Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks.
BACKGROUND
One of the most challenging tasks in the exploration of anti-aging is to discover drugs that can promote longevity and delay the incidence of age-associated diseases of human. Up to date, a number of drugs, including some antioxidants, metabolites and synthetic compounds, have been found to effectively delay the aging of nematodes and insects.


RESULTS
We proposed a label propagation algorithm on drug-protein network to infer drugs that can extend the lifespan of C. elegans. We collected a set of drugs of which functions on lifespan extension of C. elegans have been reliably determined, and then built a large-scale drug-protein network by collecting a set of high-confidence drugprotein interactions. A label propagation algorithm was run on the drug-protein bipartite network to predict new drugs with lifespan-extending effect on C. elegans. We calibrated the performance of the proposed method by conducting performance comparison with two classical models, kNN and SVM. We also showed that the screened drugs significantly mediate in the aging-related pathways, and have higher chemical similarities to the effective drugs than ineffective drugs in promoting longevity of C. elegans. Moreover, we carried out wet-lab experiments to verify a screened drugs, 2- Bromo-4'-nitroacetophenone, and found that it can effectively extend the lifespan of C. elegans. These results showed that our method is effective in screening lifespanextending drugs in C. elegans.


CONCLUSIONS
In this paper, we proposed a semi-supervised algorithm to predict drugs with lifespan-extending effects on C. elegans. In silico empirical evaluations and in vivo experiments in C. elegans have demonstrated that our method can effectively narrow down the scope of candidate drugs needed to be verified by wet lab experiments.
Changzhou University, Jiangsu, 213164, China.|Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China.
Hui
H
Liu
Changzhou University, Jiangsu, 213164, China.
Mengmeng
M
Guo
Changzhou University, Jiangsu, 213164, China.
Ting
T
Xue
Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China.
Jihong
J
Guan
Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China. @fudan.edu.cn.
Libo
L
Luo
Changzhou University, Jiangsu, 213164, China. llb213001@163.com.|Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China. llb213001@163.com.
Ziheng
Z
Zhuang
D000375:Aging / Q000187:drug effects; D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000187:drug effects* / Q000378:metabolism / Q000502:physiology*; D029742:Caenorhabditis elegans Proteins / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D004353:Drug Evaluation, Preclinical; D020407:Internet; D008136:Longevity / Q000187:drug effects*; D011485:Protein Binding; D060388:Support Vector Machine
D029742:Caenorhabditis elegans Proteins
Caenorhabditis elegans; Drug targets; Label propagation; Longevity