Unique Molecular Identifier-Based High-Resolution HLA Typing and Transcript Quantitation Using Long-Read Sequencing

Benchmarking long-read sequencing strategies for obtaining ASV-resolved rRNA operons from environmental microeukaryotes

The use of short-read metabarcoding for classifying microeukaryotes is challenged by the lack of comprehensive 18S rRNA reference databases. While recent advances in high-throughput long-read sequencing provide the potential to greatly increase the phylogenetic coverage of these databases, the performance of different sequencing technologies and subsequent bioinformatics processing remain to be evaluated, primarily because of the absence of well-defined eukaryotic mock communities. To address this challenge, we created a eukaryotic rRNA operon clone-library and turned it into a precisely defined synthetic eukaryotic mock community. This mock community was then used to evaluate the performance of three long-read sequencing strategies (PacBio circular consensus sequencing and two Nanopore approaches using unique molecular identifiers) and three tools for resolving amplicons sequence variants (ASVs) (USEARCH, VSEARCH, and DADA2). We investigated the sensitivity of the sequencing techniques based on the number of detected mock taxa, and the accuracy of the different ASV-calling tools with a specific focus on the presence of chimera among the final rRNA operon ASVs. Based on our findings, we provide recommendations and best practice protocols for how to cost-effectively obtain essentially error-free rRNA operons in high-throughput. An agricultural soil sample was used to demonstrate that the sequencing and bioinformatic results from the mock community also translates to highly diverse natural samples, which enables us to identify previously undescribed microeukaryotic lineages.

Closing the gap: Solving complex medically relevant genes at scale

Comprehending the mechanism behind human diseases with an established heritable component represents the forefront of personalized medicine. Nevertheless, numerous medically important genes are inaccurately represented in short-read sequencing data analysis due to their complexity and repetitiveness or the so-called 'dark regions' of the human genome. The advent of PacBio as a long-read platform has provided new insights, yet HiFi whole-genome sequencing (WGS) cost remains frequently prohibitive. We introduce a targeted sequencing and analysis framework, Twist Alliance Dark Genes Panel (TADGP), designed to offer phased variants across 389 medically important yet complex autosomal genes. We highlight TADGP accuracy across eleven control samples and compare it to WGS. This demonstrates that TADGP achieves variant calling accuracy comparable to HiFi-WGS data, but at a fraction of the cost. Thus, enabling scalability and broad applicability for studying rare diseases or complementing previously sequenced samples to gain insights into these complex genes. TADGP revealed several candidate variants across all cases and provided insight into LPA diversity when tested on samples from rare disease and cardiovascular disease cohorts. In both cohorts, we identified novel variants affecting individual disease-associated genes (e.g., IKZF1, KCNE1). Nevertheless, the annotation of the variants across these 389 medically important genes remains challenging due to their underrepresentation in ClinVar and gnomAD. Consequently, we also offer an annotation resource to enhance the evaluation and prioritization of these variants. Overall, we can demonstrate that TADGP offers a cost-efficient and scalable approach to routinely assess the dark regions of the human genome with clinical relevance.

Nucleotide alterations in the HLA-C class I gene can cause aberrant splicing and marked changes in RNA levels in a polymorphic context-dependent manner

Polymorphisms of HLA genes, which play a crucial role in presenting peptides with diverse sequences in their peptide-binding pockets, are also thought to affect HLA gene expression, as many studies have reported associations between HLA gene polymorphisms and their expression levels. In this study, we devised an ectopic expression assay for the HLA class I genes in the context of the entire gene, and used the assay to show that the HLA-C*03:03:01 and C*04:01:01 polymorphic differences observed in association studies indeed cause different levels of RNA expression. Subsequently, we investigated the C*03:23N null allele, which was previously noted for its reduced expression, attributed to an alternate exon 3 3' splice site generated by G/A polymorphism at position 781 within the exon 3. We conducted a thorough analysis of the splicing patterns of C*03:23N, and revealed multiple aberrant splicing, including the exon 3 alternative splicing, which overshadowed its canonical counterpart. After confirming a significant reduction in RNA levels caused by the G781A alteration in our ectopic assay, we probed the function of the G-rich sequence preceding the canonical exon 3 3' splice site. Substituting the G-rich sequence with a typical pyrimidine-rich 3' splice site sequence on C*03:23N resulted in a marked elevation in RNA levels, likely due to the enhanced preference for the canonical exon 3 3' splice site over the alternate site. However, the same substitution led to a reduction in RNA levels for C*03:03:01. These findings suggested the dual roles of the G-rich sequence in RNA expression, and furthermore, underscore the importance of studying polymorphism effects within the framework of the entire gene, extending beyond conventional mini-gene reporter assays.

Utilizing proficiency testing survey data to create advanced educational content: the virtual crossmatch challenge model

Proficiency testing (PT) surveys include data from laboratories across the world and are ideal for creating advanced educational content, beyond just consensus grading. Educational challenges provide a unique opportunity to probe common laboratory practices and risk assessment, especially in cases where there is no "analyte" tested. Human leukocyte antigen (HLA) compatibility evaluation between donor and recipient pairs has been traditionally assessed using T-cell and B-cell physical crossmatches. However, advancements in our ability to identify and characterize HLA antibodies using solid phase assays, in combination with changing deceased donor allocation schemes and improved HLA typing, have shifted the paradigm from performing physical crossmatches to the use of the virtual crossmatch (VXM). VXM is a compatibility assessment relying on the interpretation of pre-transplant HLA laboratory data and as such, it is not an "analyte". However, VXM results are used in clinical decision-making. The VXM assessment depends on patient characteristics as well as laboratory and transplant center practices but must ensure safe transplantation outcomes while maintaining equity in access to transplantation. In this manuscript, we describe the American Society for Histocompatibility and Immunogenetics (ASHI) PT Educational VXM Challenge, as a model for creating educational content using PT survey data. We discuss the different components of the VXM Challenge and highlight major findings and learning points acquired from ASHI VXM Challenges performed between 2018-2022, such as the lack of correlation between the VXM and the physical crossmatch in the presence of low level donor-specific antibodies (DSA), or when the DSA were aimed against donor alleles that are not present on the antibody panel, and in the presence of an antibody to a shared eplet. Finally, we show that the VXM Educational Challenge serves as a valuable tool to highlight the strengths and pitfalls of the VXM assessment and reveals differences in testing and result interpretation among participating HLA laboratories.

Allele-specific quantification of human leukocyte antigen transcript isoforms by nanopore sequencing

Introduction

While tens of thousands of HLA alleles have been identified by DNA sequencing, the contribution of alternative splicing to HLA diversity is not well characterized. In this study, we sought to determine if long-read sequencing could be used to accurately quantify allele-specific HLA transcripts in primary human lymphocytes.

Methods

cDNA libraries were prepared from peripheral blood lymphocytes from 12 donors and sequenced by nanopore long-read sequencing. HLA reads were aligned to donor-specific reference sequences based on the known type of each donor. Allele-specific exon utilization was calculated as the proportion of reads aligning to each allele containing known exons, and transcript isoforms were quantified based on patterns of exon utilization within individual reads.

Results

Splice variants were rare among class I HLA genes (median exon retention rate 99%-100%), except for several HLA-C alleles with exon 5 spliced out of up to 15% of reads. Splice variants were also rare among class II HLA genes (median exon retention rate 98%-100%), except for HLA-DQB1. Consistent with previous work, exon 5 of HLA-DQB1 was spliced out in alleles with a mutated splice acceptor site at rs28688207. Surprisingly, a 28% loss of exon 5 was also observed in HLA-DQB1 alleles with an intact splice acceptor site at rs28688207.

Discussion

We describe a simple bioinformatic workflow to quantify allele-specific expression of HLA transcript isoforms. Further studies are warranted to characterize the repertoire of HLA transcripts expressed in different cell types and tissues across diverse populations.

The application of long-read sequencing in clinical settings

Long-read DNA sequencing technologies have been rapidly evolving in recent years, and their ability to assess large and complex regions of the genome makes them ideal for clinical applications in molecular diagnosis and therapy selection, thereby providing a valuable tool for precision medicine. In the third-generation sequencing duopoly, Oxford Nanopore Technologies and Pacific Biosciences work towards increasing the accuracy, throughput, and portability of long-read sequencing methods while trying to keep costs low. These trades have made long-read sequencing an attractive tool for use in research and clinical settings. This article provides an overview of current clinical applications and limitations of long-read sequencing and explores its potential for point-of-care testing and health care in remote settings.

Applications of advanced technologies for detecting genomic structural variation

Chromosomal structural variation (SV) encompasses a heterogenous class of genetic variants that exerts strong influences on human health and disease. Despite their importance, many structural variants (SVs) have remained poorly characterized at even a basic level, a discrepancy predicated upon the technical limitations of prior genomic assays. However, recent advances in genomic technology can identify and localize SVs accurately, opening new questions regarding SV risk factors and their impacts in humans. Here, we first define and classify human SVs and their generative mechanisms, highlighting characteristics leveraged by various SV assays. We next examine the first-ever gapless assembly of the human genome and the technical process of assembling it, which required third-generation sequencing technologies to resolve structurally complex loci. The new portions of that "telomere-to-telomere" and subsequent pangenome assemblies highlight aspects of SV biology likely to develop in the near-term. We consider the strengths and limitations of the most promising new SV technologies and when they or longstanding approaches are best suited to meeting salient goals in the study of human SV in population-scale genomics research, clinical, and public health contexts. It is a watershed time in our understanding of human SV when new approaches are expected to fundamentally change genomic applications.

A current and future perspective on T cell receptor repertoire profiling

T cell receptors (TCR) play a vital role in the immune system's ability to recognize and respond to foreign antigens, relying on the highly polymorphic rearrangement of TCR genes. The recognition of autologous peptides by adaptive immunity may lead to the development and progression of autoimmune diseases. Understanding the specific TCR involved in this process can provide insights into the autoimmune process. RNA-seq (RNA sequencing) is a valuable tool for studying TCR repertoires by providing a comprehensive and quantitative analysis of the RNA transcripts. With the development of RNA technology, transcriptomic data must provide valuable information to model and predict TCR and antigen interaction and, more importantly, identify or predict neoantigens. This review provides an overview of the application and development of bulk RNA-seq and single-cell (SC) RNA-seq to examine the TCR repertoires. Furthermore, discussed here are bioinformatic tools that can be applied to study the structural biology of peptide/TCR/MHC (major histocompatibility complex) and predict antigenic epitopes using advanced artificial intelligence tools.

Shared graft-versus-leukemia minor histocompatibility antigens in DISCOVeRY-BMT

T-cell responses to minor histocompatibility antigens (mHAs) mediate graft-versus-leukemia (GVL) effects and graft-versus-host disease (GVHD) in allogeneic hematopoietic cell transplantation. Therapies that boost T-cell responses improve allogeneic hematopoietic cell transplant (alloHCT) efficacy but are limited by concurrent increases in the incidence and severity of GVHD. mHAs with expression restricted to hematopoietic tissue (GVL mHAs) are attractive targets for driving GVL without causing GVHD. Prior work to identify mHAs has focused on a small set of mHAs or population-level single-nucleotide polymorphism-association studies. We report the discovery of a large set of novel GVL mHAs based on predicted immunogenicity, tissue expression, and degree of sharing among donor-recipient pairs (DRPs) in the DISCOVeRY-BMT data set of 3231 alloHCT DRPs. The total number of predicted mHAs varied by HLA allele, and the total number and number of each class of mHA significantly differed by recipient genomic ancestry group. From the pool of predicted mHAs, we identified the smallest sets of GVL mHAs needed to cover 100% of DRPs with a given HLA allele. We used mass spectrometry to search for high-population frequency mHAs for 3 common HLA alleles. We validated 24 predicted novel GVL mHAs that are found cumulatively within 98.8%, 60.7%, and 78.9% of DRPs within DISCOVeRY-BMT that express HLA-A∗02:01, HLA-B∗35:01, and HLA-C∗07:02, respectively. We confirmed the immunogenicity of an example novel mHA via T-cell coculture with peptide-pulsed dendritic cells. This work demonstrates that the identification of shared mHAs is a feasible and promising technique for expanding mHA-targeting immunotherapeutics.

HLA/MHC and KIR characterization in humans and non-human primates using Oxford Nanopore Technologies and Pacific Biosciences sequencing platforms

The gene products of the HLA/MHC and KIR multigene families are important modulators of the immune system and are associated with health and disease. Characterization of the genes encoding these receptors has been integrated into different biomedical applications, including transplantation and reproduction biology, immune therapies and in fundamental research into disease susceptibility or resistance. Conventional short-read sequencing strategies have shown their value in high throughput typing, but are insufficient to uncover the entire complexity of the highly polymorphic HLA/MHC and KIR gene systems. The implementation of single-molecule and real-time sequencing platforms, offered by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), revolutionized the fields of genomics and transcriptomics. Using fundamentally distinct principles, these platforms generate long-read data that can unwire the plasticity of the HLA/MHC and KIR genes, including high-resolution characterization of genes, alleles, phased haplotypes, transcription levels and epigenetics modification patterns. These insights might have profound clinical relevance, such as improved matching of donors and patients in clinical transplantation, but could also lift disease association studies to a higher level. Even more, a comprehensive characterization may refine animal models in preclinical studies. In this review, the different HLA/MHC and KIR characterization approaches using PacBio and ONT platforms are described and discussed.

Comparison between qPCR and RNA-seq reveals challenges of quantifying HLA expression

Human leukocyte antigen (HLA) class I and II loci are essential elements of innate and acquired immunity. Their functions include antigen presentation to T cells leading to cellular and humoral immune responses, and modulation of NK cells. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. The exons encoding the peptide-binding groove have been the main focus for determining HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on quantitative PCR (qPCR). Adoption of alternative high-throughput technologies such as RNA-seq has been hampered by technical issues due to the extreme polymorphism at HLA genes. Recently, however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. This opens an exciting opportunity to quantify HLA expression in large datasets but also brings questions on whether RNA-seq results are comparable to those by qPCR. In this study, we analyze three classes of expression data for HLA class I genes for a matched set of individuals: (a) RNA-seq, (b) qPCR, and (c) cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C (0.2 ≤ rho ≤ 0.53). We discuss technical and biological factors which need to be accounted for when comparing quantifications for different molecular phenotypes or using different techniques.

HLA allele-specific expression: Methods, disease associations, and relevance in hematopoietic stem cell transplantation

Varying HLA allele-specific expression levels are associated with human diseases, such as graft versus host disease (GvHD) in hematopoietic stem cell transplantation (HSCT), cytotoxic T cell response and viral load in HIV infection, and the risk of Crohn’s disease. Only recently, RNA-based next generation sequencing (NGS) methodologies with accompanying bioinformatics tools have emerged to quantify HLA allele-specific expression replacing the quantitative PCR (qPCR) -based methods. These novel NGS approaches enable the systematic analysis of the HLA allele-specific expression changes between individuals and between normal and disease phenotypes. Additionally, analyzing HLA allele-specific expression and allele-specific expression loss provide important information for predicting efficacies of novel immune cell therapies. Here, we review available RNA sequencing-based approaches and computational tools for NGS to quantify HLA allele-specific expression. Moreover, we explore recent studies reporting disease associations with differential HLA expression. Finally, we discuss the role of allele-specific expression in HSCT and how considering the expression quantification in recipient-donor matching could improve the outcome of HSCT.