SARS-CoV-2 Mutations and COVID-19 Clinical Outcome: Mutation Global Frequency Dynamics and Structural Modulation Hold the Key.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an enormous burden on the healthcare system worldwide as a consequence of its new emerging variants of concern (VOCs) since late 2019. Elucidating viral genome characteristics and its influence on disease severity and clinical outcome has been one of the crucial aspects toward pandemic management. Genomic surveillance holds the key to identify the spectrum of mutations vis-à-vis disease outcome. Here, in our study, we performed a comprehensive analysis of the mutation distribution among the coronavirus disease 2019 (COVID-19) recovered and mortality patients. In addition to the clinical data analysis, the significant mutations within the two groups were analyzed for their global presence in an effort to understand the temporal dynamics of the mutations globally in comparison with our cohort. Interestingly, we found that all the mutations within the recovered patients showed significantly low global presence, indicating the possibility of regional pool of mutations and the absence of preferential selection by the virus during the course of the pandemic. In addition, we found the mutation S194L to have the most significant occurrence in the mortality group, suggesting its role toward a severe disease progression. Also, we discovered three mutations within the mortality patients with a high cohort and global distribution, which later became a part of variants of interest (VOIs)/VOCs, suggesting its significant role in enhancing viral characteristics. To understand the possible mechanism, we performed molecular dynamics (MD) simulations of nucleocapsid mutations, S194L and S194*, from the mortality and recovered patients, respectively, to examine its impacts on protein structure and stability. Importantly, we observed the mutation S194* within the recovered to be comparatively unstable, hence showing a low global frequency, as we observed. Thus, our study provides integrative insights about the clinical features, mutations significantly associated with the two different clinical outcomes, its global presence, and its possible effects at the structural level to understand the role of mutations in driving the COVID-19 pandemic.

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification.

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.