Cell GenomicsGenomicsPeer-reviewed journal article
eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis
Katie L. BurnhamiD, Nikhil MilindiD, Wanseon LeeiD, Andrew John KwokiD, Kiki Cano-GameziD, Yuxin MiiD, Cyndi G. GeogheganiD, Ping ZhangiD, Jenni Addison, Helen F. GalleyiD, Sally HalliD, Siân Roughton, Jane Taylor, Heather M. Tennant, Nigel R. Webster, Achyut GuleriiD, Natalia Waddington, Dilshan Arawwawala, John Durcan, Christine Mitchell-Inwang, Alasdair Short, Susan Smolen, Karen Swan, Sarah WilliamsiD, Emily Errington, , Sarah Mappleback
Abstract
Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene expression-based patient subgroups (Sepsis Response Signatures: SRS) informative for outcome and underlying pathophysiology. Here we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-seq data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs, and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. These included HIF1A and CEBPB, which were associated with progenitor and immature neutrophil subsets respectively, further implicating glycolysis and emergency granulopoiesis in SRS1. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection. Understanding the regulatory networks underlying patient heterogeneity provides additional information for developing immunomodulatory treatments and a personalised medicine approach to treating sepsis.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 21
- FWCI
- 5.55
- Field percentile
- top 3%
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