Human Leukocyte Antigen Peptide Research Articles

High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response.

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

CD8+ T cell memory induced by successive SARS-CoV-2 mRNA vaccinations is characterized by shifts in clonal dominance

mRNA vaccines against the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) elicit strong Tcell responses. However, a clonal-resolution analysis of Tcell responses to mRNA vaccination has not been performed. Here, we temporally track the CD8+ Tcell repertoire in individuals who received three shots of the BNT162b2 mRNA vaccine through longitudinal Tcell receptor sequencing with peptide-human leukocyte antigen (HLA) tetramer analysis. We demonstrate a shift in Tcell responses between the clonotypes with different kinetics: from early responders that expand rapidly after the first shot to main responders that greatly expand after the second shot. Although the main responders re-expand after the third shot, their clonal diversity is skewed, and newly elicited third responders partially replace them. Furthermore, this shift in clonal dominance occurs not only between, but also within, clonotypes specific for spike epitopes. Our study will be a valuable resource for understanding vaccine-induced Tcell responses in general.

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200μl of plasma, in just 30min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.

Genome-wide association analysis reveals the associations of NPHP4, TYW1-AUTS2 and SEMA6D for Behçet's disease and HLA-B*46:01 for its intestinal involvement

BackgroundIntestinal involvement in Behçet's disease (BD) is associated with poor prognosis and is more prevalent in East Asian than in Mediterranean populations. Identifying the genetic causes of intestinal BD is important for understanding the pathogenesis and for appropriate treatment of BD patients. MethodsWe performed genome-wide association studies (GWAS) and imputation/replication genotyping of human leukocyte antigen (HLA) alleles for 1,689 Korean and Turkish patients with BD (including 379 patients with intestinal BD) and 2,327 healthy controls, followed by replication using 593 Japanese patients with BD (101 patients with intestinal BD) and 737 healthy controls. Stratified cross-phenotype analyses were performed for 1) overall BD, 2) intestinal BD, and 3) intestinal BD without association of overall BD. ResultsWe identified three novel genome-wide significant susceptibility loci including NPHP4 (rs74566205; P=1.36 × 10−8), TYW1–AUTS2 (rs60021986; P=1.14 × 10−9), and SEMA6D (rs4143322; P=5.54 × 10−9) for overall BD, and a new association with HLA-B*46:01 for intestinal BD (P=1.67 × 10−8) but not for BD without intestinal involvement. HLA peptide binding analysis revealed that Mycobacterial peptides, have a stronger binding affinity to HLA-B*46:01 compared to the known risk allele HLA-B*51:01. ConclusionsHLA-B*46:01 is associated with the development of intestinal BD; NPHP4, TYW1–AUTS2, and SEMA6D are susceptibility loci for overall BD.

Associations of functional human leucocyte antigen class I groups with HIV viral load in a heterogeneous cohort.

Human leucocyte antigen (HLA) class I alleles are the main host genetic factors involved in controlling HIV-1 viral load (VL). Nevertheless, HLA diversity has proven a significant challenge in association studies. We assessed how accounting for binding affinities of HLA class I alleles to HIV-1 peptides facilitate association testing of HLA with HIV-1 VL in a heterogeneous cohort. Cohort from the Strategic Timing of AntiRetroviral Treatment (START) study. We imputed HLA class I alleles from host genetic data (2546 HIV+ participants) and sampled immunopeptidomes from 2079 host-paired viral genomes (targeted amplicon sequencing). We predicted HLA class I binding affinities to HIV-1 and unspecific peptides, grouping alleles into functional clusters through consensus clustering. These functional HLA class I clusters were used to test associations with HIV VL. We identified four clades totaling 30 HLA alleles accounting for 11.4% variability in VL. We highlight HLA-B∗57:01 and B∗57:03 as functionally similar but yet overrepresented in distinct ethnic groups, showing when combined a protective association with HIV+ VL (log, β -0.25; adj. P-value < 0.05). We further demonstrate only a slight power reduction when using unspecific immunopeptidomes, facilitating the use of the inferred functional HLA groups in other studies. The outlined computational approach provides a robust and efficient way to incorporate HLA function and peptide diversity, aiding clinical association studies in heterogeneous cohorts. To facilitate access to the proposed methods and results we provide an interactive application for exploring data.

An ultra-high-throughput screen for the evaluation of peptide HLA-Binder interactions

Peptide human leukocyte antigen (pHLA) targeting therapeutics like T-cell receptor based adoptive cell therapy or bispecific T cell engaging receptor molecules hold great promise for the treatment of cancer. Comprehensive pre-clinical screening of therapeutic candidates is important to ensure patient safety but is challenging because of the size of the potential off-target space. By combining stabilized peptide-receptive HLA molecules with microarray printing and screening, we have developed an ultra-high-throughput screening platform named ValidaTe that enables large scale evaluation of pHLA-binder interactions. We demonstrate its potential by measuring and analyzing over 30.000 binding curves for a high-affinity T cell Engaging Receptor towards a large pHLA library. Compared to a dataset obtained by conventional bio-layer interferometry measurements, we illustrate that a massively increased throughput (over 650 fold) is obtained by our microarray screening, paving the way for use in pre-clinical safety screening of pHLA-targeting drugs.

Identification of HPV16 E1 and E2-specific T cells in the oropharyngeal cancer tumor microenvironment

BackgroundHigh-risk human papillomavirus (HPV) is a primary cause of an increasing number of oropharyngeal squamous cell carcinomas (OPSCCs). The viral etiology of these cancers provides the opportunity for antigen-directed therapies...

Benchmarking Bioinformatics Pipelines in Data-Independent Acquisition Mass Spectrometry for Immunopeptidomics

Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex [human leukocyte antigen (HLA) in humans]. These HLA–peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.

Rheumatic disease Risk factors, pathophysiology and management

Rheumatic diseases, because of their systemic inflammatory component, are associated with several shared comorbidities. The high affinity of shared epitope for citrullinated peptides could accelerate the amount of human leukocyte antigen peptide complexes on the surface of antigen-presenting cells, thus leading to a (possible joint-specific) T- cell response. Molecules such as receptor activator of nuclear factor κB ligand, prostaglandins, and matrix metalloproteinases are induced by pro-inflammatory cytokines, involving tumor necrosis factor and interleukin 6, and mediate signs and symptoms of the disease, involving pain and swelling, and degradation of cartilage and bone. Steroids at dosages equivalent to less than 10 mg of prednisone daily are highly effective for relieving symptoms of rheumatoid arthritis and can slow joint injury.

Graft Versus Leukaemia (GvL): Identification & Characterisation of Alloreactive Antigens and Cognate T Cell Responses in Acute Myeloid Leukemia

Allogeneic stem cell transplantation (allo-SCT) is the most established, curative T cell cancer immunotherapy. The curative effect of allo-SCT relies on a graft-versus-leukemia (GvL) effect; but this can be difficult to dissociate from graft-versus-host disease (GvHD). For decades, the key unsolved challenge has been to identify common alloreactive peptide-human leukocyte antigen (HLA) complexes targeted by T cells that mediate GvL but not GvHD. We developed a workflow for unbiased identification of alloreactive antigens and analysis of cognate donor alloreactive T cells and their T cell receptors (TCRs; Fig 1). We studied sequential bone marrow and peripheral blood samples, collected over 15 years, from 12 high-risk AML patients who remain in continuous complete remission following allo-SCT (all >2 years). All patients had relapsed at least once pre-allo-SCT and/or were AML flow cytometry minimal residual disease (MRD)+ pre-allo-SCT. 7 had received donor lymphocyte infusions. No patients developed >1 grade GvHD. We performed whole exome sequencing of DNA from isolated patient T cells (patient germline), AML blasts and donor DNA to identify: (i) AML-specific coding variants; and (ii) germline coding single nucleotide polymorphisms (SNPs), mismatched between patient and donor. 98.7% of the mismatched variants were germline. More variants were identified in unrelated compared with sibling donors (mean: 5744 vs 3253 variants). To narrow the list of variants taken forward for functional studies, we applied two sequential filtering steps: (i) we restricted analysis to variants expressed in the patients' AML cells (determined by RNA-Seq). 28.7% of all mismatched coding variants were expressed in AML cells; and (ii) we used in silico prediction of HLA-binding affinities of peptides spanning variant amino acids to patient-specific HLA-I and -II using NetMHCpan 4.0 and NetMHCIIpan 3.0. To functionally identify alloreactive antigens, we then screened ~200-900 peptides per patient-donor pair (total 5300 peptides) for recognition by T cells present in patient peripheral blood collected 2-7 years after allo-SCT. This enriches for the identification of long-lived anti-AML responses at a time when patients were both infection- and GvHD- free. Response screening, using IFNγ ELISPOT assays, identified T cell responses to 24 germline, mismatched epitopes. In all cases, responses were only observed when the amino acid encoded by the patient SNP, but not the donor allele, was present in the peptide. The phenotype of the epitope-specific T cells was established by intracellular cytokine staining (ICS) and flow cytometry analysis, and the HLA restriction of each T cell response was determined using HLA-I, -DR, -DP and -DQ blocking antibodies. Of the 24 alloreactive peptides,19 elicited CD4+ HLA class II restricted responses and 3 were targeted by CD8+ class I restricted T cells. Two remain to be characterised (Fig 2). The population allele frequency of the germline variant SNPs targeted varies from 0.5% to 72% in the Caucasian population. Likewise, some of the restricting HLA alleles are very common (e.g. a HLA-DP allele presenting 3 epitope peptides is present in ~66% of the Caucasian population). ICS showed that epitope-specific CD4+ T cells produce Th1 cytokines (IFNg, TNFa) and degranulate (express CD107a) in response to peptide stimulation (Fig 2). Next, we performed single cell 5'RNA-seq with TCR-seq of epitope-restricted T cells (isolated via IFN-g catch assay) for all CD4+ epitope-specific T cells. Alloreactive CD4+ T cells have cytokine-producing and cytotoxic effector potential, expressing MIP1a and b, IFNg, granzyme B and FasL (Fig 2). Finally, lentiviral-based gene transfer of one of the candidate alloreactive epitope-specific TCRs into third party primary T cells confers the capacity to generate IFNg and TNFa in response to peptide stimulation, and to recognise primary AML cell-derived antigen in the presence of monocyte-derived dendritic cells (Fig 2). In vitro and in vivo killing assays are in progress. The high proportion of CD4+ T Cell responses identified by our pipeline is concordant with downregulation of HLA II expression on AML blasts at relapse post-allo-SCT, which is a common mechanism of immune evasion, found in one-third of patients. Our approach dissects the biology of alloreactive anti-AML GvL and GvHD responses and to manipulate them for therapeutic purposes. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Quantitative immunopeptidomics reveals a tumor stroma-specific target for T cell therapy.

T cell receptor (TCR)-based immunotherapy has emerged as a promising therapeutic approach for the treatment of patients with solid cancers. Identifying peptide-human leukocyte antigen (pHLA) complexes highly presented on tumors and rarely expressed on healthy tissue in combination with high-affinity TCRs that when introduced into T cells can redirect T cells to eliminate tumor but not healthy tissue is a key requirement for safe and efficacious TCR-based therapies. To discover promising shared tumor antigens that could be targeted via TCR-based adoptive T cell therapy, we employed population-scale immunopeptidomics using quantitative mass spectrometry across ~1500 tumor and normal tissue samples. We identified an HLA-A*02:01-restricted pan-cancer epitope within the collagen type VI α-3 (COL6A3) gene that is highly presented on tumor stroma across multiple solid cancers due to a tumor-specific alternative splicing event that rarely occurs outside the tumor microenvironment. T cells expressing natural COL6A3-specific TCRs demonstrated only modest activity against cells presenting high copy numbers of COL6A3 pHLAs. One of these TCRs was affinity-enhanced, enabling transduced T cells to specifically eliminate tumors in vivo that expressed similar copy numbers of pHLAs as primary tumor specimens. The enhanced TCR variants exhibited a favorable safety profile with no detectable off-target reactivity, paving the way to initiate clinical trials using COL6A3-specific TCRs to target an array of solid tumors.

Abstract 2810: Modeling pHLA:TCR interactions for effective TCR therapies: Leveraging AI and molecular dynamics

Abstract Background: The purpose of our study is to aid the development of adoptive T-cell receptor (TCR) -based cancer therapies with an AI platform that models binding between peptide-human leukocyte antigen (pHLA) complexes and TCRs. One of the main challenges in the field is finding the right pHLA target and the TCR that binds it. The development of in silico methods that can reduce the cost and time of laboratory research is the ‘holy grail’ of novel TCR therapies design. Molecular Dynamics (MD) simulation is a computational method allowing to gain a direct insight into the movements of protein complexes and giving the possibility to study the dynamics of the pHLA:TCR interaction. The combination of the MD and AI models helps us to increase the accuracy of the pHLA:TCR binding prediction. Methods: We conducted large-scale MD simulations of the crystal structures of 130 pHLA:TCR complexes available in the Protein Data Bank (PDB). Next, we analyzed the obtained trajectories in terms of formed bonds and other physico-chemical properties. This led us to postulate an MD model of interaction further used as an inductive bias for the AI binding prediction model, in which we applied deep learning on public single-cell pHLA:TCR datasets to estimate the probability of binding.In order to validate and improve our platform, we are building a pHLA:TCR interaction database with paired alpha and beta chain TCR sequences from a cohort of 100 colorectal cancer patients (NCT04994093). Results: We established the agretopicity and epitopicity of peptide residues within the pHLA:TCR systems based on the characteristics of the bonds formed between peptides and HLAs or TCRs in MD simulations. Additionally, the incorporation of other molecular properties, e.g. solvent accessible surface area, improved our AI pHLA:TCR model. In order to refine our results, we also account for the HLA type and physico-chemical properties of peptides. The introduction of the detailed TCR-peptide interaction matrix allowed the model to generalize to previously unseen pHLA targets. Conclusions: Rational design of effective TCR therapies can benefit from novel pHLA:TCR models for which the predictive accuracy is driven by the synergistic AI-MD approach. Citation Format: Alexander Myronov, Sławomir Stachura, Anna Sanecka-Duin, Oskar Gniewek, Łukasz Grochowalski, Maciej Jasiński, Paulina Król, Joanna Marczyńska-Grzelak, Piotr Skoczylas, Daniel Wojciechowski, Giovanni Mazzocco, Mikołaj Mizera, Jan Kaczmarczyk, Agnieszka Blum. Modeling pHLA:TCR interactions for effective TCR therapies: Leveraging AI and molecular dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2810.

A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design

Human leukocyte antigen (HLA) can recognize and bind foreign peptides to present them to specialized immune cells, then initiate an immune response. Computational prediction of the peptide and HLA (pHLA) binding can speed up immunogenic peptide screening and facilitate vaccine design. However, there is a lack of an automatic program to optimize mutated peptides with higher affinity to the target HLA allele. Here, to fill this gap, we develop the TransMut framework—composed of TransPHLA for pHLA binding prediction and an automatically optimized mutated peptides (AOMP) program—which can be generalized to any binding and mutation task of biomolecules. First, TransPHLA is developed by constructing a transformer-based model to predict pHLA binding, which is superior to 14 previous methods on pHLA binding prediction and neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed by exploiting the attention scores generated by TransPHLA to automatically optimize mutated peptides with higher affinity to the target HLA allele and with high homology to the source peptide. The proposed framework may automatically generate potential peptide vaccines for experimentalists.

Immunopeptidomic analysis of influenza A virus infected human tissues identifies internal proteins as a rich source of HLA ligands.

CD8+ and CD4+ T cells provide cell-mediated cross-protection against multiple influenza strains by recognising epitopes bound as peptides to human leukocyte antigen (HLA) class I and -II molecules respectively. Two challenges in identifying the immunodominant epitopes needed to generate a universal T cell influenza vaccine are: A lack of cell models susceptible to influenza infection which present population-prevalent HLA allotypes, and an absence of a reliable in-vitro method of identifying class II HLA peptides. Here we present a mass spectrometry-based proteomics strategy for identifying viral peptides derived from the A/H3N2/X31 and A/H3N2/Wisconsin/67/2005 strains of influenza. We compared the HLA-I and -II immunopeptidomes presented by ex-vivo influenza challenged human lung tissues. We then compared these with directly infected immortalised macrophage-like cell line (THP1) and primary dendritic cells fed apoptotic influenza-infected respiratory epithelial cells. In each of the three experimental conditions we identified novel influenza class I and II HLA peptides with motifs specific for the host allotype. Ex-vivo infected lung tissues yielded few class-II HLA peptides despite significant numbers of alveolar macrophages, including directly infected ones, present within the tissues. THP1 cells presented HLA-I viral peptides derived predominantly from internal proteins. Primary dendritic cells presented predominantly viral envelope-derived HLA class II peptides following phagocytosis of apoptotic infected cells. The most frequent viral source protein for HLA-I and -II was matrix 1 protein (M1). This work confirms that internal influenza proteins, particularly M1, are a rich source of CD4+ and CD8+ T cell epitopes. Moreover, we demonstrate the utility of two ex-vivo fully human infection models which enable direct HLA-I and -II immunopeptide identification without significant viral tropism limitations. Application of this epitope discovery strategy in a clinical setting will provide more certainty in rational vaccine design against influenza and other emergent viruses.

Unravelling the Proteomics of HLA-B*57:01+ Antigen Presenting Cells during Abacavir Medication.

Type B adverse drug reactions (ADRs) are unpredictable based on the drug’s pharmacology and represent a key challenge in pharmacovigilance. For human leukocyte antigen (HLA)-mediated type B ADRs, it is assumed that the protein/small-molecule interaction alters the biophysical and mechanistic properties of the antigen presenting cells. Sophisticated methods enabled the molecular appreciation of HLA-mediated ADRs; in several instances, the drug molecule occupies part of the HLA peptide binding groove and modifies the recruited peptide repertoire thereby causing a strong T-cell-mediated immune response that is resolved upon withdrawal of medication. The severe ADR in HLA-B*57:01+ patients treated with the antiretroviral drug abacavir (ABC) in anti-HIV therapy is an example of HLA-drug-T cell cooperation. However, the long-term damages of the HLA-B*57:01-expressing immune cells following ABC treatment remain unexplained. Utilizing full proteome sequencing following ABC treatment of HLA-B*57:01+ cells, we demonstrate stringent proteomic alteration of the HLA/drug presenting cells. The proteomic content indisputably reflects the cellular condition; this knowledge directs towards individual pharmacovigilance for the development of personalized and safe medication.

Self-mediated positive selection of T cells sets an obstacle to the recognition of nonself

Adaptive immune recognition is mediated by the binding of peptide-human leukocyte antigen complexes by T cells. Positive selection of T cells in the thymus is a fundamental step in the generation of a responding T cell repertoire: only those T cells survive that recognize human peptides presented on the surface of cortical thymic epithelial cells. We propose that while this step is essential for optimal immune function, the process results in a defective T cell repertoire because it is mediated by self-peptides. To test our hypothesis, we focused on amino acid motifs of peptides in contact with T cell receptors. We found that motifs rarely or not found in the human proteome are unlikely to be recognized by the immune system just like the ones that are not expressed in cortical thymic epithelial cells or not presented on their surface. Peptides carrying such motifs were especially dissimilar to human proteins. Importantly, we present our main findings on two independent T cell activation datasets and directly demonstrate the absence of naïve T cells in the repertoire of healthy individuals. We also show that T cell cross-reactivity is unable to compensate for the absence of positively selected T cells. Additionally, we show that the proposed mechanism could influence the risk for different infectious diseases. In sum, our results suggest a side effect of T cell positive selection, which could explain the nonresponsiveness to many nonself peptides and could improve the understanding of adaptive immune recognition.

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through \u2018reverse phenotyping\u2019

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

Phenotype, specificity and avidity of antitumour CD8+ T cells in melanoma.

Interactions between T cell receptors (TCRs) and their cognate tumour antigens are central to antitumour immune responses1-3; however, the relationship between phenotypic characteristics and TCR properties is not well elucidated. Here we show, by linking the antigenic specificity of TCRs and the cellular phenotype of melanoma-infiltrating lymphocytes at single-cell resolution, that tumour specificity shapes the expression state of intratumoural CD8+ T cells. Non-tumour-reactive T cells were enriched for viral specificities and exhibited a non-exhausted memory phenotype, whereas melanoma-reactive lymphocytes predominantly displayed an exhausted state that encompassed diverse levels of differentiation but rarely acquired memory properties. These exhausted phenotypes were observed bothamong clonotypes specific for public overexpressed melanoma antigens (shared across differenttumours) or personal neoantigens (specific for each tumour). The recognition of such tumour antigens was provided by TCRs with avidities inversely related to the abundance of cognate targets in melanoma cells and proportional to the binding affinity of peptide-human leukocyte antigen (HLA) complexes. The persistence of TCR clonotypes in peripheral blood was negatively affected by the level of intratumoural exhaustion, and increased in patients with a poor response to immune checkpoint blockade, consistent with chronic stimulation mediated by residual tumour antigens. By revealing how the quality and quantity of tumour antigens drive the features of T cell responses within the tumour microenvironment, we gain insights into the properties of the anti-melanoma TCR repertoire.