Topological alternate centrality measure capturing drug targets in the network of MAPK pathways.

A new centrality of the nodes in the network is proposed called alternate centrality, which can isolate effective drug targets in the complex signalling network. Alternate centrality metric defined over the network substructure (four nodes - motifs). The nodes involving in alternative activation in the motifs gain in metric values. Targeting high alternative centrality nodes hypothesised to be destructive free to the network due to their alternative activation mechanism. Overlapping and crosstalk among the gene products in the conserved network of MAPK pathways selected for the study. In silico knock-out of high alternate centrality nodes causing rewiring in the network is investigated using MCF-7 breast cancer cell line-based data. Degree of top alternate centrality nodes lies between the degree of bridging and pagerank nodes. Node deletion of high alternate centrality on the centralities such as eccentricity, closeness, betweenness, stress, centroid and radiality causes low perturbation. The authors identified the following alternate centrality nodes ERK1, ERK2, MEKK2, MKK5, MKK4, MLK3, MLK2, MLK1, MEKK4, MEKK1, TAK1, P38alpha, ZAK, DLK, LZK, MLTKa/b and P38beta as efficient drug targets for breast cancer. Alternate centrality identifies effective drug targets and is free from intertwined biological processes and lethality.

[1]  Falk Schreiber,et al.  MAVisto: a tool for the exploration of network motifs , 2005, Bioinform..

[2]  Robert Preissner,et al.  WITHDRAWN—a resource for withdrawn and discontinued drugs , 2015, Nucleic Acids Res..

[3]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[4]  W-C Hwang,et al.  Identification of Information Flow‐Modulating Drug Targets: A Novel Bridging Paradigm for Drug Discovery , 2008, Clinical pharmacology and therapeutics.

[5]  P. Sundaramurthy,et al.  Dynamic Modeling and Simulation of JNK and P38 Kinase Cascades With Feedbacks and Crosstalks , 2010, IEEE Transactions on NanoBioscience.

[6]  Takashi Nakakuki,et al.  Quantitative transcriptional control of ErbB receptor signaling undergoes graded to biphasic response for cell differentiation. , 2006, The Journal of Biological Chemistry.

[7]  Pier Federico Gherardini,et al.  Deep Proteomics of Breast Cancer Cells Reveals that Metformin Rewires Signaling Networks Away from a Pro-growth State. , 2016, Cell systems.

[8]  R. Linding,et al.  Network‐based drugs and biomarkers , 2010, The Journal of pathology.

[9]  V. M. Chandrasekaran,et al.  Identification of cluster of proteins in the network of MAPK pathways as cancer drug targets , 2017 .

[10]  W. Cao,et al.  TAK1 inhibitor NG25 enhances doxorubicin-mediated apoptosis in breast cancer cells , 2016, Scientific Reports.

[11]  Jeffrey K. Aronson,et al.  Post-marketing withdrawal of anti-obesity medicinal products because of adverse drug reactions: a systematic review , 2016, BMC Medicine.

[12]  Julio Saez-Rodriguez,et al.  Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks , 2012, BMC Systems Biology.

[13]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[14]  P. Bonacich.  Factoring and weighting approaches to status scores and clique identification , 1972 .

[15]  P. Brown,et al.  Novel Agents for the Prevention of Breast Cancer: Targeting Transcription Factors and Signal Transduction Pathways , 2004, Journal of Mammary Gland Biology and Neoplasia.

[16]  Giovanni Scardoni,et al.  Identifying critical traffic jam areas with node centralities interference and robustness , 2012, Networks Heterog. Media.

[17]  Juancarlos Chan,et al.  Gene Ontology Consortium: going forward , 2014, Nucleic Acids Res..

[18]  Sergey Brin,et al.  Reprint of: The anatomy of a large-scale hypertextual web search engine , 2012, Comput. Networks.

[19]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[20]  F. Giancotti.  Deregulation of cell signaling in cancer , 2014, FEBS letters.

[21]  Brad T. Sherman,et al.  DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.

[22]  J. Downward.  The ins and outs of signalling , 2001, Nature.

[23]  David Haussler,et al.  ENCODE Data in the UCSC Genome Browser: year 5 update , 2012, Nucleic Acids Res..

[24]  Amy M Mingo-Sion,et al.  Inhibition of JNK reduces G2/M transit independent of p53, leading to endoreduplication, decreased proliferation, and apoptosis in breast cancer cells , 2004, Oncogene.

[25]  Jeffrey K Aronson,et al.  Worldwide withdrawal of medicinal products because of adverse drug reactions: a systematic review and analysis , 2016, Critical reviews in toxicology.

[26]  Chun-Hsi Huang,et al.  Current innovations and future challenges of network motif detection , 2015, Briefings Bioinform..

[27]  R. Schiff,et al.  Molecular changes in tamoxifen-resistant breast cancer: relationship between estrogen receptor, HER-2, and p38 mitogen-activated protein kinase. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[28]  S. Hilsenbeck,et al.  Inhibition of the p38 kinase suppresses the proliferation of human ER-negative breast cancer cells. , 2009, Cancer research.

[29]  A. Shimbel.  Structural parameters of communication networks , 1953 .

[30]  D. K. Arrell,et al.  Network Systems Biology for Drug Discovery , 2010, Clinical pharmacology and therapeutics.

[31]  M. Zelen,et al.  Rethinking centrality: Methods and examples☆ , 1989 .

[32]  A. Esparís-Ogando,et al.  Therapeutic potential of ERK5 targeting in triple negative breast cancer , 2014, Oncotarget.