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J Nanopart Res (2012) 14:913 DOI 10.1007/s11051-012-0913-6
RESEARCH PAPER
Effects of TiO2 nanoparticles on the growth and metabolism of three species of freshwater algae Bradley J. Cardinale • Raven Bier Courtney Kwan
•
Received: 19 July 2011 / Accepted: 8 May 2012 Ó Springer Science+Business Media B.V. 2012
Abstract We examined how TiO2 nanoparticles (nTiO2) impact the growth and metabolism of three species of freshwater green algae (Scenedesmus quadricauda, Chlamydomonas moewusii, and Chlorella vulgaris) that are widespread throughout North America. We exposed laboratory cultures to five initial concentrations of nTiO2 (0, 50, 100, 200, and 300 ppm) and measured impacts on species population growth rates, as well as on metabolic rates of gross primary production (GPP) and respiration (R). Population growth rates were consistently reduced by nTiO2, with reduction ranging from 11 to 27 % depending on the species. But the mechanisms of reduction differed among species. For Chlamydomonas, nTiO2 reduced both GPP and R, but effects on
Electronic supplementary material The online version of this article (doi:10.1007/s11051-012-0913-6) contains supplementary material, which is available to authorized users. B. J. Cardinale (&) School of Natural Resources & Environment, University of Michigan, 440 Church Street, Ann Arbor, MI 48109, USA e-mail: [email protected] R. Bier Department of Biology, Duke University, Durham, NC 27708, USA R. Bier C. Kwan Department of Ecology, Evolution & Marine Biology, University of California, Santa Barbara, CA 93106, USA
GPP were stronger. As a consequence, carbon was respired more quickly than it was fixed, leading to reduced growth. In contrast, nTiO2 stimulated both GPP and R in Chorella. But because R was stimulated to a greater extent than GPP, carbon loss again exceeded fixation, leading to reduced growth. For Scenedesmus, nTiO2 had no significant impact on R, but reduced GPP. This pattern also caused carbon loss to exceed fixation. Results suggest that nTiO2 may generally suppress the growth of pelagic algae, but these impacts are manifest through contrasting effects on species-specific metabolic functions. Because growth and metabolism of algae are fundamental to the functioning of ecosystems and the structure of aquatic food-webs, our study suggests nTiO2 has potential to alter important community and ecosystem properties of freshwater habitats. Keywords Titanium dioxide Nanomaterials Lakes Primary production Nanobiotechnology
Introduction The rapidly expanding field of nanotechnology is producing a wealth of new consumer products and pharmaceuticals (Rejeski 2009). Despite the many benefits these products have for society, there is growing concern that some types of nanomaterials could have unintended, perhaps negative, impacts on natural ecosystems as they inevitably make their way
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into the environment (Colvin 2003; Nel et al. 2006; Warheit 2010). This concern stems from the fact that most nanomaterials have physical and chemical characteristics that differ from their bulk forms (Handy and Shaw 2007; Farre et al. 2009), and some of these characteristics (e.g., high reactivity, ability to penetrate membranes, photoactivity) have potential to exert adverse impacts on living organisms (Nel et al. 2006; Scown et al. 2010). Given this possibility, an increasing amount of research has focused on understanding what impacts nanomaterials may have on different types of natural ecosystems (Guzman et al. 2006; Handy et al. 2008; Mueller and Nowack 2008). One nanomaterial that has received considerable attention is titanium dioxide (nTiO2). nTiO2 ranks among the most abundant of engineered nanomaterials (Borm et al. 2006; Mueller and Nowack 2008), and it is increasingly used in the production of solar cells, cosmetics, pharmaceuticals, paints, and food-products (Ju-Nam and Lead 2008; Sugibayashi et al. 2008). Although environmental concentrations of nTiO2 are largely undocumented and a matter of debate (Mueller and Nowack 2008), there is some evidence that release of nTiO2 from commercial products can lead to measurable, and sometimes significant, concentrations in urban runoff (Kaegi et al. 2008). This runoff ultimately flows into streams and lakes; however, once nTiO2 enters freshwater habitats, the literature is conflicted regarding what impacts this material may have on aquatic organisms. Standard toxicity tests performed with model metazoans (mostly Daphnia) have ranged from studies that show no effects of nTiO2 on growth or mortality (Griffitt et al. 2008; Heinlaan et al. 2008), to studies that have documented strong impacts on physiology (Lovern et al. 2007) and mortality (Hall et al. 2009). Similar contrasts are apparent in studies focused on lower trophic levels, such as algae that form the base of the food-web. For these organisms, one body of work suggests that nTiO2 may be toxic to algae (Hund-Rinke and Simon 2006; Velzeboer et al. 2008), and this is usually attributed to the photocatalytic properties of nTiO2 that generate reactive oxygen species in the presence of UV light (Reeves et al. 2008; Brunet et al. 2009). A separate body of work has demonstrated that nTiO2 can facilitate electron transport in plants and, in turn, can stimulate photosynthesis (Hong et al. 2005; Lei et al. 2007). Certain types of algae have even been
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engineered to incorporate nTiO2 into their tissues to enhance photo-efficiency (Jeffryes et al. 2008). Given these contrasting results, it is not yet clear what the general impacts of nTiO2 are on freshwater algae or how any potential impacts might be manifest. Here we report the results of an experiment in which we examined how TiO2 nanoparticles influence the growth and metabolism of three ecologically important species of freshwater green algae (Chlamydomonas moewusii, Chlorella vulgaris, and Scendesmus quadricauda). We exposed monocultures of each species to three increasing concentrations of nTiO2 and then measured increases in population biomass during exponential phases of growth. Growth rates were then related to measured rates of photosynthesis and respiration (R) in the algal cultures. We show that nTiO2 consistently reduced the population growth rates of all three species, but it did so by altering differing aspects their metabolic processes. Our study contributes to a more general understanding of how nanomaterials influence primary producers while, at the same time, emphasizing that the mechanisms that underlie these impacts can be species specific.
Methods and materials Focal species Our experiment focused on three species of Chlorophycean green algae that rank among the most widespread of all algae (Scenedesmus quadricauda, Chlamydomonas moewusii, and Chlorella vulgaris). For example, in a 2007 survey of 1,028 N, American lakes by the U.S. EPA (National Lakes Assessment, http://water.epa.gov/type/lakes/lakessurvey_index.cfm), Scenedesmus occurred in ca. 53 % of all lakes, Chlorella occurred in 31 %, and Chlamydomonas in 23 %. Aside from their widespread distributions, these three species are commonly used in laboratory studies due to their availability and ease of culture. The particular strains used in our experiment were obtained from stock cultures at Carolina Biological Supply (C. vulgaris) or the Culture Collection of Algae at the University of Texas (http://www.sbs.utexas.edu/utex/) and were maintained in laboratory cultures for ca. 3 months prior to the start of this experiment.
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Experimental units The experimental units used for this study were 150 mL borosilicate glass jars that were filled with 130 mL of sterilized soil water media (SWM). SWM is a widely used growth media for culturing algae, and it is made by steeping greenhouse potting soil for 24 h in UV sterilized 18 MX water to leach micro- and macronutrients that are required for growth (Watanabe 2005). On potential drawback of SWM is that it is not a ‘‘synthetic’’ media where the concentrations of all elements are controlled directly by the researcher. But SWM has several advantages. First, most species of algae grow readily in SWM, which contrasts with most synthetic media that are formulated to maximize the growth of just one species or taxonomic group. Second, SWM mimics many physical and chemical properties of lake water that have the potential to influence the aggregation and settlement of nTiO2 (see Table S1, which details the physical and chemical parameters required to replicate SWM). In addition to the characterization of the media, we have previously published a detailed characterization of the behavior of TiO2 nanoparticles in SWM, as well as several other types of fluid media (Keller et al. 2010). The Keller et al. study was performed concurrently with our experiment and analyzed not only the same batch of SWM used in this experiment, but also the same TiO2 nanoparticles (Evonik Degussa Corp. 4168063098, 98 % pure TiO2, 82 % anatase/18 % rutile, mean size = 27 nm ± SD 4). In that companion study, we showed that nTiO2 aggregates within hours, forming equilibrium particle sizes of ca. 300 nm, which settle from solution at a rate of *0.6 % per h. For convenience, information on rates of aggregation and sedimentation of nTiO2 are provided in the Supplemental Information (Figs. S1–S2). Experimental design Each of the three species of algae was inoculated in 15 jars at initial values of 20.89 lg of biomass mL-1. Three replicate jars for each species were then randomly assigned to each of five concentrations of nTiO2 (0, 50, 100, 200, or 300 ppm). This range of nTiO2 is orders of magnitude greater than the expected concentrations in freshwater habitats that have been predicted from nanoparticle release models (Mueller and Nowack 2008), but is comparable to the span of
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concentrations that have been used in prior ecotoxicological studies (see Kahru and Dubourguier 2010 for a summary). Nevertheless, it is worth noting that the level of exposure in this experiment may not reflect realistic scenarios, except under extreme cases such as point-source spills. Stock dispersions were produced by adding increasing amounts of TiO2 nanoparticles to 1.0 L of deionized water so that comparable volumes could be added to cultures. The stock solution was sonicated for 30 min (Branson 2510, 40 kHz), after which, inoculations were dispensed into the water of the appropriate containers using a pipette. After jars were inoculated with algae and nTiO2, they were covered with loose fitting rubber stoppers to prevent cross-contamination of algal species. Each jar was then placed in a randomized position on an orbital shaker table that provided continuous stirring at a rate of 30 revolutions per min. The shaker table was located in an environmental walk-in chamber where temperature was held at a constant 18 °C. Two GE cool white fluorescent lamps were hung 20 cm above the shaker table and set to a 16:8 light/dark cycle. These lamps emit light over the entire visible spectrum and emit in the upper UV spectrum at low intensities (Fig. S2, see 360–400 nm where intensities of [0.2 lW cm-2 are apparent). Response variables To characterize the growth rates of each algal species, we measured the fluorescence of a 3-mL subsample drawn from each culture every 2–3 days during the exponential phases of algal growth (15–22 days depending on the species) using an Turner Designs Aquafluor fluorometer. Measures of fluorescence were converted to chlorophyll a, which is a widely used proxy of algal biomass (Wetzel and Likens 2000). On days 9 and 10 of the experiment, algal primary production was measured using the light/dark bottle incubation technique that characterizes rates of phytoplankton production via changes in the concentration of dissolved O2 (Wetzel 1983; Bott 1996). We began by measuring initial levels of O2 (to a resolution of 0.01 mg L-1) in each culture jar using a YSI model 556 probe. Jars were then sealed with air-tight stoppers, covered with tin foil to prevent exposure to light, and set back on the shaker table for a 1-h incubation. At the end of the incubation, we again measured levels of dissolved oxygen to assess the
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amount of respiration in the cultures, R, as the rate at which O2 was consumed in the dark. Jars were then sealed a second time, placed back on the shaker table, and incubated for a second 1-h period while exposed to cool white fluorescent lights. Dissolved oxygen concentrations were measured at the end of the light incubation to assess net primary production, NPP, as the rate of O2 accumulation in the light. Gross primary production (GPP) was then calculated as GPP = R ? NPP. It should be noted that our cultures were not axenic (which is difficult, and often impractical, to accomplish). As such, while estimates of GPP can unambiguously be associated with algal metabolism per se, estimates of R represent the sum of hetero- and autotrophic dark R in the cultures.
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measurement (15 days for Chlamydomonas and Chlorella, and 22 days for Scenedesmus, which grew more slowly). Repeated measures analysis of variance revealed significant time 9 nTiO2 interactions for all three species (Table 1), with each coefficient for the interaction being negative. This indicates that the presence of nTiO2 generally inhibited the growth rates of the algae. To better understand the magnitude of inhibition, Fig. 1d–f re-plots estimated growth rates from the rmANOVAs as a function of nTiO2 concentration. Linear regressions confirm the declining growth rates with increasing nTiO2 (albeit, with considerable scatter) and indicate that, relative to the zero nTiO2 controls, growth was reduced by 11–27 % over the 50–300 ppm range of Ti additions.
Data analyses Effect of nTiO2 on metabolism To determine whether algal growth rates varied as a function of nTiO2 concentration, we used a repeated measures analysis of variance (rmANOVA) with an autoregressive covariance structure AR(1) to model the rate of increase in each species biomass as a function of time, nTiO2 concentration, and the time 9 nTiO2 interaction. Significant interaction terms were taken as evidence that nTiO2 alters a species growth rate. General linear models were then used to estimate how rates of GPP and R varied as a function of nTiO2 concentrations. All estimates of GPP and R were standardized by the amount of chlorophyll a in a culture to give the mass-specific rates of each metabolic function. We did this because the first set of analyses (rmANOVA’s) focused on explaining changes in biomass per se, whereas the second set of analyses (General Linear Model’s) focused on explaining changes in metabolism that might explain variation in biomass. All analyses were performed using SAS, v. 9.1.3.
Results Effect of nTiO2 on algal growth TiO2 nanoparticles reduced the growth of all three algal species. Figure 1a–c shows the natural logarithm of the biomass of each species. The linearity of the growth curves indicate that each species was in exponential phases of growth during the period of
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While TiO2 nanoparticles generally reduced the growth of algae, the impacts on metabolic processes were species specific. For Chlamydomonas, the massspecific rate of GPP declined significantly with increasing concentrations of nTiO2 (Fig. 1g; Table 2A). Mass-specific rates of R in cultures of Chlamydomonas also tended to decline with increasing nTiO2 (Fig. 1g; Table 2B); however, these declines were neither as fast nor as significant as those for GPP. As a result, cultures exposed to increasing levels of nTiO2 tended to respire more than they produced, which likely explains the why growth rates declined for this species. In contrast to Chlamydomonas, metabolic processes in cultures of Chlorella were generally stimulated by the presence of nTiO2 (Fig. 1h; Table 2C, D). However, mass-specific rates of R increased almost two times faster than mass-specific rates of GPP. As a result, cultures exposed to increasing levels of nTiO2 once again tended to respire more than they produced. For cultures of Scenedesmus, mass-specific rates of GPP showed a general tendency to decline with increasing concentrations of nTiO2, but the P-value for this trend was 0.09 (Fig. 1I; Table 2E). At the same time, rates of mass-specific R in the cultures were insensitive to nTiO2 (Table 2F). The combination of decreasing GPP and constant, but generally higher values of R, again ensured that cultures exposed to increasing levels of nTiO2 respired more than they produced.
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A
0.24
Chlamydomonas TiO2 x time,P < 0.01
7
D
G Chlamydomonas
Chlamydomonas 0.004
0.21
5
0.18
4 0.15 y = 0.22 - 0.0002x
8
0.00
B Chlorella TiO2 x time P < 0.01
7 6
0 50 100 200 300 ppm
5 4 3 0
8
C
0.30
E Chlorella
0.27
0.24 y = 0.27 - 0.0001x 0.00
0.18
Scenedesmus TiO2 x time,P = 0.01
7
Growth rate (ln chl-a day-1)
Algal biomass, ln(µg chl-a L-1)
3 0
F Scenedesmus
0.16
6 5
0.14
10
15
20
H Chlorella R
0.002
0.001
GPP
0.000
0.005
I Scenedesmus
0.004 0.003 R
0.000
0.00
Time, days
0.003
GPP
y = 0.15 - 0.0001x
5
GPP 0.000
0.001
0.12
0
R
0.002
0.002
4 3 0
Mass specific metabolic rate (µg O2L -1hr-1 µg chl-a-1)
6
0
100
200
Nano-TiO2 (ppm)
300
0
100
200
300
Nano-TiO2 (ppm)
Fig. 1 Impacts of nTiO2 on the growth rates and metabolism of three species of freshwater algae. Panels a–c show the log rate of increase in the biomass of each species during the phases of exponential growth as a function of initial nTiO2 concentrations added to the experimental microcosms. The significance of the nTiO2 9 time interaction from a repeated measures analysis of variance is noted (see Table 1 for corresponding statistical analyses). Panels d–f re-plot the growth rates from a to c as a
function of nTiO2. The equations for a simple linear regression are given to simply illustrate the magnitude of growth inhibition in the cultures. Panels g–i show how the mass-specific rates of gross primary production (GPP) and respiration (R) in the algal cultures change as a function of nTiO2. Corresponding statistical analyses are given in Table 2. Data points in d–i represent the mean of N = 3 replicate experimental units ±1SEM
Discussion
a greater extent than R. In Chlorella, nTiO2 had the opposite effect and stimulated the species metabolism. But R was stimulated to a greater extent than photosynthesis. In Scenedesmus, only photosynthesis was inhibited by nTiO2. These divergent effects on metabolism all had the same end result, which was to increase the rate of carbon loss relative to gain, resulting in growth inhibition. Collectively, these results suggest that the ultimate impact of nTiO2 on freshwater algae may be quite predictable. However, the mechanistic basis for these impacts could be difficult to understand and predict without a good deal of species-specific information on pathways of metabolism.
We have shown that the presence of TiO2 nanoparticles in freshwater microcosms consistently inhibited the growth rates of three common species of green algae. These reductions were not only significant, but relatively large in magnitude, ranging from 11 to 27 % reductions during the short interval of exponential population growth. While there were general negative effects of nTiO2 on algal growth, the underlying changes in metabolism that led to growth inhibition were unique to each species. In Chlamydomonas, nTiO2 generally shut-down metabolism of the alga, but photosynthesis was reduced to
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Page 6 of 8 Table 1 Results of repeated measures ANOVA’s (analysis of variance) showing how the biomass of three algal species varied as a function of time, concentrations of nano-TiO2, and the time 9 nTiO2 interaction
J Nanopart Res (2012) 14:913
Species/effect
Coefficient
Standard error
df
t Value
Pr [ t
A. Chlamydomonas Intercept
3.1002
0.06228
116
49.78
\0.01
Time (days)
0.2230
0.00621
116
35.92
\0.01
nTiO2 (ppm)
-0.0001
0.00037
116
-0.26
0.80
Time 9 nTiO2
-0.0002
0.00004
116
-4.94
\0.01
B. Chlorella 3.3541
0.06831
116
49.10
\0.01
Time (days) nTiO2 (ppm)
0.2710 -0.0001
0.00681 0.00041
116 116
39.79 -0.27
\0.01 0.79
Time 9 nTiO2
-0.0001
0.00004
116
-2.96
\0.01
Intercept
C. Scenedesmus
These analyses correspond to the data shown in Fig. 1a–c
Table 2 Results of general linear models showing how biomass-specific rates of gross primary production (GPP) and respiration (R) change as a function of nTiO2 for three species of algae. Units for GPP and R are in lg O2 L-1 h-1 per lg chlorophyll a
Intercept
3.1566
0.07409
116
42.61
\0.01
Time (days)
0.1408
0.00585
116
24.09
\0.01
nTiO2 (ppm)
-0.0002
0.00042
116
-0.47
0.64
Time 9 nTiO2
-0.0001
0.00003
116
-2.52
0.01
Species/effect
Estimate
Standard error
t Value
Pr [ |t|
A. Chlamydomonas (GPP) Intercept nTiO2 (ppm)
3.03E-3
3.23E-4
9.37
\0.01
-6.93E-6
1.92E-6
-3.62
\0.01
B. Chlamydomonas (R) 3.94E-3
3.91E-4
10.10
\0.01
-4.84E-6
2.31E-6
-2.09
0.06
4.03E-4 1.98E-6
1.21E-4 7.10E-7
3.34 2.77
\0.01 0.02
Intercept
9.84E-4
1.93E-4
5.10
\0.01
nTiO2 (ppm)
3.64E-6
1.14E-6
3.19
\0.01
Intercept nTiO2 (ppm) C. Chlorella (GPP) Intercept nTiO2 (ppm) D. Chlorella (R)
E. Scenedesmus (GPP) Intercept nTiO2 (ppm)
1.79E-3
3.29E-4
5.46
\0.01
-3.49E-6
1.84E-6
-1.89
0.09
2.75E-3
6.29E-4
4.37
\0.01
-1.48E-6
3.53E-6
-0.42
0.68
F. Scenedesmus (R) Intercept These analyses correspond to the data shown in Fig. 1g–i
nTiO2 (ppm)
Results of our study are qualitatively consistent with a select number of prior studies that have suggested nTiO2 can decrease the growth of certain types of pelagic algae (Hund-Rinke and Simon 2006; Velzeboer et al. 2008). In these past studies, reduced growth of algae has been attributed to the photocatalytic properties of TiO2 that generate reactive oxygen species (ROS) that disrupt cell membranes (Reeves
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et al. 2008; Brunet et al. 2009) and/or interfere with photosynthesis (Kim and Lee 2005; Hund-Rinke and Simon 2006; Neal 2008). Such effects require that electrons on the surface of Ti become excited by UV light at ca. 380 nm (Hashimoto et al. 2005). The fluorescent lamps used to grow algae in this study do emit 0.2–0.4 lW cm-2 at 360–380 nm (see Supplemental Fig. S2), which some have suggested is
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sufficient to stimulate minimal photocatalytic activity of Ti (see Fig. 6 of (Hashimoto et al. 2005). Because we did not measure redox reactions with any direct assay, we can neither confirm nor refute photocatalysis as a potential mechanism of toxicity. But even if photocatalysis was operating in this study, it could not possibly explain the contrasting responses of R and photosynthesis among the three algal taxa. In particular, the fact that these metabolic processes were stimulated in one taxon (Chlorella) suggests that some metabolic processes of select algae may respond positively to nTiO2. When other studies have shown stimulation of algal growth by nTiO2, it has been explained as electron transport at the surface of Ti enhancing the efficiency of photosynthesis (Hong et al. 2005; Lei et al. 2007; Gao et al. 2008). Thus, to understand why nTiO2 affects algal fitness, future work will need to consider the balance of potentially opposing impacts (positive and negative) on photosynthesis and R. Like any experiment that uses an overly simplified model system, our study has inherent limitations. While algal monocultures growing in glass jars are useful model systems, they are an over-simplification of natural lake ecosystems that have dozens of species interacting in media that can be far more complex. Natural phytoplankton communities are exposed to light intensities that greatly exceed what can be replicated in the lab, which almost certainly influences the reactivity of nTiO2 through exposure to UV. Furthermore, while the range of nTiO2 used in this study is comparable to the span of concentrations that have been used in many prior ecotoxicological studies (see Kahru and Dubourguier 2010 for a summary), the concentrations are high than expected based on nanoparticle release models (Mueller and Nowack 2008). Thus, results of our study are best interpreted as showing the potential impacts of nTiO2 under idealized conditions at potentially higher exposures—and whether or not these impacts can be generalized to realistic scenarios remains to be seen. But even as we work to extend the reality and complexity of study systems, this study suggests that TiO2 nanoparticles may have consistent negative impacts on the growth of freshwater green algae, but for reasons that are qualitatively different among species. Acknowledgments This project was supported by the National Science Foundation and Environmental Protection
Page 7 of 8 Agency under Cooperative Agreement # NSF-EF0830117. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the Environmental Protection Agency.
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RESEARCH PAPER
Effects of TiO2 nanoparticles on the growth and metabolism of three species of freshwater algae Bradley J. Cardinale • Raven Bier Courtney Kwan
•
Received: 19 July 2011 / Accepted: 8 May 2012 Ó Springer Science+Business Media B.V. 2012
Abstract We examined how TiO2 nanoparticles (nTiO2) impact the growth and metabolism of three species of freshwater green algae (Scenedesmus quadricauda, Chlamydomonas moewusii, and Chlorella vulgaris) that are widespread throughout North America. We exposed laboratory cultures to five initial concentrations of nTiO2 (0, 50, 100, 200, and 300 ppm) and measured impacts on species population growth rates, as well as on metabolic rates of gross primary production (GPP) and respiration (R). Population growth rates were consistently reduced by nTiO2, with reduction ranging from 11 to 27 % depending on the species. But the mechanisms of reduction differed among species. For Chlamydomonas, nTiO2 reduced both GPP and R, but effects on
Electronic supplementary material The online version of this article (doi:10.1007/s11051-012-0913-6) contains supplementary material, which is available to authorized users. B. J. Cardinale (&) School of Natural Resources & Environment, University of Michigan, 440 Church Street, Ann Arbor, MI 48109, USA e-mail: [email protected] R. Bier Department of Biology, Duke University, Durham, NC 27708, USA R. Bier C. Kwan Department of Ecology, Evolution & Marine Biology, University of California, Santa Barbara, CA 93106, USA
GPP were stronger. As a consequence, carbon was respired more quickly than it was fixed, leading to reduced growth. In contrast, nTiO2 stimulated both GPP and R in Chorella. But because R was stimulated to a greater extent than GPP, carbon loss again exceeded fixation, leading to reduced growth. For Scenedesmus, nTiO2 had no significant impact on R, but reduced GPP. This pattern also caused carbon loss to exceed fixation. Results suggest that nTiO2 may generally suppress the growth of pelagic algae, but these impacts are manifest through contrasting effects on species-specific metabolic functions. Because growth and metabolism of algae are fundamental to the functioning of ecosystems and the structure of aquatic food-webs, our study suggests nTiO2 has potential to alter important community and ecosystem properties of freshwater habitats. Keywords Titanium dioxide Nanomaterials Lakes Primary production Nanobiotechnology
Introduction The rapidly expanding field of nanotechnology is producing a wealth of new consumer products and pharmaceuticals (Rejeski 2009). Despite the many benefits these products have for society, there is growing concern that some types of nanomaterials could have unintended, perhaps negative, impacts on natural ecosystems as they inevitably make their way
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into the environment (Colvin 2003; Nel et al. 2006; Warheit 2010). This concern stems from the fact that most nanomaterials have physical and chemical characteristics that differ from their bulk forms (Handy and Shaw 2007; Farre et al. 2009), and some of these characteristics (e.g., high reactivity, ability to penetrate membranes, photoactivity) have potential to exert adverse impacts on living organisms (Nel et al. 2006; Scown et al. 2010). Given this possibility, an increasing amount of research has focused on understanding what impacts nanomaterials may have on different types of natural ecosystems (Guzman et al. 2006; Handy et al. 2008; Mueller and Nowack 2008). One nanomaterial that has received considerable attention is titanium dioxide (nTiO2). nTiO2 ranks among the most abundant of engineered nanomaterials (Borm et al. 2006; Mueller and Nowack 2008), and it is increasingly used in the production of solar cells, cosmetics, pharmaceuticals, paints, and food-products (Ju-Nam and Lead 2008; Sugibayashi et al. 2008). Although environmental concentrations of nTiO2 are largely undocumented and a matter of debate (Mueller and Nowack 2008), there is some evidence that release of nTiO2 from commercial products can lead to measurable, and sometimes significant, concentrations in urban runoff (Kaegi et al. 2008). This runoff ultimately flows into streams and lakes; however, once nTiO2 enters freshwater habitats, the literature is conflicted regarding what impacts this material may have on aquatic organisms. Standard toxicity tests performed with model metazoans (mostly Daphnia) have ranged from studies that show no effects of nTiO2 on growth or mortality (Griffitt et al. 2008; Heinlaan et al. 2008), to studies that have documented strong impacts on physiology (Lovern et al. 2007) and mortality (Hall et al. 2009). Similar contrasts are apparent in studies focused on lower trophic levels, such as algae that form the base of the food-web. For these organisms, one body of work suggests that nTiO2 may be toxic to algae (Hund-Rinke and Simon 2006; Velzeboer et al. 2008), and this is usually attributed to the photocatalytic properties of nTiO2 that generate reactive oxygen species in the presence of UV light (Reeves et al. 2008; Brunet et al. 2009). A separate body of work has demonstrated that nTiO2 can facilitate electron transport in plants and, in turn, can stimulate photosynthesis (Hong et al. 2005; Lei et al. 2007). Certain types of algae have even been
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engineered to incorporate nTiO2 into their tissues to enhance photo-efficiency (Jeffryes et al. 2008). Given these contrasting results, it is not yet clear what the general impacts of nTiO2 are on freshwater algae or how any potential impacts might be manifest. Here we report the results of an experiment in which we examined how TiO2 nanoparticles influence the growth and metabolism of three ecologically important species of freshwater green algae (Chlamydomonas moewusii, Chlorella vulgaris, and Scendesmus quadricauda). We exposed monocultures of each species to three increasing concentrations of nTiO2 and then measured increases in population biomass during exponential phases of growth. Growth rates were then related to measured rates of photosynthesis and respiration (R) in the algal cultures. We show that nTiO2 consistently reduced the population growth rates of all three species, but it did so by altering differing aspects their metabolic processes. Our study contributes to a more general understanding of how nanomaterials influence primary producers while, at the same time, emphasizing that the mechanisms that underlie these impacts can be species specific.
Methods and materials Focal species Our experiment focused on three species of Chlorophycean green algae that rank among the most widespread of all algae (Scenedesmus quadricauda, Chlamydomonas moewusii, and Chlorella vulgaris). For example, in a 2007 survey of 1,028 N, American lakes by the U.S. EPA (National Lakes Assessment, http://water.epa.gov/type/lakes/lakessurvey_index.cfm), Scenedesmus occurred in ca. 53 % of all lakes, Chlorella occurred in 31 %, and Chlamydomonas in 23 %. Aside from their widespread distributions, these three species are commonly used in laboratory studies due to their availability and ease of culture. The particular strains used in our experiment were obtained from stock cultures at Carolina Biological Supply (C. vulgaris) or the Culture Collection of Algae at the University of Texas (http://www.sbs.utexas.edu/utex/) and were maintained in laboratory cultures for ca. 3 months prior to the start of this experiment.
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Experimental units The experimental units used for this study were 150 mL borosilicate glass jars that were filled with 130 mL of sterilized soil water media (SWM). SWM is a widely used growth media for culturing algae, and it is made by steeping greenhouse potting soil for 24 h in UV sterilized 18 MX water to leach micro- and macronutrients that are required for growth (Watanabe 2005). On potential drawback of SWM is that it is not a ‘‘synthetic’’ media where the concentrations of all elements are controlled directly by the researcher. But SWM has several advantages. First, most species of algae grow readily in SWM, which contrasts with most synthetic media that are formulated to maximize the growth of just one species or taxonomic group. Second, SWM mimics many physical and chemical properties of lake water that have the potential to influence the aggregation and settlement of nTiO2 (see Table S1, which details the physical and chemical parameters required to replicate SWM). In addition to the characterization of the media, we have previously published a detailed characterization of the behavior of TiO2 nanoparticles in SWM, as well as several other types of fluid media (Keller et al. 2010). The Keller et al. study was performed concurrently with our experiment and analyzed not only the same batch of SWM used in this experiment, but also the same TiO2 nanoparticles (Evonik Degussa Corp. 4168063098, 98 % pure TiO2, 82 % anatase/18 % rutile, mean size = 27 nm ± SD 4). In that companion study, we showed that nTiO2 aggregates within hours, forming equilibrium particle sizes of ca. 300 nm, which settle from solution at a rate of *0.6 % per h. For convenience, information on rates of aggregation and sedimentation of nTiO2 are provided in the Supplemental Information (Figs. S1–S2). Experimental design Each of the three species of algae was inoculated in 15 jars at initial values of 20.89 lg of biomass mL-1. Three replicate jars for each species were then randomly assigned to each of five concentrations of nTiO2 (0, 50, 100, 200, or 300 ppm). This range of nTiO2 is orders of magnitude greater than the expected concentrations in freshwater habitats that have been predicted from nanoparticle release models (Mueller and Nowack 2008), but is comparable to the span of
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concentrations that have been used in prior ecotoxicological studies (see Kahru and Dubourguier 2010 for a summary). Nevertheless, it is worth noting that the level of exposure in this experiment may not reflect realistic scenarios, except under extreme cases such as point-source spills. Stock dispersions were produced by adding increasing amounts of TiO2 nanoparticles to 1.0 L of deionized water so that comparable volumes could be added to cultures. The stock solution was sonicated for 30 min (Branson 2510, 40 kHz), after which, inoculations were dispensed into the water of the appropriate containers using a pipette. After jars were inoculated with algae and nTiO2, they were covered with loose fitting rubber stoppers to prevent cross-contamination of algal species. Each jar was then placed in a randomized position on an orbital shaker table that provided continuous stirring at a rate of 30 revolutions per min. The shaker table was located in an environmental walk-in chamber where temperature was held at a constant 18 °C. Two GE cool white fluorescent lamps were hung 20 cm above the shaker table and set to a 16:8 light/dark cycle. These lamps emit light over the entire visible spectrum and emit in the upper UV spectrum at low intensities (Fig. S2, see 360–400 nm where intensities of [0.2 lW cm-2 are apparent). Response variables To characterize the growth rates of each algal species, we measured the fluorescence of a 3-mL subsample drawn from each culture every 2–3 days during the exponential phases of algal growth (15–22 days depending on the species) using an Turner Designs Aquafluor fluorometer. Measures of fluorescence were converted to chlorophyll a, which is a widely used proxy of algal biomass (Wetzel and Likens 2000). On days 9 and 10 of the experiment, algal primary production was measured using the light/dark bottle incubation technique that characterizes rates of phytoplankton production via changes in the concentration of dissolved O2 (Wetzel 1983; Bott 1996). We began by measuring initial levels of O2 (to a resolution of 0.01 mg L-1) in each culture jar using a YSI model 556 probe. Jars were then sealed with air-tight stoppers, covered with tin foil to prevent exposure to light, and set back on the shaker table for a 1-h incubation. At the end of the incubation, we again measured levels of dissolved oxygen to assess the
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amount of respiration in the cultures, R, as the rate at which O2 was consumed in the dark. Jars were then sealed a second time, placed back on the shaker table, and incubated for a second 1-h period while exposed to cool white fluorescent lights. Dissolved oxygen concentrations were measured at the end of the light incubation to assess net primary production, NPP, as the rate of O2 accumulation in the light. Gross primary production (GPP) was then calculated as GPP = R ? NPP. It should be noted that our cultures were not axenic (which is difficult, and often impractical, to accomplish). As such, while estimates of GPP can unambiguously be associated with algal metabolism per se, estimates of R represent the sum of hetero- and autotrophic dark R in the cultures.
J Nanopart Res (2012) 14:913
measurement (15 days for Chlamydomonas and Chlorella, and 22 days for Scenedesmus, which grew more slowly). Repeated measures analysis of variance revealed significant time 9 nTiO2 interactions for all three species (Table 1), with each coefficient for the interaction being negative. This indicates that the presence of nTiO2 generally inhibited the growth rates of the algae. To better understand the magnitude of inhibition, Fig. 1d–f re-plots estimated growth rates from the rmANOVAs as a function of nTiO2 concentration. Linear regressions confirm the declining growth rates with increasing nTiO2 (albeit, with considerable scatter) and indicate that, relative to the zero nTiO2 controls, growth was reduced by 11–27 % over the 50–300 ppm range of Ti additions.
Data analyses Effect of nTiO2 on metabolism To determine whether algal growth rates varied as a function of nTiO2 concentration, we used a repeated measures analysis of variance (rmANOVA) with an autoregressive covariance structure AR(1) to model the rate of increase in each species biomass as a function of time, nTiO2 concentration, and the time 9 nTiO2 interaction. Significant interaction terms were taken as evidence that nTiO2 alters a species growth rate. General linear models were then used to estimate how rates of GPP and R varied as a function of nTiO2 concentrations. All estimates of GPP and R were standardized by the amount of chlorophyll a in a culture to give the mass-specific rates of each metabolic function. We did this because the first set of analyses (rmANOVA’s) focused on explaining changes in biomass per se, whereas the second set of analyses (General Linear Model’s) focused on explaining changes in metabolism that might explain variation in biomass. All analyses were performed using SAS, v. 9.1.3.
Results Effect of nTiO2 on algal growth TiO2 nanoparticles reduced the growth of all three algal species. Figure 1a–c shows the natural logarithm of the biomass of each species. The linearity of the growth curves indicate that each species was in exponential phases of growth during the period of
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While TiO2 nanoparticles generally reduced the growth of algae, the impacts on metabolic processes were species specific. For Chlamydomonas, the massspecific rate of GPP declined significantly with increasing concentrations of nTiO2 (Fig. 1g; Table 2A). Mass-specific rates of R in cultures of Chlamydomonas also tended to decline with increasing nTiO2 (Fig. 1g; Table 2B); however, these declines were neither as fast nor as significant as those for GPP. As a result, cultures exposed to increasing levels of nTiO2 tended to respire more than they produced, which likely explains the why growth rates declined for this species. In contrast to Chlamydomonas, metabolic processes in cultures of Chlorella were generally stimulated by the presence of nTiO2 (Fig. 1h; Table 2C, D). However, mass-specific rates of R increased almost two times faster than mass-specific rates of GPP. As a result, cultures exposed to increasing levels of nTiO2 once again tended to respire more than they produced. For cultures of Scenedesmus, mass-specific rates of GPP showed a general tendency to decline with increasing concentrations of nTiO2, but the P-value for this trend was 0.09 (Fig. 1I; Table 2E). At the same time, rates of mass-specific R in the cultures were insensitive to nTiO2 (Table 2F). The combination of decreasing GPP and constant, but generally higher values of R, again ensured that cultures exposed to increasing levels of nTiO2 respired more than they produced.
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8
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A
0.24
Chlamydomonas TiO2 x time,P < 0.01
7
D
G Chlamydomonas
Chlamydomonas 0.004
0.21
5
0.18
4 0.15 y = 0.22 - 0.0002x
8
0.00
B Chlorella TiO2 x time P < 0.01
7 6
0 50 100 200 300 ppm
5 4 3 0
8
C
0.30
E Chlorella
0.27
0.24 y = 0.27 - 0.0001x 0.00
0.18
Scenedesmus TiO2 x time,P = 0.01
7
Growth rate (ln chl-a day-1)
Algal biomass, ln(µg chl-a L-1)
3 0
F Scenedesmus
0.16
6 5
0.14
10
15
20
H Chlorella R
0.002
0.001
GPP
0.000
0.005
I Scenedesmus
0.004 0.003 R
0.000
0.00
Time, days
0.003
GPP
y = 0.15 - 0.0001x
5
GPP 0.000
0.001
0.12
0
R
0.002
0.002
4 3 0
Mass specific metabolic rate (µg O2L -1hr-1 µg chl-a-1)
6
0
100
200
Nano-TiO2 (ppm)
300
0
100
200
300
Nano-TiO2 (ppm)
Fig. 1 Impacts of nTiO2 on the growth rates and metabolism of three species of freshwater algae. Panels a–c show the log rate of increase in the biomass of each species during the phases of exponential growth as a function of initial nTiO2 concentrations added to the experimental microcosms. The significance of the nTiO2 9 time interaction from a repeated measures analysis of variance is noted (see Table 1 for corresponding statistical analyses). Panels d–f re-plot the growth rates from a to c as a
function of nTiO2. The equations for a simple linear regression are given to simply illustrate the magnitude of growth inhibition in the cultures. Panels g–i show how the mass-specific rates of gross primary production (GPP) and respiration (R) in the algal cultures change as a function of nTiO2. Corresponding statistical analyses are given in Table 2. Data points in d–i represent the mean of N = 3 replicate experimental units ±1SEM
Discussion
a greater extent than R. In Chlorella, nTiO2 had the opposite effect and stimulated the species metabolism. But R was stimulated to a greater extent than photosynthesis. In Scenedesmus, only photosynthesis was inhibited by nTiO2. These divergent effects on metabolism all had the same end result, which was to increase the rate of carbon loss relative to gain, resulting in growth inhibition. Collectively, these results suggest that the ultimate impact of nTiO2 on freshwater algae may be quite predictable. However, the mechanistic basis for these impacts could be difficult to understand and predict without a good deal of species-specific information on pathways of metabolism.
We have shown that the presence of TiO2 nanoparticles in freshwater microcosms consistently inhibited the growth rates of three common species of green algae. These reductions were not only significant, but relatively large in magnitude, ranging from 11 to 27 % reductions during the short interval of exponential population growth. While there were general negative effects of nTiO2 on algal growth, the underlying changes in metabolism that led to growth inhibition were unique to each species. In Chlamydomonas, nTiO2 generally shut-down metabolism of the alga, but photosynthesis was reduced to
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Page 6 of 8 Table 1 Results of repeated measures ANOVA’s (analysis of variance) showing how the biomass of three algal species varied as a function of time, concentrations of nano-TiO2, and the time 9 nTiO2 interaction
J Nanopart Res (2012) 14:913
Species/effect
Coefficient
Standard error
df
t Value
Pr [ t
A. Chlamydomonas Intercept
3.1002
0.06228
116
49.78
\0.01
Time (days)
0.2230
0.00621
116
35.92
\0.01
nTiO2 (ppm)
-0.0001
0.00037
116
-0.26
0.80
Time 9 nTiO2
-0.0002
0.00004
116
-4.94
\0.01
B. Chlorella 3.3541
0.06831
116
49.10
\0.01
Time (days) nTiO2 (ppm)
0.2710 -0.0001
0.00681 0.00041
116 116
39.79 -0.27
\0.01 0.79
Time 9 nTiO2
-0.0001
0.00004
116
-2.96
\0.01
Intercept
C. Scenedesmus
These analyses correspond to the data shown in Fig. 1a–c
Table 2 Results of general linear models showing how biomass-specific rates of gross primary production (GPP) and respiration (R) change as a function of nTiO2 for three species of algae. Units for GPP and R are in lg O2 L-1 h-1 per lg chlorophyll a
Intercept
3.1566
0.07409
116
42.61
\0.01
Time (days)
0.1408
0.00585
116
24.09
\0.01
nTiO2 (ppm)
-0.0002
0.00042
116
-0.47
0.64
Time 9 nTiO2
-0.0001
0.00003
116
-2.52
0.01
Species/effect
Estimate
Standard error
t Value
Pr [ |t|
A. Chlamydomonas (GPP) Intercept nTiO2 (ppm)
3.03E-3
3.23E-4
9.37
\0.01
-6.93E-6
1.92E-6
-3.62
\0.01
B. Chlamydomonas (R) 3.94E-3
3.91E-4
10.10
\0.01
-4.84E-6
2.31E-6
-2.09
0.06
4.03E-4 1.98E-6
1.21E-4 7.10E-7
3.34 2.77
\0.01 0.02
Intercept
9.84E-4
1.93E-4
5.10
\0.01
nTiO2 (ppm)
3.64E-6
1.14E-6
3.19
\0.01
Intercept nTiO2 (ppm) C. Chlorella (GPP) Intercept nTiO2 (ppm) D. Chlorella (R)
E. Scenedesmus (GPP) Intercept nTiO2 (ppm)
1.79E-3
3.29E-4
5.46
\0.01
-3.49E-6
1.84E-6
-1.89
0.09
2.75E-3
6.29E-4
4.37
\0.01
-1.48E-6
3.53E-6
-0.42
0.68
F. Scenedesmus (R) Intercept These analyses correspond to the data shown in Fig. 1g–i
nTiO2 (ppm)
Results of our study are qualitatively consistent with a select number of prior studies that have suggested nTiO2 can decrease the growth of certain types of pelagic algae (Hund-Rinke and Simon 2006; Velzeboer et al. 2008). In these past studies, reduced growth of algae has been attributed to the photocatalytic properties of TiO2 that generate reactive oxygen species (ROS) that disrupt cell membranes (Reeves
123
et al. 2008; Brunet et al. 2009) and/or interfere with photosynthesis (Kim and Lee 2005; Hund-Rinke and Simon 2006; Neal 2008). Such effects require that electrons on the surface of Ti become excited by UV light at ca. 380 nm (Hashimoto et al. 2005). The fluorescent lamps used to grow algae in this study do emit 0.2–0.4 lW cm-2 at 360–380 nm (see Supplemental Fig. S2), which some have suggested is
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sufficient to stimulate minimal photocatalytic activity of Ti (see Fig. 6 of (Hashimoto et al. 2005). Because we did not measure redox reactions with any direct assay, we can neither confirm nor refute photocatalysis as a potential mechanism of toxicity. But even if photocatalysis was operating in this study, it could not possibly explain the contrasting responses of R and photosynthesis among the three algal taxa. In particular, the fact that these metabolic processes were stimulated in one taxon (Chlorella) suggests that some metabolic processes of select algae may respond positively to nTiO2. When other studies have shown stimulation of algal growth by nTiO2, it has been explained as electron transport at the surface of Ti enhancing the efficiency of photosynthesis (Hong et al. 2005; Lei et al. 2007; Gao et al. 2008). Thus, to understand why nTiO2 affects algal fitness, future work will need to consider the balance of potentially opposing impacts (positive and negative) on photosynthesis and R. Like any experiment that uses an overly simplified model system, our study has inherent limitations. While algal monocultures growing in glass jars are useful model systems, they are an over-simplification of natural lake ecosystems that have dozens of species interacting in media that can be far more complex. Natural phytoplankton communities are exposed to light intensities that greatly exceed what can be replicated in the lab, which almost certainly influences the reactivity of nTiO2 through exposure to UV. Furthermore, while the range of nTiO2 used in this study is comparable to the span of concentrations that have been used in many prior ecotoxicological studies (see Kahru and Dubourguier 2010 for a summary), the concentrations are high than expected based on nanoparticle release models (Mueller and Nowack 2008). Thus, results of our study are best interpreted as showing the potential impacts of nTiO2 under idealized conditions at potentially higher exposures—and whether or not these impacts can be generalized to realistic scenarios remains to be seen. But even as we work to extend the reality and complexity of study systems, this study suggests that TiO2 nanoparticles may have consistent negative impacts on the growth of freshwater green algae, but for reasons that are qualitatively different among species. Acknowledgments This project was supported by the National Science Foundation and Environmental Protection
Page 7 of 8 Agency under Cooperative Agreement # NSF-EF0830117. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the Environmental Protection Agency.
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