Filters
Technology
Platform
GSE40452
Mus musculus
30 Downloadable Samples
Affymetrix HT Mouse Genome 430A Array (htmg430a)
Description
Individual genetic variation affects gene expression and cell phenotype by acting within complex molecular circuits, but this relationship is still largely unknown. Here, we combine genomic and meso-scale profiling with novel computational methods to detect genetic variants that affect the responsiveness of gene expression to stimulus (responsiveness QTLs) and position them in circuit diagrams. We apply this approach to study individual variation in transcriptional responsiveness to three different pathogen components in the model response of primary bone marrow dendritic cells (DCs) from recombinant inbred mice strains. We show that reQTLs are common both in cis (affecting a single target gene) and in trans (pleiotropically affecting co-regulated gene modules) and are specific to some stimuli but not others. Leveraging the stimulus-specific activity of reQTLs and the differential responsiveness of their associated targets, we show how to position reQTLs within the context of known pathways in this regulatory circuit. For example, we find that a pleiotropic trans-acting genetic factor in chr1:129-165Mb affects the responsiveness of 35 anti-viral genes only during an anti-viral like stimulus. Using RNAi we uncover RGS16 the likely causal gene in this interval, and an activator of the antiviral response. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in other complex circuits in primary mammalian cells.
Publication Title
Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli.
Sample Metadata Fields
Age, Specimen part
View Samples- Mus musculus
- 46 Downloadable Samples
Illumina HiSeq 2000
Description
Hundreds of immune cell types work in coordination to maintain tissue homeostasis. Upon infection, dramatic changes occur with the localization, migration and proliferation of the immune cells to first alert the body of the danger, confine it to limit spreading, and finally extinguish the threat and bring the tissue back to homeostasis. Since current technologies can follow the dynamics of only a limited number of cell types, we have yet to grasp the full complexity of global in vivo cell dynamics in normal developmental processes and disease. Here we devise a computational method, digital cell quantification (DCQ), which combines genomewide gene expression data with an immune cell compendium to infer in vivo dynamical changes in the quantities of 213 immune cell subpopulations. DCQ was applied to study global immune cell dynamics in mice lungs at ten time points during a 7-day time course of flu infection. We find dramatic changes in quantities of 70 immune cell types, including various innate, adaptive and progenitor immune cells. We focus on the previously unreported dynamics of four immune dendritic cell subtypes, and suggest a specific role for CD103+CD11b- cDCs in early stages of disease and CD8+ pDC in late stages of flu infection. Overall design: To study pathogenesis of Influenza infection, C57BL/6 mice (5 weeks) were infected intranasally with 4x103 PFU of influenza PR8 virus. We measured using RNA-Seq global gene expression in lung tissue at ten time points during a 7-day time course of infection, two infected individuals in each time point and four un-infected individuals as control. The lung organ was removed and transferred immediately into RNA Latter solution (Invitrogen).
Publication Title
Digital cell quantification identifies global immune cell dynamics during influenza infection.
Sample Metadata Fields
Age, Specimen part, Cell line, Subject, Time
View Samples- Mus musculus
- 28 Downloadable Samples
- Illumina HiSeq 2000
Description
Hundreds of immune cell types work in coordination to maintain tissue homeostasis. Upon infection, dramatic changes occur with the localization, migration and proliferation of the immune cells to first alert the body of the danger, confine it to limit spreading, and finally extinguish the threat and bring the tissue back to homeostasis. Since current technologies can follow the dynamics of only a limited number of cell types, we have yet to grasp the full complexity of global in vivo cell dynamics in normal developmental processes and disease. Here we devise a computational method, digital cell quantification (DCQ), which combines genomewide gene expression data with an immune cell compendium to infer in vivo dynamical changes in the quantities of 213 immune cell subpopulations. DCQ was applied to study global immune cell dynamics in mice lungs at ten time points during a 7-day time course of flu infection. We find dramatic changes in quantities of 70 immune cell types, including various innate, adaptive and progenitor immune cells. We focus on the previously unreported dynamics of four immune dendritic cell subtypes, and suggest a specific role for CD103+CD11b- cDCs in early stages of disease and CD8+ pDC in late stages of flu infection. Overall design: To better understand the physiological role of these differential dynamic changes in the DCs, we measured the genome-wide RNA expression of all four DC subpopulations from lung of influenza infected mice at four time points following infections (two mice per time-point). For sorting dendritic cells from lungs, the lungs from infected and control uninfected C57BL/6J mice were immersed in cold PBS, cut into small pieces in 5 ml DMEM media containing 10% Bovine Fetal Serum, the cell suspensions were grinded using 1ml syringe cup on a 70 µm cell strainers (BD Falcon). The cells were washed with ice cold PBS. Remaining red blood cells were lysed using ammonium chloride solution (Sigma). Cells were harvested, immersed 1ml FACS buffer [PBS+2% FBS, 1mM EDTA], Fc receptors were blocked with anti-mouse CD16/CD32, washed with FACS buffer and divided into two tubes for sorting cDC and pDC cells.
Publication Title
Digital cell quantification identifies global immune cell dynamics during influenza infection.
Sample Metadata Fields
Age, Specimen part, Cell line, Subject, Time
View Samples- Mus musculus
- 8 Downloadable Samples
- Illumina HiSeq 2500
Description
The host immune response against an infection requires the coordinated action of many diverse cell subsets that dynamically adapt to the pathogen threat. Here we combined WGCNA and DCQ to analyse time-resolved mouse splenic transcriptomes in acute and chronic LCMV infections. This approach allowed to better characterize the dynamic cell events occurring in complex tissues such as the induction of the adaptive T cell response which requires the coordination of monocytes/macrophages and CD8+ T cells. Overall design: mRNA profiles of CD8 T cells and macrophages (in duplicate days 0 and 7 post-infection) from C57BL/6 mice infected with 2x10E2 pfu of LCMV strain Docile, generated by deep sequencing.
Publication Title
Linking Cell Dynamics With Gene Coexpression Networks to Characterize Key Events in Chronic Virus Infections.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 169 Downloadable Samples
- Affymetrix HT Human Genome U133A Array (hthgu133a)
Description
We defined the major transcriptional responses in primary human bronchial epithelial cells (HBECs) after either infection with influenza or treatment with relevant ligands. We used four different strategies, each highlighting distinct aspects of the response. (1) cells were infected with the wild-type PR8 influenza virus that can mount a complete replicative cycle. (2) cells were transfected with viral RNA (vRNA) isolated from influenza particles. This does not result in the production of viral proteins or particles and identifies the effect of RNA-sensing pathways (e.g., RIG-I.). (3) Cells were treated with interferon beta (IFNb), to distinguish the portion of the response which is mediated through Type I IFNs. (4) Cells were infected with a PR8 virus lacking the NS1 gene (DNS1). The NS1 protein normally inhibits vRNA- or IFNb-induced pathways, and its deletion can reveal an expanded response to infection.
Publication Title
A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection.
Sample Metadata Fields
Specimen part, Disease, Time
View Samples- Homo sapiens
- 16 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
The mechanisms regulating breast cancer differentiation state are poorly understood. Of particular interest are molecular regulators controlling the highly aggressive and poorly differentiated traits of basal-like breast carcinomas. Here we show that the Polycomb factor EZH2 maintains the differentiation state of basal-like breast cancer cells, and promotes the expression of progenitor-associated and basal-lineage genes. Specifically, EZH2 regulates the composition of basal-like breast cancer cell populations by promoting a bi-lineage differentiation state, in which cells co-express basal- and luminal-lineage markers. We show that human basal-like breast cancers contain a subpopulation of bi-lineage cells, and that EZH2-deficient cells give rise to tumors with a decreased proportion of such cells. Bi-lineage cells express genes that are active in normal luminal progenitors, and possess increased colony formation capacity, consistent with a primitive differentiation state. We found that GATA3, a driver of luminal differentiation, performs a function opposite to EZH2, acting to suppress bi-lineage identity and luminal progenitor gene expression. GATA3 levels increase upon EZH2 silencing, leading to the observed decrease in bi-lineage cell numbers. Our findings reveal a novel role for EZH2 in controlling basal-like breast cancer differentiation state and intra-tumoral cell composition.
Publication Title
EZH2 promotes a bi-lineage identity in basal-like breast cancer cells.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Mus musculus
- 74 Downloadable Samples
- NextSeq 500
Description
Single-cell expression profiling is a rich resource of cellular heterogeneity. While profiling every sample under study is advantageous, such workflow is time consuming and costly. We devised CPM - a deconvolution algorithm in which cellular heterogeneity is inferred from bulk expression data based on pre-existing collection of single-cell RNA-seq profiles. We applied CPM to investigate individual variation in heterogeneity of murine lung cells during in vivo influenza virus infection, revealing that the relations between cell quantities and clinical outcomes varies in a gradual manner along the cellular activation process. Validation experiments confirmed these gradual changes along the cellular activation trajectory. Additional analysis suggests that clinical outcomes relate to the rate of cell activation at the early stages of this process. These findings demonstrate the utility of CPM as a mapping deconvolution tool at single-cell resolution, and highlight the importance of such fine cell landscape for understanding diversity of clinical outcomes. Overall design: Lungs gene expression of Collaborative Cross mice taken 48h after the infection with either the influenza virus or PBS.
Publication Title
Cell composition analysis of bulk genomics using single-cell data.
Sample Metadata Fields
Specimen part, Subject, Time
View Samples- Mus musculus
- 53 Downloadable Samples
- Illumina HiSeq 2000
Description
A central challenge in pharmaceutical research is to investigate genetic variation in response to drugs. The Collaborative Cross (CC) mouse reference population is a promising model for pharmacogenomic studies because of its large amount of genetic variation, genetic reproducibility, and dense recombination sites. While the CC lines are phenotypically diverse, their genetic diversity in drug disposition processes, such as detoxification reactions, is still largely uncharacterized. Here we systematically measured RNA-sequencing expression profiles from livers of 29 CC lines under baseline conditions. We then leveraged a reference collection of metabolic biotransformation pathways to map potential relations between drugs and their underlying expression quantitative trait loci (eQTLs). By applying this approach on proximal eQTLs, including eQTLs acting on the overall expression of genes and on the expression of particular transcript isoforms, we were able to construct the organization of hepatic eQTL-drug connectivity across the CC population. The analysis revealed a substantial impact of genetic variation acting on drug biotransformation, allowed mapping of potential joint genetic effects in the context of individual drugs, and demonstrated crosstalk between drug metabolism and lipid metabolism. Our findings provide a resource for investigating drug disposition in the CC strains, and offer a new paradigm for integrating biotransformation reactions to corresponding variations in DNA sequences. Overall design: This dataset includes RNA-Seq data of mRNA that were extracted from the liver of 55 male mice. The 55 mice belong to 29 different collaborative cross strains. The number of individual mice per strains is 3 for 3 strains, 2 for 16 strains, and 1 for 8 strains. All the mice are naïve without any special treatment.
Publication Title
Dissecting the Effect of Genetic Variation on the Hepatic Expression of Drug Disposition Genes across the Collaborative Cross Mouse Strains.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Mus musculus
- 8 Downloadable Samples
- NextSeq 500
Description
The balance between protecting tissue integrity and efficient immune response is critical for host survival. Here we investigate the role of extracellular matrix (ECM) proteolysis in achieving this balance in the lung during influenza virus infection using a combined genomic and proteomic approach. We followed the transcriptional dynamics and ECM- related responses in a mouse model of influenza virus infection, integrated with whole tissue imaging and functional assays. Our study identifies MT1-MMP as a prominent host-ECM-remodeling collagenase in influenza virus infection. We show that selective inhibition of MT1-MMP-driven ECM proteolysis protects the tissue from infection-related structural and compositional damage. Inhibition of MT1-MMP did not significantly alter the immune response or cytokine expression, indicating its dominant role in ECM remodeling. We demonstrate that the available treatment for influenza virus (Tamiflu/ Oseltamivir) does not prevent lung ECM damage and is less effective than anti-MT1-MMP treatment in influenza virus and Streptococcus pneumoniae coinfection paradigms. Importantly, combination therapy of Tamiflu with anti-MT1-MMP shows a strong synergistic effect and results in complete recovery in mice. This study highlights the importance of tissue tolerance agents for surviving infectious diseases, and the potential of such host-pathogen therapy combination for respiratory infections. Overall design: Overall 8 samples were included, in duplicates, both infected and non-infected control cells were includeda. Both MT1-MMP positive and MT1-MMP negative were tested were non-infectdd, MT1-MMP negative cells served as controls.
Publication Title
Extracellular Matrix Proteolysis by MT1-MMP Contributes to Influenza-Related Tissue Damage and Mortality.
Sample Metadata Fields
Specimen part, Cell line, Treatment, Subject
View Samples- Mus musculus
- 4 Downloadable Samples
- Affymetrix HT Mouse Genome 430A Array (htmg430a)
Description
We used microarrays to detail the global programme of gene expression underlying Polo-like kinase inhibition with BI 2536 compound in mouse bone marrow-derived dendritic cells (BMDCs) stimulated with Toll-like receptor agonists LPS and poly(I:C).
Publication Title
Systematic discovery of TLR signaling components delineates viral-sensing circuits.
Sample Metadata Fields
Specimen part
View Samples