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Comparative analysis of transcriptomic responses to sub-lethal levels of six environmentally relevant pesticides in Saccharomyces cerevisiae

Gil, Fátima N., Gonçalves, Alina C., Becker, Jörg D., Viegas, Cristina A.
Ecotoxicology 2018 v.27 no.7 pp. 871-889
Saccharomyces cerevisiae, active ingredients, adverse effects, alachlor, carbofuran, data collection, diuron, ecosystems, eukaryotic cells, genes, insecticides, mechanism of action, metolachlor, microarray technology, models, pollution, prediction, pyrimethanil, reverse transcriptase polymerase chain reaction, risk, toxicity, transcription (genetics), transcriptomics, yeasts, Europe, North America
Accidental spills and misuse of pesticides may lead to current and/or legacy environmental contamination and may pose concerns regarding possible risks towards non-target microbes and higher eukaryotes in ecosystems. The present study was aimed at comparing transcriptomic responses to effects of sub-lethal levels of six environmentally relevant pesticide active substances in the Saccharomyces cerevisiae eukaryotic model. The insecticide carbofuran, the fungicide pyrimethanil and the herbicides alachlor, S-metolachlor, diuron and methyl(4-chloro-2-methylphenoxy)acetate were studied. Some are currently used agricultural pesticides, while others are under restricted utilization or banned in Europe and/or North America albeit being used in other geographical locations. In the present work transcriptional profiles representing genome-wide responses in a standardized yeast population upon 2 h of exposure to concentrations of each compound exerting equivalent toxic effects, i.e., inhibition of growth by 20% relative to the untreated control cells, were examined. Hierarchical clustering and Venn analyses of the datasets of differentially expressed genes pointed out transcriptional patterns distinguishable between the six active substances. Functional enrichment analyses allowed predicting mechanisms of pesticide toxicity and response to pesticide stress in the yeast model. In general, variations in transcript numbers of selected genes assessed by Real-Time quantitative reverse transcription polymerase chain reaction confirmed microarray data and correlated well with growth inhibitory effects. A possible biological relevance of mechanistic predictions arising from these comparative transcriptomic analyses is discussed in the context of better understanding potential modes of action and adverse side-effects of pesticides.