Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Noncoding RNAs improve the predictive power of network medicine

Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.

Inhibition of Caspase-1-mediated pyroptosis promotes osteogenic differentiation, offering a therapeutic target for osteoporosis

BACKGROUND

Pyroptosis, an emerging inflammatory form of cell death, has been previously demonstrated to stimulate a massive inflammatory response, thus hindering the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Nevertheless, the impact of pyroptosis in thwarting osteogenic differentiation and exacerbating the advancement of osteoporosis (OP) remains enigmatic.

METHODS

We evaluated the expression levels of pyroptosis-associated indicators, including NOD-like receptor family pyrin domain-containing protein 3 (NLRP3), CASPASE-1, IL-1β, and IL-18, in specimens obtained from femoral heads of OP patients, as well as in an ovariectomy-induced mouse model of OP. Subsequently, the precise roles of pyroptosis in osteogenic differentiation were investigated using bioinformatics analysis, alongside morphological and biochemical assessments.

RESULTS

The pivotal pyroptotic proteins, including NLRP3, Caspase-1, IL-1β, and IL-18, exhibited significant upregulation within the bone tissue samples of clinical OP cases, as well as in the femoral tissues of ovariectomy (OVX)-induced mouse OP model, displaying a negatively associated with compromised osteogenic capacity, as represented by lessened bone mass, suppressed expression of osteogenic proteins such as Runt-related transcription factor 2 (RUNX2), Alkaline phosphatase (ALP), Osterix (OSX), and Osteopontin (OPN), and increased lipid droplets. Moreover, bioinformatics analysis substantiated shared gene expression patterns between pyroptosis and OP pathology, encompassing NLRP3, Caspase-1, IL-1β, IL-18, etc. Furthermore, our in vitro investigation using ST2 cells revealed that dexamethasone treatment prominently induced pyroptosis while impeding osteogenic differentiation. Notably, gene silencing of Caspase-1 effectively counteracted the inhibitory effects of dexamethasone on osteogenic differentiation, as manifested by increased ALP activity and enhanced expression of RUNX2, ALP, OSX, and OPN.

CONCLUSION

Our findings unequivocally underscore that inhibition of Caspase-1-mediated pyroptosis promotes osteogenic differentiation, providing a promising therapeutic target for managing OP.

Relations between Structure/Composition and Mechanics in Osteoarthritic Regenerated Articular Tissue: A Machine Learning Approach

In the context of a large animal model of early osteoarthritis (OA) treated by orthobiologics, the purpose of this study was to reveal relations between articular tissues structure/composition and cartilage viscoelasticity. Twenty-four sheep, with induced knee OA, were treated by mesenchymal stem cells in various preparations-adipose-derived mesenchymal stem cells (ADSCs), stromal vascular fraction (SVF), and amniotic endothelial cells (AECs)-and euthanized at 3 or 6 months to evaluate the (i) biochemistry of synovial fluid; (ii) histology, immunohistochemistry, and histomorphometry of articular cartilage; and (iii) viscoelasticity of articular cartilage. After performing an initial analysis to evaluate the correlation and multicollinearity between the investigated variables, this study used machine learning (ML) models-Variable Selection Using Random Forests (VSURF) and Extreme Gradient Boosting (XGB)-to classify variables according to their importance and employ them for interpretation and prediction. The experimental setup revealed a potential relation between cartilage elastic modulus and cartilage thickness (CT), synovial fluid interleukin 6 (IL6), and prostaglandin E2 (PGE2), and between cartilage relaxation time and CT and PGE2. SVF treatment was the only limit on the deleterious OA effect on cartilage viscoelastic properties. This work provides indications to future studies aiming to highlight these and other relationships and focusing on advanced regeneration targets.

Transcriptomic and Metabolomic Changes Reveal the Immunomodulatory Function of Casein Phosphopeptide-Selenium Chelate in Beagle Dogs

Casein phosphopeptide-selenium chelate (CPP-Se) is an organic compound produced by the chelation of casein phosphopeptide with selenium. This compound showed the ability to modulate canine immune response in our previous study; but its effect on the peripheral blood transcriptome and serum metabolome was unknown. This study aims to reveal the potential mechanism behind the immunomodulatory function of CPP-Se. We have identified 341 differentially expressed genes (DEGs) in CPP-Se groups as compared to the control group which comprised 110 up-regulated and 231 down-regulated genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis found that DEGs were mainly involved in immune-related signaling pathways. Moreover, the immune-related DEGs and hub genes were identified. Similarly, metabolomics identified 53 differentially expressed metabolites (DEMs) in the CPP-Se group, of which 17 were up-regulated and 36 were down-regulated. The pathways mainly enriched by DEMs were primary bile acid biosynthesis, tryptophan metabolism, and other amino acids metabolic pathways. Combined analysis of transcriptomic and metabolomic data showed that the DEGs and DEMs were commonly enriched in fatty acid biosynthesis, pyrimidine metabolism, glutathione metabolism, and glycerolipid metabolic pathways. Taken together, our findings provided a theoretical basis for further understanding of the immunomodulatory function of CPP-Se as well as a scientific reference for the future use of CPP-Se in pet foods as a dietary supplement to modulate the immunity.

Bioinformatics analysis of ceRNA regulatory network of baicalin in alleviating pathological joint alterations in CIA rats

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inffammation of synovium, leading to cartilage damage, bone erosion,even joint destruction and deformity. The conventional treatment modalities in RA are associated with side effects, emphasizing the need for alternative therapeutic remedies. Baicalin possesses multiple pharmacological effects and the advantage of low toxicity. This study aimed to reveal the potential gene regulatory mechanisms underlying the alleviating effects of baicalin in joint pathological alterations in Collagen-Induced Arthritis (CIA) rat models. At 28 days after the primary immunization, 60mg/kg/d of baicalin was administered via intraperitoneal injection once daily for 40 days, and the pathological alterations of hind paw joints were examined with X-ray imaging. Subsequently, the synovial tissue of knee joints was isolated, from which total RNA was extracted, and mRNA and miRNA sequencing libraries were established. Finally, High-throughput transcriptome sequencing(RNA-seq) technology was performed, and the lncRNAs/miRNAs/mRNAs competing endogenous RNA(ceRNA) regulatory network was analyzed. The CIA model was successfully established, and baicalin treatment significantly alleviated the destruction of distal joints of CIA rat models (p < 0.01). We found that 3 potential ceRNA regulatory networks of baicalin were established, including lncRNA ENSRNOT00000076420/miR-144-3p/Fosb, lncRNA MSTRG.1448.13/miR-144-3p/Atp2b2 and lncRNA MSTRG.1448.13/miR-144-3p/Shanks. The validation results from synovial tissue of CIA rats were consistent with the RNA-Seq results. Overall, this study revealed potentially important genes and ceRNA regulatory network that mediate the alleviating effects of baicalin on joint pathological alterations in CIA rats.

Understanding the function of the GABAergic system and its potential role in rheumatoid arthritis

Rheumatoid arthritis (RA) is a highly disabling chronic autoimmune disease. Multiple factors contribute to the complex pathological process of RA, in which an abnormal autoimmune response, high survival of inflammatory cells, and excessive release of inflammatory factors lead to a severe chronic inflammatory response. Clinical management of RA remains limited; therefore, exploring and discovering new mechanisms of action could enhance clinical benefits for patients with RA. Important bidirectional communication occurs between the brain and immune system in inflammatory diseases such as RA, and circulating immune complexes can cause neuroinflammatory responses in the brain. The gamma-aminobutyric acid (GABA)ergic system is a part of the nervous system that primarily comprises GABA, GABA-related receptors, and GABA transporter (GAT) systems. GABA is an inhibitory neurotransmitter that binds to GABA receptors in the presence of GATs to exert a variety of pathophysiological regulatory effects, with its predominant role being neural signaling. Nonetheless, the GABAergic system may also have immunomodulatory effects. GABA/GABA-A receptors may inhibit the progression of inflammation in RA and GATs may promote inflammation. GABA-B receptors may also act as susceptibility genes for RA, regulating the inflammatory response of RA via immune cells. Furthermore, the GABAergic system may modulate the abnormal pain response in RA patients. We also summarized the latest clinical applications of the GABAergic system and provided an outlook on its clinical application in RA. However, direct studies on the GABAergic system and RA are still lacking; therefore, we hope to provide potential therapeutic options and a theoretical basis for RA treatment by summarizing any potential associations.

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

Rheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic inflammation that affects synovial tissues of multiple joints. Granzymes (Gzms) are serine proteases that are released into the immune synapse between cytotoxic lymphocytes and target cells. They enter target cells with the help of perforin to induce programmed cell death in inflammatory and tumor cells. Gzms may have a connection with RA. First, increased levels of Gzms have been found in the serum (GzmB), plasma (GzmA, GzmB), synovial fluid (GzmB, GzmM), and synovial tissue (GzmK) of patients with RA. Moreover, Gzms may contribute to inflammation by degrading the extracellular matrix and promoting cytokine release. They are thought to be involved in RA pathogenesis and have the potential to be used as biomarkers for RA diagnosis, although their exact role is yet to be fully elucidated. The purpose of this review was to summarize the current knowledge regarding the possible role of the granzyme family in RA, with the aim of providing a reference for future research on the mechanisms of RA and the development of new therapies.

Absent in melanoma 2 (AIM2) in rheumatoid arthritis: novel molecular insights and implications

Absent in melanoma 2 (AIM2), a member of the Pyrin and HIN domain protein family, is a cytoplasmic receptor that recognizes double-stranded DNA. AIM2 exhibits limited expression under physiological conditions but is widely expressed in many human diseases, including autoimmune diseases, and plays an essential role in the immune response. Rheumatoid arthritis (RA) is an autoimmune disease that poses a severe threat to physical and mental health, and is caused by several genetic and metabolic factors. Multiple immune cells interact to form a complex inflammatory network that mediates inflammatory responses and bone destruction. Abnormal AIM2 expression in multiple immune cell populations (T cells, B cells, fibroblast-like synoviocytes, monocytes, and macrophages) may regulate multiple functional responses in RA through mechanisms such as pyroptosis, PANoptosis, and regulation of other molecules. In this review, we describe and summarize the functional regulation and impact of AIM2 expression in immune cells to improve our understanding of the complex pathological mechanisms. These insights may provide potential directions for the development of new clinical diagnostic strategies for RA.

Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

Methods

Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes.

Results

A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis.

Conclusion

15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis.

AIM2-inflammasome role in systemic lupus erythematous and rheumatoid arthritis

The inflammasome AIM2 regulates multiple aspects of innate immune functions and serves as a critical mediator of inflammatory responses. AIM2 inflammasome activation leads to the production of pro-inflammatory cytokines, IL-1β and IL-18 and participates triggering a pyroptosis response needed to counteract excessive cell proliferation. In addition, AIM2 expression and activation is wide regulated since alteration in its activity may derived in pathological consequences. Consequently, deregulated AIM2 activation contributes to the pathogenic processes of various inflammatory diseases. In this review, we will discuss the activation and function of AIM2 inflammasome, as well as its contribution in rheumatoid arthritis and systemic lupus erythematous pathology. Finally, we highlight the participation of the AIM2-inflammasome at the level of joint in rheumatoid arthritis and at kidney in systemic lupus erythematous. The development of therapeutic strategies based on modulation of AIM2-inflammasome activity should have a tissue-specific focus.

Computation-Based Discovery of Potential Targets for Rheumatoid Arthritis and Related Molecular Screening and Mechanism Analysis of Traditional Chinese Medicine

This study is aimed at screening potential therapeutic ingredients in traditional Chinese medicine (TCM) and identifying the key rheumatoid arthritis (RA) targets using computational simulations. Data for TCM-active ingredients with clear pharmacological effects were collected. Absorption, distribution, metabolism, excretion, and toxicity were evaluated. Potential RA targets were identified using the Gene Expression Omnibus (GEO) database, protein–protein interaction network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and potential TCM ingredients using AutoDock Vina. To examine the mechanisms underlying small molecules, target prediction, Gene Ontology, KEGG, and network modeling analyses were conducted; the effects were verified in rat synovial cells using cell proliferation assay. The activities of tumor necrosis factor TNF-α and IL-1β and alterations in cellular target protein levels were detected by ELISA and Western blotting, respectively. In total, data for 432 TCM active ingredients with clear pharmacological effects were obtained. Five critical RA-related genes were identified; CCL5 and CXCL10 were selected for molecular docking. Target prediction and network-based proximity analysis showed that dioscin could modulate 22 known RA clinical targets. Dioscin, asiaticoside, and ginsenoside Re could effectively inhibit in vitro cell proliferation and secretion of TNF-α and IL-1β in RA rat synovial cells. Using bioinformatics and computer-aided drug design, the potential small anti-RA molecules and their mechanisms of action were comprehensively identified. Dioscin could significantly inhibit proliferation and induce apoptosis in RA rat synovial cells by reducing TNF-α and IL-1β secretion and inhibiting abnormal CCL5, CXCL10, CXCR2, and IL2 expression.

Non-Coding RNAs in Rheumatoid Arthritis: Implications for Biomarker Discovery

Recent advances in gene expression analysis techniques and increased access to technologies such as microarrays, qPCR arrays, and next-generation sequencing, in the last decade, have led to increased awareness of the complexity of the inflammatory responses that lead to pathology. This finding is also the case for rheumatic diseases, importantly and specifically, rheumatoid arthritis (RA). The coincidence in major genetic and epigenetic regulatory events leading to RA’s inflammatory state is now well-recognized. Research groups have characterized the gene expression profile of early RA patients and identified a group of miRNAs that is particularly abundant in the early stages of the disease and miRNAs associated with treatment responses. In this perspective, we summarize the current state of RNA-based biomarker discovery and the context of technology adoption/implementation due to the COVID-19 pandemic. These advances have great potential for clinical application and could provide preclinical disease detection, follow-up, treatment targets, and biomarkers for treatment response monitoring.

Inhibition of Histone H3 Lysine-27 Demethylase Activity Relieves Rheumatoid Arthritis Symptoms via Repression of IL6 Transcription in Macrophages

Rheumatoid arthritis (RA) occurs in about 5 per 1,000 people and can lead to severe joint damage and disability. However, the knowledge of pathogenesis and treatment for RA remains limited. Here, we found that histone demethylase inhibitor GSK-J4 relieved collagen induced arthritis (CIA) symptom in experimental mice model, and the underlying mechanism is related to epigenetic transcriptional regulation in macrophages. The role of epigenetic regulation has been introduced in the process of macrophage polarization and the pathogenesis of inflammatory diseases. As a repressive epigenetic marker, tri-methylation of lysine 27 on histone H3 (H3K27me3) was shown to be important for transcriptional gene expression regulation. Here, we comprehensively analyzed H3K27me3 binding promoter and corresponding genes function by RNA sequencing in two differentially polarized macrophage populations. The results revealed that H3K27me3 binds on the promoter regions of multiple critical cytokine genes and suppressed their transcription, such as IL6, specifically in M-CSF derived macrophages but not GM-CSF derived counterparts. Our results may provide a new approach for the treatment of inflammatory and autoimmune disorders.

Potential biomarkers that discriminate rheumatoid arthritis and osteoarthritis based on the analysis and validation of datasets

Background

Rheumatoid arthritis (RA) and osteoarthritis (OA) share some similar arthritic symptoms, but different mechanisms underlie the pathogenesis of these two diseases. Analysis of differentially expressed molecules in rheumatoid arthritis and osteoarthritis may assist in improving diagnosis and treatment strategies in clinical practice.

Methods

Microarray and RNA-seq data were acquired from the gene expression omnibus database. Differentially expressed genes (DEGs) were identified using Bioconductor packages. Receiver operating characteristic curves were plotted to assess performance. Gene ontology enrichment analysis was conducted using the clusterProfiler application. During validation, synovial fluid was harvested from patients who had undergone in-hospital joint replacement, in which the expression of proteins was measured using enzyme-linked immunosorbent assays.

Results

Compared with OA samples, RA samples showed 14 genes to be upregulated and 3 to be downregulated. Gene ontology analysis indicated that DEGs principally included molecules responsible for the regulation of a synovial tissue inflammatory response. Seven genes displayed a good discriminatory power with an AUC higher than 0.90. ADAMDEC1 was the biomarker that most clearly discriminated RA from OA in the database, exhibiting an AUC of 0.999, a sensitivity of 100%, and a specificity of 97.8%. Following validation, the expression levels of ADAMDEC1 in the synovial fluid from RA patients were significantly higher than those in the synovial fluid from OA patients (P < 0.05). At the cut-off value of 1957 pg/mL, ADAMDEC1 expression in the synovial fluid discriminated RA from OA with an AUC of 0.951, a specificity of 88.6%, and a sensitivity of 92.9%.

Conclusion

The differential expression of genes in RA compared with OA indicates potential targets for molecular diagnosis and treatment. The presence of ADAMDEC1 in synovial fluid is a good biomarker of RA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05277-x.

Identification and development of a novel 5-gene diagnostic model based on immune infiltration analysis of osteoarthritis

Background

Osteoarthritis (OA), which is due to the progressive loss and degeneration of articular cartilage, is the leading cause of disability worldwide. Therefore, it is of great significance to explore OA biomarkers for the prevention, diagnosis, and treatment of OA.

Methods and materials

The GSE129147, GSE57218, GSE51588, GSE117999, and GSE98918 datasets with normal and OA samples were downloaded from the Gene Expression Omnibus (GEO) database. The GSE117999 and GSE98918 datasets were integrated, and immune infiltration was evaluated. The differentially expressed genes (DEGs) were analyzed using the limma package in R, and weighted gene co-expression network analysis (WGCNA) was used to explore the co-expression genes and co-expression modules. The co-expression module genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and hub genes were identified by the degree, MNC, closeness, and MCC algorithms. The hub genes were used to construct a diagnostic model based on support vector machines.

Results

The Immune Score in the OA samples was significantly higher than in the normal samples, and a total of 2313 DEGs were identified. Through WGCNA, we found that the yellow module was significantly positively correlated with the OA samples and Immune Score and negatively correlated with the normal samples. The 142 DEGs of the yellow module were related to biological processes such as regulation of inflammatory response, positive regulation of inflammatory response, blood vessel morphogenesis, endothelial cell migration, and humoral immune response. The intersections of the genes obtained by the 4 algorithms resulted in 5 final hub genes, and the diagnostic model constructed with these 5 genes showed good performance in the training and validation cohorts.

Conclusions

The 5-gene diagnostic model can be used to diagnose OA and guide clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03183-9.

Identification of Potential Key Genes in the Pathogenesis of Chronic Obstructive Pulmonary Disease Through Bioinformatics Analysis

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease with high morbidity and mortality. The etiology of COPD is complex, and the pathogenesis mechanisms remain unclear. In this study, we used rat and human COPD gene expression data from our laboratory and the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between individuals with COPD and healthy individuals. Then, protein–protein interaction (PPI) networks were constructed, and hub genes were identified. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. A total of 198 DEGs were identified, and a PPI network with 144 nodes and 355 edges was constructed. Twelve hub genes were identified by the cytoHubba plugin in Cytoscape. Of these genes, CCR3, CCL2, COL4A2, VWF, IL1RN, IL2RA, and CCL13 were related to inflammation or immunity, or tissue-specific expression in lung tissue, and their messenger RNA (mRNA) levels were validated by qRT-PCR. COL4A2, VWF, and IL1RN were further verified by the GEO dataset GSE76925, and the ceRNA network was constructed with Cytoscape. These three genes were consistent with COPD rat model data compared with control data, and their dysregulation direction was reversed when the COPD rat model was treated with effective-component compatibility of Bufei Yishen formula III. This bioinformatics analysis strategy may be useful for elucidating novel mechanisms underlying COPD. We pinpointed three key genes that may play a role in COPD pathogenesis and therapy, which deserved to be further studied.

Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis

Background

Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated.

Methods

In this study, we conducted various bioinformatics analyses to identify hub biomarkers and immune infiltration in OA. The gene expression profiles of synovial tissues from 29 healthy controls and 36 OA samples were obtained from the gene expression omnibus database to identify differentially expressed genes (DEGs). The CIBERSORT algorithm was used to explore the association between immune infiltration and arthritis.

Results

Eighteen hub DEGs were identified as critical biomarkers for OA. Through gene ontology and pathway enrichment analyses, it was found that these DEGs were primarily involved in PI3K-Akt signaling pathway and Rap1 signaling pathway. Furthermore, immune infiltration analysis revealed differences in immune infiltration between patients with OA and healthy controls. The hub gene ZNF160 was closely related to immune cells, especially mast cell activation in OA.

Conclusion

Overall, this study presented a novel method to identify hub DEGs and their correlation with immune infiltration, which may provide novel insights into the diagnosis and treatment of patients with OA.

Isopsoralen ameliorates rheumatoid arthritis by targeting MIF

BACKGROUND

Isopsoralen (IPRN), one of the active ingredients of Psoralea corylifolia Linn, has anti-inflammatory properties. We attempted to investigate the inhibitory effects of IPRN on rheumatoid arthritis (RA) and characterize its potential mechanism.

METHODS

RA fibroblast-like synoviocytes (FLSs) and mice with collagen-induced arthritis (CIA) were used as in vitro and in vivo models to analyze the antiarthritic effect of IPRN. Histological analysis of the inflamed joints from mice with CIA was performed using microcomputed tomography (micro-CT) and hematoxylin-eosin (HE) staining. RNA sequencing (RNA-Seq), network pharmacology analysis, molecular docking, drug affinity responsive target stability (DARTS) assay, and cellular thermal shift assay (CETSA) were performed to evaluate the targets of IPRN.

RESULTS

IPRN ameliorated the inflammatory phenotype of RA FLSs by inhibiting their cytokine production, migration, invasion, and proangiogenic ability. IPRN also significantly reduced the severity of CIA in mice by decreasing paw thickness, arthritis score, bone damage, and serum inflammatory cytokine levels. A mechanistic study demonstrated that macrophage migration inhibitory factor (MIF), a key protein in the inflammatory process, was the specific target by which IPRN exerted its anti-inflammatory effects in RA FLSs.

CONCLUSION

Our study demonstrates the antiarthritic effect of IPRN, which suggests the therapeutic potential of IPRN in RA.

Cell-Free DNA in Rheumatoid Arthritis

Endogenous DNA derived from the nuclei or mitochondria is released into the bloodstream following cell damage or death. Extracellular DNA, called cell-free DNA (cfDNA), is associated with various pathological conditions. Recently, multiple aspects of cfDNA have been assessed, including cfDNA levels, integrity, methylation, and mutations. Rheumatoid arthritis (RA) is the most common form of autoimmune arthritis, and treatment of RA has highly varied outcomes. cfDNA in patients with RA is elevated in peripheral blood and synovial fluid and is associated with disease activity. Profiling of cfDNA in patients with RA may then be utilized in various aspects of clinical practice, such as the prediction of prognosis and treatment responses; monitoring disease state; and as a diagnostic marker. In this review, we discuss cfDNA in patients with RA, particularly the sources of cfDNA and the correlation of cfDNA with RA pathogenesis. We also highlight the potential of analyzing cfDNA profiles to guide individualized treatment approaches for RA.

Identification of differentially expressed genes and pathways crosstalk analysis in Rheumatoid and Osteoarthritis using next-generation sequencing and protein-protein networks

Osteoarthritis occurs when protective cartilage of bones worn out. Similarlty, cartilage damage occurs mainly in the pannus cartilage in rheumatoid arthritis. It is a potentially debilitating condition, affecting women two to three times more often than men. The cause and prognosis of rheumatoid and osteoarthritis are still poorly known. However, advances in the study of disease pathogenesis have encouraged the creation of new therapeutics with improved outcomes. The purpose of this study is to investigate the differentially expressed genes potentially involved in dysregulated rheumatoid arthritis (RA) and their association to other types of arthritis, including osteoarthritis (OA). Complete RNAs were isolated for RNA expression profiling using next-generation sequencing from human primary cultured normal and RA chondrocytes. From RNA sequencing results 250 differentially expressed genes were identified using bioinformatics analysis, of which 32 were found to be significantly playing role in RA pathogenesis and its associated diseases. Molecular ontologies of the identified genes showed they are connected to Innate immune response, Protein phosphorylation, Transcription initiation from RNA polymerase II promoter, Immune response, Neoplasms of bones, as well as osteorthritis, and Rheumatoid arthritis. Among the identified genes, TRAF1, TRAF2, BAMP, STX11, MEOX2, AES, REL, FHL3, PNMA1, SGTA, LZTS2, SIAH2, PNMA1, and TFCP2 were found to be highly enriched in the protein-protein interaction network. The significant cross talks were found in Hypertrophic cardiomyopathy, Small cell lung cancer, Proteasome, p53 signaling pathway, Arrhythmogenic right ventricular cardiomyopathy, Small cell lung cancer, SNARE interactions in vesicular transport, RIG-I-like receptor signaling pathway, and Hypertrophic cardiomyopathy pathways. The results offer new opportunities for target gene control in RA and OA cartilage destruction.

Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis

Background

Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive.

Objective

This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion.

Methods

Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the “LIMMA” package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA.

Results

We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion (P < 0.05). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA.

Conclusions

We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

Comprehensive Analysis of Gene Expression Changes and Validation in Hepatocellular Carcinoma

Aim

This study aimed to analyze the involvement of hub genes in hepatocellular carcinoma.

Methods

Four series were used in this study: GSE45267, GSE84402, and GSE101685 from GPL570 platform in the Gene Expression Omnibus and the other from The Cancer Genome Atlas. The gene audition was completed using R software and Venn diagrams. The outcome, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes preliminary analyses of differentially expressed genes were performed using the R software. A string image was obtained using the Search Tool for the Retrieval of Interacting Genes. The protein–protein interaction network was examined using Cytoscape software. The corrplot package was used to analyze the correlation of genes. Human Protein Atlas was used to confirm the protein levels. Univariate Cox regression was used to analyze whether these genes were related to survival. UALCAN was used to confirm the effect of these genes on patient survival.

Results

A total of 107 differentially expressed genes from 491 patients with hepatocellular carcinoma and 119 normal individuals were selected in this study. Cytoscape revealed 25 central nodes from the 107 genes. CCNB1, CDK1, CCNA2, PTTG1, and CDC20 were selected based on the cell cycle pathway. A significant correlation was found among the 6 DEGs. The transcription levels and protein levels of these genes were verified in cells and human tissue samples. The overall survival for these genes was analyzed using univariate Cox regression and UALCAN.

Conclusion

CCNB1, CDK1, CDC20, PTTG1, CCNA2, and TTK were overexpressed and correlated in hepatocellular carcinoma cells and tumors. The results might help explore the prognosis and diagnostic markers of HCC.

Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis

BACKGROUND

Rheumatoid arthritis (RA) is the most common chronic autoimmune connective tissue disease. However, early RA is difficult to diagnose due to the lack of effective biomarkers. This study aimed to identify new biomarkers and mechanisms for RA disease progression at the transcriptome level through a combination of microarray and bioinformatics analyses.

METHODS

Microarray datasets for synovial tissue in RA or osteoarthritis (OA) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Tissue/organ-specific genes were recognized by BioGPS. Enrichment analyses were performed and protein-protein interaction (PPI) networks were constructed to understand the functions and enriched pathways of DEGs and to identify hub genes. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value for the early diagnosis of RA were validated by GEO datasets. The ggpubr package was used to perform statistical analyses with Student's t-test.

RESULTS

A total of 275 DEGs were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. Among these DEGs, 71 tissue/organ-specific expressed genes were recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEGs are mostly enriched in immune response, immune-related biological process, immune system, and cytokine signal pathways. Fifteen hub genes and gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be potential biomarkers for diagnosis of early RA. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might be potential RNA regulatory pathways to regulate the disease progression of early RA.

CONCLUSIONS

This work identified three haematologic/immune system-specific expressed genes, namely, GZMA, PRC1, and TTK, as potential biomarkers for the early diagnosis and treatment of RA and provided insight into the mechanisms of disease development in RA at the transcriptome level. In addition, we proposed that NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK are potential RNA regulatory pathways that control disease progression in early RA.

Expression of AIM2 in Rheumatoid Arthritis and Its Role on Fibroblast-Like Synoviocytes

Objectives

To determine differences in AIM2 inflammasome expression levels between rheumatoid arthritis (RA) and osteoarthritis (OA) and to investigate the role of AIM2 in RA fibroblast-like synoviocytes (RA-FLS).

Methods

Serum AIM2 levels among health controls (HC, n = 20), OA (n = 25), and RA (n =49) patients were compared via ELISA. The different expression levels of AIM2, ASC, caspase-1, and IL-1β between RA and OA synovium were semiquantified by qRT-PCR and immunohistochemical (IHC) staining. IHC staining was recorded by H scores, and its correlation with the ESR and CRP levels of RA patients was determined. SiRNA AIM2 was transferred to RA-FLS and its effects on the proliferation and migration via CCK-8 assay and Transwell test, respectively.

Results

In RA sera, the HC expressed higher level of AIM2 than OA and RA patients, and ASC, caspase-1, and IL-1β expressed higher in RA patients than HC; no significant differences were observed between sera of OA and RA patients. However, in affected knee synovium, AIM2, ASC, caspase-1, and IL-1β were expressed higher in RA than that of OA. Moreover, the H scores of AIM2, ASC, and IL-1β were positively correlated with the ESR and CRP levels in RA patients. The proliferation of FLS was significantly inhibited after transferring with AIM2 siRNA to FLS. There were no differences in apoptosis and migration assay between the si-AIM2 group and the control group.

Conclusion

AIM2 inflammasome pathway involves in the pathogenesis of RA. si-AIM2 inhibits the proliferation of RA-FLS, which may be a promising therapeutic strategy for the treatment of RA.

Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold-change | >1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

Promising targets and drugs in rheumatoid arthritis: a module-based and cumulatively scoring approach

AIMS

Rheumatoid arthritis (RA) is a systematic autoimmune disorder, characterized by synovial inflammation, bone and cartilage destruction, and disease involvement in multiple organs. Although numerous drugs are employed in RA treatment, some respond little and suffer from severe side effects. This study aimed to screen the candidate therapeutic targets and promising drugs in a novel method.

METHODS

We developed a module-based and cumulatively scoring approach that is a deeper-layer application of weighted gene co-expression network (WGCNA) and connectivity map (CMap) based on the high-throughput datasets.

RESULTS

Four noteworthy RA-related modules were identified, revealing the immune- and infection-related biological processes and pathways involved in RA. HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DRB1, BLNK, BTK, CD3D, CD4, IL2RG, INPP5D, LCK, PTPRC, RAC2, SYK, and VAV1 were recognized as the key hub genes with high connectivity in gene regulation networks and gene pathway networks. Moreover, the long noncoding RNAs (lncRNAs) in the RA-related modules, such as FAM30A and NEAT1, were identified as the indispensable interactors with the hub genes. Finally, candidate drugs were screened by developing a cumulatively scoring approach based on the selected modules. Niclosamide and the other compounds of T-type calcium channel blocker, IKK inhibitor, and PKC activator, HIF activator, and proteasome inhibitor, which harbour the similar gene signature with niclosamide, were promising drugs with high specificity and broad coverage for the RA-related modules.

CONCLUSION

This study provides not only the promising targets and drugs for RA but also a novel methodological insight into the target and drug screening.Cite this article: Bone Joint Res 2020;9(8):501-514.

Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis

Background

Abatacept (ABA) is a biological disease-modifying antirheumatic drug (bDMARD) for rheumatoid arthritis (RA). The aim of this study was to identify molecules that are associated with therapeutic responses to ABA in patients with RA.

Methods

Peripheral blood was collected using a PAX gene Blood RNA kit from 45 bDMARD-naïve patients with RA at baseline and at 6 months after the initiation of ABA treatment. Gene expression levels of responders (n = 27) and non-responders (n = 8) to ABA treatment among patients with RA at baseline were compared using a microarray. The gene expression levels were confirmed using real-time quantitative polymerase chain reaction (RT-qPCR).

Results

Gene expression analysis revealed that the expression levels of 218 genes were significantly higher and those of 392 genes were significantly lower in the responders compared to the non-responders. Gene ontology analysis of the 218 genes identified “response to type I interferon (IFN)” with 24 type I IFN-related genes. RT-qPCR confirmed that there was a strong correlation between the score calculated using the 24 genes and that using OAS3, MX1, and IFIT3 (type I IFN score) (rho with the type I IFN score 0.981); the type I IFN score was significantly decreased after treatment with ABA in the responders (p < 0.05), but not in the non-responders. The receiver operating characteristic curve analysis of the type I IFN score showed that sensitivity, specificity, and AUC (95% confidence interval) for the responders were 0.82, 1.00, and 0.92 (0.82–1.00), respectively. Further, RT-qPCR demonstrated higher expression levels of BATF2, LAMP3, CD83, CLEC4A, IDO1, IRF7, STAT1, STAT2, and TNFSF10 in the responders, all of which are dendritic cell-related genes or type I IFN-related genes with significant biological implications.

Conclusion

Type I IFN score and expression levels of the nine genes may serve as novel biomarkers associated with a clinical response to ABA in patients with RA.

Identification of Key Candidate Genes in Dairy Cow in Response to Escherichia coli Mastitis by Bioinformatical Analysis

At present, bovine mastitis is one of the most costly diseases affecting animal health and welfare. Escherichia coli (E. coli) is considered to be one of the main pathogens causing mastitis with clinical signs in dairy cattle. However, the cure rate of E. coli mastitis is low, and the pathogenesis of E. coli mastitis is not completely known. In order to develop new strategies for the rapid detection of E. coli mastitis, a comprehensive molecular investigation of E. coli mastitis is necessary. Hence, this study integrated three microarray data sets to identify the potential key candidate genes in dairy cow in response to E. coli mastitis. Differentially expressed genes (DEGs) were screened in mammary gland tissues with live E. coli infection. Furthermore, the pathways enrichment of DEGs were analyzed, and the protein–protein interaction (PPI) network was performed. In total, 105 shared DEGs were identified from the three data sets. The DEGs were significantly enriched in biological processes mainly involved in immunity. The PPI network of DEGs was constructed with 102 nodes and 546 edges. The module with the highest score through MCODE analysis was filtered from PPI; 18 central node genes were identified. However, in addition to immune-related pathways, some of the 18 DEGs were involved in signaling pathways triggered by other diseases. Considering the specificity of biomarkers for rapid detection, IL8RB, CXCL6, and MMP9 were identified as the most potential biomarker for E. coli mastitis. In conclusion, the novel DEGs and pathways identified in this study can help to improve the diagnosis and treatment strategies for E. coli mastitis in cattle.