Login / Signup

Structural Characteristics and Phylogenetic Analysis of the Mitochondrial Genomes of Four Krisna Species (Hemiptera: Cicadellidae: Iassinae).

Yanqiong YangJia-Jia WangRenhuai DaiXianyi Wang
Published in: Genes (2023)
Krisna species are insects that have piercing-sucking mouthparts and belong to the Krisnini tribe in the Iassinae subfamily of leafhoppers in the Cicadellidae family. In this study, we sequenced and compared the mitochondrial genomes (mitogenomes) of four Krisna species. The results showed that all four mitogenomes were composed of cyclic double-stranded molecules and contained 13 protein-coding genes (PCGs) and 22 and 2 genes coding for tRNAs and rRNAs, respectively. Those mitogenomes exhibited similar base composition, gene size, and codon usage patterns for the protein-coding genes. The analysis of the nonsynonymous substitution rate (Ka)/synonymous substitution rate (Ks) showed that evolution occurred the fastest in ND4 and the slowest in COI . 13 PCGs that underwent purification selection were suitable for studying phylogenetic relationships within Krisna . ND2 , ND6 , and ATP6 had highly variable nucleotide diversity, whereas COI and ND1 exhibited the lowest diversity. Genes or gene regions with high nucleotide diversity can provide potential marker candidates for population genetics and species delimitation in Krisna . Analyses of parity and neutral plots showed that both natural selection and mutation pressure affected the codon usage bias. In the phylogenetic analysis, all subfamilies were restored to a monophyletic group; the Krisnini tribe is monophyletic, and the Krisna genus is paraphyletic. Our study provides novel insights into the significance of the background nucleotide composition and codon usage patterns in the CDSs of the 13 mitochondrial PCGs of the Krisna genome, which could enable the identification of a different gene organization and may be used for accurate phylogenetic analysis of Krisna species.
Keyphrases
  • genome wide identification
  • genome wide
  • bioinformatics analysis
  • genome wide analysis
  • oxidative stress
  • transcription factor
  • dna methylation
  • copy number
  • amino acid
  • risk assessment