(100a) A Systems-Level Analysis Approach for Identifying Genetic Targets to Treat Biofilm-Forming Pathogens: An Application to Pseudomonas Aeruginosa
The investigation of biofilm formation is an urgent research theme in systems biology, as biofilms are heavily involved in bacterial chronic inflammatory and infectious diseases (Sauer et al., 2007). It is reported that nearly 65% of all nosocomial infections in the USA are associated with biofilms. Prevention of the initiation of biofilm formation is the most important step for combating biofilm-forming pathogens, as the ability of pathogens to resist antibiotics is enhanced 10 to 1000 times once they form biofilms (Thien and O’toole, 2001). Both experimental approaches (Gerdes et al., 2003) and model-based approaches (Edwards and Palsson, 2000) have been developed to identify genes essential for bacterial growth in the planktonic state. However, inhibition of some essential planktonic-growth genes might prevent the growth of microorganisms in the planktonic state but may not prevent the formation of biofilms. In these cases, microorganisms can still switch from planktonic growth to the biofilm lifestyle before antibiotics completely inhibit biological functions associated with these genes. For example, Zhang et al., 2007 has reported that biofilm formation is a general strategy utilized by pathogens to survive stress such as that imposed by antibiotic treatment. On the other hand, a large amount of genes are involved in biofilm formation. Even though we inhibit some of these biofilm-associated genes, we cannot eliminate pathogenicity as these genes might not be essential for microorganism metabolism. Therefore, the ideal gene targets to treat biofilm-forming pathogens should be those that prevent the formation of biofilms and also eliminate pathogens in the planktonic state.
In this work, we develop a systems-level analysis approach to determine target genes that are essential for bacterial metabolism but which do not induce biofilm formation when they are inactivated. The proposed approach is based on flux balance analysis (FBA) (Edwards and Palsson, 2000), artificial center hint and run sampling (ACHR) (Becker et al., 2007), and hierarchical clustering. Specifically, fluxes of reactions positively associated with biofilm formation are used as sensors to monitor the trend of gene-inhibition mutants to form biofilms. Mechanisms for inhibition mutants of essential genes to form biofilms are investigated by identifying those biofilm-associated reactions whose fluxes surge for most gene-inhibition mutants. Target genes to treat biofilm-forming pathogens are then defined as those essential planktonic-growth genes whose mutants cannot induce the formation of biofilms.
The proposed approach was applied to identify genetic targets to treat Pseudomonas aeruginosa infections, as it is one of the leading causes of nosocomial infections in hospitalized patients and P. aeruginosa displays resistance to a wide array of antibiotics (Cao et al., 2004). The model presented in Oberhardt et al., 2008, was used to predict the trend for P. aeruginosa to switch from the planktonic state to the biofilm growth mode when essential planktonic-growth genes are inhibited. It is interesting to find that that inhibition of most essential planktonic-growth genes induces the formation of biofilms. Specifically, P. aeruginosa tends to survive the essential-gene inhibition treatment (i.e., a pseudo antibiotic-treatment) by mainly increasing the fluxes through 10 reactions that regulate amino acid metabolism, acetate metabolism, hydrogen peroxide, and ABC phosphate transporter. This phenomenon indicates the adaptability of P. aeruginosa toward unfavorable environments. This work presents the first computational approach to address the mechanism under this phenomenon. In addition, three essential planktonic-growth genes, i.e., PA1756 (cysH), PA3686 (adk), and PA2023 (galU), cannot induce biofilm formation when they are inhibited. They constitute gene targets to treat P. aeruginosa as inhibition of them can eliminate pathogens without inducing biofilm formation. All these three genes have been validated by existing data as potential drug targets for their crucial role in the survival or virulence of P. aeruginosa (Bonofiglio et al., 2005; Markaryan et al., 2001; Ren et al., 2005; Williams et al., 2002).
Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2:727-738, 2007
Bonofiglio L, García E, Mollerach M. Biochemical characterization of the pneumococcal glucose 1-phosphate uridylyltransferase (GalU) essential for capsule biosynthesis. Curr Microbiol 51(4):217-221, 2005
Cao B, Wang H, Sun H, Zhu Y, Che M. Risk factors and clinical outcomes of nosocomial multi-drug resistant Pseudomonas aeruginosa infections. J Hosp Infect 57(2): 112-118, 2004
Edwards JS, Palsson BO. Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1:1-1, 2000
Gerdes SY, Scholle MD, Campbell JW, et al. Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J Bacteriol 185(19): 5673-5684, 2003
Markaryan A, Zaborina O, Punj V, Chakrabarty AM. Adenylate kinase as a virulence factor of Pseudomonas aeruginosa. J Bacteriol 183(11):3345-3352, 2001
Oberhardt MA, Puchalka J, Fryer KE, Martins dos Santos VAP, Papin JA. Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol 190(8): 2790–2803, 2008
Ren D, Zuo R, Gonzalez Barrios AF, Bedzyk LA, Eldridge GR, Pasmore ME, Wood TK. Differential Gene Expression for Investigation of Escherichia coli Biofilm Inhibition by Plant Extract Ursolic Acid. Appl Environ Microb 71(7): 4022–4034, 2005
Sauer K, Rickard AH, Davies DG. Biofilms and biocomplexity. Microbe 2:347–353, 2007
Thien FCM, O'Toole GA. Mechanisms of biofilm resistance to antimicrobial agents. Trends Microbiol 9(1): 34-39, 2001
Williams SJ, Senaratne RH, Mougous JD, Riley LW, Bertozzi CR. 5’-Adenosinephosphosulfate lies at a metabolic branch point in mycobacteria. J Biol Chem 277(36):32606-32615, 2002
Zhang X, Garcia-Contreras R, Wood TK. YcfR (BhsA) Influences Escherichia coli Biofilm Formation through Stress Response and Surface Hydrophobicity. J Bacteriol 189(8): 3051-3062, 2007
See more of this Group/Topical: Food, Pharmaceutical & Bioengineering Division - See also TI: Comprehensive Quality by Design in Pharmaceutical Development and Manufacture