Role of IFIT1 and IFIT3 in systemic lupus erythematosus: modeling a diagnosis and exploring immune regulation

MX2 and IRF7 are Associated With Disease Activity and Renal Involvement in Systemic Lupus Erythematosus: An Exploratory Study

Background Systemic lupus erythematosus (SLE) has multiple phenotypes, one of which is lupus nephritis (LN), a very serious complication with a high mortality rate. Patients with LN may require different treatment regimens than patients with SLE, as there are significant differences between SLE and LN pathogenesis; however, to date, no biomarkers specific for LN have been characterized. Identification of immunological markers of LN could therefore facilitate more individualized treatment strategies. Materials and Methods Using public datasets (GSE121239), we screened patients based on SELENA‐SLEDAI scores into mild (SLELA), moderate‐to‐severe (SLEHA), and healthy control (HC) groups. Bioinformatics analyses identified differentially expressed genes (DEGs), which were analyzed for functional pathway enrichment and immune cell infiltration. We validated these findings in an independent clinical cohort of 40 SLE patients (20 with LN and 20 without LN) and 20 HC individuals. LN diagnosis was confirmed by renal biopsy or clinical criteria. We assessed disease activity using clinical indicators and measured the expression of MX2, IRF7, and TRIM69 in peripheral blood mononuclear cells (PBMCs) via real‐time quantitative PCR (n = 40). Serum interferon‐α (IFN‐α) levels were measured by ELISA, and protein expression in kidney tissue was detected by immunofluorescence (IF). Results We observed differential expression of 32 disease activity‐related genes in SLE. Univariate logistic regression and ROC curve analysis indicated that MX2 (AUC = 0.95, 95% CI: 0.92–0.98), TRIM69 (AUC = 0.93, 95% CI: 0.89–0.96), and IRF7 (AUC = 0.91, 95% CI: 0.87–0.95) were associated with SLE diagnosis. Gene ontology functional enrichment analysis suggested that the type I IFN pathway is aberrantly activated in SLE. Disease activity‐related genes such as MX2, IRF7, and IFIT3 are functionally enriched in the type I IFN pathway, and abnormal infiltration of neutrophils is involved in SLE pathogenesis. Disease activity‐related genes such as MX2 and IRF7 were positively correlated with immune cell infiltration, including neutrophils and memory B cells. RT‐qPCR and ELISA showed significantly higher expression of IFN‐α, MX2, and IRF7 in LN patients than in SLE patients. IF assays showed that IFN‐α, MX2, and IRF7 were expressed at significantly higher levels in the kidneys of LN patients than in SLE patients. Conclusions Our study identifies MX2 and IRF7 as candidate biomarkers associated with high systemic disease activity in SLE. In our validation cohort, their expression was significantly elevated in patients with LN, suggesting they may serve as potential noninvasive indicators warranting further longitudinal investigation for LN risk assessment.

Differential methylation of immune-related genes and its clinical significance in systemic lupus erythematosus: insights from TET2, FOXP3, and IFI44L genes

Identification and Validation of ISG20 as a Shared Immune-Related Crosstalk Marker in Rheumatoid Arthritis and Inflammatory Bowel Disease

Purpose There is a bidirectional association between rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). Patients with RA exhibit a higher prevalence of IBD, those with IBD are at a significantly increased risk of developing RA. The shared molecular mechanisms and key bridging molecules underlying remain to be explored. Methods Transcriptomic datasets from GEO were analyzed using WGCNA, differential expression, and immune-related genes to identify shared RA-IBD signatures. Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to prioritize diagnostic markers. Single-cell RNA sequencing datasets were used to localize the cellular source of the hub gene. Subsequently, two independent large-scale peripheral blood datasets were retrieved to further provide transcriptomic profiles of circulating immune cells, serving as a “bridge” between RA and IBD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Findings were validated using collagen-induced arthritis (CIA) and dextran sulfate sodium (DSS) mice models, as well as in vitro TNF-α stimulation of MH7A and Caco-2 cells. Results Intersection analysis identified 19 core immune-driven genes shared between RA and IBD, both diseases are characterized by a highly activated immune profile, particularly T cell lineages. Among these, ISG20 emerged as a critical molecular link, exhibiting high diagnostic efficacy for RA and IBD. Single-cell transcriptomic analysis further revealed that ISG20 is predominantly expressed in T cell populations in both RA synovium and IBD colon. In peripheral blood datasets, 29 “bridge genes” were identified and found to be significantly enriched in chemokine signaling. Experimental validation confirmed that ISG20 mRNA levels were significantly upregulated in inflamed synovial tissues of CIA and colon of DSS mice, and in TNF-α-stimulated MH7A and Caco-2 cells. Conclusion ISG20 serves as a crosstalk molecular link across the RA and IBD. These findings provide molecular landscape for shared pathogenesis and diagnostic window for patients predisposed to the RA-IBD co-occurrence clinical phenotype.

Multimodal Interrogation of Autoimmune Disease Patients Reveals Highly Diverse Biomarker Signatures 2259778

This study interrogated human plasma samples from AID patients and age- and sex-matched healthy controls for biomarker signatures and identified three inflammatory clusters among the analyzed patient samples indicating the underlying AID endotypes.

ZBTB16-associated NK cell alterations reveal shared immunometabolic signatures linking primary Sjögren’s syndrome and type 1 diabetes mellitus

Background Primary Sjögren’s syndrome (pSS) and type 1 diabetes mellitus (T1DM) share immune-inflammatory features, yet conserved pathogenic signatures linking these autoimmune disorders remain incompletely understood. The present research sought to uncover common molecular markers and dissect the underlying immune-metabolic cross-talk underlying pSS and T1DM. Methods Gene expression profiles of patients with pSS and T1DM were retrieved from the Gene Expression Omnibus database, normalized, and corrected for batch effects prior to downstream analyses. Overlapping potential biomarkers were screened by integrating differential expression analysis, weighted gene co-expression network analysis and least absolute shrinkage and selection operator regression. Functional enrichment based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases was implemented to interpret gene biological properties, and a protein–protein interaction network was further established afterwards. Diagnostic performance was evaluated using receiver operating characteristic analysis. Experimental validation was conducted in non-obese diabetic (NOD) mice using quantitative PCR, immunohistochemistry, and flow cytometry. The CIBERSORT algorithm was adopted to quantify immune cell infiltration levels. Results ZBTB16 was identified as a shared hub biomarker in both pSS and T1DM and exhibited favorable diagnostic performance. Experimental validation confirmed significantly reduced ZBTB16 expression in peripheral blood mononuclear cells, salivary gland tissues, and pancreatic tissues of NOD mice. Gene Set Enrichment Analysis indicated that ZBTB16-associated signatures were enriched in mitochondrial-related processes, neuroactive ligand-receptor interactions, and ribosome-related pathways. Immune infiltration analysis revealed that resting natural killer (NK) cells were positively correlated with ZBTB16 expression in both diseases. Flow cytometric analysis further confirmed a reduced proportion of resting NK cells in peripheral blood of NOD mice, consistent with the CIBERSORT-based prediction. Conclusion This study identifies ZBTB16 as a shared biomarker linking pSS and T1DM. Reduced resting NK-cell abundance was consistently observed in both computational and experimental analyses, and bioinformatic correlation analysis suggested a positive association with ZBTB16 expression. These findings provide evidence for shared molecular and immunological signatures underlying the two autoimmune disorders and support further investigation of the biological role and diagnostic value of ZBTB16 in pSS and T1DM.

A compact five-gene immuno-fibrotic profile distinguishing systemic sclerosis subtypes with PXDN validated as a fibrosis-associated target

Introduction Systemic sclerosis (SSc) is a heterogeneous autoimmune disease characterized by a paucity of reliable biomarkers for accurate diagnosis and subtype stratification. The considerable clinical variability between diffuse and limited cutaneous subtypes underscores an urgent need for molecular tools that can dissect this heterogeneity and guide therapeutic strategies. Methods To address this, we employed a multi-step computational approach. Initially, Weighted Gene Co-expression Network Analysis (WGCNA) was applied to the GSE130955 dataset to identify disease-associated modules. This was followed by the application of three machine-learning algorithms—LASSO regression, random forest, and SVM-RFE—to the GSE181549 dataset for hub gene selection. Immune cell infiltration was estimated using CIBERSORT, and the expression of candidate genes was validated at single-cell resolution using the GSE138669 dataset. Experimental validation was performed for the top candidate, PXDN, assessing its protein expression in human fibroblasts, SSc patient skin, and a bleomycin-induced murine fibrosis model. Finally, molecular docking with AutoDock Vina was conducted to evaluate the binding affinity of small molecules to PXDN. Results WGCNA identified a module strongly correlated with SSc status (r = 0.73, p < 2e-16), which was enriched in immune chemotaxis and extracellular matrix organization pathways. The convergence of the three machine-learning algorithms nominated a five-gene signature (CPXM1, ELN, GSTM5, PXDN, PDE7B), which demonstrated high diagnostic accuracy (AUC = 0.985, 95% CI: 0.965-1) and effectively distinguished diffuse from limited cutaneous SSc. Single-cell analysis confirmed predominant expression of these genes in fibroblast and macrophage populations within SSc lesional skin. Experimentally, PXDN was found to be upregulated by TGF-β1 in human fibroblasts and was significantly elevated in skin and lung tissues from SSc and IPF patients, as well as in the bleomycin-induced mouse model. Although molecular docking nominated Protokylol hydrochloride as a compound satisfying distal-cavity geometric criteria, this serves as a hypothesis-generating finding rather than a confirmed inhibitor. Discussion Our integrative analysis identifies a concise immunofibrotic gene signature that robustly distinguishes SSc and its major subtypes. This signature highlights a potential nexus of immune-stromal interactions that may underlie disease heterogeneity, offering candidate biomarkers for molecular stratification and therapeutic targeting. Notably, PXDN emerges as a particularly promising target, warranting further experimental investigation to validate its functional role and therapeutic potential in SSc.