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At least 19 recordsLinked to original sources
PubMed2025

An In-Depth Review of the Genetics of the Non-Classical HLA Class I Gene HLA-E and Its Effects on Haematopoietic Cell Transplant Outcomes.

HLA-E is a non-classical HLA class I gene with limited reported genetic variability and few published studies into full-gene sequencing or population allele frequencies. Two protein variants, HLA-E*01:01 and HLA-E*01:03, are very common, accounting for 94%-100% of observed alleles in most studies performed to date. Frequently utilised exon-based sequencing strategies have led to the assumption of HLA-E being a near bi-allelic gene; however, recent full-gene sequencing studies have shown a greater degree of genetic variability than initially imagined. We carried out a literature review of HLA-E genotype and ethnicity data, which suggested HLA-E*01:03 is more common in Asian and, in particular, East Asian populations. Furthermore, HLA-E*01:03:02 is more frequently observed than HLA-E*01:03:01 in European and American populations, whereas HLA-E*01:03:01 is found at higher frequencies in Asian populations. It has been proposed that HLA-E may have a role in Haematopoietic Cell Transplantation (HCT) due to its interaction with NK and CD8+ T cells and its non-canonical peptide binding repertoire. Here we also review published literature into the effects of HLA-E genetics on HCT outcomes. Heterogeneity between cohorts muddies the waters; hence, studies report confounding effects of HLA-E genotype and matching on HCT outcomes. The need for further HLA-E sequencing of larger cohorts is evident to gain useful insight into the true genetic variability of HLA-E and its impact on HCT.

Humans↗
PubMed2026

HLA class I expression and tumor immune infiltration together shape colon cancer immune contexture.

BACKGROUND: The relative contribution of HLA class I molecules-including classical HLA class Ia (HLA-A, -B, -C) and non-classical HLA class Ib (HLA-E, -F, -G)-to shaping the tumor immune microenvironment in colon cancer remains insufficiently defined. We investigated how their expression patterns relate to immune infiltration and clinical outcome. METHODS: In a retrospective cohort of 280 colon cancers, we assessed HLA class Ia and class Ib expression and quantified CD45-positive immune cell infiltration by immunohistochemistry. These features were correlated with clinicopathological variables, microsatellite instability (MSI) status, and previously established genomic immune signatures. RESULTS: High HLA class Ia expression and increased CD45-positive cell infiltration were each associated with improved overall, disease-specific, and progression-free survival. CD45-positive density correlated strongly with Immunologic Constant of Rejection scores. HLA class Ia loss was more frequent in advanced stages and in MSI-H tumors. Among HLA class Ib molecules, only HLA-E expression was associated with favorable disease-specific and progression-free survival. Integrative analysis identified three immune phenotypes with distinct prognostic profiles; tumors characterized by high HLA class Ia expression, low HLA class Ib expression, and high CD45-positive infiltration had the best outcomes. CONCLUSION: Colon cancer immunogenicity is shaped by coordinated patterns of HLA class I expression and immune infiltration. Integrating HLA class Ia/Ib expression with immune cell density provides a refined stratification of tumor immune phenotypes and may support personalized immunotherapeutic decision-making.

Antigen presentation↗
PubMed2025

ESMpHLA: Evolutionary Scale Model-Based Deep Learning Prediction of HLA Class I Binding Peptides.

The recognition of endogenous peptides by HLA class I plays a crucial role in CD8+ T cell immune responses and human adaptive cell immune. Thus, the prediction of HLA class I-peptide binding affinities is always the core issue for the research of immune recognition and vaccine development. In this study, an evolutionary scale model (ESM) combined with parallel CNN blocks and a cross attention mechanism was used to construct a novel ESMpHLA model for predicting HLA class I binding peptides. Based on the 91,560 binding peptides of 41 HLA-A alleles, 56,731 of 50 HLA-B alleles and 2444 of 10 HLA-C alleles, the ESMpHLA model was successfully established and achieved satisfying prediction performances with the overall accuracy and AUC values of 0.874 and 0.938 for the test dataset. The results indicate that the ESMpHLA model performs well in dealing with different HLA class I 2-field alleles as well as the peptides with different lengths. Then, the generalisation ability of the ESMpHLA model was validated by an independent test dataset compiled from recent IEDB weekly benchmark datasets. The results showed that the ESMpHLA model achieved the highest ROC-AUC and PR-AUC values when compared with the latest BVMHC, CapsNet-MHC, STMHCpan and BVLSTM models. In addition, two ensemble models were also established by integrating the above 5 deep learning models using soft-voting and hard-voting strategies.

Humans↗
PubMed2026

Novel HLA class I and II insights into the pathogenesis of systemic sclerosis-associated interstitial lung disease.

OBJECTIVES: Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of mortality in systemic sclerosis (SSc), yet its genetic architecture remains incompletely understood. Therefore, given the key role of the major histocompatibility complex (MHC) in SSc, we aimed to perform a comprehensive MHC-wide association study in the largest SSc-ILD cohort to date. METHODS: We analysed 2412 patients with SSc-ILD⁺, 3550 patients with SSc-ILD⁻, and 15,076 controls of European ancestry from 10 international cohorts. After quality control, the MHC region was imputed, and inverse variance weighted meta-analysis was performed. Subsequently, conditional stepwise analyses, adjustment for antitopoisomerase autoantibody (ATA) status, and functional annotation of significant single-nucleotide polymorphisms were performed. Finally, we constructed a composite score combining genetic, clinical, and demographic variables to predict SSc-ILD. RESULTS: After conditional analysis, we detected 12 significant associations within class I and class II human leukocyte antigen (HLA) genes. ATA adjustment reduced the significance of class II HLA variants, whereas class I HLA variants remained unaffected. Finally, the built composite score had an area under the curve of 0.754, significantly outperforming the models including any of the variables alone. CONCLUSIONS: In this study, we identify genetic mechanisms underlying SSc-ILD that support the potential implication of CD8+ T cells and ATAs in its pathogenesis. Moreover, we also demonstrate the enhanced efficacy of integrating genetic information into predictive models to detect patients at high risk of SSc-ILD. These findings provide new insights into disease pathogenesis and suggest potential biomarkers and therapeutic targets for improved patient management.

Humans↗
PubMed2026

HLA class I escape drives the evolution of SARS-CoV-2 in human populations.

The role of escape from the cytotoxic T cell (CTL) response in Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) evolution remains controversial. Here, we study the origin and spread of SARS-CoV-2 variants whose mutations reduce presentation by the human leukocyte antigen (HLA) class I alleles common in human populations. We find that 35% of mutations that are characteristic of the variants of concern, and 39% of all subsequent viral mutations, facilitate escape of viral epitopes from presentation. Mutations allowing escape from more common HLA alleles reach higher frequencies, particularly in those countries where these HLA alleles are more frequent, indicating that escape is selected by the local genetic composition of the human host population. We also show that viral mutations that accumulated in general-population transmission chains matched population HLA class I allele frequencies as well as, or better than, mutations acquired during persistent infections, suggesting that selection in favor of escape mutations is not limited to immunocompromised individuals. Together, these data reveal CTL escape as a facet of selection, a driver of evolution, and an epidemiological concern for SARS-CoV-2.

Humans↗
PubMed2025

Donor HLA Class I Evolutionary Divergence and Late Allograft Rejection After Liver Transplantation in Children: An Emulated Target Trial.

HLA evolutionary divergence (HED), a continuous metric quantifying the differences between each amino acid of two homologous HLA alleles, reflects the importance of the immunopeptidome presented to T lymphocytes. It has been associated with rejection after liver transplantation. This retrospective cohort study aimed to analyse the potential effect of donor or recipient HED on liver transplant rejection in a new series of patients transplanted during childhood and followed in adulthood. The study included 120 children who had been transplanted between 1991 and 2010 and were followed by routine biopsies and histological evaluations with a median of 14.1 years post-LT. Liver biopsies were performed routinely 1, 5, 10 and 20 years after transplantation and in the event of liver dysfunction. HED was calculated using the physicochemical Grantham distance for donor and recipient Class I (HLA-A, -B, -C) and Class II (HLA-DRB1, -DQB1) alleles. The influence of HED on rejection was analysed using inverse probability weighting (IPW) and target trial emulation using the g method. Based on the IPW score, donor HED class I was correlated with the occurrence of late (> 90 days) rejection (HR, 1.19, 95% CI: 1.01-1.40) independently of HLA mismatches, donor age and initial induction. The emulated target trial confirmed that donor HED Class I has a causal effect on liver graft rejection and this relationship was observed long-term.

Humans↗
PubMed2025

Polygenic Risk Scores and HLA Class II Variants are Biomarkers of Corticosteroid Response in Childhood Nephrotic Syndrome.

INTRODUCTION: Nephrotic syndrome (NS), a common glomerular disease in children, is classified based on response to corticosteroid therapy as either steroid-sensitive nephrotic syndrome (SSNS), or steroid-resistant nephrotic syndrome (SRNS). However, there are currently no reliable predictors of therapy response at initial clinical presentation. METHODS: We conducted genome-wide association studies, developed polygenic risk scores (PRS) for therapy response and analyzed classical HLA alleles in 1,997 (994 discovery and 1,003 replication/validation cohorts) previously unstudied children with NS and 3,558 ancestry-matched controls. RESULTS: A significant association with HLA loci defined by variants in HLA-DQB1, HLA-DRB1, and HLA-DQA1 were found for SSNS (but not SRNS), along with a second immune-related SSNS locus: CLEC16A. A PRS that discriminates between SSNS and SRNS was validated in two independent cohorts. The HLA haplotype HLA- DRB1*07:01~DQA1*02:01~DQB1*02:02 was associated with ~4 times the risk of developing SSNS. A model incorporating HLA haplotype, PRS score, and age at onset of the disease was the best predictor of steroid responsiveness with an AUC of 0.68-0.70 and an overall classification accuracy of SSNS versus SRNS of 67-71%. CONCLUSIONS: Our findings confirm that SSNS (unlike SRNS) is an immune-mediated HLA-associated disorder. The PRS for therapy response and HLA haplotype can serve as biomarkers and provide a foundation for more accurate diagnoses and tailored and individualized treatment.

HLA haplotype↗
PubMed2025

Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA.

COVID-19, severe acute respiratory syndrome coronavirus 2, rapidly spread worldwide. Severe and critical patients are expected to rapidly deteriorate. Although several studies have attempted to uncover the mechanisms underlying COVID-19 severity, most have focused on the perturbations of single genes. However, the complex mechanism of COVID-19 involves numerous perturbed genes in a molecular network rather than a single abnormal gene. Thus, we aimed to identify COVID-19 severity-specific markers in the Japanese population using gene network analysis. In order to reveal the severity-specific molecular interplays, we developed a novel computational network biology strategy that measures dissimilarity between networks based on the comprehensive information of gene network (i.e., expression levels of genes and network structure) by using Kullback-Leibler divergence. Monte Carlo simulations demonstrated the effectiveness of our strategy for differential gene network analysis. We applied this method to publicly available whole blood RNA-seq data from the Japan coronavirus disease 2019 Task Force and identified differentially regulated molecular interplays between 368 severe and 105 non-severe samples. Our analysis suggests the gene network between HLA class II, CIITA, and CD74 as a COVID-19 severity specific molecular marker. Although the association between HLA class II and COVID-19 has been demonstrated, our data analysis revealed that the molecular interplay of HLA class II with its target and/or regulator is a crucial marker for COVID-19 severity. Our findings from computational network biology analysis suggest that suppression and activation of the molecular interplay between HLA class II, CIITA, and CD74 provide crucial clues to uncover the mechanisms of COVID-19 severity.

Humans↗
PubMed2025

Extensive Analysis of Genetic Diversity in HLA-DMA, HLA-DMB, HLA-DOA and HLA-DOB: Characterisation of 236 Novel Alleles.

HLA-DMA, -DMB, -DOA and -DOB are non-classical HLA Class II genes that play a crucial role in the selection of highly stable HLA Class II/peptide complexes on antigen-presenting cells. Although the genes were initially thought to have a limited diversity with less than 13 alleles per gene documented in the IPD-IMGT/HLA Database in 2022, recent studies suggest a potential impact of certain alleles on the outcome of hematopoietic cell transplantation. To gain a deeper understanding of allelic diversity, we sequenced HLA-DMA, -DMB, -DOA and -DOB of 1880 potential stem cell donors from Germany, Poland, Great Britain and Chile, achieving full-gene resolution. Remarkably, we identified 3968 previously undescribed sequences, including 28 distinct novel proteins. The observed allele frequencies were consistent across all studied populations with one dominating protein for each gene: HLA-DMA*01:01 (> 77%), HLA-DMB*01:01 (> 63%), HLA-DOA*01:01 (> 97%) and HLA-DOB*01:01 (> 77%). Notably, a much higher diversity was observed in full-genomic resolution. Finally, we submitted 51 distinct novel sequences for HLA-DMA, 58 for HLA-DMB, 80 for HLA-DOA and 47 for HLA-DOB to the IPD-IMGT/HLA Database. This comprehensive reference database update will not only simplify future genotyping of HLA-DMA, -DMB, -DOA and -DOB but will hopefully also enhance our understanding of the complex process of peptide selection and loading to the HLA Class II proteins.

Humans↗