Back to Search Start Over

New Zealand Tree and Giant Wētā (Orthoptera) Transcriptomics Reveal Divergent Selection Patterns in Metabolic Loci.

Authors :
Twort VG
Newcomb RD
Buckley TR
Source :
Genome biology and evolution [Genome Biol Evol] 2019 Apr 01; Vol. 11 (4), pp. 1293-1306.
Publication Year :
2019

Abstract

Exposure to low temperatures requires an organism to overcome physiological challenges. New Zealand wētā belonging to the genera Hemideina and Deinacrida are found across a wide range of thermal environments and therefore subject to varying selective pressures. Here we assess the selection pressures across the wētā phylogeny, with a particular emphasis on identifying genes under positive or diversifying selection. We used RNA-seq to generate transcriptomes for all 18 Deinacrida and Hemideina species. A total of 755 orthologous genes were identified using a bidirectional best-hit approach, with the resulting gene set encompassing a diverse range of functional classes. Analysis of ortholog ratios of synonymous to nonsynonymous amino acid changes found 83 genes that are under positive selection for at least one codon. A wide variety of Gene Ontology terms, enzymes, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways are represented among these genes. In particular, enzymes involved in oxidative phosphorylation, melanin synthesis, and free-radical scavenging are represented, consistent with physiological and metabolic changes that are associated with adaptation to alpine environments. Structural alignment of the transcripts with the most codons under positive selection revealed that the majority of sites are surface residues, and therefore have the potential to influence the thermostability of the enzyme, with the exception of prophenoloxidase where two residues near the active site are under selection. These proteins provide interesting candidates for further analysis of protein evolution.<br /> (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Details

Language :
English
ISSN :
1759-6653
Volume :
11
Issue :
4
Database :
MEDLINE
Journal :
Genome biology and evolution
Publication Type :
Academic Journal
Accession number :
30957857
Full Text :
https://doi.org/10.1093/gbe/evz070