From bench to bedside: Calprotectin (S100A8/S100A9) as a biomarker in rheumatoid arthritis

Inciarte-Mundo, J.; Frade‐Sosa, Beatriz; Sanmartí, Raimón

doi:10.3389/fimmu.2022.1001025

Cited by 33 publications

(27 citation statements)

References 151 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CX3CR1 [666], CD177 [690], PF4 [691], FFAR2 [692], MPO (myeloperoxidase) [693], F11 [694], S100A8 [695], VEGFD (vascular endothelial growth factor D) [674], IL1A [696], BPI (bactericidal permeability increasing protein) [697], AQP4 [698], BDNF (brain derived neurotrophic factor) [699], CXCL10 [700], RNASE2 [701], FCGR3B [702], S100A9 [703], IL1B [704], CXCR2 [705], GPIHBP1 [294], CD36 [706], TRIB3 [707], PCSK9 [708], FGF2 [709], FASN (fatty acid synthase) [710], PNPLA3 [711], HSPA6 [712], VIP (vasoactive intestinal peptide) [713], TLR3 [683], ADRB1 [328], SPOCK2 [714], TLR8 [715], CCR2 [716], IFIT3 [717], NEK7 [718], TLR7 [687], EFNB2 [719], CAV1 [720], CR1 [721] and AQP5 [722] plays essential roles in viral respiratory diseases. Previous study confirmed that CX3CR1 [723], S100A12 [724], CD177 [725], PF4 [726], MPO (myeloperoxidase) [727], CD5L [728], F11 [729], S100A8 [730], PGLYRP1 [731], GPR15 [732], BPI (bactericidal permeability increasing protein) [733], AQP4 [734], BDNF (brain derived neurotrophic factor) [735], CXCL10 [736], FCGR3B [737], S100A9 [738], IL1B [739], CXCR1 [740], CXCR2 [741], AFF3 [742], WNT3A [743], FCN3 [744], AZGP1 [745], CD36 [746], PCSK9 [747], GPX3 [748], FGF2 [749], SHH (sonic hedgehog signaling molecule) [750], SLC7A11 [751], VIP (vasoactive intestinal peptide) [752], KL (klotho) [753], APOA1 [754], RASGRF1 [75...…”

Section: Discussionmentioning

confidence: 69%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

View full text Add to dashboard Cite

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

show abstract

Section: Discussionmentioning

confidence: 69%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The present study has found that RAGE exhibits variable influence on DAMP release. S100A8/A9 is a heterodimeric DAMP, released from cells in response to stress or during cell death, with neutrophils being the primary source ( 79 ). Extracellular S100A8/A9 has strong antimicrobial activity and promotes local inflammation through binding of RAGE and TLR4 ( 80 ).…”

Section: Discussionmentioning

confidence: 99%

RAGE contributes to allergen driven severe neutrophilic airway inflammation via NLRP3 inflammasome activation in mice

et al. 2023

View full text Add to dashboard Cite

BackgroundAsthma is a major public healthcare burden, affecting over 300 million people worldwide. While there has been great progress in the treatment of asthma, subsets of patients who present with airway neutrophilia, often have more severe disease, and tend to be resistant to conventional corticosteroid treatments. The receptor for advanced glycation endproducts (RAGE) plays a central role in the pathogenesis of eosinophilic asthma, however, it’s role in neutrophilic asthma remains largely uninvestigated.MethodsA mouse model of severe steroid resistant neutrophilic airway disease (SSRNAD) using the common fungal allergen Alternaria alternata (AA) was employed to evaluate the effects of genetic ablation of RAGE and pharmacological inhibition of the NLRP3 inflammasome on neutrophilic airway inflammation.ResultsAA exposure induced robust neutrophil-dominant airway inflammation and increased BALF levels of Th1/Th17 cytokines in wild-type mice, which was significantly reduced in RAGE-/- mice. Serum levels of IgE and IgG1 were increased similarly in both wild-type and RAGE-/- mice. Pharmacological inhibition of NLRP3 blocked the effects of AA exposure and NLRP3 inflammasome activation was RAGE-dependent. Neutrophil extracellular traps were elevated in the BALF of wild-type but not RAGE-/- mice and an atypical population of SiglecF+ neutrophils were identified in the BALF. Lastly, time-course studies found that RAGE expression promoted sustained neutrophil accumulation in the BALF of mice in response to AA.

show abstract

“…Both S100A9 and S100A12 can regulate immune response and in ammation by inducing production of pro-in ammatory cytokines for neutrophil, monocyte and lymphocyte recruitment [30]. It was reported that S100A9 and S100A12 were signi cantly up-regulated and played an important role in pathogenesis of many in ammatory disease, such as rheumatoid, atherosclerosis and in ammatory bowel disease [31][32][33][34][35]. Although it still hasn't been widely applied in clinical diagnosis, the serum levels of S100A8/9 and S100A12 have already been proposed being potential biomarker for sJIA [7,36].…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Diagnostic Biomarkers for Systemic Juvenile Idiopathic Arthritis by Integrative Transcriptomic Analysis

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

Introduction: Currently the diagnostic criteria for systemic juvenile idiopathic arthritis (sJIA) is lack of specificity. Diagnostic biomarkers are needed to be identified to help with the early diagnosis of sJIA and prevent lethal complications like MAS. The aim of this study was to identify potential diagnostic biomarkers of sJIA. Methods A JIA cohort study from Gene Expression Omnibus (GEO) database was adopted to identify hub genes of sJIA comparing to healthy or non-sJIA JIA group by using integrated bioinformatic analysis which combined differentially expressed gene (DEG) analysis, weighted co-expression network analysis (WGCNA) and protein-protein network interaction (PPI) analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen out biomarker genes with most diagnostic potential for sJIA. A prediction model based on the selected genes was constructed and validated in three independent GEO cohort to testify their potency as reliable diagnostic markers to distinguish sJIA patients from healthy population as well as other different types of JIA. Also, CIBERSORT was applied to evaluate the immune cells infiltration and the correlation coefficient between three diagnostic genes and each immune cell subgroup was calculated in the correlation analysis. Results Totally 761 DEGs were acquired by comparing the gene expression profiles in peripheral blood mononuclear cell (PBMC) samples between the sJIA patients and the health controls, the up-regulated genes in sJIA group were mostly enriched in innate immunity and erythrocyte related biological process, while the down-regulated genes were mostly enriched in nature killer cells related biological process. Up to 22 hub genes were identified via combining DEGs with WGCNA and PPI network analysis. All the hub genes were processed to LASSO regression analysis and eventually three genes, 5’-Aminolevulinate Synthase 2 (ALAS2), S100 Calcium Binding Protein A9 (S100A9) and S100 Calcium Binding Protein A12 (S100A12) were screened out as the most potential diagnostic genes. The three genes-based prediction nomogram model was verified and presented good diagnostic performance in all three independent validation datasets. Erythrocyte related gene ALAS2 was with the most significance among all three genes, and specifically higher in sJIA patients comparing with the health controls and other JIA categories. Immune related genes S100A9 and S100A12 also showed significant difference in most conditions, but the difference was less dramatic when comparing with polyarthritis. ALAS2 was also highly expressed in familial hemophagocytic lymphohistiocytosis (FHLH) and systemic lupus erythematosus (SLE), which can develop to MAS and lead to hemophagocytosis. While S100A9 and S100A12 were commonly up-regulated in inflammatory disease. Conclusions ALAS2, S100A9 and S100A12 were highly relevant to sJIA and showed better performance in diagnosis of sJIA when applied comprehensively. ALAS2 may be associated with the predisposition to hemophagocytosis in sJIA, while S100A9 and S100A12 were mainly associated with the hyperinflammation.

show abstract

From bench to bedside: Calprotectin (S100A8/S100A9) as a biomarker in rheumatoid arthritis

Cited by 33 publications

References 151 publications

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

RAGE contributes to allergen driven severe neutrophilic airway inflammation via NLRP3 inflammasome activation in mice

Identification of Potential Diagnostic Biomarkers for Systemic Juvenile Idiopathic Arthritis by Integrative Transcriptomic Analysis

Contact Info

Product

Resources

About