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Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow
Xie, Chenglong1,2,3,4,5; Zhuang, Xu Xu6; Niu, Zhangming7,8; Ai, Ruixue2; Lautrup, Sofie2; Zheng, Shuangjia9; Jiang, Yinghui8; Han, Ruiyu2; Gupta, Tanima Sen2; Cao, Shuqin2; Lagartos-Donate, Maria Jose2; Cai, Cui Zan6; Xie, Li Ming6; Caponio, Domenica2; Wang, Wen Wen10; Schmauck-Medina, Tomas2; Zhang, Jianying2; Wang, He ling2; Lou, Guofeng2; Xiao, Xianglu8; Zheng, Wenhua11; Palikaras, Konstantinos12; Yang, Guang13,14; Caldwell, Kim A.15,16; Caldwell, Guy A.15,16; Shen, Han Ming17,18; Nilsen, Hilde2,19; Lu, Jia Hong6; Fang, Evandro F.2,19,20
2022-01
Source PublicationNature Biomedical Engineering
ISSN2157-846X
Volume6Issue:1Pages:76-93
Abstract

A reduced removal of dysfunctional mitochondria is common to aging and age-related neurodegenerative pathologies such as Alzheimer’s disease (AD). Strategies for treating such impaired mitophagy would benefit from the identification of mitophagy modulators. Here we report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds. From a library of naturally occurring compounds, the workflow allowed us to identify 18 small molecules, and among them two potent mitophagy inducers (Kaempferol and Rhapontigenin). In nematode and rodent models of AD, we show that both mitophagy inducers increased the survival and functionality of glutamatergic and cholinergic neurons, abrogated amyloid-β and tau pathologies, and improved the animals’ memory. Our findings suggest the existence of a conserved mechanism of memory loss across the AD models, this mechanism being mediated by defective mitophagy. The computational–experimental screening and validation workflow might help uncover potent mitophagy modulators that stimulate neuronal health and brain homeostasis.

DOI10.1038/s41551-021-00819-5
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000739783800003
Scopus ID2-s2.0-85122380150
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Cited Times [WOS]:31   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Health Sciences
Institute of Chinese Medical Sciences
Corresponding AuthorLu, Jia Hong; Fang, Evandro F.
Affiliation1.Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
2.Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, Lørenskog, Norway
3.Institute of Aging, Wenzhou Medical University, Wenzhou, China
4.Oujiang Laboratory, Wenzhou, Zhejiang, China
5.Key Laboratory of Alzheimer’s Disease of Zhejiang Province, Wenzhou, China
6.State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao
7.Aladdin Healthcare Technologies Ltd., London, United Kingdom
8.MindRank AI Ltd., Hangzhou, Zhejiang, China
9.School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
10.Center of Traditional Chinese Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
11.Faculty of Health Sciences, University of Macau, Taipa, Macao
12.Department of Physiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
13.Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom
14.National Heart and Lung Institute, Imperial College London, London, United Kingdom
15.Department of Biological Sciences, The University of Alabama, Tuscaloosa, United States
16.Departments of Neurology and Neurobiology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center for Research on the Basic Biology of Aging, University of Alabama at Birmingham School of Medicine, Birmingham, United States
17.Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
18.Faculty of Health Sciences, University of Macau, Macao
19.The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway
20.Department of Geriatrics, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
Corresponding Author AffilicationInstitute of Chinese Medical Sciences
Recommended Citation
GB/T 7714
Xie, Chenglong,Zhuang, Xu Xu,Niu, Zhangming,et al. Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow[J]. Nature Biomedical Engineering,2022,6(1):76-93.
APA Xie, Chenglong.,Zhuang, Xu Xu.,Niu, Zhangming.,Ai, Ruixue.,Lautrup, Sofie.,Zheng, Shuangjia.,Jiang, Yinghui.,Han, Ruiyu.,Gupta, Tanima Sen.,Cao, Shuqin.,Lagartos-Donate, Maria Jose.,Cai, Cui Zan.,Xie, Li Ming.,Caponio, Domenica.,Wang, Wen Wen.,Schmauck-Medina, Tomas.,Zhang, Jianying.,Wang, He ling.,Lou, Guofeng.,...&Fang, Evandro F..(2022).Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow.Nature Biomedical Engineering,6(1),76-93.
MLA Xie, Chenglong,et al."Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow".Nature Biomedical Engineering 6.1(2022):76-93.
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