No abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic of coronavirus disease in 2019 (COVID-19). Genome surveillance is a key method to track the spread of SARS-CoV-2 variants. Genetic diversity and evolution of SARS-CoV-2 were analyzed based on 260,673 whole-genome sequences, which were sampled from 62 countries between 24 December 2019 and 12 January 2021. We found that amino acid (AA) substitutions were observed in all SARS-CoV-2 proteins, and the top six proteins with the highest substitution rates were ORF10, nucleocapsid, ORF3a, spike glycoprotein, RNA-dependent RNA polymerase, and ORF8. Among 25,629 amino acid substitutions at 8484 polymorphic sites across the coding region of the SARS-CoV-2 genome, the D614G (93.88%) variant in spike and the P323L (93.74%) variant in RNA-dependent RNA polymerase were the dominant variants on six continents. As of January 2021, the genomic sequences of SARS-CoV-2 could be divided into at least 12 different clades. Distributions of SARS-CoV-2 clades were featured with temporal and geographical dynamics on six continents. Overall, this large-scale analysis provides a detailed mapping of SARS-CoV-2 variants in different geographic areas at different time points, highlighting the importance of evaluating highly prevalent variants in the development of SARS-CoV-2 antiviral drugs and vaccines.

On June 8, 2018, an NS3/4A protease inhibitor called danoprevir was approved in China to treat the infections of HCV genotype (GT) 1b – the most common HCV genotype worldwide. Based on phase 2 and 3 clinical trials, the 12-week regimen of ritonavir-boosted danoprevir (danoprevir/r) plus peginterferon alpha-2a and ribavirin offered 97.1% (200/206) of sustained virologic response at post-treatment week 12 (SVR12) in treatment-naïve non-cirrhotic patients infected with HCV genotype 1b. Adverse events such as anemia, fatigue, fever, and headache were associated with the inclusion of peginterferon alpha-2a and ribavirin in the danoprevir-based regimen. Moreover, drug resistance to danoprevir could be traced to amino acid substitutions (Q80K/R, R155K, D168A/E/H/N/T/V) near the drug-binding pocket of HCV NS3 protease. Despite its approval, the clinical use of danoprevir is currently limited to its combination with peginterferon alpha-2a and ribavirin, thereby driving its development towards interferon-free, ribavirin-free regimens with improved tolerability and adherence. In the foreseeable future, pan-genotypic direct-acting antivirals with better clinical efficacy and less adverse events will be available to treat HCV infections worldwide.

Despite the active development of SARS-CoV-2 surveillance methods (e.g., Nextstrain, GISAID, Pangolin), the global emergence of various SARS-CoV-2 viral lineages that potentially cause antiviral and vaccine failure has driven the need for accurate and efficient SARS-CoV-2 genome sequence classifiers. This study presents an optimized method that accurately identifies the viral lineages of SARS-CoV-2 genome sequences using existing schemes. For Nextstrain and GISAID clades, a template matching-based method is proposed to quantify the differences between viral clades and to play an important role in classification evaluation. Furthermore, to improve the typing accuracy of SARS-CoV-2 genome sequences, an ensemble model that integrates a combination of machine learning-based methods (such as Random Forest and Catboost) with optimized weights is proposed for Nextstrain, Pangolin, and GISAID clades. Cross-validation is applied to optimize the parameters of the machine learning-based method and the weight settings of the ensemble model. To improve the efficiency of the model, in addition to the one-hot encoding method, we have proposed a nucleotide site mutation-based data structure that requires less computational resources and performs better in SARS-CoV-2 genome sequence typing. Based on an accumulated database of >1 million SARS-CoV-2 genome sequences, performance evaluations show that the proposed system has a typing accuracy of 99.879%, 97.732%, and 96.291% for Nextstrain, Pangolin, and GISAID clades, respectively. A single prediction only takes an average of <20 ms on a portable laptop. Overall, this study provides an efficient and accurate SARS-CoV-2 genome sequence typing system that benefits current and future surveillance of SARS-CoV-2 variants.