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Microbiol Spectr
. 2023 Jan 9;e0342622.
doi: 10.1128/spectrum.03426-22. Online ahead of print.
Genomic Analysis of the Suspicious SARS-CoV-2 Sequences in the Public Sequencing Database
Xiao Sun # 1 2 , Chuanwen Kan # 1 2 , Wentai Ma 1 2 , Zhenglin Du 3 , Mingkun Li 1 2 3
Affiliations
- PMID: 36622170
- DOI: 10.1128/spectrum.03426-22
Abstract
SARS-CoV-2 has infected more than 600 million people. However, the origin of the virus is still unclear; knowing where the virus came from could help us prevent future zoonotic epidemics. Sequencing data, particularly metagenomic data, can profile the genomes of all species in the sample, including those not recognized at the time, thus allowing for the identification of the progenitor of SARS-CoV-2 in samples collected before the pandemic. We analyzed the data from 5,196 SARS-CoV-2-positive sequencing runs in the NCBI's SRA database with collection dates prior to 2020 or unknown. We found that the mutation patterns obtained from these suspicious SARS-CoV-2 reads did not match the genome characteristics of an unknown progenitor of the virus, suggesting that they may derive from circulating SARS-CoV-2 variants or other coronaviruses. Despite a negative result for tracking the progenitor of SARS-CoV-2, the methods developed in the study could assist in pinpointing the origin of various pathogens in the future. IMPORTANCE Sequences that are homologous to the SARS-CoV-2 genome were found in numerous sequencing runs that were not associated with the SARS-CoV-2 studies in the public database. It is unclear whether they are derived from the possible progenitor of SARS-CoV-2 or contamination of more recent SARS-CoV-2 variants circulated in the population due to the lack of information on the collection, library preparation, and sequencing processes. We have developed a computational framework to infer the evolutionary relationship between sequences based on the comparison of mutations, which enabled us to rule out the possibility that these suspicious sequences originate from unknown progenitors of SARS-CoV-2.
Keywords: SARS-CoV-2; evolutionary relationship inference; metagenomics; progenitor; sequencing data.