Final Report: Dynamical Systems Models Based on Energy Budgets for Ecotoxicological Impact Assessment

EPA Grant Number: R835797
Title: Dynamical Systems Models Based on Energy Budgets for Ecotoxicological Impact Assessment
Investigators: Nisbet, Roger M. , Muller, Erik B , Whitehead, Andrew
Institution: University of California - Santa Barbara , University of California - Davis
EPA Project Officer: Lasat, Mitch
Project Period: June 1, 2015 through May 31, 2018 (Extended to May 31, 2019)
Project Amount: $799,723
RFA: Systems-Based Research for Evaluating Ecological Impacts of Manufactured Chemicals (2014) RFA Text |  Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Ecosystems , Safer Chemicals

Objective:

The specific objectives of this project are:

Objective 1: Formulate and test new theory relating organismal dynamics to suborganismal responses to toxicant exposure

- 1a) Develop new Dynamic Energy Budget (DEB) models with mechanistic connections to suborganismal processes including those currently used in Adverse Outcome Pathway (AOP) studies

- 1b) Use data from literature and new experiments to determine for two model organisms the extent to which transcriptomic data relate to DEB model parameters and organismal dynamics

- 1c) Use the new DEB models for qualitative prediction of “tipping points” caused by failure of feedback processes within an organism.

Objective 2: Use individual-based population models to predict possible population level responses to exposure to toxicants

- 2a) Formulate and test individual-based population models to project effects on interacting phytoplankton and zooplankton populations

- 2b) Use models from objective 2a to investigate the likelihood of “tipping points” representing abrupt extinctions

- 2c) Develop models of adaptation to stress that take account of sub-organismal regulatory processes and thereby provide tools for evaluating the likelihood of evolutionary rescue in chronically polluted environments.

Objective 3: Investigate applicability of new concepts to non-model organisms.

- 3a) Determine the additional information required for generalizing findings from objectives 1 and 2.

Summary/Accomplishments (Outputs/Outcomes):

 Early in the project, in collaboration with two other investigators, researchers completed an extensive review of prior work on how the effects of exposure to chemical stressors are expressed across levels of biological organization [1]. They developed a new theory on “tipping points”[2], a key prerequisite for work on specific objectives 1 and 2. Investigators started a wide-ranging collaboration on many aspects of the work through a working group at the National Institute for Mathematical and Biological Synthesis (NIMBioS), co-led by PI Nisbet and Dr. C. Murphy (Michigan State University) another STAR grantee. Two papers from the NIMBioS working group proposed new methodology for making mechanistic connections between DEB models and suborganismal processes including those currently used in Adverse Outcome Pathway (AOP) studies ([3], [4]). The key new feature was identification of a variable characterizing cellular “damage” in DEB theory that connects key events in an AOP. Motivated by this connection, researchers developed new formalism for modeling damage dynamics [5]. A further output linking suborganismal to organismal processes was a new representation of regulation of reproduction in fish that describes time‐resolved measurements of wet weight of body, ovaries and liver, egg diameter and plasma content of vitellogenin and oestradiol [15].

With the modeling methodology in place, the subsequent focus of much of the research was on applying and testing it. Application to the Atlantic killifish Fundulus heteroclitus was performed in collaboration with Dr. Diane Nacci’s group (EPA Atlantic Ecology Division). Researchers tested the models using dioxin-like chemicals (DLCs), which are of particular interest in this species due to the well-documented large intraspecific variability in sensitivity of F. heteroclitus to DLCs. The accepted AOP for DLC exposure is through activation of the Aryl hydrocarbon receptor (AhR) pathway, however the precise toxic mechanism of DLCs is poorly understood. Data from the Nacci lab indicated that sublethal exposure to PCB126 during embryonic development reduces growth rate in larval killifish, not exposed to the stressor. Investigators connected AOP Key Events (KEs) to DEB processes through a representation of damage dynamics, driven by the internal toxicant concentration. They used data on CYP1A induction rates (an enzyme that is a hallmark of AhR pathway activation) to parametrize damage production. The model predicts regulated but increasing concentrations of damage in response to increasing toxicant exposure and also “tipping points” of internal toxicant concentration above which damage outpaces regulatory feedbacks, leading to mortality. A feedback mechanism whereby damage impacts DEB parameters was hypothesized from transcriptomic information, using DAVID analysis to identify a handful of significantly enriched gene clusters, giving a broad outline of impacted pathways.

The model fits data on effects on sublethal growth of larval fish and relates the tipping point in damage dynamics to the onset of lethal effects. Work is well advanced in testing the model’s ability to use low-level data to predict the response of fish from a population previously shown to be resistant to DLCs.

Research on applying the methodology to Daphnia spp. had two components. Researchers used data from an extensive set of chronic toxicity studies on Daphnia exposed to waterborne silver nanoparticles [6] to parametrize and test a DEB model that underpins an individual based population model. The primary finding was that feedbacks mediated by indirect effects on phytoplankton may to some extent mitigate the impacts of exposure. Second, in collaboration with five members of the NIMBioS working group, researchers designed a sampling regime for following changes in gene expression in experiments on individual Daphnia exposed to fly ash. These experiments are complete and researchers  are awaiting analyses of the transcriptomic samples.

Two studies contributed to objective 3: investigating applicability of new concepts to non-model organisms. Recognizing some challenging issues in relating data on lipids to standard DEB models, investigators developed a new model, DEBlipid, based on meta-analysis of data on lipid composition from a diverse range of fish species [7]. They are also developing a new model of inter-specific variation in no-effect concentration (NEC). That model was motivated by a publication in 2015 by J. Baas and S.A.L.M. Kooijman, who found that for several compounds, NEC co-varies negatively with mass-specific metabolic rate. Investigators hypothesized that an organism’s response to increasing stress involves a cascade of failures of metabolic regulatory processes, represented mathematically by tipping points in damage dynamics. They have derived a formula that predicts the variation of NEC with organism size, but requires knowledge of the bioconcentration factor for non-model organisms. One immediate area of application will be to help interpret species sensitivity distributions (SSDs); researchers are pursuing this possibility using published SSDs for several engineered nanoparticles.

Leveraged activities enhanced by this award included an application of the representation of “damage” dynamics to an important disease [8], a DEB model of coral bleaching [9], and applications of DEB theory and related dynamic models in projects performed in collaboration with investigators in the University of California Center for Environmental Implications of Nanotechnology (UC CEIN). UC CEIN related work include a DEB-based explanation of non-monotone dose-response in soybean exposed to metal oxide nanoparticles [10], and a study of the predictive value of high content screening of marine phytoplankton exposed to metal oxide nanoparticles [11]. Researchers developed DEB-inspired models of mortality in amphipods exposed to CuCl2 and to several copper nanoparticles, and remediation of cadmium toxicity in phytoplankton by sulfidized nano-iron [12,13]. The latter study inspired a new project, in collaboration with Dr. P. Antczac (University of Liverpool, UK), in which the investigators conducted an experiment exposing Chlamydomonas reinhardtii, a naturally abundant and widely studied freshwater green algae, to copper (CuCl2) and collected samples for whole organismal effects (biomass measured by chlorophyll), metabolomic response, nutrient availability (PO4), and copper concentration at multiple time points. The metabolomic analysis is complete and allows researchers to connect the dynamic response of algae to its suborganismal signal, identifying specific pathways, including several relating to photosynthesis, that investigators hypothesize could explain copper-induced changes in energy use as predicted by DEB models. This connection between suborganismal (metabolomic) and organismal-level information is enhancing the ability to utilize existing data on molecular responses of algae to toxicants to predict effects on whole cells and on populations of algae.

Conclusions:

The key findings were:

• We formulated new, systems-based, methodology based on DEB theory that relates whole-organism performance to suborganismal information.

• We connected genomic data on a model organism (the estuarine fish Fundulus) to processes in DEB models, and tested the new theory tested on data for fish exposed as embryos to a dioxing-like chemical.

• We predicted organismal “tipping points” caused by failure of physiological and population regulatory processes that lead to mortality, and tested the theory on Fundulus.

• We leveraged the methodology form this award to model responses of model organisms (Daphnia and microlage) exposed to engineered nanomaterials.


Journal Articles on this Report : 13 Displayed | Download in RIS Format

Other project views: All 39 publications 15 publications in selected types All 13 journal articles
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Journal Article Adeleye AS, Stevenson LM, Su, YM, Nisbet RM, Zhang YL, Keller AA. Influence of phytoplankton on fate and effects of modified zerovalent iron nanoparticles. Environmental Science & Technology 2016;50(11):5597-5605. R835797 (2016)
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  • Journal Article Civitello DJ, Fatima H, Johnson LR, Nisbet RM, Rohr JR. Bioenergetic theory predicts infection dynamics of human schistosomes in intermediate host snails across ecological gradients. Ecology Letters 2018;21(5):692-701. R835797 (2017)
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  • Journal Article Cunning R, Muller EB, Gates RD, Nisbet RM. A dynamic bioenergetic model for coral-Symbiodinium symbioses and coral bleaching as an alternate stable state. Journal of Theoretical Biology 2017;431:49-62. R835797 (2017)
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  • Journal Article Klanjscek T, Muller EB, Holden PA, Nisbet RM. Host-symbiont interaction model explains non-monotonic response of soybean growth and seed production to nano-CeO2 exposure. Environmental Science & Technology 2017;51(9):4944-4950. R835797 (2016)
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  • Journal Article Martin BT, Heintz R, Danner EM, Nisbet RM. Integrating lipid storage into general representations of fish energetics. Journal of Animal Ecology 2017;86(4):812-825. R835797 (2016)
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  • Journal Article Miller RJ, Muller EB, Cole B, Martin T, Nisbet R, Bielmyer-Fraser GK, Jarvis TA, Keller AA, Cherr G, Lenihan HS. Photosynthetic efficiency predicts toxic effects of metal nanomaterials in phytoplankton. Aquatic Toxicology 2017;183:85-93. R835797 (2015)
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  • Journal Article Murphy CA, Nisbet RM, Antczak P, Garcia-Reyero N, Gergs A, Lika K, Mathews T, Muller EB, Nacci D, Peace A, Remien CH, Schultz IR, Stevenson LM, Watanabe KH. Incorporating suborganismal processes into dynamic energy budget models for ecological risk assessment. Integrated Environmental Assessment and Management 2018;14(5):615-624. R835797 (2017)
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  • Journal Article Nisbet RM. Challenges for dynamic energy budget theory: omment on "Physics of metabolic organization" by Marko Jusup et al. Physics of Life Reviews 2017;20:72-74. R835797 (2016)
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  • Journal Article Rohr JR, Salice CJ, Nisbet RM. The pros and cons of ecological risk assessment based on data from different levels of biological organization. Critical Reviews in Toxicology 2016;46(9):756-784. R835797 (2015)
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  • Journal Article Rohr JR, Salice CJ, Nisbet RM. Chemical safety must extend to ecosystems. Science 2017;356(6341):917. R835797 (2016)
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  • Journal Article Stevenson LM, Krattenmaker KE, Johnson E, Bowers AJ, Adeleye AS, McCauley E, Nisbet RM. Standardized toxicity testing may underestimate ecotoxicity:environmentally relevant food rations increase the toxicity of silver nanoparticles to Daphnia. Environmental Toxicology and Chemistry 2017;36(11):3008-2018. R835797 (2016)
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  • Journal Article Stevenson LM, Adeleye AS, Su Y, Zhang Y, Keller AA, Nisbet RM. Remediation of cadmium toxicity by sulfidized nano-iron: the importance of organic material. ACS Nano 2017;11(10):10558-10567. R835797 (2017)
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  • Journal Article Klanjscek T, Muller EB, Nisbet RM. Feedbacks and tipping points in organismal response to oxidative stress. Journal of Theoretical Biology 2016;404:361-374. R835797 (2015)
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  • Supplemental Keywords:

    Dynamic Energy Budget (DEB); Adverse Outcome Pathway (AOP); Ecotoxicology

    Progress and Final Reports:

    Original Abstract
  • 2015 Progress Report
  • 2016 Progress Report
  • 2017 Progress Report