Immune Profile of SARS-CoV-2 Variants of Concern.

The spread of the current Sars-Cov-2 pandemics leads to the development of mutations that are constantly monitored because they could affect the efficacy of vaccines. Three recently identified mutated strains, known as variants of concern, are rapidly spreading worldwide. Here, we study possible effects of these mutations on the immune response to Sars-Cov-2 infection using NetTepi a computational method based on artificial neural networks that considers binding and stability of peptides obtained by proteasome degradation for widely represented HLA class I alleles present in human populations as well as the T-cell propensity of viral peptides that measures their immune response. Our results show variations in the number of potential highly ranked peptides ranging between 0 and 20% depending on the specific HLA allele. The results can be useful to design more specific vaccines.

Evidence of a SARS-CoV-2 double Spike mutation D614G/S939F potentially affecting immune response of infected subjects.

OBJECTIVES: Despite extensive efforts to monitor the diffusion of COVID-19, the actual wave of infection is worldwide characterized by the presence of emerging SARS-CoV-2 variants. The present study aims to describe the presence of yet undiscovered SARS-CoV-2 variants in Italy. METHODS: Next Generation Sequencing was performed on 16 respiratory samples from occasionally employed within the Bangladeshi community present in Ostia and Fiumicino towns. Computational strategy was used to identify all potential epitopes for reference and mutated Spike proteins. A simulation of proteasome activity and the identification of possible cleavage sites along the protein guided to a combined score involving binding affinity, peptide stability and T-cell propensity. RESULTS: Retrospective sequencing analysis revealed a double Spike D614G/S939F mutation in COVID-19 positive subjects present in Ostia while D614G mutation was evidenced in those based in Fiumicino. Unlike D614G, S939F mutation affects immune response by the slight but significant modulation of T-cell propensity and the selective enrichment of potential binding epitopes for some HLA alleles. CONCLUSION: Collectively, our findings mirror further the importance of deep sequencing of SARS-CoV-2 genome as a unique approach to monitor the appearance of specific mutations as for those herein reported for Spike protein. This might have implications on both the type of immune response triggered by the viral infection and the severity of the related illness.

Estimating the Binding of Sars-CoV-2 Peptides to HLA Class I in Human Subpopulations Using Artificial Neural Networks.

Epidemiological studies show that SARS-CoV-2 infection leads to severe symptoms only in a fraction of patients, but the determinants of individual susceptibility to the virus are still unknown. The major histocompatibility complex (MHC) class I exposes viral peptides in all nucleated cells and is involved in the susceptibility to many human diseases. Here, we use artificial neural networks to analyze the binding of SARS-CoV-2 peptides with polymorphic human MHC class I molecules. In this way, we identify two sets of haplotypes present in specific human populations: the first displays weak binding with SARS-CoV-2 peptides, while the second shows strong binding and T cell propensity. Our work offers a useful support to identify the individual susceptibility to COVID-19 and illustrates a mechanism underlying variations in the immune response to SARS-CoV-2. A record of this paper's transparent peer review process is included in the Supplemental Information.