Differential RNAi screening provides insights into the rewiring of signalling networks during oxidative stress

Mutant p53-Associated Molecular Mechanisms of ROS Regulation in Cancer Cells

The TP53 tumor suppressor gene is the most frequently altered gene in tumors and an increasing number of studies highlight that mutant p53 proteins can acquire oncogenic properties, referred to as gain-of-function (GOF). Reactive oxygen species (ROS) play critical roles as intracellular messengers, regulating numerous signaling pathways linked to metabolism and cell growth. Tumor cells frequently display higher ROS levels compared to healthy cells as a result of their increased metabolism as well as serving as an oncogenic agent because of its damaging and mutational properties. Several studies reported that in contrast with the wild type protein, mutant p53 isoforms fail to exert antioxidant activities and rather increase intracellular ROS, driving a pro-tumorigenic survival. These pro-oxidant oncogenic abilities of GOF mutant p53 include signaling and metabolic rewiring, as well as the modulation of critical ROS-related transcription factors and antioxidant systems, which lead ROS unbalance linked to tumor progression. The studies summarized here highlight that GOF mutant p53 isoforms might constitute major targets for selective therapeutic intervention against several types of tumors and that ROS enhancement driven by mutant p53 might represent an “Achilles heel” of cancer cells, suggesting pro-oxidant drugs as a therapeutic approach for cancer patients bearing the mutant TP53 gene.

Chemoresistance of Lung and Breast Cancer Cells Growing Under Prolonged Periods of Serum Starvation

The efficacy of chemotherapy is hindered by both tumor heterogeneity and acquired or intrinsic multi-drug resistance caused by the contribution of multidrug resistance proteins and stemness-associated prosurvival markers. Therefore, targeting multi-drug resistant cells would be much more effective against cancer. In this study, we characterized the chemoresistance properties of adherent (anchorage-dependent) lung H460 and breast MCF-7 cancer cells growing under prolonged periods of serum starvation (PPSS). We found that under PPSS, both cell lines were highly resistant to Paclitaxel, Colchicine, Hydroxyurea, Obatoclax, Wortmannin, and LY294002. Levels of several proteins associated with increased stemness such as Sox2, MDR1, ABCG2, and Bcl-2 were found to be elevated in H460 cells but not in MCF-7 cells. While pharmacological inhibition of either MDR1, ABCG2, Bcl-2 with Verapamil, Sorafenib, or Obatoclax, respectively decreased the levels of their target proteins under routine culture conditions as expected, such inhibition did not reverse PX resistance in PPSS conditions. Paradoxically, treatment with inhibitors in serum-starved conditions produced an elevation of their respective target proteins. In addition, we found that Digitoxin, an FDA approved drug that decrease the viability of cancer cells growing under PPSS, downregulates the expression of Sox2, MDR1, phospho- AKT, Wnt5a/b, and β-catenin. Our data suggest that PPSS-induced chemoresistance is the result of extensive rewiring of intracellular signaling networks and that multi-resistance can be effectively overcome by simultaneously targeting multiple targets of the rewired network. Furthermore, our PPSS model provides a simple and useful tool to screen drugs for their ability to target multiple pathways of cancer resistance. J. Cell. Physiol. 232: 2033-2043, 2017. © 2016 Wiley Periodicals, Inc.

High-Content Imaging and RNAi Screens for Investigating Kinase Network Plasticity

High-content imaging connects the information-rich method of microscopy with the systematic objective principles of software-driven analysis. Suited to automation and, therefore, considerable scale-up of study size, this approach can deliver multiparametric data over cell populations or at the level of the individual cell and has found considerable utility in reverse genetic and pharmacological screens. Here we present a method to screen small interfering RNA (siRNA) libraries allowing subsequent observation of the impact of each knockdown on two interlinked, high-content, G1-/S-phase cell cycle transition assays related to cyclin-dependent kinase (CDK) 2 activity. We show how plasticity within the network governing the activity of this kinase can be detected by combining modifier siRNAs with a siRNA library. The method uses fluorescent immunostaining of a nuclear antigen, CyclinA, following cell fixation while also preserving the fluorescence of a stably expressed fluorescent protein-tagged reporter for CDK2 activity. We provide methodology for data extraction and handling including an R-script that converts the multidimensional data into four simple binary outcomes, on which a hit-mining strategy can be built. The workflow described can in principle be adopted to yield quantitative single-cell-resolved data and mining for outcomes relating to a broad range of other similar readouts and signaling contexts.

A sensitised RNAi screen reveals a ch-TOG genetic interaction network required for spindle assembly

How multiple spindle assembly pathways are integrated to drive bipolar spindle assembly is poorly understood. We performed an image-based double RNAi screen to identify genes encoding Microtubule-Associated Proteins (MAPs) that interact with the highly conserved ch-TOG gene to regulate bipolar spindle assembly in human cells. We identified a ch-TOG centred network of genetic interactions which promotes ensures centrosome-mediated microtubule polymerisation, leading to the incorporation of microtubules polymerised by all pathways into a bipolar structure. Our genetic screen also reveals that ch-TOG maintains a dynamic microtubule population, in part, through modulating HSET activity. ch-TOG ensures that spindle assembly is robust to perturbation but sufficiently dynamic such that spindles can explore a diverse shape space in search of structures that can align chromosomes.

Inferring signalling networks from images

The mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost-effective means to infer connectivity between genes on a systems-level is by quantifying the similarity between high-dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens.