Diagnostic biomarkers and mechanistic understanding of biofilm formation in prosthetic joint infection

About the Project

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the Faculty of Science and Health, and will be supervised by Professor Sam Robson (Professor of Genomics and Bioinformatics, University of Portsmouth), Dr Robbie Baldock (Senior Lecturer in Biomedical Sciences, University of Portsmouth) and Dr Robin Rumney (Lecturer in Musculoskeletal Biology and Regenerative Medicine, University of Southampton).

The work on this project could involve:

• Metagenomic sequencing of pathogens and host RNA sequencing from clinical samples using Oxford Nanopore Technologies sequencing platforms to identify potential biomarkers for prosthetic joint infection.
• Development of machine learning models for prediction of prosthetic joint infection incorporating sequencing data and patient metadata.
• Metabolic analyses of clinical samples to identify biomarkers of immune response and inflammation as potential targets for diagnosis of prosthetic joint infection.
• Co-culturing of biofilm community members with cell types linked to periprosthetic tissue (e.g., osteoblasts, osteoclasts, endothelial cells, skeletal stem cells) for in vitro assessment of the role of biofilm formation on immune response.

Project description

The use of medical implants in modern medicine has become an increasingly common occurrence. Hip and knee arthroplasty account for a large number of medical implant surgeries, with over 200,000 performed annually in the UK and Wales.

Prosthetic joint infection (PJI) represents one of the most common reasons for failure among hip and knee arthroplasty, with an incidence of around 0.5-2% (increasing significantly in cases of revision arthroplasty), and an estimated cost to the NHS for revision surgery of between ~£1,000 and £165,000. Infection can occur early (with about a third diagnosed within the first 6 months) or late (over a year after surgery), and no specific early markers for infection onset exist. Current diagnostic tests require infection to have already taken hold and may often be highly invasive.

Given the significant costs to the NHS for corrective revision surgery, the added suffering and risks to patients from surgery, and the risk of enhancing antimicrobial resistance through use of broad-spectrum antibiotics, a more specific predictive test for early onset of infection is required.

We have previously demonstrated detection of bacteria from biofilms associated with PJI through 16S rRNA sequencing of patient samples from peripheral blood. In this project, you will work with a comprehensive set of clinical samples to identify novel predictive biomarkers for PJI. You will utilise a range of tools, including targeted amplicon and metagenomic nanopore sequencing approaches from Oxford Nanopore Technologies, and real-time metabolic analyses using the Agilent Seahorse XF. In addition, you will conduct pilot in vitro studies to explore the impact of pathogens isolated from PJI cases on distinct bone cell populations. This is an exciting opportunity to work in a highly impactful multi-site clinical research study, develop a range of biomolecular skills, and incorporate both wet and dry lab skills to address an important clinical need.

General admissions criteria

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

You will require experience working in a microbiological lab with excellent aseptic technique. Experience with cell culture, and/or previous training in molecular biology techniques will be highly desirable and candidates with prior experience will be favoured. Previous experience with computational analyses and bioinformatics would be a benefit for success in this project. In particular expertise using R or python for data analysis, experience working in a Unix environment, experience with working on a high-performance cluster and use of load balancing software (e.g., SLURM), and experience with common bioinformatics tools (e.g., BLAST, samtools, etc.) will be considered favourably. Experience with development of machine learning algorithms and artificial intelligence will also be of benefit to the successful candidate.

How to Apply

We’d encourage you to contact Professor Sam Robson (samuel.robson@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Pharmacy, pharmacology and biomedical sciences PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

When applying please quote project code: MPB10041026