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Combinatorial drug design targeting multiple cancer signaling networks controlled by mitochondrial Hsp90.

ABSTRACT: Although therapeutically targeting a single signaling pathway that drives tumor development and/or progression has been effective for a number of cancers, in many cases this approach has not been successful. Targeting networks of signaling pathways, instead of isolated pathways, may overcome this problem, which is probably due to the extreme heterogeneity of human tumors. However, the possibility that such networks may be spatially arranged in specialized subcellular compartments is not often considered in pathway-oriented drug discovery and may influence the design of new agents. Hsp90 is a chaperone protein that controls the folding of proteins in multiple signaling networks that drive tumor development and progression. Here, we report the synthesis and properties of Gamitrinibs, a class of small molecules designed to selectively target Hsp90 in human tumor mitochondria. Gamitrinibs were shown to accumulate in the mitochondria of human tumor cell lines and to inhibit Hsp90 activity by acting as ATPase antagonists. Unlike Hsp90 antagonists not targeted to mitochondria, Gamitrinibs exhibited a "mitochondriotoxic" mechanism of action, causing rapid tumor cell death and inhibiting the growth of xenografted human tumor cell lines in mice. Importantly, Gamitrinibs were not toxic to normal cells or tissues and did not affect Hsp90 homeostasis in cellular compartments other than mitochondria. Therefore, combinatorial drug design, whereby inhibitors of signaling networks are targeted to specific subcellular compartments, may generate effective anticancer drugs with novel mechanisms of action.

SUBMITTER: Kang BH

PROVIDER: S-EPMC2648691 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Combinatorial drug design targeting multiple cancer signaling networks controlled by mitochondrial Hsp90.

Kang Byoung Heon BH Plescia Janet J Song Ho Young HY Meli Massimiliano M Colombo Giorgio G Beebe Kristin K Scroggins Bradley B Neckers Len L Altieri Dario C DC

The Journal of clinical investigation 20090223 3

Although therapeutically targeting a single signaling pathway that drives tumor development and/or progression has been effective for a number of cancers, in many cases this approach has not been successful. Targeting networks of signaling pathways, instead of isolated pathways, may overcome this problem, which is probably due to the extreme heterogeneity of human tumors. However, the possibility that such networks may be spatially arranged in specialized subcellular compartments is not often co ...[more]

PMID: 19229106

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Project description:Macroautophagy/autophagy (hereafter autophagy), the process of mass degradation of unnecessary elements within the cell, is often dysregulated in many diseases such as cancer, atherosclerosis, and neurodegenerative diseases. Hence, autophagy modulating agents have a great potential to be therapeutic agents for the autophagy-related diseases. Here we report that an anti-depressant drug sertraline (Sert) is an autophagy-inducing agent. Mechanistically, Sert potentially binds to and antagonizes the mitochondrial VDAC1 (voltage dependent anion channel 1), resulting in reduced cellular ATP (adenosine triphosphate) level, activation of AMP-activated protein kinase (AMPK) and inhibition of its downstream, MTOR (mechanistic target of rapamycin kinase)-RPS6KB1 (ribosomal protein S6 kinase B1) signaling pathway. Cells lacking VDAC1 expression completely abrogate the modulatory effect of Sert on AMPK-MTOR pathway and autophagy-inducing activity. We further show that Sert suppresses tauopathy by promoting the autophagic degradation of MAPT (microtubule associated protein tau) protein via inducing autophagy. Our study demonstrates the potential of Sert as a novel small molecule autophagy-inducing agent and provides a new drug candidate to treat autophagy related diseases by targeting VDAC1.Abbreviations: AMP: adenosine monophosphate; AMPK: AMP-activated protein kinase; ATP: adenosine triphosphate; Baf: bafilomycin A1; BiFC: biomolecular fluorescence complementation; CAMKK2/CAMKKB: calcium/calmodulin dependent protein kinase kinase 2; CC: compound C; DARTS: drug affinity responsive target stability; HUVECs: human umbilical vein endothelial cells; Inda: indatraline; STK11/LKB1: serine/threonine kinase 11; MAPT: microtubule associated protein tau; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; 3-MA: 3-methyladenine; MEFs: mouse embryonic fibroblasts; MTOR: mechanistic target of rapamycin kinase; PI3K: phosphoinositide 3-kinase; Rapa: rapamycin; Sert: sertraline; RPS6KB1: ribosomal protein S6 kinase B1; SQSTM1/p62: sequestosome 1; SLC6A4/SERT1: solute carrier family 6 member 4; TFEB: transcription factor EB; VDAC1: voltage dependent anion channel 1; WT: wild-type; WM: wortmannin.

Project description:MOTIVATION:Perturbation experiments constitute the central means to study cellular networks. Several confounding factors complicate computational modeling of signaling networks from this data. First, the technique of RNA interference (RNAi), designed and commonly used to knock-down specific genes, suffers from off-target effects. As a result, each experiment is a combinatorial perturbation of multiple genes. Second, the perturbations propagate along unknown connections in the signaling network. Once the signal is blocked by perturbation, proteins downstream of the targeted proteins also become inactivated. Finally, all perturbed network members, either directly targeted by the experiment, or by propagation in the network, contribute to the observed effect, either in a positive or negative manner. One of the key questions of computational inference of signaling networks from such data are, how many and what combinations of perturbations are required to uniquely and accurately infer the model? RESULTS:Here, we introduce an enhanced version of linear effects models (LEMs), which extends the original by accounting for both negative and positive contributions of the perturbed network proteins to the observed phenotype. We prove that the enhanced LEMs are identified from data measured under perturbations of all single, pairs and triplets of network proteins. For small networks of up to five nodes, only perturbations of single and pairs of proteins are required for identifiability. Extensive simulations demonstrate that enhanced LEMs achieve excellent accuracy of parameter estimation and network structure learning, outperforming the previous version on realistic data. LEMs applied to Bartonella henselae infection RNAi screening data identified known interactions between eight nodes of the infection network, confirming high specificity of our model and suggested one new interaction. AVAILABILITY AND IMPLEMENTATION:https://github.com/EwaSzczurek/LEM. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

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Combinatorial drug design targeting multiple cancer signaling networks controlled by mitochondrial Hsp90.

Publications

Combinatorial drug design targeting multiple cancer signaling networks controlled by mitochondrial Hsp90.

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