Dataset Information

Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures.

ABSTRACT: The lung is critical in surveillance and initial defense against pathogens. In humans, as in mice, individual genetic differences strongly modulate pulmonary responses to infectious agents, severity of lung disease, and potential allergic reactions. In a first step towards understanding genetic predisposition and pulmonary molecular networks that underlie individual differences in disease vulnerability, we performed a global analysis of normative lung gene expression levels in inbred mouse strains and a large family of BXD strains that are widely used for systems genetics. Our goal is to provide a key community resource on the genetics of the normative lung transcriptome that can serve as a foundation for experimental analysis and allow predicting genetic predisposition and response to pathogens, allergens, and xenobiotics.Steady-state polyA+ mRNA levels were assayed across a diverse and fully genotyped panel of 57 isogenic strains using the Affymetrix M430 2.0 array. Correlations of expression levels between genes were determined. Global expression QTL (eQTL) analysis and network covariance analysis was performed using tools and resources in GeneNetwork http://www.genenetwork.org.Expression values were highly variable across strains and in many cases exhibited a high heritability factor. Several genes which showed a restricted expression to lung tissue were identified. Using correlations between gene expression values across all strains, we defined and extended memberships of several important molecular networks in the lung. Furthermore, we were able to extract signatures of immune cell subpopulations and characterize co-variation and shared genetic modulation. Known QTL regions for respiratory infection susceptibility were investigated and several cis-eQTL genes were identified. Numerous cis- and trans-regulated transcripts and chromosomal intervals with strong regulatory activity were mapped. The Cyp1a1 P450 transcript had a strong trans-acting eQTL (LOD 11.8) on Chr 12 at 36 ± 1 Mb. This interval contains the transcription factor Ahr that has a critical mis-sense allele in the DBA/2J haplotype and evidently modulates transcriptional activation by AhR.Large-scale gene expression analyses in genetic reference populations revealed lung-specific and immune-cell gene expression profiles and suggested specific gene regulatory interactions.

SUBMITTER: Alberts R

PROVIDER: S-EPMC3105947 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures.

Alberts Rudi R Lu Lu L Williams Robert W RW Schughart Klaus K

Respiratory research 20110502

<h4>Background</h4>The lung is critical in surveillance and initial defense against pathogens. In humans, as in mice, individual genetic differences strongly modulate pulmonary responses to infectious agents, severity of lung disease, and potential allergic reactions. In a first step towards understanding genetic predisposition and pulmonary molecular networks that underlie individual differences in disease vulnerability, we performed a global analysis of normative lung gene expression levels in ...[more]

PMID: 21535883

Similar Datasets

Project description:Inflammatory bowel disease (IBD) is the common name for chronic disorders associated with the inflammation of the gastrointestinal tract. IBD is triggered by environmental factors in genetically susceptible individuals and has a significant number of incidences worldwide. Crohn's disease (CD) and ulcerative colitis (UC) are the two distinct types of IBD. While involvement in ulcerative colitis is limited to the colon, Crohn's disease may involve the whole gastrointestinal tract. Although these two disorders differ in macroscopic inflammation patterns, they share various molecular pathogenesis, yet the diagnosis can remain unclear, and it is important to reveal their molecular signatures in the network level. Improved molecular understanding may reveal disease type-specific and even individual-specific targets. To this aim, we determine the subnetworks specific to UC and CD by mapping transcriptome data to protein-protein interaction (PPI) networks using two different approaches [KeyPathwayMiner (KPM) and stringApp] and perform the functional enrichment analysis of the resulting disease type-specific subnetworks. TP63 was identified as the hub gene in the UC-specific subnet and p63 tumor protein, being in the same family as p53 and p73, has been studied in literature for the risk associated with colorectal cancer and IBD. APP was identified as the hub gene in the CD-specific subnet, and it has an important role in the pathogenesis of Alzheimer's disease (AD). This relation suggests that some similar genetic factors may be effective in both AD and CD. Last, in order to understand the biological meaning of these disease-specific subnets, they were functionally enriched. It is important to note that chemokines-special types of cytokines-and antibacterial response are important in UC-specific subnets, whereas cytokines and antimicrobial responses as well as cancer-related pathways are important in CD-specific subnets. Overall, these findings reveal the differences between IBD subtypes at the molecular level and can facilitate diagnosis for UC and CD as well as provide potential molecular targets that are specific to disease subtypes.

Project description:An integrative analysis focused on multi-tissue transcriptomics has not been done for asthma. Tissue-specific DEGs remain undetected in many multi-tissue analyses, which influences identification of disease-relevant pathways and potential drug candidates. Transcriptome data from 609 cases and 196 controls, generated using airway epithelium, bronchial, nasal, airway macrophages, distal lung fibroblasts, proximal lung fibroblasts, CD4+ lymphocytes, CD8+ lymphocytes from whole blood and induced sputum samples, were retrieved from Gene Expression Omnibus (GEO). Differentially regulated asthma-relevant genes identified from each sample type were used to identify (a) tissue-specific and tissue-shared asthma pathways, (b) their connection to GWAS-identified disease genes to identify candidate tissue for functional studies, (c) to select surrogate sample for invasive tissues, and finally (d) to identify potential drug candidates via connectivity map analysis. We found that inter-tissue similarity in gene expression was more pronounced at pathway/functional level than at gene level with highest similarity between bronchial epithelial cells and lung fibroblasts, and lowest between airway epithelium and whole blood samples. Although public-domain gene expression data are limited by inadequately annotated per-sample demographic and clinical information which limited the analysis, our tissue-resolved analysis clearly demonstrated relative importance of unique and shared asthma pathways, At the pathway level, IL-1b signaling and ERK signaling were significant in many tissue types, while Insulin-like growth factor and TGF-beta signaling were relevant in only airway epithelial tissue. IL-12 (in macrophages) and Immunoglobulin signaling (in lymphocytes) and chemokines (in nasal epithelium) were the highest expressed pathways. Overall, the IL-1 signaling genes (inflammatory) were relevant in the airway compartment, while pro-Th2 genes including IL-13 and STAT6 were more relevant in fibroblasts, lymphocytes, macrophages and bronchial biopsies. These genes were also associated with asthma in the GWAS catalog. Support Vector Machine showed that DEGs based on macrophages and epithelial cells have the highest and lowest discriminatory accuracy, respectively. Drug (entinostat, BMS-345541) and genetic perturbagens (KLF6, BCL10, INFB1 and BAMBI) negatively connected to disease at multi-tissue level could potentially repurposed for treating asthma. Collectively, our study indicates that the DEGs, perturbagens and disease are connected differentially depending on tissue/cell types. While most of the existing literature describes asthma transcriptome data from individual sample types, the present work demonstrates the utility of multi-tissue transcriptome data. Future studies should focus on collecting transcriptomic data from multiple tissues, age and race groups, genetic background, disease subtypes and on the availability of better-annotated data in the public domain.

Dataset Information

Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures.

Publications

Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets