Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses

[Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database]

OBJECTIVE

To identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms.

METHODS

The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA).

RESULTS

We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (R=0.68, P=2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment.

CONCLUSION

CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration.

Methods

We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis.

Results

Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis.

Conclusion

RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

The effect of food on the pharmacokinetics of WXFL10203614, a potential selective JAK1 inhibitor, in healthy Chinese subjects

Objective: This study was performed to investigate the effect of food on the pharmacokinetics (PK) of WXFL10203614 in healthy Chinese subjects. Methods: This was a randomized, open-label, single-dose, two-treatment (fed vs fasted), two-period, two-sequence, crossover study. 14 eligible subjects were averagely randomized into 2 sequences and then received 10 mg WXFL10203614 under fasted or fed condition. In each period, the blood samples were collected from 0 h (pre-dose) and serially up to 72 h post-dose, and plasma concentrations were detected using the high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method. The effect of food on the PK profile and safety of WXFL10203614 were assessed. Results: 70 subjects were screened, and 14 subjects (10 male and 4 female) were enrolled and completed the study. Under the fasted condition, WXFL10203614 was absorbed rapidly with a T_max of 0.98 h. The absorption rate was slower, T_max delayed by 2.98 h, and the C_max decreased by 16.3% when WXFL10203614 administered after the high-fat and high-calorie diet, other PK parameters were not affected. The 90% confidence intervals (CIs) for the ratio (fed/fasted) of geometric means of the C_max, AUC_0-t and AUC_0-∞ were 0.73-1.01, 0.90-1.03 and 0.90-1.03, indicating that the high-fat and high-calorie diet might impact the absorption process of WXFL10203614. Although the C_max was slightly decreased, there was no significant difference in the C_max under fasted and fed conditions. Thus, it was not considered clinically significant owing to the small magnitude of changes in C_max. All Treatment-emergent adverse events (TEAEs) were mild and resolved spontaneously without treatment. Conclusion: Food had no clinically relevant effects on drug system exposure of WXFL10203614. It was well tolerated under fasted and fed conditions in healthy Chinese subjects, so WXFL10203614 could be administered orally with or without food. Clinical Trial Registration: http://www.chinadrugtrials.org.cn/index.html, identifier CTR20191636.

Safety, tolerability and pharmacokinetics of WXFL10203614 in healthy Chinese subjects: A randomized, double-blind, placebo-controlled phase Ⅰ study

Objective: This study was conducted to investigate the safety, tolerability and pharmacokinetics (PK) of WXFL10203614 after single and multiple oral doses in healthy Chinese subjects.

Methods: A single-center, randomized, double-blind, placebo-controlled phase Ⅰ study was performed on healthy Chinese subjects. In the single-dose study, Subjects were randomized into 7 dose levels of WXFL10203614 (1 mg group, n = 2; 2, 5, 10, 17, 25 and 33 mg groups with placebo, 8 subjects per group, 2 of them given placebo). In the multiple-dose study, subjects received 5 or 10 mg WXFL10203614 once daily (QD), 5 mg twice daily (BID) or placebo for 7 consecutive days. Safety, tolerability and PK of WXFL10203614 were all assessed.

Results: A total of 592 subjects were screened, 50 subjects were enrolled in the single-dose study and 30 in the multiple-dose study. All adverse events (AEs) were mild or moderate and resolved spontaneously. No Serious Adverse Events (SAEs) or deaths were reported during the study. WXFL10203614 was absorbed rapidly after dosing with Tmax of 0.48–0.98 h, Cmax, AUC0-t and AUC0-∞ were all increased in a dose-related manner over the range of 1–33 mg. Renal excretion was the major route of elimination of WXFL10203614. Steady-state PK parameters (Cmax,ss, AUC0-t,ss and AUC0-∞,ss) were elevated after once-daily administration of 5–10 mg WXFL10203614 and non- and weak drug accumulations were observed, whereas moderate drug accumulation occurred in the 5 mg BID group.

Conclusion: WXFL10203614 exhibited good safety, tolerability and favorable PK profiles in healthy Chinese subjects, supporting further clinical development in patients with rheumatoid arthritis.

Clinical Trials Registration Number:http://www.chinadrugtrials.org.cn/index.html, #CTR20190069 and CTR20200143.

Identification and validation of potential hub genes in rheumatoid arthritis by bioinformatics analysis

OBJECTIVE

Rheumatoid arthritis (RA) is considered to be a chronic immune disease pathologically characterized by synovial inflammation and bone destruction. At present, the potential pathogenesis of RA is still unclear. Hub genes are recognized to play a pivotal role in the occurrence and progression of RA.

METHODS

Firstly, we attempted to screen hub genes that are associated with RA, to clarify the underlying pathological mechanisms of RA, and to offer potential treatment methods for RA. We acquired these datasets (GSE12021, GSE55235, and GSE55457) of RA patients and healthy samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were recognized via R software. Then, Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were utilized to deeply explore the underlying biological functions and pathways closely associated with RA. In addition, a protein-protein interaction (PPI) network was built to further evaluate and screen for hub genes. Finally, on the basis of the results of PPI analysis, we confirmed the mRNA expression levels of five hub genes in the synovial tissue of rats modeled with RA.

RESULTS

In the human microarray datasets, LCK, JAK2, SOCS3, STAT1, and EGFR were identified as hub genes associated with RA by bioinformatics analysis. Furthermore, we verified the differential expression levels of hub genes in rat synovial tissues via qRT-PCR (P < 0.05).

CONCLUSIONS

Our findings suggest that the hub genes LCK, JAK2, SOCS3, STAT1, and EGFR might have vital roles in the progression of RA and may offer novel therapeutic treatments for RA.

Transcriptomic profiling revealed the role of apigenin-4'-O-α-L-rhamnoside in inhibiting the activation of rheumatoid arthritis fibroblast-like synoviocytes via MAPK signaling pathway

BACKGROUND

Activated fibroblast-like synoviocyte (FLS) played a significant role in the pathogenesis and progression of rheumatoid arthritis (RA). Apigenin-4'-O-α-L-rhamnoside showed remarkable effects against RA, however, no relevant studies on pharmacology of apigenin-4'-O-α-L-rhamnoside yet, the effects and underlying molecular mechanism of apigenin-4'-O-α-L-rhamnoside on RA are still unclear.

PURPOSE

This study aimed to investigate the therapeutic effects and mechanisms of apigenin-4'-O-α-L-rhamnoside on RA-FLS cells by transcriptomic analysis.

METHODS

In vitro, RA-FLS cell viability and migration were measured by CCK-8 and scratch assays, respectively. The effects of apigenin-4'-O-α-L-rhamnoside on inflammatory levels of MMP-1, MMP-3, RANKL and TNF-α in RA-FLS cells were detected using ELISA kits. High-throughput transcriptome analysis was performed to screen the key genes and related pathways of apigenin-4'-O-α-L-rhamnoside inhibit RA-FLSs, and the result of which were validated by RT-qPCR and western blot. Furthermore, in vivo, we also evaluated the effects of apigenin-4'-O-α-L-rhamnoside in rat with CIA.

RESULTS

Apigenin-4'-O-α-L-rhamnoside significantly suppressed RA-FLS migration, exerted remarkable inhibiting effects on the expression levels on MMP-1, MMP3, RANKL and TNF-α in RA-FLS cells. It seemed that MAPK signaling pathway might be closely related to the pathogenesis of RA by down-regulated relevant core targets (MAPK1, HRAS, ATF-2, p38 and JNK). Moreover, apigenin-4'-O-α-L-rhamnoside attenuated the severity of arthritis in CIA rat.

CONCLUSION

Apigenin-4'-O-α-L-rhamnoside inhibited pro-inflammatory cytokine, chemokine and MMPs factors production of RA-FLS by targeting the MAPK signaling pathway, which provided a scientific basis for potential application in the treatment of RA.

RNA-seq and Network Analysis Reveal Unique Chemokine Activity Signatures in the Synovial Tissue of Patients With Rheumatoid Arthritis

Purpose

This study aimed to provide a comprehensive understanding of the genome-wide expression patterns in the synovial tissue samples of patients with rheumatoid arthritis (RA) to investigate the potential mechanisms regulating RA occurrence and development.

Methods

Transcription profiles of the synovial tissue samples from nine patients with RA and 15 patients with osteoarthritis (OA) (control) from the East Asian population were generated using RNA sequencing (RNA-seq). Gene set enrichment analysis (GSEA) was used to analyze all the detected genes and the differentially expressed genes (DEGs) were identified using DESeq. To further analyze the DEGs, the Gene Ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. The protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the hub genes were identified by topology clustering with the Molecular Complex Detection (MCODE)-Cytoscape. The most important hub genes were validated using quantitative real-time PCR (qRT-PCR).

Results

Of the 17,736 genes detected, 851 genes were identified as the DEGs (474 upregulated and 377 downregulated genes) using the false discovery rate (FDR) approach. GSEA revealed that the significantly enriched gene sets that positively correlated with RA were CD40 signaling overactivation, Th1 cytotoxic module, overactivation of the immune response, adaptive immune response, effective vs. memory CD8+ T cells (upregulated), and naïve vs. effective CD8+ T cells (downregulated). Biological process enrichment analysis showed that the DEGs were significantly enriched for signal transduction (P = 3.01 × 10-6), immune response (P = 1.65 × 10-24), and inflammatory response (P = 5.76 × 10-10). Molecule function enrichment analysis revealed that the DEGs were enriched in calcium ion binding (P = 1.26 × 10-5), receptor binding (P = 1.26 × 10-5), and cytokine activity (P = 2.01 × 10-3). Cellular component enrichment analysis revealed that the DEGs were significantly enriched in the plasma membrane (P = 1.91 × 10-31), an integral component of the membrane (P = 7.39 × 10-13), and extracellular region (P = 7.63 × 10-11). The KEGG pathway analysis showed that the DEGs were enriched in the cytokine-cytokine receptor interaction (P = 3.05 × 10-17), chemokine signaling (P = 3.50 × 10-7), T-cell receptor signaling (P = 5.17 × 10-4), and RA (P = 5.17 × 10-4) pathways. We confirmed that RA was correlated with the upregulation of the PPI network hub genes, such as CXCL13, CXCL6, CCR5, CXCR5, CCR2, CXCL3, and CXCL10, and the downregulation of the PPI network hub gene such as SSTR1.

Conclusion

This study identified and validated the DEGs in the synovial tissue samples of patients with RA, which highlighted the activity of a subset of chemokine genes, thereby providing novel insights into the molecular mechanisms of RA pathogenesis and identifying potential diagnostic and therapeutic targets for RA.