ABSTRACT
Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.
ABSTRACT
The Coronavirus disease 2019 (COVID-19) outbreak caused by Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2) poses a worldwide human health crisis, causing respiratory illness with a high mortality rate. To investigate the factors governing codon usage bias in all the respiratory viruses, including SARS-CoV-2 isolates from different geographical locations (~62K), including two recently emerging strains from the United Kingdom (UK), i.e., VUI202012/01 and South Africa (SA), i.e., 501.Y.V2 codon usage bias (CUBs) analysis was performed. The analysis includes RSCU analysis, GC content calculation, ENC analysis, dinucleotide frequency and neutrality plot analysis. We were motivated to conduct the study to fulfil two primary aims: first, to identify the difference in codon usage bias amongst all SARS-CoV-2 genomes and, secondly, to compare their CUBs properties with other respiratory viruses. A biased nucleotide composition was found as most of the highly preferred codons were A/U-ending in all the respiratory viruses studied here. Compared with the human host, the RSCU analysis led to the identification of 11 over-represented codons and 9 under-represented codons in SARS-CoV-2 genomes. Correlation analysis of ENC and GC3s revealed that mutational pressure is the leading force determining the CUBs. The present study results yield a better understanding of codon usage preferences for SARS-CoV-2 genomes and discover the possible evolutionary determinants responsible for the biases found among the respiratory viruses, thus unveils a unique feature of the SARS-CoV-2 evolution and adaptation. To the best of our knowledge, this is the first attempt at comparative CUBs analysis on the worldwide genomes of SARS-CoV-2, including novel emerged strains and other respiratory viruses.
ABSTRACT
While our battle with the COVID-19 pandemic continues, a multitude of Omics data has been generated from patient samples in various studies, which remains to be translated. We conducted a meta-analysis of published transcriptome and proteome profiles of nasal swab and bronchioalveolar lavage fluid (BALF) samples of COVID-19 patients, to shortlist high confidence upregulated host factors. Subsequently, mRNA overexpression of selected genes was validated in nasal swab/BALF samples from a cohort of COVID-19 positive/negative, symptomatic/asymptomatic individuals. Analysis of these data revealed S100 family genes (S100A6, S100A8, S100A9, and S100P) as prognostic markers of COVID-19 disease. Furthermore, Thioredoxin gene (TXN) was identified as a significant upregulated host factor in our overlap analysis. An FDA-approved drug Auranofin, which inhibits Thioredoxin reduction, was found to mitigate SARS-CoV-2 replication in vitro and in vivo in the hamster challenge model. Overall, this study translates COVID-19 host response Big Data into potential clinical interventions.
ABSTRACT
To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India), conducted a sero-survey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS CoV2 anti-nucleocapsid (anti-NC) antibodies; 95% with surrogate neutralization activity. Three-fourth recalled no symptoms. Repeat serology tests at 3 (n=346) and 6 (n=35) months confirmed stability of antibody response and neutralization potential. Local sero-positivity was higher in densely populated cities and was inversely correlated with a 30 day change in regional test positivity rates (TPR). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of sero-positivity were high-exposure work (Odds Ratio, 95% CI, p value; 2{middle dot}23, 1{middle dot}92-2{middle dot}59, 6{middle dot}5E-26), use of public transport (1{middle dot}79, 1{middle dot}43-2{middle dot}24, 2{middle dot}8E-06), not smoking (1{middle dot}52, 1{middle dot}16-1{middle dot}99, 0{middle dot}02), non-vegetarian diet (1{middle dot}67, 1{middle dot}41-1{middle dot}99, 3{middle dot}0E-08), and B blood group (1{middle dot}36,1{middle dot}15-1{middle dot}61, 0{middle dot}001). Impact StatementWidespread asymptomatic and undetected SARS-CoV2 infection affected more than a 100 million Indians by September 2020. Declining new cases thereafter may be due to persisting humoral immunity amongst sub-communities with high exposure. FundingCouncil of Scientific and Industrial Research, India (CSIR)
ABSTRACT
The ongoing pandemic of the coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). We have performed an integrated sequence-based analysis of SARS-CoV2 genomes from different geographical locations in order to identify its unique features absent in SARS-CoV and other related coronavirus family genomes, conferring unique infection, facilitation of transmission, virulence and immunogenic features to the virus. The phylogeny of the genomes yields some interesting results. Systematic gene level mutational analysis of the genomes has enabled us to identify several unique features of the SARS-CoV2 genome, which includes a unique mutation in the spike surface glycoprotein (A930V (24351C>T)) in the Indian SARS-CoV2, absent in other strains studied here. We have also predicted the impact of the mutations in the spike glycoprotein function and stability, using computational approach. To gain further insights into host responses to viral infection, we predict that antiviral host-miRNAs may be controlling the viral pathogenesis. Our analysis reveals nine host miRNAs which can potentially target SARS-CoV2 genes. Interestingly, the nine miRNAs do not have targets in SARS and MERS genomes. Also, hsa-miR-27b is the only unique miRNA which has a target gene in the Indian SARS-CoV2 genome. We also predicted immune epitopes in the genomes