(254c) A Systems Biology Approach for Investigation of the Influence of Environmental Nutrients On the Biofilm Formation of P. Aeruginosa

A Systems Biology Approach for Investigation of the Influence of Environmental Nutrients on the Biofilm Formation of P. aeruginosa

Zhaobin Xu, Xin Fang, Thomas K. Wood, Zuyi (Jacky) Huang*

Abstract:

Biofilms have been frequently associated with human infections such as those of implanted devices. (Costerton et al., 2005) Antibiotic resistance of pathogens is strongly enhanced once they form biofilms. (Thien and O’toole, 2001) Investigation of the switch from planktonic to biofilm growth mode can facilitate the treatment of biofilm-associated pathogens. Ines Thiele from University of Iceland used the planktonic model to predict the growth of Pseudomonas aeruginosa in the biofilm environment and performed gene knockout to determine the gene target that can stop microbial growth in biofilms (Sigurdsson et al., 2012). However, the biofilm formation of the mutants has not been considered in this approach. Our group (Xu et al., 2013) presented the first systems biology approach to quantify the biofilm formation ability of single mutants

The availability of nutrients in the environment influences biofilm formation: The experimental results presented by Kristich et al. (Kristich et al., 2004) indicated that the nutrients that were more dilute tended to promote biofilm formation relative to nutrient-rich media. Some strains of Escherichia coli K-12 and Vibrio cholerae will not form biofilms in minimal medium unless supplemented with amino acids (Watnick et al., 1999). The availability of ions such as iron also plays an important role in biofilm formation (Banin et al., 2005). However, little research has been conducted to quantify the influence of nutrients such as amino acids, iron, phosphate, and sulfate on the microbial biofilm formation.

In this work, we develop a systems-level analysis approach to determine the influence of nutrients on the biofilm formation of P. aeruginosa. This work extends our previous approach to further quantify the biofilm formation ability of P. aeruginosa based on  the availability of amino acids, iron, phosphate, and sulfate changes in the surrounding environment. Using flux balanced analysis(Edwards and Palsson, 2000), artificial hit and run sampling (Becker et al., 2007), we propose an approach to quantify the biofilm formation ability from the profile of flux change through those biofilm-associated reactions, and then further quantify the biofilm formation ability of P. aeruginosa in an environment with different availability of nutrients. Specifically, we predict the biofilm formation of P. aeruginosa upon the change of uptake rates of amino acids, iron, phosphate and sulfate. The change of uptake rates in the flux balance analysis mimic the change of availability of nutrients in the environment.

Our approach predict that P. aeruginosa switches from planktonic to biofilm growth upon the addition of most of amino acids into the minimal medium. This prediction is consistent with the experimental data presented in Bernier et al., 2011, which shows the amino acid effect on biofilm formation of P. aeruginosa.  Our approach also predicted that the biofilm formation ability of planktonic P. aeruginosa is enhanced when limited ions such as iron, phosphate and sulfate are available in the surrounding environment. In particular, upon the limited availability of these ions, P. aeruginosa is predicted to slow down its growth rate but increase itsbiofilm formation. This may reveal the strategy used by P. aeruginosa to survive in the hostile environment.  In addition, the developed platform can be used to predict the biofilm formation of P. aeruginosa in the environment conditions with the combination of different nutrient elements. This approach can provide directions for experimental work to quantify the planktonic-biofilm transition under a specific nutrient environment for other microorganisms based on cell metabolism.

References:

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Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ.(2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2:727-738.

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Xu Z, Fang X, Wood TK, Huang ZJ (2013) A systems level approach for investigating Pseudomonas aeruginosa biofilm formation. PLoS One. 8(2):e57050.