Download Abstracts - Keble ASC

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