Dissecting the role of CSF2RB expression in human regulatory T cells

Dynamic responses to rejection in the transplanted human heart revealed through spatial transcriptomics

Allograft rejection following solid-organ transplantation is a major cause of graft dysfunction and mortality. Current approaches to diagnosis rely on histology, which exhibits wide diagnostic variability and lacks access to molecular phenotypes that may stratify therapeutic response. Here, we leverage image-based spatial transcriptomics at sub-cellular resolution in longitudinal human cardiac biopsies to characterize transcriptional heterogeneity in 62 adult and pediatric heart transplant (HT) recipients during and following histologically-diagnosed rejection. Across 28 cell types, we identified significant differences in abundance in CD4 + and CD8 + T cells, fibroblasts, and endothelial cells across different biological classes of rejection (cellular, mixed, antibody-mediated). We observed a broad overlap in cellular transcriptional states across histologic rejection severity and biological class and significant heterogeneity within rejection severity grades that would qualify for immunomodulatory treatment. Individuals who had resolved rejection after therapy had a distinct transcriptomic profile relative to those with persistent rejection, including 216 genes across 6 cell types along pathways of inflammation, IL6-JAK-STAT3 signaling, IFNα/IFNγ response, and TNFα signaling. Spatial transcriptomics also identified genes linked to long-term prognostic outcomes post-HT. These results underscore importance of subtyping immunologic states during rejection to stratify immune-cardiac interactions following HT that are therapeutically relevant to short- and long-term rejection-related outcomes.

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

Juvenile Idiopathic Arthritis (JIA) is an autoimmune condition characterised by flares of joint inflammation. However, no reliable biomarker exists to predict the erratic disease course. Normally, regulatory T cells (Tregs) maintain tolerance, with altered Tregs associated with autoimmunity. Treg signatures have shown promise in monitoring other conditions, therefore a Treg gene/protein signature could offer novel biomarker potential for predicting disease activity in JIA. Machine learning on our nanoString Treg 48-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 on test data). 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 (cJADAS<0.5). To investigate altered protein expression as a surrogate measure for Treg fitness in JIA, we utilised spectral flow cytometry and unbiased clustering analysis. Three Treg clusters were of interest in active JIA PB, including TIGIThighCD226highCD25low Teff-like Tregs, CD39-TNFR2-Helioshigh, and 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 potentially predict inactive individuals that flared by 9-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 contributing to Treg fitness. Ultimately, PB Treg fitness measures could serve as routine biomarkers to guide disease and treatment management to sustain remission in JIA.

Exploration of the shared diagnostic genes and mechanisms between periodontitis and primary Sjögren's syndrome by integrated comprehensive bioinformatics analysis and machine learning

BACKGROUND

Accumulating evidence has showed a bidirectional link between periodontitis (PD) and primary Sjögren's syndrome (pSS), but the mechanisms of their occurrence remain unclear. Hence, this study aimed to investigate the shared diagnostic genes and potential mechanisms between PD and pSS using bioinformatics methods.

METHODS

Gene expression data for PD and pSS were acquired from the Gene Expression Omnibus (GEO) database. Differential expression genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were utilized to search common genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore biological functions. Three machine learning algorithms (least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF)) were used to further identify shared diagnostic genes, and these genes were assessed via receiver operating characteristic (ROC) curves in discovery and validation datasets. CIBERSORT was employed for immune cell infiltration analysis. Transcription factors (TFs)-genes and miRNAs-genes regulatory networks were conducted by NetworkAnalyst. Finally, relevant drug targets were predicted by DSigDB.

RESULTS

Based on DEGs, 173 overlapping genes were obtained and primarily enriched in immune- and inflammation-related pathways. WGCNA revealed 34 common disease-related genes, which were enriched in similar biological pathways. Intersecting the DEGs with WGCNA results yielded 22 candidate genes. Moreover, three machine learning algorithms identified three shared genes (CSF2RB, CXCR4, and LYN) between PD and pSS, and these genes demonstrated good diagnostic performance (AUC>0.85) in both discovery and validation datasets. The immune cell infiltration analysis showed significant dysregulation in several immune cell populations. Regulatory network analysis highlighted that WRNIP1 and has-mir-155-5p might be pivotal co-regulators of the three shared gene expressions. Finally, the top 10 potential gene-targeted drugs were screened.

CONCLUSION

CSF2RB, CXCR4, and LYN may serve as potential biomarkers for the concurrent diagnosis of PD and pSS. Additionally, we identified common molecular mechanisms, TFs, miRNAs, and candidate drugs between PD and pSS, which may provide novel insights and targets for future research on the pathogenesis, diagnosis, and therapy of both diseases.

Transactivation DNA-binding protein-related genes were associated with salivary gland injury in primary Sjögren syndrome

The purpose was to identify transactivation DNA-binding protein-related genes in salivary gland injury in primary Sjögren syndrome (pSS) in southwest China. We downloaded the datasets of GSE7451, GSE23117, and GSE40611. In order to screen the candidate genes, 2 kinds of machine learning algorithms were used. We collected blood from 28 patients and 20 controls to verify the expression of candidate genes using quantitative real-time polymerase chain reaction. The receiver operating characteristic curve was used to evaluate the diagnostic efficiency. Correlations between candidate genes and immune cells were examined. A total of 31 differentially expressed genes were obtained. Through different algorithms, 6 genes including IFIT1, CSF2RB, TRIM22, PPM1H, VAMP7, and C21orf2 were getted. Validation results suggested that the expression of CSF2RB, VAMP7, IFIT1, C21orf2, and TRIM22 was significantly increased in pSS. The area under the curve of CSF2RB was 0.937 and that of TRIM22 was 0.915. Immune infiltration analysis showed that the percentage of activated mast cells was lower than the controls (P = .025). Correlation analysis suggested that CSF2RB was associated with immune cell infiltration. The expression of CSF2RB was significantly upregulated, which could be related to the increase of γδ T cells. We revealed that CSF2RB could be the candidate gene of pSS. CSF2RB was involved by regulating various immune cells. The expression of CSF2RB was significantly upregulated, which was related to the increase of γδ T cells.

RNA-seq analysis reveals modulation of inflammatory pathways by an enriched-triterpene natural extract in mouse and human macrophage cell lines

Chronic inflammation is crucial in developing insulin resistance and type 2 diabetes. Previous studies have shown that a leaf extract of Eucalyptus tereticornis, with ursolic acid (UA), oleanolic acid (OA), and ursolic acid lactone (UAL) as the main molecules (78 %) mixed with unknown minor metabolites (22 %), provided superior anti-inflammatory, hypoglycemic, and hypolipidemic effects than reconstituted triterpenoid mixtures in macrophage cell lines and a pre-diabetic mouse model. Further identification of the molecular mechanisms of action of this mixture of triterpenes is required. This study aims to analyse the RNA expression profiles of mouse and human macrophage cell lines treated with the natural extract and its components. Activated macrophage cell lines were treated with the natural extract, UA, OA, UAL or a triterpene mixture (M1). RNA was extracted and sequenced using the DNBseq platform and the EnrichR software to perform gene enrichment analysis using the Gene Ontology database, Kyoto Encyclopedia of Genes and Genomes, and Reactome. To conduct clustering analysis, we standardised the normalised counts of each gene and applied k-means clustering. The combination of molecules in the natural extract has an additive or synergic effect that affects the expression of up-regulated genes by macrophage activation. Triterpenes (M1) regulated 76 % of human and 68 % of mouse genes, while uncharacterised minority molecules could regulate 24 % of human and 32 % of mouse genes. The extract inhibited the expression of many cytokines (IL6, IL1, OSM), chemokines (CXCL3), inflammatory mediators (MMP8 and MMP13) and the JAK-STAT signalling pathway in both models. The natural extract has a more powerful immunomodulatory effect than the triterpene mixture, increasing the number of genes regulated in mouse and human models. Our study shows that Eucalyptus tereticornis extract is a promising option for breaking the link between inflammation and insulin resistance.

Pathogenesis and novel therapeutics of regulatory T cell subsets and interleukin-2 therapy in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous multisystem inflammatory disease with wide variability in clinical manifestations. Natural arising CD4+ regulatory T cells (Tregs) play a critical role in maintaining peripheral tolerance by suppressing inflammation and preventing autoimmune responses in SLE. Additionally, CD8+ regulatory T cells, type 1 regulatory T cells (Tr1), and B regulatory cells also have a less well-defined role in the pathogenesis of SLE. Elucidation of the roles of various Treg subsets dedicated to immune homeostasis will provide a novel therapeutic approach that governs immune tolerance for the remission of active lupus. Diminished interleukin (IL)-2 production is associated with a depleted Treg cell population, and its reversibility by IL-2 therapy provides important reasons for the treatment of lupus. This review focuses on the pathogenesis and new therapeutics of human Treg subsets and low-dose IL-2 therapy in clinical benefits with SLE.