Codon usage pattern of the ancestor of green plants revealed through Rhodophyta.
Codon usage bias
Optimal codons
Rhodophyta
Translational accuracy
tRNA modification
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
11 Sep 2023
11 Sep 2023
Historique:
received:
30
04
2023
accepted:
14
08
2023
medline:
13
9
2023
pubmed:
12
9
2023
entrez:
11
9
2023
Statut:
epublish
Résumé
Rhodophyta are among the closest known relatives of green plants. Studying the codons of their genomes can help us understand the codon usage pattern and characteristics of the ancestor of green plants. By studying the codon usage pattern of all available red algae, it was found that although there are some differences among species, high-bias genes in most red algae prefer codons ending with GC. Correlation analysis, Nc-GC3s plots, parity rule 2 plots, neutrality plot analysis, differential protein region analysis and comparison of the nucleotide content of introns and flanking sequences showed that the bias phenomenon is likely to be influenced by local mutation pressure and natural selection, the latter of which is the dominant factor in terms of translation accuracy and efficiency. It is worth noting that selection on translation accuracy could even be detected in the low-bias genes of individual species. In addition, we identified 15 common optimal codons in seven red algae except for G. sulphuraria for the first time, most of which were found to be complementary and bound to the tRNA genes with the highest copy number. Interestingly, tRNA modification was found for the highly degenerate amino acids of all multicellular red algae and individual unicellular red algae, which indicates that highly biased genes tend to use modified tRNA in translation. Our research not only lays a foundation for exploring the characteristics of codon usage of the red algae as green plant ancestors, but will also facilitate the design and performance of transgenic work in some economic red algae in the future.
Identifiants
pubmed: 37697255
doi: 10.1186/s12864-023-09586-w
pii: 10.1186/s12864-023-09586-w
pmc: PMC10496412
doi:
Substances chimiques
Amino Acids
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
538Subventions
Organisme : huipeng Yao
ID : 2023YFH0103
Organisme : huipeng Yao
ID : 2023YFH0103
Organisme : huipeng Yao
ID : 2023YFH0103
Organisme : huipeng Yao
ID : 2023YFH0103
Organisme : huipeng Yao
ID : 2023YFH0103
Organisme : huipeng Yao
ID : 2023YFH0103
Organisme : huipeng Yao
ID : 2023YFH0103
Organisme : huipeng Yao
ID : 2023YFH0103
Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
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