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Genetically proxied HTRA1 protease activity and circulating levels independently predict risk of ischemic stroke and coronary artery disease

Nature Cardiovascular Research (2024)Cite this article

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Abstract

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.

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Fig. 1: Analytic workflow of human phenotypes linked to rare and common HTRA1 variants.
Fig. 2: Consequences of rare missense protease domain variants on enzymatic activity.
Fig. 3: The HTRA1 protomer–protomer interface is a hotspot for severe loss of function mutations.
Fig. 4: PheWAS of imputed HTRA1 protease activity reveals associations with neurovascular, skeletal and CAD-related traits.
Fig. 5: A common variant in HTRA1 shows co-localization with five neurovascular phenotypes.
Fig. 6: Genetically proxied HTRA1 activity and levels independently predict risk of stroke and CAD.

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Data availability

The data that support the findings of this study are available in the manuscript and its online supplements. Source data underlying Fig. 2 and Extended Data Figs. 2 and 3 are provided as Source Data files. The structure of HTRA1 is publicly available from the Protein Data Bank (https://www.rcsb.org/structure/3TJO). All UK Biobank raw and derived data in this study are available from the UK Biobank (http://www.ukbiobank.ac.uk/). MyCode data are not publicly available due to ethical and institutional review board regulations but are available upon reasonable request. BioBank Japan PheWAS data are available at https://pheweb.jp/. eQTL data can be accessed at https://gtexportal.org/home/ and https://eqtlgen.org/. pQTL data are available at https://www.decode.com/summarydata/.

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Acknowledgements

We thank A. Nottebrock and B. Lindner for technical support. We acknowledge the UK Biobank Resource for providing access to their data under application numbers 2532 and 36993. This study was supported by Deutsche Forschungsgemeinschaft (GZ: MA 7973/2-1 to R.M. and GZ: DI 722/20-1 to M.D., ID 497256604); the European Union’s Horizon Europe (European Innovation Council) program under grant agreement number 101115381 (to R.M. and M.D.); and the flagship P4-medicine project DigiMed Bayern. C.D.A is supported by a grant from the US National Institutes of Health (R01NS103942). The BioBank Japan project is supported by the Ministry of Education, Culture, Sports, Sciences and Technology (MEXT) of the Japanese government and the Japan Agency for Medical Research and Development (AMED) under grant numbers JP18km0605001 and JP23tm0624002. This work was also funded by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198, to M.D.); ERA-NET NEURON (MatriSVDs, DI 722/22-1 to M.D.); the Leducq Fondation (grant 22CVD01 to M.D.); the Vascular Dementia Research Foundation (to M.D.); and the CRC 1123 (B3; to M.D.). We thank all patients engaged in the MyCode Community Health Initiative and members of the MyCode research team. We also acknowledge the Geisinger–Regeneron DiscovEHR collaboration contributors who have been critical in the generation of the data used for this study.

Author information

Author notes

  1. These authors contributed equally: Rainer Malik, Nathalie Beaufort.

Authors and Affiliations

  1. Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich, Munich, Germany

    Rainer Malik, Nathalie Beaufort, Marios K. Georgakis & Martin Dichgans

  2. Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA

    Jiang Li

  3. Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan

    Koki Tanaka, Yunye He, Masaru Koido & Yoichiro Kamatani

  4. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan

    Masaru Koido & Chikashi Terao

  5. Institute of Medical Science, University of Tokyo, Tokyo, Japan

    BioBank Japan

  6. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Christopher D. Anderson

  7. Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Boston, MA, USA

    Christopher D. Anderson

  8. McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA

    Christopher D. Anderson

  9. Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA

    Christopher D. Anderson

  10. Department of Neurology, Pennsylvania State University, Hershey, PA, USA

    Ramin Zand

  11. Department of Neurology, Neuroscience Institute, Geisinger Health System, Danville, PA, USA

    Ramin Zand

  12. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

    Martin Dichgans

  13. German Center for Cardiovascular Research (DZHK), Munich, Germany

    Martin Dichgans

  14. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

    Martin Dichgans

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Contributions

R.M. designed the study and performed statistical analyses. N.B. designed the study and performed biochemical analyses. J.L. and R.Z. designed and performed MyCode-related statistical analyses. M.K.G. and C.D.A. provided statistical advice and access to UK Biobank data. K.T., Y.H., M.K., C.T. and Y.K. designed and performed BioBank Japan–related statistical analyses. M.D. designed, conceived and supervised the study. R.M., N.B. and M.D. wrote and edited the first version of the manuscript. All authors interpreted the data and revised the manuscript for intellectual content.

Corresponding author

Correspondence to Martin Dichgans.

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Competing interests

C.D.A. has received sponsored research support from Bayer AG and has consulted for ApoPharma, unrelated to the content of this manuscript. All other authors declare no competing interests.

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Nature Cardiovascular Research thanks Stephanie Debette, Braxton Mitchell, Muredach Reilly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Forest plot of rare HTRA1 variant association with ischemic stroke and comparison with classical risk factors.

Depicted is a forest plot with the standardized beta and standard error for each of the classical risk factors for ischemic stroke on the x-axis. SBP: systolic blood pressure (n = 502,536 independent samples), DBP: diastolic blood pressure (n = 502,536 independent samples), LDL: low-density lipoprotein levels (n = 502,509 independent samples), HDL: high density lipoprotein levels (n = 502,509 independent samples), BMI: body mass index (n = 502,475 independent samples). Data are presented as standardized beta ±SEM.

Extended Data Fig. 2 Biochemical characterization of UKB variants – Representative examples.

a, HTRA1 levels in secretomes from HEK293E cells transfected to overexpress HTRA1 were analyzed by anti-Myc immunoblot. b, Secretomes from cells transfected to overexpress LTBP1 were treated with secretomes from cells transfected to overexpress HTRA1 for 24 h at 37 °C. LTBP1 processing was assessed by anti-V5 immunoblot (IB). c, HTRA1 protease activity was determined as the ratio cleaved to intact LTBP1, normalized to HTRA1 levels. a–c, Secretomes from non-transfected cells (Ctrl) and from cells transfected to overexpress wt HTRA1 or the inactive variant S328A (SA) were included in each run. a, b, Representative immunoblots are depicted. c, Histogram depicts the average activity + s.d. measured in n = 5–6 experiments (see Source Data for details on sample size); circles: data points. The activity of wt HTRA1 (set to 1) and SA are marked by dashed lines.

Source data

Extended Data Fig. 3 HTRA1 variants G213R and G213V are not secreted in transfected HEK293E cells.

HEK293E cells were transfected to overexpress wt HTRA1, the active site mutant S328A, or the UKB variants G213R or G213V. Non-transfected cells served as control (Ctrl). Cells lysates and secretomes were collected and analyzed by anti-Myc immunoblot. Actin served as loading control for the intracellular fraction. Images are representative of n = 2 (cell lysates) or n = 5 (cell secretomes) independent experiments.

Source data

Extended Data Fig. 4 HTRA1 variants causing moderate to strong loss of protease activity are enriched for variants linked to familial or sporadic cSVD.

Percentage of variants previously identified in familial or sporadic cSVD cases in each protease activity category.

Extended Data Fig. 5 Loss of enzymatic activity linked to missense protease domain variants in HTRA1 correlates with ischemic stroke risk as measured by the LT-FH phenotype and with WMH burden.

a, For each of the 76 variants with protease activity measurements, the effect size on HTRA1 activity (x-axis) and the LT-FH phenotype (y-axis) is displayed. b, For each of the 13 protease activity lowering variants found in the UKB imaging dataset, the effect size on HTRA1 activity (x-axis) and the logWMH volume (y-axis) is displayed. a, b, Correlation was computed using the Pearson’s correlation coefficient. P-value is derived from a two-tailed test.

Extended Data Fig. 6 Generalized additive model analysis of the LT-FH ischemic stroke phenotype (a) or of logWMH volume (b) and HTRA1 protease activity.

Loess-smoothed GAM generalized additive model curve. For each individual with European ancestry in the UKB, we predicted HTRA1 protease activity based on their genotype. HTRA1 activity for individuals without a rare HTRA1 protease domain mutation was set to 100%. Wild-type HTRA1 activity was set to 100%. Error bands represent 95% confidence intervals.

Extended Data Fig. 7 Effect sizes of significant Phecodes in UK Biobank (discovery, total n = 425,338 independent samples) and MyCode (replication, total n = 167,780 independent samples) for genetically proxied HTRA1 activity.

The x-axis holds information on the effect size and the associated 95% confidence interval derived by logistic regression. P-values < 0.05 were corrected using Firth’s correction. Data are presented as standardized beta ± s.e.m.

Extended Data Fig. 8 GTEx information on rs2672592 and expression changes in multi-tissue analysis.

The x-axis represents the normalized effect size (NES) and associated 95% confidence interval derived from linear regression. P-values were not corrected for multiple testing. Data are presented as normalized effect size ±95% confidence interval.

Extended Data Fig. 9 rs2672592 is an eQTL and pQTL for HTRA1.

For any ischemic stroke, coronary artery disease, blood HTRA1 eQTL and plasma HTRA1 pQTL, the HTRA1 gene ±150 kb is depicted on the x-axis. The y-axis depicts the -log10 p-value of the respective GWAS derived from logistic or mixed models. The dashed lines depict genome-wide significance (p = 5E-8).

Extended Data Fig. 10 Effect sizes of significant Phecodes in UK Biobank (discovery, total n = 425,338 independent samples) and MyCode (replication, total n = 167,780 independent samples) for genetically proxied HTRA1 levels.

The x-axis holds information on the effect size and the associated 95% confidence interval derived by logistic regression. P-values < 0.05 were corrected using Firth’s correction. Data are presented as standardized beta ± s.e.m.

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Malik, R., Beaufort, N., Li, J. et al. Genetically proxied HTRA1 protease activity and circulating levels independently predict risk of ischemic stroke and coronary artery disease. Nat Cardiovasc Res (2024). https://doi.org/10.1038/s44161-024-00475-3

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