Clinico-Genomic Analysis Reveals Mutations Associated with COVID-19 Disease Severity: Possible Modulation by RNA Structure

Autor: Priyanka Mehta, Shanmukh Alle, Anusha Chaturvedi, Aparna Swaminathan, Sheeba Saifi, Ranjeet Maurya, Partha Chattopadhyay, Priti Devi, Ruchi Chauhan, Akshay Kanakan, Janani Srinivasa Vasudevan, Ramanathan Sethuraman, Subramanian Chidambaram, Mashrin Srivastava, Avinash Chakravarthi, Johnny Jacob, Madhuri Namagiri, Varma Konala, Sujeet Jha, U. Deva Priyakumar, P. K. Vinod, Rajesh Pandey
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Pathogens, Vol 10, Iss 9, p 1109 (2021)
Druh dokumentu: article
ISSN: 2076-0817
DOI: 10.3390/pathogens10091109
Popis: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and preventionas the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support.
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