Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis

TPX2 upregulates MMP13 to promote the progression of lipopolysaccharide-induced osteoarthritis

Purpose

This study seeks to identify potential clinical biomarkers for osteoarthritis (OA) using bioinformatics and investigate OA mechanisms through cellular assays.

Methods

Differentially Expressed Genes (DEGs) from GSE52042 (four OA samples, four control samples) were screened and analyzed with protein-protein interaction (PPI) analysis. Overlapping genes in GSE52042 and GSE206848 (seven OA samples, and seven control samples) were identified and evaluated using Gene Set Enrichment Analysis (GSEA) and clinical diagnostic value analysis to determine the hub gene. Finally, whether and how the hub gene impacts LPS-induced OA progression was explored by in vitro experiments, including Western blotting (WB), co-immunoprecipitation (Co-IP), flow cytometry, etc.

Result

Bioinformatics analysis of DEGs (142 up-regulated and 171 down-regulated) in GSE52042 identified two overlapping genes (U2AF2, TPX2) that exhibit significant clinical diagnostic value. These genes are up-regulated in OA samples from both GSE52042 and GSE206848 datasets. Notably, TPX2, which AUC = 0.873 was identified as the hub gene. In vitro experiments have demonstrated that silencing TPX2 can alleviate damage to chondrocytes induced by lipopolysaccharide (LPS). Furthermore, there is a protein interaction between TPX2 and MMP13 in OA. Excessive MMP13 can attenuate the effects of TPX2 knockdown on LPS-induced changes in OA protein expression, cell growth, and apoptosis.

Conclusion

In conclusion, our findings shed light on the molecular mechanisms of OA and suggested TPX2 as a potential therapeutic target. TPX2 could promote the progression of LPS-induced OA by up-regulating the expression of MMP13, which provides some implications for clinical research.

Identification of the potential regulatory interactions in rheumatoid arthritis through a comprehensive analysis of lncRNA-related ceRNA networks

OBJECTIVE

This study aimed at constructing a network of competing endogenous RNA (ceRNA) in the synovial tissues of rheumatoid arthritis (RA). It seeks to discern potential biomarkers and explore the long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) axes that are intricately linked to the pathophysiological mechanisms underpinning RA, and providing a scientific basis for the pathogenesis and treatment of RA.

METHODS

Microarray data pertaining to RA synovial tissue, GSE103578, GSE128813, and GSE83147, were acquired from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). Conducted to discern both differentially expressed lncRNAs (DELncRNAs) and differentially expressed genes (DEGs). A ceRNA network was obtained through key lncRNAs, key miRNAs, and key genes. Further investigations involved co-expression analyses to uncover the lncRNA-miRNA-mRNA axes contributing to the pathogenesis of RA. To delineate the immune-relevant facets of this axis, we conducted an assessment of key genes, emphasizing those with the most substantial immunological correlations, employing the GeneCards database. Finally, gene set enrichment analysis (GSEA) was executed on the identified key lncRNAs to elucidate their functional implications in RA.

RESULTS

The 2 key lncRNAs, 7 key miRNAs and 6 key genes related to the pathogenesis of RA were obtained, as well as 2 key lncRNA-miRNA-mRNA axes (KRTAP5-AS1-hsa-miR-30b-5p-PNN, XIST-hsa-miR-511-3p/hsa-miR-1277-5p-F2RL1). GSEA of two key lncRNAs obtained biological processes and signaling pathways related to RA synovial lesions.

CONCLUSION

The findings of this investigation hold promise in furnishing a foundational framework and guiding future research endeavors aimed at comprehending the etiology and therapeutic interventions for RA.

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.

A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation

In this study, we aimed to identify transplantation tolerance (TOL)-related gene signature and use it to predict the different types of renal allograft rejection performances in kidney transplantation. Gene expression data were obtained from the Gene Expression Omnibus (GEO) database, differently expressed genes (DEGs) were performed, and the gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted. The machine learning methods were combined to analyze the feature TOL-related genes and verify their predictive performance. Afterward, the gene expression levels and predictive performances of TOL-related genes were conducted in the context of acute rejection (AR), chronic rejection (CR), and graft loss through heatmap plots and the receiver operating characteristic (ROC) curves, and their respective immune infiltration results were also performed. Furthermore, the TOL-related gene signature for graft survival was conducted to discover gene immune cell enrichment. A total of 25 TOL-related DEGs were founded, and the GO and KEGG results indicated that DEGs mainly enriched in B cell-related functions and pathways. 7 TOL-related gene signature was constructed and performed delightedly in TOL groups and different types of allograft rejection. The immune infiltration analysis suggested that gene signature was correlated with different types of immune cells. The Kaplan-Meier (KM) survival analysis demonstrated that BLNK and MZB1 were the prognostic TOL-related genes. Our study proposed a novel gene signature that may influence TOL in kidney transplantation, providing possible guidance for immunosuppressive therapy in kidney transplant patients.

Interaction between N6-methyladenosine and autophagy in the regulation of bone and tissue degeneration

Bone and tissue degeneration are the most common skeletal disorders that seriously affect people’s quality of life. N6-methyladenosine (m6A) is one of the most common RNA modifications in eukaryotic cells, affecting the alternative splicing, translation, stability and degradation of mRNA. Interestingly, increasing number of evidences have indicated that m6A modification could modulate the expression of autophagy-related (ATG) genes and promote autophagy in the cells. Autophagy is an important process regulating intracellular turnover and is evolutionarily conserved in eukaryotes. Abnormal autophagy results in a variety of diseases, including cardiomyopathy, degenerative disorders, and inflammation. Thus, the interaction between m6A modification and autophagy plays a prominent role in the onset and progression of bone and tissue degeneration. In this review, we summarize the current knowledge related to the effect of m6A modification on autophagy, and introduce the role of the crosstalk between m6A modification and autophagy in bone and tissue degeneration. An in-depth knowledge of the above crosstalk may help to improve our understanding of their effects on bone and tissue degeneration and provide novel insights for the future therapeutics.

The Protective Effect of the Soluble Egg Antigen of Schistosoma japonicum in A Mouse Skin Transplantation Model

Background

Organ transplantation is currently an effective method for treating organ failure. Long-term use of immunosuppressive drugs has huge side effects, which severely restricts the long-term survival of patients. Schistosoma can affect the host’s immune system by synthesizing, secreting, or excreting a variety of immunomodulatory molecules, but its role in transplantation was not well defined. In order to explore whether Schistosoma-related products can suppress rejection and induce long-term survival of the transplant, we used soluble egg antigen (SEA) of Schistosoma japonicum in mouse skin transplantation models.

Materials and methods

Each mouse was intraperitoneally injected with 100 μg of SEA three times a week for four consecutive weeks before allogenic skin transplant. Skin transplants were performed on day 0 to observe graft survival. Pathological examination of skin grafts was conducted 7 days post transplantation. The skin grafts were subjected to mRNA sequencing. Bioinformatics analysis was conducted and the expression of hub genes was verified by qPCR. Flow cytometry analysis was performed to evaluate the immune status and validate the results from bioinformatic analysis.

Results

The mean survival time (MST) of mouse skin grafts in the SEA-treated group was 11.67 ± 0.69 days, while that of the control group was 8.00 ± 0.36 days. Pathological analysis showed that Sj SEA treatment led to reduced inflammatory infiltration within skin grafts 7 days after allogenic skin transplantation. Bioinformatics analysis identified 86 DEGs between the Sj SEA treatment group and the control group, including 39 upregulated genes and 47 downregulated genes. Further analysis revealed that Sj SEA mediated regulation on cellular response to interferon-γ, activation of IL-17 signaling and chemokine signaling pathways, as well as cytokine–cytokine receptor interaction. Flow cytometry analysis showed that SEA treatment led to higher percentages of CD4⁺IL-4⁺ T cells and CD4⁺Foxp3⁺ T cells and decreased CD4⁺IFN-γ⁺ T cells in skin transplantation.

Conclusion

Sj SEA treatment suppressed rejection and prolonged skin graft survival by regulating immune responses. Sj SEA treatment might be a potential new therapeutic strategy to facilitate anti-rejection therapy and even to induce tolerance.

The Regulatory Role of Ferroptosis in Bone Homeostasis

Ferroptosis is an iron-dependent form of programmed cell death and an important type of biological catabolism. Through the action of divalent iron or ester oxygenase, ferroptosis can induce lipid peroxidation and cell death, regulating a variety of physiological processes. The role of ferroptosis in the modulation of bone homeostasis is a significant topic of interest. Herein, we review and discuss recent studies exploring the mechanisms and functions of ferroptosis in different bone-related cells, including mesenchymal stem cells, osteoblasts, osteoclasts, and osteocytes. The association between ferroptosis and disorders of bone homeostasis is also explored in this review. Overall, we aim to provide a detailed overview of ferroptosis, summarizing recent understanding on its role in regulation of bone physiology and bone disease pathogenesis.

A computational model revealing the immune-related hub genes and key pathways involved in rheumatoid arthritis (RA)

Rheumatoid arthritis (RA) has one of the highest disability rates among inflammatory joint disorders. However, the reason and possible molecular events are still unclear. There are various treatment options available, but no complete cure. To obtain early diagnosis and successful medication in RA, it is necessary to explore gene susceptibility and pathogenic factors. The main intend of our work is to explore the immune-related hub genes with similar functions that are differentially expressed in RA patients. Three datasets such as GSE21959, GSE55457, and GSE77298, were taken to analyze the differently expressed genes (DEGs) among 55 RA and 33 control samples. We obtained 331 upregulated and 275 downregulated DEGs from three Gene Expression Omnibus (GEO) datasets using the R package. Furthermore, a protein-protein interaction network was built for upregulated and downregulated DEGs using Cytoscape. Subsequently, MCODE analysis was performed and obtained the top two modules in each DEG's upregulated and downregulated protein-protein interactions (PPIs) network. CytoNCA and cytoHubba were performed and identified overlapping DEGs. In addition, we narrowed down DEGs by filtering with immune-related genes and identified DE-IRGs. Gene ontology (GO) and KEGG pathway analysis in upregulated and downregulated DEGs were executed with the DAVID platform. Our study obtained the nine most significant DE-IRGs in RA such as CXCR4, CDK1, BUB1, BIRC5, AGTR1, EGFR, EDNRB, KALRN, and GHSR. Among them, CXCR4, CDK1, BUB1, and BIRC5 are overexpressed in RA and may contribute to the pathophysiology of the disease. Similarly, AGTR1, EGFR, EDNRB, KALRN, and GHSR are all low expressed in RA and may have a contribution to pathogenesis. GO, KEGG functional enrichment, and GeneMANIA showed that the dysregulated process of DE-IRGs causes RA development and progression. These findings may be helpful in future studies in RA diagnosis and therapy.

Epigenetic Regulation of Immune and Inflammatory Responses in Rheumatoid Arthritis

Purpose

Rheumatoid arthritis (RA) is a disease associated with multiple factors. Epigenetics can affect gene expression without altering the DNA sequence. In this study, we aimed to comprehensively analyze epigenetic regulation in RA.

Methods

Using the Gene Expression Omnibus database, we identified a methylation chip, RNA-sequencing, and miRNA microarray for RA. First, we searched for DNA methylation, genes, and miRNAs associated with RA using differential analysis. Second, we determined the regulatory networks for RA-specific methylation, miRNA, and m6A using cross-analysis. Based on these three regulatory networks, we built a comprehensive epigenetic regulatory network and identified hub genes.

Results

Using a differential analysis, we identified 16,852 differentially methylated sites, 4877 differentially expressed genes, and 32 differentially expressed miRNAs. The methylation-expression regulatory network was mainly associated with the PI3K-Akt and T-cell receptor signaling pathways. The miRNA expression regulatory network was mainly related to the MAPK and chemokine signaling pathways. M6A regulatory network was mainly associated with the MAPK signaling pathway. Additionally, five hub genes were identified in the epigenetic regulatory network: CHD3, SETD1B, FBXL19, SMARCA4, and SETD1A. Functional analysis revealed that these five genes were associated with immune cells and inflammatory responses.

Conclusion

We constructed a comprehensive epigenetic network associated with RA and identified core regulatory genes. This study provides a new direction for future research on the epigenetic mechanisms of RA.

Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis

Aims

This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis.

Methods

Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined.

Results

A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways.

Conclusion

This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.

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.

Bioinformatics Analysis Identified the Hub Genes, mRNA–miRNA–lncRNA Axis, and Signaling Pathways Involved in Rheumatoid Arthritis Pathogenesis

Objective

Rheumatoid arthritis (RA) is a nonspecific, chronic, systemic autoimmune disease characterized by symmetric polyarticular synovitis. Bioinformatics analysis of potential biomarkers, mRNA–miRNA–lncRNA axes, and signaling pathways in the pathogenesis of RA provides potential targets and theoretical basis for further research on RA.

Methods

The GSE1919 and GSE77298 datasets were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo). Perl was used to perform data merging, and R was used to perform batch correction. The “limma” package of R was used to screen differentially expressed genes, and the “clusterProfiler” package was used to perform enrichment analysis of the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein–protein interaction network, Cytoscape was used for module analysis, and R was used to screen for hub genes. GraphPad Prism was used to plot the receiver operating characteristic curve of the hub genes. Gene set enrichment analysis and competitive endogenous RNA network analysis were performed on hub genes with the greatest diagnostic values. The hub gene with the greatest diagnostic value was verified using immunohistochemical staining.

Results

We obtained nine hub genes (ITGB2, VAMP8, HLA-A, PTAFR, SYK, FCER1G, HLA-DPB1, LCP2, and ACTR2) and four mRNA–miRNA–lncRNA axes (ITGB2-hsa-miR-486-3p-SNHG3, ITGB2-hsa-miR-338-5p-XIST, ITGB2-hsa-miR-5581-3p-XIST, and ITGB2-hsa-miR-1226-5p-XIST) related to the pathogenesis of RA. The nine hub genes were highly expressed, and ITGB2 had the highest diagnostic value for RA. We also identified signaling pathways related to the pathogenesis of RA: Fc epsilon Rl and chemokine signaling pathways. The immunohistochemical results showed that ITGB2 expression was significantly upregulated in RA.

Conclusion

The hub genes, mRNA–miRNA–lncRNA axes, and signaling pathways related to RA pathogenesis identified in this study provide a new research direction for the mechanism, diagnosis, and treatment of RA.

Screening of potential hub genes in pulmonary thromboembolism

Pulmonary thromboembolism (PTE) is a fatal clinical syndrome that usually occurs in elderly individuals. The present study aimed to identify functional and key genes involved in the early diagnosis of PTE using bioinformatics analysis. The GSE84738 dataset was retrieved from the Gene Expression Omnibus database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were subsequently performed. In addition, Cytoscape software v.3.7.2 was used to construct a protein-protein interaction (PPI) network. Serum samples from patients with PTE and healthy individuals were collected and the expression levels of Toll-like receptor (TLR)4, TLR2, IL-1β, JUN, prostaglandin-endoperoxide synthase 2 (PTGS2), osteopontin (SPP1) and endothelin-1 (ET-1) were analyzed by reverse transcription-quantitative PCR. A total of 160 upregulated and 159 downregulated differentially expressed genes were identified between patients with PTE and healthy individuals. TNF, IL-1β, JUN, TLR4, PTGS2, vascular cell adhesion molecule 1, SPP1, ryanodine receptor 2, TLR2 and ET-1 were considered as hub genes, which are defined as the genes with the highest degree of interaction in the enrichment and PPI network analyses. The top 10 common genes with the highest degree in the PPI network and the top 10 genes in modules 1 and 2 were TLR4, TLR2, IL-1β, JUN, PTGS2, SPP1 and ET-1. Taken together, the present study suggested that TLR4, TLR2, IL-1β and SPP1 were enriched in patients with PTE, thus providing novel potential biomarkers for the diagnosis of PTE.

Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

Purpose

Ischemic cardiomyopathy (ICM) is considered to be the most common cause of heart failure, with high prevalence and mortality. This study aimed to investigate the different expressed genes (DEGs) and pathways in the pathogenesis of ICM using bioinformatics analysis.

Methods

The control and ICM datasets GSE116250, GSE46224 and GSE5406 were collected from the gene expression omnibus (GEO) database. DEGs were identified using limma package of R software, and co-expressed genes were identified using Venn diagrams. Then, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore the biological functions and signaling pathways. Protein-protein interaction (PPI) networks were assembled with Cytoscape software to identify hub genes related to the pathogenesis of ICM. RT-PCR of Heart tissues (n=2 for non-failing controls and n=4 for ischemic cardiomyopathy patients) was used to validate the bioinformatic results.

Results

A total of 844 DEGs were screened from GSE116250, of which 447 were up-regulated genes and 397 were down-regulated genes, respectively. A total of 99 DEGs were singled out from GSE46224, of which 58 were up-regulated genes and 41 were down-regulated genes, respectively. Thirty DEGs were screened from GSE5406, including 10 genes with up-regulated expression and 20 genes with down-regulated expression. Five up-regulated and 3 down-regulated co-expressed DEGs were intersected in three datasets. GO and KEGG pathway analyses revealed that DEGs are mainly enriched in collagen fibril organization, protein digestion and absorption, AGE-RAGE signaling pathway and other related pathways. Collagen alpha-1(III) chain (COL3A1), collagen alpha-2(I) chain (COL1A2) and lumican (LUM) are the three hub genes in all three datasets through PPI network analysis. The expression of 5 DEGs (SERPINA3, FCN3, COL3A1, HBB, MXRA5) in heart tissues by qRT-PCR results was consistent with our GEO analysis, while expression of 3 DEGs (ASPN, LUM, COL1A2) was opposite with GEO analysis.

Conclusion

These findings from this bioinformatics network analysis investigated key hub genes, which contributed to better understanding the mechanism and new therapeutic targets of ICM.

Identification of hub genes in rheumatoid arthritis through an integrated bioinformatics approach

Background

Rheumatoid arthritis (RA) is a common chronic autoimmune disease characterized by inflammation of the synovial membrane. However, the etiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify the key genes involved in RA.

Methods

GSE77298 was downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen the differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by using the DAVID online tool. The STRING database was used to analyze the interaction of differentially encoded proteins. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape. Gene set enrichment analysis (GSEA) was performed to identify relevant biological functions. qRT-PCR analysis was also performed to verify the expression of identified hub DEGs.

Results

A total of 4062 differentially expressed genes were selected, including 1847 upregulated genes and 2215 downregulated genes. In the biological process, DEGs were mainly concentrated in the fields of muscle filament sliding, muscle contraction, intracellular signal transduction, cardiac muscle contraction, signal transduction, and skeletal muscle tissue development. In the cellular components, DEGs were mainly concentrated in the parts of cytosol, Z disk, membrane, extracellular exosome, mitochondrion, and M band. In molecular functions, DEGs were mainly concentrated in protein binding, structural constituent of muscle, actin binding, and actin filament binding. KEGG pathway analysis shows that DEGs mainly focuses on pathways about lysosome, Wnt/β-catenin signaling pathway, and NF-κB signaling pathway. CXCR3, GNB4, and CXCL16 were identified as the core genes that involved in the progression of RA. By qRT-PCR analysis, we found that CXCR3, GNB4, and CXCL16 were significantly upregulated in RA tissue as compared to healthy controls.

Conclusion

In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the progression of RA, and provide candidate targets for diagnosis and treatment of RA.

Location, location, location: how the tissue microenvironment affects inflammation in RA

Current treatments for rheumatoid arthritis (RA) do not work well for a large proportion of patients, or at all in some individuals, and cannot cure or prevent this disease. One major obstacle to developing better drugs is a lack of complete understanding of how inflammatory joint disease arises and progresses. Emerging evidence indicates an important role for the tissue microenvironment in the pathogenesis of RA. Each tissue is made up of cells surrounded and supported by a unique extracellular matrix (ECM). These complex molecular networks define tissue architecture and provide environmental signals that programme site-specific cell behaviour. In the synovium, a main site of disease activity in RA, positional and disease stage-specific cellular diversity exist. Improved understanding of the architecture of the synovium from gross anatomy to the single-cell level, in parallel with evidence demonstrating how the synovial ECM is vital for synovial homeostasis and how dysregulated signals from the ECM promote chronic inflammation and tissue destruction in the RA joint, has opened up new ways of thinking about the pathogenesis of RA. These new ideas provide novel therapeutic approaches for patients with difficult-to-treat disease and could also be used in disease prevention.

Identification of hub genes and therapeutic drugs in rheumatoid arthritis patients

OBJECTIVES

Rheumatoid arthritis (RA) is considered a chronic autoimmune inflammatory disease that causes great morbidity and shortens life expectancy; however, the precise pathogenesis of RA remains unclear. This study aimed to select hub genes correlated with the development of RA.

METHODS

Two gene expression profiles, GSE55235 and GSE12021, obtained from the Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs) in control and RA samples using GEO2R, followed by other bioinformatics methods, including functional enrichment analysis, protein-protein interaction (PPI) networks, miRNA-hub gene network, and drug-hub gene interactions. In addition, qRT-PCR was finally conducted to confirm the reliability and validity of the expression level of the novel DEGs via freshly collected heparinized blood samples of healthy controls and RA patients.

RESULTS

A sum of 136 upregulated and 37 downregulated DEGs were selected. Functional enrichment analysis indicated that all the upregulated DEGs were correlated with immune response, B cell receptor signalling pathway, and adaptive immune response. KEGG pathway enrichment analysis revealed that the upregulated DEGs were mostly related to cytokine-cytokine receptor interaction, primary immunodeficiency, chemokine signalling pathways, and cell adhesion molecules (CAMs). In total, 12 hub genes (IL15, KLRK1, GZMA, CXCR6, IGHV4-38-2, IGLL5, CXCL13, CXCL11, MS4A1, SDC1, SLAMF1, and PDCD1LG2) were identified and all these hub genes were upregulated, of which IGLL5 and IGHV4-38-2 were first reported to be correlated with the pathogenic mechanism and prognosis of RA. Furthermore, we also used qRT-PCR to validate the overexpression of IGLL5 and IGHV4-38-2 in RA patients compared to the healthy controls. In the miRNA-hub gene network, hsa-miR-1185-5p and hsa-miR-3679-5p might inhibit the expression of IGLL5 during the progression of RA. The 15 most promising candidate drugs, which were all approved by the Food and Drug Administration, may assist with the treatment of RA.

CONCLUSIONS

Overall, these findings may assist with developing diagnostic, prognostic, and therapeutic biomarkers for RA. Key Points • IGLL5 and IGHV4-38-2 were first reported to be correlated with the pathogenic mechanism and prognosis of RA. • Besides, hsa-miR-1185-5p and hsa-miR-3679-5p may inhibit the expression of IGLL5 during the progression of RA.

Integrated Bioinformatics-Based Identification of Potential Diagnostic Biomarkers Associated with Diabetic Foot Ulcer Development

The present study was designed to detect possible biomarkers associated with diabetic foot ulcer (DFU) incidence in an effort to develop novel treatments for this condition. The GSE7014 and GSE29221 gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, after which differentially expressed genes (DEGs) were identified between DFU and healthy samples. These DEGs were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes. In total, 1192 DEGs were identified in the GSE7014 dataset (900 upregulated, 292 downregulated), while 1177 were identified in the GSE29221 dataset (257 upregulated, 919 downregulated). GO analyses revealed these DEGs to be significantly enriched in biological processes including sarcomere organization, muscle filament sliding, and the regulation of cardiac conduction, molecular functions including structural constituent of muscle, protein binding, and calcium ion binding, and cellular components including Z disc, myosin filament, and M band. These DEGs were also enriched in the adrenergic signaling in cardiomyoctes, dilated cardiomyopathy, and tight junction KEGG pathways. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with DFU development.

Functional Analysis of Estrogen Receptor 1 in Diabetic Wound Healing: A Knockdown Cell-Based and Bioinformatic Study

Background

Diabetic wound (DW) treatment is a serious challenge for clinicians, and the underlying mechanisms of DWs remain elusive. We sought to identify the critical genes in the development of DWs and provide potential targets for DW therapies.

Material/Methods

Datasets of GSE38396 from the Gene Expression Omnibus (GEO) database were reviewed. Pathway analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology term analyses were carried out, and Cytoscape software (Cytoscape 3.7.2) was used to construct the protein interaction network. Serum samples from patients with diabetes and control participants were collected, and the expression of estrogen receptor 1 (ESR1) was measured by quantitative reverse-transcription polymerase chain reaction. In addition, the function of ESR1 in human skin fibroblasts was investigated in vitro.

Results

Eight samples were analyzed using the Morpheus online tool, which identified 637 upregulated and 448 downregulated differentially expressed genes. The top 5 KEGG pathways of upregulated differentially expressed genes were associated with sphingolipid metabolism, estrogen signaling, ECM-receptor interaction, MAPK signaling, and PI3K-Akt signaling. The hub genes for DWs were JUN, ESR1, CD44, SMARCA4, MMP2, BMP4, GSK3B, WDR5, PTK2, and PTGS2. JUN, MMP2, and ESR1 were the upregulated hub genes, and ESR1 was found to be consistently enriched in DW patients. Inhibition of ESR1 had a stimulative role in human skin fibroblasts.

Conclusions

ESR1 was identified as a crucial gene in the development of DWs, which suggests potential therapeutic targets for DW healing.

Inhibition of miRNA-152-3p enhances diabetic wound repair via upregulation of PTEN

Diabetic foot ulcer (DFU) is a major complication of diabetes in the elderly population. The aim of this study was to investigate the potential mechanism of DFU at the molecular level and explore a feasible therapy for it. Using data from the Gene Expression Omnibus (GEO) database, we found that phosphatase and tensin homolog (PTEN) is differentially expressed between diabetic patients and those without diabetes. We also found that PTEN expression is regulated by glucose stimulation. In addition, decreased function of human umbilical vein endothelial cells (HUVECs) was found to be associated with reduction of PTEN. We identified microRNA-152-3p (miR-152-3p) to be a putative upstream negative regulator of PTEN, and in vivo and in vitro results indicated that miR-152-3p antagonist could restore HUVEC function and accelerate wound repair. Thus, miR-152-3p-induced downregulation of PTEN appears responsible for the delayed wound healing in DFU, and miR-152-3p inhibition may effectively accelerate wound repair, thereby providing a potential target for DFU therapy.

Identification of drug targets and potential molecular mechanisms for Wantong Jingu Tablet extract in treatment of rheumatoid arthritis: bioinformatics analysis of fibroblast-like synoviocytes

Background

Rheumatoid arthritis-fibroblast-like synoviocytes (RA-FLSs) play important roles in pathogenesis of rheumatoid arthritis (RA). Wantong Jingu Tablet (WJT), a mixture of traditional Chinese medicine, is a potentially effective therapy for RA, but its underlying mechanism is unclear. In this study, we explore the effects of WJT on human RA-FLSs and the underlying molecular mechanism.

Methods

The major components of WJT were determined using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Cell proliferative ability was evaluated by CCK-8, colony formation assay, and EdU incorporation assay. Cell apoptotic capacity was examined by caspase-3 and caspase-9 activity test. Protein levels of Bax and Bcl-2 were investigated by western blotting. High-throughput sequencing and bioinformatics analysis were conducted to screen and identify targeted genes, followed by identification by qRT-PCR and western blotting.

Results

In this study, we have identified 346 compounds in WJT. Our results showed that WJT inhibited the RA-FLSs proliferation, and promoted apoptosis in a dose- and time-dependent manner. More importantly, 184 differentially expressed genes (DEGs) has been screened after WJT treatment based on DEGSeq2 and 278 DEGs was identified by DEGSeq2 combined with WGCNA. Then, 10 hub genes were identified based on two different analyses, while the expression levels of only SMC3, THOC1, BUB1, and STAG2 were decreased after WJT treatment, which was identical to the sequencing profiles.

Conclusions

WJT exerted its anti-proliferation and pro-apoptosis effects possibly through suppressing the expression of SMC3, THOC1, BUB1, and STAG2 in RA-FLSs. Thus, therapeutics targeting these genes may be a promising strategy for rescuing RA.

Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis

BACKGROUND

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis.

MATERIALS AND METHODS

To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired from the Gene Expression Omnibus (GEO) database. DEGs between T2DM islet and normal islet were picked out by the GEO2R tool and Venn diagram software. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify the pathways and functional annotation of DEGs. Then, protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).

RESULTS

In total, we identified 36 DEGs in the three datasets, including 32 up-regulated genes and four down-regulated genes. The improved functions and pathways of the DEGs enriched in cytokine-cytokine receptor interaction, pathways in cancer, PI3K-Akt signaling pathway, and Rheumatoid arthritis. Among them, ten hub genes with a high degree of connectivity were selected. Furthermore, via the re-analysis of DAVID, four genes (IL6, MMP3, MMP1, and IL11) were significantly enriched in the Rheumatoid arthritis pathway.

CONCLUSION

Our study, based on the GEO database, identified four significant up-regulated DEGs and provided novel targets for diagnosis and treatment of T2DM.

Analysis of metabolite profiles of Saccharomyces cerevisiae strains suitable for butanol production

Butanol has advantages over ethanol as a biofuel. Although butanol is naturally produced by some Clostridium species, clostridial fermentation has inherent characteristics that prevent its industrial application. Butanol-producing Saccharomyces cerevisiae strains may be a solution to this problem. The aim of this study was to evaluate the ability of wild-type and industrial Brazilian strains of S. cerevisiae to produce n-butanol using glycine as co-substrate and evaluate the relationship between the production of this alcohol and other metabolites in fermented broth. Of the 48 strains analyzed, 25 were able to produce n-butanol in a glycine-containing medium. Strains exhibited different profiles of n-butanol, isobutanol, ethanol, glycerol and acetic acid production. Some wild-type strains showed substantial n-butanol production capability, for instance UFMG-CM-Y267, which produced about 12.7 mg/L of butanol. Although this concentration is low, it demonstrates that wild-type S. cerevisiae can synthesize butanol, suggesting that selection and genetic modification of this microorganism could yield promising results. The findings presented here may prove useful for future studies aimed at optimizing S. cerevisiae strains for butanol production.