Kim Clarke

A Systems Biology Approach Identifies Molecular Networks Defining Skeletal Muscle Abnormalities in Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory process of the lung inducing persistent airflow limitation. Extensive systemic effects, such as skeletal muscle dysfunction, often characterize these patients and severely limit life expectancy. Despite considerable research efforts, the molecular basis of muscle degeneration in COPD is still a matter of intense debate. In this study, we have applied a network biology approach to model the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. Our model shows that failure to co-ordinately activate expression of several tissue remodelling and bioenergetics pathways is a specific landmark of COPD diseased muscles. Our findings also suggest that this phenomenon may be linked to an abnormal expression of a number of histone modifiers, which we discovered correlate with oxygen utilization. These observations raised the interesting possibility that cell hypoxia may be a key factor driving skeletal muscle degeneration in COPD patients.

eIF6 coordinates insulin sensitivity and lipid metabolism by coupling translation to transcription

Insulin regulates glycaemia, lipogenesis and increases mRNA translation. Cells with reduced eukaryotic initiation factor 6 (eIF6) do not increase translation in response to insulin. The role of insulin-regulated translation is unknown. Here we show that reduction of insulin-regulated translation in mice heterozygous for eIF6 results in normal glycaemia, but less blood cholesterol and triglycerides. eIF6 controls fatty acid synthesis and glycolysis in a cell autonomous fashion. eIF6 acts by exerting translational control of adipogenic transcription factors like C/EBPβ, C/EBPδ and ATF4 that have G/C rich or uORF sequences in their 5′ UTR. The outcome of the translational activation by eIF6 is a reshaping of gene expression with increased levels of lipogenic and glycolytic enzymes. Finally, eIF6 levels modulate histone acetylation and amounts of rate-limiting fatty acid synthase (Fasn) mRNA. Since obesity, type 2 diabetes, and cancer require a Fasn-driven lipogenic state, we propose that eIF6 could be a therapeutic target for these diseases.

Characterization of mutations in the PAS domain of the EvgS sensor kinase selected by laboratory evolution for acid resistance in Escherichia coli

Laboratory‐based evolution and whole‐genome sequencing can link genotype and phenotype. We used evolution of acid resistance in exponential phase Escherichia coli to study resistance to a lethal stress. Iterative selection at pH 2.5 generated five populations that were resistant to low pH in early exponential phase. Genome sequencing revealed multiple mutations, but the only gene mutated in all strains was evgS, part of a two‐component system that has already been implicated in acid resistance. All these mutations were in the cytoplasmic PAS domain of EvgS, and were shown to be solely responsible for the resistant phenotype, causing strong upregulation at neutral pH of genes normally induced by low pH. Resistance to pH 2.5 in these strains did not require the transporter GadC, or the sigma factor RpoS. We found that EvgS‐dependent constitutive acid resistance to pH 2.5 was retained in the absence of the regulators GadE or YdeO, but was lost if the oxidoreductase YdeP was also absent. A deletion in the periplasmic domain of EvgS abolished the response to low pH, but not the activity of the constitutive mutants. On the basis of these results we propose a model for how EvgS may become activated by low pH.

A systems biology approach reveals a link between systemic cytokines and skeletal muscle energy metabolism in a rodent smoking model and human COPD

BackgroundA relatively large percentage of patients with chronic obstructive pulmonary disease (COPD) develop systemic co-morbidities that affect prognosis, among which muscle wasting is particularly debilitating. Despite significant research effort, the pathophysiology of this important extrapulmonary manifestation is still unclear. A key question that remains unanswered is to what extent systemic inflammatory mediators might play a role in this pathology.Cigarette smoke (CS) is the main risk factor for developing COPD and therefore animal models chronically exposed to CS have been proposed for mechanistic studies and biomarker discovery. Although mice have been successfully used as a pre-clinical in vivo model to study the pulmonary effects of acute and chronic CS exposure, data suggest that they may be inadequate models for studying the effects of CS on peripheral muscle function. In contrast, recent findings indicate that the guinea pig model (Cavia porcellus) may better mimic muscle wasting.MethodsWe have used a systems biology approach to compare the transcriptional profile of hindlimb skeletal muscles from a Guinea pig rodent model exposed to CS and/or chronic hypoxia to COPD patients with muscle wasting.ResultsWe show that guinea pigs exposed to long-term CS accurately reflect most of the transcriptional changes observed in dysfunctional limb muscle of severe COPD patients when compared to matched controls. Using network inference, we could then show that the expression profile in whole lung of genes encoding for soluble inflammatory mediators is informative of the molecular state of skeletal muscles in the guinea pig smoking model. Finally, we show that CXCL10 and CXCL9, two of the candidate systemic cytokines identified using this pre-clinical model, are indeed detected at significantly higher levels in serum of COPD patients, and that their serum protein level is inversely correlated with the expression of aerobic energy metabolism genes in skeletal muscle.ConclusionsWe conclude that CXCL10 and CXCL9 are promising candidate inflammatory signals linked to the regulation of central metabolism genes in skeletal muscles. On a methodological level, our work also shows that a system level analysis of animal models of diseases can be very effective to generate clinically relevant hypothesis.

Dual roles for CXCL4 chemokines and CXCR3 in angiogenesis and invasion of pancreatic cancer

The CXCL4 paralog CXCL4L1 is a less studied chemokine that has been suggested to exert an antiangiogenic function. However, CXCL4L1 is also expressed in patient tumors, tumor cell lines, and murine xenografts, prompting a more detailed analysis of its role in cancer pathogenesis. We used genetic and antibody-based approaches to attenuate CXCL4L1 in models of pancreatic ductal adenocarcinoma (PDAC). Mechanisms of expression were assessed in cell coculture experiments, murine, and avian xenotransplants, including through an evaluation of CpG methylation and mutation of critical CpG residues. CXCL4L1 gene expression was increased greatly in primary and metastatic PDAC. We found that myofibroblasts triggered cues in the tumor microenvironment, which led to induction of CXCL4L1 in tumor cells. CXCL4L1 expression was also controlled by epigenetic modifications at critical CpG islands, which were mapped. CXCL4L1 inhibited angiogenesis but also affected tumor development more directly, depending on the tumor cell type. In vivo administration of an mAb against CXCL4L1 demonstrated a blockade in the growth of tumors positive for CXCR3, a critical receptor for CXCL4 ligands. Our findings define a protumorigenic role in PDAC development for endogenous CXCL4L1, which is independent of its antiangiogenic function. Cancer Res; 76(22); 6507-19. ©2016 AACR.

Eukaryotic translation initiation factor 6 is a novel regulator of reactive oxygen species-dependent megakaryocyte maturation

Ribosomopathies constitute a class of inherited disorders characterized by defects in ribosome biogenesis and function. Classically, bone marrow (BM) failure is a clinical symptom shared between these syndromes, including Shwachman–Bodian–Diamond syndrome (SBDS). Eukaryotic translation initiation factor 6 (eIF6) is a critical translation factor that rescues the quasilethal effect of the loss of the SBDS protein.

ChainRank, a chain prioritisation method for contextualisation of biological networks.

BackgroundAdvances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).ResultsOur method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation.ConclusionsChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank.

Inference of Low and High-Grade Glioma Gene Regulatory Networks Delineates the Role of Rnd3 in Establishing Multiple Hallmarks of Cancer

Gliomas are a highly heterogeneous group of brain tumours that are refractory to treatment, highly invasive and pro-angiogenic. Glioblastoma patients have an average survival time of less than 15 months. Understanding the molecular basis of different grades of glioma, from well differentiated, low-grade tumours to high-grade tumours, is a key step in defining new therapeutic targets. Here we use a data-driven approach to learn the structure of gene regulatory networks from observational data and use the resulting models to formulate hypothesis on the molecular determinants of glioma stage. Remarkably, integration of available knowledge with functional genomics datasets representing clinical and pre-clinical studies reveals important properties within the regulatory circuits controlling low and high-grade glioma. Our analyses first show that low and high-grade gliomas are characterised by a switch in activity of two subsets of Rho GTPases. The first one is involved in maintaining normal glial cell function, while the second is linked to the establishment of multiple hallmarks of cancer. Next, the development and application of a novel data integration methodology reveals novel functions of RND3 in controlling glioma cell migration, invasion, proliferation, angiogenesis and clinical outcome.

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

Abstract The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.

Kinome-wide transcriptional profiling of uveal melanoma reveals new vulnerabilities to targeted therapeutics

Metastatic uveal melanoma (UM) is invariably fatal, usually within a year of diagnosis. There are currently no effective therapies, and clinical studies employing kinase inhibitors have so far demonstrated limited success. This is despite common activating mutations in GNAQ/11 genes, which trigger signalling pathways that might predispose tumours to a variety of targeted drugs. In this study, we have profiled kinome expression network dynamics in various human ocular melanomas. We uncovered a shared transcriptional profile in human primary UM samples and across a variety of experimental cell‐based models. The poor overall response of UM cells to FDA‐approved kinase inhibitors contrasted with much higher sensitivity to the bromodomain inhibitor JQ1, a broad transcriptional repressor. Mechanistically, we identified a repressed FOXM1‐dependent kinase subnetwork in JQ1‐exposed cells that contained multiple cell cycle‐regulated protein kinases. Consistently, we demonstrated vulnerability of UM cells to inhibitors of mitotic protein kinases within this network, including the investigational PLK1 inhibitor BI6727. We conclude that analysis of kinome‐wide signalling network dynamics has the potential to reveal actionable drug targets and inhibitors of potential therapeutic benefit for UM patients.