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A comprehensive global perspective on phylogenomics and evolutionary dynamics of Small ruminant morbillivirus

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Article number17
<mark>Journal publication date</mark>8/01/2020
<mark>Journal</mark>Scientific Reports
Issue number1
Volume10
Number of pages17
Publication statusPublished
Original languageEnglish

Abstract

A string of complete genome sequences of Small ruminant morbillivirus (SRMV) have been reported from different parts of the globe including Asia, Africa and the Middle East. Despite individual genome sequence-based analysis, there is a paucity of comparative genomic and evolutionary analysis to provide overarching and comprehensive evolutionary insights. Therefore, we first enriched the existing database of complete genome sequences of SRMVs with Pakistan-originated strains and then explored overall nucleotide diversity, genomic and residue characteristics, and deduced an evolutionary relationship among strains representing a diverse geographical region worldwide. The average number of pairwise nucleotide differences among the whole genomes was found to be 788.690 with a diversity in nucleotide sequences (0.04889 ± S.D. 0.00468) and haplotype variance (0.00001). The RNA-dependent-RNA polymerase (L) gene revealed phylogenetic relationship among SRMVs in a pattern similar to those of complete genome and the nucleoprotein (N) gene. Therefore, we propose another useful molecular marker that may be employed for future epidemiological investigations. Based on evolutionary analysis, the mean evolution rate for the complete genome, N, P, M, F, H and L genes of SRMV was estimated to be 9.953 × 10–4, 1.1 × 10–3, 1.23 × 10–3, 2.56 × 10–3, 2.01 × 10–3, 1.47 × 10–3 and 9.75 × 10–4 substitutions per site per year, respectively. A recombinant event was observed in a Pakistan-originated strain (KY967608) revealing Indian strains as major (98.1%, KR140086) and minor parents (99.8%, KT860064). Taken together, outcomes of the study augment our knowledge and current understanding towards ongoing phylogenomic and evolutionary dynamics for better comprehensions of SRMVs and effective disease control interventions.