Understanding the causal linkage of Alzheimer?s disease (AD)-associated genetic variants identified in genome-wide association studies and genome sequencing studies to molecular alterations in human neural cells is believed to give greater insight into Alzheimer?s biology and to shed light into molecular targets for AD therapy. As a consequence, it is critical to identify and develop more effective ways to rapidly screen variants for impact on potential biological and pathological functions prior to any in-depth mechanistic studies. Our proposal aims to fill such critical gap and proposes to develop and implement novel functional proteomics approaches that inquire into proteomewide pathologic alterations induced by individual genetic variants for Alzheimer?s and AD-related cell stressors, or by a combination thereof, by using the chaperome as a sensor for pathologic molecular alterations. To achieve our goals we here aim to: 1. Develop a comprehensive chaperome-directed chemical bait toolset for Alzheimer?s functional proteomics; and 2. Develop, test and implement computational methods most optimal to identify and investigate the molecular signature, including functionally altered (downregulated or upregulated) proteins, protein pathways and/or protein networks, and changes in protein-protein interactions and protein complex formation derived from the chemical chaperomics datasets. In line with the requirements of this FOA, we aim to deliver an effective and integrated omics platform (baits, reagents, protocols and bioinformatics) to screen protein functions, protein-protein interaction, protein complexes and their regulation by AD genetic variants.
Our aim i s to share the toolset and methodology with the scientific community to facilitate proteomewide nonbiased genotype-phenotype functional analyses.
This proposal is responsive to the goals of PAR-18-516 that encourages research to develop high-throughput functional assays that enable one to assess in an unbiased way the function of AD genetic variants in iPSCs- derived cells. Specifically, NIA is interested in identifying and developing more effective and integrated platforms to generate unbiased molecular datasets that can be integrated with genomic and physiological data prior to any in-depth mechanistic studies.
