Autumn 2016 Projects

Almost every meal we eat involves plants, from our morning cornflakes to the late night take-away chips. Globally, 90% of all calorie intake comes directly from crop plants. The onward rise in global population means that we must continuously grow greater quantities of crops. When these crops fail, millions of deaths can result.

One of the chief reasons for catastrophic crop failure is disease. Examples include the historical potato famine of Ireland and the more recent Cassava famine in Uganda. However, plants do not stand idly by. As with all organisms, they have developed complex ways to fight disease.

We are interested in the way that plants respond to being infected by microbes. Plant cells move special molecules to the site of infection with the aim of repelling the invader. But how do these resources get to the infection? How do they fight the microbes? And what goes wrong when disease wins?

In this project we will directly look into how the immune defences move to sites of infection. We will do this using a combination of traditional biology techniques and mathematics, which will allow us to investigate how we could improve the plant immune response in order to develop stronger resistance and safeguard crop production for the future.

Decades of fruitless effort to develop drugs to cure dementia have led many researchers to question whether relying on animal experiments is the best way forward. An alternative approach focuses on identifying risk factors that cause dementia in the first place. If we can understand what causes dementia, then we can intervene to delay or even prevent it. Various health and lifestyle factors are linked with dementia risk (e.g. obesity). However, we do not know if they cause dementia or simply occur at greater rates in people who also develop dementia. The distinction is important, as efforts to modify these risk factors will only reduce dementia risk if they are truly causal. To improve the design of future clinical trials we will use advanced modelling and take into account each person’s individual characteristics. This will allow us to understand what truly drives dementia risk and predict who is most likely to benefit from particular interventions and in what circumstances. If the clinical trials prove successful, this form of ‘precision medicine’ has enormous potential to help reduce the growing burden of dementia by enhancing clinical practice.

Medical imaging, such as magnetic resonance imaging (MRI), uses computer-based technology to create and display images of the human body. This enables the potential for further analysis of the images where programmes can be used to identify breaks in the bones, identify diseases and assess the response to treatment. However, to train computers to undertake these complex tasks involves expert users of medical imaging to identify (outline) the shapes of structures, such as bones in the spine, to enable accurate information to develop these programmes. This is a time consuming and expensive practice for a small number of experts.  However, it is possible that large numbers of non-experts could provide these outlines with a small amount of training.  This might appeal to some members of the community who are interested in medical imaging, in helping research or in learning anatomy for their studies or personal interest. This study explores whether non-experts can be easily trained to accurately outline anatomy on medical images of the spine from magnetic resonance imaging scans.

Dementia is an alarming healthcare issue: it might affect more than one million people in the UK by 2025, at the actual rate. Alzheimer’s disease represents over 60% of all cases of dementia; it provokes the gradual death of nerve cells in the brain, which results in the person progressively losing the ability to think, until eventually death incurs. In healthy individuals, each nerve cell contains filaments of an elastic protein, called tau, that gives them a typical “branchy” shape. In Alzheimer’s disease, such protein becomes insoluble, filling the nerve cells with tangles and provoking their death. We are using a mathematical model of brain function and light-based methods to investigate the relationship between brain physiology and the progressive accumulation of such tangles. This study will be conducted on mice, as these animals, under certain conditions, can develop tangles in few months, while in humans it would take years. These techniques require advanced computing and microscopic imaging; we have been successfully employing them as effective tools for neuronal function modelling and the detection of a sticky toxic protein found in Alzheimer’s disease, respectively.

In this project, they will be applied to another model of progressive cognitive decline, as we continue developing our research towards their application in humans.

This research is attempting to better understand the biology of sleep and its links to disease. Too much, too little or poor quality sleep is associated with several human diseases, in particular disorders such as obesity and Type 2 diabetes. For example, individuals who sleep less than 6 hours per night have a 75% increased risk of obesity. Another aspect of sleep patterns is our individual circadian rhythm – the 24 hour cycle of changes in hormones, body temperature and most body systems - which regulates our feelings of wakefulness and sleepiness. Disrupting our circadian rhythms is strongly associated with disease. For example, shift-workers are 40% more likely to develop heart disease.

The aim of this project is to identify the genetic markers influencing sleep duration and timing. We can then test whether individuals that carry these genetic markers and (a) work night shifts or (b) survive on little (less than 6hrs) or excessive (more than 9hrs) sleep are also more likely to suffer adverse health effects.

To do this we will analyse more than 70 million genetic markers in 500,000 individuals from the UK Biobank study. We will investigate a range of diseases, including cancer, heart disease, obesity, type 2 diabetes and mental health.

This work will increase our understanding of the biology of sleep. More importantly it will identify genetic markers that increase the chance of developing disease in people with specific sleep patterns. This not only has applications for the development of drugs but could also inform public health policy. By providing evidence for a causal link between sleep, disease and lifestyle, we could have a significant impact on policy makers by directing public health efforts towards improving sleep quality as one way of reducing the problem of diseases like obesity, heart disease and mental health.