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

Laghmouchi, Aicha; Kester, Michel G.D.; Hoogstraten, Conny; Hageman, Lois; Klerk, Wendy de; Huisman, Wesley; Koster, Eva A.S.; Ru, Arnoud H. de; Balen, Peter van; Klobuch, Sebastian; Veelen, Peter A. van; Falkenburg, J.H. Frederik; Jedema, Inge

doi:10.3389/fimmu.2022.831822

Cited by 11 publications

(13 citation statements)

References 41 publications

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“…Of note, these limitations are also affecting state-of-the-art MHC-II ligand predictors (see examples in Figures 4B, S4B and S6). As such our results provide both a refined view of the main MHC-II binding specificities, in line with the recent classification proposed for HLA-DP alleles (Laghmouchi et al, 2022; Meurer et al, 2021), and a robust implementation of these observations into an accurate MHC-II ligand prediction tool (MixMHC2pred-2.0).…”

Section: Discussionsupporting

confidence: 85%

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

Racle

Guillaume

Schmidt

et al. 2022

Preprint

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CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC-II molecules. The high polymorphism of MHC-II genes represents an important hurdle towards accurate prediction and identification of CD4+ T-cell epitopes in different individuals and different species. 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 human, mouse, cattle and chicken. 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 MHC-II 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 enabled us to discover and characterize several viral and bacterial epitopes following the aforementioned reverse binding mode.

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Section: Discussionsupporting

confidence: 85%

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

Racle

Guillaume

Schmidt

et al. 2022

Preprint

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“…The raw files were obtained from PRIDE (23) under the accession PXD030591 as provided by Laghmouchi et al . (22), subsequently, peptides were identified from each allele using MHCQuant (24) ( Material and Methods ). ( A ) The predictive accuracy of the four tools on elution data from proteins encoded by HLA-DP proteins (y-axis) where accuracy was measured using the area under the receiver operating curve AUC (ROC) (x-axis).…”

Section: Resultsmentioning

confidence: 99%

“…3. The third dataset (benchmarking-3) was assembled from an HLA-DP immunopeptidomic dataset obtained from Laghmouchi et al (22). The dataset contained 13 different immunopeptidomic datasets each of which was obtained from a different HLA-DP protein.…”

Section: Development Of Pia-m Architecturementioning

confidence: 99%

Predicting Peptide HLA-II Presentation Using Immunopeptidomics, Transcriptomics and Deep Multimodal Learning

ElAbd

Wendorff

Koudelka

et al. 2022

Preprint

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The human leukocyte antigen (HLA) class II proteins present peptides to CD4+ T cells through an interaction with T cell receptors (TCRs). Thus, HLA proteins are key players in shaping immunogenicity and immunodominance. Nevertheless, factors governing peptide presentation by HLA-II proteins are still poorly understood. To address this problem, we profiled the blood transcriptome and immunopeptidome of 20 healthy individuals and integrated the profiles with publicly available immunopeptidomics datasets. In depth multi-omics analysis identified expression levels and subcellular locations as import sequence-independent features governing presentation. Levering this knowledge, we developed the Peptide Immune Annotator Multimodal (PIA-M) tool, as a novel pan multimodal transformer-based framework that utilises sequence-dependent along with sequence-independent features to model presentation by HLA-II proteins. PIA-M illustrated a consistently superior performance relative to existing tools across two independent test datasets (area under the curve: 0.93 vs. 0.84 and 0.95 vs. 0.86), respectively. Besides achieving a higher predictive accuracy, PIA-M with its Rust-based pre-processing engine, had significantly shorter runtimes. PIA-M is freely available with a permissive licence as a standalone pipeline and as a webserver (https://hybridcomputing.ikmb.uni-kiel.de/pia). In conclusion, PIA-M enables a new state-of-the-art accuracy in predicting peptide presentation by HLA-II proteins in vivo.

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“…Each one of these TCE groups of DPB1 alleles is characterized by distinct structural features that differentiate one from the other group and influence the peptides bound to the DP protein. Most recently, the immunopeptidome of the alleles that belong to each one of these groups has been characterized (van Balen et al, 2020;Meurer et al, 2021;Laghmouchi et al, 2022). Considering that in our study of AD we have identified HLA-DPA1 and DPB1 alleles and specific residues relevant to the disease, we were interested to investigate as to whether any of the structural elements (alleles or residues) of the DP molecules correlated with the TCE groups, therefore, establishing a relationship between the DP structural elements of our study with the functional grouping of DPs including the peptide repertoire that characterizes each one of the TCE groups.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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

Cited by 11 publications

References 41 publications

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

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

Predicting Peptide HLA-II Presentation Using Immunopeptidomics, Transcriptomics and Deep Multimodal Learning

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

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