Dynamic Monitoring of Metal Oxide Nanoparticle Toxicity by Label

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Dynamic Monitoring of Metal Oxide Nanoparticle Toxicity by Label Free Impedance Sensing Joanna M. Seiffert, Marc-Olivier Baradez, Volker Nischwitz, Tamara Lekishvili, Heidi Goenaga-Infante, and Damian Marshall* LGC, Science and Technology Division, Queens Road, Teddington, Middlesex TW11 0LY, U.K. ABSTRACT: The increased use of nanoparticles in industrial and medical products is driving the need for accurate, high throughput in vitro testing procedures to screen new particles for potential toxicity. While approaches using standard viability assays have been widely used, there have been increased reports of the interactions of nanoparticles with their soluble labels or optical readouts which raise concerns over the potential generation of false positive results. Here, we describe the use of an impedance spectroscopy approach to provide real-time reagent free detection of toxicity for a panel of metal oxide nanoparticles (ZnO, CuO, and TiO2). Using this approach, we show how impedance measurements can be used to track nanoparticle toxicity over time with comparable IC50 values to those of standard assays (ZnO-55 μg/mL, CuO28 μg/mL) as well as being used to identify a critical 6 h period following exposure during which the nanoparticles trigger rapid cellular responses. Through targeted analysis during this response period and the use of a novel image analysis approach, we show how the ZnO and CuO nanoparticles trigger the active export of intracellular glutathione via an increase in the activity of the ATP dependent MRP/1 efflux pumps. The loss of glutathione leads to increased production of reactive oxygen species which after 2.5 h triggers the cells to enter apoptosis resulting in a dose dependent cytotoxic response. This targeted testing strategy provides comprehensive information beyond that achieved with standard toxicity assays and indicates the potential for cell-nanoparticle interactions that could occur following in vivo exposure.

’ INTRODUCTION Over 1300 consumer products containing nanomaterials are currently available globally with an estimated rise of 50% predicted in line with their growing commercial applications.1 In particular, metallic nanoparticles are increasingly used to create industrial products ranging from catalysts, paints, sunscreens, medical devices, and solar cells, to coatings and cosmetics.2,3 This raises the chance of human exposure, which may occur during any stage of the lifecycle of a nanomaterial, from handling the raw materials and secondary manufacturing processes, through to routine use of the consumer products and disposal and waste management.4 It has been reported that nanoparticles can cross cellular barriers.5 8 Therefore, it has become increasingly important to ensure that they do not pose a risk to human health if they are accidently inhaled, ingested or absorbed through the skin. A wide range of metal nanoparticle properties have been linked to the development of adverse biological effects including surface area and reactivity,9,10 shape,11 crystallinity,12 redox potential,13 agglomeration,14 dissolution,15 and composition.16 These characteristics have been shown in vivo to trigger cellular responses including membrane damage, cytotoxicity, inflammation, and fibrosis.17 However, the role of specific nanoparticle properties in these toxic responses has proved difficult to elucidate, due to the complex partico-kinetic interactions of nanoparticle properties in biological systems.18 Until this becomes possible, it has been recommended that nanoparticle toxicity assessment is performed on a case by case basis, with the individual nanoparticle properties r 2011 American Chemical Society

being fully characterized. This has greatly increased the need for integrated testing schemes, with reliable and accurate high throughput in vitro tests, capable of predicting the in vivo effects of nanoparticles. While traditional high throughput in vitro screening assays can offer rapid analysis of toxicity at the cellular level, the suitability of these assays for nanotoxicology has not been fully validated. Generally, the assays provide an indirect measurement of cell number, relying on the cellular metabolism of tetrazolium salts (MTT and WST-1 assays) or resazurin (Alamar Blue assays) or measuring the integrity of the plasma membrane (neutral red uptake and lactate dehydrogenase assays). In addition, the MTT, WST-1, and Alamar blue assays measure cell number based on metabolism in different cellular compartments, which means that, depending upon their specific properties, nanoparticles that accumulate or impact on discrete parts of the cells (e.g., cell membrane, mitochondria, or nucleus) can affect data comparability generated using different assays. Recent studies have also reported that nanoparticles can interact with the soluble indicators inherent in these assays, such as the reported chemical reduction of Formazin by carbon nanotubes in the MTT assay,19 further limiting the comparability and reproducibility of toxicity data. Methods which rely on absorbance or fluorescence based readouts can also be limited by interferences due to the optical Received: August 18, 2011 Published: November 04, 2011 140

dx.doi.org/10.1021/tx200355m | Chem. Res. Toxicol. 2012, 25, 140–152

Chemical Research in Toxicology properties of the nanoparticle as reported for carbon black nanoparticles in HEK cells.20 Therefore, the use of any testing methods for nanoparticles needs to be carefully evaluated. Despite worldwide efforts to utilize reagent based tests, comparability of toxicology data for nanoparticles remains poor with the lack of standardization of cell culture models an ongoing issue for nanotoxicologists.21 Alternatives to reagent assays for measuring cell number, such as impedance spectroscopy based Real Time Cell Electronic Sensing (RT-CES), could potentially overcome some of the limitations of traditional viability screens. RT-CES is a high throughput noninvasive technology in which cells are seeded onto the surface of gold electrodes, which are fabricated into the wells of standard format 96 well plates. Attachment of the cells to the gold electrodes is detected by the resistance or impedance to a low voltage current which is passed over the surface of the electrodes, due to the insulating properties of the cells.22 The number of cells remains proportional to the level of impedance up to the point when a confluent monolayer is formed.23 Since the insulating properties of the cells are based on whole cell morphology, cellular responses such as cell death, proliferation, cell spreading, and migration, which also cause a change in morphology or attachment, can also be detected by impedance measurements.24,25 Impedance measurements have several potential advantages for nanotoxicology over conventional toxicity screening assays. First, they are unaffected by the chemical reactivity or differing absorptive properties of nanoparticles, as soluble indicators are not required. Second, since cellular quantification is based on whole cell morphology, differing optical properties of nano particles are no longer a limiting factor for assessing the linearity of dose responses. Third, the system is online with continuous sampling possible throughout the exposure period, thus providing valuable information about the early rates and mechanisms of cellular events. These are overlooked with discrete end point sampling techniques. Sensitivity is also extremely high, as the cells are grown on an electrode array which covers 90% of the well surface area.22 Additionally, recent studies have shown that impedance measurements can also detect changes in plasma membrane cholesterol such as those which occur during GPCR signaling,26,27 as well as changes occurring in the membrane during early apoptosis.28,29 The reason for the change in impedance in these latter studies is not completely understood, but it is thought to reflect changes in the capacitance of the altered plasma membrane, resulting in a change in the insulating properties of the cells.28 Despite the advantages that impedance based measurements can offer over traditional cell viability assays their use in nanotoxicology has so far been limited. In this study, we investigated the feasibility of impedance spectroscopy to measure the toxicity of three industrially and environmentally relevant metal oxide nanoparticles, CuO, ZnO, and TiO2, which were selected for their reported variable biocompatibilities in cancer cell lines and in vivo.30,31 Using A549 cells as a model lung epithelial cell line, we show that impedance spectroscopy can measure the formation of confluent cell monolayers on the gold electrode surface prior to nanoparticle exposure, with the impedance values directly correlating to cell number in a linear manner. Following nanoparticle exposure, impedance spectroscopy also gives comparable toxicity measurements to a range of standard assays when compared after 24 h. However, because impedance spectroscopy provides kinetic measurements throughout the exposure period, we also show that the cells elicit an early and pronounced dose dependent response to the presence of CuO and ZnO nanoparticles

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over the first 5 6 h of exposure. By analyzing the stress responses in the cells during this period in combination with novel quantitative imaging techniques, we show how the mechanism of toxicity in A549 cells can be ascertained. This kind of information is significantly more informative than standard end-point viability assays and may help predict how nanoparticles will respond during exposure in vivo potentially reducing the reliance on animal studies.

’ MATERIALS AND METHODS Nanoparticle Characterization. Commercially available nano cupric oxide (CuO), nano zinc oxide (ZnO), and anastase nano titanium dioxide (TiO2) were purchased from Sigma-Aldrich, U.K. The primary nanopowders were characterized for primary crystallite size (TEM), surface area (BET), and density by the manufacturer. Suspensions of the nanoparticles in F12K cell culture medium [LGC Standards, U.K.] containing 10% Hyclone FBS [Thermo Fisher, U.K.] (F12K 10% FBS) were freshly prepared and sonicated prior to use. Dissolution of the nanoparticles in F12K -10% FBS over a 24 h period was measured by total element determination using collision reaction cell inductively coupled plasma mass spectrometry (ICP-MS). Briefly, 1 mL aliquots of nanoparticle suspensions (100 μg/mL) were prepared in F12K 10% FBS. Once prepared, an initial control sample (0 h control) was taken, and the particles were separated from the soluble fraction by centrifugation at 33,000g for 15 min at room temperature. The remaining samples were shaken gently in an incubator set at 37 °C/5% CO2 for either 4 or 24 h. After the incubations, these particulates were also separated from their soluble fractions by centrifugation. All of the samples (nanoparticles and the soluble fractions) were then prepared using microwave assisted acid digestion (Discover System, CEM, USA) to dissolve the nanoparticles and to mineralize organic matrix (from the cell culture medium), and the metal concentration was quantified in triplicate by ICP-MS (Agilent 7500, Agilent Technologies, Japan). The size distribution of nanoparticles in the cell culture medium was characterized by asymmetric Flow Field Flow Fractionation (FFF) coupled to ICP-MS. Size fractionation was performed with a metal-free AF2000 MT system (Postnova, Germany) using deionized water as carrier and a regenerated cellulose membrane with 10 kDa molecular weight cut off. The cross-flow was kept constant during sample eluting and reduced to 0 at the end of the fractionation for cleaning of the channel. Approximate size distributions of the nanoparticles were calculated based on retention times in the FFF compared to NIST gold nanoparticle standards (nominal sizes 10 nm, 30 and 60 nm, NIST RM 8011, RM 8012, RM 8013). The isotopes m/z 63Cu, 65Cu, 64Zn, 66Zn, 46Ti, and 49 Ti were monitored by ICP-MS in transient signal mode. Nanoparticle Dispersion. Nanoparticle samples were prepared in accordance with BS ISO 14887 (2000) guidance. Briefly, approximately 10 15 mg of particulate material was transferred to a preweighed 50 mL tube under sterile conditions and reweighed. The powder was wetted with a few drops of F12K 10% FBS and agitated with a sterile spatula to remove visible aggregates. A suspension was made by the addition of 5 mL of F12K 10% FBS prewarmed to 37 °C, and the suspension was stirred with a spatula and then vortex mixed for 15 s. The tubes containing the particle suspensions were then placed into a sonicating water bath for 10 min set at 37 °C, followed by 15 s of vortex mixing and a further 10 min in a sonicating water bath at 37 °C. After this time, the volume of the suspensions was adjusted to create a stock solution of 500 μg/mL. The samples were vortex mixed again for 15 s to make a homogeneous suspension and then immediately diluted to create exposure solutions of 100, 75, 50, and 25 μg/mL. Analysis of size distribution and dissolution was also performed on nanoparticle suspensions (100 μg/mL) made using this dispersion method (Table 1). The final suspensions were 141

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Table 1. Characterization Data for CuO, ZnO, and TiO2 Nanoparticles metal ion concentration (μM) in cell culture medium ( standard deviation (ICP-MS)

nanoparticle

primary crystallite size (nm) surface area

density

size distribution in cell culture medium

agglomeration

m2/g (BET)

(g/cm3)

(nm) (FFF)

state

type

(TEM)

cupric oxide (CuO)

>50

25 40

6.32

range: 110 170

0h

4h

24 h

agglomerated

18.7 ( 0.3

36.2 ( 0.3

106.6 ( 2.6

agglomerated

2.7 ( 0.5

229.0 ( 14.0

208.2 ( 1.5

agglomerated but small fraction

0.7 ( 0.3

3.5 ( 0.8

4.9 ( 0.2

peak: 150 zinc oxide (ZnO)

>50

10.8

not specified

range: 110 180 peak: 150

anastase titanium dioxide (TiO2)

>25

200 220

3.9

range: 25 170 peak: 70

with primary size

540 ( 10 nm using a Tecan 2000 infinite plate reader. The WST-1 and Alamar Blue (aBlue) assays were performed by incubating the cells with 100 μL of a 1:10 dilution of the WST-1 or aBlue reagent in F12K 10% FBS. The optical density of the metabolized WST-1 product was detected at 420/690 nm, while the fluorescent aBlue product was detected at 560/ 590 nm both using a Tecan 2000 infinite plate reader, Actin Labeling and Nuclear Morphology. Confluent A549 cells were treated with CuO, ZnO, or TiO2 nanoparticle suspensions (0, 25, or 100 μg/mL) for 2.5, 5.5, or 24 h. After washing three times with PBS at 37 °C, nanoparticle treated cells were fixed by incubation for 20 min with 3.7% Paraformaldehyde. Cells were then washed twice with PBS and permeablized with 0.5% Triton X-100 for 10 min followed by washing in 0.1% triton X-100. A blocking step was performed by incubating with 2% BSA for 10 min, and the actin cytoskeleton was stained by incubation with 100 μL of Phalloidin (50 μg/mL) containing 2% BSA for 20 min. Cell nuclei were counter stained with DAPI in a Vector shield solution (Vector Laboratories) for 20 min at room temperature. Cells were stored at 4 °C in the dark prior to imaging. Cells were imaged at 590 nm (phalloidin) and 460 nm (DAPI) using a Zeiss axio observer microscope. A minimum of 25 individual image sets were processed using Matlab R2008b (The MathWorks, USA) for each treatment group, and changes in nuclear morphology were analyzed using principle component analysis. An average of 1250 nuclei were measured for 15 parameters describing area (area, convex area, and equivalent diameter), perimeter length, shape (eccentricity, extent, major, and minor axis length of fitting ellipse), roundness (solidity), and roughness (6 Fourier parameters describing the shape of nuclear outline) per treatment. Detection of Apoptosis. Confluent A549 cell monolayers were treated with CuO, ZnO, or TiO2 nanoparticle suspensions at 0 or 100 μg/mL at 37 °C, 5% CO2 for 3 h. After the exposure period, the cells were incubated with cell culture media containing 1 μM Yo-Pro-1 (Invitrogen, UK) for 1 h at 37 °C, 5% CO2. The cells were then washed three times in PBS and fluorescent cells imaged at 490 500 nm using a Zeiss axio observer microscope. Measurement of Intracellular ROS. To establish the kinetics of ROS in real time, confluent cells were preloaded with 25 μm H2carboxy-DCF-DA (DCF-DA) [Molecular Probes, U.K.] (in phenol free F12K media) for 30 min. The preloaded cells were then washed twice with PBS at 37 °C and exposed to 100 μL of nanoparticles (0, 25 μg/mL) or tert-butyl-hydroperoxide (TBHP). In order to determine if nanoparticles interfered with the optical readout in this assay, the backgrounds of the nanoparticles on the cells were also measured in parallel without the fluorescent indicator. The cells were placed in a Tecan 2000 infinite microplate reader at 37 °C and the fluorescence measured at 485/539 nm every 15 min for a period of 3 h.

vortex mixed again for 15 s immediately prior to their use in exposure experiments. Cell Culture. The immortalized epithelial lung cell line, A549 (LGC Standards, U.K.) was cultured in vented T75 flasks under submerged culture conditions in F12K 10% FBS. The cells were incubated in a humidified, 5% CO2 atmosphere at 37 °C until they were 80% confluent with media changes every 48 72 h. Subculturing and preparation of cell suspensions were performed using a 0.25% Trypsin EDTA solution [Sigma-Aldrich], and the viability of the cell suspensions was determined using an Automated Cell Viability Analyzer [Vicell; Beckman Coulter]. Cells were seeded onto 96 well assays plates at a density of 80,000 cells per cm2 for analysis unless otherwise stated. Impedance Measurements. A549 cells were seeded directly onto the gold microelectrode array surface of a real time cell electronic sensing plate (E-Plate, Acea Bioscience USA). The plate was loaded into an impedance station housed in a 37 °C/5% CO2 environment for the duration of the experiment. Impedance measurements were taken every hour for the initial 24 h after seeding, to check that a confluent monolayer had been established in each well. After 24 h, the cells were exposed to nanoparticles at a concentration of 0, 25, 50, 75, or 100 μg/mL in F12K 10% FBS or 0.1% Saponin (Sigma-Aldrich) and returned to the impedance station. Impedance measurements were taken every hour for a further 24 h to measure nanoparticle dependent changes in cell number. To ensure the nanoparticles did not interfere with the impedance measurement, control wells containing nanoparticles without cells were run in parallel with the samples. Toxicity related to the initial dissolution of the nanoparticles was examined by exposing the confluent cells to soluble Cu and Zn ions in the range of (0 50 μM, Cu) and (150 225 μM, Zn) and monitoring the level of impedance for 24 h Cytotoxicity Using Standard Viability Assays. After exposure to the nanoparticles, the cells were washed three times with PBS at 37 °C prior to the addition of the soluble reagents. This was necessary to prevent interferences with some of the optical assay measurements (data not shown). All reagents were incubated with the cells at 37 °C/5% CO2, in a 96 well plate format. For the MTT assay, cells were incubated with 100 μL of 0.5 mg/mL MTT [3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide]) solution in F12K 10% FBS, for 4 h. After this time, the medium was carefully removed, and the formazin crystals were solubilized with 100 μL of DMSO. The optical density at 570 and 690 nm was measured using a Tecan 2000 infinite plate reader. For the neutral red assay, cells were incubated with 250 μL of a 25 μg/mL solution of neutral red solution (3-amino-7-dimethylamino-2 methylphenazine hydrochloride) in F12K 10% FBS, for 3 h. The neutral red solution was then removed, and the cells were washed twice with PBS. Neutral red was solubilized by the addition of 100 μL of Desorb solution to each well of the plate, and the optical density in the wells was read at 142



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Figure 1. Dynamic monitoring of nanoparticle toxicity by impedance spectroscopy. (A) The use of impedance spectroscopy to monitoring both cell attachment (Stage I) and the formation of a confluent monolayer (Stage II) using A549 cells at 80,000 cells/cm2. (B) Linear relationship between the impedance values (cell index) and cell number after 18 h post-seeding for A549 cells. (C,D,E) Dynamic changes in impedance following the exposure of confluent A549 cells to 0, 25, 50, 75, and 100 μg/mL suspensions of nano CuO (C), nano ZnO (D), and nano TiO2 (E) for 24 h. Data in C, D, and E are expressed as the average cell index of three triplicate experiments. Standard deviations are not shown but were less than 5% of the value in all cases. (F) Comparison of IC50 values for CuO and ZnO obtained using impedance spectroscopy and standard viability assays after 24 h exposure. Data is expressed as the average IC50 of three triplicate experiments (1 standard deviation.

Measurement of Intracellular and Extracellular Glutathione.

485/535 nm for Calcein and 530/620 nm for PI. At least 25 images were taken in each well so that a significantly representative proportion of the surface area of each experimental well was imaged. Importantly the same light intensity/brightness and exposure settings were used for each treatment group to allow image sets to be compared. An average of 6225 ( 396 cells were analyzed per treatment group. The images were processed using Matlab software to remove high frequency vesicular staining, and the mean cytoplasmic fluorescence intensity from the center of each cell was measured and analyzed. Thresholds for nonsignificant random variability in fluorescence measurements were estimated for each treatment as the expanded uncertainty in fluorescence measurement within and between wells for control and treatment conditions for each separate 96 well plate, using a coverage factor k = 3. Significance of effects was assessed by post hoc power analysis as described by Faul et al.63 using the GPower 3.1 package.

Briefly, confluent cells were exposed to nanoparticle suspensions or controls for 2.5, 5.5, or 24 h in 24 well plates. Cells were washed and lysed by freeze thawing, followed by acidification with sulphosalicyclic acid, and then subject to sonication and vortex mixing. Cells were centrifuged at 33,000 rpm for 5 min prior to measuring total glutathione in the supernatant. Measurement of intracellular and extracellular glutathione was measured according to the method of Tietze et al.62

MRP-1/P-Glycoprotein Activity and Membrane Integrity. Confluent A549 cell monolayers in 96 well plates were exposed to either nanoparticle suspensions in phenol free F12K 10% FBS for 2 h or nanoparticle suspensions in F12K 10% FBS containing the MRP-1/ PgP inhibitor, verapamil hydrochloride, at a final concentration of 400 μm for 2 h. At 2 h exposure, 100 μL aliquots of media containing Calcein-AM and propidium iodide (PI) (4 μM and 5 μM respectively) were added to the cells without removing the medium. All reagents were incubated with the cells for a further 30 min. After this time, the supernatant was removed, the cells were washed twice with PBS at 37 °C, and the live cells were imaged immediately using a Zeiss axio observer microscope at

’ RESULTS Nanoparticle properties are known to change during their preparation for experimental analysis. We therefore characterized 143



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Figure 2. Early elevation in cell index is not associated with morphological changes or loss of membrane integrity. (A) Impedance measurements following exposure of A549 cells to a concentration range of copper ions (0 50 μM, left graph) or zinc ions (150 225 μM, right graph). The orange line shows impedance measurements for CuO or ZnO in the absence of cells, and the gray line shows impedance changes for A549 cells exposed to 100 μg/mL CuO or ZnO for comparison. (B) Propidium iodide (PI) staining showing membrane integrity of confluent A549 cells exposed to nano CuO, nano ZnO (100 μg/mL), or TBHP (400 μM) for 2.5 h. (C) Morphology of the actin cyto cytoskeleton following exposure to CuO, ZnO, and TiO2 nanoparticles (100 μg/mL) for 2.5 or 5.5 h. Cells were fixed and stained with Phalloidin. (CuO = copper oxide; ZnO = zinc oxide; TBHP = tertbutylhydroperoxide, Ti = titanium dioxide).

three types of metallic nanoparticles (ZnO, CuO and TiO2) for their physicochemical properties in cell culture media (size distribution and dissolution) and compared the data to their primary nanopowder characteristics (size, surface area) (Table 1). This analysis showed that primary CuO and ZnO nanoparticles were 50 nm in size, while the TiO2 nanoparticles were smaller with a primary diameter below 25 nm. Once dispersed in cell culture media, all the nanoparticles became polydispersed with TiO2 nanoparticles having the largest range of sizes (approximately 25 170 nm), although the maximum abundance was around 70 nm. CuO nanoparticles had a narrower size range but were generally larger than TiO2 (110 170 nm) with a peak around 150 nm. ZnO nanoparticles had a size distribution similar to that of CuO in the range of 110 180 nm with a peak around 150 nm. The larger size distribution in cell medium compared to the primary size is an indication that agglomeration had occurred.18 The time dependent dissolution data at 0, 4, and 24 h, confirmed the high solubility of CuO and ZnO in F12K 10% FBS. ZnO had the highest rate of dissolution with the soluble fractions at 4 h containing 217 μM of Zn ions compared to 36.2 μM of Cu ions, (approximately 50% and 16% of the total metal in the whole

suspensions for ZnO and CuO, respectively). CuO continued to dissolve after 4 h, and by 24 h, the soluble fraction contained 107 μM Cu ions (47.0% of the total metal), and ZnO did not continue to dissolve after 4 h. TiO2 had very low solubility compared to that of CuO and ZnO nanoparticles in F12K 10% FBS, with the soluble fractions containing 0.7 ( 0.3, 3.5 ( 0.8, and 4.9 ( 0.2 μM at 0, 4, and 24 h, respectively, which was approximately 0.4, 1.7, and 2.4% of the total metal at these timepoints. The application of impedance based analysis for measuring the toxicity of metal nanoparticles is shown in Figure 1. When A549 cells are seeded onto the gold electrode array of the impedance assay plate at 80,000 cells/cm2, there is a sharp increase in impedance over the first 18 h as the cells attach and spread on the surface of the plate (Figure 1A, stage 1). After this point, there is a plateau in the impedance measurements as the cells form a stable confluent monolayer (Figure 1A, stage 2). At the point where the cells form a confluent monolayer, the cell impedance measurements can be directly related to cell number in a linear manner (Figure 1B). Measurement of nanoparticle toxicity starting 24 h after cell seeding onto the electrode array shows that both CuO 144



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Figure 3. CuO and ZnO but not anastase TiO2 nanoparticles induce nuclear changes in A549 cells. (A) Yo-Pro-1 staining showing the presence of apoptotic cells (green) following exposure to nanoparticles for 3 h. (B) Principle component analysis of the size and shape of the nuclei of cells following treatment with 0 μg/mL (blank, gray clusters), 25 μg/mL (low dose, blue clusters), or 100 μg/mL (high dose, pink clusters) of CuO, ZnO, or TiO2 nanoparticle for 2.5 or 5.5 h. The scale bar in A = 150 μm.

(Figure 1C) and ZnO (Figure 1D) induce dynamic dose dependent changes in impedance. These are characterized by a transient increase in impedance immediately after exposure of the cells, followed by a dose dependent decrease in impedance related to toxicity, in the range of 25 100 μg/mL for CuO and 50 100 μg/mL for ZnO. In contrast, anastase TiO2 nanoparticles did not induce changes in impedance relative to untreated cells, up to the highest dose tested (100 μg/mL), indicating that they are nontoxic in the A549 cells (Figure 1E). The IC50 values for CuO (28 μg/mL) and ZnO (55 μg/mL) after a 24 h exposure indicate that CuO had greater cytotoxicity than ZnO in the A549 cells and were comparable to IC50 data obtained using standard toxicity assays (Figure 1F). One of the most notable features of the impedance toxicity measurements for CuO and ZnO is the significant increase in cell index immediately following the exposure of the cells to the nanoparticles. In the absence of cells, exposure of the impedance plates to CuO and ZnO nanoparticles up to 100 μg/mL causes no detectable change in impedance (Figure 2A, orange line) indicating that the system is measuring a cellular response. To understand if this response is linked to the nanoparticles or the dissolved ions, the cells were exposed to an ion concentration dose range comparable to the dissolution levels measured after 4 h using ICP-MS (Cu, 0 50 μM; Zn, 150 225 μM). This exposure, monitored over 24 h, did not result in an impedance

increase following exposure to either Cu or Zn ions and did not lead to significant cell toxicity (Figure 2A). To examine if the impedance change is related to either the integrity of the cellular membrane or changes in cell morphology, the cells were exposed to 100 μg/mL of CuO or ZnO for 2.5 h and examined by staining with propidium iodide (PI) (membrane integrity, Figure 2B) or phalloidin (morphology, Figure 2C). Image analysis of the cells showed no intercalation of the nuclear DNA by PI following nanoparticle exposure indicating that the cell membranes are still intact (Figure 2B). In comparison, positive control cells exposed to the membrane damaging agent tert-butyl-hydroperoxide (TBHP) showed numerous cells with membrane damage indicated by PI labeled nuclei. Morphological analysis also showed that after 2.5 h exposure to ZnO and CuO there is no change in cell morphology compared to TiO2 treated cells and untreated controls (Figure 2C). However, after 5.5 h exposure, which coincides with the return to baseline for the CuO and ZnO impedance measurements, some of the ZnO treated cells had started to undergo morphological changes and contained polymerized F-Actin (Figure 2C arrows). To investigate the relationship between the impedance changes and the toxicity of the nanoparticles, the cells were labeled with Yo-Pro-1 to detect cells undergoing apoptosis following exposure to 100 μg/mL of nanoparticles for 3 h (Figure 3A). This analysis showed that at the peak of the impedance change initiated by CuO and ZnO there are signs of particle induced toxicity, evident by 145



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Figure 4. Early elevation in cell index is linked with ROS generation and intracellular glutathione depletion. (A) Dynamic kinetics of intracellular ROS generation by TBHP at doses ranging from 0 to 200 μM. Standard deviations are omitted but were less than 5% of the value in all cases. (B) Dynamic intracellular ROS generation by CuO, ZnO, and TiO2 nanoparticles (25 μg/mL). (C) Intracellular reduced and oxidized glutathione, following exposure to CuO and ZnO nanoparticles (25 or 100 μg/mL) or TBHP (400 μM) for 2.5 h. (D) Glutathione detected in the extracellular compartment at 2.5 h post-exposure to CuO and ZnO nanoparticles (25 or 100 μg/mL) or TBHP (400 μM). Data in B, C, and D are expressed as the average value ( 1 standard deviation.

the presence of fluorescently labeled Yo-Pro-1 positive cells. In contrast, the nontoxic TiO2 did not induce apoptosis. To look in more detail at the number of cells in an early apoptotic state due to nanoparticle induced toxicity, the morphology of cell nuclei was examined following exposure to 0 μg/mL (blank controls), 25 μg/mL (low dose), or 100 μg/mL (high dose) of CuO, ZnO or TiO2 (Figure 3B). Morphological characterization of the nuclei for each nanoparticle treatment was analyzed using principal component analysis (PCA) after 2.5 and 5.5 h with an average of 1250 nuclei analyzed per exposure condition per time-point (>30,000 nuclei examined in total) to create clusters within the PCA space which represent the overall nuclei morphology. PCA analysis showed that following exposure to TiO2 there is a strong overlap in the nuclear morphology clusters with the nontreated controls with no significant change in morphology at either time point. Following exposure of the cells to ZnO, there are shifts in the morphology clusters at the two time points. However, only the high dose treatment after 5.5 h shows a significant change in nuclear morphology compared to the untreated controls (P < 0.005). In comparison, exposure of the cells to CuO lead to significant changes in nuclear morphology following both low and high dose treatments at the 2.5 h time point (P < 0.005) and the 5.5 h time point (P < 0.005 for low dose and P < 0.0005 for high dose). The increase in impedance measured following the exposure of the cells to CuO and ZnO suggests that a cellular response is rapidly initiated by the presence of the nanoparticles. To understand

the cellular mechanisms that are being affected, the generation of reactive oxygen species (ROS) and depletion of intracellular glutathione were examined in cells treated for 2.5 h with either ZnO, CuO or TiO2 nanoparticles or with the ROS generating compound TBHP (Figure 4). TBHP induced a dose dependent increase in ROS from 50 to 200 μM (Figure 4A), with the maximum rate for each concentration reached by 60 90 min. After this time, the level of ROS remained stable over the 2.5 h exposure period. In cells exposed to CuO and ZnO nanoparticles, intracellular ROS was generated, although the kinetics were slower than for TBHP (Figure 4B). Nano CuO and ZnO dependent ROS generation was only measurable from 60 to 90 min but did increase linearly during the exposure period. Depletion of reduced and oxidized glutathione following nanoparticle and TBHP exposure at 2.5 h is shown in Figure 4C. Cells exposed to TBHP (200 μM) had depleted all their intracellular glutathione at 2.5 h (reduced + oxidized), presumably through leakage from the cells due to a loss of membrane integrity as demonstrated by previous PI staining. Low doses (25 μg/mL) of CuO or ZnO did not induce any glutathione loss at 2.5 h, although there was evidence of a small amount of glutathione oxidation relative to the controls. In contrast, cells exposed to high doses of nano CuO and ZnO (100 μg/mL) depleted over half their glutathione by 2.5 h, although CuO treated cells showed increased levels of oxidative stress compared with that of ZnO treated cells, with the remaining glutathione present in an oxidized form following CuO treatment. Because of the observed 146



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Figure 5. Quantification of calcein fluorescence in A549 cells. (A) Comparison of the level of calcein fluorescence in cells treated with 100 μg/mL TiO2 or CuO nanoparticles for 2.5 h. (B) Bootstrap estimation of the reproducibility of fluorescence data for different numbers of images. (C) Example of the process by which fluorescence intensity was recorded for individual cells within each image showing the noise reduction and sampling points used. (D) Fluorescence intensity data from each cell recorded as a dot plot of SD intensity vs mean intensity. The population of cells with a relevant fluorescent intensity was gated to distinguish it from the background signal which has a characteristic pattern on the plot.

In the first instance, cells were treated with 100 μg/mL of either TiO2 or CuO for 2.5 h (Figure 5A). This showed differences in the levels of fluorescence detectable within the cells, with TiO2 treatment leading to increased levels of cellular fluorescence compared to that of CuO treated cells. However, the cell to cell variability in fluorescence within each image sample made it difficult to obtain statistically relevant data. We therefore expanded the image data set until cell to cell variability was no longer a significant factor (Figure 5B). This showed that analysis of 25 individual images for each nanoparticle treatment would reduce the coefficient of variability due to random sampling to 3% allowing statistically relevant data to be obtained. To improve the analysis further, each individual image was processed to remove noise and artifacts, and the fluorescent intensity in the center of each cell was analyzed (Figure 5C). This allowed the large amounts of data

depletion of intracellular glutathione, the levels in the extracellular compartment were also measured. This showed the presence of significant levels of glutathione in the extracellular medium following a 2.5 h exposure to both the high and low doses of CuO and ZnO nanoparticles (Figure 4D). The loss of intracellular glutathione in cells with intact membranes suggests that it is being actively removed via an energy dependent mechanism such as MRP-1 efflux pumps. However, measuring the activity of these pumps is difficult with no direct assay available during the early stages of cellular exposure to nanoparticles. It is known that MRP-1/PgP efflux pumps can actively transport compounds such as Calcein-AM which is a fluorescent dye commonly used for cell viability assays. We therefore examined the possibility of linking calcein build up within cells to the activity of the MRP-1/PgP efflux pumps using fluorescent imaging (Figure 5). 147



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Figure 6. CuO and ZnO nanoparticles increase MRP-1 activity in A549 cells following a 2.5 h exposure. (A) A549 cells exposed to 100 μg/mL of CuO or ZnO show reduced uptake of Calcein-AM compared to that of untreated cells (blank). In the presence of verapamil, the uptake of Calcein-AM is increased indicating reduced MRP active pump activity. (B) Graph showing the level of fluorescence in an average of 1300 cells treated with 100 μg/mL ZnO, CuO, or vehicle control in the presence and absence of verapamil. Power values >0.9 show significant changes. (C) Changes in MRP-1 activity in cells treated with 100 μg/mL of CuO, ZnO, or TiO2 for either 2.5 h (black bars) or 5.5 h (crossed bars). Gray shaded areas show background noise levels in each treatment group.

generated for the thousands of cells in each image set to be analyzed as dot plots of standard deviation of fluorescent intensity versus mean intensity (Figure 5D). To assess whether the image based calcein assay is measuring the activity of MRP-1/PgP efflux pumps, cells were treated with 100 μg/mL of CuO, ZnO, or a nontreatment control in the absence and presence of the MRP-1/PgP inhibitor verapamil (Figure 6A). Control cells exhibited high levels of cellular fluorescence which was unchanged in the presence of verapamil, indicating no detectable MRP-1/PgP pump activity. In contrast, cells treated with either CuO or ZnO had low levels of cellular fluorescence in the absence of verapamil but much higher levels with verapamil present, which were similar to the fluorescence levels in the control cells. To show that these changes in fluorescence in the presence of verapamil were significant, the fluorescent intensity from an average of 1300 individual cells per treatment was analyzed (Figure 6B). This showed that cells treated with CuO and ZnO had significantly lower fluorescence in the absence of verapamil (power values >0.9), indicating significantly higher levels of MRP-1/PgP pump activity. In the presence of verapamil, there was no significant difference in the levels of fluorescence of CuO and ZnO treated cells and control cells (power (2) Lee, C. J., Lee, T. J., Lyu, S. C., Zhang, Y., Ruh, H., and Lee, H. (2002) Field emission from well-aligned zinc oxide nanowires grown at low temperature. J. Appl. Phys. Lett. 81, 3648–3650. 150



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(19) W€orle-Knirsch, J., Pulskamp, K., and Krug, H. (2006) Oops they did it again! Carbon nanotubes hoax scientists in viability assays. Nano Lett. 6 (6), 1261–1268. (20) Monteiro-Riviere, N. A., Inman, A. O., and Zhang, L. W. (2009) Limitations and relative utility of screening assays to assess engineered nanoparticle toxicity in a human cell line. Toxicol. App. Pharmacol. 234 (2), 222–235. (21) Alvarez, P. J., Colvin, V., Lead, J., and Stone, V. (2009) Research priorities to advance eco-responsible nanotechnology. ACS Nano 3 (7), 1616–1619. (22) Xing, J. Z., Zhu, L., Jackson, J. A., Gabos, S., Sun, X. J., Wang, X. B., and Xu, X. (2005) Dynamic monitoring of cytotoxicity on microelectronic sensors. Chem. Res. Toxicol. 2, 154–161. (23) Huang, L., Xie, L., Boyd, J. M., and Li, X.-F. (2008) Cellelectronic sensing of particle-induced cellular responses. Analyst 133, 643–648. (24) Solly, K., Wang, X., Xu, X., Strulovici, B., and Zheng, W. (2004) Application of real-time cell electronic sensing (RT-CES) technology to cell-based assays. Assay Drug Dev. Technol. 2, 363–372. (25) Wegener, J., Keese, C. R., and Giaever, I. (2000) Electric cell substrate impedance sensing (ECIS) as a noninvasive means to monitor the kinetics of cell spreading to artificial surfaces. Exp. Cell Res. 259, 158–166. (26) Yu, N, Atienza, J., Bernard, J., Blanc, S., Zhu, J., Wang, X, Xu, X., and Abassi, Y. (2006) Real-time monitoring of morphological changes in living cells by electronic cell sensor arrays: an approach to study G protein- coupled receptors. Anal. Chem. 78 (1), 35–43. (27) Fang, Y., Frutos, A. G., and Verklereen, V. (2008) Label-free cell-based assays for GPCR screening. Combinat. Chem. High Throughput Screen. 11, 357–369. (28) Wang, X., Becker, F. F., and Gascoyne, P. R. C. (2002) Membrane dielectric changes indicate induced apoptosis in HL-60 cells more sensitively than surface phosphatidylserine expression or DNA fragmentation. Biochim. Biophys. Acta 1564 (2), 412–420. (29) Arndt, S., Seebach, J., Psathaki, K., Galla, H.-J., and Wegener, J. (2004) Bioelectrical impedance assay to monitor changes in cell shape during apoptosis. Biosensor. Bioelectronic. 19, 583–594. (30) Karlsson, H. L., Cronholm, P., Gustafsson, J., and M€oller, L. (2009) Copper oxide nanoparticles are highly toxic: a comparison between metal oxide nanoparticles and carbon nanotubes. Chem. Res. Toxicol. 21, 1726–1732. (31) Cho, Wan-Seob, Duffin, R., Poland, C. A., Duschl, A., Oostingh, G. J., Macnee, M., Megson, I. L., and Donnaldson, K. (2011) Differential pro-inflammatory effects of metal oxide nanoparticles and their soluble ions in vitro and in vivo; zinc and copper nanoparticles, but not their ions, recruit eosinophils to the lungs. Nanotoxicol., DOI: 10.3109/ 17435390.2011.552810. (32) Oberd€ orster, G., Maynard, A., Donaldson, K., Castranova, V., Fitzpatrick, J., Kevin Ausman, K., Carter, J., Karn, B., Kreyling, W., Lai, D., Olin, S., Monteiro-Riviere, N., Warheit, D., and Yang, H. (2005) Principles for characterizing the potential human health effects from exposure to nanomaterials: elements of a screening strategy. Particle Fibre Toxicol. 2, 8. (33) Nel, A., Xia, T., and M€adler, L. (2006) Toxic potential of materials at the Nanolevel. Science 311 (5761), 622–627. (34) Kroll, A., Pillukat, M. H., Hahn, D., and Schnekenburger, J. (2009) Current in vitro methods in nanoparticle risk assessment: Limitations and challenges. Eur. J. Pharm. Biopharm. 72 (2), 370–377. (35) Male, K. B., Lachance, M., Hrapoviv, B., Sunahara., S., and Luong, J. H. T. (2008) Assessment of cytotoxicity of quantum dots and gold nanoparticles using cell-based impedance spectroscopy. Anal. Chem. 80, 5487–5493. (36) Wu, C., Shi, L., Li, Q., and Jiang, H. (2010) Probing the dynamic effect of cys-CdTe quantum dots toward cancer cells in vitro. Chem. Res. Toxicol. 23, 82–88. (37) Abassi, Y. A., Biao, B., Zhang, W., Ye, P., Kirstein, S. L., Gaylord, M. R., Feinstein, S. C., Wang, X., and Xu, X. (2009) Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target effects. Chem Biol. 16 (7), 712–723.

(3) Xu, C. X., Sun, X. W., Chen, B. J., Shum, P., Li, S., and Hu, X. (2004) Nanostructural zinc oxide and its electrical and optical properties. J. Appl. Phys. 95, 661–666. (4) Park, B. (2007) Current and Future Applications of Nanotechnology, in Nanotechnology: Consequences for Human Health and the Environment (Hester, R. E., and Harrison, R. M., Eds.) Royal Society of Chemistry Publishing, London, U.K. (5) Kreyling, W. G., Semmler, M., Erbe, F., Mayer, P., Takenaka, S, Schulz, H., Oberdorster, G., and Ziesenis, A. (2002) Translocation of ultrafine insoluble iridium particles from lung epithelium to extrapulmonary organs is size dependent but very low. J. Toxicol. Environ. Health A. 65, 1513–1530. (6) Fenart, L., Casanova, A, Dehouck, B., Duhen, C, Slupek, Cecchelli, R., and Betbeder, D. (1999) Evaluation of effect of charge and lipid coating on ability of 60-nm nanoparticles to cross an in vitro model of the blood-brain barrier. J. Pharmacol. Exp. Ther. 291, 1017–1022. (7) De Jong, W. H., Hagens, W. I., Krystek, P., Burger, M. C., Sips, A. J., Geertsma, R. E., and Particle (2008) size-dependent organ distribution of gold nanoparticles after intravenous administration. Biomaterial 12, 1912–1919. (8) Wick, P., Malek, A., Manser, P., Meili, D., Maeder-Althaus, X., Diener, L., Diener, P-A, Zisch, A, Krug, H. F., and von Mandach, U. (2010) Barrier capacity of human placenta for nanosized materials. Environ. Health Perspect. 118, 432–436. (9) Duffin, R., Tran, L., Brown, D., Stone, V., and Donaldson, K. (2007) Pro-inflammogenic effects of low-toxicity and metal nanoparticles in vivo and in vitro: highlighting the role of particle surface area and surface reactivity. Inhal. Toxicol. 10, 849–56. (10) Montellier, C., Tran, L., MacNee, W., Faux, S., Jones, A., Miller, B., and Donaldson, K. (2007) The pro-inflammatory effects of lowtoxicity low-solubility particles, nanoparticles and fine particles, on epithelial cells in vitro: the role of surface area. Occup. Environ. Med. 9, 609–15. (11) Poland, C. A., Duffin, R., Kinloch, I., Maynard, A., Wallace, W. A., Seaton, A., Stone, V., Brown, S., Macnee, W., and Donaldson, K. (2008) Carbon nanotubes introduced into the abdominal cavity of mice show asbestos like pathogenicity in a pilot study. Nat. Nanotechnol. 3, 423–428. (12) Jiang, J., Oberd€orster, G., Elder, A., Gelein, R., Pamela Mercer, P., and Biswas, P. (2008) Does nanoparticle activity depend upon size and crystal phase? Nanotoxicol. 2 (1), 33–42. (13) Ayres, J. G., Borm, P., Cassee, F. R., Castranova, V., Donaldson, K., Ghio, A., Harrison, R. M., Hider, R., Kelly, F. J., Ingeborg M. Kooter, I. M., Marano, F., Maynard, R. L., Mudway, I. S., Nel, A., Sioutas, C., Smith, S., Baeza-Squiban, A., Cho, A., Duggan, S., and Froines, J. (2008) Evaluating the toxicity of airborne particulate matter and nanoparticles by measuring oxidative stress potential: a workshop report and consensus statement. Inhal. Toxicol. 20 (1), 75–99. (14) Limbach, L., Li, Y., Grass, R., Brunner, T., Hinterman, M., Muller, M., Gunther, D, and Stark, W. (2005) Oxide nanoparticle uptake in human lung fibroblasts: effect on particle size, agglomeration, and diffusion at low concentrations. Environ. Sci. Technol. 39 (23), 9370–9376. (15) Xia, T., Kovochich, M., Liong, M., Madler, L., Gilbert, B., Shi, H., Yeh, J. I., Zink, J. I., and Nel, A. E. (2008) Comparison of the mechanism of toxicity of zinc oxide and cerium oxide nanoparticles based on dissolution and oxidative stress properties. ACS Nano 2, 2121–2134. (16) Limbach, L,K, Wick, P., Manser, P., Grass, R. N., Bruinink, A., and Stark, W. J. (2007) Exposure of engineered nanoparticles to human lung epithelial cells: influence of chemical composition and catalytic activity on oxidative stress. Environ. Sci. Technol. 41 (11), 4158–4163. (17) Cho, W-S, Duffin, R., Poland, C. A., Howie, S. E. M., MacNee, W., Bradley, M., Megson, I. L., and Donaldson, K. (2010) Metal oxide nanoparticles induce unique inflammatory footprints in the lung: important implications for nanoparticle testing. Environ. Health Perspect. 118, 1699–1706. (18) Teeguarden, J. G., Hinderliter, P. M., Orr, G., Thrall, B. D., and Pounds, J. G. (2007) Particokinetics in vitro: dosimetry considerations for in vitro nanoparticle toxicology assessments. Toxicol. Sci. 95, 300–312. 151



ARTICLE

monitoring of membrane cholesterol reveals new insights into epidermal differentiation. J. Invest. Dermatol. 130 (5), 1268–78. (58) Peters, M. F., and Scott, C. W. (2009) Evaluating cellular impedance assays for detection of GPCR pleiotropic signalling and functional selectivity. J. Biomol. Screen. 14 (3), 246–255. (59) Tarantola, M., Schneider, D., Sunnick, E., Adam, H., Pierrat, S., Rosman, C., Breus, V., S€onnichsen, C., Basche, T., Wegener, J., and Janshoff, A. (2009) Cytotoxicity of metal and semiconductor nanoparticles indicated by cellular micromotility. ACS Nano 3 (1), 213–22. (60) Reno, F., Burattini, S., Rossi, S., Luchetti, F., Columbaro, M., Santi, S., Papa, S., and Falcieri, E. (1998) Phospholipid rearrangement of apoptotic membrane does not depend on nuclear activity. Histochem. Cell Biol. 110, 467–476. (61) Drenou, B., Blancheteau, V., Burgess, D. H., Fauchet, R., Charron, D. J., and Mooney, N. A. (1999) A caspase-independent pathway of MHC class II antigen-mediated apoptosis of human B lymphocytes. J. Immunol. 163, 4115–4124. (62) Tietze, F. (1969) Enzymatic method for quantitative determination of nanogram quantities of total and oxidised glutathione: application to mammalian blood and other tissues. Anal. Biochem. 27, 502–522. (63) Faul, F, Erdfelder, E, Buchner, A, and Lang, A. L. (2009) Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Res. Methods 41 (4), 1149–1160.

(38) Bhaskar, L., Krishnan, V., and Thampan, R. (2007) Cytoskeletal elements and intracellular transport. J. Cell. Biochem. 101 (5), 1097–1108. (39) Maeno, E., Ishizaki, Y., Toku Kanaseki, T., Akihiro Hazama, A., and Yasunobu Okada, Y. (2000) Normotonic cell shrinkage because of disordered volume regulation is an early prerequisite to apoptosis. Proc. Natl. Acad. Sci. U.S.A. 97 (17), 9487–9492. (40) Jeng, H. A., and Swanson, J. (2006) Toxicity of metal oxide nanoparticles in mammalian cells. J. Environ. Sci. Health, Part A 41 (12), 2699–2711. (41) Stone, V., and Donaldson, K. (2006) Nanotoxicology: Signs of stress. Nat. Nanotechnol. 1, 23–24. (42) Stohs, S. J., and Bagchi, D. (1995) Oxidative mechanisms in the toxicity of metal ions. Free Radical Biol. Med. 18 (2), 321–336. (43) Bishop, G. M., Dringen, R., and Robinson, S. R. (2007) Zinc stimulates the production of toxic reactive oxygen species (ROS) and inhibits glutathione reductase in astrocytes. Free Radical Biol. Med. 42, 1222–1230. (44) Singh, A, Misra, V, Thimmulappa, R. K., Lee, H, Ames, S, Hoque, M. O., Herman, J. G., Baylin, S. B., Sidransky, D, Gabrielson, E, Brock, M. V., and Biswal, S. (2006) Dysfunctional KEAP1-NRF2 interaction in non-small-cell lung cancer. PLoS Med. 3 (10), e420. (45) Marchan, R., Hammond, C. L., and Ballatori, N. (2008) Multidrug resistance-associated protein 1 as a major mediator of basal and apoptotic glutathione release. Biochim. Biophys. Acta 1778 (10), 2413–2420. (46) Ghibelli, L., Fanelli, C., Rotillo, E., Lafavia, E., Copolla, S., Colussi, C., Civitareale, P., and Ciriolo, M. R. (1998) Rescue of cells from apoptosis by inhibition of active GSH extrusion. FASEB J. 12, 479–486. (47) Hammond, C. L., Marchan, R., Krance, S. M., and Ballatori, N. (2007) Glutathione export during apoptosis requires functional multidrug resistance-associated proteins. J. Biol. Chem. 282 (19), 14337–14347. (48) Filomeni, G., Rotilio, G., and Ciriolo, M. R. (2002) Cell signalling and the glutathione redox system. Biochem. Pharmacol. 64 (5 6), 1057–1064. (49) Ballatori, N., Krance, S. M., Marchan, R., and Hammond, C. L. (2009) Plasma membrane glutathione transporters and their roles in cell physiology and pathophysiology. Mol. Aspects Med. 30 (1 2), 13–28. (50) Scheffer, G. L., Pijnenborg, A. C., Smit, E. F., M€uller, M., Postma, D. S., Timens, W., van der Valk, P., de Vries, E. G., and Scheper, R. J. (2002) Multidrug resistance related molecules in human and murine lung. J. Clin. Pathol. 332–339. (51) Kang, H.-K., Lee, E., Pyo, H., and Lim, S.-J. (2001) Cyclooxygenase independent down-regulation of multidrug resistance associated protein-1 expression by celecoxib in human lung cancer cells. Mol. Cancer. Ther. 4, 1358–1363. (52) Leslie, E. M., Deeley, R. G., and Cole, S. P. C. (2001) Toxicological relevance of the multidrug resistance protein 1, MRP1 (ABCC1) and related transporters. Toxicol. 167, 3–23. (53) Zaman, G. J. R., Lankelema, J., van Tellingen, O., Beijnen, J., Dekker, H., Paulusma, C., Oude Elferinkii, R. P. J., Bass, F., and Borst, P. (1995) Role of glutathione in the export of compounds from cells by the multidrug-resistance-associated protein. Proc. Natl. Acad. Sci. U.S.A. 92, 7690–7694. (54) Salomon, J. J., and Ehrhardt, C. (2010) Nanoparticles attenuate P-glycoprotein/MDR1 function in A549 human alveolar epithelial cells. Eur. J. Pharm. Biopharm. 77 (3), 392–397. (55) Meng, H., Liong, M., Xia, T., Li, Z., Ji, Z., Zink, J. I., and Nel, A. E. (2010) Engineered design of mesoporous silica nanoparticles to deliver doxorubicin and P-Glycoprotein siRNA to overcome drug resistance in a cancer cell line. ACS Nano 4 (8), 4539–4550. (56) Kagan, V. E., Borisenko, G. G., Serinkan, B. F., Tyurina, Y. Y., Tyurin, V. A., Jiang, J., Liu, S. X., Shvedova, A. A., Fabisiak, J. P., Uthaisang, W., and Fadeel, B. (2003) Appetizing rancidity of apoptotic cells for macrophages: oxidation, externalization, and recognition of phosphatidylserine. Am. J. Physiol. Lung Cell Mol. Physiol. 285 (1), L1–17. (57) Sp€orl, F, Wunderskirchner, M, Ullrich, O, B€omke, G, Breitenbach, U, Blatt, T, Wenck, H, Wittern, K. P., and Schrader, A. (2010) Real-time 152


Dynamic Monitoring of Metal Oxide Nanoparticle Toxicity by Label

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