Allele-Specific Quantification of HLA-DRB1 Transcripts Reveals Imbalanced Allelic Expression That Modifies the Amino Acid Effects in HLA-DRβ1.
Alanine
/ genetics
Alleles
Anti-Citrullinated Protein Antibodies
/ immunology
Arthritis, Rheumatoid
/ genetics
Asian People
Gene Dosage
Gene Expression
HLA-DRB1 Chains
/ genetics
Heterozygote
Humans
Logistic Models
Quantitative Trait Loci
RNA, Messenger
/ metabolism
RNA-Seq
Republic of Korea
White People
Journal
Arthritis & rheumatology (Hoboken, N.J.)
ISSN: 2326-5205
Titre abrégé: Arthritis Rheumatol
Pays: United States
ID NLM: 101623795
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
12
01
2020
accepted:
22
09
2020
pubmed:
2
10
2020
medline:
23
3
2021
entrez:
1
10
2020
Statut:
ppublish
Résumé
HLA association fine-mapping studies have shown the effects of missense variants in HLA-DRB1 on rheumatoid arthritis (RA) susceptibility, prognosis, and autoantibody production. However, the phenotypic effects of expression changes in HLA-DRB1 remain poorly understood. Therefore, we investigated the allele-specific expression of HLA-DRB1 and its effect on an HLA-DRβ1 structure-associated trait in RA. We quantified the allele-specific expression of each HLA-DRB1 3-field classic allele in 48 Korean RA patients with anti-citrullinated protein antibodies (ACPAs) and 319 healthy European subjects by using both RNA sequencing and HLA-DRB1 genotype data to calculate the relative expression strength of multiple HLA-DRB1 alleles (n = 14 in Koreans and n = 25 in Europeans) in each population. The known association between ACPA level and alanine at position 74 of HLA-DRβ1 in ACPA-positive RA was revisited to understand the phenotypic effect of allele-specific expression of HLA-DRB1 by modeling multivariate logistic regression with the genomic dosage or relative expression dosage of Ala-74 in 2 independent sets of 1,723 Korean RA patients with ACPA. The relative expression strength was highly allele-specific, causing imbalanced allelic expression in HLA-DRB1 heterozygotes. The association between HLA-DRβ1 Ala-74 and ACPA level in RA was better explained by relative expression dosage of Ala-74 than by the genomic dosage (change in Akaike's information criterion = -6.98). Moreover, the expression variance of Ala-74 in Ala-74 heterozygotes with no genomic variance of Ala-74 was significantly associated with ACPA level (P = 2.26 × 10 Our findings illustrate the advantage of integrating quantitative and qualitative changes in HLA-DRB1 into a single model for understanding HLA-DRB1 associations.
Substances chimiques
Anti-Citrullinated Protein Antibodies
0
HLA-DRB1 Chains
0
RNA, Messenger
0
Alanine
OF5P57N2ZX
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
381-391Informations de copyright
© 2020, American College of Rheumatology.
Références
Kubinak JL, Ruff JS, Hyzer CW, Slev PR, Potts WK. Experimental viral evolution to specific host MHC genotypes reveals fitness and virulence trade-offs in alternative MHC types. Proc Natl Acad Sci U S A 2012;109:3422-7.
Shiina T, Hosomichi K, Inoko H, Kulski JK. The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet 2009;54:15-39.
Scott DL, Symmons DP, Coulton BL, Popert AJ. Long-term outcome of treating rheumatoid arthritis: results after 20 years. Lancet 1987;1:1108-11.
Kim K, Bang SY, Lee HS, Bae SC. Update on the genetic architecture of rheumatoid arthritis [review]. Nat Rev Rheumatol 2017;13:13-24.
Terao C, Suzuki A, Ikari K, Kochi Y, Ohmura K, Katayama M, et al. An association between amino acid position 74 of HLA-DRB1 and anti-citrullinated protein antibody levels in Japanese patients with anti-citrullinated protein antibody-positive rheumatoid arthritis. Arthritis Rheumatol 2015;67:2038-45.
Van der Helm-van Mil AH, Verpoort KN, Breedveld FC, Huizinga TW, Toes RE, de Vries RR. The HLA-DRB1 shared epitope alleles are primarily a risk factor for anti-cyclic citrullinated peptide antibodies and are not an independent risk factor for development of rheumatoid arthritis. Arthritis Rheum 2006;54:1117-21.
Anderson KM, Roark CL, Portas M, Aubrey MT, Rosloniec EF, Freed BM. A molecular analysis of the shared epitope hypothesis: binding of arthritogenic peptides to DRB1*04 alleles. Arthritis Rheumatol 2016;68:1627-36.
Castel SE, Cervera A, Mohammadi P, Aguet F, Reverter F, Wolman A, et al. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat Genet 2018;50:1327-34.
Benovoy D, Kwan T, Majewski J. Effect of polymorphisms within probe-target sequences on olignonucleotide microarray experiments. Nucleic Acids Res 2008;36:4417-23.
Arnett FC, Edworthy SM, Bloch DA, McShane DJ, Fries JF, Cooper NS, et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 1988;31:315-24.
Riedemann JP, Munoz S, Kavanaugh A. The use of second generation anti-CCP antibody (anti-CCP2) testing in rheumatoid arthritis: a systematic review. Clin Exp Rheumatol 2005;23:S69-76.
Gourraud PA, Khankhanian P, Cereb N, Yang SY, Feolo M, Maiers M, et al. HLA diversity in the 1000 genomes dataset. PLoS One 2014;9:e97282.
Abi-Rached L, Gouret P, Yeh JH, di Cristofaro J, Pontarotti P, Picard C, et al. Immune diversity sheds light on missing variation in worldwide genetic diversity panels. PLoS One 2018;13:e0206512.
Lappalainen T, Sammeth M, Friedlander MR, 't Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 2013;501:506-11.
Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al, on behalf of the 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 2015;526:68-74.
Currenti J, Chopra A, John M, Leary S, McKinnon E, Alves E, et al. Deep sequence analysis of HIV adaptation following vertical transmission reveals the impact of immune pressure on the evolution of HIV. PLoS Pathog 2019;15:e1008177.
Cooper D. High resolution HLA typing from second generation sequencing data [PhD thesis]. Perth (Australia): Murdoch Univ.; 2015.
Moon S, Kim YJ, Han S, Hwang MY, Shin DM, Park MY, et al. The Korea Biobank Array: design and identification of coding variants associated with blood biochemical traits. Sci Rep 2019;9:1382.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012;9:357-9.
Saaty TL. How to make a decision: the analytic hierarchy process. Eur J Oper Res 1990;48:9-26.
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 2013;14:R36.
Anders S, Pyl PT, Huber W. HTSeq: a Python framework to work with high-throughput sequencing data. Bioinformatics 2015;31:166-9.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40.
Gao S, Zhang Z, Cao C. Calculating weights methods in complete matrices and incomplete matrices. J Softw 2010;5:304-11.
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489:57-74.
Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 2004;33:261-304.
Kralovicova J, Marsh SG, Waller MJ, Hammarstrom L, Vorechovsky I. The HLA-DRA*0102 allele: correct nucleotide sequence and associated HLA haplotypes. Tissue Antigens 2002;60:266-7.
Dimas AS, Stranger BE, Beazley C, Finn RD, Ingle CE, Forrest MS, et al. Modifier effects between regulatory and protein-coding variation. PLoS Genet 2008;4:e1000244.
Lappalainen T, Montgomery SB, Nica AC, Dermitzakis ET. Epistatic selection between coding and regulatory variation in human evolution and disease. Am J Hum Genet 2011;89:459-63.