Tuberculosis (TB) is now the single largest infectious disease killer globally, with over 1.5 million people dying of the disease in 2014. In common with all infectious diseases, the use of antibiotics of the last 70 years has led to the emergence of resistance.
The cell wall of M.tuberculosis is unusually thick; the consequences of this are that the bacteria grow very slowly, and therefore conventional lab-based diagnostics takes around a month to determine if an infection is resistant, and secondly, there is a comparatively small panel of antibiotics that can be used to treat the disease.
The Modernising Medical Microbiology (MMM) group here at the John Radcliffe Hospital are pioneering the use of genetic sequencing to infer the resistance phenotype of clinical TB cases by examining the bacterial genome for known resistance-causing mutations. The MMM group, through Derrick Crook, is leading the international TB consortium, CRyPTIC, that has been funded by the Wellcome Trust and the Gates Foundation. This large project, which will run until 2021, will collect samples from 100,000+ patients with TB worldwide and, for each, sequence the genome of the pathogen and determine its susceptibility to a panel of antibiotics using a specially designed 96-well plate.
Each well has a different concentration of antibiotic, hence the effectiveness of each antibiotic is usually determined by visually assessing which concentration of antibiotic prevents the growth of the bacterium. The problem is this is a subjective task, and relying on a single assessment is likely to introduce too much inconsistency into the final dataset, reducing our ability to infer which mutations in the TB genome confer resistance.
In April 2017 we launched BashTheBug.net, a Citizen Science project that invites anyone to help us beat TB by looking at a photograph of part of the plate and then telling us which is the first well in which TB is no longer growing. The project has been highly successful; in its first six months nearly 7,500 people from around the world have classified 430,000 images. This is just a small step; to classify all the images expected to be produced by CRyPTIC will require between 10 and 20 million classifications.
The student will
This is a highly interdisciplinary project and therefore no candidate is expected to have all the skills and knowledge necessary at the start; these will be gained through the training opportunities outlined below.
The student will be exposed to and trained in
The student would need to collaborate and work with a large virtual team made up of
The student would be encouraged to collaborate with the growing number of international partners in CRyPTIC and take advantage of the numerous training courses available through the department and university.
Project reference number: 955
|Dr Philip Fowler||Experimental Medicine Division||Oxford University, John Radcliffe Hospital||GBRemail@example.com|
|Professor David Clifton||Institute of Biomedical Engineering||University of Oxford||GBRfirstname.lastname@example.org|
|Professor (Ann) Sarah Walker||Experimental Medicine Division||Oxford University, John Radcliffe Hospital||GBRemail@example.com|
There are no publications listed for this DPhil project.