Nature CommunicationsGenomicsPeer-reviewed journal article
Transposon invasion of primate genomes shaped human inflammatory enhancers and susceptibility to inflammatory diseases
Mengliang YeiD, Maxime RotivaliD, Sebastián AmigorenaiD, Elina ZuevaiD
Abstract
Human immune inflammatory response reflects the evolutionary adaptation of immune-cell regulatory elements1, where recent mutations can control both pathogen defence and susceptibility to chronic inflammatory and autoimmune diseases2-4. The impact of the deeper evolutionary history of these elements within primate genomes on human inflammatory responses remains poorly understood. Evolutionary young transposons have uniquely reshaped primate genomes5 and spread novel cis-regulatory sequences6. To understand how these events influenced human inflammation, we traced sequence changes in annotated human immune-cell enhancers back to macaque. We show that Alu elements and endogenous retroviruses dispersed motifs for the inflammation-related NF-{kappa}B and IRF1, redefining their binding patterns and contributing most prominently to great ape-specific binding sites. After the human-macaque split, many of these motifs shifted toward higher predicted binding affinity. In humans, population genetics analyses reveal that positive selection favors alleles, often Alu-derived, that increase enhancer affinity toward NF-{kappa}B. Enhancers containing Alu elements are more likely to undergo positive selection at the locus level, particularly when associated with chronic inflammatory diseases. As the most mutable enhancer sequences, Alus harbor disproportionately high numbers of single nucleotide polymorphisms and significantly contribute to selected alleles, some associated with chronic inflammatory diseases. We propose that the invasion of primate-specific transposons has created unique opportunities to adapt inflammatory responses in rapidly evolving great apes, with ancestral Alus continuing to influence evolutionary potential in humans.
- organism: human
- organism: mouse
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 7
- FWCI
- 3.75
- Field percentile
- top 7%
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