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Principal Investigator: Ritz, Beate R
Institute Receiving Award University Of California Los Angeles
Location Los Angeles, CA
Grant Number R21ES030175
Funding Organization National Institute of Environmental Health Sciences
Award Funding Period 01 Feb 2019 to 31 Jan 2022
DESCRIPTION (provided by applicant): Project Abstract Our PEG study is among the largest Parkinson's disease (PD) population-based study with exceptional high-quality disease characterization and in-depth exposure assessment. Patients are diagnosed and examined (multiple times) by a UCLA movement disorder specialist. We developed a longitudinal geographic information system (GIS) based assessment for pesticide exposures that links state-mandated information on type, date, and location of all agricultural pesticide applications in California recorded since 1974 to land use maps and study participants' residences and work places. Here we propose to combine a powerful new metabolomics tool and system biology analytic methods to identify signatures for toxic exposures that evoke long-term biologic responses. The metabolomics data we will generate will help us identify metabolic profiles for chronic environmental exposures for both PD patients and population controls. This will generate a first understanding of the metabolic consequences of chronic low dose pesticide exposure in PD. However, biologic processes, including biologic responses to chronic toxicant exposures and those involved in disease development, are highly dynamic and interactive systems. The PEG study is uniquely capable to begin investigating these multidimensional networks linking exposure and disease. We have already generated genome and epigenome profiles for 550 PD patients and 250 controls. Here we newly propose to generate and analyze serum based metabolome profiles (targeted and untargeted) for these same 800 study participants characterizing environmental pesticide exposures via metabolome wide association analyses. We aim to develop a metabolite signature of environmental exposure using supervised machine learning methods, and also determine if these are disease specific or found in both exposed cases and controls. Furthermore, incorporating our genome and epigenome information, we propose to use biological systems analysis to identify multi-omics network patterns that distinguish environmental exposures that contribute to PD onset and progression. We expect this to show a chronic response pattern across different molecular layers and are influenced by many environmental factors. Combining multi-omic measures based on multidimensional network and system analyses will address the gaps in our current knowledge concerning molecular mechanisms responsible for the effects of chronic low dose exposures in PD.
Science Code(s)/Area of Science(s) Primary: 77 - Metabolomics Technology
Secondary: 03 - Carcinogenesis/Cell Transformation
Publications See publications associated with this Grant.
Program Officer Yuxia Cui
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