Nature GeneticsGenomicsPeer-reviewed journal article
Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation
Blagoje SoskiciD, Eddie Cano-GameziD, Deborah J. SmythiD, Kirsty Ambridge, Ziying KeiD, Julie C. MatteiD, Lara Bossini‐CastilloiD, Joanna KaplanisiD, Lucia Ramirez-NavarroiD, Anna LorenciD, Nikolina Nakić, Jorge Esparza-Gordillo, Wendy C. RowaniD, David R. WilléiD, David F. ToughiD, Paola G. BronsoniD, Gosia TrynkaiD
Abstract
During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 190
- FWCI
- 22.02
- Field percentile
- top 1%
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