Evolutionary insights into a non-coding deletion of SARS-CoV-2 B.1.1.7

Three prevalent SARS-CoV-2 Variants of Concern (VOCs) were emerged and caused epidemic waves. It is essential to uncover the key genetic changes that cause the high transmissibility of VOCs. However, different viral mutations are generally tightly linked so traditional population genetic methods may not reliably detect beneficial mutation. In this study, we proposed a new pandemic-scale phylogenomic approach to detect mutations crucial to transmissibility. We analyzed 3,646,973 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata. Based on the sequential occurrence order of mutations and the instantaneously accelerated furcation rate, the analysis revealed that two non-coding mutations at the position of 28271 (g.a28271-/t) might be crucial for the high transmissibility of Alpha, Delta and Omicron VOCs. Both two mutations cause an A-to-T change at the core Kozak site of the N gene. The analysis also revealed that the non-coding mutations (g.a28271-/t) alone are unlikely to cause high viral transmissibility, indicating epistasis or multilocus interaction in viral transmissibility. A convergent evolutionary analysis revealed that g.a28271-/t, S:P681H/R and N:R203K/M occur independently in the three-VOC lineages, suggesting a potential interaction among these mutations. Therefore, this study unveils that non-synonymous and non-coding mutations could affect the transmissibility synergistically.

An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

Comprehensive analyses of viral genomes can provide a global picture on SARS-CoV-2 transmission and help to predict the oncoming trends of pandemic. This molecular tracing is mainly conducted through extensive phylogenetic network analyses. However, the rapid accumulation of SARS-CoV-2 genomes presents an unprecedented data size and complexity that has exceeded the capacity of existing methods in constructing evolution network through virus genotyping. Here we report a Viral genome Evolution Network Analysis System (VENAS), which uses Hamming distances adjusted by the minor allele frequency to construct viral genome evolution network. The resulting network was topologically clustered and divided using community detection algorithm, and potential evolution paths were further inferred with a network disassortativity trimming algorithm. We also employed parallel computing technology to achieve rapid processing and interactive visualization of >10,000 viral genomes, enabling accurate detection and subtyping of the viral mutations through different stages of Covid-19 pandemic. In particular, several core viral mutations can be independently identified and linked to early transmission events in Covid-19 pandemic. As a general platform for comprehensive viral genome analysis, VENAS serves as a useful computational tool in the current and future pandemics.