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一种协同的机器学习加跨物种湿实验室验证方法确定了抑制阿尔茨海默病的神经元线粒体自噬诱导剂。

A synergized machine learning plus cross-species wet-lab validation approach identifies neuronal mitophagy inducers inhibiting Alzheimer disease.

机构信息

Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, Lørenskog, Norway.

State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau SAR, China.

出版信息

Autophagy. 2022 Apr;18(4):939-941. doi: 10.1080/15548627.2022.2031382. Epub 2022 Feb 7.

DOI:10.1080/15548627.2022.2031382
PMID:35130128
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9037405/
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 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.

摘要

受损线粒体的识别和清除失败会导致阿尔茨海默病(AD)患者记忆力减退以及 Aβ 和 MAPT/Tau 病理学,目前对此存在未满足的治疗需求。恢复线粒体自噬以消除受损线粒体可以消除代谢功能障碍、神经退行性变,并且随后可能抑制或减缓 AD 模型中的认知能力下降。我们开发了一种高通量机器学习方法,结合跨物种筛选平台,从天然产物库中发现新型的诱导线粒体自噬的化合物,并进一步通过实验验证了潜在的候选化合物。两种先导化合物山奈酚和瑞鲍迪苷元以 PINK1 依赖性方式诱导神经元线粒体自噬,并减少 AD 模型中的 Aβ 和 MAPT/Tau 病理学。我们的组合方法为鉴定有效的线粒体自噬诱导剂以维持大脑健康提供了一种快速、经济高效且高度准确的方法。

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本文引用的文献

1
Amelioration of Alzheimer's disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow.通过机器学习和跨物种工作流程鉴定的诱导细胞自噬改善阿尔茨海默病病理学。
Nat Biomed Eng. 2022 Jan;6(1):76-93. doi: 10.1038/s41551-021-00819-5. Epub 2022 Jan 6.