Detection of Dichlorvos Adducts in a Hepatocyte Cell Line

The toxicity of dichlorvos (DDVP), an organophosphate (OP) pesticide, classically results from modification of the serine in the active sites of cholinesterases. However, DDVP also forms adducts on unrelated targets such as transferrin and albumin, suggesting that DDVP could cause perturbations in cellular processes by modifying noncholinesterase targets. Here we identify novel DDVP-modified targets in lysed human hepatocyte-like cells (HepaRG) using a direct liquid chromatography–mass spectrometry (LC–MS) assay of cell lysates incubated with DDVP or using a competitive pull-down experiments with a biotin-linked organophosphorus compound (10-fluoroethoxyphosphinyl-<i>N</i>-biotinamidopentyldecanamide; FP-biotin), which competes with DDVP for similar binding sites. We show that DDVP forms adducts to several proteins important for the cellular metabolic pathways and differentiation, including glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and actin. We validated the results using purified proteins and enzymatic assays. The study not only identified novel DDVP-modified targets but also suggested that the modification directly inhibits the enzymes. The current approach provides information for future hypothesis-based studies to understand the underlying mechanism of toxicity of DDVP in non-neuronal tissues. The MS data have been deposited to the ProteomeXchange with identifier PXD001107

Exposure to Cobalt Causes Transcriptomic and Proteomic Changes in Two Rat Liver Derived Cell Lines

<div><p>Cobalt is a transition group metal present in trace amounts in the human diet, but in larger doses it can be acutely toxic or cause adverse health effects in chronic exposures. Its use in many industrial processes and alloys worldwide presents opportunities for occupational exposures, including military personnel. While the toxic effects of cobalt have been widely studied, the exact mechanisms of toxicity remain unclear. In order to further elucidate these mechanisms and identify potential biomarkers of exposure or effect, we exposed two rat liver-derived cell lines, H4-II-E-C3 and MH1C1, to two concentrations of cobalt chloride. We examined changes in gene expression using DNA microarrays in both cell lines and examined changes in cytoplasmic protein abundance in MH1C1 cells using mass spectrometry. We chose to closely examine differentially expressed genes and proteins changing in abundance in both cell lines in order to remove cell line specific effects. We identified enriched pathways, networks, and biological functions using commercial bioinformatic tools and manual annotation. Many of the genes, proteins, and pathways modulated by exposure to cobalt appear to be due to an induction of a hypoxic-like response and oxidative stress. Genes that may be differentially expressed due to a hypoxic-like response are involved in Hif-1α signaling, glycolysis, gluconeogenesis, and other energy metabolism related processes. Gene expression changes linked to oxidative stress are also known to be involved in the NRF2-mediated response, protein degradation, and glutathione production. Using microarray and mass spectrometry analysis, we were able to identify modulated genes and proteins, further elucidate the mechanisms of toxicity of cobalt, and identify biomarkers of exposure and effect <i>in vitro</i>, thus providing targets for focused <i>in vivo</i> studies.</p></div

Enriched Pathways.

<p>Enriched IPA canonical pathways are listed for the transcriptomic, proteomic, and combined data. We considered a pathway to be enriched at a p<.05 and contain more than 2 changing molecules.</p

Modulated Extracellular Transcripts and Proteins.

<p>We identified 26 extracellular proteins and/or genes which encode extracellular protein whose expression was modulated in response to cobalt exposure. We propose them as candidate biomarkers of exposure or effect. We focused on extracellular proteins as they have the best potential to be identified through non-invasive methods.</p

Oral Toxicity Database.

<p>An extensive database was created to access and prioritize the oral hazard of industrial chemicals based on the toxicity, stability, and usage of the chemical. This prioritization was limited to only pure elements and metal compounds.</p

Network of modulated genes and proteins related to HIF-1α.

<p>Many of the differentially expressed genes and/or proteins changing in abundance are regulated by HIF-1α. The arrows show the direction of the relationship, the color indicates the direction of change of the gene or protein with red being an increase and green being a decrease, and the intensity of the color indicating the degree of change.</p

Gel electrophoresis of extracted BALF obtained at 24 hr posttreatment from a control rat (PBS instilled) and a rat exposed intratracheally to 50 mg of DEP/kg body weight

<p><b>Copyright information:</b></p><p>Taken from "Proteomic Analysis of Bronchoalveolar Lavage Fluid: Effect of Acute Exposure to Diesel Exhaust Particles in Rats"</p><p></p><p>Environmental Health Perspectives 2007;115(5):756-763.</p><p>Published online 5 Feb 2007</p><p>PMCID:PMC1867966.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p> The four numbered bands in the diesel lane were excised and analyzed by LC/MS

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

<div><p>Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.</p></div

Mapping toxicity pathways of liver fibrosis using integrated gene expression and protein-protein interaction network analysis

<p>Liver fibrosis is a common pathologic feature observed in a wide spectrum of liver injuries. Identifying toxicity pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential biomarkers. Towards this end, we analyzed DrugMatrix, a toxicogenomics database with gene expression data from rats after exposure to diverse chemicals and drugs. We used differential expression and co-expression analyses to identify liver fibrosis-relevant genes. These genes were then mapped to protein-protein interaction (PPI) networks to identify network modules associated with liver fibrosis. We identified a network module that was enriched with known liver fibrosis genes such as Timp1 and Lgals3. The network module also supports the published disease mechanism and identified potential new genes associated with liver fibrosis. Using our network analysis we were able to link compounds such as carbon tetrachloride to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. The gene expression pattern of genes in the network module was in agreement with external liver fibrosis data from Gene Expression Omnibus (GEO). These results demonstrated that our integrated gene expression and PPI network analysis approach has the potential to aid in predictive toxicity, biomarker identification, and to identify toxicity pathways. </p

Number of fibrosis-relevant genes from differential and co-expression analysis.

<p>Number of genes in the liver fibrosis-relevant differentially expressed gene list and liver fibrosis-relevant co-expressed gene list and the overlap between them.</p