocoa bean fermentation metagenome reveals new insights into its bacterial and fungal community diversity.

    Directory of Open Access Journals (Sweden)

    Koen Illeghems

    Full Text Available This is the first report on the phylogenetic analysis of the community diversity of a single spontaneous cocoa bean box fermentation sample through a metagenomic approach involving 454 pyrosequencing. Several sequence-based and composition-based taxonomic profiling tools were used and evaluated to avoid software-dependent results and their outcome was validated by comparison with previously obtained culture-dependent and culture-independent data. Overall, this approach revealed a wider bacterial (mainly γ-Proteobacteria and fungal diversity than previously found. Further, the use of a combination of different classification methods, in a software-independent way, helped to understand the actual composition of the microbial ecosystem under study. In addition, bacteriophage-related sequences were found. The bacterial diversity depended partially on the methods used, as composition-based methods predicted a wider diversity than sequence-based methods, and as classification methods based solely on phylogenetic marker genes predicted a more restricted diversity compared with methods that took all reads into account. The metagenomic sequencing analysis identified Hanseniaspora uvarum, Hanseniaspora opuntiae, Saccharomyces cerevisiae, Lactobacillus fermentum, and Acetobacter pasteurianus as the prevailing species. Also, the presence of occasional members of the cocoa bean fermentation process was revealed (such as Erwinia tasmaniensis, Lactobacillus brevis, Lactobacillus casei, Lactobacillus rhamnosus, Lactococcus lactis, Leuconostoc mesenteroides, and Oenococcus oeni. Furthermore, the sequence reads associated with viral communities were of a restricted diversity, dominated by Myoviridae and Siphoviridae, and reflecting Lactobacillus as the dominant host. To conclude, an accurate overview of all members of a cocoa bean fermentation process sample was revealed, indicating the superiority of metagenomic sequencing over previously used techniques.

  5. Exploring neighborhoods in the metagenome universe.

    Science.gov (United States)

    Aßhauer, Kathrin P; Klingenberg, Heiner; Lingner, Thomas; Meinicke, Peter

    2014-07-14

    The variety of metagenomes in current databases provides a rapidly growing source of information for comparative studies. However, the quantity and quality of supplementary metadata is still lagging behind. It is therefore important to be able to identify related metagenomes by means of the available sequence data alone. We have studied efficient sequence-based methods for large-scale identification of similar metagenomes within a database retrieval context. In a broad comparison of different profiling methods we found that vector-based distance measures are well-suitable for the detection of metagenomic neighbors. Our evaluation on more than 1700 publicly available metagenomes indicates that for a query metagenome from a particular habitat on average nine out of ten nearest neighbors represent the same habitat category independent of the utilized profiling method or distance measure. While for well-defined labels a neighborhood accuracy of 100% can be achieved, in general the neighbor detection is severely affected by a natural overlap of manually annotated categories. In addition, we present results of a novel visualization method that is able to reflect the similarity of metagenomes in a 2D scatter plot. The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space. Our study suggests that for inspection of metagenome neighborhoods the profiling methods and distance measures can be chosen to provide a convenient interpretation of results in terms of the underlying features. Furthermore, supplementary metadata of metagenome samples in the future needs to comply with readily available ontologies for fine-grained and standardized annotation. To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to the research community, we included the proposed methods in our CoMet-Universe server for comparative metagenome analysis.

  6. Expanding the marine virosphere using metagenomics.

    Directory of Open Access Journals (Sweden)

    Carolina Megumi Mizuno

    Full Text Available Viruses infecting prokaryotic cells (phages are the most abundant entities of the biosphere and contain a largely uncharted wealth of genomic diversity. They play a critical role in the biology of their hosts and in ecosystem functioning at large. The classical approaches studying phages require isolation from a pure culture of the host. Direct sequencing approaches have been hampered by the small amounts of phage DNA present in most natural habitats and the difficulty in applying meta-omic approaches, such as annotation of small reads and assembly. Serendipitously, it has been discovered that cellular metagenomes of highly productive ocean waters (the deep chlorophyll maximum contain significant amounts of viral DNA derived from cells undergoing the lytic cycle. We have taken advantage of this phenomenon to retrieve metagenomic fosmids containing viral DNA from a Mediterranean deep chlorophyll maximum sample. This method allowed description of complete genomes of 208 new marine phages. The diversity of these genomes was remarkable, contributing 21 genomic groups of tailed bacteriophages of which 10 are completely new. Sequence based methods have allowed host assignment to many of them. These predicted hosts represent a wide variety of important marine prokaryotic microbes like members of SAR11 and SAR116 clades, Cyanobacteria and also the newly described low GC Actinobacteria. A metavirome constructed from the same habitat showed that many of the new phage genomes were abundantly represented. Furthermore, other available metaviromes also indicated that some of the new phages are globally distributed in low to medium latitude ocean waters. The availability of many genomes from the same sample allows a direct approach to viral population genomics confirming the remarkable mosaicism of phage genomes.

  7. Prediction of Wild-type Enzyme Characteristics

    DEFF Research Database (Denmark)

    Geertz-Hansen, Henrik Marcus

    of biotechnology, including enzyme discovery and characterization. This work presents two articles on sequence-based discovery and functional annotation of enzymes in environmental samples, and two articles on analysis and prediction of enzyme thermostability and cofactor requirements. The first article presents...... a sequence-based approach to discovery of proteolytic enzymes in metagenomes obtained from the Polar oceans. We show that microorganisms living in these extreme environments of constant low temperature harbour genes encoding novel proteolytic enzymes with potential industrial relevance. The second article...... presents a web server for the processing and annotation of functional metagenomics sequencing data, tailored to meet the requirements of non-bioinformaticians. The third article presents analyses of the molecular determinants of enzyme thermostability, and a feature-based prediction method of the melting...

  8. Back to the Future of Soil Metagenomics.\

    Czech Academy of Sciences Publication Activity Database

    Nesme J, J.; Achouak, W.; Agathos SN, S.N.; Bailey, M.; Baldrian, Petr; Brunel, D.; Frostegård, Å.; Heulin, T.; Jansson JK, J.K.; Jurkevitch, E.; Kruus, K.L.; Kowalchuk, G.A.; Lagares, A.; Lapin-Scott, H.M.; Lemanceau, P.; Le Paslier, D.; Mandic-Mulec, I.; Murrell, J.C.; Myrold, D.D.; Nalin, R.; Nannipieri, P.; Neufeld, J.D.; O'Gara, F.; Parnell, J.J.; Pühler, A.; Pylro, V.; Ramos, J.L.; Roesch, L.F.; Schloter, M.; Schleper, C.; Sczyrba, A.; Sessitsch, A.; Sjöling, S.; Sørensen, J.; Sørensen, S.J.; Tebbe, C.C.; Topp, E.; Tsiamis, G.; van Elsas, J.D.; van Keulen, G.; Widmer, F.; Wagner, M.; Zhang, T.; Zhang, X.; Zhao, L; Zhu, Y-G.; Vogel, T.M.; Simonet, P.

    2016-01-01

    Roč. 7, FEB 10 (2016), s. 73 ISSN 1664-302X Institutional support: RVO:61388971 Keywords : metagenomic * soil microbiology; terrestrial microbiology * metagenomic; soil microbiology; terrestrial microbiology Subject RIV: EE - Microbiology, Virology Impact factor: 4.076, year: 2016

  9. Vast diversity of prokaryotic virus genomes encoding double jelly-roll major capsid proteins uncovered by genomic and metagenomic sequence analysis.

    Science.gov (United States)

    Yutin, Natalya; Bäckström, Disa; Ettema, Thijs J G; Krupovic, Mart; Koonin, Eugene V

    2018-04-10

    Analysis of metagenomic sequences has become the principal approach for the study of the diversity of viruses. Many recent, extensive metagenomic studies on several classes of viruses have dramatically expanded the visible part of the virosphere, showing that previously undetected viruses, or those that have been considered rare, actually are important components of the global virome. We investigated the provenance of viruses related to tail-less bacteriophages of the family Tectiviridae by searching genomic and metagenomics sequence databases for distant homologs of the tectivirus-like Double Jelly-Roll major capsid proteins (DJR MCP). These searches resulted in the identification of numerous genomes of virus-like elements that are similar in size to tectiviruses (10-15 kilobases) and have diverse gene compositions. By comparison of the gene repertoires, the DJR MCP-encoding genomes were classified into 6 distinct groups that can be predicted to differ in reproduction strategies and host ranges. Only the DJR MCP gene that is present by design is shared by all these genomes, and most also encode a predicted DNA-packaging ATPase; the rest of the genes are present only in subgroups of this unexpectedly diverse collection of DJR MCP-encoding genomes. Only a minority encode a DNA polymerase which is a hallmark of the family Tectiviridae and the putative family "Autolykiviridae". Notably, one of the identified putative DJR MCP viruses encodes a homolog of Cas1 endonuclease, the integrase involved in CRISPR-Cas adaptation and integration of transposon-like elements called casposons. This is the first detected occurrence of Cas1 in a virus. Many of the identified elements are individual contigs flanked by inverted or direct repeats and appear to represent complete, extrachromosomal viral genomes, whereas others are flanked by bacterial genes and thus can be considered as proviruses. These contigs come from metagenomes of widely different environments, some dominated by

  10. Metagenomic Signatures of Microbial Communities in Deep-Sea Hydrothermal Sediments of Azores Vent Fields.

    Science.gov (United States)

    Cerqueira, Teresa; Barroso, Cristina; Froufe, Hugo; Egas, Conceição; Bettencourt, Raul

    2018-01-21

    The organisms inhabiting the deep-seafloor are known to play a crucial role in global biogeochemical cycles. Chemolithoautotrophic prokaryotes, which produce biomass from single carbon molecules, constitute the primary source of nutrition for the higher organisms, being critical for the sustainability of food webs and overall life in the deep-sea hydrothermal ecosystems. The present study investigates the metabolic profiles of chemolithoautotrophs inhabiting the sediments of Menez Gwen and Rainbow deep-sea vent fields, in the Mid-Atlantic Ridge. Differences in the microbial community structure might be reflecting the distinct depth, geology, and distance from vent of the studied sediments. A metagenomic sequencing approach was conducted to characterize the microbiome of the deep-sea hydrothermal sediments and the relevant metabolic pathways used by microbes. Both Menez Gwen and Rainbow metagenomes contained a significant number of genes involved in carbon fixation, revealing the largely autotrophic communities thriving in both sites. Carbon fixation at Menez Gwen site was predicted to occur mainly via the reductive tricarboxylic acid cycle, likely reflecting the dominance of sulfur-oxidizing Epsilonproteobacteria at this site, while different autotrophic pathways were identified at Rainbow site, in particular the Calvin-Benson-Bassham cycle. Chemolithotrophy appeared to be primarily driven by the oxidation of reduced sulfur compounds, whether through the SOX-dependent pathway at Menez Gwen site or through reverse sulfate reduction at Rainbow site. Other energy-yielding processes, such as methane, nitrite, or ammonia oxidation, were also detected but presumably contributing less to chemolithoautotrophy. This work furthers our knowledge of the microbial ecology of deep-sea hydrothermal sediments and represents an important repository of novel genes with potential biotechnological interest.

  11. Metagenomic insights into the carbohydrate-active enzymes carried by the microorganisms adhering to solid digesta in the rumen of cows.

    Directory of Open Access Journals (Sweden)

    Lingling Wang

    Full Text Available The ruminal microbial community is a unique source of enzymes that underpin the conversion of cellulosic biomass. In this study, the microbial consortia adherent on solid digesta in the rumen of Jersey cattle were subjected to an activity-based metagenomic study to explore the genetic diversity of carbohydrolytic enzymes in Jersey cows, with a particular focus on cellulases and xylanases. Pyrosequencing and bioinformatic analyses of 120 carbohydrate-active fosmids identified genes encoding 575 putative Carbohydrate-Active Enzymes (CAZymes and proteins putatively related to transcriptional regulation, transporters, and signal transduction coupled with polysaccharide degradation and metabolism. Most of these genes shared little similarity to sequences archived in databases. Genes that were predicted to encode glycoside hydrolases (GH involved in xylan and cellulose hydrolysis (e.g., GH3, 5, 9, 10, 39 and 43 were well represented. A new subfamily (S-8 of GH5 was identified from contigs assigned to Firmicutes. These subfamilies of GH5 proteins also showed significant phylum-dependent distribution. A number of polysaccharide utilization loci (PULs were found, and two of them contained genes encoding Sus-like proteins and cellulases that have not been reported in previous metagenomic studies of samples from the rumens of cows or other herbivores. Comparison with the large metagenomic datasets previously reported of other ruminant species (or cattle breeds and wallabies showed that the rumen microbiome of Jersey cows might contain differing CAZymes. Future studies are needed to further explore how host genetics and diets affect the diversity and distribution of CAZymes and utilization of plant cell wall materials.

  12. A genome-wide gene function prediction resource for Drosophila melanogaster.

    Directory of Open Access Journals (Sweden)

    Han Yan

    2010-08-01

    Full Text Available Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

  13. Metagenomics reveals pervasive bacterial populations and reduced community diversity across the Alaska tundra ecosystem

    Directory of Open Access Journals (Sweden)

    Eric Robert Johnston

    2016-04-01

    Full Text Available How soil microbial communities contrast with respect to taxonomic and functional composition within and between ecosystems remains an unresolved question that is central to predicting how global anthropogenic change will affect soil functioning and services. In particular, it remains unclear how small-scale observations of soil communities based on the typical volume sampled (1-2 grams are generalizable to ecosystem-scale responses and processes. This is especially relevant for remote, northern latitude soils, which are challenging to sample and are also thought to be more vulnerable to climate change compared to temperate soils. Here, we employed well-replicated shotgun metagenome and 16S rRNA gene amplicon sequencing to characterize community composition and metabolic potential in Alaskan tundra soils, combining our own datasets with those publically available from distant tundra and temperate grassland and agriculture habitats. We found that the abundance of many taxa and metabolic functions differed substantially between tundra soil metagenomes relative to those from temperate soils, and that a high degree of OTU-sharing exists between tundra locations. Tundra soils were an order of magnitude less complex than their temperate counterparts, allowing for near-complete coverage of microbial community richness (~92% breadth by sequencing, and the recovery of twenty-seven high-quality, almost complete (>80% completeness population bins. These population bins, collectively, made up to ~10% of the metagenomic datasets, and represented diverse taxonomic groups and metabolic lifestyles tuned toward sulfur cycling, hydrogen metabolism, methanotrophy, and organic matter oxidation. Several population bins, including members of Acidobacteria, Actinobacteria, and Proteobacteria, were also present in geographically distant (~100-530 km apart tundra habitats (full genome representation and up to 99.6% genome-derived average nucleotide identity. Collectively

  14. Biochemical Characterization of a Family 15 Carbohydrate Esterase from a Bacterial Marine Arctic Metagenome.

    Directory of Open Access Journals (Sweden)

    Concetta De Santi

    Full Text Available The glucuronoyl esterase enzymes of wood-degrading fungi (Carbohydrate Esterase family 15; CE15 form part of the hemicellulolytic and cellulolytic enzyme systems that break down plant biomass, and have possible applications in biotechnology. Homologous enzymes are predicted in the genomes of several bacteria, however these have been much less studied than their fungal counterparts. Here we describe the recombinant production and biochemical characterization of a bacterial CE15 enzyme denoted MZ0003, which was identified by in silico screening of a prokaryotic metagenome library derived from marine Arctic sediment. MZ0003 has high similarity to several uncharacterized gene products of polysaccharide-degrading bacterial species, and phylogenetic analysis indicates a deep evolutionary split between these CE15s and fungal homologs.MZ0003 appears to differ from previously-studied CE15s in some aspects. Some glucuronoyl esterase activity could be measured by qualitative thin-layer chromatography which confirms its assignment as a CE15, however MZ0003 can also hydrolyze a range of other esters, including p-nitrophenyl acetate, which is not acted upon by some fungal homologs. The structure of MZ0003 also appears to differ as it is predicted to have several large loop regions that are absent in previously studied CE15s, and a combination of homology-based modelling and site-directed mutagenesis indicate its catalytic residues deviate from the conserved Ser-His-Glu triad of many fungal CE15s. Taken together, these results indicate that potentially unexplored diversity exists among bacterial CE15s, and this may be accessed by investigation of the microbial metagenome. The combination of low activity on typical glucuronoyl esterase substrates, and the lack of glucuronic acid esters in the marine environment suggest that the physiological substrate of MZ0003 and its homologs is likely to be different from that of related fungal enzymes.

  15. Gut metagenomes of type 2 diabetic patients have characteristic single-nucleotide polymorphism distribution in Bacteroides coprocola.

    Science.gov (United States)

    Chen, Yaowen; Li, Zongcheng; Hu, Shuofeng; Zhang, Jian; Wu, Jiaqi; Shao, Ningsheng; Bo, Xiaochen; Ni, Ming; Ying, Xiaomin

    2017-02-01

    Gut microbes play a critical role in human health and disease, and researchers have begun to characterize their genomes, the so-called gut metagenome. Thus far, metagenomics studies have focused on genus- or species-level composition and microbial gene sets, while strain-level composition and single-nucleotide polymorphism (SNP) have been overlooked. The gut metagenomes of type 2 diabetes (T2D) patients have been found to be enriched with butyrate-producing bacteria and sulfate reduction functions. However, it is not known whether the gut metagenomes of T2D patients have characteristic strain patterns or SNP distributions. We downloaded public gut metagenome datasets from 170 T2D patients and 174 healthy controls and performed a systematic comparative analysis of their metagenome SNPs. We found that Bacteroides coprocola, whose relative abundance did not differ between the groups, had a characteristic distribution of SNPs in the T2D patient group. We identified 65 genes, all in B. coprocola, that had remarkably different enrichment of SNPs. The first and sixth ranked genes encode glycosyl hydrolases (GenBank accession EDU99824.1 and EDV02301.1). Interestingly, alpha-glucosidase, which is also a glycosyl hydrolase located in the intestine, is an important drug target of T2D. These results suggest that different strains of B. coprocola may have different roles in human gut and a specific set of B. coprocola strains are correlated with T2D.

  16. Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis

    Directory of Open Access Journals (Sweden)

    Li He

    2014-01-01

    Full Text Available For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.

  17. Metagenomic evidence for sulfur lithotrophy by Epsilonproteobacteria as the major energy source for primary productivity in a sub-aerial arctic glacial deposit, Borup Fiord Pass.

    Science.gov (United States)

    Wright, Katherine E; Williamson, Charles; Grasby, Stephen E; Spear, John R; Templeton, Alexis S

    2013-01-01

    We combined free enenergy calculations and metagenomic analyses of an elemental sulfur (S(0)) deposit on the surface of Borup Fiord Pass Glacier in the Canadian High Arctic to investigate whether the energy available from different redox reactions in an environment predicts microbial metabolism. Many S, C, Fe, As, Mn, and [Formula: see text] oxidation reactions were predicted to be energetically feasible in the deposit, and aerobic oxidation of S(0) was the most abundant chemical energy source. Small subunit ribosomal RNA (SSU rRNA) gene sequence data showed that the dominant phylotypes were Sulfurovum and Sulfuricurvum, both Epsilonproteobacteria known to be capable of sulfur lithotrophy. Sulfur redox genes were abundant in the metagenome, but sox genes were significantly more abundant than reverse dsr (dissimilatory sulfite reductase)genes. Interestingly, there appeared to be habitable niches that were unoccupied at the depth of genome coverage obtained. Photosynthesis and [Formula: see text] oxidation should both be energetically favorable, but we found few or no functional genes for oxygenic or anoxygenic photosynthesis, or for [Formula: see text] oxidation by either oxygen (nitrification) or nitrite (anammox). The free energy, SSU rRNA gene and quantitative functional gene data are all consistent with the hypothesis that sulfur-based chemolithoautotrophy by Epsilonproteobacteria (Sulfurovum and Sulfuricurvum) is the main form of primary productivity at this site, instead of photosynthesis. This is despite the presence of 24-h sunlight, and the fact that photosynthesis is not known to be inhibited by any of the environmental conditions present. This is the first time that Sulfurovum and Sulfuricurvum have been shown to dominate a sub-aerial environment, rather than anoxic or sulfidic settings. We also found that Flavobacteria dominate the surface of the sulfur deposits. We hypothesize that this aerobic heterotroph uses enough oxygen to create a microoxic

  18. Metagenomic evidence for sulfur lithotrophy by Epsilonproteobacteria as the major energy source for primary productivity in a sub-aerial arctic glacial deposit, Borup Fiord Pass

    Directory of Open Access Journals (Sweden)

    Katherine E Wright

    2013-04-01

    Full Text Available We combined free energy calculations and metagenomic analyses of an elemental sulfur (S0 deposit on the surface of Borup Fiord Pass Glacier in the Canadian High Arctic to investigate whether the energy available from different redox reactions in an environment predicts microbial metabolism. Many S, C, Fe, As, Mn and NH4+ oxidation reactions were predicted to be energetically feasible in the deposit, and aerobic oxidation of S0 was the most abundant chemical energy source. Small subunit ribosomal RNA (SSU rRNA gene sequence data showed that the dominant phylotypes were Sulfurovum and Sulfuricurvum, both Epsilonproteobacteria known to be capable of sulfur lithotrophy. Sulfur redox genes were abundant in the metagenome, but sox genes were significantly more abundant than reverse dsr genes. Interestingly, there appeared to be habitable niches that were unoccupied at the depth of genome coverage obtained. Photosynthesis and NH4+ oxidation should both be energetically favorable, but we found few or no functional genes for oxygenic or anoxygenic photosynthesis, or for NH4+ oxidation by either oxygen (nitrification or nitrite (anammox. The free energy, SSU rRNA gene and quantitative functional gene data are all consistent with the hypothesis that sulfur-based chemolithoautotrophy by Epsilonproteobacteria (Sulfurovum and Sulfuricurvum is the main form of primary productivity at this site, instead of photosynthesis. This is despite the presence of 24-hour sunlight, and the fact that photosynthesis is not known to be inhibited by any of the environmental conditions present. This is the first time that Sulfurovum and Sulfuricurvum have been shown to dominate a sub-aerial environment, rather than anoxic or sulfidic settings. We also found that Flavobacteria dominate the surface of the sulfur deposits. We hypothesize that this aerobic heterotroph uses enough oxygen to create a microoxic environment in the sulfur below, where the Epsilonproteobacteria can

  19. Human milk metagenome: a functional capacity analysis

    Science.gov (United States)

    2013-01-01

    Background Human milk contains a diverse population of bacteria that likely influences colonization of the infant gastrointestinal tract. Recent studies, however, have been limited to characterization of this microbial community by 16S rRNA analysis. In the present study, a metagenomic approach using Illumina sequencing of a pooled milk sample (ten donors) was employed to determine the genera of bacteria and the types of bacterial open reading frames in human milk that may influence bacterial establishment and stability in this primal food matrix. The human milk metagenome was also compared to that of breast-fed and formula-fed infants’ feces (n = 5, each) and mothers’ feces (n = 3) at the phylum level and at a functional level using open reading frame abundance. Additionally, immune-modulatory bacterial-DNA motifs were also searched for within human milk. Results The bacterial community in human milk contained over 360 prokaryotic genera, with sequences aligning predominantly to the phyla of Proteobacteria (65%) and Firmicutes (34%), and the genera of Pseudomonas (61.1%), Staphylococcus (33.4%) and Streptococcus (0.5%). From assembled human milk-derived contigs, 30,128 open reading frames were annotated and assigned to functional categories. When compared to the metagenome of infants’ and mothers’ feces, the human milk metagenome was less diverse at the phylum level, and contained more open reading frames associated with nitrogen metabolism, membrane transport and stress response (P milk metagenome also contained a similar occurrence of immune-modulatory DNA motifs to that of infants’ and mothers’ fecal metagenomes. Conclusions Our results further expand the complexity of the human milk metagenome and enforce the benefits of human milk ingestion on the microbial colonization of the infant gut and immunity. Discovery of immune-modulatory motifs in the metagenome of human milk indicates more exhaustive analyses of the functionality of the human

  20. A retrospective metagenomics approach to studying Blastocystis

    DEFF Research Database (Denmark)

    Andersen, Lee O'Brien; Bonde, Ida; Nielsen, Henrik Bjørn

    2015-01-01

    a selection of 316 human faecal samples, hence representing genes originating from a single subtype. The 316 faecal samples were from 236 healthy individuals, 13 patients with Crohn's disease (CD) and 67 patients with ulcerative colitis (UC). The prevalence of Blastocystis was 20.3% in the healthy individuals......Blastocystis is a common single-celled intestinal parasitic genus, comprising several subtypes. Here, we screened data obtained by metagenomic analysis of faecal DNA for Blastocystis by searching for subtype-specific genes in coabundance gene groups, which are groups of genes that covary across...... and 14.9% in patients with UC. Meanwhile, Blastocystis was absent in patients with CD. Individuals with intestinal microbiota dominated by Bacteroides were much less prone to having Blastocystis-positive stool (Matthew's correlation coefficient = -0.25, P

  1. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

    DEFF Research Database (Denmark)

    Nielsen, Henrik Bjørn; Almeida, Mathieu; Juncker, Agnieszka

    2014-01-01

    of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify...

  2. Metagenomic analysis of bacterial community structure and diversity of lignocellulolytic bacteria in Vietnamese native goat rumen

    NARCIS (Netherlands)

    Do, Huyen Thi; Dao, Khoa Trong; Nguyen, Viet Khanh Hoang; Le Ngoc, Giang; Nguyen, Phuong Thi Mai; Le, Lam Tung; Phung, Nguyet Thu; M. van Straalen, Nico; Roelofs, Dick; Truong, Hai Nam

    2017-01-01

    Objective: In a previous study, analysis of Illumina sequenced metagenomic DNA data of bacteria in Vietnamese goats' rumen showed a high diversity of putative lignocellulolytic genes. In this study, taxonomy speculation of microbial community and lignocellulolytic bacteria population in the rumen

  3. An enrichment of CRISPR and other defense-related features in marine sponge-associated microbial metagenomes

    Directory of Open Access Journals (Sweden)

    Hannes Horn

    2016-11-01

    Full Text Available Many marine sponges are populated by dense and taxonomically diverse microbial consortia. We employed a metagenomics approach to unravel the differences in the functional gene repertoire among three Mediterranean sponge species, Petrosia ficiformis, Sarcotragus foetidus, Aplysina aerophoba and seawater. Different signatures were observed between sponge and seawater metagenomes with regard to microbial community composition, GC content, and estimated bacterial genome size. Our analysis showed further a pronounced repertoire for defense systems in sponge metagenomes. Specifically, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR, restriction modification, DNA phosphorothioation and phage growth limitation systems were enriched in sponge metagenomes. These data suggest that defense is an important functional trait for an existence within sponges that requires mechanisms to defend against foreign DNA from microorganisms and viruses. This study contributes to an understanding of the evolutionary arms race between viruses/phages and bacterial genomes and it sheds light on the bacterial defenses that have evolved in the context of the sponge holobiont.

  4. Vinasse fertirrigation alters soil resistome dynamics: an analysis based on metagenomic profiles.

    Science.gov (United States)

    Braga, Lucas P P; Alves, Rafael F; Dellias, Marina T F; Navarrete, Acacio A; Basso, Thiago O; Tsai, Siu M

    2017-01-01

    Every year around 300 Gl of vinasse, a by-product of ethanol distillation in sugarcane mills, are flushed into more than 9 Mha of sugarcane cropland in Brazil. This practice links fermentation waste management to fertilization for plant biomass production, and it is known as fertirrigation. Here we evaluate public datasets of soil metagenomes mining for changes in antibiotic resistance genes (ARGs) of soils from sugarcane mesocosms repeatedly amended with vinasse. The metagenomes were annotated using the ResFam database. We found that the abundance of open read frames (ORFs) annotated as ARGs changed significantly across 43 different families ( p -value resistome.

  5. Tuning the performance of a natural treatment process using metagenomics for improved trace organic chemical attenuation

    KAUST Repository

    Drewes, Jorg

    2014-02-01

    By utilizing high-throughput sequencing and metagenomics, this study revealed how the microbial community characteristics including composition, diversity, as well as functional genes in managed aquifer recharge (MAR) systems can be tuned to enhance removal of trace organic chemicals of emerging concern (CECs). Increasing the humic content of the primary substrate resulted in higher microbial diversity. Lower concentrations and a higher humic content of the primary substrate promoted the attenuation of biodegradable CECs in laboratory and field MAR systems. Metagenomic results indicated that the metabolic capabilities of xenobiotic biodegradation were significantly promoted for the microbiome under carbon-starving conditions. © IWA Publishing 2014.

  6. Metagenomics and development of the gut microbiota in infants

    DEFF Research Database (Denmark)

    Vallès, Y.; Gosalbes, M. J.; de Vries, Lisbeth Elvira

    2012-01-01

    Clin Microbiol Infect 2012; 18 (Suppl. 4): 21–26 The establishment of a balanced intestinal microbiota is essential for numerous aspects of human health, yet the microbial colonization of the gastrointestinal tract of infants is both complex and highly variable among individuals. In addition......, the gastrointestinal tract microbiota is often exposed to antibiotics, and may be an important reservoir of resistant strains and of transferable resistance genes from early infancy. We are investigating by means of diverse metagenomic approaches several areas of microbiota development in infants, including...

  7. A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors

    Science.gov (United States)

    2017-02-01

    affecting the function of Fanconi Anemia (FA) genes ( FANCA /B/C/D2/E/F/G/I/J/L/M, PALB2) or DNA damage response genes involved in HR 5 (ATM, ATR...Award Number: W81XWH-10-1-0585 TITLE: A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors...To) 15 July 2010 – 2 Nov.2016 4. TITLE AND SUBTITLE A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP

  8. Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

    Directory of Open Access Journals (Sweden)

    Teng Shaolei

    2013-01-01

    Full Text Available Abstract Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs and Support Vector Machines (SVMs were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.

  9. Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

    DEFF Research Database (Denmark)

    Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V

    2007-01-01

    was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...

  10. Marine metagenomics: strategies for the discovery of novel enzymes with biotechnological applications from marine environments

    Directory of Open Access Journals (Sweden)

    Dobson Alan DW

    2008-08-01

    Full Text Available Abstract Metagenomic based strategies have previously been successfully employed as powerful tools to isolate and identify enzymes with novel biocatalytic activities from the unculturable component of microbial communities from various terrestrial environmental niches. Both sequence based and function based screening approaches have been employed to identify genes encoding novel biocatalytic activities and metabolic pathways from metagenomic libraries. While much of the focus to date has centred on terrestrial based microbial ecosystems, it is clear that the marine environment has enormous microbial biodiversity that remains largely unstudied. Marine microbes are both extremely abundant and diverse; the environments they occupy likewise consist of very diverse niches. As culture-dependent methods have thus far resulted in the isolation of only a tiny percentage of the marine microbiota the application of metagenomic strategies holds great potential to study and exploit the enormous microbial biodiversity which is present within these marine environments.

  11. Novel polyhydroxyalkanoate copolymers produced in Pseudomonas putida by metagenomic polyhydroxyalkanoate synthases.

    Science.gov (United States)

    Cheng, Jiujun; Charles, Trevor C

    2016-09-01

    Bacterially produced biodegradable polyhydroxyalkanoates (PHAs) with versatile properties can be achieved using different PHA synthases (PhaCs). This work aims to expand the diversity of known PhaCs via functional metagenomics and demonstrates the use of these novel enzymes in PHA production. Complementation of a PHA synthesis-deficient Pseudomonas putida strain with a soil metagenomic cosmid library retrieved 27 clones expressing either class I, class II, or unclassified PHA synthases, and many did not have close sequence matches to known PhaCs. The composition of PHA produced by these clones was dependent on both the supplied growth substrates and the nature of the PHA synthase, with various combinations of short-chain-length (SCL) and medium-chain-length (MCL) PHA. These data demonstrate the ability to isolate diverse genes for PHA synthesis by functional metagenomics and their use for the production of a variety of PHA polymer and copolymer mixtures.

  12. Metagenomic and proteomic analyses to elucidate the mechanism of anaerobic benzene degradation

    Energy Technology Data Exchange (ETDEWEB)

    Abu Laban, Nidal [Helmholtz (Germany)

    2011-07-01

    This paper presents the mechanism of anaerobic benzene degradation using metagenomic and proteomic analyses. The objective of the study is to find out the microbes and biochemistry involved in benzene degradation. Hypotheses are proposed for the initial activation mechanism of benzene under anaerobic conditions. Two methods for degradation, molecular characterization and identification of benzene-degrading enzymes, are described. The physiological and molecular characteristics of iron-reducing enrichment culture are given and the process is detailed. Metagenome analysis of iron-reducing culture is presented using a pie chart. From the metagenome analysis of benzene-degrading culture, putative mobile element genes were identified in the aromatic-degrading configurations. Metaproteomic analysis of iron-reducing cultures and the anaerobic benzene degradation pathway are also elucidated. From the study, it can be concluded that gram-positive bacteria are involved in benzene degradation under iron-reducing conditions and that the catalysis mechanism of putative anaerobic benzene carboxylase needs further investigation.

  13. Genomic and metagenomic technologies to explore the antibiotic resistance mobilome.

    Science.gov (United States)

    Martínez, José L; Coque, Teresa M; Lanza, Val F; de la Cruz, Fernando; Baquero, Fernando

    2017-01-01

    Antibiotic resistance is a relevant problem for human health that requires global approaches to establish a deep understanding of the processes of acquisition, stabilization, and spread of resistance among human bacterial pathogens. Since natural (nonclinical) ecosystems are reservoirs of resistance genes, a health-integrated study of the epidemiology of antibiotic resistance requires the exploration of such ecosystems with the aim of determining the role they may play in the selection, evolution, and spread of antibiotic resistance genes, involving the so-called resistance mobilome. High-throughput sequencing techniques allow an unprecedented opportunity to describe the genetic composition of a given microbiome without the need to subculture the organisms present inside. However, bioinformatic methods for analyzing this bulk of data, mainly with respect to binning each resistance gene with the organism hosting it, are still in their infancy. Here, we discuss how current genomic methodologies can serve to analyze the resistance mobilome and its linkage with different bacterial genomes and metagenomes. In addition, we describe the drawbacks of current methodologies for analyzing the resistance mobilome, mainly in cases of complex microbiotas, and discuss the possibility of implementing novel tools to improve our current metagenomic toolbox. © 2016 New York Academy of Sciences.

  14. Tapping uncultured microorganisms through metagenomics for drug ...

    African Journals Online (AJOL)

    African Journal of Biotechnology ... Microorganisms are major source of bioactive natural products, and several ... This review highlights the recent methodologies, limitations, and applications of metagenomics for the discovery of new drugs.

  15. Tapping uncultured microorganisms through metagenomics for drug ...

    African Journals Online (AJOL)

    bdelnasser

    reached the market using this new technology. For these reasons and others, the interest in natural products has ..... Functional metagenomic library screening strategy ..... Bertrand H, Poly F, Van VT, Lombard N, Nalin R, Vogel TM, Simonet P.

  16. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression.

    Science.gov (United States)

    Guilloux, Jean-Philippe; Bassi, Sabrina; Ding, Ying; Walsh, Chris; Turecki, Gustavo; Tseng, George; Cyranowski, Jill M; Sibille, Etienne

    2015-02-01

    Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).

  17. Year-long metagenomic study of river microbiomes across land use and water quality

    Directory of Open Access Journals (Sweden)

    Thea eVan Rossum

    2015-12-01

    Full Text Available Select bacteria, such as Escherichia coli or coliforms, have been widely used as sentinels of low water quality; however, there are concerns regarding their predictive accuracy for the protection of human and environmental health. To develop improved monitoring systems, a greater understanding of bacterial community structure, function and variability across time is required in the context of different pollution types, such as agricultural and urban contamination. Here, we present a year-long survey of free-living bacterial DNA collected from seven sites along rivers in three watersheds with varying land use in Southwestern Canada. This is the first study to examine the bacterial metagenome in flowing freshwater (lotic environments over such a time span, providing an opportunity to describe bacterial community variability as a function of land use and environmental conditions. Characteristics of the metagenomic data, such as sequence composition and average genome size, vary with sampling site, environmental conditions, and water chemistry. For example, average genome size was correlated with hours of daylight in the agricultural watershed and, across the agriculturally and urban-affected sites, k-mer composition clustering corresponded to nutrient concentrations. In addition to indicating a community shift, this change in average genome size has implications in terms of the normalisation strategies required, and considerations surrounding such strategies in general are discussed. When comparing abundances of gene functional groups between high- and low-quality water samples collected from an agricultural area, the latter had a higher abundance of nutrient metabolism and bacteriophage groups, possibly reflecting an increase in agricultural runoff. This work presents a valuable dataset representing a year of monthly sampling across watersheds and an analysis targeted at establishing a foundational understanding of how bacterial lotic communities

  18. Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases.

    Directory of Open Access Journals (Sweden)

    Nikol Snoeren

    Full Text Available BACKGROUND AND OBJECTIVES: This study was designed to identify and validate gene signatures that can predict disease free survival (DFS in patients undergoing a radical resection for their colorectal liver metastases (CRLM. METHODS: Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France and the University Medical Center Utrecht (The Netherlands. Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort. RESULT: No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort. CONCLUSIONS: No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.

  19. Challenges and Opportunities of Airborne Metagenomics

    OpenAIRE

    Behzad, Hayedeh; Gojobori, Takashi; Mineta, Katsuhiko

    2015-01-01

    Recent metagenomic studies of environments, such as marine and soil, have significantly enhanced our understanding of the diverse microbial communities living in these habitats and their essential roles in sustaining vast ecosystems. The increase in the number of publications related to soil and marine metagenomics is in sharp contrast to those of air, yet airborne microbes are thought to have significant impacts on many aspects of our lives from their potential roles in atmospheric events su...

  20. Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrroly-sine containing genes

    DEFF Research Database (Denmark)

    Have, Christian Theil; Zambach, Sine; Christiansen, Henning

    2013-01-01

    for prediction of pyrrolysine incorporating genes in genomes of bacteria and archaea leading to insights about the factors driving pyrrolysine translation and identification of new gene candidates. The method predicts known conserved genes with high recall and predicts several other promising candidates...... for experimental verification. The method is implemented as a computational pipeline which is available on request....

  1. Enrichment allows identification of diverse, rare elements in metagenomic resistome-virulome sequencing.

    Science.gov (United States)

    Noyes, Noelle R; Weinroth, Maggie E; Parker, Jennifer K; Dean, Chris J; Lakin, Steven M; Raymond, Robert A; Rovira, Pablo; Doster, Enrique; Abdo, Zaid; Martin, Jennifer N; Jones, Kenneth L; Ruiz, Jaime; Boucher, Christina A; Belk, Keith E; Morley, Paul S

    2017-10-17

    Shotgun metagenomic sequencing is increasingly utilized as a tool to evaluate ecological-level dynamics of antimicrobial resistance and virulence, in conjunction with microbiome analysis. Interest in use of this method for environmental surveillance of antimicrobial resistance and pathogenic microorganisms is also increasing. In published metagenomic datasets, the total of all resistance- and virulence-related sequences accounts for enrichment system that incorporates unique molecular indices to count DNA molecules and correct for enrichment bias. The use of the bait-capture and enrichment system significantly increased on-target sequencing of the resistome-virulome, enabling detection of an additional 1441 gene accessions and revealing a low-abundance portion of the resistome-virulome that was more diverse and compositionally different than that detected by more traditional metagenomic assays. The low-abundance portion of the resistome-virulome also contained resistance genes with public health importance, such as extended-spectrum betalactamases, that were not detected using traditional shotgun metagenomic sequencing. In addition, the use of the bait-capture and enrichment system enabled identification of rare resistance gene haplotypes that were used to discriminate between sample origins. These results demonstrate that the rare resistome-virulome contains valuable and unique information that can be utilized for both surveillance and population genetic investigations of resistance. Access to the rare resistome-virulome using the bait-capture and enrichment system validated in this study can greatly advance our understanding of microbiome-resistome dynamics.

  2. Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

    Directory of Open Access Journals (Sweden)

    Kim Seon-Young

    2009-05-01

    Full Text Available Abstract Background Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature. Results A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+ patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures Conclusion Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.

  3. HuMiChip: Development of a Functional Gene Array for the Study of Human Microbiomes

    Energy Technology Data Exchange (ETDEWEB)

    Tu, Q.; Deng, Ye; Lin, Lu; Hemme, Chris L.; He, Zhili; Zhou, Jizhong

    2010-05-17

    Microbiomes play very important roles in terms of nutrition, health and disease by interacting with their hosts. Based on sequence data currently available in public domains, we have developed a functional gene array to monitor both organismal and functional gene profiles of normal microbiota in human and mouse hosts, and such an array is called human and mouse microbiota array, HMM-Chip. First, seed sequences were identified from KEGG databases, and used to construct a seed database (seedDB) containing 136 gene families in 19 metabolic pathways closely related to human and mouse microbiomes. Second, a mother database (motherDB) was constructed with 81 genomes of bacterial strains with 54 from gut and 27 from oral environments, and 16 metagenomes, and used for selection of genes and probe design. Gene prediction was performed by Glimmer3 for bacterial genomes, and by the Metagene program for metagenomes. In total, 228,240 and 801,599 genes were identified for bacterial genomes and metagenomes, respectively. Then the motherDB was searched against the seedDB using the HMMer program, and gene sequences in the motherDB that were highly homologous with seed sequences in the seedDB were used for probe design by the CommOligo software. Different degrees of specific probes, including gene-specific, inclusive and exclusive group-specific probes were selected. All candidate probes were checked against the motherDB and NCBI databases for specificity. Finally, 7,763 probes covering 91.2percent (12,601 out of 13,814) HMMer confirmed sequences from 75 bacterial genomes and 16 metagenomes were selected. This developed HMM-Chip is able to detect the diversity and abundance of functional genes, the gene expression of microbial communities, and potentially, the interactions of microorganisms and their hosts.

  4. Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

    Science.gov (United States)

    Basyuni, M.; Wati, R.

    2018-03-01

    The present study evaluates the bioinformatics methods to analyze twenty-four predicted polyprenol reductase genes from higher plants on GenBank as well as predicted the structure, composition, similarity, subcellular localization, and phylogenetic. The physicochemical properties of plant polyprenol showed diversity among the observed genes. The percentage of the secondary structure of plant polyprenol genes followed the ratio order of α helix > random coil > extended chain structure. The values of chloroplast but not signal peptide were too low, indicated that few chloroplast transit peptide in plant polyprenol reductase genes. The possibility of the potential transit peptide showed variation among the plant polyprenol reductase, suggested the importance of understanding the variety of peptide components of plant polyprenol genes. To clarify this finding, a phylogenetic tree was drawn. The phylogenetic tree shows several branches in the tree, suggested that plant polyprenol reductase genes grouped into divergent clusters in the tree.

  5. deFUME: Dynamic exploration of functional metagenomic sequencing data

    DEFF Research Database (Denmark)

    van der Helm, Eric; Geertz-Hansen, Henrik Marcus; Genee, Hans Jasper

    2015-01-01

    is time consuming and constitutes a major bottleneck for experimental researchers in the field. Here we present the deFUME web server, an easy-to-use web-based interface for processing, annotation and visualization of functional metagenomics sequencing data, tailored to meet the requirements of non......-bioinformaticians. The web-server integrates multiple analysis steps into one single workflow: read assembly, open reading frame prediction, and annotation with BLAST, InterPro and GO classifiers. Analysis results are visualized in an online dynamic web-interface. The deFUME webserver provides a fast track from raw sequence...

  6. A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

    Science.gov (United States)

    Glusman, Gustavo; Qin, Shizhen; El-Gewely, M. Raafat; Siegel, Andrew F; Roach, Jared C; Hood, Leroy; Smit, Arian F. A

    2006-01-01

    The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.” PMID:16543943

  7. Quantitative metagenomic analyses based on average genome size normalization

    DEFF Research Database (Denmark)

    Frank, Jeremy Alexander; Sørensen, Søren Johannes

    2011-01-01

    provide not just a census of the community members but direct information on metabolic capabilities and potential interactions among community members. Here we introduce a method for the quantitative characterization and comparison of microbial communities based on the normalization of metagenomic data...... marine sources using both conventional small-subunit (SSU) rRNA gene analyses and our quantitative method to calculate the proportion of genomes in each sample that are capable of a particular metabolic trait. With both environments, to determine what proportion of each community they make up and how......). These analyses demonstrate how genome proportionality compares to SSU rRNA gene relative abundance and how factors such as average genome size and SSU rRNA gene copy number affect sampling probability and therefore both types of community analysis....

  8. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

    Science.gov (United States)

    Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

    2017-11-24

    Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

  9. Prediction of highly expressed genes in microbes based on chromatin accessibility

    DEFF Research Database (Denmark)

    Willenbrock, Hanni; Ussery, David

    2007-01-01

    BACKGROUND: It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed...

  10. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

    Directory of Open Access Journals (Sweden)

    Assaf Gottlieb

    2017-11-01

    Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort

  11. Metagenomic analysis of microbial communities yields insight into impacts of nanoparticle design

    Science.gov (United States)

    Metch, Jacob W.; Burrows, Nathan D.; Murphy, Catherine J.; Pruden, Amy; Vikesland, Peter J.

    2018-01-01

    Next-generation DNA sequencing and metagenomic analysis provide powerful tools for the environmentally friendly design of nanoparticles. Herein we demonstrate this approach using a model community of environmental microbes (that is, wastewater-activated sludge) dosed with gold nanoparticles of varying surface coatings and morphologies. Metagenomic analysis was highly sensitive in detecting the microbial community response to gold nanospheres and nanorods with either cetyltrimethylammonium bromide or polyacrylic acid surface coatings. We observed that the gold-nanoparticle morphology imposes a stronger force in shaping the microbial community structure than does the surface coating. Trends were consistent in terms of the compositions of both taxonomic and functional genes, which include antibiotic resistance genes, metal resistance genes and gene-transfer elements associated with cell stress that are relevant to public health. Given that nanoparticle morphology remained constant, the potential influence of gold dissolution was minimal. Surface coating governed the nanoparticle partitioning between the bioparticulate and aqueous phases.

  12. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

    Science.gov (United States)

    Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

    Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

  13. Exploration of soil metagenome diversity for prospection of enzymes involved in lignocellulosic biomass conversion

    Energy Technology Data Exchange (ETDEWEB)

    Alvarez, T.M.; Squina, F.M. [Laboratorio Nacional de Luz Sincrotron (LNLS), Campinas, SP (Brazil); Paixao, D.A.A.; Franco Cairo, J.P.L.; Buchli, F.; Ruller, R. [Laboratorio Nacional de Ciencia e Tecnologia do Bioetanol (CTBE), Campinas, SP (Brazil); Prade, R. [Oklahoma State University, Sillwater, OK (United States)

    2012-07-01

    Full text: Metagenomics allows access to genetic information encoded in DNA of microorganisms recalcitrant to cultivation. They represent a reservoir of novel biocatalyst with potential application in environmental friendly techniques aiming to overcome the dependence on fossil fuels and also to diminish air and water pollution. The focus of our work is the generation of a tool kit of lignocellulolytic enzymes from soil metagenome, which could be used for second generation ethanol production. Environmental samples were collected at a sugarcane field after harvesting, where it is expected that the microbial population involved on lignocellulose degradation was enriched due to the presence of straws covering the soil. Sugarcane Bagasse-Degrading-Soil (SBDS) metagenome was massively-parallel-454-Roche-sequenced. We identified a full repertoire of genes with significant match to glycosyl hydrolases catalytic domain and carbohydrate-binding modules. Soil metagenomics libraries cloned into pUC19 were screened through functional assays. CMC-agar screening resulted in positive clones, revealing new cellulases coding genes. Through a CMC-zymogram it was possible to observe that one of these genes, nominated as E-1, corresponds to an enzyme that is secreted to the extracellular medium, suggesting that the cloned gene carried the original signal peptide. Enzymatic assays and analysis through capillary electrophoresis showed that E-1 was able to cleave internal glycosidic bonds of cellulose. New rounds of functional screenings through chromogenic substrates are being conducted aiming the generation of a library of lignocellulolytic enzymes derived from soil metagenome, which may become key component for development of second generation biofuels. (author)

  14. Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

    Science.gov (United States)

    Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

    2009-02-01

    Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.

  15. Functional metagenomic profiling of intestinal microbiome in extreme ageing

    Science.gov (United States)

    Rampelli, Simone; Candela, Marco; Turroni, Silvia; Biagi, Elena; Collino, Sebastiano; Franceschi, Claudio; O'Toole, Paul W; Brigidi, Patrizia

    2013-01-01

    Age-related alterations in human gut microbiota composition have been thoroughly described, but a detailed functional description of the intestinal bacterial coding capacity is still missing. In order to elucidate the contribution of the gut metagenome to the complex mosaic of human longevity, we applied shotgun sequencing to total fecal bacterial DNA in a selection of samples belonging to a well-characterized human ageing cohort. The age-related trajectory of the human gut microbiome was characterized by loss of genes for shortchain fatty acid production and an overall decrease in the saccharolytic potential, while proteolytic functions were more abundant than in the intestinal metagenome of younger adults. This altered functional profile was associated with a relevant enrichment in “pathobionts”, i.e. opportunistic pro-inflammatory bacteria generally present in the adult gut ecosystem in low numbers. Finally, as a signature for long life we identified 116 microbial genes that significantly correlated with ageing. Collectively, our data emphasize the relationship between intestinal bacteria and human metabolism, by detailing the modifications in the gut microbiota as a consequence of and/or promoter of the physiological changes occurring in the human host upon ageing. PMID:24334635

  16. Microbial survival strategies in ancient permafrost: insights from metagenomics.

    Science.gov (United States)

    Mackelprang, Rachel; Burkert, Alexander; Haw, Monica; Mahendrarajah, Tara; Conaway, Christopher H; Douglas, Thomas A; Waldrop, Mark P

    2017-10-01

    In permafrost (perennially frozen ground) microbes survive oligotrophic conditions, sub-zero temperatures, low water availability and high salinity over millennia. Viable life exists in permafrost tens of thousands of years old but we know little about the metabolic and physiological adaptations to the challenges presented by life in frozen ground over geologic time. In this study we asked whether increasing age and the associated stressors drive adaptive changes in community composition and function. We conducted deep metagenomic and 16 S rRNA gene sequencing across a Pleistocene permafrost chronosequence from 19 000 to 33 000 years before present (kyr). We found that age markedly affected community composition and reduced diversity. Reconstruction of paleovegetation from metagenomic sequence suggests vegetation differences in the paleo record are not responsible for shifts in community composition and function. Rather, we observed shifts consistent with long-term survival strategies in extreme cryogenic environments. These include increased reliance on scavenging detrital biomass, horizontal gene transfer, chemotaxis, dormancy, environmental sensing and stress response. Our results identify traits that may enable survival in ancient cryoenvironments with no influx of energy or new materials.

  17. Functional metagenomic profiling of intestinal microbiome in extreme ageing.

    Science.gov (United States)

    Rampelli, Simone; Candela, Marco; Turroni, Silvia; Biagi, Elena; Collino, Sebastiano; Franceschi, Claudio; O'Toole, Paul W; Brigidi, Patrizia

    2013-12-01

    Age-related alterations in human gut microbiota composition have been thoroughly described, but a detailed functional description of the intestinal bacterial coding capacity is still missing. In order to elucidate the contribution of the gut metagenome to the complex mosaic of human longevity, we applied shotgun sequencing to total fecal bacterial DNA in a selection of samples belonging to a well-characterized human ageing cohort. The age-related trajectory of the human gut microbiome was characterized by loss of genes for shortchain fatty acid production and an overall decrease in the saccharolytic potential, while proteolytic functions were more abundant than in the intestinal metagenome of younger adults. This altered functional profile was associated with a relevant enrichment in "pathobionts", i.e. opportunistic pro-inflammatory bacteria generally present in the adult gut ecosystem in low numbers. Finally, as a signature for long life we identified 116 microbial genes that significantly correlated with ageing. Collectively, our data emphasize the relationship between intestinal bacteria and human metabolism, by detailing the modifications in the gut microbiota as a consequence of and/or promoter of the physiological changes occurring in the human host upon ageing.

  18. A seven-gene CpG-island methylation panel predicts breast cancer progression

    International Nuclear Information System (INIS)

    Li, Yan; Melnikov, Anatoliy A.; Levenson, Victor; Guerra, Emanuela; Simeone, Pasquale; Alberti, Saverio; Deng, Youping

    2015-01-01

    DNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors. CpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes. Seven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression. Our findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings. The online version of this article (doi:10.1186/s12885-015-1412-9) contains supplementary

  19. Concerted down-regulation of immune-system related genes predicts metastasis in colorectal carcinoma

    International Nuclear Information System (INIS)

    Fehlker, Marion; Huska, Matthew R; Jöns, Thomas; Andrade-Navarro, Miguel A; Kemmner, Wolfgang

    2014-01-01

    This study aimed at the identification of prognostic gene expression markers in early primary colorectal carcinomas without metastasis at the time point of surgery by analyzing genome-wide gene expression profiles using oligonucleotide microarrays. Cryo-conserved tumor specimens from 45 patients with early colorectal cancers were examined, with the majority of them being UICC stage II or earlier and with a follow-up time of 41–115 months. Gene expression profiling was performed using Whole Human Genome 4x44K Oligonucleotide Microarrays. Validation of microarray data was performed on five of the genes in a smaller cohort. Using a novel algorithm based on the recursive application of support vector machines (SVMs), we selected a signature of 44 probes that discriminated between patients developing later metastasis and patients with a good prognosis. Interestingly, almost half of the genes was related to the patients’ immune response and showed reduced expression in the metastatic cases. Whereas up to now gene signatures containing genes with various biological functions have been described for prediction of metastasis in CRC, in this study metastasis could be well predicted by a set of gene expression markers consisting exclusively of genes related to the MHC class II complex involved in immune response. Thus, our data emphasize that the proper function of a comprehensive network of immune response genes is of vital importance for the survival of colorectal cancer patients

  20. Genomic Features That Predict Allelic Imbalance in Humans Suggest Patterns of Constraint on Gene Expression Variation

    Science.gov (United States)

    Fédrigo, Olivier; Haygood, Ralph; Mukherjee, Sayan; Wray, Gregory A.

    2009-01-01

    Variation in gene expression is an important contributor to phenotypic diversity within and between species. Although this variation often has a genetic component, identification of the genetic variants driving this relationship remains challenging. In particular, measurements of gene expression usually do not reveal whether the genetic basis for any observed variation lies in cis or in trans to the gene, a distinction that has direct relevance to the physical location of the underlying genetic variant, and which may also impact its evolutionary trajectory. Allelic imbalance measurements identify cis-acting genetic effects by assaying the relative contribution of the two alleles of a cis-regulatory region to gene expression within individuals. Identification of patterns that predict commonly imbalanced genes could therefore serve as a useful tool and also shed light on the evolution of cis-regulatory variation itself. Here, we show that sequence motifs, polymorphism levels, and divergence levels around a gene can be used to predict commonly imbalanced genes in a human data set. Reduction of this feature set to four factors revealed that only one factor significantly differentiated between commonly imbalanced and nonimbalanced genes. We demonstrate that these results are consistent between the original data set and a second published data set in humans obtained using different technical and statistical methods. Finally, we show that variation in the single allelic imbalance-associated factor is partially explained by the density of genes in the region of a target gene (allelic imbalance is less probable for genes in gene-dense regions), and, to a lesser extent, the evenness of expression of the gene across tissues and the magnitude of negative selection on putative regulatory regions of the gene. These results suggest that the genomic distribution of functional cis-regulatory variants in the human genome is nonrandom, perhaps due to local differences in evolutionary

  1. Prediction of highly expressed genes in microbes based on chromatin accessibility

    Directory of Open Access Journals (Sweden)

    Ussery David W

    2007-02-01

    Full Text Available Abstract Background It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes. Results We find that absolute gene expression levels are correlated with the position preference in many microbial genomes. It is postulated that in these regions, the DNA may be more accessible to the transcriptional machinery. Moreover, ribosomal proteins and ribosomal RNA are encoded by DNA having significantly lower position preference values than other genes in fast-replicating microbes. Conclusion This insight into DNA structure-dependent gene expression in microbes may be exploited for predicting the expression of non-translated genes such as non-coding RNAs that may not be predicted by any of the conventional codon usage bias approaches.

  2. Accurate prediction of secondary metabolite gene clusters in filamentous fungi

    DEFF Research Database (Denmark)

    Andersen, Mikael Rørdam; Nielsen, Jakob Blæsbjerg; Klitgaard, Andreas

    2013-01-01

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify...... used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom....

  3. Gene prediction and RFX transcriptional regulation analysis using comparative genomics

    OpenAIRE

    Chu, Jeffrey Shih Chieh

    2011-01-01

    Regulatory Factor X (RFX) is a family of transcription factors (TF) that is conserved in all metazoans, in some fungi, and in only a few single-cellular organisms. Seven members are found in mammals, nine in fishes, three in fruit flies, and a single member in nematodes and fungi. RFX is involved in many different roles in humans, but a particular function that is conserved in many metazoans is its regulation of ciliogenesis. Probing over 150 genomes for the presence of RFX and ciliary genes ...

  4. Clustering gene expression data based on predicted differential effects of GV interaction.

    Science.gov (United States)

    Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

    2005-02-01

    Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

  5. Marine Metagenome as A Resource for Novel Enzymes

    KAUST Repository

    Alma’ abadi, Amani D.; Gojobori, Takashi; Mineta, Katsuhiko

    2015-01-01

    the metagenomics approach has many limitations, it is expected to provide not only scientific insights but also economic benefits, especially in industry. This review highlights the importance of metagenomics in mining microbial lipases, as an example, by using

  6. Metagenomic insights into evolution of heavy metal-contaminated groundwater microbial community

    Energy Technology Data Exchange (ETDEWEB)

    Hemme, C.L.; Deng, Y.; Gentry, T.J.; Fields, M.W.; Wu, L.; Barua, S.; Barry, K.; Green-Tringe, S.; Watson, D.B.; He, Z.; Hazen, T.C.; Tiedje, J.M.; Rubin, E.M.; Zhou, J.

    2010-07-01

    Understanding adaptation of biological communities to environmental change is a central issue in ecology and evolution. Metagenomic analysis of a stressed groundwater microbial community reveals that prolonged exposure to high concentrations of heavy metals, nitric acid and organic solvents ({approx}50 years) has resulted in a massive decrease in species and allelic diversity as well as a significant loss of metabolic diversity. Although the surviving microbial community possesses all metabolic pathways necessary for survival and growth in such an extreme environment, its structure is very simple, primarily composed of clonal denitrifying {gamma}- and {beta}-proteobacterial populations. The resulting community is overabundant in key genes conferring resistance to specific stresses including nitrate, heavy metals and acetone. Evolutionary analysis indicates that lateral gene transfer could have a key function in rapid response and adaptation to environmental contamination. The results presented in this study have important implications in understanding, assessing and predicting the impacts of human-induced activities on microbial communities ranging from human health to agriculture to environmental management, and their responses to environmental changes.

  7. Metagenomic Insights into Evolution of a Heavy Metal-Contaminated Groundwater Microbial Community

    Energy Technology Data Exchange (ETDEWEB)

    Hemme, Christopher L.; Deng, Ye; Gentry, Terry J.; Fields, Matthew W.; Wu, Liyou; Barua, Soumitra; Barry, Kerrie; Tringe, Susannah G.; Watson, David B.; He, Zhili; Hazen, Terry C.; Tiedje, James M.; Rubin, Edward M.; Zhou, Jizhong

    2010-02-15

    Understanding adaptation of biological communities to environmental change is a central issue in ecology and evolution. Metagenomic analysis of a stressed groundwater microbial community reveals that prolonged exposure to high concentrations of heavy metals, nitric acid and organic solvents (~;;50 years) have resulted in a massive decrease in species and allelic diversity as well as a significant loss of metabolic diversity. Although the surviving microbial community possesses all metabolic pathways necessary for survival and growth in such an extreme environment, its structure is very simple, primarily composed of clonal denitrifying ?- and ?-proteobacterial populations. The resulting community is over-abundant in key genes conferring resistance to specific stresses including nitrate, heavy metals and acetone. Evolutionary analysis indicates that lateral gene transfer could be a key mechanism in rapidly responding and adapting to environmental contamination. The results presented in this study have important implications in understanding, assessing and predicting the impacts of human-induced activities on microbial communities ranging from human health to agriculture to environmental management, and their responses to environmental changes.

  8. Cloning, expression and characterization of a novel esterase from a South China Sea sediment metagenome

    Science.gov (United States)

    Zhang, Hao; Li, Fuchao; Chen, Huaxin; Zhao, Jin; Yan, Jinfei; Jiang, Peng; Li, Ronggui; Zhu, Baoli

    2015-07-01

    Lipolytic enzymes, including esterases and lipases, represent a group of hydrolases that catalyze the cleavage and formation of ester bonds. A novel esterase gene, scsEst01, was cloned from a South China Sea sediment metagenome. The scsEst01 gene consisted of 921 bp encoding 307 amino acid residues. The predicted amino acid sequence shared less than 90% identity with other lipolytic enzymes in the NCBI nonredundant protein database. ScsEst01 was successfully co-expressed in Escherichia coli BL21 (DE3) with chaperones (dnaK-dnaJ-grpE) to prevent the formation of inclusion bodies. The recombinant protein was purified on an immobilized metal ion affinity column containing chelating Sepharose charged with Ni2+. The enzyme was characterized using p -nitrophenol butyrate as a substrate. ScsEst01 had the highest lipolytic activity at 35°C and pH 8.0, indicative of a meso-thermophilic alkaline esterase. ScsEst01 was thermostable at 20°C. The lipolytic activity of scsEst01 was strongly increased by Fe2+, Mn2+ and 1% Tween 80 or Tween 20.

  9. Metagenomic sequencing reveals the relationship between microbiota composition and quality of Chinese Rice Wine.

    Science.gov (United States)

    Hong, Xutao; Chen, Jing; Liu, Lin; Wu, Huan; Tan, Haiqin; Xie, Guangfa; Xu, Qian; Zou, Huijun; Yu, Wenjing; Wang, Lan; Qin, Nan

    2016-05-31

    Chinese Rice Wine (CRW) is a common alcoholic beverage in China. To investigate the influence of microbial composition on the quality of CRW, high throughput sequencing was performed for 110 wine samples on bacterial 16S rRNA gene and fungal Internal Transcribed Spacer II (ITS2). Bioinformatic analyses demonstrated that the quality of yeast starter and final wine correlated with microbial taxonomic composition, which was exemplified by our finding that wine spoilage resulted from a high proportion of genus Lactobacillus. Subsequently, based on Lactobacillus abundance of an early stage, a model was constructed to predict final wine quality. In addition, three batches of 20 representative wine samples selected from a pool of 110 samples were further analyzed in metagenomics. The results revealed that wine spoilage was due to rapid growth of Lactobacillus brevis at the early stage of fermentation. Gene functional analysis indicated the importance of some pathways such as synthesis of biotin, malolactic fermentation and production of short-chain fatty acid. These results led to a conclusion that metabolisms of microbes influence the wine quality. Thus, nurturing of beneficial microbes and inhibition of undesired ones are both important for the mechanized brewery.

  10. Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

    International Nuclear Information System (INIS)

    Yu, Jack X; Sieuwerts, Anieta M; Zhang, Yi; Martens, John WM; Smid, Marcel; Klijn, Jan GM; Wang, Yixin; Foekens, John A

    2007-01-01

    Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different

  11. Analysis and prediction of gene splice sites in four Aspergillus genomes

    DEFF Research Database (Denmark)

    Wang, Kai; Ussery, David; Brunak, Søren

    2009-01-01

    Several Aspergillus fungal genomic sequences have been published, with many more in progress. Obviously, it is essential to have high-quality, consistently annotated sets of proteins from each of the genomes, in order to make meaningful comparisons. We have developed a dedicated, publicly available......, splice site prediction program called NetAspGene, for the genus Aspergillus. Gene sequences from Aspergillus fumigatus, the most common mould pathogen, were used to build and test our model. Compared to many animals and plants, Aspergillus contains smaller introns; thus we have applied a larger window...... better splice site prediction than other available tools. NetAspGene will be very helpful for the study in Aspergillus splice sites and especially in alternative splicing. A webpage for NetAspGene is publicly available at http://www.cbs.dtu.dk/services/NetAspGene....

  12. Oxytocin receptor gene variation predicts subjective responses to MDMA.

    Science.gov (United States)

    Bershad, Anya K; Weafer, Jessica J; Kirkpatrick, Matthew G; Wardle, Margaret C; Miller, Melissa A; de Wit, Harriet

    2016-12-01

    3,4-Methylenedioxymethamphetamine (MDMA, "ecstasy") enhances desire to socialize and feelings of empathy, which are thought to be related to increased oxytocin levels. Thus, variation in the oxytocin receptor gene (OXTR) may influence responses to the drug. Here, we examined the influence of a single OXTR nucleotide polymorphism (SNP) on responses to MDMA in humans. Based on findings that carriers of the A allele at rs53576 exhibit reduced sensitivity to oxytocin-induced social behavior, we hypothesized that these individuals would show reduced subjective responses to MDMA, including sociability. In this three-session, double blind, within-subjects study, healthy volunteers with past MDMA experience (N = 68) received a MDMA (0, 0.75 mg/kg, and 1.5 mg/kg) and provided self-report ratings of sociability, anxiety, and drug effects. These responses were examined in relation to rs53576. MDMA (1.5 mg/kg) did not increase sociability in individuals with the A/A genotype as it did in G allele carriers. The genotypic groups did not differ in responses at the lower MDMA dose, or in cardiovascular or other subjective responses. These findings are consistent with the idea that MDMA-induced sociability is mediated by oxytocin, and that variation in the oxytocin receptor gene may influence responses to the drug.

  13. Microbial Diversity and Biochemical Potential Encoded by Thermal Spring Metagenomes Derived from the Kamchatka Peninsula

    Directory of Open Access Journals (Sweden)

    Bernd Wemheuer

    2013-01-01

    Full Text Available Volcanic regions contain a variety of environments suitable for extremophiles. This study was focused on assessing and exploiting the prokaryotic diversity of two microbial communities derived from different Kamchatkian thermal springs by metagenomic approaches. Samples were taken from a thermoacidophilic spring near the Mutnovsky Volcano and from a thermophilic spring in the Uzon Caldera. Environmental DNA for metagenomic analysis was isolated from collected sediment samples by direct cell lysis. The prokaryotic community composition was examined by analysis of archaeal and bacterial 16S rRNA genes. A total number of 1235 16S rRNA gene sequences were obtained and used for taxonomic classification. Most abundant in the samples were members of Thaumarchaeota, Thermotogae, and Proteobacteria. The Mutnovsky hot spring was dominated by the Terrestrial Hot Spring Group, Kosmotoga, and Acidithiobacillus. The Uzon Caldera was dominated by uncultured members of the Miscellaneous Crenarchaeotic Group and Enterobacteriaceae. The remaining 16S rRNA gene sequences belonged to the Aquificae, Dictyoglomi, Euryarchaeota, Korarchaeota, Thermodesulfobacteria, Firmicutes, and some potential new phyla. In addition, the recovered DNA was used for generation of metagenomic libraries, which were subsequently mined for genes encoding lipolytic and proteolytic enzymes. Three novel genes conferring lipolytic and one gene conferring proteolytic activity were identified.

  14. Natural history bycatch: a pipeline for identifying metagenomic sequences in RADseq data

    Directory of Open Access Journals (Sweden)

    Iris Holmes

    2018-04-01

    Full Text Available Background Reduced representation genomic datasets are increasingly becoming available from a variety of organisms. These datasets do not target specific genes, and so may contain sequences from parasites and other organisms present in the target tissue sample. In this paper, we demonstrate that (1 RADseq datasets can be used for exploratory analysis of tissue-specific metagenomes, and (2 tissue collections house complete metagenomic communities, which can be investigated and quantified by a variety of techniques. Methods We present an exploratory method for mining metagenomic “bycatch” sequences from a range of host tissue types. We use a combination of the pyRAD assembly pipeline, NCBI’s blastn software, and custom R scripts to isolate metagenomic sequences from RADseq type datasets. Results When we focus on sequences that align with existing references in NCBI’s GenBank, we find that between three and five percent of identifiable double-digest restriction site associated DNA (ddRAD sequences from host tissue samples are from phyla to contain known blood parasites. In addition to tissue samples, we examine ddRAD sequences from metagenomic DNA extracted snake and lizard hind-gut samples. We find that the sequences recovered from these samples match with expected bacterial and eukaryotic gut microbiome phyla. Discussion Our results suggest that (1 museum tissue banks originally collected for host DNA archiving are also preserving valuable parasite and microbiome communities, (2 that publicly available RADseq datasets may include metagenomic sequences that could be explored, and (3 that restriction site approaches are a useful exploratory technique to identify microbiome lineages that could be missed by primer-based approaches.

  15. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer

    DEFF Research Database (Denmark)

    Yu, Jun; Feng, Qiang; Wong, Sunny Hei

    2017-01-01

    known associations of Fusobacterium nucleatum and Peptostreptococcus stomatis with CRC, we found significant associations with several species, including Parvimonas micra and Solobacterium moorei. We identified 20 microbial gene markers that differentiated CRC and control microbiomes, and validated 4...... in the independent Chinese cohort with AUC=0.84 and OR of 23. These genes were enriched in early-stage (I-II) patient microbiomes, highlighting the potential for using faecal metagenomic biomarkers for early diagnosis of CRC. CONCLUSIONS: We present the first metagenomic profiling study of CRC faecal microbiomes...

  16. Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

    Directory of Open Access Journals (Sweden)

    Thomas H A Ederveen

    Full Text Available Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4% and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.

  17. Assembling the Marine Metagenome, One Cell at a Time

    Energy Technology Data Exchange (ETDEWEB)

    Woyke, Tanja; Xie, Gary; Copeland, Alex; Gonzalez, Jose M.; Han, Cliff; Kiss, Hajnalka; Saw, Jimmy H.; Senin, Pavel; Yang, Chi; Chatterji, Sourav; Cheng, Jan-Fang; Eisen, Jonathan A.; Sieracki, Michael E.; Stepanauskas, Ramunas

    2010-06-24

    The difficulty associated with the cultivation of most microorganisms and the complexity of natural microbial assemblages, such as marine plankton or human microbiome, hinder genome reconstruction of representative taxa using cultivation or metagenomic approaches. Here we used an alternative, single cell sequencing approach to obtain high-quality genome assemblies of two uncultured, numerically significant marine microorganisms. We employed fluorescence-activated cell sorting and multiple displacement amplification to obtain hundreds of micrograms of genomic DNA from individual, uncultured cells of two marine flavobacteria from the Gulf of Maine that were phylogenetically distant from existing cultured strains. Shotgun sequencing and genome finishing yielded 1.9 Mbp in 17 contigs and 1.5 Mbp in 21 contigs for the two flavobacteria, with estimated genome recoveries of about 91percent and 78percent, respectively. Only 0.24percent of the assembling sequences were contaminants and were removed from further analysis using rigorous quality control. In contrast to all cultured strains of marine flavobacteria, the two single cell genomes were excellent Global Ocean Sampling (GOS) metagenome fragment recruiters, demonstrating their numerical significance in the ocean. The geographic distribution of GOS recruits along the Northwest Atlantic coast coincided with ocean surface currents. Metabolic reconstruction indicated diverse potential energy sources, including biopolymer degradation, proteorhodopsin photometabolism, and hydrogen oxidation. Compared to cultured relatives, the two uncultured flavobacteria have small genome sizes, few non-coding nucleotides, and few paralogous genes, suggesting adaptations to narrow ecological niches. These features may have contributed to the abundance of the two taxa in specific regions of the ocean, and may have hindered their cultivation. We demonstrate the power of single cell DNA sequencing to generate reference genomes of uncultured

  18. Viral Metagenomics: MetaView Software

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, C; Smith, J

    2007-10-22

    The purpose of this report is to design and develop a tool for analysis of raw sequence read data from viral metagenomics experiments. The tool should compare read sequences of known viral nucleic acid sequence data and enable a user to attempt to determine, with some degree of confidence, what virus groups may be present in the sample. This project was conducted in two phases. In phase 1 we surveyed the literature and examined existing metagenomics tools to educate ourselves and to more precisely define the problem of analyzing raw read data from viral metagenomic experiments. In phase 2 we devised an approach and built a prototype code and database. This code takes viral metagenomic read data in fasta format as input and accesses all complete viral genomes from Kpath for sequence comparison. The system executes at the UNIX command line, producing output that is stored in an Oracle relational database. We provide here a description of the approach we came up with for handling un-assembled, short read data sets from viral metagenomics experiments. We include a discussion of the current MetaView code capabilities and additional functionality that we believe should be added, should additional funding be acquired to continue the work.

  19. Preliminary High-Throughput Metagenome Assembly

    Energy Technology Data Exchange (ETDEWEB)

    Dusheyko, Serge; Furman, Craig; Pangilinan, Jasmyn; Shapiro, Harris; Tu, Hank

    2007-03-26

    Metagenome data sets present a qualitatively different assembly problem than traditional single-organism whole-genome shotgun (WGS) assembly. The unique aspects of such projects include the presence of a potentially large number of distinct organisms and their representation in the data set at widely different fractions. In addition, multiple closely related strains could be present, which would be difficult to assemble separately. Failure to take these issues into account can result in poor assemblies that either jumble together different strains or which fail to yield useful results. The DOE Joint Genome Institute has sequenced a number of metagenomic projects and plans to considerably increase this number in the coming year. As a result, the JGI has a need for high-throughput tools and techniques for handling metagenome projects. We present the techniques developed to handle metagenome assemblies in a high-throughput environment. This includes a streamlined assembly wrapper, based on the JGI?s in-house WGS assembler, Jazz. It also includes the selection of sensible defaults targeted for metagenome data sets, as well as quality control automation for cleaning up the raw results. While analysis is ongoing, we will discuss preliminary assessments of the quality of the assembly results (http://fames.jgi-psf.org).

  20. Shotgun metagenomic data streams: surfing without fear

    Energy Technology Data Exchange (ETDEWEB)

    Berendzen, Joel R [Los Alamos National Laboratory

    2010-12-06

    Timely information about bio-threat prevalence, consequence, propagation, attribution, and mitigation is needed to support decision-making, both routinely and in a crisis. One DNA sequencer can stream 25 Gbp of information per day, but sampling strategies and analysis techniques are needed to turn raw sequencing power into actionable knowledge. Shotgun metagenomics can enable biosurveillance at the level of a single city, hospital, or airplane. Metagenomics characterizes viruses and bacteria from complex environments such as soil, air filters, or sewage. Unlike targeted-primer-based sequencing, shotgun methods are not blind to sequences that are truly novel, and they can measure absolute prevalence. Shotgun metagenomic sampling can be non-invasive, efficient, and inexpensive while being informative. We have developed analysis techniques for shotgun metagenomic sequencing that rely upon phylogenetic signature patterns. They work by indexing local sequence patterns in a manner similar to web search engines. Our methods are laptop-fast and favorable scaling properties ensure they will be sustainable as sequencing methods grow. We show examples of application to soil metagenomic samples.

  1. Bioreactor microbial ecosystems for thiocyanate and cyanide degradation unravelled with genome-resolved metagenomics.

    Science.gov (United States)

    Kantor, Rose S; van Zyl, A Wynand; van Hille, Robert P; Thomas, Brian C; Harrison, Susan T L; Banfield, Jillian F

    2015-12-01

    Gold ore processing uses cyanide (CN(-) ), which often results in large volumes of thiocyanate- (SCN(-) ) contaminated wastewater requiring treatment. Microbial communities can degrade SCN(-) and CN(-) , but little is known about their membership and metabolic potential. Microbial-based remediation strategies will benefit from an ecological understanding of organisms involved in the breakdown of SCN(-) and CN(-) into sulfur, carbon and nitrogen compounds. We performed metagenomic analysis of samples from two laboratory-scale bioreactors used to study SCN(-) and CN(-) degradation. Community analysis revealed the dominance of Thiobacillus spp., whose genomes harbour a previously unreported operon for SCN(-) degradation. Genome-based metabolic predictions suggest that a large portion of each bioreactor community is autotrophic, relying not on molasses in reactor feed but using energy gained from oxidation of sulfur compounds produced during SCN(-) degradation. Heterotrophs, including a bacterium from a previously uncharacterized phylum, compose a smaller portion of the reactor community. Predation by phage and eukaryotes is predicted to affect community dynamics. Genes for ammonium oxidation and denitrification were detected, indicating the potential for nitrogen removal, as required for complete remediation of wastewater. These findings suggest optimization strategies for reactor design, such as improved aerobic/anaerobic partitioning and elimination of organic carbon from reactor feed. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

  2. Resolving prokaryotic taxonomy without rRNA: longer oligonucleotide word lengths improve genome and metagenome taxonomic classification.

    Science.gov (United States)

    Alsop, Eric B; Raymond, Jason

    2013-01-01

    Oligonucleotide signatures, especially tetranucleotide signatures, have been used as method for homology binning by exploiting an organism's inherent biases towards the use of specific oligonucleotide words. Tetranucleotide signatures have been especially useful in environmental metagenomics samples as many of these samples contain organisms from poorly classified phyla which cannot be easily identified using traditional homology methods, including NCBI BLAST. This study examines oligonucleotide signatures across 1,424 completed genomes from across the tree of life, substantially expanding upon previous work. A comprehensive analysis of mononucleotide through nonanucleotide word lengths suggests that longer word lengths substantially improve the classification of DNA fragments across a range of sizes of relevance to high throughput sequencing. We find that, at present, heptanucleotide signatures represent an optimal balance between prediction accuracy and computational time for resolving taxonomy using both genomic and metagenomic fragments. We directly compare the ability of tetranucleotide and heptanucleotide world lengths (tetranucleotide signatures are the current standard for oligonucleotide word usage analyses) for taxonomic binning of metagenome reads. We present evidence that heptanucleotide word lengths consistently provide more taxonomic resolving power, particularly in distinguishing between closely related organisms that are often present in metagenomic samples. This implies that longer oligonucleotide word lengths should replace tetranucleotide signatures for most analyses. Finally, we show that the application of longer word lengths to metagenomic datasets leads to more accurate taxonomic binning of DNA scaffolds and have the potential to substantially improve taxonomic assignment and assembly of metagenomic data.

  3. Resolving prokaryotic taxonomy without rRNA: longer oligonucleotide word lengths improve genome and metagenome taxonomic classification.

    Directory of Open Access Journals (Sweden)

    Eric B Alsop

    Full Text Available Oligonucleotide signatures, especially tetranucleotide signatures, have been used as method for homology binning by exploiting an organism's inherent biases towards the use of specific oligonucleotide words. Tetranucleotide signatures have been especially useful in environmental metagenomics samples as many of these samples contain organisms from poorly classified phyla which cannot be easily identified using traditional homology methods, including NCBI BLAST. This study examines oligonucleotide signatures across 1,424 completed genomes from across the tree of life, substantially expanding upon previous work. A comprehensive analysis of mononucleotide through nonanucleotide word lengths suggests that longer word lengths substantially improve the classification of DNA fragments across a range of sizes of relevance to high throughput sequencing. We find that, at present, heptanucleotide signatures represent an optimal balance between prediction accuracy and computational time for resolving taxonomy using both genomic and metagenomic fragments. We directly compare the ability of tetranucleotide and heptanucleotide world lengths (tetranucleotide signatures are the current standard for oligonucleotide word usage analyses for taxonomic binning of metagenome reads. We present evidence that heptanucleotide word lengths consistently provide more taxonomic resolving power, particularly in distinguishing between closely related organisms that are often present in metagenomic samples. This implies that longer oligonucleotide word lengths should replace tetranucleotide signatures for most analyses. Finally, we show that the application of longer word lengths to metagenomic datasets leads to more accurate taxonomic binning of DNA scaffolds and have the potential to substantially improve taxonomic assignment and assembly of metagenomic data.

  4. Predicting Genes Involved in Human Cancer Using Network Contextual Information

    Directory of Open Access Journals (Sweden)

    Rahmani Hossein

    2012-03-01

    Full Text Available Protein-Protein Interaction (PPI networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1 Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself; and (2 Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes that uses the features proposed here outperforms the current state-of-the-art methods by more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.

  5. Stable isotope probing in the metagenomics era: a bridge towards improved bioremediation

    Science.gov (United States)

    Uhlik, Ondrej; Leewis, Mary-Cathrine; Strejcek, Michal; Musilova, Lucie; Mackova, Martina; Leigh, Mary Beth; Macek, Tomas

    2012-01-01

    Microbial biodegradation and biotransformation reactions are essential to most bioremediation processes, yet the specific organisms, genes, and mechanisms involved are often not well understood. Stable isotope probing (SIP) enables researchers to directly link microbial metabolic capability to phylogenetic and metagenomic information within a community context by tracking isotopically labeled substances into phylogenetically and functionally informative biomarkers. SIP is thus applicable as a tool for the identification of active members of the microbial community and associated genes integral to the community functional potential, such as biodegradative processes. The rapid evolution of SIP over the last decade and integration with metagenomics provides researchers with a much deeper insight into potential biodegradative genes, processes, and applications, thereby enabling an improved mechanistic understanding that can facilitate advances in the field of bioremediation. PMID:23022353

  6. MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

    Directory of Open Access Journals (Sweden)

    Yang Yi-Fan

    2007-03-01

    Full Text Available Abstract Background Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. Results This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs and Translation Initiation Sites (TISs. The former is based on a linguistic "Entropy Density Profile" (EDP model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. Conclusion Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.

  7. An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

    Science.gov (United States)

    Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

    2018-05-14

    A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

  8. Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

    Directory of Open Access Journals (Sweden)

    David M Mutch

    Full Text Available BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss could always be differentiated from non-responders (<4 kgs weight loss. We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier improved prediction accuracy to 80.9%+/-2.2%. CONCLUSION: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

  9. Phylogenomic detection and functional prediction of genes potentially important for plant meiosis.

    Science.gov (United States)

    Zhang, Luoyan; Kong, Hongzhi; Ma, Hong; Yang, Ji

    2018-02-15

    Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. The integration of weighted human gene association networks based on link prediction.

    Science.gov (United States)

    Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

    2017-01-31

    Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

  11. Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

    Science.gov (United States)

    Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

    2018-02-02

    Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.

  12. Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower

    Directory of Open Access Journals (Sweden)

    Patrick Thorwarth

    2018-02-01

    Full Text Available Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding.

  13. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jing; Ma, Zihao; Carr, Steven A.; Mertins, Philipp; Zhang, Hui; Zhang, Zhen; Chan, Daniel W.; Ellis, Matthew J. C.; Townsend, R. Reid; Smith, Richard D.; McDermott, Jason E.; Chen, Xian; Paulovich, Amanda G.; Boja, Emily S.; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Rodland, Karin D.; Liebler, Daniel C.; Zhang, Bing

    2016-11-11

    Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies

  14. FANTOM: Functional and taxonomic analysis of metagenomes

    Directory of Open Access Journals (Sweden)

    Sanli Kemal

    2013-02-01

    Full Text Available Abstract Background Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context. Results Here we introduce FANTOM, a tool that allows for exploratory and comparative analysis of metagenomics abundance data integrated with metadata information and biological databases. Importantly, FANTOM can make use of any hierarchical database and it comes supplied with NCBI taxonomic hierarchies as well as KEGG Orthology, COG, PFAM and TIGRFAM databases. Conclusions The software is implemented in Python, is platform independent, and is available at http://www.sysbio.se/Fantom.

  15. Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer

    International Nuclear Information System (INIS)

    Karlsson, Elin; Delle, Ulla; Danielsson, Anna; Olsson, Björn; Abel, Frida; Karlsson, Per; Helou, Khalil

    2008-01-01

    It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved. The research on these tumours was approved by the Medical Faculty Research

  16. lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

    Directory of Open Access Journals (Sweden)

    Silu Zhang

    2018-01-01

    Full Text Available Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs, which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

  17. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

    Science.gov (United States)

    Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

    2012-01-01

    Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

  18. Can Thrifty Gene(s or Predictive Fetal Programming for Thriftiness Lead to Obesity?

    Directory of Open Access Journals (Sweden)

    Ulfat Baig

    2011-01-01

    Full Text Available Obesity and related disorders are thought to have their roots in metabolic “thriftiness” that evolved to combat periodic starvation. The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype or anticipatory fetal programming. The assumption of thriftiness is implicit in obesity research. We examine here, with the help of a mathematical model, the conditions for evolution of thrifty genes or fetal programming for thriftiness. The model suggests that a thrifty gene cannot exist in a stable polymorphic state in a population. The conditions for evolution of thrifty fetal programming are restricted if the correlation between intrauterine and lifetime conditions is poor. Such a correlation is not observed in natural courses of famine. If there is fetal programming for thriftiness, it could have evolved in anticipation of social factors affecting nutrition that can result in a positive correlation.

  19. EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles.

    Science.gov (United States)

    Dittmar, W James; McIver, Lauren; Michalak, Pawel; Garner, Harold R; Valdez, Gregorio

    2014-07-01

    The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server 'EvoCor', to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. Molecular cloning, expression, and characterization of four novel thermo-alkaliphilic enzymes retrieved from a metagenomic library.

    Science.gov (United States)

    Maruthamuthu, Mukil; van Elsas, Jan Dirk

    2017-01-01

    Enzyme discovery is a promising approach to aid in the deconstruction of recalcitrant plant biomass in an industrial process. Novel enzymes can be readily discovered by applying metagenomics on whole microbiomes. Our goal was to select, examine, and characterize eight novel glycoside hydrolases that were previously detected in metagenomic libraries, to serve biotechnological applications with high performance. Here, eight glycosyl hydrolase family candidate genes were selected from metagenomes of wheat straw-degrading microbial consortia using molecular cloning and subsequent gene expression studies in Escherichia coli. Four of the eight enzymes had significant activities on either p NP-β-d-galactopyranoside, p NP-β-d-xylopyranoside, p NP-α-l-arabinopyranoside or p NP-α-d-glucopyranoside. These proteins, denoted as proteins 1, 2, 5 and 6, were his-tag purified and their nature and activities further characterized using molecular and activity screens with the p NP-labeled substrates. Proteins 1 and 2 showed high homologies with (1) a β-galactosidase (74%) and (2) a β-xylosidase (84%), whereas the remaining two (5 and 6) were homologous with proteins reported as a diguanylate cyclase and an aquaporin, respectively. The β-galactosidase- and β-xylosidase-like proteins 1 and 2 were confirmed as being responsible for previously found thermo-alkaliphilic glycosidase activities of extracts of E. coli carrying the respective source fosmids. Remarkably, the β-xylosidase-like protein 2 showed activities with both p NP-Xyl and p NP-Ara in the temperature range 40-50 °C and pH range 8.0-10.0. Moreover, proteins 5 and 6 showed thermotolerant α-glucosidase activity at pH 10.0. In silico structure prediction of protein 5 revealed the presence of a potential "GGDEF" catalytic site, encoding α-glucosidase activity, whereas that of protein 6 showed a "GDSL" site, encoding a 'new family' α-glucosidase activity. Using a rational screening approach, we identified and

  1. Metagenomic Detection Methods in Biopreparedness Outbreak Scenarios

    DEFF Research Database (Denmark)

    Karlsson, Oskar Erik; Hansen, Trine; Knutsson, Rickard

    2013-01-01

    In the field of diagnostic microbiology, rapid molecular methods are critically important for detecting pathogens. With rapid and accurate detection, preventive measures can be put in place early, thereby preventing loss of life and further spread of a disease. From a preparedness perspective...... of a clinical sample, creating a metagenome, in a single week of laboratory work. As new technologies emerge, their dissemination and capacity building must be facilitated, and criteria for use, as well as guidelines on how to report results, must be established. This article focuses on the use of metagenomics...

  2. Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis.

    Science.gov (United States)

    Wen, Chengping; Zheng, Zhijun; Shao, Tiejuan; Liu, Lin; Xie, Zhijun; Le Chatelier, Emmanuelle; He, Zhixing; Zhong, Wendi; Fan, Yongsheng; Zhang, Linshuang; Li, Haichang; Wu, Chunyan; Hu, Changfeng; Xu, Qian; Zhou, Jia; Cai, Shunfeng; Wang, Dawei; Huang, Yun; Breban, Maxime; Qin, Nan; Ehrlich, Stanislav Dusko

    2017-07-27

    The assessment and characterization of the gut microbiome has become a focus of research in the area of human autoimmune diseases. Ankylosing spondylitis is an inflammatory autoimmune disease and evidence showed that ankylosing spondylitis may be a microbiome-driven disease. To investigate the relationship between the gut microbiome and ankylosing spondylitis, a quantitative metagenomics study based on deep shotgun sequencing was performed, using gut microbial DNA from 211 Chinese individuals. A total of 23,709 genes and 12 metagenomic species were shown to be differentially abundant between ankylosing spondylitis patients and healthy controls. Patients were characterized by a form of gut microbial dysbiosis that is more prominent than previously reported cases with inflammatory bowel disease. Specifically, the ankylosing spondylitis patients demonstrated increases in the abundance of Prevotella melaninogenica, Prevotella copri, and Prevotella sp. C561 and decreases in Bacteroides spp. It is noteworthy that the Bifidobacterium genus, which is commonly used in probiotics, accumulated in the ankylosing spondylitis patients. Diagnostic algorithms were established using a subset of these gut microbial biomarkers. Alterations of the gut microbiome are associated with development of ankylosing spondylitis. Our data suggest biomarkers identified in this study might participate in the pathogenesis or development process of ankylosing spondylitis, providing new leads for the development of new diagnostic tools and potential treatments.

  3. Comparative metagenome of a stream impacted by the urbanization phenomenon

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    Julliane Dutra Medeiros

    Full Text Available Abstract Rivers and streams are important reservoirs of freshwater for human consumption. These ecosystems are threatened by increasing urbanization, because raw sewage discharged into them alters their nutrient content and may affect the composition of their microbial community. In the present study, we investigate the taxonomic and functional profile of the microbial community in an urban lotic environment. Samples of running water were collected at two points in the São Pedro stream: an upstream preserved and non-urbanized area, and a polluted urbanized area with discharged sewage. The metagenomic DNA was sequenced by pyrosequencing. Differences were observed in the community composition at the two sites. The non-urbanized area was overrepresented by genera of ubiquitous microbes that act in the maintenance of environments. In contrast, the urbanized metagenome was rich in genera pathogenic to humans. The functional profile indicated that the microbes act on the metabolism of methane, nitrogen and sulfur, especially in the urbanized area. It was also found that virulence/defense (antibiotic resistance and metal resistance and stress response-related genes were disseminated in the urbanized environment. The structure of the microbial community was altered by uncontrolled anthropic interference, highlighting the selective pressure imposed by high loads of urban sewage discharged into freshwater environments.

  4. A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence

    International Nuclear Information System (INIS)

    Reis, Patricia P; Simpson, Colleen; Goldstein, David; Brown, Dale; Gilbert, Ralph; Gullane, Patrick; Irish, Jonathan; Jurisica, Igor; Kamel-Reid, Suzanne; Waldron, Levi; Perez-Ordonez, Bayardo; Pintilie, Melania; Galloni, Natalie Naranjo; Xuan, Yali; Cervigne, Nilva K; Warner, Giles C; Makitie, Antti A

    2011-01-01

    Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence. We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients. We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Over-expression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test). Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence

  5. MITEs in the promoters of effector genes allow prediction of novel virulence genes in Fusarium oxysporum

    NARCIS (Netherlands)

    Schmidt, S.M.; Houterman, P.M.; Schreiver, I.; Ma, L.; Amyotte, S.; Chellappan, B.; Boeren, S.; Takken, F.L.W.; Rep, M.

    2013-01-01

    Background The plant-pathogenic fungus Fusarium oxysporum f.sp.lycopersici (Fol) has accessory, lineage-specific (LS) chromosomes that can be transferred horizontally between strains. A single LS chromosome in the Fol4287 reference strain harbors all known Fol effector genes. Transfer of this

  6. Probability-based collaborative filtering model for predicting gene-disease associations.

    Science.gov (United States)

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-12-28

    Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

  7. Computational prediction and experimental validation of Ciona intestinalis microRNA genes

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    Pasquinelli Amy E

    2007-11-01

    Full Text Available Abstract Background This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development. Results In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling. Conclusion The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.

  8. An ensemble method to predict target genes and pathways in uveal melanoma

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    Wei Chao

    2018-04-01

    Full Text Available This work proposes to predict target genes and pathways for uveal melanoma (UM based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC, a causal inference method (IDA and a regression method (Lasso utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.

  9. Biotechnological applications of functional metagenomics in the food and pharmaceutical industries.

    Science.gov (United States)

    Coughlan, Laura M; Cotter, Paul D; Hill, Colin; Alvarez-Ordóñez, Avelino

    2015-01-01

    Microorganisms are found throughout nature, thriving in a vast range of environmental conditions. The majority of them are unculturable or difficult to culture by traditional methods. Metagenomics enables the study of all microorganisms, regardless of whether they can be cultured or not, through the analysis of genomic data obtained directly from an environmental sample, providing knowledge of the species present, and allowing the extraction of information regarding the functionality of microbial communities in their natural habitat. Function-based screenings, following the cloning and expression of metagenomic DNA in a heterologous host, can be applied to the discovery of novel proteins of industrial interest encoded by the genes of previously inaccessible microorganisms. Functional metagenomics has considerable potential in the food and pharmaceutical industries, where it can, for instance, aid (i) the identification of enzymes with desirable technological properties, capable of catalyzing novel reactions or replacing existing chemically synthesized catalysts which may be difficult or expensive to produce, and able to work under a wide range of environmental conditions encountered in food and pharmaceutical processing cycles including extreme conditions of temperature, pH, osmolarity, etc; (ii) the discovery of novel bioactives including antimicrobials active against microorganisms of concern both in food and medical settings; (iii) the investigation of industrial and societal issues such as antibiotic resistance development. This review article summarizes the state-of-the-art functional metagenomic methods available and discusses the potential of functional metagenomic approaches to mine as yet unexplored environments to discover novel genes with biotechnological application in the food and pharmaceutical industries.

  10. Biotechnological applications of functional metagenomics in the food and pharmaceutical industries

    Directory of Open Access Journals (Sweden)

    Laura M Coughlan

    2015-06-01

    Full Text Available Microorganisms are found throughout nature, thriving in a vast range of environmental conditions. The majority of them are unculturable or difficult to culture by traditional methods. Metagenomics enables the study of all microorganisms, regardless of whether they can be cultured or not, through the analysis of genomic data obtained directly from an environmental sample, providing knowledge of the species present and allowing the extraction of information regarding the functionality of microbial communities in their natural habitat. Function-based screenings, following the cloning and expression of metagenomic DNA in a heterologous host, can be applied to the discovery of novel proteins of industrial interest encoded by the genes of previously inaccessible microorganisms. Functional metagenomics has considerable potential in the food and pharmaceutical industries, where it can, for instance, aid (i the identification of enzymes with desirable technological properties, capable of catalysing novel reactions or replacing existing chemically synthesized catalysts which may be difficult or expensive to produce, and able to work under a wide range of environmental conditions encountered in food and pharmaceutical processing cycles including extreme conditions of temperature, pH, osmolarity, etc; (ii the discovery of novel bioactives including antimicrobials active against microorganisms of concern both in food and medical settings; (iii the investigation of industrial and societal issues such as antibiotic resistance development. This review article summarizes the state-of-the-art functional metagenomic methods available and discusses the potential of functional metagenomic approaches to mine as yet unexplored environments to discover novel genes with biotechnological application in the food and pharmaceutical industries.

  11. Separating metagenomic short reads into genomes via clustering

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    Tanaseichuk Olga

    2012-09-01

    Full Text Available Abstract Background The metagenomics approach allows the simultaneous sequencing of all genomes in an environmental sample. This results in high complexity datasets, where in addition to repeats and sequencing errors, the number of genomes and their abundance ratios are unknown. Recently developed next-generation sequencing (NGS technologies significantly improve the sequencing efficiency and cost. On the other hand, they result in shorter reads, which makes the separation of reads from different species harder. Among the existing computational tools for metagenomic analysis, there are similarity-based methods that use reference databases to align reads and composition-based methods that use composition patterns (i.e., frequencies of short words or l-mers to cluster reads. Similarity-based methods are unable to classify reads from unknown species without close references (which constitute the majority of reads. Since composition patterns are preserved only in significantly large fragments, composition-based tools cannot be used for very short reads, which becomes a significant limitation with the development of NGS. A recently proposed algorithm, AbundanceBin, introduced another method that bins reads based on predicted abundances of the genomes sequenced. However, it does not separate reads from genomes of similar abundance levels. Results In this work, we present a two-phase heuristic algorithm for separating short paired-end reads from different genomes in a metagenomic dataset. We use the observation that most of the l-mers belong to unique genomes when l is sufficiently large. The first phase of the algorithm results in clusters of l-mers each of which belongs to one genome. During the second phase, clusters are merged based on l-mer repeat information. These final clusters are used to assign reads. The algorithm could handle very short reads and sequencing errors. It is initially designed for genomes with similar abundance levels and then

  12. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    Science.gov (United States)

    Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

    2018-03-01

    In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and

  13. Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

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    Karacali Bilge

    2007-10-01

    Full Text Available Abstract Background Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a all genes on the microarray platform and b a list of known disease-related genes (a priori selection. We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. Results Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform. Conclusion Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine

  14. Entropy-based gene ranking without selection bias for the predictive classification of microarray data

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    Serafini Maria

    2003-11-01

    Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

  15. Metagenomic data of fungal internal transcribed spacer from serofluid dish, a traditional Chinese fermented food

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    Peng Chen

    2016-03-01

    Full Text Available Serofluid dish (or Jiangshui, in Chinese, a traditional food in the Chinese culture for thousands of years, is made from vegetables by fermentation. In this work, microorganism community of the fermented serofluid dish was investigated by the culture-independent method. The metagenomic data in this article contains the sequences of fungal internal transcribed spacer (ITS regions of rRNA genes from 12 different serofluid dish samples. The metagenome comprised of 50,865 average raw reads with an average of 8,958,220 bp and G + C content is 45.62%. This is the first report on metagenomic data of fungal ITS from serofluid dish employing Illumina platform to profile the fungal communities of this little known fermented food from Gansu Province, China. The Metagenomic data of fungal internal transcribed spacer can be accessed at NCBI, SRA database accession no. SRP067411. Keywords: Serofluid dish, Jiangshui, Fungal ITS, Cultivation-independent, Microbial diversity

  16. Minimal gene selection for classification and diagnosis prediction based on gene expression profile

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    Alireza Mehridehnavi

    2013-01-01

    Conclusion: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.

  17. Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge.

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    Mika Gustafsson

    Full Text Available BACKGROUND: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. METHODOLOGY/PRINCIPAL FINDINGS: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the "elastic net". Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance. CONCLUSIONS/SIGNIFICANCE: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

  18. Assembly of viral genomes from metagenomes

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    Saskia L Smits

    2014-12-01

    Full Text Available Viral infections remain a serious global health issue. Metagenomic approaches are increasingly used in the detection of novel viral pathogens but also to generate complete genomes of uncultivated viruses. In silico identification of complete viral genomes from sequence data would allow rapid phylogenetic characterization of these new viruses. Often, however, complete viral genomes are not recovered, but rather several distinct contigs derived from a single entity, some of which have no sequence homology to any known proteins. De novo assembly of single viruses from a metagenome is challenging, not only because of the lack of a reference genome, but also because of intrapopulation variation and uneven or insufficient coverage. Here we explored different assembly algorithms, remote homology searches, genome-specific sequence motifs, k-mer frequency ranking, and coverage profile binning to detect and obtain viral target genomes from metagenomes. All methods were tested on 454-generated sequencing datasets containing three recently described RNA viruses with a relatively large genome which were divergent to previously known viruses from the viral families Rhabdoviridae and Coronaviridae. Depending on specific characteristics of the target virus and the metagenomic community, different assembly and in silico gap closure strategies were successful in obtaining near complete viral genomes.

  19. Assembly of viral genomes from metagenomes

    NARCIS (Netherlands)

    S.L. Smits (Saskia); R. Bodewes (Rogier); A. Ruiz-Gonzalez (Aritz); V. Baumgärtner (Volkmar); M.P.G. Koopmans D.V.M. (Marion); A.D.M.E. Osterhaus (Albert); A. Schürch (Anita)

    2014-01-01

    textabstractViral infections remain a serious global health issue. Metagenomic approaches are increasingly used in the detection of novel viral pathogens but also to generate complete genomes of uncultivated viruses. In silico identification of complete viral genomes from sequence data would allow

  20. A 65‑gene signature for prognostic prediction in colon adenocarcinoma.

    Science.gov (United States)

    Jiang, Hui; Du, Jun; Gu, Jiming; Jin, Liugen; Pu, Yong; Fei, Bojian

    2018-04-01

    The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival‑related genes were selected from the DEGs using the Cox regression method. A co‑expression network of survival‑related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan‑Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival‑related genes selected. The co‑expression network of survival‑related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator‑activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine‑cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65‑gene signature was established using this co‑expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e‑12) and the GSE17538 dataset (P=1.67e‑6). The 65‑gene signature included kallikrein‑related peptidase 6 (KLK6), collagen type XI α1 (COL11A1), cartilage

  1. Establishing Genotype-to-Phenotype Relationships in Bacteria Causing Hospital-Acquired Pneumonia: A Prelude to the Application of Clinical Metagenomics

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    Etienne Ruppé

    2017-11-01

    Full Text Available Clinical metagenomics (CMg, referred to as the application of next-generation sequencing (NGS to clinical samples, is a promising tool for the diagnosis of hospital-acquired pneumonia (HAP. Indeed, CMg allows identifying pathogens and antibiotic resistance genes (ARGs, thereby providing the information required for the optimization of the antibiotic regimen. Hence, provided that CMg would be faster than conventional culture, the probabilistic regimen used in HAP could be tailored faster, which should lead to an expected decrease of mortality and morbidity. While the inference of the antibiotic susceptibility testing from metagenomic or even genomic data is challenging, a limited number of antibiotics are used in the probabilistic regimen of HAP (namely beta-lactams, aminoglycosides, fluoroquinolones, glycopeptides and oxazolidinones. Accordingly, based on the perspective of applying CMg to the early diagnostic of HAP, we aimed at reviewing the performances of whole genomic sequencing (WGS of the main HAP-causing bacteria (Enterobacteriaceae, Pseudomonas aeruginosa, Acinetobacter baumannii, Stenotrophomonas maltophilia and Staphylococcus aureus for the prediction of susceptibility to the antibiotic families advocated in the probabilistic regimen of HAP.

  2. Glycoside Hydrolases from a targeted Compost Metagenome, activity-screening and functional characterization

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    Dougherty Michael J

    2012-07-01

    Full Text Available Abstract Background Metagenomics approaches provide access to environmental genetic diversity for biotechnology applications, enabling the discovery of new enzymes and pathways for numerous catalytic processes. Discovery of new glycoside hydrolases with improved biocatalytic properties for the efficient conversion of lignocellulosic material to biofuels is a critical challenge in the development of economically viable routes from biomass to fuels and chemicals. Results Twenty-two putative ORFs (open reading frames were identified from a switchgrass-adapted compost community based on sequence homology to related gene families. These ORFs were expressed in E. coli and assayed for predicted activities. Seven of the ORFs were demonstrated to encode active enzymes, encompassing five classes of hemicellulases. Four enzymes were over expressed in vivo, purified to homogeneity and subjected to detailed biochemical characterization. Their pH optima ranged between 5.5 - 7.5 and they exhibit moderate thermostability up to ~60-70°C. Conclusions Seven active enzymes were identified from this set of ORFs comprising five different hemicellulose activities. These enzymes have been shown to have useful properties, such as moderate thermal stability and broad pH optima, and may serve as the starting points for future protein engineering towards the goal of developing efficient enzyme cocktails for biomass degradation under diverse process conditions.

  3. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer

    NARCIS (Netherlands)

    Knauer, Michael; Mook, Stella; Rutgers, Emiel J. T.; Bender, Richard A.; Hauptmann, Michael; van de Vijver, Marc J.; Koornstra, Rutger H. T.; Bueno-de-Mesquita, Jolien M.; Linn, Sabine C.; van 't Veer, Laura J.

    2010-01-01

    Multigene assays have been developed and validated to determine the prognosis of breast cancer. In this study, we assessed the additional predictive value of the 70-gene MammaPrint signature for chemotherapy (CT) benefit in addition to endocrine therapy (ET) from pooled study series. For 541

  4. [The value of 5-HTT gene polymorphism for the assessment and prediction of male adolescence violence].

    Science.gov (United States)

    Yu, Yue; Liu, Xiang; Yang, Zhen-xing; Qiu, Chang-jian; Ma, Xiao-hong

    2012-08-01

    To establish an adolescent violence crime prediction model, and to assess the value of serotonin transporter (5-HTT) gene polymorphism for the assessment and prediction of violent crime. Investigative tools were used to analyze the difference in personality dimensions, social support, coping styles, aggressiveness, impulsivity, and family condition scale between 223 adolescents with violence behavior and 148 adolescents without violence behavior. The distribution of 5-HTT gene polymorphisms (5-HTTLPR and 5-HTTVNTR) was compared between the two groups. The role of 5-HTT gene polymorphism on adolescent personality, impulsion and aggression scale also was also analyzed. Stepwise logistic regression was used to establish a predictive model for adolescent violent crime. Significant difference was found between the violence group and the control group on multiple dimensions of psychology and environment scales. However, no statistical difference was found with regard to the 5-HTT genotypes and alleles between adolescents with violent behaviors and normal controls. The rate of prediction accuracy was not significantly improved when 5-HTT gene polymorphism was taken into the model. The violent crime of adolescents was closely related with social and environmental factors. No association was found between 5-HTT polymorphisms and adolescent violence criminal behavior.

  5. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

    Science.gov (United States)

    Zhu, Mingzhu; Dahmen, Jeremy L; Stacey, Gary; Cheng, Jianlin

    2013-09-22

    High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.

  6. Coregulation of terpenoid pathway genes and prediction of isoprene production in Bacillus subtilis using transcriptomics

    Energy Technology Data Exchange (ETDEWEB)

    Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. S.; Ahring, Birgitte K.; Linggi, Bryan E.

    2013-06-19

    The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding the system level regulation and control of the pathway. To address this limitation, we examined Bacillus subtilis grown under multiple conditions and then determined the relationship between altered isoprene production and the pattern of gene expression. We found that terpenoid genes appeared to fall into two distinct subsets with opposing correlations with respect to the amount of isoprene produced. The group whose expression levels positively correlated with isoprene production included dxs, the gene responsible for the commitment step in the pathway, as well as ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. This analysis showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model which accurately predicts production of this secondary metabolite across many simulated environmental conditions.

  7. A comparative analysis of the intestinal metagenomes present in guinea pigs (Cavia porcellus) and humans (Homo sapiens)

    DEFF Research Database (Denmark)

    Hildebrand, Falk; Ebersbach, Tine; Nielsen, Henrik Bjørn

    2012-01-01

    Background: Guinea pig (Cavia porcellus) is an important model for human intestinal research. We have characterized the faecal microbiota of 60 guinea pigs using Illumina shotgun metagenomics, and used this data to compile a gene catalogue of its prevalent microbiota. Subsequently, we compared th...

  8. HOX Gene Promoter Prediction and Inter-genomic Comparison: An Evo-Devo Study

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    Marla A. Endriga

    2010-10-01

    Full Text Available Homeobox genes direct the anterior-posterior axis of the body plan in eukaryotic organisms. Promoter regions upstream of the Hox genes jumpstart the transcription process. CpG islands found within the promoter regions can cause silencing of these promoters. The locations of the promoter regions and the CpG islands of Homeo sapiens sapiens (human, Pan troglodytes (chimpanzee, Mus musculus (mouse, and Rattus norvegicus (brown rat are compared and related to the possible influence on the specification of the mammalian body plan. The sequence of each gene in Hox clusters A-D of the mammals considered were retrieved from Ensembl and locations of promoter regions and CpG islands predicted using Exon Finder. The predicted promoter sequences were confirmed via BLAST and verified against the Eukaryotic Promoter Database. The significance of the locations was determined using the Kruskal-Wallis test. Among the four clusters, only promoter locations in cluster B showed significant difference. HOX B genes have been linked with the control of genes that direct the development of axial morphology, particularly of the vertebral column bones. The magnitude of variation among the body plans of closely-related species can thus be partially attributed to the promoter kind, location and number, and gene inactivation via CpG methylation.

  9. Effectiveness of gene expression profiling for response prediction of rectal cancer to preoperative radiotherapy

    International Nuclear Information System (INIS)

    Ojima, Eiki; Inoue, Yasuhiro; Miki, Chikao; Kusunoki, Masato; Mori, Masaki

    2007-01-01

    Our aim was to determine whether the expression levels of specific genes could predict clinical radiosensitivity in human colorectal cancer. Radioresistant colorectal cancer cell lines were established by repeated X-ray exposure (total, 100 Gy), and the gene expressions of the parent and radioresistant cell lines were compared in a microarray analysis. To verify the microarray data, we carried out a reverse transcriptase-polymerase chain reaction analysis of identified genes in clinical samples from 30 irradiated rectal cancer patients. A comparison of the intensity data for the parent and three radioresistant cell lines revealed 17 upregulated and 142 downregulated genes in all radioresistant cell lines. Next, we focused on two upregulated genes, PTMA (prothymosin α) and EIF5a2 (eukaryotic translation initiation factor 5A), in the radioresistant cell lines. In clinical samples, the expression of PTMA was significantly higher in the minor effect group than in the major effect group (P=0.004), but there were no significant differences in EIF5a2 expression between the two groups. We identified radiation-related genes in colorectal cancer and demonstrated that PTMA may play an important role in radiosensitivity. Our findings suggest that PTMA may be a novel marker for predicting the effectiveness of radiotherapy in clinical cases. (author)

  10. Paired hormone response elements predict caveolin-1 as a glucocorticoid target gene.

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    Marinus F van Batenburg

    2010-01-01

    Full Text Available Glucocorticoids act in part via glucocorticoid receptor binding to hormone response elements (HREs, but their direct target genes in vivo are still largely unknown. We developed the criterion that genomic occurrence of paired HREs at an inter-HRE distance less than 200 bp predicts hormone responsiveness, based on synergy of multiple HREs, and HRE information from known target genes. This criterion predicts a substantial number of novel responsive genes, when applied to genomic regions 10 kb upstream of genes. Multiple-tissue in situ hybridization showed that mRNA expression of 6 out of 10 selected genes was induced in a tissue-specific manner in mice treated with a single dose of corticosterone, with the spleen being the most responsive organ. Caveolin-1 was strongly responsive in several organs, and the HRE pair in its upstream region showed increased occupancy by glucocorticoid receptor in response to corticosterone. Our approach allowed for discovery of novel tissue specific glucocorticoid target genes, which may exemplify responses underlying the permissive actions of glucocorticoids.

  11. Metagenomics, metatranscriptomics and single cell genomics reveal functional response of active Oceanospirillales to Gulf oil spill

    Energy Technology Data Exchange (ETDEWEB)

    Mason, Olivia U.; Hazen, Terry C.; Borglin, Sharon; Chain, Patrick S. G.; Dubinsky, Eric A.; Fortney, Julian L.; Han, James; Holman, Hoi-Ying N.; Hultman, Jenni; Lamendella, Regina; Mackelprang, Rachel; Malfatti, Stephanie; Tom, Lauren M.; Tringe, Susannah G.; Woyke, Tanja; Zhou, Jizhong; Rubin, Edward M.; Jansson, Janet K.

    2012-06-12

    The Deepwater Horizon oil spill in the Gulf of Mexico resulted in a deep-sea hydrocarbon plume that caused a shift in the indigenous microbial community composition with unknown ecological consequences. Early in the spill history, a bloom of uncultured, thus uncharacterized, members of the Oceanospirillales was previously detected, but their role in oil disposition was unknown. Here our aim was to determine the functional role of the Oceanospirillales and other active members of the indigenous microbial community using deep sequencing of community DNA and RNA, as well as single-cell genomics. Shotgun metagenomic and metatranscriptomic sequencing revealed that genes for motility, chemotaxis and aliphatic hydrocarbon degradation were significantly enriched and expressed in the hydrocarbon plume samples compared with uncontaminated seawater collected from plume depth. In contrast, although genes coding for degradation of more recalcitrant compounds, such as benzene, toluene, ethylbenzene, total xylenes and polycyclic aromatic hydrocarbons, were identified in the metagenomes, they were expressed at low levels, or not at all based on analysis of the metatranscriptomes. Isolation and sequencing of two Oceanospirillales single cells revealed that both cells possessed genes coding for n-alkane and cycloalkane degradation. Specifically, the near-complete pathway for cyclohexane oxidation in the Oceanospirillales single cells was elucidated and supported by both metagenome and metatranscriptome data. The draft genome also included genes for chemotaxis, motility and nutrient acquisition strategies that were also identified in the metagenomes and metatranscriptomes. These data point towards a rapid response of members of the Oceanospirillales to aliphatic hydrocarbons in the deep sea.

  12. Data on gut metagenomes of the patients with alcoholic dependence syndrome and alcoholic liver cirrhosis

    Directory of Open Access Journals (Sweden)

    Alexander V. Tyakht

    2017-04-01

    Full Text Available Alcoholism is associated with significant changes in gut microbiota composition. Metagenomic sequencing allows to assess the altered abundance levels of bacterial taxa and genes in a culture-independent way. We collected 99 stool samples from the patients with alcoholic dependence syndrome (n=72 and alcoholic liver cirrhosis (n=27. Each of the samples was surveyed using “shotgun” (whole-genome sequencing on SOLiD platform. The reads are deposited in the ENA (project ID: PRJEB18041.

  13. Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

    Science.gov (United States)

    Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

    2012-07-15

    Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of EOperon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. A novel method to discover fluoroquinolone antibiotic resistance (qnr genes in fragmented nucleotide sequences

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    Boulund Fredrik

    2012-12-01

    Full Text Available Abstract Background Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. Results In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. Conclusions The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/.

  15. Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

    Science.gov (United States)

    Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

    2016-02-19

    Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

  16. Cell-specific prediction and application of drug-induced gene expression profiles.

    Science.gov (United States)

    Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

    2018-01-01

    Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

  17. In silico prediction of novel therapeutic targets using gene-disease association data.

    Science.gov (United States)

    Ferrero, Enrico; Dunham, Ian; Sanseau, Philippe

    2017-08-29

    Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target

  18. Prediction of metastasis from low-malignant breast cancer by gene expression profiling

    DEFF Research Database (Denmark)

    Thomassen, Mads; Tan, Qihua; Eiriksdottir, Freyja

    2007-01-01

    examined in these studies is the low-risk patients for whom outcome is very difficult to predict with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study, 26 tumors from low-risk patients...... with different characteristics and risk, expression-based classification specifically developed in low-risk patients have higher predictive power in this group.......Promising results for prediction of outcome in breast cancer have been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly...

  19. Metagenomic potential for and diversity of N-cycle driving microorganisms in the Bothnian Sea sediment.

    Science.gov (United States)

    Rasigraf, Olivia; Schmitt, Julia; Jetten, Mike S M; Lüke, Claudia

    2017-08-01

    The biological nitrogen cycle is driven by a plethora of reactions transforming nitrogen compounds between various redox states. Here, we investigated the metagenomic potential for nitrogen cycle of the in situ microbial community in an oligotrophic, brackish environment of the Bothnian Sea sediment. Total DNA from three sediment depths was isolated and sequenced. The characterization of the total community was performed based on 16S rRNA gene inventory using SILVA database as reference. The diversity of diagnostic functional genes coding for nitrate reductases (napA;narG), nitrite:nitrate oxidoreductase (nxrA), nitrite reductases (nirK;nirS;nrfA), nitric oxide reductase (nor), nitrous oxide reductase (nosZ), hydrazine synthase (hzsA), ammonia monooxygenase (amoA), hydroxylamine oxidoreductase (hao), and nitrogenase (nifH) was analyzed by blastx against curated reference databases. In addition, Polymerase chain reaction (PCR)-based amplification was performed on the hzsA gene of anammox bacteria. Our results reveal high genomic potential for full denitrification to N 2 , but minor importance of anaerobic ammonium oxidation and dissimilatory nitrite reduction to ammonium. Genomic potential for aerobic ammonia oxidation was dominated by Thaumarchaeota. A higher diversity of anammox bacteria was detected in metagenomes than with PCR-based technique. The results reveal the importance of various N-cycle driving processes and highlight the advantage of metagenomics in detection of novel microbial key players. © 2017 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

  20. Metagenomic exploration of viruses throughout the Indian Ocean.

    Directory of Open Access Journals (Sweden)

    Shannon J Williamson

    Full Text Available The characterization of global marine microbial taxonomic and functional diversity is a primary goal of the Global Ocean Sampling Expedition. As part of this study, 19 water samples were collected aboard the Sorcerer II sailing vessel from the southern Indian Ocean in an effort to more thoroughly understand the lifestyle strategies of the microbial inhabitants of this ultra-oligotrophic region. No investigations of whole virioplankton assemblages have been conducted on waters collected from the Indian Ocean or across multiple size fractions thus far. Therefore, the goals of this study were to examine the effect of size fractionation on viral consortia structure and function and understand the diversity and functional potential of the Indian Ocean virome. Five samples were selected for comprehensive metagenomic exploration; and sequencing was performed on the microbes captured on 3.0-, 0.8- and 0.1 µm membrane filters as well as the viral fraction (<0.1 µm. Phylogenetic approaches were also used to identify predicted proteins of viral origin in the larger fractions of data from all Indian Ocean samples, which were included in subsequent metagenomic analyses. Taxonomic profiling of viral sequences suggested that size fractionation of marine microbial communities enriches for specific groups of viruses within the different size classes and functional characterization further substantiated this observation. Functional analyses also revealed a relative enrichment for metabolic proteins of viral origin that potentially reflect the physiological condition of host cells in the Indian Ocean including those involved in nitrogen metabolism and oxidative phosphorylation. A novel classification method, MGTAXA, was used to assess virus-host relationships in the Indian Ocean by predicting the taxonomy of putative host genera, with Prochlorococcus, Acanthochlois and members of the SAR86 cluster comprising the most abundant predictions. This is the first study

  1. Muscle myeloid type I interferon gene expression may predict therapeutic responses to rituximab in myositis patients.

    Science.gov (United States)

    Nagaraju, Kanneboyina; Ghimbovschi, Svetlana; Rayavarapu, Sree; Phadke, Aditi; Rider, Lisa G; Hoffman, Eric P; Miller, Frederick W

    2016-09-01

    To identify muscle gene expression patterns that predict rituximab responses and assess the effects of rituximab on muscle gene expression in PM and DM. In an attempt to understand the molecular mechanism of response and non-response to rituximab therapy, we performed Affymetrix gene expression array analyses on muscle biopsy specimens taken before and after rituximab therapy from eight PM and two DM patients in the Rituximab in Myositis study. We also analysed selected muscle-infiltrating cell phenotypes in these biopsies by immunohistochemical staining. Partek and Ingenuity pathway analyses assessed the gene pathways and networks. Myeloid type I IFN signature genes were expressed at higher levels at baseline in the skeletal muscle of rituximab responders than in non-responders, whereas classic non-myeloid IFN signature genes were expressed at higher levels in non-responders at baseline. Also, rituximab responders have a greater reduction of the myeloid and non-myeloid type I IFN signatures than non-responders. The decrease in the type I IFN signature following administration of rituximab may be associated with the decreases in muscle-infiltrating CD19(+) B cells and CD68(+) macrophages in responders. Our findings suggest that high levels of myeloid type I IFN gene expression in skeletal muscle predict responses to rituximab in PM/DM and that rituximab responders also have a greater decrease in the expression of these genes. These data add further evidence to recent studies defining the type I IFN signature as both a predictor of therapeutic responses and a biomarker of myositis disease activity. Published by Oxford University Press on behalf British Society for Rheumatology 2016. This work is written by US Government employees and is in the public domain in the US.

  2. Predictive gene signatures: molecular markers distinguishing colon adenomatous polyp and carcinoma.

    Directory of Open Access Journals (Sweden)

    Janice E Drew

    Full Text Available Cancers exhibit abnormal molecular signatures associated with disease initiation and progression. Molecular signatures could improve cancer screening, detection, drug development and selection of appropriate drug therapies for individual patients. Typically only very small amounts of tissue are available from patients for analysis and biopsy samples exhibit broad heterogeneity that cannot be captured using a single marker. This report details application of an in-house custom designed GenomeLab System multiplex gene expression assay, the hCellMarkerPlex, to assess predictive gene signatures of normal, adenomatous polyp and carcinoma colon tissue using archived tissue bank material. The hCellMarkerPlex incorporates twenty-one gene markers: epithelial (EZR, KRT18, NOX1, SLC9A2, proliferation (PCNA, CCND1, MS4A12, differentiation (B4GANLT2, CDX1, CDX2, apoptotic (CASP3, NOX1, NTN1, fibroblast (FSP1, COL1A1, structural (ACTG2, CNN1, DES, gene transcription (HDAC1, stem cell (LGR5, endothelial (VWF and mucin production (MUC2. Gene signatures distinguished normal, adenomatous polyp and carcinoma. Individual gene targets significantly contributing to molecular tissue types, classifier genes, were further characterised using real-time PCR, in-situ hybridisation and immunohistochemistry revealing aberrant epithelial expression of MS4A12, LGR5 CDX2, NOX1 and SLC9A2 prior to development of carcinoma. Identified gene signatures identify aberrant epithelial expression of genes prior to cancer development using in-house custom designed gene expression multiplex assays. This approach may be used to assist in objective classification of disease initiation, staging, progression and therapeutic responses using biopsy material.

  3. PRGdb 3.0: a comprehensive platform for prediction and analysis of plant disease resistance genes.

    Science.gov (United States)

    Osuna-Cruz, Cristina M; Paytuvi-Gallart, Andreu; Di Donato, Antimo; Sundesha, Vicky; Andolfo, Giuseppe; Aiese Cigliano, Riccardo; Sanseverino, Walter; Ercolano, Maria R

    2018-01-04

    The Plant Resistance Genes database (PRGdb; http://prgdb.org) has been redesigned with a new user interface, new sections, new tools and new data for genetic improvement, allowing easy access not only to the plant science research community but also to breeders who want to improve plant disease resistance. The home page offers an overview of easy-to-read search boxes that streamline data queries and directly show plant species for which data from candidate or cloned genes have been collected. Bulk data files and curated resistance gene annotations are made available for each plant species hosted. The new Gene Model view offers detailed information on each cloned resistance gene structure to highlight shared attributes with other genes. PRGdb 3.0 offers 153 reference resistance genes and 177 072 annotated candidate Pathogen Receptor Genes (PRGs). Compared to the previous release, the number of putative genes has been increased from 106 to 177 K from 76 sequenced Viridiplantae and algae genomes. The DRAGO 2 tool, which automatically annotates and predicts (PRGs) from DNA and amino acid with high accuracy and sensitivity, has been added. BLAST search has been implemented to offer users the opportunity to annotate and compare their own sequences. The improved section on plant diseases displays useful information linked to genes and genomes to connect complementary data and better address specific needs. Through, a revised and enlarged collection of data, the development of new tools and a renewed portal, PRGdb 3.0 engages the plant science community in developing a consensus plan to improve knowledge and strategies to fight diseases that afflict main crops and other plants. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  4. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  5. Predictive gene lists for breast cancer prognosis: A topographic visualisation study

    Directory of Open Access Journals (Sweden)

    Lowe David

    2008-04-01

    Full Text Available Abstract Background The controversy surrounding the non-uniqueness of predictive gene lists (PGL of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE and the Locally Linear Embedding(LLE techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion The random correlation effect to an arbitrary outcome induced by small subset selection from very high

  6. MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

    Directory of Open Access Journals (Sweden)

    Kohlbacher Oliver

    2009-09-01

    Full Text Available Abstract Background Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy. Results We extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations. Conclusion MultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2.

  7. Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

    Science.gov (United States)

    Mutch, David M.; Temanni, M. Ramzi; Henegar, Corneliu; Combes, Florence; Pelloux, Véronique; Holst, Claus; Sørensen, Thorkild I. A.; Astrup, Arne; Martinez, J. Alfredo; Saris, Wim H. M.; Viguerie, Nathalie; Langin, Dominique; Zucker, Jean-Daniel; Clément, Karine

    2007-01-01

    Background The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. Methodology/Principal Findings The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8–12 kgs weight loss) could always be differentiated from non-responders (diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition. PMID:18094752

  8. CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer.

    Science.gov (United States)

    Nguyen, Minh Nam; Choi, Tae Gyu; Nguyen, Dinh Truong; Kim, Jin-Hwan; Jo, Yong Hwa; Shahid, Muhammad; Akter, Salima; Aryal, Saurav Nath; Yoo, Ji Youn; Ahn, Yong-Joo; Cho, Kyoung Min; Lee, Ju-Seog; Choe, Wonchae; Kang, Insug; Ha, Joohun; Kim, Sung Soo

    2015-10-13

    Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies.

  9. The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

    Science.gov (United States)

    Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

    2017-01-01

    Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

  10. Genome sequence analysis of predicted polyprenol reductase gene from mangrove plant kandelia obovata

    Science.gov (United States)

    Basyuni, M.; Sagami, H.; Baba, S.; Oku, H.

    2018-03-01

    It has been previously reported that dolichols but not polyprenols were predominated in mangrove leaves and roots. Therefore, the occurrence of larger amounts of dolichol in leaves of mangrove plants implies that polyprenol reductase is responsible for the conversion of polyprenol to dolichol may be active in mangrove leaves. Here we report the early assessment of probably polyprenol reductase gene from genome sequence of mangrove plant Kandelia obovata. The functional assignment of the gene was based on a homology search of the sequences against the non-redundant (nr) peptide database of NCBI using Blastx. The degree of sequence identity between DNA sequence and known polyprenol reductase was confirmed using the Blastx probability E-value, total score, and identity. The genome sequence data resulted in three partial sequences, termed c23157 (700 bp), c23901 (960 bp), and c24171 (531 bp). The c23157 gene showed the highest similarity (61%) to predicted polyprenol reductase 2- like from Gossypium raimondii with E-value 2e-100. The second gene was c23901 to exhibit high similarity (78%) to the steroid 5-alpha-reductase Det2 from J. curcas with E-value 2e-140. Furthermore, the c24171 gene depicted highest similarity (79%) to the polyprenol reductase 2 isoform X1 from Jatropha curcas with E- value 7e-21.The present study suggested that the c23157, c23901, and c24171, genes may encode predicted polyprenol reductase. The c23157, c23901, c24171 are therefore the new type of predicted polyprenol reductase from K. obovata.

  11. Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

    Directory of Open Access Journals (Sweden)

    Borlawsky Tara B

    2010-10-01

    Full Text Available Abstract Background Chronic lymphocytic leukemia (CLL is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. Results In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. Conclusions We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

  12. Methylation of cancer-stem-cell-associated Wnt target genes predicts poor prognosis in colorectal cancer patients

    NARCIS (Netherlands)

    de Sousa E Melo, Felipe; Colak, Selcuk; Buikhuisen, Joyce; Koster, Jan; Cameron, Kate; de Jong, Joan H.; Tuynman, Jurriaan B.; Prasetyanti, Pramudita R.; Fessler, Evelyn; van den Bergh, Saskia P.; Rodermond, Hans; Dekker, Evelien; van der Loos, Chris M.; Pals, Steven T.; van de Vijver, Marc J.; Versteeg, Rogier; Richel, Dick J.; Vermeulen, Louis; Medema, Jan Paul

    2011-01-01

    Gene signatures derived from cancer stem cells (CSCs) predict tumor recurrence for many forms of cancer. Here, we derived a gene signature for colorectal CSCs defined by high Wnt signaling activity, which in agreement with previous observations predicts poor prognosis. Surprisingly, however, we

  13. Strain-Level Discrimination of Shiga Toxin-Producing Escherichia coli in Spinach Using Metagenomic Sequencing.

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    Susan R Leonard

    Full Text Available Consumption of fresh bagged spinach contaminated with Shiga toxin-producing Escherichia coli (STEC has led to severe illness and death; however current culture-based methods to detect foodborne STEC are time consuming. Since not all STEC strains are considered pathogenic to humans, it is crucial to incorporate virulence characterization of STEC in the detection method. In this study, we assess the comprehensiveness of utilizing a shotgun metagenomics approach for detection and strain-level identification by spiking spinach with a variety of genomically disparate STEC strains at a low contamination level of 0.1 CFU/g. Molecular serotyping, virulence gene characterization, microbial community analysis, and E. coli core gene single nucleotide polymorphism (SNP analysis were performed on metagenomic sequence data from enriched samples. It was determined from bacterial community analysis that E. coli, which was classified at the phylogroup level, was a major component of the population in most samples. However, in over half the samples, molecular serotyping revealed the presence of indigenous E. coli which also contributed to the percent abundance of E. coli. Despite the presence of additional E. coli strains, the serotype and virulence genes of the spiked STEC, including correct Shiga toxin subtype, were detected in 94% of the samples with a total number of reads per sample averaging 2.4 million. Variation in STEC abundance and/or detection was observed in replicate spiked samples, indicating an effect from the indigenous microbiota during enrichment. SNP analysis of the metagenomic data correctly placed the spiked STEC in a phylogeny of related strains in cases where the indigenous E. coli did not predominate in the enriched sample. Also, for these samples, our analysis demonstrates that strain-level phylogenetic resolution is possible using shotgun metagenomic data for determining the genomic relatedness of a contaminating STEC strain to other

  14. An Improved Methodology to Overcome Key Issues in Human Fecal Metagenomic DNA Extraction

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    Jitendra Kumar

    2016-12-01

    Full Text Available Microbes are ubiquitously distributed in nature, and recent culture-independent studies have highlighted the significance of gut microbiota in human health and disease. Fecal DNA is the primary source for the majority of human gut microbiome studies. However, further improvement is needed to obtain fecal metagenomic DNA with sufficient amount and good quality but low host genomic DNA contamination. In the current study, we demonstrate a quick, robust, unbiased, and cost-effective method for the isolation of high molecular weight (>23 kb metagenomic DNA (260/280 ratio >1.8 with a good yield (55.8 ± 3.8 ng/mg of feces. We also confirm that there is very low human genomic DNA contamination (eubacterial: human genomic DNA marker genes = 227.9:1 in the human feces. The newly-developed method robustly performs for fresh as well as stored fecal samples as demonstrated by 16S rRNA gene sequencing using 454 FLX+. Moreover, 16S rRNA gene analysis indicated that compared to other DNA extraction methods tested, the fecal metagenomic DNA isolated with current methodology retains species richness and does not show microbial diversity biases, which is further confirmed by qPCR with a known quantity of spike-in genomes. Overall, our data highlight a protocol with a balance between quality, amount, user-friendliness, and cost effectiveness for its suitability toward usage for culture-independent analysis of the human gut microbiome, which provides a robust solution to overcome key issues associated with fecal metagenomic DNA isolation in human gut microbiome studies.

  15. Genome diversity of marine phages recovered from Mediterranean metagenomes: Size matters.

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    Mario López-Pérez

    2017-09-01

    Full Text Available Marine viruses play a critical role not only in the global geochemical cycles but also in the biology and evolution of their hosts. Despite their importance, viral diversity remains underexplored mostly due to sampling and cultivation challenges. Direct sequencing approaches such as viromics has provided new insights into the marine viral world. As a complementary approach, we analysed 24 microbial metagenomes (>0.2 μm size range obtained from six sites in the Mediterranean Sea that vary by depth, season and filter used to retrieve the fraction. Filter-size comparison showed a significant number of viral sequences that were retained on the larger-pore filters and were different from those found in the viral fraction from the same sample, indicating that some important viral information is missing using only assembly from viromes. Besides, we were able to describe 1,323 viral genomic fragments that were more than 10Kb in length, of which 36 represented complete viral genomes including some of them retrieved from a cross-assembly from different metagenomes. Host prediction based on sequence methods revealed new phage groups belonging to marine prokaryotes like SAR11, Cyanobacteria or SAR116. We also identified the first complete virophage from deep seawater and a new endemic clade of the recently discovered Marine group II Euryarchaeota virus. Furthermore, analysis of viral distribution using metagenomes and viromes indicated that most of the new phages were found exclusively in the Mediterranean Sea and some of them, mostly the ones recovered from deep metagenomes, do not recruit in any database probably indicating higher variability and endemicity in Mediterranean bathypelagic waters. Together these data provide the first detailed picture of genomic diversity, spatial and depth variations of viral communities within the Mediterranean Sea using metagenome assembly.

  16. Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Pingzhao Hu

    2006-01-01

    biological pathways. In particular, we observed that by integrating information from the insulin signalling pathway into our prediction model, we achieved better prediction of prostate cancer. Conclusions: Our data integration methodology provides an efficient way to identify biologically sound and statistically significant pathways from gene expression data. The significant gene expression phenotypes identified in our study have the potential to characterize complex genetic alterations in prostate cancer.

  17. Mining anaerobic digester consortia metagenomes for secreted carbohydrate active enzymes

    DEFF Research Database (Denmark)

    Wilkens, Casper; Busk, Peter Kamp; Pilgaard, Bo

    thermophilic and mesophilic ADs a wide variety of carbohydrate active enzyme functions were discovered in the metagenomic sequencing of the microbial consortia. The most dominating type of glycoside hydrolases were β-glucosidases (up to 27%), α-amylases (up to 10%), α-glucosidases (up to 8%), α......, and food wastes (Alvarado et al., 2014). The processes and the roles of the microorganisms that are involved in biomass conversion and methane production in ADs are still not fully understood. We are investigating thermophilic and mesophilic ADs that use wastewater surplus sludge for methane production...... was done with the Peptide Pattern Recognition (PPR) program (Busk and Lange, 2013), which is a novel non-alignment based approach that can predict function of e.g. CAZymes. PPR identifies a set of short conserved sequences, which can be used as a finger print when mining genomes for novel enzymes. In both...

  18. A function-based screen for seeking RubisCO active clones from metagenomes: novel enzymes influencing RubisCO activity.

    Science.gov (United States)

    Böhnke, Stefanie; Perner, Mirjam

    2015-03-01

    Ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) is a key enzyme of the Calvin cycle, which is responsible for most of Earth's primary production. Although research on RubisCO genes and enzymes in plants, cyanobacteria and bacteria has been ongoing for years, still little is understood about its regulation and activation in bacteria. Even more so, hardly any information exists about the function of metagenomic RubisCOs and the role of the enzymes encoded on the flanking DNA owing to the lack of available function-based screens for seeking active RubisCOs from the environment. Here we present the first solely activity-based approach for identifying RubisCO active fosmid clones from a metagenomic library. We constructed a metagenomic library from hydrothermal vent fluids and screened 1056 fosmid clones. Twelve clones exhibited RubisCO activity and the metagenomic fragments resembled genes from Thiomicrospira crunogena. One of these clones was further analyzed. It contained a 35.2 kb metagenomic insert carrying the RubisCO gene cluster and flanking DNA regions. Knockouts of twelve genes and two intergenic regions on this metagenomic fragment demonstrated that the RubisCO activity was significantly impaired and was attributed to deletions in genes encoding putative transcriptional regulators and those believed to be vital for RubisCO activation. Our new technique revealed a novel link between a poorly characterized gene and RubisCO activity. This screen opens the door to directly investigating RubisCO genes and respective enzymes from environmental samples.

  19. Isolation of xylose isomerases by sequence- and function-based screening from a soil metagenomic library

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    Parachin Nádia

    2011-05-01

    Full Text Available Abstract Background Xylose isomerase (XI catalyses the isomerisation of xylose to xylulose in bacteria and some fungi. Currently, only a limited number of XI genes have been functionally expressed in Saccharomyces cerevisiae, the microorganism of choice for lignocellulosic ethanol production. The objective of the present study was to search for novel XI genes in the vastly diverse microbial habitat present in soil. As the exploitation of microbial diversity is impaired by the ability to cultivate soil microorganisms under standard laboratory conditions, a metagenomic approach, consisting of total DNA extraction from a given environment followed by cloning of DNA into suitable vectors, was undertaken. Results A soil metagenomic library was constructed and two screening methods based on protein sequence similarity and enzyme activity were investigated to isolate novel XI encoding genes. These two screening approaches identified the xym1 and xym2 genes, respectively. Sequence and phylogenetic analyses revealed that the genes shared 67% similarity and belonged to different bacterial groups. When xym1 and xym2 were overexpressed in a xylA-deficient Escherichia coli strain, similar growth rates to those in which the Piromyces XI gene was expressed were obtained. However, expression in S. cerevisiae resulted in only one-fourth the growth rate of that obtained for the strain expressing the Piromyces XI gene. Conclusions For the first time, the screening of a soil metagenomic library in E. coli resulted in the successful isolation of two active XIs. However, the discrepancy between XI enzyme performance in E. coli and S. cerevisiae suggests that future screening for XI activity from soil should be pursued directly using yeast as a host.

  20. High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved.

    Science.gov (United States)

    Turaev, Dmitrij; Rattei, Thomas

    2016-06-01

    The systems biology of microbial communities, organismal communities inhabiting all ecological niches on earth, has in recent years been strongly facilitated by the rapid development of experimental, sequencing and data analysis methods. Novel experimental approaches and binning methods in metagenomics render the semi-automatic reconstructions of near-complete genomes of uncultivable bacteria possible, while advances in high-resolution amplicon analysis allow for efficient and less biased taxonomic community characterization. This will also facilitate predictive modeling approaches, hitherto limited by the low resolution of metagenomic data. In this review, we pinpoint the most promising current developments in metagenomics. They facilitate microbial systems biology towards a systemic understanding of mechanisms in microbial communities with scopes of application in many areas of our daily life. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Laboratory procedures to generate viral metagenomes.

    Science.gov (United States)

    Thurber, Rebecca V; Haynes, Matthew; Breitbart, Mya; Wegley, Linda; Rohwer, Forest

    2009-01-01

    This collection of laboratory protocols describes the steps to collect viruses from various samples with the specific aim of generating viral metagenome sequence libraries (viromes). Viral metagenomics, the study of uncultured viral nucleic acid sequences from different biomes, relies on several concentration, purification, extraction, sequencing and heuristic bioinformatic methods. No single technique can provide an all-inclusive approach, and therefore the protocols presented here will be discussed in terms of hypothetical projects. However, care must be taken to individualize each step depending on the source and type of viral-particles. This protocol is a description of the processes we have successfully used to: (i) concentrate viral particles from various types of samples, (ii) eliminate contaminating cells and free nucleic acids and (iii) extract, amplify and purify viral nucleic acids. Overall, a sample can be processed to isolate viral nucleic acids suitable for high-throughput sequencing in approximately 1 week.

  2. Genomics and metagenomics in medical microbiology.

    Science.gov (United States)

    Padmanabhan, Roshan; Mishra, Ajay Kumar; Raoult, Didier; Fournier, Pierre-Edouard

    2013-12-01

    Over the last two decades, sequencing tools have evolved from laborious time-consuming methodologies to real-time detection and deciphering of genomic DNA. Genome sequencing, especially using next generation sequencing (NGS) has revolutionized the landscape of microbiology and infectious disease. This deluge of sequencing data has not only enabled advances in fundamental biology but also helped improve diagnosis, typing of pathogen, virulence and antibiotic resistance detection, and development of new vaccines and culture media. In addition, NGS also enabled efficient analysis of complex human micro-floras, both commensal, and pathological, through metagenomic methods, thus helping the comprehension and management of human diseases such as obesity. This review summarizes technological advances in genomics and metagenomics relevant to the field of medical microbiology. Copyright © 2013 Elsevier B.V. All rights reserved.

  3. A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

    Directory of Open Access Journals (Sweden)

    Ruzzo Walter L

    2006-03-01

    Full Text Available Abstract Background As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. Methods In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. Results We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. Conclusion Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.

  4. Phylogeny-guided (meta)genome mining approach for the targeted discovery of new microbial natural products.

    Science.gov (United States)

    Kang, Hahk-Soo

    2017-02-01

    Genomics-based methods are now commonplace in natural products research. A phylogeny-guided mining approach provides a means to quickly screen a large number of microbial genomes or metagenomes in search of new biosynthetic gene clusters of interest. In this approach, biosynthetic genes serve as molecular markers, and phylogenetic trees built with known and unknown marker gene sequences are used to quickly prioritize biosynthetic gene clusters for their metabolites characterization. An increase in the use of this approach has been observed for the last couple of years along with the emergence of low cost sequencing technologies. The aim of this review is to discuss the basic concept of a phylogeny-guided mining approach, and also to provide examples in which this approach was successfully applied to discover new natural products from microbial genomes and metagenomes. I believe that the phylogeny-guided mining approach will continue to play an important role in genomics-based natural products research.

  5. A highly optimized grid deployment: the metagenomic analysis example.

    Science.gov (United States)

    Aparicio, Gabriel; Blanquer, Ignacio; Hernández, Vicente

    2008-01-01

    Computational resources and computationally expensive processes are two topics that are not growing at the same ratio. The availability of large amounts of computing resources in Grid infrastructures does not mean that efficiency is not an important issue. It is necessary to analyze the whole process to improve partitioning and submission schemas, especially in the most critical experiments. This is the case of metagenomic analysis, and this text shows the work done in order to optimize a Grid deployment, which has led to a reduction of the response time and the failure rates. Metagenomic studies aim at processing samples of multiple specimens to extract the genes and proteins that belong to the different species. In many cases, the sequencing of the DNA of many microorganisms is hindered by the impossibility of growing significant samples of isolated specimens. Many bacteria cannot survive alone, and require the interaction with other organisms. In such cases, the information of the DNA available belongs to different kinds of organisms. One important stage in Metagenomic analysis consists on the extraction of fragments followed by the comparison and analysis of their function stage. By the comparison to existing chains, whose function is well known, fragments can be classified. This process is computationally intensive and requires of several iterations of alignment and phylogeny classification steps. Source samples reach several millions of sequences, which could reach up to thousands of nucleotides each. These sequences are compared to a selected part of the "Non-redundant" database which only implies the information from eukaryotic species. From this first analysis, a refining process is performed and alignment analysis is restarted from the results. This process implies several CPU years. The article describes and analyzes the difficulties to fragment, automate and check the above operations in current Grid production environments. This environment has been

  6. Metagenomic profiling reveals lignocellulose degrading system in a microbial community associated with a wood-feeding beetle.

    Directory of Open Access Journals (Sweden)

    Erin D Scully

    Full Text Available The Asian longhorned beetle (Anoplophoraglabripennis is an invasive, wood-boring pest that thrives in the heartwood of deciduous tree species. A large impediment faced by A. glabripennis as it feeds on woody tissue is lignin, a highly recalcitrant biopolymer that reduces access to sugars and other nutrients locked in cellulose and hemicellulose. We previously demonstrated that lignin, cellulose, and hemicellulose are actively deconstructed in the beetle gut and that the gut harbors an assemblage of microbes hypothesized to make significant contributions to these processes. While lignin degrading mechanisms have been well characterized in pure cultures of white rot basidiomycetes, little is known about such processes in microbial communities associated with wood-feeding insects. The goals of this study were to develop a taxonomic and functional profile of a gut community derived from an invasive population of larval A. glabripennis collected from infested host trees and to identify genes that could be relevant for the digestion of woody tissue and nutrient acquisition. To accomplish this goal, we taxonomically and functionally characterized the A. glabripennis midgut microbiota through amplicon and shotgun metagenome sequencing and conducted a large-scale comparison with the metagenomes from a variety of other herbivore-associated communities. This analysis distinguished the A. glabripennis larval gut metagenome from the gut communities of other herbivores, including previously sequenced termite hindgut metagenomes. Genes encoding enzymes were identified in the A. glabripennis gut metagenome that could have key roles in woody tissue digestion including candidate lignin degrading genes (laccases, dye-decolorizing peroxidases, novel peroxidases and β-etherases, 36 families of glycoside hydrolases (such as cellulases and xylanases, and genes that could facilitate nutrient recovery, essential nutrient synthesis, and detoxification. This community

  7. Genomic and metagenomic challenges and opportunities for bioleaching: a mini-review.

    Science.gov (United States)

    Cárdenas, Juan Pablo; Quatrini, Raquel; Holmes, David S

    2016-09-01

    High-throughput genomic technologies are accelerating progress in understanding the diversity of microbial life in many environments. Here we highlight advances in genomics and metagenomics of microorganisms from bioleaching heaps and related acidic mining environments. Bioleaching heaps used for copper recovery provide significant opportunities to study the processes and mechanisms underlying microbial successions and the influence of community composition on ecosystem functioning. Obtaining quantitative and process-level knowledge of these dynamics is pivotal for understanding how microorganisms contribute to the solubilization of copper for industrial recovery. Advances in DNA sequencing technology provide unprecedented opportunities to obtain information about the genomes of bioleaching microorganisms, allowing predictive models of metabolic potential and ecosystem-level interactions to be constructed. These approaches are enabling predictive phenotyping of organisms many of which are recalcitrant to genetic approaches or are unculturable. This mini-review describes current bioleaching genomic and metagenomic projects and addresses the use of genome information to: (i) build metabolic models; (ii) predict microbial interactions; (iii) estimate genetic diversity; and (iv) study microbial evolution. Key challenges and perspectives of bioleaching genomics/metagenomics are addressed. Copyright © 2016 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.

  8. MetaQUAST: evaluation of metagenome assemblies.

    Science.gov (United States)

    Mikheenko, Alla; Saveliev, Vladislav; Gurevich, Alexey

    2016-04-01

    During the past years we have witnessed the rapid development of new metagenome assembly methods. Although there are many benchmark utilities designed for single-genome assemblies, there is no well-recognized evaluation and comparison tool for metagenomic-specific analogues. In this article, we present MetaQUAST, a modification of QUAST, the state-of-the-art tool for genome assembly evaluation based on alignment of contigs to a reference. MetaQUAST addresses such metagenome datasets features as (i) unknown species content by detecting and downloading reference sequences, (ii) huge diversity by giving comprehensive reports for multiple genomes and (iii) presence of highly relative species by detecting chimeric contigs. We demonstrate MetaQUAST performance by comparing several leading assemblers on one simulated and two real datasets. http://bioinf.spbau.ru/metaquast aleksey.gurevich@spbu.ru Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  9. Phylogenetic convolutional neural networks in metagenomics.

    Science.gov (United States)

    Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare

    2018-03-08

    Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

  10. Bayesian mixture analysis for metagenomic community profiling.

    Science.gov (United States)

    Morfopoulou, Sofia; Plagnol, Vincent

    2015-09-15

    Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix sofia.morfopoulou.10@ucl.ac.uk Supplementary data are available at Bionformatics online. © The Author 2015. Published by Oxford University Press.

  11. Metagenomic analysis reveals that modern microbialites and polar microbial mats have similar taxonomic and functional potential

    Directory of Open Access Journals (Sweden)

    Richard Allen White III

    2015-09-01

    Full Text Available Within the subarctic climate of Clinton Creek, Yukon, Canada, lies an abandoned and flooded open-pit asbestos mine that harbors rapidly growing microbialites. To understand their formation we completed a metagenomic community profile of the microbialites and their surrounding sediments. Assembled metagenomic data revealed that bacteria within the phylum Proteobacteria numerically dominated this system, although the relative abundances of taxa within the phylum varied among environments. Bacteria belonging to Alphaproteobacteria and Gammaproteobacteria were dominant in the microbialites and sediments, respectively. The microbialites were also home to many other groups associated with microbialite formation including filamentous cyanobacteria and dissimilatory sulfate-reducing Deltaproteobacteria, consistent with the idea of a shared global microbialite microbiome. Other members were present that are typically not associated with microbialites including Gemmatimonadetes and iron-oxidizing Betaproteobacteria, which participate in carbon metabolism and iron cycling. Compared to the sediments, the microbialite microbiome has significantly more genes associated with photosynthetic processes (e.g., photosystem II reaction centers, carotenoid and chlorophyll biosynthesis and carbon fixation (e.g., CO dehydrogenase. The Clinton Creek microbialite communities had strikingly similar functional potentials to non-lithifying microbial mats from the Canadian High Arctic and Antarctica, but are functionally distinct, from non-lithifying mats or biofilms from Yellowstone. Clinton Creek microbialites also share metabolic genes (R2 0.900. These metagenomic profiles from an anthropogenic microbialite-forming ecosystem provide context to microbialite formation on a human-relevant timescale.

  12. Metagenomics of Bacterial Diversity in Villa Luz Caves with Sulfur Water Springs

    Directory of Open Access Journals (Sweden)

    Giuseppe D’Auria

    2018-01-01

    Full Text Available New biotechnology applications require in-depth preliminary studies of biodiversity. The methods of massive sequencing using metagenomics and bioinformatics tools offer us sufficient and reliable knowledge to understand environmental diversity, to know new microorganisms, and to take advantage of their functional genes. Villa Luz caves, in the southern Mexican state of Tabasco, are fed by at least 26 groundwater inlets, containing 300–500 mg L-1 H2S and <0.1 mg L-1 O2. We extracted environmental DNA for metagenomic analysis of collected samples in five selected Villa Luz caves sites, with pH values from 2.5 to 7. Foreign organisms found in this underground ecosystem can oxidize H2S to H2SO4. These include: biovermiculites, a bacterial association that can grow on the rock walls; snottites, that are whitish, viscous biofilms hanging from the rock walls, and sacks or bags of phlegm, which live within the aquatic environment of the springs. Through the emergency food assistance program (TEFAP pyrosequencing, a total of 20,901 readings of amplification products from hypervariable regions V1 and V3 of 16S rRNA bacterial gene in whole and pure metagenomic DNA samples were generated. Seven bacterial phyla were identified. As a result, Proteobacteria was more frequent than Acidobacteria. Finally, acidophilic Proteobacteria was detected in UJAT5 sample

  13. Metagenomic exploration reveals a marked change in the river resistome and mobilome after treated wastewater discharges.

    Science.gov (United States)

    Lekunberri, Itziar; Balcázar, José Luis; Borrego, Carles M

    2018-03-01

    Mobile genetic elements (MGEs) are key agents in the spread of antibiotic resistance genes (ARGs) across environments. Here we used metagenomics to compare the river resistome (collection of all ARGs) and mobilome (e.g., integrases, transposases, integron integrases and insertion sequence common region "ISCR" elements) between samples collected upstream (n = 6) and downstream (n = 6) of an urban wastewater treatment plant (UWWTP). In comparison to upstream metagenomes, downstream metagenomes showed a drastic increase in the abundance of ARGs, as well as markers of MGEs, particularly integron integrases and ISCR elements. These changes were accompanied by a concomitant prevalence of 16S rRNA gene signatures of bacteria affiliated to families encompassing well-known human and animal pathogens. Our results confirm that chronic discharges of treated wastewater severely impact the river resistome affecting not only the abundance and diversity of ARGs but also their potential spread by enriching the river mobilome in a wide variety of MGEs. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Characterization of Bacterial Hydrocarbon Degradation Potential in the Red Sea Through Metagenomic and Cultivation Methods

    KAUST Repository

    Bianchi, Patrick

    2018-01-01

    The focus of this thesis is on the characterization at the metagenomic level of the water column of the Red Sea and on the isolation and characterization of novel hydrocarbon-degrading species and genomes adapted to the unique environmental characteristics of the basin. The presence of metabolic genes responsible of both linear and aromatic hydrocarbon degradation has been evaluated from a metagenomic survey and a meta-analysis of already available datasets. In parallel, water column-based microcosms have been established with crude oil as the sole carbon source, with aim to isolate potential novel bacterial species and provide new genome-based insights on the hydrocarbon degradation potential available in the Red Sea.

  15. The Human Gut Antibiotic Resistome in the Metagenomic Era: Progress and Perspectives

    Directory of Open Access Journals (Sweden)

    Yongfei Hu

    2016-04-01

    Full Text Available The human gut is populated by a vast number of bacteria, which play a critical role in human health. In recent years, attention has focused on the gut bacteria as a reservoir of antibiotic resistance genes (ARGs. Both culture-dependent and culture-independent methods have been applied to investigate numerous ARGs, collectively called the antibiotic resistome, harbored by gut bacteria. This has led to an increased understanding of the overall profile of the gut antibiotic resistome, although it remains incompletely understood. In this review, we summarize the recent research findings on the human gut antibiotic resistome, with an emphasis on progress achieved using the culture-independent metagenomic strategy. We also describe the features of different available ARG databases used for annotation in metagenomic analysis, discuss the potential problems and limitations in current research, and suggest several directions for future investigation.

  16. GenomePeek—an online tool for prokaryotic genome and metagenome analysis

    Directory of Open Access Journals (Sweden)

    Katelyn McNair

    2015-06-01

    Full Text Available As more and more prokaryotic sequencing takes place, a method to quickly and accurately analyze this data is needed. Previous tools are mainly designed for metagenomic analysis and have limitations; such as long runtimes and significant false positive error rates. The online tool GenomePeek (edwards.sdsu.edu/GenomePeek was developed to analyze both single genome and metagenome sequencing files, quickly and with low error rates. GenomePeek uses a sequence assembly approach where reads to a set of conserved genes are extracted, assembled and then aligned against the highly specific reference database. GenomePeek was found to be faster than traditional approaches while still keeping error rates low, as well as offering unique data visualization options.

  17. Metagenomics Study on the Polymorphism of Gut Microbiota and Their Function on Human Health

    DEFF Research Database (Denmark)

    Feng, Qiang

    diversity and functional complexity of the gut microbiome. Facilitated by the Next Generation Sequencing (NGS) technologies and the progress of bioinformatics in the past decade, we have acquired substantial achievements in metagenomic studies on human gut microbiome and established the fundamentals of our...... understanding of the interactions between gut microbes and human body, and also the importance of this interaction on human health. As one of the milestones, the first integrated gene catalog in the human gut microbiome was constructed in 2010 in the scheme of the Metagenomics of Human Intestinal Tract (Meta......’ are shared in the population. These microorganisms participate in various metabolic pathways and activities of the immune system and the nervous system of our bodies,and have fundamental impacts on our health. For example, an association study between gut microbiome and type 2 diabetes (T2D) highlighted...

  18. Metagenomic Analysis of Chicken Gut Microbiota for Improving Metabolism and Health of Chickens — A Review

    Directory of Open Access Journals (Sweden)

    Ki Young Choi

    2015-09-01

    Full Text Available Chicken is a major food source for humans, hence it is important to understand the mechanisms involved in nutrient absorption in chicken. In the gastrointestinal tract (GIT, the microbiota plays a central role in enhancing nutrient absorption and strengthening the immune system, thereby affecting both growth and health of chicken. There is little information on the diversity and functions of chicken GIT microbiota, its impact on the host, and the interactions between the microbiota and host. Here, we review the recent metagenomic strategies to analyze the chicken GIT microbiota composition and its functions related to improving metabolism and health. We summarize methodology of metagenomics in order to obtain bacterial taxonomy and functional inferences of the GIT microbiota and suggest a set of indicator genes for monitoring and manipulating the microbiota to promote host health in future.

  19. Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

    Directory of Open Access Journals (Sweden)

    Monteiro Santos Erika

    2012-02-01

    Full Text Available Abstract Background Lynch syndrome (LS is the most common form of inherited predisposition to colorectal cancer (CRC, accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1, mutS homolog 2 (MSH2, postmeiotic segregation increased 1 (PMS1, post-meiotic segregation increased 2 (PMS2 and mutS homolog 6 (MSH6. Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome. Methods Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 genes. The probability of detecting a mutation was calculated using the PREMM, Barnetson, MMRpro, Wijnen and Myriad models. To evaluate the sensitivity and specificity of the models, receiver operating characteristic curves were constructed. Results Of the 88 patients included in this analysis, 31 mutations were identified: 16 were found in the MSH2 gene, 15 in the MLH1 gene and no pathogenic mutations were identified in the MSH6 gene. It was observed that the AUC for the PREMM (0.846, Barnetson (0.850, MMRpro (0.821 and Wijnen (0.807 models did not present significant statistical difference. The Myriad model presented lower AUC (0.704 than the four other models evaluated. Considering thresholds of ≥ 5%, the models sensitivity varied between 1 (Myriad and 0.87 (Wijnen and specificity ranged from 0 (Myriad to 0.38 (Barnetson. Conclusions The Barnetson, PREMM, MMRpro and Wijnen models present similar AUC. The AUC of the Myriad model is statistically inferior to the four other models.

  20. Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

    Energy Technology Data Exchange (ETDEWEB)

    Monteiro Santos, Erika Maria [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); International Center of Research and Training (CIPE), AC Camargo Hospital, Sao Paulo (Brazil); Silva Junior, Wilson Araujo da [Sao Paulo University, Department of Genetics, Medical School of Ribeirao Preto, Ribeirao Preto (Brazil); Carraro, Dirce Maria [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); International Center of Research and Training (CIPE), AC Camargo Hospital, Sao Paulo (Brazil); Rossi, Benedito Mauro; Valentin, Mev Dominguez [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Carneiro, Felipe [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); International Center of Research and Training (CIPE), AC Camargo Hospital, Sao Paulo (Brazil); Oliveira, Ligia Petrolini de [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Oliveira Ferreira, Fabio de; Junior, Samuel Aguiar [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Hereditary Colorectal Cancer Registry, AC Camargo Hospital, Sao Paulo (Brazil); Nakagawa, Wilson Toshihiko [Hereditary Colorectal Cancer Registry, AC Camargo Hospital, Sao Paulo (Brazil); Gomy, Israel [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Sao Paulo University, Department of Genetics, Medical School of Ribeirao Preto, Ribeirao Preto (Brazil); Faria Ferraz, Victor Evangelista de [Sao Paulo University, Department of Genetics, Medical School of Ribeirao Preto, Ribeirao Preto (Brazil)

    2012-02-09

    Lynch syndrome (LS) is the most common form of inherited predisposition to colorectal cancer (CRC), accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), postmeiotic segregation increased 1 (PMS1), post-meiotic segregation increased 2 (PMS2) and mutS homolog 6 (MSH6). Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome. Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 genes. The probability of detecting a mutation was calculated using the PREMM, Barnetson, MMRpro, Wijnen and Myriad models. To evaluate the sensitivity and specificity of the models, receiver operating characteristic curves were constructed. Of the 88 patients included in this analysis, 31 mutations were identified: 16 were found in the MSH2 gene, 15 in the MLH1 gene and no pathogenic mutations were identified in the MSH6 gene. It was observed that the AUC for the PREMM (0.846), Barnetson (0.850), MMRpro (0.821) and Wijnen (0.807) models did not present significant statistical difference. The Myriad model presented lower AUC (0.704) than the four other models evaluated. Considering thresholds of ≥ 5%, the models sensitivity varied between 1 (Myriad) and 0.87 (Wijnen) and specificity ranged from 0 (Myriad) to 0.38 (Barnetson). The Barnetson, PREMM, MMRpro and Wijnen models present similar AUC. The AUC of the Myriad model is statistically inferior to the four other models.

  1. Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

    International Nuclear Information System (INIS)

    Monteiro Santos, Erika Maria; Silva Junior, Wilson Araujo da; Carraro, Dirce Maria; Rossi, Benedito Mauro; Valentin, Mev Dominguez; Carneiro, Felipe; Oliveira, Ligia Petrolini de; Oliveira Ferreira, Fabio de; Junior, Samuel Aguiar; Nakagawa, Wilson Toshihiko; Gomy, Israel; Faria Ferraz, Victor Evangelista de

    2012-01-01

    Lynch syndrome (LS) is the most common form of inherited predisposition to colorectal cancer (CRC), accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), postmeiotic segregation increased 1 (PMS1), post-meiotic segregation increased 2 (PMS2) and mutS homolog 6 (MSH6). Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome. Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 gene