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Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

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Quant. Biol.    2018, Vol. 6 Issue (4) : 321-333    https://doi.org/10.1007/s40484-018-0159-0
RESEARCH ARTICLE
Subnetwork identification and chemical modulation for neural regeneration: A study combining network guided forest and heat diffusion model
Hui Wang1, Gang Wang1,2,3, Li-Da Zhu1, Xuan Xu1, Bo Diao2,3(), Hong-Yu Zhang1()
1. Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
2. Department of Clinical Experiment, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
3. Hubei Key Laboratory of Central Nervous System Tumor and Intervention, Wuhan 430070, China
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Abstract

Background: The induction of neural regeneration is vital to the repair of spinal cord injury (SCI). While compared with peripheral nervous system (PNS), the regenerative capacity of the central nervous system (CNS) is extremely limited. This indicates that modulating the molecular pathways underlying PNS repair may lead to the discovery of potential treatment for CNS injury.

Methods: Based on the gene expression profiles of dorsal root ganglion (DRG) after a sciatic nerve injury, we utilized network guided forest (NGF) to rank genes in terms of their capacity of distinguishing injured DRG from sham-operated controls. Gene importance scores deriving from NGF were used as initial heat in a heat diffusion model (HotNet2) to infer the subnetworks underlying neural regeneration in the DRG. After potential regulators of the subnetworks were found through Connectivity Map (cMap), candidate compounds were experimentally evaluated for their capacity to regenerate the damaged neurons.

Results: Gene ontology analysis of the subnetworks revealed ubiquinone biosynthetic process is crucial for neural regeneration. Moreover, almost half of the genes in these subnetworks are found to be related to neural regeneration via text mining. After screening compounds that are likely to modulate gene expressions of the subnetworks, three compounds were selected for the experiment. Of them, trichostatin A, a histone deacetylase inhibitor, was validated to enhance neurite outgrowth in vivo via an optic nerve crush mouse model.

Conclusions: Our study identified subnetworks underlying neural regeneration, and validated a compound can promote neurite outgrowth by modulating these subnetworks. This work also suggests an alternative approach for drug repositioning that can be easily extended to other disease phenotypes.

Keywords network guided forest      HotNet2      neural regeneration      axon growth      neurotrophic factors     
Corresponding Author(s): Bo Diao,Hong-Yu Zhang   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Online First Date: 30 November 2018    Issue Date: 10 December 2018
 Cite this article:   
Hui Wang,Gang Wang,Li-Da Zhu, et al. Subnetwork identification and chemical modulation for neural regeneration: A study combining network guided forest and heat diffusion model[J]. Quant. Biol., 2018, 6(4): 321-333.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-018-0159-0
https://academic.hep.com.cn/qb/EN/Y2018/V6/I4/321
Fig.1  Figure 1. The workflow of our drug screening process.
Model AUC ACCURACY
Network-guided forest 0.892±0.009 0.821±0.0147
Random forest 0.875±0.168 0.814±0.0253
Tab.1  Classification accuracy of NGF and RF (Number of trees is 1000)
Fig.2  Area under ROC and gene importance scores obtained by NGF.
Fig.3  Six subnetworks identified by Hotnet2 and their enriched gene ontology (GO) terms.
Fig.4  HE staining of optic nerve in mice.
Fig.5  Immunofluorescence staining of optic nerve regeneration.
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