Bioinformatic Approaches to Small Molecule Profiling of Cardiometabolic Disease

心脏代谢疾病小分子分析的生物信息学方法

• 批准号: 8111964
• 负责人: Rahul Chandrakant Deo
• 金额: $ 13.7万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8111964
• 关键词: Address; Adopted; Anabolism; Atherosclerosis; Biochemistry; Bioinformatics; Biological; Biological Markers; Biology; Biophysics; Cardiology; Classification; Clinical; Complex; Computational Biology; Computing Methodologies; Data; Data Analyses; Data Set; Development; Development Plans; Diabetes Mellitus; Diagnosis; Diagnostic; Disease; Disease Marker; Early Diagnosis; Epidemic; Functional disorder; General Hospitals; Genes; Genetic; Glucose; Glucose Intolerance; Goals; Health; Hereditary Disease; Heterogeneity; Human; Individual; Information Networks; Institutes; Insulin; Insulin Resistance; Investigation; Lead; Life; Liquid substance; Maps; Mass Spectrum Analysis; Massachusetts; Measures; Medicine; Mentors; Mentorship; Metabolic; Metabolic Pathway; Molecular; Monitor; Myocardial Infarction; OGTT; Obesity; Pathway interactions; Patients; Pharmacology; Phenotype; Physiological; Physiology; Process; Purines; Reaction; Research; Research Personnel; Research Training; Resolution; Risk; Role; Sampling; Stimulus; Stroke; Subgroup; Surveys; Techniques; Technology; Testing; Work; base; beta-Alanine; career; data integration; design; enzyme pathway; gamma-Aminobutyric Acid; human disease; improved; insight; medical schools; metabolomics; molecular phenotype; novel; novel marker; nutrition; outcome forecast; professor; prognostic; programs; protein protein interaction; purine; purine metabolism; rapid detection; response; small molecule; treatment response

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：该提案描述了Rahul Deo的五年发展计划，以实现心脏代谢（CM）疾病计算生物学研究者的独立性。 Deo博士是马萨诸塞州综合医院（MGH）的心脏病学研究员。本文所述的路径将使他能够利用哈佛医学院（HMS）（HMS）和MGH的临床优势的生物信息学研究和培训机会，以分子生物物理学和复杂疾病遗传学的背景为基础。 Deo博士将由HMS生物学化学和分子药理学系副教授Frederick'Fritz'Roth和HMS的副教授，哈佛医学院医学院的副教授，哈佛大学和MIT的代谢局平台主任Robert Gerszten。罗斯博士是大型“ OMIC”数据集的计算生物学的公认专家，而Gerszten博士是代谢组学专家，特别适用于CM疾病。除了在过去五年中与许多代谢组学项目密切合作，DRS。罗斯（Roth）和格斯兹滕（Gerszten）都有强大的指导记录。 Deo博士还将与Drs密切合作。 Marc Vidal，Joseph Loscalzo，Isaac Kohane和Calum Macrae，他们将就执行拟议的研究计划的执行提供职业指导和科学建议。该研究计划将强调使用生物信息学技术和代谢物分析，以推动CM疾病的表征和分类。人们越来越认识到我们目前的疾病分类方法不足以描述人类疾病的范围和异质性。代谢组学 - 对生物流体样品的代谢产物水平的分析 - 是从患者中获得定量分子表型来解决这种复杂性的一种非侵入性方法。该研究计划旨在评估以下假设：以前针对大型高通量生物学“ OMIC”数据开发的现代计算方法的应用，用于分析代谢物分析数据将有助于我们改善疾病阐明。具体而言，该程序提出：1）使用数据集成和网络方法来表征对心脏代谢（CM）扰动的生物学反应，以及2）使用相关的生物信息学分析技术来构建和测试与对照患者区分CM疾病患者的代谢物分类器 公共卫生相关性：拟议的研究愿意通过使用定量生物学数据和生物信息学分析来诊断CM疾病，以解决我们当前“诊断解决方案”的局限性。相同的计算方法可以用来细分表面上相似但病因不同的CM疾病形式，从而解决了疾病异质性问题并接近个性化医学的目标。