HLA and proteasome expression body map

Abstract

BackgroundThe presentation of HLA peptide complexes to T cells is a highly regulated and tissue specific process involving multiple transcriptionally controlled cellular components. The extensive polymorphism of HLA genes and the complex composition of the proteasome make it difficult to map their expression profiles across tissues.MethodsHere we applied a tailored gene quantification pipeline to 4323 publicly available RNA-Seq datasets representing 55 normal tissues and cell types to examine expression profiles of (classical and non-classical) HLA class I, class II and proteasomal genes.ResultsWe generated the first comprehensive expression atlas of antigen presenting-related genes across 56 normal tissues and cell types, including immune cells, pancreatic islets, platelets and hematopoietic stem cells. We found a surprisingly heterogeneous HLA expression pattern with up to 100-fold difference in intra-tissue median HLA abundances. Cells of the immune system and lymphatic organs expressed the highest levels of classical HLA class I (HLA-A,-B,-C), class II (HLA-DQA1,-DQB1,-DPA1,-DPB1,-DRA,-DRB1) and non-classical HLA class I (HLA-E,-F) molecules, whereas retina, brain, muscle, megakaryocytes and erythroblasts showed the lowest abundance. In contrast, we identified a distinct and highly tissue-restricted expression pattern of the non-classical class I gene HLA-G in placenta, pancreatic islets, pituitary gland and testis. While the constitutive proteasome showed relatively constant expression across all tissues, we found the immunoproteasome to be enriched in lymphatic organs and almost absent in immune privileged tissues.ConclusionsHere, we not only provide a reference catalog of tissue and cell type specific HLA expression, but also highlight extremely variable expression of the basic components of antigen processing and presentation in different cell types. Our findings indicate that low expression of classical HLA class I molecules together with lack of immunoproteasome components as well as upregulation of HLA-G may be of key relevance to maintain tolerance in immune privileged tissues.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0354-x) contains supplementary material, which is available to authorized users.

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

“…For example, for the GEUVADIS original quantifications, HLA-B is twice as expressed as HLA-A, and HLA-DPA1 is more expressed than HLA-DRB1 ( Figure S1). Our results are more in accordance with previous HLA-personalized approaches which shows, for example, that HLA-DR is always more expressed than HLA-DP and HLA-DQ [32]. We found that, although the expression estimates using the reference transcriptome were usually lower than using the personalized index, the correlation between indices was greater than 0.87 for every locus except for HLA-DQA1 ( Figure 4).…”

“…A strategy to overcome these challenges is the mapping of reads to an HLA-personalized reference, rather than to a single reference genome. For example, seq2HLA is a tool developed by [30] to provide in-silico HLA types and expression estimates, and later applied to demonstrate that different tumors types are associated with different HLA expression levels [31], and also to provide a large catalog of HLA expression in 56 human tissues and cell types [32]. AltHapAlignR [33] is another recently described algorithm which infers the MHC references which are the closest to the individual's MHC haplotypes, and maps reads to them.…”

scHLAcount: Allele-specific HLA expression from single-cell gene expression data

“…We demonstrate that HLA genes present cell-type specific expression (Boegel et al 2018) and that HLA loss of expression can be evaluated per-cell and per-cluster. Using five AML samples published in (Petti et al 2019) for which HLA class I and class II genotypes were provided by the authors, we demonstrate the ability to find cell type specific allele bias when cell types have been annotated using marker genes.…”

“…Due to the nature of 3’ GEX data, nearly all reads are sequenced from the opposite end of the HLA-A transcript from the variable sites used to define HLA types S1. These variable sites are mostly located in exons 2 and 3, while the 3’ end of the transcripts are mostly homologous between the class I genes Boegel et al 2018. As a result of the coverage distribution of 3’ GEX data, very few HLA-A molecules could be assigned to an allele.…”

Extraordinary diversity of HLA class I gene expression in single cells contribute to the plasticity and adaptability of human immune system

“…In transplant practices, the importance of RNA or protein expression levels of HLA genes have been gradually realized. There are some reports on HLA typing using RNA-seq data and quantification of HLA genes as well [18][19][20][21]. However, to date, few reports have been available on the HLA typing at the singlecell level by using scRNA-seq data.…”