Environ Sci Pollut Res (2013) 20:803–811
DOI 10.1007/s11356-012-1280-7
RESEARCH ARTICLE
Mechanisms of toxicity of triphenyltin chloride (TPTC) determined
by a live cell reporter array
Guanyong Su & Xiaowei Zhang & Jason C. Raine &
Liqun Xing & Eric Higley & Markus Hecker &
John P. Giesy & Hongxia Yu
Received: 8 August 2012 / Accepted: 24 September 2012 / Published online: 6 November 2012
# Springer-Verlag Berlin Heidelberg 2012
Abstract Triphenyltin chloride (TPTC), which has been extensively used in industry and agriculture, can occur at concentrations in the environment sufficient to be toxic. Here,
potency of TPTC to modulate genes in a library containing
1,820 modified green fluorescent protein (GFP)-expressing
promoter reporter vectors constructed from Escherichia coli
K12 strains was determined. Exposure to TPTC resulted in 22
(fold change > 2) or 71 (fold change > 1.5) differentially
expressed genes. The no observed transcriptional effect
(NOTEC) and median transcriptional effect concentrations
(TEC50) were determined to be 0.036 and 0.45 mg/L in E.
coli. These responses were 1,230 and 97 times more sensitive
than the acute median effect concentration (EC50) required to
inhibit growth of cells, which demonstrated that this live cell
array represents a sensitive method to assess toxic potency of
chemicals. The 71 differentially expressed genes could be
classified into seven functional groups. Of all the altered
genes, three groups which encoded for catalytic enzymes,
regulatory proteins, and structural proteins accounted for
28 %, 18 %, and 14 % of all altered genes, respectively. The
pattern of differential expression observed during this study
was used to elucidate the mechanism of toxicity of TPTC. To
determine potential relationships among genes that were
changed greater than 2.0-fold by exposure to TPTC, a correlation network analysis was constructed, and four genes were
related to aroH, which is the primary target for metabolic
regulation of aromatic biosynthesis by feedback inhibition in
bacteria. The genes rnC, cld, and glgS were selected as potential biomarkers for TPTC, since their expression was more
than 2.0-fold greater after exposure to TPTC.
Keywords High throughput . NOTEC . Biomarker .
Correlation network . Toxicity assessment . Bacterial .
Genomics . Organotin
Responsible editor: Philippe Garrigues
Electronic supplementary material The online version of this article
(doi:10.1007/s11356-012-1280-7) contains supplementary material,
which is available to authorized users.
G. Su : X. Zhang : L. Xing : J. P. Giesy : H. Yu
State Key Laboratory of Pollution Control and Resource Reuse &
School of the Environment, Nanjing University,
Nanjing, China
J. P. Giesy
Department of Biomedical Veterinary Sciences and Toxicology
Centre, University of Saskatchewan,
Saskatoon, SK S7N 5B3, Canada
G. Su : J. C. Raine : E. Higley : M. Hecker : J. P. Giesy
Toxicology Centre,
University of Saskatchewan,
Saskatoon, SK S7N 5B3, Canada
J. P. Giesy
Department of Biology and Chemistry and State Key Laboratory
in Marine Pollution, City University of Hong Kong,
83 Tat Chee Avenue,
Kowloon, Hong Kong SAR, China
M. Hecker
School of Environment and Sustainability,
University of Saskatchewan,
Saskatoon, SK S7N 5B3, Canada
X. Zhang (*) : H. Yu (*)
School of the Environment, Nanjing University,
Nanjing 210089, China
e-mail: howard50003250@yahoo.com
e-mail: yuhx@nju.edu.cn
804
Introduction
Organotin compounds are widely used with a worldwide
production that has increased almost tenfold over the past
40 years (Liu et al. 2006). These compounds are extensively
used in industry and agriculture as biocides, fungicides, antifouling agents in boat paint, wood preservatives, catalysts, and
stabilizers for polyvinylchloride polymers (Fent and Muller
1991; Grote et al. 2007; Sano et al. 2010). Of the organotin
compounds, triphenyltin chloride (TPTC) is one of the most
potent. Concentrations less than 40 ngTPTC/ml can affect the
immune system of cultivated clams (Tapes philippinarum)
(Cima et al. 1998) and enhance histone acetyltransferase
activity, which is a type endocrine-disruption (Osada et al.
2005). TPTC can also inhibit gap junctional intercellular
communication in WB-F344 rat liver epithelial cells (Lee et
al. 2010) and cause loss of post-implantation of embryos,
failure to implant, and effects on body mass, size, and structure of testicles and lesser fertility of Holtzmann rats (Ema
2000). Because of bio-concentration and accumulation potentials (Horiguchi et al. 1997; Shim et al. 2000), TPTC could
also pose a risk to human health through dietary exposure.
While it has been established that TPTC is more toxic than
some other pollutants, the mechanisms by which it causes
toxicity remain mostly unknown.
Genome-wide transcriptional investigations using whole
cell arrays is a useful toxicogenomic approach to characterize modes of toxic action of chemicals (Zhang et al. 2011; Su
et al. 2012). Whole cell arrays consist of an assortment of
genetically engineered microorganisms tailored to respond
to activation of specific promoters. Fusion of stress promoters to reporter genes (such as fluorescent proteins) is
the basic concept for detection of cellular signaling (Elad et
al. 2010). Compared with microarray technology, whole cell
arrays avoid complex protocols of pre-treatment and highcost experimental materials, have fewer interferences, and
can provide temporal resolution (Onnis-Hayden et al. 2009).
Furthermore, the short testing time (less than 3 h) makes live
cell arrays rapid, economical, high-throughput biosensor
systems for detecting toxicity and determining effects on
specific signaling pathways. Consequently, this assay is not
specific to bacteria and can represent responses of systems
that are conserved in multiple organisms, including metazoans (Zhang et al. 2011).
Here, toxicity of TPTC was assessed by application of a
comprehensive cell array of transcriptional fusions of GFP
to each of 1,820 different gene promoters in Escherichia
coli K12. Profiles of concentration- and time-dependent
expression of genes caused by TPTC were obtained over
3-h exposures to 0.1, 1, or 10 mg TPTC/L. All altered genes
were classified into seven groups according to their known
function (Supplementary Table 1), and the pattern of which
was hypothesized to be indicative of the specific molecular
Environ Sci Pollut Res (2013) 20:803–811
signaling pathways affected by exposure to TPTC. Based on
these profiles of gene expression, a correlation network was
generated to elucidate potential correlations of differentially
expressed molecular pathways.
Materials and methods
Microbial live cell array
The microbial promoter collection was produced by
researchers at the Weizmann Institute of Science (Rehovot,
Israel) and includes more than 1,900, out of 2,500 promoters in the entire genome of E. coli K12 strain MG1655
(Zaslaver et al. 2006). Each of the reporter strains is
coupled with a bright, fast-folding GFP fused to a fulllength copy of an E. coli promoter in a low-copy plasmid.
This enables measurement of gene expression within
minutes with high accuracy and reproducibility. All clones
were grown at 37 °C in lysogeny broth (LB)–Lennox
media plus 25 mg/L kanamycin.
Exposure to TPTC
Triphenyltin chloride was purchased from Sigma Aldrich
(#245712, St. Louis, MO, USA). A TPTC stock solution
(20,000 mg/L) was prepared in dimethyl sulfoxide (DMSO),
and other stock solutions were made by serial dilution with
DMSO. Chemical-induced effects on growth of cells were
assessed by measuring optical density (OD) at 600 nm by a
Fluostar OPTIMA microplate reader (BMG Labtech, Offenburg, Germany). For each concentration, three replications
were conducted. Specifically, growth and division of E. coli
was determined after 4 h of incubation at 37 °C. The OD600
value is the most commonly used to estimate the E. coli cell
density and corresponds with cell number in a given E. coli
culture volume (Luo et al. 2011; Su et al. 2012).
To measure expression of genes, assay plates were prepared
by adding 72 μL of LB medium to each well in black 384-well
optical bottom plates (NUNC, Rochester, NY, USA). E. coli
strains were inoculated in the 384-well plate from a 96-well
stock plate by disposable replicators (Genetix, San Jose, CA,
USA). Cells were incubated at 37°C for a 2.5 h in 384-well
plates, and then 3.8 μL of DMSO (solvent control) or TPTC
stock solutions were added into individual wells of the 384well plates to make final concentrations of 0, 0.1, 1, or 10 mg
TPTC/L. GFP intensity in each well was consecutively monitored every 10 min during 3 h by use of the Fluostar OPTIMA
120 microplate reader (excitation/emission, 545 nm/590 nm).
Effects of TPTC on growth of cells were assessed by OD
measurement prior to the promoter reporter assay. None of
the three concentrations of TPTC caused significant effects
on growth of cells during 10 h of exposure.
Environ Sci Pollut Res (2013) 20:803–811
805
Data analyses
All data analyses have been described previously (Zhang et
al. 2011; Su et al. 2012). To select the promoter reporters
that were significantly differentially expressed in response
to exposure with TPTC, a linear regression model was
applied. The response measured as GFP fluorescence intensity was fitted to a function of time for each promoter
reporter strain. Classification and visualization of the gene
expression were derived by ToxClust (Zhang et al. 2009).
Determination of NOTEC
The No Observed Transcriptional Effect Concentration
(NOTEC) was calculated based on the number of promoter
strains in the library of 1,820 genes that were significantly
altered by TPTC. The No Observable Effect Concentration
(NOEC) based on inhibition of growth by TPTC was determined. Then, the percentage of genes differentially expressed
at different concentrations relative to the NOEC was calculated. Finally, a generalized linear binomial model was used to
assess the concentration-dependent response curve of the percentage of the differentially expressed genes. The NOTEC
was calculated as the maximum concentration of TPTC at
which less than 5 % of the genes were differentially expressed
upon chemical exposure compared with control (Su et al.
2012).
Pathway analysis
Lists of genes were developed for further analysis based on
statistical significance and 1.5- or 2.0-fold change cutoffs.
Differentially expressed genes were classified into seven
groups based on their biological functions (www.ecogene.org,
www.geneontology.org, and www.ecoliwiki.net; Supplementary Table 1). Correlation network analyses were conducted using
the “GeneNet” package by R software (http://cran.r-project.org/
web/packages/GeneNet/). This method was used for analyzing
gene expression (time series) data with focus on the inference
of gene networks (Opgen-Rhein and Strimmer 2007).
Results
Inhibition of E. coli growth by TPTC
TPTC inhibited growth of E. coli cells in a concentrationdependent manner (Fig. 1). The median effect concentration
(EC50), NOEC, and lowest observed effect concentration of
TPTC on cell growth were 43.7, 10.0, and 20.0 mg/L, respectively. Three concentrations, 0.1, 1, and 10 mg/L, were selected as exposure concentrations to assess the effects of TPTC on
transcriptional expression profiles of E. coli. At these
Fig. 1 Inhibition profile of E. coli growth by different concentrations
of TPTC (data points were shown with mean values of three
replications)
concentrations, cell growth would not be affected. The NOEC
was included to enable determination of the NOTEC.
Gene expression profiles
Expression of genes by the microbial reporter strains was
modulated by TPTC in a time- and concentration-dependent
manner. Exposure to TPTC resulted in fewer upregulated
promoter strains than downregulated strains during the 3h exposure for genes selected by both the 1.5-fold (Fig. 2b)
and 2.0-fold cut-offs (Fig. 2a). Of the 22 promoter reporter
strains selected using a 2.0-fold cut-off, 2 and 20 strains were
up- and downregulated, respectively. Of the 71 promoter
reporter strains selected by application of a 1.5-fold cut-off,
the greatest downregulation of as much as eightfold relative to
the controls was observed for rrnC. Furthermore, this gene
was separated from all other groups when data was subjected
to analysis by ToxClast. Other than rrnC, expression of 16 and
54 genes were up- and downregulated, respectively (Fig. 2b).
Alteration of gene expression by TPTC was concentrationdependent. Using a 2.0-fold change as a cut-off, exposure to 0.1,
1.0, or 10 mg, TPTC/L significantly altered expression of 2, 6,
and 21 promoters, respectively. Only one strain was responsive
to all three concentrations (Fig. 3). Of the 1,820 genes, 71 were
differentially expressed with a maximum absolute fold change
of at least 1.5 (Fig. 3). Among those genes, 17, 33, and 71
promoters were differentially expressed after exposure to 0.1,
1.0, or 10 mg TPTC/L, respectively, and 17 strains were responsive to all three concentrations. Exposure to 0.1 or 1.0 mg
TPTC/L resulted in one gene, yhhY, being completely different
from those modulated by exposure to 10 mg TPTC/L (Fig. 3).
Determination of no observed transcriptional effect
concentration
Ratios of differentially expressed genes were 24 %,
47 %, and 100 % after exposure to 0.1, 1.0, or 10 mg
806
a
10 mg/L
1 mg/L
0.1 mg/L
Conc.
Fold Change
30
2
1
0.5
0.2
0
0.1
Fig. 2 Real-time gene
expression profiles of
differentially expressed gens in
E. coli after exposure to 0.1,
1.0, or 10 mg TPTC/L as
represented by the lower,
middle, and upper bands in
each gene column, respectively.
Classification and visualization
of the gene expression were
derived by ToxClust.
Dissimilarity between genes
was calculated by the
Manhattan distance between the
gene expressions at all the
concentration versus time
combinations. Fold change of
gene expression is indicated by
color gradient, and the time
course of expression changes is
indicated from left to right. a
Clustering of the timedependent expression of the
TPTC altered genes selected by
2.0-fold change cut-off. b
Clustering of the timedependent expression of the
TPTC altered genes selected by
1.5-fold change cut-off
Environ Sci Pollut Res (2013) 20:803–811
60
90
120
Time (min)
150
180
rmf
skp
ybcW
lacZ
yjdI
mglB
insA_7
menG
uspF
yifE
11
cld
glgS
wrbA
add
galU
aqpZ
mcrA
yhhY
yhcG
rrnC
serB
aroH
TPTC/L, respectively. The NOTEC was 0.036 mg TPTC/
L, a concentration at which fewer than 5 % of genes
were differentially expressed relative to controls. The
median transcriptional effect concentration (TEC50) was
0.45 mg TPTC/L.
In previous publications, acute toxicity of TPTC ranged
from 2.0 to 4,455 μg/L in nine aquatic species that
included algae, fish, mollusks, worms, daphnids, and
mysid shrimp (Fig. 4) (Goel and Prasad 1978; Goel and
Srivastava 1981; Wong et al. 1982; Devries et al. 1991;
Nagase et al. 1991; Fargasova 2002, 1997). Of these
species, green algae (Ankistrodesmusfalcatus ssp. acicul)
were the most sensitive species with an EC50 of 2.0 μg
TPTC/L. Based on effects of TPTC on cell growth, E. coli
was less sensitive than all other species and endpoints
(Supplementary Table 3). However, the transcriptional
endpoints, NOTEC and TEC50, in E. coli measured in
this study were 1,230- and 97-fold more sensitive than the
acute EC50 for inhibition of cell growth. Aligned onto the
sensitive distribution curve of aquatic species, the NOTEC
and TEC50 are equivalent to the 47th and 74th centile
species, respectively.
Classification of differentially expressed genes
All differentially expressed genes were classified into one of
seven groups: enzyme (pepE, aroH, serB, add, gcvT, lacZ,
cysK, serA, dcd, dmsA, ligB, fpr, galU, gadB, metA, icd, lipA,
cueO, aroK, and ttdA), regulatory protein (cysB, cspD, ftsK,
gadX, phoP, mcrA, rmf, rsd, wrbA, fdhD, flgM, manX, and cld),
structural protein (rpsT, rpsU, mglB, ytfF, b1403, ompC, ompA,
skp, osmC, and aqpZ), rRNA or tRNA (argW, rrnB, rrnD, and
rrnC), stress responsive pathway (evgA, uspB, uspA), amino
acids formation (menG), and unclear function (yfbV, mntR,
yhgF, yjdI, yhcG, yafK, insA_7, yaiE, b3007, yhhY, yeaU, glgS,
proQ, yedW, yedP, ymcC, yfiE, uspF, yifE, and ybcW), which
accounted for 28 %, 18 %, 14 %, 6 %, 4 %, 1 %, and 28 % of
the 71 genes altered by TPTC, respectively. Among these
differentially expressed genes, 22 (31 % of 71) genes’ fold
change was greater than 2.0 (Supplementary Table 4).
Correlation network
A correlation network was constructed to analyze highdimensional data from E. coli gene expression (fold
Environ Sci Pollut Res (2013) 20:803–811
Fig. 2 (continued)
807
b
2
1
0.5
0.2
0.1
Fold Change
b1403
fdhD
ymcC
mntR
yedP
yaiE
gadX
add
dcd
argW
icd
flgM
yafK
cysB
yjdI
lacZ
ttdA
yhhY
yfiE
osmC
serA
ftsK
uspB
mglB
insA_7
ytfF
yeaU
uspA
pepE
yfbV
cueO
ligB
gcvT
yhgF
metA
phoP
fpr
b3007
cysK
serB
aroH
wrbA
skp
menG
cld
glgS
rmf
ybcW
aqpZ
mcrA
rrnD
gadB
manX
proQ
uspF
yifE
ompA
yedW
lipA
rpsU
cspD
evgA
rsd
ompC
aroK
rrnB
rpsT
yhcG
dmsA
galU
rrnC
change >2) after exposure to TPTC based on graphical
Gaussian models (Opgen-Rhein and Strimmer 2007)
(Fig. 5). From their expression profiles after 3 h, correlation
coefficients between each two related nodes (genes) were
calculated. Six of the 22 genes that were altered by more
than 2.0-fold were directly related with aroH, and four
genes (wrbA, cld, rmf, and glgs) exhibited significant
correlations with aroH protein (r>0.89). And four genes
(uspF, aqpZ, galU, aroH) were associated with rrnC. Functions of these genes are given in Supplementary Table 1.
Fig. 3 Concentration-dependent promoter activity of reporter strains
in TPTC exposure. (Venn diagram displayed the differentially
expressed genes selected by 1.5- or 2.0-fold change cut-off at three
different TPTC concentrations including 0.1, 1, and 10 mg/L, which
were marked with red, green, and blue, respectively)
Fig. 4 Species sensitivity distribution using the ecotoxicity data of
TPTC and the data acquired in the present study. Probit model was
fitted for different species. NOTEC and TEC 50 are represented as blue
and red asterisks on the fitted curve, respectively. Detailed ecotoxicity
data are available in the Supplementary Table 2)
808
Fig. 5 Sparse graphical Gaussian model for 22 genes inferred from an
E. coli live cell array data set with 19 data points
Discussion
The fact that transcriptional endpoints measured in this study
were more sensitive than the acute EC50 based on inhibition
of growth of cells demonstrated that effects of TPTC on
transcription as expressed by the NOTEC represent a sensitive
endpoint to assess toxicity of this chemical. This result is
consistent with previous reports that have demonstrated that
the NOTEC is more sensitive than conventional endpoints,
since it reflects sub-lethal and molecular level responses to a
toxicant (Lobenhofer et al. 2004; Poynton et al. 2008). The
relatively inexpensive live cell array could provide a sensitive
tool to assess the toxicity of environmental chemicals in a
short time (3 h). Future studies should evaluate the sensitivity
of live cell array relative to several commonly used test
species for additional chemicals.
The gene expression profile suggested that TPTC can cause
toxicity to E. coli through modulation of enzymes in biochemical
reactions, including pepE, aroH, serB, add, gcvT, lacZ, cysK,
serA, dcd, dmsA, ligB, fpr, galU, gadB, metA, icd, lipA, cueO,
aroK, and ttdA, formation of regulatory proteins including cysB,
cspD, ftsK, gadX, phoP, mcrA, rmf, rsd, wrbA, fdhD, flgM,
manX, and cld, and structural proteins, including rpsT, rpsU,
mglB, ytfF, b1403, ompC, ompA, skp, osmC, and aqpZ, rRNA or
tRNA, including argW, rrnB, rrnD, and rrnC, stress responsive
pathways, evgA, uspB, and uspA, and formation of amino acids
(menG). To our knowledge, this is the first report using the E.
coli whole cell assay to assess toxicity of TPTC. However, the
toxic action of TPTC can be grouped into four general categories: (1) effects on immune function (Nishida et al. 1990), (2)
Environ Sci Pollut Res (2013) 20:803–811
clastogens (Sasaki et al. 1994), (3) cytotoxicity (Snoeij et al.
1985), and (4) inhibition of intercellular gap junctions.
Modulation of enzymes in biochemical reactions might be
one of the most important TPTC-induced toxic pathways,
since genes falling into this functional group accounted for
28 % of the total differentially expressed genes. After exposure to TPTC, 19 enzymes were altered, including αaspartyldipeptidase (pepE), 3-deoxy-D-arabino-heptulosonate-7- phosphate synthase (aroH), 3-phosphoserine
phosphatase (serB), adenosine deaminase (add), aminomethyltransferase (gcvT), β-galactosidase (lacZ), cysteine synthase A (cysK), D -3-phosphoglycerate dehydrogenase
(serA), deoxycytidine triphosphate deaminase (dcd), dimethyl sulfoxidereductase (dmsA), DNA ligase (ligB),
ferredoxin-NADP reductase (fpr), glucose-1-phosphate uridylyltransferase (galU), glutamate decarboxylase B subunit
(gadB), homoserinetranssuccinylase (icd), isocitrate dehydrogenase (metA), isocitrate dehydrogenase (metA), lipoate
synthase (lipA), shikimate kinase I (aroK), L-tartrate dehydratase (ttdA). Glucose-1-phosphate uridylyltransferase
(galU) is an enzyme associated with glycogenesis, and its
downregulation by TPTC would inhibit synthesis of UDPglucose from glucose-1-phosphate and UTP (Thoden and
Holden 2007). As a senescence-associated enzyme, downregulation of β-galactosidase (lacZ) might imply that E. coli
is senescent after exposure to TPTC (Pardee et al. 1959).
Transcriptional activities of these two genes were both
downregulated more than 2.0-fold by TPTC. Expression of
3-phosphoserine phosphatase (serB) was upregulated by
more than 2.0-fold, which indicates that a phosphoserine
phosphatase process might be disturbed by exposure to
TPTC (Veiga-da-Cunha et al. 2004). 3-Phosphoserine phosphatase has been shown to be a breast cancer marker molecule (Pestlin et al. 2005), and its altered expression might be
indicative of another new toxicity mechanism by TPTC.
The fact that nearly 18 % of all altered genes were classified
as “regulatory proteins” suggested that TPTC can also cause
toxicity through its disturbance of transcriptional regulators,
activators, or inhibitors, especially for processes involving
DNA. Four genes, cld, mcrA, rmf, and wrbA, were downregulated more than 2.0-fold. Based on downregulation of cld, the
length of the O-antigen component of lipopolysaccharides
would be disturbed after exposure of E. coli to TPTC (Raetz
and Whitfield 2002). As a nuclease, downregulation of mcrA
protein suggested the potential damage to DNA of bacteria
after exposure of TPTC (Anton and Raleigh 2004). The fact
that the ribosome modulation factor was shown to influence
survival of E. coli under acid stress has already been shown by
others (Yamagishi et al. 1993). Thus, downregulation of rmf
could be indicative of damage to this bacterium. Disturbance
of wrbA was proposed to be implicated in protection against
oxidative stress (Burnett et al. 1974).
Structural proteins, such as membrane proteins and water
major intrinsic protein (MIP) channels, represented another
Environ Sci Pollut Res (2013) 20:803–811
protein group altered by TPTC. aqpZ encodes for water MIP
channels (Hovijitra et al. 2009), which was downregulated
by less than 0.5-fold. aqpZ protein’s downregulation would
disturb the cell’s osmoregulatory capacity since it allows E.
coli to adapt to osmotic variations by rapid diffusion of
water molecules. Otherwise, the fact that both skp and ompA
were downregulated by TPTC demonstrated that skp can
bind outer membrane proteins, such as ompA. Lack of skp
protein would lead to accumulation of protein aggregates in
the periplasm, which also implies that skp can recognize
early folding intermediates of outer membrane proteins
(Schafer et al. 1999). The gene mglB, which encodes for
the D-galactose-binding periplasmic protein, was downregulated by less than 0.5-fold. Downregulation of expression of
this protein would inhibit transport of galactose and glucose.
TPTC elicited transcriptional alteration of a group of
“rRNA or tRNA” genes, which produced RNA after expression. All genes in this category were downregulated by
TPTC. rrnC, as one of seven ribosomal RNA operons (Yeon
et al. 2008), was suppressed less than 0.2-fold. This suggested that inhibition of decoding mRNA into amino acids
might be a mechanism of toxicity for TPTC. The gene
menG, which is related to adenosylmethionine and belongs
to the group of “amino acids formation,” was downregulated
by TPTC. This suggested that inhibition of adenosylmethionine formation was another toxicity pathway of TPTC.
TPTC altered transcription of a group of stress responsive
genes, which can be divided into three categories according
to previous studies (Onnis-Hayden et al. 2009): detoxification (uspB), drug resistance/sensitivity (evgA), and general
stress (uspA). Both uspB and evgA are related to compound-/
chemical-induced mortality or stress, such as response to
antibiotics, and were downregulated after exposure to
TPTC. However, uspA was upregulated by TPTC, which
implied that biochemical and biophysical homeostasis of the
cell were disturbed after a 3-h exposure to TPTC.
After construction of the correlation network, significant
relationships among genes altered by TPTC were observed.
Through the process of the network construction, a graphical
Gaussian model, also known as covariance selection or concentration graph, was employed. Based on the constructed
network, two genes, aroH and rrnC, seemed to have been
very actively involved in the response to the exposure with
TPTC and were related with six and four other genes, respectively. The gene aroH encodes 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase (DAHPS), which is feedbackregulated by tyrosine and phenylalanine (Shumilin et al.
2004) and is the primary target for metabolic regulation of
aromatic biosynthesis by feedback inhibition in bacteria and
fungi (Keith et al. 1991). Based on the observed significant
correlations, it is hypothesized that disturbance of DAHPS
would affect four genes including wrbA, cld, rmf, and glgs.
Fold change of expression of rrnC was less than 0.2, and
809
downregulation of expression of this gene might contribute to
or is affected by four related genes (uspF, aqpZ, galU, aroH)
through the gene network.
The results indicate that the E. coli whole cell array has
the potential to identify novel biomarkers for determination
of specific chemical classes in environmental media (Gou et
al. 2010; Watson and Mutti 2004). Three general principles
are proposed for selection of biomarkers of chemical pollution based on use of the E. coli array: (1) The endpoints need
to be chemical-specific; (2) the magnitude of changes in gene
expression should be related to the concentration of chemical; and (3) the change in gene expression should be great
enough that it can be monitored easily. Based on these
criteria, rrnC, cld, and glgS are recommended as potential
biomarkers for TPTC, as their fold changes in expression
were greater than 2 and proportional to concentrations of
TPTC between 0.1 and 10 mg/L (Supplementary Figures 1A,
1B, 1C). cld and glgS genes encode proteins that regulate the
length of the O-antigen component of lipopolysaccharide
chains and can serve as a predictor of synthesis of glycogen.
The gene rrnC directly encodes rRNA. However, to date,
there were no reports that expression of these three genes is
regulated by some specific chemicals. Suitability of these
genes as biomarkers remains to be validated and requires
further investigation into the consistency and TPTCspecificity of their responses. For this purpose, field TPTCsample tests will be performed with these genes in our future
work.
Acknowledgments The research was supported by grants from National Natural Science Foundation of China (NSFC) (grant no.
21007025), Jiangsu Provincial Key Technology R&D Program
(#BE2011776), Jiangsu Provincial Environment Monitoring Station
(Project # 1012), National Science and Technology Major Project (No.
2008ZX08526-003), and a Discovery Grant from the National Science
and Engineering Research Council of Canada (Project # 326415-07), and
a grant from the Western Economic Diversification Canada (Projects #
6578 and 6807). The authors wish to acknowledge the support of an
instrumentation grant from the Canada Foundation for Infrastructure.
Prof. Giesy was supported by the program of 2012 "High Level Foreign
Experts" (#GDW20123200120) funded by the State Administration of
Foreign Experts Affairs, P.R. China. He was also supported by the
Canada Research Chair program, an at-large Chair Professorship at the
Department of Biology and Chemistry and State Key Laboratory in
Marine Pollution, City University of Hong Kong, and the Einstein Professor Program of the Chinese Academy of Sciences. Prof. Zhang was
supported by a Program for New Century Excellence Talents in Universities (Ministry of Education, China). Mr. Guanyong Su was supported by
the Shanghai Tongji Gao Tingyao Environmental Science and Technology Development Foundation (STGEF).
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Mechanisms of Toxicity of Triphenyltin Chloride (TPTC)
Determined by a Live Cell Reporter Array
Guanyong Su1,2, Xiaowei Zhang1,*, Jason C. Raine2, Liqun Xing1, Eric Higley2,
Markus Hecker2,3, John P. Giesy1,2,,4,5, Hongxia Yu1,*
1
State Key Laboratory of Pollution Control and Resource Reuse & School of the
Environment, Nanjing University, Nanjing, China
2
Toxicology Centre, University of Saskatchewan, Saskatoon, SK S7N 5B3, Canada
3
School of Environment and Sustainability, University of Saskatchewan, Saskatoon,
SK, S7N 5B3Canada
4
Department of Biomedical Veterinary Sciences and Toxicology Centre, University of
Saskatchewan, Saskatoon, SK S7N 5B3, Canada
5
Department of Biology and Chemistry and State Key Laboratory in Marine Pollution,
City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR,
China
Corresponding author:
School of the Environment, Nanjing University, Nanjing, 210089, China
Tel: 86-25-89680623
Fax: 86-25-83707304
E-mail:
howard50003250@yahoo.com (Xiaowei Zhang)
yuhx@nju.edu.cn (Hongxia Yu)
Supporting Table 1: Description of Gene Function Groups
Function Group
Enzyme
Regulatory Protein
Structure Protein
rRNA or tRNA
Stress Responsive
Pathway
Amino Acids Formation
Function Unclear
Description
Genes encoded enzymes, which played an important role
in biochemical reaction.
Genes produced protein after gene expression, which were
transcriptional regulators, activators or inhibitors,
especially for DNA process.
Genes produced protein after gene expression, which were
part of organism structure, for example, membrane
protein, water MIP channel,
Genes in the “RNA” group didn't produce protein but
RNA, which might be rRNA or tRNA.
Stress responsive genes.
Genes were related to the formation of amino acids.
Genes' functions were still not very clear right now.
Supporting Table 2: Functions of 71 differentially expressed genes
Type
1
pepE
Protein
function
Classification
(alpha)-aspartyl dipeptidase
Enzyme
3-deoxy-D-arabino-heptulosonate-7-phosphate synthase, tryptophan
2
aroH
Protein
Enzyme
repressible
3
serB
Protein
3-phosphoserine phosphatase
Enzyme
4
add
Protein
adenosine deaminase
Enzyme
5
gcvT
Protein
aminomethyltransferase
Enzyme
6
lacZ
Protein
beta-galactosidase, lac operon
Enzyme
7
cysK
Protein
cysteine synthase A,O-acetylserine sulfhydrolase A subunit
Enzyme
8
serA
Protein
D-3-phosphoglycerate dehydrogenase
Enzyme
9
dcd
Protein
Deoxycytidine triphosphate deaminase
Enzyme
10
dmsA
Protein
dimethyl sulfoxide reductase, chain A
Enzyme
11
ligB
Protein
DNA ligase
Enzyme
12
fpr
Protein
ferredoxin-NADP reductase
Enzyme
13
galU
Protein
glucose-1-phosphate uridylyltransferase
Enzyme
14
gadB
Protein
glutamate decarboxylase B subunit
Enzyme
15
metA
Protein
homoserine transsuccinylase
Enzyme
16
icd
Protein
isocitrate dehydrogenase
Enzyme
17
lipA
Protein
lipoate synthase
Enzyme
18
cueO
Protein
multicopper oxidase with role in copper homeostasis
Enzyme
19
aroK
Protein
shikimate kinase I
Enzyme
20
ttdA
Protein
L-tartrate dehydratase
Enzyme
21
cysB
Protein
Cys regulon transcriptional activator
Regulatory Protein
22
cspD
Protein
DNA replication inhibitor
Regulatory Protein
DNA-binding membrane protein required for chromosome resolution and
23
ftsK
Protein
Regulatory Protein
partitioning
24
gadX
Protein
DNA-binding transcriptional dual regulator
Regulatory Protein
25
phoP
Protein
PhoP transcriptional regulator PhoP transcriptional dual regulator
Regulatory Protein
26
mcrA
Protein
restriction of DNA at 5-methylcytosine residues
Regulatory Protein
27
rmf
Protein
ribosome modulation factor
Regulatory Protein
28
rsd
Protein
regulator of sigma D stationary phase protein, binds sigma 70 RNA
Regulatory Protein
polymerase subunit
29
wrbA
Protein
The purified WrbA protein has NAD(P)H:quinone oxidoreductase activity
Regulatory Protein
30
fdhD
31
flgM
Protein
affects formate dehydrogenase-N
Regulatory Protein
Protein
Negative regulator of flagellin synthesis
Regulatory Protein
32
manX
Protein
PTS system mannose-specific EIIAB component
Regulatory Protein
33
cld
Protein
regulator of length of O-antigen component of lipopolysaccharide chains
Regulatory Protein
34
rpsT
Protein
30S ribosomal subunit protein S20
Structure Protein
35
rpsU
Protein
30S ribosomal subunit protein S21
Structure Protein
36
mglB
Protein
D-galactose-binding periplasmic protein
Structure Protein
37
ytfF
Protein
inner membrane protein
Structure Protein
38
b1403
Protein
IS21 protein 2
Structure Protein
39
ompC
Protein
outer membrane porin protein C
Structure Protein
40
ompA
Protein
outer membrane protein 3a (II*Gd)
Structure Protein
41
skp
Protein
periplasmic molecular chaperone for outer membrane proteins
Structure Protein
42
osmC
Protein
resistance protein, osmotically inducible
Structure Protein
43
aqpZ
Protein
water MIP channel
Structure Protein
The 5S and 23S rRNAs are the RNA components of the large subunit (50S
44
rrnB
rRNA
subunit) of the E. coli ribosome.There are seven ribosomal RNA (rRNA)
RNA
operons, called rrnA, rrnB, rrnC, rrnD, rrnE, rrnG, and rrnH
The 5S and 23S rRNAs are the RNA components of the large subunit (50S
45
rrnD
rRNA
subunit) of the E. coli ribosome.There are seven ribosomal RNA (rRNA)
RNA
operons, called rrnA, rrnB, rrnC, rrnD, rrnE, rrnG, and rrnH
The 5S and 23S rRNAs are the RNA components of the large subunit (50S
46
rrnC
rRNA
subunit) of the E. coli ribosome.There are seven ribosomal RNA (rRNA)
RNA
operons, called rrnA, rrnB, rrnC, rrnD, rrnE, rrnG, and rrnH
47
argW
tRNA
48
evgA
Protein
tRNA(argW) is one of seven arginine tRNAs
RNA
response regulator in two-component regulatory system with EvgS，
Environmental
involved in acid resistance, osmotic adaption, and drug resistance
Stress Response
Component of
49
uspB
Protein
ethanol tolerance protein
Organism
Environmental
50
uspA
Protein
universal stress global stress response regulator
Stress Response
S-adenosylmethionine: 2-demethylmenaquinone methyltransferase proteinE.
Amino Acids
51
menG
Protein
The interaction of RraA with the degradosome is facilitated by protein-RNA
Formation
remodeling via the ATPase activity of RhlB
52
yfbV
Protein
conserved inner membrane protein
Function Unclear
53
mntR
Protein
conserved protein
Function Unclear
54
yhgF
Protein
conserved protein (3rd module)
Function Unclear
55
yjdI
Protein
conserved protein;Uncharacterized protein yjdI
Function Unclear
56
yhcG
Protein
function unknow
Function Unclear
57
yafK
Protein
hypothetical protein
Function Unclear
58
insA_7
Protein
hypothetical protein
Function Unclear
59
yaiE
Protein
hypothetical protein，UPF0345 family，function unknown
Function Unclear
60
b3007
Protein
unknown CDS
Function Unclear
61
yhhY
Protein
predicted acetyltransferase
Function Unclear
62
yeaU
Protein
predicted dehydrogenase D-malate dehydrogenase (decarboxylating)
Function Unclear
63
glgS
Protein
predicted glycogen synthesis protein
Function Unclear
64
proQ
Protein
predicted structural transport element
Function Unclear
65
yedW
Protein
putative 2-component transcriptional regulator（yedV）
Function Unclear
66
yedP
Protein
Putative mannosyl-3-phosphoglycerate phosphatase
Function Unclear
67
ymcC
Protein
putative synthetase
Function Unclear
68
yfiE
Protein
putative transcriptional regulator (LysR family)
Function Unclear
69
uspF
Protein
putative universal stress protein F
Function Unclear
70
yifE
Protein
Similar to Yersinia pestis KIM, hypothetical protein y0333
Function Unclear
71
ybcW
Protein
Uncharacterized protein
Function Unclear
Supporting Table 3: References of NOEC for different species exposure to TPTC
Species Scientific
Species
Name
Group
Ankistrodesmus
falcatus ssp.
acicul (Wong et al.
Concentration
(Days)
(ng/mL)
EC50
8
2.0
4.0×10-1
EC50
NR
9.1×10-1
1.8×10-1
EC50
12
1.1×103
2.3×102
EC50
12
3.5×102
7.0×101
Fish
LC50
2
6.4×101
1.4
1.4
Fish
LOEC
110
2.3×10-1
9.3×10-2
9.3×10-2
Molluscs
LC50
1
6.2×10-4
1.3×10-5
Moss or
Fungi
Scenedesmus
Algae
quadricauda
Moss or
(Fargasova 1997)
Fungi
Scenedesmus
Algae
quadricauda
Moss or
(Fargasova 2002)
Fungi
Scenedesmus
Algae
quadricauda
Moss or
(Fargasova 2002)
Fungi
(De Vries et al. 1991)
Endpoint
of NOEC
(ng/mL)a
Mean of
(ng/mL)
Algae
1982)
Oryzias latipes
Calculation
Duration
4.0×10-1
1.0×102
Oncorhynchus
mykiss (Nagase et al.
1991)
Indoplanorbis
exustus (Goel and
Prasad 1978)
3.7
Indoplanorbis
exustus (Goel and
Molluscs
LC50
1
3.5×102
7.4
Molluscs
LC50
1
4.3×101
9.1×10-1
9.1×10-1
Worms
LC50
4
2.4
5.1×10-2
5.1×10-2
Srivastava 1981)
Lymnaea
acuminate (Goel and
Srivastava 1981)
Tubifex tubifex
(Fargasova 1997)
a
NOEC was calculated following “Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of
Aquatic Organisms and their Uses”, which was drafted by U.S. Environmental Protection Agency.
References:
De Vries H, Penninks AH, Snoeij NJ, Seinen W (1991) COMPARATIVE TOXICITY OF ORGANOTIN
COMPOUNDS TO RAINBOW TROUT ONCORHYNCHUS-MYKISS YOLK SAC FRY. Science of the
Total Environment 103 (2-3):229-244
Fargasova A (1997) Comparative study of ecotoxicological effect of triorganotin compounds on various
biological subjects. Ecotoxicology and Environmental Safety 36 (1):38-42
Fargasova A (2002) Structure-affected algicidal activity of triorganotin compounds. Bulletin of
Environmental
Contamination
and
Toxicology
69
(5):756-762.
doi:10.1007/s00128-002-0125-3
Goel HC, Prasad R (1978) Action of Molluscicides on Freshly Laid Eggs of Snail Indoplanorbis-Exustus
(Deshayes). Indian Journal of Experimental Biology 16 (5):620-622
Goel HC, Srivastava CP (1981) Laboratory evaluation of some molluscicides against french water snails,
Indoplanorbis and Lymnaea species. J Commun Dis 13 (2):121-127
Nagase H, Hamasaki T, Sato T, Kito H, Yoshioka Y, Ose Y (1991) Structure-Activity-Relationships for
Organotin Compounds on the Red Killifish Oryzias-Latipes. Applied Organometallic Chemistry
5 (2):91-97
Wong PTS, Chau YK, Kramar O, Bengert GA (1982) Structure-Toxicity Relationship of Tin-Compounds
on Algae. Canadian Journal of Fisheries and Aquatic Sciences 39 (3):483-488
Supporting Table 4: Classification of differentially expressed genes and proportion of each
groups basing on the function of genes (see in Supplementary Table 1) and fold change.
Fold Change
1.5 - 2.0
2.0 - 3.0
3.0 - 4.0
7.0 - 8.0
Genes
cysK, dcd, dmsA, gadB, fpr, gcvT, pepE, metA, serA, ttdA, icd, ligB,
lipA, cueO, aroK, fdhD, flgM, ftsK, gadX, manX, rsd, phoP, cysB,
cspD, ompA, ompC, osmC, ytfF, rpsT, rpsU, b1403, rrnB, rrnD,
argW, uspA, uspB, evgA, yaiE, yeaU, yedP, yedW, yfbV, proQ, yfiE,
yhgF, ymcC, b3007, yafK, mntR
lacZ, serB, galU, aroH, add, wrbA, cld, rmf, mglB, skp, aqpZ, glgS,
insA_7, uspF, yhcG, yhhY, yifE, yjdI
mcrA, menG, ybcW
rrnC
Number
49
18
3
1
Each color represents one toxic pathway induced by TPTC, and detailed description of each
toxic pathway can be found in Supplementary Table 1. О “enzyme”; О “Regulatory
Protein”; О “Component of Organism”; О “rRNA or tRNA”; О “Environmental Stress
Response”; О “Amino Acids Formation”; О “Function Unclear”.
Supporting Figure 1: Gene expression profiles of three potential biomarkers.