Biology subjects

Wedderburn, L. R.

Publications and source records attributed to Wedderburn, L. R..

7 recordsLinked to original sources

bioRxiv2025

Functional and dysfunctional T regulatory cell states in human tissues in RA and other autoimmune arthritic diseases

Regulatory T cells (Tregs), characterized by FOXP3 expression, are essential for maintaining immune homeostasis by controlling inflammation. However, in autoimmune diseases such as rheumatoid arthritis (RA), impaired Treg function contributes to immune dysregulation and disease pathology. While most studies of human Tregs have focused on blood, here we analyzed Tregs in synovial tissues from RA patients using single cell RNA sequencing (scRNAseq). We identified two predominant Treg states, CD25hiCXCR6pos Tregs with strong suppressive function, and CD25loAREGpos Tregs, a dysfunctional state exclusively enriched in synovial tissues but not in blood. Computational and in vitro analyses revealed that cortisol induced AREG expression, suppressed glycolysis, and impaired the suppressive function of CD25loAREGpos Tregs. In turn, AREG promoted an IL-33+ inflammatory phenotype in synovial fibroblasts. Importantly, we found that TNFR2 engagement can prevent or reverse this dysfunctional Treg state. In contrast to CD25loAREGpos Tregs, CD25hiCXCR6pos Tregs were highly suppressive, showed coordinated abundance with macrophages in synovial tissue, and functionally interacted with membrane-bound TNF expressed by macrophages, which promoted their functional suppressive state. These two Treg subsets were similarly found in the synovial tissue in Juvenile Idiopathic Arthritis (JIA), another inflammatory arthritic disorder, indicating conserved mechanisms across arthritic diseases. Together, our findings define distinct pathways driving divergent functional and dysfunctional Treg states in inflamed tissues and point to interventions that may prevent or reverse the development of the dysfunctional state.

immunology↗

bioRxiv2025

Spatially resolved fetal and maternal cell contributions to severe Preeclampsia

The molecular and cellular pathophysiology of the fetal-maternal interface in preeclamsia remains poorly understood, but it is increasingly clear that both fetus and mother make independent contributions to it. Here, we spatially distinguish them from each other and from gestational age for the severe form of the disease, interrogating tissues and cell types beyond the placenta for the Early and Late presentation of the condition. In addition to concerted hypoxia, angiogenic imbalance, fibrosis and aberrant metabolism in the placenta, new maternal immune signatures, including changes in macrophages, mitochondrial dysfunction and interferon signalling in the placenta, myometrium or chorioamniotic membranes, offer an explanation to systemic inflammation and endothelial dysfunction in the mother. These tissue and cell-specific responses are potential targets for therapy, with their prompt consideration in Early gestational disease likely beneficial due to its severity. Thus, timely intervention during gestation could change the extremely poor prognosis of severe disease.

molecular biology↗

bioRxiv2025

Spatial transcriptomic analysis of muscle biopsy from treatment-naive juvenile dermatomyositis patients reveals mitochondrial abnormalities despite disease-related interferon driven signature

ObjectivesThis study aimed to investigate the spatial transcriptomic landscape of muscle tissue from treatment-naive juvenile dermatomyositis (JDM) patients in comparison to healthy paediatric muscle tissue. MethodsMuscle biopsies from three JDM patients and three age-matched controls were analysed using the Nanostring GeoMx(R) Digital Spatial Profiler. Regions of interest were selected based on muscle fibres without immune cells, immune cell infiltration and CD68+ macrophage enrichment. Differential gene expression, pathway analysis and pathways clustering analysis were conducted. Key findings were validated in 19 cases of JDM using immunohistochemistry and chemical stains, and a bulk RNAseq dataset of four cases of JDM. ResultsJDM muscle tissues exhibited significant interferon pathway activation and mitochondrial dysfunction compared to controls. A 15-gene interferon signature was significantly elevated in JDM muscle and macrophage-enriched regions, correlating with clinical weakness. In contrast, mitochondrial dysregulation, characterized by downregulated respiratory chain pathways, was present regardless of interferon activity or muscle strength. The interferon-driven and mitochondrial signatures were replicated in an independent RNAseq dataset from JDM muscle; lack of association between interferon signature and mitochondrial dysregulation was validated in 19 cases by conventional staining methods. Clustering analysis revealed distinct transcriptomic profiles between JDM and control tissues, as well as between JDM patients with varying clinical phenotypes. ConclusionsThis study highlights mitochondrial dysfunction as a consistent pathological feature in JDM muscle, which may be independent of interferon-driven inflammation. These findings highlight the potential for mitochondrial-targeted therapies in JDM management and emphasise the need for further studies to explore their therapeutic value. KEY MESSAGESWhat is already known on this topic O_LIJuvenile dermatomyositis (JDM) involves interferon-driven inflammation and immune-mediated muscle damage. C_LIO_LIMitochondrial abnormalities in blood immune cells persist despite treatment and contribute to disease pathology. C_LI What this study adds O_LIMitochondrial dysfunction is present in both muscle fibres and tissue-infiltrating immune cells within JDM muscle. C_LIO_LIThese abnormalities are detectable even in clinically less severe muscle weakness. C_LIO_LIDegree of mitochondrial abnormality at transcript and protein level may be independent of strength of IFN-driven signal. C_LIO_LIMitochondrial dysregulation detected at transcriptional level correlates with abnormal transcription of muscle (sarcomere) and the subcellular peroxisome organelle. C_LI How this study might affect research, practice, or policy O_LITargeting mitochondrial dysfunction could enhance treatment outcomes for JDM, especially in patients whose disease is refractory to current therapies. C_LIO_LIInsights from this study support the development of stratification tools to detect aspects of pathology which are not tightly correlated with IFN-driven pathology. C_LI

immunology↗

bioRxiv2024

Oestrogen influences B cell class-switching in individuals with an XX sex chromosome complement

Sex differences in humoral immunity are well-documented, though the mechanisms underpinning these differences remain ill-defined. Here, we demonstrate that post-pubertal cisgender females have higher levels of class-switched B cells compared to age-matched cisgender males. However, whilst sex chromosome-encoded genes characterise most of the differences in total B cell transcriptomes between cisgender-females and -males, sex differences in class-switched B cells are only observed post-pubertally. Accordingly, B cells express high levels of oestrogen receptor 2 (ESR2) and genes known to regulate B cell class-switching are enriched for ESR2-binding sites. Using a gender-diverse cohort of young people, we show that in transgender males (XX chromosomal background), blockade of natal oestrogen reduced the frequency of class-switched B cells, whilst gender-affirming oestradiol treatment in transgender females (XY chromosomal background), did not increase the frequency of class-switched B cells. These data demonstrate that sex hormones and chromosomes work in tandem to impact immune responses, with oestrogen only supporting B cell class-switching on an XX chromosomal background. eTOC summarySex hormones and chromosomes work in tandem to impact immune responses, with oestrogen influencing B cell class-switching exclusively on an XX chromosomal background.

immunology↗

bioRxiv2024

Treg fitness as a biomarker for disease activity in Juvenile Idiopathic Arthritis

Juvenile Idiopathic Arthritis (JIA) is an autoimmune condition characterised by persistent flares of joint inflammation. However, no reliable biomarker exists to predict the erratic disease course. Normally, regulatory T cells (Tregs) maintain immune tolerance, with altered Tregs associated with autoimmunity. Treg signatures have shown promise in monitoring other autoimmune conditions, therefore a Treg gene and/or protein signature could offer novel biomarker potential for predicting disease activity in JIA. Machine learning on our nanoString Treg gene signature on peripheral blood (PB) Tregs generated a model to distinguish active JIA (active joint count, AJC[≥]1) Tregs from healthy controls (HC, AUC=0.9875). Biomarker scores from this model successfully differentiated inactive (AJC=0) from active JIA PB Tregs. Moreover, scores correlated with clinical activity scores (cJADAS), and discriminated subclinical disease (AJC=0, cJADAS[≥]0.5) from remission (AUC=0.8980, Sens=0.8571, Spec= 0.8571). To investigate altered Treg fitness in JIA by protein expression, we utilised spectral flow cytometry and unbiased analysis. Three Treg clusters were increased in active JIA PB, including CD226highCD25low effector-like Tregs and CD39-TNFR2-Helioshigh, while a 4-1BBlowTIGITlowID2intermediate Treg cluster predominated in inactive JIA PB (AJC=0). The ratio of these Treg clusters correlated to cJADAS, and higher ratios could predict inactive individuals that flared by 6-month follow-up. Thus, we demonstrate altered Treg signatures and subsets as an important factor, and useful biomarker, for disease progression versus remission in JIA, revealing genes and proteins important in Treg fitness. Ultimately, PB Treg fitness measures could serve as routine biomarkers to guide disease and treatment management to sustain remission in JIA.

immunology↗

bioRxiv2023

The immune landscape of the inflamed joint defined by spectral flow cytometry

Cellular phenotype and function are altered in different microenvironments. For targeted therapies it is important to understand site-specific cellular adaptations. Juvenile Idiopathic Arthritis (JIA) is characterised by joint inflammation, with frequent inadequate treatment responses. To comprehensively assess the inflammatory immune landscape, we designed a 37-parameter spectral flow cytometry panel delineating mononuclear cells from JIA synovial fluid (SF), compared to JIA and healthy control blood. Synovial monocytes and NK cells lack the Fc-receptor CD16, suggesting antibody-mediated targeting may be ineffective. B cells and DCs, both in small frequencies in SF, undergo maturation with high 4-1BB, CD71, CD39 expression, supporting T cell activation. SF effector and regulatory T cells were highly active with newly described co-receptor combinations that may alter function, and suggestion of metabolic reprogramming via CD71, TNFR2 and PD-1. Most SF effector phenotypes, as well as an identified CD4-Foxp3+ T cell population, were restricted to the inflamed joint, yet specific SF-predominant Treg (CD4+Foxp3+) subpopulations were increased in blood of active but not inactive JIA, suggesting possible recirculation and loss of immunoregulation at distal sites. This first comprehensive dataset of the site-specific inflammatory landscape at protein level will inform functional studies and the development of targeted therapeutics to restore immunoregulatory balance and achieve remission in JIA. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/569010v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@66c213org.highwire.dtl.DTLVardef@4f99b5org.highwire.dtl.DTLVardef@1f475bborg.highwire.dtl.DTLVardef@5d533f_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Imputation of cell-type specific expression using RNA-seq data from mixed cell populations

Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (eg cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available. Author SummaryNumerous studies have demonstrated that gene expression in particular subsets of immune cells plays a critical role in the development of diseases and response to treatment. By profiling gene expression from these cells, we can identify disease-relevant genes, comprehend their functions in the disease or response to treatment, and potentially pave the way for screening and patient stratification for prevention and treatment. However, the current cost of single-cell RNA sequencing is too high for large-scale expression profiling analysis. Therefore, an alternative approach is to computationally estimate cell-type specific expression from mixed cell populations, which has been less explored in the field. With this in mind, we proposed using machine learning approaches, multiple response LASSO and ridge, and applied them to synthesised datasets and real-world data where gene expression was measured in mixed and pure cell populations of the same subjects. We compared them to standard methods in the field, and evaluated the accuracy of predicted expression as well as the ability to reconstruct differentially expressed gene signals. Our results revealed that the LASSO/ridge algorithms performed better than existing methods in recovering differentially expressed gene signals, highlighting their potential applications to impute the cell-type expression.

bioinformatics↗