HostâSymbiont Interaction Model Explains Non-monotonic Response

Subscriber access provided by University of Pennsylvania Libraries

Article

Host-symbiont interaction model explains non-monotonic response of soybean growth and seed production to nano-CeO2 exposure Tin Klanjscek, Erik B Muller, Patricia A. Holden, and Roger M. Nisbet Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b06618 • Publication Date (Web): 23 Mar 2017 Downloaded from http://pubs.acs.org on March 26, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 25

Environmental Science & Technology

1

Host-symbiont interaction model

2

explains non-monotonic response of

3

soybean growth and seed production

4

to nano-CeO2 exposure

5

Tin Klanjšček*1,2, Erik B. Muller3, Patricia A. Holden4, Roger M. Nisbet1

6

1

7

Santa Barbara, CA 93106-9610, USA

8

2

9

3

Department of Ecology, Evolution and Marine Biology, University of California Santa Barbara,

Ruđer Bošković Institute, Zagreb, 10000 Croatia Marine Science Institute, University of California Santa Barbara, Santa Barbara, CA 93106-

10

9610, USA

11

4

12

Barbara, Santa Barbara, CA 93106-5131, USA

13

* corresponding author, Tin Klanjscek, ORCID: 0000-0002-9341-344X

14

Address: Ruđer Bošković Institute, P.O. Box 180, Bijenička 54, HR-10000 Zagreb, Croatia.

15

E-mail: [email protected], [email protected]

16

Phone: +38514561131; Fax: +38514680242

Bren School of Environmental Science and Management, University of California Santa

17

ACS Paragon Plus Environment

1


Page 2 of 25

18

Abstract

19

Recent nanotoxicity studies have demonstrated non-monotonic dose-response mechanisms for

20

planted soybean that have a symbiotic relationship with bacteroids in their root nodules:

21

reduction of growth and seed production was greater for low, as compared to high, exposures.

22

To investigate mechanistic underpinnings of the observed patterns, we formulated an energy

23

budget model coupled to a toxicokinetic module describing bioaccumulation, and two

24

toxicodynamic modules describing respectively toxic effects on host plant and symbionts. By

25

fitting data on plants exposed to engineered CeO2 nanoparticles to the newly formulated model,

26

we show that the non-monotonic patterns can be explained as the interaction of two, individually

27

monotonic, dose-response processes: one for the plant, and the other for the symbiont. We

28

further validate the newly formulated model by showing that, without the need for additional

29

parameters, the model successfully predicts changes in dinitrogen fixation potential as a

30

function of exposure (dinitrogen fixation potential data not used in model fitting). The symbiont

31

buffers overall toxicity only when, in the absence of exposure to a toxicant, it has a parasitic

32

interaction with the host plant. If the interaction is mutualistic or commensal, there is no

33

buffering and only monotonic toxic responses are possible. Since the model is based on general

34

biological principles, we expect it to be applicable to other similar symbiotic systems, especially

35

other nodule-forming legumes.

36

Introduction

37

Ecological nanotoxicology bridges multiple levels of biological organization to help understand

38

and minimize negative impacts of an ever-increasing input of engineered nanomaterials (ENMs)

39

on ecosystem function and resulting ecosystem services(1). Recent efforts in ecological

40

nanotoxicology include coherent experimental and theoretical efforts to investigate mechanisms

41

of ENM action and toxicity on many aquatic and terrestrial (eco)systems (e.g. Xia et al. (2) and


2

Page 3 of 25


42

references therein). These efforts have identified a number of mechanisms specific to ENMs

43

and materials that comprise ENMs. In particular, elevated oxidative stress has for long been

44

identified as a major contributor to cellular damage resulting from ENM exposure (3–6). The

45

cascading effects of oxidative stress from biochemical, to individual, to population-level scales

46

of biological organization have been modeled using Dynamic Energy Budget (DEB) models (7–

47

9). These models assume that exposure increases oxidative stress, which results in damage to

48

cells and potentially to whole organisms; in particular, Klanjscek et al. (10) developed theory

49

showing that with only some non-restrictive assumptions, correlation between exposure, ROS,

50

and cellular damage is expected. In the absence of other confounding factors, this would at the

51

organism level translate into a monotonic dose-response pattern, i.e. negative effects that

52

increase with exposure. Such monotonic responses in many endpoints to metal oxide

53

nanoparticles have been reported for a number of plants (see Du et al. (11) for a comprehensive

54

review).

55

Recent studies on effects of CeO2 nanoparticles on soybean revealed a much more complex

56

pattern of impacts. Prior publications (6,12) report on a large number of endpoints

57

characterizing the effects on soybean of exposure to three levels of CeO2 nanoparticle. Of

58

particular importance was their finding that dinitrogen fixation potential was impacted, as has

59

been observed for some other plants (13) (but see Moll et al. (14), who found no change in

60

dinitrogen fixation by clover in response to nano-CeO2 exposure). Since dinitrogen fixation is an

61

important function directly related to ecosystem services (food production and nutrient cycling),

62

the impact could have far-reaching consequences. Prior publications (6,12) report that some

63

measured quantities such as bioaccumulation, oxidative stress, and leaf damage, exhibit

64

monotonic dose-responses. Other endpoints, however, exhibit a non-monotone, zig-zagged or

65

U-shaped dose-response. Stem length, pods and seeds per plant, leaf cover, dry mass, and

66

growth rate exhibit the largest effects for the smallest exposure (0.1 g/kg), while increasing the


3


Page 4 of 25

67

exposure (to 0.5 g/kg and 1 g/kg) reduces the toxic effect. Effects of the exposure on the plant

68

may interact with the control on dinitrogen fixation to produce the atypical correlations between

69

exposure, oxidative stress, and other measured quantities (6).

70

From a modelling perspective, the non-monotone patterns suggest two competing processes,

71

one of which promotes, while the other mitigates the response. For example, Conolly and Lutz

72

(15) give four instances of non-monotone dose-responses involving competing molecular

73

processes. In the soybean studies described above (6,12), the promoting process is most

74

pronounced for low exposures, and the second process mitigates responses for mid- and high

75

exposures (with the potential for the promoting process to again dominate for large exposures).

76

Noting that endpoints exhibiting ‘standard’ toxicity patterns relate to mechanisms of toxicity (e.g.

77

oxidative stress), while the non-monotone patterns (e.g. growth rate) relate to energy utilization,

78

we hypothesize that understanding the origins of the patterns requires a coherent inclusion of

79

mechanisms governing both energetics and toxicity.

80

We investigate the hypothesis by creating a simple model that is able to integrate

81

measurements relating to both toxicity and energetics. The model differs from previous models

82

describing non-monotonic dose-response curves, in that one of the processes modeled involves

83

inter-organism interactions rather than molecular kinetics. Starting with a bioenergetic module

84

capturing energy utilization for growth and reproduction, we (i) implement a rudimentary

85

symbiotic interaction between the plant and bacteroids, (ii) integrate a toxicokinetic module (16)

86

for cerium bioaccumulation, and (iii) couple the toxicokinetic module to a toxicodynamic module

87

(17) that describes toxic effects on the plant and bacteroids. We fit the model to the measured

88

growth curves, bioaccumulation, and measures of reproductive output. We use data on

89

dinitrogen fixation potential to ground-truth the model and guide discussion of our results.

90


4

Page 5 of 25


Methods

91 92

The model consists of three integrated modules: a bioenergetic module describing acquisition

93

and utilization of energy, a toxicokinetic module describing the distribution of cerium within the

94

compartments of the energy budget module, and a toxicodynamic module capturing the toxic

95

effects (Figure 1). An overview of model equations, state variables, parameters, and units is

96

provided in Table 1.

97


5


Page 6 of 25

98 99

Figure 1: Overview of the model with energy budget, toxicokinetic, and toxicodynamic modules. Net

100

production flux (P) is the rate of production of utilizable photosynthesized energy left after subtracting the

101

maintenance needs of the plant, and accounting for the net energy loss or gain from supporting dinitrogen

102

fixation by bacteroids. The production flux is either committed to growth of the above- and below-ground

103

biomass (both proportional to total plant somatic biomass (excluding seeds), W), or reproductive output (S

104

or ∑, see text for definitions), with the ratio of the two commitments determined by Θ. Bioaccumulation

105

due to exposure of the root increases the bioaccumulated toxicant, Q, which affects the production flux by

106

decreasing photosynthesis and/or increasing maintenance, and reducing the interaction with N2-fixing

107

bacteroids (e.g. by instituting bacteroid mortality in response to the toxicant burden).

108 109

Our bioenergetics module tracks the utilization of energy from photosynthesis (Figure 1).

110

Photosynthesis results in gross production of photosynthate, Φ, part of which is used to meet

111

maintenance requirements and provide energy to support dinitrogen fixation by bacteroids. We

112

use the term production flux (P) to represent the rate at which the remaining energy becomes

113

available to support growth and reproduction, with a fraction Θ of the production flux assigned to

114

reproduction, and (1- Θ) to increase in plant biomass (growth). Both reproduction and growth

115

are assumed to be proportional to energy invested. We further assume:


6

Page 7 of 25


116

1. environmental conditions are constant;

117

2. the ratio of above and below-ground biomass is constant (see Figure S1);

118

3. production flux is proportional to plant biomass;

119

4. bioaccumulation is proportional to the root biomass;

120

5. investment to reproduction starts at a fixed age denoted by tS;

121

6. in the absence of exposure to a toxicant, bacteroid biomass is proportional to below-

122 123 124

ground biomass, but contributes negligibly to the weight of the plant; 7. both the energy demands of the bacteroids and their contributions to the production flux are proportional to their biomass.

125

Assumption 6 ensures that the dynamics of bacteroid biomass can be represented

126

mathematically from the below-ground biomass, while assumption 2 ensures that both above-

127

and below-ground biomass can be represented by a single state variable (plant biomass).

128

Hence, through assumptions 2 and 6, the two state variables of the bioenergetic model (plant

129

biomass and reproductive output) account for four compartments: above-ground and below-

130

ground biomass, reproductive buffer, and bacteroids. Although we do not explicitly include

131

nitrogen dynamics in the model, the parameters linked to assumption 7 allow both positive and

132

negative net impact of N2-fixing bacteroids on production flux. We make no prior assumptions

133

about the impact of bacteroids on plant energy budgets, but infer this impact from the data and

134

model fits. The symbionts may, depending on species and environment, operate as mutualists

135

or parasites.

136

Protein biosynthesis represents a major cost of seed production and is, therefore, important for

137

estimating energy committed to reproduction. Data on seed production collected by the

138

experiment in Priester et al. (12) included seed count for each plant, and protein density of

139

seeds (mg protein per g of seed dry weight). However, total seed protein content could not be

140

determined because total seed weight per plant was not measured.


7


Page 8 of 25

141

To extract as much information on seeds as possible, we used additional assumptions to create

142

two plausible measures of reproduction. First, by assuming that all seeds are of equal size,

143

weight, and protein density, we could use the recorded seed count, S, as a measure of

144

reproduction. Second, we assumed all seeds have equal mass. Then, we defined scaled seed

145

protein, ∑, as the seed count multiplied by the protein density. The scaled seed protein

146

represents total protein content of seeds scaled to mass of a single seed, thus representing

147

protein content. Since ∑ is the product of the two endpoints (scaled seed protein and seed

148

count), it scores both seed count and protein content.

149

The toxicokinetic module accounts for uptake of toxicant by the plant. Cerium, administered in

150

the form of nano-CeO2 has been shown to bioaccumulate primarily in the root, with some

151

transport to the aboveground biomass (12) including in the form of nano-CeO2 (18) that exerts

152

oxidative stress in the leaves (6). Using a common approach in cases where physico-chemical

153

details of toxicant transport are not known (e.g. Ashauer and Escher (19) and Jager et al. (20)),

154

we assumed that (i) the concentration of bioavailable cerium is proportional to nominal

155

concentration, (ii) toxicant uptake by the plant is a linear function of outside toxicant

156

concentration and the soil-plant interface area (which, in turn, is proportional to root biomass –

157

giving rise to Assumption 4 above), and (iii) the bioaccumulated toxicant is evenly distributed in

158

the root, with no transport to above-ground biomass.

159

The toxicodynamic module has two sub-modules that capture effects of bioaccumulated toxicant

160

on the plant and on the bacteroids. The plant sub-module (fP) reduces the production flux, while

161

the bacteroid sub-module (fR) reduces the total activity of bacteroids and their net contribution to

162

the production flux (shown to be negative during parameter estimation). While specifying

163

functional forms for the toxicodynamic sub-modules, we focused on minimizing the number of

164

parameters while keeping the overall response to exposure flexible yet realistic. The functional

165

forms needed to have no effect when there is no exposure (i.e. fP,R(0)=1), and converge to


8

Page 9 of 25


166

maximum effect for large internal cerium concentration. Hill functions satisfy the requirements,

167

and are often utilized when investigating concentration-dependent effects of biological systems.

168

We used Hill functions to construct the toxicodynamic sub-modules presented in the main text

169

(Table 1). In the SI, we assess sensitivity of our analysis on the choice of functional form of the

170

toxicodynamic module by considering four alternative functional forms featuring (i) constant

171

negative effect upon exposure, (ii) exponential decay as a function of bioaccumulated toxicant

172

concentration, (iii) exponential decay towards a non-zero asymptote, and (iv) logarithmic

173

response. A total of 10 combinations of functional forms for the two sub-modules were

174

investigated (SI, Table S1).

175

Additional analysis using the original choice of toxicodynamc functions was performed to

176

ascertain the sensitivity of the unimodal effect to model parameters. We used two metrics to

177

characterize the strength of the unimodal response: the magnitudes of the initial decline, and of

178

the subsequent recovery. Details are in section “Parameter Sensitivity Analysis” of the SI.

179

Parameter estimation

180 181

The parameters in Table 1 were estimated by fitting to the following data from Priester et al.

182

(12): final biomass measurements of the leaf, stem, root, and nodule biomass, final

183

concentrations of cerium in roots and leaves, timing of flowering, final seed count, and time

184

series of plant height. Additionally, we used data on final fraction of protein in seeds from the

185

same experiments (6).

186

The fraction of below-ground biomass (qW) was estimated as the mean of final ratios of below-

187

ground and total dry weights (without seed weight). Time of maturation (ts) was taken to be

188

equal to the date of first observation of flowers. The time series used as a proxy for growth

189

(stem height) was converted to the state variable (biomass, W) using parameter δ calculated by


9


Page 10 of 25

190

least squares fitting of the total biomass to final height measurements (Figure S1, bottom left

191

plot). The average of the initial plant height measurements for each treatment was used as the

192

initial height for all plants in that treatment in simulations, and converted to W using δ. The

193

toxicant partitioning coefficient (vQ) was estimated as the mean of the ratios of measured final

194

toxicant concentrations in leaf and root biomass using data for medium and high exposures (the

195

low exposure data were not used because bioaccumulation was extremely low). Note that

196

photosynthate (Φ) is only implicitly used, and that units of Φ (photosynthate units, ups) can be

197

set without any loss of generality. Since only the linear combination of yW and φ is used, we

198

choose such ups that φyW=1 ups(J day)-1.

199

Models were fitted to data by maximizing likelihoods while assuming deviations between models

200

and data were due to additive normally distributed error. First, growth-related parameters, pT

201

and Θ. were estimated from stem length data series. Parameters of the toxicokinetic and

202

toxicodynamic modules (specific to each module), reproductive yield (yS and y∑), and the net

203

energetic effect of dinitrogen fixation to the production flux (in terms of proportion, qR), were

204

estimated using growth series of exposed plants, data on bioaccumulation, and the reproductive

205

output by maximizing a sum of (1) log-likelihood function for growth series, and (2) log-likelihood

206

function for endpoints (bioaccumulation and a reproductive output). Both log-likelihood functions

207

were based on additive normally distributed error, with optimizations performed using the

208

Nelder-Mead simplex algorithm.

209

Note that growth data only allow estimates of the total production flux, so they could not be used

210

to fit the net contribution of bacteroids to plant growth, qR. Importantly, however, responses of

211

growth and reproduction to exposure allowed us to differentiate between contributions of

212

bacteroids and the plant to the production flux.


10

Page 11 of 25


Results

213 214

Our hypothesis on the type of the symbiotic interaction is sufficient to explain the

215

observed patterns in growth, bioaccumulation, and reproduction. The model fits presented

216

in the first three plot columns of Figure 2 (titles ‘Growth’, ‘Bioaccumulation’, and ‘Reproduction’)

217

illustrate that the model is able to capture important features of the observed dynamics:

218

•

non-monotonic (negative) effect of exposure on plant (stem height) growth that is stronger for low exposures than for higher exposures,

219 220

•

bioaccumulation pattern that increases monotonically with exposure, and

221

•

non-monotonic pattern (lowest exposure begets the strongest effect) of reproductive

222

responses to exposure.

223

The reproductive patterns were simulated using a single set of initial conditions for all

224

exposures. The agreement between the data and the model improves further when the initial

225

condition is allowed to vary to match initial conditions used for the growth curves (Figure S3).

226

227 228

Figure 2: Model fits of the impact of nano-CeO2 exposure on soybean growth, bioaccumulation, and

229

reproduction, and predictions of bacteroid activity. Each row shows data and model runs for growth (first

230

column), bioaccumulation (second column), reproduction (third column), and predicted N2-fixing bacteroid

231

activity (fourth column). Top row has been simulated using scaled seed protein, and the second row using


11


Page 12 of 25

232

seed count as a measure of reproductive output. Toxicodynamic modules listed in Table 1 (with only two

233

free parameters) have been used. Data plotted in the first three columns were used to fit the model:

234

growth curves (first column), cerium bioaccumulation (second column), and two measures of reproduction

235

(third column): recorded seed count (top), and scaled seed protein (bottom). The last column compares

236

predicted bacteroid activity with data on measured N2 fixation potential (normalized to root nodule dry

237

biomass (12)) as a proportion of the largest mean. The black curve represents model predictions of root

238

nodule bacteroid activity (normalized to maximum value) as a function of exposure. Blue lines, stars:

239

control (no exposure). Red lines, circles: low exposure (0.1 g/kg). Green lines, triangles upwards: medium

240

exposure (0.5 g/kg). Cyan lines, triangles downwards: high exposure (1 g/kg). Black lines: simulations.

241

In the absence of exposure, contribution of root nodule symbioses to plant growth is

242

negative (for particular experimental conditions). Although it is impossible to use only data

243

for the control to distinguish relative contributions to the production flux of the plant’s own

244

assimilation and of the net contribution from bacteroid activity, the data on toxicity help

245

distinguish the two. In particular, the increase in plant growth and reproduction observed at the

246

highest relative to the intermediate exposure level can only be achieved for negative

247

contributions of the symbioses to the production flux (qR0), our model would predict

346

that all toxicity patterns should be monotonic. However, mechanistic insight into the transition

347

from parasitism to mutualism would require a model that recognizes explicitly the nitrogen

348

fluxes, for example by invoking the formalism of dynamic energy budget theory as in one study

349

of the symbiosis of corals and their symbionts (29).

350

All legume-bacteroid symbioses are clearly potentially susceptible to the environmentally-

351

induced toxicity buffering mediated by the symbiotic interactions modeled here, but other similar

352

symbiotic systems may be susceptible as well. Plants and ectomycorrhizal fungi respond to

353

manipulation of the fungal community (30) and plant growth could thereby be buffered against

354

toxicity in environments where a fungal species (directly or indirectly) competes with the plant

355

for specific nutrients. Indeed, buffering of toxicity for a focal species through indirect effects


16

Page 17 of 25


356

mediated by interaction with natural enemies is common in nature (31). We have here

357

demonstrated that similar responses are likely when a host organism interacts with its endo-

358

symbionts.

359

Supporting Information: cover sheet; argumentation to support height as a measure of weight;

360

list of alternative toxicodynamic modules; simulations for all toxicodynamic modules with a

361

unique initial condition; simulations for all toxicodynamic modules with interpolated initial

362

conditions; parameter values; parameter sensitivity analysis; and toxicokinetics and

363

toxicodynamics.

364

ACKNOWLEDGEMENTS

365

We thank J.H. Priester for providing data in digital form and helping with useful information and

366

insights, Y. Wang and J. Rohr for valuable discussions and advice, and T. Jager for sharing

367

statistical programs. This research was primarily funded by the National Science Foundation

368

(NSF) and the U.S. Environmental Protection Agency (EPA) under Cooperative Agreements

369

DBI-0830117 and 1266377 (to P.A.H and R.M.N.) and by the US Environmental Protection

370

Agency under STAR grant 835797 to RMN and EM. Additional funding was provided by the

371

Croatian National Science Foundation (HRZZ) under the project 2202-ACCTA. Any opinions,

372

findings, and conclusions expressed in this material are those of the author(s) and do not

373

necessarily reflect those of the NSF, the EPA, or the HRZZ. This work has not been subjected

374

to EPA review and no official endorsement should be inferred.

375

REFERENCES

376

(1)

Holden, P. A.; Nisbet, R. M.; Lenihan, H. S.; Miller, R. J.; Cherr, G. N.; Schimel, J. P.;

377

Gardea-Torresdey, J. L. Ecological nanotoxicology: integrating nanomaterial hazard

378

considerations across the subcellular, population, community, and ecosystems levels. Acc.

379

Chem. Res. 2013, 46 (3), 813–822.


17


380

(2)

Page 18 of 25

Xia, T.; Malasarn, D.; Lin, S.; Ji, Z.; Zhang, H.; Miller, R. J.; Keller, A. A.; Nisbet, R. M.;

381

Harthorn, B. H.; Godwin, H. A.; et al. Implementation of a multidisciplinary approach to

382

solve complex nano EHS problems by the UC Center for the Environmental Implications of

383

Nanotechnology. Small Weinh. Bergstr. Ger. 2013, 9 (9–10), 1428–1443.

384

(3)

2006, 311 (5761), 622–627.

385 386

Nel, A.; Xia, T.; Mädler, L.; Li, N. Toxic Potential of Materials at the Nanolevel. Science

(4)

Priester, J. H.; Stoimenov, P. K.; Mielke, R. E.; Webb, S. M.; Ehrhardt, C.; Zhang, J. P.;

387

Stucky, G. D.; Holden, P. A. Effects of Soluble Cadmium Salts Versus CdSe Quantum

388

Dots on the Growth of Planktonic Pseudomonas aeruginosa. Environ. Sci. Technol. 2009,

389

43 (7), 2589–2594.

390

(5)

Kaweeteerawat, C.; Ivask, A.; Liu, R.; Zhang, H.; Chang, C. H.; Low-Kam, C.; Fischer, H.;

391

Ji, Z.; Pokhrel, S.; Cohen, Y.; et al. Toxicity of metal oxide nanoparticles in Escherichia coli

392

correlates with conduction band and hydration energies. Environ. Sci. Technol. 2015, 49

393

(2), 1105–1112.

394

(6)

Priester, J. H.; Moritz, S. C.; Espinosa, K.; Ge, Y.; Wang, Y.; Nisbet, R.M.; Schimel, J.P.;

395

Goggi, A.S.; Gardea-Torresdeyc, J.L.; Holden, P.A. Damage assessment for soybean

396

cultivated in soil with either CeO2 or ZnO manufactured nanomaterials. Sci. Total Environ.

397

2017, 579, 1756–1768.

398

(7)

van Leeuwen, I. M. M. van; Vera, J.; Wolkenhauer, O. Dynamic energy budget approaches

399

for modelling organismal ageing. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2010, 365

400

(1557), 3443–3454.

401

(8)

Klanjscek, T.; Nisbet, R. M.; Priester, J. H.; Holden, P. A. Modeling Physiological

402

Processes That Relate Toxicant Exposure and Bacterial Population Dynamics. PLOS ONE

403

2012, 7 (2), e26955.


18

Page 19 of 25

404


(9)

Klanjscek, T.; Nisbet, R. M.; Priester, J. H.; Holden, P. A. Dynamic energy budget

405

approach to modeling mechanisms of CdSe quantum dot toxicity. Ecotoxicol. Lond. Engl.

406

2013, 22 (2), 319–330.

407 408 409

(10) Klanjscek, T.; Muller, E. B.; Nisbet, R. M. Feedbacks and tipping points in organismal response to oxidative stress. J. Theor. Biol. 2016, 404, 361–374. (11) Du, W.; Tan, W.; Peralta-Videa, J. R.; Gardea-Torresdey, J. L.; Ji, R.; Yin, Y.; Guo, H.

410

Interaction of metal oxide nanoparticles with higher terrestrial plants: Physiological and

411

biochemical aspects. Plant Physiol. Biochem. 2017, 110, 210–225.

412

(12) Priester, J. H.; Ge, Y.; Mielke, R. E.; Horst, A. M.; Moritz, S. C.; Espinosa, K.; Gelb, J.;

413

Walker, S. L.; Nisbet, R. M.; An, Y.-J.; et al. Soybean susceptibility to manufactured

414

nanomaterials with evidence for food quality and soil fertility interruption. Proc. Natl. Acad.

415

Sci. 2012, 109 (37), 14734–14735.

416 417 418

(13) Dimkpa, C. O. Can nanotechnology deliver the promised benefits without negatively impacting soil microbial life? J. Basic Microbiol. 2014, 54 (9), 889–904. (14) Moll, J.; Gogos, A.; Bucheli, T. D.; Widmer, F.; van der Heijden, M. G. A. Effect of

419

nanoparticles on red clover and its symbiotic microorganisms. J. Nanobiotechnology 2016,

420

14, 36.

421

(15) Conolly, R. B.; Lutz, W. K. Nonmonotonic Dose-Response Relationships: Mechanistic

422

Basis, Kinetic Modeling, and Implications for Risk Assessment. Toxicol. Sci. 2004, 77 (1),

423

151–157.

424

(16) Jager, T.; Heugens, E. H. W.; Kooijman, S. A. L. M. Making Sense of Ecotoxicological Test

425

Results: Towards Application of Process-based Models. Ecotoxicology 2006, 15 (3), 305–

426

314.

427 428

(17) Boelsterli, U. Mechanistic Toxicology: The molecular basis of how chemicals disrupt biological targets; 2003; pp 258–263.


19


429

Page 20 of 25

(18) Hernandez-Viezcas, J. A.; Castillo-Michel, H.; Andrews, J. C.; Cotte, M.; Rico, C.; Peralta-

430

Videa, J. R.; Ge, Y.; Priester, J. H.; Holden, P. A.; Gardea-Torresdey, J. L. In Situ

431

Synchrotron X-ray Fluorescence Mapping and Speciation of CeO2 and ZnO Nanoparticles

432

in Soil Cultivated Soybean (Glycine max). ACS Nano 2013, 7 (2), 1415–1423.

433

(19) Ashauer, R.; Escher, B. I. Advantages of toxicokinetic and toxicodynamic modelling in

434

aquatic ecotoxicology and risk assessment. J. Environ. Monit. JEM 2010, 12 (11), 2056–

435

2061.

436

(20) Jager, T.; Albert, C.; Preuss, T. G.; Ashauer, R. General Unified Threshold Model of

437

Survival - a Toxicokinetic-Toxicodynamic Framework for Ecotoxicology. Environ. Sci.

438

Technol. 2011, 45 (7), 2529–2540.

439

(21) Amarger, N.; Mariotti, A.; Mariotti, F.; Durr, J. C.; Bourguignon, C.; Lagacherie, B. Estimate

440

of symbiotically fixed nitrogen in field grown soybeans using variations in 15N natural

441

abundance. Plant Soil 1979, 52 (2), 269–280.

442

(22) Manguiat, I. J.; Mendoza, D. M.; Perez, A. M.; Yoneyama, T. Comparison of 15N natural

443

abundance and difference methods for estimating N2 fixation of soybeans grown in a

444

tropical soil. Soil Sci. Plant Nutr. 1988, 34 (4), 593–604.

445

(23) Zhu, B.; Cheng, W. Nodulated soybean enhances rhizosphere priming effects on soil

446

organic matter decomposition more than non-nodulated soybean. Soil Biol. Biochem. 51,

447

56–65.

448

(24) Kohl, D. H.; Shearer, G.; Harper, J. E. Estimates of N2 Fixation Based on Differences in

449

the Natural Abundance of 15N in Nodulating and Nonnodulating Isolines of Soybeans 1.

450

Plant Physiol. 1980, 66 (1), 61–65.

451 452

(25) Djordjevic, M. A.; Gabriel, D. W.; Rolfe, B. G. Rhizobium-The Refined Parasite of Legumes. Annu. Rev. Phytopathol. 1987, 25 (1), 145–168.


20

Page 21 of 25


453

(26) Salon, C.; Jean-Christophe, A.; Larmure, A.; Ourry, A.; Prudent, M.; Voisin, A.-S. Plant N

454

Fluxes and Modulation by Nitrogen, Heat and Water Stresses: A Review Based on

455

Comparison of Legumes and Non Legume Plants. 2011.

456 457 458

(27) Abd-Alla, M. H.; Issa, A. A.; Ohyama, T. Impact of Harsh Environmental Conditions on Nodule Formation and Dinitrogen Fixation of Legumes. 2014. (28) Peralta-Videa, J. R.; Hernandez-Viezcas, J. A.; Zhao, L.; Diaz, B. C.; Ge, Y.; Priester, J.

459

H.; Holden, P. A.; Gardea-Torresdey, J. L. Cerium dioxide and zinc oxide nanoparticles

460

alter the nutritional value of soil cultivated soybean plants. Plant Physiol. Biochem. PPB

461

2014, 80, 128–135.

462

(29) Muller; Doyle, F. J.; Nisbet, R. M.; Edmunds, P.; Kooijman, S. Dynamic energy budgets of

463

syntrophic symbiotic relationships between heterotrophic hosts and photoautotrophic

464

symbionts. Comp. Biochem. Physiol. – Mol. Integr. Physiol. Suppl. 2009, 153A, S145–

465

S145.

466 467 468 469

(30) Moeller, H. V.; Peay, K. G. Competition-function tradeoffs in ectomycorrhizal fungi. PeerJ 2016, 4, e2270. (31) Fleeger, J. W.; Carman, K. R.; Nisbet, R. M. Indirect effects of contaminants in aquatic ecosystems. Sci. Total Environ. 2003, 317 (1–3), 207–233.

470 471 472


21


473

Page 22 of 25

Table 1: Summary of model dynamics.

MODEL EQUATIONS State variable Total plant dry biomass, W (g) Seed count, S (count), or Scaled seed protein (∑)

Dynamics

Parameters and units

dW = yW (1 − Θ) P dt

yW - growth yield (g/J)

dS = yS ΘP dt

yS - reproductive yield (seeds/J) yΣ - scaled seed protein yield in

OR

milligrams of protein per Joule time

dΣ = yΣ Θ P dt Bioaccumulated cerium in the plant, Q (mg of

gram of plant dry mass

dQ = vQ qW X AW dt

( mg Jg )

vQ - bioaccumulation rate constant

    mg(Ce)     g(root) day  g (CeO2 ) kg ( soil )    

cerium)

qW - fraction of below-ground biomass in total biomass (g(root)/g(plant))

X A - environmental toxicant concentration (g(CeO2)/kg(soil)) FUNCTIONALS Gross production of photosynthate, Φ

Φ = ϕW

φ - biomass-specific gross production rate (ups/(day g))


22

Page 23 of 25


(ups/day) Net production flux, P

P = pT

(J/day)

fW + f R qR Φ (1 + qR )

pT –net production yield (J/ups) qR – net contribution or detraction to the production flux of supporting bacteroids and dinitrogen fixation (n.d.)

Fraction of P dedicated to reproduction (dimensionless)

Below-ground cerium

θ - reproductive switch rate (day-1) t < tS 0  Θ = θ (t − t S ) tS ≤ t ≤ θ −1 + tS ,  tS – age at the onset of reproduction otherwise 1

(

(day)

CB =

concentration, CB (mg

qQ Q

qQ – cerium partitioning coefficient

qW W

between above- and below-ground

Ce/g biomass) Reduction of

)

biomass (n.d.)

fP =

photosynthesis and/or increase in plant

1 γP

 C  1+  B   CP 50 

.

CP50 – below-ground toxicant concentration (proportional to total concentration) for which the

maintenance in response

production (P) is reduced by 50%

to the burden (CB)

(mg/g)

(dimensionless)

γ P - strength of the effect, set to γ P = 1 in fitting (n.d.)

Reduction of net energetic cost (or benefit) of dinitrogen

fR =

1 γR

 C  1+  B   CR 50 

.

CR50 – below-ground cerium concentration for which 50% of bacteroids die (mg/g)


23


fixation in response to

γ R - strength of the effect,

the burden (CB)

set to γ R = 2 in fitting (n.d.)

Page 24 of 25

(dimensionless) Although ten different sets of the toxicodynamic modules (fP and fR) were investigated (Table S1, results shown in Figure S2 and Figure S3), only modules used to generate Figure 2 are listed below; for the nine other combinations of toxicodynamic modules, see Table S1. The units of reproductive yield listed in the table apply only when seeds are considered as the reproductive endpoint. Parameter values for the toxicodynamic modules used for Figure 2 are given in Table S2. The abbreviation ‘ups’ denotes (proprietary) units of photosynthate, and ‘n.d.’ denotes ‘non-dimensional’.


24

Page 25 of 25


TOC/Abstract art 83x47mm (300 x 300 DPI)


HostâSymbiont Interaction Model Explains Non-monotonic Response

Recommend Documents

HostâSymbiont Interaction Model Explains Non-monotonic Response