Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis

Yitan Lu; Ziyun Zhou; Qi Li; Bin Yang; Xing Xu; Yu Zhu; Mengjun Xie; Yuwan Qi; Fei Xiao; Wenying Yan; Zhongjie Liang; Qifei Cong; Guang Hu

doi:10.1016/j.jpha.2025.101295

Volume 15 Issue 6

Jun. 2025

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Yitan Lu, Ziyun Zhou, Qi Li, Bin Yang, Xing Xu, Yu Zhu, Mengjun Xie, Yuwan Qi, Fei Xiao, Wenying Yan, Zhongjie Liang, Qifei Cong, Guang Hu. Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis[J]. Journal of Pharmaceutical Analysis, 2025, 15(6): 101295. doi: 10.1016/j.jpha.2025.101295

Citation:

Yitan Lu, Ziyun Zhou, Qi Li, Bin Yang, Xing Xu, Yu Zhu, Mengjun Xie, Yuwan Qi, Fei Xiao, Wenying Yan, Zhongjie Liang, Qifei Cong, Guang Hu. Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis[J]. Journal of Pharmaceutical Analysis, 2025, 15(6): 101295. doi: 10.1016/j.jpha.2025.101295

Citation:

Yitan Lu, Ziyun Zhou, Qi Li, Bin Yang, Xing Xu, Yu Zhu, Mengjun Xie, Yuwan Qi, Fei Xiao, Wenying Yan, Zhongjie Liang, Qifei Cong, Guang Hu. Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis[J]. Journal of Pharmaceutical Analysis, 2025, 15(6): 101295. doi: 10.1016/j.jpha.2025.101295

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Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis

doi: 10.1016/j.jpha.2025.101295

Yitan Lu^a,
Ziyun Zhou^a,
Qi Li^b,
Bin Yang^a,
Xing Xu^a,
Yu Zhu^c,
Mengjun Xie^b,
Yuwan Qi^b,
Fei Xiao^a,
Wenying Yan^d,e,
Zhongjie Liang^{a,e
,
,},
Qifei Cong^{b,f,g
,
,},
Guang Hu^{a,e,h,i
,
,}

a. MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou, 215123, China;
b. Institute of Neuroscience and Jiangsu Key Laboratory of Neuropsychiatric Diseases, Soochow University, Suzhou, 215123, China;
c. Department of Pharmacology, University of Cambridge, Cambridge, UK;
d. Department of Bioinformatics, Center for Systems Biology, School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, 215123, China;
e. Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, 215123, China;
f. Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China;
g. Department of Nephrology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China;
h. Key Laboratory of Alkene-carbon Fibres-based Technology & Application for Detection of Major Infectious Diseases, Soochow University, Suzhou, 215123, China;
i. Jiangsu Key Laboratory of Infection and Immunity, Soochow University, Suzhou, 215123, China

Funds:

This work was supported by the National Natural Science Foundation of China (Grant Nos.: 32271292, 31872723, 32200778, and 22377089), the Jiangsu Students Innovation and Entrepreneurship Training Program, China (Program No.: 202210285081Z), the Project of MOE Key Laboratory of Geriatric Diseases and Immunology, China (Project No.: JYN202404), Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions, Natural Science Foundation of Jiangsu Province, China (Project No.: BK20220494), Suzhou Medical and Health Technology Innovation Project, China (Grant No.: SKY2022107), the Clinical Research Center of Neurological Disease in The Second Affiliated Hospital of Soochow University, China (Grant No.: ND2022A04), State Key Laboratory of Drug Research (Grant No.: SKLDR-2023-KF-05), Jiangsu Shuangchuang Program for Doctor, Young Science Talents Promotion Project of Jiangsu Science and Technology Association (Program No.: TJ-2023-019), Young Science Talents Promotion Project of Suzhou Science and Technology Association, Suzhou International Joint Laboratory for Diagnosis and Treatment of Brain Diseases, and startup funding (Grant Nos.: NH21500221, NH21500122, and NH21500123) to Qifei Cong.

Received Date: Sep. 06, 2024
Accepted Date: Apr. 04, 2025
Rev Recd Date: Mar. 01, 2025
Available Online: Jul. 18, 2025
Publish Date: Apr. 09, 2025

Abstract

Abstract

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the “therapeutic module” of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.
- Network perturbations,
- Mechanism of action,
- Multiple sclerosis,
- HTR2B

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References(50)

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