Emergence of SARS-CoV-2 Variants Are Induced by Coinfections With Dengue.

IF 2.3 Q3 BIOCHEMICAL RESEARCH METHODS
Bioinformatics and Biology Insights Pub Date : 2024-09-11 eCollection Date: 2024-01-01 DOI:10.1177/11779322241272399
Hassan M Al-Emran, Fazlur Rahman, Laxmi Sarkar, Prosanto Kumar Das, Provakar Mondol, Suriya Yesmin, Pipasha Sultana, Toukir Ahammed, Rasel Parvez, Md Shazid Hasan, Shovon Lal Sarkar, M Shaminur Rahman, Anamica Hossain, Mahmudur Rahman, Ovinu Kibria Islam, Md Tanvir Islam, Shireen Nigar, Selina Akter, A S M Rubayet Ul Alam, Mohammad Mahfuzur Rahman, Iqbal Kabir Jahid, M Anwar Hossain
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引用次数: 0

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has accumulated a series of point mutations and evolved into several variants of concern (VOCs), some of which are more transmissible and potentially more severe than the original strain. The most notable VOCs are Alpha, Beta, Gamma, Delta, and Omicron, which have spread to various parts of the world. This study conducted surveillance in Jashore, Bangladesh to identify the prevalence of SARS-CoV-2 coinfected with dengue virus and their genomic effect on the emergence of VOCs. A hospital-based COVID-19 surveillance from June to August, 2021 identified 9 453 positive patients in the surveillance area. The study enrolled 572 randomly selected COVID-19-positive patients, of which 11 (2%) had dengue viral coinfection. Whole genome sequences of SARS-CoV-2 were analyzed and compared between coinfection positive and negative group. In addition, we extracted 185 genome sequences from GISAID to investigate the cross-correlation function between SARS-CoV-2 mutations and VOC; multiple ARIMAX(p,d,q) models were developed to estimate the average number of amino acid (aa) substitution among different SARS-CoV-2 VOCs. The results of the study showed that the coinfection group had an average of 30.6 (±1.7) aa substitutions in SARS-CoV-2, whereas the dengue-negative COVID-19 group had that average of 25.6 (±1.8; P < .01). The coinfection group showed a significant difference of aa substitutions in open reading frame (ORF) and N-protein when compared to dengue-negative group (P = .03). Our ARIMAX models estimated that the emergence of SARS-CoV-2 variants Delta required additional 9 to 12 aa substitutions than Alpha, Beta, or Gamma variant. The emergence of Omicron accumulated additional 19 (95% confidence interval [CI]: 15.74, 21.95) aa substitution than Delta. Increased number of point mutations in SARS-CoV-2 genome identified from coinfected cases could be due to the compromised immune function of host and induced adaptability of pathogens during coinfections. As a result, new variants might be emerged when series of coinfection events occur during concurrent two epidemics.

登革热并发感染诱发 SARS-CoV-2 变体的出现
2019 年末出现的严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)已积累了一系列点突变,并进化成多个令人担忧的变种(VOCs),其中一些变种的传播性更强,可能比原始毒株更严重。最显著的 VOCs 是 Alpha、Beta、Gamma、Delta 和 Omicron,它们已传播到世界各地。本研究在孟加拉国贾肖尔进行了监测,以确定 SARS-CoV-2 与登革热病毒混合感染的流行率及其基因组对 VOCs 出现的影响。2021 年 6 月至 8 月期间,一项以医院为基础的 COVID-19 监测在监测地区发现了 9 453 名阳性患者。研究随机选取了572名COVID-19阳性患者,其中11人(2%)合并登革热病毒感染。我们分析了 SARS-CoV-2 的全基因组序列,并对合并感染阳性组和阴性组进行了比较。此外,我们还从GISAID中提取了185个基因组序列,以研究SARS-CoV-2突变与VOC之间的交叉相关函数;建立了多个ARIMAX(p,d,q)模型,以估计不同SARS-CoV-2 VOC之间的平均氨基酸(aa)替换数。研究结果表明,合并感染组的 SARS-CoV-2 平均有 30.6 (±1.7) 个氨基酸取代,而登革热阴性 COVID-19 组平均有 25.6 (±1.8; P P = .03)。据我们的 ARIMAX 模型估计,与 Alpha、Beta 或 Gamma 变种相比,SARS-CoV-2 变种 Delta 的出现需要额外的 9 至 12 个 aa 替换。Omicron变种的出现比Delta变种多了19个(95%置信区间[CI]:15.74,21.95)aa的置换。从合并感染病例中发现的 SARS-CoV-2 基因组点突变数量增加,可能是由于合并感染期间宿主的免疫功能受损和病原体的适应性增强。因此,在两种病毒同时流行期间发生一系列合并感染事件时,可能会出现新的变种。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioinformatics and Biology Insights
Bioinformatics and Biology Insights BIOCHEMICAL RESEARCH METHODS-
CiteScore
6.80
自引率
1.70%
发文量
36
审稿时长
8 weeks
期刊介绍: Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on bioinformatics methods and their applications which must pertain to biological insights. All papers should be easily amenable to biologists and as such help bridge the gap between theories and applications.
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