HLA Amino Acid Polymorphisms and Kidney Allograft Survival

Graph-Based Imputation Methods and Their Applications to Single Donors and Families

The outcome of Hematopoietic Stem Cell (HSCT) and organ transplant is strongly affected by the matching of the HLA alleles of the donor and the recipient. However, donors and sometimes recipients are often typed at low resolution, with some alleles either missing or ambiguous. Thus, imputation methods are required to detect the most probably high-resolution HLA haplotypes consistent with a typing. Such imputation algorithms require predefined haplotype frequencies. As such, the phasing of the typing is required for both imputation and frequency generation.We have developed a new approach to HLA haplotype and genotype imputation, where first all candidate phases of a typing are explicated, and then the ambiguity within each phase is solved. This ambiguity is solved through a graph structure of all partial haplotypes and the haplotypes consistent with them.This phasing approach was used to produce an imputation algorithm (GRIMM-Graph Imputation and Matching). GRIMM was then combined with the possibility of combining information from multiple races to produce MR-GRIMM (Multi-Race GRIMM). When family information is available, the phasing of each family member can be restricted by the others. We propose GRAMM (GRaph-bAsed faMily iMputation) to phase alleles in family pedigree HLA typing data and in mother-cord blood unit pairs. Finally, we combined MR-GRIMM with an expectation-maximization (EM) algorithm to estimate haplotype frequencies sharing information between races to produce MR-GRIMME (MR-GRIMM EM).We have shown that these algorithms naturally combine information between races and family members. The accuracy of each of these algorithms is significantly better than its current parallel methods. MR-GRIMM leads to high accuracy in matching predictions. GRAMM better imputes family members than either MR-GRIMM or any existing algorithm and has practically no phasing errors. MR-GRIMME obtains a higher likelihood than existing algorithms.MR-GRIMM, MR-GRIMME, and GRAMM are available as servers or through stand-alone versions in GITHUB and PyPi, as detailed in the appropriate sections.

GRAMM: A new method for analysis of HLA in families

Recently, haplo-identical transplantation with multiple HLA mismatches has become a viable option for stem cell transplants. Haplotype sharing detection requires the imputation of donor and recipient. We show that even in high-resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low-resolution typings. Similarly, in related donors, the parents' haplotypes should be imputed to determine what haplotype each child inherited. We propose graph-based family imputation (GRAMM) to phase alleles in family pedigree HLA typing data, and in mother-cord blood unit pairs. We show that GRAMM has practically no phasing errors when pedigree data are available. We apply GRAMM to simulations with different typing resolutions as well as paired cord-mother typings, and show very high phasing accuracy, and improved allele imputation accuracy. We use GRAMM to detect recombination events and show that the rate of falsely detected recombination events (false-positive rate) in simulations is very low. We then apply recombination detection to typed families to estimate the recombination rate in Israeli and Australian population datasets. The estimated recombination rate has an upper bound of 10%-20% per family (1%-4% per individual).

Detection of Nonsynonymous Single Variants in Human HLA-DRB1 Exon 2 Associated with Renal Transplant Rejection

Background: HLA-DRB1 is the most polymorphic gene in the human leukocyte antigen (HLA) class II, and exon 2 is critical because it encodes antigen-binding sites. This study aimed to detect functional or marker genetic variants of HLA-DRB1 exon 2 in renal transplant recipients (acceptance and rejection) using Sanger sequencing. Methods: This hospital-based case-control study collected samples from two hospitals over seven months. The 60 participants were equally divided into three groups: rejection, acceptance, and control. The target regions were amplified and sequenced by PCR and Sanger sequencing. Several bioinformatics tools have been used to assess the impact of non-synonymous single-nucleotide variants (nsSNVs) on protein function and structure. The sequences data that support the findings of this study with accession numbers (OQ747803-OQ747862) are available in National Center for Biotechnology Information (GenBank database). Results: Seven SNVs were identified, two of which were novel (chr6(GRCh38.p12): 32584356C>A (K41N) and 32584113C>A (R122R)). Three of the seven SNVs were non-synonymous and found in the rejection group (chr6(GRCh38.p12): 32584356C>A (K41N), 32584304A>G (Y59H), and 32584152T>A (R109S)). The nsSNVs had varying effects on protein function, structure, and physicochemical parameters and could play a role in renal transplant rejection. The chr6(GRCh38.p12):32584152T>A variant showed the greatest impact. This is because of its conserved nature, main domain location, and pathogenic effects on protein structure, function, and stability. Finally, no significant markers were identified in the acceptance samples. Conclusion: Pathogenic variants can affect intramolecular/intermolecular interactions of amino acid residues, protein function/structure, and disease risk. HLA typing based on functional SNVs could be a comprehensive, accurate, and low-cost method for covering all HLA genes while shedding light on previously unknown causes in many graft rejection cases.

HLA amino acid Mismatch-Based risk stratification of kidney allograft failure using a novel Machine learning algorithm

OBJECTIVE

While associations between HLA antigen-level mismatches (Ag-MM) and kidney allograft failure are well established, HLA amino acid-level mismatches (AA-MM) have been less explored. Ag-MM fails to consider the substantial variability in the number of MMs at polymorphic amino acid (AA) sites within any given Ag-MM category, which may conceal variable impact on allorecognition. In this study we aim to develop a novel Feature Inclusion Bin Evolver for Risk Stratification (FIBERS) and apply it to automatically discover bins of HLA amino acid mismatches that stratify donor-recipient pairs into low versus high graft survival risk groups.

METHODS

Using data from the Scientific Registry of Transplant Recipients, we applied FIBERS on a multiethnic population of 166,574 kidney transplants between 2000 and 2017. FIBERS was applied (1) across all HLA-A, B, C, DRB1, and DQB1 locus AA-MMs with comparison to 0-ABDR Ag-MM risk stratification, (2) on AA-MMs within each HLA locus individually, and (3) using cross validation to evaluate FIBERS generalizability. The predictive power of graft failure risk stratification was evaluated while adjusting for donor/recipient characteristics and HLA-A, B, C, DRB1, and DQB1 Ag-MMs as covariates.

RESULTS

FIBERS's best-performing bin (on AA-MMs across all loci) added significant predictive power (hazard ratio = 1.10, Bonferroni adj. p < 0.001) in stratifying graft failure risk (where low-risk is defined as zero AA-MMs and high-risk is one or more AA-MMs) even after adjusting for Ag-MMs and donor/recipient covariates. The best bin also categorized more than twice as many patients to the low-risk category, compared to traditional 0-ABDR Ag mismatching (∼24.4% vs ∼ 9.1%). When HLA loci were binned individually, the bin for DRB1 exhibited the strongest risk stratification; relative to zero AA-MM, one or more MMs in the bin yielded HR = 1.11, p < 0.005 in a fully adjusted Cox model. AA-MMs at HLA-DRB1 peptide contact sites contributed most to incremental risk of graft failure. Additionally, FIBERS points to possible risk associated with HLA-DQB1 AA-MMs at positions that determine specificity of peptide anchor residues and HLA-DQ heterodimer stability.

CONCLUSION

FIBERS's performance suggests potential for discovery of HLA immunogenetics-based risk stratification of kidney graft failure that outperforms traditional assessment.

Seeking Standardized Definitions for HLA-incompatible Kidney Transplants: A Systematic Review

BACKGROUND

There is no standard definition for "HLA incompatible" transplants. For the first time, we systematically assessed how HLA incompatibility was defined in contemporary peer-reviewed publications and its prognostic implication to transplant outcomes.

METHODS

We combined 2 independent searches of MEDLINE, EMBASE, and the Cochrane Library from 2015 to 2019. Content-expert reviewers screened for original research on outcomes of HLA-incompatible transplants (defined as allele or molecular mismatch and solid-phase or cell-based assays). We ascertained the completeness of reporting on a predefined set of variables assessing HLA incompatibility, therapies, and outcomes. Given significant heterogeneity, we conducted narrative synthesis and assessed risk of bias in studies examining the association between death-censored graft failure and HLA incompatibility.

RESULTS

Of 6656 screened articles, 163 evaluated transplant outcomes by HLA incompatibility. Most articles reported on cytotoxic/flow T-cell crossmatches (n = 98). Molecular genotypes were reported for selected loci at the allele-group level. Sixteen articles reported on epitope compatibility. Pretransplant donor-specific HLA antibodies were often considered (n = 143); yet there was heterogeneity in sample handling, assay procedure, and incomplete reporting on donor-specific HLA antibodies assignment. Induction (n = 129) and maintenance immunosuppression (n = 140) were frequently mentioned but less so rejection treatment (n = 72) and desensitization (n = 70). Studies assessing death-censored graft failure risk by HLA incompatibility were vulnerable to bias in the participant, predictor, and analysis domains.

CONCLUSIONS

Optimization of transplant outcomes and personalized care depends on accurate HLA compatibility assessment. Reporting on a standard set of variables will help assess generalizability of research, allow knowledge synthesis, and facilitate international collaboration in clinical trials.

Fine mapping of the HLA locus in Parkinson's disease in Europeans

We fine mapped the leukocyte antigen (HLA) region in 13,770 Parkinson’s disease (PD) patients, 20,214 proxy-cases, and 490,861 controls of European origin. Four HLA types were associated with PD after correction for multiple comparisons, HLA-DQA1*03:01, HLA-DQB1*03:02, HLA-DRB1*04:01, and HLA-DRB1*04:04. Haplotype analyses followed by amino acid analysis and conditional analyses suggested that the association is protective and primarily driven by three specific amino acid polymorphisms present in most HLA-DRB1*04 subtypes—11V, 13H, and 33H (OR = 0.87, 95% CI: 0.83–0.90, p < 8.23 × 10⁻⁹ for all three variants). No other effects were present after adjustment for these amino acids. Our results suggest that specific HLA-DRB1 variants are associated with reduced risk of PD, providing additional evidence for the role of the immune system in PD. Although effect size is small and has no diagnostic significance, understanding the mechanism underlying this association may lead to the identification of new targets for therapeutics development.

Association of Bw4/Bw6 mismatch across class I HLA loci with renal graft outcomes in first time transplants

Bw4 and Bw6 are strongly immunogenic epitopes routinely assigned based on HLA-B typing results per Organ Procurement and Transplantation Network (OPTN) policies. These public epitopes and their variants are shared by some cross-reactive HLA-A and -C antigens. Although epitope mismatch has been associated with poor transplant outcomes, previous studies did not find such associations for Bw4/6 mismatch as defined by HLA-B antigens only. We hypothesized that a broader definition for Bw4/Bw6 mismatch that includes cross-reactive HLA-A and -C antigens may reveal the risk associated with these epitopes. In this retrospective cohort study, we examined kidney transplantations between 2000 and 2016 in the OPTN database and determined the association of Bw4/6 mismatch across all class I HLA antigens and renal graft outcomes. Even by this broader definition, Bw4/6 mismatch was not independently associated with 1-year graft rejection (adjusted OR: 0.99, 95%CI 0.93-1.06) or death-censored graft survival (adjusted HR: 1.02, 95%CI 1.00-1.05). There was no significant association among recipients who were already sensitized at transplant either. Our findings suggest that Bw4/6 mismatch alone is not associated with poor renal graft outcomes despite their strong immunogenicity, and the load of epitope mismatches over a certain threshold is likely required to cause adverse clinical consequences.

Unique pathogen peptidomes facilitate pathogen-specific selection and specialization of MHC alleles

A key component of pathogen-specific adaptive immunity in vertebrates is the presentation of pathogen-derived antigenic peptides by major histocompatibility complex (MHC) molecules. The excessive polymorphism observed at MHC genes is widely presumed to result from the need to recognize diverse pathogens, a process called pathogen-driven balancing selection. This process assumes that pathogens differ in their peptidomes—the pool of short peptides derived from the pathogen’s proteome—so that different pathogens select for different MHC variants with distinct peptide-binding properties. Here, we tested this assumption in a comprehensive data set of 51.9 Mio peptides, derived from the peptidomes of 36 representative human pathogens. Strikingly, we found that 39.7% of the 630 pairwise comparisons among pathogens yielded not a single shared peptide and only 1.8% of pathogen pairs shared more than 1% of their peptides. Indeed, 98.8% of all peptides were unique to a single pathogen species. Using computational binding prediction to characterize the binding specificities of 321 common human MHC class-I variants, we investigated quantitative differences among MHC variants with regard to binding peptides from distinct pathogens. Our analysis showed signatures of specialization toward specific pathogens especially by MHC variants with narrow peptide-binding repertoires. This supports the hypothesis that such fastidious MHC variants might be maintained in the population because they provide an advantage against particular pathogens. Overall, our results establish a key selection factor for the excessive allelic diversity at MHC genes observed in natural populations and illuminate the evolution of variable peptide-binding repertoires among MHC variants.

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.

Substituting imputation of HLA antigens for high-resolution HLA typing: Evaluation of a multiethnic population and implications for clinical decision making in transplantation

Molecular mismatch analysis for assessment of histocompatibility in transplantation requires high-resolution HLA typing. Algorithms to "guesstimate" high-resolution from low-resolution typing exist, but their accuracy remains unknown. We converted high-resolution, sequence-based, HLA typing of 310 subjects from an ethnically heterogeneous population to low-resolution equivalents and tested the ability of the NMDP HaploStats and HLA Matchmaker programs to impute/reproduce the measured high-resolution HLA type, using the more common "winner-takes-all" approach. Only 35.6% of the HaploStats imputed HLA-A, -B, -C, -DRB1, and -DQB1 haplotypes had no mistakes, and the accuracy was significantly lower for non-Caucasians (29.1%) compared to Caucasians (45.2%) (odds ratio [OR], 0.5; 95% confidence interval [CI], 0.3-0.8; P = .004). HLA Matchmaker was not able to provide high-resolution haplotypes for 45.2% of Caucasian subjects and 63.5% of non-Caucasian subjects (P = .002). Of those with an imputed result, only 10.3% of Caucasians and 4.8% of non-Caucasians had accurate 10-allele high-resolution output. Eplet analysis revealed additional, inaccurate eplets in 37% of individuals, with 22.5% showing at least 2 additional, inaccurate eplets; incorrect eplets were more common among non-Caucasians (OR, 1.8; 95% CI, 1.1-2.9; P = .018). Given this high error rate, caution should be taken before using imputation tools for clinical or research purposes, especially for non-Caucasian individuals.

Common, intermediate and well-documented HLA alleles in world populations: CIWD version 3.0.0

A catalog of common, intermediate and well-documented (CIWD) HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, -DQB1 and -DPB1 alleles has been compiled from over 8 million individuals using data from 20 unrelated hematopoietic stem cell volunteer donor registries. Individuals are divided into seven geographic/ancestral/ethnic groups and data are summarized for each group and for the total population. P (two-field) and G group assignments are divided into one of four frequency categories: common (≥1 in 10 000), intermediate (≥1 in 100 000), well-documented (≥5 occurrences) or not-CIWD. Overall 26% of alleles in IPD-IMGT/HLA version 3.31.0 at P group resolution fall into the three CIWD categories. The two-field catalog includes 18% (n = 545) common, 17% (n = 513) intermediate, and 65% (n = 1997) well-documented alleles. Full-field allele frequency data are provided but are limited in value by the variations in resolution used by the registries. A recommended CIWD list is based on the most frequent category in the total or any of the seven geographic/ancestral/ethnic groups. Data are also provided so users can compile a catalog specific to the population groups that they serve. Comparisons are made to three previous CWD reports representing more limited population groups. This catalog, CIWD version 3.0.0, is a step closer to the collection of global HLA frequencies and to a clearer view of HLA diversity in the human population as a whole.

Naming HLA diversity: A review of HLA nomenclature

The development of a standardized HLA nomenclature has been critical in our understanding of the HLA system and in facilitating the clinical applications of HLA. The Nomenclature Committee for Factors of the HLA System, established in 1968, has overseen the development and usage of nomenclature based on serologic specificities, cellular responses, and DNA sequences. Their decisions have been guided by community consensus reached through 17 international workshops beginning in 1964 and continuing today. Two websites provide a curated database of the sequences of over 26,000 HLA alleles and a reference site for the current nomenclature. This review covers the major steps in the development of the HLA nomenclature as well as the efforts of other groups to extend its usefulness for research and clinical applications.

A blueprint for electronic utilization of ambiguous molecular HLA typing data in organ allocation systems and virtual crossmatch

Virtual crossmatch (VXM) compares a transplant candidate's unacceptable antigens to the HLA typing of the donor before an organ offer is accepted and, in selected cases, supplant a prospective physical crossmatch. However, deceased donor typing can be ambiguous, leading to uncertainty in compatibility prediction. We have developed a prototype web application that utilizes ambiguous HLA molecular typing data to predict which unacceptable antigens are present in the donor HLA genotype as donor-specific antibodies (DSA). The application compares a candidate's listed unacceptable antigens to computed probabilities of all possible two-field donor HLA alleles and UNOS antigens. The VIrtual CrossmaTch for mOleculaR HLA typing (VICTOR) tool can be accessed at http://www.transplanttoolbox.org/victor. We reanalyzed historical VXM cases where a transplant center's manual interpretation of molecular typing results influenced offer evaluation. We found that interpretation of ambiguous donor molecular typing data using imputation could one day influence VXM decisions if the DSA predictions were rigorously validated. Standardized interpretation of molecular typing data, if applied to the match run, could also change which offers are made. HLA typing ambiguity has been an underappreciated source of immunological risk in organ transplantation. The VICTOR tool can serve as a testbed for development of allocation policies with the aim of decreasing offers refused due to HLA incompatibility.

Epitopes as characterized by antibody-verified eplet mismatches determine risk of kidney transplant loss

To optimize strategies that mitigate the risk of graft loss associated with HLA incompatibility, we evaluated whether sequence defined HLA targets (eplets) that result in donor-specific antibodies are associated with transplant outcomes. To define this, we fit multivariable Cox proportional hazard models in a cohort of 118 382 United States first kidney transplant recipients to assess risk of death-censored graft failure by increments of ten antibody-verified eplet mismatches. To verify robustness of our findings, we conducted sensitivity analysis in this United States cohort and assessed the role of antibody-verified eplet mismatches as autonomous predictors of transplant glomerulopathy in an independent Canadian cohort. Antibody-verified eplet mismatches were found to be independent predictors of death-censored graft failure with hazard ratios of 1.231 [95% confidence interval 1.195, 1. 268], 1.268 [1.231, 1.305] and 1.411 [1.331, 1.495] for Class I (HLA-A, B, and C), -DRB1 and -DQB1 loci, respectively. To address linkage disequilibrium between HLA-DRB1 and -DQB1, we fit models in a subcohort without HLA-DQB1 eplet mismatches and found hazard ratios for death-censored graft failure of 1.384 [1.293, 1.480] for each additional antibody-verified HLA-DRB1 eplet mismatch. In a subcohort without HLA-DRB1 mismatches, the hazard ratio was 1.384 [1.072, 1.791] for each additional HLA-DQB1 mismatch. In the Canadian cohort, antibody-verified eplet mismatches were independent predictors of transplant glomerulopathy with hazard ratios of 5.511 [1.442, 21.080] for HLA-DRB1 and 3.640 [1.574, 8.416] for -DRB1/3/4/5. Thus, donor-recipient matching for specific HLA eplets appears to be a feasible and clinically justifiable strategy to mitigate risk of graft loss.

Comparison of sequence-specific oligonucleotide probe vs next generation sequencing for HLA-A, B, C, DRB1, DRB3/B4/B5, DQA1, DQB1, DPA1, and DPB1 typing: Toward single-pass high-resolution HLA typing in support of solid organ and hematopoietic cell transplant programs

Many clinical laboratories supporting solid organ transplant programs use multiple HLA genotyping technologies, depending on individual laboratory needs. Sequence‐specific primers and quantitative polymerase chain reaction (qPCR) serve the rapid turnaround necessary for deceased donor workup, while sequence‐specific oligonucleotide probe (SSOP) technology is widely employed for higher volumes. When clinical need mandates high‐resolution data, Sanger sequencing‐based typing (SBT) has been the “gold standard.” However, all those methods commonly yield ambiguous typing results that utilize valuable laboratory resources when resolution is required. In solid organ transplantation, high‐resolution typing may provide critical information for highly sensitized patients with donor‐specific anti‐HLA antibodies (DSA), particularly when DSA involve HLA alleles not discriminated by SSOP typing. Arguments against routine use of SBT include assay complexity, long turnaround times (TAT), and increased costs. Here, we compare a next generation sequencing (NGS) technology with SSOP for accuracy, effort, turnaround time, and level of resolution for genotyping of 11 HLA loci among 289 specimens from five clinical laboratories. Results were concordant except for SSOP misassignments in eight specimens and 21 novel sequences uniquely identified by NGS. With few exceptions, SSOP generated ambiguous results while NGS provided unambiguous three‐field allele assignments. For complete HLA genotyping of up to 24 samples by either SSOP or NGS, bench work was completed on day 1 and typing results were available on day 2. This study provides compelling evidence that, although not viable for STAT typing of deceased donors, a single‐pass NGS HLA typing method has direct application for solid organ transplantation.