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Computational Identification of Kinases That Control Axon Growth in Mouse.
SLAS Discov. 2020 Aug; 25(7):792-800.SD

Abstract

The determination of signaling pathways and transcriptional networks that control various biological processes is a major challenge from both basic science and translational medicine perspectives. Because such analysis can point to critical disease driver nodes to target for therapeutic purposes, we combined data from phenotypic screening experiments and gene expression studies of mouse neurons to determine information flow through a molecular interaction network using a network propagation approach. We hypothesized that differences in information flow between control and injured conditions prioritize relevant driver nodes that cause this state change. Identifying paths likely taken from potential source nodes to a set of transcription factors (TFs), called sinks, we found that kinases are enriched among source genes sending significantly different amounts of information to TFs in an axonal injury model. Additionally, TFs found to be differentially active during axon growth were enriched in the set of sink genes that received significantly altered amounts of information from source genes. Notably, such enrichment levels hold even when restricting the set of source genes to only those kinases observed to support or hamper neurite growth. That way, we found a set of 71 source genes that send significantly different levels of information to axon growth-relevant TFs. We analyzed their information flow changes in response to axonal injury and their influences on TFs predicted to facilitate or antagonize axon growth. Finally, we drew a network diagram of the interactions and changes in information flow between these source genes and their axon growth-relevant sink TFs.

Authors+Show Affiliations

Department of Computer Science, University of Miami, Miami, FL, USA.Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, FL, USA.Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, FL, USA. Miami Institute of Data Science and Computing, University of Miami, Miami, FL, USA. Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA. Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, FL, USA. Miami Institute of Data Science and Computing, University of Miami, Miami, FL, USA. Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA. Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.Department of Computer Science, University of Miami, Miami, FL, USA. Miami Institute of Data Science and Computing, University of Miami, Miami, FL, USA. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA. Department of Biology, University of Miami, Miami, FL, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32613890

Citation

Devkota, Prajwal, et al. "Computational Identification of Kinases That Control Axon Growth in Mouse." SLAS Discovery : Advancing Life Sciences R & D, vol. 25, no. 7, 2020, pp. 792-800.
Devkota P, Danzi MC, Lemmon VP, et al. Computational Identification of Kinases That Control Axon Growth in Mouse. SLAS Discov. 2020;25(7):792-800.
Devkota, P., Danzi, M. C., Lemmon, V. P., Bixby, J. L., & Wuchty, S. (2020). Computational Identification of Kinases That Control Axon Growth in Mouse. SLAS Discovery : Advancing Life Sciences R & D, 25(7), 792-800. https://doi.org/10.1177/2472555220930697
Devkota P, et al. Computational Identification of Kinases That Control Axon Growth in Mouse. SLAS Discov. 2020;25(7):792-800. PubMed PMID: 32613890.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Computational Identification of Kinases That Control Axon Growth in Mouse. AU - Devkota,Prajwal, AU - Danzi,Matt C, AU - Lemmon,Vance P, AU - Bixby,John L, AU - Wuchty,Stefan, Y1 - 2020/07/02/ PY - 2020/7/3/pubmed PY - 2020/7/3/medline PY - 2020/7/3/entrez KW - CNS and PNS diseases KW - gene expression KW - high-content screening KW - kinases KW - transcription factors SP - 792 EP - 800 JF - SLAS discovery : advancing life sciences R & D JO - SLAS Discov VL - 25 IS - 7 N2 - The determination of signaling pathways and transcriptional networks that control various biological processes is a major challenge from both basic science and translational medicine perspectives. Because such analysis can point to critical disease driver nodes to target for therapeutic purposes, we combined data from phenotypic screening experiments and gene expression studies of mouse neurons to determine information flow through a molecular interaction network using a network propagation approach. We hypothesized that differences in information flow between control and injured conditions prioritize relevant driver nodes that cause this state change. Identifying paths likely taken from potential source nodes to a set of transcription factors (TFs), called sinks, we found that kinases are enriched among source genes sending significantly different amounts of information to TFs in an axonal injury model. Additionally, TFs found to be differentially active during axon growth were enriched in the set of sink genes that received significantly altered amounts of information from source genes. Notably, such enrichment levels hold even when restricting the set of source genes to only those kinases observed to support or hamper neurite growth. That way, we found a set of 71 source genes that send significantly different levels of information to axon growth-relevant TFs. We analyzed their information flow changes in response to axonal injury and their influences on TFs predicted to facilitate or antagonize axon growth. Finally, we drew a network diagram of the interactions and changes in information flow between these source genes and their axon growth-relevant sink TFs. SN - 2472-5560 UR - https://www.unboundmedicine.com/medline/citation/32613890/Computational_Identification_of_Kinases_That_Control_Axon_Growth_in_Mouse L2 - https://journals.sagepub.com/doi/10.1177/2472555220930697?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -
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