Insights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV2 - Naushin, Salwa; Sardana, Viren; Ujjainiya, Rajat; Bhatheja, Nitin; Kutum, Rintu; Bhaskar, Akash Kumar; Pradhan, Shalini; Prakash, Satyartha; Khan, Raju; Rawat, Birendra Singh; Tallapaka, Karthik Bharadwaj; Anumalla, Mahesh; et al. (eLife, 2021)
To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey 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% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, <0.0001), use of public transport (1.79, 1.43-2.24, <0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, <0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

Dimerization of SARS-CoV-2 nucleocapsid protein affects sensitivity of ELISA based diagnostics of COVID-19 - Khan, Wajihul; Khan, Nida; Mishra, Avinash; Gupta, Surbhi; Bansode, Vikrant; Mehta, Deepa; Bhambure, Rahul; Rathore, Anurag (International Journal of Biological Macromolecules, 2022)
Nucleocapsid protein (N protein) is the primary antigen of the virus for development of sensitive diagnostic assays of COVID-19. In this paper, we demonstrate the significant impact of dimerization of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) N-protein on sensitivity of enzyme-linked immunosorbent assay (ELISA) based diagnostics. The expressed purified protein from E. coli is composed of dimeric and monomeric forms, which have been further characterized using biophysical and immunological techniques. Indirect ELISA indicated elevated susceptibility of the dimeric form of the nucleocapsid protein for identification of protein-specific monoclonal antibody as compared to the monomeric form. This finding also confirmed with the modelled structure of monomeric and dimeric nucleocapsid protein via HHPred software and its solvent accessible surface area, which indicates higher stability and antigenicity of the dimeric type as compared to the monomeric form. The sensitivity and specificity of the ELISA at 95% CI are 99.0% (94.5-99.9) and 95.0% (83.0-99.4), respectively, for the highest purified dimeric form of the N protein. As a result, using the highest purified dimeric form will improve the sensitivity of the current nucleocapsid-dependent ELISA for COVID-19 diagnosis, and manufacturers should monitor and maintain the monomer-dimer composition for accurate and robust diagnostics.

Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys - Singh, Prateek; Ujjainiya, Rajat; Prakash, Satyartha; Naushin, Salwa; Sardana, Viren; Bhatheja, Nitin; Singh, Ajay Pratap; Barman, Joydeb; Kumar, Kartik; Gayali, Saurabh; Khan, Raju; Rawat, Birendra Singh; et al. (Computers in Biology and Medicine, 2022)
Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% 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. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.
Keywords: BBV152; Covaxin; COVID-19; Ensemble methods; Infection; machine learning; SARS-CoV-2