Genetically proxied HTRA1 protease activity and circulating levels independently predict risk of ischemic stroke and coronary artery disease.
High-Temperature Requirement A Serine Peptidase 1
/ genetics
Humans
Ischemic Stroke
/ genetics
Coronary Artery Disease
/ genetics
Genetic Predisposition to Disease
Genome-Wide Association Study
Female
Male
Middle Aged
Japan
/ epidemiology
Risk Assessment
Aged
Risk Factors
Polymorphism, Single Nucleotide
Phenotype
United Kingdom
/ epidemiology
Loss of Function Mutation
Journal
Nature cardiovascular research
ISSN: 2731-0590
Titre abrégé: Nat Cardiovasc Res
Pays: England
ID NLM: 9918284280206676
Informations de publication
Date de publication:
Jun 2024
Jun 2024
Historique:
received:
31
10
2023
accepted:
23
04
2024
medline:
28
8
2024
pubmed:
28
8
2024
entrez:
28
8
2024
Statut:
ppublish
Résumé
Genetic variants in HTRA1 are associated with stroke risk. However, the mechanisms mediating this remain largely unknown, as does the full spectrum of phenotypes associated with genetic variation in HTRA1. Here we show that rare HTRA1 variants are linked to ischemic stroke in the UK Biobank and BioBank Japan. Integrating data from biochemical experiments, we next show that variants causing loss of protease function associated with ischemic stroke, coronary artery disease and skeletal traits in the UK Biobank and MyCode cohorts. Moreover, a common variant modulating circulating HTRA1 mRNA and protein levels enhances the risk of ischemic stroke and coronary artery disease while lowering the risk of migraine and macular dystrophy in genome-wide association study, UK Biobank, MyCode and BioBank Japan data. We found no interaction between proxied HTRA1 activity and levels. Our findings demonstrate the role of HTRA1 for cardiovascular diseases and identify two mechanisms as potential targets for therapeutic interventions.
Identifiants
pubmed: 39196222
doi: 10.1038/s44161-024-00475-3
pii: 10.1038/s44161-024-00475-3
doi:
Substances chimiques
High-Temperature Requirement A Serine Peptidase 1
EC 3.4.21.-
HTRA1 protein, human
EC 3.4.21.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
701-713Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
Références
GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 20, 795–820 (2021).
doi: 10.1016/S1474-4422(21)00252-0
Roth, G. A. et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: update from the GBD 2019 study. J. Am. Coll. Cardiol. 76, 2982–3021 (2020).
pubmed: 33309175
pmcid: 7755038
doi: 10.1016/j.jacc.2020.11.010
Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).
pubmed: 29531354
pmcid: 5968830
doi: 10.1038/s41588-018-0058-3
Mishra, A. et al. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature 611, 115–123 (2022).
pubmed: 36180795
pmcid: 9524349
doi: 10.1038/s41586-022-05165-3
Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).
pubmed: 36474045
pmcid: 9729111
doi: 10.1038/s41588-022-01233-6
Verdura, E. et al. Heterozygous HTRA1 mutations are associated with autosomal dominant cerebral small vessel disease. Brain 138, 2347–2358 (2015).
pubmed: 26063658
doi: 10.1093/brain/awv155
Hara, K. et al. Association of HTRA1 mutations and familial ischemic cerebral small-vessel disease. N. Engl. J. Med. 360, 1729–1739 (2009).
pubmed: 19387015
doi: 10.1056/NEJMoa0801560
Nozaki, H. et al. Distinct molecular mechanisms of HTRA1 mutants in manifesting heterozygotes with CARASIL. Neurology 86, 1964–1974 (2016).
pubmed: 27164673
doi: 10.1212/WNL.0000000000002694
Tan, R. Y. Y. et al. How common are single gene mutations as a cause for lacunar stroke? A targeted gene panel study. Neurology 93, e2007–e2020 (2019).
pubmed: 31719132
pmcid: 6913325
doi: 10.1212/WNL.0000000000008544
Coste, T. et al. Heterozygous HTRA1 nonsense or frameshift mutations are pathogenic. Brain 144, 2616–2624 (2021).
pubmed: 34270682
doi: 10.1093/brain/awab271
Malik, R. et al. Whole-exome sequencing reveals a role of HTRA1 and EGFL8 in brain white matter hyperintensities. Brain 144, 2670–2682 (2021).
pubmed: 34626176
pmcid: 8557338
doi: 10.1093/brain/awab253
Hautakangas, H. et al. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat. Genet. 54, 152–160 (2022).
pubmed: 35115687
pmcid: 8837554
doi: 10.1038/s41588-021-00990-0
Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48, 134–143 (2016).
pubmed: 26691988
doi: 10.1038/ng.3448
Beaufort, N. et al. Cerebral small vessel disease-related protease HtrA1 processes latent TGF-β binding protein 1 and facilitates TGF-β signaling. Proc. Natl Acad. Sci. USA 111, 16496–16501 (2014).
pubmed: 25369932
pmcid: 4246310
doi: 10.1073/pnas.1418087111
Uemura, M. et al. HTRA1-related cerebral small vessel disease: a review of the literature. Front. Neurol. 11, 545 (2020).
pubmed: 32719647
pmcid: 7351529
doi: 10.3389/fneur.2020.00545
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
pubmed: 25826379
pmcid: 4380465
doi: 10.1371/journal.pmed.1001779
Hujoel, M. L. A., Gazal, S., Loh, P. R., Patterson, N. & Price, A. L. Liability threshold modeling of case–control status and family history of disease increases association power. Nat. Genet. 52, 541–547 (2020).
pubmed: 32313248
pmcid: 7210076
doi: 10.1038/s41588-020-0613-6
Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).
pubmed: 28189464
pmcid: 5350590
doi: 10.1016/j.je.2016.12.005
Schwartz, M. L. B. et al. A model for genome-first care: returning secondary genomic findings to participants and their healthcare providers in a large research cohort. Am. J. Hum. Genet. 103, 328–337 (2018).
pubmed: 30100086
pmcid: 6128218
doi: 10.1016/j.ajhg.2018.07.009
Williams, M. S. et al. Patient-centered precision health in a learning health care system: Geisinger’s genomic medicine experience. Health Aff. (Millwood) 37, 757–764 (2018).
pubmed: 29733722
doi: 10.1377/hlthaff.2017.1557
Engelter, S. T. et al. Epidemiology of aphasia attributable to first ischemic stroke: incidence, severity, fluency, etiology, and thrombolysis. Stroke 37, 1379–1384 (2006).
pubmed: 16690899
doi: 10.1161/01.STR.0000221815.64093.8c
Easton, J. D. et al. Definition and evaluation of transient ischemic attack: a scientific statement for healthcare professionals from the American Heart Association/American Stroke Association Stroke Council; Council on Cardiovascular Surgery and Anesthesia; Council on Cardiovascular Radiology and Intervention; Council on Cardiovascular Nursing; and the Interdisciplinary Council on Peripheral Vascular Disease. The American Academy of Neurology affirms the value of this statement as an educational tool for neurologists. Stroke 40, 2276–2293 (2009).
pubmed: 19423857
doi: 10.1161/STROKEAHA.108.192218
Vincent, M. B. & Hadjikhani, N. Migraine aura and related phenomena: beyond scotomata and scintillations. Cephalalgia 27, 1368–1377 (2007).
pubmed: 17944958
pmcid: 3761083
doi: 10.1111/j.1468-2982.2007.01388.x
Rannikmae, K. et al. Beyond the brain: systematic review of extracerebral phenotypes associated with monogenic cerebral small vessel disease. Stroke 51, 3007–3017 (2020).
pubmed: 32842921
doi: 10.1161/STROKEAHA.120.029517
Traylor, M. et al. Genetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies. Lancet Neurol. 20, 351–361 (2021).
pubmed: 33773637
pmcid: 8062914
doi: 10.1016/S1474-4422(21)00031-4
Vosa, 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
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
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
doi: 10.1126/science.aaz1776
Kerimov, N. et al. eQTL Catalogue 2023: new datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs. PLoS Genet. 19, e1010932 (2023).
pubmed: 37721944
pmcid: 10538656
doi: 10.1371/journal.pgen.1010932
Kato, T. et al. Candesartan prevents arteriopathy progression in cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy model. J. Clin. Invest. 131, e140555 (2021).
Zellner, A. et al. CADASIL brain vessels show a HTRA1 loss-of-function profile. Acta Neuropathol. 136, 111–125 (2018).
pubmed: 29725820
doi: 10.1007/s00401-018-1853-8
Grau, S. et al. The role of human HtrA1 in arthritic disease. J. Biol. Chem. 281, 6124–6129 (2006).
pubmed: 16377621
doi: 10.1074/jbc.M500361200
Tom, I. et al. Development of a therapeutic anti-HtrA1 antibody and the identification of DKK3 as a pharmacodynamic biomarker in geographic atrophy. Proc. Natl Acad. Sci. USA 117, 9952–9963 (2020).
pubmed: 32345717
pmcid: 7211935
doi: 10.1073/pnas.1917608117
Joutel, A., Haddad, I., Ratelade, J. & Nelson, M. T. Perturbations of the cerebrovascular matrisome: a convergent mechanism in small vessel disease of the brain? J. Cereb. Blood Flow. Metab. 36, 143–157 (2016).
pubmed: 25853907
pmcid: 4758555
doi: 10.1038/jcbfm.2015.62
Verdura, E. et al. Disruption of a miR-29 binding site leading to COL4A1 upregulation causes pontine autosomal dominant microangiopathy with leukoencephalopathy. Ann. Neurol. 80, 741–753 (2016).
pubmed: 27666438
doi: 10.1002/ana.24782
Gould, D. B. et al. Role of COL4A1 in small-vessel disease and hemorrhagic stroke. N. Engl. J. Med. 354, 1489–1496 (2006).
pubmed: 16598045
doi: 10.1056/NEJMoa053727
Verbeek, E. et al. COL4A2 mutation associated with familial porencephaly and small-vessel disease. Eur. J. Hum. Genet. 20, 844–851 (2012).
pubmed: 22333902
pmcid: 3400734
doi: 10.1038/ejhg.2012.20
Jeanne, M. et al. COL4A2 mutations impair COL4A1 and COL4A2 secretion and cause hemorrhagic stroke. Am. J. Hum. Genet. 90, 91–101 (2012).
pubmed: 22209247
pmcid: 3257894
doi: 10.1016/j.ajhg.2011.11.022
Aloui, C. et al. End-truncated LAMB1 causes a hippocampal memory defect and a leukoencephalopathy. Ann. Neurol. 90, 962–975 (2021).
pubmed: 34606115
doi: 10.1002/ana.26242
Yang, W. et al. Coronary-heart-disease-associated genetic variant at the COL4A1/COL4A2 locus affects COL4A1/COL4A2 expression, vascular cell survival, atherosclerotic plaque stability and risk of myocardial infarction. PLoS Genet. 12, e1006127 (2016).
pubmed: 27389912
pmcid: 4936713
doi: 10.1371/journal.pgen.1006127
Dichgans, M., Pulit, S. L. & Rosand, J. Stroke genetics: discovery, biology, and clinical applications. Lancet Neurol. 18, 587–599 (2019).
pubmed: 30975520
doi: 10.1016/S1474-4422(19)30043-2
Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).
pubmed: 26343387
pmcid: 4589895
doi: 10.1038/ng.3396
Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).
pubmed: 21378990
pmcid: 3119261
doi: 10.1038/ng.784
CARDIoGRAMplusC4D Consortiumet al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45, 25–33 (2013).
doi: 10.1038/ng.2480
Poepsel, S. et al. Determinants of amyloid fibril degradation by the PDZ protease HTRA1. Nat. Chem. Biol. 11, 862–869 (2015).
pubmed: 26436840
doi: 10.1038/nchembio.1931
Jones, A. et al. Increased expression of multifunctional serine protease, HTRA1, in retinal pigment epithelium induces polypoidal choroidal vasculopathy in mice. Proc. Natl Acad. Sci. USA 108, 14578–14583 (2011).
pubmed: 21844367
pmcid: 3167497
doi: 10.1073/pnas.1102853108
Menon, M. et al. Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration. Nat. Commun. 10, 4902 (2019).
pubmed: 31653841
pmcid: 6814749
doi: 10.1038/s41467-019-12780-8
Khanani, A. M. et al. Phase 1 study of the anti-HtrA1 antibody-binding fragment FHTR2163 in geographic atrophy secondary to age-related macular degeneration. Am. J. Ophthalmol. 232, 49–57 (2021).
pubmed: 34214452
doi: 10.1016/j.ajo.2021.06.017
Cheng, Q. et al. Selective organ targeting (SORT) nanoparticles for tissue-specific mRNA delivery and CRISPR–Cas gene editing. Nat. Nanotechnol. 15, 313–320 (2020).
pubmed: 32251383
pmcid: 7735425
doi: 10.1038/s41565-020-0669-6
Mulder, W. J. M., Ochando, J., Joosten, L. A. B., Fayad, Z. A. & Netea, M. G. Therapeutic targeting of trained immunity. Nat. Rev. Drug Discov. 18, 553–566 (2019).
pubmed: 30967658
pmcid: 7069501
doi: 10.1038/s41573-019-0025-4
Ding, R. et al. scQTLbase: an integrated human single-cell eQTL database. Nucleic Acids Res. 52, D1010–D1017 (2024).
pubmed: 37791879
doi: 10.1093/nar/gkad781
Lizio, M. et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 16, 22 (2015).
pubmed: 25723102
pmcid: 4310165
doi: 10.1186/s13059-014-0560-6
Zheng, Z. et al. QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes. Nucleic Acids Res. 48, D983–D991 (2020).
pubmed: 31598699
doi: 10.1093/nar/gkz888
Abraham, G., Qiu, Y. & Inouye, M. FlashPCA2: principal component analysis of Biobank-scale genotype datasets. Bioinformatics 33, 2776–2778 (2017).
pubmed: 28475694
doi: 10.1093/bioinformatics/btx299
Rannikmae, K. et al. Accuracy of identifying incident stroke cases from linked health care data in UK Biobank. Neurology 95, e697–e707 (2020).
pubmed: 32616677
pmcid: 7455356
doi: 10.1212/WNL.0000000000009924
Verweij, N., Eppinga, R. N., Hagemeijer, Y. & van der Harst, P. Identification of 15 novel risk loci for coronary artery disease and genetic risk of recurrent events, atrial fibrillation and heart failure. Sci. Rep. 7, 2761 (2017).
pubmed: 28584231
pmcid: 5459820
doi: 10.1038/s41598-017-03062-8
Bak, S., Gaist, D., Sindrup, S. H., Skytthe, A. & Christensen, K. Genetic liability in stroke: a long-term follow-up study of Danish twins. Stroke 33, 769–774 (2002).
pubmed: 11872902
doi: 10.1161/hs0302.103619
Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).
pubmed: 34017140
doi: 10.1038/s41588-021-00870-7
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
pubmed: 27268795
pmcid: 4893825
doi: 10.1186/s13059-016-0974-4
Ioannidis, N. M. et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016).
pubmed: 27666373
pmcid: 5065685
doi: 10.1016/j.ajhg.2016.08.016
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
pubmed: 32461654
pmcid: 7334197
doi: 10.1038/s41586-020-2308-7
Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).
pubmed: 21737059
pmcid: 3135811
doi: 10.1016/j.ajhg.2011.05.029
Lee, S. et al. Optimal unified approach for rare-variant association testing with application to small-sample case–control whole-exome sequencing studies. Am. J. Hum. Genet. 91, 224–237 (2012).
pubmed: 22863193
pmcid: 3415556
doi: 10.1016/j.ajhg.2012.06.007
Liu, Y. et al. ACAT: a fast and powerful p value combination method for rare-variant analysis in sequencing studies. Am. J. Hum. Genet. 104, 410–421 (2019).
pubmed: 30849328
pmcid: 6407498
doi: 10.1016/j.ajhg.2019.01.002
Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49, 1458–1467 (2017).
pubmed: 28892062
doi: 10.1038/ng.3951
He, Y. et al. East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease. Nat. Genet. 55, 2129–2138 (2023).
pubmed: 38036781
pmcid: 10703676
doi: 10.1038/s41588-023-01569-7
Loh, P. R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
pubmed: 27694958
pmcid: 5096458
doi: 10.1038/ng.3679
Terao, C. et al. Population-specific reference panel improves imputation quality and enhances locus discovery and fine-mapping. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-3194976/v1 (2023).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
pubmed: 27571263
pmcid: 5157836
doi: 10.1038/ng.3656
He, Y., Koido, M., Shimmori, Y. & Kamatami, Y. GWASLab: a Python package for processing and visualizing GWAS summary statistics. Preprint at Jxiv https://doi.org/10.51094/jxiv.370 (2023).
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
pubmed: 21988835
pmcid: 3261699
doi: 10.1038/msb.2011.75
Packer, R. J. et al. DeepPheWAS: an R package for phenotype generation and association analysis for phenome-wide association studies. Bioinformatics 39, btad073 (2023).
pubmed: 36744935
pmcid: 10070035
doi: 10.1093/bioinformatics/btad073
Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).
pubmed: 28008009
doi: 10.1126/science.aaf6814
Haggerty, C. M. et al. Genomics-first evaluation of heart disease associated with titin-truncating variants. Circulation 140, 42–54 (2019).
pubmed: 31216868
pmcid: 6602806
doi: 10.1161/CIRCULATIONAHA.119.039573
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
Gleason, K. J., Yang, F., Pierce, B. L., He, X. & Chen, L. S. Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits. Genome Biol. 21, 236 (2020).
pubmed: 32912334
pmcid: 7488447
doi: 10.1186/s13059-020-02125-w