New Developments in Transcriptomic Analysis of Synovial Tissue

hsa_circ_0087100/hsa-miR-6743-5p affects Th1 cell differentiation by regulating STAT1 in diabetic retinopathy

Objective: To elucidate the role of the competitive endogenous RNA (ceRNA) network in immune infiltration of diabetic retinopathy (DR). Methods: We obtained differentially expressed (DE) circRNAs, miRNAs and mRNAs from the Gene Expression Omnibus database. Then, we identified immune infiltration by CIBERSORT and single-sample gene set enrichment analysis and discovered co-expression genes by weighted gene co-expression network analysis. Furthermore, STAT1-mediated Th1 differentiation was determined in DR cell models, DR patients and DR mouse models. Results: hsa_circ_0087100/hsa-miR-6743-5p/STAT1 was involved in immune infiltration of Th1 cells. Aberrant expression of the ceRNA network and STAT1-mediated Th1 differentiation was thus verified in vitro and in vivo. Conclusion: hsa_circ_0087100/hsa-miR-6743-5p/STAT1 may affect Th1 cell differentiation in DR.

Exploring the mechanism of Celastrol in the treatment of rheumatoid arthritis based on systems pharmacology and multi-omics

To explore the molecular network mechanism of Celastrol in the treatment of rheumatoid arthritis (RA) based on a novel strategy (integrated systems pharmacology, proteomics, transcriptomics and single-cell transcriptomics). Firstly, the potential targets of Celastrol and RA genes were predicted through the database, and the Celastrol-RA targets were obtained by taking the intersection. Then, transcriptomic data and proteomic data of Celastrol treatment of RA were collected. Subsequently, Celastrol-RA targets, differentially expressed genes, and differentially expressed proteins were imported into Metascape for enrichment analysis, and related networks were constructed. Finally, the core targets of Celastrol-RA targets, differentially expressed genes, and differentially expressed proteins were mapped to synoviocytes of RA mice to find potential cell populations for Celastrol therapy. A total of 195 Celastrol-RA targets, 2068 differential genes, 294 differential proteins were obtained. The results of enrichment analysis showed that these targets, genes and proteins were mainly related to extracellular matrix organization, TGF-β signaling pathway, etc. The results of single cell sequencing showed that the main clusters of these targets, genes, and proteins could be mapped to RA synovial cells. For example, Mmp9 was mainly distributed in Hematopoietic cells, especially in Ptprn+fibroblast. The results of molecular docking also suggested that Celastrol could stably combine with molecules predicted by network pharmacology. In conclusion, this study used systems pharmacology, transcriptomics, proteomics, single-cell transcriptomics to reveal that Celastrol may regulate the PI3K/AKT signaling pathway by regulating key targets such as TNF and IL6, and then play an immune regulatory role.

Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures

Objective

To study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS.

Methods

We retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes.

Results

We obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores.

Conclusion

The study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

Activated CD90/Thy-1 fibroblasts co-express the Δ133p53β isoform and are associated with highly inflamed rheumatoid arthritis

BACKGROUND

The p53 isoform Δ133p53β is known to be associated with cancers driven by inflammation. Many of the features associated with the development of inflammation in rheumatoid arthritis (RA) parallel those evident in cancer progression. However, the role of this isoform in RA has not yet been explored. The aim of this study was to determine whether Δ133p53β is driving aggressive disease in RA.

METHODS

Using RA patient synovia, we carried out RT-qPCR and RNAScope-ISH to determine both protein and mRNA levels of Δ133p53 and p53. We also used IHC to determine the location and type of cells with elevated levels of Δ133p53β. Plasma cytokines were also measured using a BioPlex cytokine panel and data analysed by the Milliplex Analyst software.

RESULTS

Elevated levels of pro-inflammatory plasma cytokines were associated with synovia from RA patients displaying extensive tissue inflammation, increased immune cell infiltration and the highest levels of Δ133TP53 and TP53β mRNA. Located in perivascular regions of synovial sub-lining and surrounding ectopic lymphoid structures (ELS) were a subset of cells with high levels of CD90, a marker of 'activated fibroblasts' together with elevated levels of Δ133p53β.

CONCLUSIONS

Induction of Δ133p53β in CD90⁺ synovial fibroblasts leads to an increase in cytokine and chemokine expression and the recruitment of proinflammatory cells into the synovial joint, creating a persistently inflamed environment. Our results show that dysregulated expression of Δ133p53β could represent one of the early triggers in the immunopathogenesis of RA and actively perpetuates chronic synovial inflammation. Therefore, Δ133p53β could be used as a biomarker to identify RA patients more likely to develop aggressive disease who might benefit from targeted therapy to cytokines such as IL-6.

Best practices for ultrasound-guided synovial biopsy in the United States

The target organ in many forms of inflammatory arthritis is the synovium. However, synovial tissue has historically been perceived as either difficult to obtain or of little practical value. Ultrasound-guided synovial biopsy [UGSB] is a safe and well-tolerated bedside procedure that is established in Europe and rapidly growing in popularity in the United States. The technique can be mastered by rheumatologists who are already experienced in ultrasound-guided procedures such as joint aspirations. The USGB procedure allows the proceduralist to access small, medium, and large joints and is inexpensive and less invasive compared to surgical alternatives. The relative ease of obtaining this tissue, along with recent research suggesting that synovium may have more clinical and investigational utility than previously thought, has led clinicians and researchers to a new appreciation of the role of synovial biopsy in both the clinical and research setting. In this manuscript, the authors present recommendations on best practices for ultrasound-guided synovial biopsy in the United States, based on our initial training with well-established experts overseas and our own subsequent collective experience in performing numerous synovial biopsies in the United States over the past 7 years for both clinical and research indications. We envision a future where UGSB is more frequently incorporated in the standard diagnostic workup of arthritis and drives novel research initiatives.

Integrated bioinformatics analysis for novel miRNAs markers and ceRNA network in diabetic retinopathy

In order to seek a more outstanding diagnosis and treatment of diabetic retinopathy (DR), we predicted the miRNA biomarkers of DR and explored the pathological mechanism of DR through bioinformatics analysis.

Method: Based on public omics data and databases, we investigated ncRNA (non-coding RNA) functions based on the ceRNA hypothesis.

Result: Among differentially expressed miRNAs (DE-miRNAs), hsa-miR-1179, -4797-3p and -665 may be diagnosis biomarkers of DR. Functional enrichment analysis revealed differentially expressed mRNAs (DE-mRNAs) enriched in mitochondrial transport, cellular respiration and energy derivation. 18 tissue/organ-specific expressed genes, 10 hub genes and gene cluster modules were identified. The ceRNA networks lncRNA FBXL19-AS1/miR-378f/MRPL39 and lncRNA UBL7-AS1/miR-378f/MRPL39 might be potential RNA regulatory pathways in DR.

Conclusion: Differentially expressed hsa-miR-1179, -4797-3p and -665 can be used as powerful markers for DR diagnosis, and the ceRNA network: lncRNA FBXL19-AS1/UBL7-AS1-miR-378f-MRPL39 may represent an important regulatory role in DR progression.

Synovial Macrophages: Past Life, Current Situation, and Application in Inflammatory Arthritis

Inflammatory arthritis is an inflammatory disease that involves the joints and surrounding tissues. Synovial hyperplasia often presents when joints become inflamed due to immune cell infiltration. Synovial membrane is an important as well as a highly specific component of the joint, and its lesions can lead to degeneration of the joint surface, causing pain and joint disability or affecting the patients’ quality of life in severe cases. Synovial macrophages (SMs) are one of the cellular components of the synovial membrane, which not only retain the function of macrophages to engulf foreign bodies in the joint cavity, but also interact with synovial fibroblasts (SFs), T cells, B cells, and other inflammatory cells to promote the production of a variety of pro-inflammatory cytokines and chemokines, such as TNF-α, IL-1β, IL-8, and IL-6, which are involved in the pathogenic process of inflammatory arthritis. SMs from different tissue sources have differently differentiated potentials and functional expressions. This article provides a summary on studies pertaining to SMs in inflammatory arthritis, and explores their role in its treatment, in order to highlight novel treatment modalities for the disease.

Evaluation of Biomarkers and Immune Microenvironment of Osteoarthritis: Evidence From Omics Data and Machine Learning

Objectives: This study aimed to identify novel biomarkers for osteoarthritis (OA) and explore potential pathological immune cell infiltration. Methods: We identified differentially expressed genes (DEGs) between OA and normal synovial tissues using the limma package in R, and performed enrichment analyses to understand the functions and enriched pathways of DEGs. Weighted gene co-expression network analysis (WGCNA) and distinct machine-learning algorithms were then used to identify hub modules and candidate biomarkers. We assessed the diagnostic value of the candidate biomarkers using receiver operating characteristic (ROC) analysis. We then used the CIBERSORT algorithm to analyze immune cell infiltration patterns, and the Wilcoxon test to screen out hub immune cells that might affect OA occurrence. Finally, the expression levels of hub biomarkers were confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Results: We identified 102 up-regulated genes and 110 down-regulated genes. The functional enrichment analysis results showed that DEGs are enriched mainly in immune response pathways. Combining the results of the algorithms and ROC analysis, we identified GUCA1A and NELL1 as potential diagnostic biomarkers for OA, and validated their diagnosibility using an external dataset. Construction of a TF-mRNA-miRNA network enabled prediction of potential candidate compounds targeting hub biomarkers. Immune cell infiltration analyses revealed the expression of hub biomarkers to be correlated with CD8 T cells, memory B cells, M0/M2 macrophages, resting mast cells and resting dendritic cells. qRT-PCR results showed both GUCA1A and NELL1 were significantly increased in OA samples (p < 0.01). All validations are consistent with the microarray hybridization, indicating that GUCA1A and NELL1 may be involved in the pathogenesis of OA. Conclusion: The findings suggest that GUCA1A and NELL1, closely related to OA occurrence and progression, represent new OA candidate markers, and that immune cell infiltration plays a significant role in the progression of OA.

Identification of Synovial Fibroblast-Associated Neuropeptide Genes and m6A Factors in Rheumatoid Arthritis Using Single-Cell Analysis and Machine Learning

Objectives

Synovial fibroblasts (SFs) play an important role in the development and progression of rheumatoid arthritis (RA). However, the pathogenic mechanism of SFs remains unclear. The objective of this study was to investigate how neuropeptides and N6-methyladenosine (m6A) played an important role in the underlying pathogenic processes of SFs that contribute to the development of RA.

Methods

Single-cell RNA sequencing data were examined using single-cell analysis and machine learning. SF subgroups were identified based on the clustering and annotation results of the single-cell analysis. Moreover, cell–cell communication was used to analyse neuropeptide-related receptor and ligand pairs on the surface of SF cell membranes. Machine learning was used to explore the m6A factors acting on these neuropeptide genes.

Results

NPR3, GHR, BDKRB2, and CALCRL, four neuropeptide genes, were shown to be differently expressed among SF subgroups. Further investigation of receptor–ligand interactions found that NPR3 (in conjunction with NPPC, OSTN, NPPB, and NPPA) and GHR (in conjunction with GH1 and GH2) may have a role in SF interactions. As predicted by machine learning, IGFBP2 and METTL3 were identified as key factors regulating m6A of NPR3 and GHR. The expression levels and enrichment pathways of METTL3 and IGFBP2 were different among SF subgroups.

Conclusions

Single-cell analysis and machine learning efficiently identified neuropeptide genes and m6A factors that perform important regulatory functions in RA. Our strategy may provide a basis for future studies to identify pathogenic cell subpopulations and molecular mechanisms in RA and other diseases.

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.

Novel NFκB Inhibitor SC75741 Mitigates Chondrocyte Degradation and Prevents Activated Fibroblast Transformation by Modulating miR-21/GDF-5/SOX5 Signaling

Osteoarthritis (OA) is a common articular disease manifested by the destruction of cartilage and compromised chondrogenesis in the aging population, with chronic inflammation of synovium, which drives OA progression. Importantly, the activated synovial fibroblast (AF) within the synovium facilitates OA through modulating key molecules, including regulatory microRNAs (miR's). To understand OA associated pathways, in vitro co-culture system, and in vivo papain-induced OA model were applied for this study. The expression of key inflammatory markers both in tissue and blood plasma were examined by qRT-PCR, western blot, immunohistochemistry, enzyme-linked immunosorbent assay (ELISA) and immunofluorescence assays. Herein, our result demonstrated, AF-activated human chondrocytes (AC) exhibit elevated NFκB, TNF-α, IL-6, and miR-21 expression as compared to healthy chondrocytes (HC). Importantly, AC induced the apoptosis of HC and inhibited the expression of chondrogenesis inducers, SOX5, TGF-β1, and GDF-5. NFκB is a key inflammatory transcription factor elevated in OA. Therefore, SC75741 (an NFκB inhibitor) therapeutic effect was explored. SC75741 inhibits inflammatory profile, protects AC-educated HC from apoptosis, and inhibits miR-21 expression, which results in the induced expression of GDF-5, SOX5, TGF-β1, BMPR2, and COL4A1. Moreover, ectopic miR-21 expression in fibroblast-like activated chondrocytes promoted osteoblast-mediated differentiation of osteoclasts in RW264.7 cells. Interestingly, in vivo study demonstrated SC75741 protective role, in controlling the destruction of the articular joint, through NFκB, TNF-α, IL-6, and miR-21 inhibition, and inducing GDF-5, SOX5, TGF-β1, BMPR2, and COL4A1 expression. Our study demonstrated the role of NFκB/miR-21 axis in OA progression, and SC75741's therapeutic potential as a small-molecule inhibitor of miR-21/NFκB-driven OA progression.

Novel insights into macrophage diversity in rheumatoid arthritis synovium

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease affecting joints and causing progressive damage and disability. Macrophages are of critical importance in the initiation and perpetuation of synovitis in RA, they can function as antigen presenting cells leading to T-cell dependent B-cell activation, assume a variety of inflammatory cell states with the production of destructive cytokines, but also contribute to tissue homeostasis/repair. The recent development of high-throughput technologies, including bulk and single cells RNA-sequencing, has broadened our understanding of synovial cell diversity, and opened novel perspectives to the discovery of new potential therapeutic targets in RA. In this review, we will focus on the relationship between the synovial macrophage infiltration and clinical disease severity and response to treatment. We will then provide a state-of-the-art picture of the biological roles of synovial macrophages and distinct macrophage subsets described in RA. Finally, we will review the effects of approved conventional and biologic drugs on the synovial macrophage component and highlight the therapeutic potential of future strategies to re-program macrophage phenotypes in RA.

TLR expression profiles are a function of disease status in rheumatoid arthritis and experimental arthritis

The role of the innate immune system has been established in the initiation and perpetuation of inflammatory disease, but less attention has been paid to its role in the resolution of inflammation and return to homeostasis. Toll-like receptor (TLR) expression profiles were analysed in tissues with differing disease status in rheumatoid arthritis (RA), ankylosing spondylitis (AS), and in experimental arthritis. TLR gene expression was measured in whole blood and monocytes, before and after TNF blockade. In RA and osteoarthritis synovia, the expression of TLRs was quantified by standard curve qPCR. In addition, four distinct stages of disease were defined and validated in collagen-induced arthritis (CIA), the gold standard animal model for RA - pre-onset, early disease, late disease and immunised mice that were resistant to the development of disease. TLR expression was measured in spleens, lymph nodes, blood cells, liver and the paws (inflamed and unaffected). In RA whole blood, the expression of TLR1, 4 and 6 was significantly reduced by TNF blockade but the differences in TLR expression profiles between responders and non-responders were less pronounced than the differences between RA and AS patients. In RA non-responders, monocytes had greater TLR2 expression prior to therapy compared to responders. The expression of TLR1, 2, 4 and 8 was higher in RA synovium compared to control OA synovium. Circulating cytokine levels in CIA resistant mice were similar to naïve mice, but anti-collagen antibodies were similar to arthritic mice. Distinct profiles of inflammatory gene expression were mapped in paws and organs with differing disease status. TLR expression in arthritic paws tended to be similar in early and late disease, with TLR1 and 2 moderately higher in late disease. TLR expression in unaffected paws varied according to gene and disease status but was generally lower in resistant paws. Disease status-specific profiles of TLR expression were observed in spleens, lymph nodes, blood cells and the liver. Notably, TLR2 expression rose then fell in the transition from naïve to pre-onset to early arthritis. TLR gene expression profiles are strongly associated with disease status. In particular, increased expression in the blood precedes clinical manifestation.

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.