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Generation of primary human intestinal T cell transcriptomes reveals differential expression at genetic risk loci for immune-mediated disease.

ABSTRACT: Genome-wide association studies (GWAS) have identified genetic variants within multiple risk loci as predisposing to intestinal inflammatory diseases, including Crohn's disease, ulcerative colitis and coeliac disease. Most risk variants affect regulation of transcription, but a critical challenge is to identify which genes and which cell types these variants affect. We aimed to characterise whole transcriptomes for each common T lymphocyte subset resident within the gut mucosa, and use these to infer biological insights and highlight candidate genes of interest within GWAS risk loci.We isolated the four major intestinal T cell populations from pinch biopsies from healthy subjects and generated transcriptomes for each. We computationally integrated these transcriptomes with GWAS data from immune-related diseases.Robust, high quality transcriptomic data were generated from 1?ng of RNA from precisely sorted cell subsets. Gene expression patterns clearly differentiated intestinal T cells from counterparts in peripheral blood and revealed distinct signalling pathways for each intestinal T cell subset. Intestinal-specific T cell transcripts were enriched in GWAS risk loci for Crohn's disease, ulcerative colitis and coeliac disease, but also specific extraintestinal immune-mediated diseases, allowing prediction of novel candidate genes.This is the first report of transcriptomes for minimally manipulated intestinal T lymphocyte subsets in humans. We have demonstrated that careful processing of mucosal biopsies allows the generation of transcriptomes from as few as 1000 highly purified cells with minimal interindividual variation. Bioinformatic integration of transcriptomic data with recent GWAS data identified specific candidate genes and cell types for inflammatory pathologies.

SUBMITTER: Raine T

PROVIDER: S-EPMC4316924 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Generation of primary human intestinal T cell transcriptomes reveals differential expression at genetic risk loci for immune-mediated disease.

Raine Tim T Liu Jimmy Z JZ Anderson Carl A CA Parkes Miles M Kaser Arthur A

Gut 20140505 2

<h4>Objective</h4>Genome-wide association studies (GWAS) have identified genetic variants within multiple risk loci as predisposing to intestinal inflammatory diseases, including Crohn's disease, ulcerative colitis and coeliac disease. Most risk variants affect regulation of transcription, but a critical challenge is to identify which genes and which cell types these variants affect. We aimed to characterise whole transcriptomes for each common T lymphocyte subset resident within the gut mucosa, ...[more]

PMID: 24799394

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Project description:In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.

Dataset Information

Generation of primary human intestinal T cell transcriptomes reveals differential expression at genetic risk loci for immune-mediated disease.

Publications

Generation of primary human intestinal T cell transcriptomes reveals differential expression at genetic risk loci for immune-mediated disease.

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