Journal of Photochemistry and Photobiology B: Biology 130 (2014) 234–240
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Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles Kavitha Pathakoti a, Ming-Ju Huang b,⇑, John D. Watts b, Xiaojia He a, Huey-Min Hwang a,⇑ a b
Department of Biology, Jackson State University, Jackson, MS 39217, USA Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS 39217, USA
a r t i c l e
i n f o
Article history: Received 4 September 2013 Received in revised form 22 November 2013 Accepted 27 November 2013 Available online 4 December 2013 Keywords: Metal oxide nanoparticles Bacteria Phototoxicity Electronegativity LUMO energy
a b s t r a c t A quantitative structure–activity relationship (QSAR) study of seventeen metal oxide nanoparticles (MNPs), in regard to their photo-induced toxicity to bacteria Escherichia coli, was developed by using quantum chemical methods. A simple and statistically significant QSAR model (F = 33.83, R2 = 0.87) was successfully developed for the dark group based on two descriptors, absolute electronegativity of the metal and the metal oxide. Similarly, a best correlation (F = 20.51, R2 = 0.804) was obtained to predict the photo-induced toxicity of MNPs by using two descriptors, molar heat capacity and average of the alpha and beta LUMO (lowest unoccupied molecular orbital) energies of the metal oxide. Revelation of these influential molecular descriptors may be useful in elucidating the mechanisms of nanotoxicity and for predicting the environmental risk associated with release of the MNPs. In addition, the developed model may have a role in the future design and manufacture of safe nanomaterials. Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction Metal oxide nanoparticles (MNPs) have been used in a large variety of applications due to their novel optical, magnetic, and electronic properties [1]. They are also used in various consumer products such as cosmetics, dental fillings, solar-driven self-cleaning coatings, textiles and also for photocatalytic degradation of various organic pollutants [2]. Various photocatalytic reactions involves oxidation–reduction reactions which may ultimately lead to overall degradation of a wide variety of organic pollutants through their interactions with MNPs via photo-generated ‘‘holes’’ or reactive oxygen species (ROS), such as hydroxyl (OH) and superoxide (O 2 ) radicals [3]. Titanium dioxide (TiO2) is one of the most widely used photocatalysts for the degradation of organic contaminants in water and air [4,5] and its phototoxicity is studied in a broad range of biological systems, from bacteria [6–8] to mammalian cell lines [9,10]. The physicochemical properties underlying the photo-induced toxicity of TiO2 and ZnO are well understood [11,12]. Briefly, when these nanoparticles (NPs) are irradiated under UV light, electrons are promoted from the valence band to the conduction band, resulting in generation of energized ‘‘holes’’ in the former (Eqs. (1)–(5)).
⇑ Corresponding authors. Tel.: +1 601 9792595; fax: +1 601 9796856 (H.-M. Hwang). Tel.: +1 601 9793492; fax: +1 601 9793674 (M.-J. Huang). E-mail addresses: [email protected] (M.-J. Huang), huey-min.hwang@ jsums.edu (H.-M. Hwang). 1011-1344/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jphotobiol.2013.11.023
þ
MOx þ hm ! hvb þ ecb ðvb valence band; cb conduction bandÞ ð1Þ +
The holes (h ) may transfer between biomolecules and MNPs, react with OH or H2O to produce hydroxyl radicals (OH): þ
ð2Þ
þ
ð3Þ
hvb þ OH ! OH hvb þ HO2 ! OH þ Hþ
The holes (h+) also react with O2 to produce singlet oxygen (1O2): þ
hvb þ O2 ! 1 O2
ð4Þ
Free electrons (e) may react with O2 to form superoxide radical anions (O 2 ):
ecb þ O2 ! O 2 ðcb conduction bandÞ
ð5Þ
In fact, production of ROS from TiO2 NPs is used in the degradation of organic pollutants and also for bacterial inactivation. Hence, cellular damage due to the phototoxicity of MNPs may generate serious health hazards with release of MNPs into natural environments. Quantitative structure activity relationships (QSARs) are used to predict the toxicity from the physicochemical properties of the studied chemicals (known as molecular descriptors). A thorough understanding of the relationship between the effects of the engineered NPs and their physicochemical properties is essential for the design of safe NPs. Although QSAR methodology is well known
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and is extensively applied in the areas of drug discovery [13] and chemical toxicity modeling [14], application of structure–activity relationship methodologies in nanosafety is still in its infancy. The majority of published studies on MNP toxicity only focus on the effect of a few NPs such as TiO2 on living cells in vitro or in vivo. Accordingly a systematic review of the phototoxicity and risk of MNPs is lacking. In this paper, we selected 17 MNPs to experimentally measure their photo-induced toxicity to Escherichia coli, after exposure to natural sunlight irradiation in comparison to dark. We used the bacterial model because bacteria can serve as surrogates in assessing potential nanotoxicity to organisms of higher order and also have an important role in elemental cycling, degradation of pollutants, and maintenance of ecological balance [15,16]. We describe herein how the cytotoxicity data were used to develop QSAR models for predicting the photo-induced cytotoxicity of MNPs to E. coli.
replaced the Pyrex tubes used early in our study. Based on the results of bacterial viability test with E. coli under the light irradiation, no statistical difference in the assay data were found between the two glassware systems. After preliminary tests with a wide range of concentrations (1– 1000 ppm), definitive toxicity tests were conducted using narrow range of four to five concentrations causing 10–90% decrease in cell viability, in order to determine the LC50 values. Cytotoxicity was expressed in terms of the logarithmic values of LC50 (the concentration of the NPs that proved to be fatal to 50% of the bacterium E. coli). Duration of exposure was two hours in dark conditions in our earlier work [17], but in our present study we have chosen 30 min duration because of the bacterial inhibition caused by light exposure. A 16% inhibition in viable count of the bacteria was observed in the light control group in comparison to the dark control group.
2. Materials and methods
2.4. Molecular descriptors
2.1. Nanoparticles
Molecular descriptors, which are numerical representations of the molecular structures, are used for performing QSAR analysis [18]. Descriptors may be obtained from calculation or by experiment. Descriptors calculated by density functional theory (DFT) have been widely used in the development of QSAR and this area has been recently reviewed [19–22]. DFT is advantageous [22] because it includes electron correlation at a much lower computational cost than wave function-based method. It also has higher predictive power than semi-empirical molecular orbital methods. The general form of the QSAR considered in this work includes three types of molecular descriptors (electronic, geometric, and thermodynamic):
All the NPs were purchased from Sigma–Aldrich and Sky Spring Nanomaterials, Inc. with sizes ranging from 15 to 90 nm except for Bi2O3 (Table S1). Stock dispersion (1200 ppm) of the NPs was prepared in distilled water by sonication (Fisher Scientific FS30, 115 V, 50/60 Hz) for 30 min at room temperature to aid dispersion. The samples were kept in the dark until use and were sonicated again for 10 min prior to experimentation. 2.2. Physicochemical characterization of the MNPs Primary particle was measured by using transmission electron microscopy (TEM). Samples were prepared by drop-coating the NP suspension onto a carbon-coated copper grid (Ted Pella, CA) and then the samples were dried overnight at room temperature. The samples were observed using a TEM (JEOL JEM-1011). The hydrodynamic diameters (z-average) were measured in distill water (at a concentration of 100 ppm in water) and zeta potentials of the MNPs were measured in both distill water and 1 mM KCl solution using Malvern Zeta Sizer (Nano-ZS, Malvern Instruments, UK). All measurements were conducted in triplicate at 25 °C and an average values was determined. 2.3. Cell viability assay E. coli (Migula) Castellani and Chalmers (ATCC#25254) strain was prepared at 37 °C overnight using Luria–Bertani (LB) broth. Then, the cultures were centrifuged at 3220 g for 10 min and resuspended in sterilized physiological saline (0.8%). Bacterial density was adjusted to 2.2 109–3.0 109 bacteria/mL as determined by colony forming units (CFU) counting on LB Petri dishes. The stock solutions of the NPs were diluted to the various concentrations in 2 mL of water. One hundred lL of freshly washed bacteria suspensions were added to the diluted solutions. The samples in Pyrex and quartz test tubes were exposed to sunlight for 30 min with agitation in a water bath at 150 r.p.m. Similarly, corresponding samples were exposed under dark conditions, by wrapping the test samples in the test tubes with aluminum foils (solar irradiation outdoors: irradiance: UVA range = 3.979–4.652 mW/cm2; UVB range = 3.1–3.7 MED/h, where an MED is defined as the minimum erythemal dose or the amount of UV radiation to produce barely perceptible erythema; 1 MED/h = 0.05833 W/m2). After 30 min of exposure time, bacterial viability was determined using plate count method CFU counting on LB petri plates. Quartz test tubes were purchased from ACE Glass Inc., Vineland, NJ. They
LogðLC 50 Þ ¼
X X X Ai EDi þ Bi GDi þ C i TDi þ D i
i
ð6Þ
i
where EDi, GDi, TDi are respectively electronic, geometric, and thermodynamic descriptors; Ai, Bi, Ci, and D are determined by least-squares fitting. The electronic descriptors considered include: vertical and adiabatic ionization potential, highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, the LUMO–HOMO difference, molecular hardness, absolute electronegativity, the average of the alpha and beta LUMO energies, the larger of the HOMO energies of the alpha and beta orbitals, and dipole moment. The geometrical descriptors considered are: mass, volume, density, surface area, and metal cation radii. The thermodynamic descriptors considered are: the enthalpy of formation of metal oxides and metal atoms, the enthalpy and Gibbs energy difference between the neutral/cation and neutral/anion, and the heat capacities. Among the 17 MNPs, there are three different types of metal oxides (MO, MO2, and M2O3). We have four MO, four MO2, and nine M2O3. We chose one MO, one MO2, and two M2O3 as our prediction set. In addition, we made sure the LC50 values for those four MNPs are not the largest or smallest values in their subsets. 2.5. Computational methods Seventeen MNPs were studied computationally with two methods. First, DFT was used with the B3LYP functional (Becke’s threeparameter exchange functional combined with the Lee–Yang–Parr correlation functional) [23,24] and the LANL2DZ (Los Alamos National Laboratory 2 double-f) basis sets. Second, the semi-empirical molecular orbital method PM6 was used. Calculations on the metal atoms were performed with the CCSD(T) method and the QZVP (quadruple-f plus polarization) basis set. All calculations were performed with the Gaussian 2009 program [25].
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As mentioned above, thirteen metal oxides were assigned to the training set and four metal oxides to the prediction set. Linear combinations of varying numbers of the descriptors were fitted to the observed dark and photo-induced (light) toxicities of MNPs to E. coli using a self-written least-squares fitting program. Electron transfer is likely to play an important role in the photoinduced toxicity of metal oxides and metal atoms. The frontier orbital energies and quantities derived therein are associated with the electron-transfer process and may be insightful. The value of the LUMO energy as well as the HOMO–LUMO gap (eHOMO eLUMO ) can be informative in judging oxidative reactivity, so these values were examined. Incorporation of the molecular orbital theory into hard-soft theory allows one to assess chemical reactivity by using the absolute electronegativity (v) and absolute hardness (g) when oxidation and reduction are involved in the process. Absolute electronegativity is defined as the average energy associated with ionization potential (I) and electron affinity (A) and is equivalent to the negative of the chemical potential (l) of the molecule. The absolute hardness is defined as half the difference between the ionization potential and the electron affinity. Within the Koopmans’ theorem approximation, these parameters can be expressed in terms of HOMO and LUMO energies that are routinely obtained from molecular orbital theory as below:
v¼
HOMO þ LUMO 2
area of the studied MNPs were provided by the vendor. Primary size was also measured in our lab using TEM and the average sizes were in the range 10–90 nm, except for Bi2O3 that was much larger, with an average size of 144 ± 7.2 nm. These sizes are in good agreement with the vendor- provided average size of the NPs. TEM images also showed large aggregation of all the studied MNPs (data not presented). The average hydrodynamic diameter of MNPs suspended in water were far larger (>1000 nm) in some of the MNPs like ZnO, Bi2O3, SiO2, SnO2, ZrO2, and Fe2O3. All of the MNPs showed negative surface charge in water, except CuO, CoO, NiO, Al2O3, In2O3 and Y2O3, whereas La2O3, ZrO2 and Cr2O3 showed a positive charge in 1 mM KCl solution. All the other MNPs did not show significant differences in zeta potential both in distill water and 1 mM KCl solution. 3.2. Nano-QSAR model Physicochemical descriptors of seventeen MNPs were obtained computationally using several methods, which are described in the supplementary section. The descriptors which we used for this study are shown in Tables S2 and S3 of the supplementary section. Table S2 shows the molecular properties of metal oxides. Table S3 shows the calculated properties (in atomic units (a.u.)) of metal atoms.
ð7Þ 3.3. Toxicity in dark condition
LUMO HOMO g¼ 2
ð8Þ
According to the hardness concept [26–28] a hardness controlled Lewis acid (or base) interacts most strongly with a hardness controlled base (or acid). Likewise, an electronegativity controlled acid (or base) interacts preferentially with an electronegativity controlled base (or acid). Thus, chemical characteristics such as absolute hardness and absolute electronegativity may be valuable indicators for toxicity prediction and for insight into the toxicity mechanisms of the metal oxides. 3. Results 3.1. Nanoparticle characterization All the physicochemical characteristics of the studied MNPs are presented in Table 1. Data of the average size and specific surface
The best correlation for a two-descriptor QSAR is given by the function:
LogðLC 50 Þ ¼ 37:697QMELECT 41:010LZELEHHO þ 6:6136
ð9Þ
ðn ¼ 13; F ¼ 33:83; R2 ¼ 0:87; SD ¼ 0:48 this is for training setÞ where n is the number of compounds submitted to the regression; R is the correlation coefficient; SD is the standard derivation; and F is the Fisher’s variance ratio. The SD for one-descriptor functions was significantly higher, while that for three descriptors was not significantly lower. Hence, this two-descriptor function offers the best compromise between a small SD and small number of descriptors. The LC50 is in units of mol/L, QMELECT is the absolute electronegativity of the metal atom (Table S3). LZELEHHO is the absolute electronegativity of the metal oxide (Table S2).
Table 1 Physicochemical characterization of metal oxide nanoparticles.
*
Metal oxide
Particle size (vendor) (nm)
Particle size TEM (nm)
Hydrodynamic size (nm)
Zeta potential (mV) (H2O)
Zeta potential (mV) (KCl)
Surface area (m2/g)
CuO ZnO TiO2 CoO In2O3 NiO V2O3 Y2O3 Al2O3 Bi2O3 La2O3 Sb2O3 SiO2 SnO2 ZrO2 Cr2O3 Fe2O3
V2O3 > CoO.
4. Discussion Table 2 The experimental and calculated dark toxicities, log(LC50), in the training set with two-parameter function (LC50 is in units of mol/L). Metal oxide
Experimental logLC50
Calculated logLC50
ZnO CuO Y2O3 In2O3 Sb2O3 Al2O3 Fe2O3 SiO2 ZrO2 TiO2 CoO Cr2O3 La2O3
5.80 4.24 5.79 2.83 3.12 2.42 2.40 2.54 2.58 2.14 3.13 2.06 4.96
5.90 4.06 4.98 2.18 3.33 1.78 3.19 2.37 3.12 2.60 3.41 2.15 4.93
Compared to other organisms, bacteria are among the most susceptible group to photodamage under natural sunlight [15] ZnO NPs showed phototoxicity to Caenorhabditis elegans under natural sunlight, when compared to the artificial light illumination [29]. In a comparative study of ecotoxicity of MNPs to bacteria, Adams et al. [7] reported that the toxicity was not related to the particle size of the NPs. In addition, Tong et al. [30] reported that the extent
Table 3 The experimental and calculated dark toxicities, log(LC50), in the prediction set with two-parameter function (LC50 is in units of mol/L). Metal oxide
Experimental logLC50
Calculated logLC50
V2O3 Bi2O3 SnO2 NiO
3.48 3.55 2.53 3.79
3.68 2.92 2.77 3.01
Table 4 The experimental and calculated photo-induced toxicities, log(LC50), in the training set with two-parameter function (LC50 is in units of mol/L).
Fig. 1. The calculated vs experimental dark toxicities, Log(LC50). The diamonds and triangles are the training and prediction set data points, respectively. R2 refers to the training set.
Metal oxide
Experimental logLC50
Calculated logLC50
ZnO CuO Y2O3 In2O3 Sb2O3 Al2O3 Fe2O3 SiO2 ZrO2 TiO2 CoO Cr2O3 La2O3
6.23 5.71 5.84 3.48 3.66 2.75 2.54 2.92 3.04 4.68 3.33 2.06 5.56
6.32 4.74 5.19 2.73 4.40 2.59 3.06 3.42 3.88 3.92 4.01 1.86 5.68
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Fig. 2. The calculated vs experimental photo-induced toxicities, Log(LC50). The diamonds and triangles are the training and prediction set data points, respectively. R2 refers to the training set.
Table 5 The experimental and calculated photo-induced toxicities, log(LC50), in the prediction set with two-parameter function (LC50 is in units of mol/L). Metal oxide
Experimental logLC50
Calculated logLC50
V2O3 Bi2O3 SnO2 NiO
3.78 4.02 3.24 3.87
2.94 2.78 3.22 4.78
of photo-induced toxicity does not correlate with the particle size of the tested TiO2 nanomaterials. Recently, a quantitative relationship between oxidative stress and antibacterial activity of NPs was studied by Li et al. [31] and a linear correlation was found between the average concentration of total ROS and the bacterial survival rates. Based on the aforementioned reports, we feel use of E. coli represents a balanced choice for the bacterial model considering environmental relevance and detection sensitivity. Hypothetically, smaller nanomaterials have lower thermal stability and are likely to be more toxic because of greater bioavailability via greater specific surface area. The percentage of surface molecules changes as a function of particle size, which reflects the importance of surface area for increased chemical and biological activity of engineered NPs. In this study, because of cost effectiveness and commercial availability, it was unfeasible to adopt all of the studied MNPs in the same size range. Use of NPs sized between 15 and 90 nm may be acceptable for the application in our study, as the fraction of surface molecules increases slowly but with dramatic elevation in the slope at diameter ZrO2 > TiO2 > Al2O3 > SiO2 [44]. Molar heat capacity change (DbCP,m) was associated with the binding reaction [45], and the molar heat capacities of minerals also represent the number of atoms within the material around room temperature [46]. The conduction band energy levels can be used to explain the toxicological potential of MNPs at cellular and whole animal levels [47]. However, it fails to explain the toxicity results from our research. TiO2, CoO and Cr2O3 are the compounds with conduction band between 4.12 to 4.84 eV. TiO2 fits in this theory; nevertheless, CoO and Cr2O3 did not show high phototoxicity compared to other MNPs. Thus, it is likely that the band gap is not the critical factor determining the phototoxicity. In the process of photo-oxidation reactions, there are significant correlations among the ionization/redox potentials with HOMO/ LUMO energy levels, which are also related to the geometries in different charge states [48]. The HOMO/LUMO energy is revealed to be associated with the phototoxicity risk prediction. NPs with small HOMO–LUMO gaps absorb energy at higher wavelengths (more visible range) of light and are photochemically less stable, whereas NPs with larger HOMO–LUMO gaps absorb at smaller wavelengths (UV; greater energy) and are photochemically reactive [49,50]. Note that the calculated HOMO–LUMO gaps are closer to the first electronic excitation energies [51]. and there is also a correlation between electron excitation energy and phototoxicity. Finally, band gap theory was also selected to predict toxicity [42,47,52]. In our earlier study we reported the toxicity of seven MNPs (ZnO, CuO, Al2O3, La2O3, Fe2O3, SnO2 and TiO2) to E. coli under dark conditions [53]. In addition, a simple nano-QSAR model was developed to relate the toxicity of 17 types of MNPs to E. coli in dark [17]. An excellent relationship was found between a single NP
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property (one molecular descriptor), the enthalpy of formation of a gaseous cation (HFM), and the concentration of NPs found to cause 50% loss in viability of the bacteria (EC50). The model performed very well on the training data (predicting 85% of the variance in the data), and internal and external validation sets (77% and 83% of the variance in the data). Since light inhibition occurred when E. coli cells were exposed to natural sunlight irradiance, exposure duration was reduced from 2 h [17] to 30 min in this study. This change translates into a reduction of exposure duration of approximately four generation time for the E. coli bacteria. The change may partially account for why different influential descriptors were obtained in this study. Although one more descriptor was involved in the QSAR models developed in this study for both dark and photo-induced toxicity of the studied MNPs, we feel this finding may lead to establishment of better estimates of the realistic health hazards associated with the release of MNPs into the natural environment. 5. Conclusions By using descriptors obtained from DFT with the B3LYP functional and coupled-cluster calculations, we established a QSAR model to predict the photo-induced toxicity of seventeen MNPs to E. coli. Our models show the HOMO and LUMO energies of metal oxide and metal atom and the heat capacity of metal oxide play an important role in the toxicity in dark/light conditions. The defined structure–activity relationships of the present study might play a role in the design of safer nanomaterials and to predict the environmental risk associated with these NPs. Acknowledgements This research was supported by the National Science Foundation (NSF-CREST Grant# HRD0833178). Most of the Gaussian 09 calculations were run on facilities provided by the Mississippi Supercomputer Center. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jphotobiol. 2013.11.023. References [1] L. Zhou, J. Xu, X. Li, F. Wang, Metal oxide nanoparticles from inorganic sources via a simple and general method, Mater. Chem. Phys. 97 (2006) 137–142. [2] The Royal Academy of Engineering The Royal Society, Nanoscience and Nanotechnologies: Opportunities and Uncertainties, Royal Society, London, 2004. [3] V. Vamathevan, R. Amal, D. Beydoun, G. Low, S. McEvoy, Photocatalytic oxidation of organics in water using pure and silver-modified titanium dioxide particles, J. Photochem. Photobiol. A Chem. 148 (2002) 233–245. [4] R. Armon, G. Weltch-Cohen, P. Bettane, Disinfection of Bacillus spp. spores in drinking water by TiO2 photocatalysis as a model for Bacillus anthracis, Water Sci. Technol. 4 (2004) 7–14. [5] V. Keller, N. Keller, M.J. Ledoux, M.C. Lett, Biological agent inactivation in a flowing air stream by photocatalysis, Chem. Commun. 2005 (2005) 2918– 2920. [6] K. Sunada, T. Watanabe, K. Hashimoto, Bactericidal activity of copperdeposited TiO2 thin film under weak UV light illumination, Environ. Sci. Technol. 37 (2003) 4785–4789. [7] L.K. Adams, D.Y. Lyon, A. Mclntosh, P.J.J. Alvarez, Comparative toxicity of nanoscale TiO2, SiO2 and ZnO water suspensions, Water Sci. Technol. 54 (2006) 327–334. [8] L. Brunet, D.Y. Lyon, E.M. Hotze, P.J.J. Alvarez, M.R. Wiesner, Comparative photoactivity and antibacterial properties of C60 fullerenes and titanium dioxide nanoparticles, Environ. Sci. Technol. 43 (2009) 4355–4360. [9] C.M. Sayes, R. Wahi, P.A. Kurian, Y. Liu, J.L. West, K.D. Ausman, D.B. Warheit, V.L. Colvin, Correlating nanoscale titania structure with toxicity: a cytotoxicity and inflammatory response study with human dermal fibroblasts and human lung epithelial cells, Toxicol. Sci. 92 (2006) 174–185.
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Contents lists available at ScienceDirect
Journal of Photochemistry and Photobiology B: Biology journal homepage: www.elsevier.com/locate/jphotobiol
Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles Kavitha Pathakoti a, Ming-Ju Huang b,⇑, John D. Watts b, Xiaojia He a, Huey-Min Hwang a,⇑ a b
Department of Biology, Jackson State University, Jackson, MS 39217, USA Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS 39217, USA
a r t i c l e
i n f o
Article history: Received 4 September 2013 Received in revised form 22 November 2013 Accepted 27 November 2013 Available online 4 December 2013 Keywords: Metal oxide nanoparticles Bacteria Phototoxicity Electronegativity LUMO energy
a b s t r a c t A quantitative structure–activity relationship (QSAR) study of seventeen metal oxide nanoparticles (MNPs), in regard to their photo-induced toxicity to bacteria Escherichia coli, was developed by using quantum chemical methods. A simple and statistically significant QSAR model (F = 33.83, R2 = 0.87) was successfully developed for the dark group based on two descriptors, absolute electronegativity of the metal and the metal oxide. Similarly, a best correlation (F = 20.51, R2 = 0.804) was obtained to predict the photo-induced toxicity of MNPs by using two descriptors, molar heat capacity and average of the alpha and beta LUMO (lowest unoccupied molecular orbital) energies of the metal oxide. Revelation of these influential molecular descriptors may be useful in elucidating the mechanisms of nanotoxicity and for predicting the environmental risk associated with release of the MNPs. In addition, the developed model may have a role in the future design and manufacture of safe nanomaterials. Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction Metal oxide nanoparticles (MNPs) have been used in a large variety of applications due to their novel optical, magnetic, and electronic properties [1]. They are also used in various consumer products such as cosmetics, dental fillings, solar-driven self-cleaning coatings, textiles and also for photocatalytic degradation of various organic pollutants [2]. Various photocatalytic reactions involves oxidation–reduction reactions which may ultimately lead to overall degradation of a wide variety of organic pollutants through their interactions with MNPs via photo-generated ‘‘holes’’ or reactive oxygen species (ROS), such as hydroxyl (OH) and superoxide (O 2 ) radicals [3]. Titanium dioxide (TiO2) is one of the most widely used photocatalysts for the degradation of organic contaminants in water and air [4,5] and its phototoxicity is studied in a broad range of biological systems, from bacteria [6–8] to mammalian cell lines [9,10]. The physicochemical properties underlying the photo-induced toxicity of TiO2 and ZnO are well understood [11,12]. Briefly, when these nanoparticles (NPs) are irradiated under UV light, electrons are promoted from the valence band to the conduction band, resulting in generation of energized ‘‘holes’’ in the former (Eqs. (1)–(5)).
⇑ Corresponding authors. Tel.: +1 601 9792595; fax: +1 601 9796856 (H.-M. Hwang). Tel.: +1 601 9793492; fax: +1 601 9793674 (M.-J. Huang). E-mail addresses: [email protected] (M.-J. Huang), huey-min.hwang@ jsums.edu (H.-M. Hwang). 1011-1344/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jphotobiol.2013.11.023
þ
MOx þ hm ! hvb þ ecb ðvb valence band; cb conduction bandÞ ð1Þ +
The holes (h ) may transfer between biomolecules and MNPs, react with OH or H2O to produce hydroxyl radicals (OH): þ
ð2Þ
þ
ð3Þ
hvb þ OH ! OH hvb þ HO2 ! OH þ Hþ
The holes (h+) also react with O2 to produce singlet oxygen (1O2): þ
hvb þ O2 ! 1 O2
ð4Þ
Free electrons (e) may react with O2 to form superoxide radical anions (O 2 ):
ecb þ O2 ! O 2 ðcb conduction bandÞ
ð5Þ
In fact, production of ROS from TiO2 NPs is used in the degradation of organic pollutants and also for bacterial inactivation. Hence, cellular damage due to the phototoxicity of MNPs may generate serious health hazards with release of MNPs into natural environments. Quantitative structure activity relationships (QSARs) are used to predict the toxicity from the physicochemical properties of the studied chemicals (known as molecular descriptors). A thorough understanding of the relationship between the effects of the engineered NPs and their physicochemical properties is essential for the design of safe NPs. Although QSAR methodology is well known
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and is extensively applied in the areas of drug discovery [13] and chemical toxicity modeling [14], application of structure–activity relationship methodologies in nanosafety is still in its infancy. The majority of published studies on MNP toxicity only focus on the effect of a few NPs such as TiO2 on living cells in vitro or in vivo. Accordingly a systematic review of the phototoxicity and risk of MNPs is lacking. In this paper, we selected 17 MNPs to experimentally measure their photo-induced toxicity to Escherichia coli, after exposure to natural sunlight irradiation in comparison to dark. We used the bacterial model because bacteria can serve as surrogates in assessing potential nanotoxicity to organisms of higher order and also have an important role in elemental cycling, degradation of pollutants, and maintenance of ecological balance [15,16]. We describe herein how the cytotoxicity data were used to develop QSAR models for predicting the photo-induced cytotoxicity of MNPs to E. coli.
replaced the Pyrex tubes used early in our study. Based on the results of bacterial viability test with E. coli under the light irradiation, no statistical difference in the assay data were found between the two glassware systems. After preliminary tests with a wide range of concentrations (1– 1000 ppm), definitive toxicity tests were conducted using narrow range of four to five concentrations causing 10–90% decrease in cell viability, in order to determine the LC50 values. Cytotoxicity was expressed in terms of the logarithmic values of LC50 (the concentration of the NPs that proved to be fatal to 50% of the bacterium E. coli). Duration of exposure was two hours in dark conditions in our earlier work [17], but in our present study we have chosen 30 min duration because of the bacterial inhibition caused by light exposure. A 16% inhibition in viable count of the bacteria was observed in the light control group in comparison to the dark control group.
2. Materials and methods
2.4. Molecular descriptors
2.1. Nanoparticles
Molecular descriptors, which are numerical representations of the molecular structures, are used for performing QSAR analysis [18]. Descriptors may be obtained from calculation or by experiment. Descriptors calculated by density functional theory (DFT) have been widely used in the development of QSAR and this area has been recently reviewed [19–22]. DFT is advantageous [22] because it includes electron correlation at a much lower computational cost than wave function-based method. It also has higher predictive power than semi-empirical molecular orbital methods. The general form of the QSAR considered in this work includes three types of molecular descriptors (electronic, geometric, and thermodynamic):
All the NPs were purchased from Sigma–Aldrich and Sky Spring Nanomaterials, Inc. with sizes ranging from 15 to 90 nm except for Bi2O3 (Table S1). Stock dispersion (1200 ppm) of the NPs was prepared in distilled water by sonication (Fisher Scientific FS30, 115 V, 50/60 Hz) for 30 min at room temperature to aid dispersion. The samples were kept in the dark until use and were sonicated again for 10 min prior to experimentation. 2.2. Physicochemical characterization of the MNPs Primary particle was measured by using transmission electron microscopy (TEM). Samples were prepared by drop-coating the NP suspension onto a carbon-coated copper grid (Ted Pella, CA) and then the samples were dried overnight at room temperature. The samples were observed using a TEM (JEOL JEM-1011). The hydrodynamic diameters (z-average) were measured in distill water (at a concentration of 100 ppm in water) and zeta potentials of the MNPs were measured in both distill water and 1 mM KCl solution using Malvern Zeta Sizer (Nano-ZS, Malvern Instruments, UK). All measurements were conducted in triplicate at 25 °C and an average values was determined. 2.3. Cell viability assay E. coli (Migula) Castellani and Chalmers (ATCC#25254) strain was prepared at 37 °C overnight using Luria–Bertani (LB) broth. Then, the cultures were centrifuged at 3220 g for 10 min and resuspended in sterilized physiological saline (0.8%). Bacterial density was adjusted to 2.2 109–3.0 109 bacteria/mL as determined by colony forming units (CFU) counting on LB Petri dishes. The stock solutions of the NPs were diluted to the various concentrations in 2 mL of water. One hundred lL of freshly washed bacteria suspensions were added to the diluted solutions. The samples in Pyrex and quartz test tubes were exposed to sunlight for 30 min with agitation in a water bath at 150 r.p.m. Similarly, corresponding samples were exposed under dark conditions, by wrapping the test samples in the test tubes with aluminum foils (solar irradiation outdoors: irradiance: UVA range = 3.979–4.652 mW/cm2; UVB range = 3.1–3.7 MED/h, where an MED is defined as the minimum erythemal dose or the amount of UV radiation to produce barely perceptible erythema; 1 MED/h = 0.05833 W/m2). After 30 min of exposure time, bacterial viability was determined using plate count method CFU counting on LB petri plates. Quartz test tubes were purchased from ACE Glass Inc., Vineland, NJ. They
LogðLC 50 Þ ¼
X X X Ai EDi þ Bi GDi þ C i TDi þ D i
i
ð6Þ
i
where EDi, GDi, TDi are respectively electronic, geometric, and thermodynamic descriptors; Ai, Bi, Ci, and D are determined by least-squares fitting. The electronic descriptors considered include: vertical and adiabatic ionization potential, highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, the LUMO–HOMO difference, molecular hardness, absolute electronegativity, the average of the alpha and beta LUMO energies, the larger of the HOMO energies of the alpha and beta orbitals, and dipole moment. The geometrical descriptors considered are: mass, volume, density, surface area, and metal cation radii. The thermodynamic descriptors considered are: the enthalpy of formation of metal oxides and metal atoms, the enthalpy and Gibbs energy difference between the neutral/cation and neutral/anion, and the heat capacities. Among the 17 MNPs, there are three different types of metal oxides (MO, MO2, and M2O3). We have four MO, four MO2, and nine M2O3. We chose one MO, one MO2, and two M2O3 as our prediction set. In addition, we made sure the LC50 values for those four MNPs are not the largest or smallest values in their subsets. 2.5. Computational methods Seventeen MNPs were studied computationally with two methods. First, DFT was used with the B3LYP functional (Becke’s threeparameter exchange functional combined with the Lee–Yang–Parr correlation functional) [23,24] and the LANL2DZ (Los Alamos National Laboratory 2 double-f) basis sets. Second, the semi-empirical molecular orbital method PM6 was used. Calculations on the metal atoms were performed with the CCSD(T) method and the QZVP (quadruple-f plus polarization) basis set. All calculations were performed with the Gaussian 2009 program [25].
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As mentioned above, thirteen metal oxides were assigned to the training set and four metal oxides to the prediction set. Linear combinations of varying numbers of the descriptors were fitted to the observed dark and photo-induced (light) toxicities of MNPs to E. coli using a self-written least-squares fitting program. Electron transfer is likely to play an important role in the photoinduced toxicity of metal oxides and metal atoms. The frontier orbital energies and quantities derived therein are associated with the electron-transfer process and may be insightful. The value of the LUMO energy as well as the HOMO–LUMO gap (eHOMO eLUMO ) can be informative in judging oxidative reactivity, so these values were examined. Incorporation of the molecular orbital theory into hard-soft theory allows one to assess chemical reactivity by using the absolute electronegativity (v) and absolute hardness (g) when oxidation and reduction are involved in the process. Absolute electronegativity is defined as the average energy associated with ionization potential (I) and electron affinity (A) and is equivalent to the negative of the chemical potential (l) of the molecule. The absolute hardness is defined as half the difference between the ionization potential and the electron affinity. Within the Koopmans’ theorem approximation, these parameters can be expressed in terms of HOMO and LUMO energies that are routinely obtained from molecular orbital theory as below:
v¼
HOMO þ LUMO 2
area of the studied MNPs were provided by the vendor. Primary size was also measured in our lab using TEM and the average sizes were in the range 10–90 nm, except for Bi2O3 that was much larger, with an average size of 144 ± 7.2 nm. These sizes are in good agreement with the vendor- provided average size of the NPs. TEM images also showed large aggregation of all the studied MNPs (data not presented). The average hydrodynamic diameter of MNPs suspended in water were far larger (>1000 nm) in some of the MNPs like ZnO, Bi2O3, SiO2, SnO2, ZrO2, and Fe2O3. All of the MNPs showed negative surface charge in water, except CuO, CoO, NiO, Al2O3, In2O3 and Y2O3, whereas La2O3, ZrO2 and Cr2O3 showed a positive charge in 1 mM KCl solution. All the other MNPs did not show significant differences in zeta potential both in distill water and 1 mM KCl solution. 3.2. Nano-QSAR model Physicochemical descriptors of seventeen MNPs were obtained computationally using several methods, which are described in the supplementary section. The descriptors which we used for this study are shown in Tables S2 and S3 of the supplementary section. Table S2 shows the molecular properties of metal oxides. Table S3 shows the calculated properties (in atomic units (a.u.)) of metal atoms.
ð7Þ 3.3. Toxicity in dark condition
LUMO HOMO g¼ 2
ð8Þ
According to the hardness concept [26–28] a hardness controlled Lewis acid (or base) interacts most strongly with a hardness controlled base (or acid). Likewise, an electronegativity controlled acid (or base) interacts preferentially with an electronegativity controlled base (or acid). Thus, chemical characteristics such as absolute hardness and absolute electronegativity may be valuable indicators for toxicity prediction and for insight into the toxicity mechanisms of the metal oxides. 3. Results 3.1. Nanoparticle characterization All the physicochemical characteristics of the studied MNPs are presented in Table 1. Data of the average size and specific surface
The best correlation for a two-descriptor QSAR is given by the function:
LogðLC 50 Þ ¼ 37:697QMELECT 41:010LZELEHHO þ 6:6136
ð9Þ
ðn ¼ 13; F ¼ 33:83; R2 ¼ 0:87; SD ¼ 0:48 this is for training setÞ where n is the number of compounds submitted to the regression; R is the correlation coefficient; SD is the standard derivation; and F is the Fisher’s variance ratio. The SD for one-descriptor functions was significantly higher, while that for three descriptors was not significantly lower. Hence, this two-descriptor function offers the best compromise between a small SD and small number of descriptors. The LC50 is in units of mol/L, QMELECT is the absolute electronegativity of the metal atom (Table S3). LZELEHHO is the absolute electronegativity of the metal oxide (Table S2).
Table 1 Physicochemical characterization of metal oxide nanoparticles.
*
Metal oxide
Particle size (vendor) (nm)
Particle size TEM (nm)
Hydrodynamic size (nm)
Zeta potential (mV) (H2O)
Zeta potential (mV) (KCl)
Surface area (m2/g)
CuO ZnO TiO2 CoO In2O3 NiO V2O3 Y2O3 Al2O3 Bi2O3 La2O3 Sb2O3 SiO2 SnO2 ZrO2 Cr2O3 Fe2O3
V2O3 > CoO.
4. Discussion Table 2 The experimental and calculated dark toxicities, log(LC50), in the training set with two-parameter function (LC50 is in units of mol/L). Metal oxide
Experimental logLC50
Calculated logLC50
ZnO CuO Y2O3 In2O3 Sb2O3 Al2O3 Fe2O3 SiO2 ZrO2 TiO2 CoO Cr2O3 La2O3
5.80 4.24 5.79 2.83 3.12 2.42 2.40 2.54 2.58 2.14 3.13 2.06 4.96
5.90 4.06 4.98 2.18 3.33 1.78 3.19 2.37 3.12 2.60 3.41 2.15 4.93
Compared to other organisms, bacteria are among the most susceptible group to photodamage under natural sunlight [15] ZnO NPs showed phototoxicity to Caenorhabditis elegans under natural sunlight, when compared to the artificial light illumination [29]. In a comparative study of ecotoxicity of MNPs to bacteria, Adams et al. [7] reported that the toxicity was not related to the particle size of the NPs. In addition, Tong et al. [30] reported that the extent
Table 3 The experimental and calculated dark toxicities, log(LC50), in the prediction set with two-parameter function (LC50 is in units of mol/L). Metal oxide
Experimental logLC50
Calculated logLC50
V2O3 Bi2O3 SnO2 NiO
3.48 3.55 2.53 3.79
3.68 2.92 2.77 3.01
Table 4 The experimental and calculated photo-induced toxicities, log(LC50), in the training set with two-parameter function (LC50 is in units of mol/L).
Fig. 1. The calculated vs experimental dark toxicities, Log(LC50). The diamonds and triangles are the training and prediction set data points, respectively. R2 refers to the training set.
Metal oxide
Experimental logLC50
Calculated logLC50
ZnO CuO Y2O3 In2O3 Sb2O3 Al2O3 Fe2O3 SiO2 ZrO2 TiO2 CoO Cr2O3 La2O3
6.23 5.71 5.84 3.48 3.66 2.75 2.54 2.92 3.04 4.68 3.33 2.06 5.56
6.32 4.74 5.19 2.73 4.40 2.59 3.06 3.42 3.88 3.92 4.01 1.86 5.68
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Fig. 2. The calculated vs experimental photo-induced toxicities, Log(LC50). The diamonds and triangles are the training and prediction set data points, respectively. R2 refers to the training set.
Table 5 The experimental and calculated photo-induced toxicities, log(LC50), in the prediction set with two-parameter function (LC50 is in units of mol/L). Metal oxide
Experimental logLC50
Calculated logLC50
V2O3 Bi2O3 SnO2 NiO
3.78 4.02 3.24 3.87
2.94 2.78 3.22 4.78
of photo-induced toxicity does not correlate with the particle size of the tested TiO2 nanomaterials. Recently, a quantitative relationship between oxidative stress and antibacterial activity of NPs was studied by Li et al. [31] and a linear correlation was found between the average concentration of total ROS and the bacterial survival rates. Based on the aforementioned reports, we feel use of E. coli represents a balanced choice for the bacterial model considering environmental relevance and detection sensitivity. Hypothetically, smaller nanomaterials have lower thermal stability and are likely to be more toxic because of greater bioavailability via greater specific surface area. The percentage of surface molecules changes as a function of particle size, which reflects the importance of surface area for increased chemical and biological activity of engineered NPs. In this study, because of cost effectiveness and commercial availability, it was unfeasible to adopt all of the studied MNPs in the same size range. Use of NPs sized between 15 and 90 nm may be acceptable for the application in our study, as the fraction of surface molecules increases slowly but with dramatic elevation in the slope at diameter ZrO2 > TiO2 > Al2O3 > SiO2 [44]. Molar heat capacity change (DbCP,m) was associated with the binding reaction [45], and the molar heat capacities of minerals also represent the number of atoms within the material around room temperature [46]. The conduction band energy levels can be used to explain the toxicological potential of MNPs at cellular and whole animal levels [47]. However, it fails to explain the toxicity results from our research. TiO2, CoO and Cr2O3 are the compounds with conduction band between 4.12 to 4.84 eV. TiO2 fits in this theory; nevertheless, CoO and Cr2O3 did not show high phototoxicity compared to other MNPs. Thus, it is likely that the band gap is not the critical factor determining the phototoxicity. In the process of photo-oxidation reactions, there are significant correlations among the ionization/redox potentials with HOMO/ LUMO energy levels, which are also related to the geometries in different charge states [48]. The HOMO/LUMO energy is revealed to be associated with the phototoxicity risk prediction. NPs with small HOMO–LUMO gaps absorb energy at higher wavelengths (more visible range) of light and are photochemically less stable, whereas NPs with larger HOMO–LUMO gaps absorb at smaller wavelengths (UV; greater energy) and are photochemically reactive [49,50]. Note that the calculated HOMO–LUMO gaps are closer to the first electronic excitation energies [51]. and there is also a correlation between electron excitation energy and phototoxicity. Finally, band gap theory was also selected to predict toxicity [42,47,52]. In our earlier study we reported the toxicity of seven MNPs (ZnO, CuO, Al2O3, La2O3, Fe2O3, SnO2 and TiO2) to E. coli under dark conditions [53]. In addition, a simple nano-QSAR model was developed to relate the toxicity of 17 types of MNPs to E. coli in dark [17]. An excellent relationship was found between a single NP
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property (one molecular descriptor), the enthalpy of formation of a gaseous cation (HFM), and the concentration of NPs found to cause 50% loss in viability of the bacteria (EC50). The model performed very well on the training data (predicting 85% of the variance in the data), and internal and external validation sets (77% and 83% of the variance in the data). Since light inhibition occurred when E. coli cells were exposed to natural sunlight irradiance, exposure duration was reduced from 2 h [17] to 30 min in this study. This change translates into a reduction of exposure duration of approximately four generation time for the E. coli bacteria. The change may partially account for why different influential descriptors were obtained in this study. Although one more descriptor was involved in the QSAR models developed in this study for both dark and photo-induced toxicity of the studied MNPs, we feel this finding may lead to establishment of better estimates of the realistic health hazards associated with the release of MNPs into the natural environment. 5. Conclusions By using descriptors obtained from DFT with the B3LYP functional and coupled-cluster calculations, we established a QSAR model to predict the photo-induced toxicity of seventeen MNPs to E. coli. Our models show the HOMO and LUMO energies of metal oxide and metal atom and the heat capacity of metal oxide play an important role in the toxicity in dark/light conditions. The defined structure–activity relationships of the present study might play a role in the design of safer nanomaterials and to predict the environmental risk associated with these NPs. Acknowledgements This research was supported by the National Science Foundation (NSF-CREST Grant# HRD0833178). Most of the Gaussian 09 calculations were run on facilities provided by the Mississippi Supercomputer Center. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jphotobiol. 2013.11.023. References [1] L. Zhou, J. Xu, X. Li, F. Wang, Metal oxide nanoparticles from inorganic sources via a simple and general method, Mater. Chem. Phys. 97 (2006) 137–142. [2] The Royal Academy of Engineering The Royal Society, Nanoscience and Nanotechnologies: Opportunities and Uncertainties, Royal Society, London, 2004. [3] V. Vamathevan, R. Amal, D. Beydoun, G. Low, S. McEvoy, Photocatalytic oxidation of organics in water using pure and silver-modified titanium dioxide particles, J. Photochem. Photobiol. A Chem. 148 (2002) 233–245. [4] R. Armon, G. Weltch-Cohen, P. Bettane, Disinfection of Bacillus spp. spores in drinking water by TiO2 photocatalysis as a model for Bacillus anthracis, Water Sci. Technol. 4 (2004) 7–14. [5] V. Keller, N. Keller, M.J. Ledoux, M.C. Lett, Biological agent inactivation in a flowing air stream by photocatalysis, Chem. Commun. 2005 (2005) 2918– 2920. [6] K. Sunada, T. Watanabe, K. Hashimoto, Bactericidal activity of copperdeposited TiO2 thin film under weak UV light illumination, Environ. Sci. Technol. 37 (2003) 4785–4789. [7] L.K. Adams, D.Y. Lyon, A. Mclntosh, P.J.J. Alvarez, Comparative toxicity of nanoscale TiO2, SiO2 and ZnO water suspensions, Water Sci. Technol. 54 (2006) 327–334. [8] L. Brunet, D.Y. Lyon, E.M. Hotze, P.J.J. Alvarez, M.R. Wiesner, Comparative photoactivity and antibacterial properties of C60 fullerenes and titanium dioxide nanoparticles, Environ. Sci. Technol. 43 (2009) 4355–4360. [9] C.M. Sayes, R. Wahi, P.A. Kurian, Y. Liu, J.L. West, K.D. Ausman, D.B. Warheit, V.L. Colvin, Correlating nanoscale titania structure with toxicity: a cytotoxicity and inflammatory response study with human dermal fibroblasts and human lung epithelial cells, Toxicol. Sci. 92 (2006) 174–185.
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