Dsouza Norine Norbert, Chellasamy Selvaa Kumar
Department of Bioinformatics, School of Biotechnology and Bioinformatics, Sector 15, CBD Belapur, Navi Mumbai, Maharashtra, India.
Department of Biotechnology, St. Xavier's College, Mumbai, Maharashtra, India.
Iran J Microbiol. 2024 Feb;16(1):104-113. doi: 10.18502/ijm.v16i1.14879.
Multiple outbreaks over two decades and a high mortality rate have emphasized the Nipah virus (NiV) as a priority research area. The study focuses on identifying the mutational landscape in sequences from NiV human isolates from different geographical regions.
Thirty-seven NiV genomes of human samples from Malaysia, Bangladesh, and India were subjected to phylogeny and metagenomic analysis to decipher the genome variability using MEGA11 software and the meta-CATS web server. Using the Single-Likelihood Ancestor Counting method, the synonymous and nonsynonymous mutations among NiV genes were identified. Further, the nonsynonymous variations were used to identify mutations in all the NiV proteins.
The NiV isolates were categorized into NiV-M, NiV-B, and NiV-I clades based on phylogenetic analysis. Metagenomic analysis revealed 1636 variations in the noncoding and coding regions of the genomes of the three clades of NiV. Further analysis of nonsynonymous mutations showed the phosphoprotein to be highly mutating, whereas the matrix protein was stable.
Deciphering the mutation pattern using a comparative genomics approach for human isolates provided valuable insight into the stability of NiV proteins which can be further used for understanding variations in host-pathogen interaction and developing effective therapeutic measures.
二十多年来的多次疫情爆发以及高死亡率凸显了尼帕病毒(NiV)作为重点研究领域的地位。本研究聚焦于确定来自不同地理区域的NiV人类分离株序列中的突变情况。
对来自马来西亚、孟加拉国和印度的37份人类样本的NiV基因组进行系统发育和宏基因组分析,使用MEGA11软件和meta-CATS网络服务器来解读基因组变异性。采用单似然祖先计数法,确定NiV基因中的同义突变和非同义突变。此外,利用非同义变异来识别所有NiV蛋白中的突变。
基于系统发育分析,NiV分离株被分为NiV-M、NiV-B和NiV-I分支。宏基因组分析揭示了NiV三个分支基因组的非编码区和编码区存在1636个变异。对非同义突变的进一步分析表明,磷蛋白高度变异,而基质蛋白则较为稳定。
采用比较基因组学方法解读人类分离株的突变模式,为NiV蛋白的稳定性提供了有价值的见解,这可进一步用于理解宿主-病原体相互作用的变异并制定有效的治疗措施。