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Molecular and clinical prediction of osteoarthritis risk after knee joint injury. (#202013)


About This PhD Project

Project Description

Osteoarthritis (OA) affects 9 million people in the UK and is the main reason for costly total joint replacement. Developing pharmacological interventions to prevent or slow disease has proved challenging, partly because OA is difficult to predict and diagnose in its early stages and tends to develop later in life. A subgroup of knee OA (~12%) is so-called ‘post-traumatic’ OA (PTOA), initiated by a significant joint injury, typically affecting younger individuals. ~50% of those with acute knee injuries such as anterior cruciate ligament (ACL) tears will develop PTOA after their injury1. We do not understand why some individuals make a full recovery, whilst others go on to develop progressive OA, though our work suggests that there is a systemic transcriptional response and a protein response within the joint (in synovial fluid) in response to joint injury, both of which vary considerably between individuals. These pathways might be associated with either damage or successful repair2. The genetic variants associated with established OA are now well described3. We are currently working to describe genetic variants specifically associated with PTOA, including polygenic risk scores and a meta-analysis looking for novel variants of PTOA. There is an unmet clinical need to understand the molecular pathways underling the initiation of PTOA to enable its prediction and treatment.

Aims


• To fully define clinical and molecular prognostic factors of post traumatic OA
• To study the relationships of proteins and molecular pathways at the time of joint injury which are associated with PTOA (or protection from it)
• To study the functional effects of genetic variants (including those known to be associated with ‘usual’ OA) on gene and protein expression at the time of joint injury
• To test the validity of incorporating novel biomarkers, genetic variants or polygenic risk scores into prognostic models of PTOA
• To develop and validate a full prognostic model for PTOA using multiple cohort data

Your primary work will be in the in Knee Injury Cohort at the Kennedy (KICK) study dataset, our longitudinal study of 150 individuals with joint injury, analyzing the completed 5 year dataset for the first time. This cohort includes high clinical phenotyping including symptomatic/imaging-based outcomes, as well as candidate protein data, peripheral blood expression data, but now also for the first time Illumina genome-wide genotypic data and SomaLogic proteomic data (5000 proteins measured in the synovial fluid at the time of joint injury). You will test replication of your findings in the Oxford Knee Injury Cohort (OxKIC) and other international joint injury cohorts, as part of their participation in international large consortia led by us (Synovial fluid To define molecular Endotypes by Unbiased Proteomics in OA (STEp UP OA) and Osteoarthritis and Sporting Knee Injury: Genomic Association with Risk (OSKGAR).
This is an exciting translational project with its ultimate aim is to define a prognostic model for PTOA for the first time, incorporating novel biomarkers such as genetic variants alongside clinical data. This work is essential in developing new treatments for this common yet poorly understood disease.

Training Opportunities


You will be based within a multi-disciplinary team in the Centre for Osteoarthritis Pathogenesis Versus Arthritis at the world-leading Kennedy Institute of Rheumatology which aims to translate scientific findings to patient benefit. Full training is given in statistical/prognostic modelling, bioinformatics and statistical genetic techniques involved in the project. A core curriculum in the first term provides foundation in musculoskeletal sciences, immunology and data analysis.

You will use techniques such as cluster and pathway analysis, and expression and protein Quantitative Trait Loci in blood and synovial fluid respectively. Using systematic literature review, Delphi approaches and existing KICK data you will fully describe prognostic factors in this setting. You will be trained in state of the art approaches to prognostic modelling.

There will be an expectation that you will present your data at regular Centre meetings, national and also international meetings. You will work in a multi-disciplinary team including laboratory technicians generating molecular data and statistical geneticists carrying out the genome wide meta-analysis, as well as other consortium members.

Key Publications


1. Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: osteoarthritis. The American journal of sports medicine. 2007;35(10):1756-69.

2. Watt FE, Paterson E, Freidin A, Kenny M, Judge A, Saklatvala J, Williams A, Vincent TL. Acute molecular changes in synovial fluid following human knee injury are associated with early clinical outcomes. Arthritis Rheumatol.2016; 68: 2129-2140.

3. Zengini, E., et al., Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Genet, 2018. 50(4): 549-558.

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