Article

Physiological and toxicological transcriptome changes in HepG2 cells exposed to copper

Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA.
Physiological Genomics (Impact Factor: 2.37). 07/2009; 38(3):386-401. DOI: 10.1152/physiolgenomics.00083.2009
Source: PubMed

ABSTRACT

Copper is an essential trace element; however, at supraphysiological levels, it can be extremely toxic. Microarray data from HepG2 cells exposed to 100, 200, 400, and 600 microM copper for 4, 8, 12 and 24 h were generated and analyzed. Principal components, K-means, and hierarchical clustering, interactome, and pathway mapping analyses indicated that these exposure conditions induce physiological and toxicological changes in the HepG2 transcriptome. As a general trend, when the level of toxicity increases, the number and diversity of affected genes, Gene Ontology categories, regulatory pathways, and complexity of interactomes increase. Physiological responses to copper include transition metal ion binding and responses to stress/stimulus, whereas toxicological responses include apoptosis, morphogenesis, and negative regulation of biomolecule metabolism. The global gene expression profile was overlaid onto biomolecular interaction networks and signal transduction cascades using pathway mapping and interactome identification. This analysis indicated that copper modulates signal transduction pathways associated with MAPK, NF-kappaB, death receptor, IGF-I, hypoxia, IL-10, IL-2, IL-6, EGF, Toll-like receptor, protein ubiquitination, xenobiotic metabolism, leukocyte extravasation, complement and coagulation, and sonic hedgehog signaling. These results provide insights into the global and molecular mechanisms regulating the physiological and toxicological responses to metal exposure.

Full-text

Available from: Min Ok Song, Jan 13, 2016
Physiological and toxicological transcriptome changes in HepG2 cells
exposed to copper
Min Ok Song,
1
Jianying Li,
2
and Jonathan H. Freedman
1
1
Laboratory of Molecular Toxicology and
2
Biostatistics Branch, National Institute of Environmental Health Sciences,
National Institutes of Health, Research Triangle Park, North Carolina
Submitted 12 May 2009; accepted in final form 13 June 2009
Song MO, Li J, Freedman JH. Physiological and toxicological
transcriptome changes in HepG2 cells exposed to copper. Physiol
Genomics 38: 386 –401, 2009. First published June 23, 2009;
doi:10.1152/physiolgenomics.00083.2009.—Copper is an essential
trace element; however, at supraphysiological levels, it can be ex-
tremely toxic. Microarray data from HepG2 cells exposed to 100, 200,
400, and 600 M copper for 4, 8, 12 and 24 h were generated and
analyzed. Principal components, K-means, and hierarchical cluster-
ing, interactome, and pathway mapping analyses indicated that these
exposure conditions induce physiological and toxicological changes
in the HepG2 transcriptome. As a general trend, when the level of
toxicity increases, the number and diversity of affected genes, Gene
Ontology categories, regulatory pathways, and complexity of interac-
tomes increase. Physiological responses to copper include transition
metal ion binding and responses to stress/stimulus, whereas toxico-
logical responses include apoptosis, morphogenesis, and negative
regulation of biomolecule metabolism. The global gene expression
profile was overlaid onto biomolecular interaction networks and
signal transduction cascades using pathway mapping and interactome
identification. This analysis indicated that copper modulates signal
transduction pathways associated with MAPK, NF-B, death recep-
tor, IGF-I, hypoxia, IL-10, IL-2, IL-6, EGF, Toll-like receptor, protein
ubiquitination, xenobiotic metabolism, leukocyte extravasation, com-
plement and coagulation, and sonic hedgehog signaling. These results
provide insights into the global and molecular mechanisms regulating
the physiological and toxicological responses to metal exposure.
signal transduction pathways; interactome; Gene Ontology; transition
metal; Cytoscape
THE TRANSITION METAL copper plays important physiological
roles, serving as a cofactor in enzymes that modify neuropep-
tides, generate cellular energy, detoxify oxygen-derived radi-
cals, mobilize iron, coagulate blood, and cross link connective
tissue (44, 54). At higher than physiological concentrations,
however, copper has a destructive potential toward cellular
macromolecules. Copper participates in redox reactions that
can generate reactive oxygen species (ROS), which damage
lipids, proteins, and DNA. Copper can also directly bind to
protein sulfhydryl and amino groups, leading to structural and
functional modifications (13, 30, 38, 41, 68). Finally, copper
can bind to DNA to form adducts and is involved in
chromatin condensation (11, 58). Thus, it is critical for
organisms to maintain homeostatic concentrations of cop-
per, because abnormally high or low levels can lead to
pathological conditions (8).
Cells defend against copper-induced toxicity by activating
the transcription of stress-responsive genes, which encode
proteins that repair intracellular damage or remove the metal
(23, 51, 64, 67, 68). The mechanism by which copper modu-
lates the expression of many of these genes is not yet clear.
Copper can activate transcription through metal- and oxidative
stress-responsive signal transduction pathways involving PKC
and MAPKs (48). The activation of copper-responsive gene
transcription may also be mediated by NF-B signaling (64).
Global gene expression profiling, through the use of DNA
microarrays, allows the monitoring of changes in the expres-
sion of thousands of genes and subsequently identifies novel
regulatory pathways. The majority of the studies examining the
genomic response to copper exposure have focused on the
toxicological response (50, 65). There is a paucity of data,
however, on the genomic response to physiological levels of
copper.
We propose that copper modulates the activity of multiple
intracellular signal transduction pathways to affect transcrip-
tion. Furthermore, the pathways affected by toxic concentra-
tions of copper may be unique from those affected by physi-
ological levels. In the present study, transcriptomes were gen-
erated using HepG2 cells exposed to four concentrations of
copper (100, 200, 400, and 600 M) for four time periods (4,
8, 12, and 24 h). These conditions were selected based on our
previous cytotoxicity results (64) and copper levels reported
from human case studies. In normal human serum, copper
levels range from 18.1 to 31.5 M (39, 49, 53); however, they
can be elevated under pathophysiological conditions. Elevated
serum copper levels of 1,300 g/100 ml (205 M) were
reported in a patient with hypercupremia associated with mul-
tiple myeloma (42). Hepatic copper concentrations as high as
1,142 g/g dry tissue in a Wilson’s disease patient and 4,788
g/g dry tissue from an individual in the terminal stages of
Indian childhood cirrhosis have been observed (27).
In humans, acute copper toxicity is rare; however, elevated
and toxic levels of copper can be encountered as a result of
environmental exposure, genetic defects, and certain neoplastic
diseases (8, 26). There are several genetic diseases of copper
metabolism that are characterized by elevated levels of intra-
cellular hepatic copper: Wilson’s disease, Indian childhood
cirrhosis, and idiopathic copper toxicosis. Patients with these
diseases present hepatic copper levels at milligram/gram con-
centrations (61). In addition to genetic disorders of copper
metabolism, other pathological conditions, including hepatic
necrosis, cholestatic cirrhosis, bile duct proliferation, hepatitis,
and hepatocellular carcinoma, are associated with elevated
copper levels (19, 20, 25). Environmental exposures to ele-
vated copper levels have been reported to be as high as 160 M
in drinking water and up to 90 mM in rivers (1). In the present
Address for reprint requests and other correspondence: J. H. Freedman,
Laboratory of Molecular Toxicology, National Institute of Environmental
Health Sciences, Mail Drop E1-05, PO Box 12233, 111 T.W. Alexander Dr.,
Research Triangle Park, NC 27709 (e-mail: freedma1@niehs.nih.gov).
Physiol Genomics 38: 386–401, 2009.
First published June 23, 2009; doi:10.1152/physiolgenomics.00083.2009.
386
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study, analysis of the copper transcriptomes from HepG2 cells
revealed that at lower concentrations (100 and 200 M) copper
modulated the expression of genes associated with physiolog-
ical adaptive responses and at higher concentrations (400 and
600 M) copper induced toxicological responses. The present
study provides insights into global, molecular mechanisms
associated with copper intoxication as well as mechanisms by
which cells maintain normal physiological levels of this essen-
tial metal.
MATERIALS AND METHODS
Cell culture, RNA isolation, and microarray hybridization
HepG2 cells (human hepatoma cell line, no. HB-8065, American
Type Culture Collection) were grown in MEM supplemented with
10% FBS, 100 M nonessential amino acids, 1 mM sodium pyruvate,
100 U/ml penicillin, and 100 g/ml streptomycin (Life Technologies,
Gaithersburg, MD). Cells were maintained in a humidified incubator
at 37°C under 5% CO
2
. To prepare RNA for microarray experiments,
HepG2 cells were grown until they were 50% confluent. Cells were
then treated with 100, 200, 400, or 600 M copper sulfate (Sigma-
Aldrich Chemical, St. Louis, MO) for 4, 8, 12, or 24 h. These times
and concentrations corresponded to exposures between a 5% lethal
dose and the 50% lethal dose (64). Three independent total RNA
samples were isolated from untreated and treated cells using RNeasy
Mini kits following the manufacturer’s instructions (Qiagen, Valen-
cia, CA). The quality of the purified RNA was determined using a
BioAnalyzer (Agilent Technologies, Palo Alto, CA), and samples
were then stored at 80°C until use.
For microarray hybridizations, 100 ng of total RNA from copper-
treated cells were amplified and labeled with Cy3 fluorescent dye, and
a common reference pool (nontreated cells) was amplified and labeled
with Cy5 using Agilent Technologies Low RNA Input Linear Am-
plification Labeling kit following the manufacturer’s protocol. The
quantity and purity of the fluorescently labeled cRNAs were evaluated
using a Nanodrop ND-100 spectrophotometer (Nanodrop Technolo-
gies, Wilmington, DE), and the size distribution was evaluated using
an Agilent Bioanalyzer. Equal amounts of Cy3- and Cy5-labeled
cRNAs were then hybridized to Agilent’s Human Microarray
(22,000-k features) for 17 h at 65°C. The hybridized microarrays
were then washed and scanned using an Agilent G2565BA scanner.
Data were then extracted using Agilent Feature Extraction software. A
total of 96 microarrays were analyzed in this study: four copper
concentrations four time points three biological replicates two
dye-swap replicates. This resulted in six microarrays for each treat-
ment condition.
Transcriptome Data Analysis
GeneSpring (version 7, Agilent Technologies) was used to identify
genes that showed significant changes in gene expression with any
treatment. Before the statistical modeling, a full-scale data quality
assessment was applied to ensure a satisfied quality level. For the
global normalization of raw microarray data, per spot- and per chip
intensity-dependent (Lowess) normalization were applied (70). A
two-sample t-test between treated and control samples was applied on
the normalized dataset; the false discovery rate (FDR) was applied as
a P value correction to handle a possible multiple testing issue. The
statistical significance threshold was set at FDR P values of 0.05. To
ensure the biological significance and comparability across platforms,
a 1.5- or 2-fold change in the level of expression was also used as
another cutoff (63). Differentially expressed genes were further de-
fined to satisfy both criteria. Microarray data presented in this publi-
cation have been deposited in the National Center for Biotechnology
Information’s Gene Expression Omnibus (GEO) (21) and are acces-
sible through GEO Series Accession No. GSE9539.
Principal component analysis. Principal component analysis
(PCA) was performed using the Partek Genomics Suite (Partek, St.
Louis, MO) (36). Gene expression data were preprocessed and nor-
malized as described above, and short-wide format data matrixes were
constructed either with a complete microarray data set (all-gene list)
or a differentially regulated genes dataset (2-fold, FDR P 0.05).
PCA was performed based on the correlation matrix, which was
standardized to a mean of 0 and a SD of 1. The number of principal
components was determined through a Scree plot to ensure that
sufficient variability had been captured (80% or above for differ-
entially expressed genes). The three components with the largest
eigenvalues were plotted.
Cluster analysis. Hierarchical (average linkage) and K-means clus-
tering of 2,132 differentially expressed genes (1.5-fold, P 0.001,
in at least 4 of 16 conditions) was performed using Cluster 3.0 (18).
As similarity measures, correlation (uncentered) was used for hierar-
chical clustering, and Euclidean distance was used for K-means
clustering. For K-means clustering, the number of clusters (K) was 13
for genes and 2 for experimental conditions. We combined clusters 12
and 13 together because they had similar expression profiles and
enriched Gene Ontology (GO) categories. Clustering results were
visualized with Java TreeView 1.0.7 (59). GO analysis of the genes in
the various clusters was performed using the Gene Ontology Tree
Machine (University of Tennessee and Oak Ridge National Labora-
tory) (72).
Ingenuity pathway analysis. The Ingenuity Pathway Analysis (IPA)
platform was used to identify significant canonical or functional
pathways from the IPA library of pathways (http://www.ingenuity.
com). A Fisher’s exact test was used to determine the probability that
the association between the copper-responsive genes and the canonical or
functional pathway occured by chance alone. The initial expression data
matrix consisted of 12,266 genes, which corresponds to genes with P
0.05 in at least 1 of 16 conditions (see Supplemental Material, Additional
Data File 1).
1
The Benjamini and Hochberg FDR was applied as a
multiple testing correction (10). This list was uploaded to Ingenuity,
and a cutoff of 1.5-fold change was then applied. IPA identified
significantly differentially regulated genes (focus genes) and then
overlaid them onto the Ingenuity Pathway Knowledge Base. Canon-
ical pathways and functional networks associated with these genes
were generated based on their connectivity.
Interactome analysis. Cytoscape with the jActiveModule plug-in was
used to identify neighborhoods in the networks associated with differen-
tially expressed genes (34, 62). The merged human interactome devel-
oped by Garrow (http://www.cytoscape.org/cgi-bin/moin.cgi/Data_Sets),
which contains 10,344 nodes and 53,526 interactions, was used in this
analysis. To identify interactomes, a list of 12,266 genes that had P
0.05 in at least 1 of 16 conditions and had expression data for all 16
conditions was used (see Supplemental Material, Additional Data File
1). Two expression data matrixes were generated: 1) a concentration-
based matrix, in which the genes identified under each of the four
exposure times at a single concentration were combined, and 2)a
time-based matrix, in which the genes identified under each of the four
concentrations at a single time were combined.
RESULTS
Gene Expression Profile
The analysis of transcriptomes of HepG2 cells exposed to
100, 200, 400, or 600 M copper for 4, 8, 12, or 24 h identified
significantly differentially expressed genes. A total of 2,257
unique genes were differentially expressed by 2-fold (FDR
P 0.05) among the 16 exposure conditions: 1,088 upregu-
1
Supplemental Material for this article is available online at the Physiolog-
ical Genomics website.
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lated genes and 1,169 downregulated genes. Exposure to 600
M copper for 12 h increased the expression of the largest
number of genes (791 genes), whereas exposure to 100 M
copper for 12 h affected the fewest (6 genes; Table 1 and
Supplemental Material, Additional Data File 2).
Most of the genes upregulated by lower copper concentra-
tions (100 and 200 M) at all exposure times were metallo-
thionein (MT) isoforms, which may be a physiological re-
sponse to maintain copper homeostasis (50). Heat shock pro-
teins (HSPs) were also upregulated at 200 M copper. In
addition, HSPs showed the highest fold change after exposure
to 400 and 600 M copper for 4 and 8 h. Upregulated genes at
the higher copper concentrations with the largest fold changes
in expression (within the top 35) included Bcl-2-associated
athanogene 3 (BAG3), suppressor of cytokine signaling 3
(SOCS3), IL-8, and growth arrest and DNA damage-induc-
ible- (GADD45G) (apoptosis); the glutamate-cysteine ligase
modifier subunit (GCLM) (cysteine metabolism); very-low-
density LDL receptor (VLDLR) (lipoprotein binding); IL-8
and cysteine-rich angiogenic inducer 61 (CYR61) (morpho-
genesis); dual-specificity phosphatase (DUSP)1, DUSP5, and
DUSP13 (MAPK signaling); Fos (FOS), EGF receptor 1
(EGR1), v-maf musculoaponeurotic fibrosarcoma oncogene
homolog B (MAFB), nuclear receptor 4A1 (NR4A1), paired
like homeodomain factor 1 (PROP1), transforming growth
factor-1 (TGFB1), basic leucine zipper transcription factor,
ATF-like (BATF), and CaM-binding transcription activator 2
(CAMTA2) (regulation of transcription); and chemokine (C-
X-C motif) ligand 2 (CXCL2), DnaJ (Hsp40) homolog B1
(DNAJB1), HSPA1A, HSPA1L, HSPA6, HSPH1, neutrophil
cytosolic factor 2 (NCF2), and adrenomedullin (ADM) (re-
sponse to stress) (Supplemental Material, Additional Data File
2). Most of these biological processes or molecular functions
are associated with toxicological responses.
Significantly downregulated genes were identified only at
the higher copper concentrations (400 and 600 M) except for
Fig. 1. Principal component analysis of copper-responsive genes. A and
B: three-dimensional representations of the first three principal components for
all genes (A) and two views of those that have a 2-fold (false discovery rate
P 0.05) change in expression (B). Copper concentrations are represented by
red symbols (100 M), blue symbols (200 M), green symbols (400 M), and
purple symbols (600 M). Cells were exposed to the four concentrations of
copper for 4 h (triangles), 8 h (squares), 12 h (diamonds), and 24 h (hexagons).
Table 1. Summary of differentially expressed genes
Responsive Genes
Downregulated Upregulated
100 M copper
4 h ND 10
8 h ND 9
12 h ND 6
24 h ND 11
200 M copper
4 h ND 29
8 h ND 8
12 h ND 20
24 h 1 19
400 M copper
4 h 6 119
8 h 37 137
12 h 97 200
24 h 113 209
600 M copper
4 h 173 361
8 h 335 384
12 h 732 791
24 h 496 500
HepG2 cells were exposed to 100, 200, 400, and 600 M copper for 4, 8,
12 and 24 h. Shown are numbers of responsive genes with a 2-fold change
in expression with false discovery rate (FDR) P 0.05. ND, none detected.
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one gene, THC1991570 [G protein-coupled receptor 110
(GPR110)], which was identified at 200 M (24 h), suggesting
that the suppression of transcription is a toxicological response.
The genes that showed the largest fold decrease in expression
included apolipoprotein A-V (APOA5), apolipoprotein C-III
(APOC3), lipase A (LIPA), solute carrier family 27A2
(SLC27A2), and phospholipase A2, group XIIB (PLA2G12B)
(lipid metabolism and transport); arginase 1 (ARG1) and phenyl-
Table 2. Significantly enriched GO categories for upregulated genes
Copper Concentration, M
100 200 400 600
4 h exposure
Transition metal ion binding (8)* Transition metal ion binding (12)* Cadmium ion binding (7)* Cell cycle arrest (7)*
Response to stimulus (10)* Cell cycle arrest (4)* Cysteine metabolism (3)*
Copper ion binding (8)* Protein kinase inhibitor activity (5)*
Cysteine metabolism (3)* Response to stress (33)*
Kinase inhibitor activity (3)* Transcription corepressor activity (10)*
Response to stress (19)* Transition metal ion binding (32)*
Transcription corepressor activity (5)* MAPK phosphatase activity (4)
Cell growth (6) Negative regulation of apoptosis (12)
Glutathione biosynthesis (2) Negative regulation of transcription (11)
Muscle cell differentiation (3) Protein folding (14)
Protein amino acid O-linked
glycosylation (3)
Regulation of erythrocyte differentiation (2)
Substrate-bound cell migration\cell
extension (2)Protein dimerization activity (12)
Regulation of transcription, DNA
dependent (26)
Taxis (5)
Transcription factor activity (15)
8 h exposure
Transition metal ion binding (7)* Transition metal ion binding (7)* Cadmium ion binding (7)* Cell fate determination (4)*
Cell fate determination (3)* Cell growth (11)*
Cell growth (6)* Cysteine metabolism (3)*
Copper ion binding (9)* MAPK phosphatase activity (4)*
Cysteine metabolism (3)* Protein folding (17)*
MAPK phosphatase activity (2)* Protein dimerization activity (20)*
Protein dimerization activity (9)* Transition metal ion binding (73)*
Protein folding (11)* Cell cycle arrest (8)
Glutathione biosynthesis (2) Negative regulation of apoptosis (10)
IGF binding (3) Protein kinase inhibitor activity (5)
LDL receptor activity (2) Regulation of erythrocyte differentiation (2)
Muscle cell differentiation (3) Regulation of transcription (70)
Oxygen and ROS metabolism (4) Ribosome assembly (3)
Regulation of nitric oxide
biosynthesis (2)
Transcription corepressor activity (9)
Ubiquitin-protein ligase activity (9)
Response to stress (19)
Transition metal ion homeostasis (3)
12 h exposure
Cadmium ion binding (2)* Transition metal ion binding (10)* Cadmium ion binding (7)* Copper ion homeostasis (2)*
Copper ion binding (2)* Copper ion binding (8)* Cysteine metabolism (3)*
Cysteine metabolism (3)* Glutathione metabolism (3)*
Glutathione biosynthesis (2)* MAPK phosphatase activity (3)*
MAPK phosphatase activity (3)* Muscle cell differentiation (3)*
Muscle cell differentiation (3)* Regulation of apoptosis (23)*
Negative regulation of apoptosis (9)* Response to stress (99)*
Response to stimulus (40)* Transition metal ion binding (57)*
Cell fate determination (4) Antibacterial humoral response (2)
Collagen catabolism (3) Calcium-dependent protein binding (3)
Hemopoiesis (7) Cell cycle arrest (8)
Peptidoglycan metabolism (3) Cell growth (14)
Ribosome assembly (2) Endosome organization and biogenesis (2)
GTPase activity (13)
Kinase inhibitor activity (4)
LDL receptor activity (3)
Protein dimerization activity (24)
Small GTPase-mediated signal
transduction (21)
Transcription corepressor activity (9)
Transcription factor activity (68)
Continued
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alanine hydroxylase (PAH) (organic acid metabolism); fibrinogen
-chain (FGA), fibrinogen -chain (FGB), fibrinogen -chain
(FGG), erythropoietin (EPO), and angiopoietin-like 1 (AN-
GPTL1) (circulation, coagulation, and wound healing); haptoglo-
bin (HP) and haptoglobin-related protein (HPR) (hemoglobin
binding); ankyrin repeat domain 15 (ANKRD15), BRCA1-asso-
ciated RING 1 (BARD1), and centromere protein F (CENPF)
(regulation of the cell cycle); and serpin peptidase inhibitor A4
(SERPINA4), serpin peptidase inhibitor I1 (SERPINI1), and re-
version-inducing cysteine-rich protein with kazal motifs (RECK)
(serine -type endopeptidase inhibitor activity) (Supplemental Ma-
terial, Additional Data File 2).
PCA
To examine the relationship among the 16 treatment condi-
tions, PCA was performed using both whole microarray and
differentially expressed gene datasets. The first three principal
components were visualized, and the genes that significantly
contributed to these principal components were identified. For
the whole microarray dataset, the first three principal compo-
nents explained only 58.9% of the variability in the data;
however, a separation between the lower (100 and 200 M)
and higher (400 and 600 M) copper concentrations along the
first principal component was observed (Fig. 1A). This trend
was more clearly demonstrated in the analysis of the differen-
tially expressed gene dataset, where the first three principal
components represented 88.7% of the variability (Fig. 1B). The
lower copper concentrations were tightly grouped in both
analyses. These results suggested that there are similar tran-
scriptional profiles after exposure to the low copper concen-
trations. In contrast, the PCA results suggested that the expres-
sion profiles for higher copper concentrations were different
from those for lower copper concentrations. Furthermore, the
expression profile for 400 M was dissimilar to that of the 600
M profile.
Sixty genes that had the largest contribution to each princi-
pal component (both positive and negative) using the differen-
tially expressed gene dataset were identified (Supplemental
Material, Additional Data File 3). GO analysis showed that the
genes contributing to the principal components were mostly
associated with cellular lipid metabolism, cholesterol biosyn-
thesis, complement activation, fatty acid binding, lipid trans-
port, mitosis, positive regulation of transcription, protein fold-
ing, small GTPase-mediated signal transduction, and xenobi-
otic metabolism. These molecular functions and biological
processes were related with a variety of physiological and
toxicological responses and were consistent with those of
differentially expressed genes (Tables 2 and 3).
GO Analysis of Differentially Expressed Genes
GO analysis was used to place the gene expression data into
a biological and functional context. Summaries of the enriched
GO categories (P 0.005) for up- and downregulated genes
are shown in Tables 2 and 3, respectively. The genes that are
contained within the GO categories can be found in the
Supplemental Material (Additional Data File 4). The number
and diversity of enriched GO categories increased in a dose-
and time-dependent manner for both up- and downregulated
genes. That is, the number of differentially expressed genes
increased and the cognate number of enriched GO categories
increased as copper exposures went from physiological to
toxicological.
At lower copper concentrations (100 and 200 M), transi-
tion metal ion binding was the only GO category identified for
upregulated genes at all exposure times. For genes upregulated
by 400 and 600 M copper at all exposure times, enriched GO
categories also included transition metal ion binding and re-
sponses to stress/stimulus. In addition, several GO categories
were significantly enriched at both high concentrations, includ-
ing cysteine metabolism, kinase inhibitor activity, transcription
corepressor activity, and cell cycle arrest at4hofexposure;
cysteine metabolism, cell fate determination, MAPK phospha-
tase activity, protein folding, and protein dimerization activity
at8hofexposure; MAPK phosphatase activity, cysteine
Table 2.—Continued
Copper Concentration, M
100 200 400 600
24 h exposure
Transition metal ion binding (8)* Transition metal ion binding (10)* Cadmium ion binding (7)* Cadmium ion binding (7)*
Calcium ion binding (22)* Cell fate determination (4)*
Cell fate determination (3)* Copper ion binding (9)*
Copper ion binding (8)* Hemopoiesis (9)*
Hemopoiesis (6)* Muscle cell differentiation (5)*
Muscle cell differentiation (3)* Positive regulation of cell proliferation (12)*
Positive regulation of cell
proliferation (7)*
Protein dimerization activity (18)*
Regulation of cell migration (5)*
Protein dimerization activity (10)* Angiogenesis (8)
Endothelial cell migration (2)* Cell growth (12)
Cysteine metabolism (3) Glycerol kinase activity (2)
Glutathione biosynthesis (2) Growth factor activity (14)
LDL receptor activity (2) GTPase regulator activity (20)
Protein amino acid O-linked
glycosylation (3)
MAP kinase phosphatase activity (3)
Protein kinase inhibitor activity (6)
Ribosome assembly (2) Small GTPase mediated signal
transduction (22)
Shown are Gene Ontology (GO) categories with P 0.005. Responsive genes were 2-fold upregulated with FDR P 0.05. Numbers in parentheses are
numbers of genes in the GO category. *Common GO categories between two or more copper concentrations.
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metabolism, glutathione biosynthesis, muscle cell differentia-
tion, and regulation of apoptosis at 12 h of exposure; and cell
fate determination, hemopoiesis, positive regulation of cell
proliferation, and protein dimerization activity at 24 h of
exposure (Table 2). There were also enriched GO categories
that mapped to specific treatment conditions: oxygen and ROS
metabolism and IGF binding (400 M, 8 h); ubiquitin-protein
ligase activity (600 M, 8 h); endosome organization and
Table 3. Significantly enriched GO categories for downregulated genes
Copper Concentration, M
400 600
4 h exposure
Spliceosomal snRNP biogenesis (2)
DNA repair (7)
Response to stress (14)
8 h exposure
Hemoglobin binding (2)* Hemoglobin binding (2)*
Polysaccharide biosynthesis (2)* Oligosaccharide biosynthesis (2)*
Negative regulation of the Wnt receptor signaling pathway (2) Acylglycerol biosynthesis (2)
Cell cycle checkpoint (7)
Centrosome duplication (2)
DNA repair (17)
DNA replication (14)
Establishment of chromosome localization (2)
G
2
phase of the mitotic cell cycle (2)
Phosphoinositide-mediated signaling (8)
Response to stress (30)
rRNA binding (3)
tRNA modification (2)
12 h exposure
Cellular lipid metabolism (12)* Cellular lipid metabolism (58)*
Bile acid catabolism (2) Lipid transport (8)*
Cholesterol binding (2) Lipoprotein metabolism (6)*
Cholesterol metabolism (4) Acyl-CoA binding (3)
Hemoglobin binding (2) Alcohol metabolism (19)
Platelet activation (2) Amine metabolism (26)
Regulation of body fluids (4) Chromatin assembly (6)
Serine-type endopeptidase inhibitor activity (5) Creatine biosynthesis (2)
Wound healing (4) Deoxyribonucleotide biosynthesis (2)
Hydrolase activity (45)
Inositol 1,4,5-trisphosphate receptor activity (2)
Inositol 1,4,5-trisphosphate-sensitive calcium-release channel activity (2)
Mammary gland development (2)
Nitrogen compound metabolism (29)
Organic acid metabolism (39)
Oxidoreductase activity (33)
Response to endogenous stimulus (15)
Response to stress (27)
Ubiquinone biosynthesis (3)
24 h exposure
Blood pressure regulation (3)* Blood pressure regulation (5)*
Cholesterol metabolism (4)* Cholesterol biosynthesis (6)*
Coagulation (5)* Coagulation (11)*
Lipid transport (5)* Lipid transport (13)*
Serine-type endopeptidase inhibitor activity (5)* Serine-type endopeptidase inhibitor activity (12)*
Steroid biosynthesis (4)* Cellular lipid metabolism (57)*
Lipoprotein metabolism (5) Alcohol metabolism (28)
Bile acid catabolism (2) B cell-mediated immunity (7)
Hemoglobin binding (2) Complement activation (7)
Lipid binding (9) Creatine biosynthesis (2)
Cytolysis (4)
Hormone metabolism (10)
Lipid transporter activity (13)
Nickel ion binding (2)
Oxidoreductase activity (52)
Positive regulation of immune response (7)
Serine-type peptidase activity (14)
Xenobiotic metabolism (5)
Shown are Gene Ontology (GO) categories with P 0.005. Responsive genes were 2-fold downregulated with FDR P 0.05. Numbers in parentheses are
numbers of genes in the GO category. *Common GO categories between two or more copper concentrations. snRNP, small nuclear ribonucleoprotein.
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biogenesis (600 M, 12 h); and angiogenesis and glycerol
kinase activity (600 M, 24 h). LDL receptor activity, ribo-
some assembly, and small GTPase-mediated signal transduc-
tion were the enriched GO categories that mapped at two or
three conditions of higher copper concentrations (Table 2).
For genes downregulated by 400 and 600 M copper, most
of the identified GO categories were related to biomolecule
metabolism including binding and transport, polysaccharide
metabolism, alcohol metabolism, lipid transport and metabo-
lism, amino acid derivative metabolism, and hormone metab-
olism (Table 3). A larger number of enriched GO categories
identified for the 400 M copper treatment also mapped at 600
M copper. In addition, as the exposure time increased, there
was a concomitant increase in the diversity of GO categories
and the number of genes contained in each category. Some of
the enriched GO categories were mapped at specific condi-
tions: negative regulation of the Wnt receptor signaling path-
way (400 M, 8 h); platelet activation (400 M, 12 h);
phosphoinositide-mediated signaling (600 M, 8 h); inositol
1,4,5-trisphosphate receptor activity (600 M, 12 h); and B
cell-mediated immunity, complement activation, and xenobi-
otic metabolism (600 M, 24 h). Hemoglobin binding and
DNA repair were mapped at two conditions of higher copper
concentrations (Table 3).
These results clearly show that copper toxicity results in the
disruption of biomolecule metabolism, regulation of the cell cycle
and transcription, and affects the expression of genes associated
with multiple intracellular signal transduction pathways.
Cluster Analysis
K-means clustering for 16 experimental conditions and
2,312 differentially expressed genes resulted in 12 clusters for
genes and 2 clusters for experimental conditions (Fig. 2). The
experimental conditions of 400 and 600 M copper (at all
exposure times except 4 h) and 100 and 200 M copper formed
separate clusters. These clusters corresponded to physiological
and toxicological responses to copper exposure.
Genes that were downregulated by 400 and 600 M copper
at 12 and 24 h of exposure grouped in clusters 1 and 8. Cluster
1 includes GO categories of alcohol dehydrogenase activity,
steroid biosynthesis, cell cycle arrest, and estrogen and xeno-
biotic metabolism (Table 4; the genes associated with each
cluster are listed in Supplemental Material, Additional Data
File 5). Genes in cluster 8 are related to lipoprotein metabo-
lism, glutathione transferase activity, and blood pressure reg-
ulation (Table 4). Another group of genes that were downregu-
lated by 400 and 600 M copper at 4, 8, and 12 h exposures
were in cluster 2 . Their biological and molecular functions
included phosphoinositide-mediated signaling, cell cycle, and
nucleotide biosynthesis (Table 4).
Genes that were upregulated by 600 M copper at 4, 8, and
12 h exposure grouped into cluster 3 and had biological
functions: ubiquitin cycle, transcriptional repressor activity,
chemokine activity, and response to stimulus (Table 4). MT
genes grouped into cluster 5 showed the highest sensitivity to
Fig. 2. K-means clustering of copper-responsive genes. K-means clustering
was performed with 2,132 genes, which were differentially regulated by
1.5-fold in at least 4 of 16 conditions and have 80% expression data in 16
conditions, using Cluster 3.0. Clustering results were visualized with Java
Treeview 1.0.7. The experimental cluster is shown on the horizontal axis and
each gene cluster is marked by a number (clusters 1–12). We performed Gene
Ontology analysis with gene lists from each cluster through the Gene Ontology
Tree Machine. Gene ontologies and associated genes can be found in Table 4
and the Supplemental Material (Additional Data File 5), respectively.
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copper exposure and were upregulated under all treatment
conditions. Cluster 7 was composed of genes upregulated
predominately by 600 M copper for 12 and 24 h. It was
enriched in GO categories of IB kinase/NF-B cascade, PKC
inhibitor activity, regulation of cell growth, cytoskeletal pro-
tein binding, and nucleotide biosynthesis (Table 4). Genes that
were upregulated by 400 and 600 M of copper at most of the
exposure times grouped into clusters 9 and 11. Genes in cluster
9 included muscle cell differentiation, regulation of cell
growth, IGF binding, regulation of transcription, and MAPK
phosphatase activity. Biological and molecular functions of the
genes in cluster 11 included cell fate determination, notch
binding, activation of MAPKKK activity, cell cycle arrest,
cysteine metabolism, protein kinase inhibitor activity, and LDL
receptor activity.
Many of the downregulated genes grouped together in clus-
ters 6 and 12. Each of these clusters had GO categories
associated with immune function, such as histamine receptor
activity in cluster 6 and the immune response in cluster 12.
Table 4. Enriched GO categories for K-means clusters
Cluster 1
Voltage-gated sodium channel activity
Alcohol dehydrogenase activity
Oxidoreductase activity\acting on the aldehyde or oxo group of donors
Aminomethyltransferase activity
Carboxylic acid metabolism
Steroid biosynthesis
Cell cycle arrest
Negative regulation of caspase activity
Estrogen metabolism
Glucuronosyltransferase activity
Xenobiotic metabolism
Neurotransmitter uptake
Serine-type endopeptidase inhibitor activity
Cluster 2
Establishment of chromosome localization
G
2
phase of the mitotic cell cycle
Mitosis
Spindle organization and biogenesis
Nucleosome assembly
Phosphoinositide-mediated signaling
Fatty acid -oxidation
Microspike biogenesis
Pyrimidine deoxyribonucleotide biosynthesis
Racemase and epimerase activity
Response to DNA damage stimulus
Ribose phosphate diphosphokinase activity
Cluster 3
1-Phosphatidylinositol-4-phosphate 5-kinase activity
Chemokine activity
Chemotaxis
Cyclin-dependent protein kinase inhibitor activity
Endosome organization and biosynthesis
NAD
ADP-ribosyltransferase activity
Negative regulation of transcription, DNA dependent
Regulation of transcription from RNA polymerase II promoter
Transcriptional repressor acitivity
Response to unfolded protein
Trans-1,2-dihydrobenzene-1,2-diol dehydrogenase activity
Transition metal ion binding
Ubiquitin cycle
Ubiquitin-dependent protein catabolism
Ubiquitin-protein ligase activity
Cluster 4
Cell adhesion
Cluster 5
Transition metal ion binding
Cluster 6
cAMP-dependent protein kinase regulator activity
Detection of stimulus during sensory perception
Histamine receptor activity
Regulation of neurotransmitter levels
Synaptic transmission\cholinergic
Cluster 7
Apical junction assembly
Cytoskeletal protein binding
Myofibril assembly
Establishment and/or maintenance of chromatin architecture
Regulation of cell growth
Positive regulation of cell proliferation
S100 binding
Glycerol kinase activity
Glycerol-3-phosphate metabolism
GTPase activator activity
Regulation of GTPase activity
IB kinase/NF-B cascade
PKC inhibitor activity
Pyridine nucleotide biosynthesis
Continued
Table 4.—Continued
Cluster 8
Bile acid catabolism
Glutathione transferase activity
Hemoglobin binding
Lipid transporter activity
Lipoprotein metabolism
Polysaccharide biosynthesis
Blood pressure regulation
Wound healing
Cluster 9
Collagen catabolism
Peptidoglycan metabolism
IGF binding
Regulation of cell growth
MAPK phosphatase activity
Myeloid cell differentiation
Protein dimerization activity
Protein folding
Response to unfolded protein
Cluster 10
Nucleosome assembly
Serine family amino acid metabolism
Cluster 11
Activation of MAPKKK activity
Alkanesulfonate biosynthesis
Cysteine metabolism
Negative regulation of apoptosis
Cell cycle arrest
Regulation of cyclin-dependent protein kinase acitivity
Cell fate determination
Notch binding
Cell motility
Growth factor activity
Chemokine activity
Inorganic anion exchanger activity
LDL receptor activity
Phosphofructokinase activity
Protein dimerization activity
Protein kinase inhibitor activity
Cluster 12
Cell migration
Immune response
Transmembrane receptor acitivity
Potassium ion binding
Voltage-gated potassium channel activity
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Average linkage hierarchical clustering of the dataset used in
the K-means analysis was also performed (Fig. 3). As shown in
the experimental conditions dendrogram, the higher (400 and
600 M) and lower (100 and 200 M) copper concentrations
were clearly separated, which is consistent with PCA and
K-means clustering. At higher copper concentrations, upregu-
lated genes formed seven clusters and down-regulated genes
formed three clusters (Fig. 3). All of the clusters, except
clusters 4 and 5, have correlations of 0.94 on the gene array
dendrogram. Correlations for clusters 4 and 5 were 0.84 and
0.90, respectively.
Enriched GO categories for the hierarchical clusters are
shown in Table 5 (genes associated with each cluster are listed
in Supplemental Material, Additional Data File 5). While most
of the GO categories identified in the hierarchical clusters
overlap with those from K-means clustering, there were several
that appeared only in the hierarchical clusters. Clusters of
downregulated genes had GO categories of electron transport
and EGF receptor activity. GO categories of upregulated genes
included calcium-dependent protein binding, cellular morpho-
genesis, chromosome organization and biogenesis, glutathione
disulfide oxidoreductase activity, G protein-coupled receptor
binding, histone deacetylase activity, protein modification, and
regulation of proteolysis.
Integration of Microarray Data Into Protein
Interaction Networks
Interactomes were identified based on copper concentration
or exposure time using Cytoscape and the jActiveModule (34).
The top 15 interactomes for each copper concentration or each
exposure time are shown in the Supplemental Material (Sup-
plemental Tables 1 and 2, respectively). GO analysis of the
core genes in the copper concentration interactomes showed
that the genes were associated with cell ion homeostasis and
protein biosynthesis at lower copper concentrations (100 and
200 M) and responses such as sulfur compound biosynthesis,
MAPK signaling, and transcriptional repressor activity at
higher concentrations (400 and 600 M) (Supplemental Ma-
terial, Supplemental Table 1). Genes of interactomes at higher
copper concentrations included IL-8, heme oxygenase (decy-
cling) 1 (HMOX1), BRCA1, and ubiquitin-conjugating en-
zyme E2C (UBE2C), which were involved in xenobiotic me-
tabolism, protein ubiquitination, leukocyte extravasation, and
hypoxia signaling. Similar to what was observed above, as the
concentration of copper increased, there was an increase in the
number of genes linked to each of the core genes. GO analysis
of interactomes by exposure time showed some exposure
time-specific GO categories, including positive regulations of
the IB kinase/NF-B cascade at 4 and 8 h and structural
constituent of the ribosome at 8 h (Supplemental Material,
Supplemental Table 2).
The first neighbors of each module (1.5-fold up- or down-
regulated) were also identified, and GO analysis of the neigh-
bors was performed (Supplemental Material, Supplemental
Tables 1 and 2 and Additional Data File 6). The number of
differentially expressed first neighbors per each active module
Fig. 3. Hierarchical clustering of copper-responsive genes. Average linkage
hierarchical clustering was performed with the dataset used in the K-means
analysis (2,132 genes, 1.5-fold change in expression in at least 4 of 16
conditions and have 80% expression data in 16 conditions). Clustering
results were visualized with Java Treeview 1.0.7. The gene cluster dendrogram
is shown on the vertical axis, and the experimental cluster dendrogram is on the
horizontal axis. Gene Ontology analysis was performed with gene lists from
each cluster through the Gene Ontology Tree Machine. The results of this
analysis are shown in Table 5 and the Supplemental Material (Additional Data
File 5).
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increased in a dose- and exposure time-dependent manner,
similar to what was observed above. The enriched GO catego-
ries for each copper concentration or exposure time from the
interactome analysis were consistent with those obtained using
the differentially regulated genes (Tables 2 and 3).
Pathway Mapping
IPA revealed that copper significantly (P 0.05) affected
the expression of genes participating in various canonical
signaling pathways (Table 6). IL-10 signaling (anti-inflamma-
tory action) was the most prevalent and was identified in 9 of
16 conditions. EGF, neurotrophin/tyrosin receptor kinase
(TRK), and IL-2 pathways were identified only at lower copper
concentrations (100 and 200 M). Toll-like receptor, IL-10,
IL-6, hypoxia, and IGF-I signaling mapped to both low and
high copper concentrations. The primary focus genes modulat-
ing these signaling pathways at lower copper concentrations
were JUN and FOS (Fig. 4; Supplemental Material, Additional
Data File 7). Death receptor, xenobiotic metabolism, and
protein ubiquitination signaling were identified only at higher
copper concentrations. This further supports the hypothesis
that copper modulates the expression of genes associated with
toxic responses at higher concentrations. Leukocyte extravasa-
tion and sonic hedgehog signaling also mapped to higher
copper concentrations. This is the first observation that copper
exposure may modulate leukocyte extravasation and sonic
hedgehog signaling pathways.
Functional networks with a significance of P 0.05 were
also identified. These networks were associated with cellular
development, cellular growth and proliferation, gene expres-
sion, cell death, cell cycle, cell signaling, lipid metabolism,
amino acid metabolism, carbohydrate metabolism, cell-to-cell
signaling and interactions, DNA replication, recombination
and repair, posttranslational modification, and free radical
scavenging (Table 7; Supplemental Material, Additional Data
File 7).
DISCUSSION
Copper is an essential metal; however, excessive levels can
lead to intracellular toxicity and pathologies. Transcriptomes
were generated from HepG2 cells exposed to four concentra-
tions of copper for four time periods. Expression profiling
showed that the number of up- and downregulated genes
increased as concentration and/or exposure time increased
(Table 1). There was also a concomitant increase in the number
and diversity of GO categories and interacting partners (Tables
2 and 3 and Supplemental Material, Supplemental Tables 1 and
2). Principal components, K-means and hierarchical clustering,
Cytoscape, and Ingenuity pathway analyses indicated that the
exposure conditions induce physiological responses at low
(100 and 200 M) copper concentrations and toxicological
responses at high (400 and 600 M) copper concentrations.
Physiological Responses to Copper Exposure
HepG2 cells were exposed to levels of copper that occur in
the environment and that humans may encounter (1). Approx-
imately 10 and 20 genes were upregulated at 100 and 200 M
copper, respectively. At these concentrations, the number of
differentially expressed genes and the diversity of GO catego-
ries were unaffected by exposure time (Tables 1–3). This
Table 5. Enriched GO categories for hierarchical clusters
Cluster 1
Cell cycle arrest
Alcohol metabolism
Phosphagen metabolism
Cellular lipid metabolism
Lipid binding
Electron transport
Blood pressure regulation
Protein tyrosine/serine/threonine phosphatase activity
Oxidoreductase activity
Glutathione transferase activity
Cluster 2
Regulation of mitosis
Caspase inhibitor activity
Cytoskeleton-dependent intracellular transport
Cellular lipid metabolism
Glycine catabolism
EGF receptor activity
Cluster 3
Phosphoinositide-mediated signaling
Response to DNA damage stimulus
RNA metabolism
Endoribonuclease activity
Cluster 4
Response to stimulus
Heat shock protein binding
G protein-coupled receptor binding
Transcription cofactor activity
Protein kinase inhibitor activity
Cluster 5
Positive regulation of IB kinase/NF-B cascade
Regulation of proteolysis
Proteasome complex (sensu Eukaryota)
Cellular morphogenesis
Calcium-dependent protein binding
Glutathione disulfide oxidoreductase activity
Metalloendopeptidase inhibitor activity
GTPase activator activity
Cholesterol metabolism
Transition metal ion binding
Transcriptional repressor activity
Cluster 6
Transition metal ion binding
Cluster 7
Chromosome organization and biogenesis
Regulation of transcription, DNA dependent
Histone deacetylase activity
Amino acid-polyamine transporter activity
Protein modification
Zinc ion binding
Transferase activity\transferring phosphorous-containing groups
Cluster 8
Cofactor activity
Caspase activity
B cell differentiation
Glycoprotein biosynthesis
tRNA aminoacylation for protein translation
Glycerol kinase activity
Cluster 9
Regulation of the cell cycle
Regulation of DNA-dependent transcription
Transcription regulator activity
Proteasome regulatory particle
Cluster 10
Cell fate determination
Protein modification
Regulation of DNA-dependent transcription
Response to abiotic stimulus
Transition metal ion binding
G protein-coupled receptor binding
Regulation of protein kinase activity
MAPK phosphatase activity
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suggests that cells can adequately accommodate these concen-
trations of copper to maintain the metal at homeostatic, non-
toxic levels. In addition, the lack of time dependence in the
response observed at 100 and 200 M copper suggests that
longer exposures would not alter the expression pattern. That
is, metal chelation by MTs and the activity of copper trans-
porters are sufficient to protect cells and cells will not be
overwhelmed at these concentrations.
The dominant physiological response to copper exposure
was an increase in the copper binding capacity. Genes encod-
ing the metal-binding protein MT were the most responsive/
sensitive to copper exposure. Changes in multiple MT genes
(MT1A, MT1B, MT1E, MT1F, MT1G, MT1J, MT1K, MT1X, and
MT2A) were observed at low copper concentrations at all
exposure times. To maintain homeostatic levels of copper, cells
use a combination of metal-regulated import, export, and
sequestration mechanisms (15). In most organisms, MTs play
central roles in the homeostasis of essential metals such as zinc
and copper (17, 24, 66). Additionally, pathway mapping
showed that neurotrophin/TRK and EGF signaling were af-
fected at lower copper concentrations. Genes that are associ-
ated with these pathways include FOS, JUN, and phosphoino-
sitide 3-kinase regulatory subunit 3 (PIK3R3) (Supplemental
Material, Additional Data File 7), factors that have been
implicated in the regulation of MT transcription (3, 16).
Toxicological Responses to Copper
HepG2 cells were exposed to supraphysiological levels of
copper, which may occur in the environment and in cases of
human genetic disease. At high copper concentrations, time
significantly affected the numbers of differentially expressed
genes (Table 1), suggesting that the toxicogenomic response to
copper is a product of both concentration and exposure time. In
addition, the number of downregulated genes increased only at
toxic levels, suggesting that the suppression of gene expression
by copper may be a toxicological response.
The cellular and molecular mechanisms underlying copper-
regulated gene expression and toxicity have been investigated
in yeast, mouse fibroblasts, and rodent strains with mutations in
Atp7b (5, 28, 33, 69). Atp7b
/
mice demonstrate intracellular
copper accumulation, low serum oxidase activity, and in-
creased copper excretion in the urine and liver pathology,
similar to Wilson’s disease patients (12, 32). Transcriptome
analysis of the Atp7b
/
mouse liver revealed copper-induced
alterations of lipid metabolism and cholesterol homeostasis,
which are also observed in Wilson’s disease patients (33). In
addition, MT genes and genes associated with the cell cycle
and chromosome structure were upregulated. Genes encoding
proteins involved in cholesterol metabolism were significantly
downregulated in the Atp7b
/
liver (32, 33). Similar changes
Table 6. Significant canonical pathways
Exposure Time, h
4 8 12 24
100
M copper
IL-10 signaling
IL-2 signaling
EGF signaling
Neurotrophin/TRK signaling
Toll-like receptor signaling
IGF-I signaling
IL-6 signaling
200
M copper
IL-10 signaling IGF-I signaling IL-10 signaling IGF-I signaling
IL-6 signaling IL-10 signaling IL-6 signaling IL-2 signaling
IL-2 signaling IL-2 signaling IL-2 signaling IL-10 signaling
EGF signaling EGF signaling EGF signaling EGF signaling
Neurotrophin/TRK signaling Neurotrophin/TRK signaling Neurotrophin/TRK signaling
Toll-like receptor signaling Toll-like receptor signaling Toll-like receptor signaling
Hypoxia signaling in the
cardiovascular system
Hypoxia signaling in the cardiovascular
system
400
M copper
IL-10 signaling Protein ubiquitination pathway Xenobiotic metabolism signaling Leukocyte extravasation signaling
Death receptor signaling Leukocyte extravasation signaling IL-10 signaling IL-10 signaling
IL-6 signaling Toll-like receptor signaling Xenobiotic metabolism signaling
Toll-like receptor signaling
IGF-I signaling
Leukocyte extravasation signaling
600
M copper
Sonic hedgehog signaling Protein ubiquitination pathway Xenobiotic metabolism signaling Leukocyte extravasation signaling
Death receptor signaling Sonic hedgehog signaling IL-10 signaling
IL-6 signaling Hypoxia signaling in the cardiovascular
system
Xenobiotic metabolism signaling
IGF-I signaling
TRK, tyrosine receptor kinase.
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were observed in the levels of expression for these genes in
copper-exposed HepG2 cells. MT genes were significantly
upregulated at all copper concentrations. Pathways associated
with lipid metabolism, cholesterol synthesis, and the cell cycle
were significantly mapped at only higher copper concentrations
(400 and 600 M).
Transcriptome changes in HepG2 cells after exposure to 100
M copper for 0 –72 h have also been studied (50). This study
(50) revealed that after 24 h of exposure, copper significantly
upregulated genes involved in heavy metal detoxification
(MTs), oxidative stress, protein modification/renaturation/
ubiquitination, electron transport, signaling, and glutathione
biosynthesis (3-fold, P 0.05). The expression data pre-
sented in this report agreed with this study in terms of heavy
metal detoxification, where MTs were significantly upregu-
lated at all 16 conditions. However, most of the other signifi-
cant upregulated genes in the other study (50) were only
affected after treatments with higher copper concentrations
(200, 400, and 600 M). HSP genes (protein modification) and
HMOX1 (oxidative stress) were upregulated by 200 Mor
higher copper concentrations. Furthermore, most of the genes
involved in glutathione biosynthesis, the ubiquitin pathway,
and signaling in the previous study (50) were upregulated only
by high copper concentrations (400 and 600 M) in the present
study. The difference in response may be attributed to differ-
ences in cell culturing. Muller et al. (50) used serum-free
medium (MEM supplemented with
L-glutamine) for incubation
with copper, whereas we used MEM with 10% heat-inactivated
FBS. Cells treated with metal in serum-containing medium
may be exposed to a lower concentration of metal than those in
serum-free medium. There are components in serum, including
-fetoprotein and albumin, that bind copper with high affinities
(4, 55). This effectively reduces the amount of copper that is
available for cellular uptake.
Toxic concentrations of copper upregulated genes associated
with transcription regulation, apoptosis, the MAPK cascade,
and morphogenesis and downregulated genes involved in the
regulation of DNA replication, DNA damage response/signal
transduction, and biomolecule metabolism (Tables 2 and 3). A
continued examination of the relation between copper exposure
and these processes will provide insights into the mechanisms
of copper hepatotoxicity.
Molecular Mechanisms of Copper Toxicity
When HepG2 cells are exposed to toxic concentrations of
copper, there is a delay in cell cycle progression and an
increase in cell death (7). In trout hepatocytes, copper exposure
leads to cell death through ROS formation (46). In the present
study, genes implicated in caspase activity, cell cycle arrest,
and cyclin-dependent protein kinase inhibition were signifi-
cantly upregulated. Likewise, genes associated with caspase
inhibitor activity and negative regulation of caspase activity
were significantly downregulated (Tables 4 and 5 and Supple-
mental Material, Additional Data File 5). Interactome, cluster-
ing, IPA, and GO analyses indicated that copper modulates
death receptor and TGF-1 signaling pathways. TGF- signal-
ing cooperates with the death receptor apoptotic pathway (Fas
and TNF) and intracellular modulators of apoptosis (p38 and
NF-B) (22, 60). Thus, our results suggest that copper-induced
apoptosis may be caused by modulation of the death receptor
cascade and TGF-1 signaling.
There is a paucity of data describing the adverse effects of
copper exposure on mammalian development, and the molec-
ular mechanisms have not been elucidated. In pregnant rats,
copper exposure caused retardation of embryonic growth and
differentiation, particularly affecting the neural tube (29). Cop-
per is also a potent teratogen for amphibians (31, 45). There are
several reports describing the effects of copper on invertebrate
development. Copper induces developmental abnormalities or
Fig. 4. Representative Ingenuity Pathway Analysis (IPA) networks. Jun and
Fos centered networks identified by IPA showing the interaction between the
significantly regulated genes at 400 M copper after4hofexposure.
Associated genes can be found in the Supplemental Material (Additional Data
File 7). See text for gene descriptions.
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Table 7. Significant functional networks
Exposure Time, h
4 8 12 24
100
M copper
Amino acid metabolism Cancer Amino acid metabolism Amino acid metabolism
Cancer Cell cycle Cancer Cancer
Carbohydrate metabolism Cell death Carbohydrate metabolism Carbohydrate metabolism
Cell cycle Cell morphology Cell cycle Cell cycle
Cell death Cell signaling Cell death Cell death
Cell morphology Cell-to-cell signaling and interactions Cell morphology Cell morphology
Cell signaling Cellular growth and proliferation Cell signaling Cell signaling
Cell-to-cell signaling and
interactions
DNA replication, recombination, and
repair
Cell-to-cell signaling and
interactions
Cellular growth and proliferation
Lipid metabolism
Cellular growth and
proliferation
Free radical scavenging Cellular growth and proliferation
Lipid metabolism DNA replication, recombination, and
repair
Free radical scavenging
DNA replication,
recombination, and repair
Free radical scavenging Gene expression
Gene expression Immune and lymphatic system
development and function
Immune response
Immune and lymphatic system
development and function
Immune response Lipid metabolism
Lipid metabolism Posttranslational modification
Posttranslational modification
200
M copper
Amino acid metabolism Amino acid metabolism Amino acid metabolism Amino acid metabolism
Cancer Cancer Cancer Cancer
Carbohydrate metabolism Carbohydrate metabolism Carbohydrate metabolism Carbohydrate metabolism
Cell cycle Cell cycle Cell cycle Cell cycle
Cell death Cell death Cell death Cell death
Cell morphology Cell morphology Cell morphology Cell morphology
Cell signaling Cell signaling Cell signaling Cell signaling
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions
Cellular growth and
proliferation
Cellular growth and proliferation Cellular growth and proliferation Cellular growth and proliferation
DNA replication, recombination, and
repair
Free radical scavenging
DNA replication, recombination, and
repair
Free radical scavenging
DNA Replication, recombination,
and repair
Free radical scavenging
DNA replication,
recombination, and repair
Free radical scavenging Gene expression Gene expression Gene expression
Gene expression Immune and lymphatic system
development and function
Immune response
Immune and lymphatic system
development and function
Immune response
Immune and lymphatic system
development and function
Immune response
Immune and lymphatic system
development and function
Immune response Lipid metabolism Lipid metabolism Lipid metabolism
Lipid metabolism Posttranslational modification Posttranslational modification
Posttranslational modification
400
M copper
Amino acid metabolism Amino acid metabolism Amino acid metabolism Amino acid metabolism
Carbohydrate metabolism Carbohydrate metabolism Carbohydrate metabolism Carbohydrate metabolism
Cell cycle Cell cycle Cell cycle Cell cycle
Cell death Cell death Cell death Cell death
Cell morphology Cell morphology Cell morphology Cell morphology
Cell signaling Cell-to-cell signaling and
interactions
Cell signaling Cell signaling
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions Cellular development
Cellular growth and proliferation Cellular development Cellular developmentCellular development
DNA replication, recombination, and
repair
Gene expression
Cellular growth and proliferation Cellular growth and proliferationCellular growth and
proliferation DNA replication, recombination, and
repair
DNA replication, recombination, and
repairDNA replication,
recombination, and repair Immune and lymphatic system
development and function
Gene expression Free radical scavenging
Immune and lymphatic system
development and function
Immune response
Gene expression
Immune and lymphatic system
development and function
Free radical scavenging
Immune responseGene expression
Lipid metabolismImmune and lymphatic system
development and function
Continued
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arrest in the sea urchin, oyster, crab, sea squirt, and in insects
(2, 9, 40, 56, 57, 71). Clustering and GO analyses showed that
at high concentrations, copper upregulated genes associated
with cell fate determination and differentiation, embryonic devel-
opment, and cell polarity, including IFN-related developmental
regulator 1 (IFRD1), myeloid cell leukemia sequence 1 (MCL1),
jagged 1 (JAG1), delta-like 1 (DLL1), tissue inhibitor of metal-
loproteinase 1 (TIMP1), MAFB, adenylate cyclase-associated
protein 1 (CAP1), FYVE, RhoGEF, PH domain-containing 6
(FGD6), and IL-11 (Tables 4 and 5 and Supplemental Material,
Additional Data File 5). JAG1 and DLL1 participate in the notch
signaling pathway, which affects the implementation of differen-
tiation, proliferation, and apoptotic programs, providing a general
developmental tool to influence organ formation and morphogen-
esis (6, 52). In addition, IPA revealed that copper could signifi-
cantly modulate sonic hedgehog signaling, which is critical in
vertebrate development (14). These results suggest that the mod-
ulation of notch and sonic hedgehog signaling may be compo-
nents of molecular mechanisms underlying copper-induced devel-
opmental abnormalities.
Effect of Copper on Signal Transduction Pathways
and Transcription
Previous studies have confirmed the effects of metals on the
MAPK and NF-B pathways (for reviews, see Refs. 48 and
68). In addition to these pathways, clustering analyses com-
bined with GO and IPA indicated that copper modulates
hypoxia, Toll-like receptor, IGF-I, EGF, death receptor,
TGF-, notch, and sonic hedgehog signaling pathways (Tables
6 and 7). Copper significantly upregulated the expression of
transcription factors including FOS, FOSB, FOSL1, JUN,
JUNB, MAFB, MAFK, MAFG, and ATF3, which regulate cell
development and differentiation. These results suggest that
copper toxicity may be a consequence of its ability to disrupt
the normal activity of multiple intracellular signal transduction
pathways and transcription factors.
Toxic concentrations of copper also caused a suppression of
gene expression. Clustering and GO analysis suggested that the
mechanistic cause of this suppression includes the upregulation
of genes associated with histone deacetylase activity (HDAC4
and HDAC10). The expression of jumonji domain-containing
2A (JMJD2A), a trimethylation-specific demethylase for his-
tone and a transcriptional repressor, also increased. Copper
also caused increased expression of Bcl-2-associated transcrip-
tion factor 1 (BCLAF1), DNA damage-inducible transcript 3
(DDIT3), E2F transcription factor 6 (E2F6), and ring finger
protein 12 (RNF12), which are transcription repressors and
corepressors. Copper can induce histone hypoacetylation by di-
rectly inhibiting histone acetyltransferase activity or via oxidative
stress (37, 43). Thus, copper-induced transcriptional repression
may be caused by changes in the chromatin structure and alter-
ations in the transcriptional machinery. Additional analyses of
target genes and pathways related with the transcriptional repres-
sion will further explain the effect of copper on transcription.
Conclusions
In this report, the genomic responses to physiological and
toxicological levels of copper were defined. At physiological
levels, the primary response is an increase in the cell’s capacity
to bind/sequester copper. At toxic levels, there is a disruption
in cell signaling, suppression of transcription, and increased
cell death. It is possible that similar transcriptional responses
would be observed in cells exposed to low or physiological
concentrations of zinc. Similar to copper, intracellular homeo-
static levels of zinc are maintained through the action of MT
and zinc transporters (47). Thus, increases in MT mRNA levels
Table 7.—Continued
Exposure Time, h
4 8 12 24
Immune response Posttranslational modification Lipid metabolism Immune response
Lipid metabolism Protein synthesis Posttranslational modification Lipid metabolism
Protein synthesis Protein synthesis Protein synthesis
600
M copper
Amino acid metabolism Amino acid metabolism Amino acid metabolism Amino acid metabolism
Carbohydrate metabolism Carbohydrate metabolism Carbohydrate metabolism Carbohydrate metabolism
Cell cycle Cell cycle Cell cycle Cell cycle
Cell death Cell death Cell death Cell death
Cell morphology Cell morphology Cell morphology Cell morphology
Cell signaling Cell signaling Cell signaling Cell signaling
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions
Cell-to-cell signaling and
interactions
Cellular development Cellular development Cellular development Cellular development
Cellular growth and
proliferation
Cellular growth and proliferation Cellular growth and proliferation Cellular growth and proliferation
DNA replication, recombination, and
repair
Free radical scavenging
DNA replication, recombination, and
repair
Free radical scavenging
DNA replication, recombination, and
repair
Gene expression
DNA replication,
recombination, and repair
Gene expression Gene expression Gene expression Immune and lymphatic system
development and function
Immune response
Immune and lymphatic system
development and function
Immune and lymphatic system
development and function
Immune and lymphatic system
development and function
Immune response Immune response Immune response Lipid metabolism
Lipid metabolism Lipid metabolism Lipid metabolism Posttranslational modification
Posttranslational modification Posttranslational modification Posttranslational modification Protein synthesis
Protein synthesis Protein synthesis Protein synthesis
399HepG2 COPPER TRANSCRIPTOMES
Physiol Genomics VOL 38 www.physiolgenomics.org
Page 14
would be expected in cells exposed to zinc. Changes in the
levels of expression for genes involved in transport, ion ho-
meostasis, metabolism, and responses to oxidative stress were
observed in studies comparing transcriptome profiles of yeast
exposed to copper and zinc. However, significantly different
responses in genes associated with ribosome biogenesis and
carbohydrate and glucose metabolism were observed. In addi-
tion, deletome analysis indicates that genes involved in vacuole
organization are essential for survival in the presence of zinc,
while copper-binding transcription factors are required to pro-
tect cells from copper toxicity (35). These results suggest that
copper and zinc will have overlapping but distinct transcrip-
tional profiles.
GRANTS
This work was supported (in part) by National Institute of Environmental
Health Sciences Grants U19-ES-011375, P42-ES-010356, and Z01-ES-102045
and by the Intramural Research Program of the National Institutes of Health.
RNA labeling, microarray hybridization, and data extraction were performed
by Cogenics (Morrisville, NC).
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  • Source
    • "Of note, CuSO 4 induced the mRNA expression of both HIF-1α (Figure 1A) and VEGF (Figure 1B) in a dose dependent manner, starting from 25 µM and reaching the strongest stimulation upon concentrations ranging from 100 to 200 µM. Taking into account these results and considering that in previous studies relevant biological responses to copper exposure were observed up to 500 µM192021, in the subsequent assays of the current study 200 µM CuSO 4 were used. First, we determined that CuSO 4 up-regulates in a time-dependent manner the mRNA expression of HIF-1α (Figure 1C) and VEGF (Figure 1D) in SkBr3 and HepG2 cells. "
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