Proteomic analysis to identify markers of exposure to cadmium sulphide quantum dots (CdS QDs)


 Background The increasing use of cadmium sulphide (CdS) quantum dot (QD)-enabled products is expected to be accompanied by their release into the environment. In this study, the whole organism Saccharomyces cerevisiae was used as a model eukaryote to study protein modulations employing 2D- gel electrophoresis and gel-free iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) proteomics following cell exposure to CdS QDs for 9 and 24 h. From both biotechnological and ecotoxicological perspectives, the use of S. cerevisiae as a model organism sheds light on the impact nanomaterials have on the biochemical responses of the exposed organism. Results Key proteins involved in essential biological pathways were downregulated, in particular after 24 h exposure. These include the major proteins of the glycolytic pathway, the components of mitochondrial respiratory chain complexes III, IV and V involved in the oxidative phosphorylation chain, the ATP-dependent molecular chaperone Hsc82 as well as other proteins responsible for protein folding and ubiquitination in the endoplasmic reticulum. Some of the proteins whose expression was altered have previously been described as strongly-adsorbed by CdS QD nanomaterial surfaces as hard corona, and involved in the cytotoxicity of this class of engineered nanomaterials. These data may be extrapolated to broader contexts and a wider range of organisms by allowing the identification of robust biomarkers of exposure to CdS QDs. Conclusions The work shows the power of the model organisms S. cerevisiae in biotechnology to ensure high levels of health and environmental safety. In fact, the double proteomic approach allowed to identify early markers of exposure to CdS QDs among all the proteins reprogrammed by the treatment.

reprogrammed by the treatment.

Background
Engineered nanomaterials (ENMs) show novel and interesting physico-chemical properties that have stimulated their use in many products currently available on the market (1) . In the past decade, ENMs have become ubiquitous and a part of our daily life in the form of components of cosmetics, food packaging, drug delivery systems, therapeutics, electronic systems, biosensors, and many other daily products (2) . The value of the global nanocomposite market is predicted to reach $5.3 billion by 2021, with a compound annual growth rate of 26.7% (3,4) .
Among the numerous types of ENMs, quantum dots (QDs) are nanocrystals of semiconducting materials measuring around 2-10 nm, composed of metals belonging to groups II-V or III-V of the periodic table. They consist of a coated semiconductor inorganic core to improve optical and electronic properties (5,6) . Owing to their narrow emission waveband, bright fluorescence tuneable according to their dimensions, high photostability and broad UV excitation, QDs were initially adopted in precision optical devices, (7) solar cells, (8) new generation LEDs and lasers (9,10) . More applications of QDs include medical diagnostic tools and imaging detection systems for biomarkers of cancer cells, (11,12,13) immunoassays, and cancer therapy, (14,15) as well as transport vehicles for DNA, proteins and drugs to degenerative cells. (16)(17)(18)(19) As QD nanomaterials applications expand, the risk of exposure of the population and dispersal in the environment increases. Like all other ENMs, QDs have been investigated for their possible toxicity to human health and the environment. (20,21) Initial studies attributed QDs toxicity mainly to the release of their Cd 2+ ions. (22) However, this theory has been disproved (23,24,25) and the issue of QD toxicity mechanisms still remains to be clarified.
There are several reports of QD's toxic impact on human cell lines, simple eukaryotes, and plants, which correlate toxicity to the surface properties, size, and functionalization of the nanomaterials. (25,28,29) It has been found that CdS QDs cause complete reprogramming of the transcriptome of Saccharomyces cerevisiae and Arabidopsis thaliana, alteration of reactive oxygen species (ROS) production in human cells, readjustment in oxidative stress responses (enzymatic and non-enzymatic) and damage to mitochondrial functionality. (23,25,29) Paesano et al. (2016) reported that CdS QDs trigger apoptosis, increase ROS concentrations, and modify the transcription of key genes in HepG2 liver cells. (26) Similar results have been reported by Zhang et al., (2016) upon in vivo and in vitro exposure to CdTe QDs of mice liver cells, (30) and by Fan et al., (2016) when exposing HL-7702, HepG2, HEK-293 cell lines to CdTe/CdS core/shell QDs. (31) There is need of a paradigm shift in nanotoxicology, as advocated by the US National Academies of Sciences (2007). Also, EU legislation promotes Intelligent or Integrated Testing Strategies (ITS) for chemicals and specifically for ENMs (REACH Directive 1907/2006. In general, toxicology regulations for the 21st century promote the use of more efficient and more ethical tests, and encourage identification of toxicity mechanisms to build evidence-based testing strategies, and promote the use of in vitro, highthroughput screening (HTS) using cell lines and model organisms such as S. cerevisiae which has 20% homology with the human genome. (32) Among the key elements in this new approach to toxicology is the widely accepted 3Rs (Reach) principle, which aims to replace, reduce, and refine animal testing. (33).
To explore the mechanism of ENMs toxicity, new in vitro and in silico approaches together with the application of HTS have been advocated. (20) In particular, "omics"-based platforms applied to model organisms have provided key information on the interaction between ENMs and living material. (25 34,35) S. cerevisiae is a unicellular eukaryote and one of the most-used model organisms for molecular biology. (36) The proteome of S. cerevisiae was one of the first to be elucidated. (37) Its genome of about 12 Mb is distributed in 16 chromosomes and has been completely sequenced and annotated. (38) The ease of use of this yeast, its rapid life cycle (about 2 h at 28°C) and the availability of several molecular and genomic tools, make it an excellent platform for toxicological studies. (32) Furthermore, the high level of functional conservation across the genomes of other eukaryotes, including humans, makes yeast a model system to assess the mechanisms of response to a wide range of molecules and ENMs. (39,29,40) Indeed, S. cerevisiae represents an ideal experimental system from a genome-wide perspective to study mechanisms through the combination of quantitative transcriptomics and proteomics. (41) Proteomic methods allow the global analysis of gene products in various physiological states of cells and yeasts can readily be analysed using proteomic tools. (37) Protein profiles provide a map of the biochemical status of stressed cells and organisms closely related to overall phenotypic responses. (42,43) In this work, a comparative proteomics analysis was employed to dissect the mechanisms of toxicity of CdS QDs in S. cerevisiae. We have investigated the most significant responses of this yeast to sub-lethal concentrations of CdS QDs at two time points, 9 and 24 h, by using 2D-gel electrophoresis followed by MALDI TOF MS-MS as well as the quantitative iTRAQ proteomics approach followed by LC-MS. Comparative proteomics utilizing gel-based and gel-free approaches has already been successfully applied to S. cerevisiae. (44)(45)(46) Our aim was to identify the proteomic alterations underlying the cell response to CdS QDs. The data facilitate understanding the detrimental effects that these ENMs have on the environment and human health.

Results And Discussion
Cell growth in the different conditions tested S. cerevisiae strain BY4742 was grown on either YPD or SD medium with CdS QD concentrations of 25 to 200 mg L − 1 . The colony spot assay showed that yeast cells grew better on YPD than on SD medium, therefore YPD was chosen for all subsequent experiments (Fig. 1A). When nystatin was added at 0.25 mg L − 1 (29) growth curve assays comparable to the control in YPD were obtained in the presence of 100 mg L − 1 of CdS QDs (Fig. 1B). The concentration of 100 mg L − 1 CdS QDs, with and without 0.25 mg L − 1 nystatin, was chosen as the treatment for subsequent analyses. (25,29) The growth and treatment selected were identical to those used in previous transcriptomics analyses, (25,28) allowing comparison between affected transcripts and proteins upon treatment with CdS QDs. Duration of the treatment was first set at 9 h, which corresponds to the exponential growth phase of the yeast cultures, and then at 24 h for the stationary phase.
Cell cultures sampled at the exponential phase showed an OD 600 value of about 2.5 for the control and 0.6 for QDs treatment, whereas cultures harvested at the stationary phase showed an OD 600 value of about 12.0 for the control and 4.5 for QDs treatment with and without nystatin.

Proteomic variations in response to CdS QDs
Qualitative and quantitative changes in the yeast proteome during CdS QDs treatments were obtained from the 2D-PAGE-based and gel-free iTRAQ approaches, respectively. (45,46) Common proteins identified with the two methods and after the two times of treatments are represented in Figs. 2 and 3 and in Table 1. Table 1 List of the differentially expressed proteins after 9 h treatment 2D-gel (A), iTRAQ (B), and 24 h treatment (iTRAQ) (C).

Proteins
Gene Identification of differentially expressed proteins with 2D-PAGE For the yeast cells exposed to CdS QDs for 9 h, with and without nystatin, the 2D-PAGE approach allowed the visualisation of around 900 spots for each sample. Subsequent . The identities of the protein spots, whose abundance was differentially modulated with a p value of ≤ 0.05 is presented in Table S1.
Identification of the differentially expressed proteins using iTRAQ labelling The time points for quantitative iTRAQ analysis were 9 and 24 h. This gel-free approach allowed processing more samples than 2D-PAGE, therefore proteome variations were also analysed under all treatments and both time points. The iTRAQ approach enables quantification at the peptide level and direct protein mapping because both types of information originate from the same MS-MS spectra. In several other iTRAQ studies, about a thousand proteins were identified. (47 48) Far more than a thousand proteins were detected here within each single iTRAQ experiment on each biological replicate ( Figure S4 and S5).
The iTRAQ experiments corresponding to 9 h of treatment allowed the identification and quantification of 1129 (934 quantified), and 1055 (835) unique proteins from the two biological replicates BR1 and BR2, respectively ( Figure S4 and Supplementary Table S2).
Of these, 849 (712) proteins were common to both biological replicates.  Figure S7B). The complementary nature of these methods was highly useful: proteins identified with 2D-PAGE and iTRAQ differ substantially as shown in Figure S6 and in Table 4, but the combined use of different techniques uncovers a higher proportion of the proteome of an organism. (49) The list of proteins identified in the two biological replicates and the proteins common to all samples are shown in Tables S2 and S4.
The iTRAQ analysis of the 24 h samples allowed the identification of 943 (886 quantified), and 1346 (1080) unique proteins from the two biological replicates BR1 and BR2, respectively ( Figure S5 and Supplementary Table S3). Of these, 562 (505) proteins were common to the two biological replicates.  Tables S3 and S4. Notably, at 24 h the modulated proteins showed a different trend to the finding after 9 h of treatment, i.e. the majority of the proteins were downregulated ( Figure S7C).
We pooled together all the proteins identified for the 9 h treatments obtained with both methods, and compared them with those obtained for the 24 h treatment resulting from the iTRAQ method. Four proteins were in common between the two methods at both 9 h and 24 h: ATP-dependent molecular chaperone Hsp82, uncharacterised oxidoreductase YMR226C, fructose-bisphosphate aldolase (Fba1), and homocysteine/cysteine synthase (Met17) ( Fig. 3 and Table S4). However, another 4 proteins were in common between the 2D -gel method at 9 h and iTRAQ at 24 h. These proteins were: carnitine O-acetyltransferase mitochondrial (Cat2), folic acid synthesis protein (Fol1), serine/threonine-protein kinase Ypk1, and succinate dehydrogenase [ubiquinone] ironsulphur subunit (Sdh2). Another 26 proteins were in common between the iTRAQ method at 9 h and iTRAQ at 24 h. The most downregulated proteins were elongation factor 1-beta (Efb1) and glutamate synthase (Glt1), and the most upregulated proteins were Sadenosylmethionine synthase 2 (Sam2) and superoxide dismutase 1 copper chaperone (Ccs1) (Fig. 3, Table 1 and Table S4).
When the enriched proteins identified at 9 h were analysed on the basis of biological processes, they were organized in five major groups: organic acid biosynthetic process (4.82%), carboxylic acid biosynthetic process (4.82%), carbohydrate metabolic process GO analysis of the differentially abundant proteins identified 'oxidoreductase activity' and 'catalytic activity' as the most perturbed biochemical functions in response to CdS QD exposure at 9 and 24 h, whilst the GO biological processes that differ between the two times of exposure corresponded to 'carbohydrate metabolic process' and 'metabolic process'. Analysis of the significant biological processes affected at 24 h revealed that the majority of the GO classes were downregulated, in particular aerobic respiration, tricarboxylic acid cycle, cellular respiration, energy derivation by oxidation of organic compounds, oxidation-reduction process, and ATP synthesis coupled proton transport.
Overall these results show that the treatment with CdS QDs is time-dependent.
In particular, two of the downregulated proteins that belong to each of the aerobic respiration, cellular respiration and tricarboxylic acid (TCA) cycle classes were citrate synthases CIT1 and CIT2. In eukaryotes, the TCA cycle occurs in the mitochondrial matrix and plays a pivotal role in the utilization of non-fermentable carbon sources via oxidative generation of reducing equivalents (NADH), driving aerobic respiration to yield ATP. (50) The TCA cycle is also an important source of biosynthetic building blocks, such as αketoglutarate, succinyl-CoA and oxaloacetate, which are required for the synthesis of glucose and amino acids. (50) Yeasts have multiple citrate synthase genes (CIT1, CIT2, and CIT3), but it is not clear how they differ in function or if any of them encode a specific methylcitrate synthase. The products of the CIT1 and CIT3 genes have been shown to be mitochondrial proteins, whereas that of the CIT2 gene is clearly peroxisomal. (51) The foregoing molecular function and biological processes mostly linked to mitochondrial function and structure represent the "core response" to CdS QDs. These data are in keeping with other results obtained from simple eukaryotic organisms and human cell lines. (25,28,26) From a physiological and molecular point of view, it has been demonstrated that ENMs increase ROS production by interacting negatively with all cell compartments, in particular by affecting cell membranes and the mitochondria and, consequently, the levels of energy production and cellular respiration. (25) The correspondence between ROS production and inhibition of respiration has been reported in the literature. For example, Fe 3 O 4 nanoparticles have an inhibitory effect on yeast growth.
The inhibition is attributed to their interaction with the mitochondria, leading to disruption of the mitochondrial respiratory chain complex IV, and consequent attenuation of ATP production. (52) In addition, it has been found that NiO NPs inhibit metabolic activity, induce intracellular accumulation of ROS, and provoke cell death in S. cerevisiae. (53) Pathway analysis of the identified proteins Metabolic pathway analysis was performed by submitting the Gene IDs of the proteins to the KEGG server (http://www.kegg.jp) for S. cerevisiae to identify the pathways that were represented more frequently. At 9 h the main pathway classes were: general metabolic pathway, biosynthesis of secondary metabolites, biosynthesis of amino acids, glycolysis and gluconeogenesis, protein biosynthesis, carbon metabolism, and protein processing in endoplasmic reticulum (ER) (Fig. 5).
At 24 h the main pathway classes were: general metabolic pathway, biosynthesis of secondary metabolites, oxidative phosphorylation, TCA cycle, glycolysis and gluconeogenesis, pyruvate metabolism, protein biosynthesis, carbon metabolism, and protein processing in endoplasmic reticulum (ER) (Fig. 5).
Of particular interest was the pathway "glycolysis and gluconeogenesis", common to the two treatment times (Fig. 6), which included 13 proteins identified at 9 h, and 11 at 24 h. as highly probable that sugar transport genes and sugar-utilising enzyme genes are simultaneously affected by the presence of Cd-QDs. (55) They conjecture that the ENO1 gene is downregulated as a consequence of transport of low levels of sugars caused by the suboptimal activity of glucose transporters due to the presence of Cd-QDs.
Conversely, the three isoforms of glyceraldehyde-3-phosphate dehydrogenase, GAPDH (Tdh1, Tdh2, Tdh3), were found to be upregulated for both treatment times. GAPDH is a glycolytic enzyme involved in glucose degradation and energy yield. It catalyses the sixth step of glycolysis, i.e. the conversion of glyceraldehyde-3-phosphate to 1,3 bisphosphoglycerate, but also displays non-glycolytic activity in certain subcellular locations.
In vitro inhibition studies of GAPDH in the presence of QDs suggest that binding of QDs to the enzyme molecules slows down the rate of the reactions catalysed by the enzyme, suggesting that QDs may act as enzyme inhibitors. (56) When human cancer cells are exposed to QDs, the loss of cellular GAPDH activity causes a metabolic perturbation during glycolysis, and the inhibition of GAPDH leads to the decrease of glycolytic rates. This suggests a possible mechanism of change in energy production from the glycolytic pathway to fermentation during QD-mediated cellular injury. This process may lead eventually to cell dysfunction and death. (56) Proteins leading to the Krebs cycle (Pdx1, Acs1, Lpd1, Ald4) or to fermentation (Adh2, Adh6, Adh7, Pdc1, Pdc5) were modulated during treatment with CdS QDs for both periods (Table S4 and Fig. 6). Pdc1 is the most prevalent form of the three yeast pyruvate decarboxylases which are involved both in the anaerobic fermentation of pyruvate to acetaldehyde and in amino acid catabolism. Pdc1, together with Tdh2 and Tdh3, was found among the proteins that constitute the hard corona in yeast during CdS QDs treatments, with a specific role in determining the toxicity of these ENMs. (57) Another pathway of particular interest is "protein processing in ER", which includes 7 proteins modulated at 9 h (2 under-abundant and 5 over-abundant) and 6 at 24 h (2 proteins with reduced levels and 3 with increased levels) (Fig. 7). Three common enzymes were found to be overexpressed at the two times of treatments: ATP-dependent molecular chaperone Hsc82, 78 kDa glucose-regulated protein homolog (Kar2) and UBX domaincontaining protein 1 (Shp1). Hsc82, a member of the Hsp90 family, acts to promote the maturation, structural maintenance and regulation of proteins involved in cell cycle control, ribosome stability and signal transduction. (58) Hsp90 proteins operate in a number of signalling pathways which are altered during exposure to metal ENMs. (59) It was shown that Hsc82 is one of the main hubs in CdS QDs sensitivity, (28) and that it is one of the hard corona proteins for CdS QDs. (57) Other two enzymes of the ER were present at higher levels at 9 h and lower levels at 24 h.
These enzymes were heat shock protein 26 (Hsp 26) and heat shock protein Ssa1, which is a ribosome-associated member of the Hsp70 family participating in the folding of newlysynthesized polypeptides. (60) In addition, the cell division control protein 48 (Cdc48) was less abundant at 9 h. showed that some processes such as protein synthesis and translocation across the ER were inhibited to reduce the stress associated to protein misfolding. (63) The majority of the modulated proteins involved in "oxidative phosphorylation" are from the 24 h treatment with CdS QDs, except for the mitochondrial succinate dehydrogenase Mitochondria are a significant organelle in QD-induced toxicity. (64,65) It has been shown that CdS QDs damage mitochondrial functionality and reduce respiration activity in yeast, (29) plants, (25) and human cells. (26) Damage to mitochondrial functions and structure caused by several types of metal-ENMs has been found in mollusc bivalve and mouse cells. (66,67) Interestingly all the proteins of the ATP synthase complex were downregulated, which indicates a reduction in the energy produced through oxidative phosphorylation, and connects with a general downregulation of the enzymes involved in the glycolytic pathway.
In summary, the upregulation of fermentation, but downregulation in the levels of glucose, manifests as a change into lactate or acetate to provide enough energy for survival. These results might bypass the imbalance in the aerobic metabolism and the TCA cycle.
Moreover, acetate is also regarded as an expedient source of energy for stressed cells. (68) These observations are consistent with the reports in which silver nanoparticles caused oxidative stress and defects in mitochondrial and endoplasmic reticulum (ER) enzymes. (69,70) In aerobic metabolism, the formation of ROS is a natural by-product, but an excess of ROS can chemically modify proteins and lipids by lipid peroxidation and oxidative stress, thus leading to damage to vital organelles such as mitochondria, the ER, and lysosomes. (71,72) Inhibition of GAPDH activity by CdS QDs Figure 9 shows that at both 9 and 24 h the activity of GAPDH in yeast cells treated with 100 mg L − 1 of CdS QDs was significantly lower than in the untreated samples (Fig. 9).
Though not highly significant, the activity of GAPDH at 9 h was higher than at 24 h.
Overall the CdS QDs treatment at both time points inhibits the glycolytic process at the level of the enzyme GAPDH, as suggested by the proteomic approach (Fig. 6). CdS QD treatment consistently altered GAPDH abundance and decreased GAPDH activity. In vitro experiments in the BY4742 yeast strain on hard corona demonstrated a strong dosedependent reduction of the enzyme activity upon CdS QDs treatment. (57) The reduction of GAPDH activity by CdS QDs could be explained by CdS QD oxidation of the GAPDH active site (cysteine 152), which is known to lower GAPDH activity and reduce the accessibility to substrates such as glyceraldehyde-3-phosphate. (56,57) ENPs can induce unfolding and a reduced activity of the identified proteins, as observed in the case of GAPDH isoforms, but CdS QD binding to hard corona proteins could mediate non-specific interactions with other cellular components. (56,57) Effect of CdS QDs on ROS generation and cell integrity in S. cerevisiae Flow cytometry analysis showed that exposure for 9 h to CdS QDs led to a very substantial overproduction of ROS, while significant but much lower ROS overproduction was observed after 24 h of treatment (CdS QDs 100 mg L − 1 ). The results indicate that growth inhibition induced by the treatment was associated with oxidative stress having intense cytotoxic effects at 9 h. Figure 10A and Figure S9 show the time-dependent changes in intracellular production of ROS compared to the untreated control.
Production of ROS by nanomaterials is considered a major factor in QDs toxicity. The deleterious action of oxidative stress starts by causing oxidative damage to biomolecules and destroying their structure, which decreases cellular defences and ultimately leads to cell death, possibly by a mechanism more similar to apoptosis. (73) Overall, our data demonstrate that QDs change the expression levels of a number of proteins by inducing oxidative stress at both treatment periods. Therefore, it is possible to correlate the dysfunction in the glycolysis pathway, the downregulation of oxidative phosphorylation and also the increase in protein misfolding in the ER, all caused by QD treatment, with the production of ROS, which impairs the oxidative balance of the cells and becomes increasingly severe with time. (74,75) Figure 10B shows that after 9 h of CdS QDs treatment, the proportion of dead cells was 30% higher with respect to the control, whilst at 24 h the proportion of dead cells increased to 54%. Together these results indicate that cell death increased with the time and dose of CdS QDs. (76,75) Robustness of markers identification using multiomic approaches The proteins that were up or down regulated following CdS QDs treatment were assessed against other omics markers identified using transcriptomics and phenomics, as reported elsewhere analyses. (28,29,25) Fig. 11 shows the levels of correlation between proteomics/transcriptomics, phenomics/transcriptomics and proteomics/phenomics markers. These data were obtained by comparing 284 significant proteins against more than 5000 haploid deletion mutants and the whole set of transcripts obtained with a yeast microarray platform. (28) The correspondences, both symmetric (++/--) and antisymmetric

Conclusions
The complexity of biological systems often makes it difficult to study their internal interactions. The choice of S. cerevisiae for this study was informed by the knowledge base for the yeast genetics and omics system, including the characterization of identified entire proteome and genome, the existence of a full set of deletion mutants which cover the entire genome. This approach facilitates the identification of 'leads' to be addressed in higher organisms and as models to study human diseases. (34) To explore the  (14) Some of the molecular markers found in this and other studies (28,29,25) suggests makes the identification of the markers are reliable and robust. There are few markers in common among proteomics, transcriptomics and phenomics but the recurrence of these within the different tests is significant. As a fact, proteomic markers are "early markers" of cellular exposure, whereas phenomic markers are "global markers" at organismal level.
These exposure markers involve systems including mitochondrial function, glycolytic cycle, and ubiquitination in the endoplasmic reticulum, which are among the AOP correlated to human pathologies and identified with the system toxicology approach. A prior complete analysis of the CdS QDs minimal inhibitory concentration was carried out, using concentrations ranging from 0 to 250 mg L − 1 (with and without nystatin). (29) Nystatin was added to facilitate the uptake of the CdS QDs. (28) The purity of the cultures was monitored by optical microscopy.

Exposure of yeast cells to different CdS QDs concentrations
Strain BY4742 was grown at 30 °C in YPD medium. After 24 h, optical densities at 600 nm (OD600) were determined using a Cary 50 UV-visible spectrophotometer (Varian, Agilent technologies, TO, Italy), and the OD600 was adjusted to 1.0 with sterile water. The cells were then serially diluted tenfold and aliquots (4 µL) of each dilution were spotted onto 2% w/v SD-agar (6.7 g L − 1 yeast nitrogen base w/v, glucose 2% w/v, histidine 20 mg L − 1 , leucine 120 mg L − 1 , lysine 60 mg L − 1 , uracil 20 mg L − 1 ) or 2% w/v YPD-agar (yeast extract 1% w/v, peptone 2% w/v, dextrose 2% w/v) in the presence or absence of CdS QDs Data Processing, protein identification, and quantification An extensive search was used rigorously to profile the MS data. (83) The MS raw data files were processed using Mascot Distiller (version 2.4.3.2, Matrix Science, London, UK). The resulting "mgf" files were converted into the ".mzXML" file format using msconvert. (84) The ".mzXML" files were searched by MyriMatch version 2.1.120 (85) and X!Tandem version 2011.12.01.1 (86)  iProphet. (87) A protein list was assembled using ProteinProphet, (88)  Sustainability, University of Parma) for helping with Figure 11. Dr. Martin Shepherd is acknowledged for helpful suggestions and language revision.

Authors' Contribution
The Author Contribution is as follows: NM, VB, MM organized the experimental setup, collaborated in writing the manuscript. VG and VS performed the experiment, analyzed the data and helped in writing the manuscript. VG and MM designed the figures. ZA and MV synthetized and analyzed the nanomaterials and helped in writing the manuscript.

Funding
The work was funded by the University of Parma under the Doctorate fellowships to VG.

Availability of data and materials
All the data acquired during this research are available from the Corresponding Author after request.

Ethics approval and Consent to Participate
We are not in need of Ethical approval under the Springer guidelines to participate to the manuscript submission.

Consent for publication
All Authors have provided their consent for the manuscript to be published.        Additional file 2: Table S1: MALDI-TOF/TOF data associated with differentially abundant proteins identified by 2d-gel at 9 h.    Venn diagrams for the differentially regulate proteins in all treatment conditions. A) 9 h iTRAQ; B) 9 h 2D-gel electrophoresis; C) 24 h iTRAQ.

Figure 3
Venn diagram comparing the number of proteins significantly modulated in 2Dgels (9 h treatment), and after iTRAQ analysis (9 and 24 h treatments).

Supplementary Files
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