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Applied and Environmental Microbiology, November 1998, p. 4346-4352, Vol. 64, No. 11
0099-2240/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
Identification and Quantification of Toxic
Chemicals by Use of Escherichia coli Carrying
lux Genes Fused to Stress Promoters
Oren
Ben-Israel,
Haggit
Ben-Israel, and
Shimon
Ulitzur*
Department of Food Engineering and
Biotechnology, The Technion, Israel Institute of Technology, Haifa
32000, Israel
Received 26 May 1998/Accepted 5 August 1998
 |
ABSTRACT |
The luxCDABE bioluminescence genes of the Vibrio
fischeri lux system have been used as a reporter system for
different stress and regulatory promoters of Escherichia
coli. Selected E. coli strains carrying
lux genes fused to different promoters were exposed to
various toxic chemicals, and the recorded luminescence was used for the
characterization of the biologic signature of each compound. Analysis
of these data with the aid of a proper algorithm allowed quantitative
and qualitative assessment of toxic chemicals. Of the 25 tested
chemicals, 23 were identified by this novel strategy in a 3-h
procedure. This system can also be adapted for the identification of
simple mixtures of toxic agents when the biologic signatures of the
individual compounds are known. This biologic recognition strategy also
provides a tool for evaluating the degree of similarity between the
modes of action of different toxic agents.
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INTRODUCTION |
A general assay for water toxicity
with intact freeze-dried Vibrio fischeri cells has been
widely applied for monitoring industrial water toxicity (7)
and genotoxicity (24). Genetically controlled bacterial
bioluminescence probes have been developed in order to detect
environmental pollutants and stress-inducing chemicals (3, 4, 25,
26). Van Dyk et al. (25, 26) and Belkin et al.
(3, 4) have fused the luxCDABE genes of
V. fischeri to various promoter genes under the control
of several global regulatory circuits, including rpoH,
soxRS, oxyR, fadR, crp,
uspA, and recA. Activation of these promoters
resulted in the development of luminescence at the intensity and with
the kinetics characteristic for each promoter. Belkin et al.
(4) have broadened this principle by applying a panel of
selected stress-responsive promoters to the detection of diverse groups
of toxicants. Other groups have applied other recombinant bacterial
sensors to the determination of specific toxic compounds, such as
certain heavy metals (9, 10, 17, 19); organic compounds,
such as naphthalene (8, 13, 14); and alkanes
(22).
The present report describes a novel method based on the genetic
fusions that were constructed by Van Dyk et al. (25, 26) and
Belkin et al. (3, 4) for the quantitative and qualitative identification of toxic chemicals and for the elucidation of their modes of action.
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MATERIALS AND METHODS |
Bacterial strains and plasmids.
Escherichia coli
RFM443 (galK2 lac74 rpsL200) (15) and
E. coli DE112 (galK2 lac74 rpsL200 tolC)
(25) were provided by R. A. LaRossa (DuPont,
Wilmington, Del.). E. coli JF699 [lacY29 proC24
tsx-63 purE41 
ompA252 his-53 rpsL97
(strR) xyl-14 metB65 cycA1 cycB2 ilv-277] (11) and E. coli SB1803 [thr-1 ara-14
leuB6 
(gpt-proA)62 lacY1 supE44 galK12
rac hisC3 rfbD1 metG83 rpsL25 kdgK51 xyl-5 mtl-1
thi-1 lpcB] (5) were provided by B. J. Bachmann
(E. coli Genetic Stock Center, Yale University, New Haven,
Conn.). E. coli DPD1006 containing the
lon'::lux (rpoH-controlled
protease) fusion plasmid pLonLux2 (25, 27), E. coli DPD2511 containing the
katG'::lux (oxyR-controlled catalase) fusion plasmid pKatGLux2 (3, 25), E. coli DPD2519 containing the
micF'::lux (in the soxRS
regulon; responsive to superoxides) fusion plasmid pMicFLux1 (3,
25), E. coli DPD2540 containing the
fabA'::lux (a
-hydroxydecanoylthioester dehydrase gene under fadR
control) fusion plasmid pFabALux6 (4, 25), E. coli DE135 containing the
uspA'::lux (universal stress) fusion plasmid pUspALux2 (25, 28), and E. coli TV1068
containing the lac'::lux
(-galactosidase) fusion plasmid pLacLux (25) were kindly
provided by S. Belkin (Hebrew University, Jerusalem, Israel) and
R. A. LaRossa. These plasmids are based on the pUCD615 plasmid
(18) carrying the V. fischeri promoterless
luxCDABE genes fused to the above-mentioned
promoter elements.
Media and conditions for growth.
The bacterial strains were
cultivated in Luria-Bertani (LB) broth (16) containing 50 µg of ampicillin (Sigma, St. Louis, Mo.) per ml. The cultures were
grown with shaking in test tubes in a reciprocal shaker (280 rpm) at
37°C. The assay medium contained dilute (3.75%) LB broth in a
salts-buffer mixture (pH 6.9) containing the following: NaCl, 10 g/liter; PIPES (1,4-piperazinediethanesulfonic acid) buffer (Sigma), 5 g/liter; CaCl2 · 2H2O, 0.15 g/liter; and MgSO4 · 7H2O, 0.1 g/liter. The dilute
medium had no significant effect on the level of the luminescence that
developed, while it minimized the interactions of the tested chemicals
with its components. Calcium and magnesium salts and a buffer were
added in order to minimize the potential influences of the hardness and
pH of drinking water samples.
Assay system.
Overnight-grown cultures were diluted 100-fold
in fresh LB broth containing ampicillin (50 µg/ml) and grown with
shaking at 37°C to the early exponential growth phase (optical
density at 600 nm, 0.2). The cells were placed on ice until used. The
chilled bacterial cultures were diluted 1:1 in cold LB broth, and
10-µl aliquots were added to wells of an opaque white microtiter
plate (Dynatech Microfluor) which contained twofold serial dilutions of
the test samples in a final volume of 150 µl of assay medium. The
microtiter plates were incubated at 27°C and analyzed at hourly intervals for luminescence by use of a temperature-controlled (27°C)
microplate recording luminometer (Lucy 1; Anthos Labtech, Salzburg, Austria).
Chemicals.
The sources of the chemicals assayed were as
follows: 2,4-dichlorophenoxyacetic acid (2,4-D),
2,4,5-trichlorophenoxyacetic acid (2,4,5-T), 2,4,6-trichlorophenol
(2,4,6-TCP), 3,5-dichlorophenol (3,5-DCP), fluoranthene, and
pentachlorophenol (PCP) were from Aldrich, Madison, Wis.;
KH2AsO4 and sodium dodecyl sulfate (SDS) were
from BDH, Poole, England; propiconazole was from Ciba-Geigy, Basel,
Switzerland; 4-nitrophenol, benzidine, and cetyltrimethylammonium bromide (CTAB) were from Fluka, Buchs, Switzerland; PbCl2
was from Mallinokrodt, St. Louis, Mo.; CdCl2,
H2O2, HgCl2, and ZnCl2 were from E. Merck AG, Darmstadt, Germany; sodium azide was from Reidel-de Haen, Seelze, Germany; 2,4-dinitrophenol (2,4-DNP), methyl
viologen, NiSO4, and proflavin hemisulfate were from Sigma; phenol was from Spectrum, Redondo Beach, Calif.; and malathion (O,O-dimethyldithiophosphate) and parathion
(diethyl-p-nitrophenyl monothiophosphate) were from Tarsis,
Tel-Aviv, Israel. All the toxicants were of analytical grade. The
toxicants were dissolved in water or ethanol. Ethanol (not exceeding
0.05%) had a negligible effect. The chemicals were doubly diluted in
the assay medium for 11 dilutions; see Table 2 for the initial
concentrations (known concentrations).
Data analysis.
The results presented are the averages of the
values obtained in two to five independent analyses. The relative
activity (RA) values were calculated from the recorded data and used
for classification of the toxicants into groups with SAS software (SAS
Institute Inc., Cary, N.C.) by use of the discriminant analysis (DA)
method (21). The generalized squared distances (GSD) were
calculated with SAS software and clustered into dendrograms by use of
the unweighted pair-group method with arithmetic averages (UPGMA) (20) with the help of Vostorg software (29).
| |
RESULTS |
Identification of toxicants on the basis of their
biologic signatures.
The rationale of our method assumes that
most of the toxicants activate certain lux-fused promoters
in a characteristic pattern. Thus, the level of luminescence reflects
the biologic signatures of these chemicals. Unknown samples were
identified by comparing their biologic signatures to those of known
standards (learning data; see below). The biologic signatures were
determined on the basis of the dependence of the luminescence obtained
at given times on the concentration of the chemical in question and on the basis of the mathematical behavior of this dependence. Curves demonstrating such dependence have been published (3, 4, 25,
26).
In order to obtain maximal sensitivity, each of the plasmids
harboring the lux-fused promoters was stored in
different E. coli mutants, and the best
plasmid-host combinations were selected. For example, it appeared that
E. coli JF699 harboring the
katG'::lux fusion was more sensitive to
heavy metals than the other examined hosts and that E. coli DE112 and E. coli SB1803 harboring the micF'::lux and
uspA'::lux fusions, respectively, were
more sensitive to phenol-related compounds. The response specificity of
these strains was mainly attributed to the different responses of
the various promoters. The different hosts contributed only minor specificity.
Each one of the seven bacterial constructs described in Table
1 was challenged with a series of
concentrations of 25 model
chemicals, and luminescence was determined
after different incubation
periods. Figure
1 shows an example of the kinetics of
light emission
by two bacterial tester strains,
E. coli
DE112 (
lon'::
lux) and
E. coli JF699 (
katG'::
lux), that
were exposed to different concentrations
of 2,4,6-TCP and
CdCl
2. During the first 3 h of incubation,
luminescence
was negligible in the controls, while the luminescence of
cells
that were incubated with the tested toxicants increased after
45 min of incubation, followed by a rapid decrease. For analytical
purposes, the luminescence that developed was examined after 1,
2, and
3 h of incubation at 27°C.
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FIG. 1.
Kinetics of E. coli DE112
(lon'::lux) induction by 2,4,6-TCP (A)
and E. coli JF699
(katG'::lux) induction by
CdCl2 (B) during 3 h of incubation at 27°C. The
toxicants were used at various concentrations. Averages from two
replicates are presented.
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Two parameters,
A and
R2, that led to
an increase in luminescence (in any member of the bacterial assay
panel for each of the
three incubation periods) were calculated for
each chemical.
A was defined as the maximum slope of
the linear regression curve
between the response ratio (RR) (the ratio
between the light level
of the tested sample and that of the control)
and the logarithm
of the concentration (for a concentration range of 1 order of
magnitude), and
R2 was defined as the
linear correlation coefficient of this regression
curve. Using these
two values, we calculated a best-fit parameter,
RA, which was found to
characterize the effect of each chemical
on a specific tester strain:
RA =
0.25 × (
R2)
2 × (log
A + 1). RA is a logarithmic function of
A and a second-order
polynomial function of
R2.
A reflects the activity of a
given toxicant in a given system,
and
R2
minimizes the nonspecific
fluctuations.
Figure
2 shows the RA
values for the 25 chemicals assayed with the members of the bacterial
panel after three incubation periods.
The 25 histograms shown represent
the characteristic biologic
signatures of the tested chemicals. In
order to confirm the specificity
of each biologic signature, the RA
values were analyzed by DA
with SAS statistical software. The DA
classified the unknown samples
into
n groups, where
n was the number of tested chemical standards
(learning
data). In each group, all the unknown chemicals that
shared common
features with a known chemical (standard) were clustered.
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FIG. 2.
Characterization of 25 chemicals in terms of activating
the promoters of seven E. coli strains: A, DE112
(micF'::lux); B, DE112
(lon'::lux); C, DE112
(fabA'::lux); D, RFM443
(lac'::lux); E, JF699
(katG'::lux); F, SB1803
(uspA'::lux); and G, SB1803
(micF'::lux). The biologic signatures
show the activation (calculated as RA) of the stress promoters by 25 chemicals after incubation for 1 h (open bars), 2 h (striped
bars), and 3 h (solid bars). The histograms show average results
for five replicates ± standard deviations.
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|
To test the validity of this approach, five independent replicates were
carried out for each chemical. Four of these were
randomly chosen as
learning data, and one replicate was evaluated
as an unknown; the
entire procedure was repeated five times. Using
this system, we
accurately identified 23.4 ± 1.5 (mean ± standard
deviation) of the 25 tested chemicals. Only eight false-negative
results were obtained out of 125 (25 × 5) examinations. Very
often,
the mistaken identities exhibited high chemical or functional
similarities to the true identities. Thus, Zn
2+ was
misclassified as Cd
2+, SDS was misclassified as
CTAB, and malathion was misclassified
as
parathion.
Estimation of the concentrations of the tested chemicals.
In
addition to the chemical identification, the procedure
described here allows an estimation of the concentrations of the tested chemicals. This estimate was determined by comparison of the
regression lines between RR and C to the regression lines of
the standards, where C is the concentration of either the
known standard (micrograms per milliliter) or the unknown chemical (in arbitrary units). This procedure was carried out for each member of the
bacterial assay panel for three incubation periods. Of the 21 values
used (seven strains, three periods), the seven upper and lower
predicted concentrations were excluded. The final predicted concentration for each replicate of each chemical was estimated by
averaging the seven remaining values by use of a method similar to the
trimmed Spearman-Karber method (12). This procedure was carried out for five replicates. The final predicted concentration for
each chemical (Table 2) was calculated by
averaging the values for the three replicates remaining after exclusion
of the higher and lower replicates, as in the trimmed
Spearman-Karber method (12). The coefficient of
correlation between the known and predicted concentrations was 0.96.
Identification of mixtures of toxicants.
The method
described here can also be adapted for the identification of a
mixture of two toxic agents. The biologic signature of a
mixture can be predicted by use of the learning data
characterizing each toxicant separately. The RR for the mixed chemicals
was predicted on the basis of the RRs for the potential individual
components of the mixture at different ratios. For mixtures of two
toxicants, the predicted RR (PRR) for each concentration of the mixture
was calculated on the basis of the RRs for the individual toxicants, RR1 and RR2, as follows:
With this identification procedure, the characterized biologic
signature (as determined by the PRR) was evaluated for each
potential
mixture of toxicants. An example of the prediction of
the biologic
signatures of two mixtures of toxicants is shown
in Fig.
3. The coefficient of correlation between
the observed
and the predicted 21 RA values for the mixtures of
CdCl
2 and PCP
was 0.93; that for the mixtures of
NaN
3 and 2,4,5-T was 0.85.
As previously shown (
3,
27), some mixtures had synergistic
effects on the level of
luminescence. We observed synergy with
a mixture of sodium azide and
2,4,5-T for strains harboring the
fusions
micF'::
lux,
lon'::
lux, and
lac'::
lux as well as with a
mixture of
CdCl
2 and PCP for strains harboring the fusions
katG'::
lux,
lon'::
lux, and
lac'::
lux.
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FIG. 3.
Estimation of the biologic signatures of
CdCl2-PCP mixtures and sodium azide-2,4,5-T mixtures,
as determined by the activation of the promoters of seven E. coli strains: A, DE112
(micF'::lux); B, DE112
(lon'::lux); C, DE112
(fabA'::lux); D, RFM443
(lac'::lux); E, JF699
(katG'::lux); F, SB1803
(uspA'::lux); and G, SB1803
(micF'::lux). The histograms show the
predicted activation and the real activation (calculated as RA) of
the stress promoters by the two mixtures at effective concentrations of
16 µg/ml (CdCl2), 32 µg/ml (PCP), 4,096 µg/ml (sodium
azide), and 2,048 µg/ml (2,4,5-T) after incubation for 1 h (open
bars), 2 h (hatched bars), and 3 h (solid bars). The biologic
signatures of the single components are shown in Fig. 2. The histograms
show average results for two replicates ± standard deviations.
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Elucidation of the modes of action of the toxic chemicals.
The degree of similarity in the modes of action of the 25 toxicants tested was evaluated according to the GSD obtained in the DA
by use of the UPGMA. This method calculates the distances between the
tested groups on the basis of the GSD of all pairs of toxicants
(20). The dendrogram shown in Fig.
4 classified the 25 tested chemicals into
several distinct clusters. The subgroups with a relative similarity
above 60% consisted of closely related chemicals. For example, five
metals (Pb2+, Hg2+, Cd2+,
As5+, and Zn2+) were clustered in one subgroup,
the chlorinated phenol derivatives (2,4-D, 2,4,6-TCP, 2,4,5-T, and PCP)
were clustered in another, and the biocides malathion, parathion, and
propiconazole were clustered in a third subgroup.
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FIG. 4.
Dendrogram for the 25 tested chemicals determined on the
basis of GSD clustering by use of the UPGMA. The dendrogram clusters
resulted from four replicates.
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DISCUSSION |
In this report, we describe a biologic strategy allowing
qualitative and quantitative identification of toxic chemicals
according to their ability to activate certain E. coli
promoters. The basis of the strategy was to monitor the specific
response of the bacterial tester strains to different toxicants and
then to define the parameters which identify the response profiles. In
most cases, the correlation between the RR and the logarithm of the
concentration of the tested analyte was linear for a certain range of
concentrations. Determining these data for each bacterial strain after
three periods of incubation enabled the characterization of each tested
sample. The identification of unknown toxicants was performed by a
computerized comparison of the biologic signatures of the tested
analytes to the learning data for all the tested chemicals. The
described method provided an accurate identification for more than 90%
of the chemicals tested. For hundreds of potential chemicals or more,
classification only to families of chemicals would be provided.
Unlike other lux-fused biosensors (8-10, 13, 14, 17,
19, 22), the suggested method overcomes the requirement for a
specific recognition probe for each of the tested analytes. By use of
the correlations between the RR and the concentrations of the tested
analytes, it is also possible to estimate the concentration of a given toxicant.
A good correlation (R2, 0.96) was found between
the known and the predicted concentrations of the 25 tested samples.
The concentrations of more than 95% of these chemicals were estimated
within the range of ±35% of the true concentrations. The accepted
variation for other water toxicity tests is ±30% of the mean in a
95% confidence interval (1).
Mixtures of two toxicants assayed at sublethal concentrations showed
new biologic signatures and a good correlation (R2,
0.85) with the profiles calculated from the individual
components (Fig. 3). This algorithm is not based on simple addition of
the RRs of the individual components; it takes into account possible synergy between components of mixtures, as previously shown by Van Dyk
et al. (27) and Belkin et al. (3), as well as
possible combinations of toxicants when one toxicant increases
luminescence and the other decreases it. Hence, when the profiles of
the individual components are known, it is possible to identify the
chemicals in a mixture of toxicants. Nevertheless, small deviations
between observed and predicted profiles will always occur, since the
described model generalizes the possible patterns of synergy that may occur.
Another advantage of the described approach is the information provided
about the modes of action of the toxic chemicals. The described method
quantifies the activities of the chemicals as a typical set of 21 channels (biologic signatures) characterizing each toxicant. Hence,
similarity between the biologic signatures of the different chemicals
represents similarity between these chemicals (Fig. 2). For example,
the heavy metals Cd2+, Pb2+, Hg2+,
and Zn2+ had similar profiles, the chlorophenoxyacetic acid
compounds 2,4,5-T and 2,4-D had similar profiles, and the biocides
malathion, parathion, and propiconazole had similar profiles. These
similarities were also reflected in the dendrogram based on the
calculated distances between the biologic signatures of the different
toxicants (Fig. 4). In addition to the similarities between common
featured chemicals, different chemicals with similar modes of action
were also clustered together, e.g., the clustering of Cd2+
and H2O2 in one subgroup. This phenomenon can
be explained by the high level of similarity of their modes of action;
both Cd2+ and H2O2 are known
activators of the oxidative stress system in different organisms
(2, 6, 23).
The strategy described in this report may be useful for prescreening
for toxic agents in environmental samples and downstream processes and
for prescreening of biologic and fermentation processes for sudden
changes in chemical composition. Suspicious findings will need to be
verified by more established physical-chemical methods. This strategy
may also be used as a simple tool for primary elucidation of the modes
of action of toxic chemicals by comparison of the biologic
signatures of the studied chemicals to the biologic signatures of
well-known chemicals.
| |
ACKNOWLEDGMENTS |
We thank R. A. LaRossa (DuPont) and S. Belkin (Hebrew
University) for kindly providing the plasmids; B. J. Bachmann
(E. coli Genetic tock Center, Yale University)
for kindly providing some E. coli strains; and S. Belkin, N. Ulitzur, S. Yannai, and J. Kuhn for critical review of the manuscript.
This work was conducted in the framework of doctorate studies in the
graduate school of the Technion.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department of
Food Engineering and Biotechnology, The Technion, Israel Institute of Technology, Haifa 32000, Israel. Phone: 972-4-8293071. Fax:
972-4-8320742. E-mail: moni{at}tx.technion.ac.il.
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Applied and Environmental Microbiology, November 1998, p. 4346-4352, Vol. 64, No. 11
0099-2240/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
