Cystic fibrosis is a genetic disease that leads to the accumulation of mucus in the lungs. Various microbes - including bacteria, fungi, and viruses; called the microbiome - inhabit this mucus, causing polymicrobial infections. Periodically, individuals with cystic fibrosis undergo pulmonary exacerbations: severe respiratory events which can include the feeling of breathlessness, the increase in sputum/mucus production, increased fever, cough etc. It is these events that individuals with cystic fibrosis frequently indicate that they wish to better understand, and that cause the majority of the morbidity and mortality in this patient population. We understand that most of these events are caused by a small number of pathogens which live within the lung microbiome. However, often individuals can be colonised with these same pathogens for years and not be affected by a single respiratory event. In this research programme, I aim to understand how this can be the case. I hypothesise that this is possible due to the interactions - or lack there of - of the pathogen with the microbes that it lives with as part of the lung microbiome. The overall aim of this work is to identify new small molecules which could be used as potential new therapeutics to better treat individuals with cystic fibrosis to prevent and/or lessen the effects of pulmonary exacerbations. To test this hypothesis, my work is split into 3 work packages (WPs): WP1: Use a large collection of cystic fibrosis lung microbiome samples to search for pairs of microbes and pathogens which are uniquely present in severe cystic fibrosis disease. I hypothesise that particular microbes found in some individuals with cystic fibrosis drive pathogens to be more able to drive disease and thus to cause more pulmonary distress. To test this hypothesis, I will look for microbes that are present in individuals who have worse disease (and more pulmonary events) when compared to those who have a milder disease phenotype. WP2: Test the effect of these microbes on cystic fibrosis pathogens in a high-throughput model of infection. I will use a fly infection model because they are small, easy to work with and more amendable to working with in high-throughput. Flies will be infected with the pathogen alone, and the time it takes to kill the fly will be logged and compared to a co-infection with the microbe and pathogen pair. If the fly dies more quickly when the microbe is added, it will signify that the presence of the microbe somehow makes the pathogen more harmful (we will confirm this by infection with the microbe alone which we predict will not cause infection and death in the fly). WP3: Next, I will find new small molecules (i.e., potential new drugs) that can inhibit these interactions. With these important interactions identified, I will next try to block them from occurring so that the pathogen is not able to trigger pulmonary events. Using the same fly model, I will test a set of structurally diverse molecules to see their effect on the time of fly death when flies are infected with microbe-pathogen pairs. These data will then be fed into a machine learning algorithm which will predict new molecules which should inhibit the microbe pathogen interaction the best. I will then make and test these molecules to ensure this is the case. By the end of this programme, I will have a better understanding of how microbes interact with each other in the cystic fibrosis lung, how these interactions drive lung disease and what types of small molecules are able to inhibit these microbe-pathogen interactions.

EPSRC : Paul Smith : EP/N509498/1 Lipids are biological molecules that have hydrophobic tails and hydrophilic headgroups. Along with proteins, lipids constitute the complex fluid mixture of biological cell membranes. There are hundreds of types of lipids in cell membranes, each with a different combination of tail and headgroup, and many serving important biological functions. The lateral organization of lipids - the way in which different lipid types mix with one another - also serves important but poorly understood biological roles, including providing platforms for transmitting signals across the membrane. Physics-based computer simulations offer a unique opportunity to study biological structures at sub-nanometer resolution. We will use computer simulations to systematically study the effect of curvature on lipid mixing and the local physical properties of the membrane. This will both add to our general understanding of membrane biophysics as well as allow us to better model the behavior of complex biological structures like red blood cells.

BBSRC : Christopher Wray : BB/T008776/1 Parasitic worm infections are an extremely common issue on livestock farms worldwide, costing farmers and economies billions in production losses. One particularly important species is the barber pole worm, named for its distinctive red and white spiral pattern similar to those seen outside a barbershop. Adult parasites live in an animal's gut, releasing thousands of eggs daily, feeding on the blood of the host. Eggs hatch to larval worms on pasture, and are ingested by another host animal, normally sheep, goat or cattle, spreading the infection. Symptoms of infection include anemia, fluid buildup, reduced growth/milk production and sudden death. Infection is difficult to diagnose before the appearance of these symptoms, with sudden death often the first sign. Thus, farmers periodically apply chemical treatments when concerned about barber-pole worm. This selects for drug-resistant worms; barber-pole worm populations have quickly developed resistance to all of the drugs to which they have been exposed, often with a few years of a new drug's introduction. Thus, monitoring drug-resistance in worm populations (on different farms) is vitally important. Currently, this is done by counting the number of eggs in faeces before and after treatment, with the expectation being that if the worm is susceptible to the drug, there will be fewer eggs present after treatment while in resistant parasites, the number of eggs will remain stable. This technique is unfortunately unreliable due to inconsistent egg numbers being counted in samples, differences in number of eggs counted based on the analyst doing the counting, and a number of other confounding factors. Thus, novel methods for monitoring drug susceptibility are needed. Recent work has identified a genetic mutation within the worm that may be linked to resistance to levamisole, one of the most commonly used drugs against the barber pole worm. In this project, we will compare how common this mutation is between two different worm populations - one which has been exposed to levamisole, and one which has not. We hypothesize that the mutation will be significantly more common in the worm populations that have been regularly exposed to levamisole and so this work will validate this mutation as a marker of resistance to levamisole. This will lead to the development of a test that could be used directly on farms to test for the presence of this mutation. If the test is positive, farmers will know they need to use different drugs. Without this kind of testing, farmers cannot know if the parasite populations on their farms are drug-resistant and thus will continue to apply these drugs, wasting money on drugs that will not improve the health of their animals.

There are many causes of cognitive decline and dementia with ageing. The common causes tend to overlap, aren't well understood and have a similar appearance on imaging. Many of the imaging features are not well-defined. We are establishing a worldwide group of experts to create a common language for describing the imaging features and conducting imaging research in order to define the role of blood vessels in dementia.

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