Dataset Information

Dissecting Genetic Control of HLA-DPB1 Expression and Its Relation to Structural Mismatch Models in Hematopoietic Stem Cell Transplantation.

ABSTRACT: HLA expression levels have been suggested to be genetically controlled by single nucleotide polymorphisms (SNP) in the untranslated regions (UTR), and expression variants have been associated with the outcome of chronic viral infection and hematopoietic stem cell transplantation (HSCT). In particular, the 3'UTR rs9277534-G/A SNP in HLA-DPB1 has been associated with graft-versus-host-disease after HSCT (Expression model); however its relevance in different immune cells and its mode of action have not been systematically addressed. In addition, there is a strong though not complete overlap between the rs9277534-G/A SNP and structural HLA-DPB1 T cell epitope (TCE) groups which have also been associated with HSCT outcome (TCE Structural model). Here we confirm and extend previous findings of significantly higher HLA-DPB1 expression in B cell lines, unstimulated primary B cells, and monocytes homozygous for rs9277534-G compared to those homozygous for rs9277534-A. However, these differences were abrogated by interferon-? stimulation or differentiation into dendritic cells. We identify at least seven 3'UTR rs9277534-G/A haplotypes differing by a total of 37 SNP, also characterized by linkage to length variants of a short tandem repeat (STR) in intron 2 and TCE group assignment. 3'UTR mapping did not show any significant differences in post-transcriptional regulation assessed by luciferase assays between two representative rs9277534-G/A haplotypes for any of eight overlapping fragments. Moreover, no evidence for alternative splicing associated with the intron 2 STR was obtained by RT-PCR. In an exemplary cohort of 379 HLA-DPB1 mismatched donor-recipient pairs, risk prediction by the Expression model and the Structural TCE model was 36.7% concordant, with the majority of discordances due to non-applicability of the Expression model. HLA-DPB1 from different TCE groups expressed in the absence of the 3'UTR at similar levels by transfected HeLa cells elicited significantly different mean alloreactive CD4+ T-cell responses, as assessed by CD137 upregulation assays in 178 independent cultures. Taken together, our data provide new insights into the cell type-specific and mechanistic basis of the association between the rs9277534-G/A SNP and HLA-DPB1 expression, and show that, despite partial overlap between both models in HSCT risk-prediction, differential alloreactivity determined by the TCE structural model occurs independently from HLA-DPB1 differential expression.

SUBMITTER: Meurer T

PROVIDER: S-EPMC6183238 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dissecting Genetic Control of HLA-DPB1 Expression and Its Relation to Structural Mismatch Models in Hematopoietic Stem Cell Transplantation.

Meurer Thuja T Arrieta-Bolaños Esteban E Metzing Maximilian M Langer Mona-May MM van Balen Peter P Falkenburg J H Frederik JHF Beelen Dietrich W DW Horn Peter A PA Fleischhauer Katharina K Crivello Pietro P

Frontiers in immunology 20181005

HLA expression levels have been suggested to be genetically controlled by single nucleotide polymorphisms (SNP) in the untranslated regions (UTR), and expression variants have been associated with the outcome of chronic viral infection and hematopoietic stem cell transplantation (HSCT). In particular, the 3'UTR rs9277534-G/A SNP in HLA-DPB1 has been associated with graft-versus-host-disease after HSCT (Expression model); however its relevance in different immune cells and its mode of action have ...[more]

PMID: 30344521

Similar Datasets

Project description:BackgroundHLA molecular mismatch (MM) is a risk factor for de novo donor-specific antibody (dnDSA) development in solid organ transplantation. HLA expression differences have also been associated with adverse outcomes in hematopoietic cell transplantation. We sought to study both MM and expression in assessing dnDSA risk.MethodsOne hundred three HLA-DP-mismatched solid organ transplantation pairs were retrospectively analyzed. MM was computed using amino acids (aa), eplets, and, supplementarily, Grantham/Epstein scores. DPB1 alleles were classified as rs9277534-A (low-expression) or rs9277534-G (high-expression) linked. To determine the associations between risk factors and dnDSA, logistic regression, linkage disequilibrium (LD), and population-based analyses were performed.ResultsA high-risk AA:GX (recipient:donor) expression combination (X = A or G) demonstrated strong association with HLA-DP dnDSA (P = 0.001). MM was also associated with HLA-DP dnDSA when evaluated by itself (eplet P = 0.007, aa P = 0.003, Grantham P = 0.005, Epstein P = 0.004). When attempting to determine the relative individual effects of the risk factors in multivariable analysis, only AA:GX expression status retained a strong association (relative risk = 18.6, P = 0.007 with eplet; relative risk = 15.8, P = 0.02 with aa), while MM was no longer significant (eplet P = 0.56, aa P = 0.51). Importantly, these risk factors are correlated, due to LD between the expression-tagging single-nucleotide polymorphism and polymorphisms along HLA-DPB1.ConclusionsThe MM and expression risk factors each appear to be strong predictors of HLA-DP dnDSA and to possess clinical utility; however, these two risk factors are closely correlated. These metrics may represent distinct ways of characterizing a common overlapping dnDSA risk profile, but they are not independent. Further, we demonstrate the importance and detailed implications of LD effects in dnDSA risk assessment and possibly transplantation overall.

Project description:HLA-C locus mismatches (MMs) are the most frequent class I disparities in unrelated hematopoietic stem cell transplantation (HSCT) and have a detrimental impact on clinical outcome. Recently, a few retrospective clinical studies have reported some variability in the immunogenicity of HLA-C incompatibilities. To get better insight into presumably permissive HLA-C MMs, we have developed a one-way in vitro mixed lymphocyte reaction (MLR) assay allowing to quantify activated CD56-CD137+CD8+ lymphocytes in HLA-C incompatible combinations. T cell-mediated alloresponses were correlated with genetic markers such as HLA-C mRNA expression and the number of amino acid (aa) MMs in the α1/α2 domains (peptide-binding region). Because of the high rate of HLA-DPB1 incompatibilities in HLA-A-, B-, C-, DRB1-, and DQB1-matched unrelated HSCT patient/donor pairs, the impact of HLA-DPB1 mismatching, a potential bystander of CD4+ T cell activation, was also considered. Heterogeneous alloresponses were measured in 63 HLA-C-mismatched pairs with a positive assay in 52% of the combinations (2.3-18.6% activated CTLs), representing 24 different HLA-A~B~DRB1~DQB1 haplotypes. There was no correlation between measured alloresponses and mRNA expression of the mismatched HLA-C alleles. The HLA-C*03:03/03:04 MM did not induce any positive alloresponse in five MLRs. We also identified HLA-C*02:02 and HLA-C*06:02 as mismatched alleles with lower immunogenicity, and HLA-C*14:02 as a more immunogenic MM. A difference of at least 10 aa residues known to impact peptide/T cell receptor (TCR) binding and a bystander HLA-DPB1 incompatibility had a significant impact on CTL alloreactivity (p = 0.021). The same HLA-C MM, when recognized by two different responders with the same HLA haplotypes, was recognized differently, emphasizing the role of the T-cell repertoire of responding cells. In conclusion, mismatched HLA-C alleles differing by 10 or more aas in the peptide/TCR-binding region, when occurring together with HLA-DPB1 incompatibilities, should be considered as high-risk MMs in unrelated HSCT.

Dataset Information

Dissecting Genetic Control of HLA-DPB1 Expression and Its Relation to Structural Mismatch Models in Hematopoietic Stem Cell Transplantation.

Publications

Dissecting Genetic Control of HLA-DPB1 Expression and Its Relation to Structural Mismatch Models in Hematopoietic Stem Cell Transplantation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets