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Modeling Mitochondrial Dysfunction in Cardiovascular Disease
The National Heart, Lung, and Blood Institute (NHLBI) convened a Working Group meeting of cardiologists, clinical investigators, and experts in mitochondrial biology and computational modeling of mitochondrial function on July 13, 2007 in Bethesda, Maryland. The purpose of the Working Group meeting on Modeling Mitochondrial Dysfunction in Cardiovascular Disease (CVD) was to advise the NHLBI on new research opportunities for improved understanding of the role of mitochondria in cardiovascular dysfunction and adaptation (e.g. ischemic injury and protection, heart failure, congenital conditions). The goal was to identify opportunities to strengthen mitochondrial research and model development as well as to facilitate the sharing of resources and study interoperability. The meeting responds to the NHLBI Strategic Plan Goals 1 and 2 to improve understanding of the molecular and physiologic basis of health and disease as well as the clinical mechanisms of disease, and thereby enable better prevention, diagnosis, and treatment. (NHLBI Strategic Plan)
The workshop began with an overview of the NHLBI’s “Workshop on the Mitochondrial Proteome” held the day before. It reported on the: (1) increased recognition that alterations in the mitochondrial proteome confer cell and organ phenotypes; (2) urgent need expressed by the community for metrics to identify proteins as either integral to the mitochondria or merely associated with the organelle; (3) importance of improved annotation and curation of mitochondrial protein and function databases; and (4) need for computer models to integrate the mitochondrial proteome with its function in biogenesis, metabolism, apoptosis, and stress sensing.
In follow-up of the above, discussions of the Working Group on Modeling Mitochondria Dysfunction in CVD revolved around three primary foci: (1) the role of mitochondria in cardiovascular diseases and stress responses/adaptations; (2) the role of mitochondria in non-cardiovascular diseases, and (3) computational tools to study mitochondria. Everyone agreed that mitochondria are an excellent model system for epigenomic investigations, through which to improve the understanding of complex biological processes underlying health and disease. The following areas of research and opportunity were identified.
- The need to advance our understanding of mitochondrial function in the normal and diseased condition using a systems biology approach. Mitochondria are a very promising model for a systems-level understanding of physiology and pathophysiology. They can be considered to operate within a relatively “closed system” with known inputs and measurable outputs. Mitochondria provide a manageable and discrete model system for a systems biology research approach to cardiovascular medicine.
- The need to promote cross-disciplinary collaboration on mitochondrial projects. An enormous amount of information has been accumulated on mitochondrial function in a variety of diseases. There is currently a paucity of integration across cell/organ type and disease state. Developing a holistic understanding of genomic, proteomic, and functional information in mitochondria requires collaborative efforts from multiple disciplines. In particular, efforts employing computational biology in close bidirectional interaction with genomic and proteomic sciences are essential to advance the field.
- The need to build new open source models and experimental tools. A biochemical “toolbox” should be developed for assessing mitochondrial biology and to interface with animal models of disease in a reasonably high throughput manner. Likewise, computational models that are user friendly and adaptable to novel experimental data must be developed. Together, these models and experimental tools are the essential combination to advance our understanding of mitochondrial function and to determine the genomic and proteomic context for this function. The data to be obtained using this approach will be the basis for constructing a mitochondrial knowledge base that provides systems level information enabling mitochondrial medicine.
- The need to bridge the translational divide by developing in vivo measurements of mitochondrial function. While animal models and computational insights remain indispensable, translational discoveries cannot proceed without quantitative measurements of mitochondrial function in humans. This requires novel experimental tools as well as thoughtful and creative interactions among basic scientists, clinicians and modelers.
- Support investigations that integrate genetic, proteomic, and functional data to analyze mitochondrial protein complexes/networks in cardiovascular diseases. This research should encompass mitochondrial function in biogenesis, metabolism, apoptosis, and stress sensing. The aim is to encourage productive interactions among cardiologists, biologists and investigators in computational biology and advance mitochondrial medicine. The particular focus of interaction should be the collection of genomic, proteomic, and functional data within the context of diseased cardiovascular phenotypes.
- Leverage existing technologies in nanotechnology, imaging, and proteomics to characterize the molecular dynamics of mitochondria and delineate their molecular anatomy in cardiovascular diseases. Capturing the dynamics of mitochondrial function in disease involves deciphering the protein functional clusters distributed in the various sub-organelle compartments and monitoring mitochondrial protein trafficking (i.e. spatial and temporal changes), as these parameters drive changes in mitochondrial phenotypes.
- Develop and manage a Mitochondrial Biology Knowledge Base. This mitochondrial knowledge base should be open source, contain ‘omic’ and functional data, provide access to computational tools for data modeling, and generalize to multiple organs and cell types (e.g., myocytes, fibroblasts, vascular endothelial cells, etc.). A good start towards community involvement would be a focused, working meeting of select individuals from the following areas of expertise: mathematical biology, bioinformatics, proteomics, genomics, metabolism and mitochondrial biology (short and long term perspective).
Foster meetings, symposia, and educational workshops. These efforts should address key issues, encourage collaborations across disciplines, and provide opportunities for training clinicians and researchers to engage in cross-disciplinary collaborative research.
A summary publication is planned in a peer reviewed journal.
Division of Cardiovascular Diseases
NHLBI Staff Contacts/Telephone
Jennie Larkin, Ph.D.
Isabella Liang, Ph.D.
Lisa Schwartz Longacre, Ph.D.
Working Group Members:
- Peipei Ping, Ph.D., F.A.H.A., UCLA.
- Robert Balaban, Ph.D., NHLBI
- Daniel Beard, Ph.D., Medical College of Wisconsin
- Eric Billings, Ph.D., NHLBI
- D. Allan Butterfield, Ph.D., University of Kentucky
- David C. Chan, M.D., Ph.D., California Institute of Technology
- Jennifer Van Eyk, Ph.D., Johns Hopkins University-Bayview campus
- Roberta A. Gottlieb, M.D., San Diego State University
- Charles L. Hoppel, M.D., Case Western Reserve University
- Daniel P. Kelly, M.D., Ph.D., Washington University
- Bradford B. Lowell, M.D., Ph.D., Harvard Medical School
- Vamsi Mootha, M.D., Massachusetts General Hospital
- Elizabeth Murphy, Ph.D., NHLBI
- Michael N. Sack, M.D., Ph.D., NHLBI
- William C. Stanley, Ph.D., University of Maryland-Baltimore
- Mark Sussman, Ph.D., San Diego State University
- Luke I. Szweda, PH.D., Oklahoma Medical Research Foundation
- Rong Tian, M.D., Ph.D., Harvard Medical School
- Raimond Winslow, Ph.D., The Johns Hopkins University School of Medicine & Whiting School of Engineering
Last Revised March 2008