Genome biologyGenomicsPeer-reviewed journal article
Population variation of miRNAs and isomiRs and their impact on human immunity to infection
Maxime RotivaliD, Katherine J. SiddleiD, Martin Silvert, Julien Pothlichet, Hélène QuachiD, Lluís Quintana‐MurciiD
Abstract
MicroRNAs (miRNAs) are key epigenetic regulators of the immune system, yet their variation and contribution to intra- and inter-population differences in immune responses is poorly characterized. Here, we generated 977 miRNA-sequencing profiles from primary monocytes, from individuals of African and European ancestry, following activation of three TLR pathways (TLR4, TLR1/2 and TLR7/8) or infection with Influenza A virus. We find that immune activation leads to important modifications in the miRNA and isomiR repertoire, particularly in response to viral challenges. These changes are, however, much weaker than those observed for protein-coding genes, suggesting stronger selective constraints on the miRNA response to stimulation. This is supported by the limited genetic control of miRNA expression variability (miR-QTLs) -- and the lower occurrence of GxE interactions -- in stark contrast with eQTLs that are largely context-dependent. We also detect marked differences in miRNA expression between populations, which are mostly driven by non-genetic factors. Yet, on average, miR-QTLs explain ~60% of population differences in expression of their cognate miRNAs, and, in some cases, evolve adaptively, as shown in Europeans for a miRNA-rich cluster on chromosome 14. Finally, integrating miRNA and mRNA data from the same individuals, we provide evidence that the canonical model of miRNA-driven transcript degradation has a minor impact on miRNA-mRNA correlations, which are, in our setting, mainly driven by co-transcription. Together, our results shed new light onto the factors driving miRNA and isomiR diversity at the population level, and constitute a useful resource for evaluating their role in host differences of immunity to infection.
- organism: human
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 30
- FWCI
- 1.54
- Field percentile
- top 19%
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