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Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Author

Listed:
  • Xiaoguang Xu

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • Chachrit Khunsriraksakul

    (Penn State College of Medicine)

  • James M. Eales

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • Sebastien Rubin

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • David Scannali

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • Sushant Saluja

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • David Talavera

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • Havell Markus

    (Penn State College of Medicine)

  • Lida Wang

    (Penn State College of Medicine)

  • Maciej Drzal

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • Akhlaq Maan

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • Abigail C. Lay

    (Faculty of Medicine, Biology and Health, University of Manchester)

  • Priscilla R. Prestes

    (Federation University Australia)

  • Jeniece Regan

    (Penn State College of Medicine)

  • Avantika R. Diwadkar

    (Penn State College of Medicine)

  • Matthew Denniff

    (University of Leicester)

  • Grzegorz Rempega

    (Medical University of Silesia)

  • Jakub Ryszawy

    (Medical University of Silesia)

  • Robert Król

    (Vascular and Transplant Surgery, Faculty of Medical Sciences in Katowice, Medical University of Silesia)

  • John P. Dormer

    (University Hospitals of Leicester)

  • Monika Szulinska

    (Metabolic Disorders Treatment and Clinical Dietetics, Karol Marcinkowski University of Medical Sciences)

  • Marta Walczak

    (Metabolic Disorders and Arterial Hypertension, Poznan University of Medical Sciences)

  • Andrzej Antczak

    (Karol Marcinkowski University of Medical Sciences)

  • Pamela R. Matías-García

    (Helmholtz Center Munich
    Helmholtz Center Munich
    partner site Munich Heart Alliance)

  • Melanie Waldenberger

    (Helmholtz Center Munich
    Helmholtz Center Munich
    partner site Munich Heart Alliance)

  • Adrian S. Woolf

    (Faculty of Biology, Medicine and Health, University of Manchester
    Manchester University NHS Foundation Trust)

  • Bernard Keavney

    (Faculty of Medicine, Biology and Health, University of Manchester
    Manchester University NHS Foundation Trust Manchester, Manchester Royal Infirmary)

  • Ewa Zukowska-Szczechowska

    (Silesian Medical College)

  • Wojciech Wystrychowski

    (Vascular and Transplant Surgery, Faculty of Medical Sciences in Katowice, Medical University of Silesia)

  • Joanna Zywiec

    (Diabetology and Nephrology, Zabrze, Medical University of Silesia)

  • Pawel Bogdanski

    (Metabolic Disorders Treatment and Clinical Dietetics, Karol Marcinkowski University of Medical Sciences)

  • A. H. Jan Danser

    (Division of Pharmacology and Vascular Medicine, Erasmus Medical Centre)

  • Nilesh J. Samani

    (University of Leicester
    Glenfield Hospital)

  • Tomasz J. Guzik

    (Jagiellonian University Medical College
    Queen’s Medical Research Institute, University of Edinburgh
    Jagiellonian University Medical College)

  • Andrew P. Morris

    (Centre for Musculoskeletal Research, Division of Musculoskeletal & Dermatological Sciences, Faculty of Medicine, Biology and Health, University of Manchester)

  • Dajiang J. Liu

    (Penn State College of Medicine)

  • Fadi J. Charchar

    (Federation University Australia
    University of Leicester
    University of Melbourne)

  • Maciej Tomaszewski

    (Faculty of Medicine, Biology and Health, University of Manchester
    Manchester University NHS Foundation Trust Manchester, Manchester Royal Infirmary)

Abstract

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

Suggested Citation

  • Xiaoguang Xu & Chachrit Khunsriraksakul & James M. Eales & Sebastien Rubin & David Scannali & Sushant Saluja & David Talavera & Havell Markus & Lida Wang & Maciej Drzal & Akhlaq Maan & Abigail C. Lay , 2024. "Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets," Nature Communications, Nature, vol. 15(1), pages 1-29, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46132-y
    DOI: 10.1038/s41467-024-46132-y
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    References listed on IDEAS

    as
    1. Yang I. Li & Garrett Wong & Jack Humphrey & Towfique Raj, 2019. "Prioritizing Parkinson’s disease genes using population-scale transcriptomic data," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    2. Tarik J. Salameh & Xiaotao Wang & Fan Song & Bo Zhang & Sage M. Wright & Chachrit Khunsriraksakul & Yijun Ruan & Feng Yue, 2020. "A supervised learning framework for chromatin loop detection in genome-wide contact maps," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    3. Alvaro N. Barbeira & Scott P. Dickinson & Rodrigo Bonazzola & Jiamao Zheng & Heather E. Wheeler & Jason M. Torres & Eric S. Torstenson & Kaanan P. Shah & Tzintzuni Garcia & Todd L. Edwards & Eli A. St, 2018. "Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics," Nature Communications, Nature, vol. 9(1), pages 1-20, December.
    4. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
    5. Chachrit Khunsriraksakul & Daniel McGuire & Renan Sauteraud & Fang Chen & Lina Yang & Lida Wang & Jordan Hughey & Scott Eckert & J. Dylan Weissenkampen & Ganesh Shenoy & Olivia Marx & Laura Carrel & B, 2022. "Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    6. Christopher N. Foley & James R. Staley & Philip G. Breen & Benjamin B. Sun & Paul D. W. Kirk & Stephen Burgess & Joanna M. M. Howson, 2021. "A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    7. Patrick Wu & QiPing Feng & Vern Eric Kerchberger & Scott D. Nelson & Qingxia Chen & Bingshan Li & Todd L. Edwards & Nancy J. Cox & Elizabeth J. Phillips & C. Michael Stein & Dan M. Roden & Joshua C. D, 2022. "Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Xiaoguang Xu & James M. Eales & Artur Akbarov & Hui Guo & Lorenz Becker & David Talavera & Fehzan Ashraf & Jabran Nawaz & Sanjeev Pramanik & John Bowes & Xiao Jiang & John Dormer & Matthew Denniff & A, 2018. "Molecular insights into genome-wide association studies of chronic kidney disease-defining traits," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    9. Zhongshang Yuan & Huanhuan Zhu & Ping Zeng & Sheng Yang & Shiquan Sun & Can Yang & Jin Liu & Xiang Zhou, 2020. "Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    10. Seong Kyu Han & Michelle T. McNulty & Christopher J. Benway & Pei Wen & Anya Greenberg & Ana C. Onuchic-Whitford & Dongkeun Jang & Jason Flannick & Noël P. Burtt & Parker C. Wilson & Benjamin D. Humph, 2023. "Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    11. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    12. Xuran Wang & Jihwan Park & Katalin Susztak & Nancy R. Zhang & Mingyao Li, 2019. "Bulk tissue cell type deconvolution with multi-subject single-cell expression reference," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    13. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    14. Joshua D. Backman & Alexander H. Li & Anthony Marcketta & Dylan Sun & Joelle Mbatchou & Michael D. Kessler & Christian Benner & Daren Liu & Adam E. Locke & Suganthi Balasubramanian & Ashish Yadav & Ni, 2021. "Exome sequencing and analysis of 454,787 UK Biobank participants," Nature, Nature, vol. 599(7886), pages 628-634, November.
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