Our ability to anticipate and estimate how pollution affects biota is intrumental in the field of ecotoxicology. Impact of chemical pollution by metals, drugs or pesticides was widely studied in different species using acute and chronic scenarios. Since the ban on tributyltin in antifouling paints, other copper (Cu)-based paints are on the market, including a new generation of booster biocides:metal pyrithiones such as Cu pyrithione (CuPT). Pyrithione acts as a Cu ionophore facilitating Cu transport across the membranes. Although some data show their occurrence in aquatic ecosystems and few studies on the toxicity of CuPT in fish are published, major gaps in knowledge remain about their toxicity and toxic pathway. Few studies were previously conducted in animals exposed to CuPT pointing to reprotoxicity, developmental malformation and mortality (Li et al. 2021, Mochida et al., 2011; Mohamat-Yusuff et al., 2018, Shin et al., 2022). However, its toxicokinetic and toxicodynamic remain to be characterized in details. 

In this context, Bourdon et al. (2023) compared in juveniles of rainbow trout (Oncorhynchus mykiss), the effects of exposure to CuPT and ionic Cu2+ at equivalent Cu2+ molar concentrations. Presented data allow to compare the toxicity threshold, the accumulation of Cu and mechanisms of toxicity of both compounds. Acute and chronic exposures showed a higher bioaccumulation of Cu in the gills, and a higher toxicity of CuPT than that of ionic Cu2+, e.g. mortality , transcription levels of genes related to oxidative stress, detoxification and Cu transport. Intriguingly, the activities of enzymatic biomarkers used as proxy of antioxidant capacity were not significantly altered, although Cu is generally expected to trigger oxidative stress. In conlusion, this study brings new knowledge pointing that the presence of CuPT in the environment could induce toxic effects in non-target species. Moreover, it support the need to study in detail the toxicity of Cu-based paints to adapt regulations concerning their use and release in aquatic environments. Because of its low solubility in water, CuPT is expected to adsorb to suspended matter and food pellets. Future research should study this route of exposure.

References

Bourdon, C., Cachot, J., Gonzalez, P., Couture, P., 2023. Characterization of the bioaccumulation and toxicity of copper pyrithione, an antifouling compound, on juveniles of rainbow trout,  bioRxiv  ver. 3 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.1101/2023.01.31.526498

Li, X., S. Ru, H. Tian, S. Zhang, Z. Lin, M. Gao and J. Wang, 2021. Combined exposure to environmentally relevant copper and 2,2′-dithiobis-pyridine induces significant reproductive toxicity in male guppy (Poecilia reticulata). Science of the Total Environment 797, https://doi.org/10.1016/j.scitotenv.2021.149131

Mochida, K., Amano, H., Onduka, T., Kakuno, A., Fujii, K., 2011. Toxicity and metabolism of copper pyrithione and its degradation product, 2,2’-dipyridyldisulfide in a marine polychaete. Chemosphere 82, 390–397, https://doi.org/10.1016/j.chemosphere.2010.09.074

Mohamat-Yusuff, F., Sarah-Nabila, Ab.G., Zulkifli, S.Z., Azmai, M.N.A., Ibrahim, W.N.W., Yusof, S., Ismail, A., 2018. Acute toxicity test of copper pyrithione on Javanese medaka and the behavioural stress symptoms. Marine Pollution Bulletin 127, 150–153, https://doi.org/10.1016/j.marpolbul.2017.11.046

Shin, D., Y. Choi, Z. Y. Soon, M. Kim, D. J. Kim and J. H. Jung, 2022. Comparative toxicity study of waterborne two booster biocides (CuPT and ZnPT) on embryonic flounder (Paralichthys olivaceus). Ecotoxicology and Environmental Safety 233, https://doi.org/10.1016/j.ecoenv.2022.113337

Our ability to anticipate and estimate how pollution affects components of ecosystems is of paramount importance in the field of ecotoxicology. Dose-response modeling is instrumental, as it allows deriving sensitivity thresholds used at the basis of regulatory risk assessment. In recent years, omics have highly influenced how the impacts of stressors are understood by revealing molecular changes at all levels of biota biological organization (Ebner et al., 2021). To allow analysis of omics data obtained using a typical dose-response design, DRomics a freely available tool for dose-response was proposed composed of both an R package and a free web application (Larras et al. 2018). Advances in this field depend both on theoretical concepts, technology and data integration.

In this context, Delignette-Muller et al. (2023) address the question of how to better integrate omics information in dose-response questions. The paper lists previous possibilities of DRomics and presents new features. It is now able to handle all types of continuous omic and continuous non-omic data (e.g. growth data). This new version proposes new visualization tools, functional annotation and improved modeling workflow for a better robustness of analysis of data with few replicates. New features are meant to help for biological interpretation at the metabolic pathway level, to compare different measurements, biological materials or experimental settings.

References

Delignette-Muller, M. L., A. Siberchicot, F. Larras and E. Billoir (2023), DRomics, a workflow to exploit dose-response omics data in ecotoxicology. bioRxiv, 2023.2002.2009.527852, ver. 4 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.1101/2023.02.09.527852

Ebner JN. (2021) Trends in the Application of "Omics" to Ecotoxicology and Stress Ecology. Genes, 12(10):1481. https://doi.org/10.3390/genes12101481

Larras F, Billoir E, Baillard V, Siberchicot A, Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M and Delignette-Muller ML (2018). DRomics: a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment. Environmental science & technology, 52(24):14461.  https://doi.org/10.1021/acs.est.8b04752

In recent years, the use of environmental DNA (eDNA) to investigate biodiversity has gained considerable interest (Thomsen and Willerslev, 2015; Mauvisseau et al., 2022). It allows for the indirect detection of species but it requires a sound understanding of eDNA behaviour and persistence in the environment. This is, however, a complex task because eDNA may be found in several states (e.g., dissolved, adsorbed, intracellular or intraorganellar), which display specific decay rates controlled by environmental factors (Harrisson et al., 2019; Mauvisseau et al. 2022). In the environment, dissolved DNA may interact with the surfaces of various sorbents, including mineral and organic particles/colloids. Current knowledge on eDNA sorption suggests that eDNA–sorbent interactions are controlled by electrostatics as well as inner-sphere complex formation (Mauvisseau et al., 2022). 

In this context, the work undertaken by Jelavic et al. (2022), focused on the adsorption of eDNA by lesser-investigated carbonaceous materials (CMs), namely soot and charcoal, as common non-mineral environmental surfaces. 

The authors aimed to study the adsorption capacity of soot and charcoal surfaces with respect to eDNA, in relation to solution parameters (i.e., pH, ionic strength, concentration/type of cations), time and eDNA length, under both non‐equilibrium and equilibrium conditions. Using such an approach, Jelavic et al. demonstrated the large adsorption capacities of CMs and the strong binding of DNA to these sorbents. The authors did not provide definitive conclusions on the mechanisms of eDNA sorption onto CMs. However, they provided new elements suggesting that, along with electrostatic interactions, hydrophobic interactions might play an important role in the adsorption of eDNA to CMs such as soot and charcoal. 

Altogether, the results presented in this paper highlight the relevance of CMs as sources of biodiversity information. In addition, it is likely that those results will also prove useful for the community to improve protocols for eDNA extraction from environmental samples that contain high fractions of CMs, e.g. urban soils. 

References

Harrison JB, Sunday JM, Rogers SM (2019) Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proceedings of the Royal Society B: Biological Sciences, 286, 20191409. https://doi.org/10.1098/rspb.2019.1409

Jelavic S, Thygesen LG, Magnin V, Findling N, Müller S, Meklesh V, Sand KK (2022) Soot and charcoal as reservoirs of extracellular DNA. ChemRxiv, ver. 5 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.26434/chemrxiv-2021-9pz8c-v5

Mauvisseau Q, Harper LR, Sander M, Hanner RH, Kleyer H, Deiner K (2022) The Multiple States of Environmental DNA and What Is Known about Their Persistence in Aquatic Environments. Environmental Science & Technology, 56, 5322–5333. https://doi.org/10.1021/acs.est.1c07638

Thomsen PF, Willerslev E (2015) Environmental DNA – An emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation, 183, 4–18. https://doi.org/10.1016/j.biocon.2014.11.019

Today, thousands of chemical substances are released into the environment because of human activities. It is thus crucial to identify all relevant chemicals that contribute to toxic effects on living organisms, also potentially disturbing the community functioning and the ecosystem services that flow from them. Once identified, chemical substances need to be associated with ecotoxicity factors. Nevertheless, getting such factors usually requires time-, resources- and animal-costly experiments that it should be possible to avoid. In this perspective, modelling approaches may be particularly helpful if they rely on easy-to-obtain information to be used as predictive variables.

Within this context, the paper of Servien et al. (2022) illustrates the use of machine learning algorithms to predict toxicity and ecotoxicity factors that were missing for a collection of compounds. Their modelling approach involve a collection of molecular descriptors as input variables. A total of 40 molecular descriptors were extracted from the TyPol database (Servien et al., 2014) as those describing the best how organic compounds behave within the environment. These molecular descriptors also have the advantage to be easily quantifiable for new chemical substances under evaluation. The performances of the proposed models were systematically checked and compared to the classical linear partial least square method, based on the calculation of the absolute error (namely, the difference between prediction and true value). This finally led to different best models (that is associated to the lowest median absolute error) according to the classification of the 526 compounds comprised in the TyPol database in five clusters. These five clusters of different sizes gather chemical substances with different but specific molecular characteristics, also corresponding to different estimates of the characterization factors both in their median and within-variability.

In a final step, predictions of characterization factors were performed for 102 missing values in the USEtox® database (Rosenbaum et al., 2008) but also referenced in TyPol. This paper highlights that the molecular descriptors that explain the most the toxicity of the chemical substances in each cluster strongly differ. Nevertheless, these predictions, whatever the cluster, appear precise enough to be considered as relevant despite everything.

As a conclusion, this paper is a promising proof-of-concept in using machine learning modelling to go beyond some constraints around the toxicity evaluation of chemical substances, especially handling non-linearities and data-demanding calculations, in in an ever-changing world that is gradually depleting its resources without sufficient concern for the short-term risks to the environment and human health.

References

Rosenbaum RK, Bachmann TM, Gold LS, Huijbregts MAJ, Jolliet O, Juraske R, Koehler A, Larsen HF, MacLeod M, Margni M, McKone TE, Payet J, Schuhmacher M, van de Meent D, Hauschild MZ (2008) USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. The International Journal of Life Cycle Assessment, 13, 532. https://doi.org/10.1007/s11367-008-0038-4

Servien R, Latrille E, Patureau D, Hélias A (2022) Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater. bioRxiv, 2021.07.20.453034, ver. 6 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.1101/2021.07.20.453034

Servien R, Mamy L, Li Z, Rossard V, Latrille E, Bessac F, Patureau D, Benoit P (2014) TyPol – A new methodology for organic compounds clustering based on their molecular characteristics and environmental behavior. Chemosphere, 111, 613–622. https://doi.org/10.1016/j.chemosphere.2014.05.020