Easy-HLA: a validated web application suite to reveal the full details of HLA typing

Anti-Human Leukocyte Antigen Antibody Detection from Terasaki's Humoral Theory to Delisting Strategies in 2024

The human leukocyte antigen (HLA) system plays a critical role in transplant immunology, influencing outcomes through various immune-mediated rejection mechanisms. Hyperacute rejection is driven by preformed donor-specific antibodies (DSAs) targeting HLAs, leading to complement activation and graft loss within hours to days. Acute rejection typically occurs within six months post-transplantation, involving cellular and humoral responses, including the formation of de novo DSAs. Chronic rejection, a key factor in long-term graft failure, often involves class II DSAs and complex interactions between the innate and adaptive immune systems. Advancements in HLA antibody detection, particularly single antigen bead (SAB) assays, have improved the sensitivity and characterization of DSAs. However, these assays face challenges like false positives from denatured antigens and false negatives due to low antibody titers or complement competition. Furthermore, molecular mismatch (MM) analysis has emerged as a potential tool for refining donor-recipient compatibility but faces some issues such as a lack of standardization. Highly sensitized patients with calculated panel-reactive antibodies (cPRA) of 100% face barriers to transplantation. Strategies like serum dilution, novel therapies (e.g., Imlifidase), and delisting approaches could refine immunological risk assessment and delisting strategies are essential to expand transplant opportunities for these patients.

HLA peptide-binding pocket diversity modulates immunological complications after cord blood transplant in acute leukaemia

Pocket motifs and their amino acid positions of HLA molecules are known to govern antigen presentation to effector cells. Our objective was to analyse their influence on the risk of graft-versus-host disease (GVHD) and relapse after umbilical cord blood transplant (UCBT). The transplant characteristics of 849 patients with acute leukaemia were obtained from the Eurocord/EBMT database. Higher acute (a) GVHD was associated with homozygosity of UCB HLA-C amino acid positions 77 and 80 (NN/KK) (p = 0.008). Severe aGVHD was associated with HLA-A pocket B YSAVMENVHY motif (p = 0.002) and NN and RR genotypes of the HLA-C amino acid positions 77 and 156 (p = 0.006 and p = 0.002). Such risk was also increased in case of recipient and UCB mismatches in P4 (p < 0.0001) and P9 (p = 0.003) pockets of HLA-DQB1 alleles. For chronic GVHD, the pocket B YYAVMEISNY motif of the HLA-B*15:01 allele and the absence of mismatch between recipient and UCB in the P6 pocket of HLA-DRB1 were associated with a lower risk (p = 0.0007 and p = 0.0004). In relapse, both UCB pocket B YFAVMENVHY belonging to HLA-A*32:01 and recipient pocket B YDSVGENYQY motif of the HLA-C*07:01 allele were associated with higher risk (p = 0.0026 and p = 0.015). We provide clues on HLA-mediated cellular interactions and their role in the development of GVHD and relapse.

Determination of the clinical relevance of donor epitope-specific HLA-antibodies in kidney transplantation

In kidney transplantation, survival rates are still partly impaired due to the deleterious effects of donor specific HLA antibodies (DSA). However, not all luminex-defined DSA appear to be clinically relevant. Further analysis of DSA recognizing polymorphic amino acid configurations, called eplets or functional epitopes, might improve the discrimination between clinically relevant vs. irrelevant HLA antibodies. To evaluate which donor epitope-specific HLA antibodies (DESAs) are clinically important in kidney graft survival, relevant and irrelevant DESAs were discerned in a Dutch cohort of 4690 patients using Kaplan-Meier analysis and tested in a cox proportional hazard (CPH) model including nonimmunological variables. Pre-transplant DESAs were detected in 439 patients (9.4%). The presence of certain clinically relevant DESAs was significantly associated with increased risk on graft loss in deceased donor transplantations (p < 0.0001). The antibodies recognized six epitopes of HLA Class I, 3 of HLA-DR, and 1 of HLA-DQ, and most antibodies were directed to HLA-B (47%). Fifty-three patients (69.7%) had DESA against one donor epitope (range 1-5). Long-term graft survival rate in patients with clinically relevant DESA was 32%, rendering DESA a superior parameter to classical DSA (60%). In the CPH model, the hazard ratio (95% CI) of clinically relevant DESAs was 2.45 (1.84-3.25) in deceased donation, and 2.22 (1.25-3.95) in living donation. In conclusion, the developed model shows the deleterious effect of clinically relevant DESAs on graft outcome which outperformed traditional DSA-based risk analysis on antigen level.

Ellipro scores of donor epitope specific HLA antibodies are not associated with kidney graft survival

In kidney transplantation, donor HLA antibodies are a risk factor for graft loss. Accessibility of donor eplets for HLA antibodies is predicted by the ElliPro score. The clinical usefulness of those scores in relation to transplant outcome is unknown. In a large Dutch kidney transplant cohort, Ellipro scores of pretransplant donor antibodies that can be assigned to known eplets (donor epitope specific HLA antibodies [DESAs]) were compared between early graft failure and long surviving deceased donor transplants. We did not observe a significant Ellipro score difference between the two cohorts, nor significant differences in graft survival between transplants with DESAs having high versus low total Ellipro scores. We conclude that Ellipro scores cannot be used to identify DESAs associated with early versus late kidney graft loss in deceased donor transplants.

Improving HLA typing imputation accuracy and eplet identification with local next-generation sequencing training data

Assessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower-resolution data either manually or with computational tools currently relying, at best, on data containing typing ambiguities. We gathered NGS typing data from 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 and DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, and DPA1 (10.5%). We developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute second field HLA typings from low- or intermediate-resolution ones. Compared with the reference tools HaploStats, HLA-EMMA, and HLA-Upgrade, HaploSFHI provided more accurate predictions across all loci on two French test sets and four European-independent test sets. Only HaploSFHI could impute DQA1, and solely HaploSFHI and HaploStats provided DRB3/4/5 imputations. The improved performance of HaploSFHI was due to our local and nonambiguous data. We provided explanations for the most common imputation errors and pinpointed the variability of a low number of low-resolution haplotypes. We thus provided guidance to select individuals for whom sequencing would optimize incompatibility assessment and cost-effectiveness of HLA typing, considering not only well-imputed second field typing(s) but also well-imputed eplets.

HLA haplotype frequencies and diversity in patients with hemoglobinopathies

The genetic diversity of the human leukocyte antigen (HLA) system was shaped by evolutionary constraints exerted by environmental factors. Analyzing HLA diversity may allow understanding of the underlying pathways and offer useful tools in transplant setting. The aim of this study was to investigate the HLA haplotype diversity in patients with sickle cell disease (SCD, N = 282) or β-thalassemia (β-Thal, N = 60), who received hematopoietic cell transplantation (HCT) reported to Eurocord and the Société Francophone de Greffe de Moelle et de Thérapie Cellulaire (SFGM-TC). We identified 405 different HLA-A-B-DRB1 haplotypes in SCD and 108 in β-Thal patients. Using data from African and European populations of the "1000 Genomes Project" for comparison with SCD and β-Thal, respectively, we found that the haplotypes HLA-A*30-B*14-DRB1*15 (OR 7.87, 95% CI: 1.66-37.3, p b = 0.035), HLA-A*23-B*08 (OR 6.59, 95% CI: 1.8-24.13, p b = 0.023), and HLA-B*14-DRB1*15 (OR 10.74, 95% CI: 3.66-31.57, p b = 0.000) were associated with SCD, and the partial haplotypes HLA-A*30-B*13 and HLA-A*68-B*53 were associated with β-Thal (OR 4.810, 95% CI: 1.55-14.91, p b = 0.033, and OR 17.52, 95% CI: 2.81-184.95, p b = 0.011). Our results confirm the extreme HLA genetic diversity in SCD patients likely due to their African ancestry. This diversity seems less accentuated in patients with β-Thal. Our findings emphasize the need to expand inclusion of donors of African descent in HCT donor registries and cord blood banks.

SARS-CoV-2 escape from cytotoxic T cells during long-term COVID-19

Evolution of SARS-CoV-2 in immunocompromised hosts may result in novel variants with changed properties. While escape from humoral immunity certainly contributes to intra-host evolution, escape from cellular immunity is poorly understood. Here, we report a case of long-term COVID-19 in an immunocompromised patient with non-Hodgkin's lymphoma who received treatment with rituximab and lacked neutralizing antibodies. Over the 318 days of the disease, the SARS-CoV-2 genome gained a total of 40 changes, 34 of which were present by the end of the study period. Among the acquired mutations, 12 reduced or prevented the binding of known immunogenic SARS-CoV-2 HLA class I antigens. By experimentally assessing the effect of a subset of the escape mutations, we show that they resulted in a loss of as much as ~1% of effector CD8 T cell response. Our results indicate that CD8 T cell escape represents a major underappreciated contributor to SARS-CoV-2 evolution in humans.

Identification of Novel Genetic Risk Factors for Focal Segmental Glomerulosclerosis in Children: Results From the Chronic Kidney Disease in Children (CKiD) Cohort

RATIONALE & OBJECTIVE

Focal segmental glomerulosclerosis (FSGS) is a major cause of pediatric nephrotic syndrome, and African Americans exhibit an increased risk for developing FSGS compared with other populations. Predisposing genetic factors have previously been described in adults. Here we performed genomic screening of primary FSGS in a pediatric African American population.

STUDY DESIGN

Prospective cohort with case-control genetic association study design.

SETTING & PARTICIPANTS

140 African American children with chronic kidney disease from the Chronic Kidney Disease in Children (CKiD) cohort, including 32 cases with FSGS.

PREDICTORS

Over 680,000 common single-nucleotide polymorphisms (SNPs) were tested for association. We also ran a pathway enrichment analysis and a human leucocyte antigen (HLA)-focused association study.

OUTCOME

Primary biopsy-proven pediatric FSGS.

ANALYTICAL APPROACH

Multivariate logistic regression models.

RESULTS

The genome-wide association study revealed 169 SNPs from 14 independent loci significantly associated with FSGS (false discovery rate [FDR]<5%). We observed notable signals for genetic variants within the APOL1 (P=8.6×10^-7; OR, 25.8 [95% CI, 7.1-94.0]), ALMS1 (P=1.3×10^-7; 13.0% in FSGS cases vs 0% in controls), and FGFR4 (P=4.3×10^-6; OR, 24.8 [95% CI, 6.3-97.7]) genes, all of which had previously been associated with adult FSGS, kidney function, or chronic kidney disease. We also highlighted novel, functionally relevant genes, including GRB2 (which encodes a slit diaphragm protein promoting podocyte structure through actin polymerization) and ITGB1 (which is linked to renal injuries). Our results suggest a major role for immune responses and antigen presentation in pediatric FSGS through (1) associations with SNPs in PTPRJ (or CD148, P=3.5×10^-7), which plays a role in T-cell receptor signaling, (2) HLA-DRB1∗11:01 association (P=6.1×10^-3; OR, 4.5 [95% CI, 1.5-13.0]), and (3) signaling pathway enrichment (P=1.3×10^-6).

LIMITATIONS

Sample size and no independent replication cohort with genomic data readily available.

CONCLUSIONS

Our genetic study has identified functionally relevant risk factors and the importance of immune regulation for pediatric primary FSGS, which contributes to a better description of its molecular pathophysiological mechanisms.

Comparative analysis of the variability of the human leukocyte antigen peptide-binding pockets in patients with acute leukaemia

The association between acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) and the human leukocyte antigens (HLA) has rarely been studied in terms of diversity of peptide-binding pockets. The objective of this study was to analyse whether motifs of HLA class I and class II peptide-binding pockets and/or their amino acid positions were differentially associated with ALL and AML. We included 849 patients from the Eurocord/European Blood and Marrow Transplant registry. The HLA peptide-binding pockets whose amino acid variability was analysed were B and F for HLA class I, P4, P6, and P9 for HLA-DRB1, and P4 and P9 for HLA-DQB1. The motif RFDRAY in P4 of HLA-DRB1*16:01/02/03/05 alleles and the motif YYVSY in P9 of HLA-DQB1*05:02/04/05 alleles, were statistically associated with ALL (corrected p value [p_c ] = 0.001 and p_c  = 0.035 respectively). The frequency of serine 57 in the P9 of HLA-DQB1 was higher in ALL (odds ratio 2.09, 95% confidence interval: 1.27-3.44; p_c  = 0.037). Our analysis suggests that specific motifs in terms of HLA class II pockets and amino acids might be unique to ALL. The associations identified in this study encourage further investigation oF the role of HLA peptide-binding pockets and their amino acids in immune processes underpinning acute leukaemia and ultimately in immunotherapy settings.

HLA-EPI: A new EPIsode in exploring donor/recipient epitopic compatibilities

The HLA system plays a pivotal role both in transplantation and immunology. While classical HLA genotypes matching is made at the allelic level, recent progresses were developed to explore antibody–antigen recognition by studying epitopes. Donor to recipient matching at the epitopic level is becoming a trending topic in the transplantation research field because anti‐HLA antibodies are epitope‐specific rather than allele‐specific. Indeed, different HLA alleles often share common epitopes. We present the HLA‐Epi tool (hla.univ-nantes.fr) to study an HLA genotype at the epitope level. Using the international HLA epitope registry (Epregistry.com.br) as a reference, we developed HLA‐Epi to easily determine epitopic and allelic compatibility levels between several HLA genotypes. The epitope database covers the most common HLA alleles (N = 2976 HLA alleles), representing more than 99% of the total observed frequency of HLA alleles. The freely accessible web tool HLA‐Epi calculates an epitopic mismatch load between different sets of potential recipient‐donor pairs at different resolution levels. We have characterized the epitopic mismatches distribution in a cohort of more than 10,000 kidney transplanted pairs from European ancestry, which showed low number of epitopic mismatches: 56.9 incompatibilities on average. HLA‐Epi allows the exploration of epitope pairing matching to better understand epitopes contribution to immune responses regulation, particularly during transplantation. This free and ready‐to‐use bioinformatics tool not only addresses limitations of other related tools, but also offers a cost‐efficient and reproducible strategy to analyze HLA epitopes as an alternative to HLA allele compatibility. In the future, this could improve sensitization prevention for allograft allocation decisions and reduce the risk of alloreactivity.

Approaching Genetics Through the MHC Lens: Tools and Methods for HLA Research

The current SARS-CoV-2 pandemic era launched an immediate and broad response of the research community with studies both about the virus and host genetics. Research in genetics investigated HLA association with COVID-19 based on in silico, population, and individual data. However, they were conducted with variable scale and success; convincing results were mostly obtained with broader whole-genome association studies. Here, we propose a technical review of HLA analysis, including basic HLA knowledge as well as available tools and advice. We notably describe recent algorithms to infer and call HLA genotypes from GWAS SNPs and NGS data, respectively, which opens the possibility to investigate HLA from large datasets without a specific initial focus on this region. We thus hope this overview will empower geneticists who were unfamiliar with HLA to run MHC-focused analyses following the footsteps of the Covid-19|HLA & Immunogenetics Consortium.

Current HLA Investigations on SARS-CoV-2 and Perspectives

The rapid, global spread of the SARS-CoV-2 virus during the current pandemic has triggered numerous efforts in clinical and research settings to better understand the host genetics' interactions and the severity of COVID-19. Due to the established major role played by MHC/HLA polymorphism in infectious disease course and susceptibility, immunologists and geneticists have teamed up to investigate its contribution to the SARS-CoV-2 infection and COVID-19 progression. A major goal of the Covid-19|HLA & Immunogenetics Consortium is to support and unify these efforts. Here, we present a review of HLA immunogenomics studies in the SARS-CoV-2 pandemic and reflect on the role of various HLA data, their limitation and future perspectives.

Graphical user interface for the haplotype frequency estimation software Hapl-o-Mat

Population-specific human leukocyte antigen (HLA) haplotype frequencies are an essential basis of advanced algorithms for donor selection in unrelated hematopoietic stem cell transplantation. In 2016, we introduced Hapl-o-Mat, a versatile tool for haplotype frequency estimation based on an expectation-maximization algorithm (https://github.com/DKMS/hapl-o-Mat). Hapl-o-Mat is specifically tailored to the analysis of HLA genes and able to cope with the heterogeneous genotyping data usually found in donor registries. To make Hapl-o-Mat accessible to a wider range of users, we designed a graphical user interface module that considerably facilitates the interaction with the application (https://github.com/DKMS/hapl-o-Mat_GUI). We further provide a precompiled version of Hapl-o-Mat that can be used on Windows personal computers without dependency on additional software libraries (https://github.com/DKMS/hapl-o-Mat_WinBin). We are confident that these new, user-oriented features will encourage more researchers to apply Hapl-o-Mat to their data, thereby increasing knowledge and public availability of population-specific HLA haplotype frequencies.

An immunogenetic view of COVID-19

Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.

The changing landscape of HLA typing: Understanding how and when HLA typing data can be used with confidence from bench to bedside

Human leukocyte antigen (HLA) genes are extraordinary for their extreme diversity and widespread impact on human health and disease. More than 30,000 HLA alleles have been officially named and more alleles continue to be discovered at a rapid pace. HLA typing systems which have been developed to detect HLA diversity have advanced rapidly and are revolutionizing our understanding of HLA's clinical importance. However, continuous improvements in knowledge and technology have created challenges for clinicians and scientists. This review explains how differences in HLA typing systems can impact the HLA types that are assigned. The consequences of differences in laboratory testing methods and reference databases are described. The challenges of using HLA types that are not equivalent are illustrated. A fundamental understanding of the continual expansion of our understanding of HLA diversity and limitations in some of the typing data is essential for using typing data appropriately in clinical and research settings.

PERHAPS: Paired-End short Reads-based HAPlotyping from next-generation Sequencing data

The identification of rare haplotypes may greatly expand our knowledge in the genetic architecture of both complex and monogenic traits. To this aim, we developed PERHAPS (Paired-End short Reads-based HAPlotyping from next-generation Sequencing data), a new and simple approach to directly call haplotypes from short-read, paired-end Next Generation Sequencing (NGS) data. To benchmark this method, we considered the APOE classic polymorphism (*1/*2/*3/*4), since it represents one of the best examples of functional polymorphism arising from the haplotype combination of two Single Nucleotide Polymorphisms (SNPs). We leveraged the big Whole Exome Sequencing (WES) and SNP-array data obtained from the multi-ethnic UK BioBank (UKBB, N=48,855). By applying PERHAPS, based on piecing together the paired-end reads according to their FASTQ-labels, we extracted the haplotype data, along with their frequencies and the individual diplotype. Concordance rates between WES directly called diplotypes and the ones generated through statistical pre-phasing and imputation of SNP-array data are extremely high (>99%), either when stratifying the sample by SNP-array genotyping batch or self-reported ethnic group. Hardy-Weinberg Equilibrium tests and the comparison of obtained haplotype frequencies with the ones available from the 1000 Genome Project further supported the reliability of PERHAPS. Notably, we were able to determine the existence of the rare APOE*1 haplotype in two unrelated African subjects from UKBB, supporting its presence at appreciable frequency (approximatively 0.5%) in the African Yoruba population. Despite acknowledging some technical shortcomings, PERHAPS represents a novel and simple approach that will partly overcome the limitations in direct haplotype calling from short read-based sequencing.

SNP-HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC-centric analyses in genomics

Genome‐wide associations studies have repeatedly identified the major histocompatibility complex genomic region (6p21.3) as key in immune pathologies. Researchers have also aimed to extend the biological interpretation of associations by focusing directly on human leukocyte antigen (HLA) polymorphisms and their combination as haplotypes. To circumvent the effort and high costs of HLA typing, statistical solutions have been developed to infer HLA alleles from single‐nucleotide polymorphism (SNP) genotyping data. Though HLA imputation methods have been developed, no unified effort has yet been undertaken to share large and diverse imputation models, or to improve methods. By training the HIBAG software on SNP + HLA data generated by the Consortium on Asthma among African‐ancestry Populations in the Americas (CAAPA) to create reference panels, we highlighted the importance of (a) the number of individuals in reference panels, with a twofold increase in accuracy (from 10 to 100 individuals) and (b) the number of SNPs, with a 1.5‐fold increase in accuracy (from 500 to 24,504 SNPs). Results showed improved accuracy with CAAPA compared to the African American models available in HIBAG, highlighting the need for precise population‐matching. The SNP‐HLA Reference Consortium is an international endeavor to gather data, enhance HLA imputation and broaden access to highly accurate imputation models for the immunogenomics community.

Association of HLA-DRB1∗09:01 with tIgE levels among African-ancestry individuals with asthma

BACKGROUND

Asthma is a complex chronic inflammatory disease of the airways. Association studies between HLA and asthma were first reported in the 1970s, and yet, the precise role of HLA alleles in asthma is not fully understood. Numerous genome-wide association studies were recently conducted on asthma, but were always limited to simple genetic markers (single nucleotide polymorphisms) and not complex HLA gene polymorphisms (alleles/haplotypes), therefore not capturing the biological relevance of this complex locus for asthma pathogenesis.

OBJECTIVE

To run the first HLA-centric association study with asthma and specific asthma-related phenotypes in a large cohort of African-ancestry individuals.

METHODS

We collected high-density genomics data for the Consortium on Asthma among African-ancestry Populations in the Americas (N = 4993) participants. Using computer-intensive machine-learning attribute bagging methods to infer HLA alleles, and Easy-HLA to infer HLA 5-gene haplotypes, we conducted a high-throughput HLA-centric association study of asthma susceptibility and total serum IgE (tIgE) levels in subjects with and without asthma.

RESULTS

Among the 1607 individuals with asthma, 972 had available tIgE levels, with a mean tIgE level of 198.7 IU/mL. We could not identify any association with asthma susceptibility. However, we showed that HLA-DRB1∗09:01 was associated with increased tIgE levels (P = 8.5 × 10-4; weighted effect size, 0.51 [0.15-0.87]).

CONCLUSIONS

We identified for the first time an HLA allele associated with tIgE levels in African-ancestry individuals with asthma. Our report emphasizes that by leveraging powerful computational machine-learning methods, specific/extreme phenotypes, and population diversity, we can explore HLA gene polymorphisms in depth and reveal the full extent of complex disease associations.