Advertisement

Ecotoxicology

, Volume 22, Issue 2, pp 319–330 | Cite as

Dynamic energy budget approach to modeling mechanisms of CdSe quantum dot toxicity

  • Tin Klanjscek
  • Roger M. Nisbet
  • John H. Priester
  • Patricia A. Holden
Article

Abstract

A mechanistic model of bacterial growth based on dynamic energy budget (DEB) theory is utilized to investigate mechanisms of toxicity of CdSe quantum dots (QDs). The model of QD toxicity is developed by extending a previously published DEB model of cadmium ion toxicity to include a separate model of QD toxic action. The extension allows for testing whether toxicity from QD exposure can be explained fully by dissolved cadmium exposure only, or if the separate effects of QDs need to be taken into account as well. Two major classes of QD toxicity mechanisms are considered: acclimation expressed through initial retardation of growth, and three separate metabolic effects that can be a result of QDs either reversibly or irreversibly associating with the cell. The model is consistent with the data, and is able to distinguish toxic effects due to QD nano-particles from the effects due to cadmium ions. Results suggest that, in contrast to ionic exposure where required acclimation remains constant as exposure increases, increase of the energy required for acclimation with exposure is the primary toxic effect of QDs. Reactive oxygen species measurements help conclude that increase in energetic cost of maintenance processes such as cellular repair and maintenance of cross-membrane gradients is the most important of the three metabolic effects of QD toxicity.

Keywords

Dynamic energy budget (DEB) Cadmium nano-particle toxicity Mechanistic model Toxicity mechanisms Pseudomonas aeruginosa bacteria Reactive oxygen species (ROS) 

Notes

Acknowledgements

We thank Erik Muller for useful discussions on the mechanisms of toxicity. The research was supported by US National Science Foundation under Grant EF-0742521. There was also support from the US National Science Foundation and the US Environmental Protection Agency under Cooperative Agreement Number EF 0830117, and Croatian Ministry for Science, Education and Sport Grant 098-0982934-2719.

Conflict of interest

Experiments performed herein comply with the current laws of the United States of America. The authors declare that they have no conflict of interest.

References

  1. Baranyi J, Roberts TA (1994) A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23:277–294CrossRefGoogle Scholar
  2. Begot C, Desnier I, Daudin JD, Labadie JC, Lebert A (1996) Methods for calculating growth parameters by optical density measurements. Journal of Microbiological Methods 25:225–232CrossRefGoogle Scholar
  3. Chen Jinfeng, Yuan Jiangang, Wu Shanshan, Lin Biyun, Yang Zhongyi (2012) Distribution of trace element contamination in sediments and riverine agricultural soils of the zhongxin river, south china, and evaluation of local plants for biomonitoring. Journal of Environmental Monitoring 14:2663–2672CrossRefGoogle Scholar
  4. Ferianc P, Farwell A, Nystrom T (1998) The cadmium stress stimulon of Escherichia coli k-12. Microbiology, 144(4):1045–1050CrossRefGoogle Scholar
  5. Gibbons Sean M, Feris Kevin, McGuirl Michele A, Morales Sergio E, Hynninen Anu, Ramsey Philip W, Gannon James E (2011) Use of microcalorimetry to determine the costs and benefits to Pseudomonas putida strain kt2440 of harboring cadmium efflux genes. Applied and Environmental Microbiology 77:108–113CrossRefGoogle Scholar
  6. Hanegraaf PPF, Muller EB (2001) The dynamics of the macromolecular composition of biomass. Journal of Theoretical Biology 212:237–251CrossRefGoogle Scholar
  7. Hills BP, Mackey BM (1995) Multi-compartment kinetic models for injury, resuscitation, induced lag and growth in bacterial cell populations. Food Microbiology 12:333–346CrossRefGoogle Scholar
  8. Jager T, Heugens EHW, Kooijman SALM (2006) Making sense of ecotoxicological test results: Towards application of process-based models. Ecotoxicology 15(3):305–314CrossRefGoogle Scholar
  9. Jager T, Klok C (2010) Extrapolating toxic effects on individuals to the population level; the role of dynamic energy budgets. Philosophical Transactions of the Royal Society B 365. doi:  10.1098/rstb.2010.0137
  10. Jager Tjalling, Zimmer Elke I (2012) Simplified dynamic energy budget model for analysing ecotoxicity data. Ecological Modelling 225:74–81CrossRefGoogle Scholar
  11. Keller Arturo A, Wang Hongtao, Zhou Dongxu, Lenihan Hunter S, Cherr Gary, Cardinale Bradley J, Miller Robert, Ji Zhaoxia (2010) Stability and aggregation of metal oxide nanoparticles in natural aqueous matrices. Environmental Science & Technology 44(6):1962–1967CrossRefGoogle Scholar
  12. Klanjscek T, Nisbet RM, Priester J, Holden PA (2012) Modeling physiological processes that relate toxicant exposure and bacterial population dynamics. PLoS ONE 7(2). doi: 10.1371/journal.pone.0026955
  13. Kooijman SALM (2010) Dynamic Energy Budget theory for metabolic organisation, 3rd ed. Cambridge University Press, Cambridge. ISBN 9780521131919Google Scholar
  14. Kooijman SALM, Bedaux JJM (1996) The analysis of aquatic toxicity data. VU University Press, AmsterdamGoogle Scholar
  15. Muller Erik B, Nisbet Roger M, Berkley Heather A (2009) Sublethal toxicant effects with dynamic energy budget theory: model formulation. Ecotoxicology 19(1):38–47. doi: 10.1007/s10646-009-0385-3 CrossRefGoogle Scholar
  16. Munoz-Cuevas M, Fernandez PS, George S, Pin C (2010) Modeling the lag period and exponential growth of listeria monocytogenes under conditions of fluctuating temperature and water activity values. Applied and Environmental Microbiology 76(9):2908–2915CrossRefGoogle Scholar
  17. Nisbet RM, Muller EB, Lika K, Kooijman SALM (2000) From molecules to ecosystems through dynamic energy budget models. Journal of Animal Ecology 69(6):913–926CrossRefGoogle Scholar
  18. Pages D, Sanchez L, Conrod S, Gidrol X, Fekete A, Schmitt-Kopplin P, Heulin T, Achouak W (2007) Exploration of intraclonal adaptation mechanisms of Pseudomonas brassicacearum facing cadmium toxicity. Environmental Microbiology 9(11):2820–2835CrossRefGoogle Scholar
  19. Priester JH, Stoimenov PK, Mielke RE, Webb SM, Ehrhardt C, Zhang JP, Stucky GD, Holden PA (2009) Effects of soluble cadmium salts versus cdse quantum dots on the growth of planktonic Pseudomonas aeruginosa. Environmental Science & Technology 43(7):2589–2594CrossRefGoogle Scholar
  20. Sousa T, Domingos T, Kooijman SALM (2008) From empirical patterns to theory: a formal metabolic theory of life. Philosophical Transactions of the Royal Society B 363(1502):2453–2464CrossRefGoogle Scholar
  21. von der Kammer Frank, Ferguson P Lee, Holden Patricia A, Masion Armand, Rogers Kim R, Klaine Stephen J, Koelmans Albert A, Horne Nina, Unrine Jason M (2012) Analysis of engineered nanomaterials in complex matrices (environment and biota): General considerations and conceptual case studies. Environmental Toxicology and Chemistry 31(1):32–49CrossRefGoogle Scholar
  22. Wang A, Crowley DE (2005) Global gene expression responses to cadmium toxicity in Escherichia coli. Journal of Bacteriology 187(9):3259–3266CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Tin Klanjscek
    • 1
    • 2
  • Roger M. Nisbet
    • 1
  • John H. Priester
    • 3
  • Patricia A. Holden
    • 3
  1. 1.Department of Ecology, Evolution and Marine BiologyUniversity of California Santa BarbaraSanta BarbaraUSA
  2. 2.Ruđer Bošković InstituteZagrebCroatia
  3. 3.Bren School of Environmental Science and ManagementUniversity of California Santa BarbaraSanta BarbaraUSA

Personalised recommendations