A rapid Q-PCR titration protocol for adenovirus and helper-dependent adenovirus vectors that produces biologically relevant results

Science.gov (United States)

Gallaher, Sean D.; Berk, Arnold J.

2013-01-01

Adenoviruses are employed in the study of cellular processes and as expression vectors used in gene therapy. The success and reproducibility of these studies is dependent in part on having accurate and meaningful titers of replication competent and helper-dependent adenovirus stocks, which is problematic due to the use of varied and divergent titration protocols. Physical titration methods, which quantify the total number of viral particles, are used by many, but are poor at estimating activity. Biological titration methods, such as plaque assays, are more biologically relevant, but are time consuming and not applicable to helper-dependent gene therapy vectors. To address this, a protocol was developed called “infectious genome titration” in which viral DNA is isolated from the nuclei of cells ~3 h post-infection, and then quantified by Q-PCR. This approach ensures that only biologically active virions are counted as part of the titer determination. This approach is rapid, robust, sensitive, reproducible, and applicable to all forms of adenovirus. Unlike other Q-PCR-based methods, titers determined by this protocol are well correlated with biological activity. PMID:23624118

Clinical Relevance of Gene Copy Number Variation in Metastatic Clear Cell Renal Cell Carcinoma.

Science.gov (United States)

Nouhaud, François-Xavier; Blanchard, France; Sesboue, Richard; Flaman, Jean-Michel; Sabourin, Jean-Christophe; Pfister, Christian; Di Fiore, Frédéric

2018-02-23

Gene copy number variations (CNVs) have been reported to be frequent in renal cell carcinoma (RCC), with potential prognostic value for some. However, their clinical utility, especially to guide treatment of metastatic disease remains to be established. Our objectives were to assess CNVs on a panel of selected genes and determine their clinical relevance in patients who underwent treatment of metastatic RCC. The genetic assessment was performed on frozen tissue samples of clear cell metastatic RCC using quantitative multiplex polymerase chain reaction of short fluorescent fragment method to detect CNVs on a panel of 14 genes of interest. The comparison of the electropherogram obtained from both tumor and normal renal adjacent tissue allowed for CNV identification. The clinical, biologic, and survival characteristics were assessed for their associations with the most frequent CNVs. Fifty patients with clear cell metastatic RCC were included. The CNV rate was 21.4%. The loss of CDKN2A and PLG was associated with a higher tumor stage (P relevance, especially those located on CDKN2A, PLG, and ALDOB, in a homogeneous cohort of patients with clear cell metastatic RCC. Copyright © 2018 Elsevier Inc. All rights reserved.

The sociobiology of genes: the gene's eye view as a unifying behavioural-ecological framework for biological evolution.

Science.gov (United States)

De Tiège, Alexis; Van de Peer, Yves; Braeckman, Johan; Tanghe, Koen B

2017-11-22

Although classical evolutionary theory, i.e., population genetics and the Modern Synthesis, was already implicitly 'gene-centred', the organism was, in practice, still generally regarded as the individual unit of which a population is composed. The gene-centred approach to evolution only reached a logical conclusion with the advent of the gene-selectionist or gene's eye view in the 1960s and 1970s. Whereas classical evolutionary theory can only work with (genotypically represented) fitness differences between individual organisms, gene-selectionism is capable of working with fitness differences among genes within the same organism and genome. Here, we explore the explanatory potential of 'intra-organismic' and 'intra-genomic' gene-selectionism, i.e., of a behavioural-ecological 'gene's eye view' on genetic, genomic and organismal evolution. First, we give a general outline of the framework and how it complements the-to some extent-still 'organism-centred' approach of classical evolutionary theory. Secondly, we give a more in-depth assessment of its explanatory potential for biological evolution, i.e., for Darwin's 'common descent with modification' or, more specifically, for 'historical continuity or homology with modular evolutionary change' as it has been studied by evolutionary developmental biology (evo-devo) during the last few decades. In contrast with classical evolutionary theory, evo-devo focuses on 'within-organism' developmental processes. Given the capacity of gene-selectionism to adopt an intra-organismal gene's eye view, we outline the relevance of the latter model for evo-devo. Overall, we aim for the conceptual integration between the gene's eye view on the one hand, and more organism-centred evolutionary models (both classical evolutionary theory and evo-devo) on the other.

Gene expression profiling of mucolipidosis type IV fibroblasts reveals deregulation of genes with relevant functions in lysosome physiology.

Science.gov (United States)

Bozzato, Andrea; Barlati, Sergio; Borsani, Giuseppe

2008-04-01

Mucolipidosis type IV (MLIV, MIM 252650) is an autosomal recessive lysosomal storage disorder that causes mental and motor retardation as well as visual impairment. The lysosomal storage defect in MLIV is consistent with abnormalities of membrane traffic and organelle dynamics in the late endocytic pathway. MLIV is caused by mutations in the MCOLN1 gene, which codes for mucolipin-1 (MLN1), a member of the large family of transient receptor potential (TRP) cation channels. Although a number of studies have been performed on mucolipin-1, the pathological mechanisms underlying MLIV are not fully understood. To identify genes that characterize pathogenic changes in mucolipidosis type IV, we compared the expression profiles of three MLIV and three normal skin fibroblasts cell lines using oligonucleotide microarrays. Genes that were differentially expressed in patients' cells were identified. 231 genes were up-regulated, and 116 down-regulated. Real-Time RT-PCR performed on selected genes in six independent MLIV fibroblasts cell lines was generally consistent with the microarray findings. This study allowed to evidence the modulation at the transcriptional level of a discrete number of genes relevant in biological processes which are altered in the disease such as endosome/lysosome trafficking, lysosome biogenesis, organelle acidification and lipid metabolism.

Mining biological databases for candidate disease genes

Science.gov (United States)

Braun, Terry A.; Scheetz, Todd; Webster, Gregg L.; Casavant, Thomas L.

2001-07-01

The publicly-funded effort to sequence the complete nucleotide sequence of the human genome, the Human Genome Project (HGP), has currently produced more than 93% of the 3 billion nucleotides of the human genome into a preliminary `draft' format. In addition, several valuable sources of information have been developed as direct and indirect results of the HGP. These include the sequencing of model organisms (rat, mouse, fly, and others), gene discovery projects (ESTs and full-length), and new technologies such as expression analysis and resources (micro-arrays or gene chips). These resources are invaluable for the researchers identifying the functional genes of the genome that transcribe and translate into the transcriptome and proteome, both of which potentially contain orders of magnitude more complexity than the genome itself. Preliminary analyses of this data identified approximately 30,000 - 40,000 human `genes.' However, the bulk of the effort still remains -- to identify the functional and structural elements contained within the transcriptome and proteome, and to associate function in the transcriptome and proteome to genes. A fortuitous consequence of the HGP is the existence of hundreds of databases containing biological information that may contain relevant data pertaining to the identification of disease-causing genes. The task of mining these databases for information on candidate genes is a commercial application of enormous potential. We are developing a system to acquire and mine data from specific databases to aid our efforts to identify disease genes. A high speed cluster of Linux of workstations is used to analyze sequence and perform distributed sequence alignments as part of our data mining and processing. This system has been used to mine GeneMap99 sequences within specific genomic intervals to identify potential candidate disease genes associated with Bardet-Biedle Syndrome (BBS).

The collective biology of the gene: Towards genetic dynamics engineering

International Nuclear Information System (INIS)

Chela-Flores, J.

1985-11-01

Chromatin dynamics is studied in terms of coupled vibrations (phonon pairing); this is shown to lead to a collective variable Δ, interpreted as a gene inhibition factor, which behaves as a biological switch turned off, not only by enzymatic action or metabolic energy, but also by means of an external probe:irradiation. We discuss the inactivation of the X chromosome and puffing. The relevance of being able to modulate Δ is emphasized, since it is equivalent to controlling chromatin dynamics without interfering with chromatin structure, unlike in the usual recombinant DNA techniques. (author)

“Zebrafishing” for Novel Genes Relevant to the Glomerular Filtration Barrier

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Nils Hanke

2013-01-01

Full Text Available Data for genes relevant to glomerular filtration barrier function or proteinuria is continually increasing in an era of microarrays, genome-wide association studies, and quantitative trait locus analysis. Researchers are limited by published literature searches to select the most relevant genes to investigate. High-throughput cell cultures and other in vitro systems ultimately need to demonstrate proof in an in vivo model. Generating mammalian models for the genes of interest is costly and time intensive, and yields only a small number of test subjects. These models also have many pitfalls such as possible embryonic mortality and failure to generate phenotypes or generate nonkidney specific phenotypes. Here we describe an in vivo zebrafish model as a simple vertebrate screening system to identify genes relevant to glomerular filtration barrier function. Using our technology, we are able to screen entirely novel genes in 4–6 weeks in hundreds of live test subjects at a fraction of the cost of a mammalian model. Our system produces consistent and reliable evidence for gene relevance in glomerular kidney disease; the results then provide merit for further analysis in mammalian models.

Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

Science.gov (United States)

Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

2016-01-01

Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

Science.gov (United States)

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

The leukemia-specific fusion gene ETV6/RUNX1 perturbs distinct key biological functions primarily by gene repression.

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Gerhard Fuka

Full Text Available BACKGROUND: ETV6/RUNX1 (E/R (also known as TEL/AML1 is the most frequent gene fusion in childhood acute lymphoblastic leukemia (ALL and also most likely the crucial factor for disease initiation; its role in leukemia propagation and maintenance, however, remains largely elusive. To address this issue we performed a shRNA-mediated knock-down (KD of the E/R fusion gene and investigated the ensuing consequences on genome-wide gene expression patterns and deducible regulatory functions in two E/R-positive leukemic cell lines. FINDINGS: Microarray analyses identified 777 genes whose expression was substantially altered. Although approximately equal proportions were either up- (KD-UP or down-regulated (KD-DOWN, the effects on biological processes and pathways differed considerably. The E/R KD-UP set was significantly enriched for genes included in the "cell activation", "immune response", "apoptosis", "signal transduction" and "development and differentiation" categories, whereas in the E/R KD-DOWN set only the "PI3K/AKT/mTOR signaling" and "hematopoietic stem cells" categories became evident. Comparable expression signatures obtained from primary E/R-positive ALL samples underline the relevance of these pathways and molecular functions. We also validated six differentially expressed genes representing the categories "stem cell properties", "B-cell differentiation", "immune response", "cell adhesion" and "DNA damage" with RT-qPCR. CONCLUSION: Our analyses provide the first preliminary evidence that the continuous expression of the E/R fusion gene interferes with key regulatory functions that shape the biology of this leukemia subtype. E/R may thus indeed constitute the essential driving force for the propagation and maintenance of the leukemic process irrespective of potential consequences of associated secondary changes. Finally, these findings may also provide a valuable source of potentially attractive therapeutic targets.

Identification of Genes Relevant to Pesticides and Biology from Global Transcriptome Data of Monochamus alternatus Hope (Coleoptera: Cerambycidae Larvae.

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Songqing Wu

Full Text Available Monochamus alternatus Hope is the main vector in China of the Pine Wilt Disease caused by the pine wood nematode Bursaphelenchus xylophilus. Although chemical control is traditionally used to prevent pine wilt disease, new strategies based in biological control are promising ways for the management of the disease. However, there is no deep sequence analysis of Monochamus alternatus Hope that describes the transcriptome and no information is available about gene function of this insect vector. We used next generation sequencing technology to sequence the whole fourth instar larva transcriptome of Monochamus alternatus Hope and successfully built a Monochamus alternatus Hope transcriptome database. In total, 105,612 unigenes were assigned for Gene Ontology (GO terms, information for 16,730 classified unigenes was obtained in the Clusters of Orthologous Groups (COGs database, and 13,024 unigenes matched with 224 predicted pathways in the Kyoto Encyclopedia of Genes and Genome (KEGG. In addition, genes related to putative insecticide resistance-related genes, RNAi, the Bt receptor, intestinal digestive enzymes, possible future insect control targets and immune-related molecules are described. This study provides valuable basic information that can be used as a gateway to develop new molecular tools for Monochamus alternatus Hope control strategies.

Integrated pathway clusters with coherent biological themes for target prioritisation.

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Yi-An Chen

Full Text Available Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.

Gene expression profiling with principal component analysis depicts the biological continuum from essential thrombocythemia over polycythemia vera to myelofibrosis

DEFF Research Database (Denmark)

Skov, Vibe; Thomassen, Mads; Riley, Caroline H

2012-01-01

The recent discovery of the Janus activating kinase 2 V617F mutation in most patients with polycythemia vera (PV) and half of those with essential thrombocythemia (ET) and primary myelofibrosis (PMF) has favored the hypothesis of a biological continuum from ET over PV to PMF. We performed gene...... with biological relevant overlaps between the different entities. Moreover, the analysis separates Janus activating kinase 2-negative ET patients from Janus activating kinase 2-positive ET patients. Functional annotation analysis demonstrates that clusters of gene ontology terms related to inflammation, immune...... system, apoptosis, RNA metabolism, and secretory system were the most significantly deregulated terms in the three different disease groups. Our results yield further support for the hypothesis of a biological continuum originating from ET over PV to PMF. Functional analysis suggests an important...

Hands-on-Entropy, Energy Balance with Biological Relevance

Science.gov (United States)

Reeves, Mark

2015-03-01

Entropy changes underlie the physics that dominates biological interactions. Indeed, introductory biology courses often begin with an exploration of the qualities of water that are important to living systems. However, one idea that is not explicitly addressed in most introductory physics or biology textbooks is important contribution of the entropy in driving fundamental biological processes towards equilibrium. From diffusion to cell-membrane formation, to electrostatic binding in protein folding, to the functioning of nerve cells, entropic effects often act to counterbalance deterministic forces such as electrostatic attraction and in so doing, allow for effective molecular signaling. A small group of biology, biophysics and computer science faculty have worked together for the past five years to develop curricular modules (based on SCALEUP pedagogy). This has enabled students to create models of stochastic and deterministic processes. Our students are first-year engineering and science students in the calculus-based physics course and they are not expected to know biology beyond the high-school level. In our class, they learn to reduce complex biological processes and structures in order model them mathematically to account for both deterministic and probabilistic processes. The students test these models in simulations and in laboratory experiments that are biologically relevant such as diffusion, ionic transport, and ligand-receptor binding. Moreover, the students confront random forces and traditional forces in problems, simulations, and in laboratory exploration throughout the year-long course as they move from traditional kinematics through thermodynamics to electrostatic interactions. This talk will present a number of these exercises, with particular focus on the hands-on experiments done by the students, and will give examples of the tangible material that our students work with throughout the two-semester sequence of their course on introductory

Molecular biology III - Oncogenes and tumor suppressor genes

International Nuclear Information System (INIS)

Giaccia, Amato J.

1996-01-01

Purpose: The purpose of this course is to introduce to radiation oncologists the basic concepts of tumorigenesis, building on the information that will be presented in the first and second part of this series of lectures. Objective: Our objective is to increase the current understanding of radiation oncologists with the process of tumorigenesis, especially focusing on genes that are altered in many tumor types that are potential candidates for novel molecular strategies. As strategies to treat cancer of cancer are becoming more sophisticated, it will be important for both the practitioner and academician to develop a basic understanding of the function of cancer 'genes'. This will be the third in a series of refresher courses that are meant to address recent advances in Cancer Biology in a way that both clinicians without previous knowledge of molecular biology or experienced researchers will find interesting. The lecture will begin with a basic overview of tumorigenesis; methods of detecting chromosome/DNA alterations, approaches used to isolate oncogenes and tumor suppressor genes, and their role in cell killing by apoptosis. Special attention will be given to oncogenes and tumor suppressor genes that are modulated by ionizing radiation and the tumor microenvironment. We will relate the biology of oncogenes and tumor suppressor genes to basic aspects of radiation biology that would be important in clinical practice. Finally, we will review recent studies on the prognostic significance of p53 mutations and apoptosis in tumor specimens. The main point of this lecture is to relate both researcher and clinician what are the therapeutic ramifications of oncogene and tumor suppressor gene mutations found in human neoptasia

Comparison of the perceived relevance of oral biology reported by students and interns of a Pakistani dental college.

Science.gov (United States)

Farooq, I; Ali, S

2014-11-01

The purpose of this study was to analyse and compare the perceived relevance of oral biology with dentistry as reported by dental students and interns and to investigate the most popular teaching approach and learning resource. A questionnaire aiming to ask about the relevance of oral biology to dentistry, most popular teaching method and learning resource was utilised in this study. Study groups encompassed second-year dental students who had completed their course and dental interns. The data were obtained and analysed statistically. The overall response rate for both groups was 60%. Both groups reported high relevance of oral biology to dentistry. Perception of dental interns regarding the relevance of oral biology to dentistry was higher than that of students. Both groups identified student presentations as the most important teaching method. Amongst the most important learning resources, textbooks were considered most imperative by interns, whereas lecture handouts received the highest importance score by students. Dental students and interns considered oral biology to be relevant to dentistry, although greater relevance was reported by interns. Year-wise advancement in dental education and training improves the perception of the students about the relevance of oral biology to dentistry. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Variation across mitochondrial gene trees provides evidence for systematic error: How much gene tree variation is biological?

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Richards, Emilie J; Brown, Jeremy M; Barley, Anthony J; Chong, Rebecca A; Thomson, Robert C

2018-02-19

The use of large genomic datasets in phylogenetics has highlighted extensive topological variation across genes. Much of this discordance is assumed to result from biological processes. However, variation among gene trees can also be a consequence of systematic error driven by poor model fit, and the relative importance of biological versus methodological factors in explaining gene tree variation is a major unresolved question. Using mitochondrial genomes to control for biological causes of gene tree variation, we estimate the extent of gene tree discordance driven by systematic error and employ posterior prediction to highlight the role of model fit in producing this discordance. We find that the amount of discordance among mitochondrial gene trees is similar to the amount of discordance found in other studies that assume only biological causes of variation. This similarity suggests that the role of systematic error in generating gene tree variation is underappreciated and critical evaluation of fit between assumed models and the data used for inference is important for the resolution of unresolved phylogenetic questions.

Expression of alcoholism-relevant genes in the liver are differently correlated to different parts of the brain.

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Wang, Lishi; Huang, Yue; Jiao, Yan; Chen, Hong; Cao, Yanhong; Bennett, Beth; Wang, Yongjun; Gu, Weikuan

2013-01-01

The purpose of this study is to investigate whether expression profiles of alcoholism-relevant genes in different parts of the brain are correlated differently with those in the liver. Four experiments were conducted. First, we used gene expression profiles from five parts of the brain (striatum, prefrontal cortex, nucleus accumbens, hippocampus, and cerebellum) and from liver in a population of recombinant inbred mouse strains to examine the expression association of 10 alcoholism-relevant genes. Second, we conducted the same association analysis between brain structures and the lung. Third, using five randomly selected, nonalcoholism-relevant genes, we conducted the association analysis between brain and liver. Finally, we compared the expression of 10 alcoholism-relevant genes in hippocampus and cerebellum between an alcohol preference strain and a wild-type control. We observed a difference in correlation patterns in expression levels of 10 alcoholism-relevant genes between different parts of the brain with those of liver. We then examined the association of gene expression between alcohol dehydrogenases (Adh1, Adh2, Adh5, and Adh7) and different parts of the brain. The results were similar to those of the 10 genes. Then, we found that the association of those genes between brain structures and lung was different from that of liver. Next, we found that the association patterns of five alcoholism-nonrelevant genes were different from those of 10 alcoholism-relevant genes. Finally, we found that the expression level of 10 alcohol-relevant genes is influenced more in hippocampus than in cerebellum in the alcohol preference strain. Our results show that the expression of alcoholism-relevant genes in liver is differently associated with the expression of genes in different parts of the brain. Because different structural changes in different parts of the brain in alcoholism have been reported, it is important to investigate whether those structural differences in

Beyond arousal and valence: the importance of the biological versus social relevance of emotional stimuli

OpenAIRE

Sakaki, Michiko; Niki, N.; Mather, M.

2012-01-01

The present study addressed the hypothesis that emotional stimuli relevant to survival or reproduction (biologically emotional stimuli) automatically affect cognitive processing (e.g., attention, memory), while those relevant to social life (socially emotional stimuli) require elaborative processing to modulate attention and memory. Results of our behavioral studies showed that (1) biologically emotional images hold attention more strongly than do socially emotional images, (2) memory for bio...

Extracting biologically significant patterns from short time series gene expression data

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McGinnis Thomas

2009-08-01

Full Text Available Abstract Background Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult. Results We developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO annotations and chromatin immunoprecipitation (ChIP-chip data. Conclusion Our approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at http://www.benoslab.pitt.edu/astro/.

A study of ruthenium complexes of some biologically relevant a-N ...

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences; Volume 112; Issue 3. A study of ruthenium complexes of some biologically relevant ∙ -N-heterocyclic ... Author Affiliations. P Sengupta1 S Ghosh1. Department of Inorganic Chemistry, Indian Association for the Cultivation of Science, Jadavpur, Calcutta 700 032, India ...

STAT3 Target Genes Relevant to Human Cancers

International Nuclear Information System (INIS)

Carpenter, Richard L.; Lo, Hui-Wen

2014-01-01

Since its discovery, the STAT3 transcription factor has been extensively studied for its function as a transcriptional regulator and its role as a mediator of development, normal physiology, and pathology of many diseases, including cancers. These efforts have uncovered an array of genes that can be positively and negatively regulated by STAT3, alone and in cooperation with other transcription factors. Through regulating gene expression, STAT3 has been demonstrated to play a pivotal role in many cellular processes including oncogenesis, tumor growth and progression, and stemness. Interestingly, recent studies suggest that STAT3 may behave as a tumor suppressor by activating expression of genes known to inhibit tumorigenesis. Additional evidence suggested that STAT3 may elicit opposing effects depending on cellular context and tumor types. These mixed results signify the need for a deeper understanding of STAT3, including its upstream regulators, parallel transcription co-regulators, and downstream target genes. To help facilitate fulfilling this unmet need, this review will be primarily focused on STAT3 downstream target genes that have been validated to associate with tumorigenesis and/or malignant biology of human cancers

Acoustic fine structure may encode biologically relevant information for zebra finches.

Science.gov (United States)

Prior, Nora H; Smith, Edward; Lawson, Shelby; Ball, Gregory F; Dooling, Robert J

2018-04-18

The ability to discriminate changes in the fine structure of complex sounds is well developed in birds. However, the precise limit of this discrimination ability and how it is used in the context of natural communication remains unclear. Here we describe natural variability in acoustic fine structure of male and female zebra finch calls. Results from psychoacoustic experiments demonstrate that zebra finches are able to discriminate extremely small differences in fine structure, which are on the order of the variation in acoustic fine structure that is present in their vocal signals. Results from signal analysis methods also suggest that acoustic fine structure may carry information that distinguishes between biologically relevant categories including sex, call type and individual identity. Combined, our results are consistent with the hypothesis that zebra finches can encode biologically relevant information within the fine structure of their calls. This study provides a foundation for our understanding of how acoustic fine structure may be involved in animal communication.

Dilution thermodynamics of the biologically relevant cation mixtures

International Nuclear Information System (INIS)

Kaczyński, Marek; Borowik, Tomasz; Przybyło, Magda; Langner, Marek

2014-01-01

Graphical abstract: - Highlights: • Dilution energetics of Ca 2+ can be altered by the aqueous phase ionic composition. • Dissipated heat upon Ca 2+ dilution is drastically reduced in the K + presence. • Reduction of the enthalpy change upon Ca 2+ dilution is K + concentration dependent. • The cooperativity of Ca 2+ hydration might be of great biological relevance providing a thermodynamic argument for the specific ionic composition of the intracellular environment. - Abstract: The ionic composition of intracellular space is rigorously controlled by a variety of processes consuming large quantities of energy. Since the energetic efficiency is an important evolutional criterion, therefore the ion fluxes within the cell should be optimized with respect to the accompanying energy consumption. In the paper we present the experimental evidence that the dilution enthalpies of the biologically relevant ions; i.e. calcium and magnesium depend on the presence of monovalent cations; i.e. sodium and potassium. The heat flow generated during the dilution of ionic mixtures was measured with the isothermal titration calorimetry. When calcium was diluted together with potassium the dilution enthalpy was drastically reduced as the function of the potassium concentration present in the solution. No such effect was observed when the potassium ions were substituted with sodium ones. When the dilution of magnesium was investigated the dependence of the dilution enthalpy on the accompanying monovalent cation was much weaker. In order to interpret experimental evidences the ionic cluster formation is postulated. The specific organization of such cluster should depend on ions charges, sizes and organization of the hydration layers

Knowledge Enrichment Analysis for Human Tissue- Specific Genes Uncover New Biological Insights

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Gong Xiu-Jun

2012-06-01

Full Text Available The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS and housekeeping (HK genes may help identify their roles in biological process or diseases and gain new biological insights.In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA and Hypergeometric Analysis (HA against three biological ontologies: Gene Ontology (GO, KEGG pathways and Disease Ontology (DO respectively.The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissuegenes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson’s disease, Huntington’s disease, Alzheimer’s disease are intensively enriched.Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of “Hypertrophic cardiomyopathy (HCM” and “Arrhythmogenic right ventricular cardiomyopathy (ARVC” are heavily enriched, which are also considered as no relationship with skeletal functions.Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon.

Clinical relevance and biology of circulating tumor cells

Science.gov (United States)

2011-01-01

Most breast cancer patients die due to metastases, and the early onset of this multistep process is usually missed by current tumor staging modalities. Therefore, ultrasensitive techniques have been developed to enable the enrichment, detection, isolation and characterization of disseminated tumor cells in bone marrow and circulating tumor cells in the peripheral blood of cancer patients. There is increasing evidence that the presence of these cells is associated with an unfavorable prognosis related to metastatic progression in the bone and other organs. This review focuses on investigations regarding the biology and clinical relevance of circulating tumor cells in breast cancer. PMID:22114869

Molecular profiles to biology and pathways: a systems biology approach.

Science.gov (United States)

Van Laere, Steven; Dirix, Luc; Vermeulen, Peter

2016-06-16

Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.

AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data

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Xia Yuannan

2006-12-01

Full Text Available Abstract Background DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challenge issue becomes how to analyze a large amount of microarray data and make biological sense of them. Affymetrix GeneChips are widely used microarrays, where a variety of statistical algorithms have been explored and used for detecting significant genes in the experiment. These methods rely solely on the quantitative data, i.e., signal intensity; however, qualitative data are also important parameters in detecting differentially expressed genes. Results AffyMiner is a tool developed for detecting differentially expressed genes in Affymetrix GeneChip microarray data and for associating gene annotation and gene ontology information with the genes detected. AffyMiner consists of the functional modules, GeneFinder for detecting significant genes in a treatment versus control experiment and GOTree for mapping genes of interest onto the Gene Ontology (GO space; and interfaces to run Cluster, a program for clustering analysis, and GenMAPP, a program for pathway analysis. AffyMiner has been used for analyzing the GeneChip data and the results were presented in several publications. Conclusion AffyMiner fills an important gap in finding differentially expressed genes in Affymetrix GeneChip microarray data. AffyMiner effectively deals with multiple replicates in the experiment and takes into account both quantitative and qualitative data in identifying significant genes. AffyMiner reduces the time and effort needed to compare data from multiple arrays and to interpret the possible biological implications associated with significant changes in a gene's expression.

Study about the relevance and the disclosure of biological assets of listed companies in BM&FBOVESPA

Directory of Open Access Journals (Sweden)

Luciana Holtz

2013-08-01

Full Text Available The main objective this article is to verify that the information content of biological assets disclosed in the financial statements are relevant and, the secondary objective perform content analysis of the notes verifying the compliance of information supplied by entities with CPC 29. The study sample was composed of publicly traded stock companies listed on the BM & FBOVESPA with data for the year 2010 and 2011. The empirical tests were conducted applying relevance models, using observations of 347 active companies characterizing a study model pooled ordinary least squares – POLS, including companies that have reported biological assets into account specific .The companies that had values of biological assets posted have had analyzed explanatory notes referring to this account. The results provide empirical evidence that the information content of biological assets disclosed by companies is not relevant to the sample. In relation the content analysis of the notes was checked a partial compliance of the standard, there is a disparity in the information disclosure practices by the companies analyzed, as well as an omission of items required by the standard. Can be inferred that loss of the relevance has occurred, in part, by the poor quality of the notes, which may make it difficult for outside users in interpreting the information disclosed.

Rethinking the central dogma: noncoding RNAs are biologically relevant.

Science.gov (United States)

Robinson, Victoria L

2009-01-01

Non-coding RNAs (ncRNAs) are a large class of functional molecules with over 100 unique classes described to date. ncRNAs are diverse in terms of their function and size. A relatively new class of small ncRNA, called microRNAs (miRNA), have received a great deal of attention in the literature in recent years. miRNAs are endogenously encoded gene families that demonstrate striking evolutionary conservation. miRNAs serve essential and diverse physiological functions such as differentiation and development, proliferation, maintaining cell type phenotypes, and many others. The discovery and ongoing investigation of miRNAs is part of a revolution in biology that is changing the basic concepts of gene expression and RNA functionality. A single miRNA can participate in controlling the expression of up to several hundred protein-coding genes by interacting with mRNAs, generally in 3' untranslated regions. Our new and developing understanding of miRNAs, and other ncRNAs, promises to lead to significant contributions to medicine. Specifically, miRNAs are likely to serve as the basis for novel therapies and diagnostic tools.

Supplementing High-Density SNP Microarrays for Additional Coverage of Disease-Related Genes: Addiction as a Paradigm

Energy Technology Data Exchange (ETDEWEB)

SacconePhD, Scott F [Washington University, St. Louis; Chesler, Elissa J [ORNL; Bierut, Laura J [Washington University, St. Louis; Kalivas, Peter J [Medical College of South Carolina, Charleston; Lerman, Caryn [University of Pennsylvania; Saccone, Nancy L [Washington University, St. Louis; Uhl, George R [Johns Hopkins University; Li, Chuan-Yun [Peking University; Philip, Vivek M [ORNL; Edenberg, Howard [Indiana University; Sherry, Steven [National Center for Biotechnology Information; Feolo, Michael [National Center for Biotechnology Information; Moyzis, Robert K [Johns Hopkins University; Rutter, Joni L [National Institute of Drug Abuse

2009-01-01

Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.

Contextual Hub Analysis Tool (CHAT: A Cytoscape app for identifying contextually relevant hubs in biological networks [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

Tanja Muetze

2016-08-01

Full Text Available Highly connected nodes (hubs in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT, which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest.   Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store (http://apps.cytoscape.org/apps/chat.

Baltic salmon activates immune relevant genes in fin tissue when responding to Gyrodactylus salaris infection

DEFF Research Database (Denmark)

Kania, Per Walther; Larsen, Thomas Bjerre; Ingerslev, Hans C.

2007-01-01

A series of immune relevant genes are expressed when the Baltic salmon responds on infections with the ectoparasite Gyrodactylus salaris which leads to a decrease of the parasite infection......A series of immune relevant genes are expressed when the Baltic salmon responds on infections with the ectoparasite Gyrodactylus salaris which leads to a decrease of the parasite infection...

Using novel descriptor accounting for ligand-receptor interactions to define and visually explore biologically relevant chemical space.

Science.gov (United States)

Rabal, Obdulia; Oyarzabal, Julen

2012-05-25

The definition and pragmatic implementation of biologically relevant chemical space is critical in addressing navigation strategies in the overlapping regions where chemistry and therapeutically relevant targets reside and, therefore, also key to performing an efficient drug discovery project. Here, we describe the development and implementation of a simple and robust method for representing biologically relevant chemical space as a general reference according to current knowledge, independently of any reference space, and analyzing chemical structures accordingly. Underlying our method is the generation of a novel descriptor (LiRIf) that converts structural information into a one-dimensional string accounting for the plausible ligand-receptor interactions as well as for topological information. Capitalizing on ligand-receptor interactions as a descriptor enables the clustering, profiling, and comparison of libraries of compounds from a chemical biology and medicinal chemistry perspective. In addition, as a case study, R-groups analysis is performed to identify the most populated ligand-receptor interactions according to different target families (GPCR, kinases, etc.), as well as to evaluate the coverage of biologically relevant chemical space by structures annotated in different databases (ChEMBL, Glida, etc.).

Unlocking the treasure trove: from genes to schizophrenia biology.

Science.gov (United States)

McCarthy, Shane E; McCombie, W Richard; Corvin, Aiden

2014-05-01

Significant progress is being made in defining the genetic etiology of schizophrenia. As the list of implicated genes grows, parallel developments in gene editing technology provide new methods to investigate gene function in model systems. The confluence of these two research fields--gene discovery and functional biology--may offer novel insights into schizophrenia etiology. We review recent advances in these fields, consider the likely obstacles to progress, and consider strategies as to how these can be overcome.

The Integrin Receptor in Biologically Relevant Bilayers

DEFF Research Database (Denmark)

Kalli, Antreas C.; Róg, Tomasz; Vattulainen, Ilpo

2017-01-01

/talin complex was inserted in biologically relevant bilayers that resemble the cell plasma membrane containing zwitterionic and charged phospholipids, cholesterol and sphingolipids to study the dynamics of the integrin receptor and its effect on bilayer structure and dynamics. The results of this study...... demonstrate the dynamic nature of the integrin receptor and suggest that the presence of the integrin receptor alters the lipid organization between the two leaflets of the bilayer. In particular, our results suggest elevated density of cholesterol and of phosphatidylserine lipids around the integrin....../talin complex and a slowing down of lipids in an annulus of ~30 Å around the protein due to interactions between the lipids and the integrin/talin F2–F3 complex. This may in part regulate the interactions of integrins with other related proteins or integrin clustering thus facilitating signal transduction...

Molecular Imaging in Synthetic Biology, and Synthetic Biology in Molecular Imaging.

Science.gov (United States)

Gilad, Assaf A; Shapiro, Mikhail G

2017-06-01

Biomedical synthetic biology is an emerging field in which cells are engineered at the genetic level to carry out novel functions with relevance to biomedical and industrial applications. This approach promises new treatments, imaging tools, and diagnostics for diseases ranging from gastrointestinal inflammatory syndromes to cancer, diabetes, and neurodegeneration. As these cellular technologies undergo pre-clinical and clinical development, it is becoming essential to monitor their location and function in vivo, necessitating appropriate molecular imaging strategies, and therefore, we have created an interest group within the World Molecular Imaging Society focusing on synthetic biology and reporter gene technologies. Here, we highlight recent advances in biomedical synthetic biology, including bacterial therapy, immunotherapy, and regenerative medicine. We then discuss emerging molecular imaging approaches to facilitate in vivo applications, focusing on reporter genes for noninvasive modalities such as magnetic resonance, ultrasound, photoacoustic imaging, bioluminescence, and radionuclear imaging. Because reporter genes can be incorporated directly into engineered genetic circuits, they are particularly well suited to imaging synthetic biological constructs, and developing them provides opportunities for creative molecular and genetic engineering.

Analysis and visualization of gene expression data using ...

African Journals Online (AJOL)

Several clustering and biclustering methods have been introduced to analyze the gene expression data by identifying the similar patterns and grouping genes into subsets that share biological significance. However, it is not clear how the different methods compare with each other with respect to the biological relevance of ...

GeneLab: Open Science For Exploration

Science.gov (United States)

Galazka, Jonathan

2018-01-01

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. The GeneLab project houses spaceflight and spaceflight-relevant multi-omics data in a publicly accessible data commons, and collaborates with NASA-funded principal investigators to maximize the omics data from spaceflight and spaceflight-relevant experiments. I will discuss the current status of GeneLab and give specific examples of how the GeneLab data system has been used to gain insight into how biology responds to spaceflight conditions.

Physical interactions among plant MADS-box transcription factors and their biological relevance

NARCIS (Netherlands)

Nougalli Tonaco, I.A.

2008-01-01

The biological interpretation of the genome starts from transcription, and many different signaling pathways are integrated at this level. Transcription factors play a central role in the transcription process, because they select the down-stream genes and determine their spatial and temporal

Developmental Testing of Liquid and Gaseous/Vaporous Decontamination on Bacterial Spores and Other Biological Warfare Agents on Military Relevant Surfaces

Science.gov (United States)

2016-02-11

Vaporous Decontamination on Bacterial Spores and Other Biological Warfare Agents on Military-Relevant Surfaces 5a. CONTRACT NUMBER 5b. GRANT... DECONTAMINATION ON BACTERIAL SPORES AND OTHER BIOLOGICAL WARFARE AGENTS ON MILITARY-RELEVANT SURFACES Page Paragraph 1. SCOPE...surfaces before and after decontamination . The protocol in this TOP is based on the developed test methodologies from Edgewood Chemical Biological

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

Directory of Open Access Journals (Sweden)

Paules Richard S

2007-11-01

Full Text Available Abstract Background A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions. Results Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV or ionizing radiation (IR-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying

IGF-I Gene Therapy in Aging Rats Modulates Hippocampal Genes Relevant to Memory Function.

Science.gov (United States)

Pardo, Joaquín; Abba, Martin C; Lacunza, Ezequiel; Ogundele, Olalekan M; Paiva, Isabel; Morel, Gustavo R; Outeiro, Tiago F; Goya, Rodolfo G

2018-03-14

In rats, learning and memory performance decline during normal aging, which makes this rodent species a suitable model to evaluate therapeutic strategies. In aging rats, insulin-like growth factor-I (IGF-I), is known to significantly improve spatial memory accuracy as compared to control counterparts. A constellation of gene expression changes underlie the hippocampal phenotype of aging but no studies on the effects of IGF-I on the hippocampal transcriptome of old rodents have been documented. Here, we assessed the effects of IGF-I gene therapy on spatial memory performance in old female rats and compared them with changes in the hippocampal transcriptome. In the Barnes maze test, experimental rats showed a significantly higher exploratory frequency of the goal hole than controls. Hippocampal RNA-sequencing showed that 219 genes are differentially expressed in 28-month-old rats intracerebroventricularly injected with an adenovector expressing rat IGF-I as compared with placebo adenovector-injected counterparts. From the differentially expressed genes, 81 were down and 138 upregulated. From those genes, a list of functionally relevant genes, concerning hippocampal IGF-I expression, synaptic plasticity as well as neuronal function was identified. Our results provide an initial glimpse at the molecular mechanisms underlying the neuroprotective actions of IGF-I in the aging brain.

Beyond arousal and valence: the importance of the biological versus social relevance of emotional stimuli.

Science.gov (United States)

Sakaki, Michiko; Niki, Kazuhisa; Mather, Mara

2012-03-01

The present study addressed the hypothesis that emotional stimuli relevant to survival or reproduction (biologically emotional stimuli) automatically affect cognitive processing (e.g., attention, memory), while those relevant to social life (socially emotional stimuli) require elaborative processing to modulate attention and memory. Results of our behavioral studies showed that (1) biologically emotional images hold attention more strongly than do socially emotional images, (2) memory for biologically emotional images was enhanced even with limited cognitive resources, but (3) memory for socially emotional images was enhanced only when people had sufficient cognitive resources at encoding. Neither images' subjective arousal nor their valence modulated these patterns. A subsequent functional magnetic resonance imaging study revealed that biologically emotional images induced stronger activity in the visual cortex and greater functional connectivity between the amygdala and visual cortex than did socially emotional images. These results suggest that the interconnection between the amygdala and visual cortex supports enhanced attention allocation to biological stimuli. In contrast, socially emotional images evoked greater activity in the medial prefrontal cortex (MPFC) and yielded stronger functional connectivity between the amygdala and MPFC than did biological images. Thus, it appears that emotional processing of social stimuli involves elaborative processing requiring frontal lobe activity.

Examination of Signatures of Recent Positive Selection on Genes Involved in Human Sialic Acid Biology.

Science.gov (United States)

Moon, Jiyun M; Aronoff, David M; Capra, John A; Abbot, Patrick; Rokas, Antonis

2018-03-28

Sialic acids are nine carbon sugars ubiquitously found on the surfaces of vertebrate cells and are involved in various immune response-related processes. In humans, at least 58 genes spanning diverse functions, from biosynthesis and activation to recycling and degradation, are involved in sialic acid biology. Because of their role in immunity, sialic acid biology genes have been hypothesized to exhibit elevated rates of evolutionary change. Consistent with this hypothesis, several genes involved in sialic acid biology have experienced higher rates of non-synonymous substitutions in the human lineage than their counterparts in other great apes, perhaps in response to ancient pathogens that infected hominins millions of years ago (paleopathogens). To test whether sialic acid biology genes have also experienced more recent positive selection during the evolution of the modern human lineage, reflecting adaptation to contemporary cosmopolitan or geographically-restricted pathogens, we examined whether their protein-coding regions showed evidence of recent hard and soft selective sweeps. This examination involved the calculation of four measures that quantify changes in allele frequency spectra, extent of population differentiation, and haplotype homozygosity caused by recent hard and soft selective sweeps for 55 sialic acid biology genes using publicly available whole genome sequencing data from 1,668 humans from three ethnic groups. To disentangle evidence for selection from confounding demographic effects, we compared the observed patterns in sialic acid biology genes to simulated sequences of the same length under a model of neutral evolution that takes into account human demographic history. We found that the patterns of genetic variation of most sialic acid biology genes did not significantly deviate from neutral expectations and were not significantly different among genes belonging to different functional categories. Those few sialic acid biology genes that

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

Science.gov (United States)

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

Hyphae-specific genes HGC1, ALS3, HWP1, and ECE1 and relevant signaling pathways in Candida albicans.

Science.gov (United States)

Fan, Yan; He, Hong; Dong, Yan; Pan, Hengbiao

2013-12-01

Fungal virulence mechanisms include adhesion to epithelia, morphogenesis, production of secretory hydrolytic enzymes, and phenotype switching, all of which contribute to the process of pathogenesis. A striking feature of the biology of Candida albicans is its ability to grow in yeast, pseudohyphal, and hyphal forms. The hyphal form plays an important role in causing disease, by invading epithelial cells and causing tissue damage. In this review, we illustrate some of the main hyphae-specific genes, namely HGC1, UME6, ALS3, HWP1, and ECE1, and their relevant and reversed signal transduction pathways in reactions stimulated by environmental factors, including pH, CO2, and serum.

The Effect of the Human Peptide GHK on Gene Expression Relevant to Nervous System Function and Cognitive Decline

Directory of Open Access Journals (Sweden)

Loren Pickart

2017-02-01

Full Text Available Neurodegeneration, the progressive death of neurons, loss of brain function, and cognitive decline is an increasing problem for senior populations. Its causes are poorly understood and therapies are largely ineffective. Neurons, with high energy and oxygen requirements, are especially vulnerable to detrimental factors, including age-related dysregulation of biochemical pathways caused by altered expression of multiple genes. GHK (glycyl-l-histidyl-l-lysine is a human copper-binding peptide with biological actions that appear to counter aging-associated diseases and conditions. GHK, which declines with age, has health promoting effects on many tissues such as chondrocytes, liver cells and human fibroblasts, improves wound healing and tissue regeneration (skin, hair follicles, stomach and intestinal linings, boney tissue, increases collagen, decorin, angiogenesis, and nerve outgrowth, possesses anti-oxidant, anti-inflammatory, anti-pain and anti-anxiety effects, increases cellular stemness and the secretion of trophic factors by mesenchymal stem cells. Studies using the Broad Institute Connectivity Map show that GHK peptide modulates expression of multiple genes, resetting pathological gene expression patterns back to health. GHK has been recommended as a treatment for metastatic cancer, Chronic Obstructive Lung Disease, inflammation, acute lung injury, activating stem cells, pain, and anxiety. Here, we present GHK’s effects on gene expression relevant to the nervous system health and function.

Gene-environment interaction and biological monitoring of occupational exposures

International Nuclear Information System (INIS)

Hirvonen, Ari

2005-01-01

Biological monitoring methods and biological limit values applied in occupational and environmental medicine have been traditionally developed on the assumption that individuals do not differ significantly in their biotransformation capacities. It has become clear, however, that this is not the case, but wide inter-individual differences exist in the metabolism of chemicals. Integration of the data on individual metabolic capacity in biological monitoring studies is therefore anticipated to represent a significant refinement of the currently used methods. We have recently conducted several biological monitoring studies on occupationally exposed subjects, which have included the determination of the workers' genotypes for the metabolic genes of potential importance for a given chemical exposure. The exposure levels have been measured by urine metabolites, adducts in blood macromolecules, and cytogenetic alterations in lymphocytes. Our studies indicate that genetic polymorphisms in metabolic genes may indeed be important modifiers of individual biological monitoring results of, e.g., carbon disulphide and styrene. The information is anticipated to be useful in insuring that the workplace is safe for everyone, including the most sensitive individuals. This knowledge could also be useful to occupational physicians, industrial hygienists, and regulatory bodies in charge of defining acceptable exposure limits for environmental and/or occupational pollutants

Quantum selfish gene (biological evolution in terms of quantum mechanics)

OpenAIRE

Ozhigov, Yuri I.

2013-01-01

I propose to treat the biological evolution of genoms by means of quantum mechanical tools. We start with the concept of meta- gene, which specifies the "selfish gene" of R.Dawkins. Meta- gene encodes the abstract living unity, which can live relatively independently of the others, and can contain a few real creatures. Each population of living creatures we treat as the wave function on meta- genes, which module squared is the total number of creatures with the given meta-gene, and the phase ...

Arbitrary protein−protein docking targets biologically relevant interfaces

International Nuclear Information System (INIS)

Martin, Juliette; Lavery, Richard

2012-01-01

Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations. In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding

Arbitrary protein−protein docking targets biologically relevant interfaces

Directory of Open Access Journals (Sweden)

Martin Juliette

2012-05-01

Full Text Available Abstract Background Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations. Results In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking

Argudas: lessons for argumentation in biology based on a gene expression use case.

Science.gov (United States)

McLeod, Kenneth; Ferguson, Gus; Burger, Albert

2012-01-25

In situ hybridisation gene expression information helps biologists identify where a gene is expressed. However, the databases that republish the experimental information online are often both incomplete and inconsistent. Non-monotonic reasoning can help resolve such difficulties - one such form of reasoning is computational argumentation. Essentially this involves asking a computer to debate (i.e. reason about) the validity of a particular statement. Arguments are produced for both sides - the statement is true and, the statement is false - then the most powerful argument is used. In this work the computer is asked to debate whether or not a gene is expressed in a particular mouse anatomical structure. The information generated during the debate can be passed to the biological end-user, enabling their own decision-making process. This paper examines the evolution of a system, Argudas, which tests using computational argumentation in an in situ gene hybridisation gene expression use case. Argudas reasons using information extracted from several different online resources that publish gene expression information for the mouse. The development and evaluation of two prototypes is discussed. Throughout a number of issues shall be raised including the appropriateness of computational argumentation in biology and the challenges faced when integrating apparently similar online biological databases. From the work described in this paper it is clear that for argumentation to be effective in the biological domain the argumentation community need to develop further the tools and resources they provide. Additionally, the biological community must tackle the incongruity between overlapping and adjacent resources, thus facilitating the integration and modelling of biological information. Finally, this work highlights both the importance of, and difficulty in creating, a good model of the domain.

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

Science.gov (United States)

Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

2018-02-16

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

De novo characterization of the spleen transcriptome of the large yellow croaker (Pseudosciaena crocea) and analysis of the immune relevant genes and pathways involved in the antiviral response

KAUST Repository

Mu, Yinnan

2014-05-12

The large yellow croaker (Pseudosciaena crocea) is an economically important marine fish in China. To understand the molecular basis for antiviral defense in this species, we used Illumia paired-end sequencing to characterize the spleen transcriptome of polyriboinosinic:polyribocytidylic acid [poly(I:C)]-induced large yellow croakers. The library produced 56,355,728 reads and assembled into 108,237 contigs. As a result, 15,192 unigenes were found from this transcriptome. Gene ontology analysis showed that 4,759 genes were involved in three major functional categories: biological process, cellular component, and molecular function. We further ascertained that numerous consensus sequences were homologous to known immune-relevant genes. Kyoto Encyclopedia of Genes and Genomes orthology mapping annotated 5,389 unigenes and identified numerous immune-relevant pathways. These immune-relevant genes and pathways revealed major antiviral immunity effectors, including but not limited to: pattern recognition receptors, adaptors and signal transducers, the interferons and interferon-stimulated genes, inflammatory cytokines and receptors, complement components, and B-cell and T-cell antigen activation molecules. Moreover, the partial genes of Toll-like receptor signaling pathway, RIG-I-like receptors signaling pathway, Janus kinase-Signal Transducer and Activator of Transcription (JAK-STAT) signaling pathway, and T-cell receptor (TCR) signaling pathway were found to be changed after poly(I:C) induction by real-time polymerase chain reaction (PCR) analysis, suggesting that these signaling pathways may be regulated by poly(I:C), a viral mimic. Overall, the antivirus-related genes and signaling pathways that were identified in response to poly(I:C) challenge provide valuable leads for further investigation of the antiviral defense mechanism in the large yellow croaker. © 2014 Mu et al.

De novo characterization of the spleen transcriptome of the large yellow croaker (Pseudosciaena crocea and analysis of the immune relevant genes and pathways involved in the antiviral response.

Directory of Open Access Journals (Sweden)

Yinnan Mu

Full Text Available The large yellow croaker (Pseudosciaena crocea is an economically important marine fish in China. To understand the molecular basis for antiviral defense in this species, we used Illumia paired-end sequencing to characterize the spleen transcriptome of polyriboinosinic:polyribocytidylic acid [poly(I:C]-induced large yellow croakers. The library produced 56,355,728 reads and assembled into 108,237 contigs. As a result, 15,192 unigenes were found from this transcriptome. Gene ontology analysis showed that 4,759 genes were involved in three major functional categories: biological process, cellular component, and molecular function. We further ascertained that numerous consensus sequences were homologous to known immune-relevant genes. Kyoto Encyclopedia of Genes and Genomes orthology mapping annotated 5,389 unigenes and identified numerous immune-relevant pathways. These immune-relevant genes and pathways revealed major antiviral immunity effectors, including but not limited to: pattern recognition receptors, adaptors and signal transducers, the interferons and interferon-stimulated genes, inflammatory cytokines and receptors, complement components, and B-cell and T-cell antigen activation molecules. Moreover, the partial genes of Toll-like receptor signaling pathway, RIG-I-like receptors signaling pathway, Janus kinase-Signal Transducer and Activator of Transcription (JAK-STAT signaling pathway, and T-cell receptor (TCR signaling pathway were found to be changed after poly(I:C induction by real-time polymerase chain reaction (PCR analysis, suggesting that these signaling pathways may be regulated by poly(I:C, a viral mimic. Overall, the antivirus-related genes and signaling pathways that were identified in response to poly(I:C challenge provide valuable leads for further investigation of the antiviral defense mechanism in the large yellow croaker.

De novo characterization of the spleen transcriptome of the large yellow croaker (Pseudosciaena crocea) and analysis of the immune relevant genes and pathways involved in the antiviral response

KAUST Repository

Mu, Yinnan; Li, Mingyu; Ding, Feng; Ding, Yang; Ao, Jingqun; Hu, Songnian; Chen, Xinhua

2014-01-01

The large yellow croaker (Pseudosciaena crocea) is an economically important marine fish in China. To understand the molecular basis for antiviral defense in this species, we used Illumia paired-end sequencing to characterize the spleen transcriptome of polyriboinosinic:polyribocytidylic acid [poly(I:C)]-induced large yellow croakers. The library produced 56,355,728 reads and assembled into 108,237 contigs. As a result, 15,192 unigenes were found from this transcriptome. Gene ontology analysis showed that 4,759 genes were involved in three major functional categories: biological process, cellular component, and molecular function. We further ascertained that numerous consensus sequences were homologous to known immune-relevant genes. Kyoto Encyclopedia of Genes and Genomes orthology mapping annotated 5,389 unigenes and identified numerous immune-relevant pathways. These immune-relevant genes and pathways revealed major antiviral immunity effectors, including but not limited to: pattern recognition receptors, adaptors and signal transducers, the interferons and interferon-stimulated genes, inflammatory cytokines and receptors, complement components, and B-cell and T-cell antigen activation molecules. Moreover, the partial genes of Toll-like receptor signaling pathway, RIG-I-like receptors signaling pathway, Janus kinase-Signal Transducer and Activator of Transcription (JAK-STAT) signaling pathway, and T-cell receptor (TCR) signaling pathway were found to be changed after poly(I:C) induction by real-time polymerase chain reaction (PCR) analysis, suggesting that these signaling pathways may be regulated by poly(I:C), a viral mimic. Overall, the antivirus-related genes and signaling pathways that were identified in response to poly(I:C) challenge provide valuable leads for further investigation of the antiviral defense mechanism in the large yellow croaker. © 2014 Mu et al.

Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

Directory of Open Access Journals (Sweden)

Sapna Kumari

Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

Simulation and estimation of gene number in a biological pathway using almost complete saturation mutagenesis screening of haploid mouse cells.

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Tokunaga, Masahiro; Kokubu, Chikara; Maeda, Yusuke; Sese, Jun; Horie, Kyoji; Sugimoto, Nakaba; Kinoshita, Taroh; Yusa, Kosuke; Takeda, Junji

2014-11-24

Genome-wide saturation mutagenesis and subsequent phenotype-driven screening has been central to a comprehensive understanding of complex biological processes in classical model organisms such as flies, nematodes, and plants. The degree of "saturation" (i.e., the fraction of possible target genes identified) has been shown to be a critical parameter in determining all relevant genes involved in a biological function, without prior knowledge of their products. In mammalian model systems, however, the relatively large scale and labor intensity of experiments have hampered the achievement of actual saturation mutagenesis, especially for recessive traits that require biallelic mutations to manifest detectable phenotypes. By exploiting the recently established haploid mouse embryonic stem cells (ESCs), we present an implementation of almost complete saturation mutagenesis in a mammalian system. The haploid ESCs were mutagenized with the chemical mutagen N-ethyl-N-nitrosourea (ENU) and processed for the screening of mutants defective in various steps of the glycosylphosphatidylinositol-anchor biosynthetic pathway. The resulting 114 independent mutant clones were characterized by a functional complementation assay, and were shown to be defective in any of 20 genes among all 22 known genes essential for this well-characterized pathway. Ten mutants were further validated by whole-exome sequencing. The predominant generation of single-nucleotide substitutions by ENU resulted in a gene mutation rate proportional to the length of the coding sequence, which facilitated the experimental design of saturation mutagenesis screening with the aid of computational simulation. Our study enables mammalian saturation mutagenesis to become a realistic proposition. Computational simulation, combined with a pilot mutagenesis experiment, could serve as a tool for the estimation of the number of genes essential for biological processes such as drug target pathways when a positive selection of

Industrial systems biology and its impact on synthetic biology of yeast cell factories

DEFF Research Database (Denmark)

Fletcher, Eugene; Krivoruchko, Anastasia; Nielsen, Jens

2016-01-01

Engineering industrial cell factories to effectively yield a desired product while dealing with industrially relevant stresses is usually the most challenging step in the development of industrial production of chemicals using microbial fermentation processes. Using synthetic biology tools......, microbial cell factories such as Saccharomyces cerevisiae can be engineered to express synthetic pathways for the production of fuels, biopharmaceuticals, fragrances, and food flavors. However, directing fluxes through these synthetic pathways towards the desired product can be demanding due to complex...... regulation or poor gene expression. Systems biology, which applies computational tools and mathematical modeling to understand complex biological networks, can be used to guide synthetic biology design. Here, we present our perspective on how systems biology can impact synthetic biology towards the goal...

Building for Biology: A Gene Therapy Trial Infrastructure

Directory of Open Access Journals (Sweden)

Samuel Taylor-Alexander

2017-06-01

Full Text Available In this article, we examine the construction of the infrastructure for a Phase II gene therapy trial for Cystic Fibrosis (CF. Tracing the development of the material technologies and physical spaces used in the trial, we show how the trial infrastructure took form at the uncertain intersection of scientific norms, built environments, regulatory negotiations, patienthood, and the biologies of both disease and therapy. We define infrastructures as material and immaterial (including symbols and affect composites that serve a selective distributive purpose and facilitate projects of making and doing. There is a politics to this distributive action, which is itself twofold, because whilst infrastructures enable and delimit the movement of matter, they also mediate the very activity for which they provide the grounds. An infrastructural focus allows us to show how purposeful connections are made in a context of epistemic and regulatory uncertainty. The gene therapy researchers were working in a context of multiple uncertainties, regarding not only how to do gene therapy, but also how to anticipate and enact ambiguous regulatory requirements in a context of limited resources (technical, spatial, and financial. At the same time, the trial infrastructure had to accommodate Cystic Fibrosis biology by bridging the gap between pathology and therapy. The consortium’s approach to treating CF required that they address concerns about contamination and safety while finding a way of getting a modified gene product into the lungs of the trial participants.

Molecular Biology In Young Women With Breast Cancer: From Tumor Gene Expression To DNA Mutations.

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Gómez-Flores-Ramos, Liliana; Castro-Sánchez, Andrea; Peña-Curiel, Omar; Mohar-Betancourt, Alejandro

2017-01-01

Young women with breast cancer (YWBC) represent roughly 15% of breast cancer (BC) cases in Latin America and other developing regions. Breast tumors occurring at an early age are more aggressive and have an overall worse prognosis compared to breast tumors in postmenopausal women. The expression of relevant proliferation biomarkers such as endocrine receptors and human epidermal growth factor receptor 2 appears to be unique in YWBC. Moreover, histopathological, molecular, genetic, and genomic studies have shown that YWBC exhibit a higher frequency of aggressive subtypes, differential tumor gene expression, increased genetic susceptibility, and specific genomic signatures, compared to older women with BC. This article reviews the current knowledge on tumor biology and genomic signatures in YWBC.

Identifying clinically relevant drug resistance genes in drug-induced resistant cancer cell lines and post-chemotherapy tissues.

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Tong, Mengsha; Zheng, Weicheng; Lu, Xingrong; Ao, Lu; Li, Xiangyu; Guan, Qingzhou; Cai, Hao; Li, Mengyao; Yan, Haidan; Guo, You; Chi, Pan; Guo, Zheng

2015-12-01

Until recently, few molecular signatures of drug resistance identified in drug-induced resistant cancer cell models can be translated into clinical practice. Here, we defined differentially expressed genes (DEGs) between pre-chemotherapy colorectal cancer (CRC) tissue samples of non-responders and responders for 5-fluorouracil and oxaliplatin-based therapy as clinically relevant drug resistance genes (CRG5-FU/L-OHP). Taking CRG5-FU/L-OHP as reference, we evaluated the clinical relevance of several types of genes derived from HCT116 CRC cells with resistance to 5-fluorouracil and oxaliplatin, respectively. The results revealed that DEGs between parental and resistant cells, when both were treated with the corresponding drug for a certain time, were significantly consistent with the CRG5-FU/L-OHP as well as the DEGs between the post-chemotherapy CRC specimens of responders and non-responders. This study suggests a novel strategy to extract clinically relevant drug resistance genes from both drug-induced resistant cell models and post-chemotherapy cancer tissue specimens.

Networks in biological systems: An investigation of the Gene Ontology as an evolving network

International Nuclear Information System (INIS)

Coronnello, C; Tumminello, M; Micciche, S; Mantegna, R.N.

2009-01-01

Many biological systems can be described as networks where different elements interact, in order to perform biological processes. We introduce a network associated with the Gene Ontology. Specifically, we construct a correlation-based network where the vertices are the terms of the Gene Ontology and the link between each two terms is weighted on the basis of the number of genes that they have in common. We analyze a filtered network obtained from the correlation-based network and we characterize its evolution over different releases of the Gene Ontology.

CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources.

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Bleda, Marta; Tarraga, Joaquin; de Maria, Alejandro; Salavert, Francisco; Garcia-Alonso, Luz; Celma, Matilde; Martin, Ainoha; Dopazo, Joaquin; Medina, Ignacio

2012-07-01

During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.

On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

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Chen, Bor-Sen; Lin, Ying-Po

2011-01-01

In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective. PMID:22084563

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Young, M; Craft, D [Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States)

2016-06-15

cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.

GeneTopics - interpretation of gene sets via literature-driven topic models

Science.gov (United States)

2013-01-01

Background Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set. Methods Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research. Results We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner. Conclusions Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly

Angiosperm phylogeny inferred from multiple genes as a tool for comparative biology.

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Soltis, P S; Soltis, D E; Chase, M W

1999-11-25

Comparative biology requires a firm phylogenetic foundation to uncover and understand patterns of diversification and evaluate hypotheses of the processes responsible for these patterns. In the angiosperms, studies of diversification in floral form, stamen organization, reproductive biology, photosynthetic pathway, nitrogen-fixing symbioses and life histories have relied on either explicit or implied phylogenetic trees. Furthermore, to understand the evolution of specific genes and gene families, evaluate the extent of conservation of plant genomes and make proper sense of the huge volume of molecular genetic data available for model organisms such as Arabidopsis, Antirrhinum, maize, rice and wheat, a phylogenetic perspective is necessary. Here we report the results of parsimony analyses of DNA sequences of the plastid genes rbcL and atpB and the nuclear 18S rDNA for 560 species of angiosperms and seven non-flowering seed plants and show a well-resolved and well-supported phylogenetic tree for the angiosperms for use in comparative biology.

Biology, Bionomics and Molecular Biology of Anopheles sinensis Wiedemann 1828 (Diptera: Culicidae), Main Malaria Vector in China.

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Feng, Xinyu; Zhang, Shaosen; Huang, Fang; Zhang, Li; Feng, Jun; Xia, Zhigui; Zhou, Hejun; Hu, Wei; Zhou, Shuisen

2017-01-01

China has set a goal to eliminate all malaria in the country by 2020, but it is unclear if current understanding of malaria vectors and transmission is sufficient to achieve this objective. Anopheles sinensis is the most widespread malaria vector specie in China, which is also responsible for vivax malaria outbreak in central China. We reviewed literature from 1954 to 2016 on An. sinensis with emphasis on biology, bionomics, and molecular biology. A total of 538 references were relevant and included. An. sienesis occurs in 29 Chinese provinces. Temperature can affect most life-history parameters. Most An. sinensis are zoophilic, but sometimes they are facultatively anthropophilic. Sporozoite analysis demonstrated An. sinensis efficacy on Plasmodium vivax transmission. An. sinensis was not stringently refractory to P. falciparum under experimental conditions, however, sporozoite was not found in salivary glands of field collected An. sinensis . The literature on An. sienesis biology and bionomics was abundant, but molecular studies, such as gene functions and mechanisms, were limited. Only 12 molecules (genes, proteins or enzymes) have been studied. In addition, there were considerable untapped omics resources for potential vector control tools. Existing information on An. sienesis could serve as a baseline for advanced research on biology, bionomics and genetics relevant to vector control strategies.

Apoptosis Gene Information System--AGIS.

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Sakharkar, Kishore R; Clement, Marie V; Chow, Vincent T K; Pervaiz, Shazib

2006-05-01

Genes implicated in apoptosis have great relevance to biology, medicine and oncology. Here, we describe a unique resource, Apoptosis Gene Information System (AGIS) that provides data for over 2400 genes involved directly or indirectly, in apoptotic pathways of more than 350 different organisms. The organization of this information system is based on the principle of one-gene, one record. AGIS will be updated on a six monthly basis as new information becomes available. AGIS can be accessed at: http://www.cellfate.org/AGIS/.

Modelling low energy electron and positron tracks in biologically relevant media

International Nuclear Information System (INIS)

Blanco, F.; Munoz, A.; Almeida, D.; Ferreira da Silva, F.; Limao-Vieira, P.; Fuss, M.C.; Sanz, A.G.; Garcia, G.

2013-01-01

This colloquium describes an approach to incorporate into radiation damage models the effect of low and intermediate energy (0-100 eV) electrons and positrons, slowing down in biologically relevant materials (water and representative biomolecules). The core of the modelling procedure is a C++ computing programme named 'Low Energy Particle Track Simulation (LEPTS)', which is compatible with available general purpose Monte Carlo packages. Input parameters are carefully selected from theoretical and experimental cross section data and energy loss distribution functions. Data sources used for this purpose are reviewed showing examples of electron and positron cross section and energy loss data for interactions with different media of increasing complexity: atoms, molecules, clusters and condense matter. Finally, we show how such a model can be used to develop an effective dosimetric tool at the molecular level (i.e. nanodosimetry). Recent experimental developments to study the fragmentation induced in biologically material by charge transfer from neutrals and negative ions are also included. (authors)

Statistical assessment of crosstalk enrichment between gene groups in biological networks.

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McCormack, Theodore; Frings, Oliver; Alexeyenko, Andrey; Sonnhammer, Erik L L

2013-01-01

Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

The Importance of Biological Databases in Biological Discovery.

Science.gov (United States)

Baxevanis, Andreas D; Bateman, Alex

2015-06-19

Biological databases play a central role in bioinformatics. They offer scientists the opportunity to access a wide variety of biologically relevant data, including the genomic sequences of an increasingly broad range of organisms. This unit provides a brief overview of major sequence databases and portals, such as GenBank, the UCSC Genome Browser, and Ensembl. Model organism databases, including WormBase, The Arabidopsis Information Resource (TAIR), and those made available through the Mouse Genome Informatics (MGI) resource, are also covered. Non-sequence-centric databases, such as Online Mendelian Inheritance in Man (OMIM), the Protein Data Bank (PDB), MetaCyc, and the Kyoto Encyclopedia of Genes and Genomes (KEGG), are also discussed. Copyright © 2015 John Wiley & Sons, Inc.

Experimental and Modeling Approaches for Understanding the Effect of Gene Expression Noise in Biological Development

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David M. Holloway

2018-04-01

Full Text Available Biological development involves numerous chemical and physical processes which must act in concert to reliably produce a cell, a tissue, or a body. To be successful, the developing organism must be robust to variability at many levels, such as the environment (e.g., temperature, moisture, upstream information (such as long-range positional information gradients, or intrinsic noise due to the stochastic nature of low concentration chemical kinetics. The latter is especially relevant to the regulation of gene expression in cell differentiation. The temporal stochasticity of gene expression has been studied in single celled organisms for nearly two decades, but only recently have techniques become available to gather temporally-resolved data across spatially-distributed gene expression patterns in developing multicellular organisms. These demonstrate temporal noisy “bursting” in the number of gene transcripts per cell, raising the question of how the transcript number defining a particular cell type is produced, such that one cell type can reliably be distinguished from a neighboring cell of different type along a tissue boundary. Stochastic spatio-temporal modeling of tissue-wide expression patterns can identify signatures for specific types of gene regulation, which can be used to extract regulatory mechanism information from experimental time series. This Perspective focuses on using this type of approach to study gene expression noise during the anterior-posterior segmentation of the fruit fly embryo. Advances in experimental and theoretical techniques will lead to an increasing quantification of expression noise that can be used to understand how regulatory mechanisms contribute to embryonic robustness across a range of developmental processes.

Distinguishing Biologically Relevant Hexoses by Water Adduction to the Lithium-Cationized Molecule.

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Campbell, Matthew T; Chen, Dazhe; Wallbillich, Nicholas J; Glish, Gary L

2017-10-03

A method to distinguish the four most common biologically relevant underivatized hexoses, d-glucose, d-galactose, d-mannose, and d-fructose, using only mass spectrometry with no prior separation/derivatization step has been developed. Electrospray of a solution containing hexose and a lithium salt generates [Hexose+Li] + . The lithium-cationized hexoses adduct water in a quadrupole ion trap. The rate of this water adduction reaction can be used to distinguish the four hexoses. Additionally, for each hexose, multiple lithiation sites are possible, allowing for multiple structures of [Hexose+Li] + . Electrospray produces at least one structure that reacts with water and at least one that does not. The ratio of unreactive lithium-cationized hexose to total lithium-cationized hexose is unique for the four hexoses studied, providing a second method for distinguishing the isomers. Use of the water adduction reaction rate or the unreactive ratio provides two separate methods for confidently (p ≤ 0.02) distinguishing the most common biologically relevant hexoses using only femtomoles of hexose. Additionally, binary mixtures of glucose and fructose were studied. A calibration curve was created by measuring the reaction rate of various samples with different ratios of fructose and glucose. The calibration curve was used to accurately measure the percentage of fructose in three samples of high fructose corn syrup (<4% error).

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

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Drier, Yotam; Domany, Eytan

2011-03-14

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

Directory of Open Access Journals (Sweden)

Yotam Drier

2011-03-01

Full Text Available The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Biology relevant to space radiation

International Nuclear Information System (INIS)

Fry, R.J.M.

1996-01-01

The biological effects of the radiations to which mankind on earth are exposed are becoming known with an increasing degree of detail. This knowledge is the basis of the estimates of risk that, in turn, fosters a comprehensive and evolving radiation protection system. The substantial body of information has been, and is being, applied to questions about the biological effects of radiation is space and the associated risk estimates. The purpose of this paper is not to recount all the biological effect of radiation but to concentrate on those that may occur as a result from exposure to the radiations encountered in space. In general, the biological effects of radiation in space are the same as those on earth. However, the evidence that the effects on certain tissues by the heaviest-charged particles can be interpreted on the basis of our knowledge about other high-LET radiation is equivocal. This specific question will be discussed in greater detail later. It is important to point out the that there are only limited data about the effects on humans of two components of the radiations in space, namely protons and heavy ions. Thus predictions of effects on space crews are based on experimental systems exposed on earth at rates and fluences that are higher than those in space and one the effects of gamma or x rays with estimates of the equivalent doses using quality factors

Single cell biology beyond the era of antibodies: relevance, challenges, and promises in biomedical research.

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Abraham, Parvin; Maliekal, Tessy Thomas

2017-04-01

Research of the past two decades has proved the relevance of single cell biology in basic research and translational medicine. Successful detection and isolation of specific subsets is the key to understand their functional heterogeneity. Antibodies are conventionally used for this purpose, but their relevance in certain contexts is limited. In this review, we discuss some of these contexts, posing bottle neck for different fields of biology including biomedical research. With the advancement of chemistry, several methods have been introduced to overcome these problems. Even though microfluidics and microraft array are newer techniques exploited for single cell biology, fluorescence-activated cell sorting (FACS) remains the gold standard technique for isolation of cells for many biomedical applications, like stem cell therapy. Here, we present a comprehensive and comparative account of some of the probes that are useful in FACS. Further, we illustrate how these techniques could be applied in biomedical research. It is postulated that intracellular molecular markers like nucleostemin (GNL3), alkaline phosphatase (ALPL) and HIRA can be used for improving the outcome of cardiac as well as bone regeneration. Another field that could utilize intracellular markers is diagnostics, and we propose the use of specific peptide nucleic acid probes (PNPs) against certain miRNAs for cancer surgical margin prediction. The newer techniques for single cell biology, based on intracellular molecules, will immensely enhance the repertoire of possible markers for the isolation of cell types useful in biomedical research.

The integration of weighted gene association networks based on information entropy.

Science.gov (United States)

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

2017-01-01

Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.

Genomic instability of osteosarcoma cell lines in culture: impact on the prediction of metastasis relevant genes.

Directory of Open Access Journals (Sweden)

Roman Muff

Full Text Available Osteosarcoma is a rare but highly malignant cancer of the bone. As a consequence, the number of established cell lines used for experimental in vitro and in vivo osteosarcoma research is limited and the value of these cell lines relies on their stability during culture. Here we investigated the stability in gene expression by microarray analysis and array genomic hybridization of three low metastatic cell lines and derivatives thereof with increased metastatic potential using cells of different passages.The osteosarcoma cell lines showed altered gene expression during in vitro culture, and it was more pronounced in two metastatic cell lines compared to the respective parental cells. Chromosomal instability contributed in part to the altered gene expression in SAOS and LM5 cells with low and high metastatic potential. To identify metastasis-relevant genes in a background of passage-dependent altered gene expression, genes involved in "Pathways in cancer" that were consistently regulated under all passage comparisons were evaluated. Genes belonging to "Hedgehog signaling pathway" and "Wnt signaling pathway" were significantly up-regulated, and IHH, WNT10B and TCF7 were found up-regulated in all three metastatic compared to the parental cell lines.Considerable instability during culture in terms of gene expression and chromosomal aberrations was observed in osteosarcoma cell lines. The use of cells from different passages and a search for genes consistently regulated in early and late passages allows the analysis of metastasis-relevant genes despite the observed instability in gene expression in osteosarcoma cell lines during culture.

Genomic instability of osteosarcoma cell lines in culture: impact on the prediction of metastasis relevant genes.

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Muff, Roman; Rath, Prisni; Ram Kumar, Ram Mohan; Husmann, Knut; Born, Walter; Baudis, Michael; Fuchs, Bruno

2015-01-01

Osteosarcoma is a rare but highly malignant cancer of the bone. As a consequence, the number of established cell lines used for experimental in vitro and in vivo osteosarcoma research is limited and the value of these cell lines relies on their stability during culture. Here we investigated the stability in gene expression by microarray analysis and array genomic hybridization of three low metastatic cell lines and derivatives thereof with increased metastatic potential using cells of different passages. The osteosarcoma cell lines showed altered gene expression during in vitro culture, and it was more pronounced in two metastatic cell lines compared to the respective parental cells. Chromosomal instability contributed in part to the altered gene expression in SAOS and LM5 cells with low and high metastatic potential. To identify metastasis-relevant genes in a background of passage-dependent altered gene expression, genes involved in "Pathways in cancer" that were consistently regulated under all passage comparisons were evaluated. Genes belonging to "Hedgehog signaling pathway" and "Wnt signaling pathway" were significantly up-regulated, and IHH, WNT10B and TCF7 were found up-regulated in all three metastatic compared to the parental cell lines. Considerable instability during culture in terms of gene expression and chromosomal aberrations was observed in osteosarcoma cell lines. The use of cells from different passages and a search for genes consistently regulated in early and late passages allows the analysis of metastasis-relevant genes despite the observed instability in gene expression in osteosarcoma cell lines during culture.

Molecular biology - Part I: Techniques, terminology, and concepts

International Nuclear Information System (INIS)

Brown, J. Martin

1996-01-01

Purpose/Objective: One of the barriers to understanding modern molecular biology is the lack of a clear understanding of the relevant terminology, techniques, and concepts. This refresher course is intended to address these deficiencies starting from a basic level. The lecture will cover many of the common uses of recombinant DNA, including gene cloning and manipulation. The goal is to enable the nonspecialist to increase his or her understanding of molecular biology in order to more fully enjoy reading current publications and/or listening seminars. Radiation biologists trying to understand a little more molecular biology should also benefit. The following concepts will be among those explained and illustrated: restriction endonucleases, gel electrophoresis, gene cloning, use of vectors such as plasmids, bacteriophage, cosmids and viruses, cDNA and genomic libraries, Southern, Northern, and Western blotting, fluorescent in situ hybridization, polymerase chain reaction (PCR), gel retardation, and reporter gene assays

StrateGene: object-oriented programming in molecular biology.

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Carhart, R E; Cash, H D; Moore, J F

1988-03-01

This paper describes some of the ways that object-oriented programming methodologies have been used to represent and manipulate biological information in a working application. When running on a Xerox 1100 series computer, StrateGene functions as a genetic engineering workstation for the management of information about cloning experiments. It represents biological molecules, enzymes, fragments, and methods as classes, subclasses, and members in a hierarchy of objects. These objects may have various attributes, which themselves can be defined and classified. The attributes and their values can be passed from the classes of objects down to the subclasses and members. The user can modify the objects and their attributes while using them. New knowledge and changes to the system can be incorporated relatively easily. The operations on the biological objects are associated with the objects themselves. This makes it easier to invoke them correctly and allows generic operations to be customized for the particular object.

Industrial systems biology and its impact on synthetic biology of yeast cell factories.

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Fletcher, Eugene; Krivoruchko, Anastasia; Nielsen, Jens

2016-06-01

Engineering industrial cell factories to effectively yield a desired product while dealing with industrially relevant stresses is usually the most challenging step in the development of industrial production of chemicals using microbial fermentation processes. Using synthetic biology tools, microbial cell factories such as Saccharomyces cerevisiae can be engineered to express synthetic pathways for the production of fuels, biopharmaceuticals, fragrances, and food flavors. However, directing fluxes through these synthetic pathways towards the desired product can be demanding due to complex regulation or poor gene expression. Systems biology, which applies computational tools and mathematical modeling to understand complex biological networks, can be used to guide synthetic biology design. Here, we present our perspective on how systems biology can impact synthetic biology towards the goal of developing improved yeast cell factories. Biotechnol. Bioeng. 2016;113: 1164-1170. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Abundances of Clinically Relevant Antibiotic Resistance Genes and Bacterial Community Diversity in the Weihe River, China

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Xiaojuan Wang

2018-04-01

Full Text Available The spread of antibiotic resistance genes in river systems is an emerging environmental issue due to their potential threat to aquatic ecosystems and public health. In this study, we used droplet digital polymerase chain reaction (ddPCR to evaluate pollution with clinically relevant antibiotic resistance genes (ARGs at 13 monitoring sites along the main stream of the Weihe River in China. Six clinically relevant ARGs and a class I integron-integrase (intI1 gene were analyzed using ddPCR, and the bacterial community was evaluated based on the bacterial 16S rRNA V3–V4 regions using MiSeq sequencing. The results indicated Proteobacteria, Actinobacteria, Cyanobacteria, and Bacteroidetes as the dominant phyla in the water samples from the Weihe River. Higher abundances of blaTEM, strB, aadA, and intI1 genes (103 to 105 copies/mL were detected in the surface water samples compared with the relatively low abundances of strA, mecA, and vanA genes (0–1.94 copies/mL. Eight bacterial genera were identified as possible hosts of the intI1 gene and three ARGs (strA, strB, and aadA based on network analysis. The results suggested that the bacterial community structure and horizontal gene transfer were associated with the variations in ARGs.

Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data.

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Tsou, Ann-Ping; Sun, Yi-Ming; Liu, Chia-Lin; Huang, Hsien-Da; Horng, Jorng-Tzong; Tsai, Meng-Feng; Liu, Baw-Juine

2006-07-01

Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.

Bacteriophage lambda: early pioneer and still relevant

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Casjens, Sherwood R.; Hendrix, Roger W.

2015-01-01

Molecular genetic research on bacteriophage lambda carried out during its golden age from the mid 1950's to mid 1980's was critically important in the attainment of our current understanding of the sophisticated and complex mechanisms by which the expression of genes is controlled, of DNA virus assembly and of the molecular nature of lysogeny. The development of molecular cloning techniques, ironically instigated largely by phage lambda researchers, allowed many phage workers to switch their efforts to other biological systems. Nonetheless, since that time the ongoing study of lambda and its relatives have continued to give important new insights. In this review we give some relevant early history and describe recent developments in understanding the molecular biology of lambda's life cycle. PMID:25742714

Physical, chemical, and biological properties of radiocerium relevant to radiation protection guidelines

International Nuclear Information System (INIS)

Anon.

1978-01-01

Present knowledge of the relevant physical, chemical, and biological properties of radiocerium as a basis for establishing radiation protection guidelines is summarized. The first section of the report reviews the chemical and physical properties of radiocerium relative to the biological behavior of internally-deposited cerium and other lanthanides. The second section of the report gives the sources of radiocerium in the environment and the pathways to man. The third section of the report describes the metabolic fate of cerium in several mammalian species as a basis for predicting its metabolic fate in man. The fourth section of the report considers the biomedical effects of radiocerium in light of extensive animal experimentation. The last two sections of the report describe the history of radiation protection guidelines for radiocerium and summarize data required for evaluating the adequacy of current radiation protection guidelines. Each section begins with a summary of the most important findings that follow

Making developmental biology relevant to undergraduates in an era of economic rationalism in Australia.

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Key, Brian; Nurcombe, Victor

2003-01-01

This report describes the road map we followed at our university to accommodate three main factors: financial pressure within the university system; desire to enhance the learning experience of undergraduates; and motivation to increase the prominence of the discipline of developmental biology in our university. We engineered a novel, multi-year undergraduate developmental biology program which was "student-oriented," ensuring that students were continually exposed to the underlying principles and philosophy of this discipline throughout their undergraduate career. Among its key features are introductory lectures in core courses in the first year, which emphasize the relevance of developmental biology to tissue engineering, reproductive medicine, therapeutic approaches in medicine, agriculture and aquaculture. State-of-the-art animated computer graphics and images of high visual impact are also used. In addition, students are streamed into the developmental biology track in the second year, using courses like human embryology and courses shared with cell biology, which include practicals based on modern experimental approaches. Finally, fully dedicated third-year courses in developmental biology are undertaken in conjunction with stand-alone practical courses where students experiencefirst-hand work in a research laboratory. Our philosophy is a "cradle-to-grave" approach to the education of undergraduates so as to prepare highly motivated, enthusiastic and well-educated developmental biologists for entry into graduate programs and ultimately post-doctoral research.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

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Lemke Ney

2009-09-01

Full Text Available Abstract Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing

Mining gene expression data by interpreting principal components

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Mortazavi Ali

2006-04-01

Full Text Available Abstract Background There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis. Results We present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset. We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.. Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation. Conclusion We provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It

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.

The asymmetric hetero-Diels-Alder reaction in the syntheses of biologically relevant compounds.

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Eschenbrenner-Lux, Vincent; Kumar, Kamal; Waldmann, Herbert

2014-10-13

The hetero-Diels-Alder reaction is one of the most powerful transformations in the chemistry toolbox for the synthesis of aza- and oxa-heterocycles embodying multiple stereogenic centers. However, as compared to other cycloadditions, in particular the dipolar cycloadditions and the Diels-Alder reaction, the hetero-Diels-Alder reaction has been much less explored and exploited in organic synthesis. Nevertheless, this powerful transformation has opened up efficient and creative routes to biologically relevant small molecules and different natural products which contain six-membered oxygen or nitrogen ring systems. Recent developments in this field, in particular in the establishment of enantioselectively catalyzed hetero-Diels-Alder cycloadditions steered by a plethora of different catalysts and the application of the resulting small molecules in chemical biology and medicinal chemistry research, are highlighted in this Minireview. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations

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Parmigiani Giovanni

2009-08-01

Full Text Available Abstract Background A major challenge in computational biology is to extract knowledge about the genetic nature of disease from high-throughput data. However, an important obstacle to both biological understanding and clinical applications is the "black box" nature of the decision rules provided by most machine learning approaches, which usually involve many genes combined in a highly complex fashion. Achieving biologically relevant results argues for a different strategy. A promising alternative is to base prediction entirely upon the relative expression ordering of a small number of genes. Results We present a three-gene version of "relative expression analysis" (RXA, a rigorous and systematic comparison with earlier approaches in a variety of cancer studies, a clinically relevant application to predicting germline BRCA1 mutations in breast cancer and a cross-study validation for predicting ER status. In the BRCA1 study, RXA yields high accuracy with a simple decision rule: in tumors carrying mutations, the expression of a "reference gene" falls between the expression of two differentially expressed genes, PPP1CB and RNF14. An analysis of the protein-protein interactions among the triplet of genes and BRCA1 suggests that the classifier has a biological foundation. Conclusion RXA has the potential to identify genomic "marker interactions" with plausible biological interpretation and direct clinical applicability. It provides a general framework for understanding the roles of the genes involved in decision rules, as illustrated for the difficult and clinically relevant problem of identifying BRCA1 mutation carriers.

Gene coexpression network analysis as a source of functional annotation for rice genes.

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Kevin L Childs

Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

Review paper Personality and genes: remarks from a biological perspective

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Grzegorz Węgrzyn

2014-10-01

Full Text Available Although there is no doubt that genes’ functions influence human personality, years of studies provided no clear picture on regulation of particular traits by specific genes. In this article, an overview of the complexity of the system of genetic control of personality is presented, and the level of complications of biological processes operating in this system is underlined. The methodology of studies devoted to determine effects of genes on personality traits is discussed, and limitations of various methods in such studies are indicated. Finally, suggestions for further research are listed and commented on. It is likely that to increase the level of our understanding of genetic mechanisms that modulate human personality, researchers conducting further studies will have to focus on using large sample sizes, performing independent replications, considering experiments on animal models, integrating cross-cultural data and epigenetic measures, and performing interdisciplinary experiments which combine methods of various disciplines, such as biology and psychology.

'Fish matters': the relevance of fish skin biology to investigative dermatology.

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Rakers, Sebastian; Gebert, Marina; Uppalapati, Sai; Meyer, Wilfried; Maderson, Paul; Sell, Anne F; Kruse, Charli; Paus, Ralf

2010-04-01

Fish skin is a multi-purpose tissue that serves numerous vital functions including chemical and physical protection, sensory activity, behavioural purposes or hormone metabolism. Further, it is an important first-line defense system against pathogens, as fish are continuously exposed to multiple microbial challenges in their aquatic habitat. Fish skin excels in highly developed antimicrobial features, many of which have been preserved throughout evolution, and infection defense principles employed by piscine skin are still operative in human skin. This review argues that it is both rewarding and important for investigative dermatologists to revive their interest in fish skin biology, as it provides insights into numerous fundamental issues that are of major relevance to mammalian skin. The basic molecular insights provided by zebrafish in vivo-genomics for genetic, regeneration and melanoma research, the complex antimicrobial defense systems of fish skin and the molecular controls of melanocyte stem cells are just some of the fascinating examples that illustrate the multiple potential uses of fish skin models in investigative dermatology. We synthesize the essentials of fish skin biology and highlight selected aspects that are of particular comparative interest to basic and clinically applied human skin research.

Occurrence of the mcr-1 Colistin Resistance Gene and other Clinically Relevant Antibiotic Resistance Genes in Microbial Populations at Different Municipal Wastewater Treatment Plants in Germany

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Norman Hembach

2017-07-01

Full Text Available Seven wastewater treatment plants (WWTPs with different population equivalents and catchment areas were screened for the prevalence of the colistin resistance gene mcr-1 mediating resistance against last resort antibiotic polymyxin E. The abundance of the plasmid-associated mcr-1 gene in total microbial populations during water treatment processes was quantitatively analyzed by qPCR analyses. The presence of the colistin resistance gene was documented for all of the influent wastewater samples of the seven WWTPs. In some cases the mcr-1 resistance gene was also detected in effluent samples of the WWTPs after conventional treatment reaching the aquatic environment. In addition to the occurrence of mcr-1 gene, CTX-M-32, blaTEM, CTX-M, tetM, CMY-2, and ermB genes coding for clinically relevant antibiotic resistances were quantified in higher abundances in all WWTPs effluents. In parallel, the abundances of Acinetobacter baumannii, Klebsiella pneumoniae, and Escherichia coli were quantified via qPCR using specific taxonomic gene markers which were detected in all influent and effluent wastewaters in significant densities. Hence, opportunistic pathogens and clinically relevant antibiotic resistance genes in wastewaters of the analyzed WWTPs bear a risk of dissemination to the aquatic environment. Since many of the antibiotic resistance gene are associated with mobile genetic elements horizontal gene transfer during wastewater treatment can't be excluded.

Improvements in algal lipid production: a systems biology and gene editing approach.

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Banerjee, Avik; Banerjee, Chiranjib; Negi, Sangeeta; Chang, Jo-Shu; Shukla, Pratyoosh

2018-05-01

In the wake of rising energy demands, microalgae have emerged as potential sources of sustainable and renewable carbon-neutral fuels, such as bio-hydrogen and bio-oil. For rational metabolic engineering, the elucidation of metabolic pathways in fine detail and their manipulation according to requirements is the key to exploiting the use of microalgae. Emergence of site-specific nucleases have revolutionized applied research leading to biotechnological gains. Genome engineering as well as modulation of the endogenous genome with high precision using CRISPR systems is being gradually employed in microalgal research. Further, to optimize and produce better algal platforms, use of systems biology network analysis and integration of omics data is required. This review discusses two important approaches: systems biology and gene editing strategies used on microalgal systems with a focus on biofuel production and sustainable solutions. It also emphasizes that the integration of such systems would contribute and compliment applied research on microalgae. Recent advances in microalgae are discussed, including systems biology, gene editing approaches in lipid bio-synthesis, and antenna engineering. Lastly, it has been attempted here to showcase how CRISPR/Cas systems are a better editing tool than existing techniques that can be utilized for gene modulation and engineering during biofuel production.

Path from schizophrenia genomics to biology: gene regulation and perturbation in neurons derived from induced pluripotent stem cells and genome editing.

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Duan, Jubao

2015-02-01

Schizophrenia (SZ) is a devastating mental disorder afflicting 1% of the population. Recent genome-wide association studies (GWASs) of SZ have identified >100 risk loci. However, the causal variants/genes and the causal mechanisms remain largely unknown, which hinders the translation of GWAS findings into disease biology and drug targets. Most risk variants are noncoding, thus likely regulate gene expression. A major mechanism of transcriptional regulation is chromatin remodeling, and open chromatin is a versatile predictor of regulatory sequences. MicroRNA-mediated post-transcriptional regulation plays an important role in SZ pathogenesis. Neurons differentiated from patient-specific induced pluripotent stem cells (iPSCs) provide an experimental model to characterize the genetic perturbation of regulatory variants that are often specific to cell type and/or developmental stage. The emerging genome-editing technology enables the creation of isogenic iPSCs and neurons to efficiently characterize the effects of SZ-associated regulatory variants on SZ-relevant molecular and cellular phenotypes involving dopaminergic, glutamatergic, and GABAergic neurotransmissions. SZ GWAS findings equipped with the emerging functional genomics approaches provide an unprecedented opportunity for understanding new disease biology and identifying novel drug targets.

Gene prioritization for livestock diseases by data integration

DEFF Research Database (Denmark)

Jiang, Li; Sørensen, Peter; Thomsen, Bo Stjerne

2012-01-01

in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to Escherichia coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes...... underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis...

Biological effects of environmentally relevant concentrations of the pharmaceutical Triclosan in the marine mussel Perna perna (Linnaeus, 1758)

Energy Technology Data Exchange (ETDEWEB)

Sanzi Cortez, Fernando, E-mail: lecotox@unisanta.br [Instituto de Pesquisas Energeticas e Nucleares IPEN-CNEN/SP, 05508-000 Sao Paulo, SP (Brazil); Laboratorio de Ecotoxicologia, Universidade Santa Cecilia, 11045-907 Santos, SP (Brazil); Dias Seabra Pereira, Camilo [Laboratorio de Ecotoxicologia, Universidade Santa Cecilia, 11045-907 Santos, SP (Brazil); Instituto do Mar, Universidade Federal de Sao Paulo, 11030-400 Santos, SP (Brazil); Ramos Santos, Aldo Ramos [Laboratorio de Ecotoxicologia, Universidade Santa Cecilia, 11045-907 Santos, SP (Brazil); Cesar, Augusto; Choueri, Rodrigo Brasil [Laboratorio de Ecotoxicologia, Universidade Santa Cecilia, 11045-907 Santos, SP (Brazil); Instituto do Mar, Universidade Federal de Sao Paulo, 11030-400 Santos, SP (Brazil); Martini, Gisela de Assis [Laboratorio de Ecotoxicologia, Universidade Santa Cecilia, 11045-907 Santos, SP (Brazil); Bohrer-Morel, Maria Beatriz [Instituto de Pesquisas Energeticas e Nucleares IPEN-CNEN/SP, 05508-000 Sao Paulo, SP (Brazil)

2012-09-15

Triclosan (5-Chloro-2-(2,4-dichlorophenoxy) phenol) is an antibacterial compound widely employed in pharmaceuticals and personal care products. Although this emerging compound has been detected in aquatic environments, scarce information is found on the effects of Triclosan to marine organisms. The aim of this study was to evaluate the toxicity of a concentration range of Triclosan through fertilization assay (reproductive success), embryo-larval development assay (early life stage) and physiological stress (Neutral Red Retention Time assay - NRRT) (adult stage) in the marine sentinel organism Perna perna. The mean inhibition concentrations for fertilization (IC{sub 50} = 0.490 mg L{sup -1}) and embryo-larval development (IC{sub 50} = 0.135 mg L{sup -1}) tests were above environmental relevant concentrations (ng L{sup -1}) given by previous studies. Differently, significant reduction on NRRT results was found at 12 ng L{sup -1}, demonstrating the current risk of the continuous introduction of Triclosan into aquatic environments, and the need of ecotoxicological studies oriented by the mechanism of action of the compound. - Highlights: Black-Right-Pointing-Pointer Triclosan causes biological adverse effects at environmental relevant concentrations. Black-Right-Pointing-Pointer Mechanisms of action oriented assays were more sensitive to detect biological damages. Black-Right-Pointing-Pointer Currently there is environmental risks concerned Triclosan in aquatic ecosystems. - Triclosan causes biological adverse effects at environmentally relevant concentrations.

Biological effects of environmentally relevant concentrations of the pharmaceutical Triclosan in the marine mussel Perna perna (Linnaeus, 1758)

International Nuclear Information System (INIS)

Sanzi Cortez, Fernando; Dias Seabra Pereira, Camilo; Ramos Santos, Aldo Ramos; Cesar, Augusto; Choueri, Rodrigo Brasil; Martini, Gisela de Assis; Bohrer-Morel, Maria Beatriz

2012-01-01

Triclosan (5-Chloro-2-(2,4-dichlorophenoxy) phenol) is an antibacterial compound widely employed in pharmaceuticals and personal care products. Although this emerging compound has been detected in aquatic environments, scarce information is found on the effects of Triclosan to marine organisms. The aim of this study was to evaluate the toxicity of a concentration range of Triclosan through fertilization assay (reproductive success), embryo-larval development assay (early life stage) and physiological stress (Neutral Red Retention Time assay - NRRT) (adult stage) in the marine sentinel organism Perna perna. The mean inhibition concentrations for fertilization (IC 50 = 0.490 mg L −1 ) and embryo-larval development (IC 50 = 0.135 mg L −1 ) tests were above environmental relevant concentrations (ng L −1 ) given by previous studies. Differently, significant reduction on NRRT results was found at 12 ng L −1 , demonstrating the current risk of the continuous introduction of Triclosan into aquatic environments, and the need of ecotoxicological studies oriented by the mechanism of action of the compound. - Highlights: ► Triclosan causes biological adverse effects at environmental relevant concentrations. ► Mechanisms of action oriented assays were more sensitive to detect biological damages. ► Currently there is environmental risks concerned Triclosan in aquatic ecosystems. - Triclosan causes biological adverse effects at environmentally relevant concentrations.

Reconstruction of biological networks based on life science data integration.

Science.gov (United States)

Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf

2010-10-27

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

Clinical Relevance of HLA Gene Variants in HBV Infection

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Li Wang

2016-01-01

Full Text Available Host gene variants may influence the natural history of hepatitis B virus (HBV infection. The human leukocyte antigen (HLA system, the major histocompatibility complex (MHC in humans, is one of the most important host factors that are correlated with the clinical course of HBV infection. Genome-wide association studies (GWASs have shown that single nucleotide polymorphisms (SNPs near certain HLA gene loci are strongly associated with not only persistent HBV infection but also spontaneous HBV clearance and seroconversion, disease progression, and the development of liver cirrhosis and HBV-related hepatocellular carcinoma (HCC in chronic hepatitis B (CHB. These variations also influence the efficacy of interferon (IFN and nucleot(side analogue (NA treatment and response to HBV vaccines. Meanwhile, discrepant conclusions were reached with different patient cohorts. It is therefore essential to identify the associations of specific HLA allele variants with disease progression and viral clearance in chronic HBV infection among different ethnic populations. A better understanding of HLA polymorphism relevance in HBV infection outcome would enable us to elucidate the roles of HLA SNPs in the pathogenesis and clearance of HBV in different areas and ethnic groups, to improve strategies for the prevention and treatment of chronic HBV infection.

Building gene co-expression networks using transcriptomics data for systems biology investigations

DEFF Research Database (Denmark)

Kadarmideen, Haja; Watson-Haigh, Nathan S.

2012-01-01

Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four......) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT...... (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended....

Hypersensitivities for Acetaldehyde and Other Agents among Cancer Cells Null for Clinically Relevant Fanconi Anemia Genes

OpenAIRE

Ghosh, Soma; Sur, Surojit; Yerram, Sashidhar R.; Rago, Carlo; Bhunia, Anil K.; Hossain, M. Zulfiquer; Paun, Bogdan C.; Ren, Yunzhao R.; Iacobuzio-Donahue, Christine A.; Azad, Nilofer A.; Kern, Scott E.

2014-01-01

Large-magnitude numerical distinctions (>10-fold) among drug responses of genetically contrasting cancers were crucial for guiding the development of some targeted therapies. Similar strategies brought epidemiological clues and prevention goals for genetic diseases. Such numerical guides, however, were incomplete or low magnitude for Fanconi anemia pathway (FANC) gene mutations relevant to cancer in FANC-mutation carriers (heterozygotes). We generated a four-gene FANC-null cancer panel, inclu...

SWIM: a computational tool to unveiling crucial nodes in complex biological networks.

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Paci, Paola; Colombo, Teresa; Fiscon, Giulia; Gurtner, Aymone; Pavesi, Giulio; Farina, Lorenzo

2017-03-20

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer.

Modular design of synthetic gene circuits with biological parts and pools.

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Marchisio, Mario Andrea

2015-01-01

Synthetic gene circuits can be designed in an electronic fashion by displaying their basic components-Standard Biological Parts and Pools of molecules-on the computer screen and connecting them with hypothetical wires. This procedure, achieved by our add-on for the software ProMoT, was successfully applied to bacterial circuits. Recently, we have extended this design-methodology to eukaryotic cells. Here, highly complex components such as promoters and Pools of mRNA contain hundreds of species and reactions whose calculation demands a rule-based modeling approach. We showed how to build such complex modules via the joint employment of the software BioNetGen (rule-based modeling) and ProMoT (modularization). In this chapter, we illustrate how to utilize our computational tool for synthetic biology with the in silico implementation of a simple eukaryotic gene circuit that performs the logic AND operation.

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

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Tran, P. B.; Berrios, D. C.; Gurram, M. M.; Hashim, J. C. M.; Raghunandan, S.; Lin, S. Y.; Le, T. Q.; Heher, D. M.; Thai, H. T.; Welch, J. D.;

Platform dependence of inference on gene-wise and gene-set involvement in human lung development

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Kho Alvin T

2009-06-01

Full Text Available Abstract Background With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age. Results We performed the comparison via sequence-based probe matching between the two platforms. "Significance grouping" was defined as a measure of comparability. Using both expression correlation and significance grouping as measures of comparability, we demonstrated that despite overall cross-platform differences at the single gene level, increased correlation between the two platforms was found in genes with higher expression level, higher probe overlap, and lower p-value. We also demonstrated that biological function as determined via KEGG pathways or GO categories is more consistent across platforms than single gene analysis. Conclusion We conclude that while the comparability of the platforms at the single gene level may be increased by increasing sample size, they are highly comparable ontologically even for subtle differences in a relatively small sample size. Biologically relevant inference should therefore be reproducible across laboratories using different platforms.

A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

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Xin Lai

2013-01-01

Full Text Available MicroRNAs (miRNAs are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.

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Mingguang Shi

Full Text Available Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes.We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002. Similar results were obtained with reversed training and testing datasets (p = 0.007. Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006, but not beneficial to the low-risk patients (p = 0.491.The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network

Reconstruction of biological networks based on life science data integration

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Kormeier Benjamin

2010-06-01

Full Text Available For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

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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.

Finding gene regulatory network candidates using the gene expression knowledge base.

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Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression

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Calvo-Dmgz D.

2012-12-01

Full Text Available DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS. The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

Cross-species comparison of biological themes and underlying genes on a global gene expression scale in a mouse model of colorectal liver metastasis and in clinical specimens

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Schirmacher Peter

2008-09-01

Full Text Available Abstract Background Invasion-related genes over-expressed by tumor cells as well as by reacting host cells represent promising drug targets for anti-cancer therapy. Such candidate genes need to be validated in appropriate animal models. Results This study examined the suitability of a murine model (CT26/Balb/C of colorectal liver metastasis to represent clinical liver metastasis specimens using a global gene expression approach. Cross-species similarity was examined between pure liver, liver invasion, tumor invasion and pure tumor compartments through overlap of up-regulated genes and gene ontology (GO-based biological themes on the level of single GO-terms and of condensed GO-term families. Three out of four GO-term families were conserved in a compartment-specific way between the species: secondary metabolism (liver, invasion (invasion front, and immune response (invasion front and liver. Among the individual GO-terms over-represented in the invasion compartments in both species were "extracellular matrix", "cell motility", "cell adhesion" and "antigen presentation" indicating that typical invasion related processes are operating in both species. This was reflected on the single gene level as well, as cross-species overlap of potential target genes over-expressed in the combined invasion front compartments reached up to 36.5%. Generally, histopathology and gene expression correlated well as the highest single gene overlap was found to be 44% in syn-compartmental comparisons (liver versus liver whereas cross-compartmental overlaps were much lower (e.g. liver versus tumor: 9.7%. However, single gene overlap was surprisingly high in some cross-compartmental comparisons (e.g. human liver invasion compartment and murine tumor invasion compartment: 9.0% despite little histolopathologic similarity indicating that invasion relevant genes are not necessarily confined to histologically defined compartments. Conclusion In summary, cross

[Applications of synthetic biology in materials science].

Science.gov (United States)

Zhao, Tianxin; Zhong, Chao

2017-03-25

Materials are the basis for human being survival and social development. To keep abreast with the increasing needs from all aspects of human society, there are huge needs in the development of advanced materials as well as high-efficiency but low-cost manufacturing strategies that are both sustainable and tunable. Synthetic biology, a new engineering principle taking gene regulation and engineering design as the core, greatly promotes the development of life sciences. This discipline has also contributed to the development of material sciences and will continuously bring new ideas to future new material design. In this paper, we review recent advances in applications of synthetic biology in material sciences, with the focus on how synthetic biology could enable synthesis of new polymeric biomaterials and inorganic materials, phage display and directed evolution of proteins relevant to materials development, living functional materials, engineered bacteria-regulated artificial photosynthesis system as well as applications of gene circuits for material sciences.

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm.

Science.gov (United States)

Tchagang, Alain B; Phan, Sieu; Famili, Fazel; Shearer, Heather; Fobert, Pierre; Huang, Yi; Zou, Jitao; Huang, Daiqing; Cutler, Adrian; Liu, Ziying; Pan, Youlian

2012-04-04

Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

Gene-ontology enrichment analysis in two independent family-based samples highlights biologically plausible processes for autism spectrum disorders.

LENUS (Irish Health Repository)

Anney, Richard J L

2012-02-01

Recent genome-wide association studies (GWAS) have implicated a range of genes from discrete biological pathways in the aetiology of autism. However, despite the strong influence of genetic factors, association studies have yet to identify statistically robust, replicated major effect genes or SNPs. We apply the principle of the SNP ratio test methodology described by O\\'Dushlaine et al to over 2100 families from the Autism Genome Project (AGP). Using a two-stage design we examine association enrichment in 5955 unique gene-ontology classifications across four groupings based on two phenotypic and two ancestral classifications. Based on estimates from simulation we identify excess of association enrichment across all analyses. We observe enrichment in association for sets of genes involved in diverse biological processes, including pyruvate metabolism, transcription factor activation, cell-signalling and cell-cycle regulation. Both genes and processes that show enrichment have previously been examined in autistic disorders and offer biologically plausibility to these findings.

Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury.

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Sweeney, Timothy E; Lofgren, Shane; Khatri, Purvesh; Rogers, Angela J

2017-08-01

The relevance of animal models to human diseases is an area of intense scientific debate. The degree to which mouse models of lung injury recapitulate human lung injury has never been assessed. Integrating data from both human and animal expression studies allows for increased statistical power and identification of conserved differential gene expression across organisms and conditions. We sought comprehensive integration of gene expression data in experimental acute lung injury (ALI) in rodents compared with humans. We performed two separate gene expression multicohort analyses to determine differential gene expression in experimental animal and human lung injury. We used correlational and pathway analyses combined with external in vitro gene expression data to identify both potential drivers of underlying inflammation and therapeutic drug candidates. We identified 21 animal lung tissue datasets and three human lung injury bronchoalveolar lavage datasets. We show that the metasignatures of animal and human experimental ALI are significantly correlated despite these widely varying experimental conditions. The gene expression changes among mice and rats across diverse injury models (ozone, ventilator-induced lung injury, LPS) are significantly correlated with human models of lung injury (Pearson r = 0.33-0.45, P human lung injury. Predicted therapeutic targets, peptide ligand signatures, and pathway analyses are also all highly overlapping. Gene expression changes are similar in animal and human experimental ALI, and provide several physiologic and therapeutic insights to the disease.

Exploring autophagy with Gene Ontology

Science.gov (United States)

2018-01-01

ABSTRACT Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular stresses and ridding cells of foreign material. Perturbations in autophagy have been implicated in a number of pathological conditions such as neurodegeneration, cardiac disease and cancer. The growing knowledge about autophagic mechanisms needs to be collected in a computable and shareable format to allow its use in data representation and interpretation. The Gene Ontology (GO) is a freely available resource that describes how and where gene products function in biological systems. It consists of 3 interrelated structured vocabularies that outline what gene products do at the biochemical level, where they act in a cell and the overall biological objectives to which their actions contribute. It also consists of ‘annotations’ that associate gene products with the terms. Here we describe how we represent autophagy in GO, how we create and define terms relevant to autophagy researchers and how we interrelate those terms to generate a coherent view of the process, therefore allowing an interoperable description of its biological aspects. We also describe how annotation of gene products with GO terms improves data analysis and interpretation, hence bringing a significant benefit to this field of study. PMID:29455577

Terminator Operon Reporter: combining a transcription termination switch with reporter technology for improved gene synthesis and synthetic biology applications.

Science.gov (United States)

Zampini, Massimiliano; Mur, Luis A J; Rees Stevens, Pauline; Pachebat, Justin A; Newbold, C James; Hayes, Finbarr; Kingston-Smith, Alison

2016-05-25

Synthetic biology is characterized by the development of novel and powerful DNA fabrication methods and by the application of engineering principles to biology. The current study describes Terminator Operon Reporter (TOR), a new gene assembly technology based on the conditional activation of a reporter gene in response to sequence errors occurring at the assembly stage of the synthetic element. These errors are monitored by a transcription terminator that is placed between the synthetic gene and reporter gene. Switching of this terminator between active and inactive states dictates the transcription status of the downstream reporter gene to provide a rapid and facile readout of the accuracy of synthetic assembly. Designed specifically and uniquely for the synthesis of protein coding genes in bacteria, TOR allows the rapid and cost-effective fabrication of synthetic constructs by employing oligonucleotides at the most basic purification level (desalted) and without the need for costly and time-consuming post-synthesis correction methods. Thus, TOR streamlines gene assembly approaches, which are central to the future development of synthetic biology.

Mining the human phenome using allelic scores that index biological intermediates

NARCIS (Netherlands)

Evans, David M; Brion, Marie Jo A; Paternoster, Lavinia; Kemp, John P; McMahon, George; Munafò, Marcus; Whitfield, John B; Medland, Sarah E; Montgomery, Grant W; Timpson, Nicholas J; St Pourcain, Beate; Lawlor, Debbie A; Martin, Nicholas G; Dehghan, Abbas; Hirschhorn, Joel; Smith, George Davey; Alizadeh, Behrooz

2013-01-01

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease

Genome-Wide Identification of Genes Probably Relevant to the Uniqueness of Tea Plant (Camellia sinensis and Its Cultivars

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Yan Wei

2015-01-01

Full Text Available Tea (Camellia sinensis is a popular beverage all over the world and a number of studies have focused on the genetic uniqueness of tea and its cultivars. However, molecular mechanisms underlying these phenomena are largely undefined. In this report, based on expression data available from public databases, we performed a series of analyses to identify genes probably relevant to the uniqueness of C. sinensis and two of its cultivars (LJ43 and ZH2. Evolutionary analyses showed that the evolutionary rates of genes involved in the pathways were not significantly different among C. sinensis, C. oleifera, and C. azalea. Interestingly, a number of gene families, including genes involved in the pathways synthesizing iconic secondary metabolites of tea plant, were significantly upregulated, expressed in C. sinensis (LJ43 when compared to C. azalea, and this may partially explain its higher content of flavonoid, theanine, and caffeine. Further investigation showed that nonsynonymous mutations may partially contribute to the differences between the two cultivars of C. sinensis, such as the chlorina and higher contents of amino acids in ZH2. Genes identified as candidates are probably relevant to the uniqueness of C. sinensis and its cultivars should be good candidates for subsequent functional analyses and marker-assisted breeding.

Ensemble attribute profile clustering: discovering and characterizing groups of genes with similar patterns of biological features

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Bissell MJ

2006-03-01

Full Text Available Abstract Background Ensemble attribute profile clustering is a novel, text-based strategy for analyzing a user-defined list of genes and/or proteins. The strategy exploits annotation data present in gene-centered corpora and utilizes ideas from statistical information retrieval to discover and characterize properties shared by subsets of the list. The practical utility of this method is demonstrated by employing it in a retrospective study of two non-overlapping sets of genes defined by a published investigation as markers for normal human breast luminal epithelial cells and myoepithelial cells. Results Each genetic locus was characterized using a finite set of biological properties and represented as a vector of features indicating attributes associated with the locus (a gene attribute profile. In this study, the vector space models for a pre-defined list of genes were constructed from the Gene Ontology (GO terms and the Conserved Domain Database (CDD protein domain terms assigned to the loci by the gene-centered corpus LocusLink. This data set of GO- and CDD-based gene attribute profiles, vectors of binary random variables, was used to estimate multiple finite mixture models and each ensuing model utilized to partition the profiles into clusters. The resultant partitionings were combined using a unanimous voting scheme to produce consensus clusters, sets of profiles that co-occured consistently in the same cluster. Attributes that were important in defining the genes assigned to a consensus cluster were identified. The clusters and their attributes were inspected to ascertain the GO and CDD terms most associated with subsets of genes and in conjunction with external knowledge such as chromosomal location, used to gain functional insights into human breast biology. The 52 luminal epithelial cell markers and 89 myoepithelial cell markers are disjoint sets of genes. Ensemble attribute profile clustering-based analysis indicated that both lists

Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology

Science.gov (United States)

2013-01-01

Background The Gene Ontology (GO) facilitates the description of the action of gene products in a biological context. Many GO terms refer to chemical entities that participate in biological processes. To facilitate accurate and consistent systems-wide biological representation, it is necessary to integrate the chemical view of these entities with the biological view of GO functions and processes. We describe a collaborative effort between the GO and the Chemical Entities of Biological Interest (ChEBI) ontology developers to ensure that the representation of chemicals in the GO is both internally consistent and in alignment with the chemical expertise captured in ChEBI. Results We have examined and integrated the ChEBI structural hierarchy into the GO resource through computationally-assisted manual curation of both GO and ChEBI. Our work has resulted in the creation of computable definitions of GO terms that contain fully defined semantic relationships to corresponding chemical terms in ChEBI. Conclusions The set of logical definitions using both the GO and ChEBI has already been used to automate aspects of GO development and has the potential to allow the integration of data across the domains of biology and chemistry. These logical definitions are available as an extended version of the ontology from http://purl.obolibrary.org/obo/go/extensions/go-plus.owl. PMID:23895341

Interspecies Systems Biology Uncovers Metabolites Affecting C. elegans Gene Expression and Life History Traits

Science.gov (United States)

Watson, Emma; MacNeil, Lesley T.; Ritter, Ashlyn D.; Yilmaz, L. Safak; Rosebrock, Adam P.; Caudy, Amy A.; Walhout, Albertha J. M.

2014-01-01

SUMMARY Diet greatly influences gene expression and physiology. In mammals, elucidating the effects and mechanisms of individual nutrients is challenging due to the complexity of both the animal and its diet. Here we used an interspecies systems biology approach with Caenorhabditis elegans and two if its bacterial diets, Escherichia coli and Comamonas aquatica, to identify metabolites that affect the animal’s gene expression and physiology. We identify vitamin B12 as the major dilutable metabolite provided by Comamonas aq. that regulates gene expression, accelerates development and reduces fertility, but does not affect lifespan. We find that vitamin B12 has a dual role in the animal: it affects development and fertility via the methionine/S-Adenosylmethionine (SAM) cycle and breaks down the short-chain fatty acid propionic acid preventing its toxic buildup. Our interspecies systems biology approach provides a paradigm for understanding complex interactions between diet and physiology. PMID:24529378

Metabolic adaptation of a human pathogen during chronic infections - a systems biology approach

DEFF Research Database (Denmark)

Thøgersen, Juliane Charlotte

modeling to uncover how human pathogens adapt to the human host. Pseudomonas aeruginosa infections in cystic fibrosis patients are used as a model system for under-­‐ standing these adaptation processes. The exploratory systems biology approach facilitates identification of important phenotypes...... by classical molecular biology approaches where genes and reactions typically are investigated in a one to one relationship. This thesis is an example of how mathematical approaches and modeling can facilitate new biologi-­‐ cal understanding and provide new surprising ideas to important biological processes....... and metabolic pathways that are necessary or related to establishment of chronic infections. Archetypal analysis showed to be successful in extracting relevant phenotypes from global gene expression da-­‐ ta. Furthermore, genome-­‐scale metabolic modeling showed to be useful in connecting the genotype...

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

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Singh, Nitesh Kumar; Ernst, Mathias; Liebscher, Volkmar; Fuellen, Georg; Taher, Leila

2016-10-20

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

Gene expression-based biological test for major depressive disorder: an advanced study

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Watanabe S

2017-02-01

Full Text Available Shin-ya Watanabe,1 Shusuke Numata,1 Jun-ichi Iga,2 Makoto Kinoshita,1 Hidehiro Umehara,1 Kazuo Ishii,3 Tetsuro Ohmori1 1Department of Psychiatry, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, 2Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Ehime, 3Department of Applied Biological Science, Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan Purpose: Recently, we could distinguished patients with major depressive disorder (MDD from nonpsychiatric controls with high accuracy using a panel of five gene expression markers (ARHGAP24, HDAC5, PDGFC, PRNP, and SLC6A4 in leukocyte. In the present study, we examined whether this biological test is able to discriminate patients with MDD from those without MDD, including those with schizophrenia and bipolar disorder.Patients and methods: We measured messenger ribonucleic acid expression levels of the aforementioned five genes in peripheral leukocytes in 17 patients with schizophrenia and 36 patients with bipolar disorder using quantitative real-time polymerase chain reaction (PCR, and we combined these expression data with our previous expression data of 25 patients with MDD and 25 controls. Subsequently, a linear discriminant function was developed for use in discriminating between patients with MDD and without MDD.Results: This expression panel was able to segregate patients with MDD from those without MDD with a sensitivity and specificity of 64% and 67.9%, respectively.Conclusion: Further research to identify MDD-specific markers is needed to improve the performance of this biological test. Keywords: depressive disorder, biomarker, gene expression, schizophrenia, bipolar disorder

A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

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Lan Chung-Yu

2008-09-01

Full Text Available Abstract Background Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs such as NF-κB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin. Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network

6,7-dimethoxy-coumarin as a probe of hydration dynamics in biologically relevant systems

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Ghose, Avisek; Amaro, Mariana; Kovaricek, Petr; Hof, Martin; Sykora, Jan

2018-04-01

Coumarin derivatives are well known fluorescence reporters for investigating biological systems due to their strong micro-environment sensitivity. Despite having wide range of environment sensitive fluorescence probes, the potential of 6,7-dimethoxy-coumarin has not been studied extensively so far. With a perspective of its use in protein studies, namely using the unnatural amino acid technology or as a substrate for hydrolase enzymes, we study acetyloxymethyl-6,7-dimethoxycoumarin (Ac-DMC). We investigate the photophysics and hydration dynamics of this dye in aerosol-OT (AOT) reverse micelles at various water contents using the time dependent fluorescence shift (TDFS) method. The TDFS response in AOT reverse micelles from water/surfactant ratio of 0 to 20 confirms its sensitivity towards the hydration and mobility of its microenvironment. Moreover, we show that the fluorophore can be efficiently quenched by halide ions. Hence, we conclude that the 6,7-dimethoxy-methylcoumarin fluorophore is useful for studying hydration parameters in biologically relevant systems.

A comprehensive experiment for molecular biology: Determination of single nucleotide polymorphism in human REV3 gene using PCR-RFLP.

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Zhang, Xu; Shao, Meng; Gao, Lu; Zhao, Yuanyuan; Sun, Zixuan; Zhou, Liping; Yan, Yongmin; Shao, Qixiang; Xu, Wenrong; Qian, Hui

2017-07-08

Laboratory exercise is helpful for medical students to understand the basic principles of molecular biology and to learn about the practical applications of molecular biology. We have designed a lab course on molecular biology about the determination of single nucleotide polymorphism (SNP) in human REV3 gene, the product of which is a subunit of DNA polymerase ζ and SNPs in this gene are associated with altered susceptibility to cancer. This newly designed experiment is composed of three parts, including genomic DNA extraction, gene amplification by PCR, and genotyping by RFLP. By combining these activities, the students are not only able to learn a series of biotechniques in molecular biology, but also acquire the ability to link the learned knowledge with practical applications. This comprehensive experiment will help the medical students improve the conceptual understanding of SNP and the technical understanding of SNP detection. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(4):299-304, 2017. © 2017 The International Union of Biochemistry and Molecular Biology.

Biology relevant to space radiation

International Nuclear Information System (INIS)

Fry, R.J.M.

1997-01-01

There are only very limited data on the health effects to humans from the two major components of the radiations in space, namely protons and heavy ions. As a result, predictions of the accompanying effects must be based either on (1) data generated through studies of experimental systems exposed on earth at rates and fluences higher than those in space, or (2) extrapolations from studies of gamma and x rays. Better information is needed about the doses, dose rates, and the energy and LET spectra of the radiations at the organ level that are anticipated to be encountered during extended space missions. In particular, there is a need for better estimates of the relationship between radiation quality and biological effects. In the case of deterministic effects, it is the threshold that is important. The possibility of the occurrence of a large solar particle event (SPE) requires that such effects be considered during extended space missions. Analyses suggest, however, that it is feasible to provide sufficient shielding so as to reduce such effects to acceptable levels, particularly if the dose rates can be limited. If these analyses prove correct, the primary biological risks will be the stochastic effects (latent cancer induction). The contribution of one large SPE to the risk of stochastic effects while undesirable will not be large in comparison to the potential total dose on a mission of long duration

Inactivation of the antibacterial and cytotoxic properties of silver ions by biologically relevant compounds.

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Geraldine Mulley

Full Text Available There has been a recent surge in the use of silver as an antimicrobial agent in a wide range of domestic and clinical products, intended to prevent or treat bacterial infections and reduce bacterial colonization of surfaces. It has been reported that the antibacterial and cytotoxic properties of silver are affected by the assay conditions, particularly the type of growth media used in vitro. The toxicity of Ag+ to bacterial cells is comparable to that of human cells. We demonstrate that biologically relevant compounds such as glutathione, cysteine and human blood components significantly reduce the toxicity of silver ions to clinically relevant pathogenic bacteria and primary human dermal fibroblasts (skin cells. Bacteria are able to grow normally in the presence of silver nitrate at >20-fold the minimum inhibitory concentration (MIC if Ag+ and thiols are added in a 1:1 ratio because the reaction of Ag+ with extracellular thiols prevents silver ions from interacting with cells. Extracellular thiols and human serum also significantly reduce the antimicrobial activity of silver wound dressings Aquacel-Ag (Convatec and Acticoat (Smith & Nephew to Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli in vitro. These results have important implications for the deployment of silver as an antimicrobial agent in environments exposed to biological tissue or secretions. Significant amounts of money and effort have been directed at the development of silver-coated medical devices (e.g. dressings, catheters, implants. We believe our findings are essential for the effective design and testing of antimicrobial silver coatings.

Gene expression patterns associated with p53 status in breast cancer

International Nuclear Information System (INIS)

Troester, Melissa A; Herschkowitz, Jason I; Oh, Daniel S; He, Xiaping; Hoadley, Katherine A; Barbier, Claire S; Perou, Charles M

2006-01-01

Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function). The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors. Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data. In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes

Interspecies systems biology uncovers metabolites affecting C. elegans gene expression and life history traits.

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Watson, Emma; MacNeil, Lesley T; Ritter, Ashlyn D; Yilmaz, L Safak; Rosebrock, Adam P; Caudy, Amy A; Walhout, Albertha J M

2014-02-13

Diet greatly influences gene expression and physiology. In mammals, elucidating the effects and mechanisms of individual nutrients is challenging due to the complexity of both the animal and its diet. Here, we used an interspecies systems biology approach with Caenorhabditis elegans and two of its bacterial diets, Escherichia coli and Comamonas aquatica, to identify metabolites that affect the animal's gene expression and physiology. We identify vitamin B12 as the major dilutable metabolite provided by Comamonas aq. that regulates gene expression, accelerates development, and reduces fertility but does not affect lifespan. We find that vitamin B12 has a dual role in the animal: it affects development and fertility via the methionine/S-Adenosylmethionine (SAM) cycle and breaks down the short-chain fatty acid propionic acid, preventing its toxic buildup. Our interspecies systems biology approach provides a paradigm for understanding complex interactions between diet and physiology. Copyright © 2014 Elsevier Inc. All rights reserved.

The Molecular Signatures Database (MSigDB) hallmark gene set collection.

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Liberzon, Arthur; Birger, Chet; Thorvaldsdóttir, Helga; Ghandi, Mahmoud; Mesirov, Jill P; Tamayo, Pablo

2015-12-23

The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.

Noise minimization in eukaryotic gene expression.

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Hunter B Fraser

2004-06-01

Full Text Available All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or "noise." Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.

Noise minimization in eukaryotic gene expression

Energy Technology Data Exchange (ETDEWEB)

Fraser, Hunter B.; Hirsh, Aaron E.; Giaever, Guri; Kumm, Jochen; Eisen, Michael B.

2004-01-15

All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or noise. Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.

Noise minimization in eukaryotic gene expression

International Nuclear Information System (INIS)

Fraser, Hunter B.; Hirsh, Aaron E.; Giaever, Guri; Kumm, Jochen; Eisen, Michael B.

2004-01-01

All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or noise. Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

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de los Reyes Benildo G

2008-04-01

Full Text Available Abstract Background Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM, facilitates the inference. Identifying NMs defined by specific transcription factors (TF establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO information. Results The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1 Gene set enrichment

A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

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Kentzoglanakis, Kyriakos; Poole, Matthew

2012-01-01

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

The role and future of in-vitro isotopic techniques in molecular biology

International Nuclear Information System (INIS)

Dar, L.; Khan, B.K.

2004-01-01

In this review we discuss isotopic in-vitro molecular biology techniques, and their advantages and applications. Isotopic methods have helped to shape molecular biology since its early days. Despite the availability of non-isotopic alternatives, isotopic methods continue to be used in molecular biology due to certain advantages, especially related to sensitivity and cost-effectiveness. Numerous techniques involving the use of isotopes help in the characterization of genes, including the detection of single nucleotide polymorphisms (SNPs) or mutations. Other isotopic molecular methods are utilized to study the phenotypic expression of gene sequences and their mutation. Emerging branches of molecular biology like functional genomics and proteomics are extremely important for exploiting the rapidly growing data derived from whole genomic sequencing of human and microbial genomes. Recent molecular biology applications like the high-throughput array techniques are relevant in the context of both structural and functional genomics. In proteomics, stable isotope based technology has found applications in the analysis of protein structure and interactions. (author)

Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data

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Hu Xiaohua

2011-07-01

the biological analysis of the identified significant genes and their related pathways demonstrated that these genes play a prominent role in AD and relate the activation patterns to AD phenotypes. It is validated that the combination of these two methods is efficient. Conclusions Unsupervised matrix factorization methods provide efficient tools to analyze high-throughput microarray dataset. According to the facts that different unsupervised approaches explore correlations in the high-dimensional data space and identify relevant subspace base on different hypotheses, integrating these methods to explore the underlying biological information from microarray dataset is an efficient approach. By combining the significant genes identified by both ICA and NMF, the biological analysis shows great efficient for elucidating the molecular taxonomy of Alzheimer’s disease and enable better experimental design to further identify potential pathways and therapeutic targets of AD.

Expression Profiling of Genes Related to Endothelial Cells Biology in Patients with Type 2 Diabetes and Patients with Prediabetes

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Sara Moradipoor

2016-01-01

Full Text Available Endothelial dysfunction appears to be an early sign indicating vascular damage and predicts the progression of atherosclerosis and cardiovascular disorders. Extensive clinical and experimental evidence suggests that endothelial dysfunction occurs in Type 2 Diabetes Mellitus (T2DM and prediabetes patients. This study was carried out with an aim to appraise the expression levels in the peripheral blood of 84 genes related to endothelial cells biology in patients with diagnosed T2DM or prediabetes, trying to identify new genes whose expression might be changed under these pathological conditions. The study covered a total of 45 participants. The participants were divided into three groups: group 1, patients with T2DM; group 2, patients with prediabetes; group 3, control group. The gene expression analysis was performed using the Endothelial Cell Biology RT2 Profiler PCR Array. In the case of T2DM, 59 genes were found to be upregulated, and four genes were observed to be downregulated. In prediabetes patients, increased expression was observed for 49 genes, with two downregulated genes observed. Our results indicate that diabetic and prediabetic conditions change the expression levels of genes related to endothelial cells biology and, consequently, may increase the risk for occurrence of endothelial dysfunction.

Mining biological networks from full-text articles.

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Czarnecki, Jan; Shepherd, Adrian J

2014-01-01

The study of biological networks is playing an increasingly important role in the life sciences. Many different kinds of biological system can be modelled as networks; perhaps the most important examples are protein-protein interaction (PPI) networks, metabolic pathways, gene regulatory networks, and signalling networks. Although much useful information is easily accessible in publicly databases, a lot of extra relevant data lies scattered in numerous published papers. Hence there is a pressing need for automated text-mining methods capable of extracting such information from full-text articles. Here we present practical guidelines for constructing a text-mining pipeline from existing code and software components capable of extracting PPI networks from full-text articles. This approach can be adapted to tackle other types of biological network.

Independent component analysis reveals new and biologically significant structures in micro array data

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Veerla Srinivas

2006-06-01

Full Text Available Abstract Background An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes. Results We applied independent component analysis (ICA to three different microarray data sets; two tumor data sets and one time series experiment. To obtain reliable components we used iterated ICA to estimate component centrotypes. We found that many of the low ranking components indeed may show a strong biological coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology (GO categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes. Conclusion Although the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA.

Gene Ontology

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Gaston K. Mazandu

2012-01-01

Full Text Available The wide coverage and biological relevance of the Gene Ontology (GO, confirmed through its successful use in protein function prediction, have led to the growth in its popularity. In order to exploit the extent of biological knowledge that GO offers in describing genes or groups of genes, there is a need for an efficient, scalable similarity measure for GO terms and GO-annotated proteins. While several GO similarity measures exist, none adequately addresses all issues surrounding the design and usage of the ontology. We introduce a new metric for measuring the distance between two GO terms using the intrinsic topology of the GO-DAG, thus enabling the measurement of functional similarities between proteins based on their GO annotations. We assess the performance of this metric using a ROC analysis on human protein-protein interaction datasets and correlation coefficient analysis on the selected set of protein pairs from the CESSM online tool. This metric achieves good performance compared to the existing annotation-based GO measures. We used this new metric to assess functional similarity between orthologues, and show that it is effective at determining whether orthologues are annotated with similar functions and identifying cases where annotation is inconsistent between orthologues.

Gregory Bateson's relevance to current molecular biology

DEFF Research Database (Denmark)

Bruni, Luis Emilio

2008-01-01

in a developmental pathway. Being a central figure in the development of cybernetic theory he collaborated with a range of researchers from the life sciences who were innovating their own disciplines by introducing cybernetic concepts in their particular fields and disciplines. In the light of this, it should...... not come as a surprise today to realize how the general ideas that he was postulating for the study of communication systems in biology fit so well with the astonishing findings of current molecular biology, for example in the field of cellular signal transduction networks. I guess this is the case due...

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells

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Montalescot Gilles

2008-06-01

Full Text Available Abstract Background In this study we assessed the respective ability of Affymetrix and Illumina microarray methodologies to answer a relevant biological question, namely the change in gene expression between resting monocytes and macrophages derived from these monocytes. Five RNA samples for each type of cell were hybridized to the two platforms in parallel. In addition, a reference list of differentially expressed genes (DEG was generated from a larger number of hybridizations (mRNA from 86 individuals using the RNG/MRC two-color platform. Results Our results show an important overlap of the Illumina and Affymetrix DEG lists. In addition, more than 70% of the genes in these lists were also present in the reference list. Overall the two platforms had very similar performance in terms of biological significance, evaluated by the presence in the DEG lists of an excess of genes belonging to Gene Ontology (GO categories relevant for the biology of monocytes and macrophages. Our results support the conclusion of the MicroArray Quality Control (MAQC project that the criteria used to constitute the DEG lists strongly influence the degree of concordance among platforms. However the importance of prioritizing genes by magnitude of effect (fold change rather than statistical significance (p-value to enhance cross-platform reproducibility recommended by the MAQC authors was not supported by our data. Conclusion Functional analysis based on GO enrichment demonstrates that the 2 compared technologies delivered very similar results and identified most of the relevant GO categories enriched in the reference list.

G Quadruplex in Plants: A Ubiquitous Regulatory Element and Its Biological Relevance.

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Yadav, Vikas; Hemansi; Kim, Nayun; Tuteja, Narendra; Yadav, Puja

2017-01-01

G quadruplexes (G4) are higher-order DNA and RNA secondary structures formed by G-rich sequences that are built around tetrads of hydrogen-bonded guanine bases. Potential G4 quadruplex sequences have been identified in G-rich eukaryotic non-telomeric and telomeric genomic regions. Upon function, G4 formation is known to involve in chromatin remodeling, gene regulation and has been associated with genomic instability, genetic diseases and cancer progression. The natural role and biological validation of G4 structures is starting to be explored, and is of particular interest for the therapeutic interventions for human diseases. However, the existence and physiological role of G4 DNA and G4 RNA in plants species have not been much investigated yet and therefore, is of great interest for the development of improved crop varieties for sustainable agriculture. In this context, several recent studies suggests that these highly diverse G4 structures in plants can be employed to regulate expression of genes involved in several pathophysiological conditions including stress response to biotic and abiotic stresses as well as DNA damage. In the current review, we summarize the recent findings regarding the emerging functional significance of G4 structures in plants and discuss their potential value in the development of improved crop varieties.

G Quadruplex in Plants: A Ubiquitous Regulatory Element and Its Biological Relevance

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Vikas Yadav

2017-07-01

Full Text Available G quadruplexes (G4 are higher-order DNA and RNA secondary structures formed by G-rich sequences that are built around tetrads of hydrogen-bonded guanine bases. Potential G4 quadruplex sequences have been identified in G-rich eukaryotic non-telomeric and telomeric genomic regions. Upon function, G4 formation is known to involve in chromatin remodeling, gene regulation and has been associated with genomic instability, genetic diseases and cancer progression. The natural role and biological validation of G4 structures is starting to be explored, and is of particular interest for the therapeutic interventions for human diseases. However, the existence and physiological role of G4 DNA and G4 RNA in plants species have not been much investigated yet and therefore, is of great interest for the development of improved crop varieties for sustainable agriculture. In this context, several recent studies suggests that these highly diverse G4 structures in plants can be employed to regulate expression of genes involved in several pathophysiological conditions including stress response to biotic and abiotic stresses as well as DNA damage. In the current review, we summarize the recent findings regarding the emerging functional significance of G4 structures in plants and discuss their potential value in the development of improved crop varieties.

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures.

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Mazandu, Gaston K; Mulder, Nicola J

2013-09-25

The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved outcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide tools that allow the integration of biological knowledge embedded in the GO structure into different biological analyses. There is a need for a unified tool that provides the scientific community with the opportunity to explore these different GO similarity measure approaches and their biological applications. We have developed DaGO-Fun, an online tool available at http://web.cbio.uct.ac.za/ITGOM, which incorporates many different GO similarity measures for exploring, analyzing and comparing GO terms and proteins within the context of GO. It uses GO data and UniProt proteins with their GO annotations as provided by the Gene Ontology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user queries. The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity measures, including topology- and annotation-based approaches to facilitate effective exploration of these measures, thus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several biological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO annotations, the clustering of functionally related genes within a set, and term enrichment analysis.

Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

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Beal, Jacob; Lu, Ting; Weiss, Ron

2011-01-01

The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

Expression of novel rice gibberellin 2-oxidase gene is under homeostatic regulation by biologically active gibberellins.

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Sakai, Miho; Sakamoto, Tomoaki; Saito, Tamio; Matsuoka, Makoto; Tanaka, Hiroshi; Kobayashi, Masatomo

2003-04-01

We have cloned two genes for gibberellin (GA) 2-oxidase from rice ( Oryza sativa L.). Expression of OsGA2ox2 was not observed. The other gene, OsGA2ox3, was expressed in every tissue examined and was enhanced by the application of biologically active GA. Recombinant OsGA2ox3 protein catalyzed the metabolism of GA(1) to GA(8) and GA(20) to GA(29)-catabolite. These results indicate that OsGA2ox3 is involved in the homeostatic regulation of the endogenous level of biologically active GA in rice.

ICGE: an R package for detecting relevant clusters and atypical units in gene expression

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Irigoien Itziar

2012-02-01

Full Text Available Abstract Background Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample... belongs to one of these previously identified clusters or to a new group. Results ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use. Conclusions We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

Equivalent Gene Expression Profiles between Glatopa™ and Copaxone®.

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Josephine S D'Alessandro

Full Text Available Glatopa™ is a generic glatiramer acetate recently approved for the treatment of patients with relapsing forms of multiple sclerosis. Gene expression profiling was performed as a means to evaluate equivalence of Glatopa and Copaxone®. Microarray analysis containing 39,429 unique probes across the entire genome was performed in murine glatiramer acetate--responsive Th2-polarized T cells, a test system highly relevant to the biology of glatiramer acetate. A closely related but nonequivalent glatiramoid molecule was used as a control to establish assay sensitivity. Multiple probe-level (Student's t-test and sample-level (principal component analysis, multidimensional scaling, and hierarchical clustering statistical analyses were utilized to look for differences in gene expression induced by the test articles. The analyses were conducted across all genes measured, as well as across a subset of genes that were shown to be modulated by Copaxone. The following observations were made across multiple statistical analyses: the expression of numerous genes was significantly changed by treatment with Copaxone when compared against media-only control; gene expression profiles induced by Copaxone and Glatopa were not significantly different; and gene expression profiles induced by Copaxone and the nonequivalent glatiramoid were significantly different, underscoring the sensitivity of the test system and the multiple analysis methods. Comparative analysis was also performed on sets of transcripts relevant to T-cell biology and antigen presentation, among others that are known to be modulated by glatiramer acetate. No statistically significant differences were observed between Copaxone and Glatopa in the expression levels (magnitude and direction of these glatiramer acetate-regulated genes. In conclusion, multiple methods consistently supported equivalent gene expression profiles between Copaxone and Glatopa.

lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

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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.

GeneLab: NASA's Open Access, Collaborative Platform for Systems Biology and Space Medicine

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Berrios, Daniel C.; Thompson, Terri G.; Fogle, Homer W.; Rask, Jon C.; Coughlan, Joseph C.

2015-01-01

NASA is investing in GeneLab1 (http:genelab.nasa.gov), a multi-year effort to maximize utilization of the limited resources to conduct biological and medical research in space, principally aboard the International Space Station (ISS). High-throughput genomic, transcriptomic, proteomic or other omics analyses from experiments conducted on the ISS will be stored in the GeneLab Data Systems (GLDS), an open-science information system that will also include a biocomputation platform with collaborative science capabilities, to enable the discovery and validation of molecular networks.

Molecular Biology at the Cutting Edge: A Review on CRISPR/CAS9 Gene Editing for Undergraduates

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Thurtle-Schmidt, Deborah M.; Lo, Te-Wen

2018-01-01

Disrupting a gene to determine its effect on an organism's phenotype is an indispensable tool in molecular biology. Such techniques are critical for understanding how a gene product contributes to the development and cellular identity of organisms. The explosion of genomic sequencing technologies combined with recent advances in genome-editing…

Oral cancer cells with different potential of lymphatic metastasis displayed distinct biologic behaviors and gene expression profiles.

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Zhuang, Zhang; Jian, Pan; Longjiang, Li; Bo, Han; Wenlin, Xiao

2010-02-01

Oral squamous cell carcinoma (OSCC) often spreads from the primary tumor to regional lymph nodes in the early stage. Better understanding of the biology of lymphatic spread of oral cancer cells is important for improving the survival rate of cancer patients. We established the cell line LNMTca8113 by repeated injections in foot pads of nude mice, which had a much higher lymphatic metastasis rate than its parental cell line Tca8113. Then, we compared the biologic behaviors of cancer cells between them. Moreover, microarray-based expression profiles between them were also compared, and a panel of differential genes was validated using real-time-PCR. In contrast to Tca8113 cells, LNMTca8113 cells were more proliferative and resistant to apoptosis in the absence of serum, and had enhanced ability of inducing capillary-like structures. Moreover, microarray-based expression profiles between them identified 1341 genes involved in cell cycle, cell adhesion, lymphangiogenesis, regulation of apoptosis, and so on. Some genes dedicating to the metastatic potential, including JAM2, TNC, CTSC, LAMB1, VEGFC, HAPLN1, ACPP, GDF9 and FGF11, were upregulated in LNMTca8113 cells. These results suggested that LNMTca8113 and Tca8113 cells were proper models for lymphatic metastasis study because there were differences in biologic behaviors and metastasis-related genes between them. Additionally, the differentially expressed gene profiles in cancer progression may be helpful in exploring therapeutic targets and provide the foundation for further functional validation of these specific candidate genes for OSCC.

Stability of silver nanoparticles: agglomeration and oxidation in biological relevant conditions

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Valenti, Laura E.; Giacomelli, Carla E.

2017-05-01

Silver nanoparticles (Ag-NP) are the most used nanomaterial in consumer products due to the intrinsic antimicrobial capacity of silver. However, Ag-NP may be also harmful to algae, aquatic species, mammalian cells, and higher plants because both Ag+ and nanoparticles are responsible of cell damages. The oxidative dissolution of Ag-NP would proceed to completion under oxic conditions, but the rate and extent of the dissolution depend on several factors. This work correlates the effect of the capping agent (albumin and citrate) with the stability of Ag-NP towards agglomeration in simulated body fluid (SBF) and oxidation in the presence of ROS species (H2O2). Capping provides colloidal stability only through electrostatic means, whereas albumin acts as bulky ligands giving steric and electrostatic repulsion, inhibiting the agglomeration in SBF. However, citrate capping protects Ag-NP from dissolution to a major extent than albumin does because of its reducing power. Moreover, citrate in solution minimizes the oxidation of albumin-coated Ag-NP even after long incubation times. H2O2-induced dissolution proceeds to completion with Ag-NP incubated in SBF, while incubation in citrate leads to an incomplete oxidation. In short, albumin is an excellent capping agent to minimize Ag-NP agglomeration whereas citrate provides a mild-reductive medium that prevents dissolution in biological relevant media as well as in the presence of ROS species. These results provide insight into how the surface properties and media composition affect the release of Ag+ from Ag-NP, related to the cell toxicity and relevant to the storage and lifetime of silver-containing nanomaterials.

Stability of silver nanoparticles: agglomeration and oxidation in biological relevant conditions

Energy Technology Data Exchange (ETDEWEB)

Valenti, Laura E.; Giacomelli, Carla E., E-mail: giacomel@fcq.unc.edu.ar [Universidad Nacional de Córdoba, Ciudad Universitaria, Instituto de Investigaciones en Físico Química de Córdoba (INFIQC) CONICET-UNC, Departamento de Fisicoquímica, Facultad de Ciencias Químicas (Argentina)

2017-05-15

Silver nanoparticles (Ag-NP) are the most used nanomaterial in consumer products due to the intrinsic antimicrobial capacity of silver. However, Ag-NP may be also harmful to algae, aquatic species, mammalian cells, and higher plants because both Ag{sup +} and nanoparticles are responsible of cell damages. The oxidative dissolution of Ag-NP would proceed to completion under oxic conditions, but the rate and extent of the dissolution depend on several factors. This work correlates the effect of the capping agent (albumin and citrate) with the stability of Ag-NP towards agglomeration in simulated body fluid (SBF) and oxidation in the presence of ROS species (H{sub 2}O{sub 2}). Capping provides colloidal stability only through electrostatic means, whereas albumin acts as bulky ligands giving steric and electrostatic repulsion, inhibiting the agglomeration in SBF. However, citrate capping protects Ag-NP from dissolution to a major extent than albumin does because of its reducing power. Moreover, citrate in solution minimizes the oxidation of albumin-coated Ag-NP even after long incubation times. H{sub 2}O{sub 2}-induced dissolution proceeds to completion with Ag-NP incubated in SBF, while incubation in citrate leads to an incomplete oxidation. In short, albumin is an excellent capping agent to minimize Ag-NP agglomeration whereas citrate provides a mild-reductive medium that prevents dissolution in biological relevant media as well as in the presence of ROS species. These results provide insight into how the surface properties and media composition affect the release of Ag{sup +} from Ag-NP, related to the cell toxicity and relevant to the storage and lifetime of silver-containing nanomaterials.

The Genome Biology of Effector Gene Evolution in Filamentous Plant Pathogens.

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Sánchez-Vallet, Andrea; Fouché, Simone; Fudal, Isabelle; Hartmann, Fanny E; Soyer, Jessica L; Tellier, Aurélien; Croll, Daniel

2018-05-16

Filamentous pathogens, including fungi and oomycetes, pose major threats to global food security. Crop pathogens cause damage by secreting effectors that manipulate the host to the pathogen's advantage. Genes encoding such effectors are among the most rapidly evolving genes in pathogen genomes. Here, we review how the major characteristics of the emergence, function, and regulation of effector genes are tightly linked to the genomic compartments where these genes are located in pathogen genomes. The presence of repetitive elements in these compartments is associated with elevated rates of point mutations and sequence rearrangements with a major impact on effector diversification. The expression of many effectors converges on an epigenetic control mediated by the presence of repetitive elements. Population genomics analyses showed that rapidly evolving pathogens show high rates of turnover at effector loci and display a mosaic in effector presence-absence polymorphism among strains. We conclude that effective pathogen containment strategies require a thorough understanding of the effector genome biology and the pathogen's potential for rapid adaptation. Expected final online publication date for the Annual Review of Phytopathology Volume 56 is August 25, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.

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Pugalendhi Ganesh Kumar

Full Text Available This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR-based method for redefining the criterion function of f-information (FI to identify the potential genes without discretizing the continuous gene expression values. The embedded procedure is implemented by means of a water swirl algorithm (WSA, which attempts to optimize the rule set and membership function required to classify samples using a fuzzy-rule-based multiclassification system (FRBMS. Two novel update equations are proposed in WSA, which have better exploration and exploitation abilities while designing a self-learning FRBMS. The efficiency of our new approach was evaluated on 13 multicategory and 9 binary datasets of cancer gene expression. Additionally, the performance of the proposed FRFI-WSA method in designing an FRBMS was compared with existing methods for gene selection and optimization such as genetic algorithm (GA, particle swarm optimization (PSO, and artificial bee colony algorithm (ABC on all the datasets. In the global cancer map with repeated measurements (GCM_RM dataset, the FRFI-WSA showed the smallest number of 16 most relevant genes associated with cancer using a minimal number of 26 compact rules with the highest classification accuracy (96.45%. In addition, the statistical validation used in this study revealed that the biological relevance of the most relevant genes associated with cancer and their linguistics detected by the proposed FRFI-WSA approach are better than those in the other methods. The simple interpretable rules with most relevant genes and effectively

A biologically relevant method for considering patterns of oceanic retention in the Southern Ocean

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Mori, Mao; Corney, Stuart P.; Melbourne-Thomas, Jessica; Klocker, Andreas; Sumner, Michael; Constable, Andrew

2017-12-01

Many marine species have planktonic forms - either during a larval stage or throughout their lifecycle - that move passively or are strongly influenced by ocean currents. Understanding these patterns of movement is important for informing marine ecosystem management and for understanding ecological processes generally. Retention of biological particles in a particular area due to ocean currents has received less attention than transport pathways, particularly for the Southern Ocean. We present a method for modelling retention time, based on the half-life for particles in a particular region, that is relevant for biological processes. This method uses geostrophic velocities at the ocean surface, derived from 23 years of satellite altimetry data (1993-2016), to simulate the advection of passive particles during the Southern Hemisphere summer season (from December to March). We assess spatial patterns in the retention time of passive particles and evaluate the processes affecting these patterns for the Indian sector of the Southern Ocean. Our results indicate that the distribution of retention time is related to bathymetric features and the resulting ocean dynamics. Our analysis also reveals a moderate level of consistency between spatial patterns of retention time and observations of Antarctic krill (Euphausia superba) distribution.

Biological pacemaker created by minimally invasive somatic reprogramming in pigs with complete heart block

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Hu, Yu-Feng; Dawkins, James Frederick; Cho, Hee Cheol; Marbán, Eduardo; Cingolani, Eugenio

2016-01-01

Somatic reprogramming by reexpression of the embryonic transcription factor T-box 18 (TBX18) converts cardiomyocytes into pacemaker cells. We hypothesized that this could be a viable therapeutic avenue for pacemaker-dependent patients afflicted with device-related complications, and therefore tested whether adenoviral TBX18 gene transfer could create biological pacemaker activity in vivo in a large-animal model of complete heart block. Biological pacemaker activity, originating from the intramyocardial injection site, was evident in TBX18-transduced animals starting at day 2 and persisted for the duration of the study (14 days) with minimal backup electronic pacemaker use. Relative to controls transduced with a reporter gene, TBX18-transduced animals exhibited enhanced autonomic responses and physiologically superior chronotropic support of physical activity. Induced sinoatrial node cells could be identified by their distinctive morphology at the site of injection in TBX18-transduced animals, but not in controls. No local or systemic safety concerns arose. Thus, minimally invasive TBX18 gene transfer creates physiologically relevant pacemaker activity in complete heart block, providing evidence for therapeutic somatic reprogramming in a clinically relevant disease model. PMID:25031269

Conditional RNAi: towards a silent gene therapy.

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Lee, Sang-Kyung; Kumar, Priti

2009-07-02

RNA interference (RNAi) has the potential to permit the downregulation of virtually any gene. While transgenic RNAi enables stable propagation of the resulting phenotype to progeny, the dominant nature of RNAi limits its use to applications where the continued suppression of gene expression does not disturb normal cell functioning. This is of particular importance when the target gene product is essential for cell survival, development or differentiation. It is therefore desirable that knockdown be externally regulatable. This review is aimed at providing an overview of the approaches for conditional RNAi in mammalian systems, with a special mention of studies employing these approaches to target therapeutically/biologically relevant molecules, their advantages and disadvantages, and a pointer towards approaches best suited for RNAi-based gene therapy.

Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

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Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

2015-03-09

Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold ([Formula: see text]). These phenotypes are more heritable ([Formula: see text]) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. Copyright © 2015 Brown et al.

Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates

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Evans, David M; Brion, Marie Jo A; Paternoster, Lavinia

2013-01-01

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease...... aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we...

Digital Gene Expression Analysis to Screen Disease Resistance-Relevant Genes from Leaves of Herbaceous Peony (Paeonia lactiflora Pall. Infected by Botrytis cinerea.

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Saijie Gong

Full Text Available Herbaceous peony (Paeonia lactiflora Pall. is a well-known traditional flower in China and is widely used for landscaping and garden greening due to its high ornamental value. However, disease spots usually appear after the flowering of the plant and may result in the withering of the plant in severe cases. This study examined the disease incidence in an herbaceous peony field in the Yangzhou region, Jiangsu Province. Based on morphological characteristics and molecular data, the disease in this area was identified as a gray mold caused by Botrytis cinerea. Based on previously obtained transcriptome data, eight libraries generated from two herbaceous peony cultivars 'Zifengyu' and 'Dafugui' with different susceptibilities to the disease were then analyzed using digital gene expression profiling (DGE. Thousands of differentially expressed genes (DEGs were screened by comparing the eight samples, and these genes were annotated using the Gene ontology (GO and Kyoto encyclopedia of genes and genomes (KEGG database. The pathways related to plant-pathogen interaction, secondary metabolism synthesis and antioxidant system were concentrated, and 51, 76, and 13 disease resistance-relevant candidate genes were identified, respectively. The expression patterns of these candidate genes differed between the two cultivars: their expression of the disease-resistant cultivar 'Zifengyu' sharply increased during the early stages of infection, while it was relatively subdued in the disease-sensitive cultivar 'Dafugui'. A selection of ten candidate genes was evaluated by quantitative real-time PCR (qRT-PCR to validate the DGE data. These results revealed the transcriptional changes that took place during the interaction of herbaceous peony with B. cinerea, providing insight into the molecular mechanisms of host resistance to gray mold.

Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data

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J. Sunil Rao

2007-01-01

Full Text Available In gene selection for cancer classifi cation using microarray data, we define an eigenvalue-ratio statistic to measure a gene’s contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalueratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

Chronic LSD alters gene expression profiles in the mPFC relevant to schizophrenia.

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Martin, David A; Marona-Lewicka, Danuta; Nichols, David E; Nichols, Charles D

2014-08-01

Chronic administration of lysergic acid diethylamide (LSD) every other day to rats results in a variety of abnormal behaviors. These build over the 90 day course of treatment and can persist at full strength for at least several months after cessation of treatment. The behaviors are consistent with those observed in animal models of schizophrenia and include hyperactivity, reduced sucrose-preference, and decreased social interaction. In order to elucidate molecular changes that underlie these aberrant behaviors, we chronically treated rats with LSD and performed RNA-sequencing on the medial prefrontal cortex (mPFC), an area highly associated with both the actions of LSD and the pathophysiology of schizophrenia and other psychiatric illnesses. We observed widespread changes in the neurogenetic state of treated animals four weeks after cessation of LSD treatment. QPCR was used to validate a subset of gene expression changes observed with RNA-Seq, and confirmed a significant correlation between the two methods. Functional clustering analysis indicates differentially expressed genes are enriched in pathways involving neurotransmission (Drd2, Gabrb1), synaptic plasticity (Nr2a, Krox20), energy metabolism (Atp5d, Ndufa1) and neuropeptide signaling (Npy, Bdnf), among others. Many processes identified as altered by chronic LSD are also implicated in the pathogenesis of schizophrenia, and genes affected by LSD are enriched with putative schizophrenia genes. Our results provide a relatively comprehensive analysis of mPFC transcriptional regulation in response to chronic LSD, and indicate that the long-term effects of LSD may bear relevance to psychiatric illnesses, including schizophrenia. Copyright © 2014 Elsevier Ltd. All rights reserved.

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome.

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Quevedo-Tumailli, Viviana F; Ortega-Tenezaca, Bernabé; González-Díaz, Humbert

2018-03-02

The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of genes are transcribed from bidirectional promoters in humans, and many more are organized into larger clusters. This raises intriguing questions previously asked by different authors. We would like to add a few more questions in this context, related to gene orientation inversions. Does gene orientation (inversion) follow a random pattern? Is it relevant to biological activity somehow? We define a new kind of network coined as the gene orientation inversion network (GOIN). GOIN's complex network encodes short- and long-range patterns of inversion of the orientation of pairs of gene in the chromosome. We selected Plasmodium falciparum as a case of study due to the high relevance of this parasite to public health (causal agent of malaria). We constructed here for the first time all of the GOINs for the genome of this parasite. These networks have an average of 383 nodes (genes in one chromosome) and 1314 links (pairs of gene with inverse orientation). We calculated node centralities and other parameters of these networks. These numerical parameters were used to study different properties of gene inversion patterns, for example, distribution, local communities, similarity to Erdös-Rényi random networks, randomness, and so on. We find clues that seem to indicate that gene orientation inversion does not follow a random pattern. We noted that some gene communities in the GOINs tend to group genes encoding for RIFIN-related proteins in the proteome of the parasite. RIFIN-like proteins are a second family of clonally variant proteins expressed on the surface of red cells infected with Plasmodium falciparum. Consequently, we used these centralities as input of machine learning (ML) models to predict the RIFIN-like activity of 5365 proteins in the proteome of Plasmodium sp. The best linear ML model found discriminates RIFIN-like from other proteins with sensitivity and

Epigenetic Modulation of Brain Gene Networks for Cocaine and Alcohol Abuse

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Sean P Farris

2015-05-01

Full Text Available Cocaine and alcohol are two substances of abuse that prominently affect the central nervous system (CNS. Repeated exposure to cocaine and alcohol leads to longstanding changes in gene expression, and subsequent functional CNS plasticity, throughout multiple brain regions. Epigenetic modifications of histones are one proposed mechanism guiding these enduring changes to the transcriptome. Characterizing the large number of available biological relationships as network models can reveal unexpected biochemical relationships. Clustering analysis of variation from whole-genome sequencing of gene expression (RNA-Seq and histone H3 lysine 4 trimethylation (H3K4me3 events (ChIP-Seq revealed the underlying structure of the transcriptional and epigenomic landscape within hippocampal postmortem brain tissue of drug abusers and control cases. Distinct sets of interrelated networks for cocaine and alcohol abuse were determined for each abusive substance. The network approach identified subsets of functionally related genes that are regulated in agreement with H3K4me3 changes, suggesting cause and effect relationships between this epigenetic mark and gene expression. Gene expression networks consisted of recognized substrates for addiction, such as the dopamine- and cAMP-regulated neuronal phosphoprotein PPP1R1B / DARPP-32 and the vesicular glutamate transporter SLC17A7 / VGLUT1 as well as potentially novel molecular targets for substance abuse. Through a systems biology based approach our results illustrate the utility of integrating epigenetic and transcript expression to establish relevant biological networks in the human brain for addiction. Future work with laboratory models may clarify the functional relevance of these gene networks for cocaine and alcohol, and provide a framework for the development of medications for the treatment of addiction.

A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool.

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Mazandu, Gaston K; Chimusa, Emile R; Mbiyavanga, Mamana; Mulder, Nicola J

2016-02-01

Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs. A-DaGO-Fun is freely available to the research community at http://web.cbio.uct.ac.za/ITGOM/adagofun. It is implemented in Linux using Python under free software (GNU General Public Licence). gmazandu@cbio.uct.ac.za or Nicola.Mulder@uct.ac.za 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.

Identification of "pathologs" (disease-related genes from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

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Socha Luis A

2004-04-01

Full Text Available Abstract Background A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%, hereditary (24%, immunological (5%, cardio-vascular (4%, or other (14%, disorders. Conclusions Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

Genes, environment and sport performance: why the nature-nurture dualism is no longer relevant.

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Davids, Keith; Baker, Joseph

2007-01-01

The historical debate on the relative influences of genes (i.e. nature) and environment (i.e. nurture) on human behaviour has been characterised by extreme positions leading to reductionist and polemic conclusions. Our analysis of research on sport and exercise behaviours shows that currently there is little support for either biologically or environmentally deterministic perspectives on elite athletic performance. In sports medicine, recent molecular biological advances in genomic studies have been over-interpreted, leading to a questionable 'single-gene-as-magic-bullet' philosophy adopted by some practitioners. Similarly, although extensive involvement in training and practice is needed at elite levels, it has become apparent that the acquisition of expertise is not merely about amassing a requisite number of practice hours. Although an interactionist perspective has been mooted over the years, a powerful explanatory framework has been lacking. In this article, we propose how the complementary nature of degenerate neurobiological systems might provide the theoretical basis for explaining the interactive influence of genetic and environmental constraints on elite athletic performance. We argue that, due to inherent human degeneracy, there are many different trajectories to achieving elite athletic performance. While the greatest training responses may be theoretically associated with the most favourable genotypes being exposed to highly specialised training environments, this is a rare and complex outcome. The concept of degeneracy provides us with a basis for understanding why each of the major interacting constraints might act in a compensatory manner on the acquisition of elite athletic performance.

Stochastic biological response to radiation. Comprehensive analysis of gene expression

International Nuclear Information System (INIS)

Inoue, Tohru; Hirabayashi, Yoko

2012-01-01

Authors explain that the radiation effect on biological system is stochastic along the law of physics, differing from chemical effect, using instances of Cs-137 gamma-ray (GR) and benzene (BZ) exposures to mice and of resultant comprehensive analyses of gene expression. Single GR irradiation is done with Gamma Cell 40 (CSR) to C57BL/6 or C3H/He mouse at 0, 0.6 and 3 Gy. BE is given orally at 150 mg/kg/day for 5 days x 2 weeks. Bone marrow cells are sampled 1 month after the exposure. Comprehensive gene expression is analyzed by Gene Chip Mouse Genome 430 2.0 Array (Affymetrix) and data are processed by programs like case normalization, statistics, network generation, functional analysis etc. GR irradiation brings about changes of gene expression, which are classifiable in common genes variable commonly on the dose change and stochastic genes variable stochastically within each dose: e.g., with Welch-t-test, significant differences are between 0/3 Gy (dose-specific difference, 455 pbs (probe set), in stochastic 2113 pbs), 0/0.6 Gy (267 in 1284 pbs) and 0.6/3 Gy (532 pbs); and with one-way analysis of variation (ANOVA) and hierarchial/dendrographic analyses, 520 pbs are shown to involve the dose-dependent 226 and dose-specific 294 pbs. It is also shown that at 3 Gy, expression of common genes are rather suppressed, including those related to the proliferation/apoptosis of B/T cells, and of stochastic genes, related to cell division/signaling. Ven diagram of the common genes of above 520 pbs, stochastic 2113 pbs at 3 Gy and 1284 pbs at 0.6 Gy shows the overlapping genes 29, 2 and 4, respectively, indicating only 35 pbs are overlapping in total. Network analysis of changes by GR shows the rather high expression of genes around hub of cAMP response element binding protein (CREB) at 0.6 Gy, and rather variable expression around CREB hub/suppressed expression of kinesin hub at 3 Gy; in the network by BZ exposure, unchanged or low expression around p53 hub and suppression

Elements determination of clinical relevance in biological tissues Dmdmdx/J dystrophic mice strains investigated by NAA

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Metairon, Sabrina

2012-01-01

In this work the determination of chemistry elements in biological tissues (whole blood, bones and organs) of dystrophic mice, used as animal model of Duchenne Muscular Dystrophy (DMD), was performed using analytical nuclear technique. The aim of this work was to determine reference values of elements of clinical (Ca, Cl, K, Mg, Na) and nutritional (Br and S) relevance in whole blood, tibia, quadriceps and hearts from Dmdmdx/J (10 males and 10 females) dystrophic mice and C57BL/6J (10 males) control group mice, using Neutron Activation Analysis technique (NAA). To show in more details the alterations that this disease may cause in these biological tissues, correlations matrixes of the DMD mdx /J mouse strain were generated and compared with C57BL/6J control group. For this study 119 samples of biological tissue were irradiated in the IEA-R1 nuclear reactor at IPEN (Sao Paulo, Brazil). The concentrations of these elements in biological tissues of Dmd mdx /J and C57B/6J mice are the first indicative interval for reference values. Moreover, the alteration in some correlation coefficients data among the elements in the health status and in the diseased status indicates a connection between these elements in whole blood, tibia, quadriceps and heart. These results may help the researchers to evaluate the efficiency of new treatments and to compare the advantages of different treatment approaches before performing tests in patients with muscular dystrophy. (author)

A Drosophila LexA Enhancer-Trap Resource for Developmental Biology and Neuroendocrine Research

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Lutz Kockel

2016-10-01

Full Text Available Novel binary gene expression tools like the LexA-LexAop system could powerfully enhance studies of metabolism, development, and neurobiology in Drosophila. However, specific LexA drivers for neuroendocrine cells and many other developmentally relevant systems remain limited. In a unique high school biology course, we generated a LexA-based enhancer trap collection by transposon mobilization. The initial collection provides a source of novel LexA-based elements that permit targeted gene expression in the corpora cardiaca, cells central for metabolic homeostasis, and other neuroendocrine cell types. The collection further contains specific LexA drivers for stem cells and other enteric cells in the gut, and other developmentally relevant tissue types. We provide detailed analysis of nearly 100 new LexA lines, including molecular mapping of insertions, description of enhancer-driven reporter expression in larval tissues, and adult neuroendocrine cells, comparison with established enhancer trap collections and tissue specific RNAseq. Generation of this open-resource LexA collection facilitates neuroendocrine and developmental biology investigations, and shows how empowering secondary school science can achieve research and educational goals.

A Comprehensive Experiment for Molecular Biology: Determination of Single Nucleotide Polymorphism in Human REV3 Gene Using PCR-RFLP

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Zhang, Xu; Shao, Meng; Gao, Lu; Zhao, Yuanyuan; Sun, Zixuan; Zhou, Liping; Yan, Yongmin; Shao, Qixiang; Xu, Wenrong; Qian, Hui

2017-01-01

Laboratory exercise is helpful for medical students to understand the basic principles of molecular biology and to learn about the practical applications of molecular biology. We have designed a lab course on molecular biology about the determination of single nucleotide polymorphism (SNP) in human REV3 gene, the product of which is a subunit of…

The expression of antibiotic resistance genes in antibiotic-producing bacteria.

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Mak, Stefanie; Xu, Ye; Nodwell, Justin R

2014-08-01

Antibiotic-producing bacteria encode antibiotic resistance genes that protect them from the biologically active molecules that they produce. The expression of these genes needs to occur in a timely manner: either in advance of or concomitantly with biosynthesis. It appears that there have been at least two general solutions to this problem. In many cases, the expression of resistance genes is tightly linked to that of antibiotic biosynthetic genes. In others, the resistance genes can be induced by their cognate antibiotics or by intermediate molecules from their biosynthetic pathways. The regulatory mechanisms that couple resistance to antibiotic biosynthesis are mechanistically diverse and potentially relevant to the origins of clinical antibiotic resistance. © 2014 John Wiley & Sons Ltd.

Systematic reconstruction of autism biology from massive genetic mutation profiles.

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Luo, Weijun; Zhang, Chaolin; Jiang, Yong-Hui; Brouwer, Cory R

2018-04-01

Autism spectrum disorder (ASD) affects 1% of world population and has become a pressing medical and social problem worldwide. As a paradigmatic complex genetic disease, ASD has been intensively studied and thousands of gene mutations have been reported. Because these mutations rarely recur, it is difficult to (i) pinpoint the fewer disease-causing versus majority random events and (ii) replicate or verify independent studies. A coherent and systematic understanding of autism biology has not been achieved. We analyzed 3392 and 4792 autism-related mutations from two large-scale whole-exome studies across multiple resolution levels, that is, variants (single-nucleotide), genes (protein-coding unit), and pathways (molecular module). These mutations do not recur or replicate at the variant level, but significantly and increasingly do so at gene and pathway levels. Genetic association reveals a novel gene + pathway dual-hit model, where the mutation burden becomes less relevant. In multiple independent analyses, hundreds of variants or genes repeatedly converge to several canonical pathways, either novel or literature-supported. These pathways define recurrent and systematic ASD biology, distinct from previously reported gene groups or networks. They also present a catalog of novel ASD risk factors including 118 variants and 72 genes. At a subpathway level, most variants disrupt the pathway-related gene functions, and in the same gene, they tend to hit residues extremely close to each other and in the same domain. Multiple interacting variants spotlight key modules, including the cAMP (adenosine 3',5'-monophosphate) second-messenger system and mGluR (metabotropic glutamate receptor) signaling regulation by GRKs (G protein-coupled receptor kinases). At a superpathway level, distinct pathways further interconnect and converge to three biology themes: synaptic function, morphology, and plasticity.

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

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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.

The association of telomere length and genetic variation in telomere biology genes.

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Mirabello, Lisa; Yu, Kai; Kraft, Peter; De Vivo, Immaculata; Hunter, David J; Prescott, Jennifer; Wong, Jason Y Y; Chatterjee, Nilanjan; Hayes, Richard B; Savage, Sharon A

2010-09-01

Telomeres cap chromosome ends and are critical for genomic stability. Many telomere-associated proteins are important for telomere length maintenance. Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) in genes encoding telomere-associated proteins (RTEL1 and TERT-CLPTM1) as markers of cancer risk. We conducted an association study of telomere length and 743 SNPs in 43 telomere biology genes. Telomere length in peripheral blood DNA was determined by Q-PCR in 3,646 participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial and Nurses' Health Study. We investigated associations by SNP, gene, and pathway (functional group). We found no associations between telomere length and SNPs in TERT-CLPTM1L or RTEL1. Telomere length was not significantly associated with specific functional groups. Thirteen SNPs from four genes (MEN1, MRE11A, RECQL5, and TNKS) were significantly associated with telomere length. The strongest findings were in MEN1 (gene-based P=0.006), menin, which associates with the telomerase promoter and may negatively regulate telomerase. This large association study did not find strong associations with telomere length. The combination of limited diversity and evolutionary conservation suggest that these genes may be under selective pressure. More work is needed to explore the role of genetic variants in telomere length regulation. Published 2010 Wiley-Liss, Inc.

Genomics-Based Discovery of Plant Genes for Synthetic Biology of Terpenoid Fragrances: A Case Study in Sandalwood oil Biosynthesis.

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Celedon, J M; Bohlmann, J

2016-01-01

Terpenoid fragrances are powerful mediators of ecological interactions in nature and have a long history of traditional and modern industrial applications. Plants produce a great diversity of fragrant terpenoid metabolites, which make them a superb source of biosynthetic genes and enzymes. Advances in fragrance gene discovery have enabled new approaches in synthetic biology of high-value speciality molecules toward applications in the fragrance and flavor, food and beverage, cosmetics, and other industries. Rapid developments in transcriptome and genome sequencing of nonmodel plant species have accelerated the discovery of fragrance biosynthetic pathways. In parallel, advances in metabolic engineering of microbial and plant systems have established platforms for synthetic biology applications of some of the thousands of plant genes that underlie fragrance diversity. While many fragrance molecules (eg, simple monoterpenes) are abundant in readily renewable plant materials, some highly valuable fragrant terpenoids (eg, santalols, ambroxides) are rare in nature and interesting targets for synthetic biology. As a representative example for genomics/transcriptomics enabled gene and enzyme discovery, we describe a strategy used successfully for elucidation of a complete fragrance biosynthetic pathway in sandalwood (Santalum album) and its reconstruction in yeast (Saccharomyces cerevisiae). We address questions related to the discovery of specific genes within large gene families and recovery of rare gene transcripts that are selectively expressed in recalcitrant tissues. To substantiate the validity of the approaches, we describe the combination of methods used in the gene and enzyme discovery of a cytochrome P450 in the fragrant heartwood of tropical sandalwood, responsible for the fragrance defining, final step in the biosynthesis of (Z)-santalols. © 2016 Elsevier Inc. All rights reserved.

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

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Chasman, Daniel I; Fuchsberger, Christian; Pattaro, Cristian; Teumer, Alexander; Böger, Carsten A; Endlich, Karlhans; Olden, Matthias; Chen, Ming-Huei; Tin, Adrienne; Taliun, Daniel; Li, Man; Gao, Xiaoyi; Gorski, Mathias; Yang, Qiong; Hundertmark, Claudia; Foster, Meredith C; O'Seaghdha, Conall M; Glazer, Nicole; Isaacs, Aaron; Liu, Ching-Ti; Smith, Albert V; O'Connell, Jeffrey R; Struchalin, Maksim; Tanaka, Toshiko; Li, Guo; Johnson, Andrew D; Gierman, Hinco J; Feitosa, Mary F; Hwang, Shih-Jen; Atkinson, Elizabeth J; Lohman, Kurt; Cornelis, Marilyn C; Johansson, Asa; Tönjes, Anke; Dehghan, Abbas; Lambert, Jean-Charles; Holliday, Elizabeth G; Sorice, Rossella; Kutalik, Zoltan; Lehtimäki, Terho; Esko, Tõnu; Deshmukh, Harshal; Ulivi, Sheila; Chu, Audrey Y; Murgia, Federico; Trompet, Stella; Imboden, Medea; Coassin, Stefan; Pistis, Giorgio; Harris, Tamara B; Launer, Lenore J; Aspelund, Thor; Eiriksdottir, Gudny; Mitchell, Braxton D; Boerwinkle, Eric; Schmidt, Helena; Cavalieri, Margherita; Rao, Madhumathi; Hu, Frank; Demirkan, Ayse; Oostra, Ben A; de Andrade, Mariza; Turner, Stephen T; Ding, Jingzhong; Andrews, Jeanette S; Freedman, Barry I; Giulianini, Franco; Koenig, Wolfgang; Illig, Thomas; Meisinger, Christa; Gieger, Christian; Zgaga, Lina; Zemunik, Tatijana; Boban, Mladen; Minelli, Cosetta; Wheeler, Heather E; Igl, Wilmar; Zaboli, Ghazal; Wild, Sarah H; Wright, Alan F; Campbell, Harry; Ellinghaus, David; Nöthlings, Ute; Jacobs, Gunnar; Biffar, Reiner; Ernst, Florian; Homuth, Georg; Kroemer, Heyo K; Nauck, Matthias; Stracke, Sylvia; Völker, Uwe; Völzke, Henry; Kovacs, Peter; Stumvoll, Michael; Mägi, Reedik; Hofman, Albert; Uitterlinden, Andre G; Rivadeneira, Fernando; Aulchenko, Yurii S; Polasek, Ozren; Hastie, Nick; Vitart, Veronique; Helmer, Catherine; Wang, Jie Jin; Stengel, Bénédicte; Ruggiero, Daniela; Bergmann, Sven; Kähönen, Mika; Viikari, Jorma; Nikopensius, Tiit; Province, Michael; Ketkar, Shamika; Colhoun, Helen; Doney, Alex; Robino, Antonietta; Krämer, Bernhard K; Portas, Laura; Ford, Ian; Buckley, Brendan M; Adam, Martin; Thun, Gian-Andri; Paulweber, Bernhard; Haun, Margot; Sala, Cinzia; Mitchell, Paul; Ciullo, Marina; Kim, Stuart K; Vollenweider, Peter; Raitakari, Olli; Metspalu, Andres; Palmer, Colin; Gasparini, Paolo; Pirastu, Mario; Jukema, J Wouter; Probst-Hensch, Nicole M; Kronenberg, Florian; Toniolo, Daniela; Gudnason, Vilmundur; Shuldiner, Alan R; Coresh, Josef; Schmidt, Reinhold; Ferrucci, Luigi; Siscovick, David S; van Duijn, Cornelia M; Borecki, Ingrid B; Kardia, Sharon L R; Liu, Yongmei; Curhan, Gary C; Rudan, Igor; Gyllensten, Ulf; Wilson, James F; Franke, Andre; Pramstaller, Peter P; Rettig, Rainer; Prokopenko, Inga; Witteman, Jacqueline; Hayward, Caroline; Ridker, Paul M; Parsa, Afshin; Bochud, Murielle; Heid, Iris M; Kao, W H Linda; Fox, Caroline S; Köttgen, Anna

2012-12-15

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.

Statistical and biological gene-lifestyle interactions of MC4R and FTO with diet and physical activity on obesity: new effects on alcohol consumption.

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Dolores Corella

Full Text Available BACKGROUND: Fat mass and obesity (FTO and melanocortin-4 receptor (MC4R and are relevant genes associated with obesity. This could be through food intake, but results are contradictory. Modulation by diet or other lifestyle factors is also not well understood. OBJECTIVE: To investigate whether MC4R and FTO associations with body-weight are modulated by diet and physical activity (PA, and to study their association with alcohol and food intake. METHODS: Adherence to Mediterranean diet (AdMedDiet and physical activity (PA were assessed by validated questionnaires in 7,052 high cardiovascular risk subjects. MC4R rs17782313 and FTO rs9939609 were determined. Independent and joint associations (aggregate genetic score as well as statistical and biological gene-lifestyle interactions were analyzed. RESULTS: FTO rs9939609 was associated with higher body mass index (BMI, waist circumference (WC and obesity (P<0.05 for all. A similar, but not significant trend was found for MC4R rs17782313. Their additive effects (aggregate score were significant and we observed a 7% per-allele increase of being obese (OR=1.07; 95%CI 1.01-1.13. We found relevant statistical interactions (P<0.05 with PA. So, in active individuals, the associations with higher BMI, WC or obesity were not detected. A biological (non-statistical interaction between AdMedDiet and rs9939609 and the aggregate score was found. Greater AdMedDiet in individuals carrying 4 or 3-risk alleles counterbalanced their genetic predisposition, exhibiting similar BMI (P=0.502 than individuals with no risk alleles and lower AdMedDiet. They also had lower BMI (P=0.021 than their counterparts with low AdMedDiet. We did not find any consistent association with energy or macronutrients, but found a novel association between these polymorphisms and lower alcohol consumption in variant-allele carriers (B+/-SE: -0.57+/-0.16 g/d per-score-allele; P=0.001. CONCLUSION: Statistical and biological interactions with PA

Extracellular membrane vesicles in blood products-biology and clinical relevance

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Emilija Krstova Krajnc

2016-01-01

Full Text Available Extracellular membrane vesicles are fragments shed from plasma membranes off all cell types that are undergoing apoptosis or are being subjected to various types of stimulation or stress.  Even in the process of programmed cell death (apoptosis, cell fall apart of varying size vesicles. They expose phosphatidylserine (PS on the outer leaflet of their membrane, and bear surface membrane antigens reflecting their cellular origin. Extracellular membrane vesicles have been isolated from many types of biological fluids, including serum, cerebrospinal fluid, urine, saliva, tears and conditioned culture medium. Flow cytometry is one of the many different methodological approaches that have been used to analyze EMVs. The method attempts to characterize the EMVs cellular origin, size, population, number, and structure. EMVs are present and accumulate in blood products (erythrocytes, platelets as well as in fresh frozen plasma during storage. The aim of this review is to highlight the importance of extracellular vesicles as a cell-to-cell communication system and the role in the pathogenesis of different diseases. Special emphasis will be given to the implication of extracellular membrane vesicles in blood products and their clinical relevance. Although our understanding of the role of  EMVs in disease is far from comprehensive, they display promise as biomarkers for different diseases in the future and also as a marker of quality and safety in the quality control of blood products.

LGscore: A method to identify disease-related genes using biological literature and Google data.

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Kim, Jeongwoo; Kim, Hyunjin; Yoon, Youngmi; Park, Sanghyun

2015-04-01

Since the genome project in 1990s, a number of studies associated with genes have been conducted and researchers have confirmed that genes are involved in disease. For this reason, the identification of the relationships between diseases and genes is important in biology. We propose a method called LGscore, which identifies disease-related genes using Google data and literature data. To implement this method, first, we construct a disease-related gene network using text-mining results. We then extract gene-gene interactions based on co-occurrences in abstract data obtained from PubMed, and calculate the weights of edges in the gene network by means of Z-scoring. The weights contain two values: the frequency and the Google search results. The frequency value is extracted from literature data, and the Google search result is obtained using Google. We assign a score to each gene through a network analysis. We assume that genes with a large number of links and numerous Google search results and frequency values are more likely to be involved in disease. For validation, we investigated the top 20 inferred genes for five different diseases using answer sets. The answer sets comprised six databases that contain information on disease-gene relationships. We identified a significant number of disease-related genes as well as candidate genes for Alzheimer's disease, diabetes, colon cancer, lung cancer, and prostate cancer. Our method was up to 40% more accurate than existing methods. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

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Gao Shouguo

2011-08-01

Full Text Available Abstract Background Bayesian Network (BN is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable. Results We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information. Conclusion our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

Chassis organism from Corynebacterium glutamicum--a top-down approach to identify and delete irrelevant gene clusters.

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Unthan, Simon; Baumgart, Meike; Radek, Andreas; Herbst, Marius; Siebert, Daniel; Brühl, Natalie; Bartsch, Anna; Bott, Michael; Wiechert, Wolfgang; Marin, Kay; Hans, Stephan; Krämer, Reinhard; Seibold, Gerd; Frunzke, Julia; Kalinowski, Jörn; Rückert, Christian; Wendisch, Volker F; Noack, Stephan

2015-02-01

For synthetic biology applications, a robust structural basis is required, which can be constructed either from scratch or in a top-down approach starting from any existing organism. In this study, we initiated the top-down construction of a chassis organism from Corynebacterium glutamicum ATCC 13032, aiming for the relevant gene set to maintain its fast growth on defined medium. We evaluated each native gene for its essentiality considering expression levels, phylogenetic conservation, and knockout data. Based on this classification, we determined 41 gene clusters ranging from 3.7 to 49.7 kbp as target sites for deletion. 36 deletions were successful and 10 genome-reduced strains showed impaired growth rates, indicating that genes were hit, which are relevant to maintain biological fitness at wild-type level. In contrast, 26 deleted clusters were found to include exclusively irrelevant genes for growth on defined medium. A combinatory deletion of all irrelevant gene clusters would, in a prophage-free strain, decrease the size of the native genome by about 722 kbp (22%) to 2561 kbp. Finally, five combinatory deletions of irrelevant gene clusters were investigated. The study introduces the novel concept of relevant genes and demonstrates general strategies to construct a chassis suitable for biotechnological application. © 2014 The Authors. Biotechnology Journal published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution-Non-Commercial-NoDerivs Licence, which permits use and distribution in any medium, provided the original work is properly cited, the use is non- commercial and no modifications or adaptations are made.

Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

International Nuclear Information System (INIS)

Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

2009-01-01

Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

A data integration approach for cell cycle analysis oriented to model simulation in systems biology

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Mosca Ettore

2007-08-01

Full Text Available Abstract Background The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies. Description In this work, we present a resource, the Cell Cycle Database, intended to support systems biology analysis on the Cell Cycle process, based on two organisms, yeast and mammalian. The database integrates information about genes and proteins involved in the cell cycle process, stores complete models of the interaction networks and allows the mathematical simulation over time of the quantitative behaviour of each component. To accomplish this task, we developed, a web interface for browsing information related to cell cycle genes, proteins and mathematical models. In this framework, we have implemented a pipeline which allows users to deal with the mathematical part of the models, in order to solve, using different variables, the ordinary differential equation systems that describe the biological process. Conclusion This integrated system is freely available in order to support systems biology research on the cell cycle and

Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

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Kim Man-Sun

2012-05-01

Full Text Available Abstract Background Network motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts. Results On the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns. Conclusions Taken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.

The clinical implications and biologic relevance of neurofilament expression in gastroenteropancreatic neuroendocrine neoplasms.

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Schimmack, Simon; Lawrence, Ben; Svejda, Bernhard; Alaimo, Daniele; Schmitz-Winnenthal, Hubertus; Fischer, Lars; Büchler, Markus W; Kidd, Mark; Modlin, Irvin

2012-05-15

Although gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) exhibit widely divergent behavior, limited biologic information (apart from Ki-67) is available to characterize malignancy. Therefore, the identification of alternative biomarkers is a key unmet need. Given the role of internexin alpha (INA) in neuronal development, the authors assessed its function in neuroendocrine cell systems and the clinical implications of its expression as a GEP-NEN biomarker. Functional assays were undertaken to investigate the mechanistic role of INA in the pancreatic BON cell line. Expression levels of INA were investigated in 50 pancreatic NENs (43 primaries, 7 metastases), 43 small intestinal NENs (25 primaries, 18 metastases), normal pancreas (n = 10), small intestinal mucosa (n = 16), normal enterochromaffin (EC) cells (n = 9), mouse xenografts (n = 4) and NEN cell lines (n = 6) using quantitative polymerase chain reaction, Western blot, and immunostaining analyses. In BON cells, decreased levels of INA messenger RNA and protein were associated with the inhibition of both proliferation and mitogen-activated protein kinase (MAPK) signaling. INA was not expressed in normal neuroendocrine cells but was overexpressed (from 2-fold to 42-fold) in NEN cell lines and murine xenografts. In pancreatic NENs, INA was overexpressed compared with pancreatic adenocarcinomas and normal pancreas (27-fold [P = .0001], and 9-fold [P = .02], respectively). INA transcripts were correlated positively with Ki-67 (correlation coefficient [r] = 0.5; P biologic information relevant to delineation of both pancreatic NEN tumor phenotypes and clinical behavior. Copyright © 2011 American Cancer Society.

Clinically relevant known and candidate genes for obesity and their overlap with human infertility and reproduction.

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Butler, Merlin G; McGuire, Austen; Manzardo, Ann M

2015-04-01

Obesity is a growing public health concern now reaching epidemic status worldwide for children and adults due to multiple problems impacting on energy intake and expenditure with influences on human reproduction and infertility. A positive family history and genetic factors are known to play a role in obesity by influencing eating behavior, weight and level of physical activity and also contributing to human reproduction and infertility. Recent advances in genetic technology have led to discoveries of new susceptibility genes for obesity and causation of infertility. The goal of our study was to provide an update of clinically relevant candidate and known genes for obesity and infertility using high resolution chromosome ideograms with gene symbols and tabular form. We used computer-based internet websites including PubMed to search for combinations of key words such as obesity, body mass index, infertility, reproduction, azoospermia, endometriosis, diminished ovarian reserve, estrogen along with genetics, gene mutations or variants to identify evidence for development of a master list of recognized obesity genes in humans and those involved with infertility and reproduction. Gene symbols for known and candidate genes for obesity were plotted on high resolution chromosome ideograms at the 850 band level. Both infertility and obesity genes were listed separately in alphabetical order in tabular form and those highlighted when involved with both conditions. By searching the medical literature and computer generated websites for key words, we found documented evidence for 370 genes playing a role in obesity and 153 genes for human reproduction or infertility. The obesity genes primarily affected common pathways in lipid metabolism, deposition or transport, eating behavior and food selection, physical activity or energy expenditure. Twenty-one of the obesity genes were also associated with human infertility and reproduction. Gene symbols were plotted on high resolution

Immediate Early Genes Anchor a Biological Pathway of Proteins Required for Memory Formation, Long-Term Depression and Risk for Schizophrenia

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Ketan K. Marballi

2018-02-01

Full Text Available While the causes of myriad medical and infectious illnesses have been identified, the etiologies of neuropsychiatric illnesses remain elusive. This is due to two major obstacles. First, the risk for neuropsychiatric disorders, such as schizophrenia, is determined by both genetic and environmental factors. Second, numerous genes influence susceptibility for these illnesses. Genome-wide association studies have identified at least 108 genomic loci for schizophrenia, and more are expected to be published shortly. In addition, numerous biological processes contribute to the neuropathology underlying schizophrenia. These include immune dysfunction, synaptic and myelination deficits, vascular abnormalities, growth factor disruption, and N-methyl-D-aspartate receptor (NMDAR hypofunction. However, the field of psychiatric genetics lacks a unifying model to explain how environment may interact with numerous genes to influence these various biological processes and cause schizophrenia. Here we describe a biological cascade of proteins that are activated in response to environmental stimuli such as stress, a schizophrenia risk factor. The central proteins in this pathway are critical mediators of memory formation and a particular form of hippocampal synaptic plasticity, long-term depression (LTD. Each of these proteins is also implicated in schizophrenia risk. In fact, the pathway includes four genes that map to the 108 loci associated with schizophrenia: GRIN2A, nuclear factor of activated T-cells (NFATc3, early growth response 1 (EGR1 and NGFI-A Binding Protein 2 (NAB2; each of which contains the “Index single nucleotide polymorphism (SNP” (most SNP at its respective locus. Environmental stimuli activate this biological pathway in neurons, resulting in induction of EGR immediate early genes: EGR1, EGR3 and NAB2. We hypothesize that dysfunction in any of the genes in this pathway disrupts the normal activation of Egrs in response to stress. This may

Immediate Early Genes Anchor a Biological Pathway of Proteins Required for Memory Formation, Long-Term Depression and Risk for Schizophrenia

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Marballi, Ketan K.; Gallitano, Amelia L.

2018-01-01

While the causes of myriad medical and infectious illnesses have been identified, the etiologies of neuropsychiatric illnesses remain elusive. This is due to two major obstacles. First, the risk for neuropsychiatric disorders, such as schizophrenia, is determined by both genetic and environmental factors. Second, numerous genes influence susceptibility for these illnesses. Genome-wide association studies have identified at least 108 genomic loci for schizophrenia, and more are expected to be published shortly. In addition, numerous biological processes contribute to the neuropathology underlying schizophrenia. These include immune dysfunction, synaptic and myelination deficits, vascular abnormalities, growth factor disruption, and N-methyl-D-aspartate receptor (NMDAR) hypofunction. However, the field of psychiatric genetics lacks a unifying model to explain how environment may interact with numerous genes to influence these various biological processes and cause schizophrenia. Here we describe a biological cascade of proteins that are activated in response to environmental stimuli such as stress, a schizophrenia risk factor. The central proteins in this pathway are critical mediators of memory formation and a particular form of hippocampal synaptic plasticity, long-term depression (LTD). Each of these proteins is also implicated in schizophrenia risk. In fact, the pathway includes four genes that map to the 108 loci associated with schizophrenia: GRIN2A, nuclear factor of activated T-cells (NFATc3), early growth response 1 (EGR1) and NGFI-A Binding Protein 2 (NAB2); each of which contains the “Index single nucleotide polymorphism (SNP)” (most SNP) at its respective locus. Environmental stimuli activate this biological pathway in neurons, resulting in induction of EGR immediate early genes: EGR1, EGR3 and NAB2. We hypothesize that dysfunction in any of the genes in this pathway disrupts the normal activation of Egrs in response to stress. This may result in

Cisplatin Resistant Spheroids Model Clinically Relevant Survival Mechanisms in Ovarian Tumors.

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Winyoo Chowanadisai

Full Text Available The majority of ovarian tumors eventually recur in a drug resistant form. Using cisplatin sensitive and resistant cell lines assembled into 3D spheroids we profiled gene expression and identified candidate mechanisms and biological pathways associated with cisplatin resistance. OVCAR-8 human ovarian carcinoma cells were exposed to sub-lethal concentrations of cisplatin to create a matched cisplatin-resistant cell line, OVCAR-8R. Genome-wide gene expression profiling of sensitive and resistant ovarian cancer spheroids identified 3,331 significantly differentially expressed probesets coding for 3,139 distinct protein-coding genes (Fc >2, FDR < 0.05 (S2 Table. Despite significant expression changes in some transporters including MDR1, cisplatin resistance was not associated with differences in intracellular cisplatin concentration. Cisplatin resistant cells were significantly enriched for a mesenchymal gene expression signature. OVCAR-8R resistance derived gene sets were significantly more biased to patients with shorter survival. From the most differentially expressed genes, we derived a 17-gene expression signature that identifies ovarian cancer patients with shorter overall survival in three independent datasets. We propose that the use of cisplatin resistant cell lines in 3D spheroid models is a viable approach to gain insight into resistance mechanisms relevant to ovarian tumors in patients. Our data support the emerging concept that ovarian cancers can acquire drug resistance through an epithelial-to-mesenchymal transition.

Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis

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Scott, RE; Ghule, PN; Stein, JL; Stein, GS

2015-01-01

The early stages of carcinogenesis are linked to defects in the cell cycle. A series of cell cycle checkpoints are involved in this process. The G1/S checkpoint that serves to integrate the control of cell proliferation and differentiation is linked to carcinogenesis and the mitotic spindle checkpoint with the development of chromosomal instability. This paper presents the outcome of systems biology studies designed to evaluate if networks of covariate cell cycle gene transcripts exist in proliferative mammalian tissues including mice, rats and humans. The GeneNetwork website that contains numerous gene expression datasets from different species, sexes and tissues represents the foundational resource for these studies (www.genenetwork.org). In addition, WebGestalt, a gene ontology tool, facilitated the identification of expression networks of genes that co-vary with key cell cycle targets, especially Cdc20 and Plk1 (www.bioinfo.vanderbilt.edu/webgestalt). Cell cycle expression networks of such covariate mRNAs exist in multiple proliferative tissues including liver, lung, pituitary, adipose and lymphoid tissues among others but not in brain or retina that have low proliferative potential. Sixty-three covariate cell cycle gene transcripts (mRNAs) compose the average cell cycle network with p = e−13 to e−36. Cell cycle expression networks show species, sex and tissue variability and they are enriched in mRNA transcripts associated with mitosis many of which are associated with chromosomal instability. PMID:25808367

The Schizophrenia-Associated BRD1 Gene Regulates Behavior, Neurotransmission, and Expression of Schizophrenia Risk Enriched Gene Sets in Mice.

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Qvist, Per; Christensen, Jane Hvarregaard; Vardya, Irina; Rajkumar, Anto Praveen; Mørk, Arne; Paternoster, Veerle; Füchtbauer, Ernst-Martin; Pallesen, Jonatan; Fryland, Tue; Dyrvig, Mads; Hauberg, Mads Engel; Lundsberg, Birgitte; Fejgin, Kim; Nyegaard, Mette; Jensen, Kimmo; Nyengaard, Jens Randel; Mors, Ole; Didriksen, Michael; Børglum, Anders Dupont

2017-07-01

The schizophrenia-associated BRD1 gene encodes a transcriptional regulator whose comprehensive chromatin interactome is enriched with schizophrenia risk genes. However, the biology underlying the disease association of BRD1 remains speculative. This study assessed the transcriptional drive of a schizophrenia-associated BRD1 risk variant in vitro. Accordingly, to examine the effects of reduced Brd1 expression, we generated a genetically modified Brd1 +/- mouse and subjected it to behavioral, electrophysiological, molecular, and integrative genomic analyses with focus on schizophrenia-relevant parameters. Brd1 +/- mice displayed cerebral histone H3K14 hypoacetylation and a broad range of behavioral changes with translational relevance to schizophrenia. These behaviors were accompanied by striatal dopamine/serotonin abnormalities and cortical excitation-inhibition imbalances involving loss of parvalbumin immunoreactive interneurons. RNA-sequencing analyses of cortical and striatal micropunches from Brd1 +/- and wild-type mice revealed differential expression of genes enriched for schizophrenia risk, including several schizophrenia genome-wide association study risk genes (e.g., calcium channel subunits [Cacna1c and Cacnb2], cholinergic muscarinic receptor 4 [Chrm4)], dopamine receptor D 2 [Drd2], and transcription factor 4 [Tcf4]). Integrative analyses further found differentially expressed genes to cluster in functional networks and canonical pathways associated with mental illness and molecular signaling processes (e.g., glutamatergic, monoaminergic, calcium, cyclic adenosine monophosphate [cAMP], dopamine- and cAMP-regulated neuronal phosphoprotein 32 kDa [DARPP-32], and cAMP responsive element binding protein signaling [CREB]). Our study bridges the gap between genetic association and pathogenic effects and yields novel insights into the unfolding molecular changes in the brain of a new schizophrenia model that incorporates genetic risk at three levels: allelic

Differential Effect of Active Smoking on Gene Expression in Male and Female Smokers

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Paul, Sunirmal; Amundson, Sally A

2015-01-01

Smoking is the second leading cause of preventable death in the United States. Cohort epidemiological studies have demonstrated that women are more vulnerable to cigarette-smoking induced diseases than their male counterparts, however, the molecular basis of these differences has remained unknown. In this study, we explored if there were differences in the gene expression patterns between male and female smokers, and how these patterns might reflect different sex-specific responses to the stress of smoking. Using whole genome microarray gene expression profiling, we found that a substantial number of oxidant related genes were expressed in both male and female smokers, however, smoking-responsive genes did indeed differ greatly between male and female smokers. Gene set enrichment analysis (GSEA) against reference oncogenic signature gene sets identified a large number of oncogenic pathway gene-sets that were significantly altered in female smokers compared to male smokers. In addition, functional annotation with Ingenuity Pathway Analysis (IPA) identified smoking-correlated genes associated with biological functions in male and female smokers that are directly relevant to well-known smoking related pathologies. However, these relevant biological functions were strikingly overrepresented in female smokers compared to male smokers. IPA network analysis with the functional categories of immune and inflammatory response gene products suggested potential interactions between smoking response and female hormones. Our results demonstrate a striking dichotomy between male and female gene expression responses to smoking. This is the first genome-wide expression study to compare the sex-specific impacts of smoking at a molecular level and suggests a novel potential connection between sex hormone signaling and smoking-induced diseases in female smokers. PMID:25621181

Integrative miRNA-Gene Expression Analysis Enables Refinement of Associated Biology and Prediction of Response to Cetuximab in Head and Neck Squamous Cell Cancer

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Loris De Cecco

2017-01-01

Full Text Available This paper documents the process by which we, through gene and miRNA expression profiling of the same samples of head and neck squamous cell carcinomas (HNSCC and an integrative miRNA-mRNA expression analysis, were able to identify candidate biomarkers of progression-free survival (PFS in patients treated with cetuximab-based approaches. Through sparse partial least square–discriminant analysis (sPLS-DA and supervised analysis, 36 miRNAs were identified in two components that clearly separated long- and short-PFS patients. Gene set enrichment analysis identified a significant correlation between the miRNA first-component and EGFR signaling, keratinocyte differentiation, and p53. Another significant correlation was identified between the second component and RAS, NOTCH, immune/inflammatory response, epithelial–mesenchymal transition (EMT, and angiogenesis pathways. Regularized canonical correlation analysis of sPLS-DA miRNA and gene data combined with the MAGIA2 web-tool highlighted 16 miRNAs and 84 genes that were interconnected in a total of 245 interactions. After feature selection by a smoothed t-statistic support vector machine, we identified three miRNAs and five genes in the miRNA-gene network whose expression result was the most relevant in predicting PFS (Area Under the Curve, AUC = 0.992. Overall, using a well-defined clinical setting and up-to-date bioinformatics tools, we are able to give the proof of principle that an integrative miRNA-mRNA expression could greatly contribute to the refinement of the biology behind a predictive model.

Giant Subependymoma Developed in a Patient with Aniridia: Analyses of PAX6 and Tumor-relevant Genes

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Maekawa, Motoko; Fujisawa, Hironori; Iwayama, Yoshimi; Tamase, Akira; Toyota, Tomoko; Osumi, Noriko; Yoshikawa, Takeo

2010-01-01

We observed an unusually large subependymoma in a female patient with congenital aniridia. To analyze the genetic mechanisms of tumorigenesis, we first examined the paired box 6 (PAX6) gene using both tumor tissue and peripheral lymphocytes. Tumor suppressor activity has been proposed for PAX6 in gliomas, in addition to its well-known role in the eye development. Using genomic quantitative PCR and loss of heterozygosity analysis, we identified hemizygous deletions in the 5′-region of PAX6. In lymphocytes, the deletion within PAX6 spanned from between exons 6 and 7 to the 5′-upstream region of the gene, but did not reach the upstream gene, RNC1, which is reported to be associated with tumors. The subependymoma had an additional de novo deletion spanning from the intron 4 to intron 6 of PAX6, although we could not completely determine whether these two deletions are on the same chromosome or not. We also examined other potentially relevant tumor suppressor genes: PTEN, TP53 and SOX2. However, we detected no exonic mutations or deletions in these genes. Collectively, we speculate that the defect in PAX6 may have contributed to the extremely large size of the subependymoma, due to a loss of tumor suppressor activity in glial cell lineage. PMID:20500513

Integration of gene expression and methylation to unravel biological networks in glioblastoma patients.

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Gadaleta, Francesco; Bessonov, Kyrylo; Van Steen, Kristel

2017-02-01

The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach, Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified, respectively, 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Although the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response, and several cancer types. Importantly, we observed significant overrepresentation of cancer-related pathways including glioma, especially in the XORnet network, suggesting a nonignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors. © 2016 WILEY PERIODICALS, INC.

The interplay of post-translational modification and gene therapy

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Osamor VC

2016-02-01

Full Text Available Victor Chukwudi Osamor,1–3 Shalom N Chinedu,3,4 Dominic E Azuh,3,5 Emeka Joshua Iweala,3,4 Olubanke Olujoke Ogunlana3,4 1Covenant University Bioinformatics Research (CUBRe Unit, Department of Computer and Information Sciences, College of Science and Technology (CST, Covenant University, Ota, Ogun State, Nigeria; 2Institute of Informatics (Computational biology and Bioinformatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw (Uniwersytet Warszawski, Warszawa, Poland; 3Covenant University Public Health and Well-being Research Group (CUPHWERG, Covenant University, 4Biochemistry and Molecular Biology Unit, Department of Biological Sciences, College of Science and Technology, Covenant University, Canaan Land, 5Department of Economics and Development Studies, Covenant University, Ota, Ogun State, Nigeria Abstract: Several proteins interact either to activate or repress the expression of other genes during transcription. Based on the impact of these activities, the proteins can be classified into readers, modifier writers, and modifier erasers depending on whether histone marks are read, added, or removed, respectively, from a specific amino acid. Transcription is controlled by dynamic epigenetic marks with serious health implications in certain complex diseases, whose understanding may be useful in gene therapy. This work highlights traditional and current advances in post-translational modifications with relevance to gene therapy delivery. We report that enhanced understanding of epigenetic machinery provides clues to functional implication of certain genes/gene products and may facilitate transition toward revision of our clinical treatment procedure with effective fortification of gene therapy delivery. Keywords: post-translational modification, gene therapy, epigenetics, histone, methylation

Effects of simulated microgravity on gene expression and biological phenotypes of a single generation Caenorhabditis elegans cultured on 2 different media.

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Tee, Ling Fei; Neoh, Hui-Min; Then, Sue Mian; Murad, Nor Azian; Asillam, Mohd Fairos; Hashim, Mohd Helmy; Nathan, Sheila; Jamal, Rahman

2017-11-01

Studies of multigenerational Caenorhabditis elegans exposed to long-term spaceflight have revealed expression changes of genes involved in longevity, DNA repair, and locomotion. However, results from spaceflight experiments are difficult to reproduce as space missions are costly and opportunities are rather limited for researchers. In addition, multigenerational cultures of C. elegans used in previous studies contribute to mixture of gene expression profiles from both larvae and adult worms, which were recently reported to be different. Usage of different culture media during microgravity simulation experiments might also give rise to differences in the gene expression and biological phenotypes of the worms. In this study, we investigated the effects of simulated microgravity on the gene expression and biological phenotype profiles of a single generation of C. elegans worms cultured on 2 different culture media. A desktop Random Positioning Machine (RPM) was used to simulate microgravity on the worms for approximately 52 to 54 h. Gene expression profile was analysed using the Affymetrix GeneChip® C. elegans 1.0 ST Array. Only one gene (R01H2.2) was found to be downregulated in nematode growth medium (NGM)-cultured worms exposed to simulated microgravity. On the other hand, eight genes were differentially expressed for C. elegans Maintenance Medium (CeMM)-cultured worms in microgravity; six were upregulated, while two were downregulated. Five of the upregulated genes (C07E3.15, C34H3.21, C32D5.16, F35H8.9 and C34F11.17) encode non-coding RNAs. In terms of biological phenotype, we observed that microgravity-simulated worms experienced minimal changes in terms of lifespan, locomotion and reproductive capabilities in comparison with the ground controls. Taking it all together, simulated microgravity on a single generation of C. elegans did not confer major changes to their gene expression and biological phenotype. Nevertheless, exposure of the worms to microgravity

Whole genome DNA methylation: beyond genes silencing

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Tirado-Magallanes, Roberto; Rebbani, Khadija; Lim, Ricky; Pradhan, Sriharsa; Benoukraf, Touati

2016-01-01

The combination of DNA bisulfite treatment with high-throughput sequencing technologies has enabled investigation of genome-wide DNA methylation at near base pair level resolution, far beyond that of the kilobase-long canonical CpG islands that initially revealed the biological relevance of this covalent DNA modification. The latest high-resolution studies have revealed a role for very punctual DNA methylation in chromatin plasticity, gene regulation and splicing. Here, we aim to outline the ...

Dose addition models based on biologically-relevant reductions in fetal testosterone accurately predict postnatal reproductive tract alterations by a phthalate mixture in rats

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Challenges in cumulative risk assessment of anti-androgenic phthalate mixtures include a lack of data on all the individual phthalates and difficulty determining the biological relevance of reduction in fetal testosterone (T) on postnatal development. The objectives of the curren...

The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes.

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Marion Ouedraogo

Full Text Available BACKGROUND: There has been a surge in studies linking genome structure and gene expression, with special focus on duplicated genes. Although initially duplicated from the same sequence, duplicated genes can diverge strongly over evolution and take on different functions or regulated expression. However, information on the function and expression of duplicated genes remains sparse. Identifying groups of duplicated genes in different genomes and characterizing their expression and function would therefore be of great interest to the research community. The 'Duplicated Genes Database' (DGD was developed for this purpose. METHODOLOGY: Nine species were included in the DGD. For each species, BLAST analyses were conducted on peptide sequences corresponding to the genes mapped on a same chromosome. Groups of duplicated genes were defined based on these pairwise BLAST comparisons and the genomic location of the genes. For each group, Pearson correlations between gene expression data and semantic similarities between functional GO annotations were also computed when the relevant information was available. CONCLUSIONS: The Duplicated Gene Database provides a list of co-localised and duplicated genes for several species with the available gene co-expression level and semantic similarity value of functional annotation. Adding these data to the groups of duplicated genes provides biological information that can prove useful to gene expression analyses. The Duplicated Gene Database can be freely accessed through the DGD website at http://dgd.genouest.org.

Breast cancer biology for the radiation oncologist

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Strauss, Jonathan [Northwestern Univ., Chicago, IL (United States). Dept. of Radiation Oncology; Small, William [Loyola Univ. Chicago, Maywood, IL (United States). Stritch School of Medicine, Cardianl Bernardin Cancer Center; Woloschak, Gayle E. (ed.) [Northwestern Univ. Feinberg, Chicago, IL (United States). School of Medicine

2015-10-01

This is the first textbook of its kind devoted to describing the biological complexities of breast cancer in a way that is relevant to the radiation oncologist. Radiation Oncology has long treated breast cancer as a single biological entity, with all treatment decisions being based on clinical and pathologic risk factors. We are now beginning to understand that biological subtypes of breast cancer may have different risks of recurrence as well as different intrinsic sensitivity to radiotherapy. Multi-gene arrays that have for years been used to predict the risk of distant recurrence and the value of systemic chemotherapy may also have utility in predicting the risk of local recurrence. Additionally, the targeted agents used to treat breast cancer may interact with radiotherapy in ways that can be beneficial or undesirable. All of these emerging issues are extensively discussed in this book, and practical evidence-based treatment recommendations are presented whenever possible.

Breast cancer biology for the radiation oncologist

International Nuclear Information System (INIS)

Strauss, Jonathan; Small, William; Woloschak, Gayle E.

2015-01-01

This is the first textbook of its kind devoted to describing the biological complexities of breast cancer in a way that is relevant to the radiation oncologist. Radiation Oncology has long treated breast cancer as a single biological entity, with all treatment decisions being based on clinical and pathologic risk factors. We are now beginning to understand that biological subtypes of breast cancer may have different risks of recurrence as well as different intrinsic sensitivity to radiotherapy. Multi-gene arrays that have for years been used to predict the risk of distant recurrence and the value of systemic chemotherapy may also have utility in predicting the risk of local recurrence. Additionally, the targeted agents used to treat breast cancer may interact with radiotherapy in ways that can be beneficial or undesirable. All of these emerging issues are extensively discussed in this book, and practical evidence-based treatment recommendations are presented whenever possible.

Novel algorithms reveal streptococcal transcriptomes and clues about undefined genes.

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Ryan, Patricia A; Kirk, Brian W; Euler, Chad W; Schuch, Raymond; Fischetti, Vincent A

2007-07-01

Bacteria-host interactions are dynamic processes, and understanding transcriptional responses that directly or indirectly regulate the expression of genes involved in initial infection stages would illuminate the molecular events that result in host colonization. We used oligonucleotide microarrays to monitor (in vitro) differential gene expression in group A streptococci during pharyngeal cell adherence, the first overt infection stage. We present neighbor clustering, a new computational method for further analyzing bacterial microarray data that combines two informative characteristics of bacterial genes that share common function or regulation: (1) similar gene expression profiles (i.e., co-expression); and (2) physical proximity of genes on the chromosome. This method identifies statistically significant clusters of co-expressed gene neighbors that potentially share common function or regulation by coupling statistically analyzed gene expression profiles with the chromosomal position of genes. We applied this method to our own data and to those of others, and we show that it identified a greater number of differentially expressed genes, facilitating the reconstruction of more multimeric proteins and complete metabolic pathways than would have been possible without its application. We assessed the biological significance of two identified genes by assaying deletion mutants for adherence in vitro and show that neighbor clustering indeed provides biologically relevant data. Neighbor clustering provides a more comprehensive view of the molecular responses of streptococci during pharyngeal cell adherence.

Epigenetics in prostate cancer: biologic and clinical relevance.

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Jerónimo, Carmen; Bastian, Patrick J; Bjartell, Anders; Carbone, Giuseppina M; Catto, James W F; Clark, Susan J; Henrique, Rui; Nelson, William G; Shariat, Shahrokh F

2011-10-01

Prostate cancer (PCa) is one of the most common human malignancies and arises through genetic and epigenetic alterations. Epigenetic modifications include DNA methylation, histone modifications, and microRNAs (miRNA) and produce heritable changes in gene expression without altering the DNA coding sequence. To review progress in the understanding of PCa epigenetics and to focus upon translational applications of this knowledge. PubMed was searched for publications regarding PCa and DNA methylation, histone modifications, and miRNAs. Reports were selected based on the detail of analysis, mechanistic support of data, novelty, and potential clinical applications. Aberrant DNA methylation (hypo- and hypermethylation) is the best-characterized alteration in PCa and leads to genomic instability and inappropriate gene expression. Global and locus-specific changes in chromatin remodeling are implicated in PCa, with evidence suggesting a causative dysfunction of histone-modifying enzymes. MicroRNA deregulation also contributes to prostate carcinogenesis, including interference with androgen receptor signaling and apoptosis. There are important connections between common genetic alterations (eg, E twenty-six fusion genes) and the altered epigenetic landscape. Owing to the ubiquitous nature of epigenetic alterations, they provide potential biomarkers for PCa detection, diagnosis, assessment of prognosis, and post-treatment surveillance. Altered epigenetic gene regulation is involved in the genesis and progression of PCa. Epigenetic alterations may provide valuable tools for the management of PCa patients and be targeted by pharmacologic compounds that reverse their nature. The potential for epigenetic changes in PCa requires further exploration and validation to enable translation to the clinic. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.

A meta-analysis of the abscopal effect in preclinical models: Is the biologically effective dose a relevant physical trigger?

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Raffaella Marconi

Full Text Available Preclinical in vivo studies using small animals are considered crucial in translational cancer research and clinical implementation of novel treatments. This is of paramount relevance in radiobiology, especially for any technological developments permitted to deliver high doses in single or oligo-fractionated regimens, such as stereotactic ablative radiotherapy (SABR. In this context, clinical success in cancer treatment needs to be guaranteed, sparing normal tissue and preventing the potential spread of disease or local recurrence. In this work we introduce a new dose-response relationship based on relevant publications concerning preclinical models with regard to delivered dose, fractionation schedule and occurrence of biological effects on non-irradiated tissue, abscopal effects.We reviewed relevant publications on murine models and the abscopal effect in radiation cancer research following PRISMA methodology. In particular, through a log-likelihood method, we evaluated whether the occurrence of abscopal effects may be related to the biologically effective dose (BED. To this aim, studies accomplished with different tumor histotypes were considered in our analysis including breast, colon, lung, fibrosarcoma, pancreas, melanoma and head and neck cancer. For all the tumors, the α / β ratio was assumed to be 10 Gy, as generally adopted for neoplastic cells.Our results support the hypothesis that the occurrence rate of abscopal effects in preclinical models increases with BED. In particular, the probability of revealing abscopal effects is 50% when a BED of 60 Gy is generated.Our study provides evidence that SABR treatments associated with high BEDs could be considered an effective strategy in triggering the abscopal effect, thus shedding light on the promising outcomes revealed in clinical practice.

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution

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Kleinjans Jos

2008-09-01

Full Text Available Abstract Background In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. Results In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh, is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. Conclusion CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution.

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Moretti, Stefano; van Leeuwen, Danitsja; Gmuender, Hans; Bonassi, Stefano; van Delft, Joost; Kleinjans, Jos; Patrone, Fioravante; Merlo, Domenico Franco

2008-09-02

In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that

DNA Array-Based Gene Profiling

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Mocellin, Simone; Provenzano, Maurizio; Rossi, Carlo Riccardo; Pilati, Pierluigi; Nitti, Donato; Lise, Mario

2005-01-01

Cancer is a heterogeneous disease in most respects, including its cellularity, different genetic alterations, and diverse clinical behaviors. Traditional molecular analyses are reductionist, assessing only 1 or a few genes at a time, thus working with a biologic model too specific and limited to confront a process whose clinical outcome is likely to be governed by the combined influence of many genes. The potential of functional genomics is enormous, because for each experiment, thousands of relevant observations can be made simultaneously. Accordingly, DNA array, like other high-throughput technologies, might catalyze and ultimately accelerate the development of knowledge in tumor cell biology. Although in its infancy, the implementation of DNA array technology in cancer research has already provided investigators with novel data and intriguing new hypotheses on the molecular cascade leading to carcinogenesis, tumor aggressiveness, and sensitivity to antiblastic agents. Given the revolutionary implications that the use of this technology might have in the clinical management of patients with cancer, principles of DNA array-based tumor gene profiling need to be clearly understood for the data to be correctly interpreted and appreciated. In the present work, we discuss the technical features characterizing this powerful laboratory tool and review the applications so far described in the field of oncology. PMID:15621987

Using Osteoclast Differentiation as a Model for Gene Discovery in an Undergraduate Cell Biology Laboratory

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Birnbaum, Mark J.; Picco, Jenna; Clements, Meghan; Witwicka, Hanna; Yang, Meiheng; Hoey, Margaret T.; Odgren, Paul R.

2010-01-01

A key goal of molecular/cell biology/biotechnology is to identify essential genes in virtually every physiological process to uncover basic mechanisms of cell function and to establish potential targets of drug therapy combating human disease. This article describes a semester-long, project-oriented molecular/cellular/biotechnology laboratory…

Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales

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Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosyst...

Genes and pathways underlying susceptibility to impaired lung function in the context of environmental tobacco smoke exposure

NARCIS (Netherlands)

K. de Jong (Kim); J.M. Vonk (Judith); M. Imboden (Medea); L. Lahousse (Lies); A. Hofman (Albert); G.G. Brusselle (Guy); N.M. Probst-Hensch (Nicole M.); D.S. Postma (Dirkje); H.M. Boezen (Marike)

2017-01-01

textabstractBackground: Studies aiming to assess genetic susceptibility for impaired lung function levels upon exposure to environmental tobacco smoke (ETS) have thus far focused on candidate-genes selected based on a-priori knowledge of potentially relevant biological pathways, such as glutathione

An ontology-driven semantic mashup of gene and biological pathway information: application to the domain of nicotine dependence.

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Sahoo, Satya S; Bodenreider, Olivier; Rutter, Joni L; Skinner, Karen J; Sheth, Amit P

2008-10-01

This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. RESOURCE PAGE: http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/

Discovering and validating biological hypotheses from coherent patterns in functional genomics data

Energy Technology Data Exchange (ETDEWEB)

Joachimiak, Marcin Pawel

2008-08-12

The area of transcriptomics analysis is among the more established in computational biology, having evolved in both technology and experimental design. Transcriptomics has a strong impetus to develop sophisticated computational methods due to the large amounts of available whole-genome datasets for many species and because of powerful applications in regulatory network reconstruction as well as elucidation and modeling of cellular transcriptional responses. While gene expression microarray data can be noisy and comparisons across experiments challenging, there are a number of sophisticated methods that aid in arriving at statistically and biologically significant conclusions. As such, computational transcriptomics analysis can provide guidance for analysis of results from newer experimental technologies. More recently, search methods have been developed to identify modules of genes, which exhibit coherent expression patterns in only a subset of experimental conditions. The latest advances in these methods allow to integrate multiple data types anddatasets, both experimental and computational, within a single statistical framework accounting for data confidence and relevance to specific biological questions. Such frameworks provide a unified environment for the exploration of specific biological hypothesis and for the discovery of coherent data patterns along with the evidence supporting them.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

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Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

TaBoo SeArch Algorithm with a Modified Inverse Histogram for Reproducing Biologically Relevant Rare Events of Proteins.

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Harada, Ryuhei; Takano, Yu; Shigeta, Yasuteru

2016-05-10

The TaBoo SeArch (TBSA) algorithm [ Harada et al. J. Comput. Chem. 2015 , 36 , 763 - 772 and Harada et al. Chem. Phys. Lett. 2015 , 630 , 68 - 75 ] was recently proposed as an enhanced conformational sampling method for reproducing biologically relevant rare events of a given protein. In TBSA, an inverse histogram of the original distribution, mapped onto a set of reaction coordinates, is constructed from trajectories obtained by multiple short-time molecular dynamics (MD) simulations. Rarely occurring states of a given protein are statistically selected as new initial states based on the inverse histogram, and resampling is performed by restarting the MD simulations from the new initial states to promote the conformational transition. In this process, the definition of the inverse histogram, which characterizes the rarely occurring states, is crucial for the efficiency of TBSA. In this study, we propose a simple modification of the inverse histogram to further accelerate the convergence of TBSA. As demonstrations of the modified TBSA, we applied it to (a) hydrogen bonding rearrangements of Met-enkephalin, (b) large-amplitude domain motions of Glutamine-Binding Protein, and (c) folding processes of the B domain of Staphylococcus aureus Protein A. All demonstrations numerically proved that the modified TBSA reproduced these biologically relevant rare events with nanosecond-order simulation times, although a set of microsecond-order, canonical MD simulations failed to reproduce the rare events, indicating the high efficiency of the modified TBSA.

Sparsity in Model Gene Regulatory Networks

International Nuclear Information System (INIS)

Zagorski, M.

2011-01-01

We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

Identification of apoptosis-related PLZF target genes

International Nuclear Information System (INIS)

Bernardo, Maria Victoria; Yelo, Estefania; Gimeno, Lourdes; Campillo, Jose Antonio; Parrado, Antonio

2007-01-01

The PLZF gene encodes a BTB/POZ-zinc finger-type transcription factor, involved in physiological development, proliferation, differentiation, and apoptosis. In this paper, we investigate proliferation, survival, and gene expression regulation in stable clones from the human haematopoietic K562, DG75, and Jurkat cell lines with inducible expression of PLZF. In Jurkat cells, but not in K562 and DG75 cells, PLZF induced growth suppression and apoptosis in a cell density-dependent manner. Deletion of the BTB/POZ domain of PLZF abrogated growth suppression and apoptosis. PLZF was expressed with a nuclear speckled pattern distinctively in the full-length PLZF-expressing Jurkat clones, suggesting that the nuclear speckled localization is required for PLZF-induced apoptosis. By microarray analysis, we identified that the apoptosis-inducer TP53INP1, ID1, and ID3 genes were upregulated, and the apoptosis-inhibitor TERT gene was downregulated. The identification of apoptosis-related PLZF target genes may have biological and clinical relevance in cancer typified by altered PLZF expression

Gene expression profiling in cells with enhanced gamma-secretase activity.

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Alexandra I Magold

2009-09-01

Full Text Available Processing by gamma-secretase of many type-I membrane protein substrates triggers signaling cascades by releasing intracellular domains (ICDs that, following nuclear translocation, modulate the transcription of different genes regulating a diverse array of cellular and biological processes. Because the list of gamma-secretase substrates is growing quickly and this enzyme is a cancer and Alzheimer's disease therapeutic target, the mapping of gamma-secretase activity susceptible gene transcription is important for sharpening our view of specific affected genes, molecular functions and biological pathways.To identify genes and molecular functions transcriptionally affected by gamma-secretase activity, the cellular transcriptomes of Chinese hamster ovary (CHO cells with enhanced and inhibited gamma-secretase activity were analyzed and compared by cDNA microarray. The functional clustering by FatiGO of the 1,981 identified genes revealed over- and under-represented groups with multiple activities and functions. Single genes with the most pronounced transcriptional susceptibility to gamma-secretase activity were evaluated by real-time PCR. Among the 21 validated genes, the strikingly decreased transcription of PTPRG and AMN1 and increased transcription of UPP1 potentially support data on cell cycle disturbances relevant to cancer, stem cell and neurodegenerative diseases' research. The mapping of interactions of proteins encoded by the validated genes exclusively relied on evidence-based data and revealed broad effects on Wnt pathway members, including WNT3A and DVL3. Intriguingly, the transcription of TERA, a gene of unknown function, is affected by gamma-secretase activity and was significantly altered in the analyzed human Alzheimer's disease brain cortices.Investigating the effects of gamma-secretase activity on gene transcription has revealed several affected clusters of molecular functions and, more specifically, 21 genes that hold significant

Toward computational cumulative biology by combining models of biological datasets.

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Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

Use of keyword hierarchies to interpret gene expression patterns.

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Masys, D R; Welsh, J B; Lynn Fink, J; Gribskov, M; Klacansky, I; Corbeil, J

2001-04-01

High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.

[Gene mutation and clinical phenotype analysis of patients with Noonan syndrome and hypertrophic cardiomyopathy].

Science.gov (United States)

Liu, X H; Ding, W W; Han, L; Liu, X R; Xiao, Y Y; Yang, J; Mo, Y

2017-10-02

Objective: To analyze the gene mutations and clinical features of patients with Noonan syndrome and hypertrophic cardiomyopathy. Method: Determined the mutation domain in five cases diagnosed with Noonan syndrome and hypertrophic cardiomyopathy and identified the relationship between the mutant domain and hypertrophic cardiomyopathy by searching relevant articles in pubmed database. Result: Three mutant genes (PTPN11 gene in chromosome 12, RIT1 gene in chromosome 1 and RAF1 gene in chromosome 3) in five cases all had been reported to be related to hypertrophic cardiomyopathy. The reported hypertrophic cardiomyopathy relevant genes MYPN, MYH6 and MYBP3 had also been found in case 1 and 2. Patients with same gene mutation had different clinical manifestations. Both case 4 and 5 had RAF1 mutation (c.770C>T). However, case 4 had special face, low IQ, mild pulmonary artery stenosis, and only mild ventricular hypertrophy. Conclusion: Noonan syndrome is a genetic heterogeneity disease. Our study identified specific gene mutations that could result in Noonan syndrome with hypertrophic cardiomyopathy through molecular biology methods. The results emphasize the importance of gene detection in the management of Noonan syndrome.

Significant Down-Regulation of “Biological Adhesion” Genes in Porcine Oocytes after IVM

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Joanna Budna

2017-12-01

Full Text Available Proper maturation of the mammalian oocyte is a compound processes determining successful monospermic fertilization, however the number of fully mature porcine oocytes is still unsatisfactory. Since oocytes’ maturation and fertilization involve cellular adhesion and membranous contact, the aim was to investigate cell adhesion ontology group in porcine oocytes. The oocytes were collected from ovaries of 45 pubertal crossbred Landrace gilts and subjected to two BCB tests. After the first test, only granulosa cell-free BCB+ oocytes were directly exposed to microarray assays and RT-qPCR (“before IVM” group, or first in vitro matured and then if classified as BCB+ passed to molecular analyses (“after IVM” group. As a result, we have discovered substantial down-regulation of genes involved in adhesion processes, such as: organization of actin cytoskeleton, migration, proliferation, differentiation, apoptosis, survival or angiogenesis in porcine oocytes after IVM, compared to oocytes analyzed before IVM. In conclusion, we found that biological adhesion may be recognized as the process involved in porcine oocytes’ successful IVM. Down-regulation of genes included in this ontology group in immature oocytes after IVM points to their unique function in oocyte’s achievement of fully mature stages. Thus, results indicated new molecular markers involved in porcine oocyte IVM, displaying essential roles in biological adhesion processes.

Gene expression in IFN-g-activated murine macrophages

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Pereira C.A.

2004-01-01

Full Text Available Macrophages are critical for natural immunity and play a central role in specific acquired immunity. The IFN-gamma activation of macrophages derived from A/J or BALB/c mice yielded two different patterns of antiviral state in murine hepatitis virus 3 infection, which were related to a down-regulation of the main virus receptor. Using cDNA hybridization to evaluate mRNA accumulation in the cells, we were able to identify several genes that are differently up- or down-regulated by IFN-gamma in A/J (267 and 266 genes, respectively, up- and down-regulated or BALB/c (297 and 58 genes, respectively, up- and down-regulated mouse macrophages. Macrophages from mice with different genetic backgrounds behave differently at the molecular level and comparison of the patterns of non-activated and IFN-gamma-activated A/J or BALB/c mouse macrophages revealed, for instance, an up-regulation and a down-regulation of genes coding for biological functions such as enzymatic reactions, nucleic acid synthesis and transport, protein synthesis, transport and metabolism, cytoskeleton arrangement and extracellular matrix, phagocytosis, resistance and susceptibility to infection and tumors, inflammation, and cell differentiation or activation. The present data are reported in order to facilitate future correlation of proteomic/transcriptomic findings as well as of results obtained from a classical approach for the understanding of biological phenomena. The possible implication of the role of some of the gene products relevant to macrophage biology can now be further scrutinized. In this respect, a down-regulation of the main murine hepatitis virus 3 receptor gene was detected only in IFN-gamma-activated macrophages of resistant mice.

Gene Circuit Analysis of the Terminal Gap Gene huckebein

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Ashyraliyev, Maksat; Siggens, Ken; Janssens, Hilde; Blom, Joke; Akam, Michael; Jaeger, Johannes

2009-01-01

The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network. PMID:19876378

Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure.

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Bourdon, Julie A; Williams, Andrew; Kuo, Byron; Moffat, Ivy; White, Paul A; Halappanavar, Sabina; Vogel, Ulla; Wallin, Håkan; Yauk, Carole L

2013-01-07

New approaches are urgently needed to evaluate potential hazards posed by exposure to nanomaterials. Gene expression profiling provides information on potential modes of action and human relevance, and tools have recently become available for pathway-based quantitative risk assessment. The objective of this study was to use toxicogenomics in the context of human health risk assessment. We explore the utility of toxicogenomics in risk assessment, using published gene expression data from C57BL/6 mice exposed to 18, 54 and 162 μg Printex 90 carbon black nanoparticles (CBNP). Analysis of CBNP-perturbed pathways, networks and transcription factors revealed concomitant changes in predicted phenotypes (e.g., pulmonary inflammation and genotoxicity), that correlated with dose and time. Benchmark doses (BMDs) for apical endpoints were comparable to minimum BMDs for relevant pathway-specific expression changes. Comparison to inflammatory lung disease models (i.e., allergic airway inflammation, bacterial infection and tissue injury and fibrosis) and human disease profiles revealed that induced gene expression changes in Printex 90 exposed mice were similar to those typical for pulmonary injury and fibrosis. Very similar fibrotic pathways were perturbed in CBNP-exposed mice and human fibrosis disease models. Our synthesis demonstrates how toxicogenomic profiles may be used in human health risk assessment of nanoparticles and constitutes an important step forward in the ultimate recognition of toxicogenomic endpoints in human health risk. As our knowledge of molecular pathways, dose-response characteristics and relevance to human disease continues to grow, we anticipate that toxicogenomics will become increasingly useful in assessing chemical toxicities and in human health risk assessment. Crown Copyright © 2012. Published by Elsevier Ireland Ltd. All rights reserved.

From early lessons to new frontiers: The worm as a treasure trove of small RNA biology

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Elaine M. Youngman

2014-11-01

Full Text Available In the past twenty years, the tiny soil nematode C. elegans has provided critical insights into our understanding of the breadth of small RNA-mediated gene regulatory activities. The first microRNA was identified in C. elegans in 1993, and the understanding that dsRNA was the driving force behind RNA-mediated gene silencing came from experiments performed in C. elegans in 1998. Likewise, early genetic screens in C. elegans for factors involved in RNAi pointed to conserved mechanisms for small RNA-mediated gene silencing pathways, placing the worm squarely among the founding fathers of a now extensive field of molecular biology. Today, the worm continues to be at the forefront of ground-breaking insight into small RNA-mediated biology. Recent studies have revealed with increasing mechanistic clarity that C. elegans possesses an extensive nuclear small RNA regulatory network that encompasses not only gene silencing but also gene activating roles. Further, a portrait is emerging whereby small RNA pathways play key roles in integrating responses to environmental stimuli and transmitting epigenetic information about such responses from one generation to the next. Here we discuss endogenous small RNA pathways in C. elegans and the insight worm biology has provided into the mechanisms employed by these pathways. We touch on the increasingly spectacular diversity of small RNA biogenesis and function, and discuss the relevance of lessons learned in the worm for human biology.

From early lessons to new frontiers: the worm as a treasure trove of small RNA biology.

Science.gov (United States)

Youngman, Elaine M; Claycomb, Julie M

2014-01-01

In the past 20 years, the tiny soil nematode Caenorhabditis elegans has provided critical insights into our understanding of the breadth of small RNA-mediated gene regulatory activities. The first microRNA was identified in C. elegans in 1993, and the understanding that dsRNA was the driving force behind RNA-mediated gene silencing came from experiments performed in C. elegans in 1998. Likewise, early genetic screens in C. elegans for factors involved in RNA interference pointed to conserved mechanisms for small RNA-mediated gene silencing pathways, placing the worm squarely among the founding fathers of a now extensive field of molecular biology. Today, the worm continues to be at the forefront of ground-breaking insight into small RNA-mediated biology. Recent studies have revealed with increasing mechanistic clarity that C. elegans possesses an extensive nuclear small RNA regulatory network that encompasses not only gene silencing but also gene activating roles. Further, a portrait is emerging whereby small RNA pathways play key roles in integrating responses to environmental stimuli and transmitting epigenetic information about such responses from one generation to the next. Here we discuss endogenous small RNA pathways in C. elegans and the insight worm biology has provided into the mechanisms employed by these pathways. We touch on the increasingly spectacular diversity of small RNA biogenesis and function, and discuss the relevance of lessons learned in the worm for human biology.

Construction of new synthetic biology tools for the control of gene expression in the cyanobacterium Synechococcus sp. strain PCC 7002.

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Zess, Erin K; Begemann, Matthew B; Pfleger, Brian F

2016-02-01

Predictive control of gene expression is an essential tool for developing synthetic biological systems. The current toolbox for controlling gene expression in cyanobacteria is a barrier to more in-depth genetic analysis and manipulation. Towards relieving this bottleneck, this work describes the use of synthetic biology to construct an anhydrotetracycline-based induction system and adapt a trans-acting small RNA (sRNA) system for use in the cyanobacterium Synechococcus sp. strain PCC 7002. An anhydrotetracycline-inducible promoter was developed to maximize intrinsic strength and dynamic range. The resulting construct, PEZtet , exhibited tight repression and a maximum 32-fold induction upon addition of anhydrotetracycline. Additionally, a sRNA system based on the Escherichia coli IS10 RNA-IN/OUT regulator was adapted for use in Synechococcus sp. strain PCC 7002. This system exhibited 70% attenuation of target gene expression, providing a demonstration of the use of sRNAs for differential gene expression in cyanobacteria. These systems were combined to produce an inducible sRNA system, which demonstrated 59% attenuation of target gene expression. Lastly, the role of Hfq, a critical component of sRNA systems in E. coli, was investigated. Genetic studies showed that the Hfq homolog in Synechococcus sp. strain PCC 7002 did not impact repression by the engineered sRNA system. In summary, this work describes new synthetic biology tools that can be applied to physiological studies, metabolic engineering, or sRNA platforms in Synechococcus sp. strain PCC 7002. © 2015 Wiley Periodicals, Inc.

Hypersensitivities for acetaldehyde and other agents among cancer cells null for clinically relevant Fanconi anemia genes.

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Ghosh, Soma; Sur, Surojit; Yerram, Sashidhar R; Rago, Carlo; Bhunia, Anil K; Hossain, M Zulfiquer; Paun, Bogdan C; Ren, Yunzhao R; Iacobuzio-Donahue, Christine A; Azad, Nilofer A; Kern, Scott E

2014-01-01

Large-magnitude numerical distinctions (>10-fold) among drug responses of genetically contrasting cancers were crucial for guiding the development of some targeted therapies. Similar strategies brought epidemiological clues and prevention goals for genetic diseases. Such numerical guides, however, were incomplete or low magnitude for Fanconi anemia pathway (FANC) gene mutations relevant to cancer in FANC-mutation carriers (heterozygotes). We generated a four-gene FANC-null cancer panel, including the engineering of new PALB2/FANCN-null cancer cells by homologous recombination. A characteristic matching of FANCC-null, FANCG-null, BRCA2/FANCD1-null, and PALB2/FANCN-null phenotypes was confirmed by uniform tumor regression on single-dose cross-linker therapy in mice and by shared chemical hypersensitivities to various inter-strand cross-linking agents and γ-radiation in vitro. Some compounds, however, had contrasting magnitudes of sensitivity; a strikingly high (19- to 22-fold) hypersensitivity was seen among PALB2-null and BRCA2-null cells for the ethanol metabolite, acetaldehyde, associated with widespread chromosomal breakage at a concentration not producing breaks in parental cells. Because FANC-defective cancer cells can share or differ in their chemical sensitivities, patterns of selective hypersensitivity hold implications for the evolutionary understanding of this pathway. Clinical decisions for cancer-relevant prevention and management of FANC-mutation carriers could be modified by expanded studies of high-magnitude sensitivities. Copyright © 2014 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

GRtoGR: a system for mapping GO relations to gene relations.

Science.gov (United States)

Taha, Kamal

2013-12-01

We introduce in this paper a biological search engine called GRtoGR. Given a set of S genes, GRtoGR would determine from GO graph the most significant Lowest Common Ancestor (LCA) of the GO terms annotating the set S. This significant LCA annotates the genes that are the most semantically related to the set S. The framework of GRtoGR refines the concept of LCA by introducing the concepts of Relevant Lowest Common Ancestor (RLCA) and Semantically Relevant Lowest Common Ancestor (SRLCA). A SRLCA is the most significant LCA of the GO terms annotating the set S. We observe that the existence of the GO terms annotating the set S is dependent on the existence of this SRLCA in GO graph. That is, the terms annotating a given set of genes usually have existence dependency relationships with the SRLCA of these terms. We evaluated GRtoGR experimentally and compared it with nine other methods. Results showed marked improvement.

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

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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.

Comprehensive Transcriptome Analysis of Sex-Biased Expressed Genes Reveals Discrete Biological and Physiological Features of Male and Female Schistosoma japonicum.

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Pengfei Cai

2016-04-01

Full Text Available Schistosomiasis is a chronic and debilitating disease caused by blood flukes (digenetic trematodes of the genus Schistosoma. Schistosomes are sexually dimorphic and exhibit dramatic morphological changes during a complex lifecycle which requires subtle gene regulatory mechanisms to fulfil these complex biological processes. In the current study, a 41,982 features custom DNA microarray, which represents the most comprehensive probe coverage for any schistosome transcriptome study, was designed based on public domain and local databases to explore differential gene expression in S. japonicum. We found that approximately 1/10 of the total annotated genes in the S. japonicum genome are differentially expressed between adult males and females. In general, genes associated with the cytoskeleton, and motor and neuronal activities were readily expressed in male adult worms, whereas genes involved in amino acid metabolism, nucleotide biosynthesis, gluconeogenesis, glycosylation, cell cycle processes, DNA synthesis and genome fidelity and stability were enriched in females. Further, miRNAs target sites within these gene sets were predicted, which provides a scenario whereby the miRNAs potentially regulate these sex-biased expressed genes. The study significantly expands the expressional and regulatory characteristics of gender-biased expressed genes in schistosomes with high accuracy. The data provide a better appreciation of the biological and physiological features of male and female schistosome parasites, which may lead to novel vaccine targets and the development of new therapeutic interventions.

Comprehensive Transcriptome Analysis of Sex-Biased Expressed Genes Reveals Discrete Biological and Physiological Features of Male and Female Schistosoma japonicum.

Science.gov (United States)

Cai, Pengfei; Liu, Shuai; Piao, Xianyu; Hou, Nan; Gobert, Geoffrey N; McManus, Donald P; Chen, Qijun

2016-04-01

Schistosomiasis is a chronic and debilitating disease caused by blood flukes (digenetic trematodes) of the genus Schistosoma. Schistosomes are sexually dimorphic and exhibit dramatic morphological changes during a complex lifecycle which requires subtle gene regulatory mechanisms to fulfil these complex biological processes. In the current study, a 41,982 features custom DNA microarray, which represents the most comprehensive probe coverage for any schistosome transcriptome study, was designed based on public domain and local databases to explore differential gene expression in S. japonicum. We found that approximately 1/10 of the total annotated genes in the S. japonicum genome are differentially expressed between adult males and females. In general, genes associated with the cytoskeleton, and motor and neuronal activities were readily expressed in male adult worms, whereas genes involved in amino acid metabolism, nucleotide biosynthesis, gluconeogenesis, glycosylation, cell cycle processes, DNA synthesis and genome fidelity and stability were enriched in females. Further, miRNAs target sites within these gene sets were predicted, which provides a scenario whereby the miRNAs potentially regulate these sex-biased expressed genes. The study significantly expands the expressional and regulatory characteristics of gender-biased expressed genes in schistosomes with high accuracy. The data provide a better appreciation of the biological and physiological features of male and female schistosome parasites, which may lead to novel vaccine targets and the development of new therapeutic interventions.

Gene-environment interaction in Major Depression: focus on experience-dependent biological systems

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Nicola eLopizzo

2015-05-01

Full Text Available Major Depressive Disorder (MDD is a multifactorial and polygenic disorder, where multiple and partially overlapping sets of susceptibility genes interact each other and with the environment, predisposing individuals to the development of the illness. Thus, MDD results from a complex interplay of vulnerability genes and environmental factors that act cumulatively throughout individual's lifetime. Among these environmental factors, stressful life experiences, especially those occurring early in life, have been suggested to exert a crucial impact on brain development, leading to permanent functional changes that may contribute to life long risk for mental health outcomes. In this review we will discuss how genetic variants (polymorphisms, SNPs within genes operating in neurobiological systems that mediate stress response and synaptic plasticity, can impact, by themselves, the vulnerability risk for MDD; we will also consider how this MDD risk can be further modulated when gene X environment interaction is taken into account. Finally, we will discuss the role of epigenetic mechanisms, and in particular of DNA methylation and miRNAs expression changes, in mediating the effect of the stress on the vulnerability risk to develop MDD. Taken together, in this review we aim to underlie the role of genetic and epigenetic processes involved in stress and neuroplasticity related biological systems on development of MDD after exposure to early life stress, thereby building the basis for future research and clinical interventions.

Investigation of the effect of ionizing radiation on gene expression variation by the 'DNA chips': feasibility of a biological dosimeter

International Nuclear Information System (INIS)

Gruel, G.

2005-01-01

After having described the different biological effects of ionizing radiation and the different approaches to biological dosimetry, and introduced 'DNA chips' or DNA micro-arrays, the author reports the characterization of gene expression variations in the response of cells to a gamma irradiation. Both main aspects of the use DNA chips are investigated: fundamental research and diagnosis. This research thesis thus proposes an analysis of the effect of ionizing radiation using DNA chips, notably by comparing gene expression modifications measured in mouse irradiated lung, heart and kidney. It reports a feasibility study of bio-dosimeter based on expression profiles

A semantic web ontology for small molecules and their biological targets.

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Choi, Jooyoung; Davis, Melissa J; Newman, Andrew F; Ragan, Mark A

2010-05-24

A wide range of data on sequences, structures, pathways, and networks of genes and gene products is available for hypothesis testing and discovery in biological and biomedical research. However, data describing the physical, chemical, and biological properties of small molecules have not been well-integrated with these resources. Semantically rich representations of chemical data, combined with Semantic Web technologies, have the potential to enable the integration of small molecule and biomolecular data resources, expanding the scope and power of biomedical and pharmacological research. We employed the Semantic Web technologies Resource Description Framework (RDF) and Web Ontology Language (OWL) to generate a Small Molecule Ontology (SMO) that represents concepts and provides unique identifiers for biologically relevant properties of small molecules and their interactions with biomolecules, such as proteins. We instanced SMO using data from three public data sources, i.e., DrugBank, PubChem and UniProt, and converted to RDF triples. Evaluation of SMO by use of predetermined competency questions implemented as SPARQL queries demonstrated that data from chemical and biomolecular data sources were effectively represented and that useful knowledge can be extracted. These results illustrate the potential of Semantic Web technologies in chemical, biological, and pharmacological research and in drug discovery.

Simulation of E. coli gene regulation including overlapping cell cycles, growth, division, time delays and noise.

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Ruoyu Luo

Full Text Available Due to the complexity of biological systems, simulation of biological networks is necessary but sometimes complicated. The classic stochastic simulation algorithm (SSA by Gillespie and its modified versions are widely used to simulate the stochastic dynamics of biochemical reaction systems. However, it has remained a challenge to implement accurate and efficient simulation algorithms for general reaction schemes in growing cells. Here, we present a modeling and simulation tool, called 'GeneCircuits', which is specifically developed to simulate gene-regulation in exponentially growing bacterial cells (such as E. coli with overlapping cell cycles. Our tool integrates three specific features of these cells that are not generally included in SSA tools: 1 the time delay between the regulation and synthesis of proteins that is due to transcription and translation processes; 2 cell cycle-dependent periodic changes of gene dosage; and 3 variations in the propensities of chemical reactions that have time-dependent reaction rates as a consequence of volume expansion and cell division. We give three biologically relevant examples to illustrate the use of our simulation tool in quantitative studies of systems biology and synthetic biology.

An ontology-driven semantic mash-up of gene and biological pathway information: Application to the domain of nicotine dependence

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Sahoo, Satya S.; Bodenreider, Olivier; Rutter, Joni L.; Skinner, Karen J.; Sheth, Amit P.

2008-01-01

Objectives This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. Methods We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Results Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Conclusion Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. Resource page http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/ PMID:18395495

Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival

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Noha Sharafeldin

2017-09-01

Full Text Available Characterization of gene-environment interactions (GEIs in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from 747 rectal cancer cases and 956 controls from the Diet, Activity and Lifestyle as a Risk Factor for Rectal Cancer study. We applied a 3-step analysis approach: first, we searched for interactions among single nucleotide polymorphisms on the pathway genes; second, we searched for interactions among the genes, both steps using Logic regression; third, we examined the GEIs significant at the 5% level using logistic regression for cancer risk and Cox proportional hazards models for survival. Permutation-based test was used for multiple testing adjustment. We identified 8 significant GEIs associated with risk among 6 genes adjusting for multiple testing: TNF (OR = 1.85, 95% CI: 1.10, 3.11, TLR4 (OR = 2.34, 95% CI: 1.38, 3.98, and EGR2 (OR = 2.23, 95% CI: 1.04, 4.78 with smoking; IGF1R (OR = 1.69, 95% CI: 1.04, 2.72, TLR4 (OR = 2.10, 95% CI: 1.22, 3.60 and EGR2 (OR = 2.12, 95% CI: 1.01, 4.46 with alcohol; and PDGFB (OR = 1.75, 95% CI: 1.04, 2.92 and MMP1 (OR = 2.44, 95% CI: 1.24, 4.81 with protein. Five GEIs were associated with survival at the 5% significance level but not after multiple testing adjustment: CXCR1 (HR = 2.06, 95% CI: 1.13, 3.75 with smoking; and KDR (HR = 4.36, 95% CI: 1.62, 11.73, TLR2 (HR = 9.06, 95% CI: 1.14, 72.11, EGR2 (HR = 2.45, 95% CI: 1.42, 4.22, and EGFR (HR = 6.33, 95% CI: 1.95, 20.54 with protein. GEIs between angiogenesis genes and smoking, alcohol, and animal protein impact rectal cancer risk. Our results support the importance of considering the biologic hypothesis to characterize GEIs associated with cancer outcomes.

Bridging cancer biology with the clinic: relative expression of a GRHL2-mediated gene-set pair predicts breast cancer metastasis.

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Xinan Yang

Full Text Available Identification and characterization of crucial gene target(s that will allow focused therapeutics development remains a challenge. We have interrogated the putative therapeutic targets associated with the transcription factor Grainy head-like 2 (GRHL2, a critical epithelial regulatory factor. We demonstrate the possibility to define the molecular functions of critical genes in terms of their personalized expression profiles, allowing appropriate functional conclusions to be derived. A novel methodology, relative expression analysis with gene-set pairs (RXA-GSP, is designed to explore the potential clinical utility of cancer-biology discovery. Observing that Grhl2-overexpression leads to increased metastatic potential in vitro, we established a model assuming Grhl2-induced or -inhibited genes confer poor or favorable prognosis respectively for cancer metastasis. Training on public gene expression profiles of 995 breast cancer patients, this method prioritized one gene-set pair (GRHL2, CDH2, FN1, CITED2, MKI67 versus CTNNB1 and CTNNA3 from all 2717 possible gene-set pairs (GSPs. The identified GSP significantly dichotomized 295 independent patients for metastasis-free survival (log-rank tested p = 0.002; severe empirical p = 0.035. It also showed evidence of clinical prognostication in another independent 388 patients collected from three studies (log-rank tested p = 3.3e-6. This GSP is independent of most traditional prognostic indicators, and is only significantly associated with the histological grade of breast cancer (p = 0.0017, a GRHL2-associated clinical character (p = 6.8e-6, Spearman correlation, suggesting that this GSP is reflective of GRHL2-mediated events. Furthermore, a literature review indicates the therapeutic potential of the identified genes. This research demonstrates a novel strategy to integrate both biological experiments and clinical gene expression profiles for extracting and elucidating the genomic

Bystander effect: Biological endpoints and microarray analysis

Energy Technology Data Exchange (ETDEWEB)

Chaudhry, M. Ahmad [Department of Medical Laboratory and Radiation Sciences, College of Nursing and Health Sciences, University of Vermont, 302 Rowell Building, Burlington, VT 05405 (United States) and DNA Microarray Facility, University of Vermont, Burlington, VT 05405 (United States)]. E-mail: mchaudhr@uvm.edu

2006-05-11

In cell populations exposed to ionizing radiation, the biological effects occur in a much larger proportion of cells than are estimated to be traversed by radiation. It has been suggested that irradiated cells are capable of providing signals to the neighboring unirradiated cells resulting in damage to these cells. This phenomenon is termed the bystander effect. The bystander effect induces persistent, long-term, transmissible changes that result in delayed death and neoplastic transformation. Because the bystander effect is relevant to carcinogenesis, it could have significant implications for risk estimation for radiation exposure. The nature of the bystander effect signal and how it impacts the unirradiated cells remains to be elucidated. Examination of the changes in gene expression could provide clues to understanding the bystander effect and could define the signaling pathways involved in sustaining damage to these cells. The microarray technology serves as a tool to gain insight into the molecular pathways leading to bystander effect. Using medium from irradiated normal human diploid lung fibroblasts as a model system we examined gene expression alterations in bystander cells. The microarray data revealed that the radiation-induced gene expression profile in irradiated cells is different from unirradiated bystander cells suggesting that the pathways leading to biological effects in the bystander cells are different from the directly irradiated cells. The genes known to be responsive to ionizing radiation were observed in irradiated cells. Several genes were upregulated in cells receiving media from irradiated cells. Surprisingly no genes were found to be downregulated in these cells. A number of genes belonging to extracellular signaling, growth factors and several receptors were identified in bystander cells. Interestingly 15 genes involved in the cell communication processes were found to be upregulated. The induction of receptors and the cell

Bystander effect: Biological endpoints and microarray analysis

International Nuclear Information System (INIS)

Chaudhry, M. Ahmad

2006-01-01

In cell populations exposed to ionizing radiation, the biological effects occur in a much larger proportion of cells than are estimated to be traversed by radiation. It has been suggested that irradiated cells are capable of providing signals to the neighboring unirradiated cells resulting in damage to these cells. This phenomenon is termed the bystander effect. The bystander effect induces persistent, long-term, transmissible changes that result in delayed death and neoplastic transformation. Because the bystander effect is relevant to carcinogenesis, it could have significant implications for risk estimation for radiation exposure. The nature of the bystander effect signal and how it impacts the unirradiated cells remains to be elucidated. Examination of the changes in gene expression could provide clues to understanding the bystander effect and could define the signaling pathways involved in sustaining damage to these cells. The microarray technology serves as a tool to gain insight into the molecular pathways leading to bystander effect. Using medium from irradiated normal human diploid lung fibroblasts as a model system we examined gene expression alterations in bystander cells. The microarray data revealed that the radiation-induced gene expression profile in irradiated cells is different from unirradiated bystander cells suggesting that the pathways leading to biological effects in the bystander cells are different from the directly irradiated cells. The genes known to be responsive to ionizing radiation were observed in irradiated cells. Several genes were upregulated in cells receiving media from irradiated cells. Surprisingly no genes were found to be downregulated in these cells. A number of genes belonging to extracellular signaling, growth factors and several receptors were identified in bystander cells. Interestingly 15 genes involved in the cell communication processes were found to be upregulated. The induction of receptors and the cell

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations.

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Sahra Uygun

2016-12-01

Full Text Available Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.

Transcriptomic network analysis of micronuclei-related genes: a case study

DEFF Research Database (Denmark)

van Leeuwen, D. M.; Pedersen, Marie; Knudsen, Lisbeth E.

2011-01-01

checkpoint and aneuploidy. The MN-related gene network was tested against a transcriptomics case study associated with MN measurements. In this case study, transcriptomic data from children and adults differentially exposed to ambient air pollution in the Czech Republic were analysed and visualised......Mechanistically relevant information on responses of humans to xenobiotic exposure in relation to chemically induced biological effects, such as micronuclei (MN) formation can be obtained through large-scale transcriptomics studies. Network analysis may enhance the analysis and visualisation...... of such data. Therefore, this study aimed to develop a 'MN formation' network based on a priori knowledge, by using the pathway tool MetaCore. The gene network contained 27 genes and three gene complexes that are related to processes involved in MN formation, e.g. spindle assembly checkpoint, cell cycle...

Seven-day human biological rhythms: An expedition in search of their origin, synchronization, functional advantage, adaptive value and clinical relevance.

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Reinberg, Alain E; Dejardin, Laurence; Smolensky, Michael H; Touitou, Yvan

2017-01-01

This fact-finding expedition explores the perspectives and knowledge of the origin and functional relevance of the 7 d domain of the biological time structure, with special reference to human beings. These biological rhythms are displayed at various levels of organization in diverse species - from the unicellular sea algae of Acetabularia and Goniaulax to plants, insects, fish, birds and mammals, including man - under natural as well as artificial, i.e. constant, environmental conditions. Nonetheless, very little is known about their derivation, functional advantage, adaptive value, synchronization and potential clinical relevance. About 7 d cosmic cycles are seemingly too weak, and the 6 d work/1 d rest week commanded from G-d through the Laws of Mosses to the Hebrews is too recent an event to be the origin in humans. Moreover, human and insect studies conducted under controlled constant conditions devoid of environmental, social and other time cues report the persistence of 7 d rhythms, but with a slightly different (free-running) period (τ), indicating their source is endogenous. Yet, a series of human and laboratory rodent studies reveal certain mainly non-cyclic exogenous events can trigger 7 d rhythm-like phenomena. However, it is unknown whether such triggers unmask, amplify and/or synchronize previous non-overtly expressed oscillations. Circadian (~24 h), circa-monthly (~30 d) and circannual (~1 y) rhythms are viewed as genetically based features of life forms that during evolution conferred significant functional advantage to individual organisms and survival value to species. No such advantages are apparent for endogenous 7 d rhythms, raising several questions: What is the significance of the 7 d activity/rest cycle, i.e. week, storied in the Book of Genesis and adopted by the Hebrews and thereafter the residents of nearby Mediterranean countries and ultimately the world? Why do humans require 1 d off per 7 d span? Do 7 d rhythms bestow functional

Cross-study analysis of gene expression data for intermediate neuroblastoma identifies two biological subtypes

International Nuclear Information System (INIS)

Warnat, Patrick; Oberthuer, André; Fischer, Matthias; Westermann, Frank; Eils, Roland; Brors, Benedikt

2007-01-01

Neuroblastoma patients show heterogeneous clinical courses ranging from life-threatening progression to spontaneous regression. Recently, gene expression profiles of neuroblastoma tumours were associated with clinically different phenotypes. However, such data is still rare for important patient subgroups, such as patients with MYCN non-amplified advanced stage disease. Prediction of the individual course of disease and optimal therapy selection in this cohort is challenging. Additional research effort is needed to describe the patterns of gene expression in this cohort and to identify reliable prognostic markers for this subset of patients. We combined gene expression data from two studies in a meta-analysis in order to investigate differences in gene expression of advanced stage (3 or 4) tumours without MYCN amplification that show contrasting outcomes (alive or dead) at five years after initial diagnosis. In addition, a predictive model for outcome was generated. Gene expression profiles from 66 patients were included from two studies using different microarray platforms. In the combined data set, 72 genes were identified as differentially expressed by meta-analysis at a false discovery rate (FDR) of 8.33%. Meta-analysis detected 34 differentially expressed genes that were not found as significant in either single study. Outcome prediction based on data of both studies resulted in a predictive accuracy of 77%. Moreover, the genes that were differentially expressed in subgroups of advanced stage patients without MYCN amplification accurately separated MYCN amplified tumours from low stage tumours without MYCN amplification. Our findings support the hypothesis that neuroblastoma consists of two biologically distinct subgroups that differ by characteristic gene expression patterns, which are associated with divergent clinical outcome

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

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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.

Fungal biology and agriculture: revisiting the field

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Yarden, O.; Ebbole, D.J.; Freeman, S.; Rodriguez, R.J.; Dickman, M. B.

2003-01-01

Plant pathology has made significant progress over the years, a process that involved overcoming a variety of conceptual and technological hurdles. Descriptive mycology and the advent of chemical plant-disease management have been followed by biochemical and physiological studies of fungi and their hosts. The later establishment of biochemical genetics along with the introduction of DNA-mediated transformation have set the stage for dissection of gene function and advances in our understanding of fungal cell biology and plant-fungus interactions. Currently, with the advent of high-throughput technologies, we have the capacity to acquire vast data sets that have direct relevance to the numerous subdisciplines within fungal biology and pathology. These data provide unique opportunities for basic research and for engineering solutions to important agricultural problems. However, we also are faced with the challenge of data organization and mining to analyze the relationships between fungal and plant genomes and to elucidate the physiological function of pertinent DNA sequences. We present our perspective of fungal biology and agriculture, including administrative and political challenges to plant protection research.

Characterization of a second physiologically relevant lactose permease gene (lacpB) in Aspergillus nidulans.

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Fekete, Erzsébet; Orosz, Anita; Kulcsár, László; Kavalecz, Napsugár; Flipphi, Michel; Karaffa, Levente

2016-05-01

In Aspergillus nidulans, uptake rather than hydrolysis is the rate-limiting step of lactose catabolism. Deletion of the lactose permease A-encoding gene (lacpA) reduces the growth rate on lactose, while its overexpression enables faster growth than wild-type strains are capable of. We have identified a second physiologically relevant lactose transporter, LacpB. Glycerol-grown mycelia from mutants deleted for lacpB appear to take up only minute amounts of lactose during the first 60 h after a medium transfer, while mycelia of double lacpA/lacpB-deletant strains are unable to produce new biomass from lactose. Although transcription of both lacp genes was strongly induced by lactose, their inducer profiles differ markedly. lacpA but not lacpB expression was high in d-galactose cultures. However, lacpB responded strongly also to β-linked glucopyranose dimers cellobiose and sophorose, while these inducers of the cellulolytic system did not provoke any lacpA response. Nevertheless, lacpB transcript was induced to higher levels on cellobiose in strains that lack the lacpA gene than in a wild-type background. Indeed, cellobiose uptake was faster and biomass formation accelerated in lacpA deletants. In contrast, in lacpB knockout strains, growth rate and cellobiose uptake were considerably reduced relative to wild-type, indicating that the cellulose and lactose catabolic systems employ common elements. Nevertheless, our permease mutants still grew on cellobiose, which suggests that its uptake in A. nidulans prominently involves hitherto unknown transport systems.

Monochloramine Cometabolism by Nitrifying Biofilm Relevant ...

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Recently, biological monochloramine removal (i.e., cometabolism) by a pure culture ammonia–oxidizing bacteria, Nitrosomonas europaea, and a nitrifying mixed–culture have been shown to increase monochloramine demand. Although important, these previous suspended culture batch kinetic experiments were not representative of drinking water distribution systems where bacteria grow predominantly as biofilm attached to pipe walls or sediments and physiological differences may exist between suspension and biofilm growth. Therefore, the current research was an important next step in extending the previous results to investigate monochloramine cometabolism by biofilm grown in annular reactors under drinking water relevant conditions. Estimated monochloramine cometabolism kinetics were similar to those of ammonia metabolism, and monochloramine cometabolism was a significant loss mechanism (25–40% of the observed monochloramine loss). These results demonstrated that monochloramine cometabolism occurred in drinking water relevant nitrifying biofilm; thus, cometabolism may be a significant contribution to monochloramine loss during nitrification episodes in distribution systems. Investigate whether or not nitrifying biofilm can biologically transform monochloramine under drinking water relevant conditions.

[Research on the relevance between the virulent genes differential expression and pathogenecity of Leptospira with microarray].

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Yu, De-li; Bao, Lang

2015-01-01

To find the change of virulent gene expression and to analyze the relevance between the virulent change and the gene expression. Grouped guinea pigs were inoculated with 1 mL Leptospira cultured in vivo, Leptospira cultured in vitro and the Leptospira culture medium through abdominal subcutaneous respectively. The survival rate, body mass and temperature change of guinea pigs in different groups were measured within 15 d after the inoculation, then the survived guinea pigs were scarified, and the organ coefficient was also measured to know the virulence of Leptospira cultured in different environment. The amplified gene segments from Leptospira were used as probes and wrote the microarray. The total RNA was extracted from Leptospira standard strain cultured in culture medium and guinea pigs. After reverse transcription to cDNA, they were labeled with Cy3 and Cy5 respectively. Labeled cDNA was mixed and hybridized with the microarray. The hybridized mircroarray was scanned and analysed. The survival rate of inoculated guinea pig was different from group to group (in vivo group: 0%; in vitro group: 88.9%; culture medium group: 100%). The guinea pigs in vivo group had a higher temperature (PLeptospira: LA1027, LA1029, LA4004, LA3050, LA3540, LA0327, LA0378, LA1650, LA3937, LA2089, LA2144, LA3576, LA0011 and gene of Loa22 were up regulation after continuously cultured in guinea pigs. The pathogenic ability of Leptospira cultured in different environment is different and the gene expression of Leptospira is different between in vivo and in vitro as well. The understanding of the meaning of this change might help to know the pathogenecity of Leptospira.

An integer optimization algorithm for robust identification of non-linear gene regulatory networks

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Chemmangattuvalappil Nishanth

2012-09-01

. Furthermore, in both the in silico and experimental case studies, the predicted gene expression profiles are in very close agreement with the dynamics of the input data. Conclusions Our integer programming algorithm effectively utilizes bootstrapping to identify robust gene regulatory networks from noisy, non-linear time-series gene expression data. With significant noise and non-linearities being inherent to biological systems, the present formulism, with the incorporation of network sparsity, is extremely relevant to gene regulatory networks, and while the formulation has been validated against in silico and E. Coli data, it can be applied to any biological system.

Towards precise classification of cancers based on robust gene functional expression profiles

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Zhu Jing

2005-03-01

Full Text Available Abstract Background Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. Results Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. Conclusion This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge

Relevance in the science classroom: A multidimensional analysis

Science.gov (United States)

Hartwell, Matthew F.

While perceived relevance is considered a fundamental component of adaptive learning, the experience of relevance and its conceptual definition have not been well described. The mixed-methods research presented in this dissertation aimed to clarify the conceptual meaning of relevance by focusing on its phenomenological experience from the students' perspective. Following a critical literature review, I propose an identity-based model of perceived relevance that includes three components: a contextual target, an identity target, and a connection type, or lens. An empirical investigation of this model that consisted of two general phases was implemented in four 9th grade-biology classrooms. Participants in Phase 1 (N = 118) completed a series of four open-ended writing activities focused on eliciting perceived personal connections to academic content. Exploratory qualitative content analysis of a 25% random sample of the student responses was used to identify the main meaning-units of the proposed model as well as different dimensions of student relevance perceptions. These meaning-units and dimensions provided the basis for the construction of a conceptual mapping sentence capturing students' perceived relevance, which was then applied in a confirmatory analysis to all other student responses. Participants in Phase 2 (N = 139) completed a closed survey designed based on the mapping sentence to assess their perceived relevance of a biology unit. The survey also included scales assessing other domain-level motivational processes. Exploratory factor analysis and non-metric multidimensional scaling indicated a coherent conceptual structure, which included a primary interpretive relevance dimension. Comparison of the conceptual structure across various groups (randomly-split sample, gender, academic level, domain-general motivational profiles) provided support for its ubiquity and insight into variation in the experience of perceived relevance among students of different

Exploring the potential relevance of human-specific genes to complex disease

Directory of Open Access Journals (Sweden)

Cooper David N

2011-01-01

Full Text Available Abstract Although human disease genes generally tend to be evolutionarily more ancient than non-disease genes, complex disease genes appear to be represented more frequently than Mendelian disease genes among genes of more recent evolutionary origin. It is therefore proposed that the analysis of human-specific genes might provide new insights into the genetics of complex disease. Cross-comparison with the Human Gene Mutation Database (http://www.hgmd.org revealed a number of examples of disease-causing and disease-associated mutations in putatively human-specific genes. A sizeable proportion of these were missense polymorphisms associated with complex disease. Since both human-specific genes and genes associated with complex disease have often experienced particularly rapid rates of evolutionary change, either due to weaker purifying selection or positive selection, it is proposed that a significant number of human-specific genes may play a role in complex disease.

The PLOS ONE Synthetic Biology Collection: Six Years and Counting

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Peccoud, Jean; Isalan, Mark

2012-01-01

Since it was launched in 2006, PLOS ONE has published over fifty articles illustrating the many facets of the emerging field of synthetic biology. This article reviews these publications by organizing them into broad categories focused on DNA synthesis and assembly techniques, the development of libraries of biological parts, the use of synthetic biology in protein engineering applications, and the engineering of gene regulatory networks and metabolic pathways. Finally, we review articles that describe enabling technologies such as software and modeling, along with new instrumentation. In order to increase the visibility of this body of work, the papers have been assembled into the PLOS ONE Synthetic Biology Collection (www.ploscollections.org/synbio). Many of the innovative features of the PLOS ONE web site will help make this collection a resource that will support a lively dialogue between readers and authors of PLOS ONE synthetic biology papers. The content of the collection will be updated periodically by including relevant articles as they are published by the journal. Thus, we hope that this collection will continue to meet the publishing needs of the synthetic biology community. PMID:22916228

Self-Relevance Constructions of Biology Concepts: Meaning-Making and Identity-Formation

Science.gov (United States)

Davidson, Yonaton Sahar

2018-01-01

Recent research supports the benefit of students' construction of relevance through writing about the connection of content to their life. However, most such research defines relevance narrowly as utility value--perceived instrumentality of the content to the student's career goals. Furthermore, the scope of phenomenological and conceptual…

Models for synthetic biology.

Science.gov (United States)

Kaznessis, Yiannis N

2007-11-06

Synthetic biological engineering is emerging from biology as a distinct discipline based on quantification. The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules. What is new is the emphasis on system behavior. The objective is the design and construction of new biological devices and systems to deliver useful applications. Numerous synthetic gene circuits have been created in the past decade, including bistable switches, oscillators, and logic gates, and possible applications abound, including biofuels, detectors for biochemical and chemical weapons, disease diagnosis, and gene therapies. More than fifty years after the discovery of the molecular structure of DNA, molecular biology is mature enough for real quantification that is useful for biological engineering applications, similar to the revolution in modeling in chemistry in the 1950s. With the excitement that synthetic biology is generating, the engineering and biological science communities appear remarkably willing to cross disciplinary boundaries toward a common goal.

FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

Science.gov (United States)

Ernst, Mathias; Du, Yang; Warsow, Gregor; Hamed, Mohamed; Endlich, Nicole; Endlich, Karlhans; Murua Escobar, Hugo; Sklarz, Lisa-Madeleine; Sender, Sina; Junghanß, Christian; Möller, Steffen; Fuellen, Georg; Struckmann, Stephan

2017-02-16

To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.

Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

Directory of Open Access Journals (Sweden)

Emre Guney

Full Text Available Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO analysis highlighted the role of functional diversity for such diseases.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

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Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Identification of highly synchronized subnetworks from gene expression data.

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Gao, Shouguo; Wang, Xujing

2013-01-01

There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously. Here we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search. Application of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization. Inclusion of interaction dynamics is important to the identification of relevant gene networks.

Computational biology

DEFF Research Database (Denmark)

Hartmann, Lars Røeboe; Jones, Neil; Simonsen, Jakob Grue

2011-01-01

Computation via biological devices has been the subject of close scrutiny since von Neumann’s early work some 60 years ago. In spite of the many relevant works in this field, the notion of programming biological devices seems to be, at best, ill-defined. While many devices are claimed or proved t...

Prognostic relevance of molecular subtypes and master regulators in pancreatic ductal adenocarcinoma

International Nuclear Information System (INIS)

Janky, Rekin’s; Binda, Maria Mercedes; Allemeersch, Joke; Van den broeck, Anke; Govaere, Olivier; Swinnen, Johannes V.; Roskams, Tania; Aerts, Stein; Topal, Baki

2016-01-01

Pancreatic cancer is poorly characterized at genetic and non-genetic levels. The current study evaluates in a large cohort of patients the prognostic relevance of molecular subtypes and key transcription factors in pancreatic ductal adenocarcinoma (PDAC). We performed gene expression analysis of whole-tumor tissue obtained from 118 surgically resected PDAC and 13 histologically normal pancreatic tissue samples. Cox regression models were used to study the effect on survival of molecular subtypes and 16 clinicopathological prognostic factors. In order to better understand the biology of PDAC we used iRegulon to identify transcription factors (TFs) as master regulators of PDAC and its subtypes. We confirmed the PDAssign gene signature as classifier of PDAC in molecular subtypes with prognostic relevance. We found molecular subtypes, but not clinicopathological factors, as independent predictors of survival. Regulatory network analysis predicted that HNF1A/B are among thousand TFs the top enriched master regulators of the genes expressed in the normal pancreatic tissue compared to the PDAC regulatory network. On immunohistochemistry staining of PDAC samples, we observed low expression of HNF1B in well differentiated towards no expression in poorly differentiated PDAC samples. We predicted IRF/STAT, AP-1, and ETS-family members as key transcription factors in gene signatures downstream of mutated KRAS. PDAC can be classified in molecular subtypes that independently predict survival. HNF1A/B seem to be good candidates as master regulators of pancreatic differentiation, which at the protein level loses its expression in malignant ductal cells of the pancreas, suggesting its putative role as tumor suppressor in pancreatic cancer. The study was registered at ClinicalTrials.gov under the number NCT01116791 (May 3, 2010). The online version of this article (doi:10.1186/s12885-016-2540-6) contains supplementary material, which is available to authorized users

Developing integrated crop knowledge networks to advance candidate gene discovery.

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Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

2016-12-01

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

Assessing therapeutic relevance of biologically interesting, ampholytic substances based on their physicochemical and spectral characteristics with chemometric tools

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Judycka, U.; Jagiello, K.; Bober, L.; Błażejowski, J.; Puzyn, T.

2018-06-01

Chemometric tools were applied to investigate the biological behaviour of ampholytic substances in relation to their physicochemical and spectral properties. Results of the Principal Component Analysis suggest that size of molecules and their electronic and spectral characteristics are the key properties required to predict therapeutic relevance of the compounds examined. These properties were used for developing the structure-activity classification model. The classification model allows assessing the therapeutic behaviour of ampholytic substances on the basis of solely values of descriptors that can be obtained computationally. Thus, the prediction is possible without necessity of carrying out time-consuming and expensive laboratory tests, which is its main advantage.

Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression

DEFF Research Database (Denmark)

Bergholdt, Regine; Brorsson, Caroline; Palleja, Albert

2012-01-01

Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated with dis......-cells. Our results provide novel insight to the mechanisms behind type 1 diabetes pathogenesis and, thus, may provide the basis for the design of novel treatment strategies.......Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated...... with disease, and they do not typically inform the broader context in which the disease genes operate. Here, we integrated type 1 diabetes GWAS data with protein-protein interactions to construct biological networks of relevance for disease. A total of 17 networks were identified. To prioritize...

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis.

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Reynier, Frédéric; Petit, Fabien; Paye, Malick; Turrel-Davin, Fanny; Imbert, Pierre-Emmanuel; Hot, Arnaud; Mougin, Bruno; Miossec, Pierre

2011-01-01

The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals. Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients. In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining.

Science.gov (United States)

Kreula, Sanna M; Kaewphan, Suwisa; Ginter, Filip; Jones, Patrik R

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to ( i ) discover novel candidate associations between different genes or proteins in the network, and ( ii ) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

BiologicalNetworks 2.0 - an integrative view of genome biology data

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Ponomarenko Julia

2010-12-01

Full Text Available Abstract Background A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems. Results Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other and their relations (interactions, co-expression, co-citations, and other. The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations. Conclusions The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org.

Identification of genes involved in the biology of atypical teratoid/rhabdoid tumours using Drosophila melanogaster

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Jeibmann, Astrid; Eikmeier, Kristin; Linge, Anna; Kool, Marcel; Koos, Björn; Schulz, Jacqueline; Albrecht, Stefanie; Bartelheim, Kerstin; Frühwald, Michael C.; Pfister, Stefan M.; Paulus, Werner; Hasselblatt, Martin

2014-06-01

Atypical teratoid/rhabdoid tumours (AT/RT) are malignant brain tumours. Unlike most other human brain tumours, AT/RT are characterized by inactivation of one single gene, SMARCB1. SMARCB1 is a member of the evolutionarily conserved SWI/SNF chromatin remodelling complex, which has an important role in the control of cell differentiation and proliferation. Little is known, however, about the pathways involved in the oncogenic effects of SMARCB1 inactivation, which might also represent targets for treatment. Here we report a comprehensive genetic screen in the fruit fly that revealed several genes not yet associated with loss of snr1, the Drosophila homologue of SMARCB1. We confirm the functional role of identified genes (including merlin, kibra and expanded, known to regulate hippo signalling pathway activity) in human rhabdoid tumour cell lines and AT/RT tumour samples. These results demonstrate that fly models can be employed for the identification of clinically relevant pathways in human cancer.

Unexpected functional similarities between gatekeeper tumour suppressor genes and proto-oncogenes revealed by systems biology.

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Zhao, Yongzhong; Epstein, Richard J

2011-05-01

Familial tumor suppressor genes comprise two subgroups: caretaker genes (CTs) that repair DNA, and gatekeeper genes (GKs) that trigger cell death. Since GKs may also induce cell cycle delay and thus enhance cell survival by facilitating DNA repair, we hypothesized that the prosurvival phenotype of GKs could be selected during cancer progression, and we used a multivariable systems biology approach to test this. We performed multidimensional data analysis, non-negative matrix factorization and logistic regression to compare the features of GKs with those of their putative antagonists, the proto-oncogenes (POs), as well as with control groups of CTs and functionally unrelated congenital heart disease genes (HDs). GKs and POs closely resemble each other, but not CTs or HDs, in terms of gene structure (Pexpression level and breadth (Pimplied suggest a common functional attribute that is strongly negatively selected-that is, a shared phenotype that enhances cell survival. The counterintuitive finding of similar evolutionary pressures affecting GKs and POs raises an intriguing possibility: namely, that cancer microevolution is accelerated by an epistatic cascade in which upstream suppressor gene defects subvert the normal bifunctionality of wild-type GKs by constitutively shifting the phenotype away from apoptosis towards survival. If correct, this interpretation would explain the hitherto unexplained phenomenon of frequent wild-type GK (for example, p53) overexpression in tumors.

Integrative biology approach identifies cytokine targeting strategies for psoriasis.

Science.gov (United States)

Perera, Gayathri K; Ainali, Chrysanthi; Semenova, Ekaterina; Hundhausen, Christian; Barinaga, Guillermo; Kassen, Deepika; Williams, Andrew E; Mirza, Muddassar M; Balazs, Mercedesz; Wang, Xiaoting; Rodriguez, Robert Sanchez; Alendar, Andrej; Barker, Jonathan; Tsoka, Sophia; Ouyang, Wenjun; Nestle, Frank O

2014-02-12

Cytokines are critical checkpoints of inflammation. The treatment of human autoimmune disease has been revolutionized by targeting inflammatory cytokines as key drivers of disease pathogenesis. Despite this, there exist numerous pitfalls when translating preclinical data into the clinic. We developed an integrative biology approach combining human disease transcriptome data sets with clinically relevant in vivo models in an attempt to bridge this translational gap. We chose interleukin-22 (IL-22) as a model cytokine because of its potentially important proinflammatory role in epithelial tissues. Injection of IL-22 into normal human skin grafts produced marked inflammatory skin changes resembling human psoriasis. Injection of anti-IL-22 monoclonal antibody in a human xenotransplant model of psoriasis, developed specifically to test potential therapeutic candidates, efficiently blocked skin inflammation. Bioinformatic analysis integrating both the IL-22 and anti-IL-22 cytokine transcriptomes and mapping them onto a psoriasis disease gene coexpression network identified key cytokine-dependent hub genes. Using knockout mice and small-molecule blockade, we show that one of these hub genes, the so far unexplored serine/threonine kinase PIM1, is a critical checkpoint for human skin inflammation and potential future therapeutic target in psoriasis. Using in silico integration of human data sets and biological models, we were able to identify a new target in the treatment of psoriasis.

Gene expression analysis of zebrafish melanocytes, iridophores, and retinal pigmented epithelium reveals indicators of biological function and developmental origin.

Directory of Open Access Journals (Sweden)

Charles W Higdon

Full Text Available In order to facilitate understanding of pigment cell biology, we developed a method to concomitantly purify melanocytes, iridophores, and retinal pigmented epithelium from zebrafish, and analyzed their transcriptomes. Comparing expression data from these cell types and whole embryos allowed us to reveal gene expression co-enrichment in melanocytes and retinal pigmented epithelium, as well as in melanocytes and iridophores. We found 214 genes co-enriched in melanocytes and retinal pigmented epithelium, indicating the shared functions of melanin-producing cells. We found 62 genes significantly co-enriched in melanocytes and iridophores, illustrative of their shared developmental origins from the neural crest. This is also the first analysis of the iridophore transcriptome. Gene expression analysis for iridophores revealed extensive enrichment of specific enzymes to coordinate production of their guanine-based reflective pigment. We speculate the coordinated upregulation of specific enzymes from several metabolic pathways recycles the rate-limiting substrate for purine synthesis, phosphoribosyl pyrophosphate, thus constituting a guanine cycle. The purification procedure and expression analysis described here, along with the accompanying transcriptome-wide expression data, provide the first mRNA sequencing data for multiple purified zebrafish pigment cell types, and will be a useful resource for further studies of pigment cell biology.

Environmental regulation of plant gene expression: an RT-qPCR laboratory project for an upper-level undergraduate biochemistry or molecular biology course.

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Eickelberg, Garrett J; Fisher, Alison J

2013-01-01

We present a novel laboratory project employing "real-time" RT-qPCR to measure the effect of environment on the expression of the FLOWERING LOCUS C gene, a key regulator of floral timing in Arabidopsis thaliana plants. The project requires four 3-hr laboratory sessions and is aimed at upper-level undergraduate students in biochemistry or molecular biology courses. The project provides students with hands-on experience with RT-qPCR, the current "gold standard" for gene expression analysis, including detailed data analysis using the common 2-ΔΔCT method. Moreover, it provides a convenient starting point for many inquiry-driven projects addressing diverse questions concerning ecological biochemistry, naturally occurring genetic variation, developmental biology, and the regulation of gene expression in nature. Copyright © 2013 Wiley Periodicals, Inc.

Application of computational systems biology to explore environmental toxicity hazards

DEFF Research Database (Denmark)

Audouze, Karine Marie Laure; Grandjean, Philippe

2011-01-01

Background: Computer-based modeling is part of a new approach to predictive toxicology.Objectives: We investigated the usefulness of an integrated computational systems biology approach in a case study involving the isomers and metabolites of the pesticide dichlorodiphenyltrichloroethane (DDT......) to ascertain their possible links to relevant adverse effects.Methods: We extracted chemical-protein association networks for each DDT isomer and its metabolites using ChemProt, a disease chemical biology database that includes both binding and gene expression data, and we explored protein-protein interactions...... using a human interactome network. To identify associated dysfunctions and diseases, we integrated protein-disease annotations into the protein complexes using the Online Mendelian Inheritance in Man database and the Comparative Toxicogenomics Database.Results: We found 175 human proteins linked to p,p´-DDT...

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

Science.gov (United States)

Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

2017-04-27

means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Local and global responses in complex gene regulation networks

Science.gov (United States)

Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

2009-04-01

An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

New Genes and New Insights from Old Genes: Update on Alzheimer Disease

Science.gov (United States)

Ringman, John M.; Coppola, Giovanni

2013-01-01

Purpose of Review: This article discusses the current status of knowledge regarding the genetic basis of Alzheimer disease (AD) with a focus on clinically relevant aspects. Recent Findings: The genetic architecture of AD is complex, as it includes multiple susceptibility genes and likely nongenetic factors. Rare but highly penetrant autosomal dominant mutations explain a small minority of the cases but have allowed tremendous advances in understanding disease pathogenesis. The identification of a strong genetic risk factor, APOE, reshaped the field and introduced the notion of genetic risk for AD. More recently, large-scale genome-wide association studies are adding to the picture a number of common variants with very small effect sizes. Large-scale resequencing studies are expected to identify additional risk factors, including rare susceptibility variants and structural variation. Summary: Genetic assessment is currently of limited utility in clinical practice because of the low frequency (Mendelian mutations) or small effect size (common risk factors) of the currently known susceptibility genes. However, genetic studies are identifying with confidence a number of novel risk genes, and this will further our understanding of disease biology and possibly the identification of therapeutic targets. PMID:23558482

Molecular biology of Homo sapiens: Abstracts of papers presented at the 51st Cold Spring Harbor symposium on quantitative biology

International Nuclear Information System (INIS)

Watson, J.D.; Siniscalco, M.

1986-01-01

This volume contains abstracts of papers presented at the 51st Cold Springs Harbor Symposium on Quantitative Biology. The topic for this meeting was the ''Molecular Biology of Homo sapiens.'' Sessions were entitled Human Gene Map, Human Cancer Genes, Genetic Diagnosis, Human Evolution, Drugs Made Off Human Genes, Receptors, and Gene Therapy. (DT)

Dealing with immunogenicity of biologicals: assessment and clinical relevance

NARCIS (Netherlands)

Wolbink, Gerrit J.; Aarden, Lucien A.; Dijkmans, B. A. C.

2009-01-01

PURPOSE OF REVIEW: In the last decade, biologicals revolutionized rheumatology. An increasing number of patients benefit from biotherapeuticals. However, some patients do not respond to treatment and others lose their response after a certain time. Immunogenicity is one of the factors linked to

Global similarity and local divergence in human and mouse gene co-expression networks

Directory of Open Access Journals (Sweden)

Koonin Eugene V

2006-09-01

Full Text Available Abstract Background A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species. Results At the global level, the topological properties of the human and mouse gene coexpression networks are, essentially, identical. For instance, both networks have topologies with small-world and scale-free properties as well as closely similar average node degrees, clustering coefficients, and path lengths. However, the human and mouse coexpression networks are highly divergent at the local level: only a small fraction ( Conclusion The dissonance between global versus local network divergence suggests that the interspecies similarity of the global network properties is of limited biological significance, at best, and that the biologically relevant aspects of the architectures of gene coexpression are specific and particular, rather than universal. Nevertheless, there is substantial evolutionary conservation of the local network structure which is compatible with the notion that gene coexpression networks are subject to purifying selection.

Systems Biology of Meridians, Acupoints, and Chinese Herbs in Disease

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Li-Ling Lin

2012-01-01

Full Text Available Meridians, acupoints, and Chinese herbs are important components of traditional Chinese medicine (TCM. They have been used for disease treatment and prevention and as alternative and complementary therapies. Systems biology integrates omics data, such as transcriptional, proteomic, and metabolomics data, in order to obtain a more global and complete picture of biological activity. To further understand the existence and functions of the three components above, we reviewed relevant research in the systems biology literature and found many recent studies that indicate the value of acupuncture and Chinese herbs. Acupuncture is useful in pain moderation and relieves various symptoms arising from acute spinal cord injury and acute ischemic stroke. Moreover, Chinese herbal extracts have been linked to wound repair, the alleviation of postmenopausal osteoporosis severity, and anti-tumor effects, among others. Different acupoints, variations in treatment duration, and herbal extracts can be used to alleviate various symptoms and conditions and to regulate biological pathways by altering gene and protein expression. Our paper demonstrates how systems biology has helped to establish a platform for investigating the efficacy of TCM in treating different diseases and improving treatment strategies.

A chronological expression profile of gene activity during embryonic mouse brain development.

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Goggolidou, P; Soneji, S; Powles-Glover, N; Williams, D; Sethi, S; Baban, D; Simon, M M; Ragoussis, I; Norris, D P

2013-12-01

The brain is a functionally complex organ, the patterning and development of which are key to adult health. To help elucidate the genetic networks underlying mammalian brain patterning, we conducted detailed transcriptional profiling during embryonic development of the mouse brain. A total of 2,400 genes were identified as showing differential expression between three developmental stages. Analysis of the data identified nine gene clusters to demonstrate analogous expression profiles. A significant group of novel genes of as yet undiscovered biological function were detected as being potentially relevant to brain development and function, in addition to genes that have previously identified roles in the brain. Furthermore, analysis for genes that display asymmetric expression between the left and right brain hemispheres during development revealed 35 genes as putatively asymmetric from a combined data set. Our data constitute a valuable new resource for neuroscience and neurodevelopment, exposing possible functional associations between genes, including novel loci, and encouraging their further investigation in human neurological and behavioural disorders.

Leaving out control groups: an internal contrast analysis of gene expression profiles in atrial fibrillation patients--a systems biology approach to clinical categorization.

Science.gov (United States)

Vanhoutte, Kurt; de Asmundis, Carlo; Francesconi, Anna; Figysl, Jurgen; Steurs, Griet; Boussy, Tim; Roos, Markus; Mueller, Andreas; Massimo, Lucio; Paparella, Gaetano; Van Caelenberg, Kristien; Chierchia, Gian Battista; Sarkozy, Andrea; Terradellas, Pedro Brugada Y; Zizi, Martin

2009-01-01

Atrial fibrillation (AF) is a frequent chronic dysrythmia with an incidence that increases with age (>40). Because of its medical and socio-economic impacts it is expected to become an increasing burden on most health care systems. AF is a multi-factorial disease for which the identification of subtypes is warranted. Novel approaches based on the broad concepts of systems biology may overcome the blurred notion of normal and pathological phenotype, which is inherent to high throughput molecular arrays analysis. Here we apply an internal contrast algorithm on AF patient data with an analytical focus on potential entry pathways into the disease. We used a RMA (Robust Multichip Average) normalized Affymetrix micro-array data set from 10 AF patients (geo_accession #GSE2240). Four series of probes were selected based on physiopathogenic links with AF entryways: apoptosis (remodeling), MAP kinase (cell remodeling), OXPHOS (ability to sustain hemodynamic workload) and glycolysis (ischemia). Annotated probe lists were polled with Bioconductor packages in R (version 2.7.1). Genetic profile contrasts were analysed with hierarchical clustering and principal component analysis. The analysis revealed distinct patient groups for all probe sets. A substantial part (54% till 67%) of the variance is explained in the first 2 principal components. Genes in PC1/2 with high discriminatory value were selected and analyzed in detail. We aim for reliable molecular stratification of AF. We show that stratification is possible based on physiologically relevant gene sets. Genes with high contrast value are likely to give pathophysiological insight into permanent AF subtypes.

An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

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Botía, Juan A; Vandrovcova, Jana; Forabosco, Paola; Guelfi, Sebastian; D'Sa, Karishma; Hardy, John; Lewis, Cathryn M; Ryten, Mina; Weale, Michael E

2017-04-12

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

When Is Hub Gene Selection Better than Standard Meta-Analysis?

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Langfelder, Peter; Mischel, Paul S.; Horvath, Steve

2013-01-01

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to

When is hub gene selection better than standard meta-analysis?

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Peter Langfelder

Full Text Available Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data. Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA in three comprehensive and unbiased empirical studies: (1 Finding genes predictive of lung cancer survival, (2 finding methylation markers related to age, and (3 finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1. However, standard meta-analysis methods perform as good as (if not better than a consensus network approach in terms of validation success (criterion 2. The article also reports a comparison of meta-analysis techniques

When is hub gene selection better than standard meta-analysis?

Science.gov (United States)

Langfelder, Peter; Mischel, Paul S; Horvath, Steve

2013-01-01

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to

Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements

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Jiang Wei

2008-08-01

Full Text Available Abstract Background With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. Results The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8 (p ≈ 0, desmin (DES (p = 2.71 × 10-6 and enolase 1 (ENO1 (p = 4.19 × 10-5], while two novel hub genes [RNA binding motif protein 9 (RBM9 (p = 1.50 × 10-4 and ribosomal protein L30 (RPL30 (p = 1.50 × 10-4] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO based analysis of the colon cancer-specific gene network and

Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.

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Jiang, Wei; Li, Xia; Rao, Shaoqi; Wang, Lihong; Du, Lei; Li, Chuanxing; Wu, Chao; Wang, Hongzhi; Wang, Yadong; Yang, Baofeng

2008-08-10

With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p approximately 0), desmin (DES) (p = 2.71 x 10(-6)) and enolase 1 (ENO1) (p = 4.19 x 10(-5))], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 x 10(-4)) and ribosomal protein L30 (RPL30) (p = 1.50 x 10(-4))] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that

Integration of molecular biology tools for identifying promoters and genes abundantly expressed in flowers of Oncidium Gower Ramsey

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Tung Shu-Yun

2011-04-01

Full Text Available Abstract Background Orchids comprise one of the largest families of flowering plants and generate commercially important flowers. However, model plants, such as Arabidopsis thaliana do not contain all plant genes, and agronomic and horticulturally important genera and species must be individually studied. Results Several molecular biology tools were used to isolate flower-specific gene promoters from Oncidium 'Gower Ramsey' (Onc. GR. A cDNA library of reproductive tissues was used to construct a microarray in order to compare gene expression in flowers and leaves. Five genes were highly expressed in flower tissues, and the subcellular locations of the corresponding proteins were identified using lip transient transformation with fluorescent protein-fusion constructs. BAC clones of the 5 genes, together with 7 previously published flower- and reproductive growth-specific genes in Onc. GR, were identified for cloning of their promoter regions. Interestingly, 3 of the 5 novel flower-abundant genes were putative trypsin inhibitor (TI genes (OnTI1, OnTI2 and OnTI3, which were tandemly duplicated in the same BAC clone. Their promoters were identified using transient GUS reporter gene transformation and stable A. thaliana transformation analyses. Conclusions By combining cDNA microarray, BAC library, and bombardment assay techniques, we successfully identified flower-directed orchid genes and promoters.

Cold Spring Harbor symposia on quantitative biology: Volume 51, Molecular biology of /ital Homo sapiens/

International Nuclear Information System (INIS)

1986-01-01

This volume is the second part of a collection of papers submitted by the participants to the 1986 Cold Spring Harbor Symposium on Quantitative Biology entitled Molecular Biology of /ital Homo sapiens/. The 49 papers included in this volume are grouped by subject into receptors, human cancer genes, and gene therapy. (DT)

Combining cell transplants or gene therapy with deep brain stimulation for Parkinson's disease.

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Rowland, Nathan C; Starr, Philip A; Larson, Paul S; Ostrem, Jill L; Marks, William J; Lim, Daniel A

2015-02-01

Cell transplantation and gene therapy each show promise to enhance the treatment of Parkinson's disease (PD). However, because cell transplantation and gene therapy generally require direct delivery to the central nervous system, clinical trial design involves unique scientific, ethical, and financial concerns related to the invasive nature of the procedure. Typically, such biologics have been tested in PD patients who have not received any neurosurgical intervention. Here, we suggest that PD patients undergoing deep brain stimulation (DBS) device implantation are an ideal patient population for the clinical evaluation of cell transplantation and gene therapy. Randomizing subjects to an experimental group that receives the biologic concurrently with the DBS implantation-or to a control group that receives the DBS treatment alone-has several compelling advantages. First, this study design enables the participation of patients likely to benefit from DBS, many of whom simultaneously meet the inclusion criteria of biologic studies. Second, the need for a sham neurosurgical procedure is eliminated, which may reduce ethical concerns, promote patient recruitment, and enhance the blinding of surgical trials. Third, testing the biologic by "piggybacking" onto an established, reimbursable procedure should reduce the cost of clinical trials, which may allow a greater number of biologics to reach this critical stage of research translation. Finally, this clinical trial design may lead to combinatorial treatment strategies that provide PD patients with more durable control over disabling motor symptoms. By combining neuromodulation with biologics, we may also reveal important treatment paradigms relevant to other diseases of the brain. © 2014 International Parkinson and Movement Disorder Society.

Molecular biology of Homo sapiens: Abstracts of papers presented at the 51st Cold Spring Harbor symposium on quantitative biology

Energy Technology Data Exchange (ETDEWEB)

Watson, J.D.; Siniscalco, M.

1986-01-01

This volume contains abstracts of papers presented at the 51st Cold Springs Harbor Symposium on Quantitative Biology. The topic for this meeting was the ''Molecular Biology of Homo sapiens.'' Sessions were entitled Human Gene Map, Human Cancer Genes, Genetic Diagnosis, Human Evolution, Drugs Made Off Human Genes, Receptors, and Gene Therapy. (DT)

Understanding Biological Regulation Through Synthetic Biology.

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Bashor, Caleb J; Collins, James J

2018-03-16

Engineering synthetic gene regulatory circuits proceeds through iterative cycles of design, building, and testing. Initial circuit designs must rely on often-incomplete models of regulation established by fields of reductive inquiry-biochemistry and molecular and systems biology. As differences in designed and experimentally observed circuit behavior are inevitably encountered, investigated, and resolved, each turn of the engineering cycle can force a resynthesis in understanding of natural network function. Here, we outline research that uses the process of gene circuit engineering to advance biological discovery. Synthetic gene circuit engineering research has not only refined our understanding of cellular regulation but furnished biologists with a toolkit that can be directed at natural systems to exact precision manipulation of network structure. As we discuss, using circuit engineering to predictively reorganize, rewire, and reconstruct cellular regulation serves as the ultimate means of testing and understanding how cellular phenotype emerges from systems-level network function. Expected final online publication date for the Annual Review of Biophysics Volume 47 is May 20, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology.

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Lovering, Ruth C; Roncaglia, Paola; Howe, Douglas G; Laulederkind, Stanley J F; Khodiyar, Varsha K; Berardini, Tanya Z; Tweedie, Susan; Foulger, Rebecca E; Osumi-Sutherland, David; Campbell, Nancy H; Huntley, Rachael P; Talmud, Philippa J; Blake, Judith A; Breckenridge, Ross; Riley, Paul R; Lambiase, Pier D; Elliott, Perry M; Clapp, Lucie; Tinker, Andrew; Hill, David P

2018-02-01

A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products. In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci. We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects. © 2018 The Authors.

SysBioCube: A Data Warehouse and Integrative Data Analysis Platform Facilitating Systems Biology Studies of Disorders of Military Relevance.

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Chowbina, Sudhir; Hammamieh, Rasha; Kumar, Raina; Chakraborty, Nabarun; Yang, Ruoting; Mudunuri, Uma; Jett, Marti; Palma, Joseph M; Stephens, Robert

2013-01-01

SysBioCube is an integrated data warehouse and analysis platform for experimental data relating to diseases of military relevance developed for the US Army Medical Research and Materiel Command Systems Biology Enterprise (SBE). It brings together, under a single database environment, pathophysio-, psychological, molecular and biochemical data from mouse models of post-traumatic stress disorder and (pre-) clinical data from human PTSD patients.. SysBioCube will organize, centralize and normalize this data and provide an access portal for subsequent analysis to the SBE. It provides new or expanded browsing, querying and visualization to provide better understanding of the systems biology of PTSD, all brought about through the integrated environment. We employ Oracle database technology to store the data using an integrated hierarchical database schema design. The web interface provides researchers with systematic information and option to interrogate the profiles of pan-omics component across different data types, experimental designs and other covariates.

Perspectives on the relevance of the circadian time structure to workplace threshold limit values and employee biological monitoring.

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Smolensky, Michael H; Reinberg, Alain E; Sackett-Lundeen, Linda

2017-01-01

The circadian time structure (CTS) and its disruption by rotating and nightshift schedules relative to work performance, accident risk, and health/wellbeing have long been areas of occupational medicine research. Yet, there has been little exploration of the relevance of the CTS to setting short-term, time-weighted, and ceiling threshold limit values (TLVs); conducting employee biological monitoring (BM); and establishing normative reference biological exposure indices (BEIs). Numerous publications during the past six decades document the CTS substantially affects the disposition - absorption, distribution, metabolism, and elimination - and effects of medications. Additionally, laboratory animal and human studies verify the tolerance to chemical, biological (contagious), and physical agents can differ extensively according to the circadian time of exposure. Because of slow and usually incomplete CTS adjustment by rotating and permanent nightshift workers, occupational chemical and other contaminant encounters occur during a different circadian stage than for dayshift workers. Thus, the intended protection of some TLVs when working the nightshift compared to dayshift might be insufficient, especially in high-risk settings. The CTS is germane to employee BM in that large-amplitude predictable-in-time 24h variation can occur in the concentration of urine, blood, and saliva of monitored chemical contaminants and their metabolites plus biomarkers indicative of adverse xenobiotic exposure. The concept of biological time-qualified (for rhythms) reference values, currently of interest to clinical laboratory pathology practice, is seemingly applicable to industrial medicine as circadian time and workshift-specific BEIs to improve surveillance of night workers, in particular. Furthermore, BM as serial assessments performed frequently both during and off work, exemplified by employee self-measurement of lung function using a small portable peak expiratory flow meter, can

Potential of genes and gene products from Trichoderma sp. and Gliocladium sp. for the development of biological pesticides.

Science.gov (United States)

Lorito, M; Hayes, C K; Zoina, A; Scala, F; Del Sorbo, G; Woo, S L; Harman, G E

1994-12-01

Fungal cell wall degrading enzymes produced by the biocontrol fungi Trichoderma harzianum and Gliocladium virens are strong inhibitors of spore germination and hyphal elongation of a number of phytopathogenic fungi. The purified enzymes include chitinolytic enzymes with different modes of action or different substrate specificity and glucanolytic enzymes with exo-activity. A variety of synergistic interactions were found when different enzymes were combined or associated with biotic or abiotic antifungal agents. The levels of inhibition obtained by using enzyme combinations were, in some cases, comparable with commercial fungicides. Moreover, the antifungal interaction between enzymes and common fungicides allowed the reduction of the chemical doses up to 200-fold. Chitinolytic and glucanolytic enzymes from T. harzianum were able to improve substantially the antifungal ability of a biocontrol strain of Enterobacter cloacae. DNA fragments containing genes encoding for different chitinolytic enzymes were isolated from a cDNA library of T. harzianum and cloned for mechanistic studies and biocontrol purposes. Our results provide additional information on the role of lytic enzymes in processes of biocontrol and strongly suggest the use of lytic enzymes and their genes for biological control of plant diseases.

Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis.

Directory of Open Access Journals (Sweden)

Feixiong Cheng

2016-09-01

Full Text Available Viruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap host genes and discover host genes that are essential for viral replication, but not for host cell survival. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase. Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline that may be potential for antiviral indication (e.g. anti-Ebola. In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics.

[Gene doping: gene transfer and possible molecular detection].

Science.gov (United States)

Argüelles, Carlos Francisco; Hernández-Zamora, Edgar

2007-01-01

The use of illegal substances in sports to enhance athletic performance during competition has caused international sports organizations such as the COI and WADA to take anti doping measures. A new doping method know as gene doping is defined as "the non-therapeutic use of genes, genetic elements and/or cells that have the capacity to enhance athletic performance". However, gene doping in sports is not easily identified and can cause serious consequences. Molecular biology techniques are needed in order to distinguish the difference between a "normal" and an "altered" genome. Further, we need to develop new analytic methods and biological molecular techniques in anti-doping laboratories, and design programs that avoid the non therapeutic use of genes.

Detection of base damage in DNA in human blood exposed to ionizing radiation at biologically relevant doses

International Nuclear Information System (INIS)

Loon, A.A.W.M. van; Lohman, P.H.M.; Groenendijk, R.H.; Schans, G.P. van der; Baan, R.A.

1991-01-01

The alkaline elution technique for the detection of DNA damage has been adapted to allow application on unlabelled blood cells. Both the induction and subsequent repair have been studied of two classes of DNA damage, viz. single-strand breaks and base damage recognized by the γ-endonuclease activity in a cell-free extract of Micrococcus luteus bacteria. The high sensitivity of the assay permitted the measurement of induction and repair of base damage after in vitro exposure of full blood under aerobic conditions to biologically relevant doses of γ-rays (1.5-4.5 Gy). After a radiation dose of 3 Gy about 50% of the base damage was removed within 1.5 h of repair. Base damage could still be detected at 24h after exposure to 15 Gy. (author)

Action video game players' visual search advantage extends to biologically relevant stimuli.

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Chisholm, Joseph D; Kingstone, Alan

2015-07-01

Research investigating the effects of action video game experience on cognition has demonstrated a host of performance improvements on a variety of basic tasks. Given the prevailing evidence that these benefits result from efficient control of attentional processes, there has been growing interest in using action video games as a general tool to enhance everyday attentional control. However, to date, there is little evidence indicating that the benefits of action video game playing scale up to complex settings with socially meaningful stimuli - one of the fundamental components of our natural environment. The present experiment compared action video game player (AVGP) and non-video game player (NVGP) performance on an oculomotor capture task that presented participants with face stimuli. In addition, the expression of a distractor face was manipulated to assess if action video game experience modulated the effect of emotion. Results indicate that AVGPs experience less oculomotor capture than NVGPs; an effect that was not influenced by the emotional content depicted by distractor faces. It is noteworthy that this AVGP advantage emerged despite participants being unaware that the investigation had to do with video game playing, and participants being equivalent in their motivation and treatment of the task as a game. The results align with the notion that action video game experience is associated with superior attentional and oculomotor control, and provides evidence that these benefits can generalize to more complex and biologically relevant stimuli. Copyright © 2015 Elsevier B.V. All rights reserved.

Chromosome-Centric Human Proteome Project Allies with Developmental Biology: A Case Study of the Role of Y Chromosome Genes in Organ Development.

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Meyfour, Anna; Pooyan, Paria; Pahlavan, Sara; Rezaei-Tavirani, Mostafa; Gourabi, Hamid; Baharvand, Hossein; Salekdeh, Ghasem Hosseini

2017-12-01

One of the main goals of Chromosome-Centric Human Proteome Project is to identify protein evidence for missing proteins (MPs). Here, we present a case study of the role of Y chromosome genes in organ development and how to overcome the challenges facing MPs identification by employing human pluripotent stem cell differentiation into cells of different organs yielding unprecedented biological insight into adult silenced proteins. Y chromosome is a male-specific sex chromosome which escapes meiotic recombination. From an evolutionary perspective, Y chromosome has preserved 3% of ancestral genes compared to 98% preservation of the X chromosome based on Ohno's law. Male specific region of Y chromosome (MSY) contains genes that contribute to central dogma and govern the expression of various targets throughout the genome. One of the most well-known functions of MSY genes is to decide the male-specific characteristics including sex, testis formation, and spermatogenesis, which are majorly formed by ampliconic gene families. Beyond its role in sex-specific gonad development, MSY genes in coexpression with their X counterparts, as single copy and broadly expressed genes, inhibit haplolethality and play a key role in embryogenesis. The role of X-Y related gene mutations in the development of hereditary syndromes suggests an essential contribution of sex chromosome genes to development. MSY genes, solely and independent of their X counterparts and/or in association with sex hormones, have a considerable impact on organ development. In this Review, we present major recent findings on the contribution of MSY genes to gonad formation, spermatogenesis, and the brain, heart, and kidney development and discuss how Y chromosome proteome project may exploit developmental biology to find missing proteins.

Synthetic biology and occupational risk.

Science.gov (United States)

Howard, John; Murashov, Vladimir; Schulte, Paul

2017-03-01

Synthetic biology is an emerging interdisciplinary field of biotechnology that involves applying the principles of engineering and chemical design to biological systems. Biosafety professionals have done an excellent job in addressing research laboratory safety as synthetic biology and gene editing have emerged from the larger field of biotechnology. Despite these efforts, risks posed by synthetic biology are of increasing concern as research procedures scale up to industrial processes in the larger bioeconomy. A greater number and variety of workers will be exposed to commercial synthetic biology risks in the future, including risks to a variety of workers from the use of lentiviral vectors as gene transfer devices. There is a need to review and enhance current protection measures in the field of synthetic biology, whether in experimental laboratories where new advances are being researched, in health care settings where treatments using viral vectors as gene delivery systems are increasingly being used, or in the industrial bioeconomy. Enhanced worker protection measures should include increased injury and illness surveillance of the synthetic biology workforce; proactive risk assessment and management of synthetic biology products; research on the relative effectiveness of extrinsic and intrinsic biocontainment methods; specific safety guidance for synthetic biology industrial processes; determination of appropriate medical mitigation measures for lentiviral vector exposure incidents; and greater awareness and involvement in synthetic biology safety by the general occupational safety and health community as well as by government occupational safety and health research and regulatory agencies.

Radiation biology. Chapter 20

Energy Technology Data Exchange (ETDEWEB)

Wondergem, J. [International Atomic Energy Agency, Vienna (Austria)

2014-09-15

Radiation biology (radiobiology) is the study of the action of ionizing radiations on living matter. This chapter gives an overview of the biological effects of ionizing radiation and discusses the physical, chemical and biological variables that affect dose response at the cellular, tissue and whole body levels at doses and dose rates relevant to diagnostic radiology.

Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology

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Rossin, Elizabeth J.; Lage, Kasper; Raychaudhuri, Soumya; Xavier, Ramnik J.; Tatar, Diana; Benita, Yair

2011-01-01

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein–protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in

Selection of reference genes for quantitative real time RT-PCR during dimorphism in the zygomycete Mucor circinelloides.

Science.gov (United States)

Valle-Maldonado, Marco I; Jácome-Galarza, Irvin E; Gutiérrez-Corona, Félix; Ramírez-Díaz, Martha I; Campos-García, Jesús; Meza-Carmen, Víctor

2015-03-01

Mucor circinelloides is a dimorphic fungal model for studying several biological processes including cell differentiation (yeast-mold transitions) as well as biodiesel and carotene production. The recent release of the first draft sequence of the M. circinelloides genome, combined with the availability of analytical methods to determine patterns of gene expression, such as quantitative Reverse transcription-Polymerase chain reaction (qRT-PCR), and the development of molecular genetic tools for the manipulation of the fungus, may help identify M. circinelloides gene products and analyze their relevance in different biological processes. However, no information is available on M. circinelloides genes of stable expression that could serve as internal references in qRT-PCR analyses. One approach to solve this problem consists in the use of housekeeping genes as internal references. However, validation of the usability of these reference genes is a fundamental step prior to initiating qRT-PCR assays. This work evaluates expression of several constitutive genes by qRT-PCR throughout the morphological differentiation stages of M. circinelloides; our results indicate that tfc-1 and ef-1 are the most stable genes for qRT-PCR assays during differentiation studies and they are proposed as reference genes to carry out gene expression studies in this fungus.

Discovering biological progression underlying microarray samples.

Directory of Open Access Journals (Sweden)

Peng Qiu

2011-04-01

Full Text Available In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD, to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression, and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the

[Detection of Staphylococcus aureus resistant to methicillin (MRSA) by molecular biology (Cepheid GeneXpert IL, GeneOhm BD, Roche LightCycler, Hyplex Evigene I2A) versus screening by culture: Economic and practical strategy for the laboratory].

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Laudat, P; Demondion, E; Jouannet, C; Charron, J; Chillou, C; Salaun, V; Mankikian, B

2012-06-01

Patients admitted in cardiac surgery and cardiac ICU at the Clinic Saint-Gatien (Tours) are screened for MRSA at the entrance by nasal swab and culture on blood agar and selective chromogenic medium made by addition of cefoxitin: BBL CHROMagar MRSA-II BD (result obtained at Day +1). We wanted to assess the molecular biology techniques available to obtain a result at day 0 for the majority of patients and to define an economic and practical strategy for the laboratory. We studied four molecular biology techniques: Cepheid GeneXpert (Cepheid) GeneOhm (BD), LightCycler (Roche) and Hyplex (I2A). Upon reception, nasal swabs were treated by culture, considered as reference, and one of the techniques of molecular biology, according to the manufacturer's notice. We conducted four studies between April 2008 and February 2009 to obtain a significant sample for each of them. By screening we mean a method that allows us to exclude MRSA carriage for patients waiting for surgery, and not to change patient management: for example, lack of isolation measures specific to entrance, no modification of antibiotic prophylaxis during surgery and no isolation measures in the immediate postoperative period. The criteria we considered for this evaluation were: (1) technician time: time to perform one or a series of sample(s) n=10 or more (about 2h for all techniques except GeneXpert 75min), level of skilled competences (no specific training for GeneXpert); (2) results: turnaround time (all molecular biology techniques), ease of reading and results interpretations (no specialized training required for GeneXpert), failure or not (12% of failure of internal controls for GeneOhm); (3) economic: cost for one or a series of sample(s) (n=10 or more), if we considered X as the reference culture cost (10 X Hyplex and LightCycler, 20 X and 40 X for GeneXpert GeneOhm); (4) NPV: 100% for GeneXpert and LightCycler. At same sensitivity, no technique, including culture, can solve alone our problem, which

Genetic mouse models relevant to schizophrenia: taking stock and looking forward.

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Harrison, Paul J; Pritchett, David; Stumpenhorst, Katharina; Betts, Jill F; Nissen, Wiebke; Schweimer, Judith; Lane, Tracy; Burnet, Philip W J; Lamsa, Karri P; Sharp, Trevor; Bannerman, David M; Tunbridge, Elizabeth M

2012-03-01

Genetic mouse models relevant to schizophrenia complement, and have to a large extent supplanted, pharmacological and lesion-based rat models. The main attraction is that they potentially have greater construct validity; however, they share the fundamental limitations of all animal models of psychiatric disorder, and must also be viewed in the context of the uncertain and complex genetic architecture of psychosis. Some of the key issues, including the choice of gene to target, the manner of its manipulation, gene-gene and gene-environment interactions, and phenotypic characterization, are briefly considered in this commentary, illustrated by the relevant papers reported in this special issue. Copyright © 2011 Elsevier Ltd. All rights reserved.

The art of curation at a biological database: Principles and application

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Sarah G. Odell

2017-09-01

Full Text Available The variety and quantity of data being produced by biological research has grown dramatically in recent years, resulting in an expansion of our understanding of biological systems. However, this abundance of data has brought new challenges, especially in curation. The role of biocurators is in part to filter research outcomes as they are generated, not only so that information is formatted and consolidated into locations that can provide long-term data sustainability, but also to ensure that the relevant data that was captured is reliable, reusable, and accessible. In many ways, biocuration lies somewhere between an art and a science. At GrainGenes (https://wheat.pw.usda.gov;https://graingenes.org, a long-time, stably-funded centralized repository for data about wheat, barley, rye, oat, and other small grains, curators have implemented a workflow for locating, parsing, and uploading new data so that the most important, peer-reviewed, high-quality research is available to users as quickly as possible with rich links to past research outcomes. In this report, we illustrate the principles and practical considerations of curation that we follow at GrainGenes with three case studies for curating a gene, a quantitative trait locus (QTL, and genomic elements. These examples demonstrate how our work allows users, i.e., small grains geneticists and breeders, to harness high-quality small grains data at GrainGenes to help them develop plants with enhanced agronomic traits.

ADAGE signature analysis: differential expression analysis with data-defined gene sets.

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Tan, Jie; Huyck, Matthew; Hu, Dongbo; Zelaya, René A; Hogan, Deborah A; Greene, Casey S

2017-11-22

Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. We designed

Estimating variation within the genes and inferring the phylogeny of 186 sequenced diverse Escherichia coli genomes

DEFF Research Database (Denmark)

Kaas, Rolf Sommer; Rundsten, Carsten Friis; Ussery, David

2012-01-01

Background Escherichia coli exists in commensal and pathogenic forms. By measuring the variation of individual genes across more than a hundred sequenced genomes, gene variation can be studied in detail, including the number of mutations found for any given gene. This knowledge will be useful...... for creating better phylogenies, for determination of molecular clocks and for improved typing techniques. Results We find 3,051 gene clusters/families present in at least 95% of the genomes and 1,702 gene clusters present in 100% of the genomes. The former 'soft core' of about 3,000 gene families is perhaps...... more biologically relevant, especially considering that many of these genome sequences are draft quality. The E. coli pan-genome for this set of isolates contains 16,373 gene clusters. A core-gene tree, based on alignment and a pan-genome tree based on gene presence/absence, maps the relatedness...

Neurodevelopmental consequences in offspring of mothers with preeclampsia during pregnancy: underlying biological mechanism via imprinting genes.

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Nomura, Yoko; John, Rosalind M; Janssen, Anna Bugge; Davey, Charles; Finik, Jackie; Buthmann, Jessica; Glover, Vivette; Lambertini, Luca

2017-06-01

Preeclampsia is known to be a leading cause of mortality and morbidity among mothers and their infants. Approximately 3-8% of all pregnancies in the US are complicated by preeclampsia and another 5-7% by hypertensive symptoms. However, less is known about its long-term influence on infant neurobehavioral development. The current review attempts to demonstrate new evidence for imprinting gene dysregulation caused by hypertension, which may explain the link between maternal preeclampsia and neurocognitive dysregulation in offspring. Pub Med and Web of Science databases were searched using the terms "preeclampsia," "gestational hypertension," "imprinting genes," "imprinting dysregulation," and "epigenetic modification," in order to review the evidence demonstrating associations between preeclampsia and suboptimal child neurodevelopment, and suggest dysregulation of placental genomic imprinting as a potential underlying mechanism. The high mortality and morbidity among mothers and fetuses due to preeclampsia is well known, but there is little research on the long-term biological consequences of preeclampsia and resulting hypoxia on the fetal/child neurodevelopment. In the past decade, accumulating evidence from studies that transcend disciplinary boundaries have begun to show that imprinted genes expressed in the placenta might hold clues for a link between preeclampsia and impaired cognitive neurodevelopment. A sudden onset of maternal hypertension detected by the placenta may result in misguided biological programming of the fetus via changes in the epigenome, resulting in suboptimal infant development. Furthering our understanding of the molecular and cellular mechanisms through which neurodevelopmental trajectories of the fetus/infant are affected by preeclampsia and hypertension will represent an important first step toward preventing adverse neurodevelopment in infants.

Gene Overexpression: Uses, Mechanisms, and Interpretation

Science.gov (United States)

2012-01-01

The classical genetic approach for exploring biological pathways typically begins by identifying mutations that cause a phenotype of interest. Overexpression or misexpression of a wild-type gene product, however, can also cause mutant phenotypes, providing geneticists with an alternative yet powerful tool to identify pathway components that might remain undetected using traditional loss-of-function analysis. This review describes the history of overexpression, the mechanisms that are responsible for overexpression phenotypes, tests that begin to distinguish between those mechanisms, the varied ways in which overexpression is used, the methods and reagents available in several organisms, and the relevance of overexpression to human disease. PMID:22419077

From gene to structure: Lactobacillus bulgaricus D-lactate dehydrogenase from yogurt as an integrated curriculum model for undergraduate molecular biology and biochemistry laboratory courses.

Science.gov (United States)

Lawton, Jeffrey A; Prescott, Noelle A; Lawton, Ping X

2018-05-01

We have developed an integrated, project-oriented curriculum for undergraduate molecular biology and biochemistry laboratory courses spanning two semesters that is organized around the ldhA gene from the yogurt-fermenting bacterium Lactobacillus bulgaricus, which encodes the enzyme d-lactate dehydrogenase. The molecular biology module, which consists of nine experiments carried out over eleven sessions, begins with the isolation of genomic DNA from L. bulgaricus in yogurt and guides students through the process of cloning the ldhA gene into a prokaryotic expression vector, followed by mRNA isolation and characterization of recombinant gene expression levels using RT-PCR. The biochemistry module, which consists of nine experiments carried out over eight sessions, begins with overexpression of the cloned ldhA gene and guides students through the process of affinity purification, biochemical characterization of the purified LdhA protein, and analysis of enzyme kinetics using various substrates and an inhibitor, concluding with a guided inquiry investigation of structure-function relationships in the three-dimensional structure of LdhA using molecular visualization software. Students conclude by writing a paper describing their work on the project, formatted as a manuscript to be submitted for publication in a scientific journal. Overall, this curriculum, with its emphasis on experiential learning, provides hands-on training with a variety of common laboratory techniques in molecular biology and biochemistry and builds experience with the process of scientific reasoning, along with reinforcement of essential transferrable skills such as critical thinking, information literacy, and written communication, all within the framework of an extended project having the look and feel of a research experience. © 2018 by The International Union of Biochemistry and Molecular Biology, 46(3):270-278, 2018. © 2018 The International Union of Biochemistry and Molecular Biology.

Biological Effects of Potato Plants Transformation with Glucose Oxidase Gene and their Resistance to Hyperthermia

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O.I. Grabelnych

2017-02-01

Full Text Available It is known that regulation of plant tolerance to adverse environmental factors is connected with short term increase of the concentration of endogenous reactive oxygen species (ROS, which are signalling molecules for the induction of protective mechanisms. Introduction and expression of heterologous gox gene, which encodes glucose oxidase enzyme in plant genome, induce constantly higher content of hydrogen peroxide in plant tissues. It is not known how the introduction of native or modified gox gene affects the plant resistance to high-temperature stress, one of the most commonly used model for the study of stress response and thermal tolerance. In this study, we investigated biological effects of transformation and evaluated the resistance to temperature stress of potato plants with altered levels of glucose oxidase expression. Transformation of potato plants by gox gene led to the more early coming out from tuber dormancy of transformed plants and slower growth rate. Transformants containing the glucose oxidase gene were more sensitive to lethal thermal shock (50 °C, 90 min than the transformant with the empty vector (pBI or untransformed plants (CK. Pre-heating of plants at 37 °C significantly weakened the damaging effect of lethal thermal shock. This attenuation was more significant in the non-transformed plants.

Identifying relevant group of miRNAs in cancer using fuzzy mutual information.

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Pal, Jayanta Kumar; Ray, Shubhra Sankar; Pal, Sankar K

2016-04-01

MicroRNAs (miRNAs) act as a major biomarker of cancer. All miRNAs in human body are not equally important for cancer identification. We propose a methodology, called FMIMS, which automatically selects the most relevant miRNAs for a particular type of cancer. In FMIMS, miRNAs are initially grouped by using a SVM-based algorithm; then the group with highest relevance is determined and the miRNAs in that group are finally ranked for selection according to their redundancy. Fuzzy mutual information is used in computing the relevance of a group and the redundancy of miRNAs within it. Superiority of the most relevant group to all others, in deciding normal or cancer, is demonstrated on breast, renal, colorectal, lung, melanoma and prostate data. The merit of FMIMS as compared to several existing methods is established. While 12 out of 15 selected miRNAs by FMIMS corroborate with those of biological investigations, three of them viz., "hsa-miR-519," "hsa-miR-431" and "hsa-miR-320c" are possible novel predictions for renal cancer, lung cancer and melanoma, respectively. The selected miRNAs are found to be involved in disease-specific pathways by targeting various genes. The method is also able to detect the responsible miRNAs even at the primary stage of cancer. The related code is available at http://www.jayanta.droppages.com/FMIMS.html .

Computational Systems Chemical Biology

OpenAIRE

Oprea, Tudor I.; May, Elebeoba E.; Leitão, Andrei; Tropsha, Alexander

2011-01-01

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically-based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology, SCB (Oprea et al., 2007).

Deregulation of obesity-relevant genes is associated with progression in BMI and the amount of adipose tissue in pigs.

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Mentzel, Caroline M Junker; Cardoso, Tainã Figueiredo; Pipper, Christian Bressen; Jacobsen, Mette Juul; Jørgensen, Claus Bøttcher; Cirera, Susanna; Fredholm, Merete

2018-02-01

The aim of this study was to elucidate the relative impact of three phenotypes often used to characterize obesity on perturbation of molecular pathways involved in obesity. The three obesity-related phenotypes are (1) body mass index (BMI), (2) amount of subcutaneous adipose tissue (SATa), and (3) amount of retroperitoneal adipose tissue (RPATa). Although it is generally accepted that increasing amount of RPATa is 'unhealthy', a direct comparison of the relative impact of the three obesity-related phenotypes on gene expression has, to our knowledge, not been performed previously. We have used multiple linear models to analyze altered gene expression of selected obesity-related genes in tissues collected from 19 female pigs phenotypically characterized with respect to the obesity-related phenotypes. Gene expression was assessed by high-throughput qPCR in RNA from liver, skeletal muscle and abdominal adipose tissue. The stringent statistical approach used in the study has increased the power of the analysis compared to the classical approach of analysis in divergent groups of individuals. Our approach led to the identification of key components of cellular pathways that are modulated in the three tissues in association with changes in the three obesity-relevant phenotypes (BMI, SATa and RPATa). The deregulated pathways are involved in biosynthesis and transcript regulation in adipocytes, in lipid transport, lipolysis and metabolism, and in inflammatory responses. Deregulation seemed more comprehensive in liver (23 genes) compared to abdominal adipose tissue (10 genes) and muscle (3 genes). Notably, the study supports the notion that excess amount of intra-abdominal adipose tissue is associated with a greater metabolic disease risk. Our results provide molecular support for this notion by demonstrating that increasing amount of RPATa has a higher impact on perturbation of cellular pathways influencing obesity and obesity-related metabolic traits compared to increase

High-temperature Ionization-induced Synthesis of Biologically Relevant Molecules in the Protosolar Nebula

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Bekaert, David V.; Derenne, Sylvie; Tissandier, Laurent; Marrocchi, Yves; Charnoz, Sebastien; Anquetil, Christelle; Marty, Bernard

2018-06-01

Biologically relevant molecules (hereafter biomolecules) have been commonly observed in extraterrestrial samples, but the mechanisms accounting for their synthesis in space are not well understood. While electron-driven production of organic solids from gas mixtures reminiscent of the photosphere of the protosolar nebula (PSN; i.e., dominated by CO–N2–H2) successfully reproduced key specific features of the chondritic insoluble organic matter (e.g., elementary and isotopic signatures of chondritic noble gases), the molecular diversity of organic materials has never been investigated. Here, we report that a large range of biomolecules detected in meteorites and comets can be synthesized under conditions typical of the irradiated gas phase of the PSN at temperatures = 800 K. Our results suggest that organic materials—including biomolecules—produced within the photosphere would have been widely dispersed in the protoplanetary disk through turbulent diffusion, providing a mechanism for the distribution of organic meteoritic precursors prior to any thermal/photoprocessing and subsequent modification by secondary parent body processes. Using a numerical model of dust transport in a turbulent disk, we propose that organic materials produced in the photosphere of the disk would likely be associated with small dust particles, which are coupled to the motion of gas within the disk and therefore preferentially lofted into the upper layers of the disk where organosynthesis occurs.

Precision control of recombinant gene transcription for CHO cell synthetic biology.

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Brown, Adam J; James, David C

2016-01-01

The next generation of mammalian cell factories for biopharmaceutical production will be genetically engineered to possess both generic and product-specific manufacturing capabilities that may not exist naturally. Introduction of entirely new combinations of synthetic functions (e.g. novel metabolic or stress-response pathways), and retro-engineering of existing functional cell modules will drive disruptive change in cellular manufacturing performance. However, before we can apply the core concepts underpinning synthetic biology (design, build, test) to CHO cell engineering we must first develop practical and robust enabling technologies. Fundamentally, we will require the ability to precisely control the relative stoichiometry of numerous functional components we simultaneously introduce into the host cell factory. In this review we discuss how this can be achieved by design of engineered promoters that enable concerted control of recombinant gene transcription. We describe the specific mechanisms of transcriptional regulation that affect promoter function during bioproduction processes, and detail the highly-specific promoter design criteria that are required in the context of CHO cell engineering. The relative applicability of diverse promoter development strategies are discussed, including re-engineering of natural sequences, design of synthetic transcription factor-based systems, and construction of synthetic promoters. This review highlights the potential of promoter engineering to achieve precision transcriptional control for CHO cell synthetic biology. Copyright © 2015. Published by Elsevier Inc.

Polymorphisms in fatty acid metabolism-related genes are associated with colorectal cancer risk

DEFF Research Database (Denmark)

Hoeft, B.; Linseisen, J.; Beckmann, L.

2010-01-01

as contributing factor to colon carcinogenesis. We examined the association between genetic variability in 43 fatty acid metabolism-related genes and colorectal risk in 1225 CRC cases and 2032 controls participating in the European Prospective Investigation into Cancer and Nutrition study. Three hundred......Colorectal cancer (CRC) is the third most common malignant tumor and the fourth leading cause of cancer death worldwide. The crucial role of fatty acids for a number of important biological processes suggests a more in-depth analysis of inter-individual differences in fatty acid metabolizing genes...... variants with CRC risk. Our results support the key role of prostanoid signaling in colon carcinogenesis and suggest a relevance of genetic variation in fatty acid metabolism-related genes and CRC risk....

Biologics in pediatric psoriasis - efficacy and safety.

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Dogra, Sunil; Mahajan, Rahul

2018-01-01

Childhood psoriasis is a special situation that is a management challenge for the treating dermatologist. As is the situation with traditional systemic agents, which are commonly used in managing severe psoriasis in children, the biologics are being increasingly used in the recalcitrant disease despite limited data on long term safety. Areas covered: We performed an extensive literature search to collect evidence-based data on the use of biologics in pediatric psoriasis. The relevant literature published from 2000 to September 2017 was obtained from PubMed, using the MeSH words 'biologics', 'biologic response modifiers' and 'treatment of pediatric/childhood psoriasis'. All clinical trials, randomized double-blind or single-blind controlled trials, open-label studies, retrospective studies, reviews, case reports and letters concerning the use of biologics in pediatric psoriasis were screened. Articles covering the use of biologics in pediatric psoriasis were screened and reference lists in the selected articles were scrutinized to identify other relevant articles that had not been found in the initial search. Articles without relevant information about biologics in general (e.g. its mechanism of action, pharmacokinetics and adverse effects) and its use in psoriasis in particular were excluded. We screened 427 articles and finally selected 41 relevant articles. Expert opinion: The available literature on the use of biologics such as anti-tumor necrosis factor (TNF)-α agents, and anti-IL-12/23 agents like ustekinumab suggests that these are effective and safe in managing severe pediatric psoriasis although there is an urgent need to generate more safety data. Dermatologists must be careful about the potential adverse effects of the biologics before administering them to children with psoriasis. It is likely that with rapidly evolving scenario of biologics in psoriasis, these will prove to be very useful molecules particularly in managing severe and recalcitrant

Unstable genes unstable mind: beyond the central dogma of molecular biology.

Science.gov (United States)

Hegde, Mahabaleshwar V; Saraph, Arundhati A

2011-08-01

Schizophrenia has a polygenic mode of inheritance and an estimated heritability of over 80%, but success in understanding its genetic underpinnings to date has been modest. Unlike in trinucleotide neurodegenerative disorders, the phenomenon of genetic anticipation observed in schizophrenia or bipolar disorder has not been explained. For the first time, we provide a plausible molecular explanation of genetic anticipation and pathophysiology of schizophrenia, at least in part, with supporting evidence. We postulate that abnormally increased numbers of CAG repeats in many genes being expressed in the brain, coding for glutamine, cumulatively press for higher demand of glutamine in the respective brain cells, resulting in a metabolic crisis and dysregulation of the glutamate-glutamine cycle. This can adversely affect the functioning of both glutamate and GABA receptors, which are known to be involved in psychosis, and may also affect glutathione levels, increasing oxidative stress. The resulting psychosis (gain in function), originating from unstable genes, is described as an effect "beyond the central dogma of molecular biology". The hypothesis explains genetic anticipation, as further expansions in subsequent generations may result in increased severity and earlier occurrence. Many other well described findings provide proof of concept. This is a testable hypothesis, does not deny any known facts and opens up new avenues of research. Copyright © 2011 Elsevier Ltd. All rights reserved.

Exploring internal features of 16S rRNA gene for identification of clinically relevant species of the genus Streptococcus

Science.gov (United States)

2011-01-01

Background Streptococcus is an economically important genus as a number of species belonging to this genus are human and animal pathogens. The genus has been divided into different groups based on 16S rRNA gene sequence similarity. The variability observed among the members of these groups is low and it is difficult to distinguish them. The present study was taken up to explore 16S rRNA gene sequence to develop methods that can be used for preliminary identification and can supplement the existing methods for identification of clinically-relevant isolates of the genus Streptococcus. Methods 16S rRNA gene sequences belonging to the isolates of S. dysgalactiae, S. equi, S. pyogenes, S. agalactiae, S. bovis, S. gallolyticus, S. mutans, S. sobrinus, S. mitis, S. pneumoniae, S. thermophilus and S. anginosus were analyzed with the purpose to define genetic variability within each species to generate a phylogenetic framework, to identify species-specific signatures and in-silico restriction enzyme analysis. Results The framework based analysis was used to segregate Streptococcus spp. previously identified upto genus level. This segregation was validated using species-specific signatures and in-silico restriction enzyme analysis. 43 uncharacterized Streptococcus spp. could be identified using this approach. Conclusions The markers generated exploring 16S rRNA gene sequences provided useful tool that can be further used for identification of different species of the genus Streptococcus. PMID:21702978

Candidate gene association study in type 2 diabetes indicates a role for genes involved in beta-cell function as well as insulin action.

Directory of Open Access Journals (Sweden)

Inês Barroso

2003-10-01

Full Text Available Type 2 diabetes is an increasingly common, serious metabolic disorder with a substantial inherited component. It is characterised by defects in both insulin secretion and action. Progress in identification of specific genetic variants predisposing to the disease has been limited. To complement ongoing positional cloning efforts, we have undertaken a large-scale candidate gene association study. We examined 152 SNPs in 71 candidate genes for association with diabetes status and related phenotypes in 2,134 Caucasians in a case-control study and an independent quantitative trait (QT cohort in the United Kingdom. Polymorphisms in five of 15 genes (33% encoding molecules known to primarily influence pancreatic beta-cell function-ABCC8 (sulphonylurea receptor, KCNJ11 (KIR6.2, SLC2A2 (GLUT2, HNF4A (HNF4alpha, and INS (insulin-significantly altered disease risk, and in three genes, the risk allele, haplotype, or both had a biologically consistent effect on a relevant physiological trait in the QT study. We examined 35 genes predicted to have their major influence on insulin action, and three (9%-INSR, PIK3R1, and SOS1-showed significant associations with diabetes. These results confirm the genetic complexity of Type 2 diabetes and provide evidence that common variants in genes influencing pancreatic beta-cell function may make a significant contribution to the inherited component of this disease. This study additionally demonstrates that the systematic examination of panels of biological candidate genes in large, well-characterised populations can be an effective complement to positional cloning approaches. The absence of large single-gene effects and the detection of multiple small effects accentuate the need for the study of larger populations in order to reliably identify the size of effect we now expect for complex diseases.

Bayesian assignment of gene ontology terms to gene expression experiments

Science.gov (United States)

Sykacek, P.

2012-01-01

Motivation: Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. Results: This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Availability: Source code under GPL license is available from the author. Contact: peter.sykacek@boku.ac.at PMID:22962488

Bayesian assignment of gene ontology terms to gene expression experiments.

Science.gov (United States)

Sykacek, P

2012-09-15

Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Source code under GPL license is available from the author. peter.sykacek@boku.ac.at.

Tunable promoters in synthetic and systems biology

DEFF Research Database (Denmark)

Dehli, Tore; Solem, Christian; Jensen, Peter Ruhdal

2012-01-01

in synthetic biology. A number of tools exist to manipulate the steps in between gene sequence and functional protein in living cells, but out of these the most straight-forward approach is to alter the gene expression level by manipulating the promoter sequence. Some of the promoter tuning tools available......Synthetic and systems biologists need standardized, modular and orthogonal tools yielding predictable functions in vivo. In systems biology such tools are needed to quantitatively analyze the behavior of biological systems while the efficient engineering of artificial gene networks is central...... for accomplishing such altered gene expression levels are discussed here along with examples of their use, and ideas for new tools are described. The road ahead looks very promising for synthetic and systems biologists as tools to achieve just about anything in terms of tuning and timing multiple gene expression...

Spatial Modeling Tools for Cell Biology

National Research Council Canada - National Science Library

Przekwas, Andrzej; Friend, Tom; Teixeira, Rodrigo; Chen, Z. J; Wilkerson, Patrick

2006-01-01

.... Scientific potentials and military relevance of computational biology and bioinformatics have inspired DARPA/IPTO's visionary BioSPICE project to develop computational framework and modeling tools for cell biology...

Streptococcus pyogenes biofilms – formation, biology,and clinical relevance

Directory of Open Access Journals (Sweden)

Tomas eFiedler

2015-02-01

Full Text Available Streptococcus pyogenes (group A streptococci, GAS is an exclusive human bacterial pathogen. The virulence potential of this species is tremendous. Interactions with humans range from asymptomatic carriage over mild and superficial infections of skin and mucosal membranes up to systemic purulent toxic-invasive disease manifestations. Particularly the latter are a severe threat for predisposed patients and lead to significant death tolls worldwide. This places GAS among the most important Gram-positive bacterial pathogens. Many recent reviews have highlighted the GAS repertoire of virulence factors, regulators and regulatory circuits/networks that enable GAS to colonize the host and to deal with all levels of the host immune defense. This covers in vitro and in vivo studies, including animal infection studies based on mice and more relevant, macaque monkeys. It is now appreciated that GAS, like many other bacterial species, do not necessarily exclusively live in a planktonic lifestyle. GAS is capable of microcolony and biofilm formation on host cells and tissues. We are now beginning to understand that this feature significantly contributes to GAS pathogenesis. In this review we will discuss the current knowledge on GAS biofilm formation, the biofilm-phenotype associated virulence factors, regulatory aspects of biofilm formation, the clinical relevance, and finally contemporary treatment regimens and future treatment options.

GeneTrailExpress: a web-based pipeline for the statistical evaluation of microarray experiments

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Kohlbacher Oliver

2008-12-01

Full Text Available Abstract Background High-throughput methods that allow for measuring the expression of thousands of genes or proteins simultaneously have opened new avenues for studying biochemical processes. While the noisiness of the data necessitates an extensive pre-processing of the raw data, the high dimensionality requires effective statistical analysis methods that facilitate the identification of crucial biological features and relations. For these reasons, the evaluation and interpretation of expression data is a complex, labor-intensive multi-step process. While a variety of tools for normalizing, analysing, or visualizing expression profiles has been developed in the last years, most of these tools offer only functionality for accomplishing certain steps of the evaluation pipeline. Results Here, we present a web-based toolbox that provides rich functionality for all steps of the evaluation pipeline. Our tool GeneTrailExpress offers besides standard normalization procedures powerful statistical analysis methods for studying a large variety of biological categories and pathways. Furthermore, an integrated graph visualization tool, BiNA, enables the user to draw the relevant biological pathways applying cutting-edge graph-layout algorithms. Conclusion Our gene expression toolbox with its interactive visualization of the pathways and the expression values projected onto the nodes will simplify the analysis and interpretation of biochemical pathways considerably.

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

Science.gov (United States)

Wagner, Florian

2015-01-01

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

[Research progress of mammalian synthetic biology in biomedical field].

Science.gov (United States)

Yang, Linfeng; Yin, Jianli; Wang, Meiyan; Ye, Haifeng

2017-03-25

Although still in its infant stage, synthetic biology has achieved remarkable development and progress during the past decade. Synthetic biology applies engineering principles to design and construct gene circuits uploaded into living cells or organisms to perform novel or improved functions, and it has been widely used in many fields. In this review, we describe the recent advances of mammalian synthetic biology for the treatment of diseases. We introduce common tools and design principles of synthetic gene circuits, and then we demonstrate open-loop gene circuits induced by different trigger molecules used in disease diagnosis and close-loop gene circuits used for biomedical applications. Finally, we discuss the perspectives and potential challenges of synthetic biology for clinical applications.

Insect parents improve the anti-parasitic and anti-bacterial defence of their offspring by priming the expression of immune-relevant genes.

Science.gov (United States)

Trauer-Kizilelma, Ute; Hilker, Monika

2015-09-01

Insect parents that experienced an immune challenge are known to prepare (prime) the immune activity of their offspring for improved defence. This phenomenon has intensively been studied by analysing especially immunity-related proteins. However, it is unknown how transgenerational immune priming affects transcript levels of immune-relevant genes of the offspring upon an actual threat. Here, we investigated how an immune challenge of Manduca sexta parents affects the expression of immune-related genes in their eggs that are attacked by parasitoids. Furthermore, we addressed the question whether the transgenerational immune priming of expression of genes in the eggs is still traceable in adult offspring. Our study revealed that a parental immune challenge did not affect the expression of immune-related genes in unparasitised eggs. However, immune-related genes in parasitised eggs of immune-challenged parents were upregulated to a higher level than those in parasitised eggs of unchallenged parents. Hence, this transgenerational immune priming of the eggs was detected only "on demand", i.e. upon parasitoid attack. The priming effects were also traceable in adult female progeny of immune-challenged parents which showed higher transcript levels of several immune-related genes in their ovaries than non-primed progeny. Some of the primed genes showed enhanced expression even when the progeny was left unchallenged, whereas other genes were upregulated to a greater extent in primed female progeny than non-primed ones only when the progeny itself was immune-challenged. Thus, the detection of transgenerational immune priming strongly depends on the analysed genes and the presence or absence of an actual threat for the offspring. We suggest that M. sexta eggs laid by immune-challenged parents "afford" to upregulate the transcription of immunity-related genes only upon attack, because they have the chance to be endowed by parentally directly transferred protective proteins

Human Gene Therapy: Genes without Frontiers?

Science.gov (United States)

Simon, Eric J.

2002-01-01

Describes the latest advancements and setbacks in human gene therapy to provide reference material for biology teachers to use in their science classes. Focuses on basic concepts such as recombinant DNA technology, and provides examples of human gene therapy such as severe combined immunodeficiency syndrome, familial hypercholesterolemia, and…

Synthetic biological networks

International Nuclear Information System (INIS)

Archer, Eric; Süel, Gürol M

2013-01-01

Despite their obvious relationship and overlap, the field of physics is blessed with many insightful laws, while such laws are sadly absent in biology. Here we aim to discuss how the rise of a more recent field known as synthetic biology may allow us to more directly test hypotheses regarding the possible design principles of natural biological networks and systems. In particular, this review focuses on synthetic gene regulatory networks engineered to perform specific functions or exhibit particular dynamic behaviors. Advances in synthetic biology may set the stage to uncover the relationship of potential biological principles to those developed in physics. (review article)

Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method

Directory of Open Access Journals (Sweden)

Carlos Roberto Arias

2012-01-01

Full Text Available Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF. The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.

Single Molecule Fluorescence: from Physical Fascination to Biological Relevance

OpenAIRE

Segers-Nolten, Gezina M.J.

2003-01-01

Confocal fluorescence microscopy is particularly well-known from the beautiful images that have been obtained with this technique from cells. Several cellular components could be nicely visualized simultaneously by staining them with different fluorophores. Not only for ensemble applications but also in single molecule research confocal fluorescence microscopy became a popular technique. In this thesis the possibilities are shown to study a complicated biological process, which is Nucleotide ...

Topical application of bFGF on acid-conditioned and non-conditioned dentin: effect on cell proliferation and gene expression in cells relevant for periodontal regeneration

Directory of Open Access Journals (Sweden)

Fernanda Regina Godoy Rocha

Full Text Available Abstract Periodontal regeneration is still a challenge in terms of predictability and magnitude of effect. In this study we assess the biological effects of combining chemical root conditioning and biological mediators on three relevant cell types for periodontal regeneration. Material and Methods: Bovine dentin slices were conditioned with 25% citric acid followed by topical application of basic fibroblast growth factor (bFGF, 10 and 50 ng. We used ELISA to assess the dynamics of bFGF release from the dentin surface and RT-qPCR to study the expression of Runx2, Col1a1, Bglap and fibronectin by periodontal ligament (PDL fibroblasts, cementoblasts and bone marrow stromal cells (BMSC grown onto these dentin slices. We also assessed the effects of topical application of bFGF on cell proliferation by quantification of genomic DNA. Results: Acid conditioning significantly increased the release of bFGF from dentin slices. Overall, bFGF application significantly (p<0.05 increased cell proliferation, except for BMSC grown on non-conditioned dentin slices. Dentin substrate discretely increased expression of Col1a1 in all cell types. Expression of Runx2, Col1a1 and Fn was either unaffected or inhibited by bFGF application in all cell types. We could not detect expression of the target genes on BMSC grown onto conditioned dentin. Conclusion: Acid conditioning of dentin improves the release of topically-applied bFGF. Topical application of bFGF had a stimulatory effect on proliferation of PDL fibroblasts, cementoblasts and BMSC, but did not affect expression of Runx2, Col1a1, Bglap and fibronectin by these cells.

Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in Omics studies and "Big data" biology.

Science.gov (United States)

Vivar, Juan C; Pemu, Priscilla; McPherson, Ruth; Ghosh, Sujoy

2013-08-01

Abstract Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association

Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Song, Mingzhou (Joe) [New Mexico State University, Las Cruces; Lewis, Chris K. [New Mexico State University, Las Cruces; Lance, Eric [New Mexico State University, Las Cruces; Chesler, Elissa J [ORNL; Kirova, Roumyana [Bristol-Myers Squibb Pharmaceutical Research & Development, NJ; Langston, Michael A [University of Tennessee, Knoxville (UTK); Bergeson, Susan [Texas Tech University, Lubbock

2009-01-01

The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. Using temporal gene expression data collected from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol, this algorithm identified genes from a major neuronal pathway as putative components of the alcohol response mechanism. Three of these genes have known associations with alcohol in the literature. Several other potentially relevant genes, highlighted and agreeing with independent results from literature mining, may play a role in the response to alcohol. Additional, previously-unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

Length bias correction in gene ontology enrichment analysis using logistic regression.

Science.gov (United States)

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

Student-oriented learning: an inquiry-based developmental biology lecture course.