In a recent study published on the bioRxiv * prepress server, researchers generated an artificial intelligence (AI) resource to characterize the T-cell epitope landscape for variants of concern (VOCs). of severe acute respiratory syndrome of coronavirus-2 (SARS-CoV-2).
Study: The T-cell epitope landscape of the worrisome variants of SARS-CoV-2. Image credit: Fit Ztudio / Shutterstock
Fund
During the 2019 coronavirus disease pandemic (COVID-19), several variants of SARS-CoV-2 have emerged with altered infectivity and immunoeving traits. To date, five variants of SARS-CoV-2 have been designated as VOCs. VOCs have been characterized by changes within the receptor binding domain (RBD) of the spike (S) protein, which often cause the mutant S protein to have an improved binding affinity or the potential for evade neutralizing antibodies (nAbs).
A variant with higher infectivity and immune evasion would be extremely difficult to contain. Therefore, it is essential to have a comprehensive vaccine against emerging variants. As such, researchers have been exploring new approaches beyond conventional antibody-focused technologies.
Research is now focusing on characterizing T-cell responses, which correlate well with robust and long-lasting immunity to SARS-CoV-2. Although nAbs caused by the vaccine or infection were considered the gold standard protection, during the last outbreak of Omicron a substantial leakage of these nAbs was observed.
More importantly, SARS-CoV-2-specific T cell responses were detected in many infected individuals without specific antibodies. Although SARS-CoV-2 mutations may decrease or negate T-cell responses at the individual / person level, the same mutations are less likely to confer a selective advantage on the virus at the population level with a diverse landscape. human leukocyte antigen. HLA haplotypes).
The study and conclusions
In the present study, a T-cell epitope landscape mapped for each SARS-CoV-2 VOC was mapped. Using advanced predictors of AI, the researchers profiled the immunogenicity of HLA class I for the five VOCs against the 156 most common HLA alleles in humans. They calculated antigen presentation (AP) scores using an AI motor that predicts the potential of the HLA epitope to show on the cell surface infected with HLA class I alleles. a pairwise comparison of the predicted AP scores between each VOC and the Wuhan ancestral strain.
Peptides with an AP score of 0.5 or higher (on a scale of 0 to 1) were considered. As a result, they considered only T cell epitopes with high AP scores in both wild-type and corresponding mutant peptides or those that showed a loss or gain of AP potential due to the mutation itself. The team noted that non-synonymous substitutions for SARS-CoV-2 proteins did not affect the potential for HLA allele to present. The AP score distributions were very similar between the wild-type peptides and their corresponding mutant peptides.
The current study only compared the subset of mutated peptides with their corresponding wild-type peptides with AP of 0.5 or higher. The subset of mutant peptides in a VOC reflected only a small fraction of the total wild-type peptides. For example, the Omicron variant had about 5% mutant peptides relative to Wuhan strain peptides. Approximately 95% or more of the peptides with an AP greater than 0.5 were identical in all VOCs.
The authors found that most of the 156 HLA alleles did not differ substantially between the two distributions of the AP score (wild-type and mutant). No HLA subpopulation had a higher risk than others due to decreased T cell responses against any given SARS-CoV-2 VOC. They observed that approximately 5% of the HLA alleles analyzed had considerable differences in the AP score distributions between wild-type and mutant peptides. This suggested that most human HLA alleles do not exhibit altered AP propensity to mutant peptides.
They then investigated the small subset of HLA alleles with the highest differences (in AP scores) to identify the most affected haplotypes. Three HLA alleles (with the highest differences) were consulted by VOC in the Allele Frequency Network (AFND) database. Although no particular ethnic group or region in the world was affected by a particular VOC, they found that three VOCs disproportionately affected Australians (Alpha, Beta and Gamma).
Finally, the mean difference in AP scores between wild-type and mutant epitopes for each non-synonymous substitution in VOCs was analyzed to investigate the effect of a specific substitution on the potential of a post-present mutant peptide. se in the cells. Although most mutations had no substantial impact, few atypical values were identified, which generally occurred together in the same epitope. For example, SARS-CoV-2 Omicron S371P, S373F, and K375N substitutions were more likely to increase the mean AP potential difference of the (candidate) epitopes.
Conclusions
The findings presented here are similar to preliminary studies based on wet laboratories, which indicated that vaccine-induced T cell responses or infections are cross-reactions against SARS-CoV-2 Omicron. In addition, these results indicated that any significant antigenic drift leading to evasion of T cell responses was unlikely to occur in emerging SARS-CoV-2 VOCs.
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bioRxiv publishes preliminary scientific reports that are not peer-reviewed and therefore should not be considered conclusive, guided by clinical practice / health-related behavior, or treated as established information.