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      Interactome Analysis of Microtubule-targeting Agents Reveals Cytotoxicity Bases in Normal Cells

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          Abstract

          Cancer causes millions of deaths annually and microtubule-targeting agents (MTAs) are the most commonly-used anti-cancer drugs. However, the high toxicity of MTAs on normal cells raises great concern. Due to the non-selectivity of MTA targets, we analyzed the interaction network in a non-cancerous human cell. Subnetworks of fourteen MTAs were reconstructed and the merged network was compared against a randomized network to evaluate the functional richness. We found that 71.4% of the MTA interactome nodes are shared, which affects cellular processes such as apoptosis, cell differentiation, cell cycle control, stress response, and regulation of energy metabolism. Additionally, possible secondary targets were identified as client proteins of interphase microtubules. MTAs affect apoptosis signaling pathways by interacting with client proteins of interphase microtubules, suggesting that their primary targets are non-tumor cells. The paclitaxel and doxorubicin networks share essential topological axes, suggesting synergistic effects. This may explain the exacerbated toxicity observed when paclitaxel and doxorubicin are used in combination for cancer treatment.

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          Most cited references36

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          Network-based classification of breast cancer metastasis

          Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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            Dynamic instability of microtubule growth.

            We report here that microtubules in vitro coexist in growing and shrinking populations which interconvert rather infrequently. This dynamic instability is a general property of microtubules and may be fundamental in explaining cellular microtubule organization.
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              Network modeling links breast cancer susceptibility and centrosome dysfunction.

              Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.
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                Author and article information

                Contributors
                Journal
                Genomics Proteomics Bioinformatics
                Genomics Proteomics Bioinformatics
                Genomics, Proteomics & Bioinformatics
                Elsevier
                1672-0229
                2210-3244
                12 December 2017
                December 2017
                12 December 2017
                : 15
                : 6
                : 352-360
                Affiliations
                Grupo de Investigaciones Biomédicas y Genética Aplicada – GIBGA, Universidad de Ciencias Aplicadas y Ambientales U.D.C.A., Bogotá 111166, Colombia
                Author notes
                [* ]Corresponding author. andresjulian1981@ 123456gmail.com
                [a]

                ORCID: 0000-0002-5790-1518.

                [b]

                ORCID: 0000-0003-1166-0920.

                Article
                S1672-0229(17)30171-7
                10.1016/j.gpb.2017.04.006
                5828656
                29246518
                83753f94-0e6f-412c-9bf1-84b8e1d5d832
                © 2017 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 November 2016
                : 17 March 2017
                : 13 April 2017
                Categories
                Original Research

                cancer treatment,microtubule-targeting agent,interactome analysis,cancer biology,apoptosis

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