Dataset Information

Identification of Environmental Chemicals Associated with the Development of Toxicant-associated Fatty Liver Disease in Rodents.

ABSTRACT: Toxicant-associated fatty liver disease (TAFLD) is a recently identified form of nonalcoholic fatty liver disease (NAFLD) associated with exposure to industrial chemicals and environmental pollutants. Numerous studies have been conducted to test the association between industrial chemicals/environmental pollutants and fatty liver disease both in vivo and in vitro.The objective of the article is to report a list of chemicals associated with TAFLD.Two federal databases of rodent toxicology studies-Toxicological Reference Database (ToxRefDB; Environmental Protection Agency) and Chemical Effects in Biological Systems (CEBS, National Toxicology Program)-were searched for liver end points. Combined, these 2 databases archive nearly 2,000 rodent studies. Toxicant-associated steatohepatitis (TASH) descriptors including fatty change, fatty necrosis, Oil red O-positive staining, steatosis, and lipid deposition were queried.Using these search terms, 123 chemicals associated with fatty liver were identified. Pesticides and solvents were the most frequently identified chemicals, while polychlorinated biphenyls (PCBs)/dioxins were the most potent. About 44% of identified compounds were pesticides or their intermediates, and >10% of pesticide registration studies in ToxRefDB were associated with fatty liver. Fungicides and herbicides were more frequently associated with fatty liver than insecticides.More research on pesticides, solvents, metals, and PCBs/dioxins in NAFLD/TAFLD is warranted due to their association with liver damage.

SUBMITTER: Al-Eryani L

PROVIDER: S-EPMC4501484 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Identification of Environmental Chemicals Associated with the Development of Toxicant-associated Fatty Liver Disease in Rodents.

Al-Eryani Laila L Wahlang Banrida B Falkner K C KC Guardiola J J JJ Clair H B HB Prough R A RA Cave Matt M

Toxicologic pathology 20141016 4

<h4>Background</h4>Toxicant-associated fatty liver disease (TAFLD) is a recently identified form of nonalcoholic fatty liver disease (NAFLD) associated with exposure to industrial chemicals and environmental pollutants. Numerous studies have been conducted to test the association between industrial chemicals/environmental pollutants and fatty liver disease both in vivo and in vitro.<h4>Objectives</h4>The objective of the article is to report a list of chemicals associated with TAFLD.<h4>Methods< ...[more]

PMID: 25326588

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Project description:BackgroundNonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients.MethodsGSE48452, GSE63067, and GSE89632 datasets including nonalcoholic steatohepatitis (NASH) and healthy controls (HC) analyzed in this study were retrieved from the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) between NASH and HC samples were obtained. Next, metabolism-related DEGs (MR-DEGs) were identified by overlapping DEGs and metabolism-related genes (MRG). Further, a protein-protein interaction (PPI) network was developed to show the interaction among MR-DEGs. Subsequently, the "Least absolute shrinkage and selection operator regression" and "Random Forest" algorithms were used to screen metabolism-related genes (MRGs) in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses (GSEA) were performed on these metabolism-related genes. Finally, the expression of metabolism-related gene was determined at the transcription level.ResultsFirst, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1, were identified using machine learning algorithms. Moreover, a regulatory network consisting of four genes, 25 microRNAs (miRNAs), and 41 transcription factors (TFs) was constructed. Finally, a significant increase in FMO1 and LPL expression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the HC group.ConclusionMetabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD.

Project description:Objective: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disease in the world, and its pathogenesis is not fully understood. Disulfidptosis is the most recently reported form of cell death and may be associated with NAFLD progression. Our study aimed to explore the molecular clusters associated with disulfidptosis in NAFLD and to construct a predictive model. Methods: First, we analyzed the expression profile of the disulfidptosis regulators and immune characteristics in NAFLD. Using 104 NAFLD samples, we investigated molecular clusters based on differentially expressed disulfidptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were then identified by using the WGCNA method. We also evaluated the performance of four machine learning models before choosing the optimal machine model for diagnosis. Nomogram, calibration curves, decision curve analysis, and external datasets were used to confirm the prediction effectiveness. Finally, the expression levels of the biomarkers were assessed in a mouse model of a high-fat diet. Results: Two differentially expressed DRGs were identified between healthy and NAFLD patients. We revealed the expression profile of DRGs in NAFLD and the correlation with 22 immune cells. In NAFLD, two clusters of molecules connected to disulfidptosis were defined. Significant immunological heterogeneity was shown by immune infiltration analysis among the various clusters. A significant amount of immunological infiltration was seen in Cluster 1. Functional analysis revealed that Cluster 1 differentially expressed genes were strongly linked to energy metabolism and immune control. The highest discriminatory performance was demonstrated by the SVM model, which had a higher area under the curve, relatively small residual and root mean square errors. Nomograms, calibration curves, and decision curve analyses were used to show how accurate the prediction of NAFLD was. Further analysis revealed that the expression of three model-related genes was significantly associated with the level of multiple immune cells. In animal experiments, the expression trends of DDO, FRK and TMEM19 were consistent with the results of bioinformatics analysis. Conclusion: This study systematically elucidated the complex relationship between disulfidptosis and NAFLD and developed a promising predictive model to assess the risk of disease in patients with disulfidptosis subtypes and NAFLD.

Dataset Information

Identification of Environmental Chemicals Associated with the Development of Toxicant-associated Fatty Liver Disease in Rodents.

Publications

Identification of Environmental Chemicals Associated with the Development of Toxicant-associated Fatty Liver Disease in Rodents.

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