Alzheimer's disease (AD) is a major national public health priority. Despite advances, clinical trials have not yielded therapies to prevent or slow disease progression with recent failures highlighting our incomplete knowledge of mechanisms. Accumulating evidence suggests the synaptic failure in AD is associated with dysregulation in multiple metabolic networks and that AD is not a singular condition but may be a combination of altered networks. Recent advances in analytical chemistry led to the emergence of a new field called metabolomics. Metabolomics allows simultaneous measurement of 100's to 1000's of metabolites for mapping perturbations in interconnected pathways and in metabolic networks enabling a systems approach to the study of AD. Over the past 4 years we assembled an interdisciplinary research team that includes experts in metabolomics, AD clinical and basic research, genetics, biochemistry and bioinformatics, and have started to define perturbations in metabolic networks across the trajectory of disease. We identified novel changes in methionine, norepinephrine, tryptophan and purine pathways, and networks in both MCI and AD subjects. We also found links between metabolic perturbations and core AD pathology markers (total tau and amyloid-beta 42) suggesting changes in these biochemical pathways might parallel or even precede formation of plaques and tangles and provide new insights into pathophysiology. Our overall goal is to leverage large investments made by the NIH in the AD Neuroimaging Initiative (ADNI) and Pharmacometabolomics Research Network taking an integrated metabolomics-genetics-imaging systems approach to define network failures in AD.
In Specific Aim 1 we propose to study three cohorts (two clinic based and one population based) to further define alterations in interconnected metabolic pathways and networks in four diagnostic groups (asymptomatic but at-risk for developing AD (APOE ?4 carriers), individuals with cognitive complaints but normal objective memory, MCI, and AD) and controls to define network alterations that track both progression of cognitive decline as well as pathology (CSF amyloid-beta and tau).
In Specific Aim 2 we will relate metabolomics data to genetic variation (GWAS) bi-directionally to determine whether novel genetic markers identified in earlier studies (e.g. ADNI) have metabolic correlates and whether metabolic phenotypes identified in this study yield novel genetic insights. We will assess models of imaging and other biomarker data with metabolomics and genetics in a systems framework.
In Specific Aim 3 we propose to use stable isotope tracers and NMR spectroscopy in ApoE4 TR and APP/PSI/ApoE4 mouse models, at three different ages, to probe these metabolic networks in both blood and spinal fluid in greater detail to determine the relative validity of the targets identified in aims 1 and 2. This innovativ, well-integrated and targeted approach is likely to transform our understanding of the heterogeneity underlying AD pathogenesis and will provide novel insights highly relevant for novel drug discovery and development.
As metabolic processes are at the core of physiology, metabolomics is ideally positioned to characterize an integrated view of metabolic failures in AD across the trajectory of disease. Such information could provide totally novel insights about disease mechanisms, disease heterogeneity, and help bridge knowledge from genetics to metabolism by providing insights about functions of genes implicated in AD pathogenesis and can lay a new path for drug discovery.
|Adler, Angela; Kirchmeier, Pia; Reinhard, Julian et al. (2018) PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases. Orphanet J Rare Dis 13:22|
|Tenenbaum, Jessica D; Blach, Colette (2018) Best practices and lessons learned from reuse of 4 patient-derived metabolomics datasets in Alzheimer's disease. Pac Symp Biocomput 23:280-291|
|Tao, Qiushan; Zhu, Haihao; Chen, Xi et al. (2018) Pramlintide: The Effects of a Single Drug Injection on Blood Phosphatidylcholine Profile for Alzheimer's Disease. J Alzheimers Dis 62:597-609|
|Barupal, Dinesh Kumar; Fan, Sili; Wancewicz, Benjamin et al. (2018) Generation and quality control of lipidomics data for the alzheimer's disease neuroimaging initiative cohort. Sci Data 5:180263|
|Toledo, Jon B; Arnold, Matthias; Kastenmüller, Gabi et al. (2017) Metabolic network failures in Alzheimer's disease: A biochemical road map. Alzheimers Dement 13:965-984|
|Li, Huang; Fang, Shiaofen; Contreras, Joey A et al. (2017) Brain explorer for connectomic analysis. Brain Inform 4:253-269|
|Du, Lei; Liu, Kefei; Yao, Xiaohui et al. (2017) Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty. Sci Rep 7:14052|
|St John-Williams, Lisa; Blach, Colette; Toledo, Jon B et al. (2017) Targeted metabolomics and medication classification data from participants in the ADNI1 cohort. Sci Data 4:170140|
|Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon et al. (2017) NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES. Proc IEEE Int Conf Acoust Speech Signal Process 2017:6170-6174|
|Liu, Kefei; Yao, Xiaohui; Yan, Jingwen et al. (2017) Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm. Graphs Biomed Image Anal Comput Anat Imaging Genet (2017) 10551:220-229|
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