Back

Prevalence of Key Spike Protein Mutations and Their Limited Effect on COVID-19 Clinical Manifestations in Sylhet, Bangladesh

Akter, S.; Zeba, Z.; Hossain, M. A.; Ofori, M. A.; Shahab, M.; Rolim Silva, G. V.; Oliveira, J. I. N.; Akash, S.; Banu, T. A.; Islam, M.; Sarkar, M. H.; Goswami, B.; Chowdhury, S. F.; Habib, M. A.; Khan, M. S.

2024-10-25 microbiology
10.1101/2024.10.25.620187 bioRxiv
Show abstract

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic, which has spread rapidly around the world and had a significant impact on public health and the economy worldwide. This study investigated the correlation between SARS-CoV-2 spike protein mutations, clinical outcomes and patient demographics in the Sylhet region of Bangladesh. We looked at the full genome sequences of 37 SARS-CoV-2 samples that were collected between January and June 2020. Specifically, we looked at five major spike protein mutations: D614G, A570D, D1118H, A222V, and P681R. The D614G mutation was the most prevalent (94.6%), followed by A570D and D1118H (both 32.4%), A222V (29.7%), and P681R (13.5%). Despite their high prevalence, we found no statistically significant associations between these mutations and clinical outcomes or demographic variables, except for possible trends for the P681R mutation. We found that age played a decisive role in recovery from COVID-19, with older patients exhibiting slower recovery rates. In terms of predictors of outcome, gender differences were observed: clinical symptoms and viral genetic mutations were more influential for men, while age and disease progression were more important for women. Common nucleotide substitutions (A23403G, C3037T, and C14408T) associated with European strains were identified, suggesting possible routes of transmission. This study contributes to our understanding of the genetics, clinical manifestations and epidemiology of SARS-CoV-2 in the Sylhet region and emphasizes the need for continuous genomic surveillance and adaptive public health strategies.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.