A synergized machine learning plus cross-species wet-lab validation approach identifies neuronal mitophagy inducers inhibiting Alzheimer disease
Ruixue Ai, Xu‐Xu Zhuang, Alexander Anisimov, Jiahong Lu, Evandro Fei Fang
Abstract
Failed recognition and clearance of damaged mitochondria contributes to memory loss as well as Aβ and MAPT/Tau pathologies in Alzheimer disease (AD), for which there is an unmet therapeutic need. Restoring mitophagy to eliminate damaged mitochondria could abrogate metabolic dysfunction, neurodegeneration and may subsequently inhibit or slow down cognitive decline in AD models. We have developed a high-throughput machine-learning approach combined with a cross-species screening platform to discover novel mitophagy-inducing compounds from a natural product library and further experimentally validated the potential candidates. Two lead compounds, kaempferol and rhapontigenin, induce neuronal mitophagy and reduce Aβ and MAPT/Tau pathologies in a PINK1-dependent manner in both C. elegans and mouse models of AD. Our combinational approach provides a fast, cost-effective, and highly accurate method for identification of potent mitophagy inducers to maintain brain health.