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Keble Networks Pharmacology Workshop on September 15 Here is the schedule of talks, now with abstracts included. The meeting will take place in the Pusey Room at Keble College. 9:55 Welcome 10 – 11 Alan Whitmore (e-Therapeutics, Long Hanborough): What is Network Pharmacology? Abstract: Drug discovery is hard and it is characterised by failure rather than success. It is this failure that accounts for the high cost of pharmaceutical development. There has been a one hundred fold decline in the productivity of pharmaceutical R&D since 1950 despite major advances in biological science and technology and the expenditure of literally hundreds of billions of dollars. This is very bad for patients, for drug companies and for the health economies of the world. Why might this be? I shall argue that a principal reason is a failure to engage with the true complexity of biological systems; and incomplete consideration of the realities of chemical biology. I shall offer up Network Pharmacology as an alternative paradigm for drug discovery. One that embraces the complexity of pathophysiology and chemical biology and which has already started to demonstrate its potential in the search for new therapies. By leveraging network science alongside modern chemoinformatic techniques I shall argue that it is possible to provide a new framework for finding novel medicines that takes the best from the current system and places it in the context of biological complexity. If the precepts of Network Pharmacology are correct this should mean that patients get better, safer new drugs more quickly and cheaply. This will be good for everyone. If they are wrong then it is likely that the current trend will continue and there will be very few, if any, new medicines. The pharmaceutical industry will become genericised, with no innovation or collateral technological benefits that can feed into other disciplines; and our children will continue to face a future filled with age-related decline, cancer and epidemic infectious disease with all the social, emotional and economic cost that this entails. 11 – 11:15 Coffee break 11:15 - 12 Shoumo Bhattacharya (Wellcome Trust Centre for Human Genetics & Target Discovery Institute): Targeting chemokine and coagulant protease networks using tick venom peptides Abstract: Graham Davies*, Kamayani Singh*, Matt Benson*, Yara Alenazi, Adrian Gray & Shoumo Bhattacharya. Wellcome Trust Centre for Human Genetics & Target Discovery Institute; * joint first authors Background: Chemokines are validated targets in atherosclerosis1 and cardiac fibrosis2, but no effective anti-chemokine drugs are available due to extensive cross-talk in the chemokine network3. The contact system proteases (FA12, KLKB1, FA11) activate the intrinsic coagulation pathway, and are validated targets for suppressing thrombosis without enhancing bleeding risk4. Ticks suppress chemokine-driven inflammation using multi-chemokine-binding salivary proteins (EVASINs), and the contact system using KUNITZ protease inhibitors5,6. We hypothesised that other tick species may have evolved novel EVASINs and KUNITZ proteins that could combinatorially target atherogenic chemokines and contact system components. Methods: We bioinformatically identified 352 EVASIN and 166 KUNITZ like peptides in tick saliva transcriptomes and created yeast expression surface display libraries. We FACS screened these libraries using chemokines expressed in atherosclerotic plaque (CCL2, CCL3, CCL4, CCL5, CCL8, CCL11, CCL17, CCL19, CCL20, CCL25, CXCL8, CXCL10, CXCL12) and with contact system proteases (FA12, KLKB1, and FA11). Results: 32 novel EVASINs and 8 novel KUNITZ peptides have been recovered in screens performed with above chemokines and contact proteases, and re-tested to confirm binding. We have expressed and purified these novel peptides from HEK293T cells, and are assaying them for binding affinity, specificity and target neutralisation using chemokine assays (receptor internalisation, chemotaxis) and coagulation assays (e.g. APTT). Conclusions & Future goals: Evolutionarily honed peptide venoms such as hirudin, conotoxin and exenatide have been successfully translated for therapeutic applications. We have developed highthroughput screening technology to identify novel tick venom peptides that bind chemokines and contact system proteases. We will characterise these peptides to obtain novel neutralising tools and potential therapeutics for atherosclerosis and thrombosis. 1. Zernecke, A. & Weber, C. Chemokines in atherosclerosis: proceedings resumed. in Arteriosclerosis, Thrombosis, and Vascular Biology Vol. 34 742-750 (2014). 2. Frangogiannis, N.G. The inflammatory response in myocardial injury, repair, and remodelling. in Nat Rev Cardiol Vol. 11 255-265 (2014). 3. Horuk, R. Chemokine receptor antagonists: overcoming developmental hurdles. in Nature Reviews Drug Discovery Vol. 8 23-33 (2009). 4. Müller, F., Gailani, D. & Renné, T. Factor XI and XII as antithrombotic targets. in Current opinion in hematology Vol. 18 349-355 (2011). 5. Decrem, Y. et al. Ir-CPI, a coagulation contact phase inhibitor from the tick Ixodes ricinus, inhibits thrombus formation without impairing hemostasis. in Journal of Experimental Medicine Vol. 206 2381-2395 (2009). 6. Copin, J.-C. et al. Treatment with Evasin-3 reduces atherosclerotic vulnerability for ischemic stroke, but not brain injury in mice. in J. Cereb. Blood Flow Metab. Vol. 33 490-498 (2013). 12 - 12:30 Brent Ryan (Department of Physiology, Anatomy and Genetics, University of Oxford): Applying network analyses to identify novel targets and pathways in Parkinson's disease Abstract: Brent Ryan, Harriet Keane, Brendan Jackson, Rosalind Roberts, Milena Cioroch, Svenja Hester, Alan Whitmore and Richard Wade-Martins Parkinson's disease is the second most common neurodegenerative disease. The disease is primarily identified by the characteristic motor symptoms, resulting in a severely decreased quality of life in addition to socio-economic challenges. These motor symptoms are a result of the death of neurons which produce the neurotransmitter dopamine. Whilst the exact reason these dopaminergic cells die is unknown, we do know that the aetiology of Parkinson's is complex, involving numerous proteins and cellular processes. Amongst these processes, cellular energy production by mitochondria and the recycling of damaged proteins by autophagy are known to be dysfunctional. The end results of these dysfunctions are increased stress in dopaminergic neurons and the aggregation of the key Parkinson's protein alpha-synuclein into Lewy bodies. The neurotoxin MPP+ is a widely used model of inducing Parkinson's disease by disrupting mitochondria and autophagy. We have modelled Parkinson's by creating a protein-protein interaction network (PPI) describing the interplay between mitochondrial dysfunction and autophagy in cells treated with MPP+. Network analysis was used to identify key proteins involved in the cross-talk between mitochondrial dysfunction and autophagy after MPP+ treatment. We tested the veracity of these predictions in the laboratory by decreasing or increasing the levels of these target proteins in MPP+-treated cells. Decreasing levels of these network proteins, in concert, increased cell susceptibility to MPP+, whereas increasing their levels rescued cells from toxicity. We have also overexpressed the Parkinson's-causing protein alpha-synuclein in mice resulting in recapitulation of several aspects of Parkinson's disease. We can observe that the brains of these mice demonstrate several Parkinson's-like cellular dysfunctions, such as mitochondrial dysfunction, autophagy dysfunction and alpha-synuclein aggregation. We have performed proteomic analysis of the brains of these mice and quantified the expression of over 2000 individual proteins. This disease protein-signature is amenable to analysis by pathway and network analysis to identify key drivers of neurodegeneration. Analysis of biological networks offers novel hypotheses regarding proteins and pathways which modulate neurodegeneration, the testing of these hypotheses in the laboratory allows us insights into both disease and network analysis. 12:30 – 13 Andrew Elliott (SABS-IDC, Oxford): A Nonparametric Significance Test for Sampled Networks Abstract: Network sampling is used in many areas such as sociology and biology to create sub-networks that are believed to relate to a process or property of interest. Many methods for creating sub-networks use a list of nodes (a seed list), containing nodes believed to be involved in the process, and a construction rule. While especially in biology there are many different ways to construct such networks, and a good amount of global information is available, by contrast methods to extract subnetworks related to a process of interest are usually ad-hoc. So how should we choose the subnetwork to focus on? The approach we take is to select the sub-networks that have summary statistics that look significantly different from ``random’’. In order to assess what would be expected under randomness we need an appropriate null model. Unfortunately the popular configuration model does not replicate the structure induced by the sampling and is hence not appropriate. Here, we construct a different null model which is appropriate for estimating the significance of summary statistics on sub-networks sampled using seed lists. As we are interested in summary statistics of the subsample, we must control for construction of seedlist. We demonstrate that not controlling for seed list construction can lead to overestimation of significance. This finding leads to a null model which is based on a resampling bootstrap procedure and a seed list correction. We also provide a statistical procedure based on Monte-Carlo tests to assess the significance of summary statistics on sub-networks constructed using seed lists. Our new null model performs favourably compared to the configuration model. We apply our method to select sub-networks which attempt to capture the protein-protein interactions which characterise Parkinson’s Disease. Using the null model allows us to select networks of interest for further study from a set of potential networks associated with Parkinson's Disease. Joint work with E. Leicht, Felix Reed-Tsochas, Gesine Reinert and Alan Whitmore. 13-14 Lunch (provided by the College) 14 - 14:45 Garrett Morris (Department of Statistics, University of Oxford): Ligand-Based Virtual Screening and Network Pharmacology Abstract: Ligand-based approaches to virtual screening are a powerful computational tool in drug discovery, but suffer from a reductionistic weakness by focussing on a single target. This talk will present some recent work that combined ultrafast molecular similarity methods with a polypharmacological approach, exploiting 3D descriptors of the binding sites of macromolecular targets of small molecules, and known side effects for existing drugs, to predict for a given small molecule their potential target(s), possible side effects, and to permit exploration of pharmacological space to assist in drug repurposing. 14:45 – 15:30 Beate Franke (Department of Statistical Science, University College London): Significance of Network Community Structure Abstract: In many sciences such as biology, physics and the social sciences, the behavior of networks is driven by a natural division into communities or what in social networks is termed homophily, the tendency of nodes to be connected based on similar characteristics. Since researchers often observe information on the nodes in addition to the interaction in the network a key question in this setting is to test for the significance of community structure. Network models where a single parameter per node governs the propensity of connection are popular in practice, because they are simple to understand and analyze. They frequently arise as null models to indicate a lack of community structure, since they cannot readily describe the division of a network into groups of nodes whose aggregate links behave in a block-like manner. Here we discuss asymptotic regimes under families of such models, and show their potential for quantifying the significance of community structure. As an important special case, we treat network modularity, which summarizes the difference between observed and expected within-community edges under such null models, and which has seen much success in practical applications of largescale network analysis. Our focus here is on statistical rather than algorithmic properties, however, in order to yield new insights into the canonical problem of testing for network community structure. Joint work with Patrick Wolfe. 15:30 – 16:00 Tea break 16 - 16:45 Mason Porter (Department of Mathematics, University of Oxford): Multilayer Networks and Network Pharmacology: Looking Forward Abstract: I'll give an introduction to the study of multilayer networks, which is one of the most active areas of network science. One can use the formalism of multilayer networks to study a set of entities that interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such ''multilayer'' features into account to try to improve our understanding of complex systems. I will discuss some theory, applications, and recent results in multilayer networks and include some forward-looking comments on the use of multilayer networks for network pharmacology. 16:45 Closing