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Mbatchou J, McPeek MS. JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression. Am J Hum Genet 2024; 111:1750-1769. [PMID: 39025064 PMCID: PMC11339629 DOI: 10.1016/j.ajhg.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.
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Affiliation(s)
- Joelle Mbatchou
- Regeneron Genetics Center, Tarrytown, NY 10591, USA; Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.
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Reynés B, García-Ruiz E, van Schothorst EM, Keijer J, Oliver P, Palou A. TLCD4 as Potential Transcriptomic Biomarker of Cold Exposure. Biomolecules 2024; 14:935. [PMID: 39199323 PMCID: PMC11352221 DOI: 10.3390/biom14080935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/26/2024] [Accepted: 07/31/2024] [Indexed: 09/01/2024] Open
Abstract
(1) Background: Cold exposure induces metabolic adaptations that can promote health benefits, including increased energy disposal due to lipid mobilization in adipose tissue (AT). This study aims to identify easily measurable biomarkers mirroring the effect of cold exposure on AT. (2) Methods: Transcriptomic analysis was performed in peripheral blood mononuclear cells (PBMCs) and distinct AT depots of two animal models (ferrets and rats) exposed to cold, and in PBMCs of cold-exposed humans. (3) Results: One week of cold exposure (at 4 °C) affected different metabolic pathways and gene expression in the AT of ferrets, an animal model with an AT more similar to humans than that of rodents. However, only one gene, Tlcd4, was affected in the same way (overexpressed) in aortic perivascular and inguinal AT depots and in PBMCs, making it a potential biomarker of interest. Subsequent targeted analysis in rats showed that 1 week at 4 °C also induced Tlcd4 expression in brown AT and PBMCs, while 1 h at 4 °C resulted in reduced Tlcd4 mRNA levels in retroperitoneal white AT. In humans, no clear effects were observed. Nevertheless, decreased PBMC TLCD4 expression was observed after acute cold exposure in women with normal weight, although this effect could be attributed to short-term fasting during the procedure. No effect was evident in women with overweight or in normal-weight men. (4) Conclusions: Our results obtained for different species point toward TLCD4 gene expression as a potential biomarker of cold exposure/fat mobilization that could tentatively be used to address the effectiveness of cold exposure-mimicking therapies.
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Affiliation(s)
- Bàrbara Reynés
- Nutrigenomics, Biomarkers and Risk Evaluation (NuBE) Group, University of the Balearic Islands, 07122 Palma, Spain; (B.R.)
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma, Spain
- CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Estefanía García-Ruiz
- Nutrigenomics, Biomarkers and Risk Evaluation (NuBE) Group, University of the Balearic Islands, 07122 Palma, Spain; (B.R.)
- CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Evert M. van Schothorst
- Human and Animal Physiology, Wageningen University, 6708 WD Wageningen, The Netherlands; (E.M.v.S.)
| | - Jaap Keijer
- Human and Animal Physiology, Wageningen University, 6708 WD Wageningen, The Netherlands; (E.M.v.S.)
| | - Paula Oliver
- Nutrigenomics, Biomarkers and Risk Evaluation (NuBE) Group, University of the Balearic Islands, 07122 Palma, Spain; (B.R.)
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma, Spain
- CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Andreu Palou
- Nutrigenomics, Biomarkers and Risk Evaluation (NuBE) Group, University of the Balearic Islands, 07122 Palma, Spain; (B.R.)
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma, Spain
- CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
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Lyu C, Joehanes R, Huan T, Levy D, Li Y, Wang M, Liu X, Liu C, Ma J. Enhancing selection of alcohol consumption-associated genes by random forest. Br J Nutr 2024; 131:2058-2067. [PMID: 38606596 PMCID: PMC11216877 DOI: 10.1017/s0007114524000795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.
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Affiliation(s)
- Chenglin Lyu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA
| | - Roby Joehanes
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA
| | - Tianxiao Huan
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA
| | - Daniel Levy
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA
| | - Yi Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Mengyao Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Xue Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jiantao Ma
- Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA
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4
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Abbas M, Diallo A, Goodney G, Gaye A. Leveraging the transcriptome to further our understanding of GWAS findings: eQTLs associated with genes related to LDL and LDL subclasses, in a cohort of African Americans. Front Genet 2024; 15:1345541. [PMID: 38384714 PMCID: PMC10879560 DOI: 10.3389/fgene.2024.1345541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/16/2024] [Indexed: 02/23/2024] Open
Abstract
Background: GWAS discoveries often pose a significant challenge in terms of understanding their underlying mechanisms. Further research, such as an integration with expression quantitative trait locus (eQTL) analyses, are required to decipher the mechanisms connecting GWAS variants to phenotypes. An eQTL analysis was conducted on genes associated with low-density lipoprotein (LDL) cholesterol and its subclasses, with the aim of pinpointing genetic variants previously implicated in GWAS studies focused on lipid-related traits. Notably, the study cohort consisted of African Americans, a population characterized by a heightened prevalence of hypercholesterolemia. Methods: A comprehensive differential expression (DE) analysis was undertaken, with a dataset of 17,948 protein-coding mRNA transcripts extracted from the whole-blood transcriptomes of 416 samples to identify mRNA transcripts associated with LDL, with further granularity delineated between small LDL and large LDL subclasses. Subsequently, eQTL analysis was conducted with a subset of 242 samples for which whole-genome sequencing data were available to identify single-nucleotide polymorphisms (SNPs) associated with the LDL-related mRNA transcripts. Lastly, plausible functional connections were established between the identified eQTLs and genetic variants reported in the GWAS catalogue. Results: DE analysis revealed 1,048, 284, and 94 mRNA transcripts that exhibited differential expression in response to LDL, small LDL, and large LDL, respectively. The eQTL analysis identified a total of 9,950 significant SNP-mRNA associations involving 6,955 SNPs including a subset 101 SNPs previously documented in GWAS of LDL and LDL-related traits. Conclusion: Through comprehensive differential expression analysis, we identified numerous mRNA transcripts responsive to LDL, small LDL, and large LDL. Subsequent eQTL analysis revealed a rich landscape of eQTL-mRNA associations, including a subset of eQTL reported in GWAS studies of LDL and related traits. The study serves as a testament to the important role of integrative genomics in unraveling the enigmatic GWAS relationships between genetic variants and the complex fabric of human traits and diseases.
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Affiliation(s)
- Malak Abbas
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Ana Diallo
- School of Nursing, Virginia Commonwealth University, Richmond, VA, United States
| | - Gabriel Goodney
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Amadou Gaye
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
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5
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Mbatchou J, McPeek MS. JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.571948. [PMID: 38187553 PMCID: PMC10769254 DOI: 10.1101/2023.12.18.571948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits and microbiome abundances. It allows for covariates, ascertainment and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, some of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.
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Affiliation(s)
- Joelle Mbatchou
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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6
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Møller AL, Vasan RS, Levy D, Andersson C, Lin H. Integrated omics analysis of coronary artery calcifications and myocardial infarction: the Framingham Heart Study. Sci Rep 2023; 13:21581. [PMID: 38062110 PMCID: PMC10703905 DOI: 10.1038/s41598-023-48848-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
Gene function can be described using various measures. We integrated association studies of three types of omics data to provide insights into the pathophysiology of subclinical coronary disease and myocardial infarction (MI). Using multivariable regression models, we associated: (1) single nucleotide polymorphism, (2) DNA methylation, and (3) gene expression with coronary artery calcification (CAC) scores and MI. Among 3106 participants of the Framingham Heart Study, 65 (2.1%) had prevalent MI and 60 (1.9%) had incident MI, median CAC value was 67.8 [IQR 10.8, 274.9], and 1403 (45.2%) had CAC scores > 0 (prevalent CAC). Prevalent CAC was associated with AHRR (linked to smoking) and EXOC3 (affecting platelet function and promoting hemostasis). CAC score was associated with VWA1 (extracellular matrix protein associated with cartilage structure in endomysium). For prevalent MI we identified FYTTD1 (down-regulated in familial hypercholesterolemia) and PINK1 (linked to cardiac tissue homeostasis and ischemia-reperfusion injury). Incident MI was associated with IRX3 (enhancing browning of white adipose tissue) and STXBP3 (controlling trafficking of glucose transporter type 4 to plasma). Using an integrative trans-omics approach, we identified both putatively novel and known candidate genes associated with CAC and MI. Replication of findings is warranted.
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Affiliation(s)
- Amalie Lykkemark Møller
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
- Department of Cardiology, Nordsjællands Hospital, Hillerød, Denmark.
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- University of Texas School of Public Health San Antonio, and Departments of Medicine and Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
| | - Daniel Levy
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Institutes of Health, Bethesda, MD, USA
| | - Charlotte Andersson
- Section of Cardiovascular Medicine, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
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7
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Keshawarz A, Bui H, Joehanes R, Ma J, Liu C, Huan T, Hwang SJ, Tejada B, Sooda M, Courchesne P, Munson PJ, Demirkale CY, Yao C, Heard-Costa NL, Pitsillides AN, Lin H, Liu CT, Wang Y, Peloso GM, Lundin J, Haessler J, Du Z, Cho M, Hersh CP, Castaldi P, Raffield LM, Wen J, Li Y, Reiner AP, Feolo M, Sharopova N, Vasan RS, DeMeo DL, Carson AP, Kooperberg C, Levy D. Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study. Sci Rep 2023; 13:12952. [PMID: 37563237 PMCID: PMC10415314 DOI: 10.1038/s41598-023-39936-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/02/2023] [Indexed: 08/12/2023] Open
Abstract
Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.
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Affiliation(s)
- Amena Keshawarz
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Helena Bui
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Roby Joehanes
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jiantao Ma
- Framingham Heart Study, Framingham, MA, USA
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Chunyu Liu
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Tianxiao Huan
- Framingham Heart Study, Framingham, MA, USA
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Shih-Jen Hwang
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brandon Tejada
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meera Sooda
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul Courchesne
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Munson
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cumhur Y Demirkale
- Mathematical and Statistical Computing Laboratory, Office of Intramural Research, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Chen Yao
- Framingham Heart Study, Framingham, MA, USA
| | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Achilleas N Pitsillides
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Honghuang Lin
- Framingham Heart Study, Framingham, MA, USA
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Ching-Ti Liu
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yuxuan Wang
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Gina M Peloso
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | | | | | - Zhaohui Du
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Michael Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- General Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexander P Reiner
- Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Mike Feolo
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA
| | - Nataliya Sharopova
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Daniel Levy
- Framingham Heart Study, Framingham, MA, USA.
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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Ma J, Huang A, Yan K, Li Y, Sun X, Joehanes R, Huan T, Levy D, Liu C. Blood transcriptomic biomarkers of alcohol consumption and cardiovascular disease risk factors: the Framingham Heart Study. Hum Mol Genet 2023; 32:649-658. [PMID: 36130209 PMCID: PMC9896471 DOI: 10.1093/hmg/ddac237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/19/2022] [Accepted: 09/15/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The relations of alcohol consumption and gene expression remain to be elucidated. MATERIALS AND METHODS We examined cross-sectional associations between alcohol consumption and whole blood derived gene expression levels and between alcohol-associated genes and obesity, hypertension, and diabetes in 5531 Framingham Heart Study (FHS) participants. RESULTS We identified 25 alcohol-associated genes. We further showed cross-sectional associations of 16 alcohol-associated genes with obesity, nine genes with hypertension, and eight genes with diabetes at P < 0.002. For example, we observed decreased expression of PROK2 (β = -0.0018; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) and PAX5 (β = -0.0014; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) per 1 g/day increase in alcohol consumption. Consistent with our previous observation on the inverse association of alcohol consumption with obesity and positive association of alcohol consumption with hypertension, we found that PROK2 was positively associated with obesity (OR = 1.42; 95%CI: 1.17, 1.72; P = 4.5e - 4) and PAX5 was negatively associated with hypertension (OR = 0.73; 95%CI: 0.59, 0.89; P = 1.6e - 3). We also observed that alcohol consumption was positively associated with expression of ABCA13 (β = 0.0012; 95%CI: 0.0007, 0.0017; P = 1.3e - 6) and ABCA13 was positively associated with diabetes (OR = 2.57; 95%CI: 1.73, 3.84; P = 3.5e - 06); this finding, however, was inconsistent with our observation of an inverse association between alcohol consumption and diabetes. CONCLUSIONS We showed strong cross-sectional associations between alcohol consumption and expression levels of 25 genes in FHS participants. Nonetheless, complex relationships exist between alcohol-associated genes and CVD risk factors.
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Affiliation(s)
- Jiantao Ma
- Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Allen Huang
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02142, USA
| | - Kaiyu Yan
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Yi Li
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Xianbang Sun
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Tianxiao Huan
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702, USA
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9
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Dorajoo R, Ihsan MO, Liu W, Lim HY, Angeli V, Park SJ, Chan JMS, Lin XY, Ong MS, Muniasamy U, Lee CH, Gurung R, Ho HH, Foo R, Liu J, Kofidis T, Lee CN, Sorokin VA. Vascular smooth muscle cells in low SYNTAX scores coronary artery disease exhibit proinflammatory transcripts and proteins correlated with IL1B activation. Atherosclerosis 2023; 365:15-24. [PMID: 36646016 DOI: 10.1016/j.atherosclerosis.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 11/22/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND AND AIMS The SYNTAX score is clinically validated to stratify number of lesions and pattern of CAD. A better understanding of the underlying molecular mechanisms influencing the pattern and complexity of coronary arteries lesions among CAD patients is needed. METHODS Human arterial biopsies from 49 patients (16 low-SYNTAX-score (LSS, <23), 16 intermediate-SYNTAX-score (ISS, 23 to 32) and 17 high-SYNTAX-score (HSS, >32)) were evaluated using Affymetrix GeneChip® Human Genome U133 Plus 2.0 microarray. The data were validated by Next-Generation Sequencing (NGS). Primary VSMC from patients with low and high SYNTAX scores were isolated and compared using immunohistochemistry, qPCR and immunoblotting to confirm mRNA and proteomic results. RESULTS The IL1B was verified as the top upstream regulator of 47 inflammatory DEGs in LSS patients and validated by another sets of patient samples using NGS analysis. The upregulated expression of IL1B was translated to increased level of IL1β protein in the LSS tissue based on immunohistochemical quantitative analysis. Plausibility of idea that IL1B in the arterial wall could be originated from VSMC was checked by exposing culture to proinflammatory conditions where IL1B came out as the top DEG (logFC = 7.083, FDR = 1.38 × 10-114). The LSS patient-derived primary VSMCs confirmed higher levels of IL1B mRNA and protein. CONCLUSIONS LSS patients could represent a group of patients where IL1B could play a substantial role in disease pathogenesis. The LSS group could represent a plausible cohort of patients for whom anti-inflammatory therapy could be considered.
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Affiliation(s)
- Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore; Health Services and Systems Research, Duke-NUS Medical School Singapore, Singapore
| | - Mario Octavianus Ihsan
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Wenting Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore; Taihe Hospital, Hubei University of Medicine and Center of Health Administration and Development Studies, School of Public Health, Hubei University of Medicine, Singapore
| | - Hwee Ying Lim
- Immunology Translational Research Program, Department of Microbiology and Immunology, National University of Singapore, Singapore
| | - Veronique Angeli
- Immunology Translational Research Program, Department of Microbiology and Immunology, National University of Singapore, Singapore
| | - Sung-Jin Park
- Translational Cardiovascular Imaging Group, Institute of Bioengineering and Bioimaging (IBB), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Joyce M S Chan
- Translational Cardiovascular Imaging Group, Institute of Bioengineering and Bioimaging (IBB), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Vascular Surgery, Singapore General Hospital, SingHealth, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Xiao Yun Lin
- Department of Cardiac, Thoracic and Vascular Surgery, National University Hospital, National University Health System, Singapore
| | - Mei Shan Ong
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Umamaheswari Muniasamy
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Chi-Hang Lee
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Cardiology, National University Hospital, National University Health System, Singapore; Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Rijan Gurung
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore; Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Hee Hwa Ho
- Department of Cardiology, Tan Tock Seng Hospital, Singapore
| | - Roger Foo
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Cardiology, National University Hospital, National University Health System, Singapore; Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Theo Kofidis
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Cardiac, Thoracic and Vascular Surgery, National University Hospital, National University Health System, Singapore; Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Chuen Neng Lee
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Cardiac, Thoracic and Vascular Surgery, National University Hospital, National University Health System, Singapore
| | - Vitaly A Sorokin
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Cardiac, Thoracic and Vascular Surgery, National University Hospital, National University Health System, Singapore.
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10
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Lai CQ, Parnell LD, Lee YC, Zeng H, Smith CE, McKeown NM, Arnett DK, Ordovás JM. The impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels. Front Genet 2023; 14:1117778. [PMID: 36873949 PMCID: PMC9975169 DOI: 10.3389/fgene.2023.1117778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023] Open
Abstract
Background: Many epigenetic loci have been associated with plasma triglyceride (TG) levels, but epigenetic connections between those loci and dietary exposures are largely unknown. This study aimed to characterize the epigenetic links between diet, lifestyle, and TG. Methods: We first conducted an epigenome-wide association study (EWAS) for TG in the Framingham Heart Study Offspring population (FHS, n = 2,264). We then examined relationships between dietary and lifestyle-related variables, collected four times in 13 years, and differential DNA methylation sites (DMSs) associated with the last TG measures. Third, we conducted a mediation analysis to evaluate the causal relationships between diet-related variables and TG. Finally, we replicated three steps to validate identified DMSs associated with alcohol and carbohydrate intake in the Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN) study (n = 993). Results: In the FHS, the EWAS revealed 28 TG-associated DMSs at 19 gene regions. We identified 102 unique associations between these DMSs and one or more dietary and lifestyle-related variables. Alcohol and carbohydrate intake showed the most significant and consistent associations with 11 TG-associated DMSs. Mediation analyses demonstrated that alcohol and carbohydrate intake independently affect TG via DMSs as mediators. Higher alcohol intake was associated with lower methylation at seven DMSs and higher TG. In contrast, increased carbohydrate intake was associated with higher DNA methylation at two DMSs (CPT1A and SLC7A11) and lower TG. Validation in the GOLDN further supports the findings. Conclusion: Our findings imply that TG-associated DMSs reflect dietary intakes, particularly alcoholic drinks, which could affect the current cardiometabolic risk via epigenetic changes. This study illustrates a new method to map epigenetic signatures of environmental factors for disease risk. Identification of epigenetic markers of dietary intake can provide insight into an individual's risk of cardiovascular disease and support the application of precision nutrition. Clinical Trial Registration: www.ClinicalTrials.gov, the Framingham Heart Study (FHS), NCT00005121; the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN), NCT01023750.
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Affiliation(s)
- Chao-Qiang Lai
- USDA ARS, Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
| | - Laurence D Parnell
- USDA ARS, Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
| | - Yu-Chi Lee
- USDA ARS, Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
| | - Haihan Zeng
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
| | - Caren E Smith
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
| | - Nicola M McKeown
- Programs of Nutrition, Department of Health Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, United States.,Nutrition Epidemiology and Data Science Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, United States
| | - José M Ordovás
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States.,IMDEA Food Institute, CEI UAM + CSIC, Madrid, Spain
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11
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Malod-Dognin N, Ceddia G, Gvozdenov M, Tomić B, Dunjić Manevski S, Djordjević V, Pržulj N. A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia. PLoS One 2023; 18:e0284084. [PMID: 37098010 PMCID: PMC10128975 DOI: 10.1371/journal.pone.0284084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 03/23/2023] [Indexed: 04/26/2023] Open
Abstract
Antithrombin resistance is a rare subtype of hereditary thrombophilia caused by prothrombin gene variants, leading to thrombotic disorders. Recently, the Prothrombin Belgrade variant has been reported as a specific variant that leads to antithrombin resistance in two Serbian families with thrombosis. However, due to clinical data scarcity and the inapplicability of traditional genome-wide association studies (GWAS), a broader perspective on molecular and phenotypic mechanisms associated with the Prothrombin Belgrade variant is yet to be uncovered. Here, we propose an integrative framework to address the lack of genomic samples and support the genomic signal from the full genome sequences of five heterozygous subjects by integrating it with subjects' phenotypes and the genes' molecular interactions. Our goal is to identify candidate thrombophilia-related genes for which our subjects possess germline variants by focusing on the resulting gene clusters of our integrative framework. We applied a Non-negative Matrix Tri-Factorization-based method to simultaneously integrate different data sources, taking into account the observed phenotypes. In other words, our data-integration framework reveals gene clusters involved with this rare disease by fusing different datasets. Our results are in concordance with the current literature about antithrombin resistance. We also found candidate disease-related genes that need to be further investigated. CD320, RTEL1, UCP2, APOA5 and PROZ participate in healthy-specific or disease-specific subnetworks involving thrombophilia-annotated genes and are related to general thrombophilia mechanisms according to the literature. Moreover, the ADRA2A and TBXA2R subnetworks analysis suggested that their variants may have a protective effect due to their connection with decreased platelet activation. The results show that our method can give insights into antithrombin resistance even if a small amount of genetic data is available. Our framework is also customizable, meaning that it applies to any other rare disease.
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Affiliation(s)
- Noël Malod-Dognin
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Department of Computer Science, University College London, London, United Kingdom
| | - Gaia Ceddia
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Maja Gvozdenov
- Institute of Molecular Genetics and Genetic Engineering (IMGGE), University of Belgrade, Belgrade, Serbia
| | - Branko Tomić
- Institute of Molecular Genetics and Genetic Engineering (IMGGE), University of Belgrade, Belgrade, Serbia
| | - Sofija Dunjić Manevski
- Institute of Molecular Genetics and Genetic Engineering (IMGGE), University of Belgrade, Belgrade, Serbia
| | - Valentina Djordjević
- Institute of Molecular Genetics and Genetic Engineering (IMGGE), University of Belgrade, Belgrade, Serbia
| | - Nataša Pržulj
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Department of Computer Science, University College London, London, United Kingdom
- ICREA, Barcelona, Spain
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12
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Jiang W, Joehanes R, Levy D, O’Connor GT, Dupuis J. Assisted clustering of gene expression data using regulatory data from partially overlapping sets of individuals. BMC Genomics 2022; 23:819. [PMID: 36496393 PMCID: PMC9734806 DOI: 10.1186/s12864-022-09026-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/18/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND As omics measurements profiled on different molecular layers are interconnected, integrative approaches that incorporate the regulatory effect from multi-level omics data are needed. When the multi-level omics data are from the same individuals, gene expression (GE) clusters can be identified using information from regulators like genetic variants and DNA methylation. When the multi-level omics data are from different individuals, the choice of integration approaches is limited. METHODS We developed an approach to improve GE clustering from microarray data by integrating regulatory data from different but partially overlapping sets of individuals. We achieve this through (1) decomposing gene expression into the regulated component and the other component that is not regulated by measured factors, (2) optimizing the clustering goodness-of-fit objective function. We do not require the availability of different omics measurements on all individuals. A certain amount of individual overlap between GE data and the regulatory data is adequate for modeling the regulation, thus improving GE clustering. RESULTS A simulation study shows that the performance of the proposed approach depends on the strength of the GE-regulator relationship, degree of missingness, data dimensionality, sample size, and the number of clusters. Across the various simulation settings, the proposed method shows competitive performance in terms of accuracy compared to the alternative K-means clustering method, especially when the clustering structure is due mostly to the regulated component, rather than the unregulated component. We further validate the approach with an application to 8,902 Framingham Heart Study participants with data on up to 17,873 genes and regulation information of DNA methylation and genotype from different but partially overlapping sets of participants. We identify clustering structures of genes associated with pulmonary function while incorporating the predicted regulation effect from the measured regulators. We further investigate the over-representation of these GE clusters in pathways of other diseases that may be related to lung function and respiratory health. CONCLUSION We propose a novel approach for clustering GE with the assistance of regulatory data that allowed for different but partially overlapping sets of individuals to be included in different omics data.
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Affiliation(s)
- Wenqing Jiang
- grid.189504.10000 0004 1936 7558Department of Biostatistics, Boston University School of Public Health, MA Boston, USA
| | - Roby Joehanes
- grid.510954.c0000 0004 0444 3861National Heart, Lung, and Blood Institute’s Framingham Heart Study, MA Framingham, USA
| | - Daniel Levy
- grid.510954.c0000 0004 0444 3861National Heart, Lung, and Blood Institute’s Framingham Heart Study, MA Framingham, USA ,grid.94365.3d0000 0001 2297 5165The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, MD Bethesda, USA
| | - George T O’Connor
- grid.189504.10000 0004 1936 7558Department of Medicine, Pulmonary Center, Boston University, MA Boston, USA
| | - Josée Dupuis
- grid.189504.10000 0004 1936 7558Department of Biostatistics, Boston University School of Public Health, MA Boston, USA
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13
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Gautvik KM, Sachse D, Hinton AC, Olstad OK, Kiel DP, Hsu YH, Utheim TP, Lary CW, Reppe S. In silico discovery of blood cell macromolecular associations. BMC Genom Data 2022; 23:57. [PMID: 35879676 PMCID: PMC9317115 DOI: 10.1186/s12863-022-01077-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 07/13/2022] [Indexed: 11/23/2022] Open
Abstract
Background Physical molecular interactions are the basis of intracellular signalling and gene regulatory networks, and comprehensive, accessible databases are needed for their discovery. Highly correlated transcripts may reflect important functional associations, but identification of such associations from primary data are cumbersome. We have constructed and adapted a user-friendly web application to discover and identify putative macromolecular associations in human peripheral blood based on significant correlations at the transcriptional level. Methods The blood transcriptome was characterized by quantification of 17,328 RNA species, including 341 mature microRNAs in 105 clinically well-characterized postmenopausal women. Intercorrelation of detected transcripts signal levels generated a matrix with > 150 million correlations recognizing the human blood RNA interactome. The correlations with calculated adjusted p-values were made easily accessible by a novel web application. Results We found that significant transcript correlations within the giant matrix reflect experimentally documented interactions involving select ubiquitous blood relevant transcription factors (CREB1, GATA1, and the glucocorticoid receptor (GR, NR3C1)). Their responsive genes recapitulated up to 91% of these as significant correlations, and were replicated in an independent cohort of 1204 individual blood samples from the Framingham Heart Study. Furthermore, experimentally documented mRNAs/miRNA associations were also reproduced in the matrix, and their predicted functional co-expression described. The blood transcript web application is available at http://app.uio.no/med/klinmed/correlation-browser/blood/index.php and works on all commonly used internet browsers. Conclusions Using in silico analyses and a novel web application, we found that correlated blood transcripts across 105 postmenopausal women reflected experimentally proven molecular associations. Furthermore, the associations were reproduced in a much larger and more heterogeneous cohort and should therefore be generally representative. The web application lends itself to be a useful hypothesis generating tool for identification of regulatory mechanisms in complex biological data sets. Supplementary Information The online version contains supplementary material available at 10.1186/s12863-022-01077-3.
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14
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Lee T, Hwang S, Seo DM, Shin HC, Kim HS, Kim JY, Uh Y. Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome. Cells 2022; 11:2867. [PMID: 36139449 PMCID: PMC9496853 DOI: 10.3390/cells11182867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/26/2022] [Accepted: 09/10/2022] [Indexed: 12/02/2022] Open
Abstract
Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene-gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene-gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.
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Affiliation(s)
- Taesic Lee
- Division of Data Mining and Computational Biology, Institute of Global Health Care and Development, Wonju Severance Christian Hospital, Wonju 26411, Korea
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Sangwon Hwang
- Artificial Intelligence Bigdata Medical Center, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Dong Min Seo
- Department of Medical Information, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | | | | | - Jang-Young Kim
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Young Uh
- Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
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15
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Yip HF, Chowdhury D, Wang K, Liu Y, Gao Y, Lan L, Zheng C, Guan D, Lam KF, Zhu H, Tai X, Lu A. ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data. Front Med (Lausanne) 2022; 9:931860. [PMID: 36072953 PMCID: PMC9441882 DOI: 10.3389/fmed.2022.931860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/28/2022] [Indexed: 11/29/2022] Open
Abstract
Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.
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Affiliation(s)
- Hiu F. Yip
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Debajyoti Chowdhury
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Kexin Wang
- National Key Clinical Specialty, Engineering Technology Research Center of Education Ministry of China, Guangzhou, China
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yujie Liu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yao Gao
- Department of Psychiatry, First Hospital, First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Liang Lan
- Department of Communication Studies, School of Communication, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Chaochao Zheng
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Daogang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, China
| | - Kei F. Lam
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Hailong Zhu
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Xuecheng Tai
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Aiping Lu
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
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16
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Navrazhina K, Renert-Yuval Y, Frew JW, Grand D, Gonzalez J, Williams SC, Garcet S, Krueger JG. Large-scale serum analysis identifies unique systemic biomarkers in psoriasis and hidradenitis suppurativa. Br J Dermatol 2022; 186:684-693. [PMID: 34254293 DOI: 10.1111/bjd.20642] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/10/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Hidradenitis suppurativa (HS) is now recognized as a systemic inflammatory disease, sharing molecular similarities with psoriasis. Direct comparison of the systemic inflammation in HS with psoriasis is lacking. OBJECTIVES To evaluate the serum proteome of HS and psoriasis, and to identify biomarkers associated with disease severity. METHODS In this cross-sectional study, 1536 serum proteins were assessed using the Olink Explore (Proximity Extension Assay) high-throughput panel in patients with moderate-to-severe HS (n = 11), patients with psoriasis (n = 10) and age- and body mass index-matched healthy controls (n = 10). RESULTS HS displayed an overall greater dysregulation of circulating proteins, with 434 differentially expressed proteins (absolute fold change ≥ 1·2; P ≤ 0·05) in patients with HS vs. controls, 138 in patients with psoriasis vs. controls and 503 between patients with HS and patients with psoriasis. Interleukin (IL)-17A levels and T helper (Th)1/Th17 pathway enrichment were comparable between diseases, while HS presented greater tumour necrosis factor- and IL-1β-related signalling. The Th17-associated markers peptidase inhibitor 3 (PI3) and lipocalin 2 (LCN2) were able to differentiate psoriasis from HS accurately. Both diseases presented increases of atherosclerosis-related proteins. Robust correlations between clinical severity scores and immune and atherosclerosis-related proteins were observed across both diseases. CONCLUSIONS HS and psoriasis share significant Th1/Th17 enrichment and upregulation of atherosclerosis-related proteins. Despite the greater body surface area involved in psoriasis, HS presents a greater serum inflammatory burden.
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Affiliation(s)
- K Navrazhina
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD program, New York, NY, USA
| | - Y Renert-Yuval
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - J W Frew
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - D Grand
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - J Gonzalez
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - S C Williams
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD program, New York, NY, USA
| | - S Garcet
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - J G Krueger
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
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17
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Castaneda AB, Petty LE, Scholz M, Jansen R, Weiss S, Zhang X, Schramm K, Beutner F, Kirsten H, Schminke U, Hwang SJ, Marzi C, Dhana K, Seldenrijk A, Krohn K, Homuth G, Wolf P, Peters MJ, Dörr M, Peters A, van Meurs JBJ, Uitterlinden AG, Kavousi M, Levy D, Herder C, van Grootheest G, Waldenberger M, Meisinger C, Rathmann W, Thiery J, Polak J, Koenig W, Seissler J, Bis JC, Franceshini N, Giambartolomei C, Hofman A, Franco OH, Penninx BWJH, Prokisch H, Völzke H, Loeffler M, O'Donnell CJ, Below JE, Dehghan A, de Vries PS. Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues. Hum Mol Genet 2022; 31:1171-1182. [PMID: 34788810 PMCID: PMC8976428 DOI: 10.1093/hmg/ddab236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/11/2021] [Accepted: 08/11/2021] [Indexed: 11/13/2022] Open
Abstract
Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.
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Affiliation(s)
- Andy B Castaneda
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Xiaoling Zhang
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,The Framingham Heart Study, Framingham, MA, USA
| | - Katharina Schramm
- Institute of Neurogenomics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | | | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ulf Schminke
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Shih-Jen Hwang
- The Framingham Heart Study, Framingham, MA, USA.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Carola Marzi
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Klodian Dhana
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Adrie Seldenrijk
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Knut Krohn
- Interdisciplinary Center of Clinical Research, University of Leipzig, Leipzig, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Petra Wolf
- Institute of Neurogenomics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Marjolein J Peters
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA, USA.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Christian Herder
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD e.V.), München-Neuherberg, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Melanie Waldenberger
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Wolfgang Rathmann
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Joachim Thiery
- LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Joseph Polak
- Tufts University School of Medicine, Boston, MA, USA
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.,Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Ulm, Germany
| | - Jochen Seissler
- Diabetes Center, Diabetes Research Group, Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-Universität, Munich, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Nora Franceshini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | | | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Institute of Social and Preventive Medicine, University of Bern, Switzerland
| | - Brenda W J H Penninx
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Holger Prokisch
- Institute of Neurogenomics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Henry Völzke
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Christopher J O'Donnell
- The Framingham Heart Study, Framingham, MA, USA.,Cardiology Section, Department of Medicine, Boston Veteran's Administration Healthcare and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK.,MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, UK.,UK Dementia Research Institute at Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN UK
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
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18
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Single-Cell Analysis Uncovers Osteoblast Factor Growth Differentiation Factor 10 as Mediator of Vascular Smooth Muscle Cell Phenotypic Modulation Associated with Plaque Rupture in Human Carotid Artery Disease. Int J Mol Sci 2022; 23:ijms23031796. [PMID: 35163719 PMCID: PMC8836240 DOI: 10.3390/ijms23031796] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 12/18/2022] Open
Abstract
(1) Background: Vascular smooth muscle cells (VSMCs) undergo a complex phenotypic switch in response to atherosclerosis environmental triggers, contributing to atherosclerosis disease progression. However, the complex heterogeneity of VSMCs and how VSMC dedifferentiation affects human carotid artery disease (CAD) risk has not been clearly established. (2) Method: A single-cell RNA sequencing analysis of CD45− cells derived from the atherosclerotic aorta of Apolipoprotein E-deficient (Apoe−/−) mice on a normal cholesterol diet (NCD) or a high cholesterol diet (HCD), respecting the site-specific predisposition to atherosclerosis was performed. Growth Differentiation Factor 10 (GDF10) role in VSMCs phenotypic switch was investigated via flow cytometry, immunofluorescence in human atherosclerotic plaques. (3) Results: scRNAseq analysis revealed the transcriptomic profile of seven clusters, five of which showed disease-relevant gene signature of VSMC macrophagic calcific phenotype, VSMC mesenchymal chondrogenic phenotype, VSMC inflammatory and fibro-phenotype and VSMC inflammatory phenotype. Osteoblast factor GDF10 involved in ossification and osteoblast differentiation emerged as a hallmark of VSMCs undergoing phenotypic switch. Under hypercholesteremia, GDF10 triggered VSMC osteogenic switch in vitro. The abundance of GDF10 expressing osteogenic-like VSMCs cells was linked to the occurrence of carotid artery disease (CAD) events. (4) Conclusions: Taken together, these results provide evidence about GDF10-mediated VSMC osteogenic switch, with a likely detrimental role in atherosclerotic plaque stability.
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19
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Comprehensive Statistical and Bioinformatics Analysis in the Deciphering of Putative Mechanisms by Which Lipid-Associated GWAS Loci Contribute to Coronary Artery Disease. Biomedicines 2022; 10:biomedicines10020259. [PMID: 35203469 PMCID: PMC8868589 DOI: 10.3390/biomedicines10020259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/22/2022] [Accepted: 01/23/2022] [Indexed: 11/17/2022] Open
Abstract
The study was designed to evaluate putative mechanisms by which lipid-associated loci identified by genome-wide association studies (GWAS) are involved in the molecular pathogenesis of coronary artery disease (CAD) using a comprehensive statistical and bioinformatics analysis. A total of 1700 unrelated individuals of Slavic origin from the Central Russia, including 991 CAD patients and 709 healthy controls were examined. Sixteen lipid-associated GWAS loci were selected from European studies and genotyped using the MassArray-4 system. The polymorphisms were associated with plasma lipids such as total cholesterol (rs12328675, rs4846914, rs55730499, and rs838880), LDL-cholesterol (rs3764261, rs55730499, rs1689800, and rs838880), HDL-cholesterol (rs3764261) as well as carotid intima-media thickness/CIMT (rs12328675, rs11220463, and rs1689800). Polymorphisms such as rs4420638 of APOC1 (p = 0.009), rs55730499 of LPA (p = 0.0007), rs3136441 of F2 (p < 0.0001), and rs6065906 of PLTP (p = 0.002) showed significant associations with the risk of CAD, regardless of sex, age, and body mass index. A majority of the observed associations were successfully replicated in large independent cohorts. Bioinformatics analysis allowed establishing (1) phenotype-specific and shared epistatic gene–gene and gene–smoking interactions contributing to all studied cardiovascular phenotypes; (2) lipid-associated GWAS loci might be allele-specific binding sites for transcription factors from gene regulatory networks controlling multifaceted molecular mechanisms of atherosclerosis.
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20
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Wang P, Castellani CA, Yao J, Huan T, Bielak LF, Zhao W, Haessler J, Joehanes R, Sun X, Guo X, Longchamps RJ, Manson JE, Grove ML, Bressler J, Taylor KD, Lappalainen T, Kasela S, Van Den Berg DJ, Hou L, Reiner A, Liu Y, Boerwinkle E, Smith JA, Peyser PA, Fornage M, Rich SS, Rotter JI, Kooperberg C, Arking DE, Levy D, Liu C. Epigenome-wide association study of mitochondrial genome copy number. Hum Mol Genet 2021; 31:309-319. [PMID: 34415308 PMCID: PMC8742999 DOI: 10.1093/hmg/ddab240] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/27/2021] [Accepted: 08/11/2021] [Indexed: 01/03/2023] Open
Abstract
We conducted cohort- and race-specific epigenome-wide association analyses of mitochondrial deoxyribonucleic acid (mtDNA) copy number (mtDNA CN) measured in whole blood from participants of African and European origins in five cohorts (n = 6182, mean age = 57-67 years, 65% women). In the meta-analysis of all the participants, we discovered 21 mtDNA CN-associated DNA methylation sites (CpG) (P < 1 × 10-7), with a 0.7-3.0 standard deviation increase (3 CpGs) or decrease (18 CpGs) in mtDNA CN corresponding to a 1% increase in DNA methylation. Several significant CpGs have been reported to be associated with at least two risk factors (e.g. chronological age or smoking) for cardiovascular disease (CVD). Five genes [PR/SET domain 16, nuclear receptor subfamily 1 group H member 3 (NR1H3), DNA repair protein, DNA polymerase kappa and decaprenyl-diphosphate synthase subunit 2], which harbor nine significant CpGs, are known to be involved in mitochondrial biosynthesis and functions. For example, NR1H3 encodes a transcription factor that is differentially expressed during an adipose tissue transition. The methylation level of cg09548275 in NR1H3 was negatively associated with mtDNA CN (effect size = -1.71, P = 4 × 10-8) and was positively associated with the NR1H3 expression level (effect size = 0.43, P = 0.0003), which indicates that the methylation level in NR1H3 may underlie the relationship between mtDNA CN, the NR1H3 transcription factor and energy expenditure. In summary, the study results suggest that mtDNA CN variation in whole blood is associated with DNA methylation levels in genes that are involved in a wide range of mitochondrial activities. These findings will help reveal molecular mechanisms between mtDNA CN and CVD.
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Affiliation(s)
- Penglong Wang
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Christina A Castellani
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario N6A 5C1, Canada
| | - Jie Yao
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Tianxiao Huan
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jeffrey Haessler
- Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xianbang Sun
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Ryan J Longchamps
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Megan L Grove
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jan Bressler
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Kent D Taylor
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario N6A 5C1, Canada
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA
- Department of Systems Biology, Columbia University, New York, NY 10034, USA
| | - Silva Kasela
- New York Genome Center, New York, NY 10013, USA
- Department of Systems Biology, Columbia University, New York, NY 10034, USA
| | - David J Van Den Berg
- Department of Population and Public Health Sciences, Center for Genetic Epidemiology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Lifang Hou
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Alexander Reiner
- Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yongmei Liu
- Department of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center, Durham, NC 27704, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Charles Kooperberg
- Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Dan E Arking
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute (NHLBI), Framingham, MA 01702, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute (NHLBI), Framingham, MA 01702, USA
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21
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Palou-Márquez G, Subirana I, Nonell L, Fernández-Sanlés A, Elosua R. DNA methylation and gene expression integration in cardiovascular disease. Clin Epigenetics 2021; 13:75. [PMID: 33836805 PMCID: PMC8034168 DOI: 10.1186/s13148-021-01064-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/29/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. RESULTS Four independent latent factors (9, 19, 21-only in women-and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. CONCLUSIONS Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.
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Affiliation(s)
- Guillermo Palou-Márquez
- Cardiovascular Epidemiology and Genetics Research Group, Hospital del Mar Medical Research Institute (IMIM), Dr Aiguader 88, 08003, Barcelona, Spain
- Pompeu Fabra University (UPF), Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Isaac Subirana
- Cardiovascular Epidemiology and Genetics Research Group, Hospital del Mar Medical Research Institute (IMIM), Dr Aiguader 88, 08003, Barcelona, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Lara Nonell
- MARGenomics, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Alba Fernández-Sanlés
- Cardiovascular Epidemiology and Genetics Research Group, Hospital del Mar Medical Research Institute (IMIM), Dr Aiguader 88, 08003, Barcelona, Spain
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Roberto Elosua
- Cardiovascular Epidemiology and Genetics Research Group, Hospital del Mar Medical Research Institute (IMIM), Dr Aiguader 88, 08003, Barcelona, Spain.
- CIBER Cardiovascular Diseases (CIBERCV), Barcelona, Spain.
- Medicine Department, Faculty of Medicine, University of Vic-Central University of Catalonia (UVic-UCC), Vic, Spain.
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22
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Wu Y, Wang H, Li Z, Cheng J, Fang R, Cao H, Cui Y. Subtypes identification on heart failure with preserved ejection fraction via network enhancement fusion using multi-omics data. Comput Struct Biotechnol J 2021; 19:1567-1578. [PMID: 33868594 PMCID: PMC8039555 DOI: 10.1016/j.csbj.2021.03.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 03/03/2021] [Accepted: 03/06/2021] [Indexed: 11/24/2022] Open
Abstract
Heart failure with preserved ejection fraction (HFpEF) is associated with multiple etiologic and pathophysiologic factors. HFpEF leads to significant cardiovascular morbidity and mortality. There are various reasons that fail to identify effective therapeutic interventions for HFpEF, primarily due to its clinical heterogeneity causing significant difficulties in determining physiologic and prognostic implications for this syndrome. Thus, identifying clinical subtypes using multi-omics data has great implications for efficient treatment and prognosis of HFpEF patients. Here we proposed to integrate mRNA, DNA methylation and microRNA (miRNA) expression data of HFpEF with a similarity network fusion (SNF) method following a network enhancement (ne-SNF) denoising technique to form a fused network. A spectral clustering method was then used to obtain clusters of patient subtypes. Experiments on HFpEF datasets demonstrated that ne-SNF significantly outperforms single data subtype analysis and other integrated methods. The identified subgroups were shown to have statistically significant differences in survival. Two HFpEF subtypes were defined: a high-risk group (16.8%) and a low-risk group (83.2%). The 5-year mortality rates were 63.3% and 33.0% for the high- and low-risk group, respectively. After adjusting for the effects of clinical covariates, HFpEF patients in the high-risk group were 2.43 times more likely to die than the low-risk group. A total of 157 differentially expressed (DE) mRNAs, 2199 abnormal methylations and 121 DE miRNAs were identified between two subtypes. They were also enriched in many HFpEF-related biological processes or pathways. The ne-SNF method provides a novel pipeline for subtype identification in integrated analysis of multi-omics data.
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Affiliation(s)
- Yongqing Wu
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Huihui Wang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Zhi Li
- Department of Hematology, Taiyuan Central Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Jinfang Cheng
- Department of Cardiology, Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Ruiling Fang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Hongyan Cao
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China.,Shanxi Provincial Key Laboratory of Major Disease Risk Assessment, Taiyuan, Shanxi 030001, PR China
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
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23
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Miles MR, Seo J, Jiang M, Wilson ZT, Little J, Hao J, Andrade J, Ueberheide B, Tseng GN. Global identification of S-palmitoylated proteins and detection of palmitoylating (DHHC) enzymes in heart. J Mol Cell Cardiol 2021; 155:1-9. [PMID: 33636221 DOI: 10.1016/j.yjmcc.2021.02.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 10/22/2022]
Abstract
High-throughput experiments suggest that almost 20% of human proteins may be S-palmitoylatable, a post-translational modification (PTM) whereby fatty acyl chains, most commonly palmitoyl chain, are linked to cysteine thiol groups that impact on protein trafficking, distribution and function. In human, protein S-palmitoylation is mediated by a group of 23 palmitoylating 'Asp-His-His-Cys' domain-containing (DHHC) enzymes. There is no information on the scope of protein S-palmitoylation, or the pattern of DHHC enzyme expression, in the heart. We used resin-assisted capture to pull down S-palmitoylated proteins from human, dog, and rat hearts, followed by proteomic search to identify proteins in the pulldowns. We identified 454 proteins present in at least 2 species-specific pulldowns. These proteins are operationally called 'cardiac palmitoylome'. Enrichment analysis based on Gene Ontology terms 'cellular component' indicated that cardiac palmitoylome is involved in cell-cell and cell-substrate junctions, plasma membrane microdomain organization, vesicular trafficking, and mitochondrial enzyme organization. Importantly, cardiac palmitoylome is uniquely enriched in proteins participating in the organization and function of t-tubules, costameres and intercalated discs, three microdomains critical for excitation-contraction coupling and intercellular communication of cardiomyocytes. We validated antibodies targeting DHHC enzymes, and detected eleven of them expressed in hearts across species. In conclusion, we provide resources useful for investigators interested in studying protein S-palmitoylation and its regulation by DHHC enzymes in the heart. We also discuss challenges in these efforts, and suggest methods and tools that should be developed to overcome these challenges.
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Affiliation(s)
- Madeleine R Miles
- Department of Physiology & Biophysics, Virginia Commonwealth University, Richmond, VA, United States
| | - John Seo
- Department of Physiology & Biophysics, Virginia Commonwealth University, Richmond, VA, United States
| | - Min Jiang
- Department of Physiology & Biophysics, Virginia Commonwealth University, Richmond, VA, United States
| | - Zachary T Wilson
- Department of Physiology & Biophysics, Virginia Commonwealth University, Richmond, VA, United States
| | - Janay Little
- Department of Physiology & Biophysics, Virginia Commonwealth University, Richmond, VA, United States
| | - Jon Hao
- Poochon Scientific, Frederick, MD, United States
| | - Joshua Andrade
- Proteomics Laboratory, Division of Advance Research Technology, New York University, School School of Medicine, New York, NY, United States
| | - Beatrix Ueberheide
- Proteomics Laboratory, Division of Advance Research Technology, New York University, School School of Medicine, New York, NY, United States; Department of Biochemistry and Molecular Pharmacology, New York University, School of Medicine, New York, NY, United States; Department of Neurology, New York University, School of Medicine, New York, NY, United States
| | - Gea-Ny Tseng
- Department of Physiology & Biophysics, Virginia Commonwealth University, Richmond, VA, United States.
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24
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Zhang X, van Rooij JGJ, Wakabayashi Y, Hwang SJ, Yang Y, Ghanbari M, Bos D, Levy D, Johnson AD, van Meurs JBJ, Kavousi M, Zhu J, O'Donnell CJ. Genome-wide transcriptome study using deep RNA sequencing for myocardial infarction and coronary artery calcification. BMC Med Genomics 2021; 14:45. [PMID: 33568140 PMCID: PMC7874462 DOI: 10.1186/s12920-020-00838-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 11/29/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Coronary artery calcification (CAC) is a noninvasive measure of coronary atherosclerosis, the proximal pathophysiology underlying most cases of myocardial infarction (MI). We sought to identify expression signatures of early MI and subclinical atherosclerosis in the Framingham Heart Study (FHS). In this study, we conducted paired-end RNA sequencing on whole blood collected from 198 FHS participants (55 with a history of early MI, 72 with high CAC without prior MI, and 71 controls free of elevated CAC levels or history of MI). We applied DESeq2 to identify coding-genes and long intergenic noncoding RNAs (lincRNAs) differentially expressed in MI and high CAC, respectively, compared with the control. RESULTS On average, 150 million paired-end reads were obtained for each sample. At the false discovery rate (FDR) < 0.1, we found 68 coding genes and 2 lincRNAs that were differentially expressed in early MI versus controls. Among them, 60 coding genes were detectable and thus tested in an independent RNA-Seq data of 807 individuals from the Rotterdam Study, and 8 genes were supported by p value and direction of the effect. Immune response, lipid metabolic process, and interferon regulatory factor were enriched in these 68 genes. By contrast, only 3 coding genes and 1 lincRNA were differentially expressed in high CAC versus controls. APOD, encoding a component of high-density lipoprotein, was significantly downregulated in both early MI (FDR = 0.007) and high CAC (FDR = 0.01) compared with controls. CONCLUSIONS We identified transcriptomic signatures of early MI that include differentially expressed protein-coding genes and lincRNAs, suggesting important roles for protein-coding genes and lincRNAs in the pathogenesis of MI.
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Affiliation(s)
- Xiaoling Zhang
- Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA.
- The National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA.
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA, 02118-2526, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
| | - Jeroen G J van Rooij
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Yoshiyuki Wakabayashi
- DNA Sequencing and Genomics Core, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Shih-Jen Hwang
- Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Yanqin Yang
- DNA Sequencing and Genomics Core, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Mohsen Ghanbari
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Daniel Levy
- Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Andrew D Johnson
- Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Maryam Kavousi
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jun Zhu
- DNA Sequencing and Genomics Core, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Christopher J O'Donnell
- Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA.
- The National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA.
- Cardiology Section, Veteran's Administration Boston Healthcare System, Boston, USA.
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25
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Li J, Salvador AM, Li G, Valkov N, Ziegler O, Yeri A, Xiao CY, Meechoovet B, Alsop E, Rodosthenous RS, Kundu P, Huan T, Levy D, Tigges J, Pico AR, Ghiran I, Silverman MG, Meng X, Kitchen R, Xu J, Keuren-Jensen KV, Shah R, Xiao J, Das S. Mir-30d Regulates Cardiac Remodeling by Intracellular and Paracrine Signaling. Circ Res 2021; 128:e1-e23. [PMID: 33092465 PMCID: PMC7790887 DOI: 10.1161/circresaha.120.317244] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
RATIONALE Previous translational studies implicate plasma extracellular microRNA-30d (miR-30d) as a biomarker in left ventricular remodeling and clinical outcome in heart failure (HF) patients, although precise mechanisms remain obscure. OBJECTIVE To investigate the mechanism of miR-30d-mediated cardioprotection in HF. METHODS AND RESULTS In rat and mouse models of ischemic HF, we show that miR-30d gain of function (genetic, lentivirus, or agomiR-mediated) improves cardiac function, decreases myocardial fibrosis, and attenuates cardiomyocyte (CM) apoptosis. Genetic or locked nucleic acid-based knock-down of miR-30d expression potentiates pathological left ventricular remodeling, with increased dysfunction, fibrosis, and cardiomyocyte death. RNA sequencing of in vitro miR-30d gain and loss of function, together with bioinformatic prediction and experimental validation in cardiac myocytes and fibroblasts, were used to identify and validate direct targets of miR-30d. miR-30d expression is selectively enriched in cardiomyocytes, induced by hypoxic stress and is acutely protective, targeting MAP4K4 (mitogen-associate protein kinase 4) to ameliorate apoptosis. Moreover, miR-30d is secreted primarily in extracellular vesicles by cardiomyocytes and inhibits fibroblast proliferation and activation by directly targeting integrin α5 in the acute phase via paracrine signaling to cardiac fibroblasts. In the chronic phase of ischemic remodeling, lower expression of miR-30d in the heart and plasma extracellular vesicles is associated with adverse remodeling in rodent models and human subjects and is linked to whole-blood expression of genes implicated in fibrosis and inflammation, consistent with observations in model systems. CONCLUSIONS These findings provide the mechanistic underpinning for the cardioprotective association of miR-30d in human HF. More broadly, our findings support an emerging paradigm involving intercellular communication of extracellular vesicle-contained miRNAs (microRNAs) to transregulate distinct signaling pathways across cell types. Functionally validated RNA biomarkers and their signaling networks may warrant further investigation as novel therapeutic targets in HF.
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Affiliation(s)
- Jin Li
- Cardiac Regeneration and Ageing Lab, Institute of Cardiovascular Sciences, School of Life Science, Shanghai University, Shanghai 200444, China
| | - Ane M. Salvador
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Guoping Li
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Nedyalka Valkov
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Olivia Ziegler
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ashish Yeri
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Chun Yang Xiao
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | | | - Eric Alsop
- Neurogenomics Division, TGen, Phoenix, AZ 85004, USA
| | - Rodosthenis S. Rodosthenous
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Piyusha Kundu
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Tianxiao Huan
- The Framingham Heart Study and The Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, NIH, Bethesda, MD 20892, USA
| | - Daniel Levy
- The Framingham Heart Study and The Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, NIH, Bethesda, MD 20892, USA
| | - John Tigges
- Division of Allergy and Inflammation, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | | | - Ionita Ghiran
- Division of Allergy and Inflammation, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Michael G. Silverman
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Xiangmin Meng
- Cardiac Regeneration and Ageing Lab, Institute of Cardiovascular Sciences, School of Life Science, Shanghai University, Shanghai 200444, China
| | - Robert Kitchen
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Jiahong Xu
- Department of Cardiology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China
| | | | - Ravi Shah
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Junjie Xiao
- Cardiac Regeneration and Ageing Lab, Institute of Cardiovascular Sciences, School of Life Science, Shanghai University, Shanghai 200444, China
| | - Saumya Das
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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26
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Nevola KT, Kiel DP, Zullo AR, Weiss S, Homuth G, Foessl I, Obermayer-Pietsch B, Motyl KJ, Lary CW. miRNA Mechanisms Underlying the Association of Beta Blocker Use and Bone Mineral Density. J Bone Miner Res 2021; 36:110-122. [PMID: 32786095 PMCID: PMC8140522 DOI: 10.1002/jbmr.4160] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 07/25/2020] [Accepted: 08/05/2020] [Indexed: 01/16/2023]
Abstract
Osteoporosis is a debilitating and costly disease that causes fractures in 33% of women and 20% of men over the age of 50 years. Recent studies have shown that beta blocker (BB) users have higher bone mineral density (BMD) and decreased risk of fracture compared with non-users. The mechanism underlying this association is thought to be due to suppression of adrenergic signaling in osteoblasts, which leads to increased BMD in rodent models; however, the mechanism in humans is unknown. Also, several miRNAs are associated with adrenergic signaling and BMD in separate studies. To investigate potential miRNA mechanisms, we performed a cross-sectional analysis using clinical data, dual-energy X-ray absorptiometry (DXA) scans, and miRNA and mRNA profiling of whole blood from the Framingham Study's Offspring Cohort. We found nine miRNAs associated with BB use and increased BMD. In parallel network analyses, we discovered a subnetwork associated with BMD and BB use containing two of these nine miRNAs, miR-19a-3p and miR-186-5p. To strengthen this finding, we showed that these two miRNAs had significantly higher expression in individuals without incident fracture compared with those with fracture in an external data set. We also noted a similar trend in association between these miRNA and Z-score as calculated from heel ultrasound measures in two external cohorts (SOS-Hip and SHIP-TREND). Because miR-19a directly targets the ADRB1 mRNA transcript, we propose BB use may downregulate ADRB1 expression in osteoblasts through increased miR-19a-3p expression. We used enrichment analysis of miRNA targets to find potential indirect effects through insulin and parathyroid hormone signaling. This analysis provides a starting point for delineating the role of miRNA on the association between BB use and BMD. © 2020 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Kathleen T. Nevola
- Graduate School of Biomedical Sciences, Tufts University, 136 Harrison Ave, Boston, MA, 02111, USA
| | - Douglas P. Kiel
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research Hebrew SeniorLife, Boston, MA, USA
| | - Andrew R. Zullo
- Department of Health Services, Policy and Practice, and Department of Epidemiology, School of Public Health, Brown University, 121 South Main Street, Providence, RI 02912, USA
- Rhode Island Hospital, Providence, RI, USA
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Ines Foessl
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Barbara Obermayer-Pietsch
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Katherine J. Motyl
- Center for Molecular Medicine, Maine Medical Center Research Institute, Maine Medical Center, Scarborough, ME, USA
| | - Christine W. Lary
- Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, Portland, ME, USA
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27
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Lai CQ, Parnell LD, Smith CE, Guo T, Sayols-Baixeras S, Aslibekyan S, Tiwari HK, Irvin MR, Bender C, Fei D, Hidalgo B, Hopkins PN, Absher DM, Province MA, Elosua R, Arnett DK, Ordovas JM. Carbohydrate and fat intake associated with risk of metabolic diseases through epigenetics of CPT1A. Am J Clin Nutr 2020; 112:1200-1211. [PMID: 32930325 PMCID: PMC7657341 DOI: 10.1093/ajcn/nqaa233] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/23/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Epigenome-wide association studies identified the cg00574958 DNA methylation site at the carnitine palmitoyltransferase-1A (CPT1A) gene to be associated with reduced risk of metabolic diseases (hypertriglyceridemia, obesity, type 2 diabetes, hypertension, metabolic syndrome), but the mechanism underlying these associations is unknown. OBJECTIVES We aimed to elucidate whether carbohydrate and fat intakes modulate cg00574958 methylation and the risk of metabolic diseases. METHODS We examined associations between carbohydrate (CHO) and fat (FAT) intake, as percentages of total diet energy, and the CHO/FAT ratio with CPT1A-cg00574958, and the risk of metabolic diseases in 3 populations (Genetics of Lipid Lowering Drugs and Diet Network, n = 978; Framingham Heart Study, n = 2331; and REgistre GIroní del COR study, n = 645) while adjusting for confounding factors. To understand possible causal effects of dietary intake on the risk of metabolic diseases, we performed meta-analysis, CPT1A transcription analysis, and mediation analysis with CHO and FAT intakes as exposures and cg00574958 methylation as the mediator. RESULTS We confirmed strong associations of cg00574958 methylation with metabolic phenotypes (BMI, triglyceride, glucose) and diseases in all 3 populations. Our results showed that CHO intake and CHO/FAT ratio were positively associated with cg00574958 methylation, whereas FAT intake was negatively correlated with cg00574958 methylation. Meta-analysis further confirmed this strong correlation, with β = 58.4 ± 7.27, P = 8.98 x 10-16 for CHO intake; β = -36.4 ± 5.95, P = 9.96 x 10-10 for FAT intake; and β = 3.30 ± 0.49, P = 1.48 x 10-11 for the CHO/FAT ratio. Furthermore, CPT1A mRNA expression was negatively associated with CHO intake, and positively associated with FAT intake, and metabolic phenotypes. Mediation analysis supports the hypothesis that CHO intake induces CPT1A methylation, hence reducing the risk of metabolic diseases, whereas FAT intake inhibits CPT1A methylation, thereby increasing the risk of metabolic diseases. CONCLUSIONS Our results suggest that the proportion of total energy supplied by CHO and FAT can have a causal effect on the risk of metabolic diseases via the epigenetic status of CPT1A.Study registration at https://www.clinicaltrials.gov/: the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN)-NCT01023750; and the Framingham Heart Study (FHS)-NCT00005121.
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Affiliation(s)
- Chao-Qiang Lai
- USDA Agricultural Research Service, Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Laurence D Parnell
- USDA Agricultural Research Service, Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Caren E Smith
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Tao Guo
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Sergi Sayols-Baixeras
- Cardiovascular Epidemiology and Genetics Research Group, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Catalonia, Spain
- CIBER Cardiovascular Diseases (CIBERCV), Barcelona, Catalonia, Spain
- Molecular Epidemiology, Department of Medical Sciences, Uppsala Universitet, Uppsala, Sweden
| | - Stella Aslibekyan
- Department of Epidemiology, School of Public Health, University of Alabama, Birmingham, AL, USA
| | - Hemant K Tiwari
- Department of Epidemiology, School of Public Health, University of Alabama, Birmingham, AL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama, Birmingham, AL, USA
| | - Carl Bender
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - David Fei
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Bertha Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama, Birmingham, AL, USA
| | - Paul N Hopkins
- Department of Cardiovascular Genetics, University of Utah, Salt Lake City, UT, USA
| | - Devin M Absher
- Hudson Alpha Institute for Biotechnology, Huntsville, AL, USA
| | - Michael A Province
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Roberto Elosua
- Cardiovascular Epidemiology and Genetics Research Group, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Catalonia, Spain
- CIBER Cardiovascular Diseases (CIBERCV), Barcelona, Catalonia, Spain
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
- IMDEA Food Institute, CEI UAM + CSIC, Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
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28
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Smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic traits. Clin Epigenetics 2020; 12:157. [PMID: 33092652 PMCID: PMC7579899 DOI: 10.1186/s13148-020-00951-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Tobacco smoking is a well-known modifiable risk factor for many chronic diseases, including cardiovascular disease (CVD). One of the proposed underlying mechanism linking smoking to disease is via epigenetic modifications, which could affect the expression of disease-associated genes. Here, we conducted a three-way association study to identify the relationship between smoking-related changes in DNA methylation and gene expression and their associations with cardio-metabolic traits. RESULTS We selected 2549 CpG sites and 443 gene expression probes associated with current versus never smokers, from the largest epigenome-wide association study and transcriptome-wide association study to date. We examined three-way associations, including CpG versus gene expression, cardio-metabolic trait versus CpG, and cardio-metabolic trait versus gene expression, in the Rotterdam study. Subsequently, we replicated our findings in The Cooperative Health Research in the Region of Augsburg (KORA) study. After correction for multiple testing, we identified both cis- and trans-expression quantitative trait methylation (eQTM) associations in blood. Specifically, we found 1224 smoking-related CpGs associated with at least one of the 443 gene expression probes, and 200 smoking-related gene expression probes to be associated with at least one of the 2549 CpGs. Out of these, 109 CpGs and 27 genes were associated with at least one cardio-metabolic trait in the Rotterdam Study. We were able to replicate the associations with cardio-metabolic traits of 26 CpGs and 19 genes in the KORA study. Furthermore, we identified a three-way association of triglycerides with two CpGs and two genes (GZMA; CLDND1), and BMI with six CpGs and two genes (PID1; LRRN3). Finally, our results revealed the mediation effect of cg03636183 (F2RL3), cg06096336 (PSMD1), cg13708645 (KDM2B), and cg17287155 (AHRR) within the association between smoking and LRRN3 expression. CONCLUSIONS Our study indicates that smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic risk factors. These findings may provide additional insights into the molecular mechanisms linking smoking to the development of CVD.
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29
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Zhang YH, Pan X, Zeng T, Chen L, Huang T, Cai YD. Identifying the RNA signatures of coronary artery disease from combined lncRNA and mRNA expression profiles. Genomics 2020; 112:4945-4958. [PMID: 32919019 DOI: 10.1016/j.ygeno.2020.09.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 07/28/2020] [Accepted: 09/05/2020] [Indexed: 12/23/2022]
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease. CAD research has greatly progressed during the past decade. mRNA is a traditional and popular pipeline to investigate various disease, including CAD. Compared with mRNA, lncRNA has better stability and thus may serve as a better disease indicator in blood. Investigating potential CAD-related lncRNAs and mRNAs will greatly contribute to the diagnosis and treatment of CAD. In this study, a computational analysis was conducted on patients with CAD by using a comprehensive transcription dataset with combined mRNA and lncRNA expression data. Several machine learning algorithms, including feature selection methods and classification algorithms, were applied to screen for the most CAD-related RNA molecules. Decision rules were also reported to provide a quantitative description about the effect of these RNA molecules on CAD progression. These new findings (CAD-related RNA molecules and rules) can help understand mRNA and lncRNA expression levels in CAD.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai 200444, China; Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China.
| | - Tao Zeng
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China.
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30
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Wu L, Yang Y, Guo X, Shu XO, Cai Q, Shu X, Li B, Tao R, Wu C, Nikas JB, Sun Y, Zhu J, Roobol MJ, Giles GG, Brenner H, John EM, Clements J, Grindedal EM, Park JY, Stanford JL, Kote-Jarai Z, Haiman CA, Eeles RA, Zheng W, Long J. An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk. Nat Commun 2020; 11:3905. [PMID: 32764609 PMCID: PMC7413371 DOI: 10.1038/s41467-020-17673-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 06/28/2020] [Indexed: 12/21/2022] Open
Abstract
It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.
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Affiliation(s)
- Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA.
| | - Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiang Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Chong Wu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Jason B Nikas
- Research & Development, Genomix Inc, Minneapolis, MN, USA
| | - Yanfa Sun
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
- College of Life Science, Longyan University, Longyan, Fujian, P. R. China
| | - Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, 207 Bouverie St, Melbourne, VIC, 3010, Australia
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Esther M John
- Department of Medicine (Oncology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Judith Clements
- Australian Prostate Cancer Research Centre-QLD, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, Australia
- Translational Research Institute, Brisbane, QLD, Australia
| | | | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Zsofia Kote-Jarai
- Division of Genetics and Epidemiology, The Institute of Cancer Research, and The Royal Marsden NHS Foundation Trust, London, UK
| | - Christopher A Haiman
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Rosalind A Eeles
- Division of Genetics and Epidemiology, The Institute of Cancer Research, and The Royal Marsden NHS Foundation Trust, London, UK
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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Sofer T, Li R, Joehanes R, Lin H, Gower AC, Wang H, Kurniansyah N, Cade BE, Lee J, Williams S, Mehra R, Patel SR, Quan SF, Liu Y, Rotter JI, Rich SS, Spira A, Levy D, Gharib SA, Redline S, Gottlieb DJ. Low oxygen saturation during sleep reduces CD1D and RAB20 expressions that are reversed by CPAP therapy. EBioMedicine 2020; 56:102803. [PMID: 32512511 PMCID: PMC7276515 DOI: 10.1016/j.ebiom.2020.102803] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 04/13/2020] [Accepted: 05/05/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Sleep Disordered Breathing (SDB) is associated with a wide range of pathophysiological changes due, in part, to hypoxemia during sleep. We sought to identify gene transcription associations with measures of SDB and hypoxemia during sleep, and study their response to treatment. METHODS In two discovery cohorts, Framingham Offspring Study (FOS; N = 571) and the Multi-Ethnic Study of Atherosclerosis (MESA; N = 580), we studied gene expression in peripheral blood mononuclear cells in association with three measures of SDB: Apnea Hypopnea Index (AHI); average oxyhemoglobin saturation (avgO2) during sleep; and minimum oxyhemoglobin saturation (minO2) during sleep. Associated genes were used for analysis of gene expression in the blood of 15 participants with moderate or severe obstructive sleep apnea (OSA) from the Heart Biomarkers In Apnea Treatment (HeartBEAT) trial. These genes were studied pre- and post-treatment (three months) with continuous positive airway pressure (CPAP). We also performed Gene Set Enrichment Analysis (GSEA) on all traits and cohort analyses. FINDINGS Twenty-two genes were associated with SDB traits in both MESA and FOS. Of these, lower expression of CD1D and RAB20 was associated with lower avgO2 in MESA and FOS. CPAP treatment increased the expression of these genes in HeartBEAT participants. Immunity and inflammation pathways were up-regulated in subjects with lower avgO2; i.e., in those with a more severe SDB phenotype (MESA), whereas immuno-inflammatory processes were down-regulated following CPAP treatment (HeartBEAT). INTERPRETATION Low oxygen saturation during sleep is associated with alterations in gene expression and transcriptional programs that are partially reversed by CPAP treatment.
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Affiliation(s)
- Tamar Sofer
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
| | - Ruitong Li
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Roby Joehanes
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA and the Framingham Heart Study, Framingham, MA, USA; Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA
| | - Honghuang Lin
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA and the Framingham Heart Study, Framingham, MA, USA; Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Adam C Gower
- Clinical and Translational Science Institute, Boston University School of Medicine, Boston, USA
| | - Heming Wang
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian E Cade
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Stephanie Williams
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Reena Mehra
- Neurologic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Sanjay R Patel
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stuart F Quan
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Avrum Spira
- Boston University School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA and the Framingham Heart Study, Framingham, MA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, University of Washington Medicine Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Computational Medicine Core, Center for Lung Biology, University of Washington Medicine Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - Daniel J Gottlieb
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
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32
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Westerman K, Fernández‐Sanlés A, Patil P, Sebastiani P, Jacques P, Starr JM, J. Deary I, Liu Q, Liu S, Elosua R, DeMeo DL, Ordovás JM. Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics. J Am Heart Assoc 2020; 9:e015299. [PMID: 32308120 PMCID: PMC7428544 DOI: 10.1161/jaha.119.015299] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 03/10/2020] [Indexed: 12/16/2022]
Abstract
Background Epigenome-wide association studies for cardiometabolic risk factors have discovered multiple loci associated with incident cardiovascular disease (CVD). However, few studies have sought to directly optimize a predictor of CVD risk. Furthermore, it is challenging to train multivariate models across multiple studies in the presence of study- or batch effects. Methods and Results Here, we analyzed existing DNA methylation data collected using the Illumina HumanMethylation450 microarray to create a predictor of CVD risk across 3 cohorts: Women's Health Initiative, Framingham Heart Study Offspring Cohort, and Lothian Birth Cohorts. We trained Cox proportional hazards-based elastic net regressions for incident CVD separately in each cohort and used a recently introduced cross-study learning approach to integrate these individual scores into an ensemble predictor. The methylation-based risk score was associated with CVD time-to-event in a held-out fraction of the Framingham data set (hazard ratio per SD=1.28, 95% CI, 1.10-1.50) and predicted myocardial infarction status in the independent REGICOR (Girona Heart Registry) data set (odds ratio per SD=2.14, 95% CI, 1.58-2.89). These associations remained after adjustment for traditional cardiovascular risk factors and were similar to those from elastic net models trained on a directly merged data set. Additionally, we investigated interactions between the methylation-based risk score and both genetic and biochemical CVD risk, showing preliminary evidence of an enhanced performance in those with less traditional risk factor elevation. Conclusions This investigation provides proof-of-concept for a genome-wide, CVD-specific epigenomic risk score and suggests that DNA methylation data may enable the discovery of high-risk individuals who would be missed by alternative risk metrics.
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Affiliation(s)
- Kenneth Westerman
- JM‐USDA Human Nutrition Research Center on Aging at Tufts UniversityBostonMA
| | - Alba Fernández‐Sanlés
- Cardiovascular Epidemiology and Genetics Research GroupREGICOR Study GroupIMIM (Hospital del Mar Medical Research Institute)BarcelonaCataloniaSpain
- Pompeu Fabra University (UPF)BarcelonaCataloniaSpain
| | - Prasad Patil
- Department of BiostatisticsBoston University School of Public HealthBostonMA
| | - Paola Sebastiani
- Department of BiostatisticsBoston University School of Public HealthBostonMA
| | - Paul Jacques
- JM‐USDA Human Nutrition Research Center on Aging at Tufts UniversityBostonMA
| | - John M. Starr
- Department of PsychologyUniversity of EdinburghUnited Kingdom
- Centre for Cognitive Ageing and Cognitive EpidemiologyUniversity of EdinburghUnited Kingdom
| | - Ian J. Deary
- Department of PsychologyUniversity of EdinburghUnited Kingdom
- Centre for Cognitive Ageing and Cognitive EpidemiologyUniversity of EdinburghUnited Kingdom
| | - Qing Liu
- Department of EpidemiologyBrown University School of Public HealthProvidenceRI
| | - Simin Liu
- Department of EpidemiologyBrown University School of Public HealthProvidenceRI
| | - Roberto Elosua
- Cardiovascular Epidemiology and Genetics Research GroupREGICOR Study GroupIMIM (Hospital del Mar Medical Research Institute)BarcelonaCataloniaSpain
- CIBER Cardiovascular Diseases (CIBERCV)MadridSpain
- Medicine DepartmentMedical SchoolUniversity of Vic‐Central University of Catalonia (UVic‐UCC)VicCataloniaSpain
| | - Dawn L. DeMeo
- Channing Division of Network MedicineDepartment of MedicineBrigham and Women’s HospitalBostonMA
| | - José M. Ordovás
- JM‐USDA Human Nutrition Research Center on Aging at Tufts UniversityBostonMA
- IMDEA AlimentaciónCEIUAMMadridSpain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
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33
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Zeng L, Yang J, Peng S, Zhu J, Zhang B, Suh Y, Tu Z. Transcriptome analysis reveals the difference between "healthy" and "common" aging and their connection with age-related diseases. Aging Cell 2020; 19:e13121. [PMID: 32077223 PMCID: PMC7059150 DOI: 10.1111/acel.13121] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 01/17/2020] [Accepted: 02/03/2020] [Indexed: 12/16/2022] Open
Abstract
A key goal of aging research was to understand mechanisms underlying healthy aging and develop methods to promote the human healthspan. One approach is to identify gene regulations unique to healthy aging compared with aging in the general population (i.e., "common" aging). Here, we leveraged Genotype-Tissue Expression (GTEx) project data to investigate "healthy" and "common" aging gene expression regulations at a tissue level in humans and their interconnection with diseases. Using GTEx donors' disease annotations, we defined a "healthy" aging cohort for each tissue. We then compared the age-associated genes derived from this cohort with age-associated genes from the "common" aging cohort which included all GTEx donors; we also compared the "healthy" and "common" aging gene expressions with various disease-associated gene expressions to elucidate the relationships among "healthy," "common" aging and disease. Our analyses showed that 1. GTEx "healthy" and "common" aging shared a large number of gene regulations; 2. Despite the substantial commonality, "healthy" and "common" aging genes also showed distinct function enrichment, and "common" aging genes had a higher enrichment for disease genes; 3. Disease-associated gene regulations were overall different from aging gene regulations. However, for genes regulated by both, their regulation directions were largely consistent, implying some aging processes could increase the susceptibility to disease development; and 4. Possible protective mechanisms were associated with some "healthy" aging gene regulations. In summary, our work highlights several unique features of GTEx "healthy" aging program. This new knowledge could potentially be used to develop interventions to promote the human healthspan.
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Affiliation(s)
- Lu Zeng
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew York
| | - Jialiang Yang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew York
| | - Shouneng Peng
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew York
| | - Jun Zhu
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew York
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew York
| | - Yousin Suh
- Department of GeneticsAlbert Einstein College of MedicineNew YorkNew York
| | - Zhidong Tu
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew York
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34
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Di Gioia M, Spreafico R, Springstead JR, Mendelson MM, Joehanes R, Levy D, Zanoni I. Endogenous oxidized phospholipids reprogram cellular metabolism and boost hyperinflammation. Nat Immunol 2019; 21:42-53. [PMID: 31768073 PMCID: PMC6923570 DOI: 10.1038/s41590-019-0539-2] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 10/07/2019] [Indexed: 12/17/2022]
Abstract
Pathogen-associated molecular patterns (PAMPs) have the capacity to couple inflammatory gene expression to changes in macrophage metabolism, both of which influence subsequent inflammatory activities. Similar to their microbial counterparts, several self-encoded damage-associated molecular patterns (DAMPs) induce inflammatory gene expression. However, whether this symmetry in host responses between PAMPs and DAMPs extends to metabolic shifts is unclear. Here we report that the self-encoded oxidized phospholipid oxPAPC alters the metabolism of macrophages exposed to lipopolysaccharide (LPS). While cells activated by LPS rely exclusively on glycolysis, macrophages exposed to oxPAPC also use mitochondrial respiration, feed the Krebs cycle with glutamine and favor the accumulation of oxaloacetate in the cytoplasm: this metabolite potentiates IL-1β production, resulting in hyperinflammation. Similar metabolic adaptions occur in vivo in hypercholesterolemic mice and human subjects. Drugs that interfere with oxPAPC-driven metabolic changes reduce atherosclerotic plaque formation in mice, thereby underscoring the importance of DAMP-mediated activities in pathophysiological conditions.
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Affiliation(s)
- Marco Di Gioia
- Division of Immunology and Division of Gastroenterology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roberto Spreafico
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - James R Springstead
- Department of Chemical and Paper Engineering, Western Michigan University, Kalamazoo, MI, USA
| | | | - Roby Joehanes
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ivan Zanoni
- Division of Immunology and Division of Gastroenterology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy.
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35
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Abstract
Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine.
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Affiliation(s)
- Kalliopi Trachana
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Rhishikesh Bargaje
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Gustavo Glusman
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Nathan D Price
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Sui Huang
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.).,Department of Biological Sciences, University of Calgary, Alberta, Canada (S.H.)
| | - Leroy E Hood
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
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36
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Huan T, Mendelson M, Joehanes R, Yao C, Liu C, Song C, Bhattacharya A, Rong J, Tanriverdi K, Keefe J, Murabito JM, Courchesne P, Larson MG, Freedman JE, Levy D. Epigenome-wide association study of DNA methylation and microRNA expression highlights novel pathways for human complex traits. Epigenetics 2019; 15:183-198. [PMID: 31282290 DOI: 10.1080/15592294.2019.1640547] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
DNA methylation (DNAm) and microRNAs (miRNAs) have been implicated in a wide-range of human diseases. While often studied in isolation, DNAm and miRNAs are not independent. We analyzed associations of expression of 283 miRNAs with DNAm at >400K CpG sites in whole blood obtained from 3565 individuals and identified 227 CpGs at which differential methylation was associated with the expression of 40 nearby miRNAs (cis-miR-eQTMs) at FDR<0.01, including 91 independent CpG sites at r2 < 0.2. cis-miR-eQTMs were enriched for CpGs in promoter and polycomb-repressed state regions, and 60% were inversely associated with miRNA expression. Bidirectional Mendelian randomization (MR) analysis further identified 58 cis-miR-eQTMCpG-miRNA pairs where DNAm changes appeared to drive miRNA expression changes and opposite directional effects were unlikely. Integration of genetic variants in joint analyses revealed an average partial between cis-miR-eQTM CpGs and miRNAs of 2% after conditioning on site-specific genetic variation, suggesting that DNAm is an important epigenetic regulator of miRNA expression. Finally, two-step MR analysis was performed to identify putatively causal CpGs driving miRNA expression in relation to human complex traits. We found that an imprinted region on 14q32 that was previously identified in relation to age at menarche is enriched with cis-miR-eQTMs. Nine CpGs and three miRNAs at this locus tested causal for age at menarche, reflecting novel epigenetic-driven molecular pathways underlying this complex trait. Our study sheds light on the joint genetic and epigenetic regulation of miRNA expression and provides insights into the relations of miRNAs to their targets and to complex phenotypes.
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Affiliation(s)
- Tianxiao Huan
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Michael Mendelson
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Roby Joehanes
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Chen Yao
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Chunyu Liu
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.,Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, MA, USA
| | - Ci Song
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Anindya Bhattacharya
- Department of Computer Science and Engineering, University of California, San Diego, CA, USA
| | - Jian Rong
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Kahraman Tanriverdi
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Joshua Keefe
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Joanne M Murabito
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,Department of Medicine, Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Paul Courchesne
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Martin G Larson
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Jane E Freedman
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Daniel Levy
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
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37
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Müller CP, Chu C, Qin L, Liu C, Xu B, Gao H, Ruggeri B, Hieber S, Schneider J, Jia T, Tay N, Akira S, Satoh T, Banaschewski T, Bokde ALW, Bromberg U, Büchel C, Quinlan EB, Flor H, Frouin V, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Artiges E, Lemaitre H, Nees F, Papadopoulos Orfanos D, Paus T, Poustka L, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Bakalkin G, Liu Y, Desrivières S, Elliott P, Eulenburg V, Levy D, Crews F, Schumann G. The Cortical Neuroimmune Regulator TANK Affects Emotional Processing and Enhances Alcohol Drinking: A Translational Study. Cereb Cortex 2019; 29:1736-1751. [PMID: 30721969 PMCID: PMC6430980 DOI: 10.1093/cercor/bhy341] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 12/14/2018] [Accepted: 12/19/2018] [Indexed: 12/22/2022] Open
Abstract
Alcohol abuse is a major public health problem worldwide. Understanding the molecular mechanisms that control regular drinking may help to reduce hazards of alcohol consumption. While immunological mechanisms have been related to alcohol drinking, most studies reported changes in immune function that are secondary to alcohol use. In this report, we analyse how the gene "TRAF family member-associated NF-κB activator" (TANK) affects alcohol drinking behavior. Based on our recent discovery in a large GWAS dataset that suggested an association of TANK, SNP rs197273, with alcohol drinking, we report that SNP rs197273 in TANK is associated both with gene expression (P = 1.16 × 10-19) and regional methylation (P = 5.90 × 10-25). A tank knock out mouse model suggests a role of TANK in alcohol drinking, anxiety-related behavior, as well as alcohol exposure induced activation of insular cortex NF-κB. Functional and structural neuroimaging studies among up to 1896 adolescents reveal that TANK is involved in the control of brain activity in areas of aversive interoceptive processing, including the insular cortex, but not in areas related to reinforcement, reward processing or impulsiveness. Our findings suggest that the cortical neuroimmune regulator TANK is associated with enhanced aversive emotional processing that better protects from the establishment of alcohol drinking behavior.
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Affiliation(s)
- Christian P Müller
- Section of Addiction Medicine, Department of Psychiatry and Psychotherapy, University Clinic, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, Erlangen, Germany
| | - Congying Chu
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
| | - Liya Qin
- Bowles Center for Alcohol Studies, School of Medicine, University of North Carolina, Chapel Hill NC, USA
| | - Chunyu Liu
- The Framingham Heart Study, 73 Mt Wayte Ave, Framingham MA, USA
- The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda MD, USA
- Boston University School of Public Health, 715 Albany St, Boston MA, USA
| | - Bing Xu
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
| | - He Gao
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Barbara Ruggeri
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
| | - Saskia Hieber
- Section of Addiction Medicine, Department of Psychiatry and Psychotherapy, University Clinic, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, Erlangen, Germany
| | - Julia Schneider
- Institute for Biochemistry and Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Fahrstrasse 17, Erlangen, Germany
| | - Tianye Jia
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
| | - Nicole Tay
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
| | - Shizuo Akira
- Laboratory of Host Defense, World Premier International Immunology Frontiern Research Center, Research Institute for Microbial Diseases, Osaka University, 1-1 Yamadaoka, Suita, Osaka, Osaka, Japan
| | - Takashi Satoh
- Laboratory of Host Defense, World Premier International Immunology Frontiern Research Center, Research Institute for Microbial Diseases, Osaka University, 1-1 Yamadaoka, Suita, Osaka, Osaka, Japan
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, James's Street, Dublin, Ireland
| | - Uli Bromberg
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, Hamburg, Germany
| | - Christian Büchel
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, Hamburg, Germany
| | - Erin Burke Quinlan
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Vincent Frouin
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany [ Physikalisch-Technische Bundesanstalt (PTB), Abbestr. 2—12, Berlin, Germany]
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud—University Paris Saclay, DIGITEO Labs, Rue Noetzlin, Gif sur Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud—Paris Saclay, University Paris Descartes; and AP-HP, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, 47-83, boulevard de l'Hôpital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud—University Paris Saclay, DIGITEO Labs, Gif sur Yvette; and Psychiatry Department, Orsay Hospital, Orsay, France
| | - Herve Lemaitre
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris-Sud Medical School, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, Mannheim, Germany
| | | | - Tomáš Paus
- Rotman Research Institute, Baycrest and Departments of Psychology and Psychiatry, University of Toronto, 3560 Bathurst Street, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, Göttingen, Germany
- Clinic for Child and Adolescent Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, Vienna, Austria
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Chemnitzer Str. 46a01187 Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Chemnitzer Str. 46a01187 Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Aras an Phiarsaigh Trinity College Dublin, Dublin, Ireland
| | - Georgy Bakalkin
- Division of Biological Research on Drug Dependence, Department of Pharmaceutical, Biosciences, Uppsala University, Husargatan 3, Uppsala, Sweden
| | - Yun Liu
- Key Laboratory of Metabolism and Molecular Medicine, Ministry of Education; Department of Biochemistry and Molecular Biology, Fudan University Shanghai Medical College, Shanghai, P.R. China
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Volker Eulenburg
- Institute for Biochemistry and Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Fahrstrasse 17, Erlangen, Germany
- Department of Anaesthesiology and Intensive Care Medicine, University of Leipzig, Liebigstrasse 20, Leipzig, Germany
| | - Daniel Levy
- The Framingham Heart Study, 73 Mt Wayte Ave, Framingham MA, USA
- The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda MD, USA
| | - Fulton Crews
- Bowles Center for Alcohol Studies, School of Medicine, University of North Carolina, Chapel Hill NC, USA
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College De Crespigny Park, London, UK
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Dergunov AD, Litvinov DY, Bazaeva EV, Dmitrieva VG, Nosova EV, Rozhkova AV, Dergunova LV. Relation of High-Density Lipoprotein Charge Heterogeneity, Cholesterol Efflux Capacity, and the Expression of High-Density Lipoprotein-Related Genes in Mononuclear Cells to the HDL-Cholesterol Level. Lipids 2018; 53:979-991. [DOI: 10.1002/lipd.12104] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 10/12/2018] [Accepted: 10/15/2018] [Indexed: 12/12/2022]
Affiliation(s)
- Alexander D. Dergunov
- National Research Centre for Preventive Medicine; 10, Petroverigsky Street, 101990 Moscow Russia
| | - Dmitry Y. Litvinov
- National Research Centre for Preventive Medicine; 10, Petroverigsky Street, 101990 Moscow Russia
| | - Ekaterina V. Bazaeva
- National Research Centre for Preventive Medicine; 10, Petroverigsky Street, 101990 Moscow Russia
| | - Veronika G. Dmitrieva
- National Research Centre for Preventive Medicine; 10, Petroverigsky Street, 101990 Moscow Russia
- Institute of Molecular Genetics of the Russian Academy of Sciences, 2, Kurchatov Square, 123182; Moscow Russia
| | - Elena V. Nosova
- Institute of Molecular Genetics of the Russian Academy of Sciences, 2, Kurchatov Square, 123182; Moscow Russia
| | - Alexandra V. Rozhkova
- Institute of Molecular Genetics of the Russian Academy of Sciences, 2, Kurchatov Square, 123182; Moscow Russia
| | - Liudmila V. Dergunova
- Institute of Molecular Genetics of the Russian Academy of Sciences, 2, Kurchatov Square, 123182; Moscow Russia
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Wang L, Perez J, Heard-Costa N, Chu AY, Joehanes R, Munson PJ, Levy D, Fox CS, Cupples LA, Liu CT. Integrating genetic, transcriptional, and biological information provides insights into obesity. Int J Obes (Lond) 2018; 43:457-467. [PMID: 30232418 PMCID: PMC6405310 DOI: 10.1038/s41366-018-0190-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 07/18/2018] [Accepted: 07/22/2018] [Indexed: 02/07/2023]
Abstract
Objective: Indices of body fat distribution are heritable, but few genetic signals have been reported from genome-wide association studies (GWAS) of computed tomography (CT) imaging measurements of body fat distribution. We aimed to identify genes associated with adiposity traits and the key drivers that are central to adipose regulatory networks. Subjects: We analyzed gene transcript expression data in blood from participants in the Framingham Heart Study, a large community-based cohort (n up to 4,303), as well as implemented an integrative analysis of these data and existing biological information. Results: Our association analyses identified unique and common gene expression signatures across several adiposity traits, including body mass index, waist-hip ratio, waist circumference, and CT-measured indices, including volume and quality of visceral and subcutaneous adipose tissues. We identified six enriched KEGG pathways and two co-expression modules for further exploration of adipose regulatory networks. The integrative analysis revealed four gene sets (Apoptosis, p53 signaling pathway, Proteasome, Ubiquitin mediated proteolysis) and two co-expression modules with significant genetic variants and 94 key drivers/genes whose local networks were enriched with adiposity-associated genes, suggesting that these enriched pathways or modules have genetic effects on adiposity. Most identified key driver genes are involved in essential biological processes such as controlling cell cycle, DNA repair and degradation of regulatory proteins and are cancer related. Conclusion: Our integrative analysis of genetic, transcriptional and biological information provides a list of compelling candidates for further follow-up functional studies to uncover the biological mechanisms underlying obesity. These candidates highlight the value of examining CT-derived and central adiposity traits.
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Affiliation(s)
- Lan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Jeremiah Perez
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | | | - Audrey Y Chu
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.,The Framingham Heart Study, Framingham, MA, 01702, USA
| | - Roby Joehanes
- Hebrew SeniorLife, Harvard Medical School, Boston, MA, 02131, USA
| | - Peter J Munson
- Mathematical and Statistical Computing Laboratory, Office of Intramural Research, Center for Information Technology, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.,The Framingham Heart Study, Framingham, MA, 01702, USA
| | - Caroline S Fox
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.,The Framingham Heart Study, Framingham, MA, 01702, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA.,The Framingham Heart Study, Framingham, MA, 01702, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA.
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40
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Kashyap S, Kumar S, Agarwal V, Misra DP, Phadke SR, Kapoor A. Protein protein interaction network analysis of differentially expressed genes to understand involved biological processes in coronary artery disease and its different severity. GENE REPORTS 2018. [DOI: 10.1016/j.genrep.2018.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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41
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Yao C, Chen G, Song C, Keefe J, Mendelson M, Huan T, Sun BB, Laser A, Maranville JC, Wu H, Ho JE, Courchesne P, Lyass A, Larson MG, Gieger C, Graumann J, Johnson AD, Danesh J, Runz H, Hwang SJ, Liu C, Butterworth AS, Suhre K, Levy D. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat Commun 2018; 9:3268. [PMID: 30111768 PMCID: PMC6093935 DOI: 10.1038/s41467-018-05512-x] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 07/09/2018] [Indexed: 01/17/2023] Open
Abstract
Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome's causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment.
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Affiliation(s)
- Chen Yao
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - George Chen
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Ci Song
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
- Department of Medical Sciences, Uppsala University, 75105, Uppsala, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, 75105, Uppsala, Sweden
| | - Joshua Keefe
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Michael Mendelson
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
- Department of Cardiology, Boston Children's Hospital, Boston, 02115, MA, USA
| | - Tianxiao Huan
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Benjamin B Sun
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Annika Laser
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | | | - Hongsheng Wu
- Computer Science and Networking, Wentworth Institute of Technology, Boston, 02115, MA, USA
| | - Jennifer E Ho
- Cardiovascular Research Center and Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, 02114, MA, USA
| | - Paul Courchesne
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Asya Lyass
- Framingham Heart Study, Framingham, 01702, MA, USA
- Department of Mathematics and Statistics, Boston University, Boston, 02115, MA, USA
| | - Martin G Larson
- Framingham Heart Study, Framingham, 01702, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, 02118, MA, USA
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Ludwigstr. 43, D-61231, Bad Nauheim, Germany
| | - Andrew D Johnson
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - John Danesh
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- British Heart Foundation Cambridge Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1RQ, UK
| | - Heiko Runz
- MRL, Merck & Co., Inc, Kenilworth, 07033, NJ, USA
| | - Shih-Jen Hwang
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Chunyu Liu
- Framingham Heart Study, Framingham, 01702, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Adam S Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO 24144, Doha, Qatar
| | - Daniel Levy
- Framingham Heart Study, Framingham, 01702, MA, USA.
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA.
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Lai CQ, Smith CE, Parnell LD, Lee YC, Corella D, Hopkins P, Hidalgo BA, Aslibekyan S, Province MA, Absher D, Arnett DK, Tucker KL, Ordovas JM. Epigenomics and metabolomics reveal the mechanism of the APOA2-saturated fat intake interaction affecting obesity. Am J Clin Nutr 2018; 108:188-200. [PMID: 29901700 PMCID: PMC6454512 DOI: 10.1093/ajcn/nqy081] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 03/28/2018] [Indexed: 12/13/2022] Open
Abstract
Background The putative functional variant -265T>C (rs5082) within the APOA2 promoter has shown consistent interactions with saturated fatty acid (SFA) intake to influence the risk of obesity. Objective The aim of this study was to implement an integrative approach to characterize the molecular basis of this interaction. Design We conducted an epigenome-wide scan on 80 participants carrying either the rs5082 CC or TT genotypes and consuming either a low-SFA (<22 g/d) or high-SFA diet (≥22 g/d), matched for age, sex, BMI, and diabetes status in the Boston Puerto Rican Health Study (BPRHS). We then validated the findings in selected participants in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study (n = 379) and the Framingham Heart Study (FHS) (n = 243). Transcription and metabolomics analyses were conducted to determine the relation between epigenetic status, APOA2 mRNA expression, and blood metabolites. Results In the BPRHS, we identified methylation site cg04436964 as exhibiting significant differences between CC and TT participants consuming a high-SFA diet, but not among those consuming low-SFA. Similar results were observed in the GOLDN Study and the FHS. Additionally, in the FHS, cg04436964 methylation was negatively correlated with APOA2 expression in the blood of participants consuming a high-SFA diet. Furthermore, when consuming a high-SFA diet, CC carriers had lower APOA2 expression than those with the TT genotype. Lastly, metabolomic analysis identified 4 pathways as overrepresented by metabolite differences between CC and TT genotypes with high-SFA intake, including tryptophan and branched-chain amino acid (BCAA) pathways. Interestingly, these pathways were linked to rs5082-specific cg04436964 methylation differences in high-SFA consumers. Conclusions The epigenetic status of the APOA2 regulatory region is associated with SFA intake and APOA2 -265T>C genotype, promoting an APOA2 expression difference between APOA2 genotypes on a high-SFA diet, and modulating BCAA and tryptophan metabolic pathways. These findings identify potential mechanisms by which this highly reproducible gene-diet interaction influences obesity risk, and contribute new insights to ongoing investigations of the relation between SFA and human health. This study was registered at clinicaltrials.gov as NCT03452787.
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Affiliation(s)
- Chao-Qiang Lai
- USDA Agricultural Research Service,Address correspondence to C-QL (e-mail )
| | - Caren E Smith
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA
| | | | - Yu-Chi Lee
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA
| | - Dolores Corella
- Department of Preventive Medicine, University of Valencia and CIBER Physiopathology of Obesity and Nutrition, Valencia, Spain
| | - Paul Hopkins
- Department of Cardiovascular Genetics, University of Utah, Salt Lake City, UT
| | - Bertha A Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama, Birmingham, AL
| | - Stella Aslibekyan
- Department of Epidemiology, School of Public Health, University of Alabama, Birmingham, AL
| | - Michael A Province
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Devin Absher
- Hudson Alpha Institute for Biotechnology, Huntsville, AL
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY
| | - Katherine L Tucker
- Department of Biomedical & Nutritional Sciences, University of Massachusetts Lowell, Lowell, MA
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA,IMDEA Food Institute, CEI UAM + CSIC, Madrid, Spain,Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
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43
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Xu C, Fang J, Shen H, Wang YP, Deng HW. EPS-LASSO: test for high-dimensional regression under extreme phenotype sampling of continuous traits. Bioinformatics 2018; 34:1996-2003. [PMID: 29385408 PMCID: PMC6454442 DOI: 10.1093/bioinformatics/bty042] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 01/20/2018] [Accepted: 01/24/2018] [Indexed: 01/19/2023] Open
Abstract
Motivation Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g. the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. Results We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. Availability and implementation The source code is available at https://github.com/xu1912/EPSLASSO. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chao Xu
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
- Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Jian Fang
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
- Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Yu-Ping Wang
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
- Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
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44
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Mendelson MM, Johannes R, Liu C, Huan T, Yao C, Miao X, Murabito JM, Dupuis J, Levy D, Benjamin EJ, Lin H. Epigenome-Wide Association Study of Soluble Tumor Necrosis Factor Receptor 2 Levels in the Framingham Heart Study. Front Pharmacol 2018; 9:207. [PMID: 29740313 PMCID: PMC5928448 DOI: 10.3389/fphar.2018.00207] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 02/23/2018] [Indexed: 12/24/2022] Open
Abstract
Background: Transmembrane tumor necrosis factor (TNF) receptors are involved in inflammatory, apoptotic, and proliferative processes. In the bloodstream, soluble TNF receptor II (sTNFR2) can modify the inflammatory response of immune cells and is predictive of cardiovascular disease risk. We hypothesize that sTNFR2 is associated with epigenetic modifications of circulating leukocytes, which may relate to the pathophysiology underlying atherogenic risk. Methods: We conducted an epigenome-wide association study of sTNFR2 levels in the Framingham Heart Study Offspring cohort (examination 8; 2005-2008). sTNFR2 was quantitated by enzyme immunoassay and DNA methylation by microarray. The concentration of sTNFR2 was loge-transformed and outliers were excluded. We conducted linear mixed effects models to test the association between sTNFR2 level and methylation at over 400,000 CpGs, adjusting for age, sex, BMI, smoking, imputed cell count, technical covariates, and accounting for familial relatedness. Results: The study sample included 2468 participants (mean age: 67 ± 9 years, 52% women, mean sTNFR2 level 2661 ± 1078 pg/ml). After accounting for multiple testing, we identified 168 CpGs (P < 1.2 × 10-7) that were differentially methylated in relation to sTNFR2. A substantial proportion (27 CpGs; 16%) are in the major histocompatibility complex region and in loci overrepresented for antigen binding molecular functions (P = 1.7 × 10-4) and antigen processing and presentation biological processes (P = 1.3 × 10-8). Identified CpGs are enriched in active regulatory regions and associated with expression of 48 cis-genes (±500 kb) in whole blood (P < 1.1 × 10-5) that coincide with genes identified in GWAS of diseases of immune dysregulation (inflammatory bowel disease, type 1 diabetes, IgA nephropathy). Conclusion: Differentially methylated loci in leukocytes associated with sTNF2 levels reside in active regulatory regions, are overrepresented in antigen processes, and are linked to inflammatory diseases.
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Affiliation(s)
- Michael M Mendelson
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Department of Cardiology, Boston Children's Hospital, Boston, MA, United States.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, United States
| | - Roby Johannes
- Hebrew SeniorLife, Harvard Medical School, Boston, MA, United States
| | - Chunyu Liu
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Tianxiao Huan
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, United States
| | - Chen Yao
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, United States
| | - Xiao Miao
- Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Joanne M Murabito
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Josée Dupuis
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Daniel Levy
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, United States
| | - Emelia J Benjamin
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Section of Cardiovascular Medicine and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.,Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Honghuang Lin
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, United States.,Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
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45
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Koupenova M, Mick E, Corkrey HA, Huan T, Clancy L, Shah R, Benjamin EJ, Levy D, Kurt-Jones EA, Tanriverdi K, Freedman JE. Micro RNAs from DNA Viruses are Found Widely in Plasma in a Large Observational Human Population. Sci Rep 2018; 8:6397. [PMID: 29686252 PMCID: PMC5913337 DOI: 10.1038/s41598-018-24765-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 04/10/2018] [Indexed: 12/19/2022] Open
Abstract
Viral infections associate with disease risk and select families of viruses encode miRNAs that control an efficient viral cycle. The association of viral miRNA expression with disease in a large human population has not been previously explored. We sequenced plasma RNA from 40 participants of the Framingham Heart Study (FHS, Offspring Cohort, Visit 8) and identified 3 viral miRNAs from 3 different human Herpesviridae. These miRNAs were mostly related to viral latency and have not been previously detected in human plasma. Viral miRNA expression was then screened in the plasma of 2763 participants of the remaining cohort utilizing high-throughput RT-qPCR. All 3 viral miRNAs associated with combinations of inflammatory or prothrombotic circulating biomarkers (sTNFRII, IL-6, sICAM1, OPG, P-selectin) but did not associate with hypertension, coronary heart disease or cancer. Using a large observational population, we demonstrate that the presence of select viral miRNAs in the human circulation associate with inflammatory biomarkers and possibly immune response, but fail to associate with overt disease. This study greatly extends smaller singular observations of viral miRNAs in the human circulation and suggests that select viral miRNAs, such as those for latency, may not impact disease manifestation.
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Affiliation(s)
- Milka Koupenova
- University of Massachusetts Medical School, Department of Medicine, Division of Cardiovascular Medicine, Worcester, MA, 01605, USA.
| | - Eric Mick
- University of Massachusetts Medical School, Department of Quantitative Health Sciences, Worcester, MA, 01605, USA
| | - Heather A Corkrey
- University of Massachusetts Medical School, Department of Medicine, Division of Cardiovascular Medicine, Worcester, MA, 01605, USA
| | - Tianxiao Huan
- National Heart, Lung, and Blood Institute, National Institutes of Health (NHLBI) and Boston University's Framingham Heart Institute, Framingham, MA, 01702, USA
- Population Sciences Branch, NHLBI, Bethesda, Maryland, 20824, USA
| | - Lauren Clancy
- University of Massachusetts Medical School, Department of Medicine, Division of Cardiovascular Medicine, Worcester, MA, 01605, USA
| | - Ravi Shah
- Beth Israel Deaconess Medical Center, Cardiovascular Institute, Boston, MA, 02215, USA
| | - Emelia J Benjamin
- Boston University School of Medicine, Department of Medicine, Boston, MA, 02118, USA
- Boston University School of Public Health, Department of Epidemiology, Boston, MA, 02118, USA
- National Heart, Lung, and Blood Institute, National Institutes of Health (NHLBI) and Boston University's Framingham Heart Institute, Framingham, MA, 01702, USA
| | - Daniel Levy
- National Heart, Lung, and Blood Institute, National Institutes of Health (NHLBI) and Boston University's Framingham Heart Institute, Framingham, MA, 01702, USA
- Population Sciences Branch, NHLBI, Bethesda, Maryland, 20824, USA
| | - Evelyn A Kurt-Jones
- University of Massachusetts Medical School, Department of Medicine, Division of Infectious Disease and Immunology, Worcester, MA, 01605, USA
| | - Kahraman Tanriverdi
- University of Massachusetts Medical School, Department of Medicine, Division of Cardiovascular Medicine, Worcester, MA, 01605, USA
| | - Jane E Freedman
- University of Massachusetts Medical School, Department of Medicine, Division of Cardiovascular Medicine, Worcester, MA, 01605, USA
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46
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Huan T, Chen G, Liu C, Bhattacharya A, Rong J, Chen BH, Seshadri S, Tanriverdi K, Freedman JE, Larson MG, Murabito JM, Levy D. Age-associated microRNA expression in human peripheral blood is associated with all-cause mortality and age-related traits. Aging Cell 2018; 17. [PMID: 29044988 PMCID: PMC5770777 DOI: 10.1111/acel.12687] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2017] [Indexed: 01/11/2023] Open
Abstract
Recent studies provide evidence of correlations of DNA methylation and expression of protein‐coding genes with human aging. The relations of microRNA expression with age and age‐related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole‐blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P < 3.3 × 10−4 (Bonferroni‐corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age‐associated mRNA expression possibly driven by age‐associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict ‘microRNA age’ that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Δage) and found that Δage was associated with all‐cause mortality (hazards ratio 1.1 per year difference, P = 4.2 × 10−5 adjusted for sex and chronological age). Additionally, Δage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole‐blood microRNA expression profiling. Age‐associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age‐related diseases.
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Affiliation(s)
- Tianxiao Huan
- The Framingham Heart Study; Framingham MA USA
- The Population Sciences Branch; Division of Intramural Research; National Heart, Lung and Blood Institute; National Institutes of Health; Bethesda MD USA
| | - George Chen
- The Framingham Heart Study; Framingham MA USA
- The Population Sciences Branch; Division of Intramural Research; National Heart, Lung and Blood Institute; National Institutes of Health; Bethesda MD USA
| | - Chunyu Liu
- The Framingham Heart Study; Framingham MA USA
- The Population Sciences Branch; Division of Intramural Research; National Heart, Lung and Blood Institute; National Institutes of Health; Bethesda MD USA
| | - Anindya Bhattacharya
- Department of Computer Science and Engineering; University of California; San Diego CA USA
| | - Jian Rong
- Department of Biostatistics; Boston University School of Public Health; Boston MA USA
| | - Brian H. Chen
- Longitudinal Studies Section; Translational Gerontology Branch; Intramural Research Program; National Institute on Aging; National Institutes of Health; Bethesda MD USA
| | - Sudha Seshadri
- Department of Medicine; Section of General Internal Medicine; Boston University School of Medicine; Boston MA USA
| | - Kahraman Tanriverdi
- Department of Medicine; University of Massachusetts Medical School; Worcester MA USA
| | - Jane E. Freedman
- Department of Medicine; University of Massachusetts Medical School; Worcester MA USA
| | - Martin G. Larson
- The Framingham Heart Study; Framingham MA USA
- Department of Biostatistics; Boston University School of Public Health; Boston MA USA
| | - Joanne M. Murabito
- The Framingham Heart Study; Framingham MA USA
- Department of Medicine; Section of General Internal Medicine; Boston University School of Medicine; Boston MA USA
| | - Daniel Levy
- The Framingham Heart Study; Framingham MA USA
- The Population Sciences Branch; Division of Intramural Research; National Heart, Lung and Blood Institute; National Institutes of Health; Bethesda MD USA
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47
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Huan T, Joehanes R, Schurmann C, Schramm K, Pilling LC, Peters MJ, Mägi R, DeMeo D, O'Connor GT, Ferrucci L, Teumer A, Homuth G, Biffar R, Völker U, Herder C, Waldenberger M, Peters A, Zeilinger S, Metspalu A, Hofman A, Uitterlinden AG, Hernandez DG, Singleton AB, Bandinelli S, Munson PJ, Lin H, Benjamin EJ, Esko T, Grabe HJ, Prokisch H, van Meurs JBJ, Melzer D, Levy D. A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking. Hum Mol Genet 2018; 25:4611-4623. [PMID: 28158590 DOI: 10.1093/hmg/ddw288] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 07/21/2016] [Accepted: 08/25/2016] [Indexed: 01/03/2023] Open
Abstract
Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a meta-analysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that the molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed the expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects.
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Affiliation(s)
- Tianxiao Huan
- Boston University’s Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Roby Joehanes
- Boston University’s Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.,Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.,Genetics of Obesity & Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Katharina Schramm
- Institute of Human Genetics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Luke C Pilling
- Epidemiology and Public Health Group, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Marjolein J Peters
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands
| | - Reedik Mägi
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands
| | | | - George T O'Connor
- Boston University School of Medicine and School of Public Health, Boston, MA, USA
| | - Luigi Ferrucci
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Alexander Teumer
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Reiner Biffar
- Department of Prosthetic Dentistry, Gerostomatology and Dental Materials, Center of Oral Health, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Christian Herder
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Epidemiology II, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Sonja Zeilinger
- Institute of Epidemiology II, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Andres Metspalu
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands
| | - Albert Hofman
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus Medical Center Rotterdam, The Netherlands
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus Medical Center Rotterdam, The Netherlands
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | | | - Peter J Munson
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, MD, USA
| | - Honghuang Lin
- Boston University School of Medicine and School of Public Health, Boston, MA, USA
| | - Emelia J Benjamin
- Boston University’s Framingham Heart Study, Framingham, MA, USA.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands.,Boston University School of Medicine and School of Public Health, Boston, MA, USA.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands
| | - Tõnu Esko
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands.,Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases DZNE, Site Rostock/Greifswald, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands
| | - David Melzer
- Epidemiology and Public Health Group, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Daniel Levy
- Boston University’s Framingham Heart Study, Framingham, MA, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
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48
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Musunuru K, Ingelsson E, Fornage M, Liu P, Murphy AM, Newby LK, Newton-Cheh C, Perez MV, Voora D, Woo D. The Expressed Genome in Cardiovascular Diseases and Stroke: Refinement, Diagnosis, and Prediction: A Scientific Statement From the American Heart Association. ACTA ACUST UNITED AC 2018; 10:HCG.0000000000000037. [PMID: 28760750 DOI: 10.1161/hcg.0000000000000037] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
There have been major advances in our knowledge of the contribution of DNA sequence variations to cardiovascular disease and stroke. However, the inner workings of the body reflect the complex interplay of factors beyond the DNA sequence, including epigenetic modifications, RNA transcripts, proteins, and metabolites, which together can be considered the "expressed genome." The emergence of high-throughput technologies, including epigenomics, transcriptomics, proteomics, and metabolomics, is now making it possible to address the contributions of the expressed genome to cardiovascular disorders. This statement describes how the expressed genome can currently and, in the future, potentially be used to diagnose diseases and to predict who will develop diseases such as coronary artery disease, stroke, heart failure, and arrhythmias.
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49
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Abstract
Studies have pointed out that the expression of genes are highly regulated, which result in a cascade of distinct patterns of coexpression forming a network. Identifying and understanding such patterns is crucial in deciphering molecular mechanisms that underlie the pathophysiology of diseases. With the advance of high throughput assay of messenger RNA (mRNA) and high performance computing, reconstructing such network from molecular data such as gene expression is now possible. This chapter discusses an overview of methods of constructing such networks, practical considerations, and an example.
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Affiliation(s)
- Roby Joehanes
- Hebrew SeniorLife, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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50
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Lin H, Lunetta KL, Zhao Q, Rong J, Benjamin EJ, Mendelson MM, Joehanes R, Levy D, Larson MG, Murabito JM. Transcriptome-wide association study of inflammatory biologic age. Aging (Albany NY) 2017; 9:2288-2301. [PMID: 29135455 PMCID: PMC5723687 DOI: 10.18632/aging.101321] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 11/02/2017] [Indexed: 05/16/2023]
Abstract
Chronic low grade inflammation is a fundamental mechanism of aging. We estimated biologic age using nine biomarkers from diverse inflammatory pathways and we hypothesized that genes associated with inflammatory biological age would provide insights into human aging. In Framingham Offspring Study participants at examination 8 (2005 to 2008), we used the Klemera-Doubal method to estimate inflammatory biologic age and we computed the difference (∆Age) between biologic age and chronologic age. Gene expression in whole blood was measured using the Affymetrix Human Exon 1.0 ST Array. We used linear mixed effect models to test associations between inflammatory ∆Age and gene expression (dependent variable) adjusting for age, sex, imputed cell counts, and technical covariates. Our study sample included 2386 participants (mean age 67A±9 years, 55% women). There were 448 genes significantly were associated with inflammatory ∆Age (P<2.8x10-6), 302 genes were positively associated and 146 genes were negatively associated. Pathway analysis among the identified genes highlighted the NOD-like receptor signaling and ubiquitin mediated proteolysis pathways. In summary, we identified 448 genes that were significantly associated with inflammatory biologic age. Future functional characterization may identify molecular interventions to delay aging and prolong healthspan in older adults.
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Affiliation(s)
- Honghuang Lin
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Kathryn L. Lunetta
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Qiang Zhao
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Jian Rong
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Emelia J. Benjamin
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Section of Cardiovascular Medicine and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Michael M. Mendelson
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Roby Joehanes
- Hebrew SeniorLife, Harvard Medical School, Boston, MA 02115, USA
| | - Daniel Levy
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Martin G. Larson
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Joanne M. Murabito
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
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