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Miniature Inverted-repeat Transposable Elements Drive Rapid MicroRNA Diversification in Angiosperms.

Zhong-Long GuoZheng KuangYihan TaoHao-Tian WangMiaomiao WanChen HaoFei ShenXiaoZeng YangLei Li
Published in: Molecular biology and evolution (2022)
MicroRNAs (miRNAs) are fast evolving endogenous small RNAs that regulate organism function and behavior in both animals and plants. Although models for de novo miRNA biogenesis have been proposed, the genomic mechanisms driving swift diversification of the miRNA repertoires in plants remain elusive. Here, by comprehensively analyzing 21 phylogenetically representative plant species, ranging from green algae to angiosperms, we systematically identified de novo miRNA events associated with 8,649 miRNA loci. We found that 399 (4.6%), 466 (5.4%), and 1,402 (16.2%) miRNAs were derived from inverted gene duplication events, long terminal repeats of retrotransposons, and miniature inverted-repeat transposable elements (MITEs), respectively. Among the miRNAs of these origins, MITEs, especially those belonging to the Mutator, Tc1/Mariner, and PIF/Harbinger superfamilies, were the predominant genomic source for de novo miRNAs in the 15 examined angiosperms but not in the six non-angiosperms. Our data further illustrated a transposition-transcription process by which MITEs are converted into new miRNAs (termed MITE-miRNAs) whereby properly sized MITEs are transcribed and therefore become potential substrates for the miRNA processing machinery by transposing into introns of active genes. By analyzing the 58,038 putative target genes for the 8,095 miRNAs, we found that the target genes of MITE-miRNAs were preferentially associated with response to environmental stimuli such as temperature, suggesting that MITE-miRNAs are pertinent to plant adaptation. Collectively, these findings demonstrate that molecular conversion of MITEs is a genomic mechanism leading to rapid and continuous changes to the miRNA repertoires in angiosperm.
Keyphrases
  • genome wide
  • copy number
  • dna methylation
  • machine learning
  • gene expression
  • climate change
  • human health
  • risk assessment