ABSTRACT
Spike protein of SARS-CoV-2 variants play critical role in the infection and transmission through its interaction with hACE2 receptor. Prior findings using molecular docking and biomolecular studies reported varied findings on the difference in the interactions among the spike variants with hACE2 receptor. Hence, it is a prerequisite to understand these interactions in a more precise manner. To this end, firstly, we performed ELISA with trimeric spike proteins of Wild (Wuhan Hu-1), Delta, C.1.2 and Omicron variants. Further, to study the interactions in a more specific manner by mimicking the natural infection, we developed hACE2 receptor expressing HEK-293T cell line and evaluated binding efficiencies of the variants and competitive binding of spike variants with D614G spike pseudotyped virus. In lines with the existing findings, we observed that Omicron had higher binding efficiency compared to Delta in both ELISA and Cellular models. Intriguingly, we found that cellular models could differentiate the subtle differences between the closely related C.1.2 and Delta in their binding to hACE2. From the analysis in receptor binding domain (RBD) revealed that a single common modification, N501Y, present in both Omicron and C.1.2 is driving the enhanced spike binding to the receptor and showed two-fold superior competitive binding than Delta. Our study using cellular model provides a precise method to evaluate the binding interactions between spike sub-lineages to hACE2 receptors and signifies the role of single common modification N501Y in RBD towards imparting superior binding efficiencies. Our approach would be instrumental in understanding the disease progression and developing therapeutics. Author SummarySpike proteins of evolving SARS-CoV2 variants demonstrated their signature binding to hACE2 receptor, in turn contributed to driving the infection and transmission. Prior studies to scale the binding efficiencies between the spike variant and the receptor had consensus in distinct variants, but discrepancies in the closely related ones. To this end, we compared spike variants-receptor interactions with ELISA, from cells expressing hACE2 receptor. Intriguingly, we found that cellular models could differentiate the subtle differences between the closely related C.1.2 and Delta in their binding to hACE2. More importantly, competitive binding studies in presence of pseudovirus, demonstrated that a single common modification, N501Y, present in both Omicron and C.1.2 showed two fold superior competitive binding than Delta. Collectively, our study suggests a precise approach to evaluate the binding interactions between spike sub-lineages to hACE2 receptor. This would be instrumental in understanding the disease progression and developing therapeutics.
ABSTRACT
BackgroundIn locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed. MethodsWe prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort. Findings426 participants were recruited, of whom 89 (21{middle dot}0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0{middle dot}72 to 0{middle dot}74) and calibration (calibration slopes: 1{middle dot}01 to 1{middle dot}05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone. InterpretationWe present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources. FundingMedecins Sans Frontieres, India. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSA living systematic review by Wynants et al. identified 137 COVID-19 prediction models, 47 of which were derived to predict whether patients with COVID-19 will have an adverse outcome. Most lacked external validation, relied on retrospective data, did not focus on patients with moderate disease, were at high risk of bias, and were not practical for use in resource-limited settings. To identify promising biochemical biomarkers which may have been evaluated independently of a prediction model and therefore not captured by this review, we searched PubMed on 1 June 2020 using synonyms of "SARS-CoV-2" AND ["biomarker" OR "prognosis"]. We identified 1,214 studies evaluating biochemical biomarkers of potential value in the prognostication of COVID-19 illness. In consultation with FIND (Geneva, Switzerland) we shortlisted seven candidates for evaluation in this study, all of which are measurable using near-patient tests which are either currently available or in late-stage development. Added value of this studyWe followed the TRIPOD guidelines to develop and validate three promising clinical prediction models to help clinicians identify which patients presenting with moderate COVID-19 can be safely managed in the community. Each model contains three easily ascertained clinical parameters (age, sex, and SpO2) and one biochemical biomarker (NLR, suPAR or IL-6), and would be practical for implementation in high-patient-throughput low resource settings. The models showed promising discrimination and calibration in the validation cohort. The inclusion of a biomarker test improved prognostication compared to a model containing the clinical parameters alone, and extended the range of contexts in which such a tool might provide utility to include situations when bed pressures are less critical, for example at earlier points in a COVID-19 surge. Implications of all the available evidencePrognostic models should be developed for clearly-defined clinical use-cases. We report the development and temporal validation of three clinical prediction models to rule-out progression to supplemental oxygen requirement amongst patients presenting with moderate COVID-19. The models are readily implementable and should prove useful in triage and resource allocation. We provide our full models to enable independent validation.
