1
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Moix S, Sadler MC, Kutalik Z, Auwerx C. Breaking down causes, consequences, and mediating effects of telomere length variation on human health. Genome Biol 2024; 25:125. [PMID: 38760657 PMCID: PMC11101352 DOI: 10.1186/s13059-024-03269-9] [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: 11/01/2023] [Accepted: 05/07/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Telomeres form repeated DNA sequences at the ends of chromosomes, which shorten with each cell division. Yet, factors modulating telomere attrition and the health consequences thereof are not fully understood. To address this, we leveraged data from 326,363 unrelated UK Biobank participants of European ancestry. RESULTS Using linear regression and bidirectional univariable and multivariable Mendelian randomization (MR), we elucidate the relationships between leukocyte telomere length (LTL) and 142 complex traits, including diseases, biomarkers, and lifestyle factors. We confirm that telomeres shorten with age and show a stronger decline in males than in females, with these factors contributing to the majority of the 5.4% of LTL variance explained by the phenome. MR reveals 23 traits modulating LTL. Smoking cessation and high educational attainment associate with longer LTL, while weekly alcohol intake, body mass index, urate levels, and female reproductive events, such as childbirth, associate with shorter LTL. We also identify 24 traits affected by LTL, with risk for cardiovascular, pulmonary, and some autoimmune diseases being increased by short LTL, while longer LTL increased risk for other autoimmune conditions and cancers. Through multivariable MR, we show that LTL may partially mediate the impact of educational attainment, body mass index, and female age at childbirth on proxied lifespan. CONCLUSIONS Our study sheds light on the modulators, consequences, and the mediatory role of telomeres, portraying an intricate relationship between LTL, diseases, lifestyle, and socio-economic factors.
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Affiliation(s)
- Samuel Moix
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
| | - Marie C Sadler
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland
| | - Zoltán Kutalik
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland.
| | - Chiara Auwerx
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland.
- Center for Integrative Genetics, UNIL, Lausanne, 1015, Switzerland.
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2
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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [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: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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3
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Prapiadou S, Živković L, Thorand B, George MJ, van der Laan SW, Malik R, Herder C, Koenig W, Ueland T, Kleveland O, Aukrust P, Gullestad L, Bernhagen J, Pasterkamp G, Peters A, Hingorani AD, Rosand J, Dichgans M, Anderson CD, Georgakis MK. Proteogenomic Data Integration Reveals CXCL10 as a Potentially Downstream Causal Mediator for IL-6 Signaling on Atherosclerosis. Circulation 2024; 149:669-683. [PMID: 38152968 PMCID: PMC10922752 DOI: 10.1161/circulationaha.123.064974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 11/17/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Genetic and experimental studies support a causal involvement of IL-6 (interleukin-6) signaling in atheroprogression. Although trials targeting IL-6 signaling are underway, any benefits must be balanced against an impaired host immune response. Dissecting the mechanisms that mediate the effects of IL-6 signaling on atherosclerosis could offer insights about novel drug targets with more specific effects. METHODS Leveraging data from 522 681 individuals, we constructed a genetic instrument of 26 variants in the gene encoding the IL-6R (IL-6 receptor) that proxied for pharmacological IL-6R inhibition. Using Mendelian randomization, we assessed its effects on 3281 plasma proteins quantified with an aptamer-based assay in the INTERVAL cohort (n=3301). Using mediation Mendelian randomization, we explored proteomic mediators of the effects of genetically proxied IL-6 signaling on coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease. For significant mediators, we tested associations of their circulating levels with incident cardiovascular events in a population-based study (n=1704) and explored the histological, transcriptomic, and cellular phenotypes correlated with their expression levels in samples from human atherosclerotic lesions. RESULTS We found significant effects of genetically proxied IL-6 signaling on 70 circulating proteins involved in cytokine production/regulation and immune cell recruitment/differentiation, which correlated with the proteomic effects of pharmacological IL-6R inhibition in a clinical trial. Among the 70 significant proteins, genetically proxied circulating levels of CXCL10 (C-X-C motif chemokine ligand 10) were associated with risk of coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease, with up to 67% of the effects of genetically downregulated IL-6 signaling on these end points mediated by decreases in CXCL10. Higher midlife circulating CXCL10 levels were associated with a larger number of cardiovascular events over 20 years, whereas higher CXCL10 expression in human atherosclerotic lesions correlated with a larger lipid core and a transcriptomic profile reflecting immune cell infiltration, adaptive immune system activation, and cytokine signaling. CONCLUSIONS Integrating multiomics data, we found a proteomic signature of IL-6 signaling activation and mediators of its effects on cardiovascular disease. Our analyses suggest the interferon-γ-inducible chemokine CXCL10 to be a potentially causal mediator for atherosclerosis in 3 vascular compartments and, as such, could serve as a promising drug target for atheroprotection.
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Affiliation(s)
- Savvina Prapiadou
- University of Patras School of Medicine, Patras, Greece
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luka Živković
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Marc J. George
- Department of Clinical Pharmacology, Division of Medicine, University College London, London, United Kingdom
| | - Sander W. van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rainer Malik
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
| | - Wolfgang Koenig
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
- German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Thor Ueland
- Thrombosis Research Center (TREC), Division of internal medicine, University hospital of North Norway, Tromsø, Norway
- Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ola Kleveland
- Clinic of Cardiology, St Olavs Hospital, Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Aukrust
- Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Lars Gullestad
- Department of Cardiology Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Jürgen Bernhagen
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany
- Munich Heart Alliance, German Center for Cardiovascular Health (DZHK e.V., partner-site Munich), Munich, Germany
| | - Aroon D. Hingorani
- Department of Clinical Pharmacology, Division of Medicine, University College London, London, United Kingdom
- Centre for Translational Genomics, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Jonathan Rosand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
| | - Christopher D. Anderson
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Marios K. Georgakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
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4
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Ying K, Liu H, Tarkhov AE, Sadler MC, Lu AT, Moqri M, Horvath S, Kutalik Z, Shen X, Gladyshev VN. Causality-enriched epigenetic age uncouples damage and adaptation. NATURE AGING 2024; 4:231-246. [PMID: 38243142 PMCID: PMC11070280 DOI: 10.1038/s43587-023-00557-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 12/12/2023] [Indexed: 01/21/2024]
Abstract
Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.
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Affiliation(s)
- Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- T. H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Hanna Liu
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
- Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA
| | - Andrei E Tarkhov
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marie C Sadler
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ake T Lu
- Altos Labs, San Diego, CA, USA
- Departments of Human Genetics and Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Steve Horvath
- Altos Labs, San Diego, CA, USA
- Departments of Human Genetics and Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Xia Shen
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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5
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Drouard G, Hagenbeek FA, Whipp AM, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. BMC Med 2023; 21:508. [PMID: 38129841 PMCID: PMC10740308 DOI: 10.1186/s12916-023-03198-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. METHODS Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. RESULTS We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. CONCLUSIONS Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Fiona A Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce M Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
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6
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Vladimir K, Perišić MM, Štorga M, Mostashari A, Khanin R. Epigenetics insights from perceived facial aging. Clin Epigenetics 2023; 15:176. [PMID: 37924108 PMCID: PMC10623707 DOI: 10.1186/s13148-023-01590-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/23/2023] [Indexed: 11/06/2023] Open
Abstract
Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.
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Affiliation(s)
- Klemo Vladimir
- LifeNome Inc., New York, 10018, NY, USA
- Faculty of Electrical Engineering and Computing, University of Zagreb, 10000, Zagreb, Croatia
| | - Marija Majda Perišić
- LifeNome Inc., New York, 10018, NY, USA
- Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000, Zagreb, Croatia
| | - Mario Štorga
- LifeNome Inc., New York, 10018, NY, USA
- Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000, Zagreb, Croatia
| | | | - Raya Khanin
- LifeNome Inc., New York, 10018, NY, USA.
- Bioinformatics Core, Memorial Sloan-Kettering Cancer Center, New York, 10065, NY, USA.
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7
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Hanssen R, Auwerx C, Jõeloo M, Sadler MC, Henning E, Keogh J, Bounds R, Smith M, Firth HV, Kutalik Z, Farooqi IS, Reymond A, Lawler K. Chromosomal deletions on 16p11.2 encompassing SH2B1 are associated with accelerated metabolic disease. Cell Rep Med 2023; 4:101155. [PMID: 37586323 PMCID: PMC10439272 DOI: 10.1016/j.xcrm.2023.101155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/08/2023] [Accepted: 07/18/2023] [Indexed: 08/18/2023]
Abstract
New approaches are needed to treat people whose obesity and type 2 diabetes (T2D) are driven by specific mechanisms. We investigate a deletion on chromosome 16p11.2 (breakpoint 2-3 [BP2-3]) encompassing SH2B1, a mediator of leptin and insulin signaling. Phenome-wide association scans in the UK (N = 502,399) and Estonian (N = 208,360) biobanks show that deletion carriers have increased body mass index (BMI; p = 1.3 × 10-10) and increased rates of T2D. Compared with BMI-matched controls, deletion carriers have an earlier onset of T2D, with poorer glycemic control despite higher medication usage. Cystatin C, a biomarker of kidney function, is significantly elevated in deletion carriers, suggesting increased risk of renal impairment. In a Mendelian randomization study, decreased SH2B1 expression increases T2D risk (p = 8.1 × 10-6). We conclude that people with 16p11.2 BP2-3 deletions have early, complex obesity and T2D and may benefit from therapies that enhance leptin and insulin signaling.
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Affiliation(s)
- Ruth Hanssen
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland; Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; University Center for Primary Care and Public Health, 1010 Lausanne, Switzerland
| | - Maarja Jõeloo
- Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia; Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Marie C Sadler
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; University Center for Primary Care and Public Health, 1010 Lausanne, Switzerland
| | - Elana Henning
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Julia Keogh
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Rebecca Bounds
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Miriam Smith
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Helen V Firth
- Department of Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust & Wellcome Sanger Institute, Cambridge, UK
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; University Center for Primary Care and Public Health, 1010 Lausanne, Switzerland
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.
| | - Katherine Lawler
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
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8
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Sadler MC, Auwerx C, Deelen P, Kutalik Z. Multi-layered genetic approaches to identify approved drug targets. CELL GENOMICS 2023; 3:100341. [PMID: 37492104 PMCID: PMC10363916 DOI: 10.1016/j.xgen.2023.100341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 07/27/2023]
Abstract
Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.
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Affiliation(s)
- Marie C. Sadler
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Chiara Auwerx
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Patrick Deelen
- Department of Genetics, University of Groningen, 9700 Groningen, the Netherlands
- Oncode Institute, 3521 Utrecht, the Netherlands
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
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9
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Drouard G, Hagenbeek FA, Whipp A, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291995. [PMID: 37425750 PMCID: PMC10327285 DOI: 10.1101/2023.06.28.23291995] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Background The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. Methods Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks. Results We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. Conclusions Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Fiona A. Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - BIOS Consortium
- Biobank-based Integrative Omics Study Consortium. Lists of authors and their affiliations appear in the supplementary material (see Additional file 1)
| | | | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J. C. de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M. Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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10
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Prapiadou S, Živković L, Thorand B, George MJ, van der Laan SW, Malik R, Herder C, Koenig W, Ueland T, Kleveland O, Aukrust P, Gullestad L, Bernhagen J, Pasterkamp G, Peters A, Hingorani AD, Rosand J, Dichgans M, Anderson CD, Georgakis MK. Proteogenomic integration reveals CXCL10 as a potentially downstream causal mediator for IL-6 signaling on atherosclerosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.24.23287543. [PMID: 37034659 PMCID: PMC10081435 DOI: 10.1101/2023.03.24.23287543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background Genetic and experimental studies support a causal involvement of interleukin-6 (IL-6) signaling in atheroprogression. While trials targeting IL-6 signaling are underway, any benefits must be balanced against an impaired host immune response. Dissecting the mechanisms that mediate the effects of IL-6 signaling on atherosclerosis could offer insights about novel drug targets with more specific effects. Methods Leveraging data from 522,681 individuals, we constructed a genetic instrument of 26 variants in the gene encoding the IL-6 receptor (IL-6R) that proxied for pharmacological IL-6R inhibition. Using Mendelian randomization (MR), we assessed its effects on 3,281 plasma proteins quantified with an aptamer-based assay in the INTERVAL cohort (n=3,301). Using mediation MR, we explored proteomic mediators of the effects of genetically proxied IL-6 signaling on coronary artery disease (CAD), large artery atherosclerotic stroke (LAAS), and peripheral artery disease (PAD). For significant mediators, we tested associations of their circulating levels with incident cardiovascular events in a population-based study (n=1,704) and explored the histological, transcriptomic, and cellular phenotypes correlated with their expression levels in samples from human atherosclerotic lesions. Results We found significant effects of genetically proxied IL-6 signaling on 70 circulating proteins involved in cytokine production/regulation and immune cell recruitment/differentiation, which correlated with the proteomic effects of pharmacological IL-6R inhibition in a clinical trial. Among the 70 significant proteins, genetically proxied circulating levels of CXCL10 were associated with risk of CAD, LAAS, and PAD with up to 67% of the effects of genetically downregulated IL-6 signaling on these endpoints mediated by decreases in CXCL10. Higher midlife circulating CXCL10 levels were associated with a larger number of cardiovascular events over 20 years, whereas higher CXCL10 expression in human atherosclerotic lesions correlated with a larger lipid core and a transcriptomic profile reflecting immune cell infiltration, adaptive immune system activation, and cytokine signaling. Conclusions Integrating multiomics data, we found a proteomic signature of IL-6 signaling activation and mediators of its effects on cardiovascular disease. Our analyses suggest the interferon-γ-inducible chemokine CXCL10 to be a potentially causal mediator for atherosclerosis in three vascular compartments and as such could serve as a promising drug target for atheroprotection.
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Affiliation(s)
- Savvina Prapiadou
- University of Patras School of Medicine, Patras, Greece
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luka Živković
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Marc J. George
- Department of Clinical Pharmacology, Division of Medicine, University College London, London, United Kingdom
| | - Sander W. van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rainer Malik
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Christian Herder
- German Center for Diabetes Research, Partner Düsseldorf, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Wolfgang Koenig
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
- German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Thor Ueland
- Thrombosis Research Center (TREC), Division of internal medicine, University hospital of North Norway, Tromsø, Norway
- Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ola Kleveland
- Clinic of Cardiology, St Olavs Hospital, Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Pal Aukrust
- Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Lars Gullestad
- Department of Cardiology Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Jürgen Bernhagen
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany
- Munich Heart Alliance, German Center for Cardiovascular Health (DZHK e.V., partner-site Munich), Munich, Germany
| | - Aroon D. Hingorani
- Department of Clinical Pharmacology, Division of Medicine, University College London, London, United Kingdom
- Centre for Translational Genomics, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Jonathan Rosand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
| | - Christopher D. Anderson
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Marios K. Georgakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-University of Munich, Munich, Germany
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11
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Auwerx C, Sadler MC, Woh T, Reymond A, Kutalik Z, Porcu E. Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations. eLife 2023; 12:81097. [PMID: 36891970 PMCID: PMC9998083 DOI: 10.7554/elife.81097] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/17/2023] [Indexed: 02/17/2023] Open
Abstract
Despite the success of genome-wide association studies (GWASs) in identifying genetic variants associated with complex traits, understanding the mechanisms behind these statistical associations remains challenging. Several methods that integrate methylation, gene expression, and protein quantitative trait loci (QTLs) with GWAS data to determine their causal role in the path from genotype to phenotype have been proposed. Here, we developed and applied a multi-omics Mendelian randomization (MR) framework to study how metabolites mediate the effect of gene expression on complex traits. We identified 216 transcript-metabolite-trait causal triplets involving 26 medically relevant phenotypes. Among these associations, 58% were missed by classical transcriptome-wide MR, which only uses gene expression and GWAS data. This allowed the identification of biologically relevant pathways, such as between ANKH and calcium levels mediated by citrate levels and SLC6A12 and serum creatinine through modulation of the levels of the renal osmolyte betaine. We show that the signals missed by transcriptome-wide MR are found, thanks to the increase in power conferred by integrating multiple omics layer. Simulation analyses show that with larger molecular QTL studies and in case of mediated effects, our multi-omics MR framework outperforms classical MR approaches designed to detect causal relationships between single molecular traits and complex phenotypes.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Marie C Sadler
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Tristan Woh
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland
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12
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Smith DA, Sadler MC, Altman RB. Promises and challenges in pharmacoepigenetics. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e18. [PMID: 37560024 PMCID: PMC10406571 DOI: 10.1017/pcm.2023.6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 08/11/2023]
Abstract
Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.
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Affiliation(s)
- Delaney A Smith
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Marie C Sadler
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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