Dataset Information

A network model for biofilm development in Escherichia coli K-12.

ABSTRACT:

Background

In nature, bacteria often exist as biofilms. Biofilms are communities of microorganisms attached to a surface. It is clear that biofilm-grown cells harbor properties remarkably distinct from planktonic cells. Biofilms frequently complicate treatments of infections by protecting bacteria from the immune system, decreasing antibiotic efficacy and dispersing planktonic cells to distant body sites. In this work, we employed enhanced Boolean algebra to model biofilm formation.

Results

The network obtained describes biofilm formation successfully, assuming - in accordance with the literature - that when the negative regulators (RscCD and EnvZ/OmpR) are off, the positive regulator (FlhDC) is on. The network was modeled under three different conditions through time with satisfactory outcomes. Each cluster was constructed using the K-means/medians Clustering Support algorithm on the basis of published Affymetrix microarray gene expression data from biofilm-forming bacteria and the planktonic state over four time points for Escherichia coli K-12.

Conclusions

The different phenotypes obtained demonstrate that the network model of biofilm formation can simulate the formation or repression of biofilm efficiently in E. coli K-12.

SUBMITTER: Shala AA

PROVIDER: S-EPMC3224578 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Publications

A network model for biofilm development in Escherichia coli K-12.

Shalá Andrew A AA Restrepo Silvia S González Barrios Andrés F AF

Theoretical biology & medical modelling 20110922

<h4>Background</h4>In nature, bacteria often exist as biofilms. Biofilms are communities of microorganisms attached to a surface. It is clear that biofilm-grown cells harbor properties remarkably distinct from planktonic cells. Biofilms frequently complicate treatments of infections by protecting bacteria from the immune system, decreasing antibiotic efficacy and dispersing planktonic cells to distant body sites. In this work, we employed enhanced Boolean algebra to model biofilm formation.<h4>R ...[more]

PMID: 21939518

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Project description:Honey has been widely used against bacterial infection for centuries. Previous studies suggested that honeys in high concentrations inhibited bacterial growth due to the presence of anti-microbial compounds, such as methylglyoxal, hydrogen peroxide, and peptides. In this study, we found that three honeys (acacia, clover, and polyfloral) in a low concentration as below as 0.5% (v/v) significantly suppress virulence and biofilm formation in enterohemorrhagic E. coli O157:H7 affecting the growth of planktonic cells while these honeys do not harm commensal E. coli K-12 biofilm formation. Transcriptome analyses show that honeys (0.5%) markedly repress quorum sensing genes (e.g., AI-2 import and indole biosynthesis), virulence genes (e.g., LEE genes), and curli genes (csgBAC). We found that glucose and fructose in honeys are key compounds to reduce the biofilm formation of E. coli O157:H7 via suppressing curli production, but not that of E. coli K-12. Additionally, we observed the temperature-dependent response of honeys and glucose on commensal E. coli K-12 biofilm formation; honey and glucose increase E. coli K-12 biofilm formation at 37Â°C, while they decrease E. coli K-12 biofilm formation at 26Â°C. These results suggest that honey can be a practical tool for reducing virulence and colonization of the pathogenic E. coli O157:H7, while honeys do not harm commensal E. coli community in the human. For the microarray experiments, E. coli O157:H7 EDL933 was inoculated in 250 ml of LB in 1000 ml flasks with overnight cultures that were diluted at 1:100. Cells were shaken with 10g of glass wool at 100 rpm and 37Â°C for 7 hrs. Cells were immediately chilled with dry ice and 95% ethanol (to prevent RNA degradation) for 30 sec before centrifugation in 50 ml centrifuge tubes at 13,000 g for 2 min; cell pellets were frozen immediately with dry ice and stored -80Â°C. RNA was isolated using Qiagen RNeasy mini Kit (Valencia, CA, USA). To eliminate DNA contamination, Qiagen RNase-free DNase I was used to digest DNA. RNA quality was assessed by Agilent 2100 bioanalyser using the RNA 6000 Nano Chip (Agilent Technologies, Amstelveen, The Netherlands), and quantity was determined by ND-1000 Spectrophotometer (NanoDrop Technologies, Inc., DE, USA).

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Dataset Information

A network model for biofilm development in Escherichia coli K-12.

Background

Results

Conclusions

Publications

A network model for biofilm development in Escherichia coli K-12.

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