Biology subjects

Goodyear, C.

Publications and source records attributed to Goodyear, C..

4 recordsLinked to original sources

bioRxiv2026

Synovial lining expressed mechanosensor PIEZO1 drives inflammation-permissive macrophage phenotypes and joint inflammation

Synovial tissue-resident macrophages regulate immune homeostasis within the joint, but can adopt an inflammation-permissive phenotype that promotes immune cell infiltration in rheumatoid arthritis (RA). Understanding the factors that drive this phenotypic switch may help prevent the localisation of inflammation in the joints of individuals at-risk of RA. We identified the mechanosensitive ion channel PIEZO1 as a potential regulator of lining-layer synovial tissue macrophage (STM) function. PIEZO1 was highly expressed in homeostatic, tissue resident TREM2pos lining-layer STMs and in its pathogenic chemokine producing TREM2low phenotype that characterises the hyperplastic lining-layer in active RA. Intra-articular injection of a PIEZO1 agonist in mice induced neutrophil and monocyte infiltration, whereas inhibition of PIEZO1 signalling restored the protective macrophage phenotype. Thus, mechanosensing via PIEZO1 is a defining feature of the joint lining-layer, and its aberrant activation by mechanical stress may lead to the localisation of inflammation within the joint, facilitating a transition from asymptomatic autoimmunity of at-risk RA to clinical disease.

immunology↗

bioRxiv2025

RNAcare: Integrating Clinical Data with Transcriptomic Evidence using Rheumatoid Arthritis as a Case Study

BackgroundGene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. ResultsOur RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing bulk RNA-Seq data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms, like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings and generating new hypotheses. ConclusionWe present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/, with its source code at https://github.com/sii-scRNA-Seq/RNAcare/.

bioinformatics↗

bioRxiv2023

A comprehensive analysis of rheumatoid arthritis B cells reveals the importance of CD11c+ve double-negative-2 B cells as the major synovial plasma cell precursor.

B cells are key pathogenic drivers of chronic inflammation in rheumatoid arthritis (RA). There is limited understanding of the relationship between synovial B cell subsets and pathogenic antibody secreting cells (ASCs). This knowledge is crucial for the development of targeted therapies. Here, we combine flow cytometry of circulating B cells with single-cell RNA and paired repertoire sequencing of over 27,000 synovial B cells from patients with established RA. Twelve B cell clusters were identified including previously recognised subsets, and a novel cluster that strongly expressed heat shock proteins. All lineages identified by trajectory analysis contribute to the DN2 B cell population, which is the major precursor to synovial ASCs. This was further supported by B cell receptor (BCR) lineage analysis, which revealed clonal relationships between DN2 cells and ASCs. This study advances our understanding of B cells in RA and reveals the origin of pathogenic ASCs in the RA synovium.

immunology↗

bioRxiv2019

Rheumatoid arthritis patients express a skewed repertoire of polyclonal, hypomutated B-cell receptors

ObjectivesThe success of B cell depletion therapy in rheumatoid arthritis (RA) therapy testifies to their importance in disease pathogenesis, but the precise B cells mediating this are unclear. For example, it is unknown if RA patients predominantly express a limited number of circulating clonally expanded populations of B cells with highly mutated B cell antigen receptors (BCRs) that would constitute a shared antigen driven response.\n\nMethodsTo address this, we have undertaken the largest study to date utilising next generation sequencing (NGS), to identify the full length of the peripheral blood BCR sequences from the antigen-binding heavy chain. Between 25,000 to 200,000 BCR sequences per patient were analysed from 127 newly diagnosed RA patients, 16 heathy controls, 16 RA patients with established disease and 8 paired blood and synovial samples. This was complemented with B cell subset analysis from an additional 64 RA patients and 22 healthy controls.\n\nResultsRA patients expressed a significantly higher percentage of circulating poorly mutated polyclonal IgG+ve variable heavy (IgG-Vh) BCR sequences, both at the time of diagnosis and following treatment. These sequences resided predominantly within TNF-alpha secreting IgG+veCD27-ve B cells, that were expanded in RA peripheral blood and enriched in the rheumatoid synovium. Surprisingly, peripheral and synovial B cell repertoires of RA patients are quite distinct, sharing very few IgG sequences.\n\nConclusionsThis is the first report to conclusively establish that a substantial component of the peripheral B cell repertoire in RA consists of polyclonal hypomutated IgG+ve BCRs that may play a critical role in driving an autoimmune mediated inflammation.

immunology↗