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Large-Scale Annotation and Evolution Analysis of MiRNA in Insects.

Xingzhou MaKang HeZhenmin ShiMeizhen LiFei LiXue-Xin Chen
Published in: Genome biology and evolution (2022)
Insects are among the most diverse and successful groups of animals and exhibit great morphological diversity and complexity. The innovation of wings and metamorphosis are some examples of the fascinating biological evolution of insects. Most microRNAs (miRNAs) contribute to canalization by conferring robustness to gene networks and thus increase the heritability of important phenotypes. Though previous studies have demonstrated how miRNAs regulate important phenotypes, little is still known about miRNA evolution in insects. Here, we used both small RNA-seq data and homology searching methods to annotate the miRNA repertoires of 152 arthropod species, including 135 insects and 17 noninsect arthropods. We identified 16,212 miRNA genes, and classified them into highly conserved (62), insect-conserved (90), and lineage-specific (354) miRNA families. The phylogenetic relationship of miRNA binary presence/absence dynamics implies that homoplastic loss of conserved miRNA families tends to occur in far-related morphologically simplified taxa, including scale insects (Coccoidea) and twisted-wing insects (Strepsiptera), leading to inconsistent phylogenetic tree reconstruction. The common ancestor of Insecta shares 62 conserved miRNA families, of which five were rapidly gained in the early winged-insects (Pterygota). We also detected extensive miRNA losses in Paraneoptera that are correlated with morphological reduction, and miRNA gains in early Endopterygota around the time holometabolous metamorphosis appeared. This was followed by abundant miRNA gains in Hymenoptera and Lepidoptera. In summary, we provide a comprehensive data set and a detailed evolutionary analysis of miRNAs in insects. These data will be important for future studies on miRNA functions associated with insect morphological innovation and trait biodiversity.
Keyphrases
  • rna seq
  • transcription factor
  • genome wide
  • gene expression
  • big data
  • single cell
  • electronic health record
  • deep learning
  • current status
  • drug induced
  • cell fate