COV2Var, a function annotation database of SARS-CoV-2 genetic variation

Abstract

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species transmission, viral immune escape, immune response level, viral transmission capability, viral evolution, host adaptability, viral protein structure, viral protein function, viral protein stability and concurrent mutations. Deep understanding the relationship between mutation sites and these factors is crucial for advancing our knowledge of SARS-CoV-2 and for developing effective responses. To fill this gap, we built COV2Var, a function annotation database of SARS-CoV-2 genetic variation, available at http://biomedbdc.wchscu.cn/COV2Var/. COV2Var aims to identify common mutations in SARS-CoV-2 variants and assess their effects, providing a valuable resource for intensive functional annotations of common mutations among SARS-CoV-2 variants.

Graphical Abstract

Figure 1.

Overview of data distribution. (A) Global distribution of sequences across 218 regions. (B) Source origins of all genome sequences. (C) Distribution of 9832 mutations in the SARS-CoV-2 genome. The y-axis represents the number of distinct mutations. (D) Positively selected mutations were detected by FEL, MEME and FUBAR methods among 9832 mutations. (E) Distribution of mutations from non-human animal hosts across the SARS-CoV-2 genome. The y-axis represents the number of sequences carrying each mutation.

Figure 2.

COV2Var pipeline overview. (A) Mutation analysis workflow leading to 9832 common mutations. (B) Main categories of common mutations among 9832 Individuals. (C) Interface and architecture of the COV2Var website.

Figure 3.

Comparative analysis of N501Y and N501T mutations on SARS-CoV-2 spike protein. (A) Temporal dynamics of sequences with the N501Y mutation. (B) Temporal dynamics of sequences with the N501T mutation. (C) Count of sequences carrying the N501Y or N501T mutation within B.1.6 sub-lineages. (D) Comparative ACE2 binding affinity of N501Y and N501T mutations. (E) Comparative immune escape of N501Y and N501T mutations.

Figure 4.

Exploring the latest variant XBB.1.16* using COV2Var. (A) Temporal dynamics of sequence counts for XBB.1.5*, XBB.1.16* and XBB.1.19* Variants. (B) Mutational differences among XBB.1.5*, XBB.1.16* and XBB.1.19* variants. (C) The position T478 in SARS-CoV-2 spike protein. SARS-CoV-2 RBD is shown in yellow, ACE2 in green. The magenta area represents the ACE2 binding interface, while the cyan region signifies the RBD binding interface. (D) Frequency changes of sequences carrying D1746Y mutation over time in XBB.1 lineage.