Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression.

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Fairfax, Benjamin P; Humburg, Peter; Makino, Seiko; Naranbhai, Vivek; Wong, Daniel; Lau, Evelyn; Jostins, Luke; Plant, Katharine; Andrews, Robert; McGee, Chris; Knight, Julian C

2014-03-07

To systematically investigate the impact of immune stimulation upon regulatory variant activity, we exposed primary monocytes from 432 healthy Europeans to interferon-γ (IFN-γ) or differing durations of lipopolysaccharide and mapped expression quantitative trait loci (eQTLs). More than half of cis-eQTLs identified, involving hundreds of genes and associated pathways, are detected specifically in stimulated monocytes. Induced innate immune activity reveals multiple master regulatory trans-eQTLs including the major histocompatibility complex (MHC), coding variants altering enzyme and receptor function, an IFN-β cytokine network showing temporal specificity, and an interferon regulatory factor 2 (IRF2) transcription factor-modulated network. Induced eQTL are significantly enriched for genome-wide association study loci, identifying context-specific associations to putative causal genes including CARD9, ATM, and IRF8. Thus, applying pathophysiologically relevant immune stimuli assists resolution of functional genetic variants.

Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

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Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

2017-08-01

This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Artificial Immune Networks: Models and Applications

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Xian Shen

2008-06-01

Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

Changing expression of vertebrate immunity genes in an anthropogenic environment: a controlled experiment.

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Hablützel, Pascal I; Brown, Martha; Friberg, Ida M; Jackson, Joseph A

2016-09-01

The effect of anthropogenic environments on the function of the vertebrate immune system is a problem of general importance. For example, it relates to the increasing rates of immunologically-based disease in modern human populations and to the desirability of identifying optimal immune function in domesticated animals. Despite this importance, our present understanding is compromised by a deficit of experimental studies that make adequately matched comparisons between wild and captive vertebrates. We transferred post-larval fishes (three-spined sticklebacks), collected in the wild, to an anthropogenic (captive) environment. We then monitored, over 11 months, how the systemic expression of immunity genes changed in comparison to cohort-matched wild individuals in the originator population (total n = 299). We found that a range of innate (lyz, defbl2, il1r-like, tbk1) and adaptive (cd8a, igmh) immunity genes were up-regulated in captivity, accompanied by an increase in expression of the antioxidant enzyme, gpx4a. For some genes previously known to show seasonality in the wild, this appeared to be reduced in captive fishes. Captive fishes tended to express immunity genes, including igzh, foxp3b, lyz, defbl2, and il1r-like, more variably. Furthermore, although gene co-expression patterns (analyzed through gene-by-gene correlations and mutual information theory based networks) shared common structure in wild and captive fishes, there was also significant divergence. For one gene in particular, defbl2, high expression was associated with adverse health outcomes in captive fishes. Taken together, these results demonstrate widespread regulatory changes in the immune system in captive populations, and that the expression of immunity genes is more constrained in the wild. An increase in constitutive systemic immune activity, such as we observed here, may alter the risk of immunopathology and contribute to variance in health in vertebrate populations exposed to

Using network properties to evaluate targeted immunization algorithms

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Bita Shams

2014-09-01

Full Text Available Immunization of complex network with minimal or limited budget is a challenging issue for research community. In spite of much literature in network immunization, no comprehensive research has been conducted for evaluation and comparison of immunization algorithms. In this paper, we propose an evaluation framework for immunization algorithms regarding available amount of vaccination resources, goal of immunization program, and time complexity. The evaluation framework is designed based on network topological metrics which is extensible to all epidemic spreading model. Exploiting evaluation framework on well-known targeted immunization algorithms shows that in general, immunization based on PageRank centrality outperforms other targeting strategies in various types of networks, whereas, closeness and eigenvector centrality exhibit the worst case performance.

Gene regulatory networks elucidating huanglongbing disease mechanisms.

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Federico Martinelli

Full Text Available Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas, especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation, sucrose metabolism (upregulation, and starch biosynthesis (upregulation. In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70 was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur.

Addiction, adolescence, and innate immune gene induction

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Fulton T Crews

2011-04-01

Full Text Available Repeated drug use/abuse amplifies psychopathology, progressively reducing frontal lobe behavioral control and cognitive flexibility while simultaneously increasing limbic temporal lobe negative emotionality. The period of adolescence is a neurodevelopmental stage characterized by poor behavioral control as well as strong limbic reward and thrill seeking. Repeated drug abuse and/or stress during this stage increase the risk of addiction and elevate activator innate immune signaling in the brain. Nuclear factor-kappa-light-chain-enhancer of activated B cells (NF-κB is a key glial transcription factor that regulates proinflammatory chemokines, cytokines, oxidases, proteases, and other innate immune genes. Induction of innate brain immune gene expression (e.g., NF-κB facilitates negative affect, depression-like behaviors, and inhibits hippocampal neurogenesis. In addition, innate immune gene induction alters cortical neurotransmission consistent with loss of behavioral control. Studies with anti-oxidant, anti-inflammatory, and anti-depressant drugs as well as opiate antagonists link persistent innate immune gene expression to key behavioral components of addiction, e.g. negative affect-anxiety and loss of frontal cortical behavioral control. This review suggests that persistent and progressive changes in innate immune gene expression contribute to the development of addiction. Innate immune genes may represent a novel new target for addiction therapy.

A drug-sensitive genetic network masks fungi from the immune system.

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Robert T Wheeler

2006-04-01

Full Text Available Fungal pathogens can be recognized by the immune system via their beta-glucan, a potent proinflammatory molecule that is present at high levels but is predominantly buried beneath a mannoprotein coat and invisible to the host. To investigate the nature and significance of "masking" this molecule, we characterized the mechanism of masking and consequences of unmasking for immune recognition. We found that the underlying beta-glucan in the cell wall of Candida albicans is unmasked by subinhibitory doses of the antifungal drug caspofungin, causing the exposed fungi to elicit a stronger immune response. Using a library of bakers' yeast (Saccharomyces cerevisiae mutants, we uncovered a conserved genetic network that is required for concealing beta-glucan from the immune system and limiting the host response. Perturbation of parts of this network in the pathogen C. albicans caused unmasking of its beta-glucan, leading to increased beta-glucan receptor-dependent elicitation of key proinflammatory cytokines from primary mouse macrophages. By creating an anti-inflammatory barrier to mask beta-glucan, opportunistic fungi may promote commensal colonization and have an increased propensity for causing disease. Targeting the widely conserved gene network required for creating and maintaining this barrier may lead to novel broad-spectrum antimycotics.

Comparison of immunization strategies in geographical networks

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Wang Bing; Aihara, Kazuyuki [Institute of Industrial Science, The University of Tokyo, Tokyo (Japan)] [ERATO Aihara Complexity Modelling Project, JST, Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505 (Japan); Kim, Beom Jun, E-mail: beomjun@skku.ed [BK21 Physics Research Division and Department of Energy Science, Sungkyunkwan University, Suwon 440-746 (Korea, Republic of)] [Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology, 100 44 Stockholm (Sweden)

2009-10-12

The epidemic spread and immunizations in geographically embedded scale-free (SF) and Watts-Strogatz (WS) networks are numerically investigated. We make a realistic assumption that it takes time which we call the detection time, for a vertex to be identified as infected, and implement two different immunization strategies: one is based on connection neighbors (CN) of the infected vertex with the exact information of the network structure utilized and the other is based on spatial neighbors (SN) with only geographical distances taken into account. We find that the decrease of the detection time is crucial for a successful immunization in general. Simulation results show that for both SF networks and WS networks, the SN strategy always performs better than the CN strategy, especially for more heterogeneous SF networks at long detection time. The observation is verified by checking the number of the infected nodes being immunized. We found that in geographical space, the distance preferences in the network construction process and the geographically decaying infection rate are key factors that make the SN immunization strategy outperforms the CN strategy. It indicates that even in the absence of the full knowledge of network connectivity we can still stop the epidemic spread efficiently only by using geographical information as in the SN strategy, which may have potential applications for preventing the real epidemic spread.

Comparison of immunization strategies in geographical networks

International Nuclear Information System (INIS)

Wang Bing; Aihara, Kazuyuki; Kim, Beom Jun

2009-01-01

The epidemic spread and immunizations in geographically embedded scale-free (SF) and Watts-Strogatz (WS) networks are numerically investigated. We make a realistic assumption that it takes time which we call the detection time, for a vertex to be identified as infected, and implement two different immunization strategies: one is based on connection neighbors (CN) of the infected vertex with the exact information of the network structure utilized and the other is based on spatial neighbors (SN) with only geographical distances taken into account. We find that the decrease of the detection time is crucial for a successful immunization in general. Simulation results show that for both SF networks and WS networks, the SN strategy always performs better than the CN strategy, especially for more heterogeneous SF networks at long detection time. The observation is verified by checking the number of the infected nodes being immunized. We found that in geographical space, the distance preferences in the network construction process and the geographically decaying infection rate are key factors that make the SN immunization strategy outperforms the CN strategy. It indicates that even in the absence of the full knowledge of network connectivity we can still stop the epidemic spread efficiently only by using geographical information as in the SN strategy, which may have potential applications for preventing the real epidemic spread.

Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction.

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Reyes-Gibby, Cielito C; Yuan, Christine; Wang, Jian; Yeung, Sai-Ching J; Shete, Sanjay

2015-06-05

Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction. Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling. Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.

Centrality measures for immunization of weighted networks

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Mohammad Khansari

2016-03-01

Full Text Available Effective immunization of individual communities with minimal cost in vaccination has made great discussion surrounding the realm of complex networks. Meanwhile, proper realization of relationship among people in society and applying it to social networks brings about substantial improvements in immunization. Accordingly, weighted graph in which link weights represent the intensity and intimacy of relationships is an acceptable approach. In this work we employ weighted graphs and a wide variety of weighted centrality measures to distinguish important individuals in contagion of diseases. Furthermore, we propose new centrality measures for weighted networks. Our experimental results show that Radiality-Degree centrality is satisfying for weighted BA networks. Additionally, PageRank-Degree and Radiality-Degree centralities showmoreacceptable performance in targeted immunization of weighted networks.

Competing spreading processes and immunization in multiplex networks

International Nuclear Information System (INIS)

Gao, Bo; Deng, Zhenghong; Zhao, Dawei

2016-01-01

Epidemic spreading on physical contact network will naturally introduce the human awareness information diffusion on virtual contact network, and the awareness diffusion will in turn depress the epidemic spreading, thus forming the competing spreading processes of epidemic and awareness in a multiplex networks. In this paper, we study the competing dynamics of epidemic and awareness, both of which follow the SIR process, in a two-layer networks based on microscopic Markov chain approach and numerical simulations. We find that strong capacities of awareness diffusion and self-protection of individuals could lead to a much higher epidemic threshold and a smaller outbreak size. However, the self-awareness of individuals has no obvious effect on the epidemic threshold and outbreak size. In addition, the immunization of the physical contact network under the interplay between of epidemic and awareness spreading is also investigated. The targeted immunization is found performs much better than random immunization, and the awareness diffusion could reduce the immunization threshold for both type of random and targeted immunization significantly.

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

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

2014-01-01

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

Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

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Hong Li

2017-05-01

Full Text Available Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein–protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs. We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant–pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

Immune system and zinc are associated with recurrent aphthous stomatitis. An assessment using a network-based approach.

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César Rivera

2017-09-01

Full Text Available Objective: The aim of this research was to identify genes, proteins and processes from the biomedical information published on recurrent aphthous stomatitis (RAS using network-based foci. Methods: The clinical context was defined using MeSH terms for RAS and biomarkers, combined with words associated with risk. A set of protein coding genes was prioritized using the Génie web server and classified with PANTHER. For defining biologically relevant proteins, protein-protein interaction networks were constructed using Reactome database and Cytoscape. Top 20 proteins were then subjected to functional enrichment using STRING. Results: From 1,075,576 gene-abstract links, 1,491 genes were prioritized. Proteins were related to signaling molecule proteins (n=221, receptor proteins (n=221 and nucleic acid binding proteins (n=169. The network constructed with these proteins included 3,963 nodes and functional analysis showed that main processes involved immune system and zinc ion binding function. Conclusions: For the first time, bioinformatics tools were used for integrating pathways and networks associated with RAS. Molecules and processes associated with immune system recur robustly in all analyzed information. The molecular zinc ion binding function could be an area for exploring more specific and effective therapeutic interventions.

Gene regulation is governed by a core network in hepatocellular carcinoma.

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Gu, Zuguang; Zhang, Chenyu; Wang, Jin

2012-05-01

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to HCC and we believe further

The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

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Frank eEmmert-Streib

2014-02-01

Full Text Available In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome $21$ is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections

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Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael

2016-01-01

Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. PMID:26751573

Evidence of inflammatory immune signaling in chronic fatigue syndrome: A pilot study of gene expression in peripheral blood

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Vernon Suzanne D

2008-09-01

Full Text Available Abstract Background Genomic profiling of peripheral blood reveals altered immunity in chronic fatigue syndrome (CFS however interpretation remains challenging without immune demographic context. The object of this work is to identify modulation of specific immune functional components and restructuring of co-expression networks characteristic of CFS using the quantitative genomics of peripheral blood. Methods Gene sets were constructed a priori for CD4+ T cells, CD8+ T cells, CD19+ B cells, CD14+ monocytes and CD16+ neutrophils from published data. A group of 111 women were classified using empiric case definition (U.S. Centers for Disease Control and Prevention and unsupervised latent cluster analysis (LCA. Microarray profiles of peripheral blood were analyzed for expression of leukocyte-specific gene sets and characteristic changes in co-expression identified from topological evaluation of linear correlation networks. Results Median expression for a set of 6 genes preferentially up-regulated in CD19+ B cells was significantly lower in CFS (p = 0.01 due mainly to PTPRK and TSPAN3 expression. Although no other gene set was differentially expressed at p Conclusion Dissection of blood microarray profiles points to B cell dysfunction with coordinated immune activation supporting persistent inflammation and antibody-mediated NK cell modulation of T cell activity. This has clinical implications as the CD19+ genes identified could provide robust and biologically meaningful basis for the early detection and unambiguous phenotyping of CFS.

The Toll-like receptor gene family is integrated into human DNA damage and p53 networks.

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Daniel Menendez

2011-03-01

Full Text Available In recent years the functions that the p53 tumor suppressor plays in human biology have been greatly extended beyond "guardian of the genome." Our studies of promoter response element sequences targeted by the p53 master regulatory transcription factor suggest a general role for this DNA damage and stress-responsive regulator in the control of human Toll-like receptor (TLR gene expression. The TLR gene family mediates innate immunity to a wide variety of pathogenic threats through recognition of conserved pathogen-associated molecular motifs. Using primary human immune cells, we have examined expression of the entire TLR gene family following exposure to anti-cancer agents that induce the p53 network. Expression of all TLR genes, TLR1 to TLR10, in blood lymphocytes and alveolar macrophages from healthy volunteers can be induced by DNA metabolic stressors. However, there is considerable inter-individual variability. Most of the TLR genes respond to p53 via canonical as well as noncanonical promoter binding sites. Importantly, the integration of the TLR gene family into the p53 network is unique to primates, a recurrent theme raised for other gene families in our previous studies. Furthermore, a polymorphism in a TLR8 response element provides the first human example of a p53 target sequence specifically responsible for endogenous gene induction. These findings-demonstrating that the human innate immune system, including downstream induction of cytokines, can be modulated by DNA metabolic stress-have many implications for health and disease, as well as for understanding the evolution of damage and p53 responsive networks.

Genome-wide analysis of immune system genes by EST profiling

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Giallourakis, Cosmas; Benita, Yair; Molinie, Benoit; Cao, Zhifang; Despo, Orion; Pratt, Henry E.; Zukerberg, Lawrence R.; Daly, Mark J.; Rioux, John D.; Xavier, Ramnik J.

2013-01-01

Profiling studies of mRNA and miRNA, particularly microarray-based studies, have been extensively used to create compendia of genes that are preferentially expressed in the immune system. In some instances, functional studies have been subsequently pursued. Recent efforts such as ENCODE have demonstrated the benefit of coupling RNA-Seq analysis with information from expressed sequence tags (ESTs) for transcriptomic analysis. However, the full characterization and identification of transcripts that function as modulators of human immune responses remains incomplete. In this study, we demonstrate that an integrated analysis of human ESTs provides a robust platform to identify the immune transcriptome. Beyond recovering a reference set of immune-enriched genes and providing large-scale cross-validation of previous microarray studies, we discovered hundreds of novel genes preferentially expressed in the immune system, including non-coding RNAs. As a result, we have established the Immunogene database, representing an integrated EST “road map” of gene expression in human immune cells, which can be used to further investigate the function of coding and non-coding genes in the immune system. Using this approach, we have uncovered a unique metabolic gene signature of human macrophages and identified PRDM15 as a novel overexpressed gene in human lymphomas. Thus we demonstrate the utility of EST profiling as a basis for further deconstruction of physiologic and pathologic immune processes. PMID:23616578

Candidate gene approach for parasite resistance in sheep--variation in immune pathway genes and association with fecal egg count.

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Kathiravan Periasamy

Full Text Available Sheep chromosome 3 (Oar3 has the largest number of QTLs reported to be significantly associated with resistance to gastro-intestinal nematodes. This study aimed to identify single nucleotide polymorphisms (SNPs within candidate genes located in sheep chromosome 3 as well as genes involved in major immune pathways. A total of 41 SNPs were identified across 38 candidate genes in a panel of unrelated sheep and genotyped in 713 animals belonging to 22 breeds across Asia, Europe and South America. The variations and evolution of immune pathway genes were assessed in sheep populations across these macro-environmental regions that significantly differ in the diversity and load of pathogens. The mean minor allele frequency (MAF did not vary between Asian and European sheep reflecting the absence of ascertainment bias. Phylogenetic analysis revealed two major clusters with most of South Asian, South East Asian and South West Asian breeds clustering together while European and South American sheep breeds clustered together distinctly. Analysis of molecular variance revealed strong phylogeographic structure at loci located in immune pathway genes, unlike microsatellite and genome wide SNP markers. To understand the influence of natural selection processes, SNP loci located in chromosome 3 were utilized to reconstruct haplotypes, the diversity of which showed significant deviations from selective neutrality. Reduced Median network of reconstructed haplotypes showed balancing selection in force at these loci. Preliminary association of SNP genotypes with phenotypes recorded 42 days post challenge revealed significant differences (P<0.05 in fecal egg count, body weight change and packed cell volume at two, four and six SNP loci respectively. In conclusion, the present study reports strong phylogeographic structure and balancing selection operating at SNP loci located within immune pathway genes. Further, SNP loci identified in the study were found to have

Local and global responses in complex gene regulation networks

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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.

Chronic ethanol exposure produces time- and brain region-dependent changes in gene coexpression networks.

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Elizabeth A Osterndorff-Kahanek

Full Text Available Repeated ethanol exposure and withdrawal in mice increases voluntary drinking and represents an animal model of physical dependence. We examined time- and brain region-dependent changes in gene coexpression networks in amygdala (AMY, nucleus accumbens (NAC, prefrontal cortex (PFC, and liver after four weekly cycles of chronic intermittent ethanol (CIE vapor exposure in C57BL/6J mice. Microarrays were used to compare gene expression profiles at 0-, 8-, and 120-hours following the last ethanol exposure. Each brain region exhibited a large number of differentially expressed genes (2,000-3,000 at the 0- and 8-hour time points, but fewer changes were detected at the 120-hour time point (400-600. Within each region, there was little gene overlap across time (~20%. All brain regions were significantly enriched with differentially expressed immune-related genes at the 8-hour time point. Weighted gene correlation network analysis identified modules that were highly enriched with differentially expressed genes at the 0- and 8-hour time points with virtually no enrichment at 120 hours. Modules enriched for both ethanol-responsive and cell-specific genes were identified in each brain region. These results indicate that chronic alcohol exposure causes global 'rewiring' of coexpression systems involving glial and immune signaling as well as neuronal genes.

Immunization strategy for epidemic spreading on multilayer networks

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Buono, C.; Braunstein, L. A.

2015-01-01

In many real-world complex systems, individuals have many kinds of interactions among them, suggesting that it is necessary to consider a layered-structure framework to model systems such as social interactions. This structure can be captured by multilayer networks and can have major effects on the spreading of process that occurs over them, such as epidemics. In this letter we study a targeted immunization strategy for epidemic spreading over a multilayer network. We apply the strategy in one of the layers and study its effect in all layers of the network disregarding degree-degree correlation among layers. We found that the targeted strategy is not as efficient as in isolated networks, due to the fact that in order to stop the spreading of the disease it is necessary to immunize more than 80% of the individuals. However, the size of the epidemic is drastically reduced in the layer where the immunization strategy is applied compared to the case with no mitigation strategy. Thus, the immunization strategy has a major effect on the layer were it is applied, but does not efficiently protect the individuals of other layers.

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

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By

Growth and recruitment in the immune network

NARCIS (Netherlands)

Boer, R.J. de; Hogeweg, P.; Perelson, A.S.

1992-01-01

The development of the immune repertoire during neonatal life involves a strong selection process among different clones. We investigate the hypothetis that repertoire selection is carried out during early life by the immune network. There are at least two processes in repertoire selection: clonal

Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus

Science.gov (United States)

Graham, Deborah S Cunninghame; Pinder, Christopher L; Tombleson, Philip; Behrens, Timothy W; Martín, Javier; Fairfax, Benjamin P; Knight, Julian C; Chen, Lingyan; Replogle, Joseph; Syvänen, Ann-Christine; Rönnblom, Lars; Graham, Robert R; Wither, Joan E; Rioux, John D; Alarcón-Riquelme, Marta E; Vyse, Timothy J

2015-01-01

Systemic lupus erythematosus (SLE; OMIM 152700) is a genetically complex autoimmune disease characterized by loss of immune tolerance to nuclear and cell surface antigens. Previous genome-wide association studies (GWAS) had modest sample sizes, reducing their scope and reliability. Our study comprised 7,219 cases and 15,991 controls of European ancestry: a new GWAS, meta-analysis with a published GWAS and a replication study. We have mapped 43 susceptibility loci, including 10 novel associations. Assisted by dense genome coverage, imputation provided evidence for missense variants underpinning associations in eight genes. Other likely causal genes were established by examining associated alleles for cis-acting eQTL effects in a range of ex vivo immune cells. We found an over-representation (n=16) of transcription factors among SLE susceptibility genes. This supports the view that aberrantly regulated gene expression networks in multiple cell types in both the innate and adaptive immune response contribute to the risk of developing SLE. PMID:26502338

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

Directory of Open Access Journals (Sweden)

Xiaodong Cai

Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

Science.gov (United States)

Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

2015-07-01

In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

Discovering implicit entity relation with the gene-citation-gene network.

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Min Song

Full Text Available In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner.

Immunizations on small worlds of tree-based wireless sensor networks

DEFF Research Database (Denmark)

Li, Qiao; Zhang, Bai-Hai; Cui, Ling-Guo

2012-01-01

, are conducted on small worlds of tree-based wireless sensor networks to combat the sensor viruses. With the former strategy, the infection extends exponentially, although the immunization effectively reduces the contagion speed. With the latter strategy, recurrent contagion oscillations occur in the small world......The sensor virus is a serious threat, as an attacker can simply send a single packet to compromise the entire sensor network. Epidemics become drastic with link additions among sensors when the small world phenomena occur. Two immunization strategies, uniform immunization and temporary immunization...

Micro Aerial Vehicle (MAV Flapping Motion Control Using an Immune Network with Different Immune Factors

Directory of Open Access Journals (Sweden)

Liguo Weng

2013-08-01

Full Text Available This paper proposes a novel Neural-Immunology/Memory Network to address the problem of motion control for flapping-wing Micro Aerial Vehicles (MAVs. This network is inspired by the human memory system as well as the immune system, and it is effective in attenuating the system errors and other lumped system uncertainties. In contrast to most existing Neural Networks, the convergence of this proposed Neural-Immunology/Memory Network can be theoretically proven. Both analyses and simulations that are based on different immune factors show that the proposed control method is effective in dealing with external disturbances, system nonlinearities, uncertainties and parameter variations.

Immunizations on small worlds of tree-based wireless sensor networks

International Nuclear Information System (INIS)

Li Qiao; Zhang Bai-Hai; Cui Ling-Guo; Fan Zhun; Vasilakos Athanasios, V.

2012-01-01

The sensor virus is a serious threat, as an attacker can simply send a single packet to compromise the entire sensor network. Epidemics become drastic with link additions among sensors when the small world phenomena occur. Two immunization strategies, uniform immunization and temporary immunization, are conducted on small worlds of tree-based wireless sensor networks to combat the sensor viruses. With the former strategy, the infection extends exponentially, although the immunization effectively reduces the contagion speed. With the latter strategy, recurrent contagion oscillations occur in the small world when the spatial-temporal dynamics of the epidemic are considered. The oscillations come from the small-world structure and the temporary immunization. Mathematical analyses on the small world of the Cayley tree are presented to reveal the epidemic dynamics with the two immunization strategies. (general)

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

Science.gov (United States)

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.

Global and local targeted immunization in networks with community structure

International Nuclear Information System (INIS)

Yan, Shu; Tang, Shaoting; Pei, Sen; Zheng, Zhiming; Fang, Wenyi

2015-01-01

Immunization plays an important role in the field of epidemic spreading in complex networks. In previous studies, targeted immunization has been proved to be an effective strategy. However, when extended to networks with community structure, it is unknown whether the superior strategy is to vaccinate the nodes who have the most connections in the entire network (global strategy), or those in the original community where epidemic starts to spread (local strategy). In this work, by using both analytic approaches and simulations, we observe that the answer depends on the closeness between communities. If communities are tied closely, the global strategy is superior to the local strategy. Otherwise, the local targeted immunization is advantageous. The existence of a transitional value of closeness implies that we should adopt different strategies. Furthermore, we extend our investigation from two-community networks to multi-community networks. We consider the mode of community connection and the location of community where epidemic starts to spread. Both simulation results and theoretical predictions show that local strategy is a better option for immunization in most cases. But if the epidemic begins from a core community, global strategy is superior in some cases. (paper)

A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

Science.gov (United States)

Larremore, Daniel B.; Clauset, Aaron; Buckee, Caroline O.

2013-01-01

The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. PMID:24130474

A network approach to analyzing highly recombinant malaria parasite genes.

Science.gov (United States)

Larremore, Daniel B; Clauset, Aaron; Buckee, Caroline O

2013-01-01

The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.

A network approach to analyzing highly recombinant malaria parasite genes.

Directory of Open Access Journals (Sweden)

Daniel B Larremore

Full Text Available The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs, and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.

Update on gene therapy of inherited immune deficiencies.

Science.gov (United States)

Engel, Barbara C; Kohn, Donald B; Podsakoff, Greg M

2003-10-01

Gene therapy has been under development as a way to correct inborn errors for many years. Recently, patients with two forms of inherited severe combined immunodeficiency (SCID), adenosine deaminase and X-linked, treated by three different clinical investigative teams, have shown significant immune reconstitution leading to protective immunity. These advances irrefutably prove the concept that hematopoietic progenitor cell gene therapy can ameliorate these diseases. However, due to proviral insertional oncogenesis, two individuals in one of the X-SCID studies developed T-cell leukemia more than two years after the gene transfer. Depending upon the results of long-term follow-up, the successes together with the side effects highlight the relative merits of this therapeutic approach.

High contaminant loads in Lake Apopka's riparian wetland disrupt gene networks involved in reproduction and immune function in largemouth bass.

Science.gov (United States)

Martyniuk, Christopher J; Doperalski, Nicholas J; Prucha, Melinda S; Zhang, Ji-Liang; Kroll, Kevin J; Conrow, Roxanne; Barber, David S; Denslow, Nancy D

2016-09-01

Lake Apopka (FL, USA) has elevated levels of some organochlorine pesticides in its sediments and a portion of its watershed has been designated a US Environmental Protection Agency Superfund site. This study assessed reproductive endpoints in Florida largemouth bass (LMB) (Micropterus salmoides floridanus) after placement into experimental ponds adjacent to Lake Apopka. LMB collected from a clean reference site (DeLeon Springs) were stocked at two periods of time into ponds constructed in former farm fields on the north shore of the lake. LMB were stocked during early and late oogenesis to determine if there were different effects of contamination on LMB that may be attributed to their reproductive stage. LMB inhabiting the ponds for ~4months had anywhere from 2 to 800 times higher contaminant load for a number of organochlorine pesticides (e.g. p, p'-DDE, methoxychlor) compared to control animals. Gonadosomatic index and plasma vitellogenin were not different between reproductively-stage matched LMB collected at reference sites compared to those inhabiting the ponds. However, plasma 17β-estradiol was lower in LMB inhabiting the Apopka ponds compared to ovary stage-matched LMB from the St. Johns River, a site used as a reference site. Sub-network enrichment analysis revealed that genes related to reproduction (granulosa function, oocyte development), endocrine function (steroid metabolism, hormone biosynthesis), and immune function (T cell suppression, leukocyte accumulation) were differentially expressed in the ovaries of LMB placed into the ponds. These data suggest that (1) LMB inhabiting the Apopka ponds showed disrupted reproduction and immune responses and that (2) gene expression profiles provided site-specific information by discriminating LMB from different macro-habitats. Copyright © 2016 Elsevier Inc. All rights reserved.

Retrieving infinite numbers of patterns in a spin-glass model of immune networks

Science.gov (United States)

Agliari, E.; Annibale, A.; Barra, A.; Coolen, A. C. C.; Tantari, D.

2017-01-01

The similarity between neural and (adaptive) immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies in parallel. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with “coordinator branches” (T-cells) and “effector branches” (B-cells), and show how the finite connectivity enables the coordinators to manage an extensive number of effectors simultaneously, even above the percolation threshold (where clonal cross-talk is not negligible). A consequence of its underlying topological sparsity is that the adaptive immune system exhibits only weak ergodicity breaking, so that also spontaneous switch-like effects as bi-stabilities are present: the latter may play a significant role in the maintenance of immune homeostasis.

Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

Directory of Open Access Journals (Sweden)

J Matthew Mahoney

2015-01-01

Full Text Available Systemic sclerosis (SSc is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected

Gene Expression by PBMC in Primary Sclerosing Cholangitis: Evidence for Dysregulation of Immune Mediated Genes

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Christopher A. Aoki

2006-01-01

Full Text Available Primary sclerosing cholangitis (PSC is a chronic disease of the bile ducts characterized by an inflammatory infiltrate and obliterative fibrosis. The precise role of the immune system in the pathogenesis of PSC remains unknown. We used RNA microarray analysis to identify immune-related genes and pathways that are differentially expressed in PSC. Messenger RNA (mRNA from peripheral blood mononuclear cells (PBMC was isolated from both patients with PSC and age and sex matched healthy controls. Samples from 5 PSC patients and 5 controls were analyzed by microarray and based upon rigorous statistical analysis of the data, relevant genes were chosen for confirmation by RT-PCR in 10 PSC patients and 10 controls. Using unsupervised hierarchical clustering, gene expression in PSC was statistically different from our control population. Interestingly, genes within the IL-2 receptor beta, IL-6 and MAP Kinase pathways were found to be differently expressed in patients with PSC compared to controls. Further, individual genes, TNF-α induced protein 6 (TNFaip6 and membrane-spanning 4-domains, subfamily A (ms4a were found to be upregulated in PSC while similar to Mothers against decapentaplegic homolog 5 (SMAD 5 was downregulated. In conclusion, several immune-related pathways and genes were differentially expressed in PSC compared to control patients, giving further evidence that this disease is systemic and immune-mediated.

NKT Cell Networks in the Regulation of Tumor Immunity

Science.gov (United States)

Robertson, Faith C.; Berzofsky, Jay A.; Terabe, Masaki

2014-01-01

CD1d-restricted natural killer T (NKT) cells lie at the interface between the innate and adaptive immune systems and are important mediators of immune responses and tumor immunosurveillance. These NKT cells uniquely recognize lipid antigens, and their rapid yet specific reactions influence both innate and adaptive immunity. In tumor immunity, two NKT subsets (type I and type II) have contrasting roles in which they not only cross-regulate one another, but also impact innate immune cell populations, including natural killer, dendritic, and myeloid lineage cells, as well as adaptive populations, especially CD8+ and CD4+ T cells. The extent to which NKT cells promote or suppress surrounding cells affects the host’s ability to prevent neoplasia and is consequently of great interest for therapeutic development. Data have shown the potential for therapeutic use of NKT cell agonists and synergy with immune response modifiers in both pre-clinical studies and preliminary clinical studies. However, there is room to improve treatment efficacy by further elucidating the biological mechanisms underlying NKT cell networks. Here, we discuss the progress made in understanding NKT cell networks, their consequent role in the regulation of tumor immunity, and the potential to exploit that knowledge in a clinical setting. PMID:25389427

NKT cell networks in the regulation of tumor immunity.

Science.gov (United States)

Robertson, Faith C; Berzofsky, Jay A; Terabe, Masaki

2014-01-01

CD1d-restricted natural killer T (NKT) cells lie at the interface between the innate and adaptive immune systems and are important mediators of immune responses and tumor immunosurveillance. These NKT cells uniquely recognize lipid antigens, and their rapid yet specific reactions influence both innate and adaptive immunity. In tumor immunity, two NKT subsets (type I and type II) have contrasting roles in which they not only cross-regulate one another, but also impact innate immune cell populations, including natural killer, dendritic, and myeloid lineage cells, as well as adaptive populations, especially CD8(+) and CD4(+) T cells. The extent to which NKT cells promote or suppress surrounding cells affects the host's ability to prevent neoplasia and is consequently of great interest for therapeutic development. Data have shown the potential for therapeutic use of NKT cell agonists and synergy with immune response modifiers in both pre-clinical studies and preliminary clinical studies. However, there is room to improve treatment efficacy by further elucidating the biological mechanisms underlying NKT cell networks. Here, we discuss the progress made in understanding NKT cell networks, their consequent role in the regulation of tumor immunity, and the potential to exploit that knowledge in a clinical setting.

NKT cell networks in the regulation of tumor immunity

Directory of Open Access Journals (Sweden)

Faith C Robertson

2014-10-01

Full Text Available CD1d-restricted natural killer T (NKT cells lie at the interface between the innate and adaptive immune systems and are important mediators of immune responses and tumor immunosurveillance. These NKT cells uniquely recognize lipid antigens, and their rapid yet specific reactions influence both innate and adaptive immunity. In tumor immunity, two NKT subsets (type I and type II have contrasting roles in which they not only cross-regulate one another, but also impact innate immune cell populations, including natural killer, dendritic and myeloid lineage cells, as well as adaptive populations, especially CD8+ and CD4+ T cells. The extent to which NKT cells promote or suppress surrounding cells affects the host’s ability to prevent neoplasia and is consequently of great interest for therapeutic development. Data have shown the potential for therapeutic use of NKT cell agonists and synergy with immune response modifiers in both pre-clinical studies and preliminary clinical studies. However, there is room to improve treatment efficacy by further elucidating the biological mechanisms underlying NKT cell networks. Here, we discuss the progress made in understanding NKT cell networks, their consequent role in the regulation of tumor immunity, and the potential to exploit that knowledge in a clinical setting.

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

Science.gov (United States)

Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

2014-12-01

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Immuno-Oncology-The Translational Runway for Gene Therapy: Gene Therapeutics to Address Multiple Immune Targets.

Science.gov (United States)

Weß, Ludger; Schnieders, Frank

2017-12-01

Cancer therapy is once again experiencing a paradigm shift. This shift is based on extensive clinical experience demonstrating that cancer cannot be successfully fought by addressing only single targets or pathways. Even the combination of several neo-antigens in cancer vaccines is not sufficient for successful, lasting tumor eradication. The focus has therefore shifted to the immune system's role in cancer and the striking abilities of cancer cells to manipulate and/or deactivate the immune system. Researchers and pharma companies have started to target the processes and cells known to support immune surveillance and the elimination of tumor cells. Immune processes, however, require novel concepts beyond the traditional "single-target-single drug" paradigm and need parallel targeting of diverse cells and mechanisms. This review gives a perspective on the role of gene therapy technologies in the evolving immuno-oncology space and identifies gene therapy as a major driver in the development and regulation of effective cancer immunotherapy. Present challenges and breakthroughs ranging from chimeric antigen receptor T-cell therapy, gene-modified oncolytic viruses, combination cancer vaccines, to RNA therapeutics are spotlighted. Gene therapy is recognized as the most prominent technology enabling effective immuno-oncology strategies.

Common gene-network signature of different neurological disorders and their potential implications to neuroAIDS.

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Vidya Sagar

Full Text Available The neurological complications of AIDS (neuroAIDS during the infection of human immunodeficiency virus (HIV are symptomized by non-specific, multifaceted neurological conditions and therefore, defining a specific diagnosis/treatment mechanism(s for this neuro-complexity at the molecular level remains elusive. Using an in silico based integrated gene network analysis we discovered that HIV infection shares convergent gene networks with each of twelve neurological disorders selected in this study. Importantly, a common gene network was identified among HIV infection, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and age macular degeneration. An mRNA microarray analysis in HIV-infected monocytes showed significant changes in the expression of several genes of this in silico derived common pathway which suggests the possible physiological relevance of this gene-circuit in driving neuroAIDS condition. Further, this unique gene network was compared with another in silico derived novel, convergent gene network which is shared by seven major neurological disorders (Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Age Macular Degeneration, Amyotrophic Lateral Sclerosis, Vascular Dementia, and Restless Leg Syndrome. These networks differed in their gene circuits; however, in large, they involved innate immunity signaling pathways, which suggests commonalities in the immunological basis of different neuropathogenesis. The common gene circuits reported here can provide a prospective platform to understand how gene-circuits belonging to other neuro-disorders may be convoluted during real-time neuroAIDS condition and it may elucidate the underlying-and so far unknown-genetic overlap between HIV infection and neuroAIDS risk. Also, it may lead to a new paradigm in understanding disease progression, identifying biomarkers, and developing therapies.

The identification of immune genes in the milk transcriptome of the Tasmanian devil (Sarcophilus harrisii

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Rehana V. Hewavisenti

2016-01-01

Full Text Available Tasmanian devil (Sarcophilus harrisii pouch young, like other marsupials, are born underdeveloped and immunologically naïve, and are unable to mount an adaptive immune response. The mother’s milk provides nutrients for growth and development as well as providing passive immunity. To better understand immune response in this endangered species, we set out to characterise the genes involved in passive immunity by sequencing and annotating the transcriptome of a devil milk sample collected during mid-lactation. At mid-lactation we expect the young to have heightened immune responses, as they have emerged from the pouch, encountering new pathogens. A total of 233,660 transcripts were identified, including approximately 17,827 unique protein-coding genes and 846 immune genes. The most highly expressed transcripts were dominated by milk protein genes such as those encoding early lactation protein, late lactation proteins, α-lactalbumin, α-casein and β-casein. There were numerous highly expressed immune genes including lysozyme, whey acidic protein, ferritin and major histocompatibility complex I and II. Genes encoding immunoglobulins, antimicrobial peptides, chemokines and immune cell receptors were also identified. The array of immune genes identified in this study reflects the importance of the milk in providing immune protection to Tasmanian devil young and provides the first insight into Tasmanian devil milk.

Microarray expression analysis of genes involved in innate immune memory in peritoneal macrophages

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Keisuke Yoshida

2016-03-01

Full Text Available Immunological memory has been believed to be a feature of the adaptive immune system for long period, but recent reports suggest that the innate immune system also exhibits memory-like reaction. Although evidence of innate immune memory is accumulating, no in vivo experimental data has clearly implicated a molecular mechanism, or even a cell-type, for this phenomenon. In this study of data deposited into Gene Expression Omnibus (GEO under GSE71111, we analyzed the expression profile of peritoneal macrophages isolated from mice pre-administrated with toll-like receptor (TLR ligands, mimicking pathogen infection. In these macrophages, increased expression of a group of innate immunity-related genes was sustained over a long period of time, and these genes overlapped with ATF7-regulated genes. We conclude that ATF7 plays an important role in innate immune memory in macrophages. Keywords: Macrophage, ATF7, Innate immune memory, Microarray

Induction of innate immune genes in brain create the neurobiology of addiction.

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Crews, F T; Zou, Jian; Qin, Liya

2011-06-01

Addiction occurs through repeated abuse of drugs that progressively reduce behavioral control and cognitive flexibility while increasing limbic negative emotion. Recent discoveries indicate neuroimmune signaling underlies addiction and co-morbid depression. Low threshold microglia undergo progressive stages of innate immune activation involving astrocytes and neurons with repeated drug abuse, stress, and/or cell damage signals. Increased brain NF-κB transcription of proinflammatory chemokines, cytokines, oxidases, proteases, TLR and other genes create loops amplifying NF-κB transcription and innate immune target gene expression. Human post-mortem alcoholic brain has increased NF-κB and NF-κB target gene message, increased microglial markers and chemokine-MCP1. Polymorphisms of human NF-κB1 and other innate immune genes contribute to genetic risk for alcoholism. Animal transgenic and genetic studies link NF-κB innate immune gene expression to alcohol drinking. Human drug addicts show deficits in behavioral flexibility modeled pre-clinically using reversal learning. Binge alcohol, chronic cocaine, and lesions link addiction neurobiology to frontal cortex, neuroimmune signaling and loss of behavioral flexibility. Addiction also involves increasing limbic negative emotion and depression-like behavior that is reflected in hippocampal neurogenesis. Innate immune activation parallels loss of neurogenesis and increased depression-like behavior. Protection against loss of neurogenesis and negative affect by anti-oxidant, anti-inflammatory, anti-depressant, opiate antagonist and abstinence from ethanol dependence link limbic affect to changes in innate immune signaling. The hypothesis that innate immune gene induction underlies addiction and affective disorders creates new targets for therapy. Copyright © 2011 Elsevier Inc. All rights reserved.

Epidemic spreading and immunization strategy in multiplex networks

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Alvarez Zuzek, Lucila G.; Buono, Camila; Braunstein, Lidia A.

2015-09-01

A more connected world has brought major consequences such as facilitate the spread of diseases all over the world to quickly become epidemics, reason why researchers are concentrated in modeling the propagation of epidemics and outbreaks in multilayer networks. In this networks all nodes interact in different layers with different type of links. However, in many scenarios such as in the society, a multiplex network framework is not completely suitable since not all individuals participate in all layers. In this paper, we use a partially overlapped, multiplex network where only a fraction of the individuals are shared by the layers. We develop a mitigation strategy for stopping a disease propagation, considering the Susceptible-Infected- Recover model, in a system consisted by two layers. We consider a random immunization in one of the layers and study the effect of the overlapping fraction in both, the propagation of the disease and the immunization strategy. Using branching theory, we study this scenario theoretically and via simulations and find a lower epidemic threshold than in the case without strategy.

Comparative analysis of the effectiveness of three immunization strategies in controlling disease outbreaks in realistic social networks.

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Zhijing Xu

Full Text Available The high incidence of emerging infectious diseases has highlighted the importance of effective immunization strategies, especially the stochastic algorithms based on local available network information. Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient. Three frequently referred stochastic immunization strategies-acquaintance immunization, community-bridge immunization, and ring vaccination-were analyzed in this work. The optimal immunization ratios for acquaintance immunization and community-bridge immunization strategies were investigated, and the effectiveness of these three strategies in controlling the spreading of epidemics were analyzed based on realistic social contact networks. The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures. However the effectiveness of acquaintance immunization and community-bridge immunization are very limited, with acquaintance immunization slightly outperforming community-bridge immunization. Ring vaccination significantly outperforms acquaintance immunization and community-bridge immunization, and the sensitivity analysis shows it could be applied to controlling the epidemics with a wide infectivity spectrum. The effectiveness of several classical stochastic immunization strategies was evaluated based on realistic contact networks for the first time in this study. These results could have important significance for epidemic control research and practice.

Comparative analysis of the effectiveness of three immunization strategies in controlling disease outbreaks in realistic social networks.

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Xu, Zhijing; Zu, Zhenghu; Zheng, Tao; Zhang, Wendou; Xu, Qing; Liu, Jinjie

2014-01-01

The high incidence of emerging infectious diseases has highlighted the importance of effective immunization strategies, especially the stochastic algorithms based on local available network information. Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient. Three frequently referred stochastic immunization strategies-acquaintance immunization, community-bridge immunization, and ring vaccination-were analyzed in this work. The optimal immunization ratios for acquaintance immunization and community-bridge immunization strategies were investigated, and the effectiveness of these three strategies in controlling the spreading of epidemics were analyzed based on realistic social contact networks. The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures). However the effectiveness of acquaintance immunization and community-bridge immunization are very limited, with acquaintance immunization slightly outperforming community-bridge immunization. Ring vaccination significantly outperforms acquaintance immunization and community-bridge immunization, and the sensitivity analysis shows it could be applied to controlling the epidemics with a wide infectivity spectrum. The effectiveness of several classical stochastic immunization strategies was evaluated based on realistic contact networks for the first time in this study. These results could have important significance for epidemic control research and practice.

Analysis of immune-related genes during Nora virus infection of Drosophila melanogaster using next generation sequencing.

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Lopez, Wilfredo; Page, Alexis M; Carlson, Darby J; Ericson, Brad L; Cserhati, Matyas F; Guda, Chittibabu; Carlson, Kimberly A

2018-01-01

Drosophila melanogaster depends upon the innate immune system to regulate and combat viral infection. This is a complex, yet widely conserved process that involves a number of immune pathways and gene interactions. In addition, expression of genes involved in immunity are differentially regulated as the organism ages. This is particularly true for viruses that demonstrate chronic infection, as is seen with Nora virus. Nora virus is a persistent non-pathogenic virus that replicates in a horizontal manner in D. melanogaster . The genes involved in the regulation of the immune response to Nora virus infection are largely unknown. In addition, the temporal response of immune response genes as a result of infection has not been examined. In this study, D. melanogaster either infected with Nora virus or left uninfected were aged for 2, 10, 20 and 30 days. The RNA from these samples was analyzed by next generation sequencing (NGS) and the resulting immune-related genes evaluated by utilizing both the PANTHER and DAVID databases, as well as comparison to lists of immune related genes and FlyBase. The data demonstrate that Nora virus infected D. melanogaster exhibit an increase in immune related gene expression over time. In addition, at day 30, the data demonstrate that a persistent immune response may occur leading to an upregulation of specific immune response genes. These results demonstrate the utility of NGS in determining the potential immune system genes involved in Nora virus replication, chronic infection and involvement of antiviral pathways.

Intestinal microbiota and immune related genes in sea cucumber (Apostichopus japonicus) response to dietary β-glucan supplementation

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Yang, Gang; Xu, Zhenjiang; Tian, Xiangli; Dong, Shuanglin; Peng, Mo

2015-01-01

β-glucan is a prebiotic well known for its beneficial outcomes on sea cucumber health through modifying the host intestinal microbiota. High-throughput sequencing techniques provide an opportunity for the identification and characterization of microbes. In this study, we investigated the intestinal microbial community composition, interaction among species, and intestinal immune genes in sea cucumber fed with diet supplemented with or without β-glucan supplementation. The results show that the intestinal dominant classes in the control group are Flavobacteriia, Gammaproteobacteria, and Alphaproteobacteria, whereas Alphaproteobacteria, Flavobacteriia, and Verrucomicrobiae are enriched in the β-glucan group. Dietary β-glucan supplementation promoted the proliferation of the family Rhodobacteraceae of the Alphaproteobacteria class and the family Verrucomicrobiaceae of the Verrucomicrobiae class and reduced the relative abundance of the family Flavobacteriaceae of Flavobacteria class. The ecological network analysis suggests that dietary β-glucan supplementation can alter the network interactions among different microbial functional groups by changing the microbial community composition and topological roles of the OTUs in the ecological network. Dietary β-glucan supplementation has a positive impact on immune responses of the intestine of sea cucumber by activating NF-κB signaling pathway, probably through modulating the balance of intestinal microbiota. - Highlights: • Dietary β-glucan supplementation increases the abundance of Rhodobacteraceae and Verrucomicrobiaceae in the intestine. • Dietary β-glucan supplementation changes the topological roles of OTUs in the ecological network. • Dietary β-glucan supplementation has a positive impact on the immune response of intestine of sea cucumber

Intestinal microbiota and immune related genes in sea cucumber (Apostichopus japonicus) response to dietary β-glucan supplementation

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Yang, Gang [The Key Laboratory of Mariculture, Ministry of Education, Fisheries College, Ocean University of China (China); Xu, Zhenjiang [Biofrontiers Institute, University of Colorado, Boulder, CO (United States); Tian, Xiangli, E-mail: xianglitian@ouc.edu.cn [The Key Laboratory of Mariculture, Ministry of Education, Fisheries College, Ocean University of China (China); Dong, Shuanglin [The Key Laboratory of Mariculture, Ministry of Education, Fisheries College, Ocean University of China (China); Peng, Mo [School of Animal Science and Technology, Jiangxi Agricultural University (China)

2015-02-27

β-glucan is a prebiotic well known for its beneficial outcomes on sea cucumber health through modifying the host intestinal microbiota. High-throughput sequencing techniques provide an opportunity for the identification and characterization of microbes. In this study, we investigated the intestinal microbial community composition, interaction among species, and intestinal immune genes in sea cucumber fed with diet supplemented with or without β-glucan supplementation. The results show that the intestinal dominant classes in the control group are Flavobacteriia, Gammaproteobacteria, and Alphaproteobacteria, whereas Alphaproteobacteria, Flavobacteriia, and Verrucomicrobiae are enriched in the β-glucan group. Dietary β-glucan supplementation promoted the proliferation of the family Rhodobacteraceae of the Alphaproteobacteria class and the family Verrucomicrobiaceae of the Verrucomicrobiae class and reduced the relative abundance of the family Flavobacteriaceae of Flavobacteria class. The ecological network analysis suggests that dietary β-glucan supplementation can alter the network interactions among different microbial functional groups by changing the microbial community composition and topological roles of the OTUs in the ecological network. Dietary β-glucan supplementation has a positive impact on immune responses of the intestine of sea cucumber by activating NF-κB signaling pathway, probably through modulating the balance of intestinal microbiota. - Highlights: • Dietary β-glucan supplementation increases the abundance of Rhodobacteraceae and Verrucomicrobiaceae in the intestine. • Dietary β-glucan supplementation changes the topological roles of OTUs in the ecological network. • Dietary β-glucan supplementation has a positive impact on the immune response of intestine of sea cucumber.

MicroRNA-mediated networks underlie immune response regulation in papillary thyroid carcinoma

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Huang, Chen-Tsung; Oyang, Yen-Jen; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

2014-09-01

Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with low death rate but increased incidence and recurrence in recent years. MicroRNAs (miRNAs) are small non-coding RNAs with diverse regulatory capacities in eukaryotes and have been frequently implied in human cancer. Despite current progress, however, a panoramic overview concerning miRNA regulatory networks in PTC is still lacking. Here, we analyzed the expression datasets of PTC from The Cancer Genome Atlas (TCGA) Data Portal and demonstrate for the first time that immune responses are significantly enriched and under specific regulation in the direct miRNA-target network among distinctive PTC variants to different extents. Additionally, considering the unconventional properties of miRNAs, we explore the protein-coding competing endogenous RNA (ceRNA) and the modulatory networks in PTC and unexpectedly disclose concerted regulation of immune responses from these networks. Interestingly, miRNAs from these conventional and unconventional networks share general similarities and differences but tend to be disparate as regulatory activities increase, coordinately tuning the immune responses that in part account for PTC tumor biology. Together, our systematic results uncover the intensive regulation of immune responses underlain by miRNA-mediated networks in PTC, opening up new avenues in the management of thyroid cancer.

Constructing an integrated gene similarity network for the identification of disease genes.

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Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

The immune gene repertoire of an important viral reservoir, the Australian black flying fox

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Papenfuss Anthony T

2012-06-01

Full Text Available Abstract Background Bats are the natural reservoir host for a range of emerging and re-emerging viruses, including SARS-like coronaviruses, Ebola viruses, henipaviruses and Rabies viruses. However, the mechanisms responsible for the control of viral replication in bats are not understood and there is little information available on any aspect of antiviral immunity in bats. Massively parallel sequencing of the bat transcriptome provides the opportunity for rapid gene discovery. Although the genomes of one megabat and one microbat have now been sequenced to low coverage, no transcriptomic datasets have been reported from any bat species. In this study, we describe the immune transcriptome of the Australian flying fox, Pteropus alecto, providing an important resource for identification of genes involved in a range of activities including antiviral immunity. Results Towards understanding the adaptations that have allowed bats to coexist with viruses, we have de novo assembled transcriptome sequence from immune tissues and stimulated cells from P. alecto. We identified about 18,600 genes involved in a broad range of activities with the most highly expressed genes involved in cell growth and maintenance, enzyme activity, cellular components and metabolism and energy pathways. 3.5% of the bat transcribed genes corresponded to immune genes and a total of about 500 immune genes were identified, providing an overview of both innate and adaptive immunity. A small proportion of transcripts found no match with annotated sequences in any of the public databases and may represent bat-specific transcripts. Conclusions This study represents the first reported bat transcriptome dataset and provides a survey of expressed bat genes that complement existing bat genomic data. In addition, these data provide insight into genes relevant to the antiviral responses of bats, and form a basis for examining the roles of these molecules in immune response to viral infection.

Candidate innate immune system gene expression in the ecological model Daphnia.

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Decaestecker, Ellen; Labbé, Pierrick; Ellegaard, Kirsten; Allen, Judith E; Little, Tom J

2011-10-01

The last ten years have witnessed increasing interest in host-pathogen interactions involving invertebrate hosts. The invertebrate innate immune system is now relatively well characterised, but in a limited range of genetic model organisms and under a limited number of conditions. Immune systems have been little studied under real-world scenarios of environmental variation and parasitism. Thus, we have investigated expression of candidate innate immune system genes in the water flea Daphnia, a model organism for ecological genetics, and whose capacity for clonal reproduction facilitates an exceptionally rigorous control of exposure dose or the study of responses at many time points. A unique characteristic of the particular Daphnia clones and pathogen strain combinations used presently is that they have been shown to be involved in specific host-pathogen coevolutionary interactions in the wild. We choose five genes, which are strong candidates to be involved in Daphnia-pathogen interactions, given that they have been shown to code for immune effectors in related organisms. Differential expression of these genes was quantified by qRT-PCR following exposure to the bacterial pathogen Pasteuria ramosa. Constitutive expression levels differed between host genotypes, and some genes appeared to show correlated expression. However, none of the genes appeared to show a major modification of expression level in response to Pasteuria exposure. By applying knowledge from related genetic model organisms (e.g. Drosophila) to models for the study of evolutionary ecology and coevolution (i.e. Daphnia), the candidate gene approach is temptingly efficient. However, our results show that detection of only weak patterns is likely if one chooses target genes for study based on previously identified genome sequences by comparison to homologues from other related organisms. Future work on the Daphnia-Pasteuria system will need to balance a candidate gene approach with more comprehensive

Identification of immune response-related genes in the Chinese oak silkworm, Antheraea pernyi by suppression subtractive hybridization.

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Liu, Qiu-Ning; Zhu, Bao-Jian; Wang, Lei; Wei, Guo-Qing; Dai, Li-Shang; Lin, Kun-Zhang; Sun, Yu; Qiu, Jian-Feng; Fu, Wei-Wei; Liu, Chao-Liang

2013-11-01

Insects possess an innate immune system that responds to invading microorganisms. In this study, a subtractive cDNA library was constructed to screen for immune response-related genes in the fat bodies of Antheraea pernyi (Lepidoptera: Saturniidae) pupa challenged with Escherichia coli. Four hundred putative EST clones were identified by suppression subtractive hybridization (SSH), including 50 immune response-related genes, three cytoskeleton genes, eight cell cycle and apoptosis genes, five respiration and energy metabolism genes, five transport genes, 40 metabolism genes, ten stress response genes, four transcription and translation regulation genes and 77 unknown genes. To verify the reliability of the SSH data, the transcription of a set of randomly selected immune response-related genes were confirmed by semi-quantitative reverse transcription-PCR (RT-PCR) and real-time quantitative reverse transcription-PCR (qRT-PCR). These identified immune response-related genes provide insight into understanding the innate immunity in A. pernyi. Copyright © 2013 Elsevier Inc. All rights reserved.

Dynamic Mobile RobotNavigation Using Potential Field Based Immune Network

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Guan-Chun Luh

2007-04-01

Full Text Available This paper proposes a potential filed immune network (PFIN for dynamic navigation of mobile robots in an unknown environment with moving obstacles and fixed/moving targets. The Velocity Obstacle method is utilized to determine imminent obstacle collision of a robot moving in the time-varying environment. The response of the overall immune network is derived by the aid of fuzzy system. Simulation results are presented to verify the effectiveness of the proposed methodology in unknown environments with single and multiple moving obstacles

Identifying genes that mediate anthracyline toxicity in immune cells

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Amber eFrick

2015-04-01

Full Text Available The role of the immune system in response to chemotherapeutic agents remains elusive. The interpatient variability observed in immune and chemotherapeutic cytotoxic responses is likely, at least in part, due to complex genetic differences. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at identifying genes underlying these chemotherapeutic cytotoxic effects on immune cells. Using genome-wide association studies (GWAS, we identified four genome-wide significant quantitative trait loci (QTL that contributed to the sensitivity of doxorubicin and idarubicin in immune cells. Of particular interest, a locus on chromosome 16 was significantly associated with cell viability following idarubicin administration (p = 5.01x10-8. Within this QTL lies App, which encodes amyloid beta precursor protein. Comparison of dose-response curves verified that T-cells in App knockout mice were more sensitive to idarubicin than those of C57BL/6J control mice (p < 0.05.In conclusion, the cellular screening approach coupled with GWAS led to the identification and subsequent validation of a gene involved in T-cell viability after idarubicin treatment. Previous studies have suggested a role for App in in vitro and in vivo cytotoxicity to anticancer agents; the overexpression of App enhances resistance, while the knockdown of this gene is deleterious to cell viability. Thus, further investigations should include performing mechanistic studies, validating additional genes from the GWAS, including Ppfia1 and Ppfibp1, and ultimately translating the findings to in vivo and human studies.

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

Gene Network for Identifying the Entropy Changes of Different Modules in Pediatric Sepsis

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Jing Yang

2016-12-01

Full Text Available Background/Aims: Pediatric sepsis is a disease that threatens life of children. The incidence of pediatric sepsis is higher in developing countries due to various reasons, such as insufficient immunization and nutrition, water and air pollution, etc. Exploring the potential genes via different methods is of significance for the prevention and treatment of pediatric sepsis. This study aimed to identify potential genes associated with pediatric sepsis utilizing analysis of gene network and entropy. Methods: The mRNA expression in the blood samples collected from 20 septic children and 30 healthy controls was quantified by using Affymetrix HG-U133A microarray. Two condition-specific protein-protein interaction networks (PINs, one for the healthy control and the other one for the children with sepsis, were deduced by combining the fundamental human PINs with gene expression profiles in the two phenotypes. Subsequently, distinct modules from the two conditional networks were extracted by adopting a maximal clique-merging approach. Delta entropy (ΔS was calculated between sepsis and control modules. Results: Then, key genes displaying changes in gene composition were identified by matching the control and sepsis modules. Two objective modules were obtained, in which ribosomal protein RPL4 and RPL9 as well as TOP2A were probably considered as the key genes differentiating sepsis from healthy controls. Conclusion: According to previous reports and this work, TOP2A is the potential gene therapy target for pediatric sepsis. The relationship between pediatric sepsis and RPL4 and RPL9 needs further investigation.

Identification of immunity related genes to study the Physalis peruviana--Fusarium oxysporum pathosystem.

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Enciso-Rodríguez, Felix E; González, Carolina; Rodríguez, Edwin A; López, Camilo E; Landsman, David; Barrero, Luz Stella; Mariño-Ramírez, Leonardo

2013-01-01

The Cape gooseberry (Physalisperuviana L) is an Andean exotic fruit with high nutritional value and appealing medicinal properties. However, its cultivation faces important phytosanitary problems mainly due to pathogens like Fusarium oxysporum, Cercosporaphysalidis and Alternaria spp. Here we used the Cape gooseberry foliar transcriptome to search for proteins that encode conserved domains related to plant immunity including: NBS (Nucleotide Binding Site), CC (Coiled-Coil), TIR (Toll/Interleukin-1 Receptor). We identified 74 immunity related gene candidates in P. peruviana which have the typical resistance gene (R-gene) architecture, 17 Receptor like kinase (RLKs) candidates related to PAMP-Triggered Immunity (PTI), eight (TIR-NBS-LRR, or TNL) and nine (CC-NBS-LRR, or CNL) candidates related to Effector-Triggered Immunity (ETI) genes among others. These candidate genes were categorized by molecular function (98%), biological process (85%) and cellular component (79%) using gene ontology. Some of the most interesting predicted roles were those associated with binding and transferase activity. We designed 94 primers pairs from the 74 immunity-related genes (IRGs) to amplify the corresponding genomic regions on six genotypes that included resistant and susceptible materials. From these, we selected 17 single band amplicons and sequenced them in 14 F. oxysporum resistant and susceptible genotypes. Sequence polymorphisms were analyzed through preliminary candidate gene association, which allowed the detection of one SNP at the PpIRG-63 marker revealing a nonsynonymous mutation in the predicted LRR domain suggesting functional roles for resistance.

Identification of immunity related genes to study the Physalis peruviana--Fusarium oxysporum pathosystem.

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Felix E Enciso-Rodríguez

Full Text Available The Cape gooseberry (Physalisperuviana L is an Andean exotic fruit with high nutritional value and appealing medicinal properties. However, its cultivation faces important phytosanitary problems mainly due to pathogens like Fusarium oxysporum, Cercosporaphysalidis and Alternaria spp. Here we used the Cape gooseberry foliar transcriptome to search for proteins that encode conserved domains related to plant immunity including: NBS (Nucleotide Binding Site, CC (Coiled-Coil, TIR (Toll/Interleukin-1 Receptor. We identified 74 immunity related gene candidates in P. peruviana which have the typical resistance gene (R-gene architecture, 17 Receptor like kinase (RLKs candidates related to PAMP-Triggered Immunity (PTI, eight (TIR-NBS-LRR, or TNL and nine (CC-NBS-LRR, or CNL candidates related to Effector-Triggered Immunity (ETI genes among others. These candidate genes were categorized by molecular function (98%, biological process (85% and cellular component (79% using gene ontology. Some of the most interesting predicted roles were those associated with binding and transferase activity. We designed 94 primers pairs from the 74 immunity-related genes (IRGs to amplify the corresponding genomic regions on six genotypes that included resistant and susceptible materials. From these, we selected 17 single band amplicons and sequenced them in 14 F. oxysporum resistant and susceptible genotypes. Sequence polymorphisms were analyzed through preliminary candidate gene association, which allowed the detection of one SNP at the PpIRG-63 marker revealing a nonsynonymous mutation in the predicted LRR domain suggesting functional roles for resistance.

Integrative analyses of leprosy susceptibility genes indicate a common autoimmune profile.

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Zhang, Deng-Feng; Wang, Dong; Li, Yu-Ye; Yao, Yong-Gang

2016-04-01

Leprosy is an ancient chronic infection in the skin and peripheral nerves caused by Mycobacterium leprae. The development of leprosy depends on genetic background and the immune status of the host. However, there is no systematic view focusing on the biological pathways, interaction networks and overall expression pattern of leprosy-related immune and genetic factors. To identify the hub genes in the center of leprosy genetic network and to provide an insight into immune and genetic factors contributing to leprosy. We retrieved all reported leprosy-related genes and performed integrative analyses covering gene expression profiling, pathway analysis, protein-protein interaction network, and evolutionary analyses. A list of 123 differentially expressed leprosy related genes, which were enriched in activation and regulation of immune response, was obtained in our analyses. Cross-disorder analysis showed that the list of leprosy susceptibility genes was largely shared by typical autoimmune diseases such as lupus erythematosus and arthritis, suggesting that similar pathways might be affected in leprosy and autoimmune diseases. Protein-protein interaction (PPI) and positive selection analyses revealed a co-evolution network of leprosy risk genes. Our analyses showed that leprosy associated genes constituted a co-evolution network and might undergo positive selection driven by M. leprae. We suggested that leprosy may be a kind of autoimmune disease and the development of leprosy is a matter of defect or over-activation of body immunity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Specific gene expression responses to parasite genotypes reveal redundancy of innate immunity in vertebrates.

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David Haase

Full Text Available Vertebrate innate immunity is the first line of defense against an invading pathogen and has long been assumed to be largely unspecific with respect to parasite/pathogen species. However, recent phenotypic evidence suggests that immunogenetic variation, i.e. allelic variability in genes associated with the immune system, results in host-parasite genotype-by-genotype interactions and thus specific innate immune responses. Immunogenetic variation is common in all vertebrate taxa and this reflects an effective immunological function in complex environments. However, the underlying variability in host gene expression patterns as response of innate immunity to within-species genetic diversity of macroparasites in vertebrates is unknown. We hypothesized that intra-specific variation among parasite genotypes must be reflected in host gene expression patterns. Here we used high-throughput RNA-sequencing to examine the effect of parasite genotypes on gene expression patterns of a vertebrate host, the three-spined stickleback (Gasterosteus aculeatus. By infecting naïve fish with distinct trematode genotypes of the species Diplostomum pseudospathaceum we show that gene activity of innate immunity in three-spined sticklebacks depended on the identity of an infecting macroparasite genotype. In addition to a suite of genes indicative for a general response against the trematode we also find parasite-strain specific gene expression, in particular in the complement system genes, despite similar infection rates of single clone treatments. The observed discrepancy between infection rates and gene expression indicates the presence of alternative pathways which execute similar functions. This suggests that the innate immune system can induce redundant responses specific to parasite genotypes.

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

Directory of Open Access Journals (Sweden)

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

Interactive visualization of gene regulatory networks with associated gene expression time series data

NARCIS (Netherlands)

Westenberg, M.A.; Hijum, van S.A.F.T.; Lulko, A.T.; Kuipers, O.P.; Roerdink, J.B.T.M.; Linsen, L.; Hagen, H.; Hamann, B.

2008-01-01

We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes,

MiR-155-regulated molecular network orchestrates cell fate in the innate and adaptive immune response to Mycobacterium tuberculosis.

Science.gov (United States)

Rothchild, Alissa C; Sissons, James R; Shafiani, Shahin; Plaisier, Christopher; Min, Deborah; Mai, Dat; Gilchrist, Mark; Peschon, Jacques; Larson, Ryan P; Bergthaler, Andreas; Baliga, Nitin S; Urdahl, Kevin B; Aderem, Alan

2016-10-11

The regulation of host-pathogen interactions during Mycobacterium tuberculosis (Mtb) infection remains unresolved. MicroRNAs (miRNAs) are important regulators of the immune system, and so we used a systems biology approach to construct an miRNA regulatory network activated in macrophages during Mtb infection. Our network comprises 77 putative miRNAs that are associated with temporal gene expression signatures in macrophages early after Mtb infection. In this study, we demonstrate a dual role for one of these regulators, miR-155. On the one hand, miR-155 maintains the survival of Mtb-infected macrophages, thereby providing a niche favoring bacterial replication; on the other hand, miR-155 promotes the survival and function of Mtb-specific T cells, enabling an effective adaptive immune response. MiR-155-induced cell survival is mediated through the SH2 domain-containing inositol 5-phosphatase 1 (SHIP1)/protein kinase B (Akt) pathway. Thus, dual regulation of the same cell survival pathway in innate and adaptive immune cells leads to vastly different outcomes with respect to bacterial containment.

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.

2013-07-18

The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.

Current approaches to gene regulatory network modelling

Directory of Open Access Journals (Sweden)

Brazma Alvis

2007-09-01

Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

Spectral Methods for Immunization of Large Networks

Directory of Open Access Journals (Sweden)

Muhammad Ahmad

2017-11-01

Full Text Available Given a network of nodes, minimizing the spread of a contagion using a limited budget is a well-studied problem with applications in network security, viral marketing, social networks, and public health. In real graphs, virus may infect a node which in turn infects its neighbour nodes and this may trigger an epidemic in the whole graph. The goal thus is to select the best k nodes (budget constraint that are immunized (vaccinated, screened, filtered so as the remaining graph is less prone to the epidemic. It is known that the problem is, in all practical models, computationally intractable even for moderate sized graphs. In this paper we employ ideas from spectral graph theory to define relevance and importance of nodes. Using novel graph theoretic techniques, we then design an efficient approximation algorithm to immunize the graph. Theoretical guarantees on the running time of our algorithm show that it is more efficient than any other known solution in the literature. We test the performance of our algorithm on several real world graphs. Experiments show that our algorithm scales well for large graphs and outperforms state of the art algorithms both in quality (containment of epidemic and efficiency (runtime and space complexity.

Genes2FANs: connecting genes through functional association networks

Science.gov (United States)

2012-01-01

Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in

Innate immune genes including a mucin-like gene, mul-1, induced by ionizing radiation in Caenorhabditis elegans.

Science.gov (United States)

Kimura, Takafumi; Takanami, Takako; Sakashita, Tetsuya; Wada, Seiichi; Kobayashi, Yasuhiko; Higashitani, Atsushi

2012-10-01

The effect of radiation on the intestine has been studied for more than one hundred years. It remains unclear, however, whether this organ uses specific defensive mechanisms against ionizing radiation. The infection with Pseudomonas aeruginosa (PA14) in Caenorhabditis elegans induces up-regulation of innate immune response genes. Here, we found that exposure to ionizing radiation also induces certain innate immune response genes such as F49F1.6 (termed mul-1), clec-4, clec-67, lys-1 and lys-2 in the intestine. Moreover, pre-treatment with ionizing radiation before seeding on PA14 lawn plate significantly increased survival rate in the nematode. We also studied transcription pathway of the mul-1 in response to ionizing radiation. Induction of mul-1 gene was highly dependent on the ELT-2 transcription factor and p38 MAPK. Moreover, the insulin/IGF-1 signal pathway works to enhance induction of this gene. The mul-1 gene showed a different induction pattern from the DNA damage response gene, ced-13, which implies that the expression of this gene might be triggered as an indirect effect of radiation. Silencing of the mul-1 gene led to growth retardation after treatment with ionizing radiation. We describe the cross-tolerance between the response to radiation exposure and the innate immune system.

Artificial immune kernel clustering network for unsupervised image segmentation

Institute of Scientific and Technical Information of China (English)

Wenlong Huang; Licheng Jiao

2008-01-01

An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the support vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coefficient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algorithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predefined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

Inducible defenses stay up late: temporal patterns of immune gene expression in Tenebrio molitor.

Science.gov (United States)

Johnston, Paul R; Makarova, Olga; Rolff, Jens

2013-12-06

The course of microbial infection in insects is shaped by a two-stage process of immune defense. Constitutive defenses, such as engulfment and melanization, act immediately and are followed by inducible defenses, archetypically the production of antimicrobial peptides, which eliminate or suppress the remaining microbes. By applying RNAseq across a 7-day time course, we sought to characterize the long-lasting immune response to bacterial challenge in the mealworm beetle Tenebrio molitor, a model for the biochemistry of insect immunity and persistent bacterial infection. By annotating a hybrid de novo assembly of RNAseq data, we were able to identify putative orthologs for the majority of components of the conserved insect immune system. Compared with Tribolium castaneum, the most closely related species with a reference genome sequence and a manually curated immune system annotation, the T. molitor immune gene count was lower, with lineage-specific expansions of genes encoding serine proteases and their countervailing inhibitors accounting for the majority of the deficit. Quantitative mapping of RNAseq reads to the reference assembly showed that expression of genes with predicted functions in cellular immunity, wound healing, melanization, and the production of reactive oxygen species was transiently induced immediately after immune challenge. In contrast, expression of genes encoding antimicrobial peptides or components of the Toll signaling pathway and iron sequestration response remained elevated for at least 7 days. Numerous genes involved in metabolism and nutrient storage were repressed, indicating a possible cost of immune induction. Strikingly, the expression of almost all antibacterial peptides followed the same pattern of long-lasting induction, regardless of their spectra of activity, signaling possible interactive roles in vivo. Copyright © 2014 Johnston et al.

Identification of Immunity Related Genes to Study the Physalis peruviana – Fusarium oxysporum Pathosystem

Science.gov (United States)

Enciso-Rodríguez, Felix E.; González, Carolina; Rodríguez, Edwin A.; López, Camilo E.; Landsman, David; Barrero, Luz Stella; Mariño-Ramírez, Leonardo

2013-01-01

The Cape gooseberry ( Physalis peruviana L) is an Andean exotic fruit with high nutritional value and appealing medicinal properties. However, its cultivation faces important phytosanitary problems mainly due to pathogens like Fusarium oxysporum, Cercosporaphysalidis and Alternaria spp. Here we used the Cape gooseberry foliar transcriptome to search for proteins that encode conserved domains related to plant immunity including: NBS (Nucleotide Binding Site), CC (Coiled-Coil), TIR (Toll/Interleukin-1 Receptor). We identified 74 immunity related gene candidates in P . peruviana which have the typical resistance gene (R-gene) architecture, 17 Receptor like kinase (RLKs) candidates related to PAMP-Triggered Immunity (PTI), eight (TIR-NBS-LRR, or TNL) and nine (CC–NBS-LRR, or CNL) candidates related to Effector-Triggered Immunity (ETI) genes among others. These candidate genes were categorized by molecular function (98%), biological process (85%) and cellular component (79%) using gene ontology. Some of the most interesting predicted roles were those associated with binding and transferase activity. We designed 94 primers pairs from the 74 immunity-related genes (IRGs) to amplify the corresponding genomic regions on six genotypes that included resistant and susceptible materials. From these, we selected 17 single band amplicons and sequenced them in 14 F. oxysporum resistant and susceptible genotypes. Sequence polymorphisms were analyzed through preliminary candidate gene association, which allowed the detection of one SNP at the PpIRG-63 marker revealing a nonsynonymous mutation in the predicted LRR domain suggesting functional roles for resistance. PMID:23844210

Genome-wide characterization and expression profiling of immune genes in the diamondback moth, Plutella xylostella (L.).

Science.gov (United States)

Xia, Xiaofeng; Yu, Liying; Xue, Minqian; Yu, Xiaoqiang; Vasseur, Liette; Gurr, Geoff M; Baxter, Simon W; Lin, Hailan; Lin, Junhan; You, Minsheng

2015-05-06

The diamondback moth, Plutella xylostella (L.), is a destructive pest that attacks cruciferous crops worldwide. Immune responses are important for interactions between insects and pathogens and information on these underpins the development of strategies for biocontrol-based pest management. Little, however, is known about immune genes and their regulation patterns in P. xylostella. A total of 149 immune-related genes in 20 gene families were identified through comparison of P. xylostella genome with the genomes of other insects. Complete and conserved Toll, IMD and JAK-STAT signaling pathways were found in P. xylostella. Genes involved in pathogen recognition were expanded and more diversified than genes associated with intracellular signal transduction. Gene expression profiles showed that the IMD pathway may regulate expression of antimicrobial peptide (AMP) genes in the midgut, and be related to an observed down-regulation of AMPs in experimental lines of insecticide-resistant P. xylostella. A bacterial feeding study demonstrated that P. xylostella could activate different AMPs in response to bacterial infection. This study has established a framework of comprehensive expression profiles that highlight cues for immune regulation in a major pest. Our work provides a foundation for further studies on the functions of P. xylostella immune genes and mechanisms of innate immunity.

MiR-155–regulated molecular network orchestrates cell fate in the innate and adaptive immune response to Mycobacterium tuberculosis

Science.gov (United States)

Rothchild, Alissa C.; Sissons, James R.; Shafiani, Shahin; Plaisier, Christopher; Min, Deborah; Mai, Dat; Gilchrist, Mark; Peschon, Jacques; Larson, Ryan P.; Bergthaler, Andreas; Baliga, Nitin S.; Urdahl, Kevin B.; Aderem, Alan

2016-01-01

The regulation of host–pathogen interactions during Mycobacterium tuberculosis (Mtb) infection remains unresolved. MicroRNAs (miRNAs) are important regulators of the immune system, and so we used a systems biology approach to construct an miRNA regulatory network activated in macrophages during Mtb infection. Our network comprises 77 putative miRNAs that are associated with temporal gene expression signatures in macrophages early after Mtb infection. In this study, we demonstrate a dual role for one of these regulators, miR-155. On the one hand, miR-155 maintains the survival of Mtb-infected macrophages, thereby providing a niche favoring bacterial replication; on the other hand, miR-155 promotes the survival and function of Mtb-specific T cells, enabling an effective adaptive immune response. MiR-155–induced cell survival is mediated through the SH2 domain-containing inositol 5-phosphatase 1 (SHIP1)/protein kinase B (Akt) pathway. Thus, dual regulation of the same cell survival pathway in innate and adaptive immune cells leads to vastly different outcomes with respect to bacterial containment. PMID:27681624

AAV2-mediated in vivo immune gene therapy of solid tumours

LENUS (Irish Health Repository)

Collins, Sara A

2010-12-20

Abstract Background Many strategies have been adopted to unleash the potential of gene therapy for cancer, involving a wide range of therapeutic genes delivered by various methods. Immune therapy has become one of the major strategies adopted for cancer gene therapy and seeks to stimulate the immune system to target tumour antigens. In this study, the feasibility of AAV2 mediated immunotherapy of growing tumours was examined, in isolation and combined with anti-angiogenic therapy. Methods Immune-competent Balb\\/C or C57 mice bearing subcutaneous JBS fibrosarcoma or Lewis Lung Carcinoma (LLC) tumour xenografts respectively were treated by intra-tumoural administration of AAV2 vector encoding the immune up-regulating cytokine granulocyte macrophage-colony stimulating factor (GM-CSF) and the co-stimulatory molecule B7-1 to subcutaneous tumours, either alone or in combination with intra-muscular (IM) delivery of AAV2 vector encoding Nk4 14 days prior to tumour induction. Tumour growth and survival was monitored for all animals. Cured animals were re-challenged with tumourigenic doses of the original tumour type. In vivo cytotoxicity assays were used to investigate establishment of cell-mediated responses in treated animals. Results AAV2-mediated GM-CSF, B7-1 treatment resulted in a significant reduction in tumour growth and an increase in survival in both tumour models. Cured animals were resistant to re-challenge, and induction of T cell mediated anti-tumour responses were demonstrated. Adoptive transfer of splenocytes to naïve animals prevented tumour establishment. Systemic production of Nk4 induced by intra-muscular (IM) delivery of Nk4 significantly reduced subcutaneous tumour growth. However, combination of Nk4 treatment with GM-CSF, B7-1 therapy reduced the efficacy of the immune therapy. Conclusions Overall, this study demonstrates the potential for in vivo AAV2 mediated immune gene therapy, and provides data on the inter-relationship between tumour

Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

Science.gov (United States)

Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

2017-10-03

With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

Convergent genetic and expression data implicate immunity in Alzheimer's disease.

Science.gov (United States)

2015-06-01

Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis. The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 × 10(-12) after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 × 10(-11)), cholesterol transport (P = 2.96 × 10(-9)), and proteasome-ubiquitin activity (P = 1.34 × 10(-6)). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002-.05). The immune response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics. Copyright © 2015. Published by Elsevier Inc.

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

Science.gov (United States)

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.

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)

Transcriptional profiling of immune-related genes in Pacific white shrimp (Litopenaeus vannamei) during ontogenesis.

Science.gov (United States)

Quispe, Ruth L; Justino, Emily B; Vieira, Felipe N; Jaramillo, Michael L; Rosa, Rafael D; Perazzolo, Luciane M

2016-11-01

We have performed here a gene expression analysis to determine the developmental stage at the main genes involved in crustacean immune response begin to be expressed and their changes in mRNA abundance during shrimp development. By using a quantitative PCR-based approach, we have measured the mRNA abundance of 24 immune-related genes from different functional categories in twelve developmental stages ranging from fertilized eggs to larval and postlarval stages and also in juveniles. We showed for the first time that the main genes from the RNAi-based post-transcriptional pathway involved in shrimp antiviral immunity are transcribed in all developmental stages, but exhibit a diverse pattern of gene expression during shrimp ontogenesis. On the other hand, hemocyte-expressed genes mainly involved in antimicrobial defenses appeared to be transcribed in larval stages, indicating that hematopoiesis initiates early in development. Moreover, transcript levels of some genes were early detected in fertilized eggs at 0-4 h post-spawning, suggesting a maternal contribution of immune-related transcripts to shrimp progeny. Altogether, our results provide important clues regarding the ontogenesis of hemocytes as well the establishment of antiviral and antimicrobial defenses in shrimp. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genes misregulated in C. elegans deficient in Dicer, RDE-4, or RDE-1 are enriched for innate immunity genes.

Science.gov (United States)

Welker, Noah C; Habig, Jeffrey W; Bass, Brenda L

2007-07-01

We describe the first microarray analysis of a whole animal containing a mutation in the Dicer gene. We used adult Caenorhabditis elegans and, to distinguish among different roles of Dicer, we also performed microarray analyses of animals with mutations in rde-4 and rde-1, which are involved in silencing by siRNA, but not miRNA. Surprisingly, we find that the X chromosome is greatly enriched for genes regulated by Dicer. Comparison of all three microarray data sets indicates the majority of Dicer-regulated genes are not dependent on RDE-4 or RDE-1, including the X-linked genes. However, all three data sets are enriched in genes important for innate immunity and, specifically, show increased expression of innate immunity genes.

Caste-, sex-, and age-dependent expression of immune-related genes in a Japanese subterranean termite, Reticulitermes speratus.

Directory of Open Access Journals (Sweden)

Yuki Mitaka

Full Text Available Insects protect themselves from microbial infections through innate immune responses, including pathogen recognition, phagocytosis, the activation of proteolytic cascades, and the synthesis of antimicrobial peptides. Termites, eusocial insects inhabiting microbe-rich wood, live in closely-related family groups that are susceptible to shared pathogen infections. To resist pathogenic infection, termite families have evolved diverse immune adaptations at both individual and societal levels, and a strategy of trade-offs between reproduction and immunity has been suggested. Although termite immune-inducible genes have been identified, few studies have investigated the differential expression of these genes between reproductive and neuter castes, and between sexes in each caste. In this study, we compared the expression levels of immune-related genes among castes, sexes, and ages in a Japanese subterranean termite, Reticulitermes speratus. Using RNA-seq, we found 197 immune-related genes, including 40 pattern recognition proteins, 97 signalling proteins, 60 effectors. Among these genes, 174 showed differential expression among castes. Comparing expression levels between males and females in each caste, we found sexually dimorphic expression of immune-related genes not only in reproductive castes, but also in neuter castes. Moreover, we identified age-related differential expression of 162 genes in male and/or female reproductives. In addition, although R. speratus is known to use the antibacterial peptide C-type lysozyme as an egg recognition pheromone, we determined that R. speratus has not only C-type, but also P-type and I-type lysozymes, as well as other termite species. Our transcriptomic analyses revealed immune response plasticity among all castes, and sex-biased expression of immune genes even in neuter castes, suggesting a sexual division of labor in the immune system of R. speratus. This study heightens the understanding of the evolution of

GENETIC SUSCEPTIBILITY TO RESPIRATORY SYNCYTIAL VIRUS BRONCHIOLITIS IN PRETERM CHILDREN IS ASSOCIATED WITH AIRWAY REMODELING GENES AND INNATE IMMUNE GENES

NARCIS (Netherlands)

Siezen, Christine L. E.; Bont, Louis; Hodemaekers, Hennie M.; Ermers, Marieke J.; Doornbos, Gerda; van't Slot, Ruben; Wijmenga, Ciska; van Hottwelingen, Hans C.; Kimpen, Jan L. L.; Kimman, Tjeerd G.; Hoebee, Barbara; Janssen, Riny

Prematurity is a risk factor for severe respiratory syncytial virus bronchiolitis. We show that genetic factors in innate immune genes (IFNA13, IFNAR2, STAT2. IL27, NFKBIA, C3, IL1RN, TLR5), in innate and adaptive immunity (IFNG), and in airway remodeling genes (ADAM33 and TGFBR1), affect disease

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

Science.gov (United States)

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-03-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

Extensive shared polymorphism at non-MHC immune genes in recently diverged North American prairie grouse

Science.gov (United States)

Minias, Piotr; Bateson, Zachary W.; Whittingham, Linda A.; Johnson, Jeff A.; Oyler-McCance, Sara J.; Dunn, Peter O.

2018-01-01

Gene polymorphisms shared between recently diverged species are thought to be widespread and most commonly reflect introgression from hybridization or retention of ancestral polymorphism through incomplete lineage sorting. Shared genetic diversity resulting from incomplete lineage sorting is usually maintained for a relatively short period of time, but under strong balancing selection it may persist for millions of years beyond species divergence (balanced trans-species polymorphism), as in the case of the major histocompatibility complex (MHC) genes. However, balancing selection is much less likely to act on non-MHC immune genes. The aim of this study was to investigate the patterns of shared polymorphism and selection at non-MHC immune genes in five grouse species from Centrocercus and Tympanuchus genera. For this purpose, we genotyped five non-MHC immune genes that do not interact directly with pathogens, but are involved in signaling and regulate immune cell growth. In contrast to previous studies with MHC, we found no evidence for balancing selection or balanced trans-species polymorphism among the non-MHC immune genes. No haplotypes were shared between genera and in most cases more similar allelic variants sorted by genus. Between species within genera, however, we found extensive shared polymorphism, which was most likely attributable to introgression or incomplete lineage sorting following recent divergence and large ancestral effective population size (i.e., weak genetic drift). Our study suggests that North American prairie grouse may have attained relatively low degree of reciprocal monophyly at nuclear loci and reinforces the rarity of balancing selection in non-MHC immune genes.

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

Directory of Open Access Journals (Sweden)

Ma'ayan Avi

2007-10-01

Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

KAUST Repository

Fujii, Chisato

2015-04-16

Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.

Combinatorial explosion in model gene networks

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Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

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.

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

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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

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato; Kuwahara, Hiroyuki; Yu, Ge; Guo, Lili; Gao, Xin

2016-01-01

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato

2016-08-25

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

Learning a Markov Logic network for supervised gene regulatory network inference.

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Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

2013-09-12

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

Mutated Genes in Schizophrenia Map to Brain Networks

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... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks ... have a high number of spontaneous mutations in genes that form a network in the front region ...

Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

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Fei Xiao

Full Text Available Combining path consistency (PC algorithms with conditional mutual information (CMI are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference, to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.

Balancing selection on immunity genes: review of the current literature and new analysis in Drosophila melanogaster.

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Croze, Myriam; Živković, Daniel; Stephan, Wolfgang; Hutter, Stephan

2016-08-01

Balancing selection has been widely assumed to be an important evolutionary force, yet even today little is known about its abundance and its impact on the patterns of genetic diversity. Several studies have shown examples of balancing selection in humans, plants or parasites, and many genes under balancing selection are involved in immunity. It has been proposed that host-parasite coevolution is one of the main forces driving immune genes to evolve under balancing selection. In this paper, we review the literature on balancing selection on immunity genes in several organisms, including Drosophila. Furthermore, we performed a genome scan for balancing selection in an African population of Drosophila melanogaster using coalescent simulations of a demographic model with and without selection. We find very few genes under balancing selection and only one novel candidate gene related to immunity. Finally, we discuss the possible causes of the low number of genes under balancing selection. Copyright © 2016 The Authors. Published by Elsevier GmbH.. All rights reserved.

Resistance Genes in Global Crop Breeding Networks.

Science.gov (United States)

Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

2017-10-01

Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data.

Science.gov (United States)

Farber, Charles R

2010-11-01

Bone mineral density (BMD) is influenced by a complex network of gene interactions; therefore, elucidating the relationships between genes and how those genes, in turn, influence BMD is critical for developing a comprehensive understanding of osteoporosis. To investigate the role of transcriptional networks in the regulation of BMD, we performed a weighted gene coexpression network analysis (WGCNA) using microarray expression data on monocytes from young individuals with low or high BMD. WGCNA groups genes into modules based on patterns of gene coexpression. and our analysis identified 11 gene modules. We observed that the overall expression of one module (referred to as module 9) was significantly higher in the low-BMD group (p = .03). Module 9 was highly enriched for genes belonging to the immune system-related gene ontology (GO) category "response to virus" (p = 7.6 × 10(-11)). Using publically available genome-wide association study data, we independently validated the importance of module 9 by demonstrating that highly connected module 9 hubs were more likely, relative to less highly connected genes, to be genetically associated with BMD. This study highlights the advantages of systems-level analyses to uncover coexpression modules associated with bone mass and suggests that particular monocyte expression patterns may mediate differences in BMD. © 2010 American Society for Bone and Mineral Research.

BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

Science.gov (United States)

Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

2015-06-12

During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

Multiscale Embedded Gene Co-expression Network Analysis.

Directory of Open Access Journals (Sweden)

Won-Min Song

2015-11-01

Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Multiscale Embedded Gene Co-expression Network Analysis.

Science.gov (United States)

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Expression of putative immune response genes during early ontogeny in the coral Acropora millepora.

Directory of Open Access Journals (Sweden)

Eneour Puill-Stephan

Full Text Available Corals, like many other marine invertebrates, lack a mature allorecognition system in early life history stages. Indeed, in early ontogeny, when corals acquire and establish associations with various surface microbiota and dinoflagellate endosymbionts, they do not efficiently distinguish between closely and distantly related individuals from the same population. However, very little is known about the molecular components that underpin allorecognition and immunity responses or how they change through early ontogeny in corals.Patterns in the expression of four putative immune response genes (apextrin, complement C3, and two CELIII type lectin genes were examined in juvenile colonies of Acropora millepora throughout a six-month post-settlement period using quantitative real-time PCR (qPCR. Expression of a CELIII type lectin gene peaked in the fourth month for most of the coral juveniles sampled and was significantly higher at this time than at any other sampling time during the six months following settlement. The timing of this increase in expression levels of putative immune response genes may be linked to allorecognition maturation which occurs around this time in A. millepora. Alternatively, the increase may represent a response to immune challenges, such as would be involved in the recognition of symbionts (such as Symbiodinium spp. or bacteria during winnowing processes as symbioses are fine-tuned.Our data, although preliminary, are consistent with the hypothesis that lectins may play an important role in the maturation of allorecognition responses in corals. The co-expression of lectins with apextrin during development of coral juveniles also raises the possibility that these proteins, which are components of innate immunity in other invertebrates, may influence the innate immune systems of corals through a common pathway or system. However, further studies investigating the expression of these genes in alloimmune-challenged corals are

Expression of putative immune response genes during early ontogeny in the coral Acropora millepora.

Science.gov (United States)

Puill-Stephan, Eneour; Seneca, François O; Miller, David J; van Oppen, Madeleine J H; Willis, Bette L

2012-01-01

Corals, like many other marine invertebrates, lack a mature allorecognition system in early life history stages. Indeed, in early ontogeny, when corals acquire and establish associations with various surface microbiota and dinoflagellate endosymbionts, they do not efficiently distinguish between closely and distantly related individuals from the same population. However, very little is known about the molecular components that underpin allorecognition and immunity responses or how they change through early ontogeny in corals. Patterns in the expression of four putative immune response genes (apextrin, complement C3, and two CELIII type lectin genes) were examined in juvenile colonies of Acropora millepora throughout a six-month post-settlement period using quantitative real-time PCR (qPCR). Expression of a CELIII type lectin gene peaked in the fourth month for most of the coral juveniles sampled and was significantly higher at this time than at any other sampling time during the six months following settlement. The timing of this increase in expression levels of putative immune response genes may be linked to allorecognition maturation which occurs around this time in A. millepora. Alternatively, the increase may represent a response to immune challenges, such as would be involved in the recognition of symbionts (such as Symbiodinium spp. or bacteria) during winnowing processes as symbioses are fine-tuned. Our data, although preliminary, are consistent with the hypothesis that lectins may play an important role in the maturation of allorecognition responses in corals. The co-expression of lectins with apextrin during development of coral juveniles also raises the possibility that these proteins, which are components of innate immunity in other invertebrates, may influence the innate immune systems of corals through a common pathway or system. However, further studies investigating the expression of these genes in alloimmune-challenged corals are needed to further

Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study

Science.gov (United States)

2012-01-01

Background This study undertakes a systematic and comprehensive analysis of brain gene expression profiles of immune/inflammation-related genes in aging and Alzheimer’s disease (AD). Methods In a well-powered microarray study of young (20 to 59 years), aged (60 to 99 years), and AD (74 to 95 years) cases, gene responses were assessed in the hippocampus, entorhinal cortex, superior frontal gyrus, and post-central gyrus. Results Several novel concepts emerge. First, immune/inflammation-related genes showed major changes in gene expression over the course of cognitively normal aging, with the extent of gene response far greater in aging than in AD. Of the 759 immune-related probesets interrogated on the microarray, approximately 40% were significantly altered in the SFG, PCG and HC with increasing age, with the majority upregulated (64 to 86%). In contrast, far fewer immune/inflammation genes were significantly changed in the transition to AD (approximately 6% of immune-related probesets), with gene responses primarily restricted to the SFG and HC. Second, relatively few significant changes in immune/inflammation genes were detected in the EC either in aging or AD, although many genes in the EC showed similar trends in responses as in the other brain regions. Third, immune/inflammation genes undergo gender-specific patterns of response in aging and AD, with the most pronounced differences emerging in aging. Finally, there was widespread upregulation of genes reflecting activation of microglia and perivascular macrophages in the aging brain, coupled with a downregulation of select factors (TOLLIP, fractalkine) that when present curtail microglial/macrophage activation. Notably, essentially all pathways of the innate immune system were upregulated in aging, including numerous complement components, genes involved in toll-like receptor signaling and inflammasome signaling, as well as genes coding for immunoglobulin (Fc) receptors and human leukocyte antigens I

Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study

Directory of Open Access Journals (Sweden)

Cribbs David H

2012-07-01

Full Text Available Abstract Background This study undertakes a systematic and comprehensive analysis of brain gene expression profiles of immune/inflammation-related genes in aging and Alzheimer’s disease (AD. Methods In a well-powered microarray study of young (20 to 59 years, aged (60 to 99 years, and AD (74 to 95 years cases, gene responses were assessed in the hippocampus, entorhinal cortex, superior frontal gyrus, and post-central gyrus. Results Several novel concepts emerge. First, immune/inflammation-related genes showed major changes in gene expression over the course of cognitively normal aging, with the extent of gene response far greater in aging than in AD. Of the 759 immune-related probesets interrogated on the microarray, approximately 40% were significantly altered in the SFG, PCG and HC with increasing age, with the majority upregulated (64 to 86%. In contrast, far fewer immune/inflammation genes were significantly changed in the transition to AD (approximately 6% of immune-related probesets, with gene responses primarily restricted to the SFG and HC. Second, relatively few significant changes in immune/inflammation genes were detected in the EC either in aging or AD, although many genes in the EC showed similar trends in responses as in the other brain regions. Third, immune/inflammation genes undergo gender-specific patterns of response in aging and AD, with the most pronounced differences emerging in aging. Finally, there was widespread upregulation of genes reflecting activation of microglia and perivascular macrophages in the aging brain, coupled with a downregulation of select factors (TOLLIP, fractalkine that when present curtail microglial/macrophage activation. Notably, essentially all pathways of the innate immune system were upregulated in aging, including numerous complement components, genes involved in toll-like receptor signaling and inflammasome signaling, as well as genes coding for immunoglobulin (Fc receptors and human

A cascade reaction network mimicking the basic functional steps of acquired immune response

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Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

2015-01-01

Biological systems use complex ‘information processing cores’ composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS which we call Adaptive Immune Response Simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system which responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner which is superficially similar to the most basic responses of the vertebrate acquired immune system, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices. PMID:26391084

Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

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Dajeong Lim

2014-01-01

Full Text Available Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7 using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60 and dihydropyrimidine dehydrogenase (DPYD are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness.

Feminizing Wolbachia: a transcriptomics approach with insights on the immune response genes in Armadillidium vulgare

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Chevalier Frédéric

2012-01-01

Full Text Available Abstract Background Wolbachia are vertically transmitted bacteria known to be the most widespread endosymbiont in arthropods. They induce various alterations of the reproduction of their host, including feminization of genetic males in isopod crustaceans. In the pill bug Armadillidium vulgare, the presence of Wolbachia is also associated with detrimental effects on host fertility and lifespan. Deleterious effects have been demonstrated on hemocyte density, phenoloxidase activity, and natural hemolymph septicemia, suggesting that infected individuals could have defective immune capacities. Since nothing is known about the molecular mechanisms involved in Wolbachia-A. vulgare interactions and its secondary immunocompetence modulation, we developed a transcriptomics strategy and compared A. vulgare gene expression between Wolbachia-infected animals (i.e., “symbiotic” animals and uninfected ones (i.e., “asymbiotic” animals as well as between animals challenged or not challenged by a pathogenic bacteria. Results Since very little genetic data is available on A. vulgare, we produced several EST libraries and generated a total of 28 606 ESTs. Analyses of these ESTs revealed that immune processes were over-represented in most experimental conditions (responses to a symbiont and to a pathogen. Considering canonical crustacean immune pathways, these genes encode antimicrobial peptides or are involved in pathogen recognition, detoxification, and autophagy. By RT-qPCR, we demonstrated a general trend towards gene under-expression in symbiotic whole animals and ovaries whereas the same gene set tends to be over-expressed in symbiotic immune tissues. Conclusion This study allowed us to generate the first reference transcriptome ever obtained in the Isopoda group and to identify genes involved in the major known crustacean immune pathways encompassing cellular and humoral responses. Expression of immune-related genes revealed a modulation of host

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

Science.gov (United States)

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

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Vipin Narang

Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

Dynamics of immune system gene expression upon bacterial challenge and wounding in a social insect (Bombus terrestris.

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Silvio Erler

2011-03-01

Full Text Available The innate immune system which helps individuals to combat pathogens comprises a set of genes representing four immune system pathways (Toll, Imd, JNK and JAK/STAT. There is a lack of immune genes in social insects (e.g. honeybees when compared to Diptera. Potentially, this might be compensated by an advanced system of social immunity (synergistic action of several individuals. The bumble bee, Bombus terrestris, is a primitively eusocial species with an annual life cycle and colonies headed by a single queen. We used this key pollinator to study the temporal dynamics of immune system gene expression in response to wounding and bacterial challenge.Antimicrobial peptides (AMP (abaecin, defensin 1, hymenoptaecin were strongly up-regulated by wounding and bacterial challenge, the latter showing a higher impact on the gene expression level. Sterile wounding down-regulated TEP A, an effector gene of the JAK/STAT pathway, and bacterial infection influenced genes of the Imd (relish and JNK pathway (basket. Relish was up-regulated within the first hour after bacterial challenge, but decreased strongly afterwards. AMP expression following wounding and bacterial challenge correlates with the expression pattern of relish whereas correlated expression with dorsal was absent. Although expression of AMPs was high, continuous bacterial growth was observed throughout the experiment.Here we demonstrate for the first time the temporal dynamics of immune system gene expression in a social insect. Wounding and bacterial challenge affected the innate immune system significantly. Induction of AMP expression due to wounding might comprise a pre-adaptation to accompanying bacterial infections. Compared with solitary species this social insect exhibits reduced immune system efficiency, as bacterial growth could not be inhibited. A negative feedback loop regulating the Imd-pathway is suggested. AMPs, the end product of the Imd-pathway, inhibited the up-regulation of the

Differential expression of immune and stress genes in the skin of Atlantic cod (Gadus morhua)

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Caipang, C.M.A.; Lazado, C.C.; Brinchmann, M.; Rombout, J.H.W.M.; Kiron, V.

2011-01-01

The present study describes the transcriptional profiles of selected immune and stress genes with putative important roles in the cutaneous immune defense of Atlantic cod (Gadus morhua). In addition it shows differential expression of many genes at the dorsal and ventral sides of fish, in general

big bang gene modulates gut immune tolerance in Drosophila.

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Bonnay, François; Cohen-Berros, Eva; Hoffmann, Martine; Kim, Sabrina Y; Boulianne, Gabrielle L; Hoffmann, Jules A; Matt, Nicolas; Reichhart, Jean-Marc

2013-02-19

Chronic inflammation of the intestine is detrimental to mammals. Similarly, constant activation of the immune response in the gut by the endogenous flora is suspected to be harmful to Drosophila. Therefore, the innate immune response in the gut of Drosophila melanogaster is tightly balanced to simultaneously prevent infections by pathogenic microorganisms and tolerate the endogenous flora. Here we describe the role of the big bang (bbg) gene, encoding multiple membrane-associated PDZ (PSD-95, Discs-large, ZO-1) domain-containing protein isoforms, in the modulation of the gut immune response. We show that in the adult Drosophila midgut, BBG is present at the level of the septate junctions, on the apical side of the enterocytes. In the absence of BBG, these junctions become loose, enabling the intestinal flora to trigger a constitutive activation of the anterior midgut immune response. This chronic epithelial inflammation leads to a reduced lifespan of bbg mutant flies. Clearing the commensal flora by antibiotics prevents the abnormal activation of the gut immune response and restores a normal lifespan. We now provide genetic evidence that Drosophila septate junctions are part of the gut immune barrier, a function that is evolutionarily conserved in mammals. Collectively, our data suggest that septate junctions are required to maintain the subtle balance between immune tolerance and immune response in the Drosophila gut, which represents a powerful model to study inflammatory bowel diseases.

Immune function genes CD99L2, JARID2 and TPO show association with autism spectrum disorder

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Ramos Paula S

2012-06-01

Full Text Available Abstract Background A growing number of clinical and basic research studies have implicated immunological abnormalities as being associated with and potentially responsible for the cognitive and behavioral deficits seen in autism spectrum disorder (ASD children. Here we test the hypothesis that immune-related gene loci are associated with ASD. Findings We identified 2,012 genes of known immune-function via Ingenuity Pathway Analysis. Family-based tests of association were computed on the 22,904 single nucleotide polymorphisms (SNPs from the 2,012 immune-related genes on 1,510 trios available at the Autism Genetic Resource Exchange (AGRE repository. Several SNPs in immune-related genes remained statistically significantly associated with ASD after adjusting for multiple comparisons. Specifically, we observed significant associations in the CD99 molecule-like 2 region (CD99L2, rs11796490, P = 4.01 × 10-06, OR = 0.68 (0.58-0.80, in the jumonji AT rich interactive domain 2 (JARID2 gene (rs13193457, P = 2.71 × 10-06, OR = 0.61 (0.49-0.75, and in the thyroid peroxidase gene (TPO (rs1514687, P = 5.72 × 10-06, OR = 1.46 (1.24-1.72. Conclusions This study suggests that despite the lack of a general enrichment of SNPs in immune function genes in ASD children, several novel genes with known immune functions are associated with ASD.

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

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Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

2012-01-01

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

The Entomopathogenic Fungi Isaria fumosorosea Plays a Vital Role in Suppressing the Immune System of Plutella xylostella: RNA-Seq and DGE Analysis of Immunity-Related Genes.

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Xu, Jin; Xu, Xiaoxia; Shakeel, Muhammad; Li, Shuzhong; Wang, Shuang; Zhou, Xianqiang; Yu, Jialin; Xu, Xiaojing; Yu, Xiaoqiang; Jin, Fengliang

2017-01-01

Most, if not all, entomopathogenic fungi have been used as alternative control agents to decrease the insect resistance and harmful effects of the insecticides on the environment. Among them, Isaria fumosorosea has also shown great potential to control different insect pests. In the present study, we explored the immune response of P. xylostella to the infection of I. fumosorosea at different time points by using RNA-Sequencing and differential gene expression technology at the genomic level. To gain insight into the host-pathogen interaction at the genomic level, five libraries of P. xylostella larvae at 12, 18, 24, and 36 h post-infection and a control were constructed. In total, 161 immunity-related genes were identified and grouped into four categories; immune recognition families, toll and Imd pathway, melanization, and antimicrobial peptides (AMPs). The results of differentially expressed immunity-related genes depicted that 15, 13, 53, and 14 up-regulated and 38, 51, 56, and 49 were down-regulated in P. xylostella at 12, 18, 24, and 36 h post-treatment, respectively. RNA-Seq results of immunity-related genes revealed that the expression of AMPs was reduced after treatment with I. fumosorosea . To validate RNA-Seq results by RT-qPCR, 22 immunity-related genes were randomly selected. In conclusion, our results demonstrate that I. fumosorosea has the potential to suppress the immune response of P. xylostella and can become a potential biopesticide for controlling P. xylostella .

The Entomopathogenic Fungi Isaria fumosorosea Plays a Vital Role in Suppressing the Immune System of Plutella xylostella: RNA-Seq and DGE Analysis of Immunity-Related Genes

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Jin Xu

2017-07-01

Full Text Available Most, if not all, entomopathogenic fungi have been used as alternative control agents to decrease the insect resistance and harmful effects of the insecticides on the environment. Among them, Isaria fumosorosea has also shown great potential to control different insect pests. In the present study, we explored the immune response of P. xylostella to the infection of I. fumosorosea at different time points by using RNA-Sequencing and differential gene expression technology at the genomic level. To gain insight into the host-pathogen interaction at the genomic level, five libraries of P. xylostella larvae at 12, 18, 24, and 36 h post-infection and a control were constructed. In total, 161 immunity-related genes were identified and grouped into four categories; immune recognition families, toll and Imd pathway, melanization, and antimicrobial peptides (AMPs. The results of differentially expressed immunity-related genes depicted that 15, 13, 53, and 14 up-regulated and 38, 51, 56, and 49 were down-regulated in P. xylostella at 12, 18, 24, and 36 h post-treatment, respectively. RNA-Seq results of immunity-related genes revealed that the expression of AMPs was reduced after treatment with I. fumosorosea. To validate RNA-Seq results by RT-qPCR, 22 immunity-related genes were randomly selected. In conclusion, our results demonstrate that I. fumosorosea has the potential to suppress the immune response of P. xylostella and can become a potential biopesticide for controlling P. xylostella.

Crowdsourcing the nodulation gene network discovery environment.

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Li, Yupeng; Jackson, Scott A

2016-05-26

The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created. The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation. Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/ , and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM

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D. Amutha Guka

2012-01-01

Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.

Global efficiency of local immunization on complex networks.

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Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J

2013-01-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

Global efficiency of local immunization on complex networks

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Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

2013-07-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

MicroRNA regulation of immune events at conception.

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Robertson, Sarah A; Zhang, Bihong; Chan, Honyueng; Sharkey, David J; Barry, Simon C; Fullston, Tod; Schjenken, John E

2017-09-01

The reproductive tract environment at conception programs the developmental trajectory of the embryo, sets the course of pregnancy, and impacts offspring phenotype and health. Despite the fundamental importance of this stage of reproduction, the rate-limiting regulatory mechanisms operating locally to control fertility and fecundity are incompletely understood. Emerging studies highlight roles for microRNAs (miRNAs) in regulating reproductive and developmental processes and in modulating the quality and strength of the female immune response. Since endometrial receptivity and robust placentation require specific adaptation of the immune response, we hypothesize that miRNAs participate in establishing pregnancy through effects on key gene networks in immune cells. Our recent studies investigated miRNAs that are induced in the peri-conception environment, focusing on miRNAs that have immune-regulatory roles-particularly miR-223, miR-155, and miR-146a. Genetic mouse models deficient in individual miRNAs are proving informative in defining roles for these miRNAs in the generation and stabilization of regulatory T cells (Treg cells) that confer adaptive immune tolerance. Overlapping and redundant functions between miRNAs that target multiple genes, combined with multiple miRNAs targeting individual genes, indicate complex and sensitive regulatory networks. Although to date most data on miRNA regulation of reproductive events are from mice, conserved functions of miRNAs across species imply similar biological pathways operate in all mammals. Understanding the regulation and roles of miRNAs in the peri-conception immune response will advance our knowledge of how environmental determinants act at conception, and could have practical applications for animal breeding as well as human fertility. © 2017 Wiley Periodicals, Inc.

Tailoring the Immune Response via Customization of Pathogen Gene Expression.

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Runco, Lisa M; Stauft, Charles B; Coleman, J Robert

2014-01-01

The majority of studies focused on the construction and reengineering of bacterial pathogens have mainly relied on the knocking out of virulence factors or deletion/mutation of amino acid residues to then observe the microbe's phenotype and the resulting effect on the host immune response. These knockout bacterial strains have also been proposed as vaccines to combat bacterial disease. Theoretically, knockout strains would be unable to cause disease since their virulence factors have been removed, yet they could induce a protective memory response. While knockout strains have been valuable tools to discern the role of virulence factors in host immunity and bacterial pathogenesis, they have been unable to yield clinically relevant vaccines. The advent of synthetic biology and enhanced user-directed gene customization has altered this binary process of knockout, followed by observation. Recent studies have shown that a researcher can now tailor and customize a given microbe's gene expression to produce a desired immune response. In this commentary, we highlight these studies as a new avenue for controlling the inflammatory response as well as vaccine development.

Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

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Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

2017-01-01

The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

Analytical connection between thresholds and immunization strategies of SIS model in random networks

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Zhou, Ming-Yang; Xiong, Wen-Man; Liao, Hao; Wang, Tong; Wei, Zong-Wen; Fu, Zhong-Qian

2018-05-01

Devising effective strategies for hindering the propagation of viruses and protecting the population against epidemics is critical for public security and health. Despite a number of studies based on the susceptible-infected-susceptible (SIS) model devoted to this topic, we still lack a general framework to compare different immunization strategies in completely random networks. Here, we address this problem by suggesting a novel method based on heterogeneous mean-field theory for the SIS model. Our method builds the relationship between the thresholds and different immunization strategies in completely random networks. Besides, we provide an analytical argument that the targeted large-degree strategy achieves the best performance in random networks with arbitrary degree distribution. Moreover, the experimental results demonstrate the effectiveness of the proposed method in both artificial and real-world networks.

CRISPR loci reveal networks of gene exchange in archaea.

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Brodt, Avital; Lurie-Weinberger, Mor N; Gophna, Uri

2011-12-21

CRISPR (Clustered, Regularly, Interspaced, Short, Palindromic Repeats) loci provide prokaryotes with an adaptive immunity against viruses and other mobile genetic elements. CRISPR arrays can be transcribed and processed into small crRNA molecules, which are then used by the cell to target the foreign nucleic acid. Since spacers are accumulated by active CRISPR/Cas systems, the sequences of these spacers provide a record of the past "infection history" of the organism. Here we analyzed all currently known spacers present in archaeal genomes and identified their source by DNA similarity. While nearly 50% of archaeal spacers matched mobile genetic elements, such as plasmids or viruses, several others matched chromosomal genes of other organisms, primarily other archaea. Thus, networks of gene exchange between archaeal species were revealed by the spacer analysis, including many cases of inter-genus and inter-species gene transfer events. Spacers that recognize viral sequences tend to be located further away from the leader sequence, implying that there exists a selective pressure for their retention. CRISPR spacers provide direct evidence for extensive gene exchange in archaea, especially within genera, and support the current dogma where the primary role of the CRISPR/Cas system is anti-viral and anti-plasmid defense. This article was reviewed by: Profs. W. Ford Doolittle, John van der Oost, Christa Schleper (nominated by board member Prof. J Peter Gogarten).

Sperm competition, immunity, selfish genes and cancer.

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Lewis, Z; Price, T A R; Wedell, N

2008-10-01

Sperm competition is widespread and has played an important role in shaping male reproductive characters such as testis size and numbers of sperm produced, and this is reflected in the rapid evolution of many reproductive genes. Additionally, sperm competition has been implicated in the rapid evolution of seminal fluids. However, our understanding of the molecular basis of many traits thought to be important in sperm competition is rudimentary. Furthermore, links between sperm competition and a range of issues not directly related to reproduction are only just beginning to be explored. These include associations between sperm competition and selfish genes, immunity and diseases such as cancer.We briefly review these topics and suggest areas we consider worthy of additional research.

The effect of alcohol on the differential expression of cluster of differentiation 14 gene, associated pathways, and genetic network.

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Diana X Zhou

Full Text Available Alcohol consumption affects human health in part by compromising the immune system. In this study, we examined the expression of the Cd14 (cluster of differentiation 14 gene, which is involved in the immune system through a proinflammatory cascade. Expression was evaluated in BXD mice treated with saline or acute 1.8 g/kg i.p. ethanol (12.5% v/v. Hippocampal gene expression data were generated to examine differential expression and to perform systems genetics analyses. The Cd14 gene expression showed significant changes among the BXD strains after ethanol treatment, and eQTL mapping revealed that Cd14 is a cis-regulated gene. We also identified eighteen ethanol-related phenotypes correlated with Cd14 expression related to either ethanol responses or ethanol consumption. Pathway analysis was performed to identify possible biological pathways involved in the response to ethanol and Cd14. We also constructed a genetic network for Cd14 using the top 20 correlated genes and present several genes possibly involved in Cd14 and ethanol responses based on differential gene expression. In conclusion, we found Cd14, along with several other genes and pathways, to be involved in ethanol responses in the hippocampus, such as increased susceptibility to lipopolysaccharides and neuroinflammation.

Genome-wide profiling of 24 hr diel rhythmicity in the water flea, Daphnia pulex: network analysis reveals rhythmic gene expression and enhances functional gene annotation.

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Rund, Samuel S C; Yoo, Boyoung; Alam, Camille; Green, Taryn; Stephens, Melissa T; Zeng, Erliang; George, Gary F; Sheppard, Aaron D; Duffield, Giles E; Milenković, Tijana; Pfrender, Michael E

2016-08-18

Marine and freshwater zooplankton exhibit daily rhythmic patterns of behavior and physiology which may be regulated directly by the light:dark (LD) cycle and/or a molecular circadian clock. One of the best-studied zooplankton taxa, the freshwater crustacean Daphnia, has a 24 h diel vertical migration (DVM) behavior whereby the organism travels up and down through the water column daily. DVM plays a critical role in resource tracking and the behavioral avoidance of predators and damaging ultraviolet radiation. However, there is little information at the transcriptional level linking the expression patterns of genes to the rhythmic physiology/behavior of Daphnia. Here we analyzed genome-wide temporal transcriptional patterns from Daphnia pulex collected over a 44 h time period under a 12:12 LD cycle (diel) conditions using a cosine-fitting algorithm. We used a comprehensive network modeling and analysis approach to identify novel co-regulated rhythmic genes that have similar network topological properties and functional annotations as rhythmic genes identified by the cosine-fitting analyses. Furthermore, we used the network approach to predict with high accuracy novel gene-function associations, thus enhancing current functional annotations available for genes in this ecologically relevant model species. Our results reveal that genes in many functional groupings exhibit 24 h rhythms in their expression patterns under diel conditions. We highlight the rhythmic expression of immunity, oxidative detoxification, and sensory process genes. We discuss differences in the chronobiology of D. pulex from other well-characterized terrestrial arthropods. This research adds to a growing body of literature suggesting the genetic mechanisms governing rhythmicity in crustaceans may be divergent from other arthropod lineages including insects. Lastly, these results highlight the power of using a network analysis approach to identify differential gene expression and provide novel

Expression profile of immune response genes in patients with Severe Acute Respiratory Syndrome

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Tai Dessmon

2005-01-01

Full Text Available Abstract Background Severe acute respiratory syndrome (SARS emerged in later February 2003, as a new epidemic form of life-threatening infection caused by a novel coronavirus. However, the immune-pathogenesis of SARS is poorly understood. To understand the host response to this pathogen, we investigated the gene expression profiles of peripheral blood mononuclear cells (PBMCs derived from SARS patients, and compared with healthy controls. Results The number of differentially expressed genes was found to be 186 under stringent filtering criteria of microarray data analysis. Several genes were highly up-regulated in patients with SARS, such as, the genes coding for Lactoferrin, S100A9 and Lipocalin 2. The real-time PCR method verified the results of the gene array analysis and showed that those genes that were up-regulated as determined by microarray analysis were also found to be comparatively up-regulated by real-time PCR analysis. Conclusions This differential gene expression profiling of PBMCs from patients with SARS strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection, as we observed a complete lack of cytokine genes usually triggered during a viral infection. Our study shows for the first time how the immune system responds to the SARS infection, and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease.

Mutational robustness of gene regulatory networks.

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Aalt D J van Dijk

Full Text Available Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive. In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

Learning gene networks under SNP perturbations using eQTL datasets.

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Lingxue Zhang

2014-02-01

Full Text Available The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network

A network of genes, genetic disorders, and brain areas.

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Satoru Hayasaka

Full Text Available The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

HPV-16 L1 genes with inactivated negative RNA elements induce potent immune responses

International Nuclear Information System (INIS)

Rollman, Erik; Arnheim, Lisen; Collier, Brian; Oeberg, Daniel; Hall, Haakan; Klingstroem, Jonas; Dillner, Joakim; Pastrana, Diana V.; Buck, Chris B.; Hinkula, Jorma; Wahren, Britta; Schwartz, Stefan

2004-01-01

Introduction of point mutations in the 5' end of the human papillomavirus type 16 (HPV-16) L1 gene specifically inactivates negative regulatory RNA processing elements. DNA vaccination of C57Bl/6 mice with the mutated L1 gene resulted in improved immunogenicity for both neutralizing antibodies as well as for broad cellular immune responses. Previous reports on the activation of L1 by codon optimization may be explained by inactivation of the regulatory RNA elements. The modified HPV-16 L1 DNA that induced anti-HPV-16 immunity may be seen as a complementary approach to protein subunit immunization against papillomavirus

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes

OpenAIRE

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-01-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix–loop–helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks e...

Discovering disease-associated genes in weighted protein-protein interaction networks

Science.gov (United States)

Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

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...

Modular and coordinated expression of immune system regulatory and signaling components in the developing and adult nervous system.

Science.gov (United States)

Monzón-Sandoval, Jimena; Castillo-Morales, Atahualpa; Crampton, Sean; McKelvey, Laura; Nolan, Aoife; O'Keeffe, Gerard; Gutierrez, Humberto

2015-01-01

During development, the nervous system (NS) is assembled and sculpted through a concerted series of neurodevelopmental events orchestrated by a complex genetic programme. While neural-specific gene expression plays a critical part in this process, in recent years, a number of immune-related signaling and regulatory components have also been shown to play key physiological roles in the developing and adult NS. While the involvement of individual immune-related signaling components in neural functions may reflect their ubiquitous character, it may also reflect a much wider, as yet undescribed, genetic network of immune-related molecules acting as an intrinsic component of the neural-specific regulatory machinery that ultimately shapes the NS. In order to gain insights into the scale and wider functional organization of immune-related genetic networks in the NS, we examined the large scale pattern of expression of these genes in the brain. Our results show a highly significant correlated expression and transcriptional clustering among immune-related genes in the developing and adult brain, and this correlation was the highest in the brain when compared to muscle, liver, kidney and endothelial cells. We experimentally tested the regulatory clustering of immune system (IS) genes by using microarray expression profiling in cultures of dissociated neurons stimulated with the pro-inflammatory cytokine TNF-alpha, and found a highly significant enrichment of immune system-related genes among the resulting differentially expressed genes. Our findings strongly suggest a coherent recruitment of entire immune-related genetic regulatory modules by the neural-specific genetic programme that shapes the NS.

Gene expression in peripheral immune cells following cardioembolic stroke is sexually dimorphic.

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Boryana Stamova

Full Text Available Epidemiological studies suggest that sex has a role in the pathogenesis of cardioembolic stroke. Since stroke is a vascular disease, identifying sexually dimorphic gene expression changes in blood leukocytes can inform on sex-specific risk factors, response and outcome biology. We aimed to examine the sexually dimorphic immune response following cardioembolic stroke by studying the differential gene expression in peripheral white blood cells.Blood samples from patients with cardioembolic stroke were obtained at ≤3 hours (prior to treatment, 5 hours and 24 hours (after treatment after stroke onset (n = 23; 69 samples and compared with vascular risk factor controls without symptomatic vascular diseases (n = 23, 23 samples (ANCOVA, false discovery rate p≤0.05, |fold change| ≥1.2. mRNA levels were measured on whole-genome Affymetrix microarrays. There were more up-regulated than down-regulated genes in both sexes, and females had more differentially expressed genes than males following cardioembolic stroke. Female gene expression was associated with cell death and survival, cell-cell signaling and inflammation. Male gene expression was associated with cellular assembly, organization and compromise. Immune response pathways were over represented at ≤3, 5 and 24 h after stroke in female subjects but only at 24 h in males. Neutrophil-specific genes were differentially expressed at 3, 5 and 24 h in females but only at 5 h and 24 h in males.There are sexually dimorphic immune cell expression profiles following cardioembolic stroke. Future studies are needed to confirm the findings using qRT-PCR in an independent cohort, to determine how they relate to risk and outcome, and to compare to other causes of ischemic stroke.

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

Science.gov (United States)

Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

The Reconstruction and Analysis of Gene Regulatory Networks.

Science.gov (United States)

Zheng, Guangyong; Huang, Tao

2018-01-01

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

Science.gov (United States)

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

2017-06-16

The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis

Introduction of optical reporter gene into cancer and immune cells using lentiviral vector

International Nuclear Information System (INIS)

Min, Jung Joon; Le, Uyenchi N.; Moon, Sung Min; Heo, Young Jun; Song, Ho Chun; Bom, Hee Seung; Kim, Yeon Soo

2004-01-01

For some applications such as gene therapy or reporter gene imaging, a gene has to be introduced into the organism of interest. Adenoviral vectors are capable of transducing both replicating and non-dividing cells. The adenoviral vectors do not integrate their DNA into host DNA, but do lead to an immune response. Lentiviruses belong to the retrovirus family and are capable of infecting both dividing and non-dividing cells. The human immunodeficiency virus (HIV) is an example of a lentavirus. A disabled HIV virus has been developed and could be used for in vivo gene delivery. A portion of the viral genome which encodes for accessory proteins canbe deleted without affecting production of the vector and efficiency of infection. Lentiviral delivery into various rodent tissues shows sustained expression of the transgene of up to six months. Furthermore, there seems to be little or no immune response with these vectors. These lentiviral vectors hold significant promise for in vivo gene delivery. We constructed lentiviral vector encoding firefly luciferase (Fluc) and eGFP. Fluc-eGFP fusion gene was inserted into multiple cloning sites of pLentiM1.3 vector. Reporter gene (Fluc-eGFP) was designed to be driven by murine CMV promoter with enhanced efficacy of transgene expression as compared to human CMV promoter. We transfected pLenti1.3-Fluc into human cervix cancer cell line (HeLa) and murine T lymphocytes. We also constructed adenovirus encoding Fluc and transfected to HeLa and T cells. This LentiM1.3-Fluc was transfected into HeLa cells and murine T lymphocytes in vitro, showing consistent expression of eGFP under the fluorescence microscopy from the 2nd day of transfection. Firefly luciferase reporter gene was not expressed in immune cells when it is mediated by adenovirus. Lentivirus was validated as a useful vector for both immune and cancer cells

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

International Nuclear Information System (INIS)

Zu Yun-Xiao; Zhou Jie

2012-01-01

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. (geophysics, astronomy, and astrophysics)

Effects of FVIII immunity on hepatocyte and hematopoietic stem cell–directed gene therapy of murine hemophilia A

Science.gov (United States)

Lytle, Allison M; Brown, Harrison C; Paik, Na Yoon; Knight, Kristopher A; Wright, J Fraser; Spencer, H Trent; Doering, Christopher B

2016-01-01

Immune responses to coagulation factors VIII (FVIII) and IX (FIX) represent primary obstacles to hemophilia treatment. Previously, we showed that hematopoietic stem cell (HSC) retroviral gene therapy induces immune nonresponsiveness to FVIII in both naive and preimmunized murine hemophilia A settings. Liver-directed adeno-associated viral (AAV)-FIX vector gene transfer achieved similar results in preclinical hemophilia B models. However, as clinical immune responses to FVIII and FIX differ, we investigated the ability of liver-directed AAV-FVIII gene therapy to affect FVIII immunity in hemophilia A mice. Both FVIII naive and preimmunized mice were administered recombinant AAV8 encoding a liver-directed bioengineered FVIII expression cassette. Naive animals receiving high or mid-doses subsequently achieved near normal FVIII activity levels. However, challenge with adjuvant-free recombinant FVIII induced loss of FVIII activity and anti-FVIII antibodies in mid-dose, but not high-dose AAV or HSC lentiviral (LV) vector gene therapy cohorts. Furthermore, unlike what was shown previously for FIX gene transfer, AAV-FVIII administration to hemophilia A inhibitor mice conferred no effect on anti-FVIII antibody or inhibitory titers. These data suggest that functional differences exist in the immune modulation achieved to FVIII or FIX in hemophilia mice by gene therapy approaches incorporating liver-directed AAV vectors or HSC-directed LV. PMID:26909355

Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

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Ettore Mosca

2017-09-01

Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

Science.gov (United States)

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

An MHC class I immune evasion gene of Marek׳s disease virus.

Science.gov (United States)

Hearn, Cari; Preeyanon, Likit; Hunt, Henry D; York, Ian A

2015-01-15

Marek׳s disease virus (MDV) is a widespread α-herpesvirus of chickens that causes T cell tumors. Acute, but not latent, MDV infection has previously been shown to lead to downregulation of cell-surface MHC class I (Virology 282:198-205 (2001)), but the gene(s) involved have not been identified. Here we demonstrate that an MDV gene, MDV012, is capable of reducing surface expression of MHC class I on chicken cells. Co-expression of an MHC class I-binding peptide targeted to the endoplasmic reticulum (bypassing the requirement for the TAP peptide transporter) partially rescued MHC class I expression in the presence of MDV012, suggesting that MDV012 is a TAP-blocking MHC class I immune evasion protein. This is the first unique non-mammalian MHC class I immune evasion gene identified, and suggests that α-herpesviruses have conserved this function for at least 100 million years. Copyright © 2014 Elsevier Inc. All rights reserved.

Fused Regression for Multi-source Gene Regulatory Network Inference.

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Kari Y Lam

2016-12-01

Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

A systems biology approach reveals that tissue tropism to West Nile virus is regulated by antiviral genes and innate immune cellular processes.

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Mehul S Suthar

2013-02-01

Full Text Available The actions of the RIG-I like receptor (RLR and type I interferon (IFN signaling pathways are essential for a protective innate immune response against the emerging flavivirus West Nile virus (WNV. In mice lacking RLR or IFN signaling pathways, WNV exhibits enhanced tissue tropism, indicating that specific host factors of innate immune defense restrict WNV infection and dissemination in peripheral tissues. However, the immune mechanisms by which the RLR and IFN pathways coordinate and function to impart restriction of WNV infection are not well defined. Using a systems biology approach, we defined the host innate immune response signature and actions that restrict WNV tissue tropism. Transcriptional profiling and pathway modeling to compare WNV-infected permissive (spleen and nonpermissive (liver tissues showed high enrichment for inflammatory responses, including pattern recognition receptors and IFN signaling pathways, that define restriction of WNV replication in the liver. Assessment of infected livers from Mavs(-/- × Ifnar(-/- mice revealed the loss of expression of several key components within the natural killer (NK cell signaling pathway, including genes associated with NK cell activation, inflammatory cytokine production, and NK cell receptor signaling. In vivo analysis of hepatic immune cell infiltrates from WT mice demonstrated that WNV infection leads to an increase in NK cell numbers with enhanced proliferation, maturation, and effector action. In contrast, livers from Mavs(-/- × Ifnar(-/- infected mice displayed reduced immune cell infiltration, including a significant reduction in NK cell numbers. Analysis of cocultures of dendritic and NK cells revealed both cell-intrinsic and -extrinsic roles for the RLR and IFN signaling pathways to regulate NK cell effector activity. Taken together, these observations reveal a complex innate immune signaling network, regulated by the RLR and IFN signaling pathways, that drives tissue

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods

Science.gov (United States)

Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E.; Re, Matteo

2014-01-01

Objective In the context of “network medicine”, gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. Materials and methods We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. Results The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different “informativeness” embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Conclusions Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further

CRISPR loci reveal networks of gene exchange in archaea

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Brodt Avital

2011-12-01

Full Text Available Abstract Background CRISPR (Clustered, Regularly, Interspaced, Short, Palindromic Repeats loci provide prokaryotes with an adaptive immunity against viruses and other mobile genetic elements. CRISPR arrays can be transcribed and processed into small crRNA molecules, which are then used by the cell to target the foreign nucleic acid. Since spacers are accumulated by active CRISPR/Cas systems, the sequences of these spacers provide a record of the past "infection history" of the organism. Results Here we analyzed all currently known spacers present in archaeal genomes and identified their source by DNA similarity. While nearly 50% of archaeal spacers matched mobile genetic elements, such as plasmids or viruses, several others matched chromosomal genes of other organisms, primarily other archaea. Thus, networks of gene exchange between archaeal species were revealed by the spacer analysis, including many cases of inter-genus and inter-species gene transfer events. Spacers that recognize viral sequences tend to be located further away from the leader sequence, implying that there exists a selective pressure for their retention. Conclusions CRISPR spacers provide direct evidence for extensive gene exchange in archaea, especially within genera, and support the current dogma where the primary role of the CRISPR/Cas system is anti-viral and anti-plasmid defense. Open peer review This article was reviewed by: Profs. W. Ford Doolittle, John van der Oost, Christa Schleper (nominated by board member Prof. J Peter Gogarten

Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer.

Science.gov (United States)

Zhou, Xionghui; Liu, Juan

2014-01-01

Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer). In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B) means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis). Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene signature for

Population genomics of the immune evasion (var genes of Plasmodium falciparum.

Directory of Open Access Journals (Sweden)

Alyssa E Barry

2007-03-01

Full Text Available Var genes encode the major surface antigen (PfEMP1 of the blood stages of the human malaria parasite Plasmodium falciparum. Differential expression of up to 60 diverse var genes in each parasite genome underlies immune evasion. We compared the diversity of the DBLalpha domain of var genes sampled from 30 parasite isolates from a malaria endemic area of Papua New Guinea (PNG and 59 from widespread geographic origins (global. Overall, we obtained over 8,000 quality-controlled DBLalpha sequences. Within our sampling frame, the global population had a total of 895 distinct DBLalpha "types" and negligible overlap among repertoires. This indicated that var gene diversity on a global scale is so immense that many genomes would need to be sequenced to capture its true extent. In contrast, we found a much lower diversity in PNG of 185 DBLalpha types, with an average of approximately 7% overlap among repertoires. While we identify marked geographic structuring, nearly 40% of types identified in PNG were also found in samples from different countries showing a cosmopolitan distribution for much of the diversity. We also present evidence to suggest that recombination plays a key role in maintaining the unprecedented levels of polymorphism found in these immune evasion genes. This population genomic framework provides a cost effective molecular epidemiological tool to rapidly explore the geographic diversity of var genes.

Effects of FVIII immunity on hepatocyte and hematopoietic stem cell–directed gene therapy of murine hemophilia A

Directory of Open Access Journals (Sweden)

Allison M Lytle

2016-01-01

Full Text Available Immune responses to coagulation factors VIII (FVIII and IX (FIX represent primary obstacles to hemophilia treatment. Previously, we showed that hematopoietic stem cell (HSC retroviral gene therapy induces immune nonresponsiveness to FVIII in both naive and preimmunized murine hemophilia A settings. Liver-directed adeno-associated viral (AAV-FIX vector gene transfer achieved similar results in preclinical hemophilia B models. However, as clinical immune responses to FVIII and FIX differ, we investigated the ability of liver-directed AAV-FVIII gene therapy to affect FVIII immunity in hemophilia A mice. Both FVIII naive and preimmunized mice were administered recombinant AAV8 encoding a liver-directed bioengineered FVIII expression cassette. Naive animals receiving high or mid-doses subsequently achieved near normal FVIII activity levels. However, challenge with adjuvant-free recombinant FVIII induced loss of FVIII activity and anti-FVIII antibodies in mid-dose, but not high-dose AAV or HSC lentiviral (LV vector gene therapy cohorts. Furthermore, unlike what was shown previously for FIX gene transfer, AAV-FVIII administration to hemophilia A inhibitor mice conferred no effect on anti-FVIII antibody or inhibitory titers. These data suggest that functional differences exist in the immune modulation achieved to FVIII or FIX in hemophilia mice by gene therapy approaches incorporating liver-directed AAV vectors or HSC-directed LV.

Reveal genes functionally associated with ACADS by a network study.

Science.gov (United States)

Chen, Yulong; Su, Zhiguang

2015-09-15

Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

Gene expression profiling provides insights into the immune mechanism of Plutella xylostella midgut to microbial infection.

Science.gov (United States)

Lin, Junhan; Xia, Xiaofeng; Yu, Xiao-Qiang; Shen, Jinhong; Li, Yong; Lin, Hailan; Tang, Shanshan; Vasseur, Liette; You, Minsheng

2018-03-20

Insect gut immunity plays a key role in defense against microorganism infection. The knowledge of insect gut immunity has been obtained mostly from Drosophila melanogaster. Little is known about gut immunity in the diamondback moth, Plutella xylostella (L.), a pest destroying cruciferous crops worldwide. In this study, expressions of the immune-related genes in the midgut of P. xylostella orally infected with Staphylococcus aureus, Escherichia coli and Pichia pastoris were profiled by RNA-seq and qRT-PCR approaches. The results revealed that the Toll, IMD, JNK and JAK-STAT pathways and possibly the prophenoloxidase activation system in P. xylostella could be activated by oral infections, and moricins, gloverins and lysozyme2 might act as important effectors against microorganisms. Subsequent knock-down of IMD showed that this gene was involved in regulating the expression of down-stream genes in the IMD pathway. Our work indicates that the Toll, IMD, JNK and JAK-STAT pathways may synergistically modulate immune responses in the P. xylostella midgut, implying a complex and diverse immune system in the midgut of insects. Copyright © 2018 Elsevier B.V. All rights reserved.

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

Science.gov (United States)

Raza, Khalid; Alam, Mansaf

2016-10-01

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation.

Science.gov (United States)

Godec, Jernej; Tan, Yan; Liberzon, Arthur; Tamayo, Pablo; Bhattacharya, Sanchita; Butte, Atul J; Mesirov, Jill P; Haining, W Nicholas

2016-01-19

Gene-expression profiling has become a mainstay in immunology, but subtle changes in gene networks related to biological processes are hard to discern when comparing various datasets. For instance, conservation of the transcriptional response to sepsis in mouse models and human disease remains controversial. To improve transcriptional analysis in immunology, we created ImmuneSigDB: a manually annotated compendium of ∼5,000 gene-sets from diverse cell states, experimental manipulations, and genetic perturbations in immunology. Analysis using ImmuneSigDB identified signatures induced in activated myeloid cells and differentiating lymphocytes that were highly conserved between humans and mice. Sepsis triggered conserved patterns of gene expression in humans and mouse models. However, we also identified species-specific biological processes in the sepsis transcriptional response: although both species upregulated phagocytosis-related genes, a mitosis signature was specific to humans. ImmuneSigDB enables granular analysis of transcriptomic data to improve biological understanding of immune processes of the human and mouse immune systems. Copyright © 2016 Elsevier Inc. All rights reserved.

Integration of biological networks and gene expression data using Cytoscape

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Cline, M.S.; Smoot, M.; Cerami, E.

2007-01-01

of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules......Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context...... and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape....

Gene Expression Contributes to the Recent Evolution of Host Resistance in a Model Host Parasite System

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Brian K. Lohman

2017-09-01

Full Text Available Heritable population differences in immune gene expression following infection can reveal mechanisms of host immune evolution. We compared gene expression in infected and uninfected threespine stickleback (Gasterosteus aculeatus from two natural populations that differ in resistance to a native cestode parasite, Schistocephalus solidus. Genes in both the innate and adaptive immune system were differentially expressed as a function of host population, infection status, and their interaction. These genes were enriched for loci controlling immune functions known to differ between host populations or in response to infection. Coexpression network analysis identified two distinct processes contributing to resistance: parasite survival and suppression of growth. Comparing networks between populations showed resistant fish have a dynamic expression profile while susceptible fish are static. In summary, recent evolutionary divergence between two vertebrate populations has generated population-specific gene expression responses to parasite infection, affecting parasite establishment and growth.

Gene expression in brain and liver produced by three different regimens of alcohol consumption in mice: comparison with immune activation.

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Elizabeth Osterndorff-Kahanek

Full Text Available Chronically available alcohol escalates drinking in mice and a single injection of the immune activator lipopolysaccharide can mimic this effect and result in a persistent increase in alcohol consumption. We hypothesized that chronic alcohol drinking and lipopolysaccharide injections will produce some similar molecular changes that play a role in regulation of alcohol intake. We investigated the molecular mechanisms of chronic alcohol consumption or lipopolysaccharide insult by gene expression profiling in prefrontal cortex and liver of C57BL/6J mice. We identified similar patterns of transcriptional changes among four groups of animals, three consuming alcohol (vs water in different consumption tests and one injected with lipopolysaccharide (vs. vehicle. The three tests of alcohol consumption are the continuous chronic two bottle choice (Chronic, two bottle choice available every other day (Chronic Intermittent and limited access to one bottle of ethanol (Drinking in the Dark. Gene expression changes were more numerous and marked in liver than in prefrontal cortex for the alcohol treatments and similar in the two tissues for lipopolysaccharide. Many of the changes were unique to each treatment, but there was significant overlap in prefrontal cortex for Chronic-Chronic Intermittent and for Chronic Intermittent-lipopolysaccharide and in liver all pairs showed overlap. In silico cell-type analysis indicated that lipopolysaccharide had strongest effects on brain microglia and liver Kupffer cells. Pathway analysis detected a prefrontal cortex-based dopamine-related (PPP1R1B, DRD1, DRD2, FOSB, PDNY network that was highly over-represented in the Chronic Intermittent group, with several genes from the network being also regulated in the Chronic and lipopolysaccharide (but not Drinking in the Dark groups. Liver showed a CYP and GST centered metabolic network shared in part by all four treatments. We demonstrate common consequences of chronic alcohol

Comparative Assessment of Induced Immune Responses Following Intramuscular Immunization with Fusion and Cocktail of LeIF, LACK and TSA Genes Against Cutaneous Leishmaniasis in BALB/c Mice.

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Maspi, Nahid; Ghaffarifar, Fatemeh; Sharifi, Zohreh; Dalimi, Abdolhossein; Dayer, Mohammad Saaid

2018-02-01

In the present study, we evaluated induced immune responses following DNA vaccine containing cocktail or fusion of LeIF, LACK and TSA genes or each gene alone. Mice were injected with 100 µg of each plasmid containing the gene of insert, plasmid DNA alone as the first control group or phosphate buffer saline as the second control group. Then, cellular and humoral responses, lesion size were measured for all groups. All vaccinated mice induced Th1 immune responses against Leishmania characterized by higher IFN-γ and IgG2a levels compared with control groups (p < 0.05). In addition, IFN-γ levels increased in groups immunized with fusion and cocktail vaccines in comparison with LACK (p < 0.001) and LeIF (p < 0.01) groups after challenge. In addition, fusion and cocktail groups produced higher IgG2a values than groups vaccinated with a gene alone (p < 0.05). Lesion progression delayed for all immunized groups compared with control groups from 5th week post-infection (p < 0.05). Mean lesion size decreased in immunized mice with fusion DNA than three groups vaccinated with one gene alone (p < 0.05). While, lesion size decreased significantly in cocktail recipient group than LeIF recipient group (p < 0.05). There was no difference in lesion size between fusion and cocktail groups. Overall, immunized mice with cocktail and fusion vaccines showed stronger Th1 response by production of higher IFN-γ and IgG2a and showed smaller mean lesion size. Therefore, use of multiple antigens can improve induced immune responses by DNA vaccination.

Transcriptional delay stabilizes bistable gene networks.

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Gupta, Chinmaya; López, José Manuel; Ott, William; Josić, Krešimir; Bennett, Matthew R

2013-08-02

Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner.

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

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Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks.

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Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina

2017-11-28

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

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Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

2012-01-01

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

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

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Mostafavi, Sara; Morris, Quaid

2012-05-01

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

Effects of date palm fruit extracts on skin mucosal immunity, immune related genes expression and growth performance of common carp (Cyprinus carpio) fry.

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Hoseinifar, Seyed Hossein; Khalili, Mohsen; Rufchaei, Rudabeh; Raeisi, Mojtaba; Attar, Marzieh; Cordero, Héctor; Esteban, M Ángeles

2015-12-01

The aim of this study was to investigate the effects of date palm fruit extracts (DPFE) on skin mucosal immunity, immune related genes expression and growth performance of fry common carp (Cyprinus carpio). One hundred and twenty specimens (4.06 ± 0.13 g) were supplied and allocated into six aquaria; specimens in three aquaria were fed non-supplemented diet (control) while the fish in the other 3 aquaria were fed with DPFE at 200 ml kg(-1). At the end of feeding trial (8 weeks) skin mucus immune parameters (total immunoglobulins, lysozyme, protease and alkaline phosphatase activity) and immune related gene expression (tumor necrosis factor α [tnfa], lysozyme [ly] and interleukin-1-beta, [il1b]) in the head-kidney were studied. The results revealed that feeding carp fry with 200 ml kg(-1) DPFE remarkably elevated the three skin mucus immune parameters tested (P 0.05) compared to control fish (fed control diet). Furthermore, growth performance parameters were significantly improved in fry fed DPFE (P < 0.05). More studies are needed to understand different aspects of DPFE administration in fry mucosal immunity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Transcriptome analysis of Pacific white shrimp (Litopenaeus vannamei) challenged by Vibrio parahaemolyticus reveals unique immune-related genes.

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Qin, Zhendong; Babu, V Sarath; Wan, Quanyuan; Zhou, Meng; Liang, Risheng; Muhammad, Asim; Zhao, Lijuan; Li, Jun; Lan, Jiangfeng; Lin, Li

2018-06-01

Pacific white shrimp (Litopenaeus vannamei) is an important cultural species worldwide. However, Vibrio spp. infections have caused a great economic loss in Pacific white shrimp culture industry. The immune responses of Pacific white shrimp to the Vibrio spp. is not fully characterized. In this study, the transcriptomic profiles of L. vannamei hemocytes were explored by injecting with or without Vibrio parahaemolyticus. Totally, 42,632 high-quality unigenes were obtained from RNAseq data. Comparative genome analysis showed 2258 differentially expressed genes (DEGs) following the Vibrio challenge, including 1017 up-regulated and 1241 down-regulated genes. Eight DEGs were randomly selected for further validation by quantitative real-time RT-PCR (qRT-PCR) and the results showed that are consistent with the RNA-seq data. Due to the lack of predictable adaptive immunity, shrimps rely on an innate immune system to defend themselves against invading microbes by recognizing and clearing them through humoral and cellular immune responses. Here we focused our studies on the humoral immunity, five genes (SR, MNK, CTL3, GILT, and ALFP) were selected from the transcriptomic data, which were significantly up-regulated by V. parahaemolyticus infection. These genes were widely expressed in six different tissues and were up-regulated by both Gram negative bacteria (V. parahaemolyticus) and Gram positive bacteria (Staphylococcus aureus). To further extend our studies, we knock-down those five genes by dsRNA in L. vannamei and analyzed the functions of specific genes against V. parahaemolyticus and S. aureus by bacterial clearance analysis. We found that the ability of L. vannamei was significantly reduced in bacterial clearance when treated with those specific dsRNA. These results indicate that those five genes play essential roles in antibacterial immunity and have its specific functions against different types of pathogens. The obtained data will shed a new light on the immunity

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

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Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo

2014-06-01

In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both

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.

Subtle variation within conserved effector operon gene products contributes to T6SS-mediated killing and immunity.

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Alteri, Christopher J; Himpsl, Stephanie D; Zhu, Kevin; Hershey, Haley L; Musili, Ninette; Miller, Jessa E; Mobley, Harry L T

2017-11-01

Type VI secretion systems (T6SS) function to deliver lethal payloads into target cells. Many studies have shown that protection against a single, lethal T6SS effector protein requires a cognate antidote immunity protein, both of which are often encoded together in a two-gene operon. The T6SS and an effector-immunity pair is sufficient for both killing and immunity. HereIn this paper we describe a T6SS effector operon that differs from conventional effector-immunity pairs in that eight genes are necessary for lethal effector function, yet can be countered by a single immunity protein. In this study, we investigated the role that the PefE T6SS immunity protein plays in recognition between two strains harboring nearly identical effector operons. Interestingly, despite containing seven of eight identical effector proteins, the less conserved immunity proteins only provided protection against their native effectors, suggesting that specificity and recognition could be dependent on variation within an immunity protein and one effector gene product. The variable effector gene product, PefD, is encoded upstream from pefE, and displays toxic activity that can be countered by PefE independent of T6SS-activity. Interestingly, while the entire pef operon was necessary to exert toxic activity via the T6SS in P. mirabilis, production of PefD and PefE alone was unable to exert this effector activity. Chimeric PefE proteins constructed from two P. mirabilis strains were used to localize immunity function to three amino acids. A promiscuous immunity protein was created using site-directed mutagenesis to change these residues from one variant to another. These findings support the notion that subtle differences between conserved effectors are sufficient for T6SS-mediated kin discrimination and that PefD requires additional factors to function as a T6SS-dependent effector.

Gene expression profiles of immune-regulatory genes in whole blood of cattle with a subclinical infection of Mycobacterium avium subsp. paratuberculosis.

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Hyun-Eui Park

Full Text Available Johne's disease is a chronic wasting disease of ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP, resulting in inflammation of intestines and persistent diarrhea. The initial host response against MAP infections is mainly regulated by the Th1 response, which is characterized by the production of IFN-γ. With the progression of disease, MAP can survive in the host through the evasion of the host's immune response by manipulating the host immune response. However, the host response during subclinical phases has not been fully understood. Immune regulatory genes, including Th17-derived cytokines, interferon regulatory factors, and calcium signaling-associated genes, are hypothesized to play an important role during subclinical phases of Johne's disease. Therefore, the present study was conducted to analyze the expression profiles of immune regulatory genes during MAP infection in whole blood. Different expression patterns of genes were identified depending on the infection stages. Downregulation of IL-17A, IL-17F, IL-22, IL-26, HMGB1, and IRF4 and upregulation of PIP5K1C indicate suppression of the Th1 response due to MAP infection and loss of granuloma integrity. In addition, increased expression of IRF5 and IRF7 suggest activation of IFN-α/β signaling during subclinical stages, which induced indoleamine 2,3-dioxygenase mediated depletion of tryptophan metabolism. Increased expression of CORO1A indicate modulation of calcium signaling, which enhanced the survival of MAP. Taken together, distinct host gene expression induced by MAP infection indicates enhanced survival of MAP during subclinical stages.

Complement C3 gene: Expression characterization and innate immune response in razor clam Sinonovacula constricta.

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Peng, Maoxiao; Niu, Donghong; Wang, Fei; Chen, Zhiyi; Li, Jiale

2016-08-01

Complement component 3 (C3) is central to the complement system, playing an important role in immune defense, immune regulation and immune pathology. Several C3 genes have been characterized in invertebrates but very few in shellfish. The C3 gene was identified from the razor clam Sinonovacula constricta, referred to here as Sc-C3. It was found to be highly homologous with the C3 gene of Ruditapes decussatus. All eight model motifs of the C3 gene were found to be included in the thiolester bond and the C345C region. Sc-C3 was widely expressed in all healthy tissues with expression being highest in hemolymph. A significant difference in expression was revealed at the umbo larvae development stage. The expression of Sc-C3 was highly regulated in the hemolymph and liver, with a distinct response pattern being noted after a challenge with Micrococcus lysodeikticus and Vibrio parahemolyticus. It is therefore suggested that a complicated and unique response pathway may be present in S. constricta. Further, serum of S. constricta containing Sc-C3 was extracted. This was activated by LPS or bacterium for verification for function. The more obvious immune function of Sc-C3 was described as an effective membrane rupture in hemocyte cells of rabbit, V. parahemolyticus and Vibrio anguillarum. Thus, Sc-C3 plays an essential role in the immune defense of S. constricta. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identification of potential crucial genes associated with steroid-induced necrosis of femoral head based on gene expression profile.

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Lin, Zhe; Lin, Yongsheng

2017-09-05

The aim of this study was to explore potential crucial genes associated with the steroid-induced necrosis of femoral head (SINFH) and to provide valid biological information for further investigation of SINFH. Gene expression profile of GSE26316, generated from 3 SINFH rat samples and 3 normal rat samples were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using LIMMA package. After functional enrichment analyses of DEGs, protein-protein interaction (PPI) network and sub-PPI network analyses were conducted based on the STRING database and cytoscape. In total, 59 up-regulated DEGs and 156 downregulated DEGs were identified. The up-regulated DEGs were mainly involved in functions about immunity (e.g. Fcer1A and Il7R), and the downregulated DEGs were mainly enriched in muscle system process (e.g. Tnni2, Mylpf and Myl1). The PPI network of DEGs consisted of 123 nodes and 300 interactions. Tnni2, Mylpf, and Myl1 were the top 3 outstanding genes based on both subgraph centrality and degree centrality evaluation. These three genes interacted with each other in the network. Furthermore, the significant network module was composed of 22 downregulated genes (e.g. Tnni2, Mylpf and Myl1). These genes were mainly enriched in functions like muscle system process. The DEGs related to the regulation of immune system process (e.g. Fcer1A and Il7R), and DEGs correlated with muscle system process (e.g. Tnni2, Mylpf and Myl1) may be closely associated with the progress of SINFH, which is still needed to be confirmed by experiments. Copyright © 2017 Elsevier B.V. All rights reserved.

Forager bees (Apis mellifera) highly express immune and detoxification genes in tissues associated with nectar processing.

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Vannette, Rachel L; Mohamed, Abbas; Johnson, Brian R

2015-11-09

Pollinators, including honey bees, routinely encounter potentially harmful microorganisms and phytochemicals during foraging. However, the mechanisms by which honey bees manage these potential threats are poorly understood. In this study, we examine the expression of antimicrobial, immune and detoxification genes in Apis mellifera and compare between forager and nurse bees using tissue-specific RNA-seq and qPCR. Our analysis revealed extensive tissue-specific expression of antimicrobial, immune signaling, and detoxification genes. Variation in gene expression between worker stages was pronounced in the mandibular and hypopharyngeal gland (HPG), where foragers were enriched in transcripts that encode antimicrobial peptides (AMPs) and immune response. Additionally, forager HPGs and mandibular glands were enriched in transcripts encoding detoxification enzymes, including some associated with xenobiotic metabolism. Using qPCR on an independent dataset, we verified differential expression of three AMP and three P450 genes between foragers and nurses. High expression of AMP genes in nectar-processing tissues suggests that these peptides may contribute to antimicrobial properties of honey or to honey bee defense against environmentally-acquired microorganisms. Together, these results suggest that worker role and tissue-specific expression of AMPs, and immune and detoxification enzymes may contribute to defense against microorganisms and xenobiotic compounds acquired while foraging.

High amino acid diversity and positive selection at a putative coral immunity gene (tachylectin-2

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Hellberg Michael E

2010-05-01

Full Text Available Abstract Background Genes involved in immune functions, including pathogen recognition and the activation of innate defense pathways, are among the most genetically variable known, and the proteins that they encode are often characterized by high rates of amino acid substitutions, a hallmark of positive selection. The high levels of variation characteristic of immunity genes make them useful tools for conservation genetics. To date, highly variable immunity genes have yet to be found in corals, keystone organisms of the world's most diverse marine ecosystem, the coral reef. Here, we examine variation in and selection on a putative innate immunity gene from Oculina, a coral genus previously used as a model for studies of coral disease and bleaching. Results In a survey of 244 Oculina alleles, we find high nonsynonymous variation and a signature of positive selection, consistent with a putative role in immunity. Using computational protein structure prediction, we generate a structural model of the Oculina protein that closely matches the known structure of tachylectin-2 from the Japanese horseshoe crab (Tachypleus tridentatus, a protein with demonstrated function in microbial recognition and agglutination. We also demonstrate that at least three other genera of anthozoan cnidarians (Acropora, Montastrea and Nematostella possess proteins structurally similar to tachylectin-2. Conclusions Taken together, the evidence of high amino acid diversity, positive selection and structural correspondence to the horseshoe crab tachylectin-2 suggests that this protein is 1 part of Oculina's innate immunity repertoire, and 2 evolving adaptively, possibly under selective pressure from coral-associated microorganisms. Tachylectin-2 may serve as a candidate locus to screen coral populations for their capacity to respond adaptively to future environmental change.

Network topologies and dynamics leading to endotoxin tolerance and priming in innate immune cells.

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Yan Fu

Full Text Available The innate immune system, acting as the first line of host defense, senses and adapts to foreign challenges through complex intracellular and intercellular signaling networks. Endotoxin tolerance and priming elicited by macrophages are classic examples of the complex adaptation of innate immune cells. Upon repetitive exposures to different doses of bacterial endotoxin (lipopolysaccharide or other stimulants, macrophages show either suppressed or augmented inflammatory responses compared to a single exposure to the stimulant. Endotoxin tolerance and priming are critically involved in both immune homeostasis and the pathogenesis of diverse inflammatory diseases. However, the underlying molecular mechanisms are not well understood. By means of a computational search through the parameter space of a coarse-grained three-node network with a two-stage Metropolis sampling approach, we enumerated all the network topologies that can generate priming or tolerance. We discovered three major mechanisms for priming (pathway synergy, suppressor deactivation, activator induction and one for tolerance (inhibitor persistence. These results not only explain existing experimental observations, but also reveal intriguing test scenarios for future experimental studies to clarify mechanisms of endotoxin priming and tolerance.

Effects of excretory/secretory products from Anisakis simplex (Nematoda) on immune gene expression in rainbow trout (Oncorhynchus mykiss)

DEFF Research Database (Denmark)

Bahlool, Qusay Zuhair Mohammad; Skovgaard, Alf; Kania, Per Walter

2013-01-01

Excretory/secretory (ES) products are molecules produced by parasitic nematodes, including larval Anisakis simplex, a parasite occurring in numerous marine fish hosts. The effects of these substances on host physiology have not been fully described. The present work elucidates the influence of ES...... substances on the fish immune system by measuring immune gene expression in spleen and liver of rainbow trout (Oncorhynchus mykiss) injected intraperitoneally with ES products isolated from A. simplex third stage larvae. The overall gene expression profile of exposed fish showed a generalized down....... This type of hydrolytic enzyme activity may play a role in nematode penetration of host tissue. In addition, based on the notion that A. simplex ES products may have an immune-depressive effect (by minimizing immune gene expression) it could also be suggested that worm enzymes directly target host immune...

Flg22-Triggered Immunity Negatively Regulates Key BR Biosynthetic Genes.

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Jiménez-Góngora, Tamara; Kim, Seong-Ki; Lozano-Durán, Rosa; Zipfel, Cyril

2015-01-01

In plants, activation of growth and activation of immunity are opposing processes that define a trade-off. In the past few years, the growth-promoting hormones brassinosteroids (BR) have emerged as negative regulators of pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI), promoting growth at the expense of defense. The crosstalk between BR and PTI signaling was described as negative and unidirectional, since activation of PTI does not affect several analyzed steps in the BR signaling pathway. In this work, we describe that activation of PTI by the bacterial PAMP flg22 results in the reduced expression of BR biosynthetic genes. This effect does not require BR perception or signaling, and occurs within 15 min of flg22 treatment. Since the described PTI-induced repression of gene expression may result in a reduction in BR biosynthesis, the crosstalk between PTI and BR could actually be negative and bidirectional, a possibility that should be taken into account when considering the interaction between these two pathways.

Paper-based synthetic gene networks.

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Pardee, Keith; Green, Alexander A; Ferrante, Tom; Cameron, D Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J

2014-11-06

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

Paper-based Synthetic Gene Networks

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Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

2014-01-01

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks.

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Saik, Olga V; Demenkov, Pavel S; Ivanisenko, Timofey V; Bragina, Elena Yu; Freidin, Maxim B; Goncharova, Irina A; Dosenko, Victor E; Zolotareva, Olga I; Hofestaedt, Ralf; Lavrik, Inna N; Rogaev, Evgeny I; Ivanisenko, Vladimir A

2018-02-13

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in

Expression of immune-response genes in lepidopteran host is suppressed by venom from an endoparasitoid, Pteromalus puparum

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Fang Qi

2010-09-01

Full Text Available Abstract Background The relationships between parasitoids and their insect hosts have attracted attention at two levels. First, the basic biology of host-parasitoid interactions is of fundamental interest. Second, parasitoids are widely used as biological control agents in sustainable agricultural programs. Females of the gregarious endoparasitoid Pteromalus puparum (Hymenoptera: Pteromalidae inject venom along with eggs into their hosts. P. puparum does not inject polydnaviruses during oviposition. For this reason, P. puparum and its pupal host, the small white butterfly Pieris rapae (Lepidoptera: Pieridae, comprise an excellent model system for studying the influence of an endoparasitoid venom on the biology of the pupal host. P. puparum venom suppresses the immunity of its host, although the suppressive mechanisms are not fully understood. In this study, we tested our hypothesis that P. puparum venom influences host gene expression in the two main immunity-conferring tissues, hemocytes and fat body. Results At 1 h post-venom injection, we recorded significant decreases in transcript levels of 217 EST clones (revealing 113 genes identified in silico, including 62 unknown contigs derived from forward subtractive libraries of host hemocytes and in transcript levels of 288 EST clones (221 genes identified in silico, including 123 unknown contigs from libraries of host fat body. These genes are related to insect immune response, cytoskeleton, cell cycle and apoptosis, metabolism, transport, stress response and transcriptional and translational regulation. We verified the reliability of the suppression subtractive hybridization (SSH data with semi-quantitative RT-PCR analysis of a set of randomly selected genes. This analysis showed that most of the selected genes were down-regulated after venom injection. Conclusions Our findings support our hypothesis that P. puparum venom influences gene expression in host hemocytes and fat body. Specifically

Methylation and Expression of Immune and Inflammatory Genes in the Offspring of Bariatric Bypass Surgery Patients

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Frédéric Guénard

2013-01-01

Full Text Available Background. Maternal obesity, excess weight gain and overnutrition during pregnancy increase risks of obesity, type 2 diabetes mellitus, and cardiovascular disease in the offspring. Maternal biliopancreatic diversion is an effective treatment for severe obesity and is beneficial for offspring born after maternal surgery (AMS. These offspring exhibit lower severe obesity prevalence and improved cardiometabolic risk factors including inflammatory marker compared to siblings born before maternal surgery (BMS. Objective. To assess relationships between maternal bariatric surgery and the methylation/expression of genes involved in the immune and inflammatory pathways. Methods. A differential gene methylation analysis was conducted in a sibling cohort of 25 BMS and 25 AMS offspring from 20 mothers. Following differential gene expression analysis (23 BMS and 23 AMS, pathway analysis was conducted. Correlations between gene methylation/expression and circulating inflammatory markers were computed. Results. Five immune and inflammatory pathways with significant overrepresentation of both differential gene methylation and expression were identified. In the IL-8 pathway, gene methylation correlated with both gene expression and plasma C-reactive protein levels. Conclusion. These results suggest that improvements in cardiometabolic risk markers in AMS compared to BMS offspring may be mediated through differential methylation of genes involved in immune and inflammatory pathways.

Influence of Immune Responses in Gene/Stem Cell Therapies for Muscular Dystrophies

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Andrea Farini

2014-01-01

Full Text Available Muscular dystrophies (MDs are a heterogeneous group of diseases, caused by mutations in different components of sarcolemma, extracellular matrix, or enzymes. Inflammation and innate or adaptive immune response activation are prominent features of MDs. Various therapies under development are directed toward rescuing the dystrophic muscle damage using gene transfer or cell therapy. Here we discussed current knowledge about involvement of immune system responses to experimental therapies in MDs.

Inferring time-varying network topologies from gene expression data.

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Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

Inference of cancer-specific gene regulatory networks using soft computing rules.

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Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Identification of human disease genes from interactome network using graphlet interaction.

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Xiao-Dong Wang

Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

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Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

2014-01-01

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

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Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

2018-03-01

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

A flood-based information flow analysis and network minimization method for gene regulatory networks.

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Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

2013-04-24

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

The quantitative basis of the Arabidopsis innate immune system to endemic pathogens depends on pathogen genetics

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Corwin, Jason A; Copeland, Daniel; Feusier, Julie

2016-01-01

The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabido......The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used....... cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence...... genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance...

System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

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Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

2018-01-01

Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

Human leukocyte antigen and cytokine receptor gene polymorphisms associated with heterogeneous immune responses to mumps viral vaccine.

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Ovsyannikova, Inna G; Jacobson, Robert M; Dhiman, Neelam; Vierkant, Robert A; Pankratz, V Shane; Poland, Gregory A

2008-05-01

Mumps outbreaks continue to occur throughout the world, including in highly vaccinated populations. Vaccination against mumps has been successful; however, humoral and cellular immune responses to mumps vaccines vary significantly from person to person. We set out to assess whether HLA and cytokine gene polymorphisms are associated with variations in the immune response to mumps viral vaccine. To identify genetic factors that might contribute to variations in mumps vaccine-induced immune responses, we performed HLA genotyping in a group of 346 healthy schoolchildren (12-18 years of age) who previously received 2 doses of live mumps vaccine. Single-nucleotide polymorphisms (minor allele frequency of >5%) in cytokine and cytokine receptor genes were genotyped for a subset of 118 children. Median values for mumps-specific antibody titers and lymphoproliferative stimulation indices were 729 IU/mL and 4.8, respectively. Girls demonstrated significantly higher mumps antibody titers than boys, indicating gender-linked genetic differences in humoral immune response. Significant associations were found between the HLA-DQB1*0303 alleles and lower mumps-specific antibody titers. An interesting finding was the association of several HLA class II alleles with mumps-specific lymphoproliferation. Alleles of the DRB1 (*0101, *0301, *0801, *1001, *1201, and *1302), DQA1 (*0101, *0105, *0401, and *0501), and DQB1 (*0201, *0402, and *0501) loci were associated with significant variations in lymphoproliferative immune responses to mumps vaccine. Additional associations were observed with single-nucleotide polymorphisms in the interleukin-10RA, interleukin-12RB1, and interleukin-12RB2 cytokine receptor genes. Minor alleles for 4 single-nucleotide polymorphisms within interleukin-10RA and interleukin-12RB genes were associated with variations in humoral and cellular immune responses to mumps vaccination. These data suggest the important role of HLA and immunoregulatory cytokine receptor

A cascade reaction network mimicking the basic functional steps of adaptive immune response.

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Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

2015-10-01

Biological systems use complex 'information-processing cores' composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS that we call an adaptive immune response simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system that responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner that is superficially similar to the most basic responses of the vertebrate AIS, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices.

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

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Wang, Yi Kan; Hurley, Daniel G; Schnell, Santiago; Print, Cristin G; Crampin, Edmund J

2013-01-01

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

A hybrid network-based method for the detection of disease-related genes

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Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

2018-02-01

Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

Kinetic parametric estimation in animal PET molecular imaging based on artificial immune network

International Nuclear Information System (INIS)

Chen Yuting; Ding Hong; Lu Rui; Huang Hongbo; Liu Li

2011-01-01

Objective: To develop an accurate,reliable method without the need of initialization in animal PET modeling for estimation of the tracer kinetic parameters based on the artificial immune network. Methods: The hepatic and left ventricular time activity curves (TACs) were obtained by drawing ROIs of liver tissue and left ventricle on dynamic 18 F-FDG PET imaging of small mice. Meanwhile, the blood TAC was analyzed by sampling the tail vein blood at different time points after injection. The artificial immune network for parametric optimization of pharmacokinetics (PKAIN) was adapted to estimate the model parameters and the metabolic rate of glucose (K i ) was calculated. Results: TACs of liver,left ventricle and tail vein blood were obtained.Based on the artificial immune network, K i in 3 mice was estimated as 0.0024, 0.0417 and 0.0047, respectively. The average weighted residual sum of squares of the output model generated by PKAIN was less than 0.0745 with a maximum standard deviation of 0.0084, which indicated that the proposed PKAIN method can provide accurate and reliable parametric estimation. Conclusion: The PKAIN method could provide accurate and reliable tracer kinetic modeling in animal PET imaging without the need of initialization of model parameters. (authors)

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon

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Satoru Koda

2017-11-01

Full Text Available We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX model with a group smoothly clipped absolute deviation (SCAD method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

Autoselection of cytoplasmic yeast virus like elements encoding toxin/antitoxin systems involves a nuclear barrier for immunity gene expression.

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Kast, Alene; Voges, Raphael; Schroth, Michael; Schaffrath, Raffael; Klassen, Roland; Meinhardt, Friedhelm

2015-05-01

Cytoplasmic virus like elements (VLEs) from Kluyveromyces lactis (Kl), Pichia acaciae (Pa) and Debaryomyces robertsiae (Dr) are extremely A/T-rich (>75%) and encode toxic anticodon nucleases (ACNases) along with specific immunity proteins. Here we show that nuclear, not cytoplasmic expression of either immunity gene (PaORF4, KlORF3 or DrORF5) results in transcript fragmentation and is insufficient to establish immunity to the cognate ACNase. Since rapid amplification of 3' ends (RACE) as well as linker ligation of immunity transcripts expressed in the nucleus revealed polyadenylation to occur along with fragmentation, ORF-internal poly(A) site cleavage due to the high A/T content is likely to prevent functional expression of the immunity genes. Consistently, lowering the A/T content of PaORF4 to 55% and KlORF3 to 46% by gene synthesis entirely prevented transcript cleavage and permitted functional nuclear expression leading to full immunity against the respective ACNase toxin. Consistent with a specific adaptation of the immunity proteins to the cognate ACNases, cross-immunity to non-cognate ACNases is neither conferred by PaOrf4 nor KlOrf3. Thus, the high A/T content of cytoplasmic VLEs minimizes the potential of functional nuclear recruitment of VLE encoded genes, in particular those involved in autoselection of the VLEs via a toxin/antitoxin principle.

Autoselection of cytoplasmic yeast virus like elements encoding toxin/antitoxin systems involves a nuclear barrier for immunity gene expression.

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Alene Kast

2015-05-01

Full Text Available Cytoplasmic virus like elements (VLEs from Kluyveromyces lactis (Kl, Pichia acaciae (Pa and Debaryomyces robertsiae (Dr are extremely A/T-rich (>75% and encode toxic anticodon nucleases (ACNases along with specific immunity proteins. Here we show that nuclear, not cytoplasmic expression of either immunity gene (PaORF4, KlORF3 or DrORF5 results in transcript fragmentation and is insufficient to establish immunity to the cognate ACNase. Since rapid amplification of 3' ends (RACE as well as linker ligation of immunity transcripts expressed in the nucleus revealed polyadenylation to occur along with fragmentation, ORF-internal poly(A site cleavage due to the high A/T content is likely to prevent functional expression of the immunity genes. Consistently, lowering the A/T content of PaORF4 to 55% and KlORF3 to 46% by gene synthesis entirely prevented transcript cleavage and permitted functional nuclear expression leading to full immunity against the respective ACNase toxin. Consistent with a specific adaptation of the immunity proteins to the cognate ACNases, cross-immunity to non-cognate ACNases is neither conferred by PaOrf4 nor KlOrf3. Thus, the high A/T content of cytoplasmic VLEs minimizes the potential of functional nuclear recruitment of VLE encoded genes, in particular those involved in autoselection of the VLEs via a toxin/antitoxin principle.

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

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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.

Immune networks: multitasking capabilities near saturation

International Nuclear Information System (INIS)

Agliari, E; Annibale, A; Barra, A; Coolen, A C C; Tantari, D

2013-01-01

Pattern-diluted associative networks were recently introduced as models for the immune system, with nodes representing T-lymphocytes and stored patterns representing signalling protocols between T- and B-lymphocytes. It was shown earlier that in the regime of extreme pattern dilution, a system with N T T-lymphocytes can manage a number N B =O(N T δ ) of B-lymphocytes simultaneously, with δ B = αN T , with a high degree of pattern dilution, in agreement with immunological findings. We use graph theory and statistical mechanical analysis based on replica methods to show that in the finite-connectivity regime, where each T-lymphocyte interacts with a finite number of B-lymphocytes as N T → ∞, the T-lymphocytes can coordinate effective immune responses to an extensive number of distinct antigen invasions in parallel. As α increases, the system eventually undergoes a second order transition to a phase with clonal cross-talk interference, where the system’s performance degrades gracefully. Mathematically, the model is equivalent to a spin system on a finitely connected graph with many short loops, so one would expect the available analytical methods, which all assume locally tree-like graphs, to fail. Yet it turns out to be solvable. Our results are supported by numerical simulations. (paper)

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.

Inferring Phylogenetic Networks from Gene Order Data

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Alexey Anatolievich Morozov

2013-01-01

Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.

Preterm Birth Reduces Nutrient Absorption With Limited Effect on Immune Gene Expression and Gut Colonization in Pigs

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Østergaard, Mette V; Cilieborg, Malene S.; Skovgaard, Kerstin

2015-01-01

The primary risk factors for necrotizing enterocolitis (NEC) are preterm birth, enteral feeding, and gut colonization. It is unclear whether feeding and colonization induce excessive expression of immune genes that lead to NEC. Using a pig model, we hypothesized that reduced gestational age would...... upregulate immune-related genes and cause bacterial imbalance after birth. Preterm (85%-92% gestation, n = 53) and near-term (95%-99% gestation, n = 69) pigs were delivered by cesarean section and euthanized at birth or after 2 days of infant formula or bovine colostrum feeding. At birth, preterm delivery...... reduced 5 of 30 intestinal genes related to nutrient absorption and innate immunity, relative to near-term pigs, whereas 2 genes were upregulated. Preterm birth also reduced ex vivo intestinal glucose and leucine uptake (40%-50%), but failed to increase cytokine secretions from intestinal explants...

Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

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Xiaosheng Wang

2010-03-01

Full Text Available Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Ni-MH batteries state-of-charge prediction based on immune evolutionary network

International Nuclear Information System (INIS)

Cheng Bo; Zhou Yanlu; Zhang Jiexin; Wang Junping; Cao Binggang

2009-01-01

Based on clonal selection theory, an improved immune evolutionary strategy is presented. Compared with conventional evolutionary strategy algorithm (CESA) and immune monoclonal strategy algorithm (IMSA), experimental results show that the proposed algorithm is of high efficiency and can effectively prevent premature convergence. A three-layer feed-forward neural network is presented to predict state-of-charge (SOC) of Ni-MH batteries. Initially, partial least square regression (PLSR) is used to select input variables. Then, five variables, battery terminal voltage, voltage derivative, voltage second derivative, discharge current and battery temperature, are selected as the inputs of NN. In order to overcome the weakness of BP algorithm, the new algorithm is adopted to train weights. Finally, under the state of dynamic power cycle, the predicted SOC and the actual SOC are compared to verify the proposed neural network with acceptable accuracy (5%).

Tissue-specific functional networks for prioritizing phenotype and disease genes.

Directory of Open Access Journals (Sweden)

Yuanfang Guan

Full Text Available Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as "functionality" and "functional relationships" are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

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

Science.gov (United States)

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

A pathway-based network analysis of hypertension-related genes

Science.gov (United States)

Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng

2016-02-01

Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.

Metabolic gene expression changes in astrocytes in Multiple Sclerosis cerebral cortex are indicative of immune-mediated signaling

KAUST Repository

Zeis, T.

2015-04-01

Emerging as an important correlate of neurological dysfunction in Multiple Sclerosis (MS), extended focal and diffuse gray matter abnormalities have been found and linked to clinical manifestations such as seizures, fatigue and cognitive dysfunction. To investigate possible underlying mechanisms we analyzed the molecular alterations in histopathological normal appearing cortical gray matter (NAGM) in MS. By performing a differential gene expression analysis of NAGM of control and MS cases we identified reduced transcription of astrocyte specific genes involved in the astrocyte–neuron lactate shuttle (ANLS) and the glutamate–glutamine cycle (GGC). Additional quantitative immunohistochemical analysis demonstrating a CX43 loss in MS NAGM confirmed a crucial involvement of astrocytes and emphasizes their importance in MS pathogenesis. Concurrently, a Toll-like/IL-1β signaling expression signature was detected in MS NAGM, indicating that immune-related signaling might be responsible for the downregulation of ANLS and GGC gene expression in MS NAGM. Indeed, challenging astrocytes with immune stimuli such as IL-1β and LPS reduced their ANLS and GGC gene expression in vitro. The detected upregulation of IL1B in MS NAGM suggests inflammasome priming. For this reason, astrocyte cultures were treated with ATP and ATP/LPS as for inflammasome activation. This treatment led to a reduction of ANLS and GGC gene expression in a comparable manner. To investigate potential sources for ANLS and GGC downregulation in MS NAGM, we first performed an adjuvant-driven stimulation of the peripheral immune system in C57Bl/6 mice in vivo. This led to similar gene expression changes in spinal cord demonstrating that peripheral immune signals might be one source for astrocytic gene expression changes in the brain. IL1B upregulation in MS NAGM itself points to a possible endogenous signaling process leading to ANLS and GGC downregulation. This is supported by our findings that, among others

Uncovering co-expression gene network modules regulating fruit acidity in diverse apples.

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Bai, Yang; Dougherty, Laura; Cheng, Lailiang; Zhong, Gan-Yuan; Xu, Kenong

2015-08-16

Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates. Guided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background.

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

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Marco Grimaldi

Full Text Available RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.

A Genome-wide Gene-Expression Analysis and Database in Transgenic Mice during Development of Amyloid or Tau Pathology

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Mar Matarin

2015-02-01

Full Text Available We provide microarray data comparing genome-wide differential expression and pathology throughout life in four lines of “amyloid” transgenic mice (mutant human APP, PSEN1, or APP/PSEN1 and “TAU” transgenic mice (mutant human MAPT gene. Microarray data were validated by qPCR and by comparison to human studies, including genome-wide association study (GWAS hits. Immune gene expression correlated tightly with plaques whereas synaptic genes correlated negatively with neurofibrillary tangles. Network analysis of immune gene modules revealed six hub genes in hippocampus of amyloid mice, four in common with cortex. The hippocampal network in TAU mice was similar except that Trem2 had hub status only in amyloid mice. The cortical network of TAU mice was entirely different with more hub genes and few in common with the other networks, suggesting reasons for specificity of cortical dysfunction in FTDP17. This Resource opens up many areas for investigation. All data are available and searchable at http://www.mouseac.org.

Frequency of single nucleotide polymorphisms of some immune response genes in a population sample from São Paulo, Brazil

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Léa Campos de Oliveira

2011-09-01

Full Text Available Objective: To present the frequency of single nucleotide polymorphismsof a few immune response genes in a population sample from SãoPaulo City (SP, Brazil. Methods: Data on allele frequencies ofknown polymorphisms of innate and acquired immunity genes werepresented, the majority with proven impact on gene function. Datawere gathered from a sample of healthy individuals, non-HLA identicalsiblings of bone marrow transplant recipients from the Hospital dasClínicas da Faculdade de Medicina da Universidade de São Paulo,obtained between 1998 and 2005. The number of samples variedfor each single nucleotide polymorphism analyzed by polymerasechain reaction followed by restriction enzyme cleavage. Results:Allele and genotype distribution of 41 different gene polymorphisms,mostly cytokines, but also including other immune response genes,were presented. Conclusion: We believe that the data presentedhere can be of great value for case-control studies, to define whichpolymorphisms are present in biologically relevant frequencies and toassess targets for therapeutic intervention in polygenic diseases witha component of immune and inflammatory responses.

Hybrid stochastic simplifications for multiscale gene networks

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Debussche Arnaud

2009-09-01

Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

De Novo assembly of the Japanese flounder (Paralichthys olivaceus spleen transcriptome to identify putative genes involved in immunity.

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Lin Huang

Full Text Available Japanese flounder (Paralichthys olivaceus is an economically important marine fish in Asia and has suffered from disease outbreaks caused by various pathogens, which requires more information for immune relevant genes on genome background. However, genomic and transcriptomic data for Japanese flounder remain scarce, which limits studies on the immune system of this species. In this study, we characterized the Japanese flounder spleen transcriptome using an Illumina paired-end sequencing platform to identify putative genes involved in immunity.A cDNA library from the spleen of P. olivaceus was constructed and randomly sequenced using an Illumina technique. The removal of low quality reads generated 12,196,968 trimmed reads, which assembled into 96,627 unigenes. A total of 21,391 unigenes (22.14% were annotated in the NCBI Nr database, and only 1.1% of the BLASTx top-hits matched P. olivaceus protein sequences. Approximately 12,503 (58.45% unigenes were categorized into three Gene Ontology groups, 19,547 (91.38% were classified into 26 Cluster of Orthologous Groups, and 10,649 (49.78% were assigned to six Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, 40,928 putative simple sequence repeats and 47, 362 putative single nucleotide polymorphisms were identified. Importantly, we identified 1,563 putative immune-associated unigenes that mapped to 15 immune signaling pathways.The P. olivaceus transciptome data provides a rich source to discover and identify new genes, and the immune-relevant sequences identified here will facilitate our understanding of the mechanisms involved in the immune response. Furthermore, the plentiful potential SSRs and SNPs found in this study are important resources with respect to future development of a linkage map or marker assisted breeding programs for the flounder.

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

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Reddy, Anupama; Huang, C Chris; Liu, Huiqing; Delisi, Charles; Nevalainen, Marja T; Szalma, Sandor; Bhanot, Gyan

2010-01-01

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily

Using an agent-based model to analyze the dynamic communication network of the immune response

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Doolittle John

2011-01-01

Full Text Available Abstract Background The immune system behaves like a complex, dynamic network with interacting elements including leukocytes, cytokines, and chemokines. While the immune system is broadly distributed, leukocytes must communicate effectively to respond to a pathological challenge. The Basic Immune Simulator 2010 contains agents representing leukocytes and tissue cells, signals representing cytokines, chemokines, and pathogens, and virtual spaces representing organ tissue, lymphoid tissue, and blood. Agents interact dynamically in the compartments in response to infection of the virtual tissue. Agent behavior is imposed by logical rules derived from the scientific literature. The model captured the agent-to-agent contact history, and from this the network topology and the interactions resulting in successful versus failed viral clearance were identified. This model served to integrate existing knowledge and allowed us to examine the immune response from a novel perspective directed at exploiting complex dynamics, ultimately for the design of therapeutic interventions. Results Analyzing the evolution of agent-agent interactions at incremental time points from identical initial conditions revealed novel features of immune communication associated with successful and failed outcomes. There were fewer contacts between agents for simulations ending in viral elimination (win versus persistent infection (loss, due to the removal of infected agents. However, early cellular interactions preceded successful clearance of infection. Specifically, more Dendritic Agent interactions with TCell and BCell Agents, and more BCell Agent interactions with TCell Agents early in the simulation were associated with the immune win outcome. The Dendritic Agents greatly influenced the outcome, confirming them as hub agents of the immune network. In addition, unexpectedly high frequencies of Dendritic Agent-self interactions occurred in the lymphoid compartment late in the

Deconstructing the pluripotency gene regulatory network

KAUST Repository

Li, Mo

2018-04-04

Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

Deconstructing the pluripotency gene regulatory network

KAUST Repository

Li, Mo; Belmonte, Juan Carlos Izpisua

2018-01-01

Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

E-cigarette use results in suppression of immune and inflammatory-response genes in nasal epithelial cells similar to cigarette smoke.

Science.gov (United States)

Martin, Elizabeth M; Clapp, Phillip W; Rebuli, Meghan E; Pawlak, Erica A; Glista-Baker, Ellen; Benowitz, Neal L; Fry, Rebecca C; Jaspers, Ilona

2016-07-01

Exposure to cigarette smoke is known to result in impaired host defense responses and immune suppressive effects. However, the effects of new and emerging tobacco products, such as e-cigarettes, on the immune status of the respiratory epithelium are largely unknown. We conducted a clinical study collecting superficial nasal scrape biopsies, nasal lavage, urine, and serum from nonsmokers, cigarette smokers, and e-cigarette users and assessed them for changes in immune gene expression profiles. Smoking status was determined based on a smoking history and a 3- to 4-wk smoking diary and confirmed using serum cotinine and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) levels. Total RNA from nasal scrape biopsies was analyzed using the nCounter Human Immunology v2 Expression panel. Smoking cigarettes or vaping e-cigarettes resulted in decreased expression of immune-related genes. All genes with decreased expression in cigarette smokers (n = 53) were also decreased in e-cigarette smokers. Additionally, vaping e-cigarettes was associated with suppression of a large number of unique genes (n = 305). Furthermore, the e-cigarette users showed a greater suppression of genes common with those changed in cigarette smokers. This was particularly apparent for suppressed expression of transcription factors, such as EGR1, which was functionally associated with decreased expression of 5 target genes in cigarette smokers and 18 target genes in e-cigarette users. Taken together, these data indicate that vaping e-cigarettes is associated with decreased expression of a large number of immune-related genes, which are consistent with immune suppression at the level of the nasal mucosa. Copyright © 2016 the American Physiological Society.

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...

Antibody study in canine distemper virus nucleocapsid protein gene-immunized mice.

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Yuan, B; Li, X Y; Zhu, T; Yuan, L; Hu, J P; Chen, J; Gao, W; Ren, W Z

2015-04-10

The gene for the nucleocapsid (N) protein of canine distemper virus was cloned into the pMD-18T vector, and positive recombinant plasmids were obtained by enzyme digestion and sequencing. After digestion by both EcoRI and KpnI, the plasmid was directionally cloned into the eukaryotic expression vector pcDNA; the positive clone pcDNA-N was screened by electrophoresis and then transfected into COS-7 cells. Immunofluorescence analysis results showed that the canine distemper virus N protein was expressed in the cytoplasm of transfected COS-7 cells. After emulsification in Freund's adjuvant, the recombinant plasmid pcDNA-N was injected into the abdominal cavity of 8-week-old BABL/c mice, with the pcDNA original vector used as a negative control. Mice were immunized 3 times every 2 weeks. The blood of immunized mice was drawn 2 weeks after completing the immunizations to measure titer levels. The antibody titer in the pcDNA-N test was 10(1.62 ± 0.164), while in the control group this value was 10(0.52 ± 0.56), indicating that specific humoral immunity was induced in canine distemper virus nucleocapsid protein-immunized mice.

Synchronous versus asynchronous modeling of gene regulatory networks.

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Garg, Abhishek; Di Cara, Alessandro; Xenarios, Ioannis; Mendoza, Luis; De Micheli, Giovanni

2008-09-01

In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Repurposed transcriptomic data facilitate discovery of innate immunity toll-like receptor (TLR) Genes across Lophotrochozoa.

Science.gov (United States)

Halanych, Kenneth M; Kocot, Kevin M

2014-10-01

The growing volume of genomic data from across life represents opportunities for deriving valuable biological information from data that were initially collected for another purpose. Here, we use transcriptomes collected for phylogenomic studies to search for toll-like receptor (TLR) genes in poorly sampled lophotrochozoan clades (Annelida, Mollusca, Brachiopoda, Phoronida, and Entoprocta) and one ecdysozoan clade (Priapulida). TLR genes are involved in innate immunity across animals by recognizing potential microbial infection. They have an extracellular leucine-rich repeat (LRR) domain connected to a transmembrane domain and an intracellular toll/interleukin-1 receptor (TIR) domain. Consequently, these genes are important in initiating a signaling pathway to trigger defense. We found at least one TLR ortholog in all but two taxa examined, suggesting that a broad array of lophotrochozoans may have innate immune systems similar to those observed in vertebrates and arthropods. Comparison to the SMART database confirmed the presence of both the LRR and the TIR protein motifs characteristic of TLR genes. Because we looked at only one transcriptome per species, discovery of TLR genes was limited for most taxa. However, several TRL-like genes that vary in the number and placement of LRR domains were found in phoronids. Additionally, several contigs contained LRR domains but lacked TIR domains, suggesting they were not TLRs. Many of these LRR-containing contigs had other domains (e.g., immunoglobin) and are likely involved in innate immunity. © 2014 Marine Biological Laboratory.

SELANSI: a toolbox for simulation of stochastic gene regulatory networks.

Science.gov (United States)

Pájaro, Manuel; Otero-Muras, Irene; Vázquez, Carlos; Alonso, Antonio A

2018-03-01

Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. antonio@iim.csic.es. © The Author(s) 2017. Published by Oxford University Press.

Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

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Sungyoung Lee

2012-12-01

Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Network graph analysis of gene-gene interactions in genome-wide association study data.

Science.gov (United States)

Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

2012-12-01

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Applying Statistical and Complex Network Methods to Explore the Key Signaling Molecules of Acupuncture Regulating Neuroendocrine-Immune Network

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Kuo Zhang

2018-01-01

Full Text Available The mechanisms of acupuncture are still unclear. In order to reveal the regulatory effect of manual acupuncture (MA on the neuroendocrine-immune (NEI network and identify the key signaling molecules during MA modulating NEI network, we used a rat complete Freund’s adjuvant (CFA model to observe the analgesic and anti-inflammatory effect of MA, and, what is more, we used statistical and complex network methods to analyze the data about the expression of 55 common signaling molecules of NEI network in ST36 (Zusanli acupoint, and serum and hind foot pad tissue. The results indicate that MA had significant analgesic, anti-inflammatory effects on CFA rats; the key signaling molecules may play a key role during MA regulating NEI network, but further research is needed.

Hericium caput-medusae (Bull.:Fr.) Pers. polysaccharide enhance innate immune response, immune-related genes expression and disease resistance against Aeromonas hydrophila in grass carp (Ctenopharyngodon idella).

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Gou, Changlong; Wang, Jiazhen; Wang, Yuqiong; Dong, Wenlong; Shan, Xiaofeng; Lou, Yujie; Gao, Yunhang

2018-01-01

The objective was to add 0, 400, 800 or 1200 mg/kg of Hericium caput-medusae polysaccharide (HCMP) to the basal diet of grass carp (Ctenopharyngodon idella) and determine effects on humoral innate immunity, expression of immune-related genes and disease resistance. Adding HCMP enhanced (P < 0.05) bactericidal activity at 1, 2 and 3 weeks and also lysozyme activity, complement C3, and SOD activity at 2 and 3 weeks. Supplementing 800 or 1200 mg/kg of HCMP for 2 or 3 weeks increased (P < 0.05) serum concentrations of total protein, albumin and globulin. Two immune-related genes (IL-1β and TNF-α) were up-regulated (P < 0.05) in HCMP supplemented groups given 800 or 1200 mg/kg HCMP after 2 and 3 weeks of feeding. Expression of anti-inflammatory cytokine IL-10 was down-regulated (P < 0.05) after receiving 800 or 1200 mg/kg HCMP for 2 or 3 weeks. Fish fed 800 mg/kg HCMP had maximal disease resistance against Aeromonas hydrophila (65.4%). In conclusion, HCMP enhanced immune response and expression of immune-related genes and increased disease resistance against Aeromonas hydrophila in grass carp, with greatest effects in fish given 800 mg/kg HCMP for 3 weeks. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana

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Singh, Pramesh; Chen, Tianlong; Arendsee, Zebulun; Wurtele, Eve S.; Bassler, Kevin E.

Orphan genes, which are genes unique to each particular species, have recently drawn significant attention for their potential usefulness for organismal robustness. Their origin and regulatory interaction patterns remain largely undiscovered. Recently, methods that use the context likelihood of relatedness to infer a network followed by modularity maximizing community detection algorithms on the inferred network to find the functional structure of regulatory networks were shown to be effective. We apply improved versions of these methods to gene expression data from Arabidopsis thaliana, identify groups (clusters) of interacting genes with related patterns of expression and analyze the structure within those groups. Focusing on clusters that contain orphan genes, we compare the identified clusters to gene ontology (GO) terms, regulons, and pathway designations and analyze their hierarchical structure. We predict new regulatory interactions and unravel the structure of the regulatory interaction patterns of orphan genes. Work supported by the NSF through Grants DMR-1507371 and IOS-1546858.

Network statistics of genetically-driven gene co-expression modules in mouse crosses

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Marie-Pier eScott-Boyer

2013-12-01

Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

Pretransplant Immune- and Apoptosis-Related Gene Expression Is Associated with Kidney Allograft Function

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Dorota Kamińska

2016-01-01

Full Text Available Renal transplant candidates present immune dysregulation, caused by chronic uremia. The aim of the study was to investigate whether pretransplant peripheral blood gene expression of immune factors affects clinical outcome of renal allograft recipients. Methods. In a prospective study, we analyzed pretransplant peripheral blood gene expression in87 renal transplant candidates with real-time PCR on custom-designed low density arrays (TaqMan. Results. Immediate posttransplant graft function (14-day GFR was influenced negatively by TGFB1 (P=0.039 and positively by IL-2 gene expression (P=0.040. Pretransplant blood mRNA expression of apoptosis-related genes (CASP3, FAS, and IL-18 and Th1-derived cytokine gene IFNG correlated positively with short- (6-month GFR CASP3: P=0.027, FAS: P=0.021, and IFNG: P=0.029 and long-term graft function (24-month GFR CASP3: P=0.003, FAS: P=0.033, IL-18: P=0.044, and IFNG: P=0.04. Conclusion. Lowered pretransplant Th1-derived cytokine and apoptosis-related gene expressions were a hallmark of subsequent worse kidney function but not of acute rejection rate. The pretransplant IFNG and CASP3 and FAS and IL-18 genes’ expression in the recipients’ peripheral blood is the possible candidate for novel biomarker of short- and long-term allograft function.

Genes involved in immunity and apoptosis are associated with human presbycusis based on microarray analysis.

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Dong, Yang; Li, Ming; Liu, Puzhao; Song, Haiyan; Zhao, Yuping; Shi, Jianrong

2014-06-01

Genes involved in immunity and apoptosis were associated with human presbycusis. CCR3 and GILZ played an important role in the pathogenesis of presbycusis, probably through regulating chemokine receptor, T-cell apoptosis, or T-cell activation pathways. To identify genes associated with human presbycusis and explore the molecular mechanism of presbycusis. Hearing function was tested by pure-tone audiometry. Microarray analysis was performed to identify presbycusis-correlated genes by Illumina Human-6 BeadChip using the peripheral blood samples of subjects. To identify biological process categories and pathways associated with presbycusis-correlated genes, bioinformatics analysis was carried out by Gene Ontology Tree Machine (GOTM) and database for annotation, visualization, and integrated discovery (DAVID). Quantitative RT-PCR (qRT-PCR) was used to validate the microarray data. Microarray analysis identified 469 up-regulated genes and 323 down-regulated genes. Both the dominant biological processes by Gene Ontology (GO) analysis and the enriched pathways by Kyoto encyclopedia of genes and genomes (KEGG) and BIOCARTA showed that genes involved in immunity and apoptosis were associated with presbycusis. In addition, CCR3, GILZ, CXCL10, and CX3CR1 genes showed consistent difference between groups for both the gene chip and qRT-PCR data. The differences of CCR3 and GILZ between presbycusis patients and controls were statistically significant (p < 0.05).

Inflammatory gene regulatory networks in amnion cells following cytokine stimulation: translational systems approach to modeling human parturition.

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Ruth Li

Full Text Available A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals.

Learning gene regulatory networks from only positive and unlabeled data

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Elkan Charles

2010-05-01

Full Text Available Abstract Background Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement. Conclusions Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

Inferring the gene network underlying the branching of tomato inflorescence.

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Laura Astola

Full Text Available The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior.

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

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Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

Inverse targeting —An effective immunization strategy

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Schneider, C. M.; Mihaljev, T.; Herrmann, H. J.

2012-05-01

We propose a new method to immunize populations or computer networks against epidemics which is more efficient than any continuous immunization method considered before. The novelty of our method resides in the way of determining the immunization targets. First we identify those individuals or computers that contribute the least to the disease spreading measured through their contribution to the size of the largest connected cluster in the social or a computer network. The immunization process follows the list of identified individuals or computers in inverse order, immunizing first those which are most relevant for the epidemic spreading. We have applied our immunization strategy to several model networks and two real networks, the Internet and the collaboration network of high-energy physicists. We find that our new immunization strategy is in the case of model networks up to 14%, and for real networks up to 33% more efficient than immunizing dynamically the most connected nodes in a network. Our strategy is also numerically efficient and can therefore be applied to large systems.

On the dynamics of a gene regulatory network

International Nuclear Information System (INIS)

Grammaticos, B; Carstea, A S; Ramani, A

2006-01-01

We examine the dynamics of a network of genes focusing on a periodic chain of genes, of arbitrary length. We show that within a given class of sigmoids representing the equilibrium probability of the binding of the RNA polymerase to the core promoter, the system possesses a single stable fixed point. By slightly modifying the sigmoid, introducing 'stiffer' forms, we show that it is possible to find network configurations exhibiting bistable behaviour. Our results do not depend crucially on the length of the chain considered: calculations with finite chains lead to similar results. However, a realistic study of regulatory genetic networks would require the consideration of more complex topologies and interactions

Signaling mechanisms underlying the robustness and tunability of the plant immune network

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Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Functional modules by relating protein interaction networks and gene expression.

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Tornow, Sabine; Mewes, H W

2003-11-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

Expression Analysis of Immune Related Genes Identified from the Coelomocytes of Sea Cucumber (Apostichopus japonicus in Response to LPS Challenge

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Ying Dong

2014-10-01

Full Text Available The sea cucumber (Apostichopus japonicus occupies a basal position during the evolution of deuterostomes and is also an important aquaculture species. In order to identify more immune effectors, transcriptome sequencing of A. japonicus coelomocytes in response to lipopolysaccharide (LPS challenge was performed using the Illumina HiSeq™ 2000 platform. One hundred and seven differentially expressed genes were selected and divided into four functional categories including pathogen recognition (25 genes, reorganization of cytoskeleton (27 genes, inflammation (41 genes and apoptosis (14 genes. They were analyzed to elucidate the mechanisms of host-pathogen interactions and downstream signaling transduction. Quantitative real-time polymerase chain reactions (qRT-PCRs of 10 representative genes validated the accuracy and reliability of RNA sequencing results with the correlation coefficients from 0.88 to 0.98 and p-value <0.05. Expression analysis of immune-related genes after LPS challenge will be useful in understanding the immune response mechanisms of A. japonicus against pathogen invasion and developing strategies for resistant markers selection.

Characterization of differentially expressed genes using high-dimensional co-expression networks

DEFF Research Database (Denmark)

Coelho Goncalves de Abreu, Gabriel; Labouriau, Rodrigo S.

2010-01-01

We present a technique to characterize differentially expressed genes in terms of their position in a high-dimensional co-expression network. The set-up of Gaussian graphical models is used to construct representations of the co-expression network in such a way that redundancy and the propagation...... that allow to make effective inference in problems with high degree of complexity (e.g. several thousands of genes) and small number of observations (e.g. 10-100) as typically occurs in high throughput gene expression studies. Taking advantage of the internal structure of decomposable graphical models, we...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...

Creating a two-layered augmented artificial immune system for application to computer network intrusion detection

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Judge, Matthew G.; Lamont, Gary B.

2009-05-01

Computer network security has become a very serious concern of commercial, industrial, and military organizations due to the increasing number of network threats such as outsider intrusions and insider covert activities. An important security element of course is network intrusion detection which is a difficult real world problem that has been addressed through many different solution attempts. Using an artificial immune system has been shown to be one of the most promising results. By enhancing jREMISA, a multi-objective evolutionary algorithm inspired artificial immune system, with a secondary defense layer; we produce improved accuracy of intrusion classification and a flexibility in responsiveness. This responsiveness can be leveraged to provide a much more powerful and accurate system, through the use of increased processing time and dedicated hardware which has the flexibility of being located out of band.

Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

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Benjamin A Logsdon

Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

Elimination of contaminating cap genes in AAV vector virions reduces immune responses and improves transgene expression in a canine gene therapy model.

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Wang, Z; Halbert, C L; Lee, D; Butts, T; Tapscott, S J; Storb, R; Miller, A D

2014-04-01

Animal and human gene therapy studies utilizing AAV vectors have shown that immune responses to AAV capsid proteins can severely limit transgene expression. The main source of capsid antigen is that associated with the AAV vectors, which can be reduced by stringent vector purification. A second source of AAV capsid proteins is that expressed from cap genes aberrantly packaged into AAV virions during vector production. This antigen source can be eliminated by the use of a cap gene that is too large to be incorporated into an AAV capsid, such as a cap gene containing a large intron (captron gene). Here, we investigated the effects of elimination of cap gene transfer and of vector purification by CsCl gradient centrifugation on AAV vector immunogenicity and expression following intramuscular injection in dogs. We found that both approaches reduced vector immunogenicity and that combining the two produced the lowest immune responses and highest transgene expression. This combined approach enabled the use of a relatively mild immunosuppressive regimen to promote robust micro-dystrophin gene expression in Duchenne muscular dystrophy-affected dogs. Our study shows the importance of minimizing AAV cap gene impurities and indicates that this improvement in AAV vector production may benefit human applications.

Immunological network analysis in HPV associated head and neck squamous cancer and implications for disease prognosis.

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Chen, Xiaohang; Yan, Bingqing; Lou, Huihuang; Shen, Zhenji; Tong, Fangjia; Zhai, Aixia; Wei, Lanlan; Zhang, Fengmin

2018-04-01

Human papillomavirus-positive (HPV+) head and neck squamous cell cancer (HNSCC) exhibits a better prognosis than HPV-negative (HPV-) HNSCC. This difference may in part be due to enhanced immune activation in the HPV+ HNSCC tumor microenvironment. To characterize differences in immune activation between HPV+ and HPV- HNSCC tumors, we identified and annotated differentially expressed genes based upon mRNA expression data from The Cancer Genome Atlas (TCGA). Immune network between immune cells and cytokines was constructed by using single sample Gene Set Enrichment Analysis and conditional mutual information. Multivariate Cox regression analysis was used to determine the prognostic value of immune microenvironment characterization. A total of 1673 differentially expressed genes were functionally annotated. We found that genes upregulated in HPV+ HNSCC are enriched in immune-associated processes. And the up-regulated gene sets were validated by Gene Set Enrichment Analysis. The microenvironment of HPV+ HNSCC exhibited greater numbers of infiltrating B and T cells and fewer neutrophils than HPV- HNSCC. These findings were validated by two independent datasets in the Gene Expression Omnibus (GEO) database. Further analyses of T cell subtypes revealed that cytotoxic T cell subtypes predominated in HPV+ HNSCC. In addition, the ratio of M1/M2 macrophages was much higher in HPV+ HNSCC. The infiltration of these immune cells was correlated with differentially expressed cytokine-associated genes. Enhanced infiltration of B cells and CD8+ T cells were identified as independent protective factors, while high neutrophil infiltration was a risk enhancing factor for HPV+ HNSCC patients. A schematic model of immunological network was established for HPV+ HNSCC to summarize our findings. Copyright © 2018 Elsevier Ltd. All rights reserved.

Common Genetic Variants Found in HLA and KIR Immune Genes in Autism Spectrum Disorder

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Anthony R Torres

2016-10-01

Full Text Available The common variant - common disease hypothesis was proposed to explain diseases with strong inheritance. This model suggests that a genetic disease is the result of the combination of several common genetic variants. Common genetic variants are described as a 5% frequency differential between diseased versus matched control populations. This theory was recently supported by an epidemiology paper stating that about 50% of genetic risk for autism resides in common variants. However, rare variants, rather than common variants, have been found in numerous genome wide genetic studies and many have concluded that the common variant—common disease hypothesis is incorrect. One interpretation is that rare variants are major contributors to genetic diseases and autism involves the interaction of many rare variants, especially in the brain. It is obvious there is much yet to be learned about autism genetics.Evidence has been mounting over the years indicating immune involvement in autism, particularly the HLA genes on chromosome 6 and KIR genes on chromosome 19. These two large multigene complexes have important immune functions and have been shown to interact to eliminate unwanted virally infected and malignant cells. HLA proteins have important functions in antigen presentation in adaptive immunity and specific epitopes on HLA class I proteins act as cognate ligands for KIR receptors in innate immunity. Data suggests that HLA alleles and KIR activating genes/haplotypes are common variants in different autism populations. For example, class I allele (HLA-A2 and HLA-G 14bp-indel frequencies are significantly increased by more than 5% over control populations (Table2. The HLA-DR4 Class II and shared epitope frequencies are significantly above the control populations (Table 2. Three activating KIR genes: 3DS1, 2DS1 and 2DS2 have increased frequencies of 15%, 22% and 14% in autism populations, respectively. There is a 6% increase in total activating KIR

Immune networks: multi-tasking capabilities at medium load

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Agliari, E.; Annibale, A.; Barra, A.; Coolen, A. C. C.; Tantari, D.

2013-08-01

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ˜ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ˜ Nδ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ frameworks are required to achieve effective retrieval.

Transcriptional networks associated with the immune system are disrupted by organochlorine pesticides in largemouth bass (Micropterus salmoides) ovary.

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Martyniuk, Christopher J; Doperalski, Nicholas J; Feswick, April; Prucha, Melinda S; Kroll, Kevin J; Barber, David S; Denslow, Nancy D

2016-08-01

were altered by p, p' DDE, MXC, and flutamide. Interestingly, immune-related gene networks were suppressed by all three chemicals. The data suggest that p, p' DDE and flutamide affected more genes in common with each other than either chemical with MXC, consistent with studies suggesting that p, p' DDE is a more potent anti-androgen than MXC. These data demonstrate that reproductive health was not affected by these specific dietary treatments, but rather the immune system, which may be a significant target of organochlorine pesticides. The interaction between the reproductive and immune systems should be considered in future studies on these legacy and persistent pesticides. Copyright © 2016 Elsevier B.V. All rights reserved.

Uncovering the liver’s role in immunity through RNA co-expression networks

Czech Academy of Sciences Publication Activity Database

Harrall, K. K.; Kechris, K. J.; Tabakoff, B.; Hoffman, P.L.; Hines, L. M.; Tsukamoto, H.; Pravenec, Michal; Printz, M.; Saba, L. M.

2016-01-01

Roč. 27, 9-10 (2016), s. 469-484 ISSN 0938-8990 R&D Projects: GA ČR(CZ) GAP301/12/0696 Institutional support: RVO:67985823 Keywords : RNA coexpression networks * liver * immunity * rat * recombinant inbred strains Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.509, year: 2016

The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network.

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David A Garfield

2013-10-01

Full Text Available Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear, allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.

Cord blood gene expression supports that prenatal exposure to perfluoroalkyl substances causes depressed immune functionality in early childhood.

Science.gov (United States)

Pennings, Jeroen L A; Jennen, Danyel G J; Nygaard, Unni C; Namork, Ellen; Haug, Line S; van Loveren, Henk; Granum, Berit

2016-01-01

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a class of synthetic compounds that have widespread use in consumer and industrial applications. PFAS are considered environmental pollutants that have various toxic properties, including effects on the immune system. Recent human studies indicate that prenatal exposure to PFAS leads to suppressed immune responses in early childhood. In this study, data from the Norwegian BraMat cohort was used to investigate transcriptomics profiles in neonatal cord blood and their association with maternal PFAS exposure, anti-rubella antibody levels at 3 years of age and the number of common cold episodes until 3 years. Genes associated with PFAS exposure showed enrichment for immunological and developmental functions. The analyses identified a toxicogenomics profile of 52 PFAS exposure-associated genes that were in common with genes associated with rubella titers and/or common cold episodes. This gene set contains several immunomodulatory genes (CYTL1, IL27) as well as other immune-associated genes (e.g. EMR4P, SHC4, ADORA2A). In addition, this study identified PPARD as a PFAS toxicogenomics marker. These markers can serve as the basis for further mechanistic or epidemiological studies. This study provides a transcriptomics connection between prenatal PFAS exposure and impaired immune function in early childhood and supports current views on PPAR- and NF-κB-mediated modes of action. The findings add to the available evidence that PFAS exposure is immunotoxic in humans and support regulatory policies to phase out these substances.

Post-capture immune gene expression studies in the deep-sea hydrothermal vent mussel Bathymodiolus azoricus acclimatized to atmospheric pressure.

Science.gov (United States)

Barros, Inês; Divya, Baby; Martins, Inês; Vandeperre, Frederic; Santos, Ricardo Serrão; Bettencourt, Raul

2015-01-01

Deep-sea hydrothermal vents are extreme habitats that are distributed worldwide in association with volcanic and tectonic events, resulting thus in the establishment of particular environmental conditions, in which high pressure, steep temperature gradients, and potentially toxic concentrations of sulfur, methane and heavy metals constitute driving factors for the foundation of chemosynthetic-based ecosystems. Of all the different macroorganisms found at deep-sea hydrothermal vents, the mussel Bathymodiolus azoricus is the most abundant species inhabiting the vent ecosystems from the Mid-Atlantic Ridge (MAR). In the present study, the effect of long term acclimatization at atmospheric pressure on host-symbiotic associations were studied in light of the ensuing physiological adaptations from which the immune and endosymbiont gene expressions were concomitantly quantified by means of real-time PCR. The expression of immune genes at 0 h, 12 h, 24 h, 36 h, 48 h, 72 h, 1 week and 3 weeks post-capture acclimatization was investigated and their profiles compared across the samples tested. The gene signal distribution for host immune and bacterial genes followed phasic changes in gene expression at 24 h, 1 week and 3 weeks acclimatization when compared to other time points tested during this temporal expression study. Analyses of the bacterial gene expression also suggested that both bacterial density and activity could contribute to shaping the intricate association between endosymbionts and host immune genes whose expression patterns seem to be concomitant at 1 week acclimatization. Fluorescence in situ hybridization was used to assess the distribution and prevalence of endosymbiont bacteria within gill tissues confirming the gradual loss of sulfur-oxidizing (SOX) and methane-oxidizing (MOX) bacteria during acclimatization. The present study addresses the deep-sea vent mussel B. azoricus as a model organism to study how acclimatization in aquaria and the

Identification of Immunity-Related Genes in Ostrinia furnacalis against Entomopathogenic Fungi by RNA-Seq Analysis

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Zhou, Fan; Wang, Guirong; An, Chunju

2014-01-01

Background The Asian corn borer (Ostrinia furnacalis (Guenée)) is one of the most serious corn pests in Asia. Control of this pest with entomopathogenic fungus Beauveria bassiana has been proposed. However, the molecular mechanisms involved in the interactions between O. furnacalis and B. bassiana are unclear, especially under the conditions that the genomic information of O. furnacalis is currently unavailable. So we sequenced and characterized the transcriptome of O. furnacalis larvae infected by B. bassiana with special emphasis on immunity-related genes. Methodology/Principal Findings Illumina Hiseq2000 was used to sequence 4.64 and 4.72 Gb of the transcriptome from water-injected and B. bassiana-injected O. furnacalis larvae, respectively. De novo assembly generated 62,382 unigenes with mean length of 729 nt. All unigenes were searched against Nt, Nr, Swiss-Prot, COG, and KEGG databases for annotations using BLASTN or BLASTX algorithm with an E-value cut-off of 10−5. A total of 35,700 (57.2%) unigenes were annotated to at least one database. Pairwise comparisons resulted in 13,890 differentially expressed genes, with 5,843 up-regulated and 8,047 down-regulated. Based on sequence similarity to homologs known to participate in immune responses, we totally identified 190 potential immunity-related unigenes. They encode 45 pattern recognition proteins, 33 modulation proteins involved in the prophenoloxidase activation cascade, 46 signal transduction molecules, and 66 immune responsive effectors, respectively. The obtained transcriptome contains putative orthologs for nearly all components of the Toll, Imd, and JAK/STAT pathways. We randomly selected 24 immunity-related unigenes and investigated their expression profiles using quantitative RT-PCR assay. The results revealed variant expression patterns in response to the infection of B. bassiana. Conclusions/Significance This study provides the comprehensive sequence resource and expression profiles of the

Identification of immunity-related genes in Ostrinia furnacalis against entomopathogenic fungi by RNA-seq analysis.

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Yang Liu

Full Text Available BACKGROUND: The Asian corn borer (Ostrinia furnacalis (Guenée is one of the most serious corn pests in Asia. Control of this pest with entomopathogenic fungus Beauveria bassiana has been proposed. However, the molecular mechanisms involved in the interactions between O. furnacalis and B. bassiana are unclear, especially under the conditions that the genomic information of O. furnacalis is currently unavailable. So we sequenced and characterized the transcriptome of O. furnacalis larvae infected by B. bassiana with special emphasis on immunity-related genes. METHODOLOGY/PRINCIPAL FINDINGS: Illumina Hiseq2000 was used to sequence 4.64 and 4.72 Gb of the transcriptome from water-injected and B. bassiana-injected O. furnacalis larvae, respectively. De novo assembly generated 62,382 unigenes with mean length of 729 nt. All unigenes were searched against Nt, Nr, Swiss-Prot, COG, and KEGG databases for annotations using BLASTN or BLASTX algorithm with an E-value cut-off of 10(-5. A total of 35,700 (57.2% unigenes were annotated to at least one database. Pairwise comparisons resulted in 13,890 differentially expressed genes, with 5,843 up-regulated and 8,047 down-regulated. Based on sequence similarity to homologs known to participate in immune responses, we totally identified 190 potential immunity-related unigenes. They encode 45 pattern recognition proteins, 33 modulation proteins involved in the prophenoloxidase activation cascade, 46 signal transduction molecules, and 66 immune responsive effectors, respectively. The obtained transcriptome contains putative orthologs for nearly all components of the Toll, Imd, and JAK/STAT pathways. We randomly selected 24 immunity-related unigenes and investigated their expression profiles using quantitative RT-PCR assay. The results revealed variant expression patterns in response to the infection of B. bassiana. CONCLUSIONS/SIGNIFICANCE: This study provides the comprehensive sequence resource and expression

Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.

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Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K H; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V; Bean, Yukie T; Beckmann, Matthias W; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S; Cramer, Daniel; Cunningham, Julie M; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F; Edwards, Robert P; Ekici, Arif B; Fasching, Peter A; Fridley, Brooke L; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G; Glasspool, Rosalind; Goode, Ellen L; Goodman, Marc T; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A T; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K; Hosono, Satoyo; Iversen, Edwin S; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K; Kelemen, Linda E; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D; Lee, Alice W; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R; McNeish, Iain A; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B; Narod, Steven A; Nedergaard, Lotte; Ness, Roberta B; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M; Permuth-Wey, Jennifer; Phelan, Catherine M; Pike, Malcolm C; Poole, Elizabeth M; Ramus, Susan J; Risch, Harvey A; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H; Rudolph, Anja; Runnebaum, Ingo B; Rzepecka, Iwona K; Salvesen, Helga B; Schildkraut, Joellen M; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C; Sucheston-Campbell, Lara E; Tangen, Ingvild L; Teo, Soo-Hwang; Terry, Kathryn L; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S; van Altena, Anne M; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S; Wicklund, Kristine G; Wilkens, Lynne R; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A; Monteiro, Alvaro N A; Freedman, Matthew L; Gayther, Simon A; Pharoah, Paul D P

2015-10-01

Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations. We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. ©2015 American Association for Cancer Research.

Signatures of selection acting on the innate immunity gene Toll-like receptor 2 (TLR2) during the evolutionary history of rodents.

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Tschirren, B; Råberg, L; Westerdahl, H

2011-06-01

Patterns of selection acting on immune defence genes have recently been the focus of considerable interest. Yet, when it comes to vertebrates, studies have mainly focused on the acquired branch of the immune system. Consequently, the direction and strength of selection acting on genes of the vertebrate innate immune defence remain poorly understood. Here, we present a molecular analysis of selection on an important receptor of the innate immune system of vertebrates, the Toll-like receptor 2 (TLR2), across 17 rodent species. Although purifying selection was the prevalent evolutionary force acting on most parts of the rodent TLR2, we found that codons in close proximity to pathogen-binding and TLR2-TLR1 heterodimerization sites have been subject to positive selection. This indicates that parasite-mediated selection is not restricted to acquired immune system genes like the major histocompatibility complex, but also affects innate defence genes. To obtain a comprehensive understanding of evolutionary processes in host-parasite systems, both innate and acquired immunity thus need to be considered. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Early Involvement of Immune/Inflammatory Response Genes in Retinal Degeneration in DBA/2J Mice

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W. Fan

2010-01-01

Full Text Available Purpose The DBA/2J (D2 mouse carries mutations in two of its genes, Tyrp1 and Gpnmb. These alterations result in the development of an immune response in the iris, leading to iris atrophy and pigment dispersion. The development of elevated intraocular pressure (IOP in this model of glaucoma is considered to be a significant factor leading to the death of retinal ganglion cells (RGCs. Changes in gene expression in the retina have already been correlated with the appearance of elevated IOP in the D2 mouse. The purpose of the present study was to determine if any changes in gene expression occur prior to the development of IOP. Methods The IOP was measured monthly using a rebound tonometer in D2 and age-matched C57/BL6 (B6 mice (normal controls. D2 animals with normal IOP at 2 and 4 M were used. In addition, mice at the age of 6–7 M were included to look for any trends in gene expression that might develop during the progression of the disease. Separate RNA samples were prepared from each of three individual retinas for each age, and gene expression profiles were determined with the aid of mouse oligonucleotide arrays (Agilent. A subset of genes was examined with the aid of real-time PCR. Immunocytochemistry was used to visualize changes in the retina for some of the gene-products. Results Four hundred and thirteen oligonucleotide probes were differentially expressed in the retinas of 4 M versus 2 M old D2 mice. The most significantly up-regulated genes (181 were associated with immune responses including interferon signaling, the complement system and the antigen presentation pathway, whereas the down-regulated genes (232 were linked to pathways related to cell death and known neurological diseases/disorders. These particular changes were not revealed in the age-matched B6 mice. By 6 M, when IOP started to increase in many of the D2 mice, more robust changes of these same genes were observed. Changes in the levels of selected genes

Early Involvement of Immune/Inflammatory Response Genes in Retinal Degeneration in DBA/2J Mice

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W. Fan

2010-03-01

Full Text Available Purpose: The DBA/2J (D2 mouse carries mutations in two of its genes, Tyrp1 and Gpnmb. These alterations result in the development of an immune response in the iris, leading to iris atrophy and pigment dispersion. The development of elevated intraocular pressure (IOP in this model of glaucoma is considered to be a significant factor leading to the death of retinal ganglion cells (RGCs. Changes in gene expression in the retina have already been correlated with the appearance of elevated IOP in the D2 mouse. The purpose of the present study was to determine if any changes in gene expression occur prior to the development of IOP. Methods: The IOP was measured monthly using a rebound tonometer in D2 and age-matched C57/BL6 (B6 mice (normal controls. D2 animals with normal IOP at 2 and 4 M were used. In addition, mice at the age of 6–7 M were included to look for any trends in gene expression that might develop during the progression of the disease. Separate RNA samples were prepared from each of three individual retinas for each age, and gene expression profiles were determined with the aid of mouse oligonucleotide arrays (Agilent. A subset of genes was examined with the aid of real-time PCR. Immunocytochemistry was used to visualize changes in the retina for some of the gene-products. Results: Four hundred and thirteen oligonucleotide probes were differentially expressed in the retinas of 4 M versus 2 M old D2 mice. The most significantly up-regulated genes (181 were associated with immune responses including interferon signaling, the complement system and the antigen presentation pathway, whereas the down-regulated genes (232 were linked to pathways related to cell death and known neurological diseases/disorders. These particular changes were not revealed in the age-matched B6 mice. By 6 M, when IOP started to increase in many of the D2 mice, more robust changes of these same genes were observed. Changes in the levels of selected genes

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

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Joeri Ruyssinck

Full Text Available One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made

Singular Perturbation Analysis and Gene Regulatory Networks with Delay

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Shlykova, Irina; Ponosov, Arcady

2009-09-01

There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study.

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Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand

2015-01-07

Network study on genes and proteins offers functional basics of the complexity of gene and protein, and its interacting partners. The gene fatty acid-binding protein 4 (fabp4) is found to be highly expressed in adipose tissue, and is one of the most abundant proteins in mature adipocytes. Our investigations on functional modules of fabp4 provide useful information on the functional genes interacting with fabp4, their biochemical properties and their regulatory functions. The present study shows that there are eight set of candidate genes: acp1, ext2, insr, lipe, ostf1, sncg, usp15, and vim that are strongly and functionally linked up with fabp4. Gene ontological analysis of network modules of fabp4 provides an explicit idea on the functional aspect of fabp4 and its interacting nodes. The hierarchal mapping on gene ontology indicates gene specific processes and functions as well as their compartmentalization in tissues. The fabp4 along with its interacting genes are involved in lipid metabolic activity and are integrated in multi-cellular processes of tissues and organs. They also have important protein/enzyme binding activity. Our study elucidated disease-associated nsSNP prediction for fabp4 and it is interesting to note that there are four rsID׳s (rs1051231, rs3204631, rs140925685 and rs141169989) with disease allelic variation (T104P, T126P, G27D and G90V respectively). On the whole, our gene network analysis presents a clear insight about the interactions and functions associated with fabp4 gene network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Gene network inference and biochemical assessment delineates GPCR pathways and CREB targets in small intestinal neuroendocrine neoplasia.

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Ignat Drozdov

Full Text Available Small intestinal (SI neuroendocrine tumors (NET are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations. All were up-regulated (p<0.035 with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10(-5 M significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10(-5 M stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D(2 and Serotonin [5-HT(2] receptor agonist, 10(-6 M stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8-2-fold for isoproterenol and forskolin. Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional

MINER: exploratory analysis of gene interaction networks by machine learning from expression data

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Sivieng Jane

2009-12-01

Full Text Available Abstract Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation, an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

Coordinations between gene modules control the operation of plant amino acid metabolic networks

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Galili Gad

2009-01-01

Full Text Available Abstract Background Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Results Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes. Conclusion Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.

An Immunization Strategy Based on Propagation Mechanism

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Yixin Zhu

2014-01-01

Full Text Available With the ubiquity of smart phones, wearable equipment, and wireless sensors, the topologies of networks composed by them change along with time. The immunization strategies in which network immune nodes are chosen by analyzing the static aggregation network topologies have been challenged. The studies about interaction propagations between two pathogens show that the interaction can change propagation threshold and the final epidemic size of each other, which provides a new thinking of immunization method. The eradication or inhibition of the virus can be achieved through the spread of its opposite party. Here, we put forward an immunization strategy whose implementation does not depend on the analysis of network topology. The immunization agents are randomly placed on a few of individuals of network and spread out from these individuals on network in a propagation method. The immunization agents prevent virus infecting their habitat nodes with certain immune success rate. The analysis and simulation of evolution equation of the model show that immune propagation has a significant impact on the spread threshold and steady-state density of virus on a finite size of BA networks. Simulations on some real-world networks also suggest that the immunization strategy is feasible and effective.

The effects of dietary Myrtle (Myrtus communis) on skin mucus immune parameters and mRNA levels of growth, antioxidant and immune related genes in zebrafish (Danio rerio).

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Safari, Roghieh; Hoseinifar, Seyed Hossein; Van Doan, Hien; Dadar, Maryam

2017-07-01

Myrtle (Myrtus communis L., Myrtaceae) is a significant plant which naturally distributed around the globe. Although numerous studies have demonstrated the benefits of myrtle in different species, studies using the oral route are rare in the literature. In the present study, we evaluated the effect of myrtle intake on the antioxidant, immune, appetite and growth related genes as well as mucosal immune responses in zebrafish (Danio rerio) model. Zebrafish were fed control or myrtle (5, 10 and 20 g kg -1 myrtle) supplemented diets for sixty days. The results showed that, oral administration of Myrtle significantly improved mucosal immune responses (the activity of lysozyme, total Ig and protease). Furthermore, fish fed 20 g kg -1 showed remarkably higher antioxidant (sod and cat) enzymes gene expression compared other treatment. There were significant difference between myrtle fed fish and control group regarding tnf-alpha and lyz expression. Also, evaluation of growth (gh and igf1) related genes revealed remarkable upregulation in 20 g kg -1 myrtle treatment compared other myrtle treatments and control group. Similar results was observed regarding the mRNA levels of appetite related genes (ghrl) in zebrafish fed 20 g kg -1 myrtle. The present results indicated that dietary administration of myrtle improved mucosal immune parameters and altered mRNA levels of selected genes. These results on zebrafish model also highlights the potential use of Myrtle supplements as additive in human diets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Life-threatening infectious diseases of childhood: single-gene inborn errors of immunity?

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Alcaïs, Alexandre; Quintana-Murci, Lluis; Thaler, David S; Schurr, Erwin; Abel, Laurent; Casanova, Jean-Laurent

2010-12-01

The hypothesis that inborn errors of immunity underlie infectious diseases is gaining experimental support. However, the apparent modes of inheritance of predisposition or resistance differ considerably among diseases and among studies. A coherent genetic architecture of infectious diseases is lacking. We suggest here that life-threatening infectious diseases in childhood, occurring in the course of primary infection, result mostly from individually rare but collectively diverse single-gene variations of variable clinical penetrance, whereas the genetic component of predisposition to secondary or reactivation infections in adults is more complex. This model is consistent with (i) the high incidence of most infectious diseases in early childhood, followed by a steady decline; (ii) theoretical modeling of the impact of monogenic or polygenic predisposition on the incidence distribution of infectious diseases before reproductive age; (iii) available molecular evidence from both monogenic and complex genetics of infectious diseases in children and adults; (iv) current knowledge of immunity to primary and secondary or latent infections; (v) the state of the art in the clinical genetics of noninfectious pediatric and adult diseases; and (vi) evolutionary data for the genes underlying single-gene and complex disease risk. With the recent advent of new-generation deep resequencing, this model of single-gene variations underlying severe pediatric infectious diseases is experimentally testable. © 2010 New York Academy of Sciences.

Introduction: Cancer Gene Networks.

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Clarke, Robert

2017-01-01

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

DMPD: Glucocorticoids and the innate immune system: crosstalk with the toll-likereceptor signaling network. [Dynamic Macrophage Pathway CSML Database

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Full Text Available 17576036 Glucocorticoids and the innate immune system: crosstalk with the toll-like...07 May 13. (.png) (.svg) (.html) (.csml) Show Glucocorticoids and the innate immune system: crosstalk with t...nd the innate immune system: crosstalk with the toll-likereceptor signaling network. Authors Chinenov Y, Rog

Transcriptome analysis of the white body of the squid Euprymna tasmanica with emphasis on immune and hematopoietic gene discovery.

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Karla A Salazar

Full Text Available In the mutualistic relationship between the squid Euprymna tasmanica and the bioluminescent bacterium Vibrio fischeri, several host factors, including immune-related proteins, are known to interact and respond specifically and exclusively to the presence of the symbiont. In squid and octopus, the white body is considered to be an immune organ mainly due to the fact that blood cells, or hemocytes, are known to be present in high numbers and in different developmental stages. Hence, the white body has been described as the site of hematopoiesis in cephalopods. However, to our knowledge, there are no studies showing any molecular evidence of such functions. In this study, we performed a transcriptomic analysis of white body tissue of the Southern dumpling squid, E. tasmanica. Our primary goal was to gain insights into the functions of this tissue and to test for the presence of gene transcripts associated with hematopoietic and immune processes. Several hematopoiesis genes including CPSF1, GATA 2, TFIID, and FGFR2 were found to be expressed in the white body. In addition, transcripts associated with immune-related signal transduction pathways, such as the toll-like receptor/NF-κβ, and MAPK pathways were also found, as well as other immune genes previously identified in E. tasmanica's sister species, E. scolopes. This study is the first to analyze an immune organ within cephalopods, and to provide gene expression data supporting the white body as a hematopoietic tissue.

Mucosal Ecological Network of Epithelium and Immune Cells for Gut Homeostasis and Tissue Healing.

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Kurashima, Yosuke; Kiyono, Hiroshi

2017-04-26

The intestinal epithelial barrier includes columnar epithelial, Paneth, goblet, enteroendocrine, and tuft cells as well as other cell populations, all of which contribute properties essential for gastrointestinal homeostasis. The intestinal mucosa is covered by mucin, which contains antimicrobial peptides and secretory IgA and prevents luminal bacteria, fungi, and viruses from stimulating intestinal immune responses. Conversely, the transport of luminal microorganisms-mediated by M, dendritic, and goblet cells-into intestinal tissues facilitates the harmonization of active and quiescent mucosal immune responses. The bacterial population within gut-associated lymphoid tissues creates the intratissue cohabitations for harmonized mucosal immunity. Intermolecular and intercellular communication among epithelial, immune, and mesenchymal cells creates an environment conducive for epithelial regeneration and mucosal healing. This review summarizes the so-called intestinal mucosal ecological network-the complex but vital molecular and cellular interactions of epithelial mesenchymal cells, immune cells, and commensal microbiota that achieve intestinal homeostasis, regeneration, and healing.

Effect of water pollution on expression of immune response genes of ...

African Journals Online (AJOL)

This research was aimed to study quality of water in Lake Qarun and effects of pollution on expression of immune genes in Egyptian sole (Solea aegyptiaca) fish. The study was carried out from August 2006 to the end of April 2007. The water samples were collected from different locations of Lake Qarun at Al-Oberge within ...

Transcriptional control in the segmentation gene network of Drosophila.

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Mark D Schroeder

2004-09-01

Full Text Available The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross- regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

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Guo Zheng

2006-01-01

Full Text Available Abstract Background It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. Results In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network to address the underlying regulations of genes that can span any unit(s of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. Conclusion We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex

The most common friend first immunization

International Nuclear Information System (INIS)

Nian Fu-Zhong; Hu Cha-Sheng

2016-01-01

In this paper, a standard susceptible-infected-recovered-susceptible(SIRS) epidemic model based on the Watts–Strogatz (WS) small-world network model and the Barabsi–Albert (BA) scale-free network model is established, and a new immunization scheme — “the most common friend first immunization” is proposed, in which the most common friend’s node is described as being the first immune on the second layer protection of complex networks. The propagation situations of three different immunization schemes — random immunization, high-risk immunization, and the most common friend first immunization are studied. At the same time, the dynamic behaviors are also studied on the WS small-world and the BA scale-free network. Moreover, the analytic and simulated results indicate that the immune effect of the most common friend first immunization is better than random immunization, but slightly worse than high-risk immunization. However, high-risk immunization still has some limitations. For example, it is difficult to accurately define who a direct neighbor in the life is. Compared with the traditional immunization strategies having some shortcomings, the most common friend first immunization is effective, and it is nicely consistent with the actual situation. (paper)

The associations between immunity-related genes and breast cancer prognosis in Korean women.

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Jaesung Choi

Full Text Available We investigated the role of common genetic variation in immune-related genes on breast cancer disease-free survival (DFS in Korean women. 107 breast cancer patients of the Seoul Breast Cancer Study (SEBCS were selected for this study. A total of 2,432 tag single nucleotide polymorphisms (SNPs in 283 immune-related genes were genotyped with the GoldenGate Oligonucleotide pool assay (OPA. A multivariate Cox-proportional hazard model and polygenic risk score model were used to estimate the effects of SNPs on breast cancer prognosis. Harrell's C index was calculated to estimate the predictive accuracy of polygenic risk score model. Subsequently, an extended gene set enrichment analysis (GSEA-SNP was conducted to approximate the biological pathway. In addition, to confirm our results with current evidence, previous studies were systematically reviewed. Sixty-two SNPs were statistically significant at p-value less than 0.05. The most significant SNPs were rs1952438 in SOCS4 gene (hazard ratio (HR = 11.99, 95% CI = 3.62-39.72, P = 4.84E-05, rs2289278 in TSLP gene (HR = 4.25, 95% CI = 2.10-8.62, P = 5.99E-05 and rs2074724 in HGF gene (HR = 4.63, 95% CI = 2.18-9.87, P = 7.04E-05. In the polygenic risk score model, the HR of women in the 3rd tertile was 6.78 (95% CI = 1.48-31.06 compared to patients in the 1st tertile of polygenic risk score. Harrell's C index was 0.813 with total patients and 0.924 in 4-fold cross validation. In the pathway analysis, 18 pathways were significantly associated with breast cancer prognosis (P<0.1. The IL-6R, IL-8, IL-10RB, IL-12A, and IL-12B was associated with the prognosis of cancer in data of both our study and a previous study. Therefore, our results suggest that genetic polymorphisms in immune-related genes have relevance to breast cancer prognosis among Korean women.

Dietary Immunogen® modulated digestive enzyme activity and immune gene expression in Litopenaeus vannamei post larvae.

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Miandare, Hamed Kolangi; Mirghaed, Ali Taheri; Hosseini, Marjan; Mazloumi, Nastaran; Zargar, Ashkan; Nazari, Sajad

2017-11-01

Pacific white shrimp Litopenaeus vannamei (Boone, 1931) is an important economical shrimp species worldwide, especially in the Middle East region, and farming activities of this species have been largely affected by diseases, mostly viral and bacterial diseases. Scientists have started to use prebiotics for bolstering the immune status of the animal. This study aimed to investigate the influence of Immunogen ® on growth, digestive enzyme activity and immune related gene expression of Litopenaeus vannamei post-larvae. All post-larvae were acclimated to the laboratory condition for 14 days. Upon acclimation, shrimps were fed on different levels of Immunogen ® (0, 0.5, 1 and 1.5 g kg -1 ) for 60 days. No significant differences were detected in weight gain, specific growth rate (SGR) and food conversion ratio (FCR) in shrimp post-larvae in which fed with different levels of Immunogen ® and control diet. The results showed that digestive enzymes activity including protease and lipase increased with different amounts of Immunogen ® in the shrimp diet. Protease activity increased with 1.5 g kg -1 Immunogen ® after 60 days and lipase activity increased with 1 and 1.5 g kg -1 Immunogen ® after 30 and 60 days of the trial respectively (P  0.05). The expression of immune related genes including, prophenoloxidase, crustin and g-type lysozyme increased with diet 1.5 g kg -1 Immunogen ® (P < 0.05) while expression of penaeidin gene increased only with experimental diet 1 g kg -1 of Immunogen ® . These results indicated that increase in digestive enzymes activity and expression of immune related genes could modulate the Immunogen ® in the innate immune system in L. vannamei in this study. Copyright © 2017. Published by Elsevier Ltd.

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....

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

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

2016-01-01

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

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Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Varroa destructor induces changes in the expression of immunity-related genes during the development of Apis mellifera worker and drone broods.

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Zaobidna, Ewa A; Żółtowska, Krystyna; Łopieńska-Biernat, Elżbieta

2017-12-20

The ectoparasitic mite Varroa destructor has emerged as the major pest of honeybees. Despite extensive research efforts, the pathogenesis of varroosis has not been fully explained. Earlier studies suggested that V. destructor infestation leads to the suppression of the host's immune system. The aim of this study was to analyze the immune responses of 14 genes in the Toll signal transduction pathways, including effector genes of antimicrobial peptides (AMPs), in developing Apis mellifera workers and drones infested with V. destructor. Four developmental stages (L5 larvae, prepupae, and 2 pupal stages) and newly emerged imagines were analyzed. In workers, the most significant changes were observed in L5 larvae in the initial stages of infestation. A significant increase in the relative expression of 10 of the 14 analyzed genes, including defensin-1 and defensin-2, was observed in infested bees relative to non-infested individuals. The immune response in drones developed at a slower rate. The expression of genes regulating cytoplasmic signal transduction increased in prepupae, whereas the expression of defensin-1 and defensin-2 effector genes increased in P3 pupae with red eyes. The expression of many immunity-related genes was silenced in successive life stages and in imagines, and it was more profound in workers than in drones. The results indicate that V. destructor significantly influences immune responses regulated by the Toll signal transduction pathway in bees. In infested bees, the observed changes in Toll pathway genes varied between life stages and the sexes.

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

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.

Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations.

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Wu, Shuang; Liu, Zhi-Ping; Qiu, Xing; Wu, Hulin

2014-01-01

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

Construction of functional linkage gene networks by data integration.

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Linghu, Bolan; Franzosa, Eric A; Xia, Yu

2013-01-01

Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

Immune networks: multi-tasking capabilities at medium load

International Nuclear Information System (INIS)

Agliari, E; Annibale, A; Barra, A; Coolen, A C C; Tantari, D

2013-01-01

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ∼ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ∼ N δ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ < 1 the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high-load regime δ = 1 we find that the system behaves as a spin-glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval. (paper)