Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2.
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ABSTRACT:
SUBMITTER: Gao A
PROVIDER: S-EPMC7956900 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic T Lymphocyte (CTL) epitopes derived from diverse pathogens, given a Human Leukocyte Antigen (HLA) genotype. The model was trained and tested on experimental data on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. The method is more accurate than publicly available models. Our model predicts that only a fraction of SARS-CoV-2 epitopes that have been predicted to bind to HLA molecules is immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but the immunogenic epitopes in the SARS-CoV-2 spike protein alone are unlikely to do so. Our model predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to those contained in low-pathogenicity coronaviruses circulating in the population. Thus, we suggest that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.
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Project description:CD8 T cells from human donors were sorted with peptide-MHC multimers based on SARS-Cov-2 epitopes. For each donor the alpha and beta chains were sequenced separately
2022-04-25 | GSE201212 | GEO
Project description:Since the first outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019, its high infectivity led to its prevalence around the world in an exceptionally short time. Efforts have been made to control the ongoing outbreak, and among them, vaccine developments are going on high priority. New clinical trials add to growing evidence that vaccines from many countries were highly effective at preventing SARS-CoV-2 virus infection. One of them is B cell-based vaccines, which were common during a pandemic. However, neutralizing antibody therapy becomes less effective when viruses mutate. In order to tackle the problem, we focused on T-cell immune mechanism. In this study, the mutated strains of the virus were selected globally from India (B.1.617.1 and B.1.617.2), United Kingdom (B.1.1.7), South Africa (B.1.351), and Brazil (P.1), and the overlapping peptides were collected based on mutation sites of S-protein. After that, residue scanning was used to predict the affinity between overlapping peptide and HLA-A*11:01, the most frequent human leukocyte antigen (HLA) allele among the Chinese population. Then, the binding free energy was evaluated with molecular docking to further verify the affinity changes after the mutations happen in the virus genomes. The affinity test results of three epitopes on spike protein from experimental validation were consistent with our predicted results, thereby supporting the inclusion of the epitope 374FSTFKCYGL382 in future vaccine design and providing a useful reference route to improve vaccine development.
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Project description:The surge of SARS-CoV-2 has created a wave of pandemic around the globe due to its high transmission rate. To contain this virus, researchers are working around the clock for a solution in the form of vaccine. Due to the impact of this pandemic, the economy and healthcare have immensely suffered around the globe. Thus, an efficient vaccine design is the need of the hour. Moreover, to have a generalised vaccine for heterogeneous human population, the virus genomes from different countries should be considered. Thus, in this work, we have performed genome-wide analysis of 10,664 SARS-CoV-2 genomes of 73 countries around the globe in order to identify the potential conserved regions for the development of peptide based synthetic vaccine viz. epitopes with high immunogenic and antigenic scores. In this regard, multiple sequence alignment technique viz. Clustal Omega is used to align the 10,664 SARS-CoV-2 virus genomes. Thereafter, entropy is computed for each genomic coordinate of the aligned genomes. The entropy values are then used to find the conserved regions. These conserved regions are refined based on the criteria that their lengths should be greater than or equal to 60 nt and their corresponding protein sequences are without any stop codons. Furthermore, Nucleotide BLAST is used to verify the specificity of the conserved regions. As a result, we have obtained 17 conserved regions that belong to NSP3, NSP4, NSP6, NSP8, RdRp, Helicase, endoRNAse, 2'-O-RMT, Spike glycoprotein, ORF3a protein, Membrane glycoprotein and Nucleocapsid protein. Finally, these conserved regions are used to identify the T-cell and B-cell epitopes with their corresponding immunogenic and antigenic scores. Based on these scores, the most immunogenic and antigenic epitopes are then selected for each of these 17 conserved regions. Hence, we have obtained 30 MHC-I and 24 MHC-II restricted T-cell epitopes with 14 and 13 unique HLA alleles and 21 B-cell epitopes for the 17 conserved regions. Moreover, for validating the relevance of these epitopes, the binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles. Also, the physico-chemical properties of the epitopes are reported along with Ramchandran plots and Z-Scores and the population coverage is shown as well. Overall, the analysis shows that the identified epitopes can be considered as potential candidates for vaccine design.
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Project description:The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.
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Project description:The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.
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Project description:Improved predictions of immunogenicity reveal SARS-Cov-2 CD8 T-cell epitopes
| PRJNA830364 | ENA
Project description:BackgroundsTo date, the effects of SARS-CoV-2 vaccines on people living with HIV (PLWH) were mainly focused on messenger RNA (mRNA) and adenovirus vector-based vaccines, and little is known about the effects of inactivated virus-based vaccine. This study was designed to determine the effects of inactivated SARS-CoV-2 vaccines on PLWH.MethodsTwenty-four HIV-positive individuals and 24 healthy donors (HD) were respectively recruited from Malipo Country People's Hospital and community in Kunming city. Enumeration of lymphocyte and CD4+CD45RO+ memory T cells were evaluated by flow cytometry. Competitive ELISA was used to measure the level of Anti-SARS-CoV-2 neutralization antibody. Spearman or Pearson correlation analysis was used to analyze the relationship between laboratory indicators and neutralization antibodies in PLWH. T-cell responses (Th1, Th2, Th17, Treg) and intracellular expression of cytokines (IL-2 and TNF-α) in CD4 or CD8 were induced by spike protein in SARS-CoV-2 (SARS-2-S) and further measured by intracellular staining.ResultsCD4, B cells, CD4+CD45RO+ memory T cells in peripheral blood of PLWH are dramatically decreased in comparison with HD. Importantly, PLWH display comparable neutralizing antibody positive rate to HD after inoculation with inactivated SARS-CoV-2 vaccine. However, PLWH showed weaker responses to vaccines exhibited by lower levels of neutralizing antibodies. Correlation analysis shows that this is possibly caused by low number of CD4 and B cells. Furthermore, SARS-2-S-induced Th2 and Th17 responses are also decreased in PLWH, while no influences on Treg and other cytokines (IL-2, TNF-α and IFN-γ) observed.ConclusionsPLWH and HD have comparable neutralizing antibodies positive rates, but PLWH display weaker responses to inactivated SARS-CoV-2 vaccines in magnitude, which suggests that a booster dose or dose adjustment are required for HIV-infected individuals, especially for those with lower counts of CD4 T and B cells.
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Project description:The role of gut microbiota in modulating the durability of COVID-19 vaccine immunity is yet to be characterised. In this cohort study, we collected blood and stool samples of 121 BNT162b2 and 40 CoronaVac vaccinees at baseline, 1 month, and 6 months post vaccination (p.v.). Neutralisation antibody, plasma cytokine and chemokines were measured and associated with the gut microbiota and metabolome composition. A significantly higher level of neutralising antibody (at 6 months p.v.) was found in BNT162b2 vaccinees who had higher relative abundances of Bifidobacterium adolescentis, Bifidobacterium bifidum, and Roseburia faecis as well as higher concentrations of nicotinic acid (Vitamin B) and γ-Aminobutyric acid (P < 0.05) at baseline. CoronaVac vaccinees with high neutralising antibodies at 6 months p.v. had an increased relative abundance of Phocaeicola dorei, a lower relative abundance of Faecalibacterium prausnitzii, and a higher concentration of L-tryptophan (P < 0.05) at baseline. A higher antibody level at 6 months p.v. was also associated with a higher relative abundance of Dorea formicigenerans at 1 month p.v. among CoronaVac vaccinees (Rho = 0.62, p = 0.001, FDR = 0.123). Of the species altered following vaccination, 79.4% and 42.0% in the CoronaVac and BNT162b2 groups, respectively, recovered at 6 months. Specific to CoronaVac vaccinees, both bacteriome and virome diversity depleted following vaccination and did not recover to baseline at 6 months p.v. (FDR < 0.1). In conclusion, this study identified potential microbiota-based adjuvants that may extend the durability of immune responses to SARS-CoV-2 vaccines.
| S-EPMC10518331 | biostudies-literature
Project description:BackgroundT-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.MethodsWe developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies.ResultsTRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP .ConclusionsThis study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.
| S-EPMC10498576 | biostudies-literature