Lysosomal degradation products induce Coxiella burnetii virulence.

Coxiella burnetii is an intracellular pathogen that replicates in a lysosome-like vacuole through activation of a Dot/Icm-type IVB secretion system and subsequent translocation of effectors that remodel the host cell. Here a genome-wide small interfering RNA screen and reporter assay were used to identify host proteins required for Dot/Icm effector translocation. Significant, and independently validated, hits demonstrated the importance of multiple protein families required for endocytic trafficking of the C. burnetii-containing vacuole to the lysosome. Further analysis demonstrated that the degradative activity of the lysosome created by proteases, such as TPP1, which are transported to the lysosome by receptors, such as M6PR and LRP1, are critical for C. burnetii virulence. Indeed, the C. burnetii PmrA/B regulon, responsible for transcriptional up-regulation of genes encoding the Dot/Icm apparatus and a subset of effectors, induced expression of a virulence-associated transcriptome in response to degradative products of the lysosome. Luciferase reporter strains, and subsequent RNA-sequencing analysis, demonstrated that particular amino acids activate the C. burnetii PmrA/B two-component system. This study has further enhanced our understanding of C. burnetii pathogenesis, the host-pathogen interactions that contribute to bacterial virulence, and the different environmental triggers pathogens can sense to facilitate virulence.

The RNA polymerase I transcription inhibitor CX-5461 cooperates with topoisomerase 1 inhibition by enhancing the DNA damage response in homologous recombination-proficient high-grade serous ovarian cancer.

BACKGROUND: Intrinsic and acquired drug resistance represent fundamental barriers to the cure of high-grade serous ovarian carcinoma (HGSC), the most common histological subtype accounting for the majority of ovarian cancer deaths. Defects in homologous recombination (HR) DNA repair are key determinants of sensitivity to chemotherapy and poly-ADP ribose polymerase inhibitors. Restoration of HR is a common mechanism of acquired resistance that results in patient mortality, highlighting the need to identify new therapies targeting HR-proficient disease. We have shown promise for CX-5461, a cancer therapeutic in early phase clinical trials, in treating HR-deficient HGSC. METHODS: Herein, we screen the whole protein-coding genome to identify potential targets whose depletion cooperates with CX-5461 in HR-proficient HGSC. RESULTS: We demonstrate robust proliferation inhibition in cells depleted of DNA topoisomerase 1 (TOP1). Combining the clinically used TOP1 inhibitor topotecan with CX-5461 potentiates a G2/M cell cycle checkpoint arrest in multiple HR-proficient HGSC cell lines. The combination enhances a nucleolar DNA damage response and global replication stress without increasing DNA strand breakage, significantly reducing clonogenic survival and tumour growth in vivo. CONCLUSIONS: Our findings highlight the possibility of exploiting TOP1 inhibition to be combined with CX-5461 as a non-genotoxic approach in targeting HR-proficient HGSC.

Supervised, semi-supervised and unsupervised inference of gene regulatory networks.

Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression data. The results reveal low prediction accuracies for unsupervised techniques with the notable exception of the Z-SCORE method on knockout data. In all other cases, the supervised approach achieved the highest accuracies and even in a semi-supervised setting with small numbers of only positive samples, outperformed the unsupervised techniques.

RMaNI: Regulatory Module Network Inference framework.

BACKGROUND: Cell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community. RESULTS: We present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmani CONCLUSION: RMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.

Nuclear stabilization of p53 requires a functional nucleolar surveillance pathway.

The nucleolar surveillance pathway monitors nucleolar integrity and responds to nucleolar stress by mediating binding of ribosomal proteins to MDM2, resulting in p53 accumulation. Inappropriate pathway activation is implicated in the pathogenesis of ribosomopathies, while drugs selectively activating the pathway are in trials for cancer. Despite this, the molecular mechanism(s) regulating this process are poorly understood. Using genome-wide loss-of-function screens, we demonstrate the ribosome biogenesis axis as the most potent class of genes whose disruption stabilizes p53. Mechanistically, we identify genes critical for regulation of this pathway, including HEATR3. By selectively disabling the nucleolar surveillance pathway, we demonstrate that it is essential for the ability of all nuclear-acting stresses, including DNA damage, to induce p53 accumulation. Our data support a paradigm whereby the nucleolar surveillance pathway is the central integrator of stresses that regulate nuclear p53 abundance, ensuring that ribosome biogenesis is hardwired to cellular proliferative capacity.

Genome-wide RNAi screen for genes regulating glycolytic response to vemurafenib in BRAFV600 melanoma cells.

Identification of mechanisms underlying sensitivity and response to targeted therapies, such as the BRAF inhibitor vemurafenib, is critical in order to improve efficacy of these therapies in the clinic and delay onset of resistance. Glycolysis has emerged as a key feature of the BRAF inhibitor response in melanoma cells, and importantly, the metabolic response to vemurafenib in melanoma patients can predict patient outcome. Here, we present a multiparameter genome-wide siRNA screening dataset of genes that when depleted improve the viability and glycolytic response to vemurafenib in BRAFV600 mutated melanoma cells. These datasets are suitable for analysis of genes involved in cell viability and glycolysis in steady state conditions and following treatment with vemurafenib, as well as computational approaches to identify gene regulatory networks that mediate response to BRAF inhibition in melanoma.

A functional genetic screen defines the AKT-induced senescence signaling network.

Exquisite regulation of PI3K/AKT/mTORC1 signaling is essential for homeostatic control of cell growth, proliferation, and survival. Aberrant activation of this signaling network is an early driver of many sporadic human cancers. Paradoxically, sustained hyperactivation of the PI3K/AKT/mTORC1 pathway in nontransformed cells results in cellular senescence, which is a tumor-suppressive mechanism that must be overcome to promote malignant transformation. While oncogene-induced senescence (OIS) driven by excessive RAS/ERK signaling has been well studied, little is known about the mechanisms underpinning the AKT-induced senescence (AIS) response. Here, we utilize a combination of transcriptome and metabolic profiling to identify key signatures required to maintain AIS. We also employ a whole protein-coding genome RNAi screen for AIS escape, validating a subset of novel mediators and demonstrating their preferential specificity for AIS as compared with OIS. As proof of concept of the potential to exploit the AIS network, we show that neurofibromin 1 (NF1) is upregulated during AIS and its ability to suppress RAS/ERK signaling facilitates AIS maintenance. Furthermore, depletion of NF1 enhances transformation of p53-mutant epithelial cells expressing activated AKT, while its overexpression blocks transformation by inducing a senescent-like phenotype. Together, our findings reveal novel mechanistic insights into the control of AIS and identify putative senescence regulators that can potentially be targeted, with implications for new therapeutic options to treat PI3K/AKT/mTORC1-driven cancers.

A Comprehensive Protocol Resource for Performing Pooled shRNA and CRISPR Screens.

This chapter details a compendium of protocols that collectively enable the reader to perform a pooled shRNA and/or CRISPR screen-with methods to identify and validate positive controls and subsequent hits; establish a viral titer in the cell line of choice; create and screen libraries, sequence strategies, and bioinformatics resources to analyze outcomes. Collectively, this provides an overarching resource from the start to finish of a screening project, making this technology possible in all laboratories.

An interactive web-based application for Comprehensive Analysis of RNAi-screen Data.

RNAi screens are widely used in functional genomics. Although the screen data can be susceptible to a number of experimental biases, many of these can be corrected by computational analysis. For this purpose, here we have developed a web-based platform for integrated analysis and visualization of RNAi screen data named CARD (for Comprehensive Analysis of RNAi Data; available at https://card.niaid.nih.gov). CARD allows the user to seamlessly carry out sequential steps in a rigorous data analysis workflow, including normalization, off-target analysis, integration of gene expression data, optimal thresholds for hit selection and network/pathway analysis. To evaluate the utility of CARD, we describe analysis of three genome-scale siRNA screens and demonstrate: (i) a significant increase both in selection of subsequently validated hits and in rejection of false positives, (ii) an increased overlap of hits from independent screens of the same biology and (iii) insight to microRNA (miRNA) activity based on siRNA seed enrichment.

Systematic Screening Identifies Dual PI3K and mTOR Inhibition as a Conserved Therapeutic Vulnerability in Osteosarcoma.

PURPOSE: Osteosarcoma is the most common cancer of bone occurring mostly in teenagers. Despite rapid advances in our knowledge of the genetics and cell biology of osteosarcoma, significant improvements in patient survival have not been observed. The identification of effective therapeutics has been largely empirically based. The identification of new therapies and therapeutic targets are urgently needed to enable improved outcomes for osteosarcoma patients. EXPERIMENTAL DESIGN: We have used genetically engineered murine models of human osteosarcoma in a systematic, genome-wide screen to identify new candidate therapeutic targets. We performed a genome-wide siRNA screen, with or without doxorubicin. In parallel, a screen of therapeutically relevant small molecules was conducted on primary murine- and primary human osteosarcoma-derived cell cultures. All results were validated across independent cell cultures and across human and mouse osteosarcoma. RESULTS: The results from the genetic and chemical screens significantly overlapped, with a profound enrichment of pathways regulated by PI3K and mTOR pathways. Drugs that concurrently target both PI3K and mTOR were effective at inducing apoptosis in primary osteosarcoma cell cultures in vitro in both human and mouse osteosarcoma, whereas specific PI3K or mTOR inhibitors were not effective. The results were confirmed with siRNA and small molecule approaches. Rationale combinations of specific PI3K and mTOR inhibitors could recapitulate the effect on osteosarcoma cell cultures. CONCLUSIONS: The approaches described here have identified dual inhibition of the PI3K-mTOR pathway as a sensitive, druggable target in osteosarcoma, and provide rationale for translational studies with these agents.

INsPeCT: INtegrative Platform for Cancer Transcriptomics.

The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChIP-seq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at http://inspect.braembl.org.au.

Complex-based analysis of dysregulated cellular processes in cancer.

BACKGROUND: Differential expression analysis of (individual) genes is often used to study their roles in diseases. However, diseases such as cancer are a result of the combined effect of multiple genes. Gene products such as proteins seldom act in isolation, but instead constitute stable multi-protein complexes performing dedicated functions. Therefore, complexes aggregate the effect of individual genes (proteins) and can be used to gain a better understanding of cancer mechanisms. Here, we observe that complexes show considerable changes in their expression, in turn directed by the concerted action of transcription factors (TFs), across cancer conditions. We seek to gain novel insights into cancer mechanisms through a systematic analysis of complexes and their transcriptional regulation. RESULTS: We integrated large-scale protein-interaction (PPI) and gene-expression datasets to identify complexes that exhibit significant changes in their expression across different conditions in cancer. We devised a log-linear model to relate these changes to the differential regulation of complexes by TFs. The application of our model on two case studies involving pancreatic and familial breast tumour conditions revealed: (i) complexes in core cellular processes, especially those responsible for maintaining genome stability and cell proliferation (e.g. DNA damage repair and cell cycle) show considerable changes in expression; (ii) these changes include decrease and countering increase for different sets of complexes indicative of compensatory mechanisms coming into play in tumours; and (iii) TFs work in cooperative and counteractive ways to regulate these mechanisms. Such aberrant complexes and their regulating TFs play vital roles in the initiation and progression of cancer. CONCLUSIONS: Complexes in core cellular processes display considerable decreases and countering increases in expression, strongly reflective of compensatory mechanisms in cancer. These changes are directed by the concerted action of cooperative and counteractive TFs. Our study highlights the roles of these complexes and TFs and presents several case studies of compensatory processes, thus providing novel insights into cancer mechanisms.

Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets.

BACKGROUND: Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. METHODS: We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. RESULTS: We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. CONCLUSIONS: Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.