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Abbott KL, Ali A, Reinfeld BI, Deik A, Subudhi S, Landis MD, Hongo RA, Young KL, Kunchok T, Nabel CS, Crowder KD, Kent JR, Madariaga MLL, Jain RK, Beckermann KE, Lewis CA, Clish CB, Muir A, Rathmell WK, Rathmell JC, Vander Heiden MG. Metabolite profiling of human renal cell carcinoma reveals tissue-origin dominance in nutrient availability. bioRxiv 2024:2023.12.24.573250. [PMID: 38187626 PMCID: PMC10769456 DOI: 10.1101/2023.12.24.573250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The tumor microenvironment is a determinant of cancer progression and therapeutic efficacy, with nutrient availability playing an important role. Although it is established that the local abundance of specific nutrients defines the metabolic parameters for tumor growth, the factors guiding nutrient availability in tumor compared to normal tissue and blood remain poorly understood. To define these factors in renal cell carcinoma (RCC), we performed quantitative metabolomic and comprehensive lipidomic analyses of tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples collected from patients. TIF nutrient composition closely resembles KIF, suggesting that tissue-specific factors unrelated to the presence of cancer exert a stronger influence on nutrient levels than tumor-driven alterations. Notably, select metabolite changes consistent with known features of RCC metabolism are found in RCC TIF, while glucose levels in TIF are not depleted to levels that are lower than those found in KIF. These findings inform tissue nutrient dynamics in RCC, highlighting a dominant role of non-cancer driven tissue factors in shaping nutrient availability in these tumors.
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Affiliation(s)
- Keene L. Abbott
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ahmed Ali
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bradley I. Reinfeld
- Medical Scientist Training Program, Vanderbilt University, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA
- Graduate Program in Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sonu Subudhi
- Steele Laboratories of Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Madelyn D. Landis
- Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA
| | - Rachel A. Hongo
- Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA
| | - Kirsten L. Young
- Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA
| | - Tenzin Kunchok
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Christopher S. Nabel
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Johnathan R. Kent
- Department of Surgery, University of Chicago Medicine, Chicago, IL, USA
| | | | - Rakesh K. Jain
- Steele Laboratories of Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kathryn E. Beckermann
- Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA
| | - Caroline A. Lewis
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Present address: UMass Chan Medical School, Program in Molecular Medicine, Worcester, MA, USA
| | | | - Alexander Muir
- Ben May Department of Cancer Research, University of Chicago, Chicago, IL, USA
| | - W. Kimryn Rathmell
- Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA
- Vanderbilt Center for Immunobiology and Vanderbilt-Ingram Cancer Center, VUMC, Nashville, TN, USA
| | - Jeffrey C. Rathmell
- Department of Pathology, Microbiology and Immunology, VUMC, Nashville, TN, USA
- Vanderbilt Center for Immunobiology and Vanderbilt-Ingram Cancer Center, VUMC, Nashville, TN, USA
| | - Matthew G. Vander Heiden
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
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2
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Mondal AK, Brock DC, Rowan S, Yang ZH, Rojulpote KV, Smith KM, Francisco SG, Bejarano E, English MA, Deik A, Jeanfavre S, Clish CB, Remaley AT, Taylor A, Swaroop A. Selective transcriptomic dysregulation of metabolic pathways in liver and retina by short- and long-term dietary hyperglycemia. iScience 2024; 27:108979. [PMID: 38333717 PMCID: PMC10850775 DOI: 10.1016/j.isci.2024.108979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/21/2023] [Accepted: 01/16/2024] [Indexed: 02/10/2024] Open
Abstract
A high glycemic index (HGI) diet induces hyperglycemia, a risk factor for diseases affecting multiple organ systems. Here, we evaluated tissue-specific adaptations in the liver and retina after feeding HGI diet to mice for 1 or 12 month. In the liver, genes associated with inflammation and fatty acid metabolism were altered within 1 month of HGI diet, whereas 12-month HGI diet-fed group showed dysregulated expression of cytochrome P450 genes and overexpression of lipogenic factors including Srebf1 and Elovl5. In contrast, retinal transcriptome exhibited HGI-related notable alterations in energy metabolism genes only after 12 months. Liver fatty acid profiles in HGI group revealed higher levels of monounsaturated and lower levels of saturated and polyunsaturated fatty acids. Additionally, HGI diet increased blood low-density lipoprotein, and diet-aging interactions affected expression of mitochondrial oxidative phosphorylation genes in the liver and disease-associated genes in retina. Thus, our findings provide new insights into retinal and hepatic adaptive mechanisms to dietary hyperglycemia.
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Affiliation(s)
- Anupam K. Mondal
- Neurobiology Neurodegeneration & Repair Laboratory, National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Daniel C. Brock
- Neurobiology Neurodegeneration & Repair Laboratory, National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Sheldon Rowan
- Laboratory for Nutrition & Vision Research, JM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
- Friedman School of Nutrition Science and Policy, and Department of Molecular and Chemical Biology, Tufts University, Boston, MA, USA
- Department of Ophthalmology, Tufts University School of Medicine, Boston, MA, USA
| | - Zhi-Hong Yang
- Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Krishna Vamsi Rojulpote
- Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Kelsey M. Smith
- Laboratory for Nutrition & Vision Research, JM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
- Friedman School of Nutrition Science and Policy, and Department of Molecular and Chemical Biology, Tufts University, Boston, MA, USA
| | - Sarah G. Francisco
- Laboratory for Nutrition & Vision Research, JM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
| | - Eloy Bejarano
- Laboratory for Nutrition & Vision Research, JM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
- School of Health Sciences and Veterinary School, Universidad CEU Cardenal Herrera, CEU Universities, Valencia, Spain
| | - Milton A. English
- Neurobiology Neurodegeneration & Repair Laboratory, National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Alan T. Remaley
- Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Allen Taylor
- Laboratory for Nutrition & Vision Research, JM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
- Friedman School of Nutrition Science and Policy, and Department of Molecular and Chemical Biology, Tufts University, Boston, MA, USA
- Department of Ophthalmology, Tufts University School of Medicine, Boston, MA, USA
| | - Anand Swaroop
- Neurobiology Neurodegeneration & Repair Laboratory, National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD, USA
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Schirmer M, Stražar M, Avila-Pacheco J, Rojas-Tapias DF, Brown EM, Temple E, Deik A, Bullock K, Jeanfavre S, Pierce K, Jin S, Invernizzi R, Pust MM, Costliow Z, Mack DR, Griffiths AM, Walters T, Boyle BM, Kugathasan S, Vlamakis H, Hyams J, Denson L, Clish CB, Xavier RJ. Linking microbial genes to plasma and stool metabolites uncovers host-microbial interactions underlying ulcerative colitis disease course. Cell Host Microbe 2024; 32:209-226.e7. [PMID: 38215740 PMCID: PMC10923022 DOI: 10.1016/j.chom.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 11/08/2023] [Accepted: 12/15/2023] [Indexed: 01/14/2024]
Abstract
Understanding the role of the microbiome in inflammatory diseases requires the identification of microbial effector molecules. We established an approach to link disease-associated microbes to microbial metabolites by integrating paired metagenomics, stool and plasma metabolomics, and culturomics. We identified host-microbial interactions correlated with disease activity, inflammation, and the clinical course of ulcerative colitis (UC) in the Predicting Response to Standardized Colitis Therapy (PROTECT) pediatric inception cohort. In severe disease, metabolite changes included increased dipeptides and tauro-conjugated bile acids (BAs) and decreased amino-acid-conjugated BAs in stool, whereas in plasma polyamines (N-acetylputrescine and N1-acetylspermidine) increased. Using patient samples and Veillonella parvula as a model, we uncovered nitrate- and lactate-dependent metabolic pathways, experimentally linking V. parvula expansion to immunomodulatory tryptophan metabolite production. Additionally, V. parvula metabolizes immunosuppressive thiopurine drugs through xdhA xanthine dehydrogenase, potentially impairing the therapeutic response. Our findings demonstrate that the microbiome contributes to disease-associated metabolite changes, underscoring the importance of these interactions in disease pathology and treatment.
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Affiliation(s)
- Melanie Schirmer
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; ZIEL - Institute for Food & Health, Technical University of Munich, 85354 Freising, Germany.
| | - Martin Stražar
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | | | - Eric M Brown
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Emily Temple
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Amy Deik
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kevin Bullock
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sarah Jeanfavre
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kerry Pierce
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shen Jin
- Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | | | - Marie-Madlen Pust
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Zach Costliow
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - David R Mack
- Division of Gastroenterology, Hepatology & Nutrition, Children's Hospital of Eastern Ontario and University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - Anne M Griffiths
- Division of Gastroenterology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Thomas Walters
- Division of Gastroenterology, Division of Gastroenterology, Hepatology and Nutrition, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Brendan M Boyle
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Nationwide Children's Hospital, Columbus, OH 43205, USA
| | - Subra Kugathasan
- Department of Pediatrics, Emory University, Atlanta, GA 30322, USA
| | - Hera Vlamakis
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jeffrey Hyams
- Connecticut Children's Medical Center, Division of Digestive Diseases, Hartford, CT 06106, USA
| | - Lee Denson
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Clary B Clish
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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4
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Semnani-Azad Z, Toledo E, Babio N, Ruiz-Canela M, Wittenbecher C, Razquin C, Wang F, Dennis C, Deik A, Clish CB, Corella D, Fitó M, Estruch R, Arós F, Ros E, García-Gavilan J, Liang L, Salas-Salvadó J, Martínez-González MA, Hu FB, Guasch-Ferré M. Plasma metabolite predictors of metabolic syndrome incidence and reversion. Metabolism 2024; 151:155742. [PMID: 38007148 PMCID: PMC10872312 DOI: 10.1016/j.metabol.2023.155742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 11/19/2023] [Accepted: 11/19/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND Metabolic Syndrome (MetS) is a progressive pathophysiological state defined by a cluster of cardiometabolic traits. However, little is known about metabolites that may be predictors of MetS incidence or reversion. Our objective was to identify plasma metabolites associated with MetS incidence or MetS reversion. METHODS The study included 1468 participants without cardiovascular disease (CVD) but at high CVD risk at enrollment from two case-cohort studies nested within the PREvención con DIeta MEDiterránea (PREDIMED) study with baseline metabolomics data. MetS was defined in accordance with the harmonized International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute criteria, which include meeting 3 or more thresholds for waist circumference, triglyceride, HDL cholesterol, blood pressure, and fasting blood glucose. MetS incidence was defined by not having MetS at baseline but meeting the MetS criteria at a follow-up visit. MetS reversion was defined by MetS at baseline but not meeting MetS criteria at a follow-up visit. Plasma metabolome was profiled by LC-MS. Multivariable-adjusted Cox regression models and elastic net regularized regressions were used to assess the association of 385 annotated metabolites with MetS incidence and MetS reversion after adjusting for potential risk factors. RESULTS Of the 603 participants without baseline MetS, 298 developed MetS over the median 4.8-year follow-up. Of the 865 participants with baseline MetS, 285 experienced MetS reversion. A total of 103 and 88 individual metabolites were associated with MetS incidence and MetS reversion, respectively, after adjusting for confounders and false discovery rate correction. A metabolomic signature comprised of 77 metabolites was robustly associated with MetS incidence (HR: 1.56 (95 % CI: 1.33-1.83)), and a metabolomic signature of 83 metabolites associated with MetS reversion (HR: 1.44 (95 % CI: 1.25-1.67)), both p < 0.001. The MetS incidence and reversion signatures included several lipids (mainly glycerolipids and glycerophospholipids) and branched-chain amino acids. CONCLUSION We identified unique metabolomic signatures, primarily comprised of lipids (including glycolipids and glycerophospholipids) and branched-chain amino acids robustly associated with MetS incidence; and several amino acids and glycerophospholipids associated with MetS reversion. These signatures provide novel insights on potential distinct mechanisms underlying the conditions leading to the incidence or reversion of MetS.
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Affiliation(s)
- Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Estefanía Toledo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Nancy Babio
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere i Virgili, Hospital Universitari Sant Joan de Reus, Reus, Spain.
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Clemens Wittenbecher
- Division of Food and Nutrition Sciences, Department of Biology, Chalmers University of Technology, Gothenburg, Sweden.
| | - Cristina Razquin
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Courtney Dennis
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Amy Deik
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Dolores Corella
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain.
| | - Montserrat Fitó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; IMIM Hospital del Mar Medical Research Institute, Grup de Risc Cardiovascular i Nutrició, Barcelona, Spain.
| | - Ramon Estruch
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain.
| | - Fernando Arós
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain; University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain.
| | - Emilio Ros
- Lipid Clinic, Department of Endocrinology and Nutrition, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain.
| | - Jesús García-Gavilan
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain.
| | - Liming Liang
- Department of Epidemiology, 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.
| | - Jordi Salas-Salvadó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere i Virgili, Hospital Universitari Sant Joan de Reus, Reus, Spain.
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, 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.
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), University of Copenhagen, Copenhagen, Denmark.
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5
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Margara-Escudero HJ, Paz-Graniel I, García-Gavilán J, Ruiz-Canela M, Sun Q, Clish CB, Toledo E, Corella D, Estruch R, Ros E, Castañer O, Arós F, Fiol M, Guasch-Ferré M, Lapetra J, Razquin C, Dennis C, Deik A, Li J, Gómez-Gracia E, Babio N, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol 2024; 23:38. [PMID: 38245716 PMCID: PMC10800064 DOI: 10.1186/s12933-023-02111-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/29/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD. METHODS The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models. RESULTS Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period. CONCLUSIONS This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease. TRIAL REGISTRATION ISRCTN35739639.
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Affiliation(s)
- Hernando J Margara-Escudero
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel Ruiz-Canela
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary B Clish
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Estefania Toledo
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Navarre, Spain
| | - Dolores Corella
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramón Estruch
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Lipid Clinic, Hospital Clínic, Barcelona, Spain
| | - Olga Castañer
- Centro de Investigación Biomédica en Red (CIBERESP) de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Fernando Arós
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Illes Balears Health Research Institute (IdISBa), Hospital Son Espases, Palma, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - José Lapetra
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain
| | - Cristina Razquin
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Courtney Dennis
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Enrique Gómez-Gracia
- Preventive Medicine and Public Health Department, School of Medicine, University of Málaga, 29010, Malaga, Spain
- Biomedical Research Institute of Malaga-IBIMA Plataforma BIONAND, 29010, Malaga, Spain
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel A Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
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6
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Singh C, Jin B, Shrestha N, Markhard AL, Panda A, Calvo SE, Deik A, Pan X, Zuckerman AL, Ben Saad A, Corey KE, Sjoquist J, Osganian S, AminiTabrizi R, Rhee EP, Shah H, Goldberger O, Mullen AC, Cracan V, Clish CB, Mootha VK, Goodman RP. ChREBP is activated by reductive stress and mediates GCKR-associated metabolic traits. Cell Metab 2024; 36:144-158.e7. [PMID: 38101397 PMCID: PMC10842884 DOI: 10.1016/j.cmet.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/24/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023]
Abstract
Common genetic variants in glucokinase regulator (GCKR), which encodes GKRP, a regulator of hepatic glucokinase (GCK), influence multiple metabolic traits in genome-wide association studies (GWASs), making GCKR one of the most pleiotropic GWAS loci in the genome. It is unclear why. Prior work has demonstrated that GCKR influences the hepatic cytosolic NADH/NAD+ ratio, also referred to as reductive stress. Here, we demonstrate that reductive stress is sufficient to activate the transcription factor ChREBP and necessary for its activation by the GKRP-GCK interaction, glucose, and ethanol. We show that hepatic reductive stress induces GCKR GWAS traits such as increased hepatic fat, circulating FGF21, and circulating acylglycerol species, which are also influenced by ChREBP. We define the transcriptional signature of hepatic reductive stress and show its upregulation in fatty liver disease and downregulation after bariatric surgery in humans. These findings highlight how a GCKR-reductive stress-ChREBP axis influences multiple human metabolic traits.
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Affiliation(s)
- Charandeep Singh
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA; Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Byungchang Jin
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA; Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nirajan Shrestha
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA; Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Andrew L Markhard
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Apekshya Panda
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Sarah E Calvo
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xingxiu Pan
- The Scintillon Institute, San Diego, CA 92121, USA
| | - Austin L Zuckerman
- The Scintillon Institute, San Diego, CA 92121, USA; Program in Mathematics and Science Education, University of California, San Diego, La Jolla, CA 92093; Program in Mathematics and Science Education, San Diego State University, San Diego, CA 92120
| | - Amel Ben Saad
- Division of Gastroenterology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Kathleen E Corey
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Julia Sjoquist
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Stephanie Osganian
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Roya AminiTabrizi
- Metabolomics Platform, Comprehensive Cancer Center, the University of Chicago, Chicago, IL 60637, USA
| | - Eugene P Rhee
- Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Nephrology Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hardik Shah
- Metabolomics Platform, Comprehensive Cancer Center, the University of Chicago, Chicago, IL 60637, USA
| | - Olga Goldberger
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Alan C Mullen
- Division of Gastroenterology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Valentin Cracan
- The Scintillon Institute, San Diego, CA 92121, USA; Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Vamsi K Mootha
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Russell P Goodman
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA; Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
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7
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Tobias DK, Hamaya R, Clish CB, Liang L, Deik A, Dennis C, Bullock K, Zhang C, Hu FB, Manson JE. Type 2 diabetes metabolomics score and risk of progression to type 2 diabetes among women with a history of gestational diabetes mellitus. Diabetes Metab Res Rev 2024; 40:e3763. [PMID: 38287718 PMCID: PMC10842268 DOI: 10.1002/dmrr.3763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/08/2023] [Accepted: 11/05/2023] [Indexed: 01/31/2024]
Abstract
BACKGROUND Several metabolites are individually related to incident type 2 diabetes (T2D) risk. We prospectively evaluated a novel T2D-metabolite pattern with a risk of progression to T2D among high-risk women with a history of gestational diabetes mellitus (GDM). METHODS The longitudinal Nurses' Health Study II cohort enroled 116,429 women in 1989 and collected blood samples from 1996 to 1999. We profiled plasma metabolites in 175 incident T2D cases and 175 age-matched controls, all with a history of GDM before the blood draw. We derived a metabolomics score from 21 metabolites previously associated with incident T2D in the published literature by scoring according to the participants' quintile (1-5 points) of each metabolite. We modelled the T2D metabolomics score categorically in quartiles and continuously per 1 standard deviation (SD) with the risk of incident T2D using conditional logistic regression models adjusting for body mass index at the blood draw, and other established T2D risk factors. RESULTS The percentage of women progressing to T2D ranged from 10% in the bottom T2D metabolomics score quartile to 78% in the highest score quartile. Adjusting for established T2D risk factors, women in the highest quartile had more than a 20-fold greater diabetes risk than women in the lowest quartile (odds ratios [OR] = 23.1 [95% CI = 8.6, 62.1]; p for trend<0.001). The continuous T2D metabolomics score was strongly and positively associated with incident T2D (adjusted OR = 2.7 per SD [95% CI = 1.9, 3.7], p < 0.0001). CONCLUSIONS A pattern of plasma metabolites among high-risk women is associated with a markedly elevated risk of progression to T2D later in life.
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Affiliation(s)
- Deirdre K. Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Nutrition Department, Harvard TH Chan School of Public Health, Boston, MA
| | - Rikuta Hamaya
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Epidemiology Department, Harvard TH Chan School of Public Health, Boston, MA
| | | | - Liming Liang
- Biostatistics Department, Harvard TH Chan School of Public Health, Boston, MA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA
| | | | | | - Cuilin Zhang
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD
| | - Frank B. Hu
- Nutrition Department, Harvard TH Chan School of Public Health, Boston, MA
- Epidemiology Department, Harvard TH Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - JoAnn E. Manson
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Epidemiology Department, Harvard TH Chan School of Public Health, Boston, MA
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8
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Zhu M, Frank MW, Radka CD, Jeanfavre S, Tse MW, Pacheco JA, Pierce K, Deik A, Xu J, Hussain S, Hussain FA, Xulu N, Khan N, Pillay V, Dong KL, Ndung’u T, Clish CB, Rock CO, Blainey PC, Bloom SM, Kwon DS. Vaginal Lactobacillus fatty acid response mechanisms reveal a novel strategy for bacterial vaginosis treatment. bioRxiv 2023:2023.12.30.573720. [PMID: 38234804 PMCID: PMC10793477 DOI: 10.1101/2023.12.30.573720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Bacterial vaginosis (BV), a common syndrome characterized by Lactobacillus-deficient vaginal microbiota, is associated with adverse health outcomes. BV often recurs after standard antibiotic therapy in part because antibiotics promote microbiota dominance by Lactobacillus iners instead of Lactobacillus crispatus, which has more beneficial health associations. Strategies to promote L. crispatus and inhibit L. iners are thus needed. We show that oleic acid (OA) and similar long-chain fatty acids simultaneously inhibit L. iners and enhance L. crispatus growth. These phenotypes require OA-inducible genes conserved in L. crispatus and related species, including an oleate hydratase (ohyA) and putative fatty acid efflux pump (farE). FarE mediates OA resistance, while OhyA is robustly active in the human vaginal microbiota and sequesters OA in a derivative form that only ohyA-harboring organisms can exploit. Finally, OA promotes L. crispatus dominance more effectively than antibiotics in an in vitro model of BV, suggesting a novel approach for treatment.
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Affiliation(s)
- Meilin Zhu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Matthew W. Frank
- Department of Host-Microbe Interactions, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Christopher D. Radka
- Department of Microbiology, Immunology, and Molecular Genetics, University of Kentucky
| | | | - Megan W. Tse
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Kerry Pierce
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jiawu Xu
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Salina Hussain
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Fatima Aysha Hussain
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Nondumiso Xulu
- HIV Pathogenesis Programme (HPP), The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa
| | - Nasreen Khan
- HIV Pathogenesis Programme (HPP), The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa
| | | | - Krista L. Dong
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Health Systems Trust, Durban, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Thumbi Ndung’u
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- HIV Pathogenesis Programme (HPP), The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa
- Africa Health Research Institute (AHRI), Durban, South Africa
- Max Planck Institute for Infection Biology, Berlin, Germany
- Division of Infection and Immunity, University College London, London, UK
| | | | - Charles O. Rock
- Department of Host-Microbe Interactions, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
- passed away on September 22, 2023
| | - Paul C. Blainey
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Seth M. Bloom
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Douglas S. Kwon
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Nagai TH, Hartigan C, Mizoguchi T, Yu H, Deik A, Bullock K, Wang Y, Cromley D, Schenone M, Cowan CA, Rader DJ, Clish CB, Carr SA, Xu YX. Chromatin regulator SMARCAL1 modulates cellular lipid metabolism. Commun Biol 2023; 6:1298. [PMID: 38129665 PMCID: PMC10739977 DOI: 10.1038/s42003-023-05665-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Biallelic mutations of the chromatin regulator SMARCAL1 cause Schimke Immunoosseous Dysplasia (SIOD), characterized by severe growth defects and premature mortality. Atherosclerosis and hyperlipidemia are common among SIOD patients, yet their onset and progression are poorly understood. Using an integrative approach involving proteomics, mouse models, and population genetics, we investigated SMARCAL1's role. We found that SmarcAL1 interacts with angiopoietin-like 3 (Angptl3), a key regulator of lipoprotein metabolism. In vitro and in vivo analyses demonstrate SmarcAL1's vital role in maintaining cellular lipid homeostasis. The observed translocation of SmarcAL1 to cytoplasmic peroxisomes suggests a potential regulatory role in lipid metabolism through gene expression. SmarcAL1 gene inactivation reduces the expression of key genes in cellular lipid catabolism. Population genetics investigations highlight significant associations between SMARCAL1 genetic variations and body mass index, along with lipid-related traits. This study underscores SMARCAL1's pivotal role in cellular lipid metabolism, likely contributing to the observed lipid phenotypes in SIOD patients.
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Affiliation(s)
- Taylor Hanta Nagai
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | | | - Taiji Mizoguchi
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Haojie Yu
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Kevin Bullock
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Yanyan Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Debra Cromley
- Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Monica Schenone
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Chad A Cowan
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Daniel J Rader
- Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Yu-Xin Xu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.
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10
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García-Gavilán JF, Babio N, Toledo E, Semnani-Azad Z, Razquin C, Dennis C, Deik A, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Lapetra J, Lamuela-Raventos R, Clish C, Ruiz-Canela M, Martínez-González MÁ, Hu F, Salas-Salvadó J, Guasch-Ferré M. Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol 2023; 22:340. [PMID: 38093289 PMCID: PMC10720204 DOI: 10.1186/s12933-023-02066-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/14/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Olive oil consumption has been inversely associated with the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, the impact of olive oil consumption on plasma metabolites remains poorly understood. This study aims to identify plasma metabolites related to total and specific types of olive oil consumption, and to assess the prospective associations of the identified multi-metabolite profiles with the risk of T2D and CVD. METHODS The discovery population included 1837 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) trial with available metabolomics data at baseline. Olive oil consumption was determined through food-frequency questionnaires (FFQ) and adjusted for total energy. A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation sample. Plasma metabolomics analyses were performed using LC-MS. Cross-sectional associations between 385 known candidate metabolites and olive oil consumption were assessed using elastic net regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite-weighted models and FFQ-derived olive oil consumption in each pair of training-validation data sets within the discovery sample. We further estimated the prospective associations of the identified plasma multi-metabolite profile with incident T2D and CVD using multivariable Cox regression models. RESULTS We identified a metabolomic signature for the consumption of total olive oil (with 74 metabolites), VOO (with 78 metabolites), and COO (with 17 metabolites), including several lipids, acylcarnitines, and amino acids. 10-CV Pearson correlation coefficients between total olive oil consumption derived from FFQs and the multi-metabolite profile were 0.40 (95% CI 0.37, 0.44) and 0.27 (95% CI 0.22, 0.31) for the discovery and validation sample, respectively. We identified several overlapping and distinct metabolites according to the type of olive oil consumed. The baseline metabolite profiles of total and extra virgin olive oil were inversely associated with CVD incidence (HR per 1SD: 0.79; 95% CI 0.67, 0.92 for total olive oil and 0.70; 0.59, 0.83 for extra virgin olive oil) after adjustment for confounders. However, no significant associations were observed between these metabolite profiles and T2D incidence. CONCLUSIONS This study reveals a panel of plasma metabolites linked to the consumption of total and specific types of olive oil. The metabolite profiles of total olive oil consumption and extra virgin olive oil were associated with a decreased risk of incident CVD in a high cardiovascular-risk Mediterranean population, though no associations were observed with T2D incidence. TRIAL REGISTRATION The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).
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Affiliation(s)
- Jesús F García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Estefanía Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Cristina Razquin
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | | | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramón Estruch
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
- Institut de Nutrició I Seguretat Alimentària (INSA-UB), Universitat de Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Lipid Clinic, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Arós
- Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 01009, Vitoria-Gasteiz, Spain
| | - Miquel Fiol
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Plataforma de Ensayos Clínicos, Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, 07120, Palma, Spain
| | - José Lapetra
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain
| | - Rosa Lamuela-Raventos
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut de Nutrició I Seguretat Alimentària (INSA-UB), Universitat de Barcelona, Barcelona, Spain
- Polyphenol Research Group, Departament de Nutrició, Ciències de L'Alimentació I Gastronomia, Universitat de Barcelon (UB), Av. de Joan XXII, 27-31, 08028, Barcelona, Spain
| | - Clary Clish
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Miguel Ángel Martínez-González
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, 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
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Øster Farimagsgade 5, 1014, Copenhagen, Denmark.
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11
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Yin Z, Rosenzweig N, Kleemann KL, Zhang X, Brandão W, Margeta MA, Schroeder C, Sivanathan KN, Silveira S, Gauthier C, Mallah D, Pitts KM, Durao A, Herron S, Shorey H, Cheng Y, Barry JL, Krishnan RK, Wakelin S, Rhee J, Yung A, Aronchik M, Wang C, Jain N, Bao X, Gerrits E, Brouwer N, Deik A, Tenen DG, Ikezu T, Santander NG, McKinsey GL, Baufeld C, Sheppard D, Krasemann S, Nowarski R, Eggen BJL, Clish C, Tanzi RE, Madore C, Arnold TD, Holtzman DM, Butovsky O. APOE4 impairs the microglial response in Alzheimer's disease by inducing TGFβ-mediated checkpoints. Nat Immunol 2023; 24:1839-1853. [PMID: 37749326 PMCID: PMC10863749 DOI: 10.1038/s41590-023-01627-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 08/15/2023] [Indexed: 09/27/2023]
Abstract
The APOE4 allele is the strongest genetic risk factor for late-onset Alzheimer's disease (AD). The contribution of microglial APOE4 to AD pathogenesis is unknown, although APOE has the most enriched gene expression in neurodegenerative microglia (MGnD). Here, we show in mice and humans a negative role of microglial APOE4 in the induction of the MGnD response to neurodegeneration. Deletion of microglial APOE4 restores the MGnD phenotype associated with neuroprotection in P301S tau transgenic mice and decreases pathology in APP/PS1 mice. MGnD-astrocyte cross-talk associated with β-amyloid (Aβ) plaque encapsulation and clearance are mediated via LGALS3 signaling following microglial APOE4 deletion. In the brains of AD donors carrying the APOE4 allele, we found a sex-dependent reciprocal induction of AD risk factors associated with suppression of MGnD genes in females, including LGALS3, compared to individuals homozygous for the APOE3 allele. Mechanistically, APOE4-mediated induction of ITGB8-transforming growth factor-β (TGFβ) signaling impairs the MGnD response via upregulation of microglial homeostatic checkpoints, including Inpp5d, in mice. Deletion of Inpp5d in microglia restores MGnD-astrocyte cross-talk and facilitates plaque clearance in APP/PS1 mice. We identify the microglial APOE4-ITGB8-TGFβ pathway as a negative regulator of microglial response to AD pathology, and restoring the MGnD phenotype via blocking ITGB8-TGFβ signaling provides a promising therapeutic intervention for AD.
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Affiliation(s)
- Zhuoran Yin
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Neta Rosenzweig
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kilian L Kleemann
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- School of Computing, University of Portsmouth, Portsmouth, UK
| | - Xiaoming Zhang
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wesley Brandão
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Milica A Margeta
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Caitlin Schroeder
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kisha N Sivanathan
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sebastian Silveira
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Gauthier
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Dania Mallah
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kristen M Pitts
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ana Durao
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shawn Herron
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Hannah Shorey
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yiran Cheng
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jen-Li Barry
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rajesh K Krishnan
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sam Wakelin
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jared Rhee
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anthony Yung
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Aronchik
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Chao Wang
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Institute for Brain Science and Disease, Chongqing Medical University, Chongqing, China
| | - Nimansha Jain
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Xin Bao
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Emma Gerrits
- Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Nieske Brouwer
- Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G Tenen
- Harvard Stem Cell Institute, Harvard Medical School, Boston, MA, USA
- Cancer Science Institute, National University of Singapore, Singapore, Singapore
| | - Tsuneya Ikezu
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Nicolas G Santander
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
- Instituto de Ciencias de la Salud, Universidad de O´Higgins, Rancagua, Chile
| | - Gabriel L McKinsey
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Caroline Baufeld
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Dean Sheppard
- Department of Medicine, Cardiovascular Research Center, University of California, San Francisco, San Francisco, CA, USA
| | - Susanne Krasemann
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf UKE, Hamburg, Germany
| | - Roni Nowarski
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bart J L Eggen
- Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Charlotte Madore
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratoire NutriNeuro, UMR1286, INRAE, Bordeaux INP, University of Bordeaux, Bordeaux, France
| | - Thomas D Arnold
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - David M Holtzman
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oleg Butovsky
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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12
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Abbott KL, Ali A, Casalena D, Do BT, Ferreira R, Cheah JH, Soule CK, Deik A, Kunchok T, Schmidt DR, Renner S, Honeder SE, Wu M, Chan SH, Tseyang T, Stoltzfus AT, Michel SLJ, Greaves D, Hsu PP, Ng CW, Zhang CJ, Farsidjani A, Kent JR, Madariaga MLL, Gramatikov IMT, Matheson NJ, Lewis CA, Clish CB, Rees MG, Roth JA, Griner LM, Muir A, Auld DS, Vander Heiden MG. Screening in serum-derived medium reveals differential response to compounds targeting metabolism. Cell Chem Biol 2023; 30:1156-1168.e7. [PMID: 37689063 PMCID: PMC10581593 DOI: 10.1016/j.chembiol.2023.08.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 06/20/2023] [Accepted: 08/16/2023] [Indexed: 09/11/2023]
Abstract
A challenge for screening new anticancer drugs is that efficacy in cell culture models is not always predictive of efficacy in patients. One limitation of standard cell culture is a reliance on non-physiological nutrient levels, which can influence cell metabolism and drug sensitivity. A general assessment of how physiological nutrients affect cancer cell response to small molecule therapies is lacking. To address this, we developed a serum-derived culture medium that supports the proliferation of diverse cancer cell lines and is amenable to high-throughput screening. We screened several small molecule libraries and found that compounds targeting metabolic enzymes were differentially effective in standard compared to serum-derived medium. We exploited the differences in nutrient levels between each medium to understand why medium conditions affected the response of cells to some compounds, illustrating how this approach can be used to screen potential therapeutics and understand how their efficacy is modified by available nutrients.
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Affiliation(s)
- Keene L Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ahmed Ali
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dominick Casalena
- Novartis Institute for BioMedical Research, Cambridge, MA 02139, USA
| | - Brian T Do
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Raphael Ferreira
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Jaime H Cheah
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Christian K Soule
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tenzin Kunchok
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Daniel R Schmidt
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Steffen Renner
- Novartis Institutes for BioMedical Research, 4056 Basel, Switzerland
| | - Sophie E Honeder
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Michelle Wu
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sze Ham Chan
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Tenzin Tseyang
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Andrew T Stoltzfus
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA
| | - Sarah L J Michel
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA
| | - Daniel Greaves
- Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK; Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Peggy P Hsu
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA; Massachusetts General Hospital Cancer Center, Boston, MA 02113, USA
| | - Christopher W Ng
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Chelsea J Zhang
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ali Farsidjani
- Novartis Institute for BioMedical Research, Cambridge, MA 02139, USA
| | - Johnathan R Kent
- Department of Surgery, University of Chicago Medicine, Chicago, IL 60637, USA
| | | | - Iva Monique T Gramatikov
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Nicholas J Matheson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK; Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Caroline A Lewis
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Matthew G Rees
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jennifer A Roth
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Alexander Muir
- Ben May Department of Cancer Research, University of Chicago, Chicago, IL 60637, USA
| | - Douglas S Auld
- Novartis Institute for BioMedical Research, Cambridge, MA 02139, USA
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA.
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13
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Younce JR, Cascella RH, Berman BD, Jinnah HA, Bellows S, Feuerstein J, Wagle Shukla A, Mahajan A, Chang FCF, Duque KR, Reich S, Richardson SP, Deik A, Stover N, Luna JM, Norris SA. Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach. Dystonia 2023; 2:11305. [PMID: 37920445 PMCID: PMC10621194 DOI: 10.3389/dyst.2023.11305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
According to expert consensus, dystonia can be classified as focal, segmental, multifocal, and generalized, based on the affected body distribution. To provide an empirical and data-driven approach to categorizing these distributions, we used a data-driven clustering approach to compare frequency and co-occurrence rates of non-focal dystonia in pre-defined body regions using the Dystonia Coalition (DC) dataset. We analyzed 1,618 participants with isolated non-focal dystonia from the DC database. The analytic approach included construction of frequency tables, variable-wise analysis using hierarchical clustering and independent component analysis (ICA), and case-wise consensus hierarchical clustering to describe associations and clusters for dystonia affecting any combination of eighteen pre-defined body regions. Variable-wise hierarchical clustering demonstrated closest relationships between bilateral upper legs (distance = 0.40), upper and lower face (distance = 0.45), bilateral hands (distance = 0.53), and bilateral feet (distance = 0.53). ICA demonstrated clear grouping for the a) bilateral hands, b) neck, and c) upper and lower face. Case-wise consensus hierarchical clustering at k = 9 identified 3 major clusters. Major clusters consisted primarily of a) cervical dystonia with nearby regions, b) bilateral hand dystonia, and c) cranial dystonia. Our data-driven approach in a large dataset of isolated non-focal dystonia reinforces common segmental patterns in cranial and cervical regions. We observed unexpectedly strong associations between bilateral upper or lower limbs, which suggests that symmetric multifocal patterns may represent a previously underrecognized dystonia subtype.
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Affiliation(s)
- J. R. Younce
- Department of Neurology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - R. H. Cascella
- School of Medicine, Washington University, St. Louis, MO, United States
| | - B. D. Berman
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, United States
| | - H. A. Jinnah
- Department of Neurology, Emory University, Atlanta, GA, United States
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | - S Bellows
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - J. Feuerstein
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - A. Wagle Shukla
- Department of Neurology, University of Florida, Gainesville, FL, United States
| | - A. Mahajan
- Rush Parkinson’s Disease and Movement Disorders Program, Rush University Medical Center, Chicago, IL, United States
| | - F. C. F. Chang
- Movement Disorders Unit, Neurology Department, Westmead Hospital & Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - K. R. Duque
- James J. and Joan A. Gardner Family Center for Parkinson’s Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, United States
| | - S. Reich
- Department of Neurology, University of Maryland, Baltimore, MD, United States
| | - S. Pirio Richardson
- Department of Neurology, University of New Mexico and New Mexico VA Healthcare System, Albuquerque, NM, United States
| | - A. Deik
- Parkinson Disease and Movement Disorders Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - N. Stover
- Department of Neurology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - J. M. Luna
- Department of Radiology, School of Medicine, Washington University, St. Louis, MO, United States
| | - S. A. Norris
- Department of Radiology, School of Medicine, Washington University, St. Louis, MO, United States
- Department of Neurology, School of Medicine, Washington University, St. Louis, MO, United States
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14
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Poganik JR, Zhang B, Baht GS, Tyshkovskiy A, Deik A, Kerepesi C, Yim SH, Lu AT, Haghani A, Gong T, Hedman AM, Andolf E, Pershagen G, Almqvist C, Clish CB, Horvath S, White JP, Gladyshev VN. Biological age is increased by stress and restored upon recovery. Cell Metab 2023; 35:807-820.e5. [PMID: 37086720 PMCID: PMC11055493 DOI: 10.1016/j.cmet.2023.03.015] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/22/2022] [Accepted: 03/20/2023] [Indexed: 04/24/2023]
Abstract
Aging is classically conceptualized as an ever-increasing trajectory of damage accumulation and loss of function, leading to increases in morbidity and mortality. However, recent in vitro studies have raised the possibility of age reversal. Here, we report that biological age is fluid and exhibits rapid changes in both directions. At epigenetic, transcriptomic, and metabolomic levels, we find that the biological age of young mice is increased by heterochronic parabiosis and restored following surgical detachment. We also identify transient changes in biological age during major surgery, pregnancy, and severe COVID-19 in humans and/or mice. Together, these data show that biological age undergoes a rapid increase in response to diverse forms of stress, which is reversed following recovery from stress. Our study uncovers a new layer of aging dynamics that should be considered in future studies. The elevation of biological age by stress may be a quantifiable and actionable target for future interventions.
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Affiliation(s)
- Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Bohan Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Gurpreet S Baht
- Department of Orthopaedic Surgery, Duke University, Durham, NC 27701, USA; Duke Molecular Physiology Institute, Duke University, Durham, NC 27701, USA
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA
| | - Csaba Kerepesi
- Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network, Budapest, 1111, Hungary
| | - Sun Hee Yim
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA
| | - Amin Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA
| | - Tong Gong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna M Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ellika Andolf
- Department of Clinical Sciences, Division of Obstetrics and Gynaecology, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Altos Labs, San Diego, CA, USA; Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - James P White
- Duke Molecular Physiology Institute, Duke University, Durham, NC 27701, USA; Department of Medicine, Duke University School of Medicine, Durham, NC 27701, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 01241, USA.
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15
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Toledo E, Wittenbecher C, Razquin C, Ruiz-Canela M, Clish CB, Liang L, Alonso A, Hernández-Alonso P, Becerra-Tomás N, Arós-Borau F, Corella D, Ros E, Estruch R, García-Rodríguez A, Fitó M, Lapetra J, Fiol M, Alonso-Gomez ÁM, Serra-Majem L, Deik A, Salas-Salvadó J, Hu FB, Martínez-González MA. Plasma lipidome and risk of atrial fibrillation: results from the PREDIMED trial. J Physiol Biochem 2023; 79:355-364. [PMID: 37004634 PMCID: PMC10300169 DOI: 10.1007/s13105-023-00958-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/16/2023] [Indexed: 04/04/2023]
Abstract
The potential role of the lipidome in atrial fibrillation (AF) development is still widely unknown. We aimed to assess the association between lipidome profiles of the Prevención con Dieta Mediterránea (PREDIMED) trial participants and incidence of AF. We conducted a nested case-control study (512 incident centrally adjudicated AF cases and 735 controls matched by age, sex, and center). Baseline plasma lipids were profiled using a Nexera X2 U-HPLC system coupled to an Exactive Plus orbitrap mass spectrometer. We estimated the association between 216 individual lipids and AF using multivariable conditional logistic regression and adjusted the p values for multiple testing. We also examined the joint association of lipid clusters with AF incidence. Hitherto, we estimated the lipidomics network, used machine learning to select important network-clusters and AF-predictive lipid patterns, and summarized the joint association of these lipid patterns weighted scores. Finally, we addressed the possible interaction by the randomized dietary intervention.Forty-one individual lipids were associated with AF at the nominal level (p < 0.05), but no longer after adjustment for multiple-testing. However, the network-based score identified with a robust data-driven lipid network showed a multivariable-adjusted ORper+1SD of 1.32 (95% confidence interval: 1.16-1.51; p < 0.001). The score included PC plasmalogens and PE plasmalogens, palmitoyl-EA, cholesterol, CE 16:0, PC 36:4;O, and TG 53:3. No interaction with the dietary intervention was found. A multilipid score, primarily made up of plasmalogens, was associated with an increased risk of AF. Future studies are needed to get further insights into the lipidome role on AF.Current Controlled Trials number, ISRCTN35739639.
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Affiliation(s)
- Estefania Toledo
- Department of Preventive Medicine and Public Health, Edificio de Investigación, University of Navarra, Planta 2, Calle Irunlarrea 1, 31008, Pamplona, Spain.
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| | - Clemens Wittenbecher
- SciLifeLab & Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Cristina Razquin
- Department of Preventive Medicine and Public Health, Edificio de Investigación, University of Navarra, Planta 2, Calle Irunlarrea 1, 31008, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, Edificio de Investigación, University of Navarra, Planta 2, Calle Irunlarrea 1, 31008, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Liming Liang
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Pablo Hernández-Alonso
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Departament de Bioquímica I Biotecnologia, Universitat Rovira I Virgili, Unitat de Nutrició Humana, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Hospital Universitari San Joan de Reus, Reus, Spain
- Unidad de Gestión Clínica de Endocrinología Y Nutrición del Hospital Virgen de La Victoria, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain
| | - Nerea Becerra-Tomás
- Departament de Bioquímica I Biotecnologia, Universitat Rovira I Virgili, Unitat de Nutrició Humana, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Hospital Universitari San Joan de Reus, Reus, Spain
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, Valencia, Spain
| | - Fernando Arós-Borau
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Bioaraba Health Research Institute Osakidetza Basque Health Service, Araba University Hospital University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
| | - Dolores Corella
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, Valencia, Spain
| | - Emilio Ros
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Endocrinology & Nutrition, Hospital Clinic, Barcelona, Spain
| | - Ramón Estruch
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | | | - Montserrat Fitó
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Cardiovascular Risk and Nutrition Group, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - José Lapetra
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013, Seville, Spain
| | - Miquel Fiol
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- IdISBa. Health Research Institute of the Balearis Islands, Palma, Spain
| | - Ángel M Alonso-Gomez
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Bioaraba Health Research Institute Osakidetza Basque Health Service, Araba University Hospital University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
| | - Luis Serra-Majem
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, & Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI) del Servicio Canario de Salud, Gobierno de Canarias, Las Palmas de Gran Canaria, Spain
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jordi Salas-Salvadó
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Departament de Bioquímica I Biotecnologia, Universitat Rovira I Virgili, Unitat de Nutrició Humana, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Hospital Universitari San Joan de Reus, Reus, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Miguel A Martínez-González
- Department of Preventive Medicine and Public Health, Edificio de Investigación, University of Navarra, Planta 2, Calle Irunlarrea 1, 31008, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- CIBER Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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16
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Abbott KL, Ali A, Casalena D, Do BT, Ferreira R, Cheah JH, Soule CK, Deik A, Kunchok T, Schmidt DR, Renner S, Honeder SE, Wu M, Chan SH, Tseyang T, Greaves D, Hsu PP, Ng CW, Zhang CJ, Farsidjani A, Gramatikov IMT, Matheson NJ, Lewis CA, Clish CB, Rees MG, Roth JA, Griner LM, Muir A, Auld DS, Heiden MGV. Screening in serum-derived medium reveals differential response to compounds targeting metabolism. bioRxiv 2023:2023.02.25.529972. [PMID: 36909640 PMCID: PMC10002634 DOI: 10.1101/2023.02.25.529972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
A challenge for screening new candidate drugs to treat cancer is that efficacy in cell culture models is not always predictive of efficacy in patients. One limitation of standard cell culture is a reliance on non-physiological nutrient levels to propagate cells. Which nutrients are available can influence how cancer cells use metabolism to proliferate and impact sensitivity to some drugs, but a general assessment of how physiological nutrients affect cancer cell response to small molecule therapies is lacking. To enable screening of compounds to determine how the nutrient environment impacts drug efficacy, we developed a serum-derived culture medium that supports the proliferation of diverse cancer cell lines and is amenable to high-throughput screening. We used this system to screen several small molecule libraries and found that compounds targeting metabolic enzymes were enriched as having differential efficacy in standard compared to serum-derived medium. We exploited the differences in nutrient levels between each medium to understand why medium conditions affected the response of cells to some compounds, illustrating how this approach can be used to screen potential therapeutics and understand how their efficacy is modified by available nutrients.
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Affiliation(s)
- Keene L. Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ahmed Ali
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dominick Casalena
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Brian T. Do
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA
| | | | - Jaime H. Cheah
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Christian K. Soule
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tenzin Kunchok
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Daniel R. Schmidt
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Steffen Renner
- Novartis Institutes for BioMedical Research, 4056 Basel, Switzerland
| | - Sophie E. Honeder
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Michelle Wu
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sze Ham Chan
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Tenzin Tseyang
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Daniel Greaves
- Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Peggy P. Hsu
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Massachusetts General Hospital Cancer Center, Boston, MA 02113, USA
| | - Christopher W. Ng
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Chelsea J. Zhang
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ali Farsidjani
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Iva Monique T. Gramatikov
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Nicholas J. Matheson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Caroline A. Lewis
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Matthew G. Rees
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Lesley Mathews Griner
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Alexander Muir
- Ben May Department of Cancer Research, University of Chicago, Chicago, IL, USA
| | - Douglas S. Auld
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Matthew G. Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Dana-Farber Cancer Institute, Boston, MA 02115, USA
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17
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García-Gavilán J, Nishi S, Paz-Graniel I, Guasch-Ferré M, Razquin C, Clish CB, Toledo E, Ruiz-Canela M, Corella D, Deik A, Drouin-Chartier JP, Wittenbecher C, Babio N, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra-Majem L, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolite Profiles Associated with the Amount and Source of Meat and Fish Consumption and the Risk of Type 2 Diabetes. Mol Nutr Food Res 2022; 66:e2200145. [PMID: 36214069 PMCID: PMC9722604 DOI: 10.1002/mnfr.202200145] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/12/2022] [Indexed: 01/18/2023]
Abstract
SCOPE Consumption of meat has been associated with a higher risk of type 2 diabetes (T2D), but if plasma metabolite profiles associated with these foods reflect this relationship is unknown. The objective is to identify a metabolite signature of consumption of total meat (TM), red meat (RM), processed red meat (PRM), and fish and examine if they are associated with T2D risk. METHODS AND RESULTS The discovery population includes 1833 participants from the PREDIMED trial. The internal validation sample includes 1522 participants with available 1-year follow-up metabolomic data. Associations between metabolites and TM, RM, PRM, and fish are evaluated with elastic net regression. Associations between the profiles and incident T2D are estimated using Cox regressions. The profiles included 72 metabolites for TM, 69 for RM, 74 for PRM, and 66 for fish. After adjusting for T2D risk factors, only profiles of TM (Hazard Ratio (HR): 1.25, 95% CI: 1.06-1.49), RM (HR: 1.27, 95% CI: 1.07-1.52), and PRM (HR: 1.27, 95% CI: 1.07-1.51) are associated with T2D. CONCLUSIONS The consumption of TM, its subtypes, and fish is associated with different metabolites, some of which have been previously associated with T2D. Scores based on the identified metabolites for TM, RM, and PRM show a significant association with T2D risk.
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Affiliation(s)
- Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Stephanie Nishi
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Unity Health Toronto, ON, Canada
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Cristina Razquin
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | | | - Estefanía Toledo
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Miguel Ruiz-Canela
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Dolores Corella
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, Santé et Société, Institut sur la Nutrition et les Aliments Fonctionnels, Faculté de Pharmacie, Université Laval, Québec, Canada
| | - Clemens Wittenbecher
- Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany,German Center for Diabetes Research, Neuherberg, Germany
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Ramon Estruch
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Internal Medicine, Institut d’Investigacions Biomèdiques August Pi Sunyer, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Lipid Clinic, Department of Endocrinology and Nutrition, Agust Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Arós
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluís Serra-Majem
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA,Department of Statistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Miguel A Martínez-González
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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18
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Guo JA, Su J, Jambhale A, Dilly J, Hennessey CJ, Shiau C, Yu P, Wang S, Wang J, Abbassi L, Neiswender J, Bertea T, Yang A, Yu Q, Westcott P, Schenkel J, Kim DY, Hoffman HI, Jaramillo GC, Uribe GA, Wu WW, Mehta A, Ting D, Pacheco JA, Deik A, Clish C, Vazquez F, Wolpin B, Regev A, Freed-Pastor WA, Mancias JD, Jacks T, Hwang WL, Aguirre AJ. Abstract A052: Systematic dissection of transcriptional states in pancreatic cancer. Cancer Res 2022. [DOI: 10.1158/1538-7445.panca22-a052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Abstract
Transcriptional states in pancreatic cancer can stratify patients by response to chemotherapy and clinical outcomes. These include the classical and basal-like states as well as a newly identified neural-like progenitor (NRP) state, which we have previously found to be enriched in primary patient tumors treated with neoadjuvant chemotherapy and radiotherapy. While several transcription factor drivers of classical and basal-like identity have been described, key regulators of the NRP state are unknown. Through in silico approaches, we identified candidate transcription factors of the NRP state, including GLIS3, a Krüppel-like zinc finger protein that mediates neuroendocrine fate during pancreatic development and differentiation of human embryonic stem cells into posterior neural progenitor cells. Our understanding of biologic and clinically-relevant attributes of transcriptional cell states remains limited by state-specific biases in various preclinical models. Existing human cell lines maintained as two-dimensional cultures tend to preferentially represent the basal-like state, whereas human three-dimensional organoid models grown in standard culture conditions best reflect the classical state. These phenotypes are therefore impacted by culture conditions as well as underlying genetic features. Furthermore, most murine pancreatic cancer models do not fully reflect the classical vs. basal-like state heterogeneity observed in humans. To enable systematic study of the classical, basal-like and NRP phenotypes, we developed isogenic KP (KrasG12D/+;Trp53FL/FL) murine organoids with a germline dCas9-VPR system to enable facile overexpression of state-specific transcription factors through CRISPR activation approaches. Quantitative PCR, RNA-sequencing, and proteomics confirmed Gata6, deltaN Trp63, and Glis3 as drivers of classical, basal-like, and NRP identity, respectively. DeltaN Trp63 organoids were further differentiated by loss of luminal morphology. Pairwise comparisons of global transcriptional alterations suggest the greatest similarities between the Gata6- and Glis3-overexpressed models, which is consistent with enhanced associations between classical and NRP states in patient tumors. Finally, although basal-like and NRP states are associated with poorer response to multi-agent chemotherapy, state-specific therapeutic sensitivities to other treatments remain incompletely defined. We therefore performed drug sensitivity assays with a panel of targeted therapies and unveiled state-specific sensitivities. These data were corroborated by drug sensitivity profiling of human patient-derived organoids and cell lines. Taken together, these results suggest a framework for defining cell state-specific vulnerabilities that may aid in stratifying and treating pancreatic cancer patients with new therapies.
Citation Format: Jimmy A. Guo, Jennifer Su, Ananya Jambhale, Julien Dilly, Connor J. Hennessey, Carina Shiau, Patrick Yu, Steven Wang, Junning Wang, Laleh Abbassi, James Neiswender, Tate Bertea, Annan Yang, Qijia Yu, Peter Westcott, Jason Schenkel, Daniel Y. Kim, Hannah I. Hoffman, Grissel Cervantes Jaramillo, Giselle A. Uribe, Westley W. Wu, Arnav Mehta, David Ting, Julian A. Pacheco, Amy Deik, Clary Clish, Francisca Vazquez, Brian Wolpin, Aviv Regev, William A. Freed-Pastor, Joseph D. Mancias, Tyler Jacks, William L. Hwang, Andrew J. Aguirre. Systematic dissection of transcriptional states in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A052.
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Affiliation(s)
- Jimmy A. Guo
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | | | | | | | - Carina Shiau
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Patrick Yu
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Steven Wang
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | | | | | - Tate Bertea
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Annan Yang
- 3Dana Farber Cancer Institute, Boston, MA,
| | - Qijia Yu
- 3Dana Farber Cancer Institute, Boston, MA,
| | | | | | | | | | | | | | | | - Arnav Mehta
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - David Ting
- 6Massachusetts General Hospital, Boston, MA,
| | | | - Amy Deik
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Clary Clish
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
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19
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Sevilla-Gonzalez MDR, Manning AK, Westerman KE, Aguilar-Salinas CA, Deik A, Clish CB. Metabolomic markers of glucose regulation after a lifestyle intervention in prediabetes. BMJ Open Diabetes Res Care 2022; 10:10/5/e003010. [PMID: 36253014 PMCID: PMC9577902 DOI: 10.1136/bmjdrc-2022-003010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/16/2022] [Indexed: 11/07/2022] Open
Abstract
INTRODUCTION Disentangling the specific factors that regulate glycemia from prediabetes to normoglycemia could improve type 2 diabetes prevention strategies. Metabolomics provides substantial insights into the biological understanding of environmental factors such as diet. This study aimed to identify metabolomic markers of regression to normoglycemia in the context of a lifestyle intervention (LSI) in individuals with prediabetes. RESEARCH DESIGN AND METHODS We conducted a single-arm intervention study with 24 weeks of follow-up. Eligible study participants had at least one prediabetes criteria according to the American Diabetes Association guidelines, and body mass index between 25 and 45 kg/m2. LSI refers to a hypocaloric diet and >150 min of physical activity per week. Regression to normoglycemia (RNGR) was defined as achieving hemoglobin A1c (HbA1c) <5.5% in the final visit. Baseline and postintervention plasma metabolomic profiles were measured using liquid chromatography-tandem mass spectrometry. To select metabolites associated with RNGR, we conducted the least absolute shrinkage and selection operator-penalized regressions. RESULTS The final sample was composed of 82 study participants. Changes in three metabolites were significantly associated with regression to normoglycemia; N-acetyl-D-galactosamine (OR=0.54; 95% CI 0.32 to 0.82), putrescine (OR=0.90, 95% CI 0.81 to 0.98), and 7-methylguanine (OR=1.06; 95% CI 1.02 to 1.17), independent of HbA1c and weight loss. In addition, metabolomic perturbations due to LSI displayed enrichment of taurine and hypotaurine metabolism pathway (p=0.03) compatible with biomarkers of protein consumption, lower red meat and animal fats and higher seafood and vegetables. CONCLUSIONS Evidence from this study suggests that specific metabolomic markers have an influence on glucose regulation in individuals with prediabetes after 24 weeks of LSI independently of other treatment effects such as weight loss.
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Affiliation(s)
- Magdalena Del Rocio Sevilla-Gonzalez
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Metabolism Program, The Broad Insitute of MIT and Harvard, Cambridge, MA, USA
- Unidad de Investigacion en Enfermedades Metabólicas, Insituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, México City, México City, Mexico
| | - Alisa K Manning
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Metabolism Program, The Broad Insitute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth E Westerman
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Metabolism Program, The Broad Insitute of MIT and Harvard, Cambridge, MA, USA
| | - Carlos Alberto Aguilar-Salinas
- Unidad de Investigacion en Enfermedades Metabólicas, Insituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, México City, México City, Mexico
| | - Amy Deik
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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20
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Shindyapina AV, Cho Y, Kaya A, Tyshkovskiy A, Castro JP, Deik A, Gordevicius J, Poganik JR, Clish CB, Horvath S, Peshkin L, Gladyshev VN. Rapamycin treatment during development extends life span and health span of male mice and Daphnia magna. Sci Adv 2022; 8:eabo5482. [PMID: 36112674 PMCID: PMC9481125 DOI: 10.1126/sciadv.abo5482] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 07/26/2022] [Indexed: 05/22/2023]
Abstract
Development is tightly connected to aging, but whether pharmacologically targeting development can extend life remains unknown. Here, we subjected genetically diverse UMHET3 mice to rapamycin for the first 45 days of life. The mice grew slower and remained smaller than controls for their entire lives. Their reproductive age was delayed without affecting offspring numbers. The treatment was sufficient to extend the median life span by 10%, with the strongest effect in males, and helped to preserve health as measured by frailty index scores, gait speed, and glucose and insulin tolerance tests. Mechanistically, the liver transcriptome and epigenome of treated mice were younger at the completion of treatment. Analogous to mice, rapamycin exposure during development robustly extended the life span of Daphnia magna and reduced its body size. Overall, the results demonstrate that short-term rapamycin treatment during development is a novel longevity intervention that acts by slowing down development and aging, suggesting that aging may be targeted already early in life.
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Affiliation(s)
| | - Yongmin Cho
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Alaattin Kaya
- Department of Biology, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Alexander Tyshkovskiy
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow 119234, Russia
| | - José P. Castro
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Jesse R. Poganik
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Leonid Peshkin
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
- Eugene Bell Center for Regenerative Biology and Tissue Engineering and National Xenopus Resource, Marine Biological Laboratory, Woods Hole, MA 02543, USA
| | - Vadim N. Gladyshev
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Corresponding author.
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21
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Ferraro G, Ali A, Luengo A, Deik A, Abbott K, Bezwada D, Blanc L, Prideaux B, Jin X, Posada J, Amoozgar Z, Ferreira R, Chen I, Naxerova K, Ng C, Westermark A, Davidson S, Fukumura D, Dartois V, Clish C, Heiden MV, Jain R. TAMI-05. FATTY ACID SYNTHESIS IS REQUIRED FOR HER2+ BREAST CANCER BRAIN METASTASIS. Neuro Oncol 2021. [DOI: 10.1093/neuonc/noab196.789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Brain metastases are refractory to therapies that control systemic disease in patients with human epidermal growth factor receptor 2-positive breast cancer and the brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for brain metastatic breast cancer growth may introduce liabilities that can be exploited for therapy. Here we assessed how metabolism differs between breast tumors in brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in the brain. We determine that this phenotype is an adaptation to decreased lipid availability in the brain relative to other tissues, resulting in site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase reduces human epidermal growth factor receptor 2-positive breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Xin Jin
- Broad Institute, Cambridge, USA
| | | | | | | | - Ivy Chen
- Harvard Medical School / MIT, Boston, USA
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22
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Ferraro GB, Ali A, Luengo A, Kodack DP, Deik A, Abbott KL, Bezwada D, Blanc L, Prideaux B, Jin X, Posada JM, Chen J, Chin CR, Amoozgar Z, Ferreira R, Chen IX, Naxerova K, Ng C, Westermark AM, Duquette M, Roberge S, Lindeman NI, Lyssiotis CA, Nielsen J, Housman DE, Duda DG, Brachtel E, Golub TR, Cantley LC, Asara JM, Davidson SM, Fukumura D, Dartois VA, Clish CB, Jain RK, Vander Heiden MG. Author Correction: Fatty acid synthesis is required for breast cancer brain metastasis. Nat Cancer 2021; 2:1243. [PMID: 35122065 DOI: 10.1038/s43018-021-00283-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Gino B Ferraro
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ahmed Ali
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Alba Luengo
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David P Kodack
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Amy Deik
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Keene L Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Divya Bezwada
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Landry Blanc
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CNRS UMR 5248, Bordeaux, France
| | - Brendan Prideaux
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
- Department of Neuroscience, Cell Biology, and Anatomy, University of Texas Medical Branch, Galveston, TX, USA
| | - Xin Jin
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Jessica M Posada
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jiang Chen
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Christopher R Chin
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zohreh Amoozgar
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Raphael Ferreira
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Ivy X Chen
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kamila Naxerova
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christopher Ng
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna M Westermark
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mark Duquette
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sylvie Roberge
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Neal I Lindeman
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Costas A Lyssiotis
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- University of Michigan, Ann Arbor, MI, USA
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - David E Housman
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dan G Duda
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Elena Brachtel
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Todd R Golub
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Lewis C Cantley
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY, USA
| | - John M Asara
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shawn M Davidson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Lewis Sigler Institute, Princeton University, Princeton, NJ, USA
| | - Dai Fukumura
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Véronique A Dartois
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Rakesh K Jain
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
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23
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Oren Y, Tsabar M, Cuoco MS, Amir-Zilberstein L, Cabanos HF, Hütter JC, Hu B, Thakore PI, Tabaka M, Fulco CP, Colgan W, Cuevas BM, Hurvitz SA, Slamon DJ, Deik A, Pierce KA, Clish C, Hata AN, Zaganjor E, Lahav G, Politi K, Brugge JS, Regev A. Cycling cancer persister cells arise from lineages with distinct programs. Nature 2021; 596:576-582. [PMID: 34381210 PMCID: PMC9209846 DOI: 10.1038/s41586-021-03796-6] [Citation(s) in RCA: 194] [Impact Index Per Article: 64.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/02/2021] [Indexed: 02/07/2023]
Abstract
Non-genetic mechanisms have recently emerged as important drivers of cancer therapy failure1, where some cancer cells can enter a reversible drug-tolerant persister state in response to treatment2. Although most cancer persisters remain arrested in the presence of the drug, a rare subset can re-enter the cell cycle under constitutive drug treatment. Little is known about the non-genetic mechanisms that enable cancer persisters to maintain proliferative capacity in the presence of drugs. To study this rare, transiently resistant, proliferative persister population, we developed Watermelon, a high-complexity expressed barcode lentiviral library for simultaneous tracing of each cell's clonal origin and proliferative and transcriptional states. Here we show that cycling and non-cycling persisters arise from different cell lineages with distinct transcriptional and metabolic programs. Upregulation of antioxidant gene programs and a metabolic shift to fatty acid oxidation are associated with persister proliferative capacity across multiple cancer types. Impeding oxidative stress or metabolic reprogramming alters the fraction of cycling persisters. In human tumours, programs associated with cycling persisters are induced in minimal residual disease in response to multiple targeted therapies. The Watermelon system enabled the identification of rare persister lineages that are preferentially poised to proliferate under drug pressure, thus exposing new vulnerabilities that can be targeted to delay or even prevent disease recurrence.
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Affiliation(s)
- Yaara Oren
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Cell Biology, Harvard Medical School, 240 Longwood Ave., Boston, MA, USA
| | - Michael Tsabar
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Systems Biology, Harvard Medical School, Boston, MA, USA,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Cuoco
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Heidie F. Cabanos
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Departments of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jan-Christian Hütter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bomiao Hu
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Pratiksha I. Thakore
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Current address: Genentech, South San Francisco, CA, USA
| | - Marcin Tabaka
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, USA,Current address: Bristol Myers Squibb, Cambridge, MA, USA
| | | | - Brandon M. Cuevas
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sara A. Hurvitz
- David Geffen School of Medicine, University of California, Los Angeles, Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Dennis J. Slamon
- David Geffen School of Medicine, University of California, Los Angeles, Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Amy Deik
- Metabolomics Platform, Broad Institute, Cambridge, MA, USA
| | | | - Clary Clish
- Metabolomics Platform, Broad Institute, Cambridge, MA, USA
| | - Aaron N. Hata
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Departments of Medicine, Harvard Medical School, Boston, MA, USA
| | - Elma Zaganjor
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Galit Lahav
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Katerina Politi
- Departments of Pathology (Section of Medical Oncology), Yale School of Medicine and Yale Cancer Center, New Haven, CT, USA
| | - Joan S. Brugge
- Department of Cell Biology, Harvard Medical School, 240 Longwood Ave., Boston, MA, USA,Ludwig Center at Harvard
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Howard Hughes Medical Institute, Chevy Chase, MD, USA. .,Genentech, South San Francisco, CA, USA.
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24
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Ferraro GB, Ali A, Luengo A, Kodack DP, Deik A, Abbott KL, Bezwada D, Blanc L, Prideaux B, Jin X, Possada JM, Chen J, Chin CR, Amoozgar Z, Ferreira R, Chen I, Naxerova K, Ng C, Westermark AM, Duquette M, Roberge S, Lyssiotis CA, Duda DG, Golub TR, Davidson SM, Fukumura D, Dartois VA, Clish CB, Heiden MGV, Jain RK. Abstract 90: Fatty acid synthesis is required for breast cancer brain metastasis. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-90] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Brain metastases are refractory to therapies that otherwise control systemic disease in patients with human epidermal growth factor receptor 2 (HER2+) breast cancer, and the unique brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for breast cancer growth in the brain microenvironment may also introduce liabilities that can be exploited for therapy. Here, we assessed how metabolism differs between breast tumors growing in the brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in the brain. We determine that this phenotype is an adaptation to decreased lipid availability in the brain relative to other tissues, which results in a site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase (FASN) reduces HER2+ breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.
Citation Format: Gino B. Ferraro, Ahmed Ali, Alba Luengo, David P. Kodack, Amy Deik, Keene L. Abbott, Divya Bezwada, Landry Blanc, Brendan Prideaux, Xin Jin, Jessica M. Possada, Jiang Chen, Christopher R. Chin, Zohreh Amoozgar, Raphael Ferreira, Ivy Chen, Kamila Naxerova, Christopher Ng, Anna M. Westermark, Mark Duquette, Sylvie Roberge, Costas A. Lyssiotis, Dan G. Duda, Todd R. Golub, Shawn M. Davidson, Dai Fukumura, Véronique A. Dartois, Clary B. Clish, Matthew G. Vander Heiden, Rakesh K. Jain. Fatty acid synthesis is required for breast cancer brain metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 90.
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Affiliation(s)
- Gino B. Ferraro
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Ahmed Ali
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Alba Luengo
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - David P. Kodack
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Amy Deik
- 3Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Keene L. Abbott
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Divya Bezwada
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Landry Blanc
- 4The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ
| | - Brendan Prideaux
- 4The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ
| | - Xin Jin
- 3Broad Institute of MIT and Harvard University, Cambridge, MA
| | | | - Jiang Chen
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Christopher R. Chin
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Zohreh Amoozgar
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Raphael Ferreira
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Ivy Chen
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Kamila Naxerova
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Christopher Ng
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Anna M. Westermark
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Mark Duquette
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Sylvie Roberge
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Costas A. Lyssiotis
- 5Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Dan G. Duda
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Todd R. Golub
- 3Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Shawn M. Davidson
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Dai Fukumura
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
| | - Véronique A. Dartois
- 4The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ
| | - Clary B. Clish
- 3Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Matthew G. Vander Heiden
- 2Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Rakesh K. Jain
- 1Massachusetts General Hospital/Harvard Medical School, Boston, MA
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25
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Jin X, Demere Z, Nair K, Ali A, Ferraro GB, Natoli T, Deik A, Petronio L, Tang AA, Zhu C, Wang L, Rosenberg D, Mangena V, Roth J, Chung K, Jain RK, Clish CB, Heiden MGV, Golub TR. Abstract NG10: A metastasis map of human cancer cell lines. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-ng10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical due to the inherent scale limitation of the in vivo models. Here we introduce an in vivo barcoding strategy capable of determining the metastatic potential of human cancer cell lines in murine xenografts at scale. We validated the robustness, scalability and reproducibility of the method, and applied it to 500 cell lines spanning 21 solid cancer types. We created a first-generation Metastasis Map (MetMap) that reveals organ-specific patterns of metastasis and allows relating those patterns to clinical and genomic features. We demonstrated the utility of MetMap by exploring the molecular basis of breast cancers capable of metastasizing to the brain - a principal cause of death in these patients. Breast cancers that were brain metastatic had unexpected genetic, expression, and metabolic evidence of altered lipid metabolism. Perturbing lipid metabolism curbed brain metastasis development and limited the outgrowth of cancer cells in the brain, suggesting a therapeutic strategy to combat the disease. These results illustrated the utility of MetMap as a step towards next-generation approach for high-throughput metastasis research.
Citation Format: Xin Jin, Zelalem Demere, Karthik Nair, Ahmed Ali, Gino B. Ferraro, Ted Natoli, Amy Deik, Lia Petronio, Andrew A. Tang, Cong Zhu, Li Wang, Danny Rosenberg, Vamsi Mangena, Jennifer Roth, Kwanghun Chung, Rakesh K. Jain, Clary B. Clish, Matthew G. Vander Heiden, Todd R. Golub. A metastasis map of human cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr NG10.
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Affiliation(s)
- Xin Jin
- 1Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Karthik Nair
- 1Broad Institute of MIT and Harvard, Cambridge, MA
| | - Ahmed Ali
- 2Massachusetts Institute of Technology, Cambridge, MA
| | | | - Ted Natoli
- 1Broad Institute of MIT and Harvard, Cambridge, MA
| | - Amy Deik
- 1Broad Institute of MIT and Harvard, Cambridge, MA
| | - Lia Petronio
- 1Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Cong Zhu
- 1Broad Institute of MIT and Harvard, Cambridge, MA
| | - Li Wang
- 1Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Vamsi Mangena
- 2Massachusetts Institute of Technology, Cambridge, MA
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26
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Sevilla-Gonzalez M, Deik A, Manning A, Clish CB. Metabolites Associated With Regression to Normoglycemia After a Lifestyle Intervention in Individuals With Prediabetes. Curr Dev Nutr 2021. [DOI: 10.1093/cdn/nzab052_010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Objectives
Prediabetes is a highly prevalent intermediate stage between normal glucose tolerance and type 2 diabetes (T2D). Lifestyle interventions (LSI) are the main focus for T2D prevention, but little is known about the aspects of LSI that can lead to the regression to a normal glucose regulation (RNGT). Metabolomics can be a helpful tool to identify the footprints of RNGR. Our aim was to identify the metabolites and lifestyle aspects that contribute to RNGR in prediabetes.
Methods
We conducted a one arm intervention study with 24 weeks of follow-up. Eligible study participants were identified by having at least one prediabetes criteria according to the American Diabetes Association, and BMI between 25 and 45 kg/m2. The LSI was a hypocaloric diet and >150 min of physical activity at a medium intensity per week. The primary outcome, RNGR was defined as HbA1c < 5.5% in the final visit. Baseline and post-intervention plasma metabolomic profiles were measured using liquid chromatography-tandem mass spectrometry (LC-MS). To select metabolites associated with RNGT we conducted the least absolute shrinkage and selection operator-penalized regression (LASSO).
Results
The final sample was composed of 82 study participants. We observed that individuals who increased their protein consumption were more likely to have RNGR compared to controls (HR 0.97 95%CI 0.94–0.99), adjusted by age, sex, HbA1c at baseline, and weight loss. At baseline, participants with RNGT had lower plasma sphinganine (OR per standard unit 0.51; 95%CI 0.27–0.96), and higher levels of myristoleic acid (OR 2.31; 95%CI 1.16 – 4.59). After the intervention, compared with controls, RNGT individuals had higher plasma levels of amino acids and other metabolites: threonine (OR 3.19; 95%CI 1.53–6.66), alanine (OR 3.09; 95%CI 1.49–6.43), proline (OR 1.95; 95%CI 1.02–3.73), and prolylglycine (OR 2.19; 95%CI 1.53–6.66), 4-guanidinobutanoic acid (OR 1.98; 95%CI 1.07–3.64), Ne, Ne-dimethyllysine (OR 2.08; 95%CI 1.14–3.80), urate (OR 2.22; 95%CI 1.13–4.36), biliverdin (OR 1.57; 95%CI 1.13–4.36), and Asymmetric dimethylarginine (OR 2.04; 95%CI 1.10–3.77).
Conclusions
Some metabolites could be associated with RNGT independently of other treatment aspects such as weight loss. Increased protein consumption could have a positive impact on the treatment of individuals with prediabetes.
Funding Sources
MGH.
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Affiliation(s)
| | - Amy Deik
- The Broad Institute of M.I.T and Harvard
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27
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Ferraro GB, Ali A, Luengo A, Kodack DP, Deik A, Abbott KL, Bezwada D, Blanc L, Prideaux B, Jin X, Posada JM, Chen J, Chin CR, Amoozgar Z, Ferreira R, Chen IX, Naxerova K, Ng C, Westermark AM, Duquette M, Roberge S, Lindeman NI, Lyssiotis CA, Nielsen J, Housman DE, Duda DG, Brachtel E, Golub TR, Cantley LC, Asara JM, Davidson SM, Fukumura D, Dartois VA, Clish CB, Jain RK, Vander Heiden MG. FATTY ACID SYNTHESIS IS REQUIRED FOR BREAST CANCER BRAIN METASTASIS. Nat Cancer 2021; 2:414-428. [PMID: 34179825 PMCID: PMC8223728 DOI: 10.1038/s43018-021-00183-y] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/08/2021] [Indexed: 02/01/2023]
Abstract
Brain metastases are refractory to therapies that control systemic disease in patients with human epidermal growth factor receptor 2 (HER2+) breast cancer, and the brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for brain metastatic breast cancer growth may introduce liabilities that can be exploited for therapy. Here, we assessed how metabolism differs between breast tumors in brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in brain. We determine that this phenotype is an adaptation to decreased lipid availability in brain relative to other tissues, resulting in a site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase (FASN) reduces HER2+ breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.
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Affiliation(s)
- Gino B Ferraro
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ahmed Ali
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Alba Luengo
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David P Kodack
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Amy Deik
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Keene L Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Divya Bezwada
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Landry Blanc
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CNRS UMR 5248, Bordeaux, France
| | - Brendan Prideaux
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
- Department of Neuroscience, Cell Biology, and Anatomy, University of Texas Medical Branch, Galveston, TX, USA
| | - Xin Jin
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Jessica M Posada
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jiang Chen
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Christopher R Chin
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zohreh Amoozgar
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Raphael Ferreira
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Ivy X Chen
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kamila Naxerova
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christopher Ng
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna M Westermark
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mark Duquette
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sylvie Roberge
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Neal I Lindeman
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Costas A Lyssiotis
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- University of Michigan, Ann Arbor, MI, USA
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - David E Housman
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dan G Duda
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Elena Brachtel
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Todd R Golub
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Lewis C Cantley
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY, USA
| | - John M Asara
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shawn M Davidson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Lewis Sigler Institute, Princeton University, Princeton, NJ, USA
| | - Dai Fukumura
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Véronique A Dartois
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Rakesh K Jain
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
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Ferraro G, Ali A, Luengo A, Kodack D, Deik A, Abbott K, Bezwada D, Blanc L, Prideaux B, Jin X, Possada J, Chen J, Chin C, Amoozgar Z, Ferreira R, Chen I, Naxerova K, Ng C, Westermark A, Duquette M, Roberge S, Lyssiotis C, Duda D, Golub T, Cantley L, Asara J, Davidson S, Fukumura D, Dartois V, Clish C, Heiden MV, Jain R. DDRE-07. FATTY ACID SYNTHESIS IS REQUIRED FOR BREAST CANCER BRAIN METASTASIS. Neurooncol Adv 2021. [PMCID: PMC7992317 DOI: 10.1093/noajnl/vdab024.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Brain metastases are refractory to therapies that otherwise control systemic disease in patients with human epidermal growth factor receptor 2 (HER2+) breast cancer, and the unique brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for breast cancer growth in the brain microenvironment may also introduce liabilities that can be exploited for therapy. Here, we assessed how metabolism differs between breast tumors growing in the brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in the brain. We determine that this phenotype is an adaptation to decreased lipid availability in the brain relative to other tissues, which results in a site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase (FASN) reduces HER2+ breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.
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Affiliation(s)
- Gino Ferraro
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Ahmed Ali
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alba Luengo
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David Kodack
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Keene Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Divya Bezwada
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Landry Blanc
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Brendan Prideaux
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Xin Jin
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Jessica Possada
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Jiang Chen
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Christopher Chin
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zohreh Amoozgar
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Raphael Ferreira
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ivy Chen
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Kamila Naxerova
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Christopher Ng
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna Westermark
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mark Duquette
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Sylvie Roberge
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Costas Lyssiotis
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Dan Duda
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Todd Golub
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Lewis Cantley
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - John Asara
- Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shawn Davidson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Boston, MA, USA
| | - Dai Fukumura
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Véronique Dartois
- The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Matthew Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rakesh Jain
- Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
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Jin X, Demere Z, Nair K, Ali A, Ferraro GB, Natoli T, Deik A, Petronio L, Tang AA, Zhu C, Wang L, Rosenberg D, Mangena V, Roth J, Chung K, Jain RK, Clish CB, Vander Heiden MG, Golub TR. A metastasis map of human cancer cell lines. Nature 2020; 588:331-336. [PMID: 33299191 PMCID: PMC8439149 DOI: 10.1038/s41586-020-2969-2] [Citation(s) in RCA: 184] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 08/26/2020] [Indexed: 12/12/2022]
Abstract
Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical owing to the complexity of in vivo models. Here we introduce an in vivo barcoding strategy that is capable of determining the metastatic potential of human cancer cell lines in mouse xenografts at scale. We validated the robustness, scalability and reproducibility of the method and applied it to 500 cell lines1,2 spanning 21 types of solid tumour. We created a first-generation metastasis map (MetMap) that reveals organ-specific patterns of metastasis, enabling these patterns to be associated with clinical and genomic features. We demonstrate the utility of MetMap by investigating the molecular basis of breast cancers capable of metastasizing to the brain—a principal cause of death in patients with this type of cancer. Breast cancers capable of metastasizing to the brain showed evidence of altered lipid metabolism. Perturbation of lipid metabolism in these cells curbed brain metastasis development, suggesting a therapeutic strategy to combat the disease and demonstrating the utility of MetMap as a resource to support metastasis research. A method in which pooled barcoded human cancer cell lines are injected into a mouse xenograft model enables simultaneous mapping of the metastatic potential of multiple cell lines, and shows that breast cancer cells that metastasize to the brain have altered lipid metabolism.
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Affiliation(s)
- Xin Jin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | | | - Karthik Nair
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ahmed Ali
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gino B Ferraro
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Ted Natoli
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lia Petronio
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew A Tang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cong Zhu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Li Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Vamsi Mangena
- Institute for Medical Engineering and Science, Picower Institute for Learning and Memory, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jennifer Roth
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kwanghun Chung
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Institute for Medical Engineering and Science, Picower Institute for Learning and Memory, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rakesh K Jain
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew G Vander Heiden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.,Dana-Farber Cancer Institute, Boston, MA, USA
| | - Todd R Golub
- Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Harvard Medical School, Boston, MA, USA. .,Dana-Farber Cancer Institute, Boston, MA, USA.
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30
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Jin X, Demere Z, Nair K, Ali A, Ferraro GB, Natoli T, Deik A, Petronio L, Tang AA, Zhu C, Wang L, Rosenberg D, Mangena V, Roth J, Chung K, Jain RK, Clish CB, Vander Heiden MG, Golub TR. Abstract PO-031: MetMap: A map of metastatic potential of human cancer cell lines. Cancer Res 2020. [DOI: 10.1158/1538-7445.epimetab20-po-031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical due to the complexity of in vivo models. Here, we introduce an in vivo barcoding strategy capable of determining the metastatic potential of human cancer cell lines in murine xenografts at scale. We validated the robustness, scalability and reproducibility of the method, and applied it to 500 cell lines spanning 21 solid cancer types. We created a first-generation Metastasis Map (MetMap) that reveals organ-specific patterns of metastasis and allows relating those patterns to clinical and genomic features. We demonstrated the utility of MetMap by exploring the molecular basis of breast cancers capable of metastasizing to the brain – a principal cause of death in these patients. We found that breast cancers capable of metastasizing to the brain had unexpected evidence of altered lipid metabolism. Perturbing lipid metabolism curbed brain metastasis development, suggesting a therapeutic strategy to combat the disease and demonstrating the utility of MetMap as a public resource to support metastasis research.
Citation Format: Xin Jin, Zelalem Demere, Karthik Nair, Ahmed Ali, Gino B. Ferraro, Ted Natoli, Amy Deik, Lia Petronio, Andrew A. Tang, Cong Zhu, Li Wang, Danny Rosenberg, Vamsi Mangena, Jennifer Roth, Kwanghun Chung, Rakesh K. Jain, Clary B. Clish, Matthew G. Vander Heiden, Todd R. Golub. MetMap: A map of metastatic potential of human cancer cell lines [abstract]. In: Abstracts: AACR Special Virtual Conference on Epigenetics and Metabolism; October 15-16, 2020; 2020 Oct 15-16. Philadelphia (PA): AACR; Cancer Res 2020;80(23 Suppl):Abstract nr PO-031.
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Affiliation(s)
- Xin Jin
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | - Karthik Nair
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Ahmed Ali
- 2Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA,
| | - Gino B. Ferraro
- 3Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA,
| | - Ted Natoli
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Amy Deik
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Lia Petronio
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | - Cong Zhu
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Li Wang
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | - Vamsi Mangena
- 4Institute for Medical Engineering and Science, Picower Institute for Learning and Memory, MIT, Cambridge, MA
| | | | - Kwanghun Chung
- 4Institute for Medical Engineering and Science, Picower Institute for Learning and Memory, MIT, Cambridge, MA
| | - Rakesh K. Jain
- 3Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA,
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Nayor M, Shah RV, Miller PE, Blodgett JB, Tanguay M, Pico AR, Murthy VL, Malhotra R, Houstis NE, Deik A, Pierce KA, Bullock K, Dailey L, Velagaleti RS, Moore SA, Ho JE, Baggish AL, Clish CB, Larson MG, Vasan RS, Lewis GD. Metabolic Architecture of Acute Exercise Response in Middle-Aged Adults in the Community. Circulation 2020; 142:1905-1924. [PMID: 32927962 PMCID: PMC8049528 DOI: 10.1161/circulationaha.120.050281] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Whereas regular exercise is associated with lower risk of cardiovascular disease and mortality, mechanisms of exercise-mediated health benefits remain less clear. We used metabolite profiling before and after acute exercise to delineate the metabolic architecture of exercise response patterns in humans. METHODS Cardiopulmonary exercise testing and metabolite profiling was performed on Framingham Heart Study participants (age 53±8 years, 63% women) with blood drawn at rest (n=471) and at peak exercise (n=411). RESULTS We observed changes in circulating levels for 502 of 588 measured metabolites from rest to peak exercise (exercise duration 11.9±2.1 minutes) at a 5% false discovery rate. Changes included reductions in metabolites implicated in insulin resistance (glutamate, -29%; P=1.5×10-55; dimethylguanidino valeric acid [DMGV], -18%; P=5.8×10-18) and increases in metabolites associated with lipolysis (1-methylnicotinamide, +33%; P=6.1×10-67), nitric oxide bioavailability (arginine/ornithine + citrulline, +29%; P=2.8×10-169), and adipose browning (12,13-dihydroxy-9Z-octadecenoic acid +26%; P=7.4×10-38), among other pathways relevant to cardiometabolic risk. We assayed 177 metabolites in a separate Framingham Heart Study replication sample (n=783, age 54±8 years, 51% women) and observed concordant changes in 164 metabolites (92.6%) at 5% false discovery rate. Exercise-induced metabolite changes were variably related to the amount of exercise performed (peak workload), sex, and body mass index. There was attenuation of favorable excursions in some metabolites in individuals with higher body mass index and greater excursions in select cardioprotective metabolites in women despite less exercise performed. Distinct preexercise metabolite levels were associated with different physiologic dimensions of fitness (eg, ventilatory efficiency, exercise blood pressure, peak Vo2). We identified 4 metabolite signatures of exercise response patterns that were then analyzed in a separate cohort (Framingham Offspring Study; n=2045, age 55±10 years, 51% women), 2 of which were associated with overall mortality over median follow-up of 23.1 years (P≤0.003 for both). CONCLUSIONS In a large sample of community-dwelling individuals, acute exercise elicits widespread changes in the circulating metabolome. Metabolic changes identify pathways central to cardiometabolic health, cardiovascular disease, and long-term outcome. These findings provide a detailed map of the metabolic response to acute exercise in humans and identify potential mechanisms responsible for the beneficial cardiometabolic effects of exercise for future study.
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Affiliation(s)
- Matthew Nayor
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Ravi V. Shah
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Patricia E. Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jasmine B. Blodgett
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Melissa Tanguay
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA
| | - Venkatesh L. Murthy
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor
- Frankel Cardiovascular Center, University of Michigan, Ann Arbor
| | - Rajeev Malhotra
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
| | - Nicholas E. Houstis
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA
| | | | | | - Lucas Dailey
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Raghava S. Velagaleti
- Cardiology Section, Department of Medicine, Boston VA Healthcare System, West Roxbury, MA
| | - Stephanie A. Moore
- Cardiology Section, Department of Medicine, Boston VA Healthcare System, West Roxbury, MA
| | - Jennifer E. Ho
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Aaron L. Baggish
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | | | - Martin G. Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
| | - Ramachandran S. Vasan
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
- Sections of Preventive Medicine and Epidemiology, and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA
| | - Gregory D. Lewis
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Pulmonary Critical Care Unit, Massachusetts General Hospital, Boston, MA
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Xu YX, Stanclift C, Nagai TH, Yu H, Vellarikkal SK, Deik A, Bullock K, Schenone M, Cowan C, Clish CB, Carr S, Kathiresan S. Interactomics Analyses of Wild-Type and Mutant A1CF Reveal Diverged Functions in Regulating Cellular Lipid Metabolism. J Proteome Res 2020; 19:3968-3980. [PMID: 32786677 DOI: 10.1021/acs.jproteome.0c00235] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Population genetic studies highlight a missense variant (G398S) of A1CF that is strongly associated with higher levels of blood triglycerides (TGs) and total cholesterol (TC). Functional analyses suggest that the mutation accelerates the secretion of very low-density lipoprotein (VLDL) from the liver by an unknown mechanism. Here, we used multiomics approaches to interrogate the functional difference between the WT and mutant A1CF. Using metabolomics analyses, we captured the cellular lipid metabolite changes induced by transient expression of the proteins, confirming that the mutant A1CF is able to relieve the TG accumulation induced by WT A1CF. Using a proteomics approach, we obtained the interactomic data of WT and mutant A1CF. Networking analyses show that WT A1CF interacts with three functional protein groups, RNA/mRNA processing, cytosolic translation, and, surprisingly, mitochondrial translation. The mutation diminishes these interactions, especially with the group of mitochondrial translation. Differential analyses show that the WT A1CF-interacting proteins most significantly different from the mutant are those for mitochondrial translation, whereas the most significant interacting proteins with the mutant are those for cytoskeleton and vesicle-mediated transport. RNA-seq analyses validate that the mutant, but not the WT, A1CF increases the expression of the genes responsible for cellular transport processes. On the contrary, WT A1CF affected the expression of mitochondrial matrix proteins and increased cell oxygen consumption. Thus, our studies confirm the previous hypothesis that A1CF plays broader roles in regulating gene expression. The interactions of the mutant A1CF with the vesicle-mediated transport machinery provide mechanistic insight in understanding the increased VLDL secretion in the A1CF mutation carriers.
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Affiliation(s)
- Yu-Xin Xu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
| | - Caroline Stanclift
- The Proteomics Platform, Broad Institute, Cambridge, Massachusetts 02142, United States
| | - Taylor Hanta Nagai
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
| | - Haojie Yu
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, United States
| | | | - Amy Deik
- The Metabolomics Program, Broad Institute, Cambridge, Massachusetts 02142, United States
| | - Kevin Bullock
- The Metabolomics Program, Broad Institute, Cambridge, Massachusetts 02142, United States
| | - Monica Schenone
- The Proteomics Platform, Broad Institute, Cambridge, Massachusetts 02142, United States
| | - Chad Cowan
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, United States
| | - Clary B Clish
- The Metabolomics Program, Broad Institute, Cambridge, Massachusetts 02142, United States
| | - Steven Carr
- The Proteomics Platform, Broad Institute, Cambridge, Massachusetts 02142, United States
| | - Sekar Kathiresan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
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Goodman RP, Markhard AL, Shah H, Sharma R, Skinner OS, Clish CB, Deik A, Patgiri A, Hsu YHH, Masia R, Noh HL, Suk S, Goldberger O, Hirschhorn JN, Yellen G, Kim JK, Mootha VK. Hepatic NADH reductive stress underlies common variation in metabolic traits. Nature 2020; 583:122-126. [PMID: 32461692 PMCID: PMC7536642 DOI: 10.1038/s41586-020-2337-2] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 03/11/2020] [Indexed: 01/21/2023]
Abstract
The cellular NADH/NAD+ ratio is fundamental to biochemistry, but the extent to which it reflects versus drives metabolic physiology in vivo is poorly understood. Here we report the in vivo application of Lactobacillus brevis (Lb)NOX1, a bacterial water-forming NADH oxidase, to assess the metabolic consequences of directly lowering the hepatic cytosolic NADH/NAD+ ratio in mice. By combining this genetic tool with metabolomics, we identify circulating α-hydroxybutyrate levels as a robust marker of an elevated hepatic cytosolic NADH/NAD+ ratio, also known as reductive stress. In humans, elevations in circulating α-hydroxybutyrate levels have previously been associated with impaired glucose tolerance2, insulin resistance3 and mitochondrial disease4, and are associated with a common genetic variant in GCKR5, which has previously been associated with many seemingly disparate metabolic traits. Using LbNOX, we demonstrate that NADH reductive stress mediates the effects of GCKR variation on many metabolic traits, including circulating triglyceride levels, glucose tolerance and FGF21 levels. Our work identifies an elevated hepatic NADH/NAD+ ratio as a latent metabolic parameter that is shaped by human genetic variation and contributes causally to key metabolic traits and diseases. Moreover, it underscores the utility of genetic tools such as LbNOX to empower studies of 'causal metabolism'.
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Affiliation(s)
- Russell P Goodman
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew L Markhard
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Hardik Shah
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Rohit Sharma
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Owen S Skinner
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Amy Deik
- Broad Institute, Cambridge, MA, USA
| | - Anupam Patgiri
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Yu-Han H Hsu
- Broad Institute, Cambridge, MA, USA
- Departments of Pediatrics and Genetics, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Ricard Masia
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Hye Lim Noh
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Sujin Suk
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Olga Goldberger
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Joel N Hirschhorn
- Broad Institute, Cambridge, MA, USA
- Departments of Pediatrics and Genetics, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Gary Yellen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jason K Kim
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
- Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Vamsi K Mootha
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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34
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Hernández‐Alonso P, Becerra‐Tomás N, Papandreou C, Bulló M, Guasch‐Ferré M, Toledo E, Ruiz‐Canela M, Clish CB, Corella D, Dennis C, Deik A, Wang DD, Razquin C, Drouin‐Chartier J, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra‐Majem L, Liang L, Martínez‐González MA, Hu FB, Salas‐Salvadó J. Plasma Metabolomics Profiles are Associated with the Amount and Source of Protein Intake: A Metabolomics Approach within the PREDIMED Study. Mol Nutr Food Res 2020; 64:e2000178. [PMID: 32378786 DOI: 10.1002/mnfr.202000178] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/15/2020] [Indexed: 01/24/2023]
Affiliation(s)
- Pablo Hernández‐Alonso
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Unidad de Gestión Clínica de Endocrinología y Nutrición del Hospital Virgen de la VictoriaInstituto de Investigación Biomédica de Málaga (IBIMA) Málaga 29010 Spain
| | - Nerea Becerra‐Tomás
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Christopher Papandreou
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Mònica Bulló
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Marta Guasch‐Ferré
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Estefanía Toledo
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Miguel Ruiz‐Canela
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Clary B. Clish
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Dolores Corella
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of Preventive MedicineUniversity of Valencia Valencia 46020 Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Amy Deik
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Dong D. Wang
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Cristina Razquin
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Jean‐Philippe Drouin‐Chartier
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF)Université Laval Québec G1V 0A6 Canada
- Faculté de PharmacieUniversité Laval Québec G1V 0A6 Canada
| | - Ramon Estruch
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of Internal MedicineDepartment of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital ClinicUniversity of Barcelona Barcelona 08036 Spain
| | - Emilio Ros
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital ClinicUniversity of Barcelona Barcelona 08036 Spain
| | - Montserrat Fitó
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Cardiovascular and Nutrition Research GroupInstitut de Recerca Hospital del Mar Barcelona 08003 Spain
| | - Fernando Arós
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of CardiologyUniversity Hospital of Alava Vitoria 01009 Spain
| | - Miquel Fiol
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Institute of Health Sciences IUNICSUniversity of Balearic Islands and Hospital Son Espases Palma de Mallorca 07122 Spain
| | - Lluís Serra‐Majem
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Research Institute of Biomedical and Health Sciences IUIBSUniversity of Las Palmas de Gran Canaria Las Palmas 35001 Spain
| | - Liming Liang
- Departments of Epidemiology and StatisticsHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Miguel A Martínez‐González
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Frank B Hu
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
- Departments of Epidemiology and StatisticsHarvard T. H. Chan School of Public Health Boston MA 02115 USA
- Channing Division for Network Medicine, Department of MedicineBrigham and Women's Hospital and Harvard Medical School Boston MA 02115 USA
| | - Jordi Salas‐Salvadó
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
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35
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Xu YX, Peloso GM, Nagai TH, Mizoguchi T, Deik A, Bullock K, Lin H, Musunuru K, Yang Q, Vasan RS, Gerszten RE, Clish CB, Rader D, Kathiresan S. EDEM3 Modulates Plasma Triglyceride Level through Its Regulation of LRP1 Expression. iScience 2020; 23:100973. [PMID: 32213464 PMCID: PMC7093811 DOI: 10.1016/j.isci.2020.100973] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 12/06/2019] [Accepted: 03/05/2020] [Indexed: 01/10/2023] Open
Abstract
Human genetics studies have uncovered genetic variants that can be used to guide biological research and prioritize molecular targets for therapeutic intervention for complex diseases. We have identified a missense variant (P746S) in EDEM3 associated with lower blood triglyceride (TG) levels in >300,000 individuals. Functional analyses in cell and mouse models show that EDEM3 deficiency strongly increased the uptake of very-low-density lipoprotein and thereby reduced the plasma TG level, as a result of up-regulated expression of LRP1 receptor. We demonstrate that EDEM3 deletion up-regulated the pathways for RNA and endoplasmic reticulum protein processing and transport, and consequently increased the cell surface mannose-containing glycoproteins, including LRP1. Metabolomics analyses reveal a cellular TG accumulation under EDEM3 deficiency, a profile consistent with individuals carrying EDEM3 P746S. Our study identifies EDEM3 as a regulator of blood TG, and targeted inhibition of EDEM3 may provide a complementary approach for lowering elevated blood TG concentrations.
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Affiliation(s)
- Yu-Xin Xu
- Center for Genomic Medicine, Massachusetts General Hospital, Simches 5.500, 185 Cambridge St., Boston, MA 02114, USA.
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Taylor H Nagai
- Center for Genomic Medicine, Massachusetts General Hospital, Simches 5.500, 185 Cambridge St., Boston, MA 02114, USA
| | - Taiji Mizoguchi
- Center for Genomic Medicine, Massachusetts General Hospital, Simches 5.500, 185 Cambridge St., Boston, MA 02114, USA
| | - Amy Deik
- The Metabolomics Program, Broad Institute, Cambridge, MA 02142, USA
| | - Kevin Bullock
- The Metabolomics Program, Broad Institute, Cambridge, MA 02142, USA
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Kiran Musunuru
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Ramachandran S Vasan
- Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA 02118, USA; Framingham Heart Study of the NHLBI and Boston University School of Medicine, Framingham, MA 01702, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Clary B Clish
- The Metabolomics Program, Broad Institute, Cambridge, MA 02142, USA
| | - Daniel Rader
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sekar Kathiresan
- Center for Genomic Medicine, Massachusetts General Hospital, Simches 5.500, 185 Cambridge St., Boston, MA 02114, USA.
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36
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Bravo CA, Hua S, Deik A, Lazar J, Hanna DB, Scott J, Chai JC, Kaplan RC, Anastos K, Robles OA, Clish CB, Kizer JR, Qi Q. Metabolomic Profiling of Left Ventricular Diastolic Dysfunction in Women With or at Risk for HIV Infection: The Women's Interagency HIV Study. J Am Heart Assoc 2020; 9:e013522. [PMID: 32063116 PMCID: PMC7070185 DOI: 10.1161/jaha.119.013522] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background People living with HIV have an increased risk of left ventricular diastolic dysfunction (LVDD) and heart failure. HIV-associated LVDD may reflect both cardiomyocyte and systemic metabolic derangements, but the underlying pathways remain unclear. Methods and Results To explore such pathways, we conducted a pilot study in the Bronx and Brooklyn sites of the WIHS (Women's Interagency HIV Study) who participated in concurrent, but separate, metabolomics and echocardiographic ancillary studies. Liquid chromatography tandem mass spectrometry-based metabolomic profiling was performed on plasma samples from 125 HIV-infected (43 with LVDD) and 35 HIV-uninfected women (9 with LVDD). Partial least squares discriminant analysis identified polar metabolites and lipids in the glycerophospholipid-metabolism and fatty-acid-oxidation pathways associated with LVDD. After multivariable adjustment, LVDD was significantly associated with higher concentrations of diacylglycerol 30:0 (odds ratio [OR], 1.60, 95% CI [1.01-2.55]); triacylglycerols 46:0 (OR 1.60 [1.04-2.48]), 48:0 (OR 1.63 [1.04-2.54]), 48:1 (OR 1.62 [1.01-2.60]), and 50:0 (OR 1.61 [1.02-2.53]); acylcarnitine C7 (OR 1.88 [1.21-2.92]), C9 (OR 1.99 [1.27-3.13]), and C16 (OR 1.80 [1.13-2.87]); as well as lower concentrations of phosphocholine (OR 0.59 [0.38-0.91]). There was no evidence of effect modification of these relationships by HIV status. Conclusions In this pilot study, women with or at risk of HIV with LVDD showed alterations in plasma metabolites in the glycerophospholipid-metabolism and fatty-acid-oxidation pathways. Although these findings require replication, they suggest that improved understanding of metabolic perturbations and their potential modification could offer new approaches to prevent cardiac dysfunction in this high-risk group.
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Affiliation(s)
- Claudio A Bravo
- Division of Cardiology Department of Medicine Columbia University Medical Center New York NY
| | - Simin Hua
- Department of Epidemiology & Population Health Albert Einstein College of Medicine Bronx NY
| | - Amy Deik
- Metabolomics Platform Broad Institute of MIT and Harvard Cambridge MA
| | - Jason Lazar
- Division of Cardiovascular Medicine State University of New York Downstate Medical Center Brooklyn NY
| | - David B Hanna
- Department of Epidemiology & Population Health Albert Einstein College of Medicine Bronx NY
| | - Justin Scott
- Metabolomics Platform Broad Institute of MIT and Harvard Cambridge MA
| | - Jin Choul Chai
- Department of Epidemiology & Population Health Albert Einstein College of Medicine Bronx NY
| | - Robert C Kaplan
- Department of Epidemiology & Population Health Albert Einstein College of Medicine Bronx NY.,Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA
| | - Kathryn Anastos
- Department of Epidemiology & Population Health Albert Einstein College of Medicine Bronx NY.,Department of Medicine Albert Einstein College of Medicine Bronx NY
| | - Octavio A Robles
- Department of Biologic Sciences Universidad de Chile Santiago Chile
| | - Clary B Clish
- Metabolomics Platform Broad Institute of MIT and Harvard Cambridge MA
| | - Jorge R Kizer
- Division of Cardiology San Francisco Veterans Affairs Health Care System University of California San Francisco San Francisco CA
| | - Qibin Qi
- Department of Epidemiology & Population Health Albert Einstein College of Medicine Bronx NY
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37
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Stefater MA, Pacheco JA, Bullock K, Pierce K, Deik A, Liu E, Clish C, Stylopoulos N. Portal Venous Metabolite Profiling After RYGB in Male Rats Highlights Changes in Gut-Liver Axis. J Endocr Soc 2020; 4:bvaa003. [PMID: 32099946 PMCID: PMC7033034 DOI: 10.1210/jendso/bvaa003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 01/21/2020] [Indexed: 12/15/2022] Open
Abstract
After Roux-en-Y gastric bypass (RYGB) surgery, the intestine undergoes structural and metabolic reprogramming and appears to enhance use of energetic fuels including glucose and amino acids (AAs), changes that may be related to the surgery’s remarkable metabolic effects. Consistently, RYGB alters serum levels of AAs and other metabolites, perhaps reflecting mechanisms for metabolic improvement. To home in on the intestinal contribution, we performed metabolomic profiling in portal venous (PV) blood from lean, Long Evans rats after RYGB vs sham surgery. We found that one-carbon metabolism (OCM), nitrogen metabolism, and arginine and proline metabolism were significantly enriched in PV blood. Nitrogen, OCM, and sphingolipid metabolism as well as ubiquinone biosynthesis were also overrepresented among metabolites uniquely affected in PV vs peripheral blood in RYGB-operated but not sham-operated animals. Peripheral blood demonstrated changes in AA metabolism, OCM, sphingolipid metabolism, and glycerophospholipid metabolism. Despite enrichment for many of the same pathways, the overall metabolite fingerprint of the 2 compartments did not correlate, highlighting a unique role for PV metabolomic profiling as a window into gut metabolism. AA metabolism and OCM were enriched in peripheral blood both from humans and lean rats after RYGB, demonstrating that these conserved pathways might represent mechanisms for clinical improvement elicited by the surgery in patients. Together, our data provide novel insight into RYGB’s effects on the gut-liver axis and highlight a role for OCM as a key metabolic pathway affected by RYGB.
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Affiliation(s)
- Margaret A Stefater
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Kevin Bullock
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Kerry Pierce
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Enju Liu
- Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, Massachusetts
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Nicholas Stylopoulos
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts.,Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, Massachusetts
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38
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Hernández-Alonso P, Papandreou C, Bulló M, Ruiz-Canela M, Dennis C, Deik A, Wang DD, Guasch-Ferré M, Yu E, Toledo E, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra-Majem L, Liang L, Clish CB, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolites Associated with Frequent Red Wine Consumption: A Metabolomics Approach within the PREDIMED Study. Mol Nutr Food Res 2019; 63:e1900140. [PMID: 31291050 PMCID: PMC6771435 DOI: 10.1002/mnfr.201900140] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 05/14/2019] [Indexed: 01/25/2023]
Abstract
SCOPE The relationship between red wine (RW) consumption and metabolism is poorly understood. It is aimed to assess the systemic metabolomic profiles in relation to frequent RW consumption as well as the ability of a set of metabolites to discriminate RW consumers. METHODS AND RESULTS A cross-sectional analysis of 1157 participants is carried out. Subjects are divided as non-RW consumers versus RW consumers (>1 glass per day RW [100 mL per day]). Plasma metabolomics analysis is performed using LC-MS. Associations between 386 identified metabolites and RW consumption are assessed using elastic net regression analysis taking into consideration baseline significant covariates. Ten-cross-validation (CV) is performed and receiver operating characteristic curves are constructed in each of the validation datasets based on weighted models. A subset of 13 metabolites is consistently selected and RW consumers versus nonconsumers are discriminated. Based on the multi-metabolite model weighted with the regression coefficients of metabolites, the area under the curve is 0.83 (95% CI: 0.80-0.86). These metabolites mainly consisted of lipid species, some organic acids, and alkaloids. CONCLUSIONS A multi-metabolite model identified in a Mediterranean population appears useful to discriminate between frequent RW consumers and nonconsumers. Further studies are needed to assess the contribution of these metabolites in health and disease.
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Affiliation(s)
- Pablo Hernández-Alonso
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Mònica Bulló
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Dong D. Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marta Guasch-Ferré
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Estefanía Toledo
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
| | - Cristina Razquin
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d’Investigacions Biomèdiques August Pi Sunyer (IDI-BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d’Investigacions Biomèdiques August Pi Sunyer (IDI-BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Arós
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluís Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Liming Liang
- Departments of Epidemiology and Statistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Miguel A Martínez-González
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
- Departments of Epidemiology and Statistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, MA, USA
| | - Jordi Salas-Salvadó
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
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Papandreou C, Hernández-Alonso P, Bulló M, Ruiz-Canela M, Yu E, Guasch-Ferré M, Toledo E, Dennis C, Deik A, Clish C, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Lapetra J, Ruano C, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolites Associated with Coffee Consumption: A Metabolomic Approach within the PREDIMED Study. Nutrients 2019; 11:E1032. [PMID: 31072000 PMCID: PMC6566346 DOI: 10.3390/nu11051032] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 05/02/2019] [Accepted: 05/03/2019] [Indexed: 02/07/2023] Open
Abstract
Few studies have examined the association of a wide range of metabolites with total and subtypes of coffee consumption. The aim of this study was to investigate associations of plasma metabolites with total, caffeinated, and decaffeinated coffee consumption. We also assessed the ability of metabolites to discriminate between coffee consumption categories. This is a cross-sectional analysis of 1664 participants from the PREDIMED study. Metabolites were semiquantitatively profiled using a multiplatform approach. Consumption of total coffee, caffeinated coffee and decaffeinated coffee was assessed by using a validated food frequency questionnaire. We assessed associations between 387 metabolite levels with total, caffeinated, or decaffeinated coffee consumption (≥50 mL coffee/day) using elastic net regression analysis. Ten-fold cross-validation analyses were used to estimate the discriminative accuracy of metabolites for total and subtypes of coffee. We identified different sets of metabolites associated with total coffee, caffeinated and decaffeinated coffee consumption. These metabolites consisted of lipid species (e.g., sphingomyelin, phosphatidylethanolamine, and phosphatidylcholine) or were derived from glycolysis (alpha-glycerophosphate) and polyphenol metabolism (hippurate). Other metabolites included caffeine, 5-acetylamino-6-amino-3-methyluracil, cotinine, kynurenic acid, glycocholate, lactate, and allantoin. The area under the curve (AUC) was 0.60 (95% CI 0.56-0.64), 0.78 (95% CI 0.75-0.81) and 0.52 (95% CI 0.49-0.55), in the multimetabolite model, for total, caffeinated, and decaffeinated coffee consumption, respectively. Our comprehensive metabolic analysis did not result in a new, reliable potential set of metabolites for coffee consumption.
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Affiliation(s)
- Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - Pablo Hernández-Alonso
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - Mònica Bulló
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
| | - Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Marta Guasch-Ferré
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Estefanía Toledo
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
| | - Clary Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
| | - Cristina Razquin
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Preventive Medicine, University of Valencia, 46010 Valencia, Spain.
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d' Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08007 Barcelona, Spain.
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08007 Barcelona, Spain.
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Cardiovascular Risk and Nutrition Research Group (CARIN), Hospital del Mar Research Institute (IMIM), 08003 Barcelona, Spain.
| | - Fernando Arós
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Cardiology, University Hospital of Álava, 01009 Vitoria, Spain.
| | - Miquel Fiol
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Illes Balears Health Research Institute (IdISBa), Hospital Son Espases, 07120 Palma de Mallorca, Spain.
| | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Family, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013 Sevilla, Spain.
| | - Cristina Ruano
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Clinical Sciences, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain.
| | - Liming Liang
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Miguel A Martínez-González
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA 02115, USA.
| | - Jordi Salas-Salvadó
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
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Li H, Ning S, Ghandi M, Kryukov GV, Gopal S, Deik A, Souza A, Pierce K, Keskula P, Hernandez D, Ann J, Shkoza D, Apfel V, Zou Y, Vazquez F, Barretina J, Pagliarini RA, Galli GG, Root DE, Hahn WC, Tsherniak A, Giannakis M, Schreiber SL, Clish CB, Garraway LA, Sellers WR. The landscape of cancer cell line metabolism. Nat Med 2019; 25:850-860. [PMID: 31068703 PMCID: PMC6629041 DOI: 10.1038/s41591-019-0404-8] [Citation(s) in RCA: 267] [Impact Index Per Article: 53.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 02/20/2019] [Indexed: 02/08/2023]
Abstract
Despite considerable efforts to identify cancer metabolic alterations that might unveil druggable vulnerabilities, systematic characterizations of metabolism as it relates to functional genomic features and associated dependencies remain uncommon. To further understand the metabolic diversity of cancer, we profiled 225 metabolites in 928 cell lines from more than 20 cancer types in the Cancer Cell Line Encyclopedia (CCLE) using liquid chromatography-mass spectrometry (LC-MS). This resource enables unbiased association analysis linking the cancer metabolome to genetic alterations, epigenetic features and gene dependencies. Additionally, by screening barcoded cell lines, we demonstrated that aberrant ASNS hypermethylation sensitizes subsets of gastric and hepatic cancers to asparaginase therapy. Finally, our analysis revealed distinct synthesis and secretion patterns of kynurenine, an immune-suppressive metabolite, in model cancer cell lines. Together, these findings and related methodology provide comprehensive resources that will help clarify the landscape of cancer metabolism.
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Affiliation(s)
- Haoxin Li
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Shaoyang Ning
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | | | | | - Shuba Gopal
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Amy Deik
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Amanda Souza
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Kerry Pierce
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Paula Keskula
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Julie Ann
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Dojna Shkoza
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Verena Apfel
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Yilong Zou
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Jordi Barretina
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Giorgio G Galli
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - David E Root
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - William C Hahn
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Marios Giannakis
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stuart L Schreiber
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Levi A Garraway
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - William R Sellers
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
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Kim W, Deik A, Gonzalez C, Gonzalez ME, Fu F, Ferrari M, Churchhouse CL, Florez JC, Jacobs SBR, Clish CB, Rhee EP. Polyunsaturated Fatty Acid Desaturation Is a Mechanism for Glycolytic NAD + Recycling. Cell Metab 2019; 29:856-870.e7. [PMID: 30686744 PMCID: PMC6447447 DOI: 10.1016/j.cmet.2018.12.023] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 11/13/2018] [Accepted: 12/27/2018] [Indexed: 12/27/2022]
Abstract
The reactions catalyzed by the delta-5 and delta-6 desaturases (D5D/D6D), key enzymes responsible for highly unsaturated fatty acid (HUFA) synthesis, regenerate NAD+ from NADH. Here, we show that D5D/D6D provide a mechanism for glycolytic NAD+ recycling that permits ongoing glycolysis and cell viability when the cytosolic NAD+/NADH ratio is reduced, analogous to lactate fermentation. Although lesser in magnitude than lactate production, this desaturase-mediated NAD+ recycling is acutely adaptive when aerobic respiration is impaired in vivo. Notably, inhibition of either HUFA synthesis or lactate fermentation increases the other, underscoring their interdependence. Consistent with this, a type 2 diabetes risk haplotype in SLC16A11 that reduces pyruvate transport (thus limiting lactate production) increases D5D/D6D activity in vitro and in humans, demonstrating a chronic effect of desaturase-mediated NAD+ recycling. These findings highlight key biologic roles for D5D/D6D activity independent of their HUFA end products and expand the current paradigm of glycolytic NAD+ regeneration.
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Affiliation(s)
- Wondong Kim
- Nephrology Division, Massachusetts General Hospital, Boston, MA 02114, USA; Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Amy Deik
- Metabolite Profiling, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Clicerio Gonzalez
- Unidad de Investigación en Diabetes y Riesgo Cardiovascular, Instituto Nacional de Salud Publica, Curenavaca, Mexico
| | | | - Feifei Fu
- Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Michele Ferrari
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Claire L Churchhouse
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Jose C Florez
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Metabolism Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Suzanne B R Jacobs
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Metabolism Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Clary B Clish
- Metabolite Profiling, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Metabolism Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
| | - Eugene P Rhee
- Nephrology Division, Massachusetts General Hospital, Boston, MA 02114, USA; Endocrine Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Metabolism Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
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42
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Bjornevik K, Zhang Z, O'Reilly ÉJ, Berry JD, Clish CB, Deik A, Jeanfavre S, Kato I, Kelly RS, Kolonel LN, Liang L, Marchand LL, McCullough ML, Paganoni S, Pierce KA, Schwarzschild MA, Shadyab AH, Wactawski-Wende J, Wang DD, Wang Y, Manson JE, Ascherio A. Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis. Neurology 2019; 92:e2089-e2100. [PMID: 30926684 DOI: 10.1212/wnl.0000000000007401] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 02/11/2019] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE To identify prediagnostic plasma metabolomic biomarkers associated with amyotrophic lateral sclerosis (ALS). METHODS We conducted a global metabolomic study using a nested case-control study design within 5 prospective cohorts and identified 275 individuals who developed ALS during follow-up. We profiled plasma metabolites using liquid chromatography-mass spectrometry and identified 404 known metabolites. We used conditional logistic regression to evaluate the associations between metabolites and ALS risk. Further, we used machine learning analyses to determine whether the prediagnostic metabolomic profile could discriminate ALS cases from controls. RESULTS A total of 31 out of 404 identified metabolites were associated with ALS risk (p < 0.05). We observed inverse associations (n = 27) with plasma levels of diacylglycerides and triacylglycerides, urate, purine nucleosides, and some organic acids and derivatives, while we found positive associations for a cholesteryl ester, 2 phosphatidylcholines, and a sphingomyelin. The number of significant associations increased to 67 (63 inverse) in analyses restricted to cases with blood samples collected within 5 years of onset. None of these associations remained significant after multiple comparison adjustment. Further, we were not able to reliably distinguish individuals who became cases from controls based on their metabolomic profile using partial least squares discriminant analysis, elastic net regression, random forest, support vector machine, or weighted correlation network analyses. CONCLUSIONS Although the metabolomic profile in blood samples collected years before ALS diagnosis did not reliably separate presymptomatic ALS cases from controls, our results suggest that ALS is preceded by a broad, but poorly defined, metabolic dysregulation years before the disease onset.
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Affiliation(s)
- Kjetil Bjornevik
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
| | - Zhongli Zhang
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Éilis J O'Reilly
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - James D Berry
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Clary B Clish
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Amy Deik
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Sarah Jeanfavre
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Ikuko Kato
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Rachel S Kelly
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Laurence N Kolonel
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Liming Liang
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Loic Le Marchand
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Marjorie L McCullough
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Sabrina Paganoni
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Kerry A Pierce
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Michael A Schwarzschild
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Aladdin H Shadyab
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jean Wactawski-Wende
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Dong D Wang
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Ying Wang
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - JoAnn E Manson
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Alberto Ascherio
- From the Departments of Nutrition (K.B., Z.Z., É.J.O., D.D.W., A.A.) and Epidemiology (L.L., J.E.M., A.A.), Harvard T.H. Chan School of Public Health, Boston, MA; School of Public Health (É.J.O.), College of Medicine, University College Cork, Ireland; Department of Neurology (J.D.B., M.A.S.), Massachusetts General Hospital, Boston; Metabolomics Platform (C.B.C., A.D., S.J., K.A.P.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA; Department of Oncology (I.K.), Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI; Channing Division of Network Medicine (R.S.K., A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Epidemiology Program (L.N.K., L.L.M.), University of Hawaii Cancer Center, Honolulu; Behavioral and Epidemiology Research Group (M.L.M.), American Cancer Society, Atlanta, GA; Department of Physical Medicine and Rehabilitation (S.P.), Spaulding Rehabilitation Hospital and Massachusetts General Hospital; Harvard Medical School (S.P., M.A.S.), Boston, MA; Family Medicine and Public Health (A.H.S.), School of Medicine, University of California San Diego; Epidemiology and Environmental Health, Public Health and Health Professions (J.W.-W.), University at Buffalo, NY; Behavioral and Epidemiology Research Group (Y.W.), American Cancer Society, Atlanta, GA; and Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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Papandreou C, Bulló M, Ruiz-Canela M, Dennis C, Deik A, Wang D, Guasch-Ferré M, Yu E, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Fiol M, Liang L, Hernández-Alonso P, Clish CB, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma metabolites predict both insulin resistance and incident type 2 diabetes: a metabolomics approach within the Prevención con Dieta Mediterránea (PREDIMED) study. Am J Clin Nutr 2019; 109:626-634. [PMID: 30796776 PMCID: PMC7307433 DOI: 10.1093/ajcn/nqy262] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/29/2018] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Insulin resistance is a complex metabolic disorder and is often associated with type 2 diabetes (T2D). OBJECTIVES The aim of this study was to test whether baseline metabolites can additionally improve the prediction of insulin resistance beyond classical risk factors. Furthermore, we examined whether a multimetabolite model predicting insulin resistance in nondiabetics can also predict incident T2D. METHODS We used a case-cohort study nested within the Prevención con Dieta Mediterránea (PREDIMED) trial in subsets of 700, 500, and 256 participants without T2D at baseline and 1 and 3 y. Fasting plasma metabolites were semiquantitatively profiled with liquid chromatography-tandem mass spectrometry. We assessed associations between metabolite concentrations and the homeostasis model of insulin resistance (HOMA-IR) through the use of elastic net regression analysis. We subsequently examined associations between the baseline HOMA-IR-related multimetabolite model and T2D incidence through the use of weighted Cox proportional hazard models. RESULTS We identified a set of baseline metabolites associated with HOMA-IR. One-year changes in metabolites were also significantly associated with HOMA-IR. The area under the curve was significantly greater for the model containing the classical risk factors and metabolites together compared with classical risk factors alone at baseline [0.81 (95% CI: 0.79, 0.84) compared with 0.69 (95% CI: 0.66, 0.73)] and during a 1-y period [0.69 (95% CI: 0.66, 0.72) compared with 0.57 (95% CI: 0.53, 0.62)]. The variance in HOMA-IR explained by the combination of metabolites and classical risk factors was also higher in all time periods. The estimated HRs for incident T2D in the multimetabolite score (model 3) predicting high HOMA-IR (median value or higher) or HOMA-IR (continuous) at baseline were 2.00 (95% CI: 1.58, 2.55) and 2.24 (95% CI: 1.72, 2.90), respectively, after adjustment for T2D risk factors. CONCLUSIONS The multimetabolite model identified in our study notably improved the predictive ability for HOMA-IR beyond classical risk factors and significantly predicted the risk of T2D.
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Affiliation(s)
- Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Mònica Bulló
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | | | - Marta Guasch-Ferré
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Departments of Nutrition, Boston, MA
| | - Edward Yu
- Departments of Nutrition, Boston, MA
| | - Cristina Razquin
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Departments of Internal Medicine, University of Barcelona, Barcelona, Spain
- Endocrinology and Nutrition, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Miquel Fiol
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- University Institute of Health Science Research (IUNICS), University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Liming Liang
- Epidemiology and Statistics, Harvard TH Chan School of Public Health, Boston, MA
| | - Pablo Hernández-Alonso
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Clary B Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Miguel A Martínez-González
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Departments of Nutrition, Boston, MA
| | - Frank B Hu
- Departments of Nutrition, Boston, MA
- Epidemiology and Statistics, Harvard TH Chan School of Public Health, Boston, MA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Jordi Salas-Salvadó
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
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Razquin C, Toledo E, Clish CB, Ruiz-Canela M, Dennis C, Corella D, Papandreou C, Ros E, Estruch R, Guasch-Ferré M, Gómez-Gracia E, Fitó M, Yu E, Lapetra J, Wang D, Romaguera D, Liang L, Alonso-Gómez A, Deik A, Bullo M, Serra-Majem L, Salas-Salvadó J, Hu FB, Martínez-González MA. Plasma Lipidomic Profiling and Risk of Type 2 Diabetes in the PREDIMED Trial. Diabetes Care 2018; 41:2617-2624. [PMID: 30327364 PMCID: PMC6245212 DOI: 10.2337/dc18-0840] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 09/07/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Specific lipid molecular changes leading to type 2 diabetes (T2D) are largely unknown. We assessed lipidome factors associated with future occurrence of T2D in a population at high cardiovascular risk. RESEARCH DESIGN AND METHODS We conducted a case-cohort study nested within the PREDIMED trial, with 250 incident T2D cases diagnosed during 3.8 years of median follow-up, and a random sample of 692 participants (639 noncases and 53 overlapping cases) without T2D at baseline. We repeatedly measured 207 plasma known lipid metabolites at baseline and after 1 year of follow-up. We built combined factors of lipid species using principal component analysis and assessed the association between these lipid factors (or their 1-year changes) and T2D incidence. RESULTS Baseline lysophosphatidylcholines and lysophosphatidylethanolamines (lysophospholipids [LPs]), phosphatidylcholine-plasmalogens (PC-PLs), sphingomyelins (SMs), and cholesterol esters (CEs) were inversely associated with risk of T2D (multivariable-adjusted P for linear trend ≤0.001 for all). Baseline triacylglycerols (TAGs), diacylglycerols (DAGs), and phosphatidylethanolamines (PEs) were positively associated with T2D risk (multivariable-adjusted P for linear trend <0.001 for all). One-year changes in these lipids showed associations in similar directions but were not significant after adjustment for baseline levels. TAGs with odd-chain fatty acids showed inverse associations with T2D after adjusting for total TAGs. CONCLUSIONS Two plasma lipid profiles made up of different lipid classes were found to be associated with T2D in participants at high cardiovascular risk. A profile including LPs, PC-PLs, SMs, and CEs was associated with lower T2D risk. Another profile composed of TAGs, DAGs, and PEs was associated with higher T2D risk.
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Affiliation(s)
- Cristina Razquin
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Estefanía Toledo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Clary B Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Christopher Papandreou
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomediques August Pi Sunyer (IDI-BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Internal Medicine, Institut d'Investigacions Biomediques August Pi Sunyer (IDI-BAPS), Barcelona, Spain
| | - Marta Guasch-Ferré
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Enrique Gómez-Gracia
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Preventive Medicine, University of Malaga, Malaga, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Research Unit, Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain
| | - Dong Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Dora Romaguera
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Instituto de Investigación Sanitaria Illes Balears (IdISBa), University Hospital of Son Espases, Palma de Mallorca, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Angel Alonso-Gómez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Mónica Bullo
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Lluis Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, and Service of Preventive Medicine, Complejo Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canary Health Service, Las Palmas de Gran Canaria, Spain
| | - Jordi Salas-Salvadó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Miguel A Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain .,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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Kim W, Deik A, Florez JC, Jacobs SB, Clish CB, Rhee EP. Polyunsaturated Fatty Acid Desaturase‐Mediated NAD
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Recycling Permits Ongoing Glycolysis and Cell Proliferation. FASEB J 2018. [DOI: 10.1096/fasebj.2018.32.1_supplement.672.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Wondong Kim
- Endocrine UnitMassachusetts General HospitalBostonMA
- Nephrology DivisionMassachusetts General HospitalBostonMA
| | - Amy Deik
- Metabolite ProfilingBroad Institute of Harvard and MITCambridgeMA
| | - Jose C. Florez
- Program in Medical and Population GeneticsBroad Institute of Harvard and MITCambridgeMA
- Metabolism ProgramBroad Institute of Harvard and MITCambridgeMA
- Diabetes Unit and Center for Genomic MedicineMassachusetts General HospitalBostonMA
| | - Suzanne B.R. Jacobs
- Program in Medical and Population GeneticsBroad Institute of Harvard and MITCambridgeMA
- Metabolism ProgramBroad Institute of Harvard and MITCambridgeMA
- Diabetes Unit and Center for Genomic MedicineMassachusetts General HospitalBostonMA
| | - Clary B. Clish
- Metabolite ProfilingBroad Institute of Harvard and MITCambridgeMA
- Metabolism ProgramBroad Institute of Harvard and MITCambridgeMA
| | - Eugene P. Rhee
- Endocrine UnitMassachusetts General HospitalBostonMA
- Metabolism ProgramBroad Institute of Harvard and MITCambridgeMA
- Nephrology DivisionMassachusetts General HospitalBostonMA
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46
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Xu YX, Redon V, Yu H, Querbes W, Pirruccello J, Liebow A, Deik A, Trindade K, Wang X, Musunuru K, Clish CB, Cowan C, Fizgerald K, Rader D, Kathiresan S. Role of angiopoietin-like 3 (ANGPTL3) in regulating plasma level of low-density lipoprotein cholesterol. Atherosclerosis 2017; 268:196-206. [PMID: 29183623 DOI: 10.1016/j.atherosclerosis.2017.08.031] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 08/02/2017] [Accepted: 08/30/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND AND AIMS Angiopoietin-like 3 (ANGPTL3) has emerged as a key regulator of lipoprotein metabolism in humans. Homozygous loss of ANGPTL3 function causes familial combined hypolipidemia characterized by low plasma levels of triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). While known effects of ANGPTL3 in inhibiting lipoprotein lipase and endothelial lipase contribute to the low TG and HDL-C, respectively, the basis of low LDL-C remains unclear. Our aim was to explore the role of ANGPTL3 in modulating plasma LDL-C. METHODS We performed RNAi-mediated gene silencing of ANGPTL3 in five mouse models and in human hepatoma cells. We validated results by deleting ANGPTL3 gene using the CRISPR/Cas9 genome editing system. RESULTS RNAi-mediated Angptl3 silencing in mouse livers resulted in very low TG, HDL-C and LDL-C, a pattern similar to the human phenotype. The effect was observed in wild-type and obese mice, while in hCETP/apolipoprotein (Apo) B-100 double transgenic mice, the silencing decreased LDL-C and TG, but not HDL-C. In a humanized mouse model (Apobec1-/- carrying human ApoB-100 transgene) deficient in the LDL receptor (LDLR), Angptl3 silencing had minimum effect on LDL-C, suggesting the effect being linked to LDLR. This observation is supported by an additive effect on LDL-C between ANGPTL3 and PCSK9 siRNAs. ANGPTL3 gene deletion induced cellular long-chain TG and ApoB-100 accumulation with elevated LDLR and LDLR-related protein (LRP) 1 expression. Consistent with this, ANGPTL3 deficiency by gene deletion or silencing reduced nascent ApoB-100 secretion and increased LDL/VLDL uptake. CONCLUSIONS Reduced secretion and increased uptake of ApoB-containing lipoproteins may contribute to the low LDL-C observed in mice and humans with genetic ANGPTL3 deficiency.
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Affiliation(s)
- Yu-Xin Xu
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Valeska Redon
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, 11-125 Translational Research Center, 3400 Civic Center Blvd, Building 421, Philadelphia, PA 19104-5158, USA
| | - Haojie Yu
- Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - William Querbes
- Alnylam Pharmaceuticals, 300 Third Street, 3rd Floor, Cambridge, MA 02142, USA
| | - James Pirruccello
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Abigail Liebow
- Alnylam Pharmaceuticals, 300 Third Street, 3rd Floor, Cambridge, MA 02142, USA
| | - Amy Deik
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Kevin Trindade
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, 11-125 Translational Research Center, 3400 Civic Center Blvd, Building 421, Philadelphia, PA 19104-5158, USA
| | - Xiao Wang
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA
| | - Kiran Musunuru
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Chad Cowan
- Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA; Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Kevin Fizgerald
- Alnylam Pharmaceuticals, 300 Third Street, 3rd Floor, Cambridge, MA 02142, USA
| | - Daniel Rader
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, 11-125 Translational Research Center, 3400 Civic Center Blvd, Building 421, Philadelphia, PA 19104-5158, USA
| | - Sekar Kathiresan
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA.
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Tang YC, Yuwen H, Wang K, Bruno PM, Bullock K, Deik A, Santaguida S, Trakala M, Pfau SJ, Zhong N, Huang T, Wang L, Clish CB, Hemann MT, Amon A. Aneuploid Cell Survival Relies upon Sphingolipid Homeostasis. Cancer Res 2017; 77:5272-5286. [PMID: 28775166 DOI: 10.1158/0008-5472.can-17-0049] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 06/13/2017] [Accepted: 07/25/2017] [Indexed: 01/26/2023]
Abstract
Aneuploidy, a hallmark of cancer cells, poses an appealing opportunity for cancer treatment and prevention strategies. Using a cell-based screen to identify small molecules that could selectively kill aneuploid cells, we identified the compound N-[2-hydroxy-1-(4-morpholinylmethyl)-2-phenylethyl]-decanamide monohydrochloride (DL-PDMP), an antagonist of UDP-glucose ceramide glucosyltransferase. DL-PDMP selectively inhibited proliferation of aneuploid primary mouse embryonic fibroblasts and aneuploid colorectal cancer cells. Its selective cytotoxic effects were based on further accentuating the elevated levels of ceramide, which characterize aneuploid cells, leading to increased apoptosis. We observed that DL-PDMP could also enhance the cytotoxic effects of paclitaxel, a standard-of-care chemotherapeutic agent that causes aneuploidy, in human colon cancer and mouse lymphoma cells. Our results offer pharmacologic evidence that the aneuploid state in cancer cells can be targeted selectively for therapeutic purposes, or for reducing the toxicity of taxane-based drug regimens. Cancer Res; 77(19); 5272-86. ©2017 AACR.
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Affiliation(s)
- Yun-Chi Tang
- The Key Laboratory of Stem Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences & Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Hui Yuwen
- The Key Laboratory of Stem Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences & Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kaiying Wang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences & Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Peter M Bruno
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Kevin Bullock
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Amy Deik
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Stefano Santaguida
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Marianna Trakala
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Sarah J Pfau
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Na Zhong
- The Key Laboratory of Stem Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences & Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences & Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lan Wang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences & Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Michael T Hemann
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Angelika Amon
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts.
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Schaefer EAK, Meixiong J, Mark C, Deik A, Motola DL, Fusco D, Yang A, Brisac C, Salloum S, Lin W, Clish CB, Peng LF, Chung RT. Apolipoprotein B100 is required for hepatitis C infectivity and Mipomersen inhibits hepatitis C. World J Gastroenterol 2016; 22:9954-9965. [PMID: 28018102 PMCID: PMC5143762 DOI: 10.3748/wjg.v22.i45.9954] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 10/01/2016] [Accepted: 10/27/2016] [Indexed: 02/06/2023] Open
Abstract
AIM To characterize the role of apolipoprotein B100 (apoB100) in hepatitis C viral (HCV) infection.
METHODS In this study, we utilize a gene editing tool, transcription activator-like effector nucleases (TALENs), to generate human hepatoma cells with a stable genetic deletion of APOB to assess of apoB in HCV. Using infectious cell culture-competent HCV, viral pseudoparticles, replicon models, and lipidomic analysis we determined the contribution of apoB to each step of the viral lifecycle. We further studied the effect of mipomersen, an FDA-approved antisense inhibitor of apoB100, on HCV using in vitro cell-culture competent HCV and determined its impact on viral infectivity with the TCID50 method.
RESULTS We found that apoB100 is indispensable for HCV infection. Using the JFH-1 fully infectious cell-culture competent virus in Huh 7 hepatoma cells with TALEN-mediated gene deletion of apoB (APOB KO), we found a significant reduction in HCV RNA and protein levels following infection. Pseudoparticle and replicon models demonstrated that apoB did not play a role in HCV entry or replication. However, the virus produced by APOB KO cells had significantly diminished infectivity as measured by the TCID-50 method compared to wild-type virus. Lipidomic analysis demonstrated that these virions have a fundamentally altered lipidome, with complete depletion of cholesterol esters. We further demonstrate that inhibition of apoB using mipomersen, an FDA-approved anti-sense oligonucleotide, results in a potent anti-HCV effect and significantly reduces the infectivity of the virus.
CONCLUSION ApoB is required for the generation of fully infectious HCV virions, and inhibition of apoB with mipomersen blocks HCV. Targeting lipid metabolic pathways to impair viral infectivity represents a novel host targeted strategy to inhibit HCV.
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Carroll JB, Deik A, Fossale E, Weston RM, Guide JR, Arjomand J, Kwak S, Clish CB, MacDonald ME. HdhQ111 Mice Exhibit Tissue Specific Metabolite Profiles that Include Striatal Lipid Accumulation. PLoS One 2015; 10:e0134465. [PMID: 26295712 PMCID: PMC4546654 DOI: 10.1371/journal.pone.0134465] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 07/10/2015] [Indexed: 01/01/2023] Open
Abstract
The HTT CAG expansion mutation causes Huntington's Disease and is associated with a wide range of cellular consequences, including altered metabolism. The mutant allele is expressed widely, in all tissues, but the striatum and cortex are especially vulnerable to its effects. To more fully understand this tissue-specificity, early in the disease process, we asked whether the metabolic impact of the mutant CAG expanded allele in heterozygous B6.HdhQ111/+ mice would be common across tissues, or whether tissues would have tissue-specific responses and whether such changes may be affected by diet. Specifically, we cross-sectionally examined steady state metabolite concentrations from a range of tissues (plasma, brown adipose tissue, cerebellum, striatum, liver, white adipose tissue), using an established liquid chromatography-mass spectrometry pipeline, from cohorts of 8 month old mutant and wild-type littermate mice that were fed one of two different high-fat diets. The differential response to diet highlighted a proportion of metabolites in all tissues, ranging from 3% (7/219) in the striatum to 12% (25/212) in white adipose tissue. By contrast, the mutant CAG-expanded allele primarily affected brain metabolites, with 14% (30/219) of metabolites significantly altered, compared to wild-type, in striatum and 11% (25/224) in the cerebellum. In general, diet and the CAG-expanded allele both elicited metabolite changes that were predominantly tissue-specific and non-overlapping, with evidence for mutation-by-diet interaction in peripheral tissues most affected by diet. Machine-learning approaches highlighted the accumulation of diverse lipid species as the most genotype-predictive metabolite changes in the striatum. Validation experiments in cell culture demonstrated that lipid accumulation was also a defining feature of mutant HdhQ111 striatal progenitor cells. Thus, metabolite-level responses to the CAG expansion mutation in vivo were tissue specific and most evident in brain, where the striatum featured signature accumulation of a set of lipids including sphingomyelin, phosphatidylcholine, cholesterol ester and triglyceride species. Importantly, in the presence of the CAG mutation, metabolite changes were unmasked in peripheral tissues by an interaction with dietary fat, implying that the design of studies to discover metabolic changes in HD mutation carriers should include metabolic perturbations.
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Affiliation(s)
- Jeffrey B. Carroll
- Center for Human Genetic Research, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts, United States of America
- Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, Washington, United States of America
- * E-mail:
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Elisa Fossale
- Center for Human Genetic Research, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts, United States of America
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Rory M. Weston
- Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, Washington, United States of America
| | - Jolene R. Guide
- Center for Human Genetic Research, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jamshid Arjomand
- CHDI Foundation, Inc., Princeton, New Jersey, United States of America
| | - Seung Kwak
- CHDI Foundation, Inc., Princeton, New Jersey, United States of America
| | - Clary B. Clish
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Marcy E. MacDonald
- Center for Human Genetic Research, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts, United States of America
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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Kalim S, Clish CB, Wenger J, Elmariah S, Yeh RW, Deferio JJ, Pierce K, Deik A, Gerszten RE, Thadhani R, Rhee EP. A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients. J Am Heart Assoc 2013; 2:e000542. [PMID: 24308938 PMCID: PMC3886735 DOI: 10.1161/jaha.113.000542] [Citation(s) in RCA: 90] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background The marked excess in cardiovascular mortality that results from uremia remains poorly understood. Methods and Results In 2 independent, nested case‐control studies, we applied liquid chromatography‐mass spectrometry‐based metabolite profiling to plasma obtained from participants of a large cohort of incident hemodialysis patients. First, 100 individuals who died of a cardiovascular cause within 1 year of initiating hemodialysis (cases) were randomly selected along with 100 individuals who survived for at least 1 year (controls), matched for age, sex, and race. Four highly intercorrelated long‐chain acylcarnitines achieved the significance threshold adjusted for multiple testing (P<0.0003). Oleoylcarnitine, the long‐chain acylcarnitine with the strongest association with cardiovascular mortality in unadjusted analysis, remained associated with 1‐year cardiovascular death after multivariable adjustment (odds ratio per SD 2.3 [95% confidence interval, 1.4 to 3.8]; P=0.001). The association between oleoylcarnitine and 1‐year cardiovascular death was then replicated in an independent sample (n=300, odds ratio per SD 1.4 [95% confidence interval, 1.1 to 1.9]; P=0.008). Addition of oleoylcarnitine to clinical variables improved cardiovascular risk prediction using net reclassification (NRI, 0.38 [95% confidence interval, 0.20 to 0.56]; P<0.0001). In physiologic profiling studies, we demonstrate that the fold change in plasma acylcarnitine levels from the aorta to renal vein and from pre‐ to post hemodialysis samples exclude renal or dialytic clearance of long‐chain acylcarnitines as confounders in our analysis. Conclusions Our data highlight clinically meaningful alterations in acylcarnitine homeostasis at the time of dialysis initiation, which may represent an early marker, effector, or both of uremic cardiovascular risk.
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Affiliation(s)
- Sahir Kalim
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
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