Genome biologyGenomicsPeer-reviewed journal article
Discovering in vivo cytokine eQTL interactions from a lupus clinical trial
Emma E. DavenportiD, Tiffany AmariutaiD, María Gutiérrez‐ArcelusiD, Kamil SlowikowskiiD, Harm-Jan WestraiD, Yang LuoiD, Ciyue SheniD, Deepak A. RaoiD, Ying ZhangiD, Stephen Pearson, David von Schack, JEAN SACCUZZO BEEBE, Nan Bing, Sally John, Michael S. VincentiD, Baohong ZhangiD, Soumya RaychaudhuriiD
Abstract
BackgroundCytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms.\n\nResultsIn a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus we measured interferon (IFN) status, anti-IL-6 drug exposure and genome-wide gene expression at three time points (379 samples from 157 individuals). First, we show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point by 64%. Then, after identifying 4,818 cis-eQTLs, we observed a statistically significant enrichment of in vivo eQTL interactions with IFN status (p<0.001 by permutation) and anti-IL-6 drug exposure (p<0.001). We observed 210 and 72 interactions for IFN and anti-IL-6 respectively (FDR<20%). Anti-IL-6 interactions have not yet been described while 99 of the IFN interactions are novel. Finally, we found transcription factor binding motifs interrupted by eQTL interaction SNPs, pointing to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs.\n\nConclusionThis study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: eQTL / molecular QTL mapping
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 46
- FWCI
- 3.22
- Field percentile
- top 7%
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