Analysis and environmental concentrations of the herbicide dichlobenil and its main metabolite 2,6-dichlorobenzamide (BAM): A review

Björklund, Erland; Anskjær, Gitte Gotholdt; Hansen, Martin; Styrishave, Bjarne; Halling-Sørensen, Bent

doi:10.1016/j.scitotenv.2011.02.008

Cited by 35 publications

(25 citation statements)

References 76 publications

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“…As important for bioaugmentation are the consequences of catabolic instability for the strain's actual performance in DWTP biofilter units where conditions are not necessarily selective, i.e., when BAM is present at trace concentrations and other nonselective AOC is expected to be higher by a factor of 20 to 100 (9,(26)(27)(28). The retention of the BbdA ϩ phenotype but loss of the Dcba ϩ phenotype implies that BAM is still degraded to 2,6-DCBA by MSH1-GFP but impaired in further DCBA conversion.…”

Section: Discussionmentioning

confidence: 99%

Genetic (In)stability of 2,6-Dichlorobenzamide Catabolism in Aminobacter sp. Strain MSH1 Biofilms under Carbon Starvation Conditions

Horemans

Raes

Brocatus

et al. 2017

Appl Environ Microbiol

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Aminobacter sp. strain MSH1 grows on and mineralizes the groundwater micropollutant 2,6-dichlorobenzamide (BAM) and is of interest for BAM removal in drinking water treatment plants (DWTPs). The BAM-catabolic genes in MSH1 are located on plasmid pBAM1, carrying bbdA, which encodes the conversion of BAM to 2,6-dichlorobenzoic acid (2,6-DCBA) (BbdA ϩ phenotype), and plasmid pBAM2, carrying gene clusters encoding the conversion of 2,6-DCBA to tricarboxylic acid (TCA) cycle intermediates (Dcba ϩ phenotype). There are indications that MSH1 easily loses its BAM-catabolic phenotype. We obtained evidence that MSH1 rapidly develops a population that lacks the ability to mineralize BAM when grown on nonselective (R2B medium) and semiselective (R2B medium with BAM) media. Lack of mineralization was explained by loss of the Dcba ϩ phenotype and corresponding genes. The ecological significance of this instability for the use of MSH1 for BAM removal in the oligotrophic environment of DWTPs was explored in lab and pilot systems. A higher incidence of BbdA ϩ Dcba Ϫ MSH1 cells was also observed when MSH1 was grown as a biofilm in flow chambers under C and N starvation conditions due to growth on nonselective residual assimilable organic carbon. Similar observations were made in experiments with a pilot sand filter reactor bioaugmented with MSH1. BAM conversion to 2,6-DCBA was not affected by loss of the DCBA-catabolic genes. Our results show that MSH1 is prone to BAM-catabolic instability under the conditions occurring in a DWTP. While conversion of BAM to 2,6-DCBA remains unaffected, BAM mineralization activity is at risk, and monitoring of metabolites is warranted.IMPORTANCE Bioaugmentation of dedicated biofiltration units with bacterial strains that grow on and mineralize micropollutants was suggested as an alternative for treating micropollutant-contaminated water in drinking water treatment plants (DWTPs). Organic-pollutant-catabolic genes in bacteria are often easily lost, especially under nonselective conditions, which affects the bioaugmentation success. In this study, we provide evidence that Aminobacter sp. strain MSH1, which uses the common groundwater micropollutant 2,6-dichlorobenzamide (BAM) as a C source, shows a high frequency of loss of its BAM-mineralizing phenotype due to the loss of genes that convert 2,6-DCBA to Krebs cycle intermediates when nonselective conditions occur. Moreover, we show that catabolic-gene loss also occurs in the oligotrophic environment of DWTPs, where growth of MSH1 depends mainly on the high fluxes of low concentrations of assimilable organic carbon, and hence show the ecological relevance of catabolic instability for using strain MSH1 for BAM removal in DWTPs.

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Section: Discussionmentioning

confidence: 99%

Genetic (In)stability of 2,6-Dichlorobenzamide Catabolism in Aminobacter sp. Strain MSH1 Biofilms under Carbon Starvation Conditions

Horemans

Raes

Brocatus

et al. 2017

Appl Environ Microbiol

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“…For example, during the treatment of drinking water, the common groundwater pollutant 2,6-dichlorobenzamide (2,6-BAM) must be removed below a threshold concentration of 0.1 µg L −1 to meet the EU Directive on Drinking Water [7]. However, the endogenous microbial communities in the sand filters (SFs) of such drinking water treatment plants are not capable of achieving sufficient BAM removal to respect this threshold [8]. Therefore, bioaugmentation of SFs has been proposed as an alternative strategy, by the addition of a specialized BAM mineralizer such as Aminobacter sp.…”

Section: Introductionmentioning

confidence: 99%

Individual-Based Modelling of Invasion in Bioaugmented Sand Filter Communities

et al. 2018

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Using experimental data obtained from in vitro bioaugmentation studies of a sand filter community of 13 bacterial species, we develop an individual-based model representing the in silico counterpart of this synthetic microbial community. We assess the inter-species interactions, first by identifying strain identity effects in the data then by synthesizing these effects into a competition structure for our model. Pairwise competition outcomes are determined based on interaction effects in terms of functionality. We also consider non-deterministic competition, where winning probabilities are assigned based on the relative intrinsic competitiveness of each strain. Our model is able to reproduce the key qualitative dynamics observed in in vitro experiments with similar synthetic sand filter communities. Simulation outcomes can be explained based on the underlying competition structures and the resulting spatial dynamics. Our results highlight the importance of community diversity and in particular evenness in stabilizing the community dynamics, allowing us to study the establishment and development of these communities, and thereby illustrate the potential of the individual-based modelling approach for addressing microbial ecological theories related to synthetic communities.

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“…Despite these advantages, several environmental dangers and some potential risk have emerged from the excessive use of these compounds. For nearly fifty years, they have been detected in the water of rivers and groundwater [1][2][3][4][5][6][7][8][9][10]. They are also found in agricultural and animal products (wheat, corn, fruits, vegetables, cereals, tea, fish, milk, eggs, meat, honey and medicinal herbs, etc.)…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network-based equation to predict the toxicity of herbicides on rats

Hamadache

Hanini

Benkortbi

et al. 2016

Chemometrics and Intelligent Laboratory Systems

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The use of herbicides is increasing around the world. The benefits achieved by the use of these herbicides are indisputable. Despite their importance in agriculture, herbicides can be dangerous to the environment and the human health, depending on their toxicity, and the degree of contamination. Also, it is essential and evident that the risk assessment of herbicides is an important task in the environmental protection. The objective of this work was to investigate and implement an Artificial Neural Network (ANN) model for the prediction of acute oral toxicity of 77 herbicides to rats. Internal and external validations of the model showed high 2 and 2 ̅̅̅ values, in the range 0.782 -0.997 for the training and the test. In addition, the major contribution of the current work was to develop artificial neural network-based equation to predict the toxicity of 13 other herbicides; the mathematical equation using the weights of the network gave very significant results, leading to an R 2 value of 0.959. The agreement between calculated and experimental values of acute toxicity confirmed the ability of ANN-based equation to predict the toxicity for herbicides that have not been tested as well as new herbicides.

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Analysis and environmental concentrations of the herbicide dichlobenil and its main metabolite 2,6-dichlorobenzamide (BAM): A review

Cited by 35 publications

References 76 publications

Genetic (In)stability of 2,6-Dichlorobenzamide Catabolism in Aminobacter sp. Strain MSH1 Biofilms under Carbon Starvation Conditions

Genetic (In)stability of 2,6-Dichlorobenzamide Catabolism in Aminobacter sp. Strain MSH1 Biofilms under Carbon Starvation Conditions

Individual-Based Modelling of Invasion in Bioaugmented Sand Filter Communities

Artificial neural network-based equation to predict the toxicity of herbicides on rats

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