Bone surface modelling and P2X7R genotyping: A new diagnostic approach for advancing objective elbow osteoarthritis classification

Abusalem, Taqwa 2024. Bone surface modelling and P2X7R genotyping: A new diagnostic approach for advancing objective elbow osteoarthritis classification. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Background. Osteoarthritis (OA) of the elbow is a painful, debilitating condition that reduces the range of motion within the elbow and can result in joint locking. It may be caused by a previous injury or, most commonly, due to microdamage induced by heavy physical activities (manual occupation, high-intensity sports, etc.). Extensive osteophytosis (formation of smooth bony growths or spurs) around the joint margins is the characteristic presentation of a specific form of elbow OA investigated here. To date, elbow OA remains challenging to treat due to the lack of early diagnostic tests and because our understanding of its pathophysiology and risk factors is still evolving. While it is acknowledged that performing physical activities contributes to disease onset, our evidence indicates that genetic predisposition is a key contributing factor and either predisposes individuals to this condition or protects others from its development. Our group has recently demonstrated that osteophyte formation is linked to purinergic signalling, specifically P2X7 receptor activity. Aims. The project aims to combine advanced imaging and genetic analysis to enhance the understanding of elbow OA. The first objective is to develop methods for extracting 3D surface data from CT scans of elbow OA patients using a software pipeline for unbiased patient categorisation. The second objective is to investigate whether specific genetic variants of the P2RX7 gene are associated with elbow OA by identifying P2X7R variants and analysing the allele distribution in affected patients. Additionally, the project seeks to establish a stable cell line expressing the pcDNA5/FRT-TG2-IRES-P2X7RRa1-IRES-P2X7Ra2 construct to elucidate the functional consequences of these genetic changes on receptor activity to inform targeted and effective therapeutic interventions. Results. The 3D surface model approach has proven instrumental in characterising unique changes in the elbow joint associated with elbow OA aetiology, allowing precise identification of osteophyte location and size for all patients. Furthermore, we successfully categorised our patients using two distinct methods: one based on the distance between specific landmarks and the other utilised the surface area affected by the osteophyte. Both categorisation techniques complement each other, offering a comprehensive assessment of osteophyte severity. Genomic DNA analysis of our patients revealed specific SNPs that are common among elbow OA patients and occur at a higher frequency than expected in the typical Caucasian population. Subsequent cloning and mRNA transcripts analysis revealed more specific information on IV allelic distribution of relevant polymorphisms, i.e., what specific allele (subunit) combinations constitute a risk factor for this trimeric receptor. Additionally, we successfully established a stable cell line expressing the pcDNA5/FRT-TG2-IRES-P2X7R construct, marking the first step in developing a bicistronic P2X7 receptor expression system. This system can be used to evaluate the impact of specific subunit combinations on receptor functionality, particularly in relation to large membrane pore formation. Conclusion. Integrating both 3D structural analysis and genetic data provides a comprehensive picture of the factors contributing to elbow OA. This information not only enhances our understanding of elbow OA but also holds the potential for facilitating early diagnosis and more accurate disease course prediction. This potential offers a promising future for elbow OA treatment, thereby improving patient outcomes in clinical practice

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Dentistry
Date of First Compliant Deposit:	31 March 2025
Last Modified:	01 Apr 2025 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/177286

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