Serologic Diagnosis of Rheumatoid Arthritis

Identification biomarkers and therapeutic targets of disulfidptosis-related in rheumatoid arthritis via bioinformatics, molecular dynamics simulation, and experimental validation

The relationship between disulfidptosis and rheumatoid arthritis (RA) remains unclear. We aimed to identified biomarkers disulfidptosis-related in RA and revealed potential targeted drugs. Two microarray datasets (GSE93272, GSE45291) related to RA were downloaded from the Gene Expression Omnibus (GEO) database. Disulfidptosis-related genes(DRGs) were extracted from FerrDb database. GSE93272 was used to identify DRGs, and GSE45291 was used to verify results. Multivariate Cox regression analysis was used to identify candidate disulfidptosis-associated hub genes. The differentiated values of DRGs were determined by receiver operator characteristic (ROC) monofactor analysis to judge their potential quality as biomarkers. RT-qPCR were used to validate the expression of hub genes. Additionally, we analyzed the connection between the hub genes and the filtration of immune cells in RA. We made predictions about the miRNAs, TFs and possible drugs that regulate the hub genes. Subsequently, molecular docking was carried out to predict the combination of drugs with hub targets. Finally, molecular dynamics simulation was conducted to further verify the findings. Oxoacyl-ACP Synthase Mitochondrial(OXSM) was identified as a biomarker with high diagnostic value, and an RA diagnostic model based on OXSM for a single gene was constructed. The model showed high accuracy in distinguishing RA and healthy controls (AUC = 0.802) and was validated by external datasets, showing excellent diagnostic power (AUC = 0.982). Twelve potential drugs against RA were recognized by comparative toxicogenomics database (CTD). Molecular docking results showed that ICG 001 had the highest binding affinity to OXSM, and molecular dynamics simulations confirmed the stability of this complexes. Furthermore, CIBERSORT analysis showed a significant correlation between immune cell infiltration and OXSM, and a regulatory network of TFs-gene-miRNAs comprising 8 miRNAs and 34 TFs was identified. Finally, the RT-qPCR results showed that OXSM was significantly increased in the peripheral blood of RA patients compared with healthy controls, consistent with the bioinformatics analysis. These studies suggest that OXSM may be a potential biomarker and therapeutic target for diagnosing RA, and ICG 001 may be a potential drug for RA. These findings may provide new avenues for the effective diagnosis and treatment of RA.

Circulating cell-free DNA as a potential biomarker for prediction of disease activity and prognosis among Egyptian rheumatoid arthritis patients

Cell-free DNA (cfDNA) has emerged as a potential biomarker for assessing disease activity and prognosis in rheumatoid arthritis (RA). However, the association between cfDNA levels and the established RA markers of inflammation and disease severity remains unclear. The current study aimed to detect plasma levels of cfDNA in patients with RA and to investigate their association with RA activity indicators (erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), disease activity score-28 (DAS28)), prognostic markers (rheumatoid factor (RF), anticitrullinated protein antibodies (ACPA)), and the musculoskeletal ultrasonographic (US7) scores. This controlled cross-sectional study included 108 RA patients and 108 healthy controls. Plasma levels of cfDNA were quantified by real-time PCR using ALU repeats. Levels of ESR, CRP, RF, and ACPA were measured using routine laboratory assays. Synovial inflammation and joint damage evaluation was performed using the US7 scoring system. Plasma levels of cfDNA were higher in RA patients than controls (P < 0.001) and significantly increased with higher DAS28 scores among all RA activity groups. Also, cfDNA levels were significantly positively correlated with ESR, CRP, RF, and ACPA levels (P-values <0.001). Regarding US7, cfDNA was significantly positively correlated with synovitis and erosion scores (P-values <0.05) but did not correlate significantly with tenosynovitis scores (P-values >0.05). In addition, plasma cfDNA was significantly higher in seropositive RA patients than in seronegative patients (P = 0.007). The odds ratio for cfDNA as a risk factor for erosions was 2.254. This study revealed that cfDNA levels are elevated in RA patients and positively associated with disease activity indicators and prognosis markers. Further research is warranted to validate these findings in larger cohorts and explore the clinical implications of cfDNA measurement in RA management.

Identification of diagnostic biomarkers of rheumatoid arthritis based on machine learning-assisted comprehensive bioinformatics and its correlation with immune cells

Background

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammatory cell infiltration, which can lead to chronic disability, joint destruction and loss of function. At present, the pathogenesis of RA is still unclear. The purpose of this study is to explore the potential biomarkers and immune molecular mechanisms of rheumatoid arthritis through machine learning-assisted bioinformatics analysis, in order to provide reference for the early diagnosis and treatment of RA disease.

Methods

RA gene chips were screened from the public gene GEO database, and batch correction of different groups of RA gene chips was performed using Strawberry Perl. DEGs were obtained using the limma package of R software, and functional enrichment analysis such as gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), and gene set (GSEA) were performed. Three machine learning methods, least absolute shrinkage and selection operator regression (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest tree (Random Forest), were used to identify potential biomarkers of RA. The validation group data set was used to verify and further confirm its expression and diagnostic value. In addition, CIBERSORT algorithm was used to evaluate the infiltration of immune cells in RA and control samples, and the correlation between confirmed RA diagnostic biomarkers and immune cells was analyzed.

Results

Through feature screening, 79 key DEGs were obtained, mainly involving virus response, Parkinson's pathway, dermatitis and cell junction components. A total of 29 hub genes were screened by LASSO regression, 34 hub genes were screened by SVM-RFE, and 39 hub genes were screened by Random Forest. Combined with the three algorithms, a total of 12 hub genes were obtained. Through the expression and diagnostic value verification in the validation group data set, 7 genes that can be used as diagnostic biomarkers for RA were preliminarily confirmed. At the same time, the correlation analysis of immune cells found that γδT cells, CD4+ memory activated T cells, activated dendritic cells and other immune cells were positively correlated with multiple RA diagnostic biomarkers, CD4+ naive T cells, regulatory T cells and other immune cells were negatively correlated with multiple RA diagnostic biomarkers.

Conclusions

The results of novel characteristic gene analysis of RA showed that KYNU, EVI2A, CD52, C1QB, BATF, AIM2 and NDC80 had good diagnostic and clinical value for the diagnosis of RA, and were closely related to immune cells. Therefore, these seven DEGs may become new diagnostic markers and immunotherapy markers for RA.

Redefining serological diagnostics with immunoaffinity proteomics

Serological diagnostics is generally defined as the detection of specific human immunoglobulins developed against viral, bacterial, or parasitic diseases. Serological tests facilitate the detection of past infections, evaluate immune status, and provide prognostic information. Serological assays were traditionally implemented as indirect immunoassays, and their design has not changed for decades. The advantages of straightforward setup and manufacturing, analytical sensitivity and specificity, affordability, and high-throughput measurements were accompanied by limitations such as semi-quantitative measurements, lack of universal reference standards, potential cross-reactivity, and challenges with multiplexing the complete panel of human immunoglobulin isotypes and subclasses. Redesign of conventional serological tests to include multiplex quantification of immunoglobulin isotypes and subclasses, utilize universal reference standards, and minimize cross-reactivity and non-specific binding will facilitate the development of assays with higher diagnostic specificity. Improved serological assays with higher diagnostic specificity will enable screenings of asymptomatic populations and may provide earlier detection of infectious diseases, autoimmune disorders, and cancer. In this review, we present the major clinical needs for serological diagnostics, overview conventional immunoassay detection techniques, present the emerging immunoassay detection technologies, and discuss in detail the advantages and limitations of mass spectrometry and immunoaffinity proteomics for serological diagnostics. Finally, we explore the design of novel immunoaffinity-proteomic assays to evaluate cell-mediated immunity and advance the sequencing of clinically relevant immunoglobulins.

Structure of type II collagen from sturgeon cartilage and its effect on adjuvant-induced rheumatoid arthritis in rats

The purpose of this paper was to extract and characterize type II collagen of sturgeon cartilage (SC-CII), and to explore the effects of taking SC-CII orally on rheumatoid arthritis (RA) in rats. SC-CII showed a triple-helix structure (RPN = 0.12), with d1 of 11.82 Å and d2 of 4.08 Å, which was analyzed by FT-IR, CD, XRD, and MS. It was constructed of the repeating tripeptide unit Gly-X-Y, where X and Y are generally Pro or Hyp, proved by amino acid composition and peptide mass fingerprinting. Furthermore, the effects of SC-CII on RA were evaluated. Ankle thickness was significantly decreased in SC-CII groups, with changes in lymphocyte proliferation also observed. Compared with the model control group, there was an evident decrease in TNF-α, IL-1β, COX-2, MCP-1, and TLR-4 mRNA levels, but no remarkable differences in APF, MMP-3, and MyD88 mRNA levels in the SC-CII groups. In addition, TNF-α, IL-1β, RF, Anti-CII Ab were significantly reduced in the SC-CII groups, proved by ELISA. Therefore, SC-CII showed alleviating effects on RA through the TLR4/MyD88-NFκB pathway.

A scoping review of autoantibodies as biomarkers for canine autoimmune disease

BACKGROUND

Autoantibody biomarkers are valuable tools used to diagnose and manage autoimmune diseases in dogs. However, prior publications have raised concerns over a lack of standardization and sufficient validation for the use of biomarkers in veterinary medicine.

OBJECTIVES

Systematically compile primary research on autoantibody biomarkers for autoimmune disease in dogs, summarize their methodological features, and evaluate their quality; synthesize data supporting their use into a resource for veterinarians and researchers.

ANIMALS

Not used.

METHODS

Five indices were searched to identify studies for evaluation: PubMed, CAB Abstracts, Web of Science, Agricola, and SCOPUS. Two independent reviewers (AET and ELC) screened titles and abstracts for exclusion criteria followed by full-text review of remaining articles. Relevant studies were classified based on study objectives (biomarker, epitope, technique). Data on study characteristics and outcomes were synthesized in independent data tables for each classification.

RESULTS

Ninety-two studies qualified for final analysis (n = 49 biomarker, n = 9 epitope, and n = 34 technique studies). A high degree of heterogeneity in study characteristics and outcomes reporting was observed. Opportunities to strengthen future studies could include: (1) routine use of negative controls, (2) power analyses to inform sample sizes, (3) statistical analyses when appropriate, and (4) multiple detection techniques to confirm results.

CONCLUSIONS

These findings provide a resource that will allow veterinary clinicians to efficiently evaluate the evidence supporting the use of autoantibody biomarkers, along with the varied methodological approaches used in their development.

Cinnamaldehyde Attenuates the Progression of Rheumatoid Arthritis through Down-Regulation of PI3K/AKT Signaling Pathway

Cinnamaldehyde (CA), as an active compound isolated from the bark of Cinnamomum cassia, has been reported to possess the anti-fungal, anti-bacterial, anti-inflammatory, anti-mutagenic, and anti-oxidant properties. However, the possible effects and underlying mechanisms of CA on rheumatoid arthritis (RA) have not been revealed yet. In the present study, we found that CA obviously improved the type II collagen-induced RA in rats, accompanied with decreasing pro-inflammatory factors, proliferation and metastasis. In addition, CA decreased the expression levels of TNF-α, IL-1β, and IL-6 in RA-FLSs. Besides, CA remarkably inhibited the proliferation, downregulated the EdU-positive cells, and promoted apoptosis of RA-FLSs by CCK-8, EdU and flow cytometry analysis. Moreover, the results of wound healing, transwell migration and invasion assays showed that CA inhibited the migration and invasion of RA-FLSs. Further, western blot experiment showed CA inhibited the activation of PI3K/AKT signaling pathway in RA-FLSs. Finally, 740Y-P, the PI3K/AKT signaling pathway activator, could reverse the effects of CA on the proliferation and metastasis in RA-FLSs. In conclusion, we confirmed that CA exhibited potential therapeutic properties against RA via suppressing proliferation and metastasis of RA-FLSs by blockage of PI3K/AKT signaling pathway. Therefore, our study provides evidence that CA may emerge as a therapeutic option for RA treatment.