3 years ago

Using time-lapse omics correlations to integrate toxicological pathways of a formulated fungicide in a soil invertebrate

Tiago Simões, Sara C. Novais, Tiago Natal-da-luz, Bart Devreese, Tjalf De Boer, Dick Roelofs, José P. Sousa, Nico M. Van Straalen, Marco F.l. Lemos

Publication date: March 2019

Source: Environmental Pollution, Volume 246

Author(s): Tiago Simões, Sara C. Novais, Tiago Natal-da-Luz, Bart Devreese, Tjalf de Boer, Dick Roelofs, José P. Sousa, Nico M. van Straalen, Marco F.L. Lemos


The use of an integrative molecular approach can actively improve the evaluation of environmental health status and impact of chemicals, providing the knowledge to develop sentinel tools that can be integrated in risk assessment studies, since gene and protein expressions represent the first response barriers to anthropogenic stress.

This work aimed to determine the mechanisms of toxic action of a widely applied fungicide formulation (chlorothalonil), following a time series approach and using a soil model arthropod, Folsomia candida. To link effects at different levels of biological organization, data were collected on reproduction, gene expression and protein levels, in a time series during exposure to a natural soil.

Results showed a mechanistic mode of action for chlorothalonil, affecting pathways of detoxification and excretion, immune response, cellular respiration, protein metabolism and oxidative stress defense, causing irregular cell signaling (JNK and NOD ½ pathways), DNA damage and abnormal cell proliferation, leading to impairment in developmental features such as molting cycle and reproduction. The omics datasets presented highly significant positive correlations between the gene expression levels at a certain time-point and the corresponding protein products 2–3 days later. The integrated omics in this study has provided useful insights into pesticide mechanisms of toxicity, evidencing the relevance of such analyses in toxicological studies, and highlighting the importance of considering a time-series when integrating these datasets.

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