PDF(Pubmed)
Abstract：
Various lineages of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have contributed to prolongation of the Coronavirus Disease 2019 (COVID-19) pandemic. Several non-synonymous mutations in SARS-CoV-2 proteins have generated multiple SARS-CoV-2 variants. In our previous report, we have shown that an evenly uneven distribution of unique protein variants of SARS-CoV-2 is geo-location or demography-specific. However, the correlation between the demographic transmutability of the SARS-CoV-2 infection and mutations in various proteins remains unknown due to hidden symmetry/asymmetry in the occurrence of mutations. This study tracked how these mutations are emerging in SARS-CoV-2 proteins in six model countries and globally. In a geo-location, considering the mutations having a frequency of detection of at least 500 in each SARS-CoV-2 protein, we studied the country-wise percentage of invariant residues. Our data revealed that since October 2020, highly frequent mutations in SARS-CoV-2 have been observed mostly in the Open Reading Frame (ORF) 7b and ORF8, worldwide. No such highly frequent mutations in any of the SARS-CoV-2 proteins were found in the UK, India, and Brazil, which does not correlate with the degree of transmissibility of the virus in India and Brazil. However, we have found a signature that SARS-CoV-2 proteins were evolving at a higher rate, and considering global data, mutations are detected in the majority of the available amino acid locations. Fractal analysis of each protein\'s normalized factor time series showed a periodically aperiodic emergence of dominant variants for SARS-CoV-2 protein mutations across different countries. It was noticed that certain high-frequency variants have emerged in the last couple of months, and thus the emerging SARS-CoV-2 strains are expected to contain prevalent mutations in the ORF3a, membrane, and ORF8 proteins. In contrast to other beta-coronaviruses, SARS-CoV-2 variants have rapidly emerged based on demographically dependent mutations. Characterization of the periodically aperiodic nature of the demographic spread of SARS-CoV-2 variants in various countries can contribute to the identification of the origin of SARS-CoV-2.
摘要：
严重急性呼吸道综合症冠状病毒-2（SARS-CoV-2）的各种谱系导致了2019年冠状病毒病（COVID-19）大流行的延长。SARS-CoV-2蛋白中的几种非同义突变产生了多种SARS-CoV-2变体。在我们之前的报告中,我们已经表明,SARS-CoV-2的独特蛋白变异体的均匀分布不均是地理位置或人口统计学特异性的.然而,由于突变发生中隐藏的对称性/不对称性,SARS-CoV-2感染的人口统计学变性与各种蛋白质突变之间的相关性仍然未知.这项研究追踪了六个模型国家和全球的SARS-CoV-2蛋白中这些突变是如何出现的。在地理位置中，考虑到每个SARS-CoV-2蛋白中检测到的突变频率至少为500，我们研究了国家/地区不变残基的百分比。我们的数据显示，自2020年10月以来，SARS-CoV-2的高度频繁突变主要在全球开放阅读框架（ORF）7b和ORF8中观察到。在英国没有发现任何SARS-CoV-2蛋白的如此频繁的突变，印度,巴西,这与病毒在印度和巴西的传播程度无关。然而,我们发现SARS-CoV-2蛋白以更高的速度进化，考虑到全球数据，在大多数可用的氨基酸位置检测到突变。对每种蛋白质的归一化因子时间序列的分形分析显示，在不同的国家中，SARS-CoV-2蛋白突变的显性变异体出现了周期性的非周期性。人们注意到，在过去的几个月中出现了某些高频变异，因此，新兴的SARS-CoV-2毒株预计在ORF3a中含有普遍的突变，膜,ORF8蛋白与其他β-冠状病毒相比,SARS-CoV-2变体已经基于人口统计学依赖性突变而迅速出现。SARS-CoV-2变体在各个国家的人口传播的周期性非周期性特征的表征可以有助于鉴定SARS-CoV-2的起源。
