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Decoding the codon usage patterns in Y-domain region of hepatitis E viruses.

Zoya Shafat1, Anwar Ahmed2, Mohammad K Parvez3

  • 1Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.

Journal, Genetic Engineering & Biotechnology
|April 11, 2022
PubMed
Summary

This study reveals Hepatitis E virus (HEV) codon usage patterns in its Y-domain region (YDR) across seven hosts. HEV exhibits a mixed codon usage strategy, influenced by both viral mutation pressure and host selection.

Keywords:
Codon usage biasMutation pressureNatural selectionNucleotide compositionYDR

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Area of Science:

  • Virology
  • Molecular Biology
  • Genomics

Background:

  • Hepatitis E virus (HEV) is a significant RNA virus.
  • The HEV genome comprises three open-reading frames (ORFs), with ORF1's Y-domain region (YDR) crucial for pathogenesis.
  • Codon usage bias is a key factor in host-pathogen interactions, yet unstudied for HEV YDR.

Purpose of the Study:

  • To investigate the synonymous codon usage patterns of the HEV YDR.
  • To analyze these patterns across seven different natural HEV hosts: Human, Rabbit, Mongoose, Pig, Wild boar, Camel, and Monkey.
  • To understand the interplay of nucleotide composition, codon bias, and host-pathogen dynamics in HEV evolution.

Main Methods:

  • Analysis of nucleotide composition in HEV YDR genomes.
  • Relative Synonymous Codon Usage (RSCU) analysis.
  • Comparative codon frequency analysis between HEV and its hosts.

Main Results:

  • HEV YDR genomes are rich in C and U nucleotides.
  • A significant bias towards C and U ending codons was observed in HEV across all hosts.
  • HEV codon usage displayed a mix of similarity and difference with host preferences, suggesting a dual strategy.
  • Mutation pressure and host natural selection appear to shape HEV YDR codon usage.

Conclusions:

  • This is the first study to analyze HEV YDR codon usage across seven natural hosts.
  • Findings provide insights into HEV molecular evolution and adaptation.
  • Understanding preferred codons can aid in predicting viral expression efficiency and host-specific adaptations.