Abstract
Background
Aluminum (Al) is toxic to animals and humans. The most common sources
of human exposure to Al are food and beverages. The intestinal
epithelium is the first barrier against Al-induced toxicity. In this
study, HT-29, a human colon cancer cell line, was selected as an in
vitro model to evaluate the Al-induced alteration in metabolomic
profiles and explore the possible mechanisms of Al toxicity.
Methods
MTT assay was performed to determine the half-maximal inhibitory
concentration of Al ions. Liquid chromatography-mass spectrometry
(LC-MS) was used for metabolomic analysis, and its results were further
confirmed using quantitative reverse transcription polymerase chain
reaction (RT-qPCR) of nine selected genes.
Results
Al inhibited the growth of the HT-29 cells, and its half-maximal dose
for the inhibition of cell proliferation was found to be four mM. This
dose was selected for further metabolomic analysis, which revealed that
81 metabolites, such glutathione (GSH), phosphatidylcholines,
phosphatidylethanolamines, and creatine, and 17 metabolic pathways,
such as the tricarboxylic acid cycle, pyruvate metabolism, and GSH
metabolism, were significantly altered after Al exposure. The RT-qPCR
results further confirmed these findings.
Conclusion
The metabolomics and RT-qPCR results indicate that the mechanisms of
Al-induced cytotoxicity in HT-29 cells include cellular apoptosis,
oxidative stress, and alteration of lipid, energy, and amino acid
metabolism.
Keywords: HT-29 cell, Heavy metal, Metabolomic, Aluminum, Cytotoxicity
Introduction
Aluminum (Al), a metal that is toxic to animals and humans, is
ubiquitous in industrialized societies, and human exposure to Al in
daily life is inevitable. The major sources of human exposure to Al
include food additives, food and beverage packaging, drinking water,
and cooking utensils ([36]Pineton de Chambrun et al., 2014; [37]EFSA,
2008). Orally ingested Al is absorbed via the intestine. Reportedly, Al
intake exceeds the health-based guidance value in the general
population worldwide, especially in children, the most vulnerable and
sensitive population ([38]Vignal, Desreumaux & Body-Malapel, 2016). The
human intestinal tract constitutes the largest interface between the
human body and the external environment and is one of the primary
barriers against the external environment ([39]Kaminsky & Zhang, 2003;
[40]Salim & Söderholm, 2011). It has been reported that 38% of ingested
Al accumulates in the intestinal mucosa and exerts negative effects on
gut homeostasis ([41]Cunat et al., 2000; [42]Powell et al., 1994), such
as alteration of gut permeability, dysbiosis of gut microbiota, and
impairment of intestinal immune function ([43]Vignal, Desreumaux &
Body-Malapel, 2016; [44]Yu et al., 2016). Ingestion of 50–475 mg/kg Al
for approximately 2 months have been reported to induce histological
alterations, such as mucosal degeneration, lymphocyte proliferation,
and focal infiltration of monocytes in the lamina propria, in the small
intestine of rodents ([45]Al-Qayim & Saadoon, 2013; [46]Perl et al.,
2004; [47]Roszak et al., 2017). Exposure to even low doses of Al (1.5
mg/kg) has been shown to aggravate intestinal damage in mouse colitis
models ([48]Pineton de Chambrun et al., 2014), such as worsening of
colitis and upregulation of the mRNA expression of inflammatory
cytokines IL1β, IL17A, and Nlrp3. Moreover, Al treatment has been shown
to suppress the growth of murine intestinal bacteria ([49]Lerner et
al., 2006). [50]Perl et al. (2004) hypothesized that Al can gain access
to microorganisms through metal-chelating systems and then increase
their pathogenicity and ability to induce an exuberant granulomatous
response. Al was found to exert deleterious effects on the intestinal
barrier integrity by inhibiting epithelial cell proliferation (both in
vivo and in vitro), inducing bacterial translocation in mesenteric
lymph nodes, and decreasing intercellular junction expression in the
colon ([51]Pineton de Chambrun et al., 2014). Al is also considered as
an environmental trigger for a group of intestinal disorders
represented by inflammatory bowel diseases ([52]Lerner, 2007; [53]Perl
et al., 2004).
Although several studies have focused on the mechanisms underlying
Al-induced intestinal injuries, the association between the altered
molecular profile of cells and Al exposure has not yet been clarified.
Metabolism is the foundation of all living systems. Metabolites, the
end products of cellular processes, reflect the system-level biological
stress response. Hence, alterations in metabolites are directly or
indirectly related to changes in cellular processes or metabolism
([54]Tripathi et al., 2008). Metabolomics, the newest “omics”
discipline, provides a comprehensive view of metabolic fluxes and a
unique insight into the environment of cells ([55]Booth et al., 2011).
For metal toxicity studies, metabolomics may be used to identify
changes in metabolic pathways that would be missed by other omics
techniques. For example, levels of proteins and mRNA in the cell do not
necessarily translate to changes in the metabolic pathways and cannot
reflect changes in the metabolic flux. Metabolomics promises to fill
this gap and provide information not accessible through other
techniques ([56]Booth et al., 2011). Metabolomics covers the
identification and quantification of endogenous low-molecular-weight
metabolites (less than ~1,000 Da), which include small compounds such
as nucleotides, lipids, and amino acids ([57]Madji Hounoum et al.,
2015). In general, potential molecular biomarkers discovered by the
clarification of metabolic changes can serve as a basis for the
comprehensive study of intestinal injury caused by metal exposure. For
example, using metabolomics, [58]Tripathi et al. (2008) showed that
after 90 days of Al treatment, the levels of citric acid, creatinine,
allantoin, succinic acid, alanine, glutamine, β-hydroxy-butyrate, and
acetoacetate in rat serum and urine significantly reduced and those of
acetate and acetone significantly increased. These overall
perturbations observed in the metabolic profile demonstrated the
impairment in the tricarboxylic acid (TCA) cycle and liver and kidney
metabolism. Similarly, [59]Lu et al. (2017) analyzed the effects of a
traditional Chinese medicine Shengmai San (SMS) on Al-induced
Alzheimer’s disease using metabolomics and showed that lipid
peroxidation was the main mechanism of SMS intervention in Alzheimer’s
disease, including inhibition of linoleic acid hydroperoxide
generation. Among the several metabolomics methods available, liquid
chromatography-mass spectrometry (LC-MS) was selected for use in our
study because it has broad metabolite coverage and high sensitivity, as
well as simple sample preparation.
The global reactions induced in intestine cells by Al exposure are
still unclear. HT-29, a human colon cancer cell line, produces the
secretory component of IgA, carcinoembryonic antigen, and mucins, which
are suitable for heavy metal toxicity study due to the disturbance of
metals on intestinal immune aspects ([60]Hsu et al., 2017; [61]Aicher
et al., 1990; [62]Forsberg et al., 2004). Many studies investigating
the mechanisms of metal toxicity in intestinal cells by metabolomics or
proteomics analyses have used HT-29 cells ([63]Ezhilarasi et al., 2016;
[64]Lee et al., 2012; [65]Brown, Yedjou & Tchounwou, 2008). In
addition, our previous study showed that HT-29 cells are responsive to
Al ([66]Yu et al., 2016). Therefore, in this study, we investigated the
Al exposure-induced global metabolic changes in the HT-29 cell line
using LC-MS and gene expression analysis. The results improve our
understanding of the mechanism of interaction between intestinal cells
and Al toxicity and aid in further exploring strategies to alleviate Al
toxicity.
Materials and Methods
Chemicals
Aluminum chloride was obtained from Sigma-Aldrich, St. Louis, MO, USA.
TRIzol was bought from Ambion, Life Technologies (Grand Island, NY,
USA). The iTaq™ universal SYBR^® Green one-step kit was purchased from
Bio-Rad (Berkeley, CA, USA). All of the materials used for cell
experimentation were bought from Shanghai Chemical Reagent Company
(Shanghai, China). MTT and dimethyl sulfoxide (DMSO) used for detecting
cell viability were purchased from Gibco, Life Technologies (Grand
Island, NY, USA). Milli-Q AdvantageA10 used to provide ultrapure water
was obtained from Millipore (Boston, MA, USA).
Cell culture
HT-29, the human colon cancer cell line, acquired from the Shanghai
cell bank of the Chinese Academy of Sciences, was cultured in RPMI-1640
medium supplemented with 10% fetal bovine serum and 1%
penicillin–streptomycin at 37 °C in a 5% CO[2] atmosphere. The cell
culture was passaged every other day by trypsinizing the cells and
diluting them at 1:3/1:2 in fresh medium to start a new cycle. The
cells were then used in the following analysis when they reached the
logarithmic growth phase.
Metabolic activity detection
MTT assay for cell viability was performed to determine the
half-maximal inhibitory concentration (IC50) of Al ion ([67]Nzengue et
al., 2008; [68]Xu et al., 2010). Briefly, the cells in the logarithmic
growth phase were added to a 96-well plate at a density of 103–104
cells per well. Once each well was coated with a monolayer, fresh
medium supplemented with sterile phosphate-buffered saline (PBS;
filtered through a 0.22-μm-pore size Millipore filter) containing Al
ions at a final concentration of 0, 2, 4, 6, 8, or 10 mM was added to
separate wells. After 24-h incubation, 20 μL of five mg/mL MTT was
added to each well, and the cells were incubated for another 4 h.
Subsequently, the medium in each well was replaced with 150-μL DMSO,
and the cell viability was determined by measuring the absorbance of
well contents at 490 nm.
Cell collection and cell extraction
The Al ion concentration of four mM was selected for further analyses
based on the results of our MTT assay and of a previous study
([69]Pineton de Chambrun et al., 2014). HT-29 cells were seeded at a
density of 106 cells per well in medium containing four mM Al ion and
were incubated for 24 h. Cells incubated in medium devoid of Al ion
were considered as control. After incubation, the cells were washed
twice with PBS and frozen in liquid nitrogen for quenching metabolism.
The procedure of extraction reported in a previous study was used with
slight modifications ([70]Van den Eede et al., 2015). Briefly, one mL
of trichloromethane–water–methanol mixture at a ratio of 1:1:4 (v/v/v)
was added to the wells for 5 min. The cells were then detached from the
wells using sterile rubber scrapers and transferred to precooled
bottles (−20 °C). Each well was rinsed with another one mL of
extraction solution, and the residue was also transferred to the
bottle. The samples were vortexed and subjected to ultrasonication in
an ice-water bath at 300 W for 3 min (6-s/4-s on/off pulses). The
samples were then transferred into 1.5-mL Eppendorf tubes and subjected
to supersonic extraction for 3 min, followed by centrifugation at
15,000 rpm for 10 min (4 °C). The supernatant (200 μL) was used for
metabolomic analysis by LC-MS.
Metabolomic analysis
Agilent 1290 Infinity Ultra High-Performance Liquid Chromatography
(UHPLC) system combined with Agilent 6538 UHD and Accurate-Mass
Q-TOF/MS were used for data processing in the positive and negative
ionization modes. The parameters of UHPLC and MS were further optimized
based on a previous study ([71]Van den Eede et al., 2015) to meet the
needs of the current conditions. A C[18] column (Waters ACQUITY
UPLC@HSS T3; 100 × 2.1 mm, 2.1 μm) was used to separate the samples.
Solvent A was 0.1% (v/v) formic acid in water, and solvent B was 0.1%
(v/v) formic acid in acetonitrile. Each time, a three μL sample was
injected into the column maintained at 40 °C. The liquid flow rate was
0.4 mL/min, with the following gradient conditions: 0 min, 5% B; 2 min,
5% B; 13 min, 95% B; 15 min, 95% B.
The capillary voltages used for the negative- and positive-ion modes
were 3,500 V and 4,000 V, respectively. The reference masses 112.985587
m/z and 1033.988109 m/z were used in the negative mode and 121.0509 m/z
and 922.0098 m/z were used in the positive mode. A quality control
sample was injected every eight samples to ensure consistent quality.
Data treatment
Agilent Mass Hunter Qualitative software was used for data conversion.
Peak alignment and integration were conducted using XCMS
([72]https://metlin.scripps.edu/) ([73]Smith et al., 2006). A 3D matrix
with retention time (RT), peak intensity, and m/z were obtained. The
following XCMS parameters were used: profmethod = bin, method = matched
Filter, step = 0.1, full width at half maximum (peak width) = 8,
bandwidth = 10, snthresh = 5, ppm = 20, mzdiff = 0.01, and minfrac =
0.8. Mass range was 50–1100 m/z, mass tolerance was 20 ppm, RT range
was 0.5–15.0 min, and RT width threshold was 0.2 min. The following
formula was used to integrate the area of the peaks: unimodal area/area
of all peaks in a single sample × 10,000. The SIMCA-P+ 14.0 software
was used for principal component analysis (PCA) and partial least
squares-discriminant analysis (PLS-DA). Two principle components for
the positive mode and two for the negative mode were used in PCA. Five
principal components for the positive mode and four for the negative
mode were used in PLS-DA. Variables with variable importance in
projection (VIP) values of >1 were considered as differential
variables. Seven-round cross-validation with 200 response permutation
tests was used to prevent model overfitting and determine the model
quality.
Metabolite screening
Metabolites were screened using univariate and multivariate analyses,
the screening criteria (VIP > 1, P < 0.05, fold change > 1.5 or <
0.67), and the mass tolerance threshold (20 mDa). The HMDB and METLIN
databases were used for metabolite identification, and the MBRole
database was used for pathway enrichment.
Verification of key enzymes by RT-qPCR
The relative expressions of nine related genes in the altered
metabolomics pathways were further validated using quantitative reverse
transcription polymerase chain reaction (RT-qPCR). The corresponding
encoded proteins were found to be succinate dehydrogenase (sdhA),
citrate synthase (CS), isocitrate dehydrogenase (IDH), lactic
dehydrogenase (LDH), pyruvate dehydrogenase (PDH), pyruvate kinase
(PK), glutamic oxaloacetic transaminase (GOT), glutathione peroxidase
(GPx), and glutathione reductase (GR). The genes of the first three,
middle three, and last three proteins are related to the TCA cycle,
glycolysis, and glutathione (GSH) metabolism, respectively. Total RNA
was extracted using the traditional TRIzol method. TAKARA RR047A kits
were used to obtain cDNA by reverse transcription. Subsequently, 20-mL
reaction mixture was prepared by combining cDNA, 2× Bio-Rad iTaq™
Universal SYBR^® Green Supermix, and specific primer pairs ([74]Table
1) and subjected to RT-qPCR. Each reaction had three parallels, and the
β-actin gene was used as the internal reference gene.
Table 1. Sequences of primers used in RT-qPCR.
Primer Sequence (5′–3′)
PDH-F TCAACTACCTGGTGCTTCG
PDH-R CATCTCCAAATGCCCTAA
sdhA-F ATTAACAGTCAAGGCGAAAG
sdhA-R ACAACCAGGTCCAAGAGC
GPx-F CCAGTCGGTGTATGCCTTCT
GPx-R GATGTCAGGCTCGATGTCAA
GR-F GCTGATTAAAGCTTTCCAAGTTGTG
GR-R GTAAAGCTCGAGGAATAGGTCTTCAC
GOT-F ACTCAAGGAGAAGCGGGTAG
GOT-R ACAGGCGTGGAGGACAAC
CS-F CGTTTCCGAGGCTTTAGT
CS-R ACAAGGTAGCTTTGCGATT
IDH-F CACTACCGCATGTACCAGAAAGG
IDH-R TCTGGTCCAGGCAAAAATGG
LDH-F TGTGCCTGTATGGAGTGG
LDH-R TTATTCCGTAAAGACCCT
PK-F GCACGCCAAGTACAACACC
PK-R CACGCTCCCACATTCCATA
β-actin-F GGGACCTGACTGACTACCTC
β-actin-R TCATACTCCTGCTTGCTGAT
[75]Open in a new tab
Note:
PDH, pyruvate dehydrogenase; sdhA, succinate dehydrogenase; GPx,
glutathione peroxidase; GR, glutathione reductase; GOT, glutamic
oxaloacetic transaminase; CS, citrate synthase; IDH, isocitrate
dehydrogenase; LDH, lactic dehydrogenase; PK, pyruvate kinase.
The relative amounts of mRNA were calculated using the following
equation:
[MATH:
Relative expression = 2∧ − [(CT
AT − CTAR) −<
/mo> (CT
CT −<
mtext> CTCR)],<
/mo> :MATH]
where AT is the target gene in the Al-treated group, AR is the
reference gene in the Al-treated group, CT is the target gene in the
control/untreated group, and CR is the reference gene in the control
group.
Results
MTT assay for cell viability
The HT-29 cell viability decreased with the increase in Al
concentration from 0 to 10 mM ([76]Fig. 1; [77]Dataset S1). Cell
viability decreased to approximately 50% at four mM of Al ion
concentration. This concentration was used to study Al cytotoxicity in
the subsequent experiment, consistent with that in a previous study
([78]Pineton de Chambrun et al., 2014).
Figure 1. Cell viability of HT-29 cells after Al exposure.
[79]Figure 1
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Viability is expressed as a cell activity percentage between the Al
group and the control group. The experimental data are expressed as the
mean value ± SD of six independent replicates.
Metabolomic characterization of cells
The metabolites in the Al-treated group were compared with those in the
control group using typical base peak intensity chromatograms. The
results are presented in [81]Fig. S1 for both the positive- and
negative-ion modes. In total, 995 and 2,272 discrepant metabolites in
the negative and positive modes, respectively, were selected by UPLC/MS
for further analysis.
First, unweighted analysis was used to evaluate the positive and
negative data. The relationships of the metabolites between the
Al-treated and control groups were identified by comparative analysis.
The R2X, a representative parameter of the PCA model, was 0.472 and
0.548 for the positive and negative modes, respectively. The PCA score
plots in [82]Fig. 2 show that six replicates of the Al-treated group
were separated from the samples in the control group. This result
indicates that Al exposure caused some alteration in the metabolic
profile of HT-29 cells.
Figure 2. PCA score plots of metabolites in HT-29 cells in the positive-ion
mode (A) and negative-ion mode (B).
[83]Figure 2
[84]Open in a new tab
Extraction of special metabolites
The Al-treated cells and untreated cells were further analyzed by
PLS-DA. The more closely the three indexes (R^2X, R^2Y, and Q^2)
approached 1, the more credible and predictable are the metabolomics
data. As shown in [85]Fig. 3, in the above case, the R^2X values were
0.725 and 0.745, the R^2Y values were 1 and 0.997, and the Q^2 values
were 0.474 and 0.823 for the positive- and negative-ion modes,
respectively. Partial least squares-discriminant analysis was
cross-validated, and the results are shown in [86]Fig. S2. All of the
metabolites were judged by their VIP values, which reflected the
proportion of their influence on the model.
Figure 3. PLS-DA score plots of metabolites in HT-29 cells in the
positive-ion mode (A) and negative-ion mode (B).
[87]Figure 3
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The following screening criteria were rigorously applied to screen the
metabolites as potential biomarkers: fold change > 1.5 or < 0.667, VIP
> 1, and P < 0.05 (relative peak areas of the metabolite contents). The
METLIN and HMDB databases were used to screen and characterize the
candidates, which were then annotated as 39 and 42 metabolites in the
positive- and negative-ion modes, respectively ([89]Tables 2 and [90]3;
[91]Dataset S2). The metabolites GSH, glutamic acid, four
phosphatidylcholines (PC (14:1), PC (14:0), PC (15:0), and PC (14:1)),
phosphatidylethanolamine (PE (22:4)), pantothenate, creatine, and
choline, were consistently downregulated after Al exposure.
Table 2. Altered metabolites of HT-29 cells after Al exposure in the
positive-ion mode.
No. Metabolites M/Z RT (min) VIP value P value FC
1 MG (18:2(9Z,12Z)/0:0/0:0) 355.28 13.29 1.33 0.012 0.15
2 PC (14:1(9Z)/P-16:0) 688.52 12.14 1.30 0.002 0.29
3 Beta-D-3-Ribofuranosyluric acid 301.08 14.35 1.30 0.005 0.29
4 N-Acryloylglycine 130.05 0.85 1.29 0.013 0.30
5 Beta-D-Glucopyranosyl-11-hydroxyjasmonic acid 389.18 0.73 1.28 0.001
0.33
6 Vaccenyl carnitine 426.36 10.69 1.27 0.001 0.34
7 8-Hydroxypinoresinol 8-glucoside 537.20 0.67 1.27 0.002 0.42
8 Creatine 263.15 0.73 1.27 0.000 0.42
9 SM (d18:1/14:0) 675.54 13.16 1.25 0.000 0.43
10 PC (o-4:0/16:0) 692.56 14.95 1.25 0.007 0.43
11 L-Glutamic acid 148.06 0.72 1.24 0.000 0.48
12 LysoPC (18:0) 524.37 11.96 1.23 0.027 0.48
13 Caffeoylcycloartenol 589.43 13.79 1.23 0.000 0.49
14 Tetradecanoylcarnitine 372.31 9.67 1.22 0.000 0.51
Myristic acid 267.17 11.01 1.22 0.015 0.51
15 3-Hydroxyphenyl-valeric acid 233.06 1.03 1.20 0.009 0.53
16 N-Hexadecanoylpyrrolidine 619.61 14.81 1.20 0.005 0.53
17 LysoPE (18:0/0:0) 482.32 11.91 1.20 0.001 0.53
18 Phenylpyruvic acid 165.05 1.17 1.19 0.003 0.54
19 LysoPC (18:2(9Z,12Z)) 520.34 10.17 1.18 0.000 0.56
20 Glyceryl lactooleate 429.32 12.51 1.17 0.003 0.57
21 Methylgingerol 309.21 11.35 1.17 0.004 0.57
22 Palmitoleoyl ethanolamide 298.27 11.75 1.17 0.026 0.57
23 PC (15:0/18:1(11Z)) 746.57 12.81 1.17 0.004 0.58
24 7-Hydroxydehydroglaucine 392.15 3.87 1.17 0.035 0.58
25 UDP-N-acetyl-alpha-D-galactosamine 630.07 0.80 1.16 0.001 0.59
26 Adrenoyl ethanolamide 376.32 12.78 1.16 0.041 0.60
27 Meta-O-Dealkylated flecainide 371.10 14.24 1.15 0.041 0.60
28 L-Agaritine 535.26 9.81 1.15 0.009 0.61
29 PE (22:4(7Z,10Z,13Z,16Z)/15:0) 754.54 14.95 1.14 0.033 0.62
30 Alpha-CEHC 279.16 11.98 1.14 0.003 0.63
31 Propionylcarnitine 218.14 1.49 1.13 0.003 0.63
32 Glycerol 1-hexadecanoate 331.28 13.71 1.13 0.028 0.63
33 Palmitoleoyl ethanolamide 320.26 10.49 1.13 0.002 0.63
34 Isolimonic acid 507.22 9.05 1.13 0.010 0.64
35 PC (22:2(13Z,16Z)/14:1(9Z)) 784.58 13.31 1.12 0.047 0.64
36 6,10,14-Trimethyl-5,9,13-pentadecatrien-2-one 263.24 12.90 1.11
0.015 0.65
37 4-Hydroxymidazolam 364.06 0.76 1.11 0.019 0.65
38 Alpha-CEHC 301.14 11.98 1.11 0.036 0.65
39 MG (18:0e/0:0/0:0) 367.32 12.39 1.09 0.005 0.66
[92]Open in a new tab
Notes:
M/Z, mass/charge number of peaks in the mass spectra; RT, retention
time of metabolites in chromatography; FC, fold change of metabolites
after Al exposure. Differences between the control and Al-treated cells
were analyzed by one-way analysis of variance, followed by the Tukey’s
post hoc test. P < 0.05 was considered as significant. The changes in
metabolite abundance are expressed as the ratio of the average content
in the treatment and control groups (n = 6). A value < 1 indicates
downregulation.
Table 3. Altered metabolites of HT-29 cells after Al exposure in the
negative-ion mode.
No. Metabolites M/Z RT (min) VIP value P value FC
1 Tiglic aldehyde 129.06 4.33 2.72 0.000 0.36
2 L-Lactic acid 135.03 0.71 1.20 0.006 0.36
3 Arsenobetaine 158.98 0.59 2.05 0.001 0.39
4 4-Pentenal 129.06 3.89 2.05 0.001 0.39
5 Hypothiocyanite 148.95 0.57 1.22 0.001 0.42
6 Ammonium peroxydisulfate 226.97 0.61 1.26 0.001 0.43
7 8-oxo-dGDP 442.01 1.04 1.70 0.003 0.43
8 Succinic acid semialdehyde 101.02 0.79 1.03 0.013 0.44
9 ADP 426.03 0.95 3.84 0.000 0.45
10 Uridine diphosphate glucose 565.05 0.82 1.34 0.027 0.49
11 Uridine 5′-diphosphate 403.00 0.88 2.05 0.000 0.50
12 Deoxycytidine 272.09 1.02 1.02 0.003 0.53
13 dCDP 386.02 0.75 1.28 0.005 0.54
14 Glutathione 306.08 1.02 7.14 0.002 0.55
15 Phosphoribosyl-AMP 540.05 1.03 1.63 0.000 0.55
16 D-Fructose 225.06 0.66 1.28 0.001 0.55
17 Gluconasturtiin 404.05 1.02 1.94 0.005 0.56
18 UDP-N-acetyl-alpha-D-galactosamine 606.08 0.82 4.73 0.008 0.56
19 NAD 709.11 1.04 1.04 0.019 0.57
20 UDP-D-galacturonate 579.03 0.88 4.51 0.000 0.58
21 Oxidized glutathione 611.15 1.03 4.50 0.010 0.58
22 L-Thyronine 254.08 2.92 1.06 0.008 0.58
23 Pantothenic acid 218.10 2.92 3.08 0.001 0.58
24 FAD 784.15 4.33 1.38 0.001 0.59
25 1,3,5-Trihydroxy-10-methylacridone 256.06 0.72 2.19 0.019 0.59
26 Citric acid 191.02 1.04 5.57 0.002 0.59
27 CDP 448.02 0.76 1.22 0.009 0.59
28 4-O-alpha-D-Galactopyranuronosyl-D-galacturonic acid 369.07 4.96
1.18 0.021 0.59
29 LysoPE (0:0/18:0) 480.31 11.90 1.18 0.013 0.61
30 Ibudilast 229.13 5.79 1.10 0.021 0.62
31 L-Tryptophan 203.08 4.08 1.08 0.001 0.63
32 N-Succinyl-L,L-2,6-diaminopimelate 289.10 4.95 3.07 0.027 0.63
33 5-Oxoprolinate 128.04 0.85 2.03 0.003 0.63
34 Alpha-D-Glucopyranoside 179.06 0.68 1.22 0.001 0.64
35 Succinic acid 117.02 1.32 2.02 0.028 0.64
36 Hydroxyhexamide 307.11 5.79 3.56 0.019 0.64
37 Penicilloic acid 333.09 7.33 1.95 0.021 0.65
38 2-O-p-Coumaroylhydroxycitric acid 353.05 5.94 1.96 0.012 0.66
39 L-Phenylalanine 164.07 2.18 1.48 0.010 0.66
40 Edetic acid 291.08 0.82 2.41 0.019 0.66
41 2-Aminomuconic acid 313.07 0.78 1.03 0.038 0.67
42 2-Phenylacetamide 180.07 1.17 1.33 0.006 0.67
[93]Open in a new tab
Notes:
M/Z, mass/charge number of peaks in the mass spectra; RT, retention
time of metabolites in chromatography; FC, fold change of metabolites
after Al exposure. Differences between the control and Al-treated cells
were analyzed by one-way analysis of variance, followed by the Tukey’s
post hoc test. P < 0.05 was considered as significant. The changes in
metabolite abundance are expressed as the ratio of the average content
in the treatment and control groups (n = 6). A value < 1 indicates
downregulation.
Pathway enrichment analysis
The MBRole-ID conversion was used to identify metabolites and enrich
the pathways in KEGG. One pathway in the positive-ion mode, namely
arginine and proline metabolism, and 17 pathways in the negative-ion
mode, including pyrimidine metabolism, the TCA cycle, pyruvate
metabolism, and GSH metabolism, were ultimately identified (all P <
0.05, [94]Fig. 4). Moreover, some metabolites, such as pyruvate,
citrate, succinate, leucine, tryptophan, phenylalanine, glutamate, were
significantly reduced after Al exposure. The results of RT-qPCR shown
in [95]Fig. 5 ([96]Dataset S3) presented the relative gene expression
levels in the Al-treated group compared with those in the control
group. Specifically, the expression of GR, PK, and LDH genes was
significantly upregulated, whereas that of the other four genes (GPx,
PDH, GOT, and IDH) was significantly downregulated (P < 0.05).
Figure 4. Significantly altered pathways of HT-29 cells after Al exposure in
the negative-ion mode.
[97]Figure 4
[98]Open in a new tab
Figure 5. Changes in the relative mRNA expression levels of related genes
after Al exposure.
[99]Figure 5
[100]Open in a new tab
The values are expressed as the mean ± SD of six independent replicates
with reference to the mRNA expression of the control group. (A)
Downregulated genes; (B) upregulated genes. The asterisks indicate
significant differences (P < 0.05).
Discussion
Al ingestion caused intestinal injuries by exacerbating intestinal
inflammation and damaging intestinal barrier function ([101]Pineton de
Chambrun et al., 2014). To elucidate the specific impact of Al exposure
on the human intestinal tract, the HT-29 cell line was chosen as an in
vitro model to investigate the global profiles of various metabolic
pathways, metabolites, and genes after Al treatment. The following
processes were found to be the most significantly altered: the TCA
cycle; glycolysis/gluconeogenesis; GSH metabolism; pyruvate metabolism;
amino acid metabolism; and biosynthesis, including those of glutamate,
leucine, phenylalanine, and tryptophan; pyrimidine metabolism; and
pantothenate and CoA biosynthesis ([102]Fig. 6).
Figure 6. Global reactions in HT-29 cells after Al exposure.
[103]Figure 6
[104]Open in a new tab
Effects of Al cytotoxicity on energy metabolism
The TCA cycle is a common pathway in all aerobic organisms used to
produce energy through the oxidation of acetyl-CoA derived from fats,
carbohydrates, and proteins into ATP and CO[2]. It also provides
precursors for the biosynthesis of certain amino acids and NADH.
Pyruvate dehydrogenase catalyzes the decarboxylation of pyruvate, the
end product of glycolysis, into acetyl-CoA. The RT-qPCR results showed
that the expression of PDH in Al-treated cells was decreased to
0.65-fold of that in the untreated cells, which consequently decreased
the formation of acetyl-CoA, resulting in the shortage of substrate for
the TCA cycle. Consequently, the levels of intermediate compounds in
the TCA cycle, such as citrate and succinate, decreased after Al
exposure. Furthermore, decreased citrate and succinate levels in turn
reduced NADH production, which is essential for the delivery of
electrons to oxygen through the electron transport chain to produce
ATP. Thus, an important mechanism of Al toxicity appears to be the
perturbation of mitochondrial ATP production. These results indicate
that Al exposure has a deleterious effect on the energy output and
causes various cellular abnormalities. Another reason that explains the
decrease in some components of the TCA cycle is the oxidation of the
TCA cycle enzymes due to Al exposure. The TCA cycle enzymes are very
sensitive to oxidative stress ([105]Tretter & Adam-Vizi, 2005). Indeed,
the relative mRNA expressions of IDH and GOT in Al-treated cells were
found to be significantly decreased to 0.42- and 0.74-fold of those in
the untreated cells, respectively ([106]Fig. 5). [107]Kil et al. (2006)
reported that heavy metal exposure decreased IDH levels in human
embryonic kidney 293 cells, impairing the conversion of citrate into
isocitrate. [108]Murakami & Yoshino (2004) found that Al inhibited IDH
expression, thereby decreasing GSH regeneration in mitochondria.
[109]Benderdour et al. (2004) reported decreased IDH expression as a
marker of oxidative stress in cardiac hypertrophy development.
Cell growth requires glycolysis for ATP generation to maintain the
energy supply and for the accumulation of glycolytic intermediates to
meet the needs of rapid cell proliferation and the need for rapid
synthesis of nucleotides, lipids, and proteins ([110]Ye et al., 2012).
In the oxidative environment, cells can use glycolysis for ATP
production, without the participation of mitochondria. The function of
this metabolic route is crucial to maintain the energy balance of
organisms in various conditions ([111]Dang & Semenza, 1999; [112]Kim et
al., 2006). [113]Mailloux & Appanna (2007) reported that glycolysis
increases with the increase in oxidative stress in Al-treated cells.
Two enzymes, PK and LDH, which are critical for ATP production in the
anaerobic environment, exhibited dramatically increased activity upon
Al exposure in HT-29 cells in our study. The study by [114]Mailloux &
Appanna (2007) reported similar results for Al-treated HepG2 cells.
Lactic dehydrogenase is also required for the recycling of NAD^+, which
is essential for activating the subsequent step of glycolysis
([115]Dang & Semenza, 1999).
Effects of Al cytotoxicity on oxidative stress and amino acid metabolism
The main mechanism of Al toxicity is the induction of oxidative stress
by producing excessive reactive oxygen species (ROS) ([116]Kumar, Bal &
Gill, 2009). Excess ROS levels cause imbalances and disturbances in
signaling processes, such as MAPK signaling pathways, leading to growth
inhibition and damage to human cells ([117]Sharma & Dietz, 2009).
Reactive oxygen species are also responsible for exacerbating DNA
damage ([118]Santos et al., 2010). Glutathione can protect cellular
components against oxidative injury by directly neutralizing ROS or
functioning as a cofactor against free radicals in cells ([119]Carrola
et al., 2016). In addition, being a thiol compound, GSH can bind to
free metal ions with high affinity and reduce metal availability, thus
indirectly reducing ROS production and alleviating oxidative stress
([120]Sharma & Dietz, 2009). In our study, GSH level was lower in
Al-treated cells than in untreated cells, suggesting that Al exposure
triggered oxidative stress. Our findings also revealed decreased levels
of glutamate, a GSH precursor, in the Al-treated cells, supporting the
hypothesis of deregulating GSH synthesis. Glutathione peroxidase
catalyzes the oxidation of GSH to GSSG, whereas GR catalyzes the
reduction of GSSG to GSH. Our RT-qPCR results showed that GPx
expression was reduced by 0.36-fold, whereas GR expression was
increased by 1.74-fold in Al-treated cells compared with untreated
cells ([121]Fig. 5). This result indicates that the decrease in GPx
expression and increase in GR expression may be a mechanism to
alleviate the Al exposure-induced oxidative stress in Al-treated cells.
Upon exposure to toxins, such as toxic metals, several species
downregulate or upregulate the synthesis of diverse metabolites and
cause the conversion of specific amino acids. For example, glycine,
alanine, serine, tryptophan, cysteine, and threonine can be converted
to pyruvate through the TCA cycle, whereas aspartate and asparagine are
converted to oxaloacetate ([122]Owen, Kalhan & Hanson, 2002). Our
results showed decreased intracellular levels of the ketogenic amino
acids leucine, tryptophan, and phenylalanine and the glycogenic amino
acid glutamate in Al-treated HT-29 cells, consistent with the results
of a previous study on human epidermal keratinocytes ([123]Carrola et
al., 2016). One previous study reported that the metabolism of
glutamine, the most abundant naturally occurring amino acid in the
body, is upregulated during the glucose shift to provide substrate for
increased lipogenesis and nucleic acid biosynthesis crucial for cell
proliferation ([124]Dakubo & Safarina, 2010). After the conversion
during the glucose shift, it may be transformed into α-ketoglutarate
and enter the TCA cycle. Thus, it can be speculated that decrease of
amino acid level may be an indirect cause for energy and lipids
metabolism alterations induced by Al exposure.
Effects of Al cytotoxicity on lipid metabolism and cellular apoptosis
Phospholipids, such as PCs, PEs, monoglyceride, and sphingomyelin (SM),
are indispensable components of cellular membranes, signaling pathways,
and cellular processes such as cell–cell interactions, cell
proliferation, cell differentiation, and cellular apoptosis ([125]Hla &
Dannenberg, 2012). Membrane lipids containing polyunsaturated fatty
acids are highly sensitive to free radicals and are susceptible to
lipid peroxidation. Our results revealed a decrease in choline and
phosphocholine levels in Al-treated cells, suggesting that cell
membrane disruption caused by Al exposure and the corresponding
oxidative stress. In particular, the levels of PCs, LysoPCs, and
PEs—crucial cellular membrane constituents—in Al-treated cells
decreased to 0.28–0.64-fold of those in untreated cells ([126]Tables 2
and [127]3), suggesting the degradation of membrane phospholipids and
cellular apoptosis. The level of SM (d18:1/14:0) in Al-treated cells
decreased to 0.43-fold of that in untreated cells, also indicating the
occurrence of cellular apoptosis. A decrease in the levels of creatine,
which plays a key role in energy transport across the mitochondrial
membrane. Notably, a decrease in choline levels can disturb
transmethylation and may also result in a decline in creatine levels.
Lower carnitine level was also observed after Al treatment. A decrease
in the carnitine level has been reported to cause accumulation of
endotoxins and disruption of several enzyme reactions ([128]Coulter,
1991; [129]Russell, 2007). Carnitine deficiency is implicated in
various conditions such as diabetes mellitus, Alzheimer’s disease, and
heart failure ([130]Ha et al., 2012; [131]Li et al., 2015). After CoA
biosynthesis from the precursor pantothenate, CoA is combined with a
long-chain fatty acid to produce acetyl-CoA that enters the TCA cycle.
Thus, disruption in the CoA biosynthesis pathway is closely related to
abnormalities in energy metabolism and fatty acid biosynthesis, as well
as imbalances in various cellular processes. Consequently, the
availability of pantothenate plays a key role in this pathway, as
indicated by a previous finding that the rate of the CoA production
step is limited by the phosphorylation of pantothenate. Previous
studies demonstrated that the lack of pantothenate has a severe impact
on energy production and fatty acids synthesis ([132]Rock et al.,
2000).
Other effects of Al cytotoxicity
The level of edetic acid, a common metal chelator, in Al-treated cells
decreased to 0.66-fold of that in the untreated cells ([133]Table 3). A
reduction in edetic acid levels would affect the trace elements levels.
Consistent with this result, Al exposure reduces the Fe, Ca, and Mg
levels in mice tissues, as well as Cu, Zn, Fe, and Ca levels in humans
serum ([134]Yu et al., 2017; [135]Metwally & Mazhar, 2007). Taken
together, our results indicate that a decrease in GSH levels and
disturbance in trace element levels may be the main mechanisms of
Al-induced oxidative stress in HT-29 cells.
The level of 8-Oxo-dGDP, generated by the oxidation and phosphorylation
of dGTP, in Al-treated cells decreased to 0.43-fold of that in the
untreated cells ([136]Table 2), suggesting that Al exposure led to
nucleotide damage directly ([137]Hayakawa et al., 1995). Moreover, Al
exposure would reduce the amount of metabolites that can metabolize
toxic substances. For example, 4-hydroxymidazolam level reduced to
0.65-fold after Al treatment. It is an active metabolite of midazolam,
can further produce to glucuronide, metabolizing several exogenous and
endogenous toxic substances and thus exerting positive and protective
effects ([138]Seo et al., 2010).
Conclusions
In conclusion, metabolite analysis of the HT-29 cells revealed
significant alterations in the TCA cycle, pyruvate metabolism, GSH
metabolism, and several metabolite levels after Al exposure. Al
exposure in our study led to cellular apoptosis, induced oxidative
stress, altered the metabolism of energy, lipids, and amino acids.
These findings explain the mechanisms of toxic effects of Al exposure
in the human gastrointestinal tract.
Supplemental Information
Supplemental Information 1. Raw data: Cell viability of HT-29 cells
after Al exposure.
Viability is expressed as a cell activity percentage between the Al
group and the control group.
[139]Click here for additional data file.^ (16KB, docx)
DOI: 10.7717/peerj.7524/supp-1
Supplemental Information 2. The altered metabolites of HT-29 cells
after Al exposure.
[140]Click here for additional data file.^ (46KB, rar)
DOI: 10.7717/peerj.7524/supp-2
Supplemental Information 3. The changes in relative mRNA expression
levels of related genes after Al exposure.
[141]Click here for additional data file.^ (16.1KB, docx)
DOI: 10.7717/peerj.7524/supp-3
Supplemental Information 4. Representational base peak intensity
chromatograms in the positive-ion mode (A) and negative-ion mode (B).
[142]Click here for additional data file.^ (592.5KB, png)
DOI: 10.7717/peerj.7524/supp-4
Supplemental Information 5. Cross-validation of the PLS-DA model in the
positive-ion mode (A) and negative-ion mode (B).
[143]Click here for additional data file.^ (377.1KB, png)
DOI: 10.7717/peerj.7524/supp-5
Acknowledgments