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Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/etap
SELDI-TOF MS-based discovery of a biomarker in Cucumis sativus seeds exposed to CuO nanoparticles Young-Sun Moon a,1 , Eun-Sil Park a,1 , Tae-Oh Kim b , Hoi-Seon Lee c,2 , Sung-Eun Lee a,∗,2 a
School of Applied Biosciences, Kyungpook National University, Daegu 702-701, Republic of Korea Department of Environmental Engineering, Kumoh National Institute of Technology, Daehak-ro 61, Gumi, Gyeongbuk 730-701, Republic of Korea c Department of Bioenvironmental Chemistry and Institute of Agricultural Science and Technology, Chonbuk National University, Jeonju 561-756, Republic of Korea b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Metal oxide nanoparticles (NPs) can inhibit plant seed germination and root elongation
Received 21 June 2014
via the release of metal ions. In the present study, two acute phytotoxicity tests, seed ger-
Received in revised form
mination and root elongation tests, were conducted on cucumber seeds (Cucumis sativus)
3 October 2014
treated with bulk copper oxide (CuO) and CuO NPs. Two concentrations of bulk CuO and
Accepted 6 October 2014
CuO NPs, 200 and 600 ppm, were used to test the inhibition rate of root germination; both
Available online 17 October 2014
concentrations of bulk CuO weakly inhibited seed germination, whereas CuO NPs significantly inhibited germination, showing a low germination rate of 23.3% at 600 ppm. Root
Keywords:
elongation tests demonstrated that CuO NPs were much stronger inhibitors than bulk CuO.
CuO nanoparticles
SELDI-TOF MS analysis showed that 34 proteins were differentially expressed in cucumber
Phytotoxicity
seeds after exposure to CuO NPs, with the expression patterns of at least 9 proteins highly
SELDI-TOF MS
differing from those in seeds treated with bulk CuO and in control plants. Therefore, these
Biomarker
9 proteins were used to identify CuO NP-specific biomarkers in cucumber plants exposed to CuO NPs. A 5977-m/z protein was the most distinguishable biomarker for determining phytotoxicity by CuO NPs. Principal component analysis (PCA) of the SELDI-TOF MS results showed variability in the modes of inhibitory action on cucumber seeds and roots. To our knowledge, this is the first study to demonstrate that the phytotoxic effect of metal oxide NPs on plants is not caused by the same mode of action as other toxins. © 2014 Elsevier B.V. All rights reserved.
∗
1 2
Corresponding author. Tel.: +82 53 950 7768; fax: +82 53 953 7233. E-mail address: [email protected] (S.-E. Lee). These authors equally contributed to this paper as first authors. These authors equally contributed to this paper as corresponding authors.
http://dx.doi.org/10.1016/j.etap.2014.10.002 1382-6689/© 2014 Elsevier B.V. All rights reserved.
e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 8 ( 2 0 1 4 ) 922–931
1.
Introduction
The oxide forms of transition metals are semiconductors, which have possible applications in magnetic storage media, solar energy transformation, and electronics (Dow and Huang, 1996; Lanje et al., 2010; Regan and Grätzel, 1991). Copper oxide (CuO) nanoparticles, which are highly efficient at heat transfer in nanofluids, are of particular interest (Lee et al., 1999). Because of the strong demand for CuO nanoparticles, a substantial amount of nanoparticles may be released into the environment before adequate short- and long-term studies of their environmental fate and ecological impact can be performed. Phytotoxicity of CuO nanoparticles has been reported for mung bean (Phaseolus radiatus), wheat (Triticum aestivum), soybean (Glycine max), chickpea (Cicer arietinum), lettuce (Lactuca sativa), radish (Raphanus sativus), and cucumber (Cucumis sativus) plants (Adhikari et al., 2012; Lee et al., 2008; Wu et al., 2012). The phytotoxic effect of CuO nanoparticles is caused by inhibition of seed germination and root elongation via higher uptake and accumulation in plant tissues (Wu et al., 2012). Phytotoxicity of nanoparticles in aqueous solutions is hypothesized to be closely associated with the concentration of the metal ions released (Ji et al., 2011). However, Wu et al. (2012) found that the phytotoxicity of CuO and NiO nanoparticles is not only related to their dissolved metal ions but also to their interactions with the seed/root surface. The nanoparticles can penetrate the cell walls and plasma membranes of the epidermal layers of roots, where they enter vascular tissues (xylem) and are taken up and translocated through the stems to the leaves (Wang et al., 2012). Cell walls consist of a porous network of polysaccharide fiber matrices; the pore sizes of plant cell walls are typically in the range of 3–8 nm (Carpita and Gibeaut, 1993). Therefore, the phytotoxicity of nanoparticles may result from penetration by nanoparticles smaller than the cell walls. This is consistent with the results of a study in which greater root toxicity correlated with smaller particle sizes of CuO nanoparticles (Dimkpa et al., 2013). For predicting phytotoxicity of nanoparticles in plants, it is important to initiate detection from the onset of exposure. Modulation of the ascorbate-glutathione cycle, membrane damage, in vivo reactive oxygen (ROS) detection, foliar H2 O2 , and proline accumulation have been studied as parameters of oxidative stress due to CuO nanoparticles in rice (Oryza sativa L.) seedlings (Shaw and Hossain, 2013). Exposure to CuO nanoparticles in rice seedlings increases APX activity, GR activity, and proline accumulation (Shaw and Hossain, 2013). Increased APX and GR enzyme activities and enhanced accumulation of proline can be used as determinants of environmental exposure of rice seedlings to CuO nanoparticles and can act as biomarkers to predict possible oxidative stress by nanoparticles. However, because these biomarker candidates are ubiquitous and easy to find under conditions of oxidative stresses, specific biomarkers for diagnosing oxidative stress or toxicity after exposure to nanoparticles in plants would be more useful. Surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF) MS is a powerful method to identify biomarkers for diagnosing human diseases, including cancers (Li et al., 2012, 2013). SELDI-TOF MS analysis has been
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applied to environmental studies, identifying biomarkers for endosulfan in Japanese rice fish (Oryzias latipes) and for heavy metals in mayfly (Ephemera orientalis) eggs (Lee et al., 2013a,b; Mo et al., 2013). In the present study, we sought to determine the phytotoxicities of CuO nanoparticles and microparticles in cucumber seeds, using seed germination and root elongation assays. In addition to these toxicity tests, protein expression was profiled by SELDI-TOF MS, in order to compare the responses in cucumbers to CuO nanoparticles with CuO microparticles. We identified specific CuO nanoparticle-induced biomarkers in cucumber in this study; to our knowledge, this is the first such endeavor into finding biomarkers specific to CuO nanoparticles in plants.
2.
Materials and methods
2.1.
Chemicals
CuO nanoparticles and CuO were purchased from Sigma–Aldrich Co. (St. Louis, MO). All other chemicals used were of technical grade.
2.2.
Cucumber seeds
Cucumber seeds were obtained from a cucumber cultivar, Jinnocksamcheock, which is moderately resistant to powdery mildew. The seeds were purchased from the KS Seed Corporation (Kyunggi-do, Korea). They were washed 10 times with distilled water to remove seed disinfectants. The average germination rate of the seeds was greater than 90%.
2.3.
Preparation of CuO nanoparticles
CuO nanoparticles were added to distilled water and dispersed by an ultrasonic vibrator for 30 min. The resulting suspension of CuO nanoparticles was stirred on a stir plate with a magnetic bar to prevent aggregation of the nanoparticles before use. Various concentrations (0, 100, 200, 400, and 600 mg/L) of CuO nanoparticles were prepared for seed germination and root elongation toxicity tests. The CuO solution was prepared as a control by dissolving CuO in distilled water.
2.4. Seed germination and root elongation assays as phytotoxicity tests Seed germination and root elongation assays were performed following a previously reported method (Adhikari et al., 2012). Prior to phytotoxicity tests, the seeds were sterilized in 5% sodium hypochlorite for 10 min; then, they were saturated with distilled water, CuO, and CuO nanoparticle solutions for 6 h. One piece of filter paper (90-mm diameter, Whatman No. 1) was added to each Petri dish (100 mm × 15 mm), and 5 mL of each concentration of a test solution was added to the filter paper. Ten soaked seeds were placed with even spacing over the top of the test solution-treated filter paper. Each treatment was performed in triplicate. The Petri dishes tested were covered and sealed with parafilm and placed in an incubator. Seed germination and root elongation were measured each
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day after incubation for 7 days. The seed germination rate was calculated and the seedling root length was measured. After the root lengths were measured, each group of tested cucumber roots was stored at −80 ◦ C until protein pattern analysis by SELDI-TOF MS.
2.5. Data analysis for the phytotoxicity of CuO nanoparticles Relative seed germination inhibition, relative root growth inhibition (%), germination index (GI), and EC50 values were calculated by previously reported methods (Pokhrel and Dubey, 2013; Wu et al., 2012). EC50 values were obtained using the USEPA software for analyzing the phytotoxicity data.
2.6.
SELDI-TOF MS analysis
2.6.1.
Sample collection and protein extraction
The procedure was slightly modified from a previously reported method (Mo et al., 2013). CuO- or CuO nanoparticleexposed whole plants, grown for 7 days, were collected and washed 3 times with deionized water. Plants were homogenized using a mortar and pestle in ice-cold phosphate buffer (0.1 M, pH 7.2) containing 0.2 M dibasic sodium phosphate and 0.2 M monobasic sodium phosphate and then centrifuged for 20 min at 10,000 × g. The cytosolic fractions were then loaded into each well of a ProteinChip® (Q10 array chips), which incorporates quaternized ammonium groups (positively charged) that act as a strong anion exchanger. Each ProteinChip “spot” was prepared by first adding 150 L of Tris–HCl binding buffer (0.1 M Tris–HCl, 0.1% Triton-X 100, pH 8) to chips, which were then incubated for 5 min at room temperature with shaking at 250 rpm. The buffer was removed from each well, to which 50 L of diluted sample was added and incubated for 30 min with shaking at 250 rpm. Samples were removed from the wells, and each well was washed 3 times with Tris–HCl washing buffer (0.1 M Tris–HCl, pH 8). After the sample-loaded protein chips were dried for 20 min, sinapinic acid (SPA), a ProteinChip energy-absorbing molecule (EAM), was added, and the chips were dried twice.
2.6.2. SELDI-TOF MS analysis conditions and data processing SELDI-TOF MS analysis and data processing were the same as previously reported (Mo et al., 2013). Briefly, SELDI-TOF MS yielded many peaks within an MS spectrum, which had the measured signal intensity on the y-axis and the calculated mass-to-ion ratio on the x-axis. Different peaks were collected and grouped into clusters containing the same molecules. Peak clustering was performed using a signal-to-noise ratio of 6 and a minimum peak threshold of 12.5%, second pass peak signal-to-noise ratio of 0.5, and mass window of 0.5%. The Biomarker Wizard of the ProteinChipTM software was then used to compare the data between different groups by using the Mann–Whitney test. Before SELDI-TOF MS analysis, the spectra were calibrated using the All-In-One Peptide Standard (Bio-Rad). The mass spectra of low-mass proteins were measured from all groups. The total ion current (TIC) was normalized for the amplitudes of all peaks between 0 and 25,000 Da using the ProteinChip
Fig. 1 – Effects of different concentrations of CuO nanoparticles on seed germination of Cucumis sativus. Values, expressed as relative seed germination inhibition (%), were determined after 3 days of exposure to the test materials (n = 30). Significant differences were observed (p < 0.05). 1, control; 2, 100 ppm CuO; 3, 600 ppm CuO; 4, 100 ppm CuO nanoparticle; 5, 600 ppm CuO nanoparticle.
data manager software (Bio-Rad). Peak detection was set at auto-detect peaks to cluster. The first pass of the signal-tonoise ratio (S/N) and valley depth were set to 3. The cluster mass window was set to a peak width of 1. The second pass of S/N and valley depth was set to 2. The non-parametric Kruskal–Wallis test was performed to compare the data of all groups. Principal component analysis (PCA) was used for log-transformed data and the variance–covariance matrix. Each cluster of peak intensities of the SELDI-TOF MS data was considered its own dimension; the ProteinChip data manager software used PCA to visualize spectra in 2- and 3dimensional graphs that illustrated the relationships between spectra based on their expression profiles (Gastinel, 2012).
2.7. Statistical analysis for acute toxic effects of nanoparticles on cucumber seeds The difference in the rates of inhibition of germination and root growth was analyzed using 1-way analysis of variance (ANOVA) (SAS Institute, 2001). When values were found to be significant using ANOVA (p < 0.05), each treatment group was compared to the control using Dunnett’s test (˛ = 0.05) (SAS Institute, 2001).
3.
Results and discussion
Seed germination and root growth inhibitory tests, which are highly sensitive, simple to perform, and inexpensive, are used to determine the phytotoxic effects of chemicals (Wang et al., 2001). The acute toxic effects of CuO nanoparticles on cucumber seeds at various concentrations were determined by seed germination and root elongation inhibition tests. All the results were statistically analyzed and are shown in Figs. 1 and 2. The relative inhibition of seed germination was determined using USEPA OPPTS 850.4200 guidelines, with seeds considered germinated when the roots of the controls
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Fig. 2 – Effects of different concentrations of CuO nanoparticles on root elongation of Cucumis sativus. Values are expressed as root elongation rate and were determined after 7 days of exposure to the test materials (n = 30). Significant differences were observed (p < 0.05). 1, control; 2, 100 ppm CuO; 3, 200 ppm CuO; 4, 400 ppm CuO; 5, 600 ppm CuO; 6, 100 ppm CuO nanoparticle; 7, 200 ppm CuO nanoparticle; 8, 400 ppm CuO nanoparticle; 9, 600 ppm CuO nanoparticle.
measured at least 20 mm in length (USEPA, 1996). As shown in Fig. 1, we chose to determine the inhibitory effects of 2 concentrations of bulk CuO and CuO nanoparticles on germination. Each concentration of bulk CuO weakly inhibited seed germination, whereas the same concentrations of CuO nanoparticles significantly inhibited germination, yielding a low germination rate of 23.3% at 600 ppm; this finding was similar to previously reported results, indicating an EC50 of 228 ppm of CuO nanoparticles for cucumber seed germination (Wu et al., 2012). At 600 ppm of bulk CuO, the seed germination rate was only inhibited by 20%, when compared to the control. Root elongation inhibition effects were analyzed using 4 different concentrations of bulk CuO and CuO nanoparticles (Fig. 2). Even at 600 ppm of bulk CuO, we did not observe 50% inhibition. However, CuO nanoparticles inhibited the rate of root elongation by 50% at 100 ppm and by 34.2% at 600 ppm (Fig. 3). The low seed germination rate and low root elongation associated with CuO nanoparticles may be caused by differentially released Cu2+ ions. The toxic effects of nanoparticles on Chlamydomonas reinhardtii and Chlorella sp. are closely correlated to the concentration of the metal ions released (Ji et al., 2011; Navarro et al., 2008). Interestingly, Wu et al. (2012) determined the concentrations of metal ions released from all tested nanoparticles, and they did not detect any metal ions released from a TiO2 nanoparticle solution but did find trace amounts of ions from Fe2 O3 and Co3 O4 solutions. Similarly, they found that Cu2+ ions were released from CuO nanoparticles during incubation with cucumber seeds, but the EC50 concentration of the Cu2+ ions released was less than 0.2 ppm for cucumber seed germination. This differed from results showing that 5–8 ppm of Cu2+ ions is necessary for a CuCl2 solution to inhibit cucumber seed germination (Wu et al., 2012). Therefore, the authors concluded that the phytotoxic
Fig. 3 – Heat map of proteins of Cucumis sativus showing significantly different expression upon exposure to bulk CuO and CuO nanoparticles, assessed using the Q10 ProteinChip (p < 0.05).
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Table 1 – Proteins differentially expressed between the CuO bulk- and CuO NP-treated cucumber plants, as indicated by SELDI-TOF MS analysis, and comparison with the mass-to-charge ratio (m/z). No. 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Groups
m/z average
Intensity average
Fold
P-Value
Control CuO 600 ppm CuO NP 600 ppm
3927.854 3927.745 3921.857
157.03 74.764 51.033
1 0.48 0.32
0.007
Control CuO 600 ppm CuO NP 600 ppm
9911.336 9894.565 9904.463
69.981 32.233 13.836
1 0.46 0.20
0.007
Control CuO 600 ppm CuO NP 600 ppm
9993.162 10,010.035 10,060.961
79.705 43.022 19.586
1 0.54 0.25
0.007
Control CuO 600 ppm CuO NP 600 ppm
10,437.931 10,433.339 10,432.624
89.658 47.261 24.7
1 0.53 0.28
0.007
Control CuO 600 ppm CuO NP 600 ppm
8209.73 8210.659 8198.931
89.478 110.681 52.931
1 1.24 0.59
0.01
Control CuO 600 ppm CuO NP 600 ppm
6085.81 6086.762 6078.478
168.184 134.41 322.866
1 0.80 2.40
0.012
Control CuO 600 ppm CuO NP 600 ppm
6572.088 6572.841 6572.428
133.997 186.164 305.189
1 1.39 2.28
0.012
Control CuO 600 ppm CuO NP 600 ppm
6589.454 6590.314 6594.666
244.05 342.78 384.221
1 1.40 1.57
0.012
Control CuO 600 ppm CuO NP 600 ppm
6744.926 6745.834 6736.43
328.236 444.965 483.679
1 1.36 1.47
0.012
Control CuO 600 ppm CuO NP 600 ppm
6825.116 6825.116 6825.116
126.685 152.247 386.688
1 1.20 2.54
0.012
Control CuO 600 ppm CuO NP 600 ppm
6942.608 6943.548 6933.951
360.347 551.7 623.855
1 1.53 1.73
0.015
Control CuO 600 ppm CuO NP 600 ppm
7982.137 7981.231 7980.848
48.194 54.141 140.206
1 1.12 2.91
0.015
Control CuO 600 ppm CuO NP 600 ppm
8158.697 8159.927 8147.155
101.77 138.452 57.471
1 1.36 0.56
0.015
Control CuO 600 ppm CuO NP 600 ppm
6516.966 6517.851 6516.59
182.278 213.985 450.113
1 1.17 2.47
0.017
Control CuO 600 ppm CuO NP 600 ppm
6555.775 6555.775 6555.775
88.364 113.288 179.751
1 1.28 2.03
0.022
Control CuO 600 ppm CuO NP 600 ppm
10,543.948 10,555.668 10,534.382
88.842 52.219 67.449
1 0.59 0.76
0.023
Control CuO 600 ppm CuO NP 600 ppm
4370.828 4371 4364.32
50.944 27.505 16.009
1 0.54 0.31
0.023
Control CuO 600 ppm CuO NP 600 ppm
6312.975 6312.975 6312.975
52.028 59.007 132.741
1 1.13 2.55
0.023
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Table 1 – (Continued) No. 19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
m/z average
Intensity average
Fold
P-Value
Control CuO 600 ppm CuO NP 600 ppm
Groups
5333.112 5326.041 5332.328
73.36 103.289 104.271
1 1.41 1.42
0.024
Control CuO 600 ppm CuO NP 600 ppm
5984.997 5985.784 5977.904
203.533 199.904 408.853
1 0.98 2.01
0.024
Control CuO 600 ppm CuO NP 600 ppm
8369.634 8370.117 8357.46
47.906 50.711 23.593
1 1.06 0.49
0.024
Control CuO 600 ppm CuO NP 600 ppm
3269.118 3270.547 3265.855
44.965 69.465 69.875
1 1.54 1.55
0.025
Control CuO 600 ppm CuO NP 600 ppm
13,341.948 13,117.191 13,429.812
12.571 12.836 19.066
1 1.02 1.51
0.025
Control CuO 600 ppm CuO NP 600 ppm
15,849.172 15,856.832 15,831.487
11.991 19.244 3.464
1 1.60 0.29
0.025
Control CuO 600 ppm CuO NP 600 ppm
16,063.801 16,060.646 16,071.178
5.037 7.368 1.518
1 1.46 0.30
0.025
Control CuO 600 ppm CuO NP 600 ppm
5556.928 5558.076 5558.037
142.624 181.979 371.961
1 1.28 2.61
0.026
Control CuO 600 ppm CuO NP 600 ppm
5615.703 5616.447 5608.802
99.536 118.212 136.725
1 1.19 1.37
0.03
Control CuO 600 ppm CuO NP 600 ppm
6443.091 6443.532 6443.224
147.745 180.248 175.658
1 1.22 1.19
0.035
Control CuO 600 ppm CuO NP 600 ppm
7419.044 7418.337 7418.325
31.63 8.722 47.821
1 0.28 1.51
0.037
Control CuO 600 ppm CuO NP 600 ppm
3474.82 3476.973 3470.054
33.665 53.112 64.549
1 1.58 1.92
0.039
Control CuO 600 ppm CuO NP 600 ppm
7999.148 8000.439 8007.959
160.105 232.845 134.749
1 1.45 0.84
0.058
Control CuO 600 ppm CuO NP 600 ppm
5543.374 5543.374 5543.374
57.986 67.019 150.287
1 1.16 0.26
0.063
Control CuO 600 ppm CuO NP 600 ppm
6328.318 6329.092 6327.786
132.544 162.078 174.418
1 1.22 1.32
0.069
Control CuO 600 ppm CuO NP 600 ppm
7843.487 7844.633 7833.648
137.399 184.298 184.241
1 1.34 1.34
0.084
effect of CuO nanoparticles on cucumber seed germination was not due solely to the release of metal ions (Wu et al., 2012). Another possible mechanism of the inhibitory action by nanoparticles on seed germination or root growth may be from plant-nanoparticle physical interactions. The size of seeds may contribute to the sensitivity to nanoparticle exposure (Shen et al., 2010). Because large seed species have
lower surface volume ratios than small seed species, large seeds have lesser exposure to nanoparticles; nanoparticle residues may change the chemistry of root surfaces, affecting the interaction between nanoparticle-bound roots and their environment (Shen et al., 2010). In other words, seed development is negatively affected because nanoparticles clog the root opening and inhibit both hydraulic and nutrient
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Fig. 4 – SELDI-TOF mass spectra of proteins of Cucumis sativus with significantly different expression on exposure to bulk CuO and CuO nanoparticles, assessed using the Q10 ProteinChip (p < 0.05).
uptake in roots. Based on the findings of several studies, the following have been found to be the principal factors that influence toxicity in plants: concentration of nanoparticles, particle size and specific surface area, physiochemical properties of nanoparticles, plant species, plant age/life cycle stage, growth media, nanoparticle stability, and diluting agents. Protein patterns of the bulk CuO- and CuO nanoparticletreated cucumber plants were obtained by SELDI-TOF MS analysis using Q10 ProteinChips and were compared to the mass-to-charge ratios (m/z) of differentially expressed proteins after exposure to nanoparticles. As shown in Table 1, 34 protein peaks were significantly different among the control, bulk CuO-treated, and CuO nanoparticle-treated cucumber plants. A treatment concentration of 600 ppm was selected because, at that concentration, CuO nanoparticles significantly inhibited cucumber seed germination and root growth (Fig. 2). All 34 peaks had intensities with P values of less than 0.084, including 30 peaks with P values of 0.05. Among the 34 peaks, 5 protein peaks with m/z values of 3927.854, 4371, 9911.336, 9993.162, and 10,437.931 were downregulated in the bulk CuOand CuO nanoparticle-treated cucumbers (Table 1). However, 18 protein peaks were overexpressed after exposure to bulk CuO and CuO nanoparticles, as compared to the control. With these 23 protein peaks, CuO nanoparticles had stronger upor downregulation effects on the cucumber plants than did the bulk CuO. It is likely that more dissolved Cu2+ ions can contribute stronger biological activities (Ji et al., 2011; Navarro et al., 2008). The remaining 9 peaks showed extreme patterns, in which one was upregulated and the other was downregulated. For example, the 5984-m/z peak (Fig. 4) was upregulated by 2-fold in the CuO nanoparticle-treated cucumber plants, whereas
the peak was downregulated by 0.98-fold by bulk CuO. This pattern was observed for the proteins with peaks of 5543, 6085, 7418, 7999, 8209, 8369, 15,849, and 16,063 m/z, as shown in Table 1. This finding helps demonstrate that the stronger phytotoxic effect of CuO nanoparticles compared to bulk CuO on cucumber seed germination and root growth is not caused only by the metal ions released (Wu et al., 2012). Approximately one-fourth of the differently expressed proteins showed extreme patterns after exposure to bulk CuO or nanoparticles, indicating that the biological mode of action of CuO nanoparticles may differ from that of the bulk CuO (Figs. 4 and 5). Using principal component analysis (PCA), the Q10 ProteinChip separated the CuO nanoparticle-treated cucumbers from the bulk CuO-treated cucumbers and the controls (Fig. 6), which suggests different modes of inhibitory mechanisms for cucumber seed germination and root growth. Biomarkers can be used as early warning signals for the presence of toxic compounds in the environment. Recent biomarker studies have been used to determine the ecological impact of nanoparticles on aquatic animals (Fan et al., 2012; Gomes et al., 2011). Metallothioneins (MTs), biomarkers found in Daphnia magna (Fan et al., 2012) and Mytilus galloprovincialis (Gomes et al., 2011), are significantly induced with increasing nanoparticle doses in both types of aquatic animals. However, these MT biomarkers are universally expressed in response to metal contamination in animals and plants, and their molecular weights vary greatly within a species (Raudenska et al., 2013). Therefore, MTs are not specific to the presence of metal nanoparticles. Alternatively, several biochemical changes have been suggested as biomarkers of nanoparticle toxicity in animals, including acetylcholinesterases (AChEs), which are neurotransmitter hydrolases (Gomes et al., 2011).
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Fig. 5 – SELDI-TOF mass spectra of proteins of Cucumis sativus with significantly different expression patterns after exposure to bulk CuO and CuO nanoparticles, assessed using the Q10 ProteinChip (p < 0.05).
In terrestrial plants, catalase (CAT) and ascorbate peroxidase (APOX) biomarkers respond to the presence of nanoparticles in the environment (Mukherjee et al., 2013). In green peas (Pisum sativum L.), CAT activity significantly decreased in leaves (p ≤ 0.05) at all the ZnO nanoparticle
concentrations tested, whereas APOX activity decreased in both roots and leaves. Bulk ZnO caused downregulation of APOX activity in roots and leaves, whereas CAT was unaffected (Mukherjee et al., 2013). Thus, CAT and APOX are presumable biomarkers of nanotoxicity in plant samples. Lee et al.
Fig. 6 – PCA plot of Cucumis sativus exposed to bulk CuO and CuO nanoparticles (NPs), using the Q10 ProteinChip in conjunction with SELDI-TOF MS analysis. Green triangle, control for the study; red square, 600 ppm of bulk CuO; blue circle, 600 ppm of CuO NPs.
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(2013a,b) showed that reactive oxygen species generated by ZnO nanoparticles could indicate the reduction in CAT activity. The authors also demonstrated that the reduced CAT activity could be a predictive biomarker of nanotoxicity. SELDI-TOF MS was used to generate a protein profile of intact samples, but MS analysis is limited in that it can only be used to identify proteins. Our SELDI-TOF MS analysis found 34 differentially expressed proteins in cucumber plants after treatment with CuO nanoparticles and bulk CuO. Among them, 9 proteins with extreme patterns were selected as presumable biomarker candidates for CuO nanoparticles, as shown in Fig. 4. Conclusively, CuO nanoparticles showed toxic effects on cucumber seed germination and root growth rate when compared to bulk CuO. SELDI-TOF MS analysis showed that the protein profiles of cucumber plants after exposure to CuO nanoparticles were statistically different from those exposed to corresponding concentrations of bulk CuO. To our knowledge, this is the first report to demonstrate differences in the toxic effects of metal nanoparticles and bulk metal ions. We found 34 proteins that were expressed with extreme patterns when cucumber plants were exposed to CuO nanoparticles and bulk CuO. Among them, the 5977-m/z protein can serve as a predictive biomarker for CuO nanoparticle exposure in cucumber plants.
Conflict of interest The authors declare that there are no conflicts of interest.
Transparency document The Transparency document associated with this article can be found in the online version.
Acknowledgements This research was supported by Kyungpook National University Research Fund, 2012. The authors express their thanks to Dajung Park for the technical support.
references
Adhikari, T., Kundu, S., Biswas, A.K., Tarafdar, J.C., Rao, A.S., 2012. Effect of copper oxide nanoparticle on seed germination of selected crops. J. Agric. Sci. Technol. A 2, 815–823. Carpita, N.C., Gibeaut, D.M., 1993. Structural models of primary cell walls in flowering plants: consistency of molecular-structure with physical properties of walls during growth. Plant J. 3, 1–30. Dimkpa, C.O., Latta, D.E., McLean, J.E., Britt, D.W., Boyanov, M.I., Anderson, A.J., 2013. Fate of CuO and ZnO nano- and microparticles in the plant environment. Environ. Sci. Technol. 47, 4734–4742. Dow, W.P., Huang, T.J., 1996. Yttria-stabilized zirconia supported copper oxide catalyst. II. Effect of oxygen vacancy of support on catalytic activity for CO oxidation. J. Catal. 160, 171–182. Fan, W.H., Cui, M.M., Shi, Z.W., Tan, C., Yang, X.P., 2012. Enhanced oxidative stress and physiological damage in Daphnia magna
by copper in the presence of nano-TiO2. J. Nanomat., http://dx.doi.org/10.1155/2012/398720, ID 398720. Gastinel, L.N., 2012. Principle component analysis in the era of omic data. In: Sanguansat, P. (Ed.), Principal component analysis – multidisciplinary applications. InTech, Available From: http://www.intechopen.com/books/principalcomponent-analysis-multidisciplinary-applications/princialcomponent-analysis-in-the-era-of-omics-data Gomes, T., Pinheiro, J.P., Cancio, I., Pereira, C.G., Cardoso, C., Bebianno, M.J., 2011. Effects of copper nanoparticles exposure in the mussel Mytilus galloprovincialis. Environ. Sci. Technol. 45, 9356–9362. Ji, J., Long, Z., Lin, D., 2011. Toxicity of oxide nanoparticles to the green algae Chlorella sp. Chem. Eng. J. 170, 525–530. Lanje, A.S., Ningthoujam, R.S., Shrama, S.J., Vasta, R.K., Pode, R.B., 2010. Luminescence properties of Sn1−x ·Fex Oz nanoparticles. Int. J. Nanotechnol. 7, 979–988. Lee, S., Choi, U.S., Li, S., Eastman, J.A., 1999. Measuring thermal conductivity of fluids containing oxide nanoparticles. J. Heat Transf. 121, 280–289. Lee, S., Kim, S., Kim, S., Lee, I., 2013a. Assessment of phytotoxicity of ZnO NPs on a medicinal plant, Fagopyrum esculentum. Environ. Sci. Pollut. Res. 20, 848–854. Lee, S.E., Choi, Y.W., Mo, H.H., Son, J., Park, K., Cho, K., 2013b. Endosulfan-induced biomarkers in Japanese rice fish (Oryzias latipes) analyzed by SELDI-TOF MS. Int. J. Biol. Sci. 9, 343–349. Lee, W.M., An, Y.J., Yoon, H., Kweon, H.S., 2008. Toxicity and bioavailability of copper nanoparticles to the terrestrial plants mung bean (Phaseolus radiates) and wheat (Triticum aestivum): plant agar test for water-insoluble nanoparticles. Environ. Toxicol. Chem. 27, 1915–1921. Li, P., Zhang, D., Guo, C., 2012. Serum biomarker screening for the diagnosis of early gastric cancer using SELDI-TOF MS. Mol. Med. Rep. 5, 1531–1535. Li, Y., Wu, J., Zhang, W., Zhang, N., Guo, H., 2013. Identification of serum CCL15 in hepatocellular carcinoma. Br. J. Cancer 108, 99–106. Mo, H.H., Lee, S.E., Son, J., Hwang, J.M., Bae, Y.J., Cho, K., 2013. Exposure of mayfly Ephemera orientalis (Ephemeroptera) eggs to heavy metals and discovery of biomarkers. Environ. Toxicol. Pharmacol. 36, 1167–1175. Mukherjee, A., Peralta-Videa, J.R., Bandyopadhyay, S., Rico, C.M., Zhao, L., Gardea-Torresdey, J.L., 2013. Physiological effects of nanoparticulate ZnO in green peas (Pisum sativum L.) cultivated in soil. Metallomics 6, 132–138. Navarro, E., Piccapietra, F., Wagner, B., Marconi, F., Kaeqi, R., Odzak, N., Sigg, L., Behra, R., 2008. Toxicity of silver nanoparticles to Chlamydomonas reinhardtii. Environ. Sci. Technol. 42, 8958–8964. Pokhrel, L.R., Dubey, B., 2013. Evaluation of developmental responses of two crop plants exposed to silver and zinc oxide nanoparticles. Sci. Total Environ. 452–453, 321–332. Raudenska, M., Gumulec, J., Podlaha, O., Sztalmachova, M., Babula, P., Eckschlager, T., Adam, V., Kizek, R., Masarik, M., 2013. Metallothionein polymorphism in pathological processes. Metallomics 6, 55–68. Regan, O., Grätzel, M., 1991. A low-cost, high-efficiency solar cell based on dye-sensitized colloidal TiO2 films. Nature 353, 737–740. SAS Institute, 2001. SAS User’s Guide. SAS Institute, NC. Shaw, A.K., Hossain, Z., 2013. Impact of nano-CuO stress on rice (Oryza sativa L.) seedling. Chemosphere 93, 906–915. Shen, C.X., Zhang, Q.F., Li, J., Bi, F.C., Yao, N., 2010. Induction of programmed cell death in Arabidopsis and rice by single wall carbon nanotubes. Am. J. Bot. 97, 1602–1609. USEPA, 1996. USEPA OPPTS 850.4200; Ecological effect test guidelines, 850 series (Proposal): seed germination/root elongation toxicity test.
e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 8 ( 2 0 1 4 ) 922–931
Wang, X.D., Sun, C., Gao, S.X., Wang, L.S., Han, S.K., 2001. Validation of germination rate and root elongation as indicator to assess phytotoxicity with Cucumis sativus. Chemosphere 44, 1711–1721. Wang, Z., Xie, X., Zhao, J., Liu, X., Feng, W., White, J.C., Xing, B., 2012. Xylem- and phloem-based transport of CuO
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nanoparticles in maize (Zea mays L.). Environ. Sci. Technol. 46, 4434–4441. Wu, S.G., Huang, L., Head, J., Chen, D.R., Kong, I.C., Tang, Y.J., 2012. Phytotoxicity of metal oxide nanoparticles is related to both dissolved metal ions and adsorption of particles on seed surfaces. J. Pet. Environ. Biotechnol. 3, 126.
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/etap
SELDI-TOF MS-based discovery of a biomarker in Cucumis sativus seeds exposed to CuO nanoparticles Young-Sun Moon a,1 , Eun-Sil Park a,1 , Tae-Oh Kim b , Hoi-Seon Lee c,2 , Sung-Eun Lee a,∗,2 a
School of Applied Biosciences, Kyungpook National University, Daegu 702-701, Republic of Korea Department of Environmental Engineering, Kumoh National Institute of Technology, Daehak-ro 61, Gumi, Gyeongbuk 730-701, Republic of Korea c Department of Bioenvironmental Chemistry and Institute of Agricultural Science and Technology, Chonbuk National University, Jeonju 561-756, Republic of Korea b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Metal oxide nanoparticles (NPs) can inhibit plant seed germination and root elongation
Received 21 June 2014
via the release of metal ions. In the present study, two acute phytotoxicity tests, seed ger-
Received in revised form
mination and root elongation tests, were conducted on cucumber seeds (Cucumis sativus)
3 October 2014
treated with bulk copper oxide (CuO) and CuO NPs. Two concentrations of bulk CuO and
Accepted 6 October 2014
CuO NPs, 200 and 600 ppm, were used to test the inhibition rate of root germination; both
Available online 17 October 2014
concentrations of bulk CuO weakly inhibited seed germination, whereas CuO NPs significantly inhibited germination, showing a low germination rate of 23.3% at 600 ppm. Root
Keywords:
elongation tests demonstrated that CuO NPs were much stronger inhibitors than bulk CuO.
CuO nanoparticles
SELDI-TOF MS analysis showed that 34 proteins were differentially expressed in cucumber
Phytotoxicity
seeds after exposure to CuO NPs, with the expression patterns of at least 9 proteins highly
SELDI-TOF MS
differing from those in seeds treated with bulk CuO and in control plants. Therefore, these
Biomarker
9 proteins were used to identify CuO NP-specific biomarkers in cucumber plants exposed to CuO NPs. A 5977-m/z protein was the most distinguishable biomarker for determining phytotoxicity by CuO NPs. Principal component analysis (PCA) of the SELDI-TOF MS results showed variability in the modes of inhibitory action on cucumber seeds and roots. To our knowledge, this is the first study to demonstrate that the phytotoxic effect of metal oxide NPs on plants is not caused by the same mode of action as other toxins. © 2014 Elsevier B.V. All rights reserved.
∗
1 2
Corresponding author. Tel.: +82 53 950 7768; fax: +82 53 953 7233. E-mail address: [email protected] (S.-E. Lee). These authors equally contributed to this paper as first authors. These authors equally contributed to this paper as corresponding authors.
http://dx.doi.org/10.1016/j.etap.2014.10.002 1382-6689/© 2014 Elsevier B.V. All rights reserved.
e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 8 ( 2 0 1 4 ) 922–931
1.
Introduction
The oxide forms of transition metals are semiconductors, which have possible applications in magnetic storage media, solar energy transformation, and electronics (Dow and Huang, 1996; Lanje et al., 2010; Regan and Grätzel, 1991). Copper oxide (CuO) nanoparticles, which are highly efficient at heat transfer in nanofluids, are of particular interest (Lee et al., 1999). Because of the strong demand for CuO nanoparticles, a substantial amount of nanoparticles may be released into the environment before adequate short- and long-term studies of their environmental fate and ecological impact can be performed. Phytotoxicity of CuO nanoparticles has been reported for mung bean (Phaseolus radiatus), wheat (Triticum aestivum), soybean (Glycine max), chickpea (Cicer arietinum), lettuce (Lactuca sativa), radish (Raphanus sativus), and cucumber (Cucumis sativus) plants (Adhikari et al., 2012; Lee et al., 2008; Wu et al., 2012). The phytotoxic effect of CuO nanoparticles is caused by inhibition of seed germination and root elongation via higher uptake and accumulation in plant tissues (Wu et al., 2012). Phytotoxicity of nanoparticles in aqueous solutions is hypothesized to be closely associated with the concentration of the metal ions released (Ji et al., 2011). However, Wu et al. (2012) found that the phytotoxicity of CuO and NiO nanoparticles is not only related to their dissolved metal ions but also to their interactions with the seed/root surface. The nanoparticles can penetrate the cell walls and plasma membranes of the epidermal layers of roots, where they enter vascular tissues (xylem) and are taken up and translocated through the stems to the leaves (Wang et al., 2012). Cell walls consist of a porous network of polysaccharide fiber matrices; the pore sizes of plant cell walls are typically in the range of 3–8 nm (Carpita and Gibeaut, 1993). Therefore, the phytotoxicity of nanoparticles may result from penetration by nanoparticles smaller than the cell walls. This is consistent with the results of a study in which greater root toxicity correlated with smaller particle sizes of CuO nanoparticles (Dimkpa et al., 2013). For predicting phytotoxicity of nanoparticles in plants, it is important to initiate detection from the onset of exposure. Modulation of the ascorbate-glutathione cycle, membrane damage, in vivo reactive oxygen (ROS) detection, foliar H2 O2 , and proline accumulation have been studied as parameters of oxidative stress due to CuO nanoparticles in rice (Oryza sativa L.) seedlings (Shaw and Hossain, 2013). Exposure to CuO nanoparticles in rice seedlings increases APX activity, GR activity, and proline accumulation (Shaw and Hossain, 2013). Increased APX and GR enzyme activities and enhanced accumulation of proline can be used as determinants of environmental exposure of rice seedlings to CuO nanoparticles and can act as biomarkers to predict possible oxidative stress by nanoparticles. However, because these biomarker candidates are ubiquitous and easy to find under conditions of oxidative stresses, specific biomarkers for diagnosing oxidative stress or toxicity after exposure to nanoparticles in plants would be more useful. Surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF) MS is a powerful method to identify biomarkers for diagnosing human diseases, including cancers (Li et al., 2012, 2013). SELDI-TOF MS analysis has been
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applied to environmental studies, identifying biomarkers for endosulfan in Japanese rice fish (Oryzias latipes) and for heavy metals in mayfly (Ephemera orientalis) eggs (Lee et al., 2013a,b; Mo et al., 2013). In the present study, we sought to determine the phytotoxicities of CuO nanoparticles and microparticles in cucumber seeds, using seed germination and root elongation assays. In addition to these toxicity tests, protein expression was profiled by SELDI-TOF MS, in order to compare the responses in cucumbers to CuO nanoparticles with CuO microparticles. We identified specific CuO nanoparticle-induced biomarkers in cucumber in this study; to our knowledge, this is the first such endeavor into finding biomarkers specific to CuO nanoparticles in plants.
2.
Materials and methods
2.1.
Chemicals
CuO nanoparticles and CuO were purchased from Sigma–Aldrich Co. (St. Louis, MO). All other chemicals used were of technical grade.
2.2.
Cucumber seeds
Cucumber seeds were obtained from a cucumber cultivar, Jinnocksamcheock, which is moderately resistant to powdery mildew. The seeds were purchased from the KS Seed Corporation (Kyunggi-do, Korea). They were washed 10 times with distilled water to remove seed disinfectants. The average germination rate of the seeds was greater than 90%.
2.3.
Preparation of CuO nanoparticles
CuO nanoparticles were added to distilled water and dispersed by an ultrasonic vibrator for 30 min. The resulting suspension of CuO nanoparticles was stirred on a stir plate with a magnetic bar to prevent aggregation of the nanoparticles before use. Various concentrations (0, 100, 200, 400, and 600 mg/L) of CuO nanoparticles were prepared for seed germination and root elongation toxicity tests. The CuO solution was prepared as a control by dissolving CuO in distilled water.
2.4. Seed germination and root elongation assays as phytotoxicity tests Seed germination and root elongation assays were performed following a previously reported method (Adhikari et al., 2012). Prior to phytotoxicity tests, the seeds were sterilized in 5% sodium hypochlorite for 10 min; then, they were saturated with distilled water, CuO, and CuO nanoparticle solutions for 6 h. One piece of filter paper (90-mm diameter, Whatman No. 1) was added to each Petri dish (100 mm × 15 mm), and 5 mL of each concentration of a test solution was added to the filter paper. Ten soaked seeds were placed with even spacing over the top of the test solution-treated filter paper. Each treatment was performed in triplicate. The Petri dishes tested were covered and sealed with parafilm and placed in an incubator. Seed germination and root elongation were measured each
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day after incubation for 7 days. The seed germination rate was calculated and the seedling root length was measured. After the root lengths were measured, each group of tested cucumber roots was stored at −80 ◦ C until protein pattern analysis by SELDI-TOF MS.
2.5. Data analysis for the phytotoxicity of CuO nanoparticles Relative seed germination inhibition, relative root growth inhibition (%), germination index (GI), and EC50 values were calculated by previously reported methods (Pokhrel and Dubey, 2013; Wu et al., 2012). EC50 values were obtained using the USEPA software for analyzing the phytotoxicity data.
2.6.
SELDI-TOF MS analysis
2.6.1.
Sample collection and protein extraction
The procedure was slightly modified from a previously reported method (Mo et al., 2013). CuO- or CuO nanoparticleexposed whole plants, grown for 7 days, were collected and washed 3 times with deionized water. Plants were homogenized using a mortar and pestle in ice-cold phosphate buffer (0.1 M, pH 7.2) containing 0.2 M dibasic sodium phosphate and 0.2 M monobasic sodium phosphate and then centrifuged for 20 min at 10,000 × g. The cytosolic fractions were then loaded into each well of a ProteinChip® (Q10 array chips), which incorporates quaternized ammonium groups (positively charged) that act as a strong anion exchanger. Each ProteinChip “spot” was prepared by first adding 150 L of Tris–HCl binding buffer (0.1 M Tris–HCl, 0.1% Triton-X 100, pH 8) to chips, which were then incubated for 5 min at room temperature with shaking at 250 rpm. The buffer was removed from each well, to which 50 L of diluted sample was added and incubated for 30 min with shaking at 250 rpm. Samples were removed from the wells, and each well was washed 3 times with Tris–HCl washing buffer (0.1 M Tris–HCl, pH 8). After the sample-loaded protein chips were dried for 20 min, sinapinic acid (SPA), a ProteinChip energy-absorbing molecule (EAM), was added, and the chips were dried twice.
2.6.2. SELDI-TOF MS analysis conditions and data processing SELDI-TOF MS analysis and data processing were the same as previously reported (Mo et al., 2013). Briefly, SELDI-TOF MS yielded many peaks within an MS spectrum, which had the measured signal intensity on the y-axis and the calculated mass-to-ion ratio on the x-axis. Different peaks were collected and grouped into clusters containing the same molecules. Peak clustering was performed using a signal-to-noise ratio of 6 and a minimum peak threshold of 12.5%, second pass peak signal-to-noise ratio of 0.5, and mass window of 0.5%. The Biomarker Wizard of the ProteinChipTM software was then used to compare the data between different groups by using the Mann–Whitney test. Before SELDI-TOF MS analysis, the spectra were calibrated using the All-In-One Peptide Standard (Bio-Rad). The mass spectra of low-mass proteins were measured from all groups. The total ion current (TIC) was normalized for the amplitudes of all peaks between 0 and 25,000 Da using the ProteinChip
Fig. 1 – Effects of different concentrations of CuO nanoparticles on seed germination of Cucumis sativus. Values, expressed as relative seed germination inhibition (%), were determined after 3 days of exposure to the test materials (n = 30). Significant differences were observed (p < 0.05). 1, control; 2, 100 ppm CuO; 3, 600 ppm CuO; 4, 100 ppm CuO nanoparticle; 5, 600 ppm CuO nanoparticle.
data manager software (Bio-Rad). Peak detection was set at auto-detect peaks to cluster. The first pass of the signal-tonoise ratio (S/N) and valley depth were set to 3. The cluster mass window was set to a peak width of 1. The second pass of S/N and valley depth was set to 2. The non-parametric Kruskal–Wallis test was performed to compare the data of all groups. Principal component analysis (PCA) was used for log-transformed data and the variance–covariance matrix. Each cluster of peak intensities of the SELDI-TOF MS data was considered its own dimension; the ProteinChip data manager software used PCA to visualize spectra in 2- and 3dimensional graphs that illustrated the relationships between spectra based on their expression profiles (Gastinel, 2012).
2.7. Statistical analysis for acute toxic effects of nanoparticles on cucumber seeds The difference in the rates of inhibition of germination and root growth was analyzed using 1-way analysis of variance (ANOVA) (SAS Institute, 2001). When values were found to be significant using ANOVA (p < 0.05), each treatment group was compared to the control using Dunnett’s test (˛ = 0.05) (SAS Institute, 2001).
3.
Results and discussion
Seed germination and root growth inhibitory tests, which are highly sensitive, simple to perform, and inexpensive, are used to determine the phytotoxic effects of chemicals (Wang et al., 2001). The acute toxic effects of CuO nanoparticles on cucumber seeds at various concentrations were determined by seed germination and root elongation inhibition tests. All the results were statistically analyzed and are shown in Figs. 1 and 2. The relative inhibition of seed germination was determined using USEPA OPPTS 850.4200 guidelines, with seeds considered germinated when the roots of the controls
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Fig. 2 – Effects of different concentrations of CuO nanoparticles on root elongation of Cucumis sativus. Values are expressed as root elongation rate and were determined after 7 days of exposure to the test materials (n = 30). Significant differences were observed (p < 0.05). 1, control; 2, 100 ppm CuO; 3, 200 ppm CuO; 4, 400 ppm CuO; 5, 600 ppm CuO; 6, 100 ppm CuO nanoparticle; 7, 200 ppm CuO nanoparticle; 8, 400 ppm CuO nanoparticle; 9, 600 ppm CuO nanoparticle.
measured at least 20 mm in length (USEPA, 1996). As shown in Fig. 1, we chose to determine the inhibitory effects of 2 concentrations of bulk CuO and CuO nanoparticles on germination. Each concentration of bulk CuO weakly inhibited seed germination, whereas the same concentrations of CuO nanoparticles significantly inhibited germination, yielding a low germination rate of 23.3% at 600 ppm; this finding was similar to previously reported results, indicating an EC50 of 228 ppm of CuO nanoparticles for cucumber seed germination (Wu et al., 2012). At 600 ppm of bulk CuO, the seed germination rate was only inhibited by 20%, when compared to the control. Root elongation inhibition effects were analyzed using 4 different concentrations of bulk CuO and CuO nanoparticles (Fig. 2). Even at 600 ppm of bulk CuO, we did not observe 50% inhibition. However, CuO nanoparticles inhibited the rate of root elongation by 50% at 100 ppm and by 34.2% at 600 ppm (Fig. 3). The low seed germination rate and low root elongation associated with CuO nanoparticles may be caused by differentially released Cu2+ ions. The toxic effects of nanoparticles on Chlamydomonas reinhardtii and Chlorella sp. are closely correlated to the concentration of the metal ions released (Ji et al., 2011; Navarro et al., 2008). Interestingly, Wu et al. (2012) determined the concentrations of metal ions released from all tested nanoparticles, and they did not detect any metal ions released from a TiO2 nanoparticle solution but did find trace amounts of ions from Fe2 O3 and Co3 O4 solutions. Similarly, they found that Cu2+ ions were released from CuO nanoparticles during incubation with cucumber seeds, but the EC50 concentration of the Cu2+ ions released was less than 0.2 ppm for cucumber seed germination. This differed from results showing that 5–8 ppm of Cu2+ ions is necessary for a CuCl2 solution to inhibit cucumber seed germination (Wu et al., 2012). Therefore, the authors concluded that the phytotoxic
Fig. 3 – Heat map of proteins of Cucumis sativus showing significantly different expression upon exposure to bulk CuO and CuO nanoparticles, assessed using the Q10 ProteinChip (p < 0.05).
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Table 1 – Proteins differentially expressed between the CuO bulk- and CuO NP-treated cucumber plants, as indicated by SELDI-TOF MS analysis, and comparison with the mass-to-charge ratio (m/z). No. 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Groups
m/z average
Intensity average
Fold
P-Value
Control CuO 600 ppm CuO NP 600 ppm
3927.854 3927.745 3921.857
157.03 74.764 51.033
1 0.48 0.32
0.007
Control CuO 600 ppm CuO NP 600 ppm
9911.336 9894.565 9904.463
69.981 32.233 13.836
1 0.46 0.20
0.007
Control CuO 600 ppm CuO NP 600 ppm
9993.162 10,010.035 10,060.961
79.705 43.022 19.586
1 0.54 0.25
0.007
Control CuO 600 ppm CuO NP 600 ppm
10,437.931 10,433.339 10,432.624
89.658 47.261 24.7
1 0.53 0.28
0.007
Control CuO 600 ppm CuO NP 600 ppm
8209.73 8210.659 8198.931
89.478 110.681 52.931
1 1.24 0.59
0.01
Control CuO 600 ppm CuO NP 600 ppm
6085.81 6086.762 6078.478
168.184 134.41 322.866
1 0.80 2.40
0.012
Control CuO 600 ppm CuO NP 600 ppm
6572.088 6572.841 6572.428
133.997 186.164 305.189
1 1.39 2.28
0.012
Control CuO 600 ppm CuO NP 600 ppm
6589.454 6590.314 6594.666
244.05 342.78 384.221
1 1.40 1.57
0.012
Control CuO 600 ppm CuO NP 600 ppm
6744.926 6745.834 6736.43
328.236 444.965 483.679
1 1.36 1.47
0.012
Control CuO 600 ppm CuO NP 600 ppm
6825.116 6825.116 6825.116
126.685 152.247 386.688
1 1.20 2.54
0.012
Control CuO 600 ppm CuO NP 600 ppm
6942.608 6943.548 6933.951
360.347 551.7 623.855
1 1.53 1.73
0.015
Control CuO 600 ppm CuO NP 600 ppm
7982.137 7981.231 7980.848
48.194 54.141 140.206
1 1.12 2.91
0.015
Control CuO 600 ppm CuO NP 600 ppm
8158.697 8159.927 8147.155
101.77 138.452 57.471
1 1.36 0.56
0.015
Control CuO 600 ppm CuO NP 600 ppm
6516.966 6517.851 6516.59
182.278 213.985 450.113
1 1.17 2.47
0.017
Control CuO 600 ppm CuO NP 600 ppm
6555.775 6555.775 6555.775
88.364 113.288 179.751
1 1.28 2.03
0.022
Control CuO 600 ppm CuO NP 600 ppm
10,543.948 10,555.668 10,534.382
88.842 52.219 67.449
1 0.59 0.76
0.023
Control CuO 600 ppm CuO NP 600 ppm
4370.828 4371 4364.32
50.944 27.505 16.009
1 0.54 0.31
0.023
Control CuO 600 ppm CuO NP 600 ppm
6312.975 6312.975 6312.975
52.028 59.007 132.741
1 1.13 2.55
0.023
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Table 1 – (Continued) No. 19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
m/z average
Intensity average
Fold
P-Value
Control CuO 600 ppm CuO NP 600 ppm
Groups
5333.112 5326.041 5332.328
73.36 103.289 104.271
1 1.41 1.42
0.024
Control CuO 600 ppm CuO NP 600 ppm
5984.997 5985.784 5977.904
203.533 199.904 408.853
1 0.98 2.01
0.024
Control CuO 600 ppm CuO NP 600 ppm
8369.634 8370.117 8357.46
47.906 50.711 23.593
1 1.06 0.49
0.024
Control CuO 600 ppm CuO NP 600 ppm
3269.118 3270.547 3265.855
44.965 69.465 69.875
1 1.54 1.55
0.025
Control CuO 600 ppm CuO NP 600 ppm
13,341.948 13,117.191 13,429.812
12.571 12.836 19.066
1 1.02 1.51
0.025
Control CuO 600 ppm CuO NP 600 ppm
15,849.172 15,856.832 15,831.487
11.991 19.244 3.464
1 1.60 0.29
0.025
Control CuO 600 ppm CuO NP 600 ppm
16,063.801 16,060.646 16,071.178
5.037 7.368 1.518
1 1.46 0.30
0.025
Control CuO 600 ppm CuO NP 600 ppm
5556.928 5558.076 5558.037
142.624 181.979 371.961
1 1.28 2.61
0.026
Control CuO 600 ppm CuO NP 600 ppm
5615.703 5616.447 5608.802
99.536 118.212 136.725
1 1.19 1.37
0.03
Control CuO 600 ppm CuO NP 600 ppm
6443.091 6443.532 6443.224
147.745 180.248 175.658
1 1.22 1.19
0.035
Control CuO 600 ppm CuO NP 600 ppm
7419.044 7418.337 7418.325
31.63 8.722 47.821
1 0.28 1.51
0.037
Control CuO 600 ppm CuO NP 600 ppm
3474.82 3476.973 3470.054
33.665 53.112 64.549
1 1.58 1.92
0.039
Control CuO 600 ppm CuO NP 600 ppm
7999.148 8000.439 8007.959
160.105 232.845 134.749
1 1.45 0.84
0.058
Control CuO 600 ppm CuO NP 600 ppm
5543.374 5543.374 5543.374
57.986 67.019 150.287
1 1.16 0.26
0.063
Control CuO 600 ppm CuO NP 600 ppm
6328.318 6329.092 6327.786
132.544 162.078 174.418
1 1.22 1.32
0.069
Control CuO 600 ppm CuO NP 600 ppm
7843.487 7844.633 7833.648
137.399 184.298 184.241
1 1.34 1.34
0.084
effect of CuO nanoparticles on cucumber seed germination was not due solely to the release of metal ions (Wu et al., 2012). Another possible mechanism of the inhibitory action by nanoparticles on seed germination or root growth may be from plant-nanoparticle physical interactions. The size of seeds may contribute to the sensitivity to nanoparticle exposure (Shen et al., 2010). Because large seed species have
lower surface volume ratios than small seed species, large seeds have lesser exposure to nanoparticles; nanoparticle residues may change the chemistry of root surfaces, affecting the interaction between nanoparticle-bound roots and their environment (Shen et al., 2010). In other words, seed development is negatively affected because nanoparticles clog the root opening and inhibit both hydraulic and nutrient
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Fig. 4 – SELDI-TOF mass spectra of proteins of Cucumis sativus with significantly different expression on exposure to bulk CuO and CuO nanoparticles, assessed using the Q10 ProteinChip (p < 0.05).
uptake in roots. Based on the findings of several studies, the following have been found to be the principal factors that influence toxicity in plants: concentration of nanoparticles, particle size and specific surface area, physiochemical properties of nanoparticles, plant species, plant age/life cycle stage, growth media, nanoparticle stability, and diluting agents. Protein patterns of the bulk CuO- and CuO nanoparticletreated cucumber plants were obtained by SELDI-TOF MS analysis using Q10 ProteinChips and were compared to the mass-to-charge ratios (m/z) of differentially expressed proteins after exposure to nanoparticles. As shown in Table 1, 34 protein peaks were significantly different among the control, bulk CuO-treated, and CuO nanoparticle-treated cucumber plants. A treatment concentration of 600 ppm was selected because, at that concentration, CuO nanoparticles significantly inhibited cucumber seed germination and root growth (Fig. 2). All 34 peaks had intensities with P values of less than 0.084, including 30 peaks with P values of 0.05. Among the 34 peaks, 5 protein peaks with m/z values of 3927.854, 4371, 9911.336, 9993.162, and 10,437.931 were downregulated in the bulk CuOand CuO nanoparticle-treated cucumbers (Table 1). However, 18 protein peaks were overexpressed after exposure to bulk CuO and CuO nanoparticles, as compared to the control. With these 23 protein peaks, CuO nanoparticles had stronger upor downregulation effects on the cucumber plants than did the bulk CuO. It is likely that more dissolved Cu2+ ions can contribute stronger biological activities (Ji et al., 2011; Navarro et al., 2008). The remaining 9 peaks showed extreme patterns, in which one was upregulated and the other was downregulated. For example, the 5984-m/z peak (Fig. 4) was upregulated by 2-fold in the CuO nanoparticle-treated cucumber plants, whereas
the peak was downregulated by 0.98-fold by bulk CuO. This pattern was observed for the proteins with peaks of 5543, 6085, 7418, 7999, 8209, 8369, 15,849, and 16,063 m/z, as shown in Table 1. This finding helps demonstrate that the stronger phytotoxic effect of CuO nanoparticles compared to bulk CuO on cucumber seed germination and root growth is not caused only by the metal ions released (Wu et al., 2012). Approximately one-fourth of the differently expressed proteins showed extreme patterns after exposure to bulk CuO or nanoparticles, indicating that the biological mode of action of CuO nanoparticles may differ from that of the bulk CuO (Figs. 4 and 5). Using principal component analysis (PCA), the Q10 ProteinChip separated the CuO nanoparticle-treated cucumbers from the bulk CuO-treated cucumbers and the controls (Fig. 6), which suggests different modes of inhibitory mechanisms for cucumber seed germination and root growth. Biomarkers can be used as early warning signals for the presence of toxic compounds in the environment. Recent biomarker studies have been used to determine the ecological impact of nanoparticles on aquatic animals (Fan et al., 2012; Gomes et al., 2011). Metallothioneins (MTs), biomarkers found in Daphnia magna (Fan et al., 2012) and Mytilus galloprovincialis (Gomes et al., 2011), are significantly induced with increasing nanoparticle doses in both types of aquatic animals. However, these MT biomarkers are universally expressed in response to metal contamination in animals and plants, and their molecular weights vary greatly within a species (Raudenska et al., 2013). Therefore, MTs are not specific to the presence of metal nanoparticles. Alternatively, several biochemical changes have been suggested as biomarkers of nanoparticle toxicity in animals, including acetylcholinesterases (AChEs), which are neurotransmitter hydrolases (Gomes et al., 2011).
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Fig. 5 – SELDI-TOF mass spectra of proteins of Cucumis sativus with significantly different expression patterns after exposure to bulk CuO and CuO nanoparticles, assessed using the Q10 ProteinChip (p < 0.05).
In terrestrial plants, catalase (CAT) and ascorbate peroxidase (APOX) biomarkers respond to the presence of nanoparticles in the environment (Mukherjee et al., 2013). In green peas (Pisum sativum L.), CAT activity significantly decreased in leaves (p ≤ 0.05) at all the ZnO nanoparticle
concentrations tested, whereas APOX activity decreased in both roots and leaves. Bulk ZnO caused downregulation of APOX activity in roots and leaves, whereas CAT was unaffected (Mukherjee et al., 2013). Thus, CAT and APOX are presumable biomarkers of nanotoxicity in plant samples. Lee et al.
Fig. 6 – PCA plot of Cucumis sativus exposed to bulk CuO and CuO nanoparticles (NPs), using the Q10 ProteinChip in conjunction with SELDI-TOF MS analysis. Green triangle, control for the study; red square, 600 ppm of bulk CuO; blue circle, 600 ppm of CuO NPs.
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(2013a,b) showed that reactive oxygen species generated by ZnO nanoparticles could indicate the reduction in CAT activity. The authors also demonstrated that the reduced CAT activity could be a predictive biomarker of nanotoxicity. SELDI-TOF MS was used to generate a protein profile of intact samples, but MS analysis is limited in that it can only be used to identify proteins. Our SELDI-TOF MS analysis found 34 differentially expressed proteins in cucumber plants after treatment with CuO nanoparticles and bulk CuO. Among them, 9 proteins with extreme patterns were selected as presumable biomarker candidates for CuO nanoparticles, as shown in Fig. 4. Conclusively, CuO nanoparticles showed toxic effects on cucumber seed germination and root growth rate when compared to bulk CuO. SELDI-TOF MS analysis showed that the protein profiles of cucumber plants after exposure to CuO nanoparticles were statistically different from those exposed to corresponding concentrations of bulk CuO. To our knowledge, this is the first report to demonstrate differences in the toxic effects of metal nanoparticles and bulk metal ions. We found 34 proteins that were expressed with extreme patterns when cucumber plants were exposed to CuO nanoparticles and bulk CuO. Among them, the 5977-m/z protein can serve as a predictive biomarker for CuO nanoparticle exposure in cucumber plants.
Conflict of interest The authors declare that there are no conflicts of interest.
Transparency document The Transparency document associated with this article can be found in the online version.
Acknowledgements This research was supported by Kyungpook National University Research Fund, 2012. The authors express their thanks to Dajung Park for the technical support.
references
Adhikari, T., Kundu, S., Biswas, A.K., Tarafdar, J.C., Rao, A.S., 2012. Effect of copper oxide nanoparticle on seed germination of selected crops. J. Agric. Sci. Technol. A 2, 815–823. Carpita, N.C., Gibeaut, D.M., 1993. Structural models of primary cell walls in flowering plants: consistency of molecular-structure with physical properties of walls during growth. Plant J. 3, 1–30. Dimkpa, C.O., Latta, D.E., McLean, J.E., Britt, D.W., Boyanov, M.I., Anderson, A.J., 2013. Fate of CuO and ZnO nano- and microparticles in the plant environment. Environ. Sci. Technol. 47, 4734–4742. Dow, W.P., Huang, T.J., 1996. Yttria-stabilized zirconia supported copper oxide catalyst. II. Effect of oxygen vacancy of support on catalytic activity for CO oxidation. J. Catal. 160, 171–182. Fan, W.H., Cui, M.M., Shi, Z.W., Tan, C., Yang, X.P., 2012. Enhanced oxidative stress and physiological damage in Daphnia magna
by copper in the presence of nano-TiO2. J. Nanomat., http://dx.doi.org/10.1155/2012/398720, ID 398720. Gastinel, L.N., 2012. Principle component analysis in the era of omic data. In: Sanguansat, P. (Ed.), Principal component analysis – multidisciplinary applications. InTech, Available From: http://www.intechopen.com/books/principalcomponent-analysis-multidisciplinary-applications/princialcomponent-analysis-in-the-era-of-omics-data Gomes, T., Pinheiro, J.P., Cancio, I., Pereira, C.G., Cardoso, C., Bebianno, M.J., 2011. Effects of copper nanoparticles exposure in the mussel Mytilus galloprovincialis. Environ. Sci. Technol. 45, 9356–9362. Ji, J., Long, Z., Lin, D., 2011. Toxicity of oxide nanoparticles to the green algae Chlorella sp. Chem. Eng. J. 170, 525–530. Lanje, A.S., Ningthoujam, R.S., Shrama, S.J., Vasta, R.K., Pode, R.B., 2010. Luminescence properties of Sn1−x ·Fex Oz nanoparticles. Int. J. Nanotechnol. 7, 979–988. Lee, S., Choi, U.S., Li, S., Eastman, J.A., 1999. Measuring thermal conductivity of fluids containing oxide nanoparticles. J. Heat Transf. 121, 280–289. Lee, S., Kim, S., Kim, S., Lee, I., 2013a. Assessment of phytotoxicity of ZnO NPs on a medicinal plant, Fagopyrum esculentum. Environ. Sci. Pollut. Res. 20, 848–854. Lee, S.E., Choi, Y.W., Mo, H.H., Son, J., Park, K., Cho, K., 2013b. Endosulfan-induced biomarkers in Japanese rice fish (Oryzias latipes) analyzed by SELDI-TOF MS. Int. J. Biol. Sci. 9, 343–349. Lee, W.M., An, Y.J., Yoon, H., Kweon, H.S., 2008. Toxicity and bioavailability of copper nanoparticles to the terrestrial plants mung bean (Phaseolus radiates) and wheat (Triticum aestivum): plant agar test for water-insoluble nanoparticles. Environ. Toxicol. Chem. 27, 1915–1921. Li, P., Zhang, D., Guo, C., 2012. Serum biomarker screening for the diagnosis of early gastric cancer using SELDI-TOF MS. Mol. Med. Rep. 5, 1531–1535. Li, Y., Wu, J., Zhang, W., Zhang, N., Guo, H., 2013. Identification of serum CCL15 in hepatocellular carcinoma. Br. J. Cancer 108, 99–106. Mo, H.H., Lee, S.E., Son, J., Hwang, J.M., Bae, Y.J., Cho, K., 2013. Exposure of mayfly Ephemera orientalis (Ephemeroptera) eggs to heavy metals and discovery of biomarkers. Environ. Toxicol. Pharmacol. 36, 1167–1175. Mukherjee, A., Peralta-Videa, J.R., Bandyopadhyay, S., Rico, C.M., Zhao, L., Gardea-Torresdey, J.L., 2013. Physiological effects of nanoparticulate ZnO in green peas (Pisum sativum L.) cultivated in soil. Metallomics 6, 132–138. Navarro, E., Piccapietra, F., Wagner, B., Marconi, F., Kaeqi, R., Odzak, N., Sigg, L., Behra, R., 2008. Toxicity of silver nanoparticles to Chlamydomonas reinhardtii. Environ. Sci. Technol. 42, 8958–8964. Pokhrel, L.R., Dubey, B., 2013. Evaluation of developmental responses of two crop plants exposed to silver and zinc oxide nanoparticles. Sci. Total Environ. 452–453, 321–332. Raudenska, M., Gumulec, J., Podlaha, O., Sztalmachova, M., Babula, P., Eckschlager, T., Adam, V., Kizek, R., Masarik, M., 2013. Metallothionein polymorphism in pathological processes. Metallomics 6, 55–68. Regan, O., Grätzel, M., 1991. A low-cost, high-efficiency solar cell based on dye-sensitized colloidal TiO2 films. Nature 353, 737–740. SAS Institute, 2001. SAS User’s Guide. SAS Institute, NC. Shaw, A.K., Hossain, Z., 2013. Impact of nano-CuO stress on rice (Oryza sativa L.) seedling. Chemosphere 93, 906–915. Shen, C.X., Zhang, Q.F., Li, J., Bi, F.C., Yao, N., 2010. Induction of programmed cell death in Arabidopsis and rice by single wall carbon nanotubes. Am. J. Bot. 97, 1602–1609. USEPA, 1996. USEPA OPPTS 850.4200; Ecological effect test guidelines, 850 series (Proposal): seed germination/root elongation toxicity test.
e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 8 ( 2 0 1 4 ) 922–931
Wang, X.D., Sun, C., Gao, S.X., Wang, L.S., Han, S.K., 2001. Validation of germination rate and root elongation as indicator to assess phytotoxicity with Cucumis sativus. Chemosphere 44, 1711–1721. Wang, Z., Xie, X., Zhao, J., Liu, X., Feng, W., White, J.C., Xing, B., 2012. Xylem- and phloem-based transport of CuO
931
nanoparticles in maize (Zea mays L.). Environ. Sci. Technol. 46, 4434–4441. Wu, S.G., Huang, L., Head, J., Chen, D.R., Kong, I.C., Tang, Y.J., 2012. Phytotoxicity of metal oxide nanoparticles is related to both dissolved metal ions and adsorption of particles on seed surfaces. J. Pet. Environ. Biotechnol. 3, 126.
