Characterisation of the novel HLA-B*07:02:01:110 allele by next-generation sequencing.

HLA-B*07:02:01:110 differs from the HLA-B*07:02:01:01 allele by two nucleotide substitutions in the 3'UTR.

Characterisation of the novel HLA-DQA1*04:14N allele by next-generation sequencing.

HLA-DQA1*04:14N differs from HLA-DQA1*04:01:01:03 by a single nucleotide deletion in codon 113 in exon 3.

Characterisation of the novel HLA-A*32:01:01:41 allele by next-generation sequencing.

HLA-A*32:01:01:41 differs from the HLA-A*32:01:01:01 allele by one nucleotide substitution in the intron 3.

HLA-DPB1 and DPA1 ~ DPB1 linkage mismatch affects the survival of recipients receiving HLA-14/14 matched unrelated donor HSCT.

To analyse the effect of HLA-DPA1 and HLA-DPB1 allelic mismatches on the outcomes of unrelated donor haematopoietic stem cell transplantation (URD-HSCT), we collected 258 recipients with haematological disease who underwent HLA-10/10 matched URD-HSCT. HLA-A, -B, -C, -DRB1, -DQB1, -DRB3/4/5, -DQA1, -DPA1 and -DPB1 typing was performed for the donors and recipients using next-generation sequencing (NGS) technology. After excluding 8 cases with DQA1 or DRB3/4/5 mismatches, we included 250 cases with HLA-14/14 matching for further analysis. Our results showed that the proportion of matched DPA1 and DPB1 alleles was only 10.4% (26/250). The remaining 89.6% of donors and recipients demonstrated DPA1 or DPB1 mismatch. In the DPA1 matched and DPB1 mismatched group, accounting for 18.8% (47/250) of the cohort, DPB1*02:01/DPB1*03:01 allelic mismatches were associated with decreased 2-year OS and increased NRM. DPB1*02:02/DPB1*05:01 and DPB1*02:01/DPB1*05:01 mismatches showed no impact on outcomes. Moreover, the specific allelic mismatches observed were consistent with the DPB1 T-cell epitope (TCE) classification as permissive and non-permissive. We innovatively established an analysis method for DPA1 ~ DPB1 linkage mismatch for cases with both DPA1 and DPB1 mismatched, accounting for 70% (175/250) of the total. DPA1*02:02 ~ DPB1*05:01/DPA1*02:01 ~ DPB1*17:01 linkage mismatches were associated with lower 2-year OS, especially among AML/MDS recipients. DPA1*02:02 ~ DPB1*05:01/DPA1*01:03 ~ DPB1*02:01 linkage mismatches showed no impact on outcomes. In conclusion, applying the DPA1 ~ DPB1 linkage mismatch analysis approach can identify different types of mismatches affecting transplant outcomes and provide valuable insight for selecting optimal donors for AML/MDS and ALL recipients.

Identification of the novel HLA-DPA1*01:195 allele by next-generation sequencing.

The novel HLA-DPA1*01:195 allele differs from HLA-DPA1*01:03:01:01 by one nucleotide substitution in exon 4.

Detection of the HLA-C*08:66 allele in a Taiwanese individual.

Two nucleotide substitutions in codon 152 of HLA-C*08:01:01:01 result in a novel allele HLA-C*08:66.

Detection of the novel HLA-DRB5*01:01:01:07 allele by Pacific Biosciences HiFi sequencing in a Chinese individual.

HLA-DRB5*01:01:01:07 differs from HLA-DRB5*01:01:01:01 by two nucleotide changes in intron 1 and intron 2.

Benchmarking NGS-Based HLA Typing Algorithms.

Knowledge of the expected accuracy of HLA typing algorithms is important when choosing between algorithms and when evaluating the HLA typing predictions of an algorithm. This chapter guides the reader through an example benchmarking study that evaluates the performances of four NGS-based HLA typing algorithms as well as outlining factors to consider, when designing and running such a benchmarking study. The code related to this benchmarking workflow can be found at https://github.com/nikolasthuesen/springers-hla-benchmark/ .

AmpliSAS and AmpliHLA: Web Server and Local Tools for MHC Typing of Non-model Species and Human Using NGS Data.

AmpliSAS and AmpliHLA are tools for automatic genotyping of MHC genes from high-throughput sequencing data. AmpliSAS is designed specifically to analyze amplicon sequencing data from non-model species and it is able to perform de novo genotyping without any previous knowledge of the reference alleles. AmpliHLA is a human specific version; it performs HLA typing by comparing sequenced variants against human reference alleles from the IMGT/HLA database. Both tools are available in AmpliSAT web-server as well as scripts for local/server installation. Here we describe the installation and deployment of AmpliSAS and AmpliHLA Perl scripts and dependencies on a local or a server computer. We will show how to run them in the command line using as examples four genotyping protocols: the first two use amplicon sequencing data to genotype the MHC genes of a passerine bird and human respectively; the third and fourth present the HLA typing of a human cell line starting from RNA and exome sequencing data respectively.

Comprehensive HLA Typing from a Current Allele Database Using Next-Generation Sequencing Data.

HLA allele information is essential for a variety of medical applications, such as genomic studies of multifactorial diseases, including immune system and inflammation-related disorders, and donor selection in organ transplantation and regenerative medicine. To obtain this information, an accurate HLA typing method that is applicable for any allele registered in HLA allele databases is needed. Here we describe a method-called HLA-HD-for determining alleles from a current HLA database using next-generation sequencing (NGS) results.

NanoHLA: A Method for Human Leukocyte Antigen Class I Genes Typing Without Error Correction Based on Nanopore Sequencing Data.

Human leukocyte antigen (HLA) typing is of great importance in clinical applications such as organ transplantation, blood transfusion, disease diagnosis and treatment, and forensic analysis. In recent years, nanopore sequencing technology has emerged as a rapid and cost-effective option for HLA typing. However, due to the principles and data characteristics of nanopore sequencing, there was a scarcity of robust and generalizable bioinformatics tools for its downstream analysis, posing a significant challenge in deciphering the thousands of HLA alleles present in the human population. To address this challenge, we developed NanoHLA as a tool for high-resolution typing of HLA class I genes without error correction based on nanopore sequencing. The method integrated the concepts of HLA type coverage analysis and the data conversion techniques employed in Nano2NGS, which was characterized by applying nanopore sequencing data to NGS-liked data analysis pipelines. In validation with public nanopore sequencing datasets, NanoHLA showed an overall concordance rate of 84.34% for HLA-A, HLA-B, and HLA-C, and demonstrated superior performance in comparison to existing tools such as HLA-LA. NanoHLA provides tools and solutions for use in HLA typing related fields, and look forward to further expanding the application of nanopore sequencing technology in both research and clinical settings. The code is available at https://github.com/langjidong/NanoHLA .

Imputation-Based HLA Typing with GWAS SNPs.

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the haplotype structure within the major histocompatibility complex (MHC) region. These methods predict HLA classical alleles using dense SNP genotypes, commonly found on array-based platforms used in genome-wide association studies (GWAS). The analysis of HLA classical alleles can be conducted on current SNP datasets at no additional cost. Here, we describe the workflow of HIBAG, an imputation method with attribute bagging, to infer a sample's HLA classical alleles using SNP data. Two examples are offered to demonstrate the functionality using public HLA and SNP data from the latest release of the 1000 Genomes project: genotype imputation using pre-built classifiers in a GWAS, and model training to create a new prediction model. The GPU implementation facilitates model building, making it hundreds of times faster compared to the single-threaded implementation.

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.

HLA Typing and Mutation Calling from Normal and Tumor Whole Genome Sequencing Data with ALPHLARD-NT.

HLA somatic mutations can alter the expression and function of HLA molecules, which in turn affect the ability of the immune system to recognize and respond to cancer cells. Therefore, it is crucial to accurately identify HLA somatic mutations to enhance our understanding of the interaction between cancer and the immune system and improve cancer treatment strategies. ALPHLARD-NT is a reliable tool that can accurately identify HLA somatic mutations as well as HLA genotypes from whole genome sequencing data of paired normal and tumor samples. Here, we provide a comprehensive guide on how to use ALPHLARD-NT and interpret the results.

Full-Length Characterization of Novel HLA-DRB1 Alleles for Reference Database Submission.

The prerequisite for successful HLA genotyping is the integrity of the large allele reference database IPD-IMGT/HLA. Consequently, it is in the laboratories' best interest that the data quality of submitted novel sequences is high. However, due to its long and variable length, the gene HLA-DRB1 presents the biggest challenge and as of today only 16% of the HLA-DRB1 alleles in the database are characterized in full length. To improve this situation, we developed a protocol for long-range PCR amplification of targeted HLA-DRB1 alleles. By subsequently combining both long-read and short-read sequencing technologies, our protocol ensures phased and error-corrected sequences of reference grade quality. This dual redundant reference sequencing (DR2S) approach is of particular importance for correctly resolving the challenging repeat regions of DRB1 intron 1. Until today, we used this protocol to characterize and submit 384 full-length HLA-DRB1 sequences to IPD-IMGT/HLA.

PIRCHE-II Risk and Acceptable Mismatch Profile Analysis in Solid Organ Transplantation.

To optimize outcomes in solid organ transplantation, the HLA genes are regularly compared and matched between the donor and recipient. However, in many cases a transplant cannot be fully matched, due to widespread variation across populations and the hyperpolymorphism of HLA alleles. Mismatches of the HLA molecules in transplanted tissue can be recognized by immune cells of the recipient, leading to immune response and possibly organ rejection. These adverse outcomes are reduced by analysis using epitope-focused models that consider the immune relevance of the mismatched HLA.PIRCHE, an acronym for Predicted Indirectly ReCognizable HLA Epitopes, aims to categorize and quantify HLA mismatches in a patient-donor pair by predicting HLA-derived T cell epitopes. Specifically, the algorithm predicts and counts the HLA-derived peptides that can be presented by the host HLA, known as indirectly-presented T cell epitopes. Looking at the immune-relevant epitopes within HLA allows a more biologically relevant understanding of immune response, and provides an expanded donor pool for a more refined matching strategy compared with allele-level matching. This PIRCHE algorithm is available for analysis of single transplantations, as well as bulk analysis for population studies and statistical analysis for comparison of probability of organ availability and risk profiles.

Characterisation of the novel HLA-B*18:243 allele using short- and long-read sequencing technologies.

The novel HLA-B*18:243 allele, first described in a potential bone marrow donor from Brazil.

A personalised delisting strategy enables successful kidney transplantation in highly sensitised patients with preformed donor-specific anti HLA antibodies.

This study investigates kidney transplant outcomes in highly sensitised patients after implementing a delisting strategy aimed at enabling transplantation despite preformed donor-specific antibodies (preDSA), with the goal of reducing acute antibody-mediated rejection (aAMR) risk. Fifty-three sensitised recipients underwent kidney transplant after delisting prohibited HLA antigens, focusing initially in low MFI antibodies (<5000), except for anti-HLA-DQ. If insufficient, higher MFI antibodies were permitted, especially for those without an immunogenic eplet pattern assigned. Delisting of Complement-fixing antibodies (C1q+) was consistently avoided. Comparison cohorts included 53 sensitised recipients without DSA (SwoDSA) and 53 non-sensitised (NS). The average waiting time prior to delisting was 4.4 ± 1.8 years, with a reduction in cPRA from 99.7 ± 0.5 to 98.1 ± 0.7, followed by transplantation within 7.2 ± 8.0 months (analysed in 34 patients). Rejection rates were similar among preDSA, SwoDSA, and NS groups (16%, 8%, and 11%, respectively; p = 0.46). However, aAMR was higher in the preDSA group (12%, 4%, and 2%, respectively; p = 0.073), only presented in recipients with DSA of MFI >5000. The highest MFI DSA were against HLA-DP (Median: 10796 MFI), with 50% of preDSA aAMR cases due to anti-DP antibodies (n = 3). Graft survival rates at 1 and 5 years in preDSA group were 94%, and 67%, comparable to SwoDSA (94%, and 70%; p = 0.69), being significantly higher in the NS group (p = 0.002). The five-year recipient survival rate was 89%, comparable to SwoDSA and NS groups (p = 0.79). A delisting strategy enables safe kidney transplant in highly sensitised patients with preDSA, with a slight increase in aAMR and comparable graft and patient survivals to non-DSA cohorts.

Detection of the HLA-A*11:127N allele in a Taiwanese individual.

A nucleotide mutation in codon 171 of HLA-A*11:01:01:01 results in a novel allele HLA-A*11:127N.

Characterisation of novel MICB alleles by next generation sequencing: MICB*055 and MICB*005:15.

Two novel MICB alleles with coding polymorphisms in exon 3 were detected by next generation sequencing.