Srinivas Ayyadevara1, Akshatha Ganne2, Rachel D Hendrix3, Meenakshisundaram Balasubramaniam4, Robert J Shmookler Reis5, Steven W Barger6. 1. Central Arkansas Veterans Healthcare System, Little Rock, AR, 72205, United States; Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States. Electronic address: AyyadevaraSrinivas@uams.edu. 2. Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States; BioInformatics Program, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States. 3. Department of Neurobiology & Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, United States. 4. Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States. 5. Central Arkansas Veterans Healthcare System, Little Rock, AR, 72205, United States; Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States; BioInformatics Program, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States; Department of Biochemistry & Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, United States. 6. Central Arkansas Veterans Healthcare System, Little Rock, AR, 72205, United States; Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States; Department of Neurobiology & Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, United States. Electronic address: BargerStevenW@uams.edu.
Abstract
BACKGROUND: Events that instigate disease may involve biochemical events distinct from changes in the steady-state levels of proteins. Even chronic degenerative disorders appear to involve changes such as post-translational modifications. NEW METHOD: We have begun a series of proteomics analyses on proteins that have been fractionated by functional status. Because Alzheimer's disease (AD) is associated with metabolic perturbations such as Type-2 diabetes, fractionation hinged on binding to phosphatidylinositol trisphosphate (PIP3), key to insulin/insulin-like growth factor signaling. We compared mice on normal chow to counterparts subjected to diet-induced obesity (DIO) or to mice expressing human Aβ1-42 from a transgene. RESULTS: The prevailing phenotypic finding in either experimental group was loss of PIP3 binding. Of the 1228 proteins that showed valid PIP3 binding in any group of mice, 55% exhibited a significant quantitative difference in the number of spectral counts as a function of DIO, 63% as function of the Aβ transgene, and 79% as a function of either variable. There was remarkable overlap among the proteins altered in the two experimental groups, and pathway analysis indicated effects on proteostasis, apoptosis, and synaptic vesicles. COMPARISON WITH EXISTING METHODS: Most proteomics approaches only identify differences in the steady-state levels of proteins. Our overlay of a functional distinction permits new levels of discovery that may achieve novel insights into physiology in an unbiased and inclusive manner. CONCLUSIONS: Proteomics analyses have revolutionized the discovery phase of biomedical research but are conventionally limited in scope. The creative use of fractionation prior to proteomic discovery is likely to provide important insights into AD and related disorders. Published by Elsevier B.V.
BACKGROUND: Events that instigate disease may involve biochemical events distinct from changes in the steady-state levels of proteins. Even chronic degenerative disorders appear to involve changes such as post-translational modifications. NEW METHOD: We have begun a series of proteomics analyses on proteins that have been fractionated by functional status. Because Alzheimer's disease (AD) is associated with metabolic perturbations such as Type-2 diabetes, fractionation hinged on binding to phosphatidylinositol trisphosphate (PIP3), key to insulin/insulin-like growth factor signaling. We compared mice on normal chow to counterparts subjected to diet-induced obesity (DIO) or to mice expressing human Aβ1-42 from a transgene. RESULTS: The prevailing phenotypic finding in either experimental group was loss of PIP3 binding. Of the 1228 proteins that showed valid PIP3 binding in any group of mice, 55% exhibited a significant quantitative difference in the number of spectral counts as a function of DIO, 63% as function of the Aβ transgene, and 79% as a function of either variable. There was remarkable overlap among the proteins altered in the two experimental groups, and pathway analysis indicated effects on proteostasis, apoptosis, and synaptic vesicles. COMPARISON WITH EXISTING METHODS: Most proteomics approaches only identify differences in the steady-state levels of proteins. Our overlay of a functional distinction permits new levels of discovery that may achieve novel insights into physiology in an unbiased and inclusive manner. CONCLUSIONS: Proteomics analyses have revolutionized the discovery phase of biomedical research but are conventionally limited in scope. The creative use of fractionation prior to proteomic discovery is likely to provide important insights into AD and related disorders. Published by Elsevier B.V.
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