In cystic fibrosis, bacteria, fungi, and viruses living together in the lung mucus can turn a normally harmless pathogen into a trigger for severe breathing crises. These pulmonary exacerbations—episodes of breathlessness, fever, and heavy coughing—cause most of the illness and death in cystic fibrosis, yet some people carry the same pathogens for years without ever having an attack. The researcher suspects the difference lies in how those pathogens interact with the other microbes around them. To test this, she will first search a large collection of lung microbiome samples for microbe–pathogen pairs that are uniquely present in severe disease. She will then use a high-throughput fly infection model to see whether certain microbes make the pathogen more lethal, and finally screen thousands of small molecules to find ones that block that harmful interaction. If successful, this work could lead to new drugs that prevent exacerbations rather than just treating them after they start. The research is fundamental science into microbial ecology, but it directly targets a clinical problem that patients themselves rank as their top priority.
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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.
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