Development of microbial engineered whole-cell systems for environmental benzene determination

https://doi.org/10.1016/j.ecoenv.2010.08.006Get rights and content

Abstract

This paper reports the development of two recombinant bacterial systems that can be used to monitor environmental benzene contamination based on Escherichia coli, which carry genes coding for benzene dioxygenase and benzene dihydrodiol dehydrogenase from Pseudomonas putida MST. E. coli strains express these two enzymes under the control of the Ptac promoter or without any induction.

These activities can be detected electrochemically or colorimetrically and used to monitor benzene pollution in environmental air samples collected from an oil refinery assessing benzene by different laboratory experimental procedures.

The procedures involving whole-cell bioassays determine the concentration of benzene through benzene dioxygenase activity, which allows for direct correlation of oxygen consumption, and through the benzene dihydrodiol dehydrogenase that causes catechol accumulation and restores NADH necessary for the activity of the first enzyme. Oxygen consumption and catechol production deriving from both enzymatic activities are related to benzene concentration and their measurements determined the sensitivity of the system. The results indicated that the sensitivity was enough to detect the benzene vapor at a lower concentration level of 0.01 mM in about 30 min. The possibility for on-line monitoring of benzene concentration by our new recombinant cells results from the fact that no particular treatment of environmental samples is required. This is a major advantage over other biosensors or assays. Moreover, the development of microbial cells that did not require any addition or effectors for the transcription of the specific enzymes, allowed these systems to be more versatile in automated environmental benzene monitoring.

Introduction

Benzene tops the list of hazardous compounds (Report on Carcinogens, 11th Edition) due to its toxicity and potential risk for human exposure. Above all, this harmful pollutant arises from vehicle emissions and is also present in large quantities in the air around fuel production and retail plants. Limiting exposure to benzene is necessary to protect the health of citizens and workers alike. Therefore extensive monitoring of contaminated areas is mandatory as well as creating adequate warnings to prevent exposure. The current methods for assessing the potential risk of benzene in contaminated sites include the analysis of benzene concentrations in the air. Usually, techniques such as passive charcoal absorption sampling, sequential extraction procedures, and chromatography-mass spectrometry are used to determine benzene concentration and estimate its average amounts. Although accurate and reproducible, analysis of benzene contamination by chromatographic methods is time-consuming, costly, and complex (Lanyon et al., 2005).

In many instances, biosensor assays have supplemented or even replaced traditional methods in extensive monitoring programs because of their fast and cost-effective techniques (Rodriguez-Mozaz et al., 2005, Sørensen et al., 2006).

Benzene biosensors are basically microbial sensors based on bacterial strains, which possess enzymatic pathways that give them the ability to use hydrocarbons as carbon sources, such as Pseudomonas spp. and other closely related species (Rogers, 2006, Tizzard and Lloyd-Jones, 2007).

In a previous paper, a whole-cell system was improved for the analysis of benzene and derivatives based on Pseudomonas putida MST cells immobilized in an agarized culture medium (Campanella et al., 1996). Moreover, our laboratory recently developed a lichen-based biosensor for benzene detection (Antonelli et al., 2005).

Microorganisms can metabolize benzene either through the formation of cis-1,2dihydrodiol or the formation of the phenol, which are both successively oxidized to catechol. In the first case, through the co-factor NAD(P)H and the use of oxygen, a dioxygenase can oxidize the benzene into the corresponding dihydrodiol that is converted to catechol by dihydrodiol dehydrogenase, restoring the NAD(P)H used previously (Fig. 1). Subsequently, the catechol is attacked by a pyrocatecase, that uses oxygen to cleave the aromatic ring, undergoing intermediates of the Krebs cycle.

Some microorganisms and plants intrinsically posses the ability to oxidize hydrocarbons to get carbon and energy. Quantification of oxygen consumption during the metabolism of hydrocarbons is one of the most sensitive methods for hydrocarbon detection. Electrochemical transducers can be used with cells or tissues immobilized onto an amperometric or potentiometric detector to measure the cellular response determined by the oxygen utilization. In our approach, bacteria and lichen tissues, which both have a benzene catabolic pathway, were coupled to a Clark oxygen electrode, the transducer for the detection of benzene in an aqueous solution.

Moreover, many engineered bacteria have been used as bio-reporters for benzene occurrence, through the construction of luminescence or fluorescence emission strains, and were applied to in situ monitoring scenarios (Kobatake et al., 1995, Ikariyama et al., 1997, Ikeno et al., 2003, Berno et al., 2004).

This paper reports the development of a benzene detection system based on Escherichia coli recombinant strains that carry a genomic region of the P. putida MST, a strain isolated in presence of α-methylstyrene but which is also able to grow on benzene (Bestetti et al., 1989). The isolated region contained genes coding for benzene dioxygenase and dihydrodiol dehydrogenase in P. putida MST. Recombinant cells were able to express these two enzymatic activities and were utilized by both biosensor and biochemical approaches in order to improve methods for detecting benzene and to monitor benzene pollution in environmental air samples collected from an oil refinery.

Section snippets

Bacterial strains and plasmids

The bacterial strains and plasmids used in this work are listed in Table 1. Recombinant plasmids (see below for the details) were constructed by standard procedures (Sambrook and Russel, 2000) using E. coli JM109 (Yanish-Perron et al., 1985) or E. coli DH5α (Quiagen) as host strain.

Preparation, amplification and cloning of DNA

The fragment of 4.3 kb containing the benzene dioxygenase and the benzene dihydrodiol dehydrogenase was isolated from the genomic DNA of P. putida MST (Bestetti et al., 1989) by PCR (Sambrook and Russel, 2000) using

Development of microbial biosensors based on E. coli engineered cells

In order to develop a microbial monitoring system for environmental benzene contamination, the benzene dioxygenase (BED) was isolated from P. putida MST. MST is a bacterial strain able to degrade benzene (Fig. 1) and uses BED to transform benzene into dihydrodiol by introducing oxygen to the aromatic ring. The dihydrodiol is dehydrogenated to catechol by the action of benzene dihydrodiol dehydrogenase (BDDH) (Bestetti et al., 1989). The consumption of oxygen by BED and the production of

Discussion

In this paper, the development and testing of two engineered microbial strains for the detection of benzene in the environment, have been described. The suitability is based on the expression of genes that code for specific enzymes under the control of the Ptac promoter or without induction and whose activity can be measured as oxygen consumption and catechol production.

The procedures involving whole-cell bioassays that can determine the occurrence of pollutants are very interesting. The

Conclusions

In conclusion, the E. coli recombinant strains developed in this paper are comparable with other aromatic whole-cell biosensors, demonstrating that the sensitivity of the systems is similar to most other systems and is within the range of micromolar. The novelty is the possibility of the electrochemical application and the possibility also offered by the colorimetric bioassay that will permit a wide range of applications of our microbial system in real cases of environmental pollution. The data

Acknowledgment

This work was supported by Grant from ISPESL 2005-2007.

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