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Xu X, Khunsriraksakul C, Eales JM, Rubin S, Scannali D, Saluja S, Talavera D, Markus H, Wang L, Drzal M, Maan A, Lay AC, Prestes PR, Regan J, Diwadkar AR, Denniff M, Rempega G, Ryszawy J, Król R, Dormer JP, Szulinska M, Walczak M, Antczak A, Matías-García PR, Waldenberger M, Woolf AS, Keavney B, Zukowska-Szczechowska E, Wystrychowski W, Zywiec J, Bogdanski P, Danser AHJ, Samani NJ, Guzik TJ, Morris AP, Liu DJ, Charchar FJ, Tomaszewski M. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat Commun 2024; 15:2359. [PMID: 38504097 PMCID: PMC10950894 DOI: 10.1038/s41467-024-46132-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
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.
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Affiliation(s)
- Xiaoguang Xu
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | | | - James M Eales
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Sebastien Rubin
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - David Scannali
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Sushant Saluja
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - David Talavera
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Havell Markus
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Lida Wang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Maciej Drzal
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Akhlaq Maan
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Abigail C Lay
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Priscilla R Prestes
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Jeniece Regan
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Avantika R Diwadkar
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Matthew Denniff
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Grzegorz Rempega
- Department of Urology, Medical University of Silesia, Katowice, Poland
| | - Jakub Ryszawy
- Department of Urology, Medical University of Silesia, Katowice, Poland
| | - Robert Król
- Department of General, Vascular and Transplant Surgery, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - John P Dormer
- Department of Cellular Pathology, University Hospitals of Leicester, Leicester, UK
| | - Monika Szulinska
- Department of Obesity, Metabolic Disorders Treatment and Clinical Dietetics, Karol Marcinkowski University of Medical Sciences, Poznan, Poland
| | - Marta Walczak
- Department of Internal Diseases, Metabolic Disorders and Arterial Hypertension, Poznan University of Medical Sciences, Poznan, Poland
| | - Andrzej Antczak
- Department of Urology and Uro-oncology, Karol Marcinkowski University of Medical Sciences, Poznan, Poland
| | - Pamela R Matías-García
- Institute of Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Center Munich, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Adrian S Woolf
- Division of Cell Matrix Biology and Regenerative Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Royal Manchester Children's Hospital and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust Manchester, Manchester Royal Infirmary, Manchester, UK
| | | | - Wojciech Wystrychowski
- Department of General, Vascular and Transplant Surgery, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - Joanna Zywiec
- Department of Internal Medicine, Diabetology and Nephrology, Zabrze, Medical University of Silesia, Katowice, Poland
| | - Pawel Bogdanski
- Department of Obesity, Metabolic Disorders Treatment and Clinical Dietetics, Karol Marcinkowski University of Medical Sciences, Poznan, Poland
| | - A H Jan Danser
- Department of Internal Medicine, Division of Pharmacology and Vascular Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Tomasz J Guzik
- Department of Internal Medicine, Jagiellonian University Medical College, Kraków, Poland
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal & Dermatological Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Fadi J Charchar
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Department of Physiology, University of Melbourne, Melbourne, Australia
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust Manchester, Manchester Royal Infirmary, Manchester, UK.
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2
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Reus LM, Boltz T, Francia M, Bot M, Ramesh N, Koromina M, Pijnenburg YAL, den Braber A, van der Flier WM, Visser PJ, van der Lee SJ, Tijms BM, Teunissen CE, Loohuis LO, Ophoff RA. Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.26.559021. [PMID: 37808647 PMCID: PMC10557608 DOI: 10.1101/2023.09.26.559021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo , measuring 5,543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n=490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n=487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders, we performed a metabolome-wide association study (MWAS), identifying 40 associations between CSF metabolites and brain traits. Similarly, over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.
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Han X, Lains I, Li J, Li J, Chen Y, Yu B, Qi Q, Boerwinkle E, Kaplan R, Thyagarajan B, Daviglus M, Joslin CE, Cai J, Guasch-Ferré M, Tobias DK, Rimm E, Ascherio A, Costenbader K, Karlson E, Mucci L, Eliassen AH, Zeleznik O, Miller J, Vavvas DG, Kim IK, Silva R, Miller J, Hu F, Willett W, Lasky-Su J, Kraft P, Richards JB, MacGregor S, Husain D, Liang L. Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration. Cell Rep Med 2023; 4:101085. [PMID: 37348500 PMCID: PMC10394104 DOI: 10.1016/j.xcrm.2023.101085] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/22/2023] [Accepted: 05/22/2023] [Indexed: 06/24/2023]
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.
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Affiliation(s)
- Xikun Han
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Ines Lains
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jinglun Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yiheng Chen
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical Center, Minneapolis, MN, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Charlotte E Joslin
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Eric Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alberto Ascherio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karen Costenbader
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Elizabeth Karlson
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Lorelei Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Miller
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Demetrios G Vavvas
- Retina Service, Ines and Fredrick Yeatts Retinal Research Laboratory, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Ivana K Kim
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Rufino Silva
- Ophthalmology Unit, Centro Hospitalar e Universitário de Coimbra (CHUC), Coimbra, Portugal; University Clinic of Ophthalmology, Faculty of Medicine, University of Coimbra (FMUC), Coimbra, Portugal; Clinical Academic Center of Coimbra (CACC), Coimbra, Portugal
| | - Joan Miller
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Frank Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Walter Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jessica Lasky-Su
- Systems Genetics and Genomics Unit, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - J Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montréal, QC, Canada; Department of Twin Research, King's College London, London, UK; Five Prime Sciences Inc, Montréal, QC, Canada
| | - Stuart MacGregor
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Deeba Husain
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA.
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Feofanova EV, Brown MR, Alkis T, Manuel AM, Li X, Tahir UA, Li Z, Mendez KM, Kelly RS, Qi Q, Chen H, Larson MG, Lemaitre RN, Morrison AC, Grieser C, Wong KE, Gerszten RE, Zhao Z, Lasky-Su J, Yu B. Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations. Nat Commun 2023; 14:3111. [PMID: 37253714 PMCID: PMC10229598 DOI: 10.1038/s41467-023-38800-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/16/2023] [Indexed: 06/01/2023] Open
Abstract
Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.
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Affiliation(s)
- Elena V Feofanova
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | - Michael R Brown
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | - Taryn Alkis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | - Astrid M Manuel
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Han Chen
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Alanna C Morrison
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | | | | | - Robert E Gerszten
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Zhongming Zhao
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Bing Yu
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA.
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Sinn M, Stanoppi M, Hauth F, Fleming JR, Funck D, Mayans O, Hartig JS. Guanidino acid hydrolysis by the human enzyme annotated as agmatinase. Sci Rep 2022; 12:22088. [PMID: 36543883 PMCID: PMC9772407 DOI: 10.1038/s41598-022-26655-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Guanidino acids such as taurocyamine, guanidinobutyrate, guanidinopropionate, and guanidinoacetate have been detected in humans. However, except for guanidionacetate, which is a precursor of creatine, their metabolism and potential functions remain poorly understood. Agmatine has received considerable attention as a potential neurotransmitter and the human enzyme so far annotated as agmatinase (AGMAT) has been proposed as an important modulator of agmatine levels. However, conclusive evidence for the assigned enzymatic activity is lacking. Here we show that AGMAT hydrolyzed a range of linear guanidino acids but was virtually inactive with agmatine. Structural modelling and direct biochemical assays indicated that two naturally occurring variants differ in their substrate preferences. A negatively charged group in the substrate at the end opposing the guanidine moiety was essential for efficient catalysis, explaining why agmatine was not hydrolyzed. We suggest to rename AGMAT as guanidino acid hydrolase (GDAH). Additionally, we demonstrate that the GDAH substrates taurocyamine, guanidinobutyrate and guanidinopropionate were produced by human glycine amidinotransferase (GATM). The presented findings show for the first time an enzymatic activity for GDAH/AGMAT. Since agmatine has frequently been proposed as an endogenous neurotransmitter, the current findings clarify important aspects of the metabolism of agmatine and guanidino acid derivatives in humans.
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Affiliation(s)
- Malte Sinn
- grid.9811.10000 0001 0658 7699Department of Chemistry, University of Konstanz, Konstanz, Germany
| | - Marco Stanoppi
- grid.9811.10000 0001 0658 7699Department of Chemistry, University of Konstanz, Konstanz, Germany
| | - Franziskus Hauth
- grid.9811.10000 0001 0658 7699Department of Chemistry, University of Konstanz, Konstanz, Germany ,grid.9811.10000 0001 0658 7699Konstanz Research School Chemical Biology (KoRS-CB), University of Konstanz, Konstanz, Germany
| | - Jennifer R. Fleming
- grid.9811.10000 0001 0658 7699Department of Biology, University of Konstanz, Konstanz, Germany
| | - Dietmar Funck
- grid.9811.10000 0001 0658 7699Department of Chemistry, University of Konstanz, Konstanz, Germany
| | - Olga Mayans
- grid.9811.10000 0001 0658 7699Konstanz Research School Chemical Biology (KoRS-CB), University of Konstanz, Konstanz, Germany ,grid.9811.10000 0001 0658 7699Department of Biology, University of Konstanz, Konstanz, Germany
| | - Jörg S. Hartig
- grid.9811.10000 0001 0658 7699Department of Chemistry, University of Konstanz, Konstanz, Germany ,grid.9811.10000 0001 0658 7699Konstanz Research School Chemical Biology (KoRS-CB), University of Konstanz, Konstanz, Germany
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6
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Hoshi RA, Liu Y, Luttmann-Gibson H, Tiwari S, Giulianini F, Andres AM, Watrous JD, Cook NR, Costenbader KH, Okereke OI, Ridker PM, Manson JE, Lee IM, Vinayagamoorthy M, Cheng S, Copeland T, Jain M, Chasman DI, Demler OV, Mora S. Association of Physical Activity With Bioactive Lipids and Cardiovascular Events. Circ Res 2022; 131:e84-e99. [PMID: 35862024 PMCID: PMC9357171 DOI: 10.1161/circresaha.122.320952] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 07/07/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations. METHODS PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www. CLINICALTRIALS gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (REGISTRATION: URL: https://www. CLINICALTRIALS gov; Unique identifier: NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation). RESULTS We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD. CONCLUSIONS We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.
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Affiliation(s)
- Rosangela A. Hoshi
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yanyan Liu
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Heike Luttmann-Gibson
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Saumya Tiwari
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92037, USA
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Allen M. Andres
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92037, USA
| | - Jeramie D. Watrous
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92037, USA
| | - Nancy R. Cook
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Karen H. Costenbader
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Olivia I. Okereke
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Paul M Ridker
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - JoAnn E. Manson
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - I-Min Lee
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | | | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Ctr, Los Angeles, CA 90048, USA
| | - Trisha Copeland
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mohit Jain
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Olga V. Demler
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland
| | - Samia Mora
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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7
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Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort. Metabolites 2022; 12:metabo12070604. [PMID: 35888728 PMCID: PMC9320943 DOI: 10.3390/metabo12070604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/24/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with >5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriched ETFDH stop gain variant p.Trp286Ter (rs1235904433-hexanoylcarnitine) and the MCCC2 stop lost variant p.Ter564GlnextTer3 (rs751970792-carnitine) have been found in patients with glutaric acidemia type II and 3-methylcrotonylglycinuria, respectively, but the loci have not been associated with the respective metabolites in a genome-wide association study (GWAS) previously. We further identified three gene-trait associations, where multiple rare variants contribute to the signal. These results not only provide further evidence for previously described associations, but also describe novel genes and mechanisms for diseases and disease-related traits.
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8
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Hysi PG, Mangino M, Christofidou P, Falchi M, Karoly ED, Mohney RP, Valdes AM, Spector TD, Menni C. Metabolome Genome-Wide Association Study Identifies 74 Novel Genomic Regions Influencing Plasma Metabolites Levels. Metabolites 2022; 12:61. [PMID: 35050183 PMCID: PMC8777659 DOI: 10.3390/metabo12010061] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 01/27/2023] Open
Abstract
Metabolites are small products of metabolism that provide a snapshot of the wellbeing of an organism and the mechanisms that control key physiological processes involved in health and disease. Here we report the results of a genome-wide association study of 722 circulating metabolite levels in 8809 subjects of European origin, providing both breadth and depth. These analyses identified 202 unique genomic regions whose variations are associated with the circulating levels of 478 different metabolites. Replication with a subset of 208 metabolites that were available in an independent dataset for a cohort of 1768 European subjects confirmed the robust associations, including 74 novel genomic regions not associated with any metabolites in previous works. This study enhances our knowledge of genetic mechanisms controlling human metabolism. Our findings have major potential for identifying novel targets and developing new therapeutic strategies.
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Affiliation(s)
- Pirro G. Hysi
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK; (P.G.H.); (M.M.); (P.C.); (M.F.); (A.M.V.)
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK; (P.G.H.); (M.M.); (P.C.); (M.F.); (A.M.V.)
- NIHR Biomedical Research Centre at Guy’s and St. Thomas’ Foundation Trust, London SE1 9RT, UK
| | - Paraskevi Christofidou
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK; (P.G.H.); (M.M.); (P.C.); (M.F.); (A.M.V.)
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK; (P.G.H.); (M.M.); (P.C.); (M.F.); (A.M.V.)
| | - Edward D. Karoly
- Discovery and Translational Sciences, Metabolon Inc., Raleigh-Durham, NC 27560, USA; (E.D.K.); (R.P.M.)
| | | | - Robert P. Mohney
- Discovery and Translational Sciences, Metabolon Inc., Raleigh-Durham, NC 27560, USA; (E.D.K.); (R.P.M.)
| | - Ana M. Valdes
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK; (P.G.H.); (M.M.); (P.C.); (M.F.); (A.M.V.)
- Inflammation, Injury and Recovery Sciences, School of Medicine, University of Nottingham, Nottingham NG5 1PB, UK
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK; (P.G.H.); (M.M.); (P.C.); (M.F.); (A.M.V.)
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK; (P.G.H.); (M.M.); (P.C.); (M.F.); (A.M.V.)
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9
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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10
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Andörfer L, Holtfreter B, Weiss S, Matthes R, Pitchika V, Schmidt CO, Samietz S, Kastenmüller G, Nauck M, Völker U, Völzke H, Csonka LN, Suhre K, Pietzner M, Kocher T. Salivary metabolites associated with a 5-year tooth loss identified in a population-based setting. BMC Med 2021; 19:161. [PMID: 34256740 PMCID: PMC8278731 DOI: 10.1186/s12916-021-02035-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss. METHODS We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models. RESULTS We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation. CONCLUSIONS Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.
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Affiliation(s)
- Leonie Andörfer
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Birte Holtfreter
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Rutger Matthes
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Vinay Pitchika
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Carsten Oliver Schmidt
- Institute for Community Medicine, SHIP/Clinical Epidemiology Research, University Medicine Greifswald, Greifswald, Germany
| | - Stefanie Samietz
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Henry Völzke
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute for Community Medicine, SHIP/Clinical Epidemiology Research, University Medicine Greifswald, Greifswald, Germany
| | - Laszlo N Csonka
- Department of Biological Sciences, Purdue University, West Lafayette, USA
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Maik Pietzner
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- Computational Medicine, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany.
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11
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Panyard DJ, Kim KM, Darst BF, Deming YK, Zhong X, Wu Y, Kang H, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Commun Biol 2021; 4:63. [PMID: 33437055 PMCID: PMC7803963 DOI: 10.1038/s42003-020-01583-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 12/09/2020] [Indexed: 02/07/2023] Open
Abstract
The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.
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Grants
- R01 AG037639 NIA NIH HHS
- UL1 TR000427 NCATS NIH HHS
- T15 LM007359 NLM NIH HHS
- T32 LM012413 NLM NIH HHS
- RF1 AG027161 NIA NIH HHS
- T32 AG000213 NIA NIH HHS
- P2C HD047873 NICHD NIH HHS
- UL1 TR002373 NCATS NIH HHS
- P30 AG062715 NIA NIH HHS
- P50 AG033514 NIA NIH HHS
- R01 AG027161 NIA NIH HHS
- R01 AG054047 NIA NIH HHS
- P30 AG017266 NIA NIH HHS
- R21 AG067092 NIA NIH HHS
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM)
- NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS)
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)
- This research is supported by National Institutes of Health (NIH) grants R01AG27161 (Wisconsin Registry for Alzheimer Prevention: Biomarkers of Preclinical AD), R01AG054047 (Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer’s Disease), R21AG067092 (Identifying Metabolomic Risk Factors in Plasma and Cerebrospinal Fluid for Alzheimer’s Disease), R01AG037639 (White Matter Degeneration: Biomarkers in Preclinical Alzheimer’s Disease), P30AG017266 (Center for Demography of Health and Aging), and P50AG033514 and P30AG062715 (Wisconsin Alzheimer’s Disease Research Center Grant), the Helen Bader Foundation, Northwestern Mutual Foundation, Extendicare Foundation, State of Wisconsin, the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS) grant UL1TR000427, and the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. This research was supported in part by the Intramural Research Program of the National Institute on Aging. Computational resources were supported by a core grant to the Center for Demography and Ecology at the University of Wisconsin-Madison (P2CHD047873). Author DJP was supported by an NLM training grant to the Bio-Data Science Training Program (T32LM012413). Author BFD was supported by an NLM training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM 5T15LM007359). Author YKD was supported by a training grant from the National Institute on Aging (T32AG000213). Author HK was supported by National Science Foundation (NSF) grant DMS-1811414 (Theory and Methods for Inferring Causal Effects with Mendelian Randomization).
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Affiliation(s)
- Daniel J Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
| | - Kyeong Mo Kim
- Department of Biotechnology, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Burcu F Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA, 90033, USA
| | - Yuetiva K Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
| | - Xiaoyuan Zhong
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI, 53706, USA
| | - Cynthia M Carlsson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI, 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI, 53705, USA
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI, 53726, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI, 53726, USA.
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI, 53706, USA.
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12
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Feofanova EV, Chen H, Dai Y, Jia P, Grove ML, Morrison AC, Qi Q, Daviglus M, Cai J, North KE, Laurie CC, Kaplan RC, Boerwinkle E, Yu B. A Genome-wide Association Study Discovers 46 Loci of the Human Metabolome in the Hispanic Community Health Study/Study of Latinos. Am J Hum Genet 2020; 107:849-863. [PMID: 33031748 PMCID: PMC7675000 DOI: 10.1016/j.ajhg.2020.09.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/10/2020] [Indexed: 02/08/2023] Open
Abstract
Variation in levels of the human metabolome reflect changes in homeostasis, providing a window into health and disease. The genetic impact on circulating metabolites in Hispanics, a population with high cardiometabolic disease burden, is largely unknown. We conducted genome-wide association analyses on 640 circulating metabolites in 3,926 Hispanic Community Health Study/Study of Latinos participants. The estimated heritability for 640 metabolites ranged between 0%-54% with a median at 2.5%. We discovered 46 variant-metabolite pairs (p value < 1.2 × 10-10, minor allele frequency ≥ 1%, proportion of variance explained [PEV] mean = 3.4%, PEVrange = 1%-22%) with generalized effects in two population-based studies and confirmed 301 known locus-metabolite associations. Half of the identified variants with generalized effect were located in genes, including five nonsynonymous variants. We identified co-localization with the expression quantitative trait loci at 105 discovered and 151 known loci-metabolites sets. rs5855544, upstream of SLC51A, was associated with higher levels of three steroid sulfates and co-localized with expression levels of SLC51A in several tissues. Mendelian randomization (MR) analysis identified several metabolites associated with coronary heart disease (CHD) and type 2 diabetes. For example, two variants located in or near CYP4F2 (rs2108622 and rs79400241, respectively), involved in vitamin E metabolism, were associated with the levels of octadecanedioate and vitamin E metabolites (gamma-CEHC and gamma-CEHC glucuronide); MR analysis showed that genetically high levels of these metabolites were associated with lower odds of CHD. Our findings document the genetic architecture of circulating metabolites in an underrepresented Hispanic/Latino community, shedding light on disease etiology.
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Affiliation(s)
- Elena V Feofanova
- Human Genetics Center, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Han Chen
- Human Genetics Center, University of Texas Health Science Center, Houston, TX 77030, USA; Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Megan L Grove
- Human Genetics Center, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Alanna C Morrison
- Human Genetics Center, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois College of Medicine, Chicago, IL 60612, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina Gilling School of Global Public Health, Chapel Hill, NC 27599, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina Gilling School of Global Public Health, Chapel Hill, NC 27599, USA; Carolina Center of Genome Sciences, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Bing Yu
- Human Genetics Center, University of Texas Health Science Center, Houston, TX 77030, USA.
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