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Russo MA, Garaci E, Frustaci A, Fini M, Costantini C, Oikonomou V, Nunzi E, Puccetti P, Romani L. Host-microbe tryptophan partitioning in cardiovascular diseases. Pharmacol Res 2023; 198:106994. [PMID: 37972721 DOI: 10.1016/j.phrs.2023.106994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
The functional interdependencies between the molecular components of a biological process demand for a network medicine platform that integrates systems biology and network science, to explore the interactions among biological components in health and disease. Access to large-scale omics datasets (genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics, etc.) has significantly advanced our opportunity along this direction. Studies utilizing these techniques have begun to provide us with a deeper understanding of how the interaction between the intestinal microbes and their host affects the cardiovascular system in health and disease. Within the framework of a multiomics network approach, we highlight here how tryptophan metabolism may orchestrate the host-microbes interaction in cardiovascular diseases and the implications for precision medicine and therapeutics, including nutritional interventions.
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Affiliation(s)
- Matteo Antonio Russo
- University San Raffaele and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele, 00166 Rome, Italy
| | - Enrico Garaci
- University San Raffaele and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele, 00166 Rome, Italy
| | - Andrea Frustaci
- University San Raffaele and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele, 00166 Rome, Italy
| | - Massimo Fini
- University San Raffaele and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele, 00166 Rome, Italy
| | - Claudio Costantini
- Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Vasileios Oikonomou
- Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Emilia Nunzi
- Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Paolo Puccetti
- Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Luigina Romani
- University San Raffaele and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele, 00166 Rome, Italy; Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy.
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2
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Huang Z, Chen K, Yang X, Cui H, Wu Y, Wang Y, Xia X, Sun H, Xie W, Li H, Zheng R, Sun Y, Han D, Shang H. Spatial metabolomics reveal mechanisms of dexamethasone against pediatric pneumonia. J Pharm Biomed Anal 2023; 229:115369. [PMID: 36996615 DOI: 10.1016/j.jpba.2023.115369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/20/2023] [Accepted: 03/25/2023] [Indexed: 03/29/2023]
Abstract
Currently, drugs are limited to treating pediatric pneumonia in clinical practice. It is urgent to find one new precise prevention and control therapy. The dynamically changing biomarkers during the development of pediatric pneumonia could help diagnose this disease, determine its severity, assess the risk of future events, and guide its treatment. Dexamethasone has been recognized as an effective agent with anti-inflammatory activity. However, its mechanisms against pediatric pneumonia remain unclear. In this study, spatial metabolomics was used to reveal the potential and characteristics of dexamethasone. Specifically, bioinformatics was first applied to find the critical biomarkers of differential expression in pediatric pneumonia. Subsequently, Desorption Electrospray Ionization mass spectrometry imaging-based metabolomics screened the differential metabolites affected by dexamethasone. Then, a gene-metabolite interaction network was built to mark functional correlation pathways for exploring integrated information and core biomarkers related to the pathogenesis and etiology of pediatric pneumonia. Further, these were validated by molecular biology and targeted metabolomics. As a result, genes of Cluster of Differentiation19, Fc fragment of IgG receptor IIb, Cluster of Differentiation 22, B-cell linker, Cluster of Differentiation 79B and metabolites of Triethanolamine, Lysophosphatidylcholine(18:1(9Z)), Phosphatidylcholine(16:0/16:0), phosphatidylethanolamine(O-18:1(1Z)/20:4(5Z,8Z,11Z,14Z)) were identified as the critical biomarkers in pediatric pneumonia. B cell receptor signaling pathway and glycerophospholipid metabolism were integrally analyzed as the main pathways of these biomarkers. The above data were illustrated using a Lipopolysaccharides-induced lung injury juvenile rat model. This work will provide evidence for the precise treatment of pediatric pneumonia.
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3
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Sethi Y, Patel N, Kaka N, Kaiwan O, Kar J, Moinuddin A, Goel A, Chopra H, Cavalu S. Precision Medicine and the future of Cardiovascular Diseases: A Clinically Oriented Comprehensive Review. J Clin Med 2023; 12:1799. [PMID: 36902588 PMCID: PMC10003116 DOI: 10.3390/jcm12051799] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/26/2023] Open
Abstract
Cardiac diseases form the lion's share of the global disease burden, owing to the paradigm shift to non-infectious diseases from infectious ones. The prevalence of CVDs has nearly doubled, increasing from 271 million in 1990 to 523 million in 2019. Additionally, the global trend for the years lived with disability has doubled, increasing from 17.7 million to 34.4 million over the same period. The advent of precision medicine in cardiology has ignited new possibilities for individually personalized, integrative, and patient-centric approaches to disease prevention and treatment, incorporating the standard clinical data with advanced "omics". These data help with the phenotypically adjudicated individualization of treatment. The major objective of this review was to compile the evolving clinically relevant tools of precision medicine that can help with the evidence-based precise individualized management of cardiac diseases with the highest DALY. The field of cardiology is evolving to provide targeted therapy, which is crafted as per the "omics", involving genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics, for deep phenotyping. Research for individualizing therapy in heart diseases with the highest DALY has helped identify novel genes, biomarkers, proteins, and technologies to aid early diagnosis and treatment. Precision medicine has helped in targeted management, allowing early diagnosis, timely precise intervention, and exposure to minimal side effects. Despite these great impacts, overcoming the barriers to implementing precision medicine requires addressing the economic, cultural, technical, and socio-political issues. Precision medicine is proposed to be the future of cardiovascular medicine and holds the potential for a more efficient and personalized approach to the management of cardiovascular diseases, contrary to the standardized blanket approach.
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Affiliation(s)
- Yashendra Sethi
- PearResearch, Dehradun 248001, India
- Department of Medicine, Government Doon Medical College, HNB Uttarakhand Medical Education University, Dehradun 248001, India
| | - Neil Patel
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Nirja Kaka
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Oroshay Kaiwan
- PearResearch, Dehradun 248001, India
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Jill Kar
- PearResearch, Dehradun 248001, India
- Department of Medicine, Lady Hardinge Medical College, New Delhi 110001, India
| | - Arsalan Moinuddin
- Vascular Health Researcher, School of Sports and Exercise, University of Gloucestershire, Cheltenham GL50 4AZ, UK
| | - Ashish Goel
- Department of Medicine, Government Doon Medical College, HNB Uttarakhand Medical Education University, Dehradun 248001, India
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Chitkara University, Punjab 140401, India
| | - Simona Cavalu
- Faculty of Medicine and Pharmacy, University of Oradea, P-ta 1 Decembrie 10, 410087 Oradea, Romania
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4
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Dai H, Younis A, Kong JD, Puce L, Jabbour G, Yuan H, Bragazzi NL. Big Data in Cardiology: State-of-Art and Future Prospects. Front Cardiovasc Med 2022; 9:844296. [PMID: 35433868 PMCID: PMC9010556 DOI: 10.3389/fcvm.2022.844296] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/24/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.
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Affiliation(s)
- Haijiang Dai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Arwa Younis
- Clinical Cardiovascular Research Center, University of Rochester Medical Center, Rochester, New York, NY, United States
| | - Jude Dzevela Kong
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Luca Puce
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Georges Jabbour
- Physical Education Department, College of Education, Qatar University, Doha, Qatar
| | - Hong Yuan
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Hong Yuan
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Postgraduate School of Public Health, Department of Health Sciences, University of Genoa, Genoa, Italy
- Section of Musculoskeletal Disease, Leeds Institute of Molecular Medicine, NIHR Leeds Musculoskeletal Biomedical Research Unit, University of Leeds, Chapel Allerton Hospital, Leeds, United Kingdom
- *Correspondence: Nicola Luigi Bragazzi
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5
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Mittas N, Chatzopoulou F, Kyritsis KA, Papagiannopoulos CI, Theodoroula NF, Papazoglou AS, Karagiannidis E, Sofidis G, Moysidis DV, Stalikas N, Papa A, Chatzidimitriou D, Sianos G, Angelis L, Vizirianakis IS. A Risk-Stratification Machine Learning Framework for the Prediction of Coronary Artery Disease Severity: Insights From the GESS Trial. Front Cardiovasc Med 2022; 8:812182. [PMID: 35118145 PMCID: PMC8804295 DOI: 10.3389/fcvm.2021.812182] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/24/2021] [Indexed: 12/28/2022] Open
Abstract
Our study aims to develop a data-driven framework utilizing heterogenous electronic medical and clinical records and advanced Machine Learning (ML) approaches for: (i) the identification of critical risk factors affecting the complexity of Coronary Artery Disease (CAD), as assessed via the SYNTAX score; and (ii) the development of ML prediction models for accurate estimation of the expected SYNTAX score. We propose a two-part modeling technique separating the process into two distinct phases: (a) a binary classification task for predicting, whether a patient is more likely to present with a non-zero SYNTAX score; and (b) a regression task to predict the expected SYNTAX score accountable to individual patients with a non-zero SYNTAX score. The framework is based on data collected from the GESS trial (NCT03150680) comprising electronic medical and clinical records for 303 adult patients with suspected CAD, having undergone invasive coronary angiography in AHEPA University Hospital of Thessaloniki, Greece. The deployment of the proposed approach demonstrated that atherogenic index of plasma levels, diabetes mellitus and hypertension can be considered as important risk factors for discriminating patients into zero- and non-zero SYNTAX score groups, whereas diastolic and systolic arterial blood pressure, peripheral vascular disease and body mass index can be considered as significant risk factors for providing an accurate estimation of the expected SYNTAX score, given that a patient belongs to the non-zero SYNTAX score group. The experimental findings utilizing the identified set of important risk factors indicate a sufficient prediction performance for the Support Vector Machine model (classification task) with an F-measure score of ~0.71 and the Support Vector Regression model (regression task) with a median absolute error value of ~6.5. The proposed data-driven framework described herein present evidence of the prediction capacity and the potential clinical usefulness of the developed risk-stratification models. However, further experimentation in a larger clinical setting is needed to ensure the practical utility of the presented models in a way to contribute to a more personalized management and counseling of CAD patients.
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Affiliation(s)
- Nikolaos Mittas
- Department of Chemistry, International Hellenic University, Kavala, Greece
| | - Fani Chatzopoulou
- Laboratory of Microbiology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Labnet Laboratories, Thessaloniki, Greece
| | - Konstantinos A. Kyritsis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Nikoleta F. Theodoroula
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas S. Papazoglou
- First Department of Cardiology, AHEPA University General Hospital of Thessaloniki, Thessaloniki, Greece
| | - Efstratios Karagiannidis
- First Department of Cardiology, AHEPA University General Hospital of Thessaloniki, Thessaloniki, Greece
| | - Georgios Sofidis
- First Department of Cardiology, AHEPA University General Hospital of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios V. Moysidis
- First Department of Cardiology, AHEPA University General Hospital of Thessaloniki, Thessaloniki, Greece
| | - Nikolaos Stalikas
- First Department of Cardiology, AHEPA University General Hospital of Thessaloniki, Thessaloniki, Greece
| | - Anna Papa
- Laboratory of Microbiology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Chatzidimitriou
- Laboratory of Microbiology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Sianos
- First Department of Cardiology, AHEPA University General Hospital of Thessaloniki, Thessaloniki, Greece
| | - Lefteris Angelis
- School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioannis S. Vizirianakis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Life and Health Sciences, University of Nicosia, Nicosia, Cyprus
- *Correspondence: Ioannis S. Vizirianakis
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6
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Knox JBL, Svendsen MN. The ethics laboratory: an educational tool for moral learning. INTERNATIONAL JOURNAL OF ETHICS EDUCATION 2022; 7:257-270. [PMCID: PMC9132602 DOI: 10.1007/s40889-022-00142-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/12/2022] [Indexed: 06/08/2023]
Abstract
This article introduces the Ethics Laboratory as an inter-sectorial and cross-disciplinary dialogical forum which can be viewed as an educational tool for moral learning. The Ethics Laboratory represents a platform for the informal, collaborative investigation, in strict confidentiality, of ethical questions that have social consequences and/or legal concerns and bridges boundaries between research communities, institutions and patients. Its methodological structure proposes an experimental, open-ended way of unpacking implied assumptions, underlying values, comparable notions and observations from different professional fields. In connection with a large social science project on precision medicine, we conducted four Ethics Laboratories followed by eight interviews with a selected number of participants. Through these interviews we learnt how this exploratory dialogical forum heighten moral awareness on issues that are shared among stakeholders who work to implement precision medicine in Denmark. Though the framework was developed specifically to foster ethical reflection within precision medicine, its dialogical structure lends itself to other professional areas and can easily be adopted and carried out.
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Affiliation(s)
| | - Mette Nordahl Svendsen
- Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Building 10, 1014 Copenhagen K, Denmark
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7
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Ricciardi C, Cuocolo R, Megna R, Cesarelli M, Petretta M. Machine learning analysis: general features, requirements and cardiovascular applications. Minerva Cardiol Angiol 2021; 70:67-74. [PMID: 33944533 DOI: 10.23736/s2724-5683.21.05637-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Artificial intelligence represents the science which will probably change the future of medicine by solving actually challenging issues. In this special article, the general features of machine learning are discussed. First, a background explanation regarding the division of artificial intelligence, machine learning and deep learning is given and a focus on the structure of machine learning subgroups is shown. The traditional process of a machine learning analysis is described, starting from the collection of data, across features engineering, modelling and till the validation and deployment phase. Due to the several applications of machine learning performed in literature in the last decades and the lack of some guidelines, the need of a standardization for reporting machine learning analysis results emerged. Some possible standards for reporting machine learning results are identified and discussed deeply; these are related to study population (number of subjects), repeatability of the analysis, validation, results, comparison with current practice. The way to the use of machine learning in clinical practice is open and the hope is that, with emerging technology and advanced digital and computational tools, available from hospitalization and subsequently after discharge, it will also be possible, with the help of increasingly powerful hardware, to build assistance strategies useful in clinical practice.
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Affiliation(s)
- Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy -
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Rosario Megna
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | - Mario Cesarelli
- Department of Information Technology and Electrical Engineering, University of Naples Federico II, Naples, Italy.,Bioengineering Unit, Institute of Care and Scientific Research Maugeri, Pavia, Italy
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8
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Kanwar MK, Kilic A, Mehra MR. Machine learning, artificial intelligence and mechanical circulatory support: A primer for clinicians. J Heart Lung Transplant 2021; 40:414-425. [PMID: 33775520 DOI: 10.1016/j.healun.2021.02.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/26/2021] [Accepted: 02/22/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) refers to the ability of machines to perform intelligent tasks, and machine learning (ML) is a subset of AI describing the ability of machines to learn independently and make accurate predictions. The application of AI combined with "big data" from the electronic health records, is poised to impact how we take care of patients. In recent years, an expanding body of literature has been published using ML in cardiovascular health care, including mechanical circulatory support (MCS). This primer article provides an overview for clinicians on relevant concepts of ML and AI, reviews predictive modeling concepts in ML and provides contextual reference to how AI is being adapted in the field of MCS. Lastly, it explains how these methods could be incorporated in the practices of medicine to improve patient outcomes.
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Affiliation(s)
- Manreet K Kanwar
- Cardiovascular Institute at Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Arman Kilic
- Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Mandeep R Mehra
- Brigham and Women's Hospital Heart and Vascular Center and Harvard Medical School, Boston, Massachusetts.
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9
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Khomtchouk BB, Tran DT, Vand KA, Might M, Gozani O, Assimes TL. Cardioinformatics: the nexus of bioinformatics and precision cardiology. Brief Bioinform 2020; 21:2031-2051. [PMID: 31802103 PMCID: PMC7947182 DOI: 10.1093/bib/bbz119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/08/2019] [Accepted: 08/13/2019] [Indexed: 12/12/2022] Open
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.
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Affiliation(s)
- Bohdan B Khomtchouk
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, University of Chicago, Chicago, IL, USA
| | - Diem-Trang Tran
- School of Computing, University of Utah, Salt Lake City, UT, USA
| | | | - Matthew Might
- Hugh Kaul Personalized Medicine Institute, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Or Gozani
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Themistocles L Assimes
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
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10
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The Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project: defining a healthy volunteer cohort. NPJ Genom Med 2020; 5:46. [PMID: 33110626 PMCID: PMC7584615 DOI: 10.1038/s41525-020-00153-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 08/31/2020] [Indexed: 01/04/2023] Open
Abstract
The integration of comprehensive genomic and phenotypic data from diverse ethnic populations offers unprecedented opportunities toward advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases are not representative of the global human population, making variant interpretation challenging, especially in underrepresented populations, such as the North African population. To address this, the Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project launched a study comprising 1000 individuals free of cardiovascular disease (CVD). Here, we present the first 391 Egyptian healthy volunteers recruited to establish a pilot phenotyped control cohort. All individuals underwent detailed clinical investigation, including cardiac magnetic resonance imaging (MRI), and were sequenced using a targeted panel of 174 genes with reported roles in inherited cardiac conditions. We identified 1262 variants in 27 cardiomyopathy genes of which 15.1% were not captured in current global and regional genetic reference databases (here: gnomAD and Great Middle Eastern Variome). The ECCO-GEN project aims at defining the genetic landscape of an understudied population and providing individual-level genetic and phenotypic data to support future studies in CVD and population genetics.
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11
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Zhao C, Li S, Zhang J, Huang Y, Zhang L, Zhao F, Du X, Hou J, Zhang T, Shi C, Wang P, Huo R, Woodman OL, Qin CX, Xu H, Huang L. Current state and future perspective of cardiovascular medicines derived from natural products. Pharmacol Ther 2020; 216:107698. [PMID: 33039419 DOI: 10.1016/j.pharmthera.2020.107698] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/25/2020] [Accepted: 09/28/2020] [Indexed: 02/06/2023]
Abstract
The contribution of natural products (NPs) to cardiovascular medicine has been extensively documented, and many have been used for centuries. Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Over the past 40 years, approximately 50% of newly developed cardiovascular drugs were based on NPs, suggesting that NPs provide essential skeletal structures for the discovery of novel medicines. After a period of lower productivity since the 1990s, NPs have recently regained scientific and commercial attention, leveraging the wealth of knowledge provided by multi-omics, combinatorial biosynthesis, synthetic biology, integrative pharmacology, analytical and computational technologies. In addition, as a crucial part of complementary and alternative medicine, Traditional Chinese Medicine has increasingly drawn attention as an important source of NPs for cardiovascular drug discovery. Given their structural diversity and biological activity NPs are one of the most valuable sources of drugs and drug leads. In this review, we briefly described the characteristics and classification of NPs in CVDs. Then, we provide an up to date summary on the therapeutic potential and the underlying mechanisms of action of NPs in CVDs, and the current view and future prospect of developing safer and more effective cardiovascular drugs based on NPs.
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Affiliation(s)
- Chunhui Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Sen Li
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; College of Chinese Medicinal Materials, Jilin Agricultural University, Changchun 130118, China
| | - Junhong Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Yuanyun Huang
- Biology Department, Cornell University, Ithaca, NY 14850, United States of America
| | - Luoqi Zhang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; College of Chinese Medicinal Materials, Jilin Agricultural University, Changchun 130118, China
| | - Feng Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Xia Du
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; Shaanxi Academy of Traditional Chinese Medicine, Xi'an 710003, China
| | - Jinli Hou
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Tong Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Chenjing Shi
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Ping Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Ruili Huo
- China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Owen L Woodman
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC 3800, Australia
| | - Cheng Xue Qin
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC 3800, Australia; School of Pharmaceutical Science, Shandong University, Shandong 250100, China; Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong 250100, China.
| | - Haiyu Xu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Luqi Huang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; China Academy of Chinese Medical Sciences, Beijing 100700, China.
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12
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Arrell DK, Rosenow CS, Yamada S, Behfar A, Terzic A. Cardiopoietic stem cell therapy restores infarction-altered cardiac proteome. NPJ Regen Med 2020; 5:5. [PMID: 32194990 PMCID: PMC7067830 DOI: 10.1038/s41536-020-0091-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 02/14/2020] [Indexed: 12/20/2022] Open
Abstract
Cardiopoietic stem cells have reached advanced clinical testing for ischemic heart failure. To profile their molecular influence on recipient hearts, systems proteomics was here applied in a chronic model of infarction randomized with and without human cardiopoietic stem cell treatment. Multidimensional label-free tandem mass spectrometry resolved and quantified 3987 proteins constituting the cardiac proteome. Infarction altered 450 proteins, reduced to 283 by stem cell treatment. Notably, cell therapy non-stochastically reversed a majority of infarction-provoked changes, remediating 85% of disease-affected protein clusters. Pathway and network analysis decoded functional reorganization, distinguished by prioritization of vasculogenesis, cardiac development, organ regeneration, and differentiation. Subproteome restoration nullified adverse ischemic effects, validated by echo-/electro-cardiographic documentation of improved cardiac chamber size, reduced QT prolongation and augmented ejection fraction post-cell therapy. Collectively, cardiopoietic stem cell intervention transitioned infarcted hearts from a cardiomyopathic trajectory towards pre-disease. Systems proteomics thus offers utility to delineate and interpret complex molecular regenerative outcomes.
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Affiliation(s)
- D. Kent Arrell
- Center for Regenerative Medicine, Mayo Clinic, Rochester, MN USA
- Marriott Heart Disease Research Program, Mayo Clinic, Rochester, MN USA
- Van Cleve Cardiac Regenerative Medicine Program, Mayo Clinic, Rochester, MN USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN USA
| | - Christian S. Rosenow
- Center for Regenerative Medicine, Mayo Clinic, Rochester, MN USA
- Marriott Heart Disease Research Program, Mayo Clinic, Rochester, MN USA
- Van Cleve Cardiac Regenerative Medicine Program, Mayo Clinic, Rochester, MN USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
| | - Satsuki Yamada
- Center for Regenerative Medicine, Mayo Clinic, Rochester, MN USA
- Marriott Heart Disease Research Program, Mayo Clinic, Rochester, MN USA
- Van Cleve Cardiac Regenerative Medicine Program, Mayo Clinic, Rochester, MN USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
- Division of Geriatric Medicine & Gerontology, Department of Medicine, Mayo Clinic, Rochester, MN USA
| | - Atta Behfar
- Center for Regenerative Medicine, Mayo Clinic, Rochester, MN USA
- Marriott Heart Disease Research Program, Mayo Clinic, Rochester, MN USA
- Van Cleve Cardiac Regenerative Medicine Program, Mayo Clinic, Rochester, MN USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN USA
| | - Andre Terzic
- Center for Regenerative Medicine, Mayo Clinic, Rochester, MN USA
- Marriott Heart Disease Research Program, Mayo Clinic, Rochester, MN USA
- Van Cleve Cardiac Regenerative Medicine Program, Mayo Clinic, Rochester, MN USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN USA
- Department of Medical Genetics, Mayo Clinic, Rochester, MN USA
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13
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Vagnozzi RJ, Pfleger J, Sadayappan S. Basic Cardiovascular Sciences Scientific Sessions 2019: Integrative Approaches to Complex Cardiovascular Diseases. Circ Res 2019; 125:924-931. [PMID: 31647770 DOI: 10.1161/circresaha.119.315977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Ronald J Vagnozzi
- From the Department of Pediatrics, Division of Molecular Cardiovascular Biology, Cincinnati Children's Hospital, OH (R.J.V.)
| | - Jessica Pfleger
- Center for Translational Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA (J.P.)
| | - Sakthivel Sadayappan
- Department of Internal Medicine, Heart, Lung and Vascular Institute, University of Cincinnati, OH (S.S.)
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14
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Leon-Mimila P, Wang J, Huertas-Vazquez A. Relevance of Multi-Omics Studies in Cardiovascular Diseases. Front Cardiovasc Med 2019; 6:91. [PMID: 31380393 PMCID: PMC6656333 DOI: 10.3389/fcvm.2019.00091] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Accepted: 06/19/2019] [Indexed: 12/21/2022] Open
Abstract
Cardiovascular diseases are the leading cause of death around the world. Despite the larger number of genes and loci identified, the precise mechanisms by which these genes influence risk of cardiovascular disease is not well understood. Recent advances in the development and optimization of high-throughput technologies for the generation of “omics data” have provided a deeper understanding of the processes and dynamic interactions involved in human diseases. However, the integrative analysis of “omics” data is not straightforward and represents several logistic and computational challenges. In spite of these difficulties, several studies have successfully applied integrative genomics approaches for the investigation of novel mechanisms and plasma biomarkers involved in cardiovascular diseases. In this review, we summarized recent studies aimed to understand the molecular framework of these diseases using multi-omics data from mice and humans. We discuss examples of omics studies for cardiovascular diseases focused on the integration of genomics, epigenomics, transcriptomics, and proteomics. This review also describes current gaps in the study of complex diseases using systems genetics approaches as well as potential limitations and future directions of this emerging field.
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Affiliation(s)
- Paola Leon-Mimila
- Division of Cardiology, David Geffen School of Medicine, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jessica Wang
- Division of Cardiology, David Geffen School of Medicine, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Adriana Huertas-Vazquez
- Division of Cardiology, David Geffen School of Medicine, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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15
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Vizirianakis IS, Miliotou AN, Mystridis GA, Andriotis EG, Andreadis II, Papadopoulou LC, Fatouros DG. Tackling pharmacological response heterogeneity by PBPK modeling to advance precision medicine productivity of nanotechnology and genomics therapeutics. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2019. [DOI: 10.1080/23808993.2019.1605828] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Ioannis S. Vizirianakis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Androulla N. Miliotou
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George A. Mystridis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eleftherios G. Andriotis
- Laboratory of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioannis I. Andreadis
- Laboratory of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lefkothea C. Papadopoulou
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios G. Fatouros
- Laboratory of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
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16
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Singh A, Dai DL, Ioannou K, Chen V, Lam KK, Hollander Z, Wilson-McManus JE, Assadian S, Toma M, Ng R, Virani S, Ignaszewski A, Tebbutt S, Bennett M, McManus BM. Ensembling Electrical and Proteogenomics Biomarkers for Improved Prediction of Cardiac-Related 3-Month Hospitalizations: A Pilot Study. Can J Cardiol 2019; 35:471-479. [DOI: 10.1016/j.cjca.2018.12.039] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 12/29/2018] [Accepted: 12/30/2018] [Indexed: 01/29/2023] Open
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Chung NC, Mirza B, Choi H, Wang J, Wang D, Ping P, Wang W. Unsupervised classification of multi-omics data during cardiac remodeling using deep learning. Methods 2019; 166:66-73. [PMID: 30853547 DOI: 10.1016/j.ymeth.2019.03.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 03/04/2019] [Indexed: 12/20/2022] Open
Abstract
Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.
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Affiliation(s)
- Neo Christopher Chung
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.
| | - Bilal Mirza
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Howard Choi
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jie Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Ding Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Peipei Ping
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine (Cardiology), University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Wei Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA.
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18
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Affiliation(s)
- Ning Ma
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sophia L Zhang
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Division of Cardiology, Department of Medicine, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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19
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Lau E, Paik DT, Wu JC. Systems-Wide Approaches in Induced Pluripotent Stem Cell Models. ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE 2018; 14:395-419. [PMID: 30379619 DOI: 10.1146/annurev-pathmechdis-012418-013046] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Human induced pluripotent stem cells (iPSCs) provide a renewable supply of patient-specific and tissue-specific cells for cellular and molecular studies of disease mechanisms. Combined with advances in various omics technologies, iPSC models can be used to profile the expression of genes, transcripts, proteins, and metabolites in relevant tissues. In the past 2 years, large panels of iPSC lines have been derived from hundreds of genetically heterogeneous individuals, further enabling genome-wide mapping to identify coexpression networks and elucidate gene regulatory networks. Here, we review recent developments in omics profiling of various molecular phenotypes and the emergence of human iPSCs as a systems biology model of human diseases.
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Affiliation(s)
- Edward Lau
- Stanford Cardiovascular Institute, and Department of Medicine, Division of Cardiology, Stanford University, Stanford, California 94305, USA;
| | - David T Paik
- Stanford Cardiovascular Institute, and Department of Medicine, Division of Cardiology, Stanford University, Stanford, California 94305, USA;
| | - Joseph C Wu
- Stanford Cardiovascular Institute, and Department of Medicine, Division of Cardiology, Stanford University, Stanford, California 94305, USA; .,Department of Radiology, Stanford University, Stanford, California 94305, USA
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20
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Affiliation(s)
- James W McNamara
- From the Department of Internal Medicine, Heart, Lung and Vascular Institute, University of Cincinnati, OH (J.W.M., S.S.)
| | - Kelly M Grimes
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, OH (K.M.G.)
| | - Sakthivel Sadayappan
- From the Department of Internal Medicine, Heart, Lung and Vascular Institute, University of Cincinnati, OH (J.W.M., S.S.)
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21
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Personalised Medicine: The Odyssey from Hope to Practice. J Pers Med 2018; 8:jpm8040031. [PMID: 30248964 PMCID: PMC6313378 DOI: 10.3390/jpm8040031] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 09/17/2018] [Accepted: 09/18/2018] [Indexed: 01/08/2023] Open
Abstract
In this endeavour, inspired by the Odyssey, we aim to embark with the reader on a journey on a ship from Troy to Ithaca, coursing through the history of the momentous events and achievements that paved the way for personalised medicine. We will set sail amidst important genetic discoveries, beginning with the discovery of the first human genome, and voyage through the projects that contributed to the progress of pharmacogenomic studies. Concurrently, we will propose methods to overcome the obstacles that are slowing the potential full implementation of accumulated knowledge into everyday practice. This journey aims to reflect on the frontiers of current genetic knowledge and the practical use of this knowledge in preventive, diagnostic and pharmacogenomic approaches to directly impact the socio-economic aspects of public health.
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Abstract
PURPOSE OF REVIEW Precision medicine is the concept of disease treatment and prevention using an individual's genomic profile in addition to personal and environmental factors. This review outlines examples of new biomarker strategies that enable the practice of precision cardiovascular medicine. RECENT FINDINGS Although commonly attributed to identifying causative genetic variants, mono-genetic causes of cardiovascular diseases (CVD) are not common and largely focused on lipoprotein analyses. Nevertheless, rare clinical presentations in families with extreme phenotypes can sometimes identify novel pathways that can serve as therapeutic targets, such as the discovery of PCSK9 inhibitors for familial hypercholesterolemia or small molecular inhibitors of myosin ATPase activities for hypertrophic cardiomyopathy. Polygenetic risks scores can also identify high-risk cohorts before their clinical manifestations. Novel metabolomic insights can also lead to unexpected modulators of CVD susceptibility, such as nutrient-induced gut microbiota-derived metabolic pathways. SUMMARY Adequate knowledge systems and data infrastructure are necessary for clinicians to take into account both genetic and environmental factors to operationalize precision medicine and to prevent CVD.
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