Defining an additivity framework for mixture research in inducible whole-cell biosensors

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Martin Betancor, K ^a   Ritz, C ^b   Fernandez Pinas F ^a   Leganes F ^a   Rodea Palomares, I ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

CELLS; GENETIC PROCEDURES; METABOLISM; PROCEDURES; REGRESSION ANALYSIS; REPRODUCIBILITY; RESEARCH; SYNECHOCOCCUS; THEORETICAL MODEL;

BIOSENSING TECHNIQUES; CELLS; MODELS, THEORETICAL; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; RESEARCH; SYNECHOCOCCUS;

EID: 84948422646     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep17200     Document Type: Article

References (32)

1. Altenburger, R. et al. Future water quality monitoring-adapting tools to deal with mixtures of pollutants in water resource management. Sci Total Environ 512-513, 540-551, doi: 10.1016/j.scitotenv.2014.12.057 (2015).
2. Kortenkamp, A., Backhaus, T. & Faust, M. State of the art report on mixture toxicity. Final Report to the European Commission under Contract Number 070307/2007/485103/ETU/D.1., (European Commission, Brussels, Belgium, 2009).
3. Rodea-Palomares, I. et al. Application of the combination index (CI)-isobologram equation to study the toxicological interactions of lipid regulators in two aquatic bioluminescent organisms. Water Res 44, 427-438, doi: 10.1016/j.watres.2009.07.026 (2010).
4. Chou, T. C. Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev 58, 621-681, doi: 10.1124/pr.58.3.10 (2006).
5. Scholze, M., Silva, E. & Kortenkamp, A. Extending the applicability of the dose addition model to the assessment of chemical mixtures of partial agonists by using a novel toxic unit extrapolation method. PloS one 9, e88808, doi: 10.1371/journal. pone.0088808 (2014).
6. Loewe, S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3, 285-290 (1953).
7. Bliss, C. I. The toxicity of poisons applied jointly. Ann Appl Biol 26, 585-615, doi: 10.1111/j.1744-7348.1939.tb06990.x (1939).
8. Greco, W. R., Bravo, G. & Parsons, J. C. The search for synergy: a critical review from a response surface perspective. Pharmacol Rev 47, 331-385 (1995).
9. Chou, T. C. & Talalay, P. Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv Enzyme Regul 22, 27-55 (1984).
10. Calabrese, E. J. Hormesis: why it is important to toxicology and toxicologists. Environ Toxicol Chem 27, 1451-1474, doi: 10.1897/07-541 (2008).
11. Hecker, M. et al. Human adrenocarcinoma (H295R) cells for rapid in vitro determination of effects on steroidogenesis: hormone production. Toxicol Appl Pharmacol 217, 114-124, doi: 10.1016/j.taap.2006.07.007 (2006).
12. McMahon, T. A. et al. The fungicide chlorothalonil is nonlinearly associated with corticosterone levels, immunity, and mortality in amphibians. Environ Health Perspect 119, 1098-1103, doi: 10.1289/ehp.1002956 (2011).
13. Silva, E., Scholze, M. & Kortenkamp, A. Activity of xenoestrogens at nanomolar concentrations in the e-screen assay. Environ Health Perspect 115, 91-97, doi: 10.1289/ehp.9363 (2007).
14. Belz, R. G., Cedergreen, N. & Sørensen, H. Hormesis in mixtures - Can it be predicted? Sci Total Environ 404, 77-87, doi: http://dx.doi.org/10.1016/j.scitotenv.2008.06.008 (2008).
15. Ohlsson, A., Cedergreen, N., Oskarsson, A. & Ulleras, E. Mixture effects of imidazole fungicides on cortisol and aldosterone secretion in human adrenocortical H295R cells. Toxicology 275, 21-28, doi: 10.1016/j.tox.2010.05.013 (2010).
16. Martin-Betancor, K., Rodea-Palomares, I., Munoz-Martin, M. A., Leganes, F. & Fernandez-Pinas, F. Construction of a selfluminescent cyanobacterial bioreporter that detects a broad range of bioavailable heavy metals in aquatic environments. Front Microbiol 6, 186, doi: 10.3389/fmicb.2015.00186 (2015).
17. van der Meer, J. R. & Belkin, S. Where microbiology meets microengineering: design and applications of reporter bacteria. Nat Rev Microbiol 8, 511-522, doi: 10.1038/nrmicro2392 (2010).
18. Roda, A. et al. Analytical strategies for improving the robustness and reproducibility of bioluminescent microbial bioreporters. Anal Bioanal Chem 401, 201-211, doi: 10.1007/s00216-011-5091-3 (2011).
19. Yagi, K. Applications of whole-cell bacterial sensors in biotechnology and environmental science. Appl Microbiol Biotechnol 73, 1251-1258, doi: 10.1007/s00253-006-0718-6 (2007).
20. Ritz, C. & Streibig, J. C. Bioassay Analysis using R. J Stat Softw 12, 17 (2005).
21. Faust, M. et al. Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants. Aquat Toxicol 56, 13-32, doi: http://dx.doi.org/10.1016/S0166-445X(01)00187-4 (2001).
22. Gonzalez-Pleiter, M. et al. Toxicity of five antibiotics and their mixtures towards photosynthetic aquatic organisms: implications for environmental risk assessment. Water Res 47, 2050-2064, doi: 10.1016/j.watres.2013.01.020 (2013).
23. Fernandez-Pinas, F., Rodea-Palomares, I., Leganes, F., Gonzalez-Pleiter, M. & Angeles Munoz-Martin, M. Evaluation of the ecotoxicity of pollutants with bioluminescent microorganisms. Adv Biochem Eng Biotechnol 145, 65-135, doi: 10.1007/978-3-662-43619-6-3 (2014).
24. Wernersson, A.-S. et al. The European technical report on aquatic effect-based monitoring tools under the water framework directive. Env Sci Eur 27, 7 (2015).
25. Brack, W. et al. The SOLUTIONS project: challenges and responses for present and future emerging pollutants in land and water resources management. Sci Total Environ 503-504, 22-31, doi: 10.1016/j.scitotenv.2014.05.143 (2015).
26. Junghans, M., Backhaus, T., Faust, M., Scholze, M. & Grimme, L. H. Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures. Aquat Toxicol 76, 93-110, doi: http://dx.doi.org/10.1016/j.aquatox.2005.10.001 (2006).
27. Altenburger, R., Scholz, S., Schmitt-Jansen, M., Busch, W. & Escher, B. I. Mixture Toxicity Revisited from a Toxicogenomic Perspective. Environ Sci Technol 46, 2508-2522, doi: 10.1021/es2038036 (2012).
28. Jouanneau, S., Durand, M. J., Courcoux, P., Blusseau, T. & Thouand, G. Improvement of the identification of four heavy metals in environmental samples by using predictive decision tree models coupled with a set of five bioluminescent bacteria. Environ Sci Technol 45, 2925-2931, doi: 10.1021/es1031757 (2011).
29. Rodea-Palomares, I. F.-P., F. González-García, C. & Leganes, F. in Nova Science Publishers, Inc. New York. USA. (2009).
30. Dawson, D. A., Allen, J. L., Schultz, T. W. & Pöch, G. Time-dependence in mixture toxicity with soft-electrophiles: 2. Effects of relative reactivity level on time-dependent toxicity and combined effects for selected Michael acceptors. J Environ Sci Health A 43, 43-52, doi: 10.1080/10934520701750371 (2007).
31. Altenburger, R., Backhaus, T., Boedeker, W., Faust, M. & Scholze, M. Simplifying complexity: Mixture toxicity assessment in the last 20 years. Environ Toxicol Chem 32, 1685-1687, doi: 10.1002/etc.2294 (2013).
32. Cedergreen, N. Quantifying Synergy: A Systematic Review of Mixture Toxicity Studies within Environmental Toxicology. PloS one 9, e96580, doi: 10.1371/journal.pone.0096580 (2014).