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Mohammadi-Shemirani P, Sood T, Paré G. From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators. Curr Atheroscler Rep 2023; 25:55-65. [PMID: 36595202 PMCID: PMC9807989 DOI: 10.1007/s11883-022-01078-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE OF REVIEW 'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed. RECENT FINDINGS Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
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Affiliation(s)
- Pedrum Mohammadi-Shemirani
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
| | - Tushar Sood
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON Canada
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Cervantes-Gracia K, Chahwan R, Husi H. Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach. Front Genet 2022; 13:828786. [PMID: 35186042 PMCID: PMC8855827 DOI: 10.3389/fgene.2022.828786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/12/2022] [Indexed: 12/24/2022] Open
Abstract
The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.
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Affiliation(s)
| | - Richard Chahwan
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- *Correspondence: Richard Chahwan, ; Holger Husi,
| | - Holger Husi
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- Division of Biomedical Sciences, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom
- *Correspondence: Richard Chahwan, ; Holger Husi,
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Lim SY, Selvaraji S, Lau H, Li SFY. Application of omics beyond the central dogma in coronary heart disease research: A bibliometric study and literature review. Comput Biol Med 2022; 140:105069. [PMID: 34847384 DOI: 10.1016/j.compbiomed.2021.105069] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 12/12/2022]
Abstract
Despite remarkable progress in disease diagnosis and treatment, coronary heart disease (CHD) remains the number one leading cause of death worldwide. Many practical challenges still faced in clinical settings necessitates the pursuit of omics studies to identify alternative/orthogonal biomarkers, as well as to discover novel insights into disease mechanisms. Albeit relatively nascent as compared to the omics frontrunners (genomics, transcriptomics, and proteomics), omics beyond the central dogma (OBCD; e.g., metabolomics, lipidomics, glycomics, and metallomics) have undeniable contributions and prospects in CHD research. In this bibliometric study, we characterised the global trends in publication/citation outputs, collaborations, and research hotspots concerning OBCD-CHD, with a focus on the more prolific fields of metabolomics and lipidomics. As for glycomics and metallomics, there were insufficient publication records on their applications in CHD research for quantitative bibliometrics analysis. Thus, we reviewed their applications in health/disease research in general, discussed and justified their potential in CHD research, and suggested important/promising research avenues. By summarising evidence obtained both quantitatively and qualitatively, this study offers a first and comprehensive picture of OBCD applications in CHD, facilitating the establishment of future research directions.
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Affiliation(s)
- Si Ying Lim
- Integrative Sciences & Engineering Programme, NUS Graduate School, National University of Singapore, University Hall, Tan Chin Tuan Wing, Singapore 119077, Singapore; Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
| | - Sharmelee Selvaraji
- Integrative Sciences & Engineering Programme, NUS Graduate School, National University of Singapore, University Hall, Tan Chin Tuan Wing, Singapore 119077, Singapore; Department of Physiology, Yong Loo Lin School of Medicine, 2 Medical Drive MD9, National University of Singapore, Singapore 117593, Singapore
| | - Hazel Lau
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
| | - Sam Fong Yau Li
- Integrative Sciences & Engineering Programme, NUS Graduate School, National University of Singapore, University Hall, Tan Chin Tuan Wing, Singapore 119077, Singapore; Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore.
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Lee T, Lee H. Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis. Biomedicines 2021; 9:biomedicines9111525. [PMID: 34829754 PMCID: PMC8614900 DOI: 10.3390/biomedicines9111525] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 01/09/2023] Open
Abstract
Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
- Correspondence: ; Tel.: +82-62-715-2213
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Climaco Pinto R, Dehghan A, Barros AS, Graça G, Diaz SO, Leite-Moreira A. Clinical Research in Cardiovascular Disease using Metabolomics. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11648-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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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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Shen L, Shen K, Bai J, Wang J, Singla RK, Shen B. Data-driven microbiota biomarker discovery for personalized drug therapy of cardiovascular disease. Pharmacol Res 2020; 161:105225. [PMID: 33007417 DOI: 10.1016/j.phrs.2020.105225] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 02/07/2023]
Abstract
Cardiovascular disease (CVD) is the most wide-spread disorder all over the world. The personalized and precision diagnosis, treatment and prevention of CVD is still a challenge. With the developing of metagenome sequencing technologies and the paradigm shifting to data-driven discovery in life science, the computer aided microbiota biomarker discovery for CVD is becoming reality. We here summarize the data resources, knowledgebases and computational models available for CVD microbiota biomarker discovery, and review the present status of the findings about the microbiota patterns associated with the therapeutic effects on CVD. The future challenges and opportunities of the translational informatics on the personalized drug usages in CVD diagnosis, prognosis and treatment are also discussed.
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Affiliation(s)
- Li Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Ke Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jinwei Bai
- Library of West-China Hospital, Sichuan University, Chengdu 610041, China
| | - Jiao Wang
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Rajeev K Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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A Genomic Approach to Characterize the Vulnerable Patient – a Clinical Update. JOURNAL OF INTERDISCIPLINARY MEDICINE 2019. [DOI: 10.2478/jim-2019-0023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
Atherosclerosis is the elemental precondition for any cardiovascular disease and the predominant cause of ischemic heart disease that often leads to myocardial infarction. Systemic risk factors play an important role in the starting and progression of atherosclerosis. The complexity of the disease is caused by its multifactorial origin. Besides the traditional risk factors, genetic predisposition is also a strong risk factor. Many studies have intensively researched cardioprotective drugs, which can relieve myocardial ischemia and reperfusion injury, thereby reducing infarct size. A better understanding of abnormal epigenetic pathways in the myocardial pathology may result in new treatment options. Individualized therapy based on genome sequencing is important for an effective future medical treatment. Studies based on multiomics help to better understand the pathophysiological mechanism of several diseases at a molecular level. Epigenomic, transcriptomic, proteomic, and metabolomic research may be essential in detecting the pathological phenotype of myocardial ischemia and ischemic heart failure.
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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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Abstract
Extensive research demonstrates unequivocally that nutrition plays a fundamental role in maintaining health and preventing disease. In parallel nutrition research provides evidence that the risks and benefits of diet and lifestyle choices do not affect people equally, as people are inherently variable in their responses to nutrition and associated interventions to maintain health and prevent disease. To simplify the inherent complexity of human subjects and their nutrition, with the aim of managing expectations for dietary guidance required to ensure healthy populations and individuals, nutrition researchers often seek to group individuals based on commonly used criteria. This strategy relies on demonstrating meaningful conclusions based on comparison of group mean responses of assigned groups. Such studies are often confounded by the heterogeneous nutrition response. Commonly used criteria applied in grouping study populations and individuals to identify mechanisms and determinants of responses to nutrition often contribute to the problem of interpreting the results of group comparisons. Challenges of interpreting the group mean using diverse populations will be discussed with respect to studies in human subjects, in vivo and in vitro model systems. Future advances in nutrition research to tackle inter-individual variation require a coordinated approach from funders, learned societies, nutrition scientists, publishers and reviewers of the scientific literature. This will be essential to develop and implement improved study design, data recording, analysis and reporting to facilitate more insightful interpretation of the group mean with respect to population diversity and the heterogeneous nutrition response.
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Liang W, Chen J, Li L, Li M, Wei X, Tan B, Shang Y, Fan G, Wang W, Liu W. Conductive Hydrogen Sulfide-Releasing Hydrogel Encapsulating ADSCs for Myocardial Infarction Treatment. ACS APPLIED MATERIALS & INTERFACES 2019; 11:14619-14629. [PMID: 30939870 DOI: 10.1021/acsami.9b01886] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Hydrogen sulfide (H2S) exhibits extensive protective actions in cardiovascular systems, such as anti-inflammatory and stimulating angiogenesis, but its therapeutic potential is severely discounted by the short half-life and the poorly controlled releasing behavior. Herein, we developed a macromolecular H2S prodrug by grafting 2-aminopyridine-5-thiocarboxamide (a small-molecule H2S donor) on partially oxidized alginate (ALG-CHO) to mimic the slow and continuous release of endogenous H2S. In addition, tetraaniline (a conductive oligomer) and adipose-derived stem cells (ADSCs) were introduced to form a stem cell-loaded conductive H2S-releasing hydrogel through the Schiff base reaction between ALG-CHO and gelatin. The hydrogel exhibited adhesive property to ensure a stable anchoring to the wet and beating hearts. After myocardial injection, longer ADSCs retention period and elevated sulfide concentration in rat myocardium were demonstrated, accompanied by upregulation of cardiac-related mRNA (Cx43, α-SMA, and cTnT) and angiogenic factors (VEGFA and Ang-1) and downregulation of inflammatory factors (tumor necrosis factor-α). Echocardiography and histological analysis strongly demonstrated an increase in the ejection fraction value and smaller infarction size, suggesting a remarkable improvement of the cardiac functions of Sprague-Dawley rats. The ADSC-loaded conductive hydrogen sulfide-releasing hydrogel dramatically promoted the therapeutic effects, offering a promising therapeutic strategy for treating myocardial infarction.
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Affiliation(s)
- Wei Liang
- School of Materials Science and Engineering, Tianjin Key Laboratory of Composite and Functional Materials , Tianjin University , Tianjin 300072 , China
| | - Jingrui Chen
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
- Tianjin State Key Laboratory of Modern Chinese Medicine , Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
| | - Lingyan Li
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
- Tianjin State Key Laboratory of Modern Chinese Medicine , Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
| | - Min Li
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
- Tianjin State Key Laboratory of Modern Chinese Medicine , Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
| | - Xiaojuan Wei
- Institute of Microsurgery on Extremities , Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University , Shanghai 200233 , China
| | - Baoyu Tan
- School of Materials Science and Engineering, Tianjin Key Laboratory of Composite and Functional Materials , Tianjin University , Tianjin 300072 , China
| | - Yingying Shang
- School of Materials Science and Engineering, Tianjin Key Laboratory of Composite and Functional Materials , Tianjin University , Tianjin 300072 , China
| | - Guanwei Fan
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
- Tianjin State Key Laboratory of Modern Chinese Medicine , Tianjin University of Traditional Chinese Medicine , Tianjin 300193 , China
| | - Wei Wang
- School of Materials Science and Engineering, Tianjin Key Laboratory of Composite and Functional Materials , Tianjin University , Tianjin 300072 , China
| | - Wenguang Liu
- School of Materials Science and Engineering, Tianjin Key Laboratory of Composite and Functional Materials , Tianjin University , Tianjin 300072 , China
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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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