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Infante T, Del Viscovo L, De Rimini ML, Padula S, Caso P, Napoli C. Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease. J Atheroscler Thromb 2020; 27:279-302. [PMID: 31723086 PMCID: PMC7192819 DOI: 10.5551/jat.52407] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 09/24/2019] [Indexed: 12/13/2022] Open
Abstract
Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.
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Affiliation(s)
- Teresa Infante
- Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Luca Del Viscovo
- Department of Precision Medicine, Section of Diagnostic Imaging, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | | | - Sergio Padula
- Department of Cardiology, A.O.R.N. Dei Colli, Monaldi Hospital, Naples, Italy
| | - Pio Caso
- Department of Cardiology, A.O.R.N. Dei Colli, Monaldi Hospital, Naples, Italy
| | - Claudio Napoli
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
- IRCCS SDN, Naples, Italy
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2
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Yan S. Integrative analysis of promising molecular biomarkers and pathways for coronary artery disease using WGCNA and MetaDE methods. Mol Med Rep 2018; 18:2789-2797. [PMID: 30015926 PMCID: PMC6102698 DOI: 10.3892/mmr.2018.9277] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 05/31/2018] [Indexed: 01/03/2023] Open
Abstract
The present study aimed to examine the molecular mechanisms of coronary artery disease (CAD). A total of four microarray datasets (training dataset no. GSE12288; validation dataset nos. GSE20680, GSE20681 and GSE42148) were downloaded from the Gene Expression Omnibus database, which included CAD and healthy samples. Weighted gene co-expression network analysis was applied to identify highly preserved modules across the four datasets. Differentially expressed genes (DEGs) with significant consistency in the four datasets were selected using the MetaDE method. The overlapping genes amongst the DEGs with significant consistency and in the preserved modules were used to construct a protein-protein interaction (PPI) network, followed by functional enrichment analysis. A total of 11 modules were established in the training dataset, and five of them were highly preserved across all four datasets, including 873 genes. There was a total of 836 DEGs with significant consistency in the four datasets. A total of 177 overlapping genes were selected, with which a PPI network was constructed. The top five genes of the PPI network were identified based on their degrees: LCK proto-oncogene, Src family tyrosine kinase (LCK), euchromatic histone lysine methyltransferase 2 (EHMT2), inosine monophosphate dehydrogenase 2 (IMPDH2), protein phosphatase 4 catalytic subunit (PPP4C) and ζ-chain of T-cell receptor associated protein kinase 70 (ZAP70). Genes in the PPI network were significantly involved in a number of Kyoto Encyclopedia Genes and Genomes pathways, including the ‘natural killer cell mediated cytotoxicity’, ‘primary immunodeficiency’ and ‘Fc gamma R-mediated phagocytosis’ pathways. LCK, EHMT2, IMPDH2, PPP4C and ZAP70 are suggested as promising molecular biomarkers for CAD. The ‘natural killer cell mediated cytotoxicity’, ‘primary immunodeficiency’ and ‘Fc gamma R-mediated phagocytosis’ pathways may serve important roles in CAD.
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Affiliation(s)
- Shilin Yan
- Department of Cardiology, Yangling Demonstration Zone Hospital, Xianyang, Shaanxi 712100, P.R. China
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6
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Zhao X, Luan YZ, Zuo X, Chen YD, Qin J, Jin L, Tan Y, Lin M, Zhang N, Liang Y, Rao SQ. Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data. GENOMICS PROTEOMICS & BIOINFORMATICS 2016; 14:349-356. [PMID: 27965104 PMCID: PMC5200919 DOI: 10.1016/j.gpb.2016.04.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Revised: 03/30/2016] [Accepted: 04/10/2016] [Indexed: 02/06/2023]
Abstract
Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer’s disease, non-alcoholic fatty liver disease, and Huntington’s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer’s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer’s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.
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Affiliation(s)
- Xiang Zhao
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Yi-Zhao Luan
- School of Life Sciences, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiaoyu Zuo
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Ye-Da Chen
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Jiheng Qin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Lv Jin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Yiqing Tan
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Meihua Lin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Naizun Zhang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yan Liang
- Maoming People's Hospital, Maoming 525000, China
| | - Shao-Qi Rao
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China; Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Bennett L, Kittas A, Muirhead G, Papageorgiou LG, Tsoka S. Detection of composite communities in multiplex biological networks. Sci Rep 2015; 5:10345. [PMID: 26012716 PMCID: PMC4446847 DOI: 10.1038/srep10345] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 03/26/2015] [Indexed: 12/23/2022] Open
Abstract
The detection of community structure is a widely accepted means of investigating the
principles governing biological systems. Recent efforts are exploring ways in which
multiple data sources can be integrated to generate a more comprehensive model of
cellular interactions, leading to the detection of more biologically relevant
communities. In this work, we propose a mathematical programming model to cluster
multiplex biological networks, i.e. multiple network slices, each with a different
interaction type, to determine a single representative partition of composite
communities. Our method, known as SimMod, is evaluated through its application to
yeast networks of physical, genetic and co-expression interactions. A comparative
analysis involving partitions of the individual networks, partitions of aggregated
networks and partitions generated by similar methods from the literature highlights
the ability of SimMod to identify functionally enriched modules. It is further shown
that SimMod offers enhanced results when compared to existing approaches without the
need to train on known cellular interactions.
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Affiliation(s)
- Laura Bennett
- Centre for Process Systems Engineering, Department of Chemical Engineering,University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Aristotelis Kittas
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom
| | - Gareth Muirhead
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom
| | - Lazaros G Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering,University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Sophia Tsoka
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom
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Mäkinen VP, Civelek M, Meng Q, Zhang B, Zhu J, Levian C, Huan T, Segrè AV, Ghosh S, Vivar J, Nikpay M, Stewart AFR, Nelson CP, Willenborg C, Erdmann J, Blakenberg S, O'Donnell CJ, März W, Laaksonen R, Epstein SE, Kathiresan S, Shah SH, Hazen SL, Reilly MP, Lusis AJ, Samani NJ, Schunkert H, Quertermous T, McPherson R, Yang X, Assimes TL. Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease. PLoS Genet 2014; 10:e1004502. [PMID: 25033284 PMCID: PMC4102418 DOI: 10.1371/journal.pgen.1004502] [Citation(s) in RCA: 154] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 05/27/2014] [Indexed: 12/13/2022] Open
Abstract
The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions. Sudden death due to heart attack ranks among the top causes of death in the world, and family studies have shown that genetics has a substantial effect on heart disease risk. Recent studies suggest that multiple genetic factors each with modest effects are necessary for the development of CAD, but the genes and molecular processes involved remain poorly understood. We conducted an integrative genomics study where we used the information of gene-gene interactions to capture groups of genes that are most likely to increase heart disease risk. We not only confirmed the importance of several known CAD risk processes such as the metabolism and transport of cholesterol, immune response, and blood coagulation, but also revealed many novel processes such as neuroprotection, cell cycle, and proteolysis that were not previously implicated in CAD. In particular, we highlight several genes such as GLO1 with key regulatory roles within these processes not detected by the first wave of genetic analyses. These results highlight the value of integrating population genetic data with diverse resources that functionally annotate the human genome. Such integration facilitates the identification of novel molecular processes involved in the pathogenesis of CAD as well as potential novel targets for the development of efficacious therapeutic interventions.
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Affiliation(s)
- Ville-Petteri Mäkinen
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- School of Molecular and Biomedical Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Mete Civelek
- Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Qingying Meng
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Candace Levian
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Tianxiao Huan
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Ayellet V. Segrè
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Sujoy Ghosh
- Department of Cardiovascular and Metabolic Research, Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, United States of America
- Program in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore
| | - Juan Vivar
- Department of Cardiovascular and Metabolic Research, Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, United States of America
| | - Majid Nikpay
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Alexandre F. R. Stewart
- John and Jennifer Ruddy Canadian Cardiovascular Research Center, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- National Institute for Health Research (NIHR) Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Christina Willenborg
- Institut für Integrative und Experimentelle Genomik, University of Lübeck, Lübeck, Germany
| | - Jeanette Erdmann
- Institut für Integrative und Experimentelle Genomik, University of Lübeck, Lübeck, Germany
- DZHK (German Research Centre for Cardiovascular Research), partner site Hamburg, Kiel, Lübeck, Germany
| | - Stefan Blakenberg
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Christopher J. O'Donnell
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
- Cardiology Division, Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Winfried März
- Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany
- Synlab Academy, Mannheim, Germany
| | - Reijo Laaksonen
- Science Center, Tampere University Hospital, Tampere, Finland
| | - Stephen E. Epstein
- Cardiovascular Research Institute, Washington Hospital Center, Washington, D.C., United States of America
| | - Sekar Kathiresan
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
- Cardiology Division, Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Svati H. Shah
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | | | - Muredach P. Reilly
- Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | | | - Aldons J. Lusis
- Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- National Institute for Health Research (NIHR) Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Heribert Schunkert
- DZHK (German Research Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Thomas Quertermous
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Ruth McPherson
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
- * E-mail: (XY); (TLA)
| | - Themistocles L. Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
- * E-mail: (XY); (TLA)
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