Subnetwork identification and chemical modulation for neural regeneration: A study combining network guided forest and heat diffusion model
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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.
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 shamoperated 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.
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.
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.
Keywordsnetwork guided forest HotNet2 neural regeneration axon growth neurotrophic factors
This work was supported by the Fundamental Research Funds for the Central Universities (No. 2662017PY115)
- 20.Ziegner, U. H., Kobayashi, R. H., Cunningham-Rundles, C., Español, T., Fasth, A., Huttenlocher, A., Krogstad, P., Marthinsen, L., Notarangelo, L. D., Pasic, S., et al. (2002) Progressive neurodegeneration in patients with primary immunodeficiency disease on IVIG treatment. Clin. Immunol., 102, 19–24CrossRefGoogle Scholar
- 27.Leiserson, M. D. M., Vandin, F., Wu, H. T., Dobson, J. R., Eldridge, J. V., Thomas, J. L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M., et al. (2015) Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet., 47, 106–114CrossRefGoogle Scholar
- 33.Chen, W., Chen, C., Xia, M., Wu, K., Chen, C., He, Q., Xue, G., Wang,W., He, Y. and Dong, Q. (2016) Interaction effects of BDNF and COMT genes on resting-state brain activity and working memory. Front. Hum. Neurosci., 10, 540Google Scholar
- 37.Burrill, J. D., Moran, L., Goulding, M. D. and Saueressig, H. (1997) PAX2 is expressed in multiple spinal cord interneurons, including a population of EN1+ interneurons that require PAX6 for their development. Development, 124, 4493–4503Google Scholar
- 38.Ziman, M. R., Rodger, J., Chen, P., Papadimitriou, J. M., Dunlop, S. A. and Beazley, L. D. (2001) Pax genes in development and maturation of the vertebrate visual system: implications for optic nerve regeneration. Histol. Histopathol., 16, 239–249Google Scholar
- 40.Iorio, F., Bosotti, R., Scacheri, E., Belcastro, V., Mithbaokar, P., Ferriero, R., Murino, L., Tagliaferri, R., Brunetti-Pierri, N., Isacchi, A., et al. (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl. Acad. Sci. USA, 107, 14621–14626CrossRefGoogle Scholar
- 44.Agudelo, M., Gandhi, N., Saiyed, Z., Pichili, V., Thangavel, S., Khatavkar, P., Yndart-Arias, A. and Nair, M. (2011) Effects of alcohol on histone deacetylase 2 (HDAC2) and the neuroprotective role of trichostatin A (TSA). Alcohol. Clin. Exp. Res., 35, 1550–1556Google Scholar
- 46.Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., et al. (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res., 45, D362–D368CrossRefGoogle Scholar
- 47.Breiman, L. I., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984) Classification and Regression Trees (CART). 1 Ed., Chapman and Hall/CRCGoogle Scholar
- 50.Vandin, F., Clay, P., Upfal, E. and Raphael, B. J. (2012) Discovery of mutated subnetworks associated with clinical data in cancer. In Proceedings of the Pacific Symposium of Biocomputing 2012, pp. 55–66. World ScientificGoogle Scholar
- 54.Templeton, J. P. and Geisert, E. E. (2012) A practical approach to optic nerve crush in the mouse. Mol. Vis., 18, 2147–2152Google Scholar