Dataset Information

Construction of a Modular Arsenic-Resistance Operon in E. coli and the Production of Arsenic Nanoparticles.

ABSTRACT: Arsenic is a widespread contaminant of both land and water around the world. Current methods of decontamination such as phytoremediation and chemical adsorbents can be resource and time intensive, and may not be suitable for some areas such as remote communities where cost and transportation are major issues. Bacterial decontamination, with strict controls preventing environmental release, may offer a cost-effective alternative or provide a financial incentive when used in combination with other remediation techniques. In this study, we have produced Escherichia coli strains containing arsenic-resistance genes from a number of sources, overexpressing them and testing their effects on arsenic resistance. While the lab E. coli strain JM109 (the "wild-type") is resistant up to 20?mM sodium arsenate, the strain containing our plasmid pEC20 is resistant up to 80?mM. When combined with our construct pArsRBCC arsenic--containing nanoparticles were observed at the cell surface; the elements of pEC20 and pArsRBCC were therefore combined in a modular construct, pArs, in order to evaluate the roles and synergistic effects of the components of the original plasmids in arsenic resistance and nanoparticle formation. We have also investigated introducing the lac operator in order to more tightly control expression from pArs. We demonstrate that our strains are able to reduce toxic forms of arsenic into stable, insoluble metallic As(0), providing one way to remove arsenate contamination, and which may also be of benefit for other heavy metals.

SUBMITTER: Edmundson MC

PROVIDER: S-EPMC4611968 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Construction of a Modular Arsenic-Resistance Operon in E. coli and the Production of Arsenic Nanoparticles.

Edmundson Matthew Charles MC Horsfall Louise L

Frontiers in bioengineering and biotechnology 20151020

Arsenic is a widespread contaminant of both land and water around the world. Current methods of decontamination such as phytoremediation and chemical adsorbents can be resource and time intensive, and may not be suitable for some areas such as remote communities where cost and transportation are major issues. Bacterial decontamination, with strict controls preventing environmental release, may offer a cost-effective alternative or provide a financial incentive when used in combination with other ...[more]

PMID: 26539432

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Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the “clustering behavior” of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is “data-driven” and does not involve manual selection of genes by the investigator. We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules.

Project description:Arsenite [As(III)] and methylarsenite [MAs(III)] are the most toxic inorganic and methylated arsenicals, respectively. As(III) and MAs(III) can be interconverted in the unicellular cyanobacterium Nostoc sp. PCC 7120 (Nostoc), which has both the arsM gene (NsarsM), which is responsible for arsenic methylation, and the arsI gene (NsarsI), which is responsible for MAs(III) demethylation. It is not clear how the cells prevent a futile cycle of methylation and demethylation. To investigate the relationship between arsenic methylation and demethylation, we constructed strains of Escherichia coli AW3110 (ΔarsRBC) expressing NsarsM or/and NsarsI. Expression of NsarsI conferred MAs(III) resistance through MAs(III) demethylation. Compared to NsArsI, NsArsM conferred higher resistance to As(III) and lower resistance to MAs(III) by methylating both As(III) and MAs(III). The major species found in solution was dimethylarsenate [DMAs(V)]. Co-expression of NsarsM and NsarsI conferred As(III) resistance at levels similar to that with NsarsM alone, although the main species found in solution after As(III) biotransformation was methylarsenate [MAs(V)] rather than DMAs(V). Co-expression of NsarsM and NsarsI conferred a higher level of resistance to MAs(III) than found with expression of NsarsM alone but lower than expression of only NsarsI. Cells co-expressing both genes converted MAs(III) to a mixture of As(III) and DMAs(V). In Nostoc NsarsM is constitutively expressed, while NsarsI is inducible by either As(III) or MAs(III). Thus, our results suggest that at low concentrations of arsenic, NsArsM activity predominates, while NsArsI activity predominates at high concentrations. We propose that coexistence of arsM and arsI genes in Nostoc could be advantageous for several reasons. First, it confers a broader spectrum of resistance to both As(III) and MAs(III). Second, at low concentrations of arsenic, the MAs(III) produced by NsArsM will possibly have antibiotic-like properties and give the organism a competitive advantage. Finally, these results shed light on the role of cyanobacteria in the arsenic biogeochemical cycle.

Dataset Information

Construction of a Modular Arsenic-Resistance Operon in E. coli and the Production of Arsenic Nanoparticles.

Publications

Construction of a Modular Arsenic-Resistance Operon in E. coli and the Production of Arsenic Nanoparticles.

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