NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity

Epigenetic dysregulation is a defining feature of tumorigenesis that is implicated in immune escape^1,2. Here, to identify factors that modulate the immune sensitivity of cancer cells, we performed in vivo CRISPR-Cas9 screens targeting 936 chromatin regulators in mouse tumour models treated with immune checkpoint blockade. We identified the H3K9 methyltransferase SETDB1 and other members of the HUSH and KAP1 complexes as mediators of immune escape^3-5. We also found that amplification of SETDB1 (1q21.3) in human tumours is associated with immune exclusion and resistance to immune checkpoint blockade. SETDB1 represses broad domains, primarily within the open genome compartment. These domains are enriched for transposable elements (TEs) and immune clusters associated with segmental duplication events, a central mechanism of genome evolution⁶. SETDB1 loss derepresses latent TE-derived regulatory elements, immunostimulatory genes, and TE-encoded retroviral antigens in these regions, and triggers TE-specific cytotoxic T cell responses in vivo. Our study establishes SETDB1 as an epigenetic checkpoint that suppresses tumour-intrinsic immunogenicity, and thus represents a candidate target for immunotherapy.

Designing multi-epitope vaccine candidates against functional amyloids in Pseudomonas aeruginosa through immunoinformatic and structural bioinformatics approach

Pseudomonas aeruginosa (P. aeruginosa) displays high drug resistance and biofilm-mediated adaptability, which makes its infections difficult to treat. Alternative intervention methods and targets have made such infections treatment manageable. One of the biofilm components, functional amyloids of Pseudomonas (Fap) is correlated positively with virulence and mucoidy phenotype found in infection in cystic fibrosis (CF) patients. Extracellular accessibility, conservation across P. aeruginosa isolates and linkage with lung infections phenotype in CF patients, makes Fap a promising intervention target. Furthermore, the reported effect of bacterial amyloid on neuronal function and immune response makes it a targetable candidate. In the current study, Fap C protein and its immediate interactions were explored to extract antigenic T-cell and B-cell epitopes. A combination of epitopes and peptide adjuvants has been linked to derive vaccine candidate structures. The vaccine candidates were validated for antigenicity, allergenicity, physiochemical properties, stability and interactions with TLRs and MHC alleles. Immunosimulation studies have demonstrated that vaccines elicit Th1 dominated response, which can assist in good prognosis of infection in CF patients.

Genetic Analysis Reveals Differences in CD8+ T Cell Epitope Regions That May Impact Cross-Reactivity of Vaccine-Induced T Cells against Wild-Type Mumps Viruses

Nowadays, mumps is re-emerging in highly vaccinated populations. Waning of vaccine-induced immunity plays a role, but antigenic differences between vaccine and mumps outbreak strains could also contribute to reduced vaccine effectiveness. CD8⁺ T cells play a critical role in immunity to viruses. However, limited data are available about sequence variability in CD8⁺ T cell epitope regions of mumps virus (MuV) proteins. Recently, the first set of naturally presented human leukocyte antigen Class I (HLA-I) epitopes of MuV was identified by us. In the present study, sequences of 40 CD8⁺ T cell epitope candidates, including previously and newly identified, obtained from Jeryl–Lynn mumps vaccine strains were compared with genomes from 462 circulating MuV strains. In 31 epitope candidates (78%) amino acid differences were detected, and in 17 (43%) of the epitope candidates the corresponding sequences in wild-type strains had reduced predicted HLA-I-binding compared to the vaccine strains. These findings suggest that vaccinated persons may have reduced T cell immunity to circulating mumps viruses due to antigenic differences.

T cell epitopes of SARS-CoV-2 spike protein and conserved surface protein of Plasmodium malariae share sequence homology

Since its emergence in late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading remarkably fast worldwide. Effective countermeasures require the rapid development of data and tools to monitor its spread and better understand immunogenic profile. However, limited information is available about the tools and target of the immune responses to SARS-CoV-2. In this study, we excogitated a new approach for analyzing phylogenetic relationships by using the whole prototype proteome sequences. Phylogenetic analysis on the whole prototype proteome sequences showed that SARS-CoV-2 was a direct descendant of Bat-CoV and was closely related to Pangolin-CoV, Bat-SL-CoV, and SARS-CoV. The pairwise comparison of SARS-CoV-2 with Bat-CoV showed an unusual replacement of the motif consisting of seven amino acids (NNLDSKV) within the spike protein of SARS-CoV-2. The replaced motif in the spike protein of SARS-CoV-2 was found in 12 other species, including a conserved surface protein of a malaria-causing pathogen, Plasmodium malariae. We further identified the T and B cell epitope sequence homology of SARS-CoV-2 spike protein with conserved surface protein of P. malariae using the Immune Epitope Database and Analysis Resource (IEDB). The shared immunodominant epitopes may provide immunity against SARS-CoV-2 infection to those previously infected with P. malariae.

Immunodominant T-cell epitopes from the SARS-CoV-2 spike antigen reveal robust pre-existing T-cell immunity in unexposed individuals

The COVID-19 pandemic has revealed a range of disease phenotypes in infected patients with asymptomatic, mild, or severe clinical outcomes, but the mechanisms that determine such variable outcomes remain unresolved. In this study, we identified immunodominant CD8 T-cell epitopes in the spike antigen using a novel TCR-binding algorithm. The predicted epitopes induced robust T-cell activation in unexposed donors demonstrating pre-existing CD4 and CD8 T-cell immunity to SARS-CoV-2 antigen. The T-cell reactivity to the predicted epitopes was higher than the Spike-S1 and S2 peptide pools in the unexposed donors. A key finding of our study is that pre-existing T-cell immunity to SARS-CoV-2 is contributed by TCRs that recognize common viral antigens such as Influenza and CMV, even though the viral epitopes lack sequence identity to the SARS-CoV-2 epitopes. This finding is in contrast to multiple published studies in which pre-existing T-cell immunity is suggested to arise from shared epitopes between SARS-CoV-2 and other common cold-causing coronaviruses. However, our findings suggest that SARS-CoV-2 reactive T-cells are likely to be present in many individuals because of prior exposure to flu and CMV viruses.

Association between HLA alleles and Epstein-Barr virus Zta-IgA serological status in healthy males from southern China

Background

Nasopharyngeal carcinoma (NPC), an Epstein–Barr virus (EBV) associated cancer, exhibits an extremely high incidence in southern Chinese. Given that human leukocyte antigen (HLA) plays critical roles in antigen presentation and relates to NPC susceptibility, it is speculated that certain HLA variants may affect EBV reactivation, which is a key pathogenic factor of NPC. Therefore, we attempted to identify HLA alleles associated with the indicator of EBV reactivation, Zta‐IgA, in healthy males from NPC endemic area.

Methods

HLA alleles of 1078 healthy males in southern China from the 21‐RCCP study were imputed using genome‐wide single nucleotide polymorphism data. EBV Zta‐IgA in blood samples were measured using an enzyme‐linked immunosorbent assay. Multiple logistic regression analysis was used to evaluate the effect of HLA allele on Zta‐IgA serological status and its potential joint association with smoking. The binding affinity for Zta‐peptide was predicted using NetMHCIIpan 4.0.

Results

HLA‐DRB1*09:01 was found to be associated with a higher risk of Zta‐IgA seropositivity (odds ratio = 1.80, 95% confidence interval = 1.32–2.45; p = 1.82 × 10⁻⁴). Compared with non‐smokers without HLA‐DRB1*09:01, the effect size increased to 2.19‐ and 3.70‐fold for the light and heavy smokers carrying HLA‐DRB1*09:01, respectively. Furthermore, HLA‐DRB1*09:01 showed a stronger binding affinity to Zta peptide than other HLA‐DRB1 alleles.

Conclusions

Our study highlighted the pivotal role of genetic HLA variants in EBV reactivation and the etiology of NPC. Smokers with HLA‐DRB1*09:01 have a significantly higher risk of being Zta‐IgA seropositive, which indicates the necessity of smoking cessation in certain high‐risk populations and also provide clues for further research on the etiology of NPC.

[In silico specificity determination of neoantigen-reactive T-lymphocytes]

Effective personalized immunotherapies of the future will need to capture not only the peculiarities of the patient's tumor but also of his immune response to it. In this study, using results of in vitro high-throughput specificity assays, and combining comparative models of pMHCs and TCRs using molecular docking, we have constructed all-atom models for the putative complexes of all their possible pairwise TCR-pMHC combinations. For the models obtained we have calculated a dataset of physics-based scores and have trained binary classifiers that perform better compared to their solely sequence-based counterparts. These structure-based classifiers pinpoint the most prominent energetic terms and structural features characterizing the type of protein-protein interactions that underlies the immune recognition of tumors by T cells.

Large-scale identification of T cell epitopes derived from SARS-CoV-2 for the development of peptide vaccines against COVID-19

BACKGROUND

Coronavirus disease-19 (COVID-19) continues to be a major public health challenge globally. The identification of SARS-CoV-2-derived T cell epitopes is of critical importance for peptide vaccines or diagnostic tools of COVID-19.

METHODS

In this study, a number of SARS-CoV-2-derived HLA-I binding peptides were predicted by NetMHCpan-4.1 and selected by Popcover to achieve pancoverage of the Chinese population. The top 5 ranked peptides derived from each protein of SARS-CoV-2 were then evaluated using PBMCs from unexposed individuals (negative for SARS-CoV-2 IgG).

RESULTS

Seven epitopes derived from 4 SARS-CoV-2 proteins were identified. Interestingly, most (5 out of 7) of the SARS-CoV-2-derived peptides with predicted affinities for HLA-I molecules were identified as HLA-II-restricted epitopes and induced CD4+ T cell-dependent responses. These results complete missing pieces of pre-existing SARS-CoV-2-specific T cells and suggest that pre-existing T cells targeting all SARS-CoV-2-encoded proteins can be discovered in unexposed populations.

CONCLUSIONS

In summary, in the current study, we present an alternative and effective strategy for the identification of T cell epitopes of SARS-CoV-2 in healthy subjects, which may indicate an important role in the development of peptide vaccines for COVID-19.

MS-Based HLA-II Peptidomics Combined With Multiomics Will Aid the Development of Future Immunotherapies

Immunotherapies have emerged to treat diseases by selectively modulating a patient's immune response. Although the roles of T and B cells in adaptive immunity have been well studied, it remains difficult to select targets for immunotherapeutic strategies. Because human leukocyte antigen class II (HLA-II) peptides activate CD4+ T cells and regulate B cell activation, proliferation, and differentiation, these peptide antigens represent a class of potential immunotherapy targets and biomarkers. To better understand the molecular basis of how HLA-II antigen presentation is involved in disease progression and treatment, systematic HLA-II peptidomics combined with multiomic analyses of diverse cell types in healthy and diseased states is required. For this reason, MS-based innovations that facilitate investigations into the interplay between disease pathologies and the presentation of HLA-II peptides to CD4+ T cells will aid in the development of patient-focused immunotherapies.

New peptides with immunomodulatory activity identified from rice proteins through peptidomic and in silico analysis

The new food-derived bio-functional peptides are urgently needed globally, but the separation and purification process for obtaining the immunopeptides from food is low efficiency and highly time-consuming. In the present study, rice proteins were extracted and identified by using liquid chromatography/tandem mass spectrometry (LC-MS/MS). Furthermore, a strategy combining immuno-prediction and in silico simulation was used to screen for peptides showing immunomodulatory activity, including inhibition of the release of nitric oxide, tumor necrosis factor-α, and the interleukins IL-6 and IL-1β in lipopolysaccharide-induced RAW264.7 mouse macrophages. This LC-MS/MS identification and immuno-prediction method may provide insights for the potential identification of more food-derived immunopeptides.

Recent progress on MHC-I epitope prediction in tumor immunotherapy

Tumor immunotherapy has now become one of the most potential therapy for those intractable cancer diseases. The antigens on the cancer cell surfaces are the keys for the immune system to recognize and eliminate them. As reported, the immunogenicity of the tumor antigens could be determined by the binding between the key epitope peptides and MHC molecules. In recent years, the approaches to anticipate the peptides from the candidate epitopes have gradually changed into more efficient methods. Including the improved conventional methods, more diverse methods were coming into view. Here we review the anticipated methods of the tumor associated epitopes that specifically bind with major histocompatibility complex (MHC) class I molecules, and the recent advances and applications of those epitope prediction methods.

Landscape of epitopes targeted by T cells in 852 individuals recovered from COVID-19: Meta-analysis, immunoprevalence, and web platform

Knowledge of the epitopes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) targeted by T cells in recovered (convalescent) individuals is important for understanding T cell immunity against coronavirus disease 2019 (COVID-19). This information can aid development and assessment of COVID-19 vaccines and inform novel diagnostic technologies. Here, we provide a unified description and meta-analysis of SARS-CoV-2 T cell epitopes compiled from 18 studies of cohorts of individuals recovered from COVID-19 (852 individuals in total). Our analysis demonstrates the broad diversity of T cell epitopes that have been recorded for SARS-CoV-2. A large majority are seemingly unaffected by current variants of concern. We identify a set of 20 immunoprevalent epitopes that induced T cell responses in multiple cohorts and in a large fraction of tested individuals. The landscape of SARS-CoV-2 T cell epitopes we describe can help guide immunological studies, including those related to vaccines and diagnostics. A web-based platform has been developed to help complement these efforts.

In vitro immunogenicity prediction: bridging between innate and adaptive immunity

Development of antidrug antibodies (ADAs) is an undesirable potential outcome of administration of biotherapeutics and involves the innate and adaptive immune systems. ADAs can have detrimental clinical consequences: they can reduce biotherapeutic efficacy or produce adverse events. Because animal models are considered poor predictors of immunogenicity in humans, in vitro assays with human innate and adaptive immune cells are commonly used alternatives that can reveal cell-mediated unwanted immune responses. Multiple methods have been developed to assess the immune cell response following exposure to biotherapeutics and estimate the potential immunogenicity of biotherapeutics. This review highlights the role of innate and adaptive immune cells as the drivers of immunogenicity and summarizes the use of these cells in assays to predict clinical ADA.

Withdrawn: Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation

This article has been withdrawn by the authors. A publication of the manuscript with the correct figures and tables has been approved and the authors state the conclusions of the manuscript remain unaffected. Specifically, errors are in Figure 6A, Supplementary Figure 10B, Supplementary Figure 10C, and Supplementary Table 5. The details of the errors are as follows: the HLA types for one sample were incorrectly assigned because of a tumor/normal mislabeling from the biobank vendor. Due to the differing HLA types between the tumor and normal sample, the sequence analysis established that the HLA alleles for this patient had been deleted (HLA LOH). The authors conclude that this was an artifact caused by the normal sample mislabeling. The corrected version can be accessed (Pyke, R.M., Mellacheruvu, D., Dea, S., Abbott, C.W., Zhang, S.V., Philips, N.A., Harris, J., Bartha, G., Desai, S., McClory, R., West, J., Snyder, M,P., Chen, R., Boyle, S.M. (2022) Precision Neoantigen Discovery Using Large-Scale Immunopeptidomics and Composite Modeling of MHC Peptide Presentation. Mol. Cell. Proteomics 22, 100506

Unique pathogen peptidomes facilitate pathogen-specific selection and specialization of MHC alleles

A key component of pathogen-specific adaptive immunity in vertebrates is the presentation of pathogen-derived antigenic peptides by major histocompatibility complex (MHC) molecules. The excessive polymorphism observed at MHC genes is widely presumed to result from the need to recognize diverse pathogens, a process called pathogen-driven balancing selection. This process assumes that pathogens differ in their peptidomes—the pool of short peptides derived from the pathogen’s proteome—so that different pathogens select for different MHC variants with distinct peptide-binding properties. Here, we tested this assumption in a comprehensive data set of 51.9 Mio peptides, derived from the peptidomes of 36 representative human pathogens. Strikingly, we found that 39.7% of the 630 pairwise comparisons among pathogens yielded not a single shared peptide and only 1.8% of pathogen pairs shared more than 1% of their peptides. Indeed, 98.8% of all peptides were unique to a single pathogen species. Using computational binding prediction to characterize the binding specificities of 321 common human MHC class-I variants, we investigated quantitative differences among MHC variants with regard to binding peptides from distinct pathogens. Our analysis showed signatures of specialization toward specific pathogens especially by MHC variants with narrow peptide-binding repertoires. This supports the hypothesis that such fastidious MHC variants might be maintained in the population because they provide an advantage against particular pathogens. Overall, our results establish a key selection factor for the excessive allelic diversity at MHC genes observed in natural populations and illuminate the evolution of variable peptide-binding repertoires among MHC variants.

A Multi-Epitope Chitosan Nanoparticles Vaccine of Canine Against Echinococcus granulosus

Cystic Echinococcosis (CE) is caused by Echinococcus granulosus (Eg), which endangers the health of the intermediate host. Therefore, effective canid vaccines against Eg infection are urgently needed to reduce the incidence of this disease. In the present work, the aim was to predict epitopes in four vaccine candidate antigens (VCAs) in Eg as a basis to design a multi-epitope canine-directed vaccine. This vaccine is based on chitosan nanoparticles (CS-NPs) and is directed against Eg infection in the definitive host. The canine-directed vaccine was designed based on Eg antigens EgM9, Eg_10196, EgA31 and EgG1Y162. Several tools in online servers were used to predict VCAs information, which was combined with B cell, CTL and Th epitopes. Considering that acquiring experimental information in canids is difficult, and that it may be possible to perform future experiments in mice, we predicted both canine and murine T cell epitopes. The multi-epitope vaccine was synthetically prepared by ionic crosslinking method, and CS-NPs was used as adjuvant. The mice were immunized by oral gavage and laser scanning confocal microscopy was used to localize the fluorescein- labeled multi-epitope peptide in the intestinal tract. The final multi-epitope vaccine was construct consist of Co1 targeting peptide, four B-cell epitopes, four canine-directed CTL epitopes and four murine-directed Th epitopes. It has been proven experimentally by this research that multi-epitope antigen concentration merged with microfold cells was high in the CS-NPs vaccine group. The present bioinformatics study is a first step towards the construction of a canine-specific multiepitope vaccine against Eg with twelve predicted epitopes. CS-NPs is a potential adjuvant with relatively safe penetration enhancement delivery and a potent immunostimulant.

BERTMHC: Improved MHC-peptide class II interaction prediction with transformer and multiple instance learning

Motivation

Increasingly comprehensive characterization of cancer-associated genetic alterations has paved the way for the development of highly specific therapeutic vaccines. Predicting precisely the binding and presentation of peptides to major histocompatibility complex (MHC) alleles is an important step toward such therapies. Recent data suggest that presentation of both class I and II epitopes are critical for the induction of a sustained effective immune response. However, the prediction performance for MHC class II has been limited compared to class I.

Results

We present a transformer neural network model which leverages self-supervised pretraining from a large corpus of protein sequences. We also propose a multiple instance learning (MIL) framework to deconvolve mass spectrometry data where multiple potential MHC alleles may have presented each peptide. We show that pretraining boosted the performance for these tasks. Combining pretraining and the novel MIL approach, our model outperforms state-of-the-art models based on peptide and MHC sequence only for both binding and cell surface presentation predictions.

Availability and implementation

Our source code is available at https://github.com/s6juncheng/BERTMHC under a noncommercial license. A webserver is available at https://bertmhc.privacy.nlehd.de/

Supplementary information

Supplementary data are available at Bioinformatics online.

Artificial intelligence in early drug discovery enabling precision medicine

Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.

Proteome-wide analysis of Coxiella burnetii for conserved T-cell epitopes with presentation across multiple host species

Background

Coxiella burnetii is the Gram-negative bacterium responsible for Q fever in humans and coxiellosis in domesticated agricultural animals. Previous vaccination efforts with whole cell inactivated bacteria or surface isolated proteins confer protection but can produce a reactogenic immune responses. Thereby a protective vaccine that does not cause aberrant immune reactions is required. The critical role of T-cell immunity in control of C. burnetii has been made clear, since either CD8⁺ or CD4⁺ T cells can empower clearance. The purpose of this study was to identify C. burnetii proteins bearing epitopes that interact with major histocompatibility complexes (MHC) from multiple host species (human, mouse, and cattle).

Results

Of the annotated 1815 proteins from the Nine Mile Phase I (RSA 493) assembly, 402 proteins were removed from analysis due to a lack of inter-isolate conservation. An additional 391 proteins were eliminated from assessment to avoid potential autoimmune responses due to the presence of host homology. We analyzed the remaining 1022 proteins for their ability to produce peptides that bind MHCI or MHCII. MHCI and MHCII predicted epitopes were filtered and compared between species yielding 777 MHCI epitopes and 453 MHCII epitopes. These epitopes were further examined for presentation by both MHCI and MHCII, and for proteins that contained multiple epitopes. There were 31 epitopes that overlapped positionally between MHCI and MHCII across host species. Of these, there were 9 epitopes represented within proteins containing ≥ 5 total epitopes, where an additional 24 proteins were also epitope dense. In all, 55 proteins were found to contain high scoring T-cell epitopes. Besides the well-studied protein Com1, most identified proteins were novel when compared to previously studied vaccine candidates.

Conclusion

These data represent the first proteome-wide evaluation of C. burnetii peptide epitopes. Furthermore, the inclusion of human, mouse, and bovine data capture a range of hosts for this zoonotic pathogen plus an important model organism. This work provides new vaccine targets for future vaccination efforts and enhances opportunities for selecting multiple T-cell epitope types to include within a vaccine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04181-w.

Systematic screening of viral and human genetic variation identifies antiretroviral resistance and immune escape link

Background

Considering the remaining threat of drug-resistantmutations (DRMs) to antiretroviral treatment (ART) efficacy, we investigated how the selective pressure of human leukocyte antigen (HLA)-restricted cytotoxic T lymphocytes drives certain DRMs' emergence and retention.

Methods

We systematically screened DRM:HLA class I allele combinations in 3997 ART-naïve Swiss HIV Cohort Study (SHCS) patients. For each pair, a logistic regression model preliminarily tested for an association with the DRM as the outcome. The three HLA:DRM pairs remaining after multiple testing adjustment were analyzed in three ways: cross-sectional logistic regression models to determine any HLA/infection time interaction, survival analyses to examine if HLA type correlated with developing specific DRMs, and via NetMHCpan to find epitope binding evidence of immune escape.

Results

Only one pair, RT-E138:HLA-B18, exhibited a significant interaction between infection duration and HLA. The survival analyses predicted two pairs with an increased hazard of developing DRMs: RT-E138:HLA-B18 and RT-V179:HLA-B35. RT-E138:HLA-B18 exhibited the greatest significance in both analyses (interaction term odds ratio [OR] 1.169 [95% confidence interval (CI) 1.075-1.273]; p-value<0.001; survival hazard ratio 12.211 [95% CI 3.523-42.318]; p-value<0.001). The same two pairs were also predicted by netMHCpan to have epitopic binding.

Conclusions

We identified DRM:HLA pairs where HLA presence is associated with the presence or emergence of the DRM, indicating that the selective pressure for these mutations alternates direction depending on the presence of these HLA alleles.

Funding

Funded by the Swiss National Science Foundation within the framework of the SHCS, and the University of Zurich, University Research Priority Program: Evolution in Action: From Genomes Ecosystems, in Switzerland.

Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs)

Background

The host’s immune system develops in equilibrium with both cellular self-antigens and non-self-antigens derived from microorganisms which enter the body during lifetime. In addition, during the years, a tumor may arise presenting to the immune system an additional pool of non-self-antigens, namely tumor antigens (tumor-associated antigens, TAAs; tumor-specific antigens, TSAs).

Methods

In the present study, we looked for homology between published TAAs and non-self-viral-derived epitopes. Bioinformatics analyses and ex vivo immunological validations have been performed.

Results

Surprisingly, several of such homologies have been found. Moreover, structural similarities between paired TAAs and viral peptides as well as comparable patterns of contact with HLA and T cell receptor (TCR) α and β chains have been observed. Therefore, the two classes of non-self-antigens (viral antigens and tumor antigens) may converge, eliciting cross-reacting CD8⁺ T cell responses which possibly drive the fate of cancer development and progression.

Conclusions

An established antiviral T cell memory may turn out to be an anticancer T cell memory, able to control the growth of a cancer developed during the lifetime if the expressed TAA is similar to the viral epitope. This may ultimately represent a relevant selective advantage for patients with cancer and may lead to a novel preventive anticancer vaccine strategy.

Improvement of Neoantigen Identification Through Convolution Neural Network

Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are limited by in vitro binding affinity data and algorithmic constraints, inevitably resulting in high false positives. In this study, we proposed a deep convolutional neural network named APPM (antigen presentation prediction model) to predict antigen presentation in the context of human leukocyte antigen (HLA) class I alleles. APPM is trained on large mass spectrometry (MS) HLA-peptides datasets and evaluated with an independent MS benchmark. Results show that APPM outperforms the methods recommended by the immune epitope database (IEDB) in terms of positive predictive value (PPV) (0.40 vs. 0.22), which will further increase after combining these two approaches (PPV = 0.51). We further applied our model to the prediction of neoantigens from consensus driver mutations and identified 16,000 putative neoantigens with hallmarks of 'drivers'.

CXCL10 potentiates immune checkpoint blockade therapy in homologous recombination-deficient tumors

Background: Homologous recombination deficiency (HRD) is a common molecular characteristic of genomic instability, and has been proven to be a biomarker for target therapy. However, until now, no research has explored the changes in the transcriptomics landscape of HRD tumors. Methods: The HRD score was established from SNP array data of breast cancer patients from the cancer genome atlas (TCGA) database. The transcriptome data of patients with different HRD scores were analyzed to identify biomarkers associated with HRD. The candidate biomarkers were validated in the gene expression omnibus (GEO) database and immunotherapy cohorts. Results: Based on data from the gene expression profile and clinical characteristics from 1310 breast cancer patients, including TCGA database and GEO database, we found that downstream targets of the cGAS-STING pathway, such as CXCL10, were upregulated in HRD tumors and could be used as a predictor of survival outcome in triple-negative breast cancer (TNBC) patients. Further comprehensive analysis of the tumor immune microenvironment (TIME) revealed that the expression of CXCL10 was positively correlated with neoantigen load and infiltrating immune cells. Finally, in vivo experimental data and clinical trial data confirmed that the expression of CXCL10 could be used as a biomarker for anti-PD-1/PD-L1 therapy. Conclusions: Together, our study not only revealed that CXCL10 is associated with HRD but also introduced a potential new perspective for identifying prognostic biomarkers of immunotherapy.

Identification of Novel Candidate CD8+ T Cell Epitopes of the SARS-CoV2 with Homology to Other Seasonal Coronaviruses

Cross-reactive T cell immunity to seasonal coronaviruses (HCoVs) may lead to immunopathology or protection during SARS-CoV2 infection. To understand the influence of cross-reactive T cell responses, we used IEDB (Immune epitope database) and NetMHCpan (ver. 4.1) to identify candidate CD8⁺ T cell epitopes, restricted through HLA-A and B alleles. Conservation analysis was carried out for these epitopes with HCoVs, OC43, HKU1, and NL63. 12/18 the candidate CD8⁺ T cell epitopes (binding score of ≥0.90), which had a high degree of homology (>75%) with the other three HCoVs were within the NSP12 and NSP13 proteins. They were predicted to be restricted through HLA-A*2402, HLA-A*201, HLA-A*206, and HLA-B alleles B*3501. Thirty-one candidate CD8⁺ T cell epitopes that were specific to SARS-CoV2 virus (<25% homology with other HCoVs) were predominantly identified within the structural proteins (spike, envelop, membrane, and nucleocapsid) and the NSP1, NSP2, and NSP3. They were predominantly restricted through HLA-B*3501 (6/31), HLA-B*4001 (6/31), HLA-B*4403 (7/31), and HLA-A*2402 (8/31). It would be crucial to understand T cell responses that associate with protection, and the differences in the functionality and phenotype of epitope specific T cell responses, presented through different HLA alleles common in different geographical groups, to understand disease pathogenesis.

Modeling clonal structure over narrow time frames via circulating tumor DNA in metastatic breast cancer

BACKGROUND

Circulating tumor DNA (ctDNA) offers minimally invasive means to repeatedly interrogate tumor genomes, providing opportunities to monitor clonal dynamics induced by metastasis and therapeutic selective pressures. In metastatic cancers, ctDNA profiling allows for simultaneous analysis of both local and distant sites of recurrence. Despite the promise of ctDNA sampling, its utility in real-time genetic monitoring remains largely unexplored.

METHODS

In this exploratory analysis, we characterize high-frequency ctDNA sample series collected over narrow time frames from seven patients with metastatic triple-negative breast cancer, each undergoing treatment with Cabozantinib, a multi-tyrosine kinase inhibitor (NCT01738438, https://clinicaltrials.gov/ct2/show/NCT01738438 ). Applying orthogonal whole exome sequencing, ultra-low pass whole genome sequencing, and 396-gene targeted panel sequencing, we analyzed 42 plasma-derived ctDNA libraries, representing 4-8 samples per patient with 6-42 days between samples. Integrating tumor fraction, copy number, and somatic variant information, we model tumor clonal dynamics, predict neoantigens, and evaluate consistency of genomic information from orthogonal assays.

RESULTS

We measured considerable variation in ctDNA tumor faction in each patient, often conflicting with RECIST imaging response metrics. In orthogonal sequencing, we found high concordance between targeted panel and whole exome sequencing in both variant detection and variant allele frequency estimation (specificity = 95.5%, VAF correlation, r = 0.949), Copy number remained generally stable, despite resolution limitations posed by low tumor fraction. Through modeling, we inferred and tracked distinct clonal populations specific to each patient and built phylogenetic trees revealing alterations in hallmark breast cancer drivers, including TP53, PIK3CA, CDK4, and PTEN. Our modeling revealed varied responses to therapy, with some individuals displaying stable clonal profiles, while others showed signs of substantial expansion or reduction in prevalence, with characteristic alterations of varied literature annotation in relation to the study drug. Finally, we predicted and tracked neoantigen-producing alterations across time, exposing translationally relevant detection patterns.

CONCLUSIONS

Despite technical challenges arising from low tumor content, metastatic ctDNA monitoring can aid our understanding of response and progression, while minimizing patient risk and discomfort. In this study, we demonstrate the potential for high-frequency monitoring of evolving genomic features, providing an important step toward scalable, translational genomics for clinical decision making.

INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results

Database search engines for bottom-up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity-based scores helps to overcome this drawback. Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity-based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity-based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator. We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity-based scores when analyzing low-intensity spectra or analytes with very similar physicochemical properties that require vast search spaces. Overall, the end-to-end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

Immunogenic profiling and designing of a novel vaccine from capsid proteins of FMDV serotype Asia-1 through reverse vaccinology

Foot-and-mouth disease is one of the devastating transboundary animal diseases causing heavy losses to the livestock industry. Different vaccines based on the inactivated FMD virus are used against this disease, but lack of immunological memory and the need for high biocontainment are the major drawbacks of these vaccines. A novel vaccine comprising recombinant antigenic regions is effective, as they lack viruses for production. Considering the fact, capsid proteins vp4, vp2, vp3, and vp1 with 3C protease of FMDV serotype Asia-1 were analyzed through reverse vaccinology approaches in this study. The sequence and structural analysis of the proteins is carried out through various bioinformatic tools and the sequence analysis has figured out the acidic nature and thermal stability of the proteins, likewise, the phylogenetic analysis helped us to trace the FMDV isolates, elucidating that selected proteins belong to the strain (Group VII), which is currently circulating in Pakistan. Next, the B-cell and MHC Class-I epitopes are identified from the antigenic proteins by immunoinformatic tools. The highly conserved, antigenic, and non-allergenic epitopes are used to design the vaccine. Accordingly, the codon adaptation and in silico cloning of the corresponding genes is performed. Thus, the bacterial expression vector could be used for efficient expression and large-scale production of the vaccine.

Nationwide Study of Drug Resistance Mutations in HIV-1 Infected Individuals under Antiretroviral Therapy in Brazil

The success of antiretroviral treatment (ART) is threatened by the emergence of drug resistance mutations (DRM). Since Brazil presents the largest number of people living with HIV (PLWH) in South America we aimed at understanding the dynamics of DRM in this country. We analyzed a total of 20,226 HIV-1 sequences collected from PLWH undergoing ART between 2008–2017. Results show a mild decline of DRM over the years but an increase of the K65R reverse transcriptase mutation from 2.23% to 12.11%. This increase gradually occurred following alterations in the ART regimens replacing zidovudine (AZT) with tenofovir (TDF). PLWH harboring the K65R had significantly higher viral loads than those without this mutation (p < 0.001). Among the two most prevalent HIV-1 subtypes (B and C) there was a significant (p < 0.001) association of K65R with subtype C (11.26%) when compared with subtype B (9.27%). Nonetheless, evidence for K65R transmission in Brazil was found both for C and B subtypes. Additionally, artificial neural network-based immunoinformatic predictions suggest that K65R could enhance viral recognition by HLA-B27 that has relatively low prevalence in the Brazilian population. Overall, the results suggest that tenofovir-based regimens need to be carefully monitored particularly in settings with subtype C and specific HLA profiles.

Generation of Epstein-Barr Virus Antigen-Specific T Cell Receptors Recognizing Immunodominant Epitopes of LMP1, LMP2A, and EBNA3C for Immunotherapy

Epstein-Barr virus (EBV) infections in healthy individuals are usually cleared by immune cells, wherein CD8⁺ T lymphocytes play the most important role. However, in some immunocompromised individuals, EBV infections can lead to the development of cancer in B, T, natural killer (NK) cells and epithelial cells. Most EBV-associated cancers express a limited number of virus-specific antigens such as latent membrane proteins (LMP1 and LMP2) and nuclear proteins (EBNA1, -2, EBNA3A, -B, -C, and EBNA-LP). These antigens represent true tumor-specific antigens and can be considered useful targets for T cell receptor (TCR) gene therapy to treat EBV-associated diseases. We used a TCR isolation platform based on a single major histocompatibility complex class I (MHC I) K562 cell library for the detection, isolation, and re-expression of TCRs targeting immunodominant peptide MHC (pMHC). Mature dendritic cells (mDCs) were pulsed with in vitro-transcribed (ivt) RNA encoding for the selected antigen to stimulate autologous T cells. The procedure allowed the mDCs to select an immunogenic epitope of the antigen for processing and presentation on the cell surface in combination with the most suitable MHC I molecule. We isolated eight EBV-specific TCRs. They recognize various pMHCs of EBV antigens LMP1, LMP2A, and EBNA3C, some of them described previously and some newly identified in this study. The TCR genes were molecularly cloned into retroviral vectors and the resultant TCR-engineered T cells secreted interferon-γ after antigen contact and were able to lyse tumor cells. The EBV-specific TCRs can be used as a basis for the generation of a TCR library, which provides a valuable source of TCRs for the production of EBV-specific T cells to treat EBV-associated diseases in patients with different MHC I types.

Nanomaterial-based delivery vehicles for therapeutic cancer vaccine development

Nanomaterial-based delivery vehicles such as lipid-based, polymer-based, inorganics-based, and bio-inspired vehicles often carry distinct and attractive advantages in the development of therapeutic cancer vaccines. Based on various delivery vehicles, specifically designed nanomaterials-based vaccines are highly advantageous in boosting therapeutic and prophylactic antitumor immunities. Specifically, therapeutic vaccines featuring unique properties have made major contributions to the enhancement of antigen immunogenicity, encapsulation efficiency, biocompatibility, and stability, as well as promoting antigen cross-presentation and specific CD8+ T cell responses. However, for clinical applications, tumor-associated antigen-derived vaccines could be an obstacle, involving immune tolerance and deficiency of tumor specificities, in achieving maximum therapeutic indices. However, when using bioinformatics predictions with emerging innovations of in silico tools, neoantigen-based therapeutic vaccines might become potent personalized vaccines for tumor treatments. In this review, we summarize the development of preclinical therapeutic cancer vaccines and the advancements of nanomaterial-based delivery vehicles for cancer immunotherapies, which provide the basis for a personalized vaccine delivery platform. Moreover, we review the existing challenges and future perspectives of nanomaterial-based personalized vaccines for novel tumor immunotherapies.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire

Accurate assessment of TCR-antigen specificity at the whole immune repertoire level lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR-peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR-peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each specific TCR-p-MHC system. When applied to simulate thymic selection of an MHC-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patient's immune repertoire.

Identification of biomarkers complementary to homologous recombination deficiency for improving the clinical outcome of ovarian serous cystadenocarcinoma

Ovarian cancer patients with homologous recombination deficiency (HRD) tumors would benefit from PARP inhibitor (PARPi) therapy. However, patients with HRD tumors account for less than 50% of the whole cohort, so new biomarkers still need to be developed. Based on the data from the SNP array and somatic mutation profiles in the ovarian cancer genome, we found that high frequency of actionable mutations existed in patients with non-HRD tumors. Through transcriptome analysis, we identified that a downstream target of the cGAS-STING pathway, CXCL11, was upregulated in HRD tumors and could be used as a predictor of survival outcome. Further comprehensive analysis of the tumor immune microenvironment (TIME) revealed that CXCL11 expression signature was closely correlated with cytotoxic cells, neoantigen load and immune checkpoint blockade (ICB). Clinical trial data confirmed that the expression of CXCL11 could be used as a biomarker for anti-PD-1/PD-L1 therapy. Finally, in vivo and in vitro experiments showed that cancer cells with PARPi treatment increased the expression of CXCL11. Collectively, our study not only provides biomarkers of ovarian cancer complementary to the HRD score but also introduces a potential new perspective for identifying prognostic biomarkers of immunotherapy.

Prior SARS-CoV-2 infection rescues B and T cell responses to variants after first vaccine dose

SARS-CoV-2 vaccine rollout has coincided with the spread of variants of concern. We investigated if single dose vaccination, with or without prior infection, confers cross protective immunity to variants. We analyzed T and B cell responses after first dose vaccination with the Pfizer/BioNTech mRNA vaccine BNT162b2 in healthcare workers (HCW) followed longitudinally, with or without prior Wuhan-Hu-1 SARS-CoV-2 infection. After one dose, individuals with prior infection showed enhanced T cell immunity, antibody secreting memory B cell response to spike and neutralizing antibodies effective against B.1.1.7 and B.1.351. By comparison, HCW receiving one vaccine dose without prior infection showed reduced immunity against variants. B.1.1.7 and B.1.351 spike mutations resulted in increased, abrogated or unchanged T cell responses depending on human leukocyte antigen (HLA) polymorphisms. Single dose vaccination with BNT162b2 in the context of prior infection with a heterologous variant substantially enhances neutralizing antibody responses against variants.

An Integrated Genomic, Proteomic, and Immunopeptidomic Approach to Discover Treatment-Induced Neoantigens

All nucleated mammalian cells express major histocompatibility complex (MHC) proteins that present peptides on cell surfaces for immune surveillance. These MHC-presented peptides (pMHC) are necessary for directing T-cell responses against cells harboring non-self antigens derived from pathogens or from somatic mutations. Alterations in tumor-specific antigen repertoires - particularly novel MHC presentation of mutation-bearing peptides (neoantigens) - can be potent targets of anti-tumor immune responses. Here we employed an integrated genomic and proteomic antigen discovery strategy aimed at measuring how interferon gamma (IFN-γ) alters antigen presentation, using a human lymphoma cell line, GRANTA-519. IFN-γ treatment resulted in 126 differentially expressed proteins (2% of all quantified proteins), which included components of antigen presentation machinery and interferon signaling pathways, and MHC molecules themselves. In addition, several proteasome subunits were found to be modulated, consistent with previous reports of immunoproteasome induction by IFN-γ exposure. This finding suggests that a modest proteomic response to IFN-γ could create larger alteration to cells' antigen/epitope repertoires. Accordingly, MHC immunoprecipitation followed by mass spectrometric analysis of eluted peptide repertoires revealed exclusive signatures of IFN-γ induction, with 951 unique peptides reproducibly presented by MHC-I and 582 presented by MHC-II. Furthermore, an additional set of pMHCs including several candidate neoantigens, distinguished control and the IFN-γ samples by their altered relative abundances. Accordingly, we developed a classification system to distinguish peptides which are differentially presented due to altered expression from novel peptides resulting from changes in antigen processing. Taken together, these data demonstrate that IFN-γ can re-shape antigen repertoires by identity and by abundance. Extending this approach to models with greater clinical relevance could help develop strategies by which immunopeptide repertoires are intentionally reshaped to improve endogenous or vaccine-induced anti-tumor immune responses and potentially anti-viral immune responses.

SARS-CoV-2 genome-wide T cell epitope mapping reveals immunodominance and substantial CD8+ T cell activation in COVID-19 patients

T cells are important for effective viral clearance, elimination of virus-infected cells and long-term disease protection. To examine the full-spectrum of CD8+ T cell immunity in COVID-19, we experimentally evaluated 3141 major histocompatibility (MHC) class I-binding peptides covering the complete SARS-CoV-2 genome. Using DNA-barcoded peptide-MHC complex (pMHC) multimers combined with a T cell phenotype panel, we report a comprehensive list of 122 immunogenic and a subset of immunodominant SARS-CoV-2 T cell epitopes. Substantial CD8+ T cell recognition was observed in COVID-19 patients, with up to 27% of all CD8+ lymphocytes interacting with SARS-CoV-2-derived epitopes. Most immunogenic regions were derived from open reading frame (ORF) 1 and ORF3, with ORF1 containing most of the immunodominant epitopes. CD8+ T cell recognition of lower affinity was also observed in healthy donors toward SARS-CoV-2-derived epitopes. This pre-existing T cell recognition signature was partially overlapping with the epitope landscape observed in COVID-19 patients and may drive the further expansion of T cell responses to SARS-CoV-2 infection. Importantly the phenotype of the SARS-CoV-2-specific CD8+ T cells, revealed a strong T cell activation in COVID-19 patients, while minimal T cell activation was seen in healthy individuals. We found that patients with severe disease displayed significantly larger SARS-CoV-2-specific T cell populations compared to patients with mild diseases and these T cells displayed a robust activation profile. These results further our understanding of T cell immunity to SARS-CoV-2 infection and hypothesize that strong antigen-specific T cell responses are associated with different disease outcomes.

Applications of Machine and Deep Learning in Adaptive Immunity

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

Distinctive clinical presentation and pathogenic specificities of anti-AK5 encephalitis

Limbic encephalitis (LE) with antibodies against adenylate kinase 5 (AK5) has been difficult to characterize because of its rarity. In this study, we identified 10 new cases and reviewed 16 previously reported patients, investigating clinical features, IgG subclasses, human leukocyte antigen (HLA), and CSF proteomic profiles. Patients with anti-AK5 LE were mostly men (20/26, 76.9%) of median age 66 years old (range 48-94). Predominant symptom was severe episodic amnesia in all patients, frequently associated with depression (17/25, 68.0%). Weight loss, asthenia, and anorexia were also highly characteristic, being present in 11/25 (44.0%) patients. Although epilepsy was always lacking at disease onset, seizures developed later in a subset of patients (4/25, 16.0%). All patients presented CSF abnormalities, such as pleocytosis (18/25, 72.0%), oligoclonal bands (18/25, 72.0%), and increased Tau (11/14, 78.6%). Temporal lobe hyper-intensities were almost always present at disease onset (23/26, 88.5%), evolving nearly invariably toward a severe atrophy in subsequent MRIs (17/19, 89.5%). This finding was in line with a poor response to immunotherapy, with only 5/25 (20.0%) patients responding. IgG1 was the predominant subclass, being the most frequently detected and the one with highest titres in nine CSF-serum paired samples. Temporal biopsy from one of our new cases showed massive lymphocytic infiltrates dominated by both CD4+ and CT8+ T-cells, intense granzyme B expression, and abundant macrophages/microglia. HLA analysis in 11 patients showed a striking association with HLA-B*08:01 (7/11, 63.6%; OR = 13.4, 95% CI [3.8-47.4]), C*07:01 (8/11, 72.7%; OR = 11.0, 95% CI [2.9-42.5]), DRB1*03:01 (8/11, 72.7%; OR = 14.4, 95% CI [3.7-55.7]), DQB1*02:01 (8/11, 72.7%; OR = 13.5, 95% CI [3.5-52.0]), and DQA1*05:01 (8/11, 72.7%; OR = 14.4, 95% CI [3.7-55.7]) alleles, which formed the extended haplotype B8-C7-DR3-DQ2 in 6/11 (54.5%) patients (OR = 16.5, 95% CI [4.8-57.1]). Finally, we compared the CSF proteomic profile of five anti-AK5 patients with that of 40 controls and 10 cases with other more common non-paraneoplastic LE (five with antibodies against leucine-rich glioma inactivated 1 and five against contactin-associated protein-like 2), as well as 10 cases with paraneoplastic neurological syndromes (five with antibodies against Yo and five against Ma2). These comparisons revealed, respectively, 31 and seven significantly up-regulated proteins in anti-AK5 LE, mapping to apoptosis pathways and innate/adaptive immune responses. These findings suggest that the clinical manifestations of anti-AK5 LE result from a distinct T-cell mediated pathogenesis, with major cytotoxicity-induced apoptosis leading to a prompt and aggressive neuronal loss, likely explaining the poor prognosis and response to immunotherapy.

Sensitive Immunopeptidomics by Leveraging Available Large-Scale Multi-HLA Spectral Libraries, Data-Independent Acquisition, and MS/MS Prediction

Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity-from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available.

Genetic variability in COVID-19-related genes in the Brazilian population

SARS-CoV-2 utilizes the angiotensin-converting enzyme 2 (ACE2) receptor and transmembrane serine protease (TMPRSS2) to infect human lung cells. Previous studies have suggested that different host ACE2 and TMPRSS2 genetic backgrounds might contribute to differences in the rate of SARS-CoV-2 infection or COVID-19 severity. Recent studies have also shown that variants in 15 genes related to type I interferon immunity to influenza virus might predispose patients toward life-threatening COVID-19 pneumonia. Other genes (SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, XCR1, IL6, CTSL, ABO, and FURIN) and HLA alleles have also been implicated in the response to infection with SARS-CoV-2. Currently, Brazil has recorded the third-highest number of COVID-19 cases worldwide. We aimed to investigate the genetic variation present in COVID-19-related genes in the Brazilian population. We analyzed 27 candidate genes and HLA alleles in 954 admixed Brazilian exomes. We used the information available in two public databases (http://www.bipmed.org and http://abraom.ib.usp.br/) and additional exomes from individuals born in southeast Brazil, the region of the country with the highest number of COVID-19 patients. Variant allele frequencies were compared with the 1000 Genomes Project phase 3 (1KGP) and gnomAD databases. We detected 395 nonsynonymous variants; of these, 325 were also found in the 1KGP and/or gnomAD. Six of these variants were previously reported to influence the rate of infection or clinical prognosis of COVID-19. The remaining 70 variants were identified exclusively in the Brazilian sample, with a mean allele frequency of 0.0025. In silico analysis revealed that seven of these variants are predicted to affect protein function. Furthermore, we identified HLA alleles previously associated with the COVID-19 response at loci DQB1 and DRB1. Our results showed genetic variability common to other populations and rare and ultrarare variants exclusively found in the Brazilian population. These findings might lead to differences in the rate of infection or response to infection by SARS-CoV-2 and should be further investigated in patients with this disease.

Genetic variants in the Brazilian population do not appear to explain the relatively high overall rates of COVID-19 cases compared to other countries, but could be relevant for risk assessment at the individual level. Iscia Lopes-Cendes of the University of Campinas and colleagues in Brazil examined gene sequences for variations in 27 genes reported to influence COVID-19 infection in Brazilians of mixed heritage and of people born in the southeast where case numbers are high. These studies included the ACE2 gene that codes for a receptor allowing SARS-CoV-2 to enter host cells. Genetic variants were also identified in two international genome databases. Seventy variants were unique to Brazilians, but computer modeling predicted only seven variants that could affect protein function. The team also looked for and found variants that could affect individual immunity to COVID-19. Similar genetic studies could help identify at-risk individuals and therapeutic targets.

Related parameters of affinity and stability prediction of HLA-A*2402 restricted antigen peptides based on molecular docking

Background

Major histocompatibility complex class I (MHC-I) plays an important role in cell immune response, and stable interaction between polypeptides and MHC-I ensures efficient presentation of polypeptide-MHC-I (pMHC-I) molecular complexes to T cells. The aim of this study was to explore ways to improve the affinity and stability of the p-Human Leukocyte Antigen (HLA)-A*2402 complex.

Methods

The peptide sequences of the restricted antigen peptides for HLA-A*2402 and the results of the in vitro competitive binding test were retrieved from the literature. The affinity values were predicted using NetMHCpan v4.1 server, and the stability values were predicted using the NetMHCstab v1.0 server. Auto Vina was used to dock peptides to HLA-A*2402 protein in a flexible docking manner, while Flexpepdock was employed to optimize the docking morphology. Maestro was used to analyze the intermolecular forces and the binding affinity of the complex, while MM-GBSA was used to calculate the binding free energy values.

Results

The intermolecular interactions that maintained the affinity and stability of peptide-HLA-A*2402 complex relied mainly on HB, followed by pi stack. The binding affinity values of molecular docking were associated with the predicted values of affinity and stability, the binding affinity and the binding free energy, as well as the intermolecular force pi-stack. The pi stack had a significant negative correlation with binding affinity and binding free energy. The replacement of the residues of the polypeptides that did not form pi-stack interactions with HLA-A*2402 improved the affinity and/or stability compared to before replacement.

Conclusions

The generation and increase in the number of pi-stacks between peptides and HLA-A*2402 molecules may help improve the affinity and stability of p-HLA-A*2402 complexes. The prediction of intermolecular forces and binding affinity of peptide-HLA by means of molecular docking is a supplement to the current commonly used prediction databases.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

Artificial intelligence in cancer research: learning at different levels of data granularity

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

Integral Use of Immunopeptidomics and Immunoinformatics for the Characterization of Antigen Presentation and Rational Identification of BoLA-DR-Presented Peptides and Epitopes

MHC peptide binding and presentation is the most selective event defining the landscape of T cell epitopes. Consequently, understanding the diversity of MHC alleles in a given population and the parameters that define the set of ligands that can be bound and presented by each of these alleles (the immunopeptidome) has an enormous impact on our capacity to predict and manipulate the potential of protein Ags to elicit functional T cell responses. Liquid chromatography-mass spectrometry analysis of MHC-eluted ligand data has proven to be a powerful technique for identifying such peptidomes, and methods integrating such data for prediction of Ag presentation have reached a high level of accuracy for both MHC class I and class II. In this study, we demonstrate how these techniques and prediction methods can be readily extended to the bovine leukocyte Ag class II DR locus (BoLA-DR). BoLA-DR binding motifs were characterized by eluted ligand data derived from bovine cell lines expressing a range of DRB3 alleles prevalent in Holstein-Friesian populations. The model generated (NetBoLAIIpan, available as a Web server at www.cbs.dtu.dk/services/NetBoLAIIpan) was shown to have unprecedented predictive power to identify known BoLA-DR-restricted CD4 epitopes. In summary, the results demonstrate the power of an integrated approach combining advanced mass spectrometry peptidomics with immunoinformatics for characterization of the BoLA-DR Ag presentation system and provide a prediction tool that can be used to assist in rational evaluation and selection of bovine CD4 T cell epitopes.

In silico comparative study of SARS-CoV-2 proteins and antigenic proteins in BCG, OPV, MMR and other vaccines: evidence of a possible putative protective effect

Background

Coronavirus Disease 2019 (COVID-19) is a viral pandemic disease that may induce severe pneumonia in humans. In this paper, we investigated the putative implication of 12 vaccines, including BCG, OPV and MMR in the protection against COVID-19. Sequences of the main antigenic proteins in the investigated vaccines and SARS-CoV-2 proteins were compared to identify similar patterns. The immunogenic effect of identified segments was, then, assessed using a combination of structural and antigenicity prediction tools.

Results

A total of 14 highly similar segments were identified in the investigated vaccines. Structural and antigenicity prediction analysis showed that, among the identified patterns, three segments in Hepatitis B, Tetanus, and Measles proteins presented antigenic properties that can induce putative protective effect against COVID-19.

Conclusions

Our results suggest a possible protective effect of HBV, Tetanus and Measles vaccines against COVID-19, which may explain the variation of the disease severity among regions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04045-3.

Protocol for purification and identification of MHC class I immunopeptidome from cancer cell lines

Major histocompatibility complexes (MHC) play a critical role in immunity by presenting peptides on the cell surface for T cell recognition. Identification of these peptides can be valuable to develop vaccines or immunotherapeutic strategies for infectious diseases and cancers. Mass spectrometry is the only tool available for unbiased identification of the immunopeptidome. Here, we describe a protocol for purification and identification of MHC class I peptides, including in-house purification of anti-MHC-antibody from hybridoma cells and the LC-MS/MS analysis of MHC-I bound peptides.

HLA-DRB1 Alleles Associated with Lower Leishmaniasis Susceptibility Share Common Amino Acid Polymorphisms and Epitope Binding Repertoires

Susceptibility for leishmaniasis is largely dependent on host genetic and immune factors. Despite the previously described association of human leukocyte antigen (HLA) gene cluster variants as genetic susceptibility factors for leishmaniasis, little is known regarding the mechanisms that underpin these associations. To better understand this underlying functionality, we first collected all known leishmaniasis-associated HLA variants in a thorough literature review. Next, we aligned and compared the protection- and risk-associated HLA-DRB1 allele sequences. This identified several amino acid polymorphisms that distinguish protection- from risk-associated HLA-DRB1 alleles. Subsequently, T cell epitope binding predictions were carried out across these alleles to map the impact of these polymorphisms on the epitope binding repertoires. For these predictions, we used epitopes derived from entire proteomes of multiple Leishmania species. Epitopes binding to protection-associated HLA-DRB1 alleles shared common binding core motifs, mapping to the identified HLA-DRB1 amino acid polymorphisms. These results strongly suggest that HLA polymorphism, resulting in differential antigen presentation, affects the association between HLA and leishmaniasis disease development. Finally, we established a valuable open-access resource of putative epitopes. A set of 14 HLA-unrestricted strong-binding epitopes, conserved across species, was prioritized for further epitope discovery in the search for novel subunit-based vaccines.

SARS-CoV-2 mutations in MHC-I-restricted epitopes evade CD8+ T cell responses

CD8⁺ T cell immunity to SARS-CoV-2 has been implicated in COVID-19 severity and virus control. Here, we identified nonsynonymous mutations in MHC-I-restricted CD8⁺ T cell epitopes after deep sequencing of 747 SARS-CoV-2 virus isolates. Mutant peptides exhibited diminished or abrogated MHC-I binding in a cell-free in vitro assay. Reduced MHC-I binding of mutant peptides was associated with decreased proliferation, IFN-γ production and cytotoxic activity of CD8⁺ T cells isolated from HLA-matched COVID-19 patients. Single cell RNA sequencing of ex vivo expanded, tetramer-sorted CD8⁺ T cells from COVID-19 patients further revealed qualitative differences in the transcriptional response to mutant peptides. Our findings highlight the capacity of SARS-CoV-2 to subvert CD8⁺ T cell surveillance through point mutations in MHC-I-restricted viral epitopes.