You are here:
Identification of Differentially Expressed Genes (DEGs) and Networks Related to Hepatic Lipid Dysfunction
Citation:
Abedini, J. AND H. El-Masri. Identification of Differentially Expressed Genes (DEGs) and Networks Related to Hepatic Lipid Dysfunction. Society of Toxicology, Baltimore, Maryland, March 10 - 14, 2019.
Impact/Purpose:
The poster presentation highlights the application of bioinformatic methods to identify key events and biochemical networks using publicly available databases. These key events can be part of an overall mode of action or adverse outcome pathway relating chemical exposure of environmental chemicals to pathological events.
Description:
The Adverse Outcome Pathway (AOP) is a conceptual framework that links a molecular initiating event, through a series of biological level key events, to an adverse outcome relevant to risk assessment. To identify key events related to hepatic lipid dysfunction we identified differentially expressed genes (DEGs) produced by chemical exposure. Pathway enrichment analysis was used to identify possible key events from the DEGs leading this adverse outcome. DrugMatrix is a database that identifies DEGs and pathological changes produced by 637 chemicals (mostly drugs) from standardized rodent chemical studies. 13 chemicals produced hepatic lipid dysfunction pathologies (p-value ≤ 0.05 and severity score greater than one). Raw microarray data collected from these exposures were further analyzed to generate a differential gene expression data set associated with hepatic lipid dysfunction. Further analysis of this gene expression set resulted in 224 upregulated genes and 175 genes that were down regulated. Meanwhile, 33 genes were both up regulated and down regulated. All identified genes were uploaded in Database for Annotation, Visualization and Integrated Discovery for gene ontology enrichments and to identify Kyoto Encyclopedia of Genes and Genome pathways. The identified pathways included steroid hormone biosynthesis, retinol metabolism, and metabolism of xenobiotics by cytochrome P450. This gene set was also analyzed using Ingenuity Pathway Analysis (IPA) software. IPA identified acetone degradation I, xenobiotic metabolism signaling, cholesterol biosynthesis, retinol biosynthesis, p53 signaling and RXR activation pathways. All identified pathways were then visualized in Cytoscape to create a network of biochemical events related to hepatic lipid dysfunction. The enrichment and clarification of the pathways and networks from the DEGs related to hepatic lipid dysfunction provide knowledge that can be used to understand the underlying key events and molecular mechanism used to develop putative AOPs for hepatic lipid dysfunction. This abstract does not necessarily reflect U.S. EPA policy.