Metabolites as drivers and targets in Rheumatoid Arthritis

2021 ◽  
Author(s):  
Megan M Hanlon ◽  
Mary Canavan ◽  
Brianne E Barker ◽  
Ursula Fearon
Keyword(s):  
Rheumatoid Arthritis ◽  
Energy Demand ◽  
Functional Disability ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Synovial Cells ◽  
Inflammatory Phenotype ◽  
Underlying Mechanisms ◽  
Novel Therapeutic Strategies

Abstract Rheumatoid Arthritis is a chronic autoimmune disease characterised by neovascularisation, immune cell infiltration and synovial hyperplasia, which leads to degradation of articular cartilage and bone, and subsequent functional disability. Dysregulated angiogenesis, synovial hypoxia and immune cell infiltration results in a ‘bioenergetic crisis’ in the inflamed joint which further exacerbates synovial invasiveness. Several studies have examined this vicious cycle between metabolism, immunity and inflammation and the role metabolites play in these interactions. To add to this complexity the inflamed synovium is a multicellular tissue with many cellular subsets having different metabolic requirements. Metabolites can shape the inflammatory phenotype of immune cell subsets during disease and act as central signaling hubs. In the RA joint the increased energy demand of stromal and immune cells leads to the accumulation of metabolites such as lactate, citrate, and succinate as well as adipocytokines which can regulate downstream signalling pathways. Transcription factors such as HIF1ɑ and mTOR can act as metabolic sensors to activate synovial cells and drive pro-inflammatory effector function, thus perpetuating chronic inflammation further. These metabolic intermediates may be potential therapeutic targets and so understanding the complex interplay between metabolites and synovial cells in RA may allow for identification of novel therapeutic strategies but also may provide significant insight into the underlying mechanisms of disease pathogenesis.

scholarly journals Construction of Two Alternative Polyadenylation Signatures to Predict the Prognosis of Sarcoma Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Chuan Hu ◽  
Chuan Liu ◽  
Jianyi Li ◽  
Tengbo Yu ◽  
Jun Dong ◽  
...  
Keyword(s):  
High Risk ◽  
Regulatory Networks ◽  
Immune Cell ◽  
Alternative Polyadenylation ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Bioinformatics Analyses ◽  
High Risk Patients ◽  
Risk Patients ◽  
Underlying Mechanisms

BackgroundIncreasing evidence indicates that alternative polyadenylation (APA) is associated with the prognosis of cancers.MethodsWe obtained gene expression and APA profiles of 259 sarcoma patients from the TCGA dataportal and TC3A database, respectively. The prognostic signatures, clinical nomograms, and regulatory networks were studied by integrated bioinformatics analyses. Then, the immune cell infiltration profile was obtained from the ImmuCellAI. The association between APA-based signature and immune cells was studied.ResultsA total of 61 and 38 APA events were identified as overall survival (OS)- and progress free-survival (PFS)-related biomarkers, respectively. Two signatures were generated. The area under the curves (AUC) values of OS signature were 0.900, 0.928, and 0.963 over 2-, 4-, and 6-years, respectively. And the AUC values of PFS signature at 2-, 4-, and 6-years were 0.826, 0.840, and 0.847, respectively. Overall and subgroup analyses indicated that high-risk patients had a worse prognosis than low-risk patients (all p-values < 0.05). In addition, immunomics analyses indicated that there are different patterns of immune cell infiltration between low- and high-risk patients. Furthermore, two clinical-APA nomograms were established and the C-indexes were 0.813 and 0.809 for OS nomogram and PFS nomogram, respectively. Finally, two APA regulatory networks were constructed. FIP1L1-VPS26B was identified as a key regulating relationship and validated in the pan-cancer analyses.ConclusionIn this study, we identified prognostic predictors based on APA events with high accuracy for risk stratification in sarcoma patients and uncovered interesting regulatory networks in sarcoma that could be underlying mechanisms. This study not only provides novel potential prognostic biomarkers but promote precision medicine and provide potential novel research interests for immunotherapy.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Fei Sun ◽  
Jian lin Zhou ◽  
Pu ji Peng ◽  
Chen Qiu ◽  
Jia rui Cao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Relative Proportion ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Synovial Tissues ◽  
Disease Specific

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.

scholarly journals Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies

2021 ◽  
Vol 12 ◽  
Author(s):  
Rongguo Yu ◽  
Jiayu Zhang ◽  
Youguang Zhuo ◽  
Xu Hong ◽  
Jie Ye ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Nk Cells ◽  
Learning Strategies ◽  
Immune Cells ◽  
Immune Cell ◽  
Bioinformatic Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Qrt Pcr

BackgroundRheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA’s diagnostic signatures and the effect of immune cell infiltration in this pathology.MethodsGene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA’s diagnostic signatures and further verify by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. Moreover, this study employed cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) website for assessing the inflammatory state of RA, and an investigation was conducted on the relationship of diagnostic signatures and infiltrating immune cells.ResultsOn the whole, 54 robust DEGs received the recognition. Lymphocyte-specific protein 1 (LSP1), Granulysin (GNLY), and Mesenchymal homobox 2 (MEOX2) (AUC = 0.955) were regarded as RA’s diagnostic markers and showed their statistically significant difference by qRT-PCR. As indicated from the immune cell infiltration analysis, resting NK cells, neutrophils, activated NK cells, T cells CD8, memory B cells, and M0 macrophages may be involved in the development of RA. Additionally, all diagnostic signatures might be different degrees of correlation with immune cells.ConclusionsIn conclusion, LSP1, GNLY, and MEOX2 are likely to be available in terms of diagnosing and treating RA, and the infiltration of immune cells mentioned above may critically impact RA development and occurrence.

scholarly journals Circulating and Synovial Pentraxin-3 (PTX3) Expression Levels Correlate With Rheumatoid Arthritis Severity and Tissue Infiltration Independently of Conventional Treatments Response

2021 ◽  
Vol 12 ◽  
Author(s):  
Marie-Astrid Boutet ◽  
Alessandra Nerviani ◽  
Gloria Lliso-Ribera ◽  
Roberto Leone ◽  
Marina Sironi ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Sequencing Analysis ◽  
Baseline Serum ◽  
Healthy Donors ◽  
Gene And Protein Expression ◽  
Lymphoid Structures

AimsTo determine the relationship between PTX3 systemic and synovial levels and the clinical features of rheumatoid arthritis (RA) in a cohort of early, treatment naïve patients and to explore the relevance of PTX3 expression in predicting response to conventional-synthetic (cs) Disease-Modifying-Anti-Rheumatic-Drugs (DMARDs) treatment.MethodsPTX3 expression was analyzed in 119 baseline serum samples from early naïve RA patients, 95 paired samples obtained 6-months following the initiation of cs-DMARDs treatment and 43 healthy donors. RNA-sequencing analysis and immunohistochemistry for PTX3 were performed on a subpopulation of 79 and 58 synovial samples, respectively, to assess PTX3 gene and protein expression. Immunofluorescence staining was performed to characterize PTX3 expressing cells within the synovium.ResultsCirculating levels of PTX3 were significantly higher in early RA compared to healthy donors and correlated with disease activity at baseline and with the degree of structural damages at 12-months. Six-months after commencing cs-DMARDs, a high level of PTX3, proportional to the baseline value, was still detectable in the serum of patients, regardless of their response status. RNA-seq analysis confirmed that synovial transcript levels of PTX3 correlated with disease activity and the presence of mediators of inflammation, tissue remodeling and bone destruction at baseline. PTX3 expression in the synovium was strongly linked to the degree of immune cell infiltration, the presence of ectopic lymphoid structures and seropositivity for autoantibodies. Accordingly, PTX3 was found to be expressed by numerous synovial cell types such as plasma cells, fibroblasts, vascular and lymphatic endothelial cells, macrophages, and neutrophils. The percentage of PTX3-positive synovial cells, although significantly reduced at 6-months post-treatment as a result of global decreased cellularity, was similar in cs-DMARDs responders and non-responders.ConclusionThis study demonstrates that, early in the disease and prior to treatment modification, the level of circulating PTX3 is a reliable marker of RA activity and predicts a high degree of structural damages at 12-months. In the joint, PTX3 associates with immune cell infiltration and the presence of ectopic lymphoid structures. High synovial and peripheral blood levels of PTX3 are associated with chronic inflammation characteristic of RA. Additional studies to determine the mechanistic link are required.

scholarly journals Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Sheng Zhou ◽  
Hongcheng Lu ◽  
Min Xiong
Keyword(s):  
Rheumatoid Arthritis ◽  
T Cells ◽  
Immune Cells ◽  
Cd4 T Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
M1 Macrophages ◽  
Tfh Cells

BackgroundRheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis.MethodsFive microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by “Batch correction” and “RobustRankAggreg” methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman’s rank correlation analysis in R software.ResultsTen significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package “UpSet”. CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells.ConclusionsCCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.

WISP1 modulates immune cell infiltration upon drug-induced liver injury (DILI)

2015 ◽  
Vol 53 (12) ◽  
Author(s):  
AB Widera ◽  
L Pütter ◽  
S Leserer ◽  
G Campos ◽  
K Rochlitz ◽  
...  
Keyword(s):  
Liver Injury ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals SFTPA1 is a potential prognostic biomarker correlated with immune cell infiltration and response to immunotherapy in lung adenocarcinoma

2021 ◽  
Author(s):  
Lu Yuan ◽  
Xixi Wu ◽  
Longshan Zhang ◽  
Mi Yang ◽  
Xiaoqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Lung Adenocarcinoma ◽  
Expression Profile ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Regulatory Pathways ◽  
Chronic Lung Diseases ◽  
Potential Biomarker

AbstractPulmonary surfactant protein A1 (SFTPA1) is a member of the C-type lectin subfamily that plays a critical role in maintaining lung tissue homeostasis and the innate immune response. SFTPA1 disruption can cause several acute or chronic lung diseases, including lung cancer. However, little research has been performed to associate SFTPA1 with immune cell infiltration and the response to immunotherapy in lung cancer. The findings of our study describe the SFTPA1 expression profile in multiple databases and was validated in BALB/c mice, human tumor tissues, and paired normal tissues using an immunohistochemistry assay. High SFTPA1 mRNA expression was associated with a favorable prognosis through a survival analysis in lung adenocarcinoma (LUAD) samples from TCGA. Further GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that SFTPA1 was involved in the toll-like receptor signaling pathway. An immune infiltration analysis clarified that high SFTPA1 expression was associated with an increased number of M1 macrophages, CD8+ T cells, memory activated CD4+ T cells, regulatory T cells, as well as a reduced number of M2 macrophages. Our clinical data suggest that SFTPA1 may serve as a biomarker for predicting a favorable response to immunotherapy for patients with LUAD. Collectively, our study extends the expression profile and potential regulatory pathways of SFTPA1 and may provide a potential biomarker for establishing novel preventive and therapeutic strategies for lung adenocarcinoma.

scholarly journals Enhanced CD4+ and CD8+ T cell infiltrate within convex hull defined pancreatic islet borders as autoimmune diabetes progresses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander J. Dwyer ◽  
Jacob M. Ritz ◽  
Jason S. Mitchell ◽  
Tijana Martinov ◽  
Mohannad Alkhatib ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Convex Hull ◽  
Pancreatic Islets ◽  
Immune Cell ◽  
Nod Mice ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Analytical Strategy ◽  
Islet Mass

AbstractThe notion that T cell insulitis increases as type 1 diabetes (T1D) develops is unsurprising, however, the quantitative analysis of CD4+ and CD8+ T cells within the islet mass is complex and limited with standard approaches. Optical microscopy is an important and widely used method to evaluate immune cell infiltration into pancreatic islets of Langerhans for the study of disease progression or therapeutic efficacy in murine T1D. However, the accuracy of this approach is often limited by subjective and potentially biased qualitative assessment of immune cell subsets. In addition, attempts at quantitative measurements require significant time for manual analysis and often involve sophisticated and expensive imaging software. In this study, we developed and illustrate here a streamlined analytical strategy for the rapid, automated and unbiased investigation of islet area and immune cell infiltration within (insulitis) and around (peri-insulitis) pancreatic islets. To this end, we demonstrate swift and accurate detection of islet borders by modeling cross-sectional islet areas with convex polygons (convex hulls) surrounding islet-associated insulin-producing β cell and glucagon-producing α cell fluorescent signals. To accomplish this, we used a macro produced with the freeware software ImageJ equipped with the Fiji Is Just ImageJ (FIJI) image processing package. Our image analysis procedure allows for direct quantification and statistical determination of islet area and infiltration in a reproducible manner, with location-specific data that more accurately reflect islet areas as insulitis proceeds throughout T1D. Using this approach, we quantified the islet area infiltrated with CD4+ and CD8+ T cells allowing statistical comparison between different age groups of non-obese diabetic (NOD) mice progressing towards T1D. We found significantly more CD4+ and CD8+ T cells infiltrating the convex hull-defined islet mass of 13-week-old non-diabetic and 17-week-old diabetic NOD mice compared to 4-week-old NOD mice. We also determined a significant and measurable loss of islet mass in mice that developed T1D. This approach will be helpful for the location-dependent quantitative calculation of islet mass and cellular infiltration during T1D pathogenesis and can be combined with other markers of inflammation or activation in future studies.

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