The Proteomic Response of Arabidopsis thaliana to Cadmium Sulfide Quantum Dots, and Its Correlation with the Transcriptomic Response

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A fuller understanding of the interaction between plants and engineered nanomaterials is of topical relevance because the latter are beginning to find applications in agriculture and the food industry. There is a growing need to establish objective safety criteria for their use. The recognition of two independent Arabidopsis thaliana mutants displaying a greater level of tolerance than the wild type plant to exposure to cadmium sulfide quantum dots (CdS QDs) has offered the opportunity to characterize the tolerance response at the physiological, transcriptomic and proteomic levels. Here, a proteomics-based comparison confirmed the conclusions drawn from an earlier transcriptomic analysis that the two mutants responded to CdS QD exposure differently both to the wild type and to each other. Just over half of the proteomic changes mirrored documented changes at the level of gene transcription, but a substantial number of transcript/gene product pairs were altered in the opposite direction. An interpretation of the discrepancies is given, along with some considerations regarding the use and significance of -omics when monitoring the potential toxicity of ENMs for health and environment.

본문

Edited by:

Manuel González-Guerrero,

Universidad Politécnica de Madrid,

Spain

Reviewed by:

Zhenzhu Xu,

Institute of Botany, Chinese Academy

of Sciences, China

Francisco Cabello-Hurtado,

University of Rennes 1, France

*Correspondence:

Marta Marmiroli

[marta.marmiroli@unipr.it](mailto:marta.marmiroli@unipr.it)

Specialty section:

This article was submitted to

Functional Plant Ecology,

a section of the journal

Frontiers in Plant Science

Received: 31 August 2015

Accepted: 22 November 2015

Published: 16 December 2015

Citation:

Marmiroli M, Imperiale D, Pagano L,

Villani M, Zappettini A and Marmiroli N

(2015) The Proteomic Response of

Arabidopsis thaliana to Cadmium

Sulfide Quantum Dots, and Its

Correlation with the Transcriptomic

Response. Front. Plant Sci. 6:1104.

[doi: 10.3389/fpls.2015.01104](http://dx.doi.org/10.3389/fpls.2015.01104)

[ORIGINAL RESEARCH](http://www.frontiersin.org/Plant_Science/editorialboard)
[published: 16 December 2015](http://www.frontiersin.org/Plant_Science/editorialboard)

[doi: 10.3389/fpls.2015.01104](http://dx.doi.org/10.3389/fpls.2015.01104)

# The Proteomic Response of Arabidopsis thaliana to Cadmium Sulfide Quantum Dots, and Its Correlation with the Transcriptomic Response

[Marta Marmiroli](http://loop.frontiersin.org/people/189742/overview) 1 [*, Davide Imperiale](http://loop.frontiersin.org/people/257084/overview) 1 [, Luca Pagano](http://loop.frontiersin.org/people/266938/overview) 1 [, Marco Villani](http://loop.frontiersin.org/people/294709/overview) 2 [, Andrea Zappettini](http://loop.frontiersin.org/people/297956/overview) 2

[and Nelson Marmiroli](http://loop.frontiersin.org/people/194216/overview) 1

1 Department of Life Sciences, University of Parma, Parma, Italy, 2 Institute of Materials for Electronics and Magnetism

(IMEM-CNR), Parma, Italy

A fuller understanding of the interaction between plants and engineered nanomaterials
is of topical relevance because the latter are beginning to find applications in agriculture
and the food industry. There is a growing need to establish objective safety criteria for
their use. The recognition of two independent Arabidopsis thaliana mutants displaying a
greater level of tolerance than the wild type plant to exposure to cadmium sulfide quantum
dots (CdS QDs) has offered the opportunity to characterize the tolerance response
at the physiological, transcriptomic, and proteomic levels. Here, a proteomics-based
comparison confirmed the conclusions drawn from an earlier transcriptomic analysis that
the two mutants responded to CdS QD exposure differently both to the wild type and to
each other. Just over half of the proteomic changes mirrored documented changes at the
level of gene transcription, but a substantial number of transcript/gene product pairs were
altered in the opposite direction. An interpretation of the discrepancies is given, along with
some considerations regarding the use and significance of -omics when monitoring the
potential toxicity of ENMs for health and environment.

Keywords: tolerant mutants, engineered nanomaterials (ENM), genotoxicology ecotoxicology, exposure markers,
comparative analysis

INTRODUCTION

Nanotechnology is regarded as transformative, but its potential long term impact on human health
and the environment remains inadequately researched (Colvin, 2003; Royal Society and Royal
Academy of Engineering, 2004). Legislative authorities still suffer from a paucity of appropriate
data to enable a science-based regulatory framework to be constructed over the release and
commercialization of nanomaterials. While much of the focus of nanotechnology has been in the
electronics industry and medical research and development, a range of potential applications in
agriculture is now opening up, including the incorporation of nanoparticles (NPs) in pesticide
formulations, their use as biosensors and devices to aid genetic manipulation and as aids to
post-harvest management (Singh Sekhon, 2014; Servin et al., 2015). A wealth of literature
over the last decade has addressed the potential toxicity of NPs and enhanced manufactured
nanomaterials [ENMs; European Parliament, 2011; Science and Technology Options Assessment
(STOA) European Parliament, 2012].

The EU Regulation 1169/2011 (to be applied in December
2016), although attempting a formal definition of ENMs, fails
to mention them in the context of food additives, and even
the proposed definitions are controversial. Concerns regarding
the adequacy of the regulation have been raised by other EU
organs [Aschberger et al., 2014; Science and Technology Options
Assessment (STOA) European Parliament, 2012].

A general lack of consumer information has been criticized
by some parties Friends of the Earth: emerging Tech Project
website, 2015 [(https://www.foe.org.au/articles/2015-09-25/new-](https://www.foe.org.au/articles/2015-09-25/new-study-raises-further-questions-about-safety-nano-ingredients-food)
[study-raises-further-questions-about-safety-nano-ingredients-fo](https://www.foe.org.au/articles/2015-09-25/new-study-raises-further-questions-about-safety-nano-ingredients-food)
[od)](https://www.foe.org.au/articles/2015-09-25/new-study-raises-further-questions-about-safety-nano-ingredients-food) as has the approach for assessing toxicity and ecotoxicity
(Fadeel and Garcia-Bennett, 2010; Saez et al., 2010; Sigg et al.,
2014). In a recent report the OECD emphasized the importance
of improved toxicity test for assessing ENMs environmental
dispersion and impact on human health [Organisation for
Economic Co-operation and Development (OECD), 2014]. The
potential risks arising from a lack of legislation have been flagged
by Abbott et al. (2012) and Hodge et al. (2014). The consensus
regarding ENMs is that hard data are still required to clarify
the nature and implications of their interaction with biological
matrices. Meanwhile, some methods aimed at improving the
performance and reducing the toxicity of medical NPs, such as
incorporating biocompatible coating materials, modifying their
surface to mitigate toxicity and building in biodegradability have
been proposed.

Quantum dots (QDs) are crystalline NPs, first synthesized in
the early 1980s for use in the electronics industry (Brus, 1984).
Cadmium sulfide quantum dots (CdS QDs) have a high surface
charge and reactivity and are very stable (Favero et al., 2006).
Their biological activity has been studied using both a plant
and a yeast model (Marmiroli et al., 2014, 2015), applying both
a mutant-based and a genome-wide transcriptomics approach.
Two Arabidopsis thaliana mutants have emerged as showing an
enhanced level of tolerance; in the first, the mutagenized genes
encoded an unknown chloroplast-localized protein, a cytoplasmlocalized calmodulin binding protein and a member of the
MYB class of transcription factors, while in the second, the
candidate genes encoded an O-glycosyl hydrolase localizing to
the endomembrane and a chloroplast-localizing ATP binding
protein. The contrasting genetic basis of tolerance in the two
mutants was taken to imply that CdS QDs tolerance can be
achieved by the activation of non-identical master switches.
A transcriptomic analysis of wild type (wt) A. thaliana plants
exposed to Cd 2+ ions revealed that a completely different gene
set was activated, meaning that the pathway leading to CdS QD
tolerance must be unrelated to that determining the response to
Cd 2+ stress.

In contrast to a wealth of transcriptomic data sets, the
understanding of the proteomic response to ENM exposure is
rather limited. In general, the statistical correlation between
transcript and protein abundance in eukaryotic cells is poor (Gygi
et al., 1999; Hajduch et al., 2010), a phenomenon ascribed largely
to the major role played by post-transcriptional modification
(Maier et al., 2009). The aim of the current study was to
supplement the documented transcriptomic and phenotypic
responses of A. thaliana to CdS QD exposure (Marmiroli et al.,

2014) with a robust set of proteomic data, collected using a
double liquid chromatography separation system well-proven to
resolve the complex protein mixture present in a plant matrix
(Marmiroli et al., 2013).

MATERIALS AND METHODS

Plant Material
A. thaliana, accession Landsberg erecta (L. Heyn) mutants atnp01
and atnp02 were isolated by screening of 378 mutant lines
obtained from the (Nottingham) European Arabidopsis Stock
Centre (uNASC; [http://arabidopsis.info/),](http://arabidopsis.info/) for resistance to CdS
QDs as described by Marmiroli et al. (2014). The same paper,
reports also a genetic and molecular characterization of the two
mutants.

Physical Properties of the CdS QDs
The CdS QDs utilized during all the experiments had a bulk
density of 4.82 g cm −3 and a diameter of about 5 nm, they
were synthesized following Villani et al. (2012). Cadmium
represented ∼78% of the dry weight. The CdS QDs were the same
batch used into the previous transcriptomic work (Marmiroli
et al., 2014).

Seed Germination, Growth, and Treatments
Twenty-five seeds of A. thaliana wild type (wt) and atnp01 and
atnp02 were sawn on Petri dishes containing Murashige and
Skoog (MS) nutrient medium (Duchefa Biochemie, Haarlem,
Netherlands) containing 1% w/v sucrose and solidified with 0.8%
w/v agar, then placed in the dark, under controlled conditions
in a growth chamber. After germination, seedlings were grown
at 24 ◦ C, with relative humidity of 30%, and under a 16-h
photoperiod (light intensity 120 µM m −2 s −1 photosynthetic
photon flux) in the same MS medium in the absence of CdS QDs
for 14 days. Seedlings were transferred to MS medium containing
80 mg L −1 CdS QDs (treatment) or 0 mg L −1 (control) and grown
for a further 21 days, as above. Plantlets were then removed from
the medium, carefully washed with distilled H2O and used for
protein extraction.

Protein Samples Preparation
Crude proteins of wt and the two mutant lines in untreated
and treated (80 mg L −1 CdS QDs) conditions were extracted
by using MgSO4-gbased buffer. A total amounts of 1 g of
frozen plants for wt and mutants and for both treatments
were finely ground in liquid nitrogen with a ceramic mortar
and pestle, adding SiO2 sand (Sigma-Aldrich, St. Louis, MO,
USA), to encourage breakage of the cell walls. The fine powder
obtained was resuspended in 50 mM Tris [tris (hydroxymethyl)
aminomethane] HCl pH 7.8, 10 mM MgSO4, 0.1% [v/v] β
mercaptoethanol and 0.1% [v/v] Protease Inhibitor Cocktail
(Sigma-Aldrich). The crude mixture was sonicated for 10 min
at 35 kHz (Transsonic T460, Elma Schmidbauer GmbH, Singen,
Germany) and then the solution was placed in ice for 40 min.
After 10 min more of sonication, the sample was centrifuged in
a precooled rotor spun at 16,000×g for 5 min at 4 ◦ C (Micorfuge

22R Centrifuge, Beckman Coulter, Fullerton, CA, USA). The
pellet, containing the larger cellular residues and SiO2 sand,
was discarded and the supernatant centrifuged at 16.000×g for
30 min at 4 ◦ C. The upper phase was pipetted into other 15 ml
tubes and stored at –20 ◦ C for further analysis. Three biological
replicates were produced for crude protein extracts from wt and
mutants.

Protein Quantification
Proteins were quantified using the Quick Start Bradford Protein
Assay (BioRad, Hercules, CA, USA); The protein-dye formed
was detected at 595 nm with spectrophotometric assay (Uvikon
931, Kontron Instruments) with a standard curve from different
dilutions of BSA (Bovine Serum Albumin; Sigma-Aldrich). The
BSA dilutions and sample dilutions were prepared in a suitable
Chromatofucusing (CF) Start buffer for the next step of twodimensional liquid chromatography (2D-LC).

PD10 Desalting Column
Protein extracts were desalted and equilibrated using PD10 Desalting Workmade disposable columns (GE-Healthcare
Biosciences, Uppsala, Sweden) containing prepacked Sephadex
G-25 Medium with exclusion limit of 5000 Da. PD-10 column
equilibration was performed by using ∼25 ml of CF Start buffer
(Eprogen, Downers Grove, IL, USA) and the sample was then
eluted with 3.5 ml of CF start buffer. The capacity of the system
allows the loading of up to 2.5 ml of sample, with a range of
loading capacity between 0.5 and 5 mg of protein per sample.

Two Dimensional Liquid Chromatography
Three milligram of protein extract were separated by 2D-LC
for each sample. Separation was performed with ProteomeLab™
PF2D by Beckman Coulter equipped with: HPCF-1D column
250 × 2.1 mm internal diameter, 300 Å pore size and HPRP
C18 column 4.66 mm length × 3.3 mm internal diameter,
1.5 µm particle size (Eprogen). Proteins are separated in the
first dimension by high-performance chromatofocusing (HPCF),
performed on an HPCF-1D column. With this technique,
proteins bound to a strong anion exchanger followed by elution
with a continuously decreasing pH (8.5–4.0) gradient. The pH
gradient was generated in the column by two buffers: Start Buffer
(SB) and Eluent Buffer (EB; Eprogen). The calibration of both
buffers was an important step: SB and EB were sonicated for
5 min and then their pH was adjusted to 8.5 and 4.0 respectively
using either a saturated solution (50 mg/ml) of iminodiacetic
acid (Sigma-Aldrich) if the buffer was too basic, or 1M NH4OH
(J.T. Baker, Deventer, Holland) if the buffer was too acidic. The
column was first equilibrated to the initial pH 8.5 using CF Start
buffer at a flow rate of 0.2 ml min −1 for 3 h. After this step,
5 ml of sample were injected the column for the first dimension
CF. Twenty minutes from sample injection, the first dimension
pump switches to the CF Eluent buffer (pH 4) at a flow rate of
0.2 ml min −1 . The interaction of the column filling with the CF
Eluent buffer produced a gradually decreasing pH gradient that
traveled through the column as a retained front. The pH gradient
affected the proteins net charge and their adsorption/desorption
to the positively-charged matrix of the column, causing protein

separation in the effluent. The pH of the mobile phase was
monitored on-line by a post-detector pH flow cell. The proteins
were eluted based on their isoeletric point (pI), measured the
absorbance at 280 nm, and collected in a 96 deep-well plate by a
fraction collector according to pre-determined pH decrements of
0.4 pH units during the gradient, or in 1 ml volumes when the pH
did not change. At the 115th min the most acidic proteins were
recovered by washing the column with 1M NaCl 30% n-propanol

[v/v] for 15 min. The column was finally washed with water for
45 min; the CF separation took of total of ∼185 min.
The eluent from the 1st dimension was injected into the 2nd
dimension, a high-performance reversed-phase chromatography
(HPRP) based on protein hydrophobicity. HPRP was carried out
in a C18 column. The mobile phase consisted of A: 0.1% TFA
(Trifluoroacetic Acid; J.T. Baker) in water and B: 0.08% TFA in
Acetonitrile (J.T. Baker). Separation was performed at 0.75 ml
min −1 with an increasing gradient of B. During the first 2 min
100% of solvent A was pumped into the column; in the next
35 min the gradient was created in the column by switching
the flow from 0 to 100% solvent B; this is followed by 100%
B for 4 min and 100% A for 9 min. In order to obtain a better
resolution, the separation was done at 50 ◦ C. The eluent from the
second dimension was monitored by a second high performance
UV/VIS detector at 214 nm, that provided a more universal and
sensitive detection of proteins via peptide bonds. Fractions were
immediately collected in eppendorf tubes for MS analysis by
using an automated fraction collector.

Protein Identification
Matrix-assisted laser desorption/ionization time-of-flight mass
spectrometry (MALDI-TOF/MS) analysis was carried out using
a 4800 Plus MALDI-TOF/TOF™ (AB SCIEX, Framingham, MA,
USA) equipment. Eluted fractions were evaporated to a final
individual volume of 10 µl, using a Speed Vac Concentrator 5301
(Eppendorf AG, Barkhausenweg, Hamburg, Germany). Protein
digestion was performed by incubating each fraction in 25 mM
NH4HCO3 and 2 mM DTT (DL-Dithiothreitol) in a water bath
at 60 ◦ C for 1 h. The alkylation of the reduced sulfhydryl groups
was carried out adding 1 mM Iodoacetamide, at 25 ◦ C, for 30 min
in the dark, and then 10 µL of Trypsin (125 µg ml −1 ) in 50 mM
NH4HCO3 were added. Digestion was carried out at 37 ◦ C for
24 h. The samples digested were then purified and concentrated
with a ZipTipC18 using the procedure recommended by the
manufacturer (Millipore Corporation, Billerica, MA, USA). Then
1 µL of each purified peptide was spotted directly onto a stainless
steel MALDI target plate with 1 µL of a saturated solution of
α-cyano-4-hydroxycinnamic acid in 0.1% TFA:ACN (2:1, v/v).
The solution was allowed to dry at room temperature and a
spot was produced. Positively charged ions were analyzed in
reflectron mode. The spectra were obtained by random scanning
of the sample surface with an ablation laser. A mass range of
10,000–100,000 Da was used, and about 400 laser shots were
averaged to improve the signal-to-noise ratio. Calibration was
performed by a ProteoMass Protein MALDI-MS Calibration
kit (Sigma-Aldrich). Two technical replicates for each spectrum
were analyzed by MS, and peptides common to all of the resolved
spectra were considered for protein identification.

Statistical and Bioinformatics Analysis
ProteoVue software (Eprogen) was utilized to convert
chromatographic intensities from the 2D-LC of each pH
fraction into a band intensity format. This produced a highly
detailed map with the dimensions of hydrophobicity and pI. The
2D-LC maps could be viewed in several colored formats where
the color intensity was proportional to the relative intensity
of each chromatographic peak. DeltaVue software (Eprogen)
was utilized for the differential analysis of corresponding
fractions from two different sample sets. This software compared
chromatogram peaks corresponding to the same protein in the
two samples, allowing quantification by a subtractive analysis. A
differential map was produced by point-to-point subtraction and
it is viewed between the two original sample sets. Mass spectra
were analyzed using the mMass software package [(http://www.](http://www.mmass.org/)
[mmass.org/;](http://www.mmass.org/) ver. 5.5, by Martin Strohalm) and the peak list for
each mass spectra were obtained. Peptide mass fingerprinting
analysis was carried out with the Mascot program [(http://](http://www.matrixscience.com)
[www.matrixscience.com).](http://www.matrixscience.com) Proteins were identified by searching
against Swiss-Prot database of A. thaliana (thale cress). The
following parameters were used for database search: mass
accuracy below 100 ppm, maximum of one missed cleavages by
trypsin, carbamidomethylation of cysteine as fixed modifications,
oxidation of methionine as variable modifications. The search
was based on the monoisotopic masses of the peptides. For
mass-spectrometry (MS) analyses, three technical replicates for
each spectrum were performed. For proteins identification, only
peptides in common to all the resolved spectra were considered.

The gene loci found in the UniProt were searched in TAIR
[database (https://www.arabidopsis.org/) for the corresponding A.](https://www.arabidopsis.org/)
thaliana proteins names, description, and GO annotations.

Heat maps of selected proteins were generated by TreeView
v1.60 software. Gene Ontology (GO) analysis (Harris et al.,
2004) visualized with pie charts were generated by VirtualPlant
[v1.3 (http://virtualplant.bio.nyu.edu/cgi-bin/vpweb/virtualplant.](http://virtualplant.bio.nyu.edu/cgi-bin/vpweb/virtualplant.cgi)
[cgi)](http://virtualplant.bio.nyu.edu/cgi-bin/vpweb/virtualplant.cgi) applying a p (calculated according to Bonferroni test)
cutoff value of 0.05. Venn diagrams were generated by
[Venny 2.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html).](http://bioinfogp.cnb.csic.es/tools/venny/index.html)
The correlation between mRNA and protein levels was calculated
using the Pearson product moment correlation coefficient in
Microsoft Excel 2010.

RESULTS

Proteomic Data Management and
Visualization
The proteomic separation identified around 600 proteins in
the extracts of wt plants and of each of the two mutants
exposed and not to CdS QDs. Coupling treated and not treated
results for wt, atnp01 and atnp02 three subset of about 1200
proteins were found. The use of DeltaVue software led to the
elaboration of a “differential map” for each genetic comparison,
where each “band” corresponded to a unique protein and where
each virtual band’s intensity was proportional to the protein’s
relative abundance, measured against its abundance in the nontreated control sample. In order to assess which of the intensity

ratios were statistically significant, their log10’s were grouped
into frequency categories, producing a normal distribution; only
those proteins associated with a ratio differing from the mean
by at least two standard deviations (±) were taken forward for
identification, following the strategy outlined by Marmiroli et al.
(2013). On this basis almost 200 proteins were selected, but of
these, only 130 were abundant enough to be subjected to MALDITOF/MS. The identification of some of the proteins using mass
fingerprinting was not possible due to low scores, so finally 88
proteins were identified with any statistical confidence. The sets
of differentially expressed proteins are listed in Table 1, and a
global heat map is presented in Figure 1B : the chosen calibrator
was the treated wt plant, because this was found to most clearly
highlight the differences between the set of samples, while
also allowed direct comparisons to be made with established
transcriptomic data (Marmiroli et al., 2014; Figure 1A ).

Venn diagrams featuring the differentially represented, both
over- and under-represented, proteins in both mutants compared
to the wt in both the treated and untreated situation are presented
in Figure 2 . There were 35 over-represented proteins in the
treated atnp01 mutant, and 47 in the atnp02 mutant; of these, 26
were in common between the two comparisons. The respective
frequencies of under-represented proteins were 44 in atnp01, 40
in atnp02, and 31 common to both mutants. In the comparison
between treated wt and atnp01 plants, nine proteins having the
same abundance.

In the comparisons involving non-treated plants, there were
44 over-represented proteins in each mutant, of which 35 were in
common. With respect to the set of under-represented proteins:
44 for atnp01, 40 for atnp02, of which 35 in common. Inspection
of the data revealed that seven of the over-represented and 11
of the under-represented proteins did not change in abundance
either as a consequence of the treatment or as a result of
a genetic difference, 17 over- and 17 under-represented were
ascribable to genetic differences and eight over- and 17 underrepresented ones to the CdS QDs exposure. The atnp01 mutation
affected eight proteins (two over-, six under-represented), while
the atnp02 mutation affected six proteins (one over-, five underrepresented). The CdS QDs treatment altered the expression level
of 12 proteins in atnp01 (four over-, eight under-represented)
and 14 in atnp02 (11 over-, three under-represented).

Functional Analysis of Differentially
Expressed Proteins
A GO analysis was conducted to assign functionality to
the set of differentially expressed proteins (Supplementary
Figures S1–S4). The most frequently encountered GO class was
biological process, followed by molecular function and cellular
components. For both the mutants, the over- and the underrepresented proteins were classified within the biological process
category as involving a cellular process, a metabolism or a
response to stimuli.

The over-represented proteins in atnp01 concerned metabolic
and cellular processes, response to stimuli and regulation
(Supplementary Figure S1), the cellular components interested
being extracellular parts, cell parts and organelles. The molecular
function of relevance were catalytic, binding but also electron

FIGURE 1 | Heat maps of A. thaliana wt and mutant lines atnp01 and atnp02 not treated and treated with 80 mg L −1 CdS QDs drawn with TreeView
software. Heat map of the transcriptomic data, the probe “wt treated” was used as calibrator (black column). Up-regulated genes compared to the calibrator are
shown in shades of red and down-regulated genes in shades of green (A). Heat map of the proteomic data, “wt treated” was used as calibrator (black column).
Proteins more abundant in the sample compared to the calibrator are shown in shades of red, and those less abundant in the sample compared to the calibrator in
shades of green (B).

FIGURE 2 | Venn diagrams for over-represented proteins in atnp01 and
atnp02 mutant lines both for control and treatment conditions (A) and
under-represented proteins in atnp01 and atnp02 mutant lines both for
control and treatment conditions (B). Within each subset a natural figure (n
∈ N) denotes the number of members (proteins) included in the subset.

carriers and antioxidants activity. The over- represented proteins
in atnp02 concerned metabolic and cellular processes and as for
atnp01 response to stimuli (Supplementary Figure S2). Also for
the cellular components and molecular functions the similarities
were remarkable (Supplementary Figure S2).

The proteomic response of the two mutants evidenced,
in the condition of treatment for atnp01, under-represented
proteins in the biological process metabolisms, cellular response
to stimuli, cellular components organization with at this level
a predominance of cell part and organelle (Supplementary
Figure S3). The molecular function involved were: catalytic

activity, binding and transport (Supplementary Figure S3). In
the mutant atnp02 the under-represented concerned proteins
of the cell metabolism and developmental process, but also
cellular components organization and response to stimuli
(Supplementary Figure S4). Cell part and organelles were the
more affected with molecular function in the class of catalytic
activity and binding as majority.

Comparison Between the Transcriptome
and the Proteome
Based on the transcriptome description provided by Marmiroli
et al. (2014), 78 of the 88 proteins were assignable an encoding
transcript ( Figure 1 ). The Pearson product moment correlation
coefficients (r) for transcript and protein representation for
the two mutants were, respectively, 0.126 and 0.197. Figure 3
shows a comparison between protein and transcript overrepresented with respect to gene product, but under-represented
with respect to transcript (column 4 and 5). In atnp01, 46
of the proteins (59%) exhibited a matching level of transcript
and protein (“concurrent” gene products), while 16 were overrepresented even though their RNA was underexpressed, and
16 behaved in the opposite manner; these 32 gene products
were termed “non-concurrent.” In atnp02, there were 44 (57%)
concurrent and 34 non-concurrent proteins, of which 12 (15%)
were over-represented with respect to gene product, but underrepresented with respect to transcript, and 22 (28%) vice versa.
In atnp02, 57% were concurrent, while of the non-concurrent
ones, 15%, the reverse holds for the 28% of the proteins ( Table 2 ).
Since the studied protein set was so much smaller than the
number of relevant transcripts, reported in Marmiroli et al.
(2014) (88 vs. 456), a correlation analysis based on either
Pearson’s P or Kendall’s τ was considered to be unsuitable.
In order to recognize an association between transcript and
protein abundance induced by the Cd QDs, the behavior of
the two mutants was compared: either the direction of change
of the transcript abundance matched that of the protein in
both mutants, or it did not. Thus, two broad groups were
defined comprising a constant, invariable, or a variable, at times
unquestionably opposite, behavior, which describes the trend
of protein production rate against the backdrop of transcript.
The final column in Figure 3 depicts the general cascade from
transcript to gene product induced by the treatment in the
two mutants. In all, for 71% of the gene products, protein
representation reflected the behavior of the matching transcript,
while for the remaining 29%, there was no apparent relationship;
in 3% of the cases, a particularly high transcript expression was
matched by a particularly low level of protein representation or
vice versa ( Table 2 ).

Identification of Specific Proteins
In Supplementary Tables S1, S2 is reported all the bibliography
relevant to each protein mentioned in this sub-heading.

The proteome of both mutants differed from that of the wt,
both when the plants were growth under control conditions
and when they were exposed to CdS QDs. In the absence of
the stress treatment, just two proteins were specifically overrepresented in atnp01 and one in atnp02 ( Figure 2A ). One

FIGURE 3 | Visualization as “heat map” of the comparison between transcriptomic data and proteomic data. In column 4 and 5, white rectangles indicate
concurrency between transcript level and protein abundance, light blue indicates that high level of transcript has a low protein abundance, blue indicates that low level
of transcript has high protein abundance. In the last column, yellow rectangles are for consistent behavior between the two mutants in the transcriptomic-proteomic
cascade, orange is for contrasting behavior, specifically, pink is for a markedly opposite trend.

TABLE 2 | Comparison between transcriptomic and proteomic data.

atnp01 atnp02 Comparisons

(%) (%) atnp01/atnp02

(%)

% Concurrent 58.97 56.41 70.51

% Non-concurrent High transcript
low protein

Low transcript
high protein

20.51 15.38 29.49

20.51 28.21

of the former was an alanine-tRNA ligase expressed in the
mitochondria and the chloroplasts, which forms part of the
response to both salinity and Cd 2+ stress; the other was N(5 ′ -phosphoribosyl) anthranilate isomerase 1, an enzyme which
catalyzes a step in the tryptophan synthesis pathway, and is
active in guard cell chloroplasts. The sole atnp02-specific overrepresented protein of unknown function was a member of the
pentatricopeptide repeat superfamily active in the mitochondria
(Supplementary Table S1). There were six atnp01-specific underrepresented proteins in the non-stressed plants (Supplementary
Table S2). These comprised (1) a calcium-binding protein CML31
localizing to the nucleus, (2,3) two pentatricopeptide repeatcontaining proteins of unknown function expressed in the
mitochondria, (4) a pentatricopeptide repeat-containing protein
member of the PCMP-E subfamily involved in RNA editing in the
chloroplast, (5) an alpha type 3 proteasome subunit active in both
the cytosol and various organelles, and involved in glycolysis,
photorespiration, proteolysis, the hyperosmotic response, the
response to various abiotic stress agents (including Cd 2+ ) and
water transport, and (6) a proton pump-interactor 3A, which may
be responsible for the regulation of plasma membrane ATPase
activity and proton transport. There were five under-represented
proteins specific to atnp02 (Supplementary Table S2). These
comprised (1) a calcium-binding protein CML42 involved in
protein binding and trichome branching, (2) a glucan endo-1,3beta-glucosidase which participates in a MAPK cascade and in
a variety of other processes, and localizes within the apoplast,
cell wall, chloroplast and vacuole, (3) a homeobox-leucine zipper
protein ATHB-7 thought to be a transcription factor acting in
a signal transduction pathway mediating the drought response,
and (4,5) the two pathogenisis-related proteins PR1 and PR5,
present in the apoplast, cell wall and extracellular regions.

Exposure to CdS QDs resulted in the specific overrepresentation of four proteins in atnp01 and 11 in atnp02
( Figure 2A ; Supplementary Table S1). The former set comprised
(1) a calcium-binding protein CML45 of unknown function,
(2) a putative defensin expressed extracellularly, (3) a KTI12
homolog expressed in both the cytoplasm and the nucleus, and
involved in 5-carbamoylmethyluridine metabolism and also
in the regulation of transcription and in tRNA modification
and, (4) a WRKY transcription factor involved in the response
to Zn 2+ . The 11 atnp02-specfic proteins were as follows:
(1) a plastid-localized arogenate dehydratase involved in
phenylalanine synthesis, in anthocyanin accumulation in
response to UV irradiation and in the vernalization response,

(2) a probable E3 ubiquitin-protein ligase, (3) an F-box/kelchrepeat protein of unknown function, (4) a mechanosensitive
ion channel protein responsible for ion transmembrane
transport, (5) a mitochondrion localizing member of the
tetratricopeptide repeat-like superfamily, (6) a mitochondrion
localizing methionine sulfoxide reductase involved in oxidationreduction, protein repair and the response to singlet oxygen,
(7) a plastid localizing phosphomethylpyrimidine synthase
involved in pathogen detection, glucosinolate and maltose
metabolism and several other processes, (8) a prefolding subunit
4, expressed in the cytosol and the nucleus, (9) a membrane
RING-H2 finger protein associated with Zn 2+ binding, (10) a
nuclear GTE1 transcription factor involved in the regulation
of germination, and (11) a nuclear WPP domain-interacting
tail-anchored protein 1 involved in lateral root development and
nucleocytoplasmic transport. Overall, a somewhat larger number
of proteins was over-represented in atnp02 than in atnp01.

Exposure to CdS QDs resulted in the specific underrepresentation of eight proteins in atnp01 and three in
atnp02 ( Figure 2B ; Supplementary Table S2). The former set
comprised: (1) an extracellular 2S seed storage and lipid
binding protein, (2) a chloroplastic ATP synthase subunit
β, involved in photosynthesis and aspects of the biotic and
abiotic stress response, (3) BIG, a cytosolic protein, involved in
auxin polar transport, auxin-activated signaling, inflorescence
morphogenesis, lateral root formation and development, and
the anti-fungal response, (4) a putative galacturonosyltransferase
2, responsible for carbohydrate and pectin synthesis and cell
wall organization, (5) a chloroplast nudix hydrolase 21, (6)
a structural constituent of the extensin-like EPR1 involved in
cell wall modification, seed lipid storage, embryo development,
dormancy and germination, and sugar-mediated signaling, and
(7,8) two RuBisCO small subunits (RBCS2B and RBCS3B).
The three specifically under-represented atnp02 proteins were:
(1) esterase/lipase ESM1 involved in photosynthesis, starch
synthesis, pest/pathogen defense, (2) an extracellular pectate
lyase, and (3) a mitochondrion localizing pentatricopeptide
repeat-containing protein acting as an adenylate cyclase.

Among the proteins showing altered representation in
both mutants (either in the control plants and/or in the
CdS QDs exposed ones), seven were over-represented and
11 under-represented ( Figures 2A,B ; Supplementary Tables S1,
S2). The former group comprised: (1) a 12S CRC protein
responsive to abscisic acid and associated with lipid storage,
protein ubiquitination, germination, seed maturation and sugarmediated signaling, (2) a calcium-dependent protein kinase
23 involved in abscisic acid-activated signaling, intracellular
signal transduction, protein phosphorylation and the response to
Cd 2, (3) glutaredoxin C14, (4) DHAR3—a chloroplast-localizing
dehydroacorbate reductase involved in protein glutathionylation
and toxin catabolism, (5) a mitochondrion localizing glycine
cleavage system H protein 2, (6) the nuclear pre-mRNA splicing
factor SLU7-A, and (7) the RuBisCO small subunit RBCS1A. The
11 down-regulated proteins were: (1) a chloroplast and cytosolic
4- alpha glucanotransferase DPE2, involved in the sensing of
the circadian rhythm, polysaccharide and starch metabolism and
cell wall organization, (2) actin2, a cytosolic protein involved in

anthocyanin accumulation, cellulose metabolism, the response
to various abiotic stresses and water transport, (3) a nuclear
ATP-dependent DNA helicase Q-like 4A involved in DNA
recombination, repair and replication, and the cellular responses
to DNA damage and low temperature stress, (4) a BTP/POZ
domain-containing plasma membrane protein responsible for
protein ubiquitination, (5) a CLIP-associated protein involved in
anthocyanin accumulation, cellulose metabolism, polysaccharide
and cell wall synthesis and root hair elongation, (6) a
nuclear DNA damage binding protein 1b involved in DNA
repair, cell division and embryo and reproductive structure
development, (7) a nuclear F-box/kelch-repeat protein belonging
to the galactose oxidase/kelch repeat superfamily, (8) a
pentatricopeptide repeat-containing protein required for the 5 ′

processing of nad9 and cox3 mRNAs in the mitochondria, (9,10)
two mitochondrial pentatricopeptide repeat-containing proteins
of unknown function, and (11) an SNC1 modifier involved in the
regulation of gene expression, glucuronoxylan metabolism, and
nuclear-transcribed mRNA catabolism.

Exposure to CdS QDs resulted in an increase in the
number of over and under-represented proteins in both
mutants ( Figures 2A,B ; Supplementary Tables S1, S2). The overrepresented proteins were eight: (1) a chloroplast-localized betaamylase 3 involved in maltose and starch synthesis and the
response to low temperature, (2) a cyclic nucleotide-gated ion
channel 6, (3) a GDP-mannose 4,6-dehydratase 2, involved in
de novo GDP-L-fucose synthesis and GDP-mannose and glucose
metabolism, (4) a nuclear LOB domain-containing protein 5 of
unknown function, (5) a chloroplast pentatricopeptide repeatcontaining protein involved in chloroplast RNA and mRNA
processing, (6) a probable beta-D-xylosidase 7 involved in
carbohydrate metabolism, (7) a probable prolyl 4-hydroxylase
7, involved in oxidation-reduction, (8) the cytoskeletal protein
TONNEAU 1-B, which is probably involved in cortical
cytoskeleton organization and microtubule organization. The
set of under-represented proteins in both mutants comprised
17 proteins: (1) a chloroplast auxilin-like protein 1, which
binds to certain heat shock proteins and is associated with
protein folding, (2) a calmodulin-like protein 1 localizing to the
mitochondria and the plasma membrane, (3,4) two extracellular
defensin-like proteins involved in anti-fungal defense, embryo
sac development and transition metal ion transport, (5) a GDPmannose 4,6-dehydratase 1, (6) a glutathione S-transferase U
24 involved in fatty acid beta-oxidation as well as in protein
and toxin catabolism, (7) a 70 kDa heat shock protein 10
involved in protein folding, peroxide neutralization and the
response to various abiotic stresses (including Cd 2+ ), (8) a
nuclear lysine-specific histone demethylase 1 involved in histone
H3-K4 methylation, histone deacetylation, oxidation-reduction
and the regulation of transcription, (9) a methyl-CpG-binding
domain-containing protein 9 involved in cell wall organization,
the regulation of transcription, embryo development, the sensing
of photoperiod, flowering and secondary shoot formation, (10)
a nuclear NF-X1-type zinc finger protein NFXL2 involved
in sensing the circadian rhythm, floral development and the
regulation of transcription, (11) oleosin GRP-17, a lipid-binding
protein involved in lipid storage, cell wall modification and pollen

development, (12) a proteasome subunit alpha type 1-A endowed
with endopeptidase and peptidase activity and involved in fatty
acid oxidation, protein catabolism and the response to As stress,
(13) an FLX-like 2 protein of unknown function expressed in the
guard cells, (14) the nuclear protein vernalization insensitive 3,
which forms part of the low temperature-induced regulation of
gene expression, (15) a RuBisCO large subunit, (16) the ROP
guanine nucleotide exchange factor 1 involved in anthocyanin
accumulation in response to UV irradiation, polysaccharide
synthesis, the regulation of hormone levels and pollen tube
growth, root hair elongation and root morphogenesis and (17)
an extracellular gamma-interferon responsive lysosomal thiol
(GILT) reductase with catalytic activity.

DISCUSSION

The most frequently reported toxicity problem associated with
ENMs is oxidative stress (Pujalté et al., 2011; Ma et al., 2015).
When taken up, they can drive down the cellular content of
antioxidants and/or increase its production of reactive oxygen
species (ROS; Maysinger and Lovric, 2007; Mahmoudi et al.,
2011; Santos et al., 2012). A better understanding of the
properties of these materials, along with technical improvements
in their synthesis, should provide the means to reduce their
hazard: examples are the use of biocompatible coating materials
and the exploitation of surface functionalization, which both
help mask the particles’ surface reactivity (Lynch et al., 2014;
Burello and Worth, 2015). The toxicity of CdS QDs has been
related to not just their small size but also their high surface
charge and reactivity, photolytic activity, shape, composition, and
mechanical stability (Favero et al., 2006; Maysinger and Lovric,
2007). Toxicity tests based on conventional pharmacokinetic
and/or pharmacodynamic approaches (Holford, 2007; Steele and
Austin, 2009) may be inadequate to identify the full range of
potential hazards posed by CdS QDs. This realization explains
the present application of a genotoxicological approach.

Transposon mutagenesis has succeeded in identifying two A.
thaliana mutants (atnp01 and atnp02) able to tolerate a level of
CdS QDs sufficient to strongly compromise the growth of a wt
plant (Marmiroli et al., 2014). Comparing the transcriptomes of
these mutants with that of the wt has provided a ready means
to define which genes which were up- or down-regulated in
one or both of the mutant(s), both in non-stressed conditions
and when the plants were exposed to CdS QDs (Marmiroli
et al., 2014). Here, the comparisons have been extended to the
protein level, by exploiting platforms able to identify not just
specific gene products but also some of their post-translationally
modified forms. Combining these data with those acquired
from other omics platforms is the aspiration of current system
biology strategies, which aim to define the complex pathways
and networks involved in response to different external stimuli
(Chen and Harmon, 2006; Jorrín-Novo et al., 2015; Wang et al.,
2015). Of particular note are the two proteins DRL and ELM,
the encoding transcripts for which were both abundant in atnp01
plants whether or not the Cd QD treatment was imposed; despite
this, both proteins were only slightly over-represented ( Figure 1 )
confirming their epistatic role (Marmiroli et al., 2014). The two

mutants differed quite markedly at the proteomic level: while
atnp01 has a mixed change in its proteins abundance, ready to
cope with general stress situations, the mutations affecting atnp02
were more closely related to the response to oxidative stress.
Many of the proteins altered in their level of expression in atnp01
were concerned with DNA transcription, lipid binding and the
auxin response; in contrast, in atnp02, although there was also
an effect on some proteins involved in DNA transcription, a
range of other functions were also modified, including protein
metabolism, cell wall formation and photosynthesis. Note that
the oxidative stress response is triggered by excessive amounts
of ROS, which not only induces changes in DNA transcription,
but also triggers the metabolism of proteins, starch and sugars
(Desikan et al., 2001; Mittler et al., 2004; Fujita et al., 2006;
Foyer and Noctor, 2011). In both mutants, there was an overrepresentation of proteins associated with the oxidative stress
response and an under-representation of those associated with
DNA and RNA processing and with cell development.

An over-representation of lytic proteins and an underrepresentation of stress-related and hormone-regulated
proteins was an unexpected feature of the CdS QD treatment.
Characteristic of an oxidative stress response was the upregulation of sugar metabolism, a disturbance in phytohormone
levels and the prominence of glutathione/ascorbate cycle related
enzymes (Couée et al., 2006; Foyer and Noctor, 2011; Villiers
et al., 2011). There was overall little commonality between
the two mutants with respect to either which proteins were
over- or which were under-represented, compared with the
WT (Supplementary Figure S5). This pointed to possibly
divergent phenotypic traits as a result of the over-represented
proteins in respect to possibly convergent traits as a result
of the under-represented proteins in atnp01 and atnp02.
Nevertheless, the numbers of altered proteins expressed in the
two mutants in plants not exposed to CdS QDs were rather
similar to one another, even though the proteins differed so
widely in type, function and cell localization (Supplementary
Figure S5).

A growing body of literature has confirmed that transcription
levels in eukaryotes are poorly correlated with the levels
of their encoded products (Griffin et al., 2002; Lan et al.,
2012). This uncoupling is assumed to reflect the action of a
number of cellular phenomena, notably the influence of RNA
secondary structure, the activity of regulatory proteins and
regulatory siRNAs, codon bias and codon adaptation, ribosomal
density, and protein half-life (Gygi et al., 1999; Hajduch et al.,
2010). The Pearson r correlation coefficients for atnp01 and
atnp02 were, respectively, 0.126 and 0.197, levels which confirm
the anticipated poor correlation between transcriptome and
proteome. As an alternative means of linking the two data

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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.

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