Development of robust analytical pipelines for the analysis of microbial community data from clinical samples

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Cystic fibrosis is an inherited disease which affects around 9,000 people in the UK. It is a recessive disorder, meaning that both parents have to carry a faulty copy of a gene for a child to be affected. Approximately 1 in 25 people carry this copy. Cystic fibrosis dramatically shortens the life of those affected and almost half do not live beyond their 30s. Cystic fibrosis has a negative effect on many parts of the body, but it particularly affects the lungs. The result of the defective gene is that thick secretions cannot be effectively cleared from the lungs, resulting in the airways becoming congested and damaged.This congestion results in frequent and severe exacerbations, sometimes requiring admission to hospital and treatment. We think exacerbations are often a result of infections, which may be caused by viruses and bacteria. One of the most common bacteria found is Pseudomonas aeruginosa. Microbiologists diagnose Pseudomonas by putting samples of sample onto plates and identifying the bacteria that grow in visible colonies. Often Pseudomonas is treated by antibiotics. Other bacteria may be found including Streptococcus and Staphylococcus.However when looking for causes of exacerbations we are limited to finding pathogens that we know about, and which grow on the types of culture plates we use. It may be that other bacteria are present that we can't see because they do not grow easily. Sometimes those bacteria may be a cause. However, in a similar way to the human gut it may be that there are "good" and "bad" bacteria. It is known that certain types of bacteria can prevent other types infecting and therefore they may help protect against exacerbations.A new technology utilises the idea of molecular barcodes which identify bacterial species from fragments of DNA in their cell. New instruments termed high-throughput sequencers permit these barcodes to be read from many samples easily and cheaply. This technology gives us a "parts list" of the bacterial species in a particular sample, and a rough idea of how frequently they occur. By reading this parts lists from patients with cystic fibrosis - when they are well, when they are very sick and when they are recovering, we may be able to tell the relative contribution of these unseen bacteria to the condition. For example a particular species increasing or reducing in abundance may be associated with recovery, giving us a potential therapeutic target.We are also interesting in seeing how patients end up being colonised with particular, commonly seen bacteria. For example in our local patients, about 30% have a particular type of Pseudomonas infection called Midlands 1. However, very little is known about how it is that so many patients end up being infected by the same strain. We are now able to sequence all the DNA in a bacterial cell (the genome) which gives very high resolution view of how it has evolved. By comparing genomes of strains from different patients, we can help determine whether patients are infecting each other with the same strain, or whether the strains are quite different and come from many different sources. We can also see how the Pseudomonas evolves whilst it is in a patients lungs. Previous studies have shown that the Pseudomonas adapts to the specific environment, which may give us clues as to why people with the same cystic fibrosis mutation have different courses and end up with more hospital admissions or exacerbations than others.The technology we are using is very new, and there are a number of difficulties with it before it can be a routine clinical test. One problem is that the machines generate plenty of sequencing "noise" which may look like species are present that aren't. I want to develop bioinformatics methods that try and increase the reliability of these techniques and generate information that would be useful for clinicians. We expect these techniques to enter the clinic within the next five years.