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Toxicology Mechanisms and Methods
ISSN: 1537-6516 (Print) 1537-6524 (Online) Journal homepage: https://www.tandfonline.com/loi/itxm20
Metabolomic analysis on the toxicological effectsof TiO2 nanoparticles in mouse fibroblast cells:from the perspective of perturbations in aminoacid metabolism
Yang Bo, Chengyu Jin, Yumin Liu, Wenjuan Yu & Hongzhang Kang
To cite this article: Yang Bo, Chengyu Jin, Yumin Liu, Wenjuan Yu & Hongzhang Kang (2014)Metabolomic analysis on the toxicological effects of TiO2 nanoparticles in mouse fibroblast cells:from the perspective of perturbations in amino acid metabolism, Toxicology Mechanisms andMethods, 24:7, 461-469, DOI: 10.3109/15376516.2014.939321
To link to this article: https://doi.org/10.3109/15376516.2014.939321
Accepted author version posted online: 26Jun 2014.Published online: 15 Jul 2014.
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Toxicol Mech Methods, 2014; 24(7): 461–469! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/15376516.2014.939321
RESEARCH ARTICLE
Metabolomic analysis on the toxicological effects of TiO2 nanoparticlesin mouse fibroblast cells: from the perspective of perturbations inamino acid metabolism
Yang Bo1,2, Chengyu Jin1*, Yumin Liu1*, Wenjuan Yu1, and Hongzhang Kang3
1Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai, China, 2School of Chemistry and Chemical Engineering, Shanghai Jiao
Tong University, Shanghai, China, and 3School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
Abstract
Titanium dioxide nanoparticles (nano-TiO2) have been widely applied in daily life andsubsequent problem on the potential health risk are raised. Studies on the toxicity of nano-TiO2
have shown that they could lead to toxic effects on human and environment. However,the mechanisms are still unclear. We investigated the change of amino acid levels in L929cells after nano-TiO2 exposure using gas chromatography with time-of-flight mass spec-trometry (GC/TOFMS)-based metabolomics approach. Spectral profiles were subjected tomultivariate statistics, namely, Principal Component Analysis (PCA), and Orthogonal Projectionsto Latent Structures-Discriminant Analysis (OPLS-DA). Using MetaboAnalyst 2.0, it was foundthat 7 metabolic pathways (impact-value 40.10) among the regulated pathways weresignificantly perturbed. Twelve distinct amino acids are identified from these pathways,including L-a-alanine, b-alanine, glycine, L-aspartate, L-methionine, L-cysteine, glutamate,L-pyroglutamate, L-asparagine, L-glutamine, S-adenosylmethionine, and L-lysine. These resultsshow that the disturbed amino acids played an important role in the nano-TiO2-inducedcytotoxicity. Along with earlier findings, we successfully used the metabolomics approaches tomanifest nano-TiO2 toxicity through triggering cellular oxidative stress, energy damage and theinhibition of DNA and RNA synthesis.
Keywords
Amino acid, GC/TOFMS, metabolomics,TiO2 nanoparticle, toxicity
History
Received 17 January 2014Revised 12 June 2014Accepted 24 June 2014Published online 15 July 2014
Introduction
Nanostructured titanium dioxide, one of the most widely
engineered nanoparticles, has been incorporated into a variety
of products such as cosmetics, drug delivery systems, and
antibacterial materials (Carbone et al., 2006; Rasmusson et al.,
2005). The unique physicochemical properties of engineered
nanoparticles are attributable to their size, surface area,
surface reactivity, charge, shape, and chemical composition
(Donaldson et al., 2001; Nel et al., 2006; Siva & Eric, 2013).
However, the quick expansion of nano-TiO2 would cause
increased occupational or nonoccupational exposure, which
inevitably leads to the risk of adverse biological effects and
toxicity (Owen & Depledge, 2005). Consequently, the toxico-
logical research of nano-TiO2 emerges in an effort to formulate
a set of design rules for the design of safe nanomaterials
(Buzea et al., 2007; Moore, 2006). Earlier toxicological
studies that focus on the biochemical level suggest that nano-
TiO2 exposures could induce organ injuries and inflammatory
responses. Parameters mainly include catalase, glutathione
S-transferase (GST), aminotransferase (ALT), aspartate ami-
notransferase (AST), lactate dehydrogenase (LDH), blood
urea nitrogen (BUN), and creatine kinase (CK) (Liu et al.,
2006, 2009; Wang et al., 2007; Zhang et al., 2009). Also,
current understanding on the molecular mechanisms implies
that nano-TiO2 induces the production of ROS by causing
inflammation or mitochondrial dysfunction (Dankovic et al.,
2007; Gui et al., 2011; Freyre-Fonseca et al., 2011). ROS-
induced signaling and activation of the interleukin family of
cytokines, Bax, caspases 3 and 9, NF-kB, and p53 would then
lead to genotoxicity through DNA damage and DNA mutation
(Kang et al., 2008; Morishige et al., 2010; Wang et al., 2011;
Yazdi et al., 2010). Furthermore, oxidative stress induced by
ROS generation may also be important in nano-TiO2-induced
genotoxicity (Liu et al., 2010). However, limited information
is available on the metabolism of nanoparticle-induced
cytotoxicity.
Metabolomics, the systematic evaluation of metabolites,
provides a temporal snapshot of these biological interactions
and reflects the real biochemical changes in the long-term
*These authors are contributed equally to this work.
Address for correspondence: Chengyu Jin and Yumin Liu, InstrumentalAnalysis Center, Shanghai Jiao Tong University, 800 DongchuanRoad, 200240, Shanghai, China. Tel: +86-21-34205699 (C.Y. Jin);86-21-34206175-504 (Y.M. Liu), Fax: +86-21-34206997. E-mail:cyjin@sjtu.edu.cn (C.Y. Jin); ymliu@sjtu.edu.cn (Y.M. Liu)
response to gene expression with concurrent immediate
response to environmental stimuli (Meyer et al., 2013;
Santos et al., 2009). As nano-TiO2 inevitably causes the
perturbation in biological pathways, metabolomics offer a
powerful systems biology approach to the screening of
biomarker patterns and the elucidation of toxicological
mechanism (Boughton et al., 2011; Yuliana et al., 2011).
Indeed, some studies have employed metabolomics to evaluate
nano-TiO2-induced toxicity in animal models. The response of
Eisenia fetida earthworms spiked with either 20 or 200 mg/kg
of TiO2 nanomaterial was examined by metabolomics. The
observed metabolic changes appeared to be consistent with
oxidative stress (Whitfield Aslund et al., 2011).
We recently reported the disorder of carbohydrate metab-
olism induced by nano-TiO2 in mouse fibroblast cells (L929
cells). The results indicated suppressed carbohydrate metab-
olism, serious damage to energy metabolism in mitochondria
and increased oxidative stress after nano-TiO2 treatment (Jin
et al., 2013). We also noticed an obvious disturbance in amino
acids, which is also pointed out in another study of nano-TiO2
toxicity using metabolomic analysis in Wistar rats (Bu et al.,
2010). Since amino acids play important roles in normal cell
physiology and function, abnormal metabolism of an amino
acid impairs growth and development, and may even cause
death (Wu, 2009). It is critical to study amino acid
metabolism to deeply understand nano-TiO2-induced cyto-
toxicity. In this study, the metabolic changes of amino acids
levels in L929 cells (widely used in the evaluation of
nanomaterial cytotoxicity) after nano-TiO2 treatment were
assessed using GC/TOF-MS in conjunction with multivariate
statistical analysis. The metabolite network of L929 cells was
predicted via MetaboAnalyst 2.0 (http://www.metaboanalyst.
ca/MetaboAnalyst/; Xia et al., 2009, 2012). Metabolomics
analysis is aimed to show how nano-TiO2 influence cellular
metabolic environments in L929 cells through amino acid
metabolism and to further uncover the potential mechanisms
involved.
Materials and methods
Chemicals and characterization of nanoparticles
Nano-TiO2 colloid (solid content: 2%, pH: 6.0–8.0) was
obtained from Sunrise Chemical Co. (Shanghai, China). The
nanoparticles were dispersed in three forms of solutions:
deionized water, phosphate buffer saline (PBS) and DMEM
with 10% FBS. Our early works showed that homogeneous
and rarely aggregated nano-TiO2 in aqueous solution were
adequate for the study on the cytotoxicity of nanoparticles in
L929 cells (Cheng-Yu et al., 2008; Jin et al., 2008).
High-sugar Dulbecco’s modified Eagle’s medium (DMEM)
and fetal bovine serum (FBS) were purchased from Hyclone
(Logan, UT). Penicillin-streptomycin was purchased from
Sangon (Shanghai, China). Ultrapure water from the Milli-Q
system (Millipore, Billerica, MA) was used in the experiments.
MTT was purchased from Sigma-Aldrich (St. Louis, MO). The
ROS assay kit was purchased from Beyotime (Jiangsu, China).
All the chemicals including methanol, pyridine, sodium
chloride, methoxyamine, and N,O-bis (trimethylsilyl) trifluor-
oacetamide (BSTFA) containing 1% trimethylchlorosilane
(TMCS) were purchased from Sigma-Aldrich (St. Louis,
MO). Also, L-2-chlorophenylalanine served as internal stand-
ard (IS) was obtained from Sigma-Aldrich.
Cell culture
Mouse fibroblast (L929) cells were obtained from the Cell
Bank of Shanghai Institutes for Biological Sciences, Chinese
Academy of Science, China. The cells were cultured in
DMEM with 10% FBS, 100 U/mL penicillin, and 100 mg/mL
streptomycin at 37 �C in a 5% CO2 humidified environment.
The cells were seeded in �100 mm dishes (Corning, Lowell,
MA) at a density of 3.0� 106 cells per dish in 9 mL culture
medium. All cells were exposed to nanoparticles after 60%
confluence. Nano-TiO2 was freshly dispersed in the cell
culture media and diluted to the concentration of 100 mg/mL.
L929 cells were cultured in media with and without (control)
nanoparticles for 48 h, which were divided into two groups:
nanoTiO2-treated L929 cells versus untreated L929 cells.
Cytotoxicity study
Toxicity analysis of nano-TiO2 was done by MTT assay. In a
typical experiment, �6000 L929 cells per well were seeded in
a 96 well plate. Cells were allowed to adhere onto the well
plate surface for 24 h followed by their exposure to different
concentrations (0, 25, 50, 75, 100, 150, 200, 300 mg/mL) of
nano-TiO2 for 48 h. Subsequently, 20 mL of a 5 mg/mL solution
of MTT in 0.01 M PBS was added to each well, and the plate
was incubated at 37 �C in 5% CO2/air for 4 h. Finally, the
medium was removed, and 200 mL dimethyl sulfoxide
(DMSO) per well was added to solubilize to formazan
crystals. The plate was shaken for 10 min, and the optical
density (OD) of the dissolved solute was measured in an
ELLSA reader (Bio-Rad Co., Hercules, CA) at 570 nm. Cells
cultured in media containing sodium chloride at the same
concentrations as the nano-TiO2 were used as the controls.
Cell viability was calculated by dividing the absorbance of
nano-TiO2-treated cells by the absorbance of untreated
controls and reported as a percentage with standard deviation.
Each experiment was done in triplicate and repeated six times.
Levels of ROS generated by nano-TiO2 in the presence of
cells were determined by a fluorometric assay using the
intracellular oxidation of 20,70-dichlorofluorescin diacetate
(DCFH-DA). DCFH-DA passively enters the cell and reacts
with ROS to produce a fluorescent compound dichloroflur-
escein (DCF). After adherence for 24 h, cells were exposed to
different concentrations of nano-TiO2 for 48 h, washed with
PBS, and then incubated with 100 mL DCFH-DA at 37 �C for
20 min. After incubation, cells were washed with PBS again.
Fluorescence data was measured by an enzyme-linked
immunosorbent assay (ELISA) using an excitation at
488 nm and an emission at 525 nm. During the entire
procedure with DCFH-DA, the plate was kept out of light
to avoid fading of the fluoroprobe. Each experiment was done
in triplicate and repeated six times.
Metabolite extraction
L929 cells (5� 105) with culture media were initially washed
in NaCl/H2O (0.9%, W/V). The cells were dissolved in
1500 mL of methanol:water (1:1, v/v) solvent and then scraped
462 Y. Bo et al. Toxicol Mech Methods, 2014; 24(7): 461–469
off from the culture plastic containers using silicon rubber
cell scrapers. The cellular metabolite L-2-chlorophenylalanine
(10mL, 0.3 mg/mL in water) was mixed and then extracted at
�4 �C by ultrasonic-breaking and extraction method. After
centrifuged at 12 000 rpm for 10 min at �4 �C, the resulting
supernatant (400 mL) was dried completely under nitrogen.
80 mL of methoxyamine (15 mg/mL in pyridine) was added
to the dried sample and vortexed for 1 min. Methoxymation
was performed at 30 �C for 60 min. Subsequently, the
sample was trimethylsilylated at 70 �C for 90 min by adding
80 mL BSTFA (containing 1% TMCS) to the solution
(Liu et al., 2012).
GC/TOFMS analysis
Each 1 mL aliquot of the derivatized sample was injected in
splitless mode into an Agilent 6890N gas chromatograph with
time-of-flight mass spectrometry (Pegasus HT, Leco Co., San
Jose, CA). The GC column was a DB 5 ms capillary column
(30 m� 250 mm i.d., 0.25mm film thickness; J&W Scientific,
Folsom, CA). Helium was used as the carrier gas at a constant
flow rate of 1.0 mL/min. The injector temperature was set at
270 �C. The GC initial column temperature was set at 80 �C for
2 min. It was raised to 180 �C at a rate of 10 �C�min�1,
to 240 �C at a rate of 5 �C�min�1, and then to 290 �C at a rate of
25 �C�min�1. Finally, it was kept at 290 �C for 9 min. Transfer
line temperature and ion source temperature were set at 260 �Cand 200 �C, respectively. Electron impact ionization (70 eV) at
full scan mode (m/z 30–550) was used to acquire mass spectra.
The dwell time for each scan was set at a rate of 12 spectra per
second and the solvent delay at 5 min.
Data processing
All GC/TOFMS files were exported into CDF format using
ChromaTOF software (v3.30, Leco Co.). CDF files were
processed using customized scripts (revised Matlab toolbox,
hierarchical multivariate curve resolution, developed by Par
Jonsson, et al.) in the MATLAB 7.0 (The MathWorks, Inc.,
Natick, MA). The data pretreatment procedures included
baseline correction, de-noising, smoothing, time-window
splitting, and multivariate curve resolution algorithm
(Jonsson et al., 2004, 2005). The resulting data containing
sample information, peak retention time and peak intensities,
were normalized to the area of the IS (IS peaks were removed
afterward), then mean-centered, and finally unit variance
scaled for further statistical analysis using Simca-P 11.5
software (Umetrics, Umea, Sweden). PCA and OPLS-DA
were performed to discriminate between nano-TiO2 treated
and the control groups. The variable importance in the
projection (VIP) values of all the data from the 7-fold cross-
validated OPLS model were taken as a coefficient for
metabolite selection. Those variables with VIP 41.0 were
considered relevant for group discrimination. After the
multivariate approaches, the significance of each metabolite
in group discrimination was further measured by the Student’s
t-test (p50.05). A list was obtained with all the metabolites
that had important contribution to the variance. Metabolite
identification from these selected variables was subsequently
achieved by NIST 05 Standard mass spectral databases
(NIST, Gaithersburg, MD) and available reference standards,
separately. Metabolic pathway analysis was then performed
via MetaboAnalyst 2.0 to identify the affected metabolic
pathways analysis and visualization.
Results
Cytotoxicity of nano-TiO2
Cytotoxicity of different concentrations of nano-TiO2 was
evaluated by the MTT assay (Figure 1A). At lower concen-
trations of nano-TiO2 (from 0 to 100 mg/mL) in the culture
medium, the viability of the cells decreased gradually from
100.0% to 63.2%. As the concentration increased from
100 mg/mL to 300mg/mL, cell viability decreased slightly to
62.9%. Whether nano-TiO2 could induce the generation of
intracellular ROS was investigated through DCFH-DA
staining (Figure 1B). The result showed a steady growth of
ROS generation along with the increasing concentration, and
reached 119.9% at the concentration of 100 mg/mL nano-TiO2.
At a higher concentration, ROS generation increased mildly
to 123.7% at the concentration of 300 mg/mL.
Figure 1. (A) Cell viability of L929 cells, as measured by MTTassay after 48 h exposure to TiO2 nanoparticles. Data are presented asthe average ± standard deviation (n¼ 6). 0.015p� 0.05 and p� 0.01 areconsidered to be statistically significant and highly significant and aredenoted as ‘‘*’’ and ‘‘**’’, respectively. (B) ROS formation in L929cells, as measured by the DCFH-DA assay after 48 h exposure to TiO2
nanoparticles. Data are presented as the average ± standard deviation(n¼ 6).
DOI: 10.3109/15376516.2014.939321 Metabolomics-based nanotoxicity research in vitro 463
Pattern recognition analysis
Metabolites in the samples were semi-quantified using
GC/TOFMS analysis. Five-hundred and eight resolved
peaks from intracellular GC-MS data were obtained for
multivariate statistical analysis. After a 48 h exposure to nano-
TiO2, peak intensities for some important amino acids such as
b-alanine, glycine, L-aspartate, L-methionine, L-pyrogluta-
mate, L-cysteine, glutamate, L-asparagine, and L-glutamine in
L929 cells were changed in TIC spectra (Figure 2), especially
at the concentration of 100 mg/mL of nano-TiO2.
The obtained GC/TOFMS data were investigated by PCA
and OPLS-DA method (Figure 3A and B). The PCA
score plot illustrated a separation tendency between the
nano-TiO2-treated and the control groups. Clear differenti-
ation of a metabolic signature between the two groups was
further evident by supervised OPLS-DA analysis. The vari-
ance explained by the components of the OPLS-DA model
were 59.3% for the X matrix (the metabolite data), and 99.6
for Y matrix (control group: 0, TiO2 nanoparticle-treated
L929 cells: 1), respectively, whereas the cumulative Q2 of the
model was 83.3% for the prediction accuracy of Y matrix,
suggesting the inter-group difference is of great multivariate
statistical significance. Based on VIP value of more than 1.0
in the OPLS-DA model and the p values (p50.05) in the
t-tests for all metabolites, 53 differential metabolites were
listed in Table 1.
Disturbed metabolites analysis
To identify the most relevant metabolic pathways involved
in metabolic disturbance of nano-TiO2-treated L929 cells,
the 53 differential metabolites were further subjected to
MetaboAnalyst 2.0. Based on pathway enrichment analysis
and pathway topology analysis with KEGG (www.genome.jp/
kegg/) pathway database, a metabolome view is shown
in Figure 4, which contains all the matched pathways.
A higher p value and impact-value reflect the more relevant
pathways affected by nano-TiO2. As the impact-value was set to
0.10, the 53 metabolites were considered to be highly
responsible for 18 metabolic pathways. The perturbed path-
ways closely related to amino acid metabolism include alanine,
aspartate and glutamate metabolism, cysteine and methionine
metabolism, b-alanine metabolism, glycine, serine and threo-
nine metabolism, lysine degradation, and glutamine and
glutamate metabolism (Figure 4). Among the identified
metabolites, 12 amino acids were involved in amino acid
metabolism, including significantly upregulated L-aspartate, L-
asparagine, L-glutamine, b-alanine, L-methionine, glutamate,
L-pyroglutamate, and the downregulated S-adenosylmethio-
nine, glycine, L-lysine, L-cysteine, L-a-alanine. The percent
change of these amino acids after nano-TiO2 exposure is shown
in Figure 5. A simplified metabolic network of amine
metabolites is shown in Figure 6, revealing disturbance of
amino acid metabolism after exposure to nano-TiO2.
Discussion
Nano-TiO2 is a widely applied in a variety of products.
Until now, toxicity studies on oxidative stress and the pro-
inflammatory effects of nano-TiO2 exist, but systematic
studies on the connection of these effects with metabolic
changes are still rare. To fill this gap, we have carried out
cytotoxicity experiments and metabolomics studies as a
model to deeply understand nano-TiO2-induced cytotoxicity
through unsupervised metabolic analysis. Our cytotoxicity
results showed that different concentrations of nano-TiO2 had
Figure 2. The total ion chromatogram (TIC) spectra of L929 cells cultured in media: 1. b-alanine; 2. glycine; 3. L-aspartate; 4. L-methionine;5. L-pyroglutamate; 6. L-cysteine; 7. glutamate; 8. L-asparagine; 9. glutamine.
464 Y. Bo et al. Toxicol Mech Methods, 2014; 24(7): 461–469
a marked effect on cell growth and induced ROS generation in
L929 cells. Consistent with our earlier work, we have found
the concentration of 100 mg/mL of nano-TiO2 as the obviously
initial checkpoint for TiO2 nanoparticle-induced cytotoxicity
(Jin et al., 2008, 2013).
Amino acids as precursors have essential involvement with
amino acid synthesis, amino sugar synthesis, oxidation as a
source of carbons, as well as purine and pyrimidine and thus
nucleic and nucleotide synthesis (Newsholme et al., 2003;
Wu, 2009). In cysteine and methionine metabolism, methio-
nine converts to S-adenosylmethionine (SAM) by an ATP-
dependent reaction, and the transsulfuration reactions of SAM
further produce cysteine from homocysteine. In our study, the
level of L-methionine was higher in the treated group.
However, the level of SAM and L-cysteine were lower,
which indicates a restraint in the regulation of cysteine and
methionine metabolism. Since glutathione (GSH) can be
synthesized from cysteine, glutamate and glycine, a defect in
transsulfuration might impair the synthesis of GSH. On the
other hand, L-pyroglutamate serves as a catalyst for catabol-
ism of GSH into the component amino acids (glycine,
cysteine and glutamate) via g-glutamyl cycle where homeo-
stasis of GSH was modulated (Griffith, 1999). As known,
GSH plays a pivotal role in maintaining the intracellular
redox state (Balendiran et al., 2004). The lowered content of
GSH might result from the consumption of oxidative stress,
and would further lead to a weakened defense against
oxidative stress and programmed cell death (Kranner et al.,
2006). Thus, GSH metabolite depletion is considered as early
stage biomarkers of oxidative stress. Accordingly, the remark-
able decreases of glycine and L-pyroglutamate in the treated
cells as compared with the control further reflects the
suppressed metabolism of GSH, indicating more oxidative
stress in the nano-TiO2 group. The result is consistent with
our earlier work that the same inclination of decreased GSH
was found in nano-TiO2 treated cells with classical assays
(Cheng-Yu et al., 2008; Jin et al., 2008). Other investigations
have also demonstrated nano-TiO2 have the ability to cause
oxidative stress both on the bronchial epithelial cell line
(Borm et al., 2006; Dagani, 2003) and on the human lung
adenocarcinoma epithelial cell line A549 (Monteiller et al.,
2007). These findings confirm that the disturbed amino acids
and the involved pathways have an active participation in
oxidative stress.
Most amino acids are deaminated by the transfer of the
amino group to a-keto acid. The a-keto acid can be used for
energy production by entering the tricarboxylic acid cycle
(TCA cycle). Figure 5 displays the metabolic disorder of
amino acids related to deamination. In these disturbed amino
acids, three (L-lysine, L-a-alanine, glycine) are converted into
acetyl-CoA and citrate, two (glutamate and L-glutamine) into
2-oxo-glutarate, one (L-methionine) into succinyl-CoA, and
two (L-aspartate and L-asparagine) into oxaloacetate, all of
which enter the TCA cycle (principally involved in alanine,
aspartate and glutamate metabolism, glutamine and glutamate
metabolism and glycine, serine and threonine metabolism and
lysine degradation). Above all, glutamine is a core link
between carbon metabolism of carbohydrates and proteins
Figure 3. (A) PCA scores plot of GCTOF-MSfrom cell extraction of the nano-TiO2-treatedand the control group (n¼ 9). Black dots,TiO2 group; red dots, control group.(B) OPLS-DA scores plot of GCTOF-MSfrom cellular metabolites of thenano-TiO2-treated and the control group(n¼ 9). Black dots, TiO2 group; red dots,control group.
DOI: 10.3109/15376516.2014.939321 Metabolomics-based nanotoxicity research in vitro 465
and plays an important role in the growth of fibroblasts
(Wolfrom et al., 1989). In this study, the levels of glutamate
and L-glutamine were significantly higher in the nanoTiO2-
treated group, whereas the level of 2-oxo-glutarate and citrate
in the TCA cycle were lower. These results suggest a block in
the process of deamination of amino acids in L929 cells and
sequentially a deficit in energy production after nano-TiO2
treatment, which further imply a potential failure in mito-
chondrial function. Our published article has demonstrated
the damage to energy metabolism in nano-TiO2-treated cells
(Jin et al., 2013). Earlier study on the toxicity of nano-TiO2
has also revealed mitochondrial injury through structural
organellar damage (Long et al., 2006).
Besides, the role of amino acids is an important precursor
of purines and pyrimidines (involved in alanine, aspartate and
glutamate metabolism, glycine, serine and threonine metab-
olism and b-alanine metabolism). In purine and pyrimidine
metabolism, certain amino acids (L-aspartate, L-glutamine,
glycine) and ribose-5-phosphate can form the common
precursor uridine-monophosphate (UMP) and inosine-mono-
phosphate (IMP), and then generate purine and pyrimidine
base, nucleosides and nucleotides (the basic constituent of
DNA and RNA). In this study, Figure 6 clearly revealed that
the abnormal amino acid metabolism was involved in the
Table 1. Alterations of intercellular metabolites induced by nano-TiO2 exposure.
Metabolites VIPa pb FCc Metabolites VIPa pb FCc
Carbohydrate Metabolism Amino acid Metabolism
myo-Inositol 1.94 9.39E-07 0.42 S-(50-adenosyl)- L-Methionine 2.01 1.37E-07 0.4a-Ketoglutarate 1.94 1.07E-06 0.21 b-Alanine 1.8 4.34E-05 7.56D-Threitol 1.9 3.73E-06 0.39 L-Aspartate 1.79 7.73E-05 2.55Ribose 1.87 3.07E-03 0.62 Glutamate 1.76 1.22E-04 4.7Sorbose 1.81 2.03E-05 0.52 L-Asparagine 1.68 2.47E-04 4.46Inositol 1.76 7.08E-05 0.53 Glycine 1.62 7.47E-04 0.55Ribose-5-phosphate 1.73 1.06E-04 0.4 L-Pyroglutamate 1.47 4.51E-03 1.622-Oxohexanoate 1.7 1.39E-04 0.44 L-Methionine 1.44 5.68E-03 1.49Fructose 1.47 4.50E-03 0.44 L-Cysteine 1.44 3.61E-03 0.48Glycerol-3-phosphate 1.43 3.18E-03 0.43 Glutamine 1.26 1.00E-02 1.73Dihydroxyacetone phosphate 1.43 6.20E-03 11.39 L-Lysine 1.25 2.00E-02 0.61Trehalose 1.39 7.43E-03 1.54 4-aminobutyrate 1.24 2.00E-02 1.41D-Gluconate 1.38 1.00E-02 0.46 Aminomalonate 1.15 4.00E-02 0.71L-Arabitol 1.37 9.27E-03 0.65 L-a-Alanine 1.08 4.00E-02 0.8
D-Sorbitol 1.3 1.00E-02 0.34 Nucleotide Metabolism
Phosphate 1.29 1.00E-02 0.74 Uridine 1.94 2.82E-06 0.52Pyruvate 1.22 2.00E-02 0.63 Uridine 50-monophosphate 1.68 4.38E-04 0.39Glucose-6-phosphate 1.21 2.00E-02 0.25 Uracil 1.47 2.15E-03 0.48Sebacate 1.19 4.00E-02 0.71 Inosine 1.35 6.49E-03 0.66Lactose 1.18 4.00E-02 1.82 Urate 1.29 9.53E-03 0.663-hydroxybutyrate 1.17 2.00E-02 0.74 Adenosine 1.18 2.00E-02 0.67Oxalate 1.16 3.00E-02 1.51 Xanthine 1.15 3.00E-02 0.45
Ethanolamine 1.15 3.00E-02 0.71 Others
3-phosphoglycerate 1.13 4.00E-02 2.91 Nicotinamide 1.32 7.42E-03 0.66Glycerol 1,3-dihexadecanoate 1.13 3.00E-02 1.54 3-Amino-2-piperidone 1.7 1.51E-04 0.57Galactose 1.12 3.00E-02 0.45 Oleic acid 1.07 4.00E-02 0.753-methyl-2-oxobutyrate 1.12 3.00E-02 0.14Arabinose 1.05 2.00E-02 0.58Citrate 1 3.42E-02 0.88
aVariable importance in the projection (VIP) was obtained from OPLS with a threshold of 1.0.bp Value was calculated from Student’s t-test.cFold change was calculated from the arithmetic mean values of each group. Fold change with a value more than 1 indicates a relatively higher
concentration present in model cell while a value less than 1 means a relatively lower concentration as compared to the controls.
Figure 4. Summary of pathway analysis with MetaboAnalyst 2.0. Thenode color is based on its p value and the node radius is determinedbased on their pathway impact values.
466 Y. Bo et al. Toxicol Mech Methods, 2014; 24(7): 461–469
Figure 5. L929 cell metabolite responses to exposure nano-TiO2 expressed as a percent change from the mean metabolite intensity observed in control(unexposed) group for amino acids. Red dots, TiO2 group; black dots, control group. Error bars represent standard error of the mean (n¼ 9).
Figure 6. Metabolic pathways related with differential metabolites between TiO2 nanoparticle-treated L929 cells and the control group.
DOI: 10.3109/15376516.2014.939321 Metabolomics-based nanotoxicity research in vitro 467
disorder of purine and pyrimidine metabolism in L929 cells
induced by nano-TiO2. Despite the remarkably higher levels
of the amino acids (L-aspartate, L-glutamine and b-alanine),
the levels of UMP and IMP were declined, along with the
lower content of uridine, uracil in purine metabolism, and
adenosine, inosine, guanosine, xanthine and its degradation
product urate in pyrimidine metabolism (Jin et al., 2013). In
our earlier publication, we have made a conclusion that the
addition of nano-TiO2 with concentrations higher than
100mg/mL to L929 cells could cause the inhibition of
nucleotide metabolism (Jin et al., 2013). These results
might suggest the inhibition of DNA and RNA synthesis,
which are in accordance with previous observation that the
chromatin (the combination of DNA, RNA and proteins) in
L929 cell was condensed and fragmented after nano-TiO2
exposure.
Conclusion
In this study, we applied GC/TOFMS-based metabolomic
approach to investigate the cytotoxicity of nano-TiO2. The
addition of nano-TiO2 to L929 cells could lead to the disorder
of amino acid metabolism, which might induce cellular
oxidative stress, damage to energy production and the
inhibition of DNA and RNA synthesis.
Declaration of interest
The authors declare no conflicts of interest.
This work was financially supported by Natural Science
Foundation of China (No. 31270491), Shanghai Natural
Science Foundation of the Science and Technology
Commission of Shanghai Municipal Government (No.
09ZR1415100), and three Fundamental Key Project of
Shanghai Jiaotong University (No. YG2010MS92,
YG2011MS66 and YG2013MS23).
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