Skip to main content
NCBI home page
Mass Spectrometry logoLink to Mass Spectrometry
. 2017 Mar 24;6(2):S0066. doi: 10.5702/massspectrometry.S0066

Comparative Proteomic Analysis of Rat Bronchoalveolar Lavage Fluid after Exposure to Zinc Oxide Nanoparticles

Yu-Min Juang 1, Han-Ju Chien 1, Cheng-Yu Yang 1, Hsiao-Chien Yeh 1, Tsun-Jen Cheng 2, Chien-Chen Lai 1,3,4,*
PMCID: PMC5448331  PMID: 28573081

Abstract

Zinc oxide nanoparticles (ZnO NPs) are one of the most widely used nanomaterials in consumer products and industrial applications. As a result of all these uses, this has raised concerns regarding their potential toxicity. We previously found that candidate markers of idiopathic pulmonary fibrosis and lung cancer were significantly up-regulated in rat bronchoalveolar lavage fluid (BALF) following exposure to ZnO NPs by using a liquid chromatography (LC)-based proteomic approach. To achieve comprehensive protein identification analysis, we conducted the two-dimensional gel electrophosis (2-DE)-based proteomic workflow to analyze the differences in BALF proteins from rats that had been exposed to a high dose of 35 nm ZnO NPs. A total of 31 differentially expressed protein spots were excised from the gels and analyzed by nanoLC-tandem mass spectrometry (MS/MS). Gene ontology (GO) annotation of these proteins showed that most of the differentially expressed proteins were involved in response to stimulus and inflammatory response processes. Moreover, pulmonary surfactant-associated protein D and gelsolin, biomarkers of idiopathic pulmonary fibrosis, were significantly up-regulated in rat BALF after ZnO NPs exposure (2.42- and 2.84-fold, respectively). The results obtained from this present study could provide a complementary consequence with our previous study and contribute to a better understanding of the molecular mechanisms involved in ZnO NP-induced lung disorders.

Keywords: bronchoalveolar lavage fluid, proteomic, 2-DE, LC-MS/MS, nanoparticles

INTRODUCTION

Zinc oxide (ZnO) nanoparticles (NPs) are widely used in many industrial fields such as ceramics and rubber and consumer products such as medicines, sunscreens and toothpaste because of their photocatalytic efficiency, biocompatibility, UV light absorption and antimicrobial properties.1) As a result of all these uses, it is therefore necessary to provide a complete understanding of ZnO NPs toxicity.

In 1997, Fine et al. first reported an adverse health effects of zinc oxide as metal fume fever in workers exposed to welding fumes.2) Currently, there are many reports suggested that the toxicity of ZnO NPs is associated with their mass and surface concentrations and the most important factor in that is zinc ion release.3–5) Furthermore, there have been an increasing number of studies demonstrated that ZnO NPs can induce lung and systemic immunomodulatory consequences in human and animal systems.1,6) For example, Roy et al. showed the ZnO NPs provided an adjuvant effect and induced Th2 responses in mice exposed to the allergen ovalbumin7); Fukui et al. reported the intratracheal instillation of ZnO NPs can induce an increase in antioxidants, including lipid peroxide, heme oxygenase-1 and alpha-tocopherol, in the lungs of exposed rat.8) Moreover, a previous study found that ZnO NPs were highest potency regarding induction of acute lung inflammation in murine inhalation model.9) Despite the above observations, the molecular and cellular mechanisms underlying the development of lung diseases after exposure to ZnO NPs are not well understood.

Bronchoalveolar lavage fluid (BALF) contains a large number of cellular and soluble components from lungs that can be used to diagnose lung diseases and represents a potential resource for the study of respiratory diseases.10,11) In 2010, Chiu et al. reported that the expression of 15 inflammatory proteins and surfactant-associated proteins was regulated in mouse BALF, which exposed to ultrafine carbon black.12) Proteomic techniques are increasingly being applied to investigate associations between differentially expressed proteins in BALF and various lung disorders such as idiopathic pulmonary fibrosis (IPF),13,14) invasive pulmonary aspergillosis,15) and acute lung injury.9,16,17) In addition, BALF has also been used to investigate the mechanisms of lung injury associated with exposure to ambient air containing oil mist,18) metal fumes,19) carbon black,20) silver NPs,21) and ZnO NPs.3,22)

A previous study showed that the oxidative stress marker, 8-hydroxydeoxyguanosine, was significantly increase in BALF of Sprague-Dawley (SD) rats that had been exposed to high dose of 35-nm ZnO particles.3) In our previous study, we used isobaric tags for relative and absolute quantitation (iTRAQ) labeling and two-dimensional liquid chromatography/tandem mass spectrometry (2D-LC/MS/MS) to investigate changes in BALF proteins after exposure to a high dose of 35-nm ZnO NPs in healthy SD rats, and found that exposure to ZnO NPs primarily affected proteins involved in immune and inflammatory processes.22) In the same study, we also found that S100A8 and S100A9, candidate markers of idiopathic pulmonary fibrosis and lung cancer,23,24) were significantly up-regulated following exposure.22) Although LC-based separation technology coupled with MS/MS offers a unique opportunity to rapidly, sensitively, and automatically identify proteins in protein mixtures,25) two-dimensional gel electrophoresis (2-DE) offers much higher resolution, allowing for the separation of up to 15,000 proteins. In addition, 2-DE allows for the separation of different protein isoforms, structures which can be either due to post-translational modifications or due to sequence variants.26) Moreover, it has been noted that both approaches should be considered to be complementary not exclusive and the results of both methods should be integrated to achieve the global proteomics analysis possible.27,28) In this study, therefore, we used 2-DE coupled with LC-MS/MS (Fig. 1) to conduct a comparative analysis of changes in protein expression in BALF from rats that had been exposed to a high dose of 35 nm ZnO NPs. We expected the results obtained in this present study could provide a complementary consequence with our previous study and contribute to a better understanding of the molecular mechanisms involved in ZnO NP-induced lung disorders.

Fig. 1. Flow chart of the experimental study design.

Fig. 1. Flow chart of the experimental study design.

Open in a new tab

EXPERIMENTAL

Experimental animals

All animal experiments were performed in Dr. Cheng Tsun-Jen’s laboratory (Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, Taiwan). Seven-week-old male Sprague-Dawley (SD) rats (285–302 g) were purchased from BioLASCO (Taiwan) and allowed to acclimate for one week before starting the experiments. The animals were then randomly divided into either a control group (N=4) or an exposure group (N=6). Rats in the control group were exposed to air filtered through a high efficiency particulate air (HEPA) filter connected to a particle generation system (see below). Rats in the exposure group were exposed to high-dose 35-nm ZnO NPs generated by the same particle generation system. The dosing was performed from 8 a.m. to 2 p.m.

The evaporation-condensation method used in this study to generate ZnO NPs (Scheme 1) has been described in detail in a previous study.3) Briefly, zinc powder was placed in a ceramic crucible and heated in a furnace. Nitrogen gas was used to carry the zinc vapor downstream through the furnace so that it could react with oxygen (compressed filtered air), resulting in the production of ZnO NPs (∼35 nm). The ZnO NPs were then mixed with filtered air in a diluting chamber before entering the exposure chamber. We used a GRIMM SMPS+C (Sequential mobility particle sizer and counter, model 5.403) to measure the particle size distribution and number concentration. The dose of ZnO NPs was based on the number concentration of particles in the chamber.3) The following physical characteristics of high-dose 35 nm ZnO NPs have been described previously3): mass concentration, 12.1 mg/m3; number concentration, 7.9×106 particles/cm3; estimated surface area concentration, 1×105 mm2/m3; geometric standard deviation, 2.0; and median diameter, 35.6 nm.

Scheme 1. Schematic representation of the ZnO NPs generation and exposure system.

Scheme 1. Schematic representation of the ZnO NPs generation and exposure system.

Open in a new tab

BALF sample collection and quantification

For BALF collection, animals were anesthetized with pentobarbital (50 mg/kg) and sacrificed 24 h after exposure. The airway was washed with 2 mL phosphate-buffered saline solution (PBS, pH 7.4) repeatedly. The recovered solution was collected and centrifuged at 1,000 rpm for 10 min at 4°C to remove the cellular portion. The supernatant was then collected and the protein concentration was measured using the Bradford method.29)

2-DE and protein spot analysis

Sample pooling is a commonly used strategy in proteomic studies to minimize individual variation.30) Thus, two pooled BALF samples from four control and six exposed rats were subjected to 2-DE analysis. The conditions for 2-DE analysis used in this study followed the reported by Magi et al. with slight modifications.14) Protein sample (200 μg) was diluted with 180 μL of immobilized pH gradient (IPG) buffer (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) (rehydration buffer [9.5 M urea, 2% CHAPS, 2% Triton X-100, 15.6 mM dithiothreitol (DTT), 0.002% Bromophenol blue trace]) and allowed to stand for 1 h at room temperature. Sample mixture (200 μL) was loaded onto an isoelectric focusing (IEF) holder (GE) and an IPG gel strip (GE, 11 cm, pH 4–7) was inserted into the holder to absorb the sample mixture, flooded with mineral oil, and then loaded onto the surface of the SDS gel. IEF was run following a stepwise incremental voltage program as follows: first step, 30 V, 12 Volt-h; second step, 200 V, 200 Volt-h; third step, 500 V, 500 Volt-h; and the forth step: 8,000 V, 20,000 Volt-h. The IPG gel strip was then dipped into DTT containing equilibrium buffer (50 mM Tris–HCl pH 8.8, 6 M urea, 30% glycerol, 2% SDS, 0.002% Bromophenol blue trace) for 15 min and then dipped into the original equilibrium buffer for 15 min. Finally, the strip was transferred to the top of a 12.5% polyacrylamide gel and held in position with molten 0.5% agarose in running buffer containing 25 mmol/L Tris, 0.192 mol/L glycine, and 0.1% SDS. Gels were run at 60 mA/gel for 30 min followed by 120 mA/gel for 4–5 h. At least three gels were run for each pooled sample.

Gels were fixed in fixing solution (30% ethanol, 10% acetic acid) for 1 h, washed twice with 30% ethanol for 20 min each time, placed in sensitizing solution (0.025% sodium dithionite Na2S2O3) in the dark for 1 min and then in silver impregnation solution (0.2% silver nitrate AgNO3, 0.003% formaldehyde (37%)) for 30 min. The gels were then placed in developer solution (3% sodium carbonate Na2CO3, 0.0005% sodium thiosulphate pentahydrate Na2S2O3. 5H2O, 0.0185% formaldehyde (37%)) until spots appeared and the reaction was stopped using 3.5% acetic acid. Gels were analyzed using ImageMaster™ 2D platinum software (Version 7.0, GE Healthcare). Proteins showing a significant increase or decrease in spot intensity (p-value<0.001) were selected.

In-gel digestion

The in-gel digestion method used in this study was based on previously reported protocols, with slight modification.31,32) Briefly, protein spots with fold-changes of >1.5 or <0.66 for three replicate silver-stained gels were sliced and put into a microtube and then washed twice with 50% acetonitrile (ACN) in 25 mM ammonium bicarbonate buffer (pH 8.0) for 10 min at room temperature. The excised gel pieces were then soaked in 100% ACN for 5 min, dried in a lyophilizer for 30 min and rehydrated in 25 mM ammonium bicarbonate buffer (pH 8.0) containing 10 ng/mL trypsin at 37°C for 16 h. After digestion, the peptides were extracted from the supernatant of the gel elution solution (50% ACN in 5.0% TFA) and vacuum-dried. The peptides were stored at −80°C until analyzed with nanospray liquid chromatography/tandem mass spectrometry (nano-LC/MS/MS).

Nanoelectrospray tandem mass spectrometry

Each dried sample was dissolved in 0.1% formic acid and analyzed by an Ultimate capillary LC system (LC Packings, Amsterdam, The Netherlands) coupled to a QSTARXL quadrupole-time of flight (Q-TOF) mass spectrometer (Applied Biosystem/MDS Sciex, Foster City, CA, USA). The peptides were separated using an RP C18 capillary column (15 cm×75 μm i.d.) with a 70-min linear ACN gradient ranging from 5–50% ACN in 0.1% formic acid at a flow rate of 200 nL/min. The eluted peptides from the capillary column were sprayed into the mass spectrometer using a PicoTip electrospray tip (FS360-20-10-D-20; New Objective, Cambridge, MA, USA). The mass spectrometer was operated in positive-ion mode with a spray voltage of 2.0 kV. The temperature of the heated laminar flow chamber was set as 100°C. Information-Dependent Acquisition-mediated LC-MS/MS screening (IDA; ABI/MDS Sciex) was used to acquire data on the three most intense ions in the full scan (1 s) with a charge state from 2–4, a mass range from 400 to 1,800 m/z, and a peak intensity greater than 15. The range of the MS/MS scan was set at 65 to 1,800 m/z. For each cycle of IDA, an exclusion period of 120 s was allotted for previously gated ions.

Protein identification and bioinformatics

The resulting MS/MS data were then compared against data in the Swiss-Prot database (Version 57.15, Mammalia, 66,524 sequences) using MASCOT software (Matrix Science, London, U.K.; version 2.3). Peptide mass tolerance and fragment tolerance were each set at ±0.2 Da. Carbamidomethyl (C) was set as a fixed modification; deamidation (NQ), oxidation (HW), and oxidation (M) were set as variable modifications. Trypsin was used as the proteolytic enzyme and one missed cleavages per peptide were allowed. Mascot’s automated decoy database searching was used, all data filtered with a built-in Percolator algorithm to calculate the posterior error probabilities for peptide identification. The false positive rates were controlled below 1% (p-value<0.001). The identified proteins were further classified using UniProt knowledge base (Swiss-Prot/TrEMBL32)) and gene ontology (GO) database.

RESULTS AND DISCUSSION

Proteomics is a powerful approach for qualitative and quantitative analysis of global protein expression in defined biological systems. LC-based shotgun proteomic analysis and gel-based proteomic analysis are the two most commonly used strategies in proteomics and both have advantages and disadvantages.26) In our previous study we used an LC-based strategy to investigate differences in rat BALF proteins after ZnO NPs exposure.22) In the present study, we used a gel-based proteomics approach that combined 2-DE and MS to analyze the changes in rat BALF proteins in order to construct a complementary knowledge to increase our understanding of the molecular mechanisms underlying the development of ZnO NP-induced lung disorders (Fig. 1).

In order to minimize individual variation, BALF samples from six exposed and four control rats were pooled into a subgroup for the 2-DE analysis. Three independently stained gels were analyzed to demonstrate the reproducibility of the experimental results, and the relative intensities of the protein spots in different gels were averaged during image analysis to identify and select the differentially expressed proteins. Several researches have demonstrated that the molecular weight of most proteins detected in abundance in BALF is greater than 36.5 kilodaltons (kDa), such as albumin (∼50%), immunoglobulins (∼30%), and transferrin (∼6%). Moreover, these abundant proteins do not play important roles in regulation of important cellular processes.10,11) In this study, however, we focused our attention on identifying changes in BALF proteins with molecular weights lower than 36.5 kDa on 2-D gels. The protein expression level in the control group (Fig. 2A) and in the exposed group (Fig. 2B) were profiled by 2-DE and then analyzed by ImageMaster™ 2D platinum software version 7.0 as described in the Experimental section. A total of 31 spots fit our criteria for selection of significantly altered proteins (fold-change ratio >1.5 or <0.66 and p<0.05 (Student’s t test)). These protein spots were excised from the stained gel, subjected to in-gel trypsin digestion, and then subjected to nanoLC/MS/MS analysis using a nanoLC/Q-TOF MS system. The locations of these 31 spots on the 2-D gels are clearly marked and numbered in Fig. 2. Protein spots 1–22 were identified as being up-regulated proteins (fold-change ratio from 1.61 to 24.82) and spots 23–31 were identified as being down-regulated proteins (fold-change ratio from 0.56 to 0.32) in the exposed group.

Fig. 2. Representative 2-DE images of BALF proteins from the control group (A) and the exposed group (B). Protein sample (200 μg) was separated by IEF in the first dimension (pH 4–7, 11 cm), then by molecular weight (MW) in the second dimension. Spots identified as differentially expressed proteins are marked and numbered in the gels. Protein spots 1–22 and 23–31 were identified as being up-regulated and down-regulated proteins, respectively, in the exposed group.

Fig. 2. Representative 2-DE images of BALF proteins from the control group (A) and the exposed group (B). Protein sample (200 μg) was separated by IEF in the first dimension (pH 4–7, 11 cm), then by molecular weight (MW) in the second dimension. Spots identified as differentially expressed proteins are marked and numbered in the gels. Protein spots 1–22 and 23–31 were identified as being up-regulated and down-regulated proteins, respectively, in the exposed group.

Open in a new tab

All of the proteins identified in this study are listed in Table 1. The amino acid sequence coverage of the identified proteins varied from 4% to 58%, representing one to fifteen unique tryptic peptide sequences. Among these identified proteins, only 11 were commonly detected by both gel-based approach and LC-based method.21) This result indicated that an integrated approach of dysregulated protein identification incorporating both methods is necessity for achievement comprehensive protein identification analysis.

Table 1. Differentially expressed proteins identified in SD rat BALF after exposure to ZnO NPs.

Spot No.a Accession No.b Protein name Therretical Mr (kDa)/pI (Experimental Mr (kDa)/pI) Unique peptides matched Mascot score (% coverage) Proteins identified in LC-MS/MS analysis Biological process Molecular function Mean spot volume: Control group Mean±S.D. Mean spot volume: Exposed group Mean±S.D. Fold changes t Test (p-value)
1 P17475 Alpha-1-antiproteinase 46.3/5.7 (51.2/5.3) 4 160 (21) v inflammatory response, response to stimulus endopeptidase inhibitor activity 0.119±0.009 0.257±0.047 2.16 1.6E-02
2 Q68FP1 Gelsolin 86.4/5.8 (51.2/6.5) 6 420 (21) cellular component organization or biogenesis actin binding 0.037±0.014 0.106±0.016 2.84 2.7E-03
3 P35248 Pulmonary surfactant-associated protein D 37.9/6.8 (43.5/6.5) 5 168 (21) immune response, response to stimulus carbohydrate binding 0.024±0.010 0.059±0.005 2.42 6.0E-03
4 Q9EQS0 Transaldolase 37.6/6.6 (36.5/6.5) 7 150 (38) metabolic process catalytic activity 0.025±0.005 0.040±0.007 1.61 2.0E-02
5 Q03626 Murinoglobulin-1 166.6/5.7 (36.0/6.3) 3 217 (7) v inflammatory response, response to stimulus catalytic activity, peptidease inhibitor activity 0.081±0.036 0.210±0.045 2.61 9.7E-03
6 P01026 Complement C3 187.8/6.1 (34.1/6.4) 10 948 (8) v inflammatory response, response to stimulus catalytic activity, endopeptidease inhibitor activity 0.040±0.018 0.162±0.018 4.06 5.6E-04
7 Q9QX79 Fetuin-B 42.4/6.7 (33.1/6.6) 3 94 (6) v metabolic process catalytic activity, endopeptidease inhibitor activity 0.032±0.008 0.102±0.020 3.20 8.6E-03
8 Q8K4I4 BPI fold-containing family A member 1 27.9/6.2 (31.0/4.9) 4 526 (18) immune response, response to stimulus lipid binding 0.019±0.002 0.038±0.003 2.04 6.6E-04
9 P12346 Serotransferrin 78.5/7.1 (31.0/6.7) 4 278 (7) inflammatory response, response to stimulus iron ion binding 0.043±0.005 0.190±0.005 4.40 1.9E-06
10 Q9Z339 Glutathione S-transferase omega-1 27.9/6.3 (29.6/6.7) 3 65 (20) response to stimulus, metabolic process catalytic activity, oxidoreductase activity 0.020±0.003 0.057±0.020 2.88 3.9E-02
11 Q8K4I4 BPI fold-containing family A member 1 27.9/6.2 (28.1/6.7) 6 777 (30) immune response, response to stimulus lipid binding 0.106±0.024 0.313±0.032 2.94 6.0E-04
12 P02651 Apolipoprotein A-IV 44.4/5.1 (27.5/5.4) 5 342 (17) v metabolic process lipid binding 0.012±0.004 0.038±0.006 3.11 2.8E-03
13 Q8K4I4 BPI fold-containing family A member 1 27.9/6.2 (25.8/5.2) 2 266 (18) immune response, response to stimulus lipid binding 0.018±0.004 0.038±0.008 2.14 1.2E-02
14 P30152 Neutrophil gelatinase-associated lipocalin 22.6/7.7 (26.0/5.6) 4 224 (33) immune response, response to stimulus, response to oxidative stress iron ion binding 0.009±0.001 0.228±0.042 24.82 5.9E-03
15 O88767 Protein deglycase DJ-1 20.2/6.3 (25.8/6.1) 3 69 (45) inflammatory response, response to stimulus, response to oxidative stress catalytic activity, superoxide dismutase copper chaperone activity 0.046±0.011 0.076±0.014 1.66 2.1E-02
16 O35244 Peroxiredoxin-6 24.9/5.6 (23.8/5.3) 6 569 (42) v response to stimulus, response to oxidative stress oxidoreductase activity, antioxidant activity 0.011±0.002 0.035±0.008 3.22 1.1E-02
17 P08427 Pulmonary surfactant-associated protein A 26.7/4.8 (23.8/4.9) 2 277 (8) response to stimulus, response to hypoxia carbohydrate binding 0.029±0.005 0.049±0.004 1.72 3.5E-03
18 P24090 Alpha-2-HS-glycoprotein 38.8/6.1 (21.0/4.2) 1 34 (4) v response to stimulus, inflamatory response catalytic activity, endopeptidease inhibitor activity 0.010±0.007 0.099±0.010 9.80 2.8E-04
19 P48199 C-reactive protein 25.7/4.9 (19.4/4.2) 1 154 (4) response to stimulus, inflamatory response lipid binding 0.010±0.003 0.035±0.006 3.59 3.1E-03
20 P24090 Alpha-2-HS-glycoprotein 38.8/6.1 (21.0/4.3) 1 45 (4) v response to stimulus, inflamatory response catalytic activity, endopeptidease inhibitor activity 0.025±0.011 0.194±0.015 7.88 8.1E-05
21 Q63716 Peroxiredoxin-1 22.3/8.3 (25.0/6.8) 15 152 (53) v response to stimulus, response to oxidative stress oxidoreductase activity, antioxidant activity 0.032±0.015 0.073±0.002 2.26 2.0E-02
22 Q8K4I4 BPI fold-containing family A member 1 27.9/6.2 (27.5/6.7) 1 148 (8) immune response, response to stimulus lipid binding 0.050±0.009 0.161±0.012 3.22 1.5E-04
23 P14668 Annexin A5 35.8/4.9 (32.2/4.9) 5 156 (17) response to stimulus lipid binding 0.046±0.009 0.021±0.007 0.47 1.0E-02
24 P04906 Glutathione S-transferase P 23.7/6.9 (26.5/6.6) 6 277 (52) response to stimulus, response to superoxide catalytic activity, glutathione transferase activity 0.159±0.025 0.051±0.014 0.32 3.4E-03
25 O88767 Protein deglycase DJ-1 20.2/6.3 (25.8/6.4) 3 69 (45) inflammatory response, response to stimulus, response to oxidative stress catalytic activity, superoxide dismutase copper chaperone activity 0.179±0.026 0.059±0.011 0.33 3.5E-03
26 P31044 Phosphatidylethanolamine-binding protein 1 20.9/5.5 (21.5/5.7) 4 354 (29) response to stimulus, metabolic process endopeptidase inhibitor activity, nucleotide binding 0.065±0.011 0.031±0.000 0.48 1.6E-02
27 P02761 Major urinary protein 21.0/5.9 (19.4/5.7) 7 345 (39) response to stimulus pheromone binding 3.316±0.233 1.745±0.061 0.53 2.4E-03
28 P07632 Superoxide dismutase [Cu–Zn] 16.1/5.9 (18.0/6.3) 8 899 (58) response to stimulus, response to superoxide catalytic activity, superoxide dismutase activity 0.902±0.095 0.509±0.039 0.56 5.1E-03
29 P02091 Hemoglobin subunit beta-1 16.1/7.9 (18.0/6.5) 2 155 (23) v transport transporter activity 0.076±0.013 0.040±0.003 0.52 1.8E-02
30 P11232 Thioredoxin 12.0/4.8 (11.0/5.0) 4 526 (58) response to stimulus, response to oxidative stress oxidoreductase activity, antioxidant activity 0.390±0.083 0.148±0.024 0.38 1.5E-02
31 P01946 Hemoglobin subunit alpha-1/2 15.5/7.8 (10.0/6.6) 4 97 (42) v transport transporter activity 0.395±0.039 0.159±0.032 0.40 7.8E-04
Open in a new tab

a) Number refer to spots depicted in Fig. 2. b) Protein accession number according to Swiss-Prot/TrEMBL database.32) c) Results are referred to ref. 21. d) The change level in optical density of the protein spots in exposed group compared with control group.

Using gene ontology analysis, the differentially expressed proteins were grouped according to well-established ontologic classification domains, namely cellular components, biological processes and molecular function (Fig. 3). As shown in Fig. 3A, most of the proteins in the cellular component domain were secreted into the extracellular region (72%). As shown in Fig. 3B, most of the proteins in the biological processes domain were involved in response to stimulus (43%), inflammatory response (17%), response to oxidative stress (15%), or immune response (10%). As shown in Fig. 3C, most of the proteins in the molecular function domain were found to play a role in binding (35%), catalytic activity (23%), enzyme inhibitor activity (16%), oxidoreductase activity (9%), or antioxidant activity (7%). The results are consistent with those in our previous study in which most differentially expressed proteins were located in the extracellular region and involved in response to stimulus.22)

Fig. 3. Gene ontology (GO) annotation of the differentially expressed proteins identified by 2-DE coupled with nanoLC-MS/MS in BALF of SD rats after exposure to ZnO NPs. These differentially expressed proteins were classified according to well-established ontologic classification domains: (A) Cell components, (B) biological processes, and (C) molecular function.

Fig. 3. Gene ontology (GO) annotation of the differentially expressed proteins identified by 2-DE coupled with nanoLC-MS/MS in BALF of SD rats after exposure to ZnO NPs. These differentially expressed proteins were classified according to well-established ontologic classification domains: (A) Cell components, (B) biological processes, and (C) molecular function.

Open in a new tab

Interestingly, a number of the proteins that were up-regulated in rat BALF after ZnO NPs exposure were inflammation-associated proteins (including alpha-1-antiproteinase (spot 1), murinoglobulin-1 (spot 5), complement C3 (spot 6), serotransferrin (spot 9), protein deglycase DJ-1 (spot 15), alpha-2-HS-glycoprotein (spots 18 and 20), and C-reactive protein (spot 19)) and immune response-associated proteins (including pulmonary surfactant-associated protein D (SP-D, spot 3), BPI fold-containing family A member 1 (spots 8, 11, 13, and 22) and neutrophil gelatinase-associated lipocalin (spot 14)) (Table 1, Table S1). Among these up-regulated proteins, Churg et al. reported that alpha-1-antiproteinase has acute anti-inflammatory effect34); Lee et al. found that the protein level of murinoglobulin-1 and complement C3 were increased in rat following oil mist exposure.18) Moreover, the changes of these three proteins are consistent with those in our previous study.22) SP-D is important in innate immune response and participate in many regulatory aspects of immune responses within the lung.12) Remarkably, a previous study showed that the concentrations of SP-D in sera are significantly increased in patients with a variety of lung diseases, including idiopathic pulmonary fibrosis (IPF), interstitial pneumonia with collagen disease (IPCD), and pulmonary alveolar proteinosis (PAP).35) In addition, SP-D has potential as useful clinical marker for the diagnosis of IPF, IPCD, and PAP.35) The fold change of SP-D observed in our study is 2.42-fold (Table 1, Fig. 4, Fig. S1B). However, in previous study, the expression of SP-D decreased 40% in mice exposed to ultrafine carbon black, it may increase airway resistance, and pulmonary dysfunction.12) Herein, we speculated that the inflammatory mechanisms were different between the treatment of ZnO NPs and ultrafine carbon black. Although the antioxidant-related proteins that reported by Fukui et al. (including lipid peroxide, heme oxygenase-1 and alpha-tocopherol)8) were not identified in our study, the other similar antioxidant proteins, peroxiredoxin-6 and -1, were observed with up-regulation in BALF of ZnO NPs exposed rats (Table 1). According to the above observations, these findings support those reported previously,1,6) namely that inhalation or instillation of ZnO NPs predominantly results in lung inflammation and systemic toxicity.

Fig. 4. Changes in abundance of gelsolin (spot 2), pulmonary surfactant-associated protein D (spot 3), and DJ-1 (spots 15 and 25) after exposure to ZnO NPs. Histograms display mean % volume contribution of each protein spot. Error bars are standard deviation of the average spot densities.

Fig. 4. Changes in abundance of gelsolin (spot 2), pulmonary surfactant-associated protein D (spot 3), and DJ-1 (spots 15 and 25) after exposure to ZnO NPs. Histograms display mean % volume contribution of each protein spot. Error bars are standard deviation of the average spot densities.

Open in a new tab

We also found that protein gelsolin (spot 2) was significantly up-regulated in rat BALF after exposure to ZnO NPs (2.84-fold, Table 1, Fig. 4, Fig. S1A). Gelsolin is a regulator of cellular cytoskeleton dynamics. Several studies have shown that the concentration of gelsolin in patients with idiopathic pulmonary fibrosis or fibrotic non-specific interstitial pneumonia is higher than in healthy individuals.36,37)

Although LC-based approach shown many advantages, one of the most important benefit of the gel-based method is that this technology can detect different molecular forms. For example, Mitsumoto et al. reported that protein deglycase DJ-1 appeared as two different major molecular forms on 2DE gel, pI 5.8 (DJ-1/5.8) and pI 6.2 (DJ-1/6.2).38) Moreover, the expression of DJ-1 with lower pI increases under endotoxin-induced inflammatory conditions in lung.38) In our study, we also founded that the exposure to ZnO NPs resulted in a 1.66-fold increase in expression of DJ-1 protein with a pI state of 6.1 (DJ-1/6.1, spot 15) and a 0.33-fold decrease in DJ-1 with a pI state of 6.4 (DJ-1/6.4, spot 25) (Table 1, Fig. 4, Fig. S1C–D).

CONCLUSION

In the present study, we used 2-DE coupled with nanoLC-MS/MS to separate, quantify and identify differentially expressed proteins in rat BALF following exposure to high-dose ZnO NPs. Our results are consistent with those from previous studies that showed that exposure to ZnO NPs mainly induces lung inflammation and immune response in rats.3,22) Although S100A8 and S100A9, candidate markers of idiopathic pulmonary fibrosis and lung cancer,23,24) were not identified, pulmonary surfactant-associated protein D and gelsolin, biomarkers of idiopathic pulmonary fibrosis,35–37) were significantly up-regulated in rat BALF after ZnO NPs exposure (2.42- and 2.84-fold, respectively). Although it is necessary to further clarify the potential health risks associated with exposure to zinc oxide nanoparticles, the results obtained by gel-based proteomic analysis in this study and those obtained by an LC-based proteomic approach in our previous study22) indicate that these inflammatory responses might induce idiopathic pulmonary fibrosis or lung cancer.

Acknowledgments

The authors thank Prof. Cheng Tsun-Jen for assistance with the animal experiments. The study was funded by a Grant from the Ministry of Science and Technology, R.O.C.

Mass Spectrom (Tokyo) 2017; 6(2): S0066

CONFLICTS OF INTEREST

The authors have declared no conflict of interest.

Abbreviations

ZnO NPs

zinc oxide nanoparticles

SD

Sprague-Dawley

BALF

bronchoalveolar lavage fluid

GO

Gene Ontology

2-DE

two-dimensional gel electrophoresis

Q-TOF

quadrupole-time of flight

References

  1. 1) R. J. Vandebriel, W. H. De Jong. A review of mammalian toxicity of ZnO nanoparticles. Nanotechnol. Sci. Appl. 5: 61–71, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. 2) J. M. Fine, T. Gordon, L. C. Chen, P. Kinney, G. Falcone, W. S. Beckett. Metal fume fever: Characterization of clinical and plasma IL-6 responses in controlled human exposures to zinc oxide fume at and below the threshold limit value. J. Occup. Environ. Med. 39: 722–726, 1997. [DOI] [PubMed] [Google Scholar]
  3. 3) M. Ho, K. Y. Wu, H. M. Chein, L. C. Chen, T. J. Cheng. Pulmonary toxicity of inhaled nanoscale and fine zinc oxide particles: Mass and surface area as an exposure metric. Inhal. Toxicol. 23: 947–956, 2011. [DOI] [PubMed] [Google Scholar]
  4. 4) T. Xia, M. Kovochich, M. Liong, L. Madler, B. Gilbert, H. Shi, J. I. Yeh, J. I. Zink, A. E. Nel. 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, 2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. 5) S. W. Wong, P. T. Leung, A. B. Djurisic, K. M. Leung. Toxicites of nano zinc oxide to five marine organisms: Influences of aggregate size and ion solubility. Anal. Bioanal. Chem. 396: 609–618, 2010. [DOI] [PubMed] [Google Scholar]
  6. 6) S. R. Saptarshi, A. Duschl, A. L. Lopata. Biological reactivity of zinc oxide nanoparticles with mammalian test systems: An overview. Nanomedicine (Lond.) 10: 2075–2092, 2015. [DOI] [PubMed] [Google Scholar]
  7. 7) R. Roy, S. Kumar, A. K. Verma, A. Sharma, B. P. Chaudhari, A. Tripathi, M. Das, P. D. Dwivedi. Zinc oxide nanoparticles provide an adjuvant effect to ovalbumin via a Th2 response in Balb/c mice. Int. Immunol. 26: 159–172, 2014. [DOI] [PubMed] [Google Scholar]
  8. 8) H. Fukui, M. Horie, S. Endoh, H. Kato, K. Fujita, K. Nishio, L. K. Komaba, J. Maru, A. Miyauhi, A. Nakamura, S. Kinugasa, Y. Yoshida, Y. Hagihara, H. Iwahashi. Association of zinc ion release and oxidative stress induced by intratracheal instillation of ZnO nanoparticles to rat lung. Chem. Biol. Interact. 198: 29–37, 2012. [DOI] [PubMed] [Google Scholar]
  9. 9) S. T. Larsen, P. Jackson, S. S. Poulsen, M. Levin, K. A. Jensen, H. Wallin, G. D. Nielsen, I. K. Koponen. Airway irritation, inflammation, and toxicity in mice following inhalation of metal oxide nanoparticles. Nanotoxicology 10: 1254–1262, 2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. 10) R. Wattiez, P. Falmagne. Proteomics of bronchoalveolar lavage fluid. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 815: 169–178, 2005. [DOI] [PubMed] [Google Scholar]
  11. 11) B. Magi, E. Bargagli, L. Bini, P. Rottoli. Proteome analysis of bronchoalveolar lavage in lung diseases. Proteomics 6: 6354–6369, 2006. [DOI] [PubMed] [Google Scholar]
  12. 12) K. H. Chiu, W. L. W. Lee, C. C. Chang, S. C. Chen, Y. C. Chang, M. N. Ho, J. F. Hsu, P. C. Liao. A label-free differential proteomic analysis of mouse bronchoalveolar lavage fluid exposed to ultrafine carbon black. Anal. Chim. Acta 673: 160–166, 2010. [DOI] [PubMed] [Google Scholar]
  13. 13) C. Landi, E. Bargagli, A. Carleo, L. Bianchi, A. Gagliardi, A. Prasse, M. G. Perari, R. M. Refini, L. Bini, P. Rottoli. A system biology study of BALF from patients affected by idiopathic pulmonary fibrosis (IPF) and healthy controls. Proteomics Clin. Appl. 8: 932–950, 2014. [DOI] [PubMed] [Google Scholar]
  14. 14) B. Magi, L. Bini, M. G. Perari, A. Fossi, J. C. Sanchez, D. Hochstrasser, S. Paesano, R. Raggiaschi, A. Santucci, V. Pallini, P. Rottoli. Bronchoalveolar lavage fluid protein composition in patients with sarcoidosis and idiopathic pulmonary fibrosis: A two-dimensional electrophoretic study. Electrophoresis 23: 3434–3444, 2002. [DOI] [PubMed] [Google Scholar]
  15. 15) D. A. Gonzales, C. De Torre, H. Wang, C. B. Devor, P. J. Munson, S. X. Ying, S. J. Kern, R. Petraitiene, D. L. Levens, T. J. Walsh, A. F. Suffredini. Protein expression profiles distinguish between experimental invasive pulmonary aspergillosis and Pseudomonas pneumonia. Proteomics 10: 4270–4280, 2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. 16) E. V. Nguyen, S. A. Gharib, S. J. Palazzo, Y. H. Chow, D. R. Goodlett, L. M. Schnapp. Proteomic profiling of bronchoalveolar lavage fluid in critically ill patients with ventilator-associated pneumonia. PLoS ONE 8: e58782, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. 17) A. Ménoret, S. Kumar, A. T. Vella. Cytochrome b5 and cytokeratin 17 are biomarkers in bronchoalveolar fluid signifying onset of acute lung injury. PLoS ONE 7: e40184, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. 18) Y. S. Lee, P. W. Chen, P. J. Tsai, S. H. Su, P. C. Liao. Proteomics analysis revealed changes in rat bronchoalveolar lavage fluid proteins associated with oil mist exposure. Proteomics 6: 2236–2250, 2006. [DOI] [PubMed] [Google Scholar]
  19. 19) W. G. Kuschner, A. D’Alessandro, H. Wong, P. D. Blanc. Early pulmonary cytokine response to zinc oxide fume inhalation. Environ. Res. 75: 7–11, 1997. [DOI] [PubMed] [Google Scholar]
  20. 20) T. T. Chen, K. J. Chuang, L. L. Chiang, C. C. Chen, C. T. Yeh, L. S. Wang, C. Gregory, T. Jones, K. BéruBé, C. N. Lee, H. C. Chuang, T. J. Cheng. Characterization of the interactions between protein and carbon black. J. Hazard. Mater. 264: 127–135, 2014. [DOI] [PubMed] [Google Scholar]
  21. 21) C. L. Su, T. T. Chen, C. C. Chang, K. J. Chuang, C. K. Wu, W. T. Liu, K. F. Ho, K. Y. Lee, S. C. Ho, H. E. Tseng, H. C. Chuang, T. J. Cheng; Taiwan CardioPulmonary Research Group (T-CPR). Comparative proteomics of inhaled silver nanoparticles in healthy and allergen provoked mice. Int. J. Nanomedicine 8: 2783–2799, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. 22) Y. M. Juang, B. H. Lai, H. J. Chien, M. Ho, T. J. Cheng, C. C. Lai. Changes in protein expression in rat bronchoalveolar lavage fluid after exposure to zinc oxide nanoparticles: An iTRAQ proteomic approach. Rapid Commun. Mass Spectrom. 28: 974–980, 2014. [DOI] [PubMed] [Google Scholar]
  23. 23) A. Hara, N. Sakamoto, Y. Ishimatsu, T. Kakugawa, S. Nakashima, S. Hara, M. Adachi, H. Fujita, H. Mukae, S. Kohno. S100A9 in BALF is a candidate biomarker of idiopathic pulmonary fibrosis. Respir. Med. 106: 571–580, 2012. [DOI] [PubMed] [Google Scholar]
  24. 24) Y. J. Su, F. Xu, J. P. Yu, D. S. Yue, X. B. Ren, C. L. Wang. Up-regulation of the expression of S100A8 and S100A9 in lung adenocarcinoma and its correlation with inflammation and other clinical features. Chin. Med. J. (Engl.) 123: 2215–2220, 2010. [PubMed] [Google Scholar]
  25. 25) E. P. Romijn, J. Krijgsveld, A. J. Heck. Recent liquid chromatographic-(tandem) mass spectrometric applications in proteomics. J. Chromatogr. A 1000: 589–608, 2003. [DOI] [PubMed] [Google Scholar]
  26. 26) P. Roepstorff. Mass spectrometry based proteomics, background, status and future needs. Protein Cell 3: 641–647, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. 27) G. A. Riding, A. Jones, M. K. Holland, J. R. Hill, S. A. Lehnert. Proteomic analysis of bovine conceptus fluids during early pregnancy. Proteomics 8: 160–177, 2008. [DOI] [PubMed] [Google Scholar]
  28. 28) A. Okamoto, T. Hasegawa, K. Yamada, M. Ohta. Application of both high-performance liquid chromatography combined with tandem mass spectrometry shotgun and 2-D polyacrylamide gel electrophoresis for streptococcal exoproteins gave reliable proteomic data. Microbiol. Immunol. 55: 84–94, 2011. [DOI] [PubMed] [Google Scholar]
  29. 29) J. E. Noble, M. J. Bailey. Quantitation of protein. Methods Enzymol. 463: 73–95, 2009. [DOI] [PubMed] [Google Scholar]
  30. 30) A. P. Diz, M. Truebano, D. O. Skibinski. The consequences of sample pooling in proteomics: An empirical study. Electrophoresis 30: 2967–2975, 2009. [DOI] [PubMed] [Google Scholar]
  31. 31) F. Gharahdaghi, C. R. Weinberg, D. A. Meagher, B. S. Imai, S. M. Mische. Mass spectrometric identification of proteins from silver-stained polyacrylamide gel: A method for the removal of silver ions to enhance sensitivity. Electrophoresis 20: 601–605, 1999. [DOI] [PubMed] [Google Scholar]
  32. 32) D. E. Terry, E. Umstot, D. M. Desiderio. Optimized sample-processing time and peptide recovery for the mass spectrometric analysis of protein digests. J. Am. Soc. Mass Spectrom. 15: 784–794, 2004. [DOI] [PubMed] [Google Scholar]
  33. 33) http//us.expasy.org.
  34. 34) A. Churg, J. Dai, K. Zay, A. Karsan, R. Hendricks, C. Yee, R. Martin, R. MacKenzie, C. Xie, L. Zhang, S. Shapiro, J. L. Wright. Alpha-1 antitrypsin and a broad spectrum metalloprotease inhibitor, RS113456, have similar acute anti-inflammatory effects. Lab. Invest. 81: 1119–1131, 2001. [DOI] [PubMed] [Google Scholar]
  35. 35) Y. Honda, Y. Kuroki, E. Matsuura, H. Nagae, H. Takahashi, T. Akino, S. Abe. Pulmonary surfactant protein D in sera and bronchoalveolar lavage fluids. Am. J. Respir. Crit. Care Med. 152: 1860–1866, 1995. [DOI] [PubMed] [Google Scholar]
  36. 36) M. Whyte. Gelsolin in idiopathic pulmonary fibrosis: A new target supports a central role for epithelial injury in disease pathogenesis. Thorax 64: 461–462, 2009. [DOI] [PubMed] [Google Scholar]
  37. 37) N. Oikonomou, A. Thanasopoulou, A. Tzouvelekis, V. Harokopos, T. Paparountas, I. Nikitopoulou, W. Witke, A. Karameris, A. Kotanidou, D. Bouros, V. Aidinis. Gelsolin expression in necessary for the development of modelled pulmonary inflammation and fibrosis. Thorax 64: 467–475, 2009. [DOI] [PubMed] [Google Scholar]
  38. 38) A. Mitsumoto, Y. Nakagawa. DJ-1 is an indicator for endogenous reactive oxygen species elicited by endotoxin. Free Radic. Res. 35: 885–893, 2001. [DOI] [PubMed] [Google Scholar]

Articles from Mass Spectrometry are provided here courtesy of Mass Spectrometry Society of Japan

RESOURCES