Environ. Sci. Technol. 2008, 42, 8486–8491
Toxicant Identification by a Luminescent Bacterial Bioreporter Panel: Application of Pattern Classification Algorithms TAL ELAD, ETAY BENOVICH, SAGI MAGRISSO, AND SHIMSHON BELKIN* Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem 91904, Israel
Received May 29, 2008. Revised manuscript received August 21, 2008. Accepted September 02, 2008.
Genetically engineered microorganisms, tailored to respond by a dose-dependent signal to the presence of toxic chemicals, are a potentially useful tool for environmental monitoring. One manifestation of this approach is based on a panel of luminescent bacterial bioreporters, harboring fusions of the luxCDABE operon to various stress-responsive gene promoters. Such sensors can report by a dose-dependent luminescent signal on the stress sensed by the cells and thus on the presence of toxic compound(s), but they lack the ability to identify the chemicals involved. Here, we demonstrate how the use of a panel of such sensors might offer a solution to this drawback. Five selected Escherichia coli reporter strains harboring fusions of selected gene promoters (grpE, nhoA, oraA, lacZ, and mipA) to luxCDABE were exposed to five model toxicants and to a toxicant-free control in a 40-repetition format. Each of the six treatments activated different promoters to different extents, producing its own unique fingerprint. Two machine learning schemes were challenged with the obtained data set: Bayesian decision theory and the nonparametric nearestneighbor technique. The Bayesian classifiers performed better and were able to identify the sample’s contents within 30 min with an error rate estimate that did not exceed 3% at a 95% confidence level and with zero false negatives. Performance in tap water and wastewater samples was similar. Given the coming of age of whole-cell sensing devices, pattern classification algorithms such as the ones described here offer a step toward the incorporation of reporter cells into future biosensor formats, including whole-cell arrays.
Introduction The awareness of the necessity for monitoring the presence of accidentally or intentionally introduced toxicants has led to an increased need for early warning devices that can detect such chemicals in the environment in general and in water in particular. There are two general approaches for monitoring chemicals in aquatic environments. The traditional approach, based on chemical or physical analysis, allows highly accurate and sensitive determination of the exact composition of any sample. However, such methodologies, while essential for regulatory purposes, fail to provide data * Corresponding author phone: 972-2-6584192; fax: 972-2-6585559; e-mail:
[email protected]. 8486
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on the bioavailability of pollutants and their effects on living systems. In order to meet this need, a complementary bioassay-based approach was promoted, according to which numerous systems have been developed, ranging from liveorganism assays, such as fish toxicity tests, to others based on subcellular components or enzymes (1-3). Unicellular microorganisms, in particular bacteria, are advantageous for such purposes. Their large population size, high growth rate, low cost, easy maintenance, and rapid response make them an appealing option for pollution monitoring. An additional attractive characteristic of bacteria is that they can be genetically engineered to respond by a detectable signal to prespecified changes in their environmental conditions. Numerous such bioreporters have been proposed, many of which are bioluminescent strains, harboring a fusion of the luxCDABE operon to various stress- or chemical-specific gene promoters (4-7). Examples include the construction of bacterial reporter strains responsive to heat shock (8), oxidative stress (9, 10), and genotoxic agents (11-13), as well as to specific toxic chemicals such as heavy metals (14, 15), BTEX (benzene, toluene, ethyl benzene, and xylene) compounds (16) and polycyclic aromatic hydrocarbons (17). Similarly to other bioassays, microbial whole-cell sensing systems test the effect of the target chemical(s), but unlike analytical techniques, they often lack the ability to actually identify the chemical. While, as indicated above, many chemical-specific bioreporters have been described, their applicability is very limited when one is trying to identify unknown pollutants; the use of numerous bioreporters specific for each of a practically unlimited spectrum of target chemicals makes no practical sense. In a partial response to this challenge, Belkin et al. (18) have suggested and described the use of a panel of specific stress-responsive luminous bacteria to detect water toxicity and indicate the type of biological stress involved. This approach was later utilized by Ben-Israel et al. (19), who used discriminant analysis to identify toxic chemicals according to the response pattern of a panel of Escherichia coli carrying lux genes fused to stress promoters. This was the first demonstration of the concept of applying a learning algorithm for the classification of a bioreporter panel’s response. However, the algorithm was described in general terms only, and a relatively limited amount of repeats was used for training. Here, we present the implementation of two welldocumented decision algorithms for the classification of the response pattern of a panel of selected recombinant luminescent bacterial bioreporters. Furthermore, we demonstrate for the first time the use of a large sample (40 repeats of each treatment), which enabled us to derive reliable error rate estimates of the classifiers’ performances with state-of-theart evaluation methods. Through the implementation of two learning algorithms, based on Bayesian decision theory and the nonparametric nearest-neighbor technique, we successfully classified the response pattern of a bioluminescent reporter strain panel and identified the toxicant in question in laboratory media as well as in spiked environmental samples.
Materials and Methods Bacterial Strains. Four E. coli RFM443 reporter strains harboring plasmid-borne nhoA, oraA, lacZ, and mipA promoters fused upstream of the Photorhabdus luminescens luxCDABE operon were screened out of a library previously constructed by Van Dyk et al. (20). Also used in the course of this study was E. coli strain TV1061 (8), containing the 10.1021/es801489a CCC: $40.75
2008 American Chemical Society
Published on Web 10/18/2008
TABLE 1. Model Toxicants toxicant
use
mode of action
LD50a (mg/kg)
concb (mg/L)
parathion dichlorvos paraquat potassium cyanide nitrogen mustard
insecticide insecticide herbicide mining, organic synthesis, electroplating chemotherapy, chemical warfare agent
acetylcholinesterase inhibition acetylcholinesterase inhibition strong oxidant; NADPH depletion cell respiration inhibition DNA alkylation
2 25 150 10 10
500 125 500 20 31
a
Calculated for a rat oral exposure (21).
b
Concentration tested in this study.
grpE promoter upstream to the plasmid-borne Vibrio fischeri luxCDABE operon. Bacteria Storage. The bacterial reporter strains were grown overnight with shaking (200 rpm) at 37 °C in 10 mL Luria-Bertani (LB) broth supplemented with 100 µg ampicillin (Sigma, St. Louis, MO) per milliliter. The overnight cultures were mixed with glycerol (Frutarom, Israel) to a final glycerol concentration of 20%, and 50 µL portions of the mixture were stored at -80 °C in 200 µL sterile Eppendorf tubes. Data Set Construction: Bacterial Responses to Model Chemicals. Five microliters of the -80 °C stored bacteria was used to inoculate 1 mL of LB broth supplemented with 100 µg/mL ampicillin (Sigma, St. Louis, MO), and the bacteria were grown overnight with shaking at 37 °C in deep 96-well microtiter plate (Costar, USA). The overnight cultures were diluted 100-fold in fresh LB to a final volume of 1 mL in the same type of plate and regrown with shaking at 37 °C for 2 h (Strain TV1061 was regrown at 30 °C). Bacterial aliquots (25 µL) were then transferred into white 384-well microtiter plates (Costar, USA) containing predetermined concentrations of five model toxicants in 25 µL of LB and a toxicant-free control (LB only). Each of the reporter strains was individually challenged with each of the model toxicants. Luminescence was measured at 4.6 min intervals for 2 h using a VICTOR2 luminometer (Wallac, Turku, Finland) and is presented in the instrument’s arbitrary relative light units (RLU). In some cases, responses are also provided as response ratios, denoting the sample’s luminescence divided by that of the control (toxicant-free LB) at the same time point. To achieve independent repeats, 40 individual cultures of each reporter strain were grown overnight, separately regrown, and exposed to the six treatments as described above. A total of 3 × 240 observations were obtained based upon the 30, 60, and 120 min luminescence data (3 time-points × 6 treatments × 40 repetitions), where each observation is represented by a 5-dimensional vector composed of the luminescence of the 5 reporters. Chemicals. Five model toxicants were selected based upon their different chemical structures and modes of action: ethyl parathion (diethoxy-(4-nitrophenoxy)-sulfanylidene-phosphorane, Liad Chemicals, Ltd., Israel), dichlorvos (1,1dichloro-2-dimethoxyphosphoryloxy-ethene, Makhteshim, Ltd., Israel), potassium cyanide (Riedel-de Haen, Germany), nitrogen mustard (2-chloro-N-(2-chloroethyl)-N-methylethanamine, Sigma, St. Louis, MO), and paraquat 1,1′dimethyl-4,4′-bipyridium Sigma, St. Louis, MO). Table 1 lists the model toxicants’ use, mode of action, LD50, and tested concentration. The responses of the reporter panel to these five toxicants and to a toxicant-free control were used for classifier design. Classifier Design. The response patterns of the 5 panel members to the different model chemicals were classified using two schemes: Bayesian decision theory and the nearest-neighbor technique (22-24). By each of the two schemes, three classifiers were designed based upon the luminescence (RLU) values measured from each panel member 30, 60, and 120 min after exposure. The feature vector was a vector of dimension 5 representing the
luminescence intensity of each of the panel members, and the possible states of nature were the six different treatments (ethyl parathion, dichlorvos, potassium cyanide, nitrogen mustard, paraquat, and a nontoxic control (LB)). The classifiers’ performances were evaluated by a repeated 10-fold cross-validation procedure as recommended by Witten and Frank (23). In practice, the sample was randomly split into 10 parts. Each part was left out in turn, and the classifiers were trained on the remaining 9/ . The 10 error rate estimates, each being the fraction 10 of test observations misclassified, were averaged to yield an overall error rate estimate. This 10-fold cross-validation process was repeated 10 times. The final error rate estimate of the classifier (i.e., its estimated error probability) was set as the average of the 10 overall error rate estimates obtained. A 5% level one-sided paired t-test was used to check whether the differences between the final error rate estimates of the various classifiers are statistically significant. To ensure the validity of the results, each pair of the 10-fold cross-validation estimates was obtained using the same split of the data for both schemes as well as for the 30-, 60-, and 120-min time-points. All the above procedures were implemented using MATLAB software (version 7.4 R2007a, The MathWorks). Environmental Samples. To test algorithm validity in liquids other than laboratory media, the reporter panel’s response to two of the model chemicals (cyanide and paraquat) was also examined in tap water and in wastewater samples, collected from Sorek-Refa’im wastewater treatment plant, Israel, at four points along the treatment process: raw sewage before and after primary sedimentation and activated sludge effluents before and after disinfection. The wastewater samples were filtered to dispose of suspended solids and bacteria. Disinfected wastewater and tap water were also amended by Na-thiosulfate (30 mg/L final concentration) to remove any residual chlorine. Bacteria were prepared as before, and their response was assayed as described above for the LB-dissolved chemicals, but exposure was conducted in a total volume of 100 µL in opaque white 96-well microtiter plates.
Results Five selected reporter strains were exposed to five toxicants and to an LB control in a 40-repetition format. Representative light development kinetics are presented in Figure 1, which exemplify different behaviors of the different sensor/toxicant pairs in terms of luminescence onset, response intensity, peak time-point, and general curve shape. Figure 2 depicts the average luminescence response after 30, 60, and 120 min of the 40 repeats. Quite clearly, the bioreporters differed in their response to each of the toxicants. In addition, each toxicant had a different effect on each bioreporter. For example, after 120 min, paraquat induced nhoA, lacZ, and mipA to different extents but did not induce grpE and oraA. In contrast, after the same time interval, grpE and oraA were induced by potassium cyanide and nitrogen mustard, respectively (Figure 2C). As a result, each of the five toxicants was characterized by its own bioluminescent fingerprint. This VOL. 42, NO. 22, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Repeated 10-Fold Cross-Validation Error Rate Estimatesa error rate estimate (%) exposure time (min)
Bayesian
nearest-neighbor
30 60 120
2.25 [2.10, 2.40] 2.50 [2.06, 2.94] 1.04 [0.79, 1.29]
9.79 [9.19, 10.39] 6.42 [6.13, 6.70] 1.87 [1.50, 2.25]
a
FIGURE 1. Representative examples of the kinetics of light development expressed in response ratio values. The response ratio is the sample’s luminescence divided by that of the control (toxicant-free LB), both measured in relative light units, RLU, at the same time point.
FIGURE 2. Response patterns of the reporter panel to five toxicants and to an LB control 30 min (A), 60 min (B), and 120 (C) min after exposure. Luminescence values (displayed in relative light units, RLU) are the average values of 40 repeats. key observation had led us to turn to pattern classification algorithms in order to try and identify the sample’s contents 8488
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Confidence intervals are indicated in square brackets.
according to the panel’s response pattern. This was carried out using two schemes: Bayesian decision theory and the nonparametric nearest-neighbor technique. Bayesian Decision Theory. Bayesian decision theory is a fundamental statistical approach to the problem of pattern classification. It is based on the assumption that the decision problem is posed in probabilistic terms and that all relevant probability parameters are known or can be estimated. From this point of view, our case involves six probability density functions, one for each of the six treatments (statistically referred to as states of nature or events). On the basis of the assumption that the panel’s luminescence in response to each of the treatments is normally distributed, the Bayesian classifiers we have designed have been required to estimate the mean and the variance of each of the six density functions according to the obtained sample. The six normal distributions are multivariate, and their mean and variance take the form of a 5-dimensional mean vector and a 5 × 5 covariance matrix, respectively. Another major theme in Bayesian decision theory is the a priori probability, which is the probability of a given event occurring. We have assigned each of the six possible events with the same a priori probability (which can be readily modified when necessary). Thus, our classifier’s decision is based solely upon the likelihood: the probability of observing a certain pattern given the occurrence of a certain event. When fed with an observation (the luminescence of each of the panel members), the classifier decides in favor of the event (a toxicant) which yields the highest likelihood. Bayesian classifiers were applied to the luminescence data collected from the 5-reporter panel 30, 60 and 120 min after exposure, and their estimated error rates, obtained in a repeated 10-fold cross-validation procedure, are summarized in Table 2. All three classifiers exhibited an error rate estimate that did not exceed 3% at a 95% confidence level, with the 120-min classifier exhibiting the lowest error rate estimate, 1.04% ( 0.25%. Additionally, no false negatives were recorded at any time-point: no toxic sample was misclassified as an LB-only control (Table S1 of the Supporting Information). The use of Bayesian decision theory in employing the responses of two E. coli gene promoters (oraA and lacZ) to discriminate between two toxicants (parathion and paraquat) is exemplified in Figure 3. Nearest-Neighbor Decision Rule. As opposed to Bayesian decision theory, the nearest-neighbor decision rule is a nonparametric procedure and can be used without assuming that the forms of the densities underlying the problem are known. According to this rule, a new and unlabeled observation is assigned with the label associated with the observation nearest to it. When applied to the data, the nearest-neighbor rule exhibited a decreasing error rate estimate with exposure time; the 30-, 60-, and 120-min classifiers yielded error rate estimates of 9.79, 6.42, and 1.87%, respectively (Table 2). Unlike the Bayesian classifiers, the nearest-neighbor classifiers were not free of false negatives. Yet, the average number of false negatives of each of the classifiers did not exceed 4, with the 120-min classifier
FIGURE 3. Bayesian decision boundary for parathion and paraquat based upon the luminescence emitted 30 min after exposure by two reporters, harboring oraA′:: and lacZ′::luxCDABE fusions. The boundary (black s) was drawn according to the obtained sample (parathion, red 1; paraquat, blue 2). exhibiting an average of only 1.9 false negatives out of 240 processed observations (Table S2 of the Supporting Information). Classifier Performance Comparison. All differences between the error rate estimates of the Bayesian classifiers and their nearest-neighbor counterparts were found to be statistically significant in a 5% level one-sided paired t-test (P values
