Research Project
10301220
PROJECT SUMMARY/ABSTRACT There is a fundamental gap in our understanding of how amyloid beta oligomers (A?O) induce neurotoxicity and neuron death in Alzheimer?s disease (AD), as evidenced by a dearth of therapies to prevent or halt AD progression. Continued existence of this knowledge gap represents a major issue for public health and the mission of the NIH because, until it is filled, development of treatments for neurodegeneration in AD will remain largely intractable. The long-term goal of this work is to discover pathways that enable resistance to A?O- induced neurotoxicity thereby allowing discovery of new AD therapeutics. The overall objective here, which is the next step in pursuit of this goal, is to build AI that accurately predicts the ability of drug candidates to cure or prevent toxicity of A?O in human stem cell-derived cortical glutamatergic neurons. To train this AI, a library of proteomic and metabolomic (hereafter referred to as multi-omic) phenotypes will be generated from neurons that are: 1) healthy, 2) A?O-treated (AD-like), or 3) drug library+A?O-treated. The central hypothesis is that some drugs at least partially palliate A?O-induced neurotoxicity, which is observable as a shift in multi-omic state toward the healthy state, and that AI can learn to predict this curative potential from drug structures. This hypothesis is based on preliminary data generated by the applicant and literature. The rationale for the proposed research is that mapping the difference in multi-omic phenotypes of healthy and A?O-stressed neurons, and mapping how chemical structures induce changes between those states, will allow AI to learn to make accurate predictions of whether additional, unmeasured molecules can improve neuron health. This will result in new and innovative approaches for prevention and treatment of AD. Guided by preliminary data and literature, this hypothesis will be tested by pursuing two specific aims: 1) validate the multi-omic phenotype landscape of healthy and AD-like neurons; and 2) build AI to discover new drugs that prevent A?O-induced neuron death in AD. The first aim will validate the human disease relevance of our model system using cell- based assays and by comparing omic profiles from our system to those observed in human AD brains. The second aim will build a map of how drugs candidates alter neural multi-omic states to use for training predictive AI. Completion of these aims will contribute (1) an in vitro system that mimics physiological milieu, and also (2) molecular ?omics? signatures of those healthy and AD-like human iPSC-derived neural cells, which are two areas of high program relevance defined in NOT-AG-19-007. This approach is innovative, in the applicant?s opinion, because it departs from the status quo by using highly translatable human iPSC-derived neurons for unbiased discovery of palliative drug candidates using a unique combination of multi-omics and AI. This contribution will be significant because it is expected to vertically advance our understanding of basic neuron stress resistance, as well as result in the first drugs that prevent A?O neural toxicity. Ultimately, such knowledge will be useful for other neurodegenerative disorders of aging.
