Monocyte chemotactic protein (MCP)-1 (CCL2) and its receptor (CCR2) are elevated in chronic heart failure facilitating lung monocyte infiltration and differentiation which may contribute to lung fibrosis

Puukila, Stephanie; Lawrence, Mark; Pasquale, C. De; Bersten, Andrew D.; Bihari, Shailesh; McEvoy-May, James; Nemec-Bakk, Ashley; Dixon, Dani‐Louise

doi:10.1016/j.cyto.2022.156060

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…On the other hand, some studies have shown an association between interstitial lung involvement and levels of MCP-1/CCL2 in patients with systemic sclerosis [ 35 ]. A recent study suggested that MCP-1/CCL2 was a predominant source for the replenishment of lung macrophages during lung remodeling [ 36 ]. Elsewhere, lung macrophages induced by MCP-1/CCL2 in patients with COVID-19 and lung involvement have been shown to share a transcriptional phenotype with macrophages stimulated by TNF alpha and IFN gamma [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Inflammatory Biomarkers in the Diagnosis and Prognosis of Rheumatoid Arthritis–Associated Interstitial Lung Disease

Mena-Vázquez

Godoy-Navarrete

Lisbona-Montañez

et al. 2023

IJMS

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Objectives: to identify inflammatory factors and soluble cytokines that act as biomarkers in the diagnosis and prognosis of rheumatoid arthritis-associated interstitial lung disease (RA-ILD). Methods: we performed a nested prospective observational case–control study of patients with RA-ILD matched by sex, age, and time since the diagnosis of RA. All participants underwent pulmonary function testing and high-resolution computed tomography. ILD was defined according to the criteria of the American Thoracic Society/European Respiratory Society; the progression of lung disease was defined as the worsening of FVC > 10% or DLCO > 15%. Inflammation-related variables included the inflammatory activity measured using the DAS28-ESR and a multiplex cytokine assay. Two Cox regression models were run to identify factors associated with ILD and the progression of ILD. Results: the study population comprised 70 patients: 35 patients with RA-ILD (cases) and 35 RA patients without ILD (controls). A greater percentage of cases had higher DAS28-ESR (p = 0.032) and HAQ values (p = 0.003). The variables associated with RA-ILD in the Cox regression analysis were disease activity (DAS28) (HR [95% CI], 2.47 [1.17–5.22]; p = 0.017) and high levels of ACPA (HR [95% CI], 2.90 [1.24–6.78]; p = 0.014), IL-18 in pg/mL (HR [95% CI], 1.06 [1.00–1.12]; p = 0.044), MCP-1/CCL2 in pg/mL (HR [95% CI], 1.03 [1.00–1.06]; p = 0.049), and SDF-1 in pg/mL (HR [95% CI], 1.00 [1.00–1.00]; p = 0.010). The only variable associated with the progression of ILD was IL-18 in pg/mL (HR [95% CI], 1.25 [1.07–1.46]; p = 0.004). Conclusion: inflammatory activity was higher in patients with RA-ILD than RA patients without ILD. Some cytokines were associated with both diagnosis and poorer prognosis in patients with RA-ILD.

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Section: Discussionmentioning

confidence: 99%

Inflammatory Biomarkers in the Diagnosis and Prognosis of Rheumatoid Arthritis–Associated Interstitial Lung Disease

Mena-Vázquez

Godoy-Navarrete

Lisbona-Montañez

et al. 2023

IJMS

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“…Extensive research has shown current therapeutic approaches in IPF included anti-inflammatory drugs, cytotoxic and immunosuppressive agents, immunomodulators, antifibrotic agents, and antioxidants targeting several crucial signaling pathways that predominantly regulate the inflammation.Genetic, aging, and environmental factors are thought to be the contributing factors to IPF [4]. However, IPF might also can be caused by many unknown causes, such as pulmonary hypertension [5], lung cancer [6], diabetes mellitus [7], dermatomyositis [8],polymyositis [9], systemic sclerosis [10],mixed connective tissue disease [11], systemic lupus erythematosus [12], rheumatoid arthritis [13],sarcoidosis [14], scleroderma [15], pneumonia [16], heart failure [17], obesity [18], viral respiratory diseases [19], gastroesophageal reflux disease [20], chronic obstructive pulmonary disease [21], and airway inflammation [22], which cannot be well solved by current drug treatment and IPF is still a complicated incurable pulmonary disease. Thus, it is necessary for us to utilize bioinformatics and next-generation sequencing (NGS) technology to explore the molecular pathogenesis or potential treatments of IPF.…”

Section: Introductionmentioning

confidence: 99%

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Giriyappagoudar,

Vastrad,

Horakeri

et al. 2023

Biomedicines

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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), and 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 analyses 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 databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate 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. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. 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.

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“…It is characterized by clinical symptoms of cough and dyspnea, declining pulmonary function with impaired gas exchange, and progressive lung scarring [3]. IPF increases the complications of developing pulmonary hypertension [4], lung cancer [5], diabetes mellitus [6], dermatomyositis [7], polymyositis [8], systemic sclerosis [9], mixed connective tissue disease [10], systemic lupus erythematosus [11], rheumatoid arthritis [12], sarcoidosis [13], scleroderma [14], pneumonia [15], heart failure [16], obesity [17], viral respiratory diseases [18], gastroesophageal reflux disease [19], chronic obstructive pulmonary disease [20] and airway inflammation [21]. Genetic, aging and environmental factors are thought to be the contributing factors to IPF [22].…”

Section: Introductionmentioning

confidence: 99%

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

Vastrad,

Vastrad

2023

Preprint

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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.

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Monocyte chemotactic protein (MCP)-1 (CCL2) and its receptor (CCR2) are elevated in chronic heart failure facilitating lung monocyte infiltration and differentiation which may contribute to lung fibrosis

Cited by 10 publications

References 38 publications

Inflammatory Biomarkers in the Diagnosis and Prognosis of Rheumatoid Arthritis–Associated Interstitial Lung Disease

Inflammatory Biomarkers in the Diagnosis and Prognosis of Rheumatoid Arthritis–Associated Interstitial Lung Disease

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

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