戊型肝炎病毒(HEV)是一种新兴的人畜共患病原体,具有多种物种和基因型,可以归类为人类,动物,和人畜共患HEV。HEV的密码子使用偏差仍不清楚。本研究旨在表征HEV的密码子使用,并阐明影响密码子使用偏差的主要驱动因素。共七种HEV基因型,HEV-1(人HEV),HEV-3和HEV-4(人畜共患HEV),HEV-8,HEV-B,HEV-C1和HEV-C2(新兴动物HEV),包括在研究中。完整的编码序列,ORF1、ORF2和ORF3相应地在GenBank中获得。除HEV-8外,其他六种基因型倾向于使用以G/C结尾的密码子。在分析相对同义密码子使用(RSCU)和主成分分析(PCA)的基础上,确定HEV基因型的密码子使用偏倚。密码子使用偏差在人类中差异很大,人畜共患,和动物HEV基因型;此外,它在某些基因型如HEV-4、HEV-8和HEV-C1中变化。此外,二核苷酸丰度表明,HEV受到翻译选择的影响,形成了独特的二核苷酸使用模式。此外,奇偶校验规则2分析(PR2),有效密码子数(ENC)-图,和中立性分析共同进行。自然选择在形成HEV密码子使用偏好中起主导作用,在HEV-1,HEV-3,HEV-B和HEV-C1中占主导地位,而与突变压力联合影响HEV-4,HEV-8和HEV-C2。我们的发现可以提供对HEV进化和密码子使用偏差的见解。
Hepatitis E virus (HEV) is an emerging zoonotic pathogen with multiple species and genotypes, which may be classified into human, animal, and zoonotic HEV. Codon usage bias of HEV remained unclear. This study aims to characterize the codon usage of HEV and elucidate the main drivers influencing the codon usage bias. A total of seven HEV genotypes, HEV-1 (human HEV), HEV-3 and HEV-4 (zoonotic HEV), HEV-8, HEV-B, HEV-C1, and HEV-C2 (emerging animal HEV), were included in the study. Complete coding sequences, ORF1, ORF2, and ORF3, were accordingly obtained in the GenBank. Except for HEV-8, the other six genotypes tended to use codons ending in G/C. Based on the analysis of relatively synonymous codon usage (RSCU) and principal component analysis (PCA), codon usage bias was determined for HEV genotypes. Codon usage bias differed widely across human, zoonotic, and animal HEV genotypes; furthermore, it varied within certain genotypes such as HEV-4, HEV-8, and HEV-C1. In addition, dinucleotide abundance revealed that HEV was affected by translation selection to form a unique dinucleotide usage pattern. Moreover, parity rule 2 analysis (PR2), effective codon number (ENC)-plot, and neutrality analysis were jointly performed. Natural selection played a leading role in forming HEV codon usage bias, which was predominant in HEV-1, HEV-3, HEV-B and HEV-C1, while affected HEV-4, HEV-8, and HEV-C2 in combination with mutation pressure. Our findings may provide insights into HEV evolution and codon usage bias.