This proposal describes a five-year development plan for Rahul Deo to achieve independence as an investigator in the computational biology of cardiometabolic (CM) disease. Dr. Deo is a Cardiology Fellow at the Massachusetts General Hospital (MGH). The path described herein will enable him to build upon his background in molecular biophysics and complex disease genetics by taking advantage of the bioinformatics research and training opportunities at Harvard Medical School (HMS) and the clinical strengths of MGH. Dr. Deo will be co-mentored by Frederick 'Fritz'Roth, an associate professor in the Department of Biological Chemistry and Molecular Pharmacology at HMS and Robert Gerszten, an associate professor in the Department of Medicine at Harvard Medical School, and Director of the Metabolomics Platform at the Broad Institute of Harvard and MIT. Dr. Roth is a recognized expert in the computational biology of large "omic" data sets while Dr. Gerszten is an expert in metabolomics, with particular application to CM disease. In addition to having worked closely together over the past five years on numerous metabolomics projects, Drs. Roth and Gerszten each have a strong record of mentorship. Dr. Deo will also work closely with Drs. Marc Vidal, Joseph Loscalzo, Isaac Kohane and Calum MacRae, who will provide career guidance and scientific advice on the execution of the proposed research plan. The research program will emphasize the use of bioinformatics techniques and metabolite profiling to advance the characterization and classification of CM disease. There is increasing recognition that our current disease categorization approaches are inadequate to describe the scope and heterogeneity of human disease. Metabolomics - the analysis of metabolite levels from biologic fluid samples - is one non-invasive way to obtain quantitative molecular phenotypes from patients to address this complexity. This research plan is designed to assess the hypothesis that the application of modern computational methods, previously developed for large high-throughput biological "omic" data, to the analysis of metabolite profiling data will help us improve disease elucidation. Specifically, this program proposes: 1) to use data integration and network approaches to characterize biologic responses to cardiometabolic (CM) perturbations and 2) to use related bioinformatic analytic techniques to build and test metabolite classifiers distinguishing CM disease patients from controls

Public Health Relevance

The proposed research aspires to address the limitations of our current "diagnostic resolution" by using quantitative biologic data and bioinformatic analysis to diagnose CM disease. The same computational approaches could be used to subdivide superficially similar but etiologically distinct forms of CM disease, thus tackling the problem of disease heterogeneity and approaching the goal of individualizing medicine.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Clinical Investigator Award (CIA) (K08)
Project #
5K08HL098361-04
Application #
8437210
Study Section
Special Emphasis Panel (ZHL1-CSR-U (M1))
Program Officer
Carlson, Drew E
Project Start
2010-07-15
Project End
2015-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
4
Fiscal Year
2013
Total Cost
$136,958
Indirect Cost
$10,145
Name
University of California San Francisco
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Roberts, Jason D; Longoria, James; Poon, Annie et al. (2015) Targeted deep sequencing reveals no definitive evidence for somatic mosaicism in atrial fibrillation. Circ Cardiovasc Genet 8:50-7
Shah, Sanjiv J; Katz, Daniel H; Selvaraj, Senthil et al. (2015) Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131:269-79
Zou, Jun; Tran, Diana; Baalbaki, Mai et al. (2015) An internal promoter underlies the difference in disease severity between N- and C-terminal truncation mutations of Titin in zebrafish. Elife 4:e09406
Deo, Rahul C (2015) Machine Learning in Medicine. Circulation 132:1920-30
Deo, Rahul C; Musso, Gabriel; Tasan, Murat et al. (2014) Prioritizing causal disease genes using unbiased genomic features. Genome Biol 15:534
Shah, Sanjiv J; Katz, Daniel H; Deo, Rahul C (2014) Phenotypic spectrum of heart failure with preserved ejection fraction. Heart Fail Clin 10:407-18
Heredia, Jose E; Mukundan, Lata; Chen, Francis M et al. (2013) Type 2 innate signals stimulate fibro/adipogenic progenitors to facilitate muscle regeneration. Cell 153:376-88
Rozenblatt-Rosen, Orit; Deo, Rahul C; Padi, Megha et al. (2012) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins. Nature 487:491-5
Deo, Rahul C; MacRae, Calum A (2011) The zebrafish: scalable in vivo modeling for systems biology. Wiley Interdiscip Rev Syst Biol Med 3:335-46
Becker, Jason R; Deo, Rahul C; Werdich, Andreas A et al. (2011) Human cardiomyopathy mutations induce myocyte hyperplasia and activate hypertrophic pathways during cardiogenesis in zebrafish. Dis Model Mech 4:400-10

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