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A Network Inference Approach to Understanding Musculoskeletal

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A Network Inference Approach to Understanding Musculoskeletal A NETWORK INFERENCE APPROACH TO UNDERSTANDING MUSCULOSKELETAL DISORDERS by NIL TURAN A thesis submitted to The University of Birmingham for the degree of Doctor of Philosophy College of Life and Environmental Sciences School of Biosciences The University of Birmingham June 2013 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Musculoskeletal disorders are among the most important health problem affecting the quality of life and contributing to a high burden on healthcare systems worldwide. Understanding the molecular mechanisms underlying these disorders is crucial for the development of efficient treatments. In this thesis, musculoskeletal disorders including muscle wasting, bone loss and cartilage deformation have been studied using systems biology approaches. Muscle wasting occurring as a systemic effect in COPD patients has been investigated with an integrative network inference approach. This work has lead to a model describing the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. This model has shown for the first time that oxygen dependent changes in the expression of epigenetic modifiers and not chronic inflammation may be causally linked to muscle dysfunction. Bone and cartilage deformation observed in ageing, arthritis and multiple myeloma (MM) patients have also been investigated by using a novel modularization approach developed within this thesis. This methodology allows integration of multi-level dataset with large interaction networks. It aims to identify sub-networks with genes differentially expressed between experimental conditions that are co-regulated across samples in different biological systems. This study has identified several potential key players such as Myc, DUSP6 and components of Notch that could enhance osteogenic differentiation in MM patients. In conclusion, this thesis present the effectiveness of systems biology approaches in understanding complex diseases and these approaches could be applied for studying other systems and datasets. ACKNOWLEDGMENTS I would like to thank people and organisations that have contributed to my scientific journey. First of all, I am grateful to my supervisor Francesco Falciani for giving me the opportunity to work in his group, his efforts to secure funding, for all of his guidance, scientific expertise, and support. I thank to my second supervisor Neil Hotchin for his support and for introducing me to the world of experimental biology. Although I have never held a pipette previously, my experience in the lab has been great fun thanks to him. Speaking about fun, I also have to mention Shabane’s company in the lab. My colleagues from the 5th floor provided laboratory facilities and help. We shared long office hours and we had delightful chats and collaborations with former and present members of our group and other colleagues from IBR and the 8th floor. Collaborating with the members of Biobridge project, Erasys project and the members of the University of Birmingham has been enriching and their input to this thesis has been invaluable; I thank Dieter Maier, Josep Roca, Anna Sotoca, Michael Weber, Joop Von Zoelen, Katrin Sameith, Philipp Antczak, Kim Clarke, Sarah Durant, Anna Stincone, Sarah Essex, Tim Williams and Kevin Chipman. I am also grateful to University of Birmingham, Biobridge and Erasys projects for providing the necessary funding, which made it possible to carry out my PhD. Special thanks go to Julie Mazzoloni, Rita Gupta and Harriet Davies for their delightful company, Elsa Boudadi for being my funniest friend, Anna Stincone for the memorable garlic juice, Philipp Antczak for being my “scientific bro”, Wazeer Varsally for his listening skills during the writing-up time, and Peter for giving me access to his drawer full of snacks. I thank my dear family: my parents Meral and Faruk, my cousin Cigdem, and my brother’s family for their love and their constant support. My brother has taught me survival skills at a very early age, which turns out to be extremely useful in my grown-up life. My love Marcin has been my inspiration, motivation and my joy. Finally, I would like to thank our Trus for keeping me warm during the writing-up time. LIST OF PUBLICATIONS [1] Nil Turan, Susana Kalko, Anna Stincone, Kim Clarke, Ayesha Sabah, Katherine Howlett, S. John Curnow, Diego A Rodriguez, Marta Cascante, Laura O’Neill, Stuart Egginton, Josep Roca, and Francesco Falciani. A systems biology approach identifies molecular networks defining skeletal muscle abnormalities in chronic obstructive pulmonary disease. PLoS Comput Biol, 7(9):e1002129, Sep 2011. [2] Dieter Maier, Wenzel Kalus, Martin Wolff, Susana G Kalko, Josep Roca, Igor Marin de Mas, Nil Turan, Marta Cascante, Francesco Falciani, Miguel Hernandez, Jordi Villà-Freixa, and Sascha Losko. Knowledge management for systems biology a general and visually driven framework applied to translational medicine.BMC Syst Biol, 5:38, 2011. [3] Tim D Williams, Nil Turan, Amer M Diab, Huifeng Wu, Carolynn Mackenzie, Katie L Bartie, Olga Hrydziuszko, Brett P Lyons, Grant D Stentiford, John M Herbert, Joseph K Abraham, Ioanna Katsiadaki, Michael J Leaver, John B Taggart, Stephen G George, Mark R Viant, Kevin J Chipman, and Francesco Falciani. Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach. PLoS Comput Biol, 7(8):e1002126, Aug 2011. [4] Sabrina Moro, J. Kevin Chipman, Philipp Antczak, Nil Turan, Wolfgang Dekant, Francesco Falciani, and Angela Mally. Identification and pathway mapping of furan target proteins reveal mitochondrial energy production and redox regulation as critical targets of furan toxicity. Toxicol Sci, 126(2):336–352, Apr 2012. CONTENTS Chapter 1: Introduction and Background .......................................................................................... 1 1.1 Human musculoskeletal system .............................................................................................. 1 1.1.1 Skeletal muscle ............................................................................................................... 1 1.1.2 Bone and cartilage ........................................................................................................... 5 1.1.3 Diseases affecting the musculoskeletal system ............................................................... 8 1.2 Muscle wasting in Chronic Obstructive Pulmonary disease (COPD) patients ....................... 9 1.2.1 Muscle wasting in COPD ................................................................................................ 9 1.2.2 Muscle characteristics of COPD patients ...................................................................... 10 1.2.3 Skeletal muscle energy metabolism in COPD .............................................................. 10 1.2.4 Potential players in skeletal muscle dysfunction and wasting in COPD patients ......... 11 1.2.5 Current therapies for muscle wasting in COPD ............................................................ 14 1.3 Bone and cartilage degradation in ageing, myeloma and arthritis patients ........................... 15 1.3.1 Background on diseases associated with bone and cartilage degradation..................... 16 1.3.2 Bone and cartilage degradation as a result of abnormal hMSCs differentiation in ageing and in diseases............................................................................................................................... 16 1.3.3 Potential role of hMSCs for treatments ......................................................................... 18 1.4 Functional genomics and systems biology approaches ......................................................... 19 1.4.1 Omics technology and challenges ................................................................................. 19 1.6.2 Microarrays and various types of technologies ............................................................. 19 1.4.3 Typical steps in microarray data analysis ..................................................................... 20 1.4.4 Microarray data processing and normalization ............................................................. 21 1.4.5 Exploring the data ......................................................................................................... 22 1.4.6 Identification of differentially expressed genes ............................................................ 23 1.4.7 Functional annotation .................................................................................................... 24 1.4.8 Methodologies for building networks from datasets ..................................................... 25 1.4.9 Constructing networks from the literature .................................................................... 26 1.4.10 Modularization approaches ........................................................................................... 27 1.5 Aims and outline
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