Relationship between HLA-DPA1 mRNA expression and susceptibility to hepatitis B

Molecular Analysis of HLA Genes in Romanian Patients with Chronic Hepatitis B Virus Infection

Hepatitis B, a persistent inflammatory liver condition, stands as a significant global health issue. In Romania, the prevalence of chronic hepatitis B virus (CHB) infection ranks among the highest in the European Union. The HLA genotype significantly impacts hepatitis B virus infection progression, indicating that certain HLA variants can affect the infection's outcome. The primary goal of the present work is to identify HLA alleles and specific amino acid residues linked to hepatitis B within the Romanian population. The study enrolled 247 patients with chronic hepatitis B; HLA typing was performed using next-generation sequencing. This study's main findings include the identification of certain HLA alleles, such as DQB1*06:03:01, DRB1*13:01:01, DQB1*06:02:01, DQA1*01:03:01, DRB5*01:01:01, and DRB1*15:01:01, which exhibit a significant protective effect against HBV. Additionally, the amino acid residue alanine at DQB1_38 is associated with a protective role, while valine presence may signal an increased risk of hepatitis B. The present findings are important in addressing the urgent need for improved methods of diagnosing and managing CHB, particularly when considering the disease's presence in diverse population groups and geographical regions.

Report from the extended HLA-DPA1 ~ promoter ~ HLA-DPB1 haplotype of the 18th international HLA and immunogenetics workshop

The primary goal of the HLA-DPA1 ~ promoter ~ HLA-DPB1 haplotype component of the 18th IHIWS was to characterise the extended haplotypes within the HLA-DP region and survey the extent of genetic diversity in this region across human populations. In this report, we analysed single-nucleotide polymorphisms (SNPs) in 255 subjects from 6 different cohorts. The results from the HLA-DP haplotype component have validated findings from the initial pilot study. SNPs in this region were inherited in strong linkage, particularly HLA-DPA1, SNP-linked promoter haplotypes and motifs in exon 2 of HLA-DPB1. We reported 17 SNP-linked haplotypes in the promoter region. Together with HLA-DPA1 and HLA-DPB1 alleles, they formed 74 distinct extended HLA-DP haplotypes in 438 sequences. We also observed the presence of region-specific alleles and promoter haplotypes. Our approach involved phasing extended SNPs including promoter SNPs, HLA-DPA1 and HLA-DPB1 alleles, in a 22 kb region, GRCh38/hg38 (chr6:33,064,111-33,086,679), followed by clustering of these SNPs as one extended haplotype. This hierarchical clustering revealed four major clades, suggesting that haplotypes within each clade may have diverged from a common ancestral haplotype and undergone similar evolutionary processes. The correlation between HLA-DPA1 and the promoter region raises questions about the role of HLA-DPA1 antigen in the heterodimer. This finding requires validation on a larger sample size specifically designed for anthropological analysis. Nevertheless, the results from this study highlight the clinical potential of selecting better-matched donors for patients awaiting haematopoietic stem cell transplants from genetically overlapping groups that share common ancestral haplotypes.

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.

Genome-Wide Association Study for Chronic Hepatitis B Infection in the Thai Population

To identify novel host genetic variants that predispose to hepatitis B virus (HBV) persistence, we performed the first genome-wide association study in the Thai population involving 318 cases of chronic hepatitis B and 309 healthy controls after quality control measures. We detected the genome-wide significant association of the HLA class II region (HLA-DPA1/DPB1, rs7770370, p-value = 7.71 × 10⁻¹⁰, OR = 0.49) with HBV chronicity. Subsequent HLA allele imputation revealed HLA-DPA1*01:03 (Pc = 1.21 × 10⁻⁶, OR = 0.53), HLA-DPB1*02:01 (Pc = 2.17 × 10⁻³, OR = 0.50), and HLA-DQB1*06:09 (Pc = 2.17 × 10⁻², OR = 0.07) as protective alleles, and HLA-DPA1*02:02 (Pc = 6.32 × 10⁻⁵, OR = 1.63), HLA-DPB1*05:01 (Pc = 1.13 × 10⁻⁴, OR = 1.72), HLA-DPB1*13:01 (Pc = 4.68 × 10⁻², OR = 1.60), and HLA-DQB1*03:03 (Pc = 1.11 × 10⁻³, OR = 1.84) as risk alleles for HBV persistence. We also detected suggestive associations in the PLSCR1 (rs35766154), PDLIM5 (rs62321986), SGPL1 (rs144998273), and MGST1 (rs1828682) loci. Among single-nucleotide polymorphisms in the PLSCR1 locus, rs1061307 was identified as the primary functional variant by in silico/in vitro functional analysis. In addition to replicating the association of the HLA class II region, we detected novel candidate loci that provide new insights into the pathophysiology of chronic hepatitis B.

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.

Variation and expression of HLA-DPB1 gene in HBV infection

Hepatitis B virus (HBV) affects approximately 68 million people in China, and 10-15% of adults infected with HBV develop chronic hepatitis B, liver cirrhosis, liver failure or hepatocellular carcinoma (HCC). HLA-DPB1 gene polymorphism and expression have been shown to be associated with HBV infection susceptibility and spontaneous clearance. The aim of this study is to evaluate the role of HLA-DPB1 gene polymorphism in HBV infection. HLA-DPB1 and rs9277535 polymorphisms were investigated in 259 patients with HBV infection and 442 healthy controls (HCs) using sequence-based typing. The mRNA of HLA-DPB1 was measured by real-time polymerase chain reaction. HLA-DPB1 genes and rs9277535 polymorphisms were all associated with HBV infection in the Sichuan Han population. rs9277535A and HLA-DPB1*04:02 played a protective role against HBV infection. rs9277535G and DPB1*05:01 were associated with susceptibility to HBV infection. rs9277535GG had significantly higher HLA-DPB1 mRNA expression in the HBV infection group compared with the HC group. HLA-DPB1*05:01 and HLA-DPB1*21:01 had significantly lower mRNA expression in the HBV infection group compared with the HC group. The meta-analysis revealed that HLA-DPB1*02:01, HLA-DPB1*02:02, HAL-DPB1*04:01 and HLA-DPB1*04:02 protected against HBV infection, while HLA-DPB1*05:01, HLA-DPB1*09:01, and HLA-DPB1*13:01 were risk factors for susceptibility to HBV infection. HLA-DPB1*02:01, HLA-DPB1*02:02, and HLA-DPB1*04:01 were associated with HBV spontaneous clearance, while HLA-DPB1*05:01 was associated with chronic HBV infection. HLA-DPB1 alleles and rs9277535 have a major effect on the risk of HBV infection, and HBV infection is associated with lower HLA-DPB1 expression. HLA-DPB1 alleles have an important role in HBV susceptibility and spontaneous clearance.

Expression estimation and eQTL mapping for HLA genes with a personalized pipeline

The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression. This results from the difficulty in mapping short reads originated from these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates obtained in this way are not biased due to divergence from the reference genome. We applied our pipeline to the GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome. Although the personalized pipeline recovers more reads, we found that using the reference transcriptome produces estimates similar to the personalized pipeline (r ≥ 0.87) with the exception of HLA-DQA1. We describe the impact of the HLA-personalized approach on downstream analyses for nine classical HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. We investigate how the eQTLs we identified explain variation in expression among lineages of HLA alleles. Finally, we discuss possible causes underlying differences between expression estimates obtained using RNA-seq, antibody-based approaches and qPCR.

The level at which a gene is expressed can have important influence on the phenotype of an organism, including its predisposition to develop diseases. One way to estimate gene expression is by quantifying the abundance of RNA. RNA-seq has become the method of choice to provide such estimates at the genomewide scale. However, the application of RNA-seq to HLA genes —key players in the immune adaptive response— has remained a rarely explored approach. This is due to the problem of mapping bias, which causes deficient read alignment at genes which are very polymorphic and different from the reference genome. This has motivated approaches that replace the single reference genome with personalized sequences, comprised of the individual’s specific HLA genotype. Here we explore the use of computational frameworks to obtain reliable expression levels for HLA genes from RNA-seq datasets. We present a pipeline in which the quantification of HLA expression is carried out using methods which account for HLA diversity, avoiding the biases of standard approaches. We then evaluate the impact of this form of quantifying HLA expression on downstream analyses. The pipeline also allows us to integrate information on eQTLs with expression levels at the HLA allele-level, which can help disentangle different contributions to disease phenotypes and help understand the regulatory architecture at the HLA region.