Linking cell dynamics with coexpression networks to characterize key events in chronic virus infections
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE123134 | GEO | 2019/05/15
REPOSITORIES: GEO
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Project description:The host immune response against infection requires the coordinated action of many diverse cell subsets that dynamically adapt to a pathogen threat. Due to the complexity of such a response, most immunological studies have focused on a few genes, proteins, or cell types. With the development of "omic"-technologies and computational analysis methods, attempts to analyze and understand complex system dynamics are now feasible. However, the decomposition of transcriptomic data sets generated from complete organs remains a major challenge. Here, we combined Weighted Gene Coexpression Network Analysis (WGCNA) and Digital Cell Quantifier (DCQ) to analyze time-resolved mouse splenic transcriptomes in acute and chronic Lymphocytic Choriomeningitis Virus (LCMV) infections. This enabled us to generate hypotheses about complex immune functioning after a virus-induced perturbation. This strategy was validated by successfully predicting several known immune phenomena, such as effector cytotoxic T lymphocyte (CTL) expansion and exhaustion. Furthermore, we predicted and subsequently verified experimentally macrophage-CD8 T cell cooperativity and the participation of virus-specific CD8+ T cells with an early effector transcriptome profile in the host adaptation to chronic infection. Thus, the linking of gene expression changes with immune cell kinetics provides novel insights into the complex immune processes within infected tissues.
| S-EPMC6509617 | biostudies-literature
Project description:Linking cell dynamics with coexpression networks to characterize key events in chronic virus infections
| PRJNA507617 | ENA
Project description:Symptomatic glycerol kinase deficiency (GKD) is associated with episodic metabolic and central nervous system deterioration. We report here the first application of Weighted Gene Co-Expression Network Analysis (WGCNA) to investigate a knockout (KO) murine model of a human genetic disease. WGCNA identified networks and key hub transcripts from liver mRNA of glycerol kinase (Gyk) KO and wild type (WT) mice. Day of life 1 (dol1) samples from KO mice contained a network module enriched for organic acid metabolism before Gyk KO mice develop organic acidemia and die on dol3-4 and the module containing Gyk was enriched with apoptotic genes. Roles for the highly connected Acot, Psat and Plk3 transcripts were confirmed in cell cultures and subsequently validated by causality testing. We provide evidence that GK may have an apoptotic moonlighting role that is lost in GKD. This systems biology strategy has improved our understanding of GKD pathogenesis and suggests possible treatments.
2009-09-10 | GSE12748 | GEO
Project description:Symptomatic glycerol kinase deficiency (GKD) is associated with episodic metabolic and central nervous system deterioration. We report here the first application of Weighted Gene Co-Expression Network Analysis (WGCNA) to investigate a knockout (KO) murine model of a human genetic disease. WGCNA identified networks and key hub transcripts from liver mRNA of glycerol kinase (Gyk) KO and wild type (WT) mice. Day of life 1 (dol1) samples from KO mice contained a network module enriched for organic acid metabolism before Gyk KO mice develop organic acidemia and die on dol3-4 and the module containing Gyk was enriched with apoptotic genes. Roles for the highly connected Acot, Psat and Plk3 transcripts were confirmed in cell cultures and subsequently validated by causality testing. We provide evidence that GK may have an apoptotic moonlighting role that is lost in GKD. This systems biology strategy has improved our understanding of GKD pathogenesis and suggests possible treatments. Male WT and KO mouse pups were sacrificed on day of life (dol) 1 and each liver was harvested. Total RNA from 4 KO and 3 WT livers was isolated individually. Affymetrix mus 430 2.0 GeneChips were used to analyze differences in liver gene expression between KO and WT mice. Dol1 and 3 Gyk KO mice represent different disease states. Dol 1 was chosen because mice are phenotypically asymptomatic with respect to Glycerol Kinase Deficiency (GKD) and allowed us to look at alterations that occur before the overt disease state. Dol 3 mice are phenotypically symptomatic with respect to GKD.
2009-09-10 | E-GEOD-12748 | biostudies-arrayexpress
Project description:The goal of this study was to identify the molecular programming using ATAC-seq of CD8 T cells responding to different viral infections. Mice were infected with either LCMV Armstrong to model an acute infection or LCMV Clone-13 to model a chronic infection. At various time points following infection, virus-specific CD8 T cells were purified and ATAC-seq performed. These data identify the changes in chromatin accessibility associated with acute infections and the establishment of functional memory versus those accessibility changes associated with chronic infection.
2017-01-31 | GSE83081 | GEO
Project description:We have discovered a small subpopulation of virus-specific CD8 T-cells that sustains the T-cell response in chronic infections. These cells are defined by - and depend on - the expression of the transcription factor Tcf1 (T cell factor 1) and show key characteristics of central memory cells while lacking an effector signature. Unlike conventional memory cells, Tcf1+ T-cells display hallmarks of an âexhaustedâ phenotype, including the expression of certain inhibitory receptors. Naive Tcf1-GFP+ P14 cells (Naive) were transferred into Vb5 recipient mice (CD45.1) prior to infection with LCMV clone 13 (c13). Tcf1-GFP+ P14 cells (chronic Tcf1+) and Tcf1-GFP- P14 cells (chronic Tcf1-) were flow sorted on day 28 post infection. Naive Tcf1-GFP+ P14 cells (Naive) were also transferred into C57BL/6 hosts (CD45.1.2) prior to infection with LCMV Armstrong (Arm). Tcf1-GFP+ P14 cells (memory Tcf1+) and Tcf1-GFP- P14 cells (memory Tcf1-) were flow sorted on day 28 post infection. Total RNA was extracted, cDNA libraries prepared and sequencing was performed using Illumina HiSeq 2500 technology.
2016-08-22 | E-GEOD-83978 | biostudies-arrayexpress
Project description:The peptide-loading complex (PLC) is a transient, multisubunit membrane complex in the endoplasmic reticulum (ER), essential for establishing a hierarchical immune response. The PLC coordinates peptide translocation into the ER with loading and editing of major histocompatibility complex class I molecules (MHC-I). After final proofreading in the PLC, stable peptide/MHC-I complexes are released to the cell surface to evoke a T-cell response against infected or malignant cells. Sampling of different MHC-I allomorphs requires the precise coordination of seven different subunits into a single macromolecular assembly, including the transporter associated with antigen processing TAP1/2, the oxidoreductase ERp57, the MHC-I heterodimer, and the chaperones tapasin and calreticulin. The molecular organization and mechanistic events in the PLC are unknown due to the compositional heterogeneity and intrinsic dynamic nature of the complex. Here, PLC from Burkitt’s lymphoma cells was isolated using an engineered viral inhibitor as bait, followed by cross-linking mass spectrometry to aid EM structure elucidation.
2017-11-07 | PXD007928 | Pride
Project description:The relevance of infections as risk factor for cerebrovascular disease is being increasingly recognized. Nonetheless, the pathogenic link between the two entities remains poorly understood. Consistent with recent advances in medicine, the present work addresses the hypothesis that infection-induced immune responses may affect human proteins associated with stroke. Applying established procedures in bioinformatics, the pathogen antigens and the human proteins were searched for common sequences using pentapeptides as probes. The resulting data demonstrate massive peptide sharing between infectious pathogens-such as Chlamydia pneumoniae, Streptococcus pneumoniae, Tannerella forsythia, Haemophilus influenzae, Influenza A virus, and Cytomegalovirus-and human proteins related to risk of ischemic and hemorrhagic stroke. Moreover, the shared peptides are also evident in a number of epitopes experimentally proven immunopositive in the human host. The present findings suggest cross-reactivity as a potential mechanistic link between infections and stroke.
| S-EPMC6528689 | biostudies-literature
Project description:Helminth infections affect more than a third of the world's population. Despite very broad phylogenetic differences among helminth parasite species, a systemic Th2 host immune response is typically associated with long-term helminth infections, also known as the "helminth effect". Many investigations have been carried out to study host gene expression profiles during helminth infections. The objective of this study is to determine if there is a common transcriptomic signature characteristic of the helminth effect across multiple helminth species and tissue types. To this end, we performed a comprehensive meta-analysis of publicly available gene expression datasets. After data processing and adjusting for study-specific effects, we identified ~700 differentially expressed genes that are changed consistently during helminth infections. Functional enrichment analyses indicate that upregulated genes are predominantly involved in various immune functions, including immunomodulation, immune signaling, inflammation, pathogen recognition and antigen presentation. Down-regulated genes are mainly involved in metabolic process, with only a few of them are involved in immune regulation. This common immune gene signature confirms previous observations and indicates that the helminth effect is robust across different parasite species as well as host tissue types. To the best of our knowledge, this study is the first comprehensive meta-analysis of host transcriptome profiles during helminth infections.
| S-EPMC4826001 | biostudies-literature
Project description:MotivationIdentifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting Events (TieDIE) that uses a network diffusion approach to connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The method computes a subnetwork of protein-protein interactions, predicted transcription factor-to-target connections and curated interactions from literature that connects genomic and transcriptomic perturbations.ResultsApplication of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets.AvailabilitySoftware is available from the Stuart lab's wiki: https://sysbiowiki.soe.ucsc.edu/tiedie.Contactjstuart@ucsc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
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