ABSTRACT Autoimmune diseases, including psoriasis (Ps), rheumatoid arthritis (RA) and ulcerative colitis (UC), pose significant health burdens worldwide. A more refined classification of these diseases is essential for enabling targeted therapeutic strategies. Traditional Chinese Medicine (TCM) is gaining increasing recognition globally, offering potential insights into disease heterogeneity. In this study, we performed comprehensive T cell receptor (TCR) and B cell receptor (BCR) repertoire sequencing in 59 participants, including patients with Ps, RA, UC and healthy controls. Patients were further stratified into Dampness and non‐Dampness groups based on standardized TCM diagnostic criteria. Repertoire composition, clonotype richness, diversity metrics, V gene usage and shared CDR3 patterns were analysed. Machine learning approaches (LASSO, GLM and OPLS‐DA) were applied to identify diagnostic biomarkers, followed by validation in an independent cohort. Compared with healthy controls, Ps, RA and UC patients exhibited reduced clonotype richness and diminished TCR/BCR diversity, indicating adaptive immune contraction. Dampness ZHENG‐specific alterations were observed, including selective IgK and IgL clonotype richness reduction in Ps with dampness, increased TRA/TRB diversity in RA with dampness, and elevated TRG clonotype counts/read proportions in UC with dampness. Stratification by Dampness ZHENG revealed distinct immune repertoire architectures characterized by increased D50 index, reduced proportions of large clones and preferential V gene usage, including TRAV20, TRAV38‐2DV8 and TRAV8‐3. Unsupervised dimensionality reduction demonstrated significant separation between Dampness and non‐Dampness groups across diseases. A three‐gene V‐segment model achieved an AUC of 0.804 and 74.7% accuracy in an independent validation cohort, supporting its potential utility as an objective biomarker for Dampness ZHENG. Our findings suggest that TCM‐based phenotyping may offer a meaningful approach for subgrouping autoimmune diseases, thereby providing a foundation for more syndrome differentiation‐based treatment strategies.
Lipeng Tang, Maojie Wang, Yuhong Yan et al.· Cell Proliferation· 0 citations
OBJECTIVE
Early diagnosis of seronegative rheumatoid arthritis (SNRA) is often challenging due to lack of reliable serological markers. The aim of this study was to explore protein biomarkers of SNRA and identify their potential as therapeutic targets.
METHODS
Third-eight seropositive (SPRA) patients and 20 SNRA patients were enrolled in our omics study. Differentially expressed proteins (DEPs) between the two groups were identified via the Astral mass Analyzer for quantitative proteomics using data-independent acquisition (DIA) mass spectrometry, and the dataset of GSE93272 was used for the validation test. A variety of bioinformatics algorithms such as random forest, support vector machine, nomogram construction, consensus clustering were applied to identify key regulators, expression patterns, and molecular subtypes. We also performed functional enrichment analysis of differentially expressed genes (DEGs) across clusters and evaluated immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). Mendelian randomization (MR) analyses were conducted to identify druggable targets, and colocalization analyses were performed to determine whether RA risk and the expression of these druggable genes were influenced by shared SNPs. Additionally, we validated these findings through both in vitro and in vivo experiments, using clinical peripheral blood samples, RAW264.7 cells, and the collagen-induced arthritis (CIA) rat model to assess the functions of key genes in RA.
RESULTS
Using quantitative proteomics, we identified 22 DEPs between SPRA and SNRA, including 12 upregulated and 10 downregulated proteins. Notably, CAMK2G protein was positively correlated with inflammation indicators in RA patients. Differential expression analysis between RA samples and controls identified nine key DEPs as potential biomarkers. A random forest classifier and a nomogram incorporating these nine biomarkers were constructed, and their clinical utility was supported by decision curve analysis. In addition, consensus clustering based on these hub genes stratified RA patients into distinct subgroups characterized by differential immune cell infiltration patterns and polygenic risk scores. Significant association between NR1H3 and disease by colocalization analysis in MR suggests its potential as druggable target. Notably, our experiments have confirmed that CAMK2G expression is associated with enhanced osteoclastogenic capacity, and enhanced CAMK2G expression accompanies joint damage in CIA rats.
CONCLUSION
Our research on polygenic risk model indicates CAMK2G protein could serve as a promising biomarker and NR1H3 might be a potential candidate for targeting therapy for RA.
Peng Zhang, Youbang Liang, Xin Li et al.· Journal of Autoimmunity· 0 citations
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