Both Simulation and Sequencing Data Reveal Multiple SARS-CoV-2 Variants Coinfection in COVID-19 Pandemic (preprint)

SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerged SARS-CoV-2 variants could increase the transmissibility, aggravate the severity, and even fade the vaccine protection. Although the coinfections of SARS-CoV-2 with other respiratory pathogens have been reported, whether multiple SARS-CoV-2 variants coinfection exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11) and April 1, 2021 (GISAID21Apr1), respectively. With the single-nucleotide variants (SNV) and network clique analysis, we constructed the single-nucleotide polymorphism (SNP) coexistence networks and noted the SNP number of the maximal clique as the coinfection index. The coinfection indices of GISAID20May11 and GISAID21Apr1 datasets were 16 and 34, respectively. Simulating the transmission routes and the mutation accumulations, we discovered the linear relationship between the coinfection index and the coinfected variant number. Based on the linear relationship, we deduced that the COVID-19 cases in the GISAID20May11 and GISAID21Apr1 datasets were coinfected with 2.20 and 3.42 SARS-CoV-2 variants on average. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients to explore the virus mutational characteristics. We found the heterozygous SNPs in 41 COVID-19 cases, which support the coinfection of SARS-CoV-2 variants and challenge the accuracy of phylogenetic analysis. In conclusion, our findings reported the coinfection of SARS-CoV-2 variants in COVID-19 patients, demonstrated the increased coinfected variants number in the epidemic, and provided clues for the prolonged viral shedding and severe symptoms in some cases.

CovProfile: profiling the viral genome and gene expressions of SARS-COV2 (preprint)

The SARS-CoV-2 virus has infected more than one million people worldwide to date. Knowing its genome and gene expressions is essential to understand the virus mechanism. Here, we propose a computational tool CovProfile to detect the viral genomic variations as well as viral gene expressions from the sequences obtained from Nanopore devices. We applied CovProfile to 11 samples, each from a terminally ill patient, and discovered that all the patients are infected by multiple viral strains, which might affect the reliability of phylogenetic analysis. Moreover, the expression of viral genes ORF1ab gene, S gene, M gene, and N gene are high among most of the samples. While performing the tests, we noticed a consistent abundance of transcript segments of MUC5B, presumably from the host, across all the samples.