Corticosteroid inhibits chemokines production in systemic sclerosis patients

Dantas, Andréa Tavares; Almeida, Anderson Rodrigues de; Sampaio, Maria Clara Pinheiro Duarte; Cordeiro, Marina Ferraz; Rocha, Laurindo Ferreira da; Oliveira, Priscilla Stela Santana de; Pereira, Michelly Cristiny; Rêgo, Moacyr Jesus Barreto de Melo; Marques, Cláudia Diniz Lopes; Pitta, Ivan da Rocha; Duarte, Ângela Luzia Branco Pinto; Pitta, Maíra Galdino da Rocha

doi:10.1016/j.steroids.2017.08.012

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…In several studies, the relationship between PPARγ and TGF-β was confirmed by experiments using PPARγ ligands, and the PPARγ agonists inhibit the ability of TGF-β1 to induce myofibroblast differentiation and collagen secretion. 10,12,25,28,29 For the quantitative relationship, there were studies of PPARγ ligand decreased intrinsic expression of TGF-β1 and TGF-β- induced phosphorylation of Akt and α-SMA and fibronectin expression as a marker of myofibroblast differentiation, in a dose-dependent manner. 10,12,28 KE1 (Activation of TGF-β Signaling) and KER with KE2 (Increase, Inflammation).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach

Jeong

García-Reyero

Burgoon

et al. 2019

Chem. Res. Toxicol.

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Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated by transforming growth factor β (TGF-β) and peroxisome proliferator-activated receptor gamma (PPARγ). Here, we developed an adverse outcome pathway (AOP) to better define the linkage of PPARγ antagonism to the adverse outcome of pulmonary fibrosis. We then conducted a systematic analysis to identify potential chemicals involved in this AOP, using the ToxCast database and deep learning artificial neural network models. We identified chemicals bearing a potential inhalation hazard and exposure hazards from the database that could be related to this AOP. For chemicals that were not present in the ToxCast database, multilayer perceptron models were developed based on the ToxCast assays related to the AOP. The reactivity of ToxCast untested chemicals was then predicted using these deep learning models. Both approaches identified a set of chemicals that could be used to validate the AOP. This study suggests that chemicals categorized using an existing database such as ToxCast can be used to validate an AOP and that deep learning approaches can be used to characterize a range of potential active chemicals for an AOP of interest.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach

Jeong

García-Reyero

Burgoon

et al. 2019

Chem. Res. Toxicol.

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Statins Inhibit Cytokines in a Dose-Dependent Response in Patients with Systemic Sclerosis

et al. 2018

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Genetic Programs Between Steroid-Sensitive and Steroid-Insensitive Interstitial Lung Disease

Tang

Wang

et al. 2023

Inflammation

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The effectiveness of corticosteroids (GCs) varies greatly in interstitial lung diseases (ILDs). In this study, we aimed to compare the gene expression profiles of patients with cryptogenic organizing pneumonia (COP), idiopathic pulmonary fibrosis (IPF), and non-specific interstitial pneumonia (NSIP) and identify the molecules and pathways responsible for GCs sensitivity in ILDs. Three datasets (GSE21411, GSE47460, and GSE32537) were selected. Differentially expressed genes (DEGs) among COP, IPF, NSIP, and healthy control (CTRL) groups were identified. Functional enrichment analysis and protein–protein interaction network analysis were performed to examine the potential functions of DEGs. There were 128 DEGs when COP versus CTRL, 257 DEGs when IPF versus CTRL, 205 DEGs when NSIP versus CTRL, and 270 DEGs when COP versus IPF. The DEGs in different ILDs groups were mainly enriched in the inflammatory response. Further pathway analysis showed that “interleukin (IL)-17 signaling pathway” (hsa04657) and “tumor necrosis factor (TNF) signaling pathway” were associated with different types of ILDs. A total of 10 genes associated with inflammatory response were identified as hub genes and their expression levels in the IPF group were higher than those in the COP group. Finally, we identified two GCs’ response-related differently expressed genes (FOSL1 and DDIT4). Our bioinformatics analysis demonstrated that the inflammatory response played a pathogenic role in the progression of ILDs. We also illustrated that the inflammatory reaction was more severe in the IPF group compared to the COP group and identified two GCs’ response-related differently expressed genes (FOSL1 and DDIT4) in ILDs.

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Corticosteroid inhibits chemokines production in systemic sclerosis patients

Cited by 8 publications

References 39 publications

Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach

Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach

Statins Inhibit Cytokines in a Dose-Dependent Response in Patients with Systemic Sclerosis

Genetic Programs Between Steroid-Sensitive and Steroid-Insensitive Interstitial Lung Disease

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