Non-prime- and prime-side profiling of Pro-Pro endopeptidase specificity using synthetic combinatorial peptide libraries and mass spectrometry

A group of bacterial proteases, the Pro-Pro endopeptidases (PPEPs), possess the unique ability to hydrolyze proline-proline bonds in proteins. Since a protease's function is largely determined by its substrate specificity, methods that can extensively characterize substrate specificity are valuable tools for protease research. Previously, we achieved an in-depth characterization of PPEP prime-side specificity. However, PPEP specificity is also determined by the non-prime-side residues in the substrate. To gain a more complete insight into the determinants of PPEP specificity, we characterized the non-prime- and prime-side specificity of various PPEPs using a combination of synthetic combinatorial peptide libraries and mass spectrometry. With this approach, we deepened our understanding of the P3-P3' specificities of PPEP-1 and PPEP-2, while identifying the endogenous substrate of PPEP-2 as the most optimal substrate in our library data. Furthermore, by employing the library approach, we investigated the altered specificity of mutants of PPEP-1 and PPEP-2. Additionally, we characterized a novel PPEP from Anoxybacillus tepidamans, which we termed PPEP-4. Based on structural comparisons, we hypothesized that PPEP-4 displays a PPEP-1-like prime-side specificity, which was substantiated by the experimental data. Intriguingly, another putative PPEP from Clostridioides difficile, CD1597, did not display Pro-Pro endoproteolytic activity. Collectively, we characterized PPEP specificity in detail using our robust peptide library method and, together with additional structural information, provide more insight into the intricate mechanisms that govern protease specificity.

The degree of HLA matching determines the incidence of cytokine release syndrome and associated nonrelapse mortality in matched related and unrelated allogeneic stem cell transplantation with post-transplant cyclophosphamide

Cytokine release syndrome (CRS) occurs frequently after haplo-identical allogeneic stem cell transplantation (alloSCT) with post-transplant cyclophosphamide (PTCy), increasing nonrelapse mortality (NRM) and decreasing survival. Data on CRS in HLA-matched alloSCT are limited and effects of specific HLA-mismatches on CRS development unknown. We hypothesized that in HLA-matched alloSCT increasing degrees of HLA-mismatching influence CRS incidence, NRM and survival. Retrospective analysis of 126 HLA-matched PTCy-alloSCT patients showed that higher degrees of HLA-mismatching significantly increased CRS incidence (26%, 75% and 90% CRS with 12/12, 10/10 and 9/10 matched donors, respectively). Maximum temperature during CRS increased with higher HLA-mismatch. Specific associations between HLA-mismatches and CRS could be determined. Grade 2 CRS and CRS-induced grade 3 fever were associated with significantly increased NRM (p < 0.001 and p = 0.003, respectively) and inferior survival (p < 0.001 and p = 0.005, respectively). NRM was mainly caused by disease conditions that may be considered CRS-induced inflammatory responses (encephalopathy, cryptogenic organizing pneumonia and multi-organ failure).

Report from the extended HLA-DPA1 ~ promoter ~ HLA-DPB1 haplotype of the 18th international HLA and immunogenetics workshop

The primary goal of the HLA-DPA1 ~ promoter ~ HLA-DPB1 haplotype component of the 18th IHIWS was to characterise the extended haplotypes within the HLA-DP region and survey the extent of genetic diversity in this region across human populations. In this report, we analysed single-nucleotide polymorphisms (SNPs) in 255 subjects from 6 different cohorts. The results from the HLA-DP haplotype component have validated findings from the initial pilot study. SNPs in this region were inherited in strong linkage, particularly HLA-DPA1, SNP-linked promoter haplotypes and motifs in exon 2 of HLA-DPB1. We reported 17 SNP-linked haplotypes in the promoter region. Together with HLA-DPA1 and HLA-DPB1 alleles, they formed 74 distinct extended HLA-DP haplotypes in 438 sequences. We also observed the presence of region-specific alleles and promoter haplotypes. Our approach involved phasing extended SNPs including promoter SNPs, HLA-DPA1 and HLA-DPB1 alleles, in a 22 kb region, GRCh38/hg38 (chr6:33,064,111-33,086,679), followed by clustering of these SNPs as one extended haplotype. This hierarchical clustering revealed four major clades, suggesting that haplotypes within each clade may have diverged from a common ancestral haplotype and undergone similar evolutionary processes. The correlation between HLA-DPA1 and the promoter region raises questions about the role of HLA-DPA1 antigen in the heterodimer. This finding requires validation on a larger sample size specifically designed for anthropological analysis. Nevertheless, the results from this study highlight the clinical potential of selecting better-matched donors for patients awaiting haematopoietic stem cell transplants from genetically overlapping groups that share common ancestral haplotypes.

Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

Assessment of HLA-DPB1 genetic variation using an HLA-DP tool and its implications in clinical transplantation

HLA-DP is a classic transplantation antigen that mediates alloreactivity through T-cell epitope (TCE) diversity and expression levels. A current challenge is to integrate these functional features into the prospective selection of unrelated donor candidates for transplantation. Genetically, HLA-DPB1 exon 2 defines the permissive and nonpermissive TCE groups, and exons 2 and 3 (in linkage with rs9277534) indicate low- and high-expression allotypes. In this study, we analyzed 356 272 exon 2-exon 3-phased sequences from individuals across 5 self-identified race and ethnicity categories: White, Hispanic, Asian or Pacific Islander, Black or African American, and American Indian or Alaskan Native. This sequence data set revealed the complex relationship between TCE and expression models and the importance of exon 3 sequence data. We also studied archived donor search lists for 2545 patients who underwent transplantation from an HLA-11/12 unrelated donor mismatched for a single HLA-DPB1 allele. Depending on the order in which the TCE and expression criteria were considered, some patients had different TCE- and expression-favorable donors. In addition, this data set revealed that many expression-favorable alternatives existed in the search lists. To improve the selection of candidate donors, we provide, disseminate, and automate our findings through our multifaceted tool called Expression of HLA-DP Assessment Tool, consisting of a public web application, Python package, and analysis pipeline.

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

CD4⁺ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4⁺ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4⁺ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3' UTR region, denoting expression levels, are associated with atopic dermatitis

Introduction: Components of the immune response have previously been associated with the pathophysiology of atopic dermatitis (AD), specifically the Human Leukocyte Antigen (HLA) Class II region via genome-wide association studies, however the exact elements have not been identified. Methods: This study examines the genetic variation of HLA Class II genes using next generation sequencing (NGS) and evaluates the resultant amino acids, with particular attention on binding site residues, for associations with AD. The Genetics of AD cohort was used to evaluate HLA Class II allelic variation on 464 subjects with AD and 384 controls. Results: Statistically significant associations with HLA-DP α and β alleles and specific amino acids were found, some conferring susceptibility to AD and others with a protective effect. Evaluation of polymorphic residues in DP binding pockets revealed the critical role of P1 and P6 (P1: α31M + (β84G or β84V) [protection]; α31Q + β84D [susceptibility] and P6: α11A + β11G [protection]) and were replicated with a national cohort of children consisting of 424 AD subjects. Independently, AD susceptibility-associated residues were associated with the G polymorphism of SNP rs9277534 in the 3' UTR of the HLA-DPB1 gene, denoting higher expression of these HLA-DP alleles, while protection-associated residues were associated with the A polymorphism, denoting lower expression. Discussion: These findings lay the foundation for evaluating non-self-antigens suspected to be associated with AD as they potentially interact with particular HLA Class II subcomponents, forming a complex involved in the pathophysiology of AD. It is possible that a combination of structural HLA-DP components and levels of expression of these components contribute to AD pathophysiology.

The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome

INTRODUCTION

The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data.

AREAS COVERED

Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches.

EXPERT OPINION

Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.

Promiscuity of Peptides Presented in HLA-DP Molecules from Different Immunogenicity Groups Is Associated With T-Cell Cross-Reactivity

In the context of HLA-DP-mismatched allogeneic stem cell transplantation, mismatched HLA-DP alleles can provoke profound allo-HLA-DP-specific immune responses from the donor T-cell repertoire leading to graft-versus-leukemia effect and/or graft-versus-host disease in the patient. The magnitude of allo-HLA-DP-specific immune responses has been shown to depend on the specific HLA-DP disparity between donor and patient and the immunogenicity of the mismatched HLA-DP allele(s). HLA-DP peptidome clustering (DPC) was developed to classify the HLA-DP molecules based on similarities and differences in their peptide-binding motifs. To investigate a possible categorization of HLA-DP molecules based on overlap of presented peptides, we identified and compared the peptidomes of the thirteen most frequently expressed HLA-DP molecules. Our categorization based on shared peptides was in line with the DPC classification. We found that the HLA-DP molecules within the previously defined groups DPC-1 or DPC-3 shared the largest numbers of presented peptides. However, the HLA-DP molecules in DPC-2 segregated into two subgroups based on the overlap in presented peptides. Besides overlap in presented peptides within the DPC groups, a substantial number of peptides was also found to be shared between HLA-DP molecules from different DPC groups, especially for groups DPC-1 and -2. The functional relevance of these findings was illustrated by demonstration of cross-reactivity of allo-HLA-DP-reactive T-cell clones not only against HLA-DP molecules within one DPC group, but also across different DPC groups. The promiscuity of peptides presented in various HLA-DP molecules and the cross-reactivity against different HLA-DP molecules demonstrate that these molecules cannot be strictly categorized in immunogenicity groups.

Tools for optimizing risk assessment in hematopoietic cell transplant - What can we get away with?

Unrelated allogeneic hematopoietic cell transplant (HCT) is a critical modality to treat hematologic malignancies. The current objective of donor selection is to match donor and recipient at the HLA (human leukocyte antigen) peptide-binding region which should lower the risk of graft-versus-host disease. However, depending on the patient's ethnicity/race, finding a matched donor is challenging, especially for HLA-DPB1 which is due to the weak linkage disequilibrium between HLA-DPB1 and the other HLA class II loci. Recent evidence, on the molecular level, has shown that certain HLA mismatches carry lower clinical risk. More specifically, there is an increasing understanding of polymorphisms of the innate and adaptive immune systems and their impact on transplant outcomes, allowing us to expand our "toolkit" for optimization of donor selection in HCT. Therefore, in this review we discuss matching strategies based on comparing donor and recipient polymorphisms that may influence innate and adaptive immune response genes in allorecognition and the role of single nucleotide polymorphisms in non-HLA genes that have the potential for providing additional tools to refine risk stratification.

Predicting HLA-DPB1 permissive probabilities through a DPB1 prediction service towards the optimization of HCT donor selection

Despite its demonstrated importance in hematopoietic cell transplantation, the HLA-DPB1 locus is only typed in one in five unrelated donors in the United States. Addressing this issue, we developed a DPB1 Prediction Service that leverages seven-locus haplotype frequencies (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1 ∼ DPB1) to extend the imputation of six-locus HLA typing (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1) to the HLA-DPB1 locus, including the novel prediction of HLA-DPB1 TCE groups to calculate donor-recipient TCE permissive match probabilities. Simulations of current-day patient searches reveal the service can fill in missing gaps for another four in five donors that appears on lists. To validate its performance, samples of 206,328 registered donors and 5,218 donor-recipient pairs with known high-resolution HLA-DPB1 typing were used for predicted-versus-observed comparisons. These comparisons demonstrated that the predictions were correct for 11.9-19.7% of HLA-DPB1 genotypes, 64.9-70.0% of TCE groups, and 61.0% of permissive match categories. Although HLA-DPB1 match predictions must be confirmed by additional typing, knowledge of TCE match probabilities facilitates rapid and improved identification of best donor options, especially for populations of color. Thus, we developed the TCE Prediction Tool user interface for a pilot program with several transplant centers to preview the accuracy and utility of this prediction framework, which provides valuable upfront optimization of donor selection.

Permissive HLA-DPB1 mismatches in HCT depend on immunopeptidome divergence and editing by HLA-DM

In hematopoietic cell transplantation (HCT), permissive HLA-DPB1 mismatches between patients and their unrelated donors are associated with improved outcomes compared with nonpermissive mismatches, but the underlying mechanism is incompletely understood. Here, we used mass spectrometry, T-cell receptor-β (TCRβ) deep sequencing, and cellular in vitro models of alloreactivity to interrogate the HLA-DP immunopeptidome and its role in alloreactive T-cell responses. We find that permissive HLA-DPB1 mismatches display significantly higher peptide repertoire overlaps compared with their nonpermissive counterparts, resulting in lower frequency and diversity of alloreactive TCRβ clonotypes in healthy individuals and transplanted patients. Permissiveness can be reversed by the absence of the peptide editor HLA-DM or the presence of its antagonist, HLA-DO, through significant broadening of the peptide repertoire. Our data establish the degree of immunopeptidome divergence between donor and recipient as the mechanistic basis for the clinically relevant permissive HLA-DPB1 mismatches in HCT and show that permissiveness is dependent on HLA-DM–mediated peptide editing. Its key role for harnessing T-cell alloreactivity to HLA-DP highlights HLA-DM as a potential novel target for cellular and immunotherapy of leukemia.

CIITA-Transduced Glioblastoma Cells Uncover a Rich Repertoire of Clinically Relevant Tumor-Associated HLA-II Antigens

CD4+ T cell responses are crucial for inducing and maintaining effective anticancer immunity, and the identification of human leukocyte antigen class II (HLA-II) cancer-specific epitopes is key to the development of potent cancer immunotherapies. In many tumor types, and especially in glioblastoma (GBM), HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in GBM. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed and sensitive mass spectrometry-based immunopeptidomics analysis, we here uncovered a remarkable breadth of the HLA-ligandome in HROG02, HROG17, and RA GBM cell lines. The effect of CIITA expression on the induction of the HLA-II presentation machinery was striking in each of the three cell lines, and it was significantly higher compared with interferon gamma (IFNɣ) treatment. In total, we identified 16,123 unique HLA-I peptides and 32,690 unique HLA-II peptides. In order to genuinely define the identified peptides as true HLA ligands, we carefully characterized their association with the different HLA allotypes. In addition, we identified 138 and 279 HLA-I and HLA-II ligands, respectively, most of which are novel in GBM, derived from known GBM-associated tumor antigens that have been used as source proteins for a variety of GBM vaccines. Our data further indicate that CIITA-expressing GBM cells acquired an antigen presenting cell-like phenotype as we found that they directly present external proteins as HLA-II ligands. Not only that CIITA-expressing GBM cells are attractive models for antigen discovery endeavors, but also such engineered cells have great therapeutic potential through massive presentation of a diverse antigenic repertoire.