Biology subjects

Gragert, L.

Publications and source records attributed to Gragert, L..

8 recordsLinked to original sources

bioRxiv2026

Classical HLA Allele and Haplotype Frequency Estimates in US Populations

The human leukocyte antigen (HLA) system is the primary determinant of donor selection in allogeneic hematopoietic cell transplantation (HCT) and plays a central role in solid organ transplantation, immune-mediated disease studies, evolutionary population genetics, and immunotherapy. Large-scale sampling of registry participants reflecting major US ancestry groups allows for characterization of the complex landscape of HLA haplotype diversity for the classical HLA class I (HLA-A, HLA-B, HLA-C) and HLA class II (HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) genes. Here we present nine-locus classical HLA allele and haplotype frequency estimates for five broad (Black, White, Asian or Pacific Islander, Hispanic and Native American) and 21 detailed US populations based on 9,671,082 donors with targeted genotyping by DNA-based methods. Frequency estimation used an expectation-maximization (EM) framework specifically adapted to handle mixed-resolution and ambiguous HLA genotyping data. Advancements in next-generation sequencing provide extensive HLA genotyping, offering new insights into the haplotype structure and diversity of the human MHC complex, expanding knowledge especially for HLA class II haplotypes. Population analyses reveal that the most common high-resolution haplotypes are predominantly population-specific, with only three haplotypes shared across the top-100 lists of all five broad population groups, and that Black populations exhibit the greatest nine-locus haplotypic diversity, a pattern that persists after controlling for differences in registry sample size. These frequencies, derived from the largest US cohort to date, support clinical decision-making and research in histocompatibility, immunogenetics, and transplantation and are publicly available at https://zenodo.org/records/17966993.

genetics↗

bioRxiv2026

GRIMM-II: A Two-Stage Real-Time Algorithm for Nine-Locus HLA Imputation and Matching with Up to Three Mismatches

BackgroundThe success of hematopoietic stem cell transplantation (HSCT) depends critically on human leukocyte antigen (HLA) matching between donor and recipient. While traditional matching focuses on five classical HLA loci (A, B, C, DRB1, DQB1), clinical practice increasingly considers extended typing at nine loci, including DPA1, DQA1, DPB1, and DRB3/4/5. Furthermore, emerging evidence supports transplantation with up to three HLA mismatches under post-transplant cyclophosphamide (PTCy) regimens. However, current donor search algorithms cannot efficiently identify donors with multiple mismatches across extended HLA loci in real-time. MethodsWe developed GRIMM-II (GRaph IMputation and Matching, version II), which comprises two novel algorithms: ML-GRIM (Multi-Locus GRIM) for HLA imputation across multiple loci, and ML-GRMA (Multi-Locus GRMA) for real-time donor-patient matching with up to three mismatches. Both algorithms employ a two-stage approach that combines efficient candidate reduction through graph-theoretic frameworks with detailed genotype comparison. ML-GRIM partitions genotypes into class I (HLA-A, B, C) and class II (remaining loci) components, enabling memory-efficient storage and rapid candidate identification. ML-GRMA searches a pre-imputed donor graph composed of donor genotypes and their sub-components, then computes asymmetric graft-versus-host (GvH) and host-versus-graft (HvG) mismatch probabilities to provide clinically relevant compatibility assessments. Both imputation and matching tools are available as a web application at https://grimmard.math.biu.ac.il/ and through GitHub repositories at https://github.com/nmdp-bioinformatics/py-graph-imputation (imputation) and https://github.com/nmdp-bioinformatics/py-graph-match (matching). ResultsWe validated ML-GRMA and ML-GRIM using the WMDA3 (World Marrow Donor Association) validation dataset, successfully reproducing all previously reported matches while identifying numerous additional candidate donors not detected by previous algorithms. Further validation of ML-GRMA using 3,000 patients with artificially introduced mismatches (0-3 allele substitutions) demonstrated 100% sensitivity and specificity in identifying matching donors at expected mismatch levels. We validated ML-GRIM using simulated nine-locus typings derived from 8,078,224 US donors in the NMDP registry. The algorithm successfully imputed genotypes across variable numbers of typed loci while incorporating multiethnic haplotype frequencies. The algorithm achieved real-time performance with typical imputation times under one second and matching times of 1-13 seconds per patient for up to three mismatches, even when searching databases exceeding 8 million donors. Notably, ML-GRMA identified substantially more potentially suitable donors than traditional algorithms by accounting for the biological reality that GvH and HvG mismatches often differ, particularly for donors homozygous at specific loci. To evaluate ML-GRIM performance with low-resolution typing, we tested it on simulated 3-locus typings from the same population. The resulting imputation accuracy correlated with the mutual information between typed loci and complete genotypes. ConclusionsGRIMM-II provides a scalable, memory-efficient solution for nine-locus HLA imputation and real-time identification of donors with up to three mismatches. The graph-based framework supports dynamic registry updates and can readily accommodate additional HLA loci and matching criteria as clinical knowledge evolves. By expanding the pool of acceptable donors while maintaining computational efficiency, GRIMM-II addresses a critical need in contemporary transplantation practice, particularly for patients from underrepresented ethnic minorities who face lower probabilities of finding perfectly matched donors.

bioinformatics↗

bioRxiv2023

Integrative Multi-omic Profiling of Two Human Decedents Receiving Pig Heart Xenografts Reveals Strong Perturbations in Early Immune-Cell and Cellular Metabolism Responses.

BackgroundRecent advances in xenotransplantation in living and decedent humans using pig xenografts have laid promising groundwork towards future emergency use and first in human trials. Major obstacles remain though, including a lack of knowledge of the genetic incompatibilities between pig donors and human recipients which may led to harmful immune responses against the xenograft or dysregulation of normal physiology. In 2022 two pig heart xenografts were transplanted into two brain-dead human decedents with a minimized immunosuppression regime, primarily to evaluate onset of hyper-acute antibody mediated rejection and sustained xenograft function over 3 days. MethodsWe performed multi-omic profiling to assess the dynamic interactions between the pig and human genomes in the first two pig heart-xenografts transplants into human decedents. To assess global and specific biological changes that may correlate with immune-related outcomes and xenograft function, we generated transcriptomic, lipidomic, proteomic and metabolomics datasets, across blood and tissue samples collected every 6 hours over the 3-day procedures. ResultsSingle-cell datasets in the 3-day pig xenograft-decedent models show dynamic immune activation processes. We observe specific scRNA-seq, snRNA-seq and geospatial transcriptomic changes of early immune-activation leading to pronounced downstream T-cell activity and hallmarks of early antibody mediated rejection (AbMR) and/or ischemia reperfusion injury (IRI) in the first xenograft recipient. Using longitudinal multiomic integrative analyses from blood in addition to antigen presentation pathway enrichment, we also observe in the first xeno-heart recipient significant cellular metabolism and liver damage pathway changes that correlate with profound physiological dysfunction whereas, these signals are not present in the other xenograft recipient. ConclusionsSingle-cell and multiomics approaches reveal fundamental insights into early molecular immune responses indicative of IRI and/or early AbMR in the first human decedent, which was not evident in the conventional histological evaluations.

systems biology↗

bioRxiv2022

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome by accurate and comprehensive HLA-DQ antigen presentation prediction

HLA class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the - and {beta}-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific MS-immunopeptidomics data. The analysis demonstrated a highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants revealed a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study has furthered our understanding of HLA-DQ specificities and has for the first time cast light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2.

bioinformatics↗

bioRxiv2020

Influence of HLA class II polymorphism on predicted cellular immunity against SARS-CoV-2 at the population and individual level

Development of effective adaptive immune responses after coronavirus disease 2019 (COVID-19) and after vaccination against SARS-CoV-2 is predicated on recognition of viral peptides, presented in the context of HLA class II molecules, by CD4+ T-cells. We capitalised on extensive high resolution HLA data deposited in the National Marrow Donor Program registry to obtain detailed information on human HLA haplotype frequencies of twenty five human populations and used a bioinformatics approach to investigate the role of HLA polymorphism on SARS-CoV-2 immunogenicity at the population and at the individual level. Within populations, we identify wide inter-individual variability in predicted CD4+ T-cell reactivity against structural, non-structural and accessory SARS-CoV-2 proteins, according to expressed HLA genotype. However, we find similar potential for anti-SARS-CoV-2 cellular immunity at the population level, across all ethnic groups examined, suggesting that HLA polymorphism is unlikely to account for observed disparities in clinical outcomes after COVID-19 among different race and ethnic groups. We predict robust immune reactivity against SARS-CoV-2 Spike protein, the basis for the majority of current vaccination efforts, both at the population and individual level, despite significant variation in Spike-derived peptide presentation by individual HLA genotypes. Finally, we provide comprehensive maps of SARS-CoV-2 proteome immunogenicity accounting for population coverage in major ethnic groups. Our findings provide important insight on the potential role of HLA polymorphism on development of protective immunity after SARS-CoV-2 infection and after vaccination and a firm basis for further experimental studies in this field.

immunology↗

bioRxiv2019

A precision virtual crossmatch decision support system for interpretation of ambiguous molecular HLA typing data

Virtual crossmatch (VXM) compares a transplant candidates 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 web application that utilizes ambiguous HLA molecular typing data to assist in VXM assessments. The application compares a candidates 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 centers manual interpretation of molecular typing results influenced offer evaluation. We found that VICTORs automated interpretation of ambiguous donor molecular typing data would influence VXM decisions. 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.

immunology↗

bioRxiv2018

Mapping molecular HLA typing data to UNOS antigen equivalents for improved virtual crossmatch

BackgroundVirtual crossmatch utilizes HLA typing and antibody screen assay data as a part of organ offers in deceased donor allocation systems. Histocompatibility labs must convert molecular HLA typings to antigen equivalencies for entry into the United Network for Organ Sharing (UNOS) UNet system. While an Organ Procurement and Transplantation Network (OPTN) policy document provides general guidelines for conversion, the process is complex because no antigen mapping table is available. We present a UNOS antigen equivalency table for all IMGT/HLA alleles at the A, B, C, DRB1, DRB3/4/5, DQA1, and DQB1 loci.\n\nMethodsAn automated script was developed to generate a UNOS antigen equivalency table. Data sources used in the conversion algorithm included the World Marrow Donor Association(WMDA) antigen table, the HLA Dictionary, and UNOS-provided tables. To validate antigen mappings, we converted National Marrow Donor Program (NMDP) high resolution allele frequencies to antigen equivalents and compared with the UNOS Calculated Panel Reactive Antibodies (CPRA) reference panel.\n\nResultsNormalized frequency similarity scores between independent NMDP and UNOS panels for 4 US population categories (Caucasian, Hispanic, African American and Asian/Pacific Islander) ranged from 0.85 to 0.97, indicating correct antigen mapping. An open source web application (ALLele to ANtigen (\"ALLAN\")) and web services were also developed to map unambiguous and ambiguous HLA typing data to UNOS antigen equivalents based on NMDP population-specific allele frequencies (http://www.transplanttoolbox.org).\n\nConclusionsThis tool sets a foundation for using molecular HLA typing to compute the virtual crossmatch and may aid in reducing typing discrepancies in UNet.

bioinformatics↗

bioRxiv2018

GRIMM: GRaph IMputation and Matching for HLA Genotypes

Motivation: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA allele sequences, therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time.\n\nResults: We present here novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, our algorithm runtime grows slowly with registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm crossvalidation dataset.\n\nAvailability: The Python, Perl and Neo4jJcode is available at https://git.com/nmdp-bioinformatics/grimm\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗