Subnetwork identification and chemical modulation for neural regeneration: A study combining network guided forest and heat diffusion model

Hui Wang, Gang Wang, Li-Da Zhu, Xuan Xu, Bo Diao, Hong-Yu Zhang

PDF(5214 KB)
PDF(5214 KB)
Quant. Biol. ›› 2018, Vol. 6 ›› Issue (4) : 321-333. DOI: 10.1007/s40484-018-0159-0
RESEARCH ARTICLE
RESEARCH ARTICLE

Subnetwork identification and chemical modulation for neural regeneration: A study combining network guided forest and heat diffusion model

Author information +
History +

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.

Author summary

Compared with peripheral nervous system, the regenerative capacity of the central nervous system is extremely limited. Thus, it may lead to the discovery of treatment for central nerve injury by modulating gene networks underlying peripheral nerve injury. Based on the gene expression profiles of dorsal root ganglion after a sciatic nerve injury, we defined gene networks underlying peripheral nerve injury. After screening compounds that were likely to modulate gene expressions of the networks, trichostatin A, a histone deacetylase inhibitor, was validated to enhance neurite outgrowth in vivo via an optic nerve crush mouse model.

Graphical abstract

Keywords

network guided forest / HotNet2 / neural regeneration / axon growth / neurotrophic factors

Cite this article

Download citation ▾
Hui Wang, Gang Wang, Li-Da Zhu, Xuan Xu, Bo Diao, Hong-Yu Zhang. Subnetwork identification and chemical modulation for neural regeneration: A study combining network guided forest and heat diffusion model. Quant. Biol., 2018, 6(4): 321‒333 https://doi.org/10.1007/s40484-018-0159-0

References

[1]
Hulsebosch, C. E. (2002) Recent advances in pathophysiology and treatment of spinal cord injury. Adv. Physiol. Educ., 26, 238–255
CrossRef Pubmed Google scholar
[2]
Smith, D. S. and Pate Skene, J. H. (1997) A transcription-dependent switch controls competence of adult neurons for distinct modes of axon growth. J. Neurosci., 17, 646–658
CrossRef Pubmed Google scholar
[3]
Silver, J. and Miller, J. H. (2004) Regeneration beyond the glial scar. Nat. Rev. Neurosci., 5, 146–156
CrossRef Pubmed Google scholar
[4]
McKerracher, L., David, S., Jackson, D. L., Kottis, V., Dunn, R. J. and Braun, P. E. (1994) Identification of myelin-associated glycoprotein as a major myelin-derived inhibitor of neurite growth. Neuron, 13, 805–811
CrossRef Pubmed Google scholar
[5]
Fitch, M. T. and Silver, J. (2008) CNS injury, glial scars, and inflammation: inhibitory extracellular matrices and regeneration failure. Exp. Neurol., 209, 294–301
CrossRef Pubmed Google scholar
[6]
Filbin, M. T. (2003) Myelin-associated inhibitors of axonal regeneration in the adult mammalian CNS. Nat. Rev. Neurosci., 4, 703–713
CrossRef Pubmed Google scholar
[7]
Wang, K. C., Koprivica, V., Kim, J. A., Sivasankaran, R., Guo, Y., Neve, R. L. and He, Z. (2002) Oligodendrocyte-myelin glycoprotein is a Nogo receptor ligand that inhibits neurite outgrowth. Nature, 417, 941–944
CrossRef Pubmed Google scholar
[8]
Schmitt, A. B., Breuer, S., Liman, J., Buss, A., Schlangen, C., Pech, K., Hol, E. M., Brook, G. A., Noth, J. and Schwaiger, F.-W. (2003) Identification of regeneration-associated genes after central and peripheral nerve injury in the adult rat. BMC Neurosci., 4, 8
CrossRef Pubmed Google scholar
[9]
Giger, R. J., Hollis, E. R. 2nd and Tuszynski, M. H. (2010) Guidance molecules in axon regeneration. Cold Spring Harb. Perspect. Biol., 2, a001867
CrossRef Pubmed Google scholar
[10]
Zuo, J., Neubauer, D., Dyess, K., Ferguson, T. A. and Muir, D. (1998) Degradation of chondroitin sulfate proteoglycan enhances the neurite-promoting potential of spinal cord tissue. Exp. Neurol., 154, 654–662
CrossRef Pubmed Google scholar
[11]
Chen, M. S., Huber, A. B., van der Haar, M. E., Frank, M., Schnell, L., Spillmann, A. A., Christ, F. and Schwab, M. E. (2000) Nogo-A is a myelin-associated neurite outgrowth inhibitor and an antigen for monoclonal antibody IN-1. Nature, 403, 434–439
CrossRef Pubmed Google scholar
[12]
Rubin, B. P., Dusart, I. and Schwab, M. E. (1994) A monoclonal antibody (IN-1) which neutralizes neurite growth inhibitory proteins in the rat CNS recognizes antigens localized in CNS myelin. J. Neurocytol., 23, 209–217
CrossRef Pubmed Google scholar
[13]
Michaelevski, I., Segal-Ruder, Y., Rozenbaum, M., Medzihradszky, K. F., Shalem, O., Coppola, G., Horn-Saban, S., Ben-Yaakov, K., Dagan, S. Y., Rishal, I., (2010) Signaling to transcription networks in the neuronal retrograde injury response. Sci. Signal., 3, ra53
CrossRef Pubmed Google scholar
[14]
Nix, P., Hisamoto, N., Matsumoto, K. and Bastiani, M. (2011) Axon regeneration requires coordinate activation of p38 and JNK MAPK pathways. Proc. Natl. Acad. Sci. USA, 108, 10738–10743
CrossRef Pubmed Google scholar
[15]
Yiu, G. and He, Z. (2006) Glial inhibition of CNS axon regeneration. Nat. Rev. Neurosci., 7, 617–627
CrossRef Pubmed Google scholar
[16]
Horner, P. J. and Gage, F. H. (2000) Regenerating the damaged central nervous system. Nature, 407, 963–970
CrossRef Pubmed Google scholar
[17]
Mattson, M. P. (1989) Acetylcholine potentiates glutamate-induced neurodegeneration in cultured hippocampal neurons. Brain Res., 497, 402–406
CrossRef Pubmed Google scholar
[18]
Connor, B. and Dragunow, M. (1998) The role of neuronal growth factors in neurodegenerative disorders of the human brain. Brain Res. Rev., 27, 1–39
CrossRef Pubmed Google scholar
[19]
Kamei, N., Tanaka, N., Oishi, Y., Hamasaki, T., Nakanishi, K., Sakai, N. and Ochi, M. (2007) BDNF, NT-3, and NGF released from transplanted neural progenitor cells promote corticospinal axon growth in organotypic cocultures. Spine, 32, 1272–1278
CrossRef Pubmed Google scholar
[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., (2002) Progressive neurodegeneration in patients with primary immunodeficiency disease on IVIG treatment. Clin. Immunol., 102, 19–24
CrossRef Pubmed Google scholar
[21]
Blesch, A., Lu, P. and Tuszynski, M. H. (2002) Neurotrophic factors, gene therapy, and neural stem cells for spinal cord repair. Brain Res. Bull., 57, 833–838
CrossRef Pubmed Google scholar
[22]
Huang, D. W., McKerracher, L., Braun, P. E. and David, S. (1999) A therapeutic vaccine approach to stimulate axon regeneration in the adult mammalian spinal cord. Neuron, 24, 639–647
CrossRef Pubmed Google scholar
[23]
Sicotte, M., Tsatas, O., Jeong, S. Y., Cai, C.-Q., He, Z. and David, S. (2003) Immunization with myelin or recombinant Nogo-66/MAG in alum promotes axon regeneration and sprouting after corticospinal tract lesions in the spinal cord. Mol. Cell. Neurosci., 23, 251–263
CrossRef Pubmed Google scholar
[24]
Chandran, V., Coppola, G., Nawabi, H., Omura, T., Versano, R., Huebner, E. A., Zhang, A., Costigan, M., Yekkirala, A., Barrett, L., (2016) A systems-level analysis of the peripheral nerve intrinsic axonal growth program. Neuron, 89, 956–970
CrossRef Pubmed Google scholar
[25]
Dutkowski, J. and Ideker, T. (2011) Protein networks as logic functions in development and cancer. PLoS Comput. Biol., 7, e1002180
CrossRef Pubmed Google scholar
[26]
Dong, X., Jiang, Z., Peng, Y. L. and Zhang, Z. (2015) Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis. Plant Physiol., 167, 1186–1203
CrossRef Pubmed Google 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., (2015) Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet., 47, 106–114
CrossRef Pubmed Google scholar
[28]
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J. P., Subramanian, A., Ross, K. N., (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 313, 1929–1935
CrossRef Pubmed Google scholar
[29]
Johnson, W. E., Li, C. and Rabinovic, A. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127
CrossRef Pubmed Google scholar
[30]
Kim, J., So, S., Lee, H. J., Park, J. C., Kim, J. J. and Lee, H. (2013) DigSee: disease gene search engine with evidence sentences (version cancer). Nucleic Acids Res., 41, W510–W517
CrossRef Pubmed Google scholar
[31]
Fleming, C. E., Saraiva, M. J. and Sousa, M. M. (2007) Transthyretin enhances nerve regeneration. J. Neurochem., 103, 831–839
CrossRef Pubmed Google scholar
[32]
Egan, M. F., Goldberg, T. E., Kolachana, B. S., Callicott, J. H., Mazzanti, C. M., Straub, R. E., Goldman, D. and Weinberger, D. R. (2001) Effect of COMT Val108/158Met genotype on frontal lobe function and risk for schizophrenia. Proc. Natl. Acad. Sci. USA, 98, 6917–6922
CrossRef Pubmed Google 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, 540
CrossRef Pubmed Google scholar
[34]
Lewin, S. L., Utley, D. S., Cheng, E. T., Verity, A. N. and Terris, D. J. (1997) Simultaneous treatment with BDNF and CNTF after peripheral nerve transection and repair enhances rate of functional recovery compared with BDNF treatment alone. Laryngoscope, 107, 992–999
CrossRef Pubmed Google scholar
[35]
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res., 44, W90–W97
CrossRef Pubmed Google scholar
[36]
Namm, A., Arend, A. and Aunapuu, M. (2013) Pax proteins in embryogenesis and their role in nervous system development. Pap. Anthropol., 22, 133–142
CrossRef Google 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, 4493–4503
Pubmed
[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, 239–249
Pubmed
[39]
Raivich, G., Bohatschek, M., Da Costa, C., Iwata, O., Galiano, M., Hristova, M., Nateri, A. S., Makwana, M., Riera-Sans, L., Wolfer, D. P., (2004) The AP-1 transcription factor c-Jun is required for efficient axonal regeneration. Neuron, 43, 57–67
CrossRef Pubmed Google scholar
[40]
Iorio, F., Bosotti, R., Scacheri, E., Belcastro, V., Mithbaokar, P., Ferriero, R., Murino, L., Tagliaferri, R., Brunetti-Pierri, N., Isacchi, A., (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl. Acad. Sci. USA, 107, 14621–14626
CrossRef Pubmed Google scholar
[41]
Sakaue, Y., Sanada, M., Sasaki, T., Kashiwagi, A. and Yasuda, H. (2003) Amelioration of retarded neurite outgrowth of dorsal root ganglion neurons by overexpression of PKCδ in diabetic rats. Neuroreport, 14, 431–436
CrossRef Pubmed Google scholar
[42]
Duan, Q., Reid, S. P., Clark, N. R., Wang, Z., Fernandez, N. F., Rouillard, A. D., Readhead, B., Tritsch, S. R., Hodos, R., Hafner, M., (2016) L1000CDS2: LINCS L1000 characteristic direction signatures search engine. NPJ Syst. Biol. Appl., 2, 16015
CrossRef Pubmed Google scholar
[43]
Sun, F., Park, K. K., Belin, S., Wang, D., Lu, T., Chen, G., Zhang, K., Yeung, C., Feng, G., Yankner, B. A., (2011) Sustained axon regeneration induced by co-deletion of PTEN and SOCS3. Nature, 480, 372–375
CrossRef Pubmed Google 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, 1550–1556
CrossRef Pubmed Google scholar
[45]
Bolstad, B. M., Collin, F., Simpson, K. M., Irizarry, R. A. and Speed, T. P. (2004) Experimental design and low-level analysis of microarray data. Int. Rev. Neurobiol., 60, 25–58
CrossRef Pubmed Google scholar
[46]
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res., 45, D362–D368
CrossRef Pubmed Google 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/CRC
[48]
Chung, F. (2007) The heat kernel as the pagerank of a graph. Proc. Natl. Acad. Sci. USA, 104, 19735–19740
CrossRef Google scholar
[49]
Vandin, F., Upfal, E. and Raphael, B. J. (2011) Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol., 18, 507–522
CrossRef Pubmed Google 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 Scientific
[51]
Huang, W., Sherman, B. T. and Lempicki, R. A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1–13
CrossRef Pubmed Google scholar
[52]
Huang, W., Sherman, B. T. and Lempicki, R. A. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44–57
CrossRef Pubmed Google scholar
[53]
Mi, H., Huang, X., Muruganujan, A., Tang, H., Mills, C., Kang, D. and Thomas, P. D. (2017) PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res., 45, D183–D189
CrossRef Pubmed Google scholar
[54]
Templeton, J. P. and Geisert, E. E. (2012) A practical approach to optic nerve crush in the mouse. Mol. Vis., 18, 2147–2152
Pubmed

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-018-0159-0.

ACKNOWLEDGEMENTS

This work was supported by the Fundamental Research Funds for the Central Universities (No. 2662017PY115)

COMPLIANCE WITH ETHICS GUIDELINES

The authors Hui Wang, Gang Wang, Li-Da Zhu, Xuan Xu, Bo Diao and Hong-Yu Zhang declare that they have no conflict of interests.
All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
AI Summary AI Mindmap
PDF(5214 KB)

Accesses

Citations

Detail

Sections
Recommended

/