Nil Turan

Deletion of Hexose-6-phosphate Dehydrogenase Activates the Unfolded Protein Response Pathway and Induces Skeletal Myopathy

Hexose-6-phosphate dehydrogenase (H6PD) is the initial component of a pentose phosphate pathway inside the endoplasmic reticulum (ER) that generates NADPH for ER enzymes. In liver H6PD is required for the 11-oxoreductase activity of 11β-hydroxysteroid dehydrogenase type 1, which converts inactive 11-oxo-glucocorticoids to their active 11-hydroxyl counterparts; consequently, H6PD null mice are relatively insensitive to glucocorticoids, exhibiting fasting hypoglycemia, increased insulin sensitivity despite elevated circulating levels of corticosterone, and increased basal and insulin-stimulated glucose uptake in muscles normally enriched in type II (fast) fibers, which have increased glycogen content. Here, we show that H6PD null mice develop a severe skeletal myopathy characterized by switching of type II to type I (slow) fibers. Running wheel activity and electrically stimulated force generation in isolated skeletal muscle are both markedly reduced. Affected muscles have normal sarcomeric structure at the electron microscopy level but contain large intrafibrillar membranous vacuoles and abnormal triads indicative of defects in structure and function of the sarcoplasmic reticulum (SR). SR proteins involved in calcium metabolism, including the sarcoplasmic/endoplasmic reticulum calcium ATPase (SERCA), calreticulin, and calsequestrin, show dysregulated expression. Microarray analysis and real-time PCR demonstrate overexpression of genes encoding proteins in the unfolded protein response pathway. We propose that the absence of H6PD induces a progressive myopathy by altering the SR redox state, thereby impairing protein folding and activating the unfolded protein response pathway. These studies thus define a novel metabolic pathway that links ER stress to skeletal muscle integrity and function.

Towards a System Level Understanding of Non-Model Organisms Sampled from the Environment: A Network Biology Approach

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

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.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

BackgroundTo enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.ResultsTo address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.ConclusionsWe generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Identification and Pathway Mapping of Furan Target Proteins Reveal Mitochondrial Energy Production and Redox Regulation as Critical Targets of Furan Toxicity

Furan, a heat-generated food contaminant, is hepatotoxic and carcinogenic in rodents. Furan is oxidized by cytochrome P450 2E1 to cis-2-butene-1,4-dial, a chemically reactive α,β-unsaturated dialdehyde, which has been identified as the key toxic metabolite of furan based on its ability to interact with tissue nucleophiles. In addition to genotoxicity, sustained cytotoxicity mediated through covalent binding of cis-2-butene-1,4-dial to critical target proteins is thought to play a key role in furan carcinogenicity. To identify putative protein targets of reactive furan metabolites, male F344/N rats (n = 5 per dose) were administered a single dose of [3,4-(14)C]-furan (20 mCi/mmol) at doses associated with hepatotoxicity following long-term exposure (0.1 and 2 mg/kg body weight [bw]). Liver proteins were separated by two-dimensional gel electrophoresis and protein spots carrying radiolabel were located by fluorography. In total, 83 discrete protein spots containing (14)C were consistently detected in livers of animals given [3,4-(14)C]-furan at 2.0 mg/kg bw, accounting for 4-5% of the proteome covered by our analyses. Protein spots were excised and digested in gel with trypsin for identification by protein mass spectrometry. Protein database search and subsequent pathway mapping identified 61 proteins localized predominantly in the cytosol and mitochondria, including structural proteins, mitochondrial enzymes involved in glucose metabolism, mitochondrial β-oxidation, and adenosine triphosphate synthesis, and proteins that participate in the maintenance of redox homeostasis and protein folding. Collectively, our data suggest that functional loss of several individual proteins and interference with pathways, most notably mitochondrial energy production, redox regulation, and protein folding, may combine to disrupt cell homeostasis and cause hepatocyte cell death.

Community detection using cooperative co-evolutionary differential evolution

In many scientific fields, from biology to sociology, community detection in complex networks has become increasingly important. This paper, for the first time, introduces Cooperative Co-evolution framework for detecting communities in complex networks. A Bias Grouping scheme is proposed to dynamically decompose a complex network into smaller subnetworks to handle large-scale networks. We adopt Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network. We also design a novel mutation operator specifically for community detection. The resulting algorithm, Cooperative Co-evolutionary DE based Community Detection (CCDECD) is evaluated on 5 small to large scale real-world social and biological networks. Experimental results show that CCDECD has very competitive performance compared with other state-of-the-art community detection algorithms.

Functional modules integrating essential cellular functions are predictive of the response of leukaemia cells to DNA damage

MOTIVATION Childhood B-precursor lymphoblastic leukaemia (ALL) is the most common paediatric malignancy. Despite the fact that 80% of ALL patients respond to anti-cancer drugs, the patho-physiology of this disease is still not fully understood. mRNA expression-profiling studies that have been performed have not yet provided novel insights into the mechanisms behind cellular response to DNA damage. More powerful data analysis techniques may be required for identifying novel functional pathways involved in the cellular responses to DNA damage. RESULTS In order to explore the possibility that unforeseen biological processes may be involved in the response to DNA damage, we have developed and applied a novel procedure for the identification of functional modules in ALL cells. We have discovered that the overall activity of functional modules integrating protein degradation and mRNA processing is predictive of response to DNA damage. AVAILABILITY Supplementary material including R code, additional results, experimental datasets, as well as a detailed description of the methodology are available at http://www.bip.bham.ac.uk/vivo/fumo.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Models and computational strategies linking physiological response to molecular networks from large-scale data

An important area of research in systems biology involves the analysis and integration of genome-wide functional datasets. In this context, a major goal is the identification of a putative molecular network controlling physiological response from experimental data. With very fragmentary mechanistic information, this is a challenging task. A number of methods have been developed, each one with the potential to address an aspect of the problem. Here, we review some of the most widely used methodologies and report new results in support of the usefulness of modularization and other modelling techniques in identifying components of the molecular networks that are predictive of physiological response. We also discuss how system identification in biology could be approached, using a combination of methodologies that aim to reconstruct the relationship between molecular pathways and physiology at different levels of the organizational complexity of the molecular network.

Protein kinase Msk1 physically and functionally interacts with the KMT2A/MLL1 methyltransferase complex and contributes to the regulation of multiple target genes

BackgroundThe KMT2A/MLL1 lysine methyltransferase complex is an epigenetic regulator of selected developmental genes, in part through the SET domain-catalysed methylation of H3K4. It is essential for normal embryonic development and haematopoiesis and frequently mutated in cancer. The catalytic properties and targeting of KMT2A/MLL1 depend on the proteins with which it complexes and the post-translational protein modifications which some of these proteins put in place, though detailed mechanisms remain unclear.ResultsKMT2A/MLL1 (both native and FLAG-tagged) and Msk1 (RPS6KA5) co-immunoprecipitated in various cell types. KMT2A/MLL1 and Msk1 knockdown demonstrated that the great majority of genes whose activity changed on KTM2A/MLL1 knockdown, responded comparably to Msk1 knockdown, as did levels of H3K4 methylation and H3S10 phosphorylation at KTM2A target genes HoxA4, HoxA5. Knockdown experiments also showed that KMT2A/MLL1 is required for the genomic targeting of Msk1, but not vice versa.ConclusionThe KMT2A/MLL1 complex is associated with, and functionally dependent upon, the kinase Msk1, part of the MAP kinase signalling pathway. We propose that Msk1-catalysed phosphorylation at H3 serines 10 and 28, supports H3K4 methylation by the KMT2A/MLL1 complex both by making H3 a more attractive substrate for its SET domain, and improving target gene accessibility by prevention of HP1- and Polycomb-mediated chromatin condensation.

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.