The common, life-shortening inherited disease cystic fibrosis (CF) is characterised by defective anion transport across cell membranes. The proposed research aims to develop chemicals which are capable of transporting anions across cell membranes, and are ready for testing in humans after safety studies are completed. Almost 11,000 people live with CF in the UK and >70,000 worldwide. The disease is caused by malfunction of a protein, the cystic fibrosis transmembrane conductance regulator (termed CFTR), which allows the transport of anions (e.g. chloride and bicarbonate) across cell membranes. When CFTR is faulty or missing from the cell membrane, ducts and tubes in the body become blocked by thick, sticky mucus. In the lungs, this triggers a vicious cycle of infection and inflammation that destroys lung tissue, leading to breathing difficulties, poor quality of life and premature death. A novel approach to treat the root cause of CF is "CFTR replacement therapy" using anionophores (anion carriers). Anionophores are synthetic small molecules which are designed to replace the action of CFTR, by picking up anions on one side of the membrane, carrying them across, and releasing them on the far side. After their delivery to the lungs by inhalation and insertion into cell membranes, anionophores could rescue normal levels of anion transport and, through a chain of effects, restore the healthy mucus which is easily cleared from the lungs. In earlier work, we and others have shown that it is indeed possible to design small molecules which insert into membranes and mediate transmembrane anion transport. Some of our systems are capable of very high activity approaching that of CFTR. Importantly, a few anionophores, with drug-like properties, are capable of efficient delivery to cell membranes, where they work for prolonged periods, transporting anions into and out of cells, without signs of toxicity. Based on our previous results, there is good reason to believe that anionophores could be used to treat CF. This project will take critical steps towards realising this goal. The work will be performed by a collaboration involving chemists and physiologists in Bristol, and a chemistry group in Sydney, Australia (funded separately). Initially we will work towards optimising activity in cells, identifying the best candidates for closer examination. We will then apply a series of tests on tissues lining ducts and tubes (as opposed to individual cells) designed to validate our hypothesis that anionophores can restore normal function in CF patients. Meanwhile we will perform in-depth studies on anionophore behaviour, in both synthetic and natural membranes, so that biomedical development can rest on firm foundations. This will include selectivity and mechanistic investigations, as well as fluorescence microscopy to ascertain anionophore distribution in cells. We will also test new delivery systems which could be used to help anionophores reach cell membranes. At the end of the project we will have set the stage for clinical studies, potentially leading to treatments for CF.

This project develops a simulation system for the MR signal in biological tissue and its dependence on molecular dynamics as influenced by tissue microarchitecture and composition. The system is an essential tool in the development of next-generation non-invasive imaging techniques. Specifically, it underpins the development and translation of the emerging paradigm of microstructure imaging. The paradigm uses mathematical models, which relate the MR signal to underlying tissue properties, to estimate and map those properties by fitting the models voxel-by-voxel to combinations of appropriately sensitised image data. The approach provides much greater biological specificity than standard MRI, thus enhancing diagnosis and treatment planning. The current generation of microstructure-imaging techniques is now starting to find widespread application in clinical studies. Prominent examples include NODDI for neuroimaging and VERDICT for cancer imaging, both developed by the investigators on this project. Those techniques are based entirely on diffusion MRI and their extension and refinement within that single contrast mechanism continues rapidly. However, a new generation of microstructure-imaging technique is just beginning to emerge that draws on multiple sources of MR contrast, for example combining diffusion MRI with relaxometry, susceptibility, etc. Such techniques offer great promise in the decades to come for the realisation of 'virtual histology' avoiding invasive procedures, such as biopsy, across a wide range of medical applications. EPSRC grant EP/E064280/1, which finished in 2011, developed the current state-of-the-art simulation system within the Camino toolkit. That system underpinned the early development of the microstructure-imaging paradigm, which led to current techniques like NODDI and VERDICT. However, the current system is insufficient to evaluate even current microstructure imaging techniques, because it excludes key effects that influence the diffusion MR signal. Moreover, its implementation limits the simulation to molecular diffusion as the only source of MR contrast, which fundamentally prevents its extension for validation of next-generation techniques. The new simulation system will use more sophisticated underlying models of tissue geometry and MR signal generation enabling it to support both modern diffusion-based microstructure-imaging applications and future multi-modal techniques. It provides a unique and invaluable validation tool allowing us to realise the full potential of quantitative non-invasive imaging in medicine and beyond. Within the project we demonstrate the new system by evaluating the performance of NODDI and VERDICT under a wide range of conditions. We also test two early examples of multi-modal microstructure imaging techniques paving the way for their robust development and eventual clinical translation.

Diabetes is a chronic metabolic disease affecting 415 million people worldwide and 3.3 million people in the UK. Ninety percent of the people suffering from diabetes have type 2 diabetes. Type 2 diabetes is characterised by the inability of muscle and fat tissues to respond to physiological levels of insulin (peripheral tissue insulin resistance), and to restore the normal levels of sugars in the bloodstream. The development of peripheral tissue insulin resistance and type 2 diabetes is strongly linked to lifestyle and to obesity, although the underlying mechanisms are incompletely resolved. In healthy individual when insulin combines with its receptor on target tissues (muscle and fat tissues) this initiates a cascade of linked reactions that ultimately result in the fusion of membrane vesicles containing the glucose transporter protein (called GLUT4) with the surface membrane of cells. This latter process increases the availability of glucose transporter molecules and thereby increases glucose transport into the cell. When fat cells are under stress, as for example when they need to store large quantities of nutrients in overweight people, they secrete small molecules, called cytokines, which can trigger inflammatory responses in the surrounding tissues. Such a cytokine is TNFa. Secretion of TNFa from fat cells has been linked to the development of a low-grade chronic inflammation in overweight subjects and to the development of insulin resistance. TNFa also has a direct effect on the fat cells themselves. It induces a cascade of events within cells, which alters the ability of the cells to respond to insulin and to increase the numbers of glucose transporters GLUT4 at the cell surface. In our recently published study, we reported our discovery that a small protein called Rab3 is important for the targeting of GLUT4 to the surface membrane of cells. More recently, we also discovered that in adipose cells TNFa treatment induces a very marked decrease in the number of Rab3 proteins in the cells. Therefore, in the current proposal, we aim to investigate the role played by Rab3 in the development of peripheral tissue insulin resistance. To achieve this aim we will use cellular models of adipose cells to investigate the molecular interactions between Rab3 and other proteins in the cell that act as molecular links between insulin action at its receptor and the GLUT4 transporter. We will investigate how these interactions are affected by treatment with TNFa or other molecules known to induce the state of insulin resistance. We will make use of unique tools that were recently developed in our laboratory to monitor the activity of Rab3 and the movement of GLUT4 to the cell surface. These experiments will allow us to understand the mechanisms of action of Rab3 in the context of the development of the insulin-resistant state. We will also investigate in humans, undergoing a diet and exercise intervention programme, whether Rab3 is affected in a manner that reveals underlying mechanisms involved in the control of modulating the insulin sensitivity in adipose tissues and skeletal muscles. The outcome from this project will have important implications for our understanding of the mechanisms of the development of peripheral tissue insulin resistance and for the development of new, targeted therapies for treatment of insulin resistant subjects and people with type 2 diabetes.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

The use of artificial intelligence (AI) to automate routine medical image analysis tasks, such as the analysis of mammograms, has been proposed to address the Radiology crisis because of low recruitment rates, the 10-year training time to produce a consultant radiologist, and the pending retirement of large parts of the current workforce. The design of these AI models tends to be centred on data rather than people, i.e., the key stakeholders patients and radiologists, where model optimisation focuses on maximising the accuracy of a generic model for the majority of patients instead of improving the performance of all sub-groups of radiologists for all patient cohorts. Note that radiologists can be divided into sub-groups depending on their experience and track record, while patients can be grouped into cohorts subject to their age, family history, previous cancer diagnosis, ethnicity, and scanner characteristics. Such design strategy has produced accurate models for the majority of patients, but its intrinsic competitive nature with radiologists' performance and lack of consideration for particular sub-groups of radiologists and patients cohorts have prevented the widespread adoption of AI models in clinical practice. We argue that the two main reasons for this weak acceptance of AI into clinical practice are: 1) radiologists have not been integrated in the design and optimisation of the models, resulting in poor cooperation between radiologists and models; and 2) patients are provided with a biased classification performance since the system performs well for the majority of patients. This proposal addresses these two problems, targeting a more usable AI model that will increase the sensitivity and specificity for all sub-groups of radiologists, improving the efficiency of the whole mammogram analysis process, and potentially allowing radiologists to join the clinical force earlier in their careers to mitigate the Radiology crisis. Also, given that patients form cohorts in unpredictable ways (depending on dataset population and imaging technology), patients will be more likely to accept AI models that produce a fair outcome for all cohorts. We focus this project on mammography, but our proposal is applicable to other similar problems, such as lung cancer screening. We will work with our collaborators from NHS and industry on an extension of our method to these similar problems during the development of this project.