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Modeling environmental impacts of engineered nanomaterials : the value of “generic models” of individual organisms Roger M. Nisbet University of California,

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Presentation on theme: "Modeling environmental impacts of engineered nanomaterials : the value of “generic models” of individual organisms Roger M. Nisbet University of California,"— Presentation transcript:

1 Modeling environmental impacts of engineered nanomaterials : the value of “generic models” of individual organisms Roger M. Nisbet University of California, Santa Barbara Work with: Tin Klanjscek, Shannon Hanna, Trish Holden, Ben Martin, Ed McCauley, Bob Miller, Erik Muller, John Priester, Louise Stevenson, and many others Funding: US Environmental Protection Agency and National Science Foundation (through UC CEIN).

2 The need for theory in ecotoxicology Contaminants impact individual organisms, populations, communities and ecosystems. Contaminants are one component of environmental stress, that typically acts simultaneously with others (e.g. temperature, pH, food availability……….) Gereral theory is required because testing cannot match rate of introduction of new chemicals: - 75,000+ chemicals registered for commercial use in US - less than 1000 have undergone complete toxicity testing - overwhelming costs of tests ($2-$4 million for in vivo studies)

3 The need for theory in ecotoxicology Contaminants impact individual organisms, populations, communities and ecosystems. Contaminants are one component of environmental stress, that typically acts simultaneously with others (e.g. temperature, pH, food availability……….) Biology-based theory is required because testing cannot match rate of introduction of new chemicals: - 75,000+ chemicals registered for commercial use in US - less than 1000 have undergone complete toxicity testing - overwhelming costs of tests ($2-$4 million for in vivo studies) Dynamics of budgets of energy and elemental matter should be a component of this theory. Kooijman’s DEB theory offers a powerful framework for this.

4 Definition Engineered nanomaterial (ENM) consists of intentionally produced particles with a characteristic dimension between 1 and 100nm and possessing properties that are not shared by non-nanoscale particles with the same chemical composition” Examples - metal oxides – TiO 2 and ZnO, (sunscreeen); Ag (antibacterial) - Quantum Dots (electronics) Properties - Size and shape dependent due to: large surface/volume - Often manufactured with coatings Ecological/environmental impact? - May impact biogeochemical fluxes (nutrient cycling) - Toxicity (e.g designed for antibacterial/antifungal properties) Nanotechnology has made the challenge tougher

5 100’s/year 1000’s/year10,000’s/day100,000’s/day High Throughput Bacterial, Cellular, Yeast, Embryo or Molecular Screening Information on potential ENM hazard Expensive in vivo testing and ecological experiments few/year Challenge for theorists: to use information from molecular and cellular studies to prioritize, guide design, and interpret ecological studies

6 Dynamic Energy Budget (DEB) Models Organism Growth Development Reproduction Survival Resources Metabolic Products DEB model equations describe the kinetics of the “reactor” that converts resources into “products”

7 Kooijman’s “standard” DEB model Feces J EA MEME MVMV MVMV somatic maintenance growth  1-  Maturity Maintenance M H M ER Maturity or Reproduction J EC Food Reserve Mobilization X

8 Kooijman’s “standard” DEB model * S.A.L.M. Kooijman (2010) Dynamic Energy Budget models for metabolic organization. Cambridge University Press. T. Sousa et al (2010)., Philosophical Transactions of the Royal Society B, 365:3413-3428.

9 Kooijman’s “standard” DEB model equations

10 COLLECTION OF MESSY ODEs

11 Dynamics of structured populations Environment: E-state variables - experienced by all organisms - Resources - Toxicants - Metabolic products Individual Organism: i-state variables - DEB state variables – ODEs in previous slides Population dynamics: p-state variables – Book-keeping - population size, age structure, distribution of i-state variables - many mathematical representations possible (IBMs, PDEs, IDEs etc.) - special assumption (ontogenetic symmetry) yields ODEs Population modeling involves assumptions on interactions of individuals and their environment

12 Messages from some UC CEIN Projects 1) Phytoplankton I. Ontogeny symmetry assumed. Suborganismal and population properties consistent 2) Phytoplankton II. Metabolic products important Algal-produced compounds mitigate toxicity. 3)Bacteria. Metabolic products important. Suborganismal data can help model selection. 4) Individual  Population projection for mussels. Ontogeny asymmetry. Population response more sensitive than individual response 5) Phytoplankton-zooplankton interactions. Ontogeny important and metabolic products important?

13 Effects of ENMs on phytoplankton populations

14 Kooijman’s “standard” DEB model * S.A.L.M. Kooijman (2010) Dynamic Energy Budget models for metabolic organization. Cambridge University Press. T. Sousa et al (2010)., Philosophical Transactions of the Royal Socitey B, 365:3413-3428.

15 Marine phytoplankton population growth * Study of 4 phytoplankton species exposed to TiO 2 and ZnO particles No effect with TiO 2 ZnO effect probably due to Zn 2+ Toxicity described by two quantities (NEC and one other) * R.J. Miller et al. (2010) Environmental Science & Technology 44: 7329–7334

16 Marine phytoplankton population growth * Study of 4 phytoplankton species exposed to TiO 2 and ZnO particles No effect with TiO 2 ZnO effect probably due to Zn 2+ DEB model Toxicity described by two quantities (NEC and one other) * R.J. Miller et al. (2010) Environmental Science & Technolgy 44: 7329–7334

17 Marine phytoplankton population growth * Study of 4 phytoplankton species exposed to TiO 2 and ZnO particles No effect with TiO 2 ZnO effect probably due to Zn 2+ DEB model Toxicity described by two quantities (NEC and one other) * R.J. Miller et al. (2010) Environmental Science & Technology 44: 7329–7334

18 ZnO mg L -1 (ppm) RF ZnO mg L -1 (ppm) Reactive oxygen species (ROS) production Membrane permeability (Cell death ) Mitochondrial membrane potential ZnO mg L -1 (ppm) Dynamic Energy Budget (DEB) modeling of NEC NEC = 223 ± 56 ppb Relative fluorescence (RF) Isochrysis galbana Expt data from Cole, Cherr et al., in prep 18 Marine phytoplankton population growth *

19 BUT – it’s not always that simple (Expts by L. Stevenson on silver ENMs and a freshwater alga) New culture One week old Two weeks old Size of AgNPs (nm) Per capita growth rate of algal cultures 5 mg/L citrate-coated AgNP New culture One week old Two weeks old Particles aggregate in older batch cultures Smaller particles more toxic than aggregates Hypothesis: algae excrete soluble organic compounds that interact with particles and dissolved metals ADDITIONAL FEEDBACK TERM(S) + NEW E-STATE INTERACTIONS

20 DOC mitigation of AgNP and Ag +

21 Effects of Cd-Se quantum dots on bacterial populations (Pseuomonas aerigunosa)

22 Strategy: Use DEB models to charcterize differences in bacterial growth response to Cd(II) and CdSe Quantum dot (QD) exposure Contrasting QD toxicity with toxicity of dissolved Cd 1-3 1. Data from J. Priester et al. Environmental Science and Technology 43:2589-2594 (2009). 2. T. Klanjscek, J. Priester, P.A. Holden and R.M. Nisbet, PlosONE, 7(2): e26955. doi:10.1371/journal.pone.0026955) 3. T. Klanjscek, J. Priester, P.A. Holden and R.M. Nisbet, Ecotoxicology, in review

23 Strategy: Use DEB models to charcterize differences in bacterial growth response to Cd(II) and CdSe Quantum dot (QD) exposure Contrasting QD toxicity with toxicity of dissolved Cd 1-3 1. Data from J. Priester et al. Environmental Science and Technology 43:2589-2594 (2009). 2. T. Klanjscek, J. Priester, P.A. Holden and R.M. Nisbet, PlosONE, 7(2): e26955. doi:10.1371/journal.pone.0026955) 3. T. Klanjscek, J. Priester, P.A. Holden and R.M. Nisbet, Ecotoxicology, in review New feedback to environment required to fit DEB model to control (zero Cd) curve

24 Kooijman’s “standard” DEB model

25 Strategy: Use DEB models to charcterize differences in bacterial growth response to Cd(II) and CdSe Quantum dot (QD) exposure Contrasting QD toxicity with toxicity of dissolved Cd 1-3 1. Data from J. Priester et al. Environmental Science and Technology 43:2589-2594 (2009). 2. T. Klanjscek, J. Priester, P.A. Holden and R.M. Nisbet, PlosONE, 7(2): e26955. doi:10.1371/journal.pone.0026955) (2012) 3. T. Klanjscek, J. Priester, P.A. Holden and R.M. Nisbet, Ecotoxicology DOI 10.1007/s10646-012-1028-7 (2013) New feedback to environment required to fit DEB model to control (zero Cd) curve Model with toxic effect on resource assimilation and mortality best fits response to Cd (II) and to ROS data

26 Modeling the effect of QDs Rule of the game: no change in Cd toxicity model QD dissolution introduces Cd 2+ in environment Cd 2+ interferes with assimilation and enters the cell → previous toxicity model QDs associate with the cell Associated QDs produce ROS affecting membrane processes ROS produced inside the cell affect all cellular processes CdSe

27 Model selection from fitting growth trajectories not possible Measurements of Reactive Oxygen Species (ROS) allow model selection Toxicity mechanism for Quantum Dots

28 Effects of metal oxide nanoparticles on populations of marine mussels (Mytilus spp.)

29 Adult marine mussels, Mytilus galloprovincialis, were exposed to ZnO NPs for 12 weeks at concentrations up to 2 mg L -1. Basic measurements on individuals(2 food levels) 1) weights of shell, gonad, somatic tissue 2) Zn distribution within organism 3) Tank clearance rates  information on food consumed. 4)Iindividual clearance rates 5)Oxygen consumption rates. Population level prediction Aims to extract enough information to project effects on lifetime reproduction (previous experience in Muller, E.B. et al. Ecotoxicology 19: 38-47 (2010)) Effects of ZnO NPs on mussel physiology (Expts. By Shannon Hanna) used to estimate parameters From DEB model

30 EC 50 EXPECTED LIFE-TIME PRODUCTION OF REPRODUCTIVE MATTER - EC 50 for a given food level -MUCH SMALLER THAN FOR INDIVIDUAL RATES (e.g. 1.5 mg/l for feeding) -Consequence of ontogenic asymmetry

31 Phytoplankton- zooplankton interactions

32 DEB-IBM predicts effects of ontogeny asymmetry * * Unpublished work by Benjamin Martin

33 Take home messages 1.Structured population models (or IBMs) can help relate sub-organismal information (cheap and fast) to population dynamics (slow, expensive and important) 2.Abstract representation of individual organism (Kooijman’s DEB theory) has practical value 3.Experiments are revealing new feedbacks involving metabolic products 4.Ontogeny asymmetry impacts levels at which toxic effects impact populations


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