Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci.
Alleles
Autoimmunity
/ genetics
CD4-Positive T-Lymphocytes
CRISPR-Cas Systems
Cell Line
Gene Expression Regulation
Genetic Loci
Genetic Variation
Genotyping Techniques
HLA Antigens
/ genetics
HLA-DQ beta-Chains
/ genetics
Humans
Immunity, Cellular
Lymphocyte Activation
/ genetics
Promoter Regions, Genetic
/ genetics
T-Lymphocytes, Regulatory
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
26
04
2019
accepted:
13
01
2020
pubmed:
19
2
2020
medline:
15
4
2020
entrez:
19
2
2020
Statut:
ppublish
Résumé
Genetic studies have revealed that autoimmune susceptibility variants are over-represented in memory CD4
Identifiants
pubmed: 32066938
doi: 10.1038/s41588-020-0579-4
pii: 10.1038/s41588-020-0579-4
pmc: PMC7135372
mid: NIHMS1549636
doi:
Substances chimiques
HLA Antigens
0
HLA-DQ beta-Chains
0
HLA-DQB1 antigen
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
247-253Subventions
Organisme : NHLBI NIH HHS
ID : HHSN268201500003C
Pays : United States
Organisme : NHGRI NIH HHS
ID : U54 HG003067
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95163
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001079
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95169
Pays : United States
Organisme : NIAID NIH HHS
ID : U19 AI111224
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95168
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK063491
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201800001C
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95165
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95159
Pays : United States
Organisme : Medical Research Council
ID : MR/R013926/1
Pays : United Kingdom
Organisme : NCATS NIH HHS
ID : UL1 TR000040
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001881
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201000001I
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95160
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL120393
Pays : United States
Organisme : NHGRI NIH HHS
ID : T32 HG002295
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95164
Pays : United States
Organisme : NIGMS NIH HHS
ID : U01 GM092691
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95162
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95161
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001420
Pays : United States
Organisme : NIAMS NIH HHS
ID : R01 AR063759
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201500003I
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG009379
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95167
Pays : United States
Organisme : NIAMS NIH HHS
ID : UH2 AR067677
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL117626
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC95166
Pays : United States
Références
Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).
pubmed: 23263488
doi: 10.1038/ng.2504
Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).
pubmed: 25751624
pmcid: 4380767
doi: 10.1038/ng.3245
Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
pubmed: 25363779
doi: 10.1038/nature13835
Simeonov, D. R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017).
pubmed: 28854172
pmcid: 5675716
doi: 10.1038/nature23875
Gutierrez-Arcelus, M., Rich, S. S. & Raychaudhuri, S. Autoimmune diseases—connecting risk alleles with molecular traits of the immune system. Nat. Rev. Genet. 17, 160–174 (2016).
pubmed: 26907721
pmcid: 4896831
doi: 10.1038/nrg.2015.33
Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).
pubmed: 24786080
pmcid: 4910825
doi: 10.1126/science.1249547
Dimas, A. S. et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246–1250 (2009).
pubmed: 19644074
pmcid: 2867218
doi: 10.1126/science.1174148
Gutierrez-Arcelus, M. et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. eLife 2, e00523 (2013).
pubmed: 23755361
pmcid: 3673336
doi: 10.7554/eLife.00523
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).
pubmed: 27863251
pmcid: 5119954
doi: 10.1016/j.cell.2016.10.026
Ishigaki, K. et al. Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat. Genet. 49, 1120–1125 (2017).
pubmed: 28553958
doi: 10.1038/ng.3885
Ye, C. J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).
pubmed: 25214635
pmcid: 4751028
doi: 10.1126/science.1254665
Hu, X. et al. Regulation of gene expression in autoimmune disease loci and the genetic basis of proliferation in CD4
pubmed: 24968232
pmcid: 4072514
doi: 10.1371/journal.pgen.1004404
Buil, A. et al. Gene–gene and gene–environment interactions detected by transcriptome sequence analysis in twins. Nat. Genet. 47, 88–91 (2015).
pubmed: 25436857
doi: 10.1038/ng.3162
Moyerbrailean, G. A. & et al. High-throughput allele-specific expression across 250 environmental conditions. Genome Res. 26, 1627–1638 (2016).
pubmed: 27934696
pmcid: 5131815
doi: 10.1101/gr.209759.116
van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).
pubmed: 26366987
pmcid: 4626402
doi: 10.1038/nmeth.3582
Knowles, D. A. et al. Allele-specific expression reveals interactions between genetic variation and environment. Nat. Methods 14, 699–702 (2017).
pubmed: 28530654
pmcid: 5501199
doi: 10.1038/nmeth.4298
Hu, X. et al. Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk. Nat. Genet. 47, 898–905 (2015).
pubmed: 26168013
pmcid: 4930791
doi: 10.1038/ng.3353
Sollid, L. M. et al. Evidence for a primary association of celiac disease to a particular HLA-DQ alpha/beta heterodimer. J. Exp. Med. 169, 345–350 (1989).
pubmed: 2909659
doi: 10.1084/jem.169.1.345
Burmester, G. R., Yu, D. T., Irani, A. M., Kunkel, H. G. & Winchester, R. J. Ia+ T cells in synovial fluid and tissues of patients with rheumatoid arthritis. Arthritis Rheumatol. 24, 1370–1376 (1981).
doi: 10.1002/art.1780241106
Yu, D. T. et al. Peripheral blood Ia-positive T cells. Increases in certain diseases and after immunization. J. Exp. Med. 151, 91–100 (1980).
pubmed: 6985649
doi: 10.1084/jem.151.1.91
Ko, H. S. Ia determinants on stimulated human T lymphocytes. Occurrence on mitogen- and antigen-activated T cells. J. Exp. Med. 150, 246–255 (1979).
pubmed: 88499
doi: 10.1084/jem.150.2.246
Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).
pubmed: 28150777
pmcid: 5349321
doi: 10.1038/nature20810
Fonseka, C. Y. et al. Mixed-effects association of single cells identifies an expanded effector CD4
pubmed: 30333237
pmcid: 6448773
doi: 10.1126/scitranslmed.aaq0305
Lanzavecchia, A., Roosnek, E., Gregory, T., Berman, P. & Abrignani, S. T cells can present antigens such as HIV gp120 targeted to their own surface molecules. Nature 334, 530–532 (1988).
pubmed: 2841610
doi: 10.1038/334530a0
LaSalle, J. M., Tolentino, P. J., Freeman, G. J., Nadler, L. M. & Hafler, D. A. Early signaling defects in human T cells anergized by T cell presentation of autoantigen. J. Exp. Med. 176, 177–186 (1992).
pubmed: 1535366
doi: 10.1084/jem.176.1.177
Brandes, M., Willimann, K. & Moser, B. Professional antigen-presentation function by human γδ T cells. Science 309, 264–268 (2005).
pubmed: 15933162
doi: 10.1126/science.1110267
Guo, M. H. et al. Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms. Proc. Natl Acad. Sci. USA 114, E327–E336 (2017).
pubmed: 28031487
Bild, D. E. et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am. J. Epidemiol. 156, 871–881 (2002).
pubmed: 12397006
doi: 10.1093/aje/kwf113
Roadmap Epigenomics Consortium, et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
doi: 10.1038/nature14248
ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
doi: 10.1038/nature11247
Wong, D. et al. Genomic mapping of the MHC transactivator CIITA using an integrated ChIP–seq and genetical genomics approach. Genome Biol. 15, 494 (2014).
pubmed: 25366989
pmcid: 4243378
doi: 10.1186/s13059-014-0494-z
GTEx Consortium, et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
doi: 10.1038/nature24277
Nédélec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669.e21 (2016).
pubmed: 27768889
doi: 10.1016/j.cell.2016.09.025
Aguiar, V. R. C., César, J., Delaneau, O., Dermitzakis, E. T. & Meyer, D. Expression estimation and eQTL mapping for HLA genes with a personalized pipeline. PLoS Genet. 15, e1008091 (2019).
pubmed: 31009447
pmcid: 6497317
doi: 10.1371/journal.pgen.1008091
Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384.e19 (2016).
pubmed: 27863249
pmcid: 5123897
doi: 10.1016/j.cell.2016.09.037
Schofield, E. C. et al. CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets. Bioinformatics 32, 2511–2513 (2016).
pubmed: 27153610
pmcid: 4978926
doi: 10.1093/bioinformatics/btw173
Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49, 600 (2017).
pubmed: 28218759
pmcid: 5374036
doi: 10.1038/ng.3795
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
pubmed: 26414678
pmcid: 4626285
doi: 10.1038/ng.3404
Castel, S. E. et al. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat. Genet. 50, 1327–1334 (2018).
pubmed: 30127527
pmcid: 6119105
doi: 10.1038/s41588-018-0192-y
Raychaudhuri, S. et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).
pubmed: 22286218
pmcid: 3288335
doi: 10.1038/ng.1076
Raj, P. et al. Regulatory polymorphisms modulate the expression of HLA class II molecules and promote autoimmunity. eLife 5, e12089 (2016).
pubmed: 26880555
pmcid: 4811771
doi: 10.7554/eLife.12089
Cavalli, G. et al. MHC class II super-enhancer increases surface expression of HLA-DR and HLA-DQ and affects cytokine production in autoimmune vitiligo. Proc. Natl Acad. Sci. USA 113, 1363–1368 (2016).
pubmed: 26787888
pmcid: 4747741
doi: 10.1073/pnas.1523482113
Vandiedonck, C. et al. Pervasive haplotypic variation in the spliceo-transcriptome of the human major histocompatibility complex. Genome Res. 21, 1042–1054 (2011).
pubmed: 21628452
pmcid: 3129247
doi: 10.1101/gr.116681.110
Pelikan, R. C. et al. Enhancer histone-QTLs are enriched on autoimmune risk haplotypes and influence gene expression within chromatin networks. Nat. Commun. 9, 2905 (2018).
pubmed: 30046115
pmcid: 6060153
doi: 10.1038/s41467-018-05328-9
Senju, S. et al. Allele-specific expression of the cytoplasmic exon of HLA-DQB1 gene. Immunogenetics 36, 319–325 (1992).
pubmed: 1644449
doi: 10.1007/BF00215661
Baecher-Allan, C., Wolf, E. & Hafler, D. A. MHC class II expression identifies functionally distinct human regulatory T cells. J. Immunol. 176, 4622–4631 (2006).
pubmed: 16585553
doi: 10.4049/jimmunol.176.8.4622
Reinherz, E. L. et al. Ia determinants on human T-cell subsets defined by monoclonal antibody. Activation stimuli required for expression. J. Exp. Med. 150, 1472–1482 (1979).
pubmed: 92523
doi: 10.1084/jem.150.6.1472
Engleman, E. G., Benike, C. J. & Charron, D. J. Ia antigen on peripheral blood mononuclear leukocytes in man. II. Functional studies of HLA-DR-positive T cells activated in mixed lymphocyte reactions. J. Exp. Med. 152, 114s–126s (1980).
pubmed: 6447741
Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLoS ONE 8, e64683 (2013).
pubmed: 23762245
pmcid: 3675122
doi: 10.1371/journal.pone.0064683
GAP Registry (The Feinstein Institute for Medical Research, accessed 27 February 2019); https://www.feinsteininstitute.org/robert-s-boas-center-for-genomics-and-human-genetics/gap-registry/
Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).
pubmed: 23558742
pmcid: 3664803
doi: 10.1093/nar/gkt214
Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).
pubmed: 30357393
doi: 10.1093/nar/gky955
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393
pmcid: 3106198
doi: 10.1093/bioinformatics/btr260
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901
pmcid: 1950838
doi: 10.1086/519795
1000 Genomes Project Consortium, et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
doi: 10.1038/nature15393
Delaneau, O., Marchini, J. & Zagury, J.-F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).
pubmed: 22138821
doi: 10.1038/nmeth.1785
Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).
pubmed: 19543373
pmcid: 2689936
doi: 10.1371/journal.pgen.1000529
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Castel, S. E., Levy-Moonshine, A., Mohammadi, P., Banks, E. & Lappalainen, T. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 16, 195 (2015).
pubmed: 26381377
pmcid: 4574606
doi: 10.1186/s13059-015-0762-6
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
doi: 10.18637/jss.v067.i01
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
pubmed: 12883005
pmcid: 170937
doi: 10.1073/pnas.1530509100
Robinson, J. et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 43, D423–D431 (2015).
pubmed: 25414341
doi: 10.1093/nar/gku1161
Dilthey, A., Cox, C., Iqbal, Z., Nelson, M. R. & McVean, G. Improved genome inference in the MHC using a population reference graph. Nat. Genet. 47, 682–688 (2015).
pubmed: 25915597
pmcid: 4449272
doi: 10.1038/ng.3257
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).
pubmed: 28846090
pmcid: 5623106
doi: 10.1038/nmeth.4396
Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).
doi: 10.1002/0471142727.mb2129s109
Schumann, K. et al. Generation of knock-in primary human T cells using Cas9 ribonucleoproteins. Proc. Natl Acad. Sci. USA 112, 10437–10442 (2015).
pubmed: 26216948
pmcid: 4547290
doi: 10.1073/pnas.1512503112
Richardson, C. D., Ray, G. J., DeWitt, M. A., Curie, G. L. & Corn, J. E. Enhancing homology-directed genome editing by catalytically active and inactive CRISPR–Cas9 using asymmetric donor DNA. Nat. Biotechnol. 34, 339–344 (2016).
pubmed: 26789497
doi: 10.1038/nbt.3481
Slowikowski, K., Hu, X. & Raychaudhuri, S. SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30, 2496–2497 (2014).
pubmed: 24813542
pmcid: 4147889
doi: 10.1093/bioinformatics/btu326
Phipson, B. & Smyth, G. K. Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol. https://doi.org/10.2202/1544-6115.1585 (2010).