FDA

The Transmissible Spongiform Encephalopathy (TSE) diseases are a group of fatal neurodegenerative diseases which include scrapie in sheep, BSE in cattle and CJD in humans. TSE diseases (also known as prion diseases) differ from other neurodegenerative disorders such as Alzheimer's disease, due to their infectious nature. Instead of an conventional infectious agent such as a bacterium or virus the TSE infectious agent (the prion) is thought to be a misfolded form of a host protein (PrP). It has been hypothesised that the abnormally folded form of the protein (PrPSc) is able to bind to the normal protein which is found in brain tissue of all mammals, and convert it into the abnormal form. PrPSc accumulates as disease progresses, and may cause the death of neurons in the brain. PrPSc is usually found in infected tissue, and can be identified microscopically by the presence of abnormal protein aggregates in sections of brain tissue, or by its resistance to digestion with proteases on immunoblotting. PrPSc co-purifies with the TSE infectious agent, and correlates with the level of infectivity present. PrPSc was therefore thought to be the sole component of the 'prion', and is currently the only diagnostic marker used for TSE disease testing. PrPSc can exist as either diffuse deposits or large amyloid aggregates in tissue, but the role of each form in disease is unknown. Conflicting studies have suggested both an infectious and a protective role for PrP amyloid in TSE disease. In addition, other experiments have shown that PrPSc is not always present in infectious tissue. These findings raise serious questions about the suitability of PrPSc as the only available diagnostic marker, and it is important for both accurate disease diagnosis and the development of new therapies and treatments for these currently incurable diseases that we identify exactly which form of PrP is associated with infectivity. In this proposal, we aim study the amyloid form of PrPSc and its association with the infectious agent. In our laboratory we have observed that transgenic mice inoculated with brain material from a case of atypical human prion disease do not develop clinical or pathological signs of disease, but do produce large amyloid aggregates in the brain. We have been unable to transmit disease from brain tissue of mice possessing these aggregates, indicating the absence of TSE infectious agent in these tissues. Current diagnostic methods would have identified these mice as TSE infected, yet we have shown the mice lack both disease and infectious agent. Our results support the hypothesis that PrP amyloid is not infectious, and may be formed by seeding from amyloid in the inoculum, or may be a host protective mechanism by which smaller more infectious aggregates are sequestered into an inert form. We therefore aim to identify the role of amyloid in TSE disease by inoculating transgenic mice with oligomeric and amyloid forms of recombinant PrP to determine whether we can induce amyloid formation in transgenic mice in the absence of infected tissue inoculum, and whether such amyloid forms of PrP are infectious. We also aim to disrupt these amyloid deposits to determine whether smaller fragments from the amyloid are infectious. The results from these experiments will aid in our understanding of the role of PrP amyloid in TSE disease. If amyloid is a protective mechanism by which the host controls TSE infectivity, treatments which target the disruption of such aggregates may instead enhance disease, and would therefore be undesirable. These results will also help to identify specific forms of PrP associated with TSE infectivity, leading to the development of accurate diagnostic tests with low risk of both false negative and false positive results which is important ethically when developing diagnostic assays for human prion disease.

The aim of "systems biology" is to understand how biological systems (e.g. cells, organs, people) work as a collection of parts by using mathematical modelling. We describe the behaviour of the parts in the form of mathematical equations using the laws of physics and chemistry, and then see how the behaviour of larger systems emerges from this. Many systems biology models for specific components have been published, but there remain significant challenges in exploiting them to understand systems as a whole. Which existing models (if any) are most appropriate for a new scientific question? How does each model behave in different situations? How well do they capture what the real systems do? At present it is very difficult to answer these questions without first downloading each model, writing programs to perform different simulated experiments, and then writing more code to compare and visualize the results. There has been nowhere to look up even simple properties for different models. We propose to build upon a pilot implementation of a system that enables such tasks to be done automatically, with results published on a website. Our approach will be demonstrated in perhaps the most mature area of systems biology: the electrical activity of heart cells, for which the first model was published in 1960 and well over 100 models are now available in public databases. The models have been hugely important in giving insights into how the heart works (and what can go wrong due to disease, age or drug side effects) and have helped in developing new treatments. The first step is to compare the different model predictions to measurements from real cells, to tell us whether we really understand how the heart's cells work. We will link real measurements to our recipes for performing equivalent experiments on the computer models, and provide an interface to display the results, indicating how well they agree both qualitatively (general appearance) and quantitatively (how well the numbers match). Cardiac models often have dozens of equations containing hundreds of parameters - key numbers governing how the models behave. How these parameters were worked out from experimental recordings (data) is, more often than not, unclear. Since many models reuse components from previous models, the original methods and data may no longer be available to anyone. This causes big problems for building on these models - if for instance we want to adapt a model to a new cell type, there is no record of which experiments were performed, or how these were analysed to produce the parameters and equations in the final model. We will extend our recipes to capture this information as well, and so be able automatically to re-calibrate a model to a given set of experiments. Crucially, our tools will use the variability in experimental measurements to calculate how models are likely to need to change to capture variations between different cells in a heart, or between different people, and explain how this variation affects predictions. Three case studies will drive development, looking at different kinds of model to give a broad picture of needs. Feedback from the wider community and an external advisory board of experts in cardiac electrophysiology will also be incorporated, building on the success of our first user workshop in September 2015. The final output will be a user-friendly online system - a "Cardiac Electrophysiology Web Lab" - providing an open community resource for researchers to use. We will also write training materials and run further workshops to help these researchers use it. Our resource will make it easier to reuse or extend existing models in appropriate ways, to develop new models, and to understand differences between heart cells. The tools will increase the impact of modelling for replacing animal experimentation and testing, e.g. in drug trials.