HLA RNAseq reveals high allele-specific variability in mRNA expression

Abstract

18The HLA gene complex is the most important, single genetic factor in susceptibility to most 19 diseases with autoimmune or autoinflammatory origin and in transplantation matching. The majority of 20 the studies have focused on the huge allelic variation in these genes; only a few studies have explored 21 differences in expression levels of HLA alleles. To study the expression levels of HLA alleles more 22 systematically we utilised two different RNA sequencing methods. Illumina RNAseq has a high 23 sequencing accuracy and depth but is limited by the short read length, whereas Oxford Nanopore's 24 technology can sequence long templates, but has a poor accuracy. We studied allelic mRNA levels of 25 HLA class I and II alleles from peripheral blood samples of 50 healthy individuals. The results 26 demonstrate large differences in mRNA expression levels between HLA alleles. The method can be 27 applied to quantitate the expression differences of HLA alleles in various tissues and to evaluate the role 28 of this type of variation in transplantation matching and susceptibility to autoimmune diseases. 29 3 30 Author Summary 31 32Even though HLA is widely studied less is known of its allele-specific expression. Due to the pivotal role 33 of HLA in infection response, autoimmunity, and transplantation biology its expression surely must play 34 a part as well. In hematopoietic stem cell transplantation the challenge often is to find a suitable HLA-35 matched donor due to the high allelic variation. Classical HLA typing methods do not take into account 36 HLA allele-specific expression. However, differential allelic expression levels could be crucial in finding 37 permissive mismatches in order to save a patient's life. Additionally, differential HLA expression levels 38 can lead into beneficial impact in viral clearance but also undesirable effects in autoimmune diseases. To 39 study HLA expression we developed a novel RNAseq-based method to systematically characterize allele-40 specific expression levels of classical HLA genes. We tested our method in a set of 50 healthy individuals 41 and found differential expression levels between HLA alleles as well as interindividual variability at the 42 gene level. Since NGS is already well adopted in HLA research the next step could be to determine HLA 43 allele-specific expression in addition to HLA allelic variation and HLA-disease association studies in 44 various cells, tissues, and diseases. 45 54 reaction (PCR) and the mean fluorescence intensity (MFI).[3-10] The differential expression of HLA 55 alleles has been associated with immunologically mediated diseases, such as Crohn's disease [11] and 56 HIV [6,12], follicular lymphoma[7], and the outcome of HSCT through the risk of graft versus host 57 disease (GvHD)[8,9]. In fact, incompatibilities between the donor and the recipient in HSCT have made 58 the expression differences of HLA molecules an interesting target for finding permissive mismatches. 59 Although currently only the qualitative HLA typing is considered in dono...

HLA RNA Sequencing With Unique Molecular Identifiers Reveals High Allele-Specific Variability in mRNA Expression

“…We would also like to thank CSC – IT Center for Science, Finland, for computational resources. This work has been previously published as a preprint in bioRxiv ( 63 ).…”

Capturing Differential Allele-Level Expression and Genotypes of All Classical HLA Loci and Haplotypes by a New Capture RNA-Seq Method

“…HLA genes also can be genotyped by amplicon sequencing using HLA transcripts as reverse-transcribed complementary DNA (cDNA) (44) and HLA RNA expression levels quantitated by amplicon sequencing using HLA locus-specific primers (45). However, the method using HLA locus-specific primers for measuring RNA levels are mostly semiquantitative because PCR efficiency can differ between the polymorphic HLA alleles (46).…”

CRISPR-based targeted haplotype-resolved assemblies of a megabase region

“…In addition to genetic variants, functional analyses of any genomic regions can also benefit greatly from our unbiased approach of constructing haplotype-resolved assemblies of targeted regions. For regions with high polymorphisms, such as the MHC, the conventional strategy of mapping short-read sequencing data to a single reference genome (e.g., hg19 or hg38) is known to yield biased alignments, leading towards inaccurate quantifications 36,37 . This has prompted recent attempts to replace a single reference genome with the computationally inferred personal genotypes as the reference, all of which showed improved accuracies of quantifications compared to the standard approach 33,[38][39][40][41] .…”