Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets.
Journal
Nature immunology
ISSN: 1529-2916
Titre abrégé: Nat Immunol
Pays: United States
ID NLM: 100941354
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
22
03
2023
accepted:
13
07
2023
medline:
28
8
2023
pubmed:
11
8
2023
entrez:
10
8
2023
Statut:
ppublish
Résumé
Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.
Identifiants
pubmed: 37563310
doi: 10.1038/s41590-023-01588-w
pii: 10.1038/s41590-023-01588-w
pmc: PMC10457199
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1540-1551Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Investigateurs
Andres Metspalu
(A)
Lili Milani
(L)
Reedik Mägi
(R)
Mari Nelis
(M)
Georgi Hudjašov
(G)
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023. The Author(s).
Références
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
pubmed: 29875488
pmcid: 6697541
doi: 10.1038/s41586-018-0175-2
Enroth, S., Johansson, A., Enroth, S. B. & Gyllensten, U. Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nat. Commun. 5, 4684 (2014).
pubmed: 25147954
doi: 10.1038/ncomms5684
Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).
pubmed: 28240269
pmcid: 5333359
doi: 10.1038/ncomms14357
Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).
pubmed: 30072576
pmcid: 6190714
doi: 10.1126/science.aaq1327
Melzer, D. et al. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet. 4, e1000072 (2008).
pubmed: 18464913
pmcid: 2362067
doi: 10.1371/journal.pgen.1000072
Lourdusamy, A. et al. Identification of cis-regulatory variation influencing protein abundance levels in human plasma. Hum. Mol. Genet. 21, 3719–3726 (2012).
pubmed: 22595970
pmcid: 6446535
doi: 10.1093/hmg/dds186
Folkersen, L. et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat. Metab. 2, 1135–1148 (2020).
pubmed: 33067605
pmcid: 7611474
doi: 10.1038/s42255-020-00287-2
Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).
pubmed: 34648354
pmcid: 9904207
doi: 10.1126/science.abj1541
Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021).
pubmed: 34857953
doi: 10.1038/s41588-021-00978-w
Zhang, J. et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat. Genet. 54, 593–602 (2022).
pubmed: 35501419
pmcid: 9236177
doi: 10.1038/s41588-022-01051-w
Gudjonsson, A. et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat. Commun. 13, 480 (2022).
pubmed: 35078996
pmcid: 8789779
doi: 10.1038/s41467-021-27850-z
Siegbahn, A. et al. Multiplex protein screening of biomarkers associated with major bleeding in patients with atrial fibrillation treated with oral anticoagulation. J. Thromb. Haemost. 19, 2726–2737 (2021).
pubmed: 34390530
doi: 10.1111/jth.15498
Pietzner, M. et al. Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat. Commun. 12, 6822 (2021).
pubmed: 34819519
pmcid: 8613205
doi: 10.1038/s41467-021-27164-0
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
pubmed: 34475573
pmcid: 8432599
doi: 10.1038/s41588-021-00913-z
The GTEx Consortium et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
pmcid: 7737656
doi: 10.1126/science.aaz1776
Peters, J. E. et al. Insight into genotype–phenotype associations through eQTL mapping in multiple cell types in health and immune-mediated disease. PLoS Genet. 12, e1005908 (2016).
pubmed: 27015630
pmcid: 4807835
doi: 10.1371/journal.pgen.1005908
Kerimov, N. et al. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet. 53, 1290–1299 (2021).
pubmed: 34493866
pmcid: 8423625
doi: 10.1038/s41588-021-00924-w
Stacey, D. et al. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res. 47, e3 (2019).
pubmed: 30239796
doi: 10.1093/nar/gky837
Rappoport, N., Simon, A. J., Amariglio, N. & Rechavi, G. The Duffy antigen receptor for chemokines, ACKR1, ‘Jeanne DARC’ of benign neutropenia. Br. J. Haematol. 184, 497–507 (2019).
pubmed: 30592023
doi: 10.1111/bjh.15730
Chen, M.-H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182, 1198–1213.e1114 (2020).
pubmed: 32888493
pmcid: 7480402
doi: 10.1016/j.cell.2020.06.045
Hassan, H. T. & Zander, A. Stem cell factor as a survival and growth factor in human normal and malignant hematopoiesis. Acta Haematol. 95, 257–262 (1996).
pubmed: 8677752
doi: 10.1159/000203893
Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).
pubmed: 31915397
pmcid: 7405896
doi: 10.1038/s41586-019-1879-7
Ferreira, R. C. et al. Functional IL6R 358Ala allele impairs classical IL-6 receptor signaling and influences risk of diverse inflammatory diseases. PLoS Genet. 9, e1003444 (2013).
pubmed: 23593036
pmcid: 3617094
doi: 10.1371/journal.pgen.1003444
Rosa, M. et al. A Mendelian randomization study of IL6 signaling in cardiovascular diseases, immune-related disorders and longevity. NPJ Genom. Med 4, 23 (2019).
pubmed: 31552141
pmcid: 6754413
doi: 10.1038/s41525-019-0097-4
Patsopoulos, A. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).
doi: 10.1126/science.aav7188
Gregory, A. P. et al. TNF receptor 1 genetic risk mirrors outcome of anti-TNF therapy in multiple sclerosis. Nature 488, 508–511 (2012).
pubmed: 22801493
pmcid: 4268493
doi: 10.1038/nature11307
Smith, G. D. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).
pubmed: 12689998
doi: 10.1093/ije/dyg070
Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).
pubmed: 29335400
pmcid: 5768719
doi: 10.1038/s41467-017-02317-2
Massimino, L. et al. The inflammatory bowel disease transcriptome and metatranscriptome meta-analysis (IBD TaMMA) framework. Nat. Computat. Sci. 1, 511–515 (2021).
doi: 10.1038/s43588-021-00114-y
Croft, M. & Siegel, R. M. Beyond TNF: TNF superfamily cytokines as targets for the treatment of rheumatic diseases. Nat. Rev. Rheumatol. 13, 217–233 (2017).
pubmed: 28275260
pmcid: 5486401
doi: 10.1038/nrrheum.2017.22
Yasuda, H. Discovery of the RANKL/RANK/OPG system. J. Bone Min. Metab. 39, 2–11 (2021).
doi: 10.1007/s00774-020-01175-1
Walsh, M. C. & Choi, Y. Biology of the RANKL-RANK-OPG system in immunity, bone, and beyond. Front. Immunol. 5, 511 (2014).
pubmed: 25368616
pmcid: 4202272
doi: 10.3389/fimmu.2014.00511
Jakubowski, A. et al. Dual role for TWEAK in angiogenic regulation. J. Cell Sci. 115, 267–274 (2002).
pubmed: 11839778
doi: 10.1242/jcs.115.2.267
Donohue, P. J. et al. TWEAK is an endothelial cell growth and chemotactic factor that also potentiates FGF-2 and VEGF-A mitogenic activity. Arterioscler. Thromb. Vasc. Biol. 23, 594–600 (2003).
pubmed: 12615668
doi: 10.1161/01.ATV.0000062883.93715.37
Domouzoglou, E. M. et al. Fibroblast growth factors in cardiovascular disease: the emerging role of FGF21. Am. J. Physiol. Heart Circ. Physiol. 309, H1029–H1038 (2015).
pubmed: 26232236
pmcid: 4747916
doi: 10.1152/ajpheart.00527.2015
Schett, G., McInnes, I. B. & Neurath, M. F. Reframing immune-mediated inflammatory diseases through signature cytokine hubs. N. Engl. J. Med. 385, 628–639 (2021).
pubmed: 34379924
doi: 10.1056/NEJMra1909094
Peters, A. L., Stunz, L. L. & Bishop, G. A. CD40 and autoimmunity: the dark side of a great activator. Semin. Immunol. 21, 293–300 (2009).
pubmed: 19595612
pmcid: 2753170
doi: 10.1016/j.smim.2009.05.012
Durie, F. H. et al. Prevention of collagen-induced arthritis with an antibody to gp39, the ligand for CD40. Science 261, 1328–1330 (1993).
pubmed: 7689748
doi: 10.1126/science.7689748
Guo, Y. et al. CD40L-dependent pathway is active at various stages of rheumatoid arthritis disease progression. J. Immunol. 198, 4490–4501 (2017).
pubmed: 28455435
doi: 10.4049/jimmunol.1601988
The Lenercept Multiple Sclerosis Study Group and The University of British Columbia MS/MRI Analysis Group. TNF neutralization in MS: results of a randomized, placebo-controlled multicenter study. Neurology 53, 457–465 (1999).
doi: 10.1212/WNL.53.3.457
Bosch, X., Saiz, A., Ramos-Casals, M. & Group, B. S. Monoclonal antibody therapy-associated neurological disorders. Nat. Rev. Neurol. 7, 165–172 (2011).
pubmed: 21263460
doi: 10.1038/nrneurol.2011.1
Singh, U. P. et al. Chemokine and cytokine levels in inflammatory bowel disease patients. Cytokine 77, 44–49 (2016).
pubmed: 26520877
doi: 10.1016/j.cyto.2015.10.008
Friedrich, M. et al. IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies. Nat. Med. 27, 1970–1981 (2021).
pubmed: 34675383
pmcid: 8604730
doi: 10.1038/s41591-021-01520-5
Pavlidis, P. et al. Interleukin-22 regulates neutrophil recruitment in ulcerative colitis and is associated with resistance to ustekinumab therapy. Nat. Commun. 13, 5820 (2022).
pubmed: 36192482
pmcid: 9530232
doi: 10.1038/s41467-022-33331-8
Richard, A. C. et al. Reduced monocyte and macrophage TNFSF15/TL1A expression is associated with susceptibility to inflammatory bowel disease. PLoS Genet. 14, e1007458 (2018).
pubmed: 30199539
pmcid: 6130856
doi: 10.1371/journal.pgen.1007458
Bamias, G. et al. Differential expression of the TL1A/DcR3 system of TNF/TNFR-like proteins in large vs. small intestinal Crohn’s disease. Dig. Liver Dis. 44, 30–36 (2012).
pubmed: 21978578
doi: 10.1016/j.dld.2011.09.002
Bamias, G. et al. High intestinal and systemic levels of decoy receptor 3 (DcR3) and its ligand TL1A in active ulcerative colitis. Clin. Immunol. 137, 242–249 (2010).
pubmed: 20675196
doi: 10.1016/j.clim.2010.07.001
Sands, B. et al. PRA023 demonstrated efficacy and favorable safety as induction therapy for moderately to severely active UC: phase 2 ARTEMIS-UC study results. European Crohn’s and Colitis Organisation https://www.ecco-ibd.eu/publications/congress-abstracts/item/op40-pra023-demonstrated-efficacy-and-favorable-safety-as-induction-therapy-for-moderately-to-severely-active-uc-phase-2-artemis-uc-study-results.html (2023).
Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).
pubmed: 24604202
pmcid: 4064786
doi: 10.1126/science.1246949
Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).
pubmed: 24604203
pmcid: 4124741
doi: 10.1126/science.1246980
de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).
pubmed: 28067908
pmcid: 5289481
doi: 10.1038/ng.3760
Hijazi, Z. et al. Screening of multiple biomarkers associated with ischemic stroke in atrial fibrillation. J. Am. Heart Assoc. 9, e018984 (2020).
pubmed: 33292046
pmcid: 7955358
doi: 10.1161/JAHA.120.018984
Sanna, S. et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat. Genet. 51, 600–605 (2019).
pubmed: 30778224
pmcid: 6441384
doi: 10.1038/s41588-019-0350-x
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
pubmed: 22426310
pmcid: 3593158
doi: 10.1038/ng.2213
Winter, D. J. rentrez: an R package for the NCBI eUtils API. R Journal 9, 520–526 (2017).
doi: 10.32614/RJ-2017-058
Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics 35, 4851–4853 (2019).
pubmed: 31233103
pmcid: 6853652
doi: 10.1093/bioinformatics/btz469
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
pubmed: 20436461
pmcid: 4840234
doi: 10.1038/nbt.1630
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
pubmed: 27019110
doi: 10.1038/ng.3538
Ochoa, D. et al. Open Targets Platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Res. 49, D1302–D1310 (2021).
pubmed: 33196847
doi: 10.1093/nar/gkaa1027
Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).
pubmed: 33536417
pmcid: 7858636
doi: 10.1038/s41467-020-20885-8
Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. 52, 1122–1131 (2020).
pubmed: 32895551
pmcid: 7610464
doi: 10.1038/s41588-020-0682-6
Robinson, J. W. et al. An efficient and robust tool for colocalisation: pair-wise conditional and colocalisation (PWCoCo). Preprint at bioRxiv https://doi.org/2022.2008.2008.503158 (2022).
Arijs, I. et al. Mucosal gene expression of antimicrobial peptides in inflammatory bowel disease before and after first infliximab treatment. PLoS ONE 4, e7984 (2009).
pubmed: 19956723
pmcid: 2776509
doi: 10.1371/journal.pone.0007984
Sands, B. E. et al. Ustekinumab as induction and maintenance therapy for ulcerative colitis. N. Engl. J. Med. 381, 1201–1214 (2019).
pubmed: 31553833
doi: 10.1056/NEJMoa1900750
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
pubmed: 25751142
pmcid: 4655817
doi: 10.1038/nmeth.3317
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
pubmed: 25260700
doi: 10.1093/bioinformatics/btu638