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The Chromosome-Level Genome of Hestina assimilis (Lepidoptera: Nymphalidae) Reveals the Evolution of Saprophagy-Related Genes in Brush-Footed Butterflies.

Lu ZhaoXiao-Dong LiTao JiangHang WangZhicuo DanSheng-Quan XuDe-Long Guan
Published in: International journal of molecular sciences (2023)
Most butterflies feed on nectar, while some saprophagous butterflies forage on various non-nectar foods. To date, little is known about the genomic and molecular shifts associated with the evolution of the saprophagous feeding strategy. Here, we assembled the high-quality chromosome-level genome of Hestina assimilis to explore its saprophagous molecular and genetic mechanisms. This chromosome-level genome of H . assimilis is 412.82 Mb, with a scaffold N50 of 15.70 Mb. In total, 98.11% of contigs were anchored to 30 chromosomes. Compared with H. assimilis and other Nymphalidae butterflies, the genes of metabolism and detoxification experienced expansions. We annotated 80 cytochrome P450 (CYP) genes in the H. assimilis genome, among which genes belonging to the CYP4 subfamily were significantly expanded ( p < 0.01). These P450 genes were unevenly distributed and mainly concentrated on chromosomes 6-9. We identified 33 olfactory receptor (OR), 20 odorant-binding protein (OBP), and six gustatory receptor (GR) genes in the H. assimilis genome, which were fewer than in the nectarivorous Danaus plexippus . A decreased number of OBP, OR, and GR genes implied that H. assimilis should resort less to olfaction and gustation than their nectarivorous counterparts, which need highly specialized olfactory and gustatory functions. Moreover, we found one site under positive selection occurred in residue 996 (phenylalanine) of GR genes exclusive to H. assimilis , which is conservative in most lineages. Our study provides support for the adaptive evolution of feeding habits in butterflies.
Keyphrases
  • genome wide
  • copy number
  • genome wide identification
  • dna methylation
  • bioinformatics analysis
  • binding protein
  • genome wide analysis
  • transcription factor
  • neural network