Role of the granzyme family in rheumatoid arthritis: Current Insights and future perspectives

Exploration of molecular biomarkers in ankylosing spondylitis transcriptomics

Background

Inflammation of the spine and sacroiliac joints is a hallmark of the chronic, progressive inflammatory illness known as ankylosing spondylitis (AS). The insidious onset and non-specific early symptoms of AS often lead to delays in diagnosis and treatment, which may result in the onset of disability. It is therefore imperative to identify new biomarkers.

Methods

In this study, datasets GSE73754 and GSE25101 were derived from the Gene Expression Omnibus (GEO). Key genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). A model was then established using LASSO regression, and then it was subjected to the receiver operating characteristic (ROC) curve analysis for evaluation of the diagnostic accuracy of the genes. Subsequently, immune infiltration analysis was conducted to demonstrate the immune infiltration status of the samples and the correlation between key genes and immune infiltration. Finally, the expression levels of key genes in peripheral blood mononuclear cells (PBMCs) and their correlation with clinical indicators were validated via RT-qPCR assay.

Results

Through WGCNA and differential expression analysis, 6 genes were identified. Ultimately, five key genes (ACSL1, SLC40A1, GZMM, TRIB1, XBP1) were determined using LASSO regression. The area under the ROC curve (AUC) for these genes was greater than 0.7, indicating favorable diagnostic performance. Immune infiltration analysis showed that AS was associated with infiltration levels of various immune cells. RT-qPCR validated that the expression of ACSL1, SLC40A1, GZMM, and XBP1 was consistent with the predictive model, whereas TRIB1 expression was contrary to the predictive model. Clinical correlation analysis of key genes revealed that ACSL1 was positively linked to hsCRP levels, GZMM was negatively linked to, hsCRP levels, and neutrophil absolute values, SLC40A1 was positively linked to ESR, hsCRP levels and neutrophil absolute values, and XBP1 was negatively linked to ESR, hsCRP levels, and neutrophil absolute values.

Conclusion

This study identified key genes that may reveal a potential association between AS and ferroptosis, demonstrating high diagnostic value. Furthermore, the expression levels of these genes in peripheral blood mononuclear cells (PBMCs) are strongly correlated with disease activity. These findings not only suggest potential biomarkers for the diagnosis of AS but also offer important references for exploring new therapeutic targets, highlighting their substantial clinical applicability.

Psoriatic arthritis subtypes are phenocopied in humanized mice

Psoriatic arthritis (PsA) is a complex inflammatory disease that challenges diagnosis and complicates the rational selection of effective therapies. Although T cells are considered active effectors in psoriasis and PsA, the role of CD8+ T cells in pathogenesis is not well understood. We selected the humanized mouse model NSG-SGM3 transgenic strain to examine psoriasis and PsA endotypes. Injection of PBMCs and sera from patients with psoriasis and PsA generated parallel skin and joint phenotypes in the recipient mouse. The transfer of human circulating memory T cells was followed by migration and accumulation in the skin and synovia of these immunodeficient mice. Unexpectedly, immunoglobulins were required for recapitulation of the clinical phenotype of psoriasiform lesions and PsA domains (dactylitis, enthesitis, bone erosion). Human CD8+ T cells expressing T-bet, IL-32 and CXCL14 were detected by spatial transcriptomics in murine synovia and by immunofluorescence in the human PsA synovia. Importantly, depletion of human CD8+ T cells prevented skin and synovial inflammation in mice humanized with PsA peripheral blood cells. The humanized model of psoriasis and PsA represents a valid platform for accelerating the understanding of disease pathogenesis, improving the design of personalized therapies, and revealing psoriatic disease targets.

Granzymes in health and diseases: the good, the bad and the ugly

Granzymes are a family of serine proteases, composed of five human members: GA, B, H, M and K. They were first discovered in the 1980s within cytotoxic granules released during NK cell- and T cell-mediated killing. Through their various proteolytic activities, granzymes can trigger different pathways within cells, all of which ultimately lead to the same result, cell death. Over the years, the initial consideration of granzymes as mere cytotoxic mediators has changed due to surprising findings demonstrating their expression in cells other than immune effectors as well as new intracellular and extracellular activities. Additional roles have been identified in the extracellular milieu, following granzyme escape from the immunological synapse or their release by specific cell types. Outside the cell, granzyme activities mediate extracellular matrix alteration via the degradation of matrix proteins or surface receptors. In certain contexts, these processes are essential for tissue homeostasis; in others, excessive matrix degradation and extensive cell death contribute to the onset of chronic diseases, inflammation, and autoimmunity. Here, we provide an overview of both the physiological and pathological roles of granzymes, highlighting their utility while also recognizing how their unregulated presence can trigger the development and/or worsening of diseases.

Upregulation of CX3CR1 expression in circulating T cells of systemic lupus erythematosus patients as a reflection of autoimmune status through characterization of cytotoxic capacity

OBJECTIVE

This study investigated CX3CR1 expression in human peripheral blood T lymphocytes and their subsets, exploring changes in SLE patients and its diagnostic potential.

METHODS

Peripheral blood samples from 31 healthy controls and 50 SLE patients were collected. RNA-Seq data from SLE patient PBMCs were used to analyze CX3CR1 expression in T cells. Flow cytometry determined CX3CR1-expressing T lymphocyte subset proportions in SLE patients and healthy controls. Subset composition and presence of GZMB, GPR56, and perforin in CX3CR1+ T lymphocytes were analyzed. T cell-clinical indicator correlations were assessed. ROC curves explored CX3CR1's diagnostic potential for SLE.

RESULTS

CX3CR1+CD8+ T cells exhibited higher GPR56, perforin, and GZMB expression than other T cell subsets. The proportion of CX3CR1+ was higher in TEMRA and lower in Tn and TCM. PMA activation reduced CX3CR1+ T cell proportions. Both RNA-Seq and flow cytometry revealed elevated CX3CR1+ T cell proportions in SLE patients. Significantly lower perforin+ and GPR56+ proportions were observed in CX3CR1+CD8+ T cells in SLE patients. CX3CR1+ T cells correlated with clinical indicators.

CONCLUSION

CX3CR1+ T cells display cytotoxic features, with heightened expression in CD8+ T cells, particularly in adult SLE patients. Increased CX3CR1 expression in SLE patient T cells suggests its potential as an adjunctive diagnostic marker for SLE.

Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis

Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman's correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes' interactions with drugs. Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA.