In time-critical infections such as severe sepsis, there is a 6% chance of mortality within each successive hour without working antibiotics. However, it often takes 24 hours to confirm a bacterial infection and a further 24 hours to determine its susceptibility to antibiotics. More broadly, delays in effective treatment for infections substantially increases the burden of morbidity in the population, and prescribing the incorrect antimicrobials leads to increasing resistance pressures with potential long-term consequences for the patient and the community at large.
These days, large clinical pathology laboratories utilise MALDI mass spectrometers to provide clinicians with species identification to inform antibiotic prescribing. These methods are generally unable to determine sub-species or antimicrobial susceptibility, and data complexity and background contaminants make it challenging to do so with conventional methodology. We have recently developed a novel suite of data science and machine learning methods that statistically assess which peaks are differentially expressed between species and strains, and which peaks provide the best predictive power for use as biomarkers (https://bit.ly/3jM69yS).
The goal of this studentship on the GW4 BioMed MRC Doctoral Training Programme (https://gw4biomed.ac.uk/) is to use these tools together with MALDI sample optimisation, genome bioinformatics analyses and gene knockout experiments, to biologically link resistance mechanisms at the genotype level to phenotype exhibited in the MALDI spectra. Focussing on clinically relevant strains of Klebsiella pnemoniae and Escherichia coli, the student will both discover and provide the necessary evidence to validate biomarkers and hence establish clinical diagnostics for antibiotic resistance using MALDI mass spectrometry. These resulting MALDI diagnostic assays will have a greatly reduced cost profile than clinical genotyping and hence could have broad implications for optimising antibiotic prescribing as well as for use in public health programmes to monitor infection outbreaks and resistance dynamics in the community.
A comprehensive multi-disciplinary supervisory team will support the student, who will benefit from being part of the richly active local research communities at the Health Data Research UK South-West Better Care Partnership (Prof Andrew Dowsey, Dr Philip Williams, Dr Maha Albur), Bristol AMR (Prof Matthew Avison; http://www.bristol.ac.uk/amr) and the Milner Centre for Evolution at Bath (Prof Ed Feil, Prof Sam Sheppard). The project would suit an applicant with a strong first degree or masters in a biological or biomedical discipline. In addition, some experience or a desire to develop skills in basic programming, data science and machine learning is desirable and will be supported through the DTP taught courses and bespoke training.
How to apply
Student applications can be made via the GW4 BioMed2 website: https://www.gw4biomed.ac.uk. The closing date for applications is 5pm on Friday, 26th November 2021. Shortlisted applicants will be invited for an informal interview (over the phone, on Skype or Zoom) between Monday 31st January 2022 and Friday 11th February 2022. The formal interviews will be held virtually on the 16th and 17th February 2022.
A GW4 BioMed2 MRC DTP studentship includes full tuition fees at the UK/Home rate, a stipend at the minimum UKRI rate (£15,609 for 21/22), a Research & Training Support Grant (RTSG) valued between £2,000-£5,000 per year and £300 annual travel and conference grant based on a 3.5-year full-time studentship. These funding arrangements will be adjusted pro-rata for part-time studentships.
The GW4 BioMed2 MRC DTP studentships are available to UK, EU and International applicants. The GW4 institutions have all agreed to cover the difference in cost between home and international tuition fees. This means that International candidates will not be expected to cover this cost.
For any informal queries about the studentship, please contact Andrew Dowsey ([Email Address Removed] )
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