Dados do Trabalho

Título

Genetic diversity of SARS-CoV-2 Spike Glycoprotein in Rio Grande do Norte, Brazil

Introdução

COVID-19 emerged in China and rapidly expanded around the globe. The rapid spread resulted in the emergence of several mutations that led to the development of new SARS-CoV-2 variants and, consequently, new difficulties in combating the infection, once these new variants are able to escape immunity elicited by previous natural infection and/or vaccine immunization. Five variants circulated in Brazil (Zeta, Gamma, Alfa, Delta, and Omicron), altering the dynamics of confirmed cases and deaths.

Objetivo (s)

Thus, this study aimed to evaluate the dynamics of the infection by SARS-CoV-2 by applying immunoinformatics and epitope prediction among all variants circulating in Rio Grande do Norte, Brazil.

Material e Métodos

The epidemiological data was obtained from the Secretary of State for Public Health of Rio Grande do Norte repository. From GISAID, we obtained and analyzed 1257 SARS-CoV-2 spike glycoprotein sequences that were clustered using the CD-HIT method, setting 99.0% identity as cutoff. Representative sequences from each cluster were used for T and B cell epitopes prediction by the NetMHCII, NetMHCIIpan 4.0, NetMHCpan 4.1, NetCLT, IEDB, BCEPred, and Ellipro servers. The IL-10PRED, INFepitope, IL-6PRED, IL-4PRED, PIP-EL, and PRE-AIP servers were utilized to analyze the in silico cytokine secretion.

Resultados e Conclusão

In Rio Grande do Norte State, the COVID-19 occurred in three waves, the first one caused by the wild-type lineages, while the second and third by Gamma and Omicron, respectively. The Spike protein sequences were classified into five clusters based on their similarity. Cluster 0 is composed of Wild-type, Zeta and Delta lineages; Cluster 1 contains all Gamma sequences; Cluster 2 and 3 are composed of Omicron and its sub-lineages; and Cluster 4 contains a few sub-lineages of Delta. By comparing the unique predicted epitopes from each cluster we observed that Cluster 0 possessed the fewest unique epitopes; Cluster 1 exhibited variability, primarily for CD8+T epitopes; Cluster 2 exhibited a greater number of unique epitopes; Clusters 3 and 4 exhibited a decreasing pattern of unique epitopes. Overall, the clusters were able to evoke pro- and anti-inflammatory profiles, which may explain the peaks and valleys in the number of confirmed cases and deaths. Thus, an immunoinformatics strategy coupled with genomic surveillance could be utilized to comprehend the diverse outcomes of SARS-CoV-2 variants during the COVID-19 pandemic.

Palavras-chave

Immunoinformatic; SAR-CoV2; Variants; Epitope prediction