Ecotoxicogenomics: Bridging the Gap between Genes and Populations

Environ. Sci. Technol. 2010, 44, 9241

Response to Comment on “Ecotoxicogenomics: Bridging the Gap between Genes and Populations” We thank Van Straalen et al. (1) for their critical comments, giving us the opportunity to emphasize once again the subtle nature of our findings (2). Following this criticism, we will argue that there is no a priori difference in the way ecotoxicological responses can be analyzed at the genetic and the individual level and that the conclusions in our paper are formulated in a much more balanced way than asserted by Van Straalen et al. In their introduction, Van Straalen et al. claim that we concluded that “the SSD for gene expression is shifted about four times above the NOEC, therefore, gene expression is a less sensitive end point than the classical sublethal end points, growth and reproduction”. The first part of their statement is correct. However, we did not conclude that gene expression is a less sensitive end point than classical sublethal end points. By contrast, we thoughtfully formulated our conclusion, also in the abstract, as “Currently, use of gene expression changes as early warning indicators of environmental effects is not underpinned due to a lack of data. To confirm the sensitivity claimed by ecotoxicogenomics, more testing at low concentrations is needed.” Therefore, the sensitivity of gene expression as measured so far, is unknown rather than low. Furthermore, Van Straalen et al. disagree with the use of Species Sensitivity Distribution approach (SSD) at the genetic level because “it is not clear at all that the data are speciesspecific traits”. However, for us, there is no reason why acrossspecies test data (at gene level) could not be explored in the same way as classical ecotoxicological end points. They continue by stating that concentrations we used for our analysis can “hardly be labelled as a lowest observed effect level” and suggest that the comparison “with ecotox-SSDs is misleading”. Yet, we clearly described the criteria for data collection for LOECs as “If more than one concentration of cadmium was tested, the lowest concentration with a response deviating significantly from the control was used”. In addition, we stated “Obviously, one cannot exclude that lower concentrations, if tested, would have induced an effect as well”. Within a certain range, there is no problem in combining exposure times and end points into the same SSD (3), as long as the criteria for data-mining are clear. Van Straalen et al. continue by claiming that “gene expression as a dose-specific property is just nonsense” because different genes will respond after hours or weeks of exposure, at low exposuresor“whentheanimalnearlydies”.Thisisperfectlysimilar to what happens at the individual level: some end points represent acuteeffects,othersrepresentchroniceffects.Individual-levelSSDs are also based on data from experiments with different exposure periods and different end points. At a given exposure concentration, for instance, reproduction might respond while growth still continues. Obviously, any kind of standardization of responses reduces variability. As reported in the Supporting Information Table S3 of our paper, we therefore used only results of acute exposure tests for the analysis. Applying more rigorous selection criteria would have increased data paucity too much. Following thesameapproachasinourpaper,LOECsforbiomarkerresponse turned out to be 35-50 times lower than individual level NOECs (4), confirming that the “SSD approach” allows for a meaningful comparison of different levels of biological organization. Van Straalen et al. discuss the difference between gene expressions which are fitness-neutral and gene expressions 10.1021/es103077x

 2010 American Chemical Society

Published on Web 11/02/2010

which are indicative of adverse effects. In our paper, we suggest potential improvements on “a standardization of gene expression assays” by applying a concept of “No Observed Transcriptional Effect Level (NOTEL)”. Yet, we welcome a discussion on other possible alternatives, inter alia, concepts like the “Normal Operating Range” (1), or the “Adverse Outcome Pathways” (5). Again, our explorations suggest that data paucity rather than a lack of concepts is the limiting factor. Van Straalen et al. end their comments stating that “Fedorenkova et al. should have been a little bit more patient and waited until ecogenomicists had solved all these problems, before applying the SSD approach to gene expression.” There appears a discrepancy between “a little bit” and “all these problems”. One may wonder why the gene-population response extrapolation apparently received limited attention so far, given its importance for several applications in environmental management (6, 7). With the large challenges ecotoxicology has to face, we cannot afford information from any level of biological organization to be excluded. Ecotoxicogenomics can play an important role, the more so if the field manages to improve links between gene expression profiles and higher levels of biological organization, develop gene profiling data at environmentally relevant concentrations, and standardize gene expression assays.

Literature Cited (1) Van Straalen, N. M.; Roelofs, D.; Van Gestel, C. A. M.; De Boer, T. E. Fitness-neutral gene expression and the importance of defining a normal operating range. Environ. Sci. Technol. 2010, 44, DOI10.1021/es102651z. (2) Fedorenkova, A.; Vonk, J. A.; Lenders, H. J. R.; Ouborg, N. J.; Breure, A. M.; Hendriks, A. J. Ecotoxicogenomics: Bridging the gap between genes and populations. Environ. Sci. Technol. 2010, 44, 4328–4333. (3) Posthuma, L., Suter, G. W., II, Traas, T. P., Eds. Species Sensitivity Distributions in Ecotoxicology; Lewis Publishers: Boca Raton, FL, 2002. (4) Smit, M. G. D.; Bechmann, R. K.; Hendriks, A. J.; Skadsheim, A.; Larsen, B. K.; Baussant, T.; Bamber, S.; Sanni, S. Relating biomarkers to whole-organism effects using species sensitivity distributions: A pilot study for marine species exposed to oil. Environ. Toxicol. Chem. 2009, 28, 1104–1109. (5) Ankley, G. T.; Bennett, R. S.; Erickson, R. J.; Hoff, D. J.; Hornung, M. W.; Johnson, R. D.; Mount, D. R.; Nichols, J. W.; Russom, C. L.; Schmieder, P. K.; Serrano, J. A.; Tietge, J. E.; Villeneuve, D. L. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 2010, 29, 730–741. (6) Nuwaysir, E. F.; Bittner, M.; Trent, J.; Barrett, J. C.; Afshari, C. A. Microarrays and toxicology: The advent of toxicogenomics. Mol. Carcinogen. 1999, 24, 153–159. (7) Van Straalen, N. M.; Roelofs, T. F. M. An Introduction to Ecological Genomics; Oxford University Press: Oxford, U.K., 2006.

Anastasia Fedorenkova,*,† J. Arie Vonk,†,‡ H. J. Rob Lenders,† N. Joop Ouborg,§ Anton M. Breure,†,‡ and A. Jan Hendriks† Department of Environmental Science, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands, RIVM, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands, and Molecular Ecology, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands * Corresponding author e-mail: [email protected]. † Department of Environmental Science, Radboud University. ‡ RIVM. § Molecular Ecology, Radboud University.

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