A targeted transcriptomics approach for the determination of mixture effects of pesticides.

While real-life exposure occurs to complex chemical mixtures, toxicological risk assessment mostly focuses on individual compounds. There is an increasing demand for in vitro tools and strategies for mixture toxicity analysis. Based on a previously established set of hepatotoxicity marker genes, we analyzed mixture effects of non-cytotoxic concentrations of different pesticides in exposure-relevant binary mixtures in human HepaRG hepatocarcinoma cells using targeted transcriptomics. An approach for mixture analysis at the level of a complex endpoint such as a transcript pattern is presented, including mixture design based on relative transcriptomic potencies and similarities. From a mechanistic point of view, goal of the study was to evaluate combinations of chemicals with varying degrees of similarity in order to determine whether differences in mechanisms of action lead to different mixtures effects. Using a model deviation ratio-based approach for assessing mixture effects, it was revealed that most data points are consistent with the assumption of dose addition. A tendency for synergistic effects was only observed at high concentrations of some combinations of the test compounds azoxystrobin, cyproconazole, difenoconazole, propiconazole and thiacloprid, which may not be representative of human real-life exposure. In summary, the findings of our study suggest that, for the pesticide mixtures investigated, risk assessment based on the general assumption of dose addition can be considered sufficiently protective for consumers. The way of data analysis presented in this paper can pave the way for a more comprehensive use of multi-gene expression data in experimental studies related to mixture toxicity.

RNA-protein correlation of liver toxicity markers in HepaRG cells.

The liver is a main target organ for the toxicity of many different compounds. While in general, in vivo testing is still routinely used for assessing the hepatotoxic potential of test chemicals, the use of in vitro models offers advantages with regard to throughput, consumption of resources, and animal welfare aspects. Using the human hepatoma cell line HepaRG, we performed a comparative evaluation of a panel of hepatotoxicity marker mRNAs and proteins after exposure of the cells to 30 different pesticidal active compounds comprising herbizides, fungicides, insecticides, and others. The panel of hepatotoxicity markers included nuclear receptor target genes, key players of fatty acid and bile acid metabolism-related pathways, as well as recently identified biomarkers of drug-induced liver injury. Moreover, marker genes and proteins were identified, for example, S100P, ANXA10, CYP1A1, and CYP7A1. These markers respond with high sensitivity to stimulation with chemically diverse test compounds already at non-cytotoxic concentrations. The potency of the test compounds, determined as an overall parameter of their ability to deregulate marker expression in vitro, was very similar between the mRNA and protein levels. Thus, this study does not only characterize the response of human liver cells to 30 different pesticides but also demonstrates that hepatotoxicity testing in human HepaRG cells yields well comparable results at the mRNA and protein levels. Furthermore, robust hepatotoxicity marker genes and proteins were identified in HepaRG cells.

Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.

Liquid chromatography coupled to tandem mass spectrometry in combination with stable-isotope labeling is an established and widely spread method to measure gene expression on the protein level. However, it is often not considered that two opposing processes are responsible for the amount of a protein in a cell--the synthesis as well as the degradation. With this work, we provide an integrative, high-throughput method--from the experimental setup to the bioinformatics analysis--to measure synthesis and degradation rates of an organism's proteome. Applicability of the approach is demonstrated with an investigation of heat shock response, a well-understood regulatory mechanism in bacteria, on the biotechnologically relevant Corynebacterium glutamicum. Utilizing a multilabeling approach using both heavy stable nitrogen as well as carbon isotopes cells are metabolically labeled in a pulse-chase experiment to trace the labels' incorporation in newly synthesized proteins and its loss during protein degradation. Our work aims not only at the calculation of protein turnover rates but also at their statistical evaluation, including variance and hierarchical cluster analysis using the rich internet application QuPE.