2008 Projects Already Assigned

Systems biology models of insulin release from pancreatic islet beta cells

Supervisor: Philip Newsholme (UCD), Zoltan Neufeld (UCD) and Gavin Davey (TCD)
Increases in extracellular glucose concentration stimulate the exocytosis of insulin release from pancreatic islet ß-cells. The most common form of human diabetes (Type II) is associated with abnormalities in the release of insulin. Recently, a number of studies have implicated mitochondria as an integral component in the mechanism of insulin release from pancreatic cells. The current model is one where increased glucose concentrations inside the ß-cell (transported via the GLUT-2 transporter) result in increased cytosolic NADH concentrations (via the glycolytic pathway) which shuttle into the mitochondria (via the malate/aspartate shuttle and the glycerol phosphate shuttle). Unusually pyruvate is further metabolised via both pyruvate dehydrogenase and pyruvate carboxylase so entering the Krebs cycle thus increasing mitochondrial matrix NADH concentrations and FADH2. This increase in reducing equivalents stimulates the electron transport chain (ETC) and through the process of oxidative phosphorylation, ATP is synthesised. Subsequently, ATP-mediated closure of KATP-channels results in depolarisation of the plasma membrane and an influx of extracellular calcium into the cytosol which triggers exocytosis of insulin-containing vesicles. Dr Davey’s group has extensive experience [1 – 4] in how mitochondria control cellular energetics in the brain. His research focuses on metabolic control analysis and the individual control that electron transport chain complexes play in mitochondrial energy thresholds and how these effect neurotransmitter release. We propose to investigate how mitochondrial electron transport chain complexes influence the release of insulin from pancreatic ß-cells. These experiments will involve some well-known mitochondrial inhibitors (rotenone, myxothiazol and KCN) that will be used to titrate out ETC complex activities in pancreatic ß-cells and the effects on insulin release will be followed using a radioimmunoassay. Concurrent with these experiments will be measurement of intracellular calcium, mitochondrial membrane potential and reactive oxygen species using fluorescent techniques. In addition, we propose to study possible connections between the mitochondrial permeability transition pore and insulin release and to focus on a number of novel compounds that interact with this pore with subsequent control over insulin release. Prof. Newsholme’s group has expertise in pancreatic beta cell metabolism and metabolic stimulus-release coupling [5 – 13]. The role of mitochondrial membrane proteins, including metabolite transporters, uncoupling proteins and the permeability transition pore with respect to metabolic regulation (and thus insulin secretion) will be investigated. In addition to the wet lab cell based approaches described above, we intend to use flux control analysis to calculate flux control coefficients for metabolite transporters (such as the aspartate/glutamate transporter) and electron transport complexes in the release of insulin from pancreatic beta cells. Using a computational model based on differential rate equations assigned to enzymes in the pathways we will construct an in silico model that will predict conditions for maximising insulin release from cells. Flux balance analysis based on the stoichiometry of the metabolic reaction network will be used to obtain theoretical limits of the systems behavior independently of the kinetic parameters. Metabolism will be modelled from bottom-up and top-down approaches using the software programs, Berkely Madonna and Mathematica. Bottom-up analysis will involve construction of models with a high number of input parameters (corresponding to metabolite concentrations and enzyme kinetics) that will allow for prediction of control points in the insulin relase pathway(s). The top-down approach will involve the determination of extreme pathways and elementary flux modes that can be used to identify the basic building blocks of the pancreatic cell metabolism (eg. oxidative phosphorylation and glycolysis) from the topological properties of the reaction network. These elementary pathways will then be studied analytically using dynamical systems techniques to identify switch-like behavior, multistability, oscillatory instabilities, thresholds, bifurcations etc. [14, 15]. Building computational models based on the parameters described above will be used to predict metabolite fluxes that control insulin release. These approaches will feedback into the experimental results and using an iterative approach we will produce a systems biology model of insulin release from pancreatic cells with verifiable experimental data. The third member of our collaborative team, Dr Zoltan Neufeld of the UCD School of Mathematics will provide expertise with respect to mathematical modeling.

Statistical Methods for the Analysis of HPLC Glycan Data for Biomarker Discovery and Validation

Supervisors: Prof. Pauline Rudd (UCD) and Prof. Brendan Murphy (UCD)
It is now widely accepted that carbohydrates play a highly specific role in many biological interactions especially on cell surfaces. Many protein-carbohydrate interactions are implicated in disease states including inflammation, autoimmunity and cancer as well as viral, bacterial and parasitic infections. However, our overall knowledge and understanding of such complex and diverse interactions is relatively poor. One main reason for this is the lack of robust and quantitative tools to study carbohydrates at the cell and organism level. Secondly, carbohydrates are difficult to derivatise, analyse and handle, therefore, the interface between state-of-art technology development and application is essential if glycobiology technology is to be adopted by the wider scientific community. We have developed a high-throughput HPLC platform for sequencing oligosaccharides structures which has been widely accepted over the last decade and is applicable in the areas of biomarker discovery, disease profiling, bio-therapeutic quality control and the bioprocessing industry. Our preliminary analysis of a large curated cancer sample set has identified several potential glycan biomakers. We have shown that the proportion of these glycan structures in the total serum glycome is associated with cancer diagnosis and disease progression. A statistical programme is now required to analysis a large collection of HPLC data to validate these biomakers. This data has highlighted the necessity to establish an integrated approach that will bring together a glycan analytical group and bioinformatics to develop a platform for glycan biomarker validation, data analysis and interpretation. The project will involve detailed HPLC glycan analysis of samples from cancer patients. The high-throughput technology includes glycan release, exoglycosidase sequencing and data analysis to quantify a pool of glycan structures. A large amount of data will be generated and novel approaches will be required to assist interpretation. We envisage the development of clustering and classification methods to identify and validate potential disease biomakers. These will be based on analytical data, HPLC profile data and multivariate statistical analysis. The project aims to establish a multi-discipline approach for the identification, analysis and validation of glycan biomakers. The student will become familiar with HPLC technologies for glycan identification and will develop a comprehensive skill set for data analysis of HPLC glycan data.

Advanced computational and NMR studies of GIP with extracellular domain of GPCR

Supervisors: Chandralal Hewage (UCD) and Jens Nielsen (UCD)
There is considerable interest in the pharmaceutical industry in the development of potential drugs for type-2 diabetes. It is estimated that more than 200,000 people in Ireland suffer from some form of diabetes. Glucose-dependent insulinotropic polypeptide (GIP) is a hormone that stimulates the secretion of insulin into the bloodstream after meal ingestion upon binding to a 7-transmembrane G-protein coupled receptor. However, this function is lost in diabetic patients. Recent crystallographic studies showed the GIP bound to the extracellular domain of the GIP receptor. We would like to use the X-ray model and the NMR derived solution structures of GIP obtained in our laboratory to study the computational dynamics, kinetics, biophysical properties and also mutational studies of the ligand. We would also like to develop a bacterial expression system for the production of the extracellular domain (ECD) of the GIP receptor and isotopically enriching the ligand (13C/15N-GIP) for ligand-ECD binding NMR studies. This project will use advanced computational modelling and molecular biology techniques which would be useful for understanding of drug binding process. The results could help in the rational design of new drugs for the treatment of type-2 diabetes and related disorders.

The Transcriptomic Phenotype of Human Adipose Tissue - to define the interaction between habitual dietary fat intake and insulin sensitivity in adipose tissue

Supervisors: Helen Roche (UCD) and Peadar Ó Gaora (TCD)
Type 2 diabetes mellitus (T2DM) is an increasingly common condition associated with greater risk of cardiovascular disease (CVD). The growing prevalence of T2DM is linked with increasing incidence of obesity which in turn predisposes to insulin resistance, the key metabolic perturbation of T2DM. Although diet is not listed specifically as a risk factor for the T2DM , there is little doubt that metabolic stressors including energy dense, high-fat diets promote obesity, insulin resistance and the metabolic syndrome.

This study will address they hypothesis that genetic background and dietary fat exposure both contribute to the gene expression phenot ype in human adipose tissue that explains insulin resistance & T2DM. It will also determine whether peripheral blood mononuclear cells (PBMC) transcriptomic profiling can accurately reflect molecular markers of insulin resistance in adipose tissue. This PhD programme will be based on samples and transcriptomic data set (n=150 Affeymatrix arrays) generated as part of LIPGENE a FP6 European Integrated Project (www.ucd.ie/lipgene).

Reproductive Biology Research Cluster

Supervisors: Brendan Loftus and Alex Evans
  • Cluster on Reproductive Biology (7.9M euro) 9 PI’s on a 5 year grant studying the molecular basis for infertility in Dairy cattle.
  • mRNA microarray studies to date are inaccurate and misleading. A more comprehensive approach (sequencing) is now possible
  • Use of next-gen seq data from transcript sequencing and CHIP-seq to compile a comprehensive picture of transcription from various tissues/developmental states, incorporating mRNA/small RNA/histone modifications/chromatin state markers/Pol II occupancy/DNAase hypersensitivity sites
  • These data will be integrated to provide a true look at the timing and role of transcriptional programming and regulation in development and fertility

Defining regulatory networks important in endocrine resistant breast cancer

Supervisors: Peadar Ó Gaora (UCD), Leonie Young (RCSI)
Every year in Europe approximately 200,000 women confront a diagnosis of breast cancer, treatment of which costs an estimated 7 billion euro per annum. Endocrine therapies, including estrogen receptor (ER) modulators and aromatase inhibitors, represent current first line treatment. However, while most patients initially respond to endocrine treatment, approximately 30-40% eventually relapse. This project addresses the question of transcriptional regulation in endocrine resistance. We have identified two transcription factors apart from ER which are important in resistance. We are currently carrying out location analysis experiments (ChIP-chip) to map the binding sites of these transcription factors in sensitive and resistant cell lines. In the first instance the student will analyse these datasets and identify the cognate genes. The data will also be integrated with those published previously which identified the binding sites of Er in breast cancer cells. A thorough analysis of the data will incorporate classification of the target genes in terms of their functional annotation (GO, KEGG, BIND etc.) and the presence of binding sites for other relevant DNA binding proteins. Mass spectrometry analysis of immunoprecipitated material will be used to identify potential interaction proteins. The proteomics data in conjunction with the mapping of binding sites will identify strong candidate interaction partners. The functional consequences of disrupting these interactions will be assessed through gene expression array experiments and proliferation assays in sensitive and resistant cell lines. Validation of in vitro experiments will be carried out on primary cultures from resistant breast tumours. The anticipated proportion of bioinformatics/wet lab work will be approximately 60/40

Novel statistical methods for the analysis of metabolomic data

Supervisors: Lorraine Brennan (UCD), Claire Gormley (UCD).
Metabolomics is a developing discipline which has major potential applications in the areas of biomarker discovery, clinical diagnostics, toxicology and nutrition. Large amounts of data are generated and novel analysis methods are necessary to help interpret the structure within the data. Novel classification and clustering methods will be employed to help identify biomarkers of disease. In particular we propose the development of statistical methods which incorporate covariates and biochemical data with metabolomic data. Model-based methods will be used to provide a statistically sound framework for clustering and classification. Existing model-based techniques will be extended to facilitate the combination of metabolomic data and `non-omic’ data (eg. biochemical data), both of which may influence the structure of the data. These methods will be developed within the Bayesian paradigm which provides a rigorous statistical framework capable of incorporating the inherent uncertainty of parameter estimates into the inference procedure. Overall, the developed methods will extend current approaches to analysing metabolomics data.

The Oxidative Stress Interactome: Modelling Atherosclerosis in Populations, from Whole Genome SNP Association Arrays

Supervisors: Alice Stanton (RCSI) and Denis Shields (UCD)
Atherosclerosis, by causing heart attacks and strokes, is a major cause of death and morbidity worldwide. There is growing evidence that oxidative stress plays a key role in both the early and late stages of atherosclerotic disease. Oxidative stress is caused by an imbalance between reactive oxygen species molecule production and breakdown. In this project we will use whole genome SNP data from large populations to study gene to gene interaction in pro-oxidant genes such as NADPH oxidase and xanthine oxidoreductase, and anti-oxidant genes such as peroxidases and superoxide dismutases, for effects on a marker of oxidant stress (urinary isoprostanes), a marker of early atherosclerosis (ultrasonic determined common carotid intima media thickness) and on actual cardiovascular events (heart attacks and strokes).

Computational analysis of protein interaction networks

Pádraig Cunningham (UCD), Gerard Cagney (UCD)
It has been proposed that an understanding of how proteins interact at the cellular level, or network level, is required to move toward an effective model of how cells function at a systems level, and in turn, the design of better therapies for multifactorial disease. We have assembled the largest network of protein interactions for any organism - the budding yeast S.cerevisiae (Collins 2007 Nature 446, 806; Krogan2006 Nature 440, 637). Yeast cells contain all the machinery needed for a eukaryotic cells to grow and replicate, and several hundred human disease homologs are encoded in its genome. In this project, unsupervised and semi-supervised machine learning techniques will be employed to investigate how the organization of the protein interaction network relates to the underlying biological functions. Specifically, we will consider the question of how the network of physically-interacting proteins is regulated by the network of genetically-interacting proteins. This problem will be addressed through the development and application of novel matrix factorizationalgorithms designed to support the analysis of data represented by multiple views. A particular emphasis will be placed on the explanation of the results in terms of the relations between the views. The output of these algorithms will subsequently be integrated with comprehensive drug-gene interaction networks. The project will involve collaboration with the Krogan Lab at University of California at San Francisco. The research student on this project will work closely with Dr. Derek Greene, a postdoc in the School of Computer Science and Informatics.

Systems biology of hypoxia: cross talk between NFkB and HIF1 in intestinal epithelial cells

Supervisors: Cormac Taylor (UCD), Ted Cox (UCD), Zoltan Neufeld (UCD)
Signalling via HIF transcription factors is critical in determining cellular responses to hypoxia. Cross talk with the NFkB pathway plays a key role. Systems biology modeling will permit the efficient design of experiments to elucidate the key parameters in this cross talk.

Organelle footprinting through global analysis of Rab protein localisation

Supervisors: Kathleen Curran and Jeremy Simpson
In order to improve the efficiency of therapeutic drug uptake we need to understand the organisation of proteins present on the outside of cells within the body:
  • This project will use high throughput microscopy-based live cell imaging and analysis to study these proteins
  • Comparing groups of candidate proteins by subcellular co-localisation will provide an insight into the organisation of protein secretion
  • Changes in cellular protein profiles can be quantified using feature analysis, and the output used to make further predictions

Multivariate Analysis of Big Alignmentss

Supervisors: Des Higgins, Jean Marc Jacqué, Pádraig Cunningham, Brendan Murphy
What:
  • Analyse LARGE alignments (many long sequences)
  • e.g. HIV
How:
  • multivariate analysis
  • machine learning
Purpose:
  • Classification e.g. classify new sequences by clinical properties or by phylogenetic sub-type
  • Data exploration e.g. relate residues to functional characteristics or measurements on sequences
Related References
  1. Davey G.P. and Tipton K.F. (2000) Mitochondria, Cell Death and DNA Damage. Radiation Research 2, 390-394.
  2. Davey G.P., Peuchen S. and Clark J.B. (1998) Energy thresholds in brain mitochondria: potential involvement in neurodegeneration Journal of Biological Chemistry 273, 12753-12757
  3. Davey G.P., Canevari L and Clark J.B. (1997) Threshold effects in synaptosomal and nonsynaptic mitochondria from hippocampal CA1 and paramedian neocortex brain regions Journal of Neurochemistry 69, 2564-2570
  4. Davey G.P. and Clark J.B. (1995) Control of oxidative phosphorylation in non-synaptic rat brain mitochondria Journal of Neurochemistry 66, 1617-1624
  5. Kiely A, McClenaghan NH, Flatt PR and Newsholme P. (2007) Pro-inflammatory cytokines increase glucose, alanine and triacylglycerol utilisation but inhibit insulin secretion in a clonal pancreatic beta cell line. J. Endocrinol 195:113-23.
  6. Newsholme P, Haber EP, Hirabara SM, Rebelato ELO, Procopio J, Morgan D, Oliveira-Emilio HC, Carpinelli AR, Curi R. (2007) Diabetes associated cell stress and dysfunction – Role of mitochondrial and non-mitochondrial ROS production and activity. J. Physiol 583: 9-24
  7. Calder P, Dimitriadis G and Newsholme P (2007) Glucose metabolism in lymphoid and inflammatory cells and tissues. Curr Opin Clin Nutr Metab Care 10: 531-540
  8. Newsholme P, Keane D, Welters H and Morgan N. (2007). Life and death decisions for the pancreatic beta cell - The role of fatty acids. Clinical Science 112: 27-42
  9. Patterson, S., Flatt, P.R., Brennan, L., Newsholme, P., and McClenaghan, N.H. (2006) Detrimental actions of metabolic syndrome risk factor, homocysteine, on pancreatic beta-cell glucose metabolism and insulin secretion. J. Endocrinology 189: 301-310
  10. Morgan D, Oliveira-Emilio HR, Keane D,. Hirata AE, Santos da Rocha M, Bordin S, Curi R. Newsholme P, and Carpinelli AR (2007) Glucose, Palmitate and Pro-Inflammatory Cytokines Modulate Expression and Activity of a Phagocyte-like NADPH Oxidase in Rat Pancreatic Islets and a Clonal Beta Cell Line. Diabetologia. 50: 359-369
  11. Brennan, L, Hewage C, Malthouse JPG, McClenaghan NH, Flatt PR and Newsholme P (2006) Investigation of the effects of sulphonylurea exposure on pancreatic beta cell metabolism. FEBS Journal. 273: 5160-5168
  12. Corless, M., Kiely A., McClenaghan, N.H., Flatt, P.R. and Newsholme P. (2006) Glutamine regulates expression of key transcription factor, signal transduction, metabolic gene and protein expression in a clonal pancreatic beta cell line. J. Endocrinology 190: 719-727
  13. Newsholme, P., Brennan, L. and Bender K. (2006). Amino acid metabolism, beta cell function and diabetes. Diabetes 55 Suppl 2: S39-47
  14. Palsson BO, Price ND and Papin JA (2003) Development of network-based pathway definitions: the need to analyze real metabolic networks. Trends in Biotechnology 21: 195-198
  15. Stelling J, Klamt S, Bettenbrock K, Schuster S and Dieter Giller E (2002) Metabolic network structure determines key aspects of functionality and regulation. Nature 420: 190-193

    16. Bioinformatic and Molecular Analysis of Alternative Transcript Expression in Malignant Melanoma

    Supervisors: Peadar Ó Gaora (UCD), William Gallagher (UCD)
    Malignant melanoma is a very aggressive form of skin cancer, which fails to respond to conventional chemotherapy and spreads rapidly to other parts of the body. In this project, we wish to study, on both bioinformatic and molecular levels, alternative splicing of transcripts in melanoma, and how these phenomena may contribute to the disease. Prof. Gallagher’s group has recently performed DNA microarray-based transcriptomic profiling using Affymetrix exon arrays on a series of isogenic melanoma cell lines that mimic progression from an early melanoma to a late metastatic stage. These exon arrays provide differential mRNA expression and alternative splicing data for all annotated transcripts/genes in the cell. The PhD student will first analyse these data to determine at the mRNA level what alterations in gene expression and alternative splicing are associated with progression, from the early to late stage melanoma cell lines. Initially, the tools for analysis of exon arrays were quite rudimentary. However, the R/BioConductor project has recently developed newer tools to more fully exploit the potential of this platform. The student will apply these techniques to identify exon skip events at the mRNA level in the melanoma cell lines. A complementary approach to the identification of splice isoforms in proteomics data has been developed recently by Dr. Ó Gaora and co-workers. Databases representing potential exon skip events in each of the human, mouse and rat genomes have been generated. These databases house the theoretical trypsin junction peptides derived from all possible exon skip events according to the latest annotations of the genomes available from Ensembl. Using the in-house proteomics pipeline, Proline, we have already identified several novel splice isoforms in breast cancer cell lines and are currently carrying out a large scale screen of exon skip events in platelets. The student will apply this approach to identify alternative splice events at the protein level in the cell lines described above. Exon skipping accounts for approximately 75% of AS events. To identify other types of AS events a new methodology will be developed. Protein identifications from standard databases (e.g. IPI) will be saved and three-frame translations of each encoding gene will be generated and saved to a temporary, sample-specific database. Unidentified spectra from that sample will then be searched against this database with a view to identifying events such as alternative 5' or 3' splice sites or intron retention. By dynamically creating a database using the prior knowledge of bona fide identifications within the sample, the search space is dramatically reduced, making such a genome-based method feasible and indeed highly practical. A set of alternatively spliced transcripts which represent different melanoma stages will then be validated by RT-PCR analysis in melanoma cell lines and tissues. To this end, Prof. Gallagher’s group has already extracted RNA from 150 melanoma tumours and 70 benign nevi (normal skin). Overall, this combined approach will provide new insights into the role of alternative transcript expression in melanoma progression. Expected split between bioinformatics and wet-lab activities would be 70/30, respectively.

    Mammalian cell culture cytomics (a systems view of cell culture)

    Supervisors: Professor Mohamed Al-Rubeai (UCD), Dr Zoltan Neufeld (UCD)
    There are substantial variations in most aspects of physiological response and the specific kind of differentiation yielding the maximum quantities of product. An important aspect of cell culture development is the monitoring, prediction and control of the state of bioreaction. The current approaches of monitoring impose severe limitation, as they only provide static qualitative information and not provide any information on the heterogeneity of the cell population, nor can it detect rare events. The correct approach for monitoring and control of mammalian cell processes concerns the identification and systematic study of relevant factors. Efficient identification depends upon the quality and quantity of information available and this in turn provides reliable indices for the interpretation of growth and metabolism. The influence of the physical or chemical environment in growth experiments is quite often wrongly interpreted because the parameters used inadequately demonstrate alterations in metabolism or structural integrity and may or may not be related directly to cell growth/death. Detailed knowledge of the metabolic and synthetic state of the cell population by flow cytometric methods will be of great value in understanding bioreaction processes as well as providing terms for adequate and sensitive process design and control. Multiparametric single cell analysis by flow cytomics provides high throughput and accurate means of analysing individual cells in suspension. Several parameters of single cells can be detected simultaneously and analysed to provide information on the relationship between them. Fluorescent labels can give quantitative data on specific target molecules and their distribution in the cell population. The aim of the project is to develop mathematical and computational models for CHO cell culture that allow the quantitative prediction of productivity level, cell growth and cell death from differential changes of molecular single cell phenotypes as well as from bioreactor culture medium properties. The theoretical models will be based on differential equations that describe the cell population dynamics coupled to stochastic models of cell cycle regulation and balance equations for feed components and waste products. Formulating such a mathematical model will be based upon current quantitative understanding and on new set of data. The data from flow cytometry (at least 10 different cellular parameters such as cell cycle, size, apoptosis, mitochondrial activity etc. measured at least at 15 points in batch cultures of at least 5 different conditions) allows for detailed quantitative comparison between theory and experiment and will be used to estimate model parameters. The models will be studied using analytical techniques from the field of nonlinear dynamical systems complemented with computer simulations, including sensitivity analysis with respect to uncertainties in the model parameters. The project will contribute to better understanding of cell culture population dynamics and has direct applications in the optimisation and control of productivity of bioreactors. It is expected that the student will spend 50% of time in computation and 50% of time in the wet lab.
© University College Dublin, 2010. Some public domain images obtained from NIH Image Bank