Enterococcus faecalis is a Gram-positive bacterium that
is getting more attention due to its “two-face” behavior.
This natural inhabitant of the gastrointestinal
mammalian tract is also an opportunist pathogen
responsible for urinary tract infections, nosocomial
infections, bacteremia and infective endocarditis (1).
Since the metabolic reconstruction of Haemophilus
influenzae was published in 1999 (2), many other
researchers have focused their attention into the
possibilities that the new era of genome-scale metabolic
models could bring to the scientific scene, both in
prokaryotic and eukaryotic organisms

Ferreira, E. C.

Portela, Carla

Rocha, I.

Villas-Bôas, S. G.

Universidade do Minho: RepositoriUM

Genome scale metabolic network reconstruction of pathogen – Enterococcus faecalis

Ferreira, Eugénio C.

Genome scale network reconstruction of the pathogen Enterococcus faecalisCarla Portela1,2, Silas G. Villas-Bôas2, Eugénio C. Ferreira1 , Isabel Rocha11IBB - Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga-Portugal2School of Biological Sciences, University of Auckland, 3A Symonds Street, Auckland 1142, New ZealandIntroductionE. faecalis is a gram-positive bacterium that is receiving moreattention due to its “two-face” behavior. This natural inhabitant ofthe mammalian gastrointestinal tract is also an opportunisticpathogen responsible for urinary tract infections, nosocomialinfections, bacteremia and infective endocarditis. Its intrinsicphysiological properties such as inherent antibiotic resistanceand exceptional ability to adapt to harsh conditions provide thisorganism with an enormous advantage during the infectionprocess.ObjectivesWe propose to reconstruct the genome scale metabolic network ofE. faecalis. The reconstruction is based on the genome sequencinginformation available, as well as online databases and literatureevidence. Later, the model will be tested and validated in thelaboratory, by performing experiments based on the simulations.The model will support the interpretation and better understanding ofthe metabolomic and proteomic data generated in our labs.MethodsBLAST searchFigure 1: Data integration of different levels of information.AcknowledgementsThis work is supported by a PhD grant from the FCT (Portuguese Science Foundation): SFRH/BD/47016/2008 and funding from HRC (Health Research Council of  New Zealand).Contactportela.carla@deb.uminho.ptResultsConclusions and future directionsThe metabolic model generated for this bacterium allows thecorrelation of flux distributions with different environmentalconditions. It is also possible to determine the minimal mediumrequired for growth.As an example, figure 2 shows the overall conversion of adefined minimal medium (shown in table 1) in bi-products andbiomass1.Additionally, metabolomic data generated by our group allowedthe identification of key compounds/reactions in the bacteriummetabolism that were beforehand unclear, namely the evidencethat E. faecalis is able to synthesize all amino acids, addingvaluable information to the model. The next step will be thevalidation of the results based on published data and also inlaboratory. As it has been previously shown for otherorganisms, the genome scale network reconstruction mayserve as a valuable tool to predict the phenotypic behaviourunder various genetic and environmental conditions, as well asto perform metabolic engineering simulations (e.g. genedeletion experiments) and drug target identification.1) Search and collect information available. Manual curation of the collected data.2) Generate SBML file to be tested in Optflux.LB Organic acids UP LB Sugars/Sugar alcohols UB LB Amino acids UB LB Others UB LB Gases/Ions UB0 MALA +∞ 0 SALC 0 0 ARG 0 GLUM H+0 LCTT +∞ 0 ARBT 0 0 ALA 0 ADE Na+0 PYR 0 ‐10 GLUC 0 0 LEU 0 NICO K+0 FORM +∞ 0 MANN 0 ‐∞ ILE 0 GABA 0 CO2 +∞0 ACET +∞ 0 TRHL 0 0 GLU 0 SLF 0 O2 00 FRUT 0 0 GLN 0 AGM PiLB Aroma compounds UP 0 SUCR 0 0 GLY 0 BET Pii0 AALD +∞ 0 MNTL 0 0 ASN 0 0 ORN 0 H2O0 DTYL +∞ 0 CELB 0 0 ASP 0 PUTR NH30 GLCN +∞ 0 MALT 0 0 SER 0 ASC0 ACTN +∞ 0 GLYC 0 0 THR 0 0 GALM 00 ETOH 0 ‐∞ MET 0 0 GALMAc 00 RIB 0 0 CYS 00 SORT 0 0 HIS 00 MurNAc 0 0 TYR 00 TRP 00 VAL 00 PHE 00 PRO 00 LYS 0The simulations were performed using the software: Optflux(www.optflux.org)Table 1: Definition of the drains reactions considering that E.faecalis is able to grow in minimalmedium and to synthesize all amino acidsOrganic acids and aroma compounds: MALA-malate, LCTT-lactate, PYR-pyruvate, FORM-formate, ACET-acetate, AALD-acetaldehyde, DTYL-diacetyl, GLCN-D-gluconate, ACTN-acetoin;Sugars and Sugar alcohols: SALC-salicin, ARBT-arbutin, GLUC-glucose, MANN-mannose, TRHL-trehalose, FRUT-fructose,SUCR-sucrose, MNTL-manitol, CELB-cellobiose, MALT-maltose, GLYC-glycerol, ETOH-ethanol, RIB-ribose, SORT-sorbitol,MurNAc-acetylmuramic acid;Aminoacids: ARG-arginine, ALA-alanine, LEU-leucine, ILE-isoleucine, GLU-glutamate, GLN-glutamine, GLY-glycine, ASN-asparagine, ASP-aspartate, SER-serine, THR-threonine, MET-methionine, CYS-systeine, HIS-histidine, TYR-tyrosine, TRP-tryptophan, VAL-valine, PHE-phenilalanine, PRO-proline, LYS-lysine;Others: GLUM-glucosamine, ADE-adenine, NICO-nicotinamide, GABA, SLF-sulfate, AGM-agmantine, BET-betaine, ORN-ornitine, PUTR-putrescine, ASC-ascorbate, GALM-galactosamine, GALMAc-acetyl-D-galactosamine;Gases/ions: Pi-phosphate, Pii-diphosphateLB – Lower Bound, UP – Upper BoundIt is currently known that E.faecalis is able to synthesize all aminoacids using minimal medium (data not shown) although it growspoorly. The current data available in the literature and annotatedgenome do not contemplate information about biosynthesis of allamino acids and therefore it was necessary to lump some of thereactions required for the biosynthesis in order for the model to workand produce the missing amino acids.OptFlux outcome: Figure 2: Snapshot of OptFlux screen displaying the overall reaction of biomass production.

Genome scale network reconstruction of the pathogen Enterococcus faecalis

  Escola de Engenharia   Semana da Escola de Engenharia  October 24 - 27, 2011    GENOME SCALE METABOLIC NETWORK RECONSTRUCTION OF PATHOGEN – ENTEROCOCCUS FAECALIS  Carla Portela, Silas Villas-Bôas,, Eugénio C. Ferreira, Isabel Rocha IBB- Institute for Biotechnology and Bioengineering,  Department of Biological Engineering E-mail: portela.carla@deb.uminho.pt   KEYWORDS Systems Biology, Genome-Scale Reconstruction, Pathogen, Metabolic Engineering.  ABSTRACT Enterococcus faecalis is a Gram-positive bacterium that is getting more attention due to its “two-face” behavior. This natural inhabitant of the gastrointestinal mammalian tract is also an opportunist pathogen responsible for urinary tract infections, nosocomial infections, bacteremia and infective endocarditis (1).  Since the metabolic reconstruction of Haemophilus influenzae was published in 1999 (2), many other researchers have focused their attention into the possibilities that the new era of genome-scale metabolic models could bring to the scientific scene, both in prokaryotic and eukaryotic organisms.  INTRODUCTION Genome scale models consist on a set of chemical reactions that are inter-connected to represent the metabolic transformations that occur in the organism. They are valuable tools that allow the better understanding of the physiology of an organism while allowing the prediction of the behaviour given certain constraints. We have formulated a genome scale reconstruction (GENRE) with 682 reactions, 655 metabolites, 516 genes (with associated metabolic function) and 584 reactions with gene association as shown in table 1. Table 1 depics the basic properties of GENRE of Enterococcus faecalis.    Table 1: Basic network properties of GENRE of pathogen Enterococcus faecalis  Basic Network Properties     Total Reactions cytosol 600 682 external 82 Metabolites cytosol 577 655 external 78 Genes 516 516 Reactions with gene association 584 584  Most of the reactions in the GENRE (86%) have one or more genes associated while 14% do not have any gene associated. These are mainly transport reactions.   RESULTS AND CONCLUSION From the total of the 682 reactions, 496 reaction (73%) have flux while 186 reaction (27%) do not carry any flux, meaning that they are not connected to the system. These reactions are in the model since there is some evidence (in literature, databases) that justifies their presence, however, it is not possible yet to connect them to the network already assembled. A matrix generated by this set of reactions can be solved by linear programming using a Flux Balance analysis (FBA) approach. To solve a linear programming problem it is necessary to have an objective function. For bacteria the main purpose is to grow and therefore, biomass equation is our objective function. Using FBA approach is possible to determine the set of fluxes that lead to an optimal growth subject to constrains.  Doing a GENRE of any organism is not a straightforward process as it requires the collection of all the available data, curate that information (with literature and databases and blast searches), analyze flux distribution and fill the gaps of the system until biomass is being formed. The data already available (from metabolomic, fluxomic and proteomic experiments) allowed the better understanding of some key control   Escola de Engenharia   Semana da Escola de Engenharia  October 24 - 27, 2011    points of the network as well as to fill some gaps in the model.   Currently, the model is able to do simulations and produce biomass. The validation of the model is initially acomplished by trying to make the predictions match what is experimentally observed. Once the model predicts with accuracy it is possible to test different experimental designs (from different growth media to gene deletions) and analyze if it is also observable in a laboratory scale.   REFERENCES 1.Paulsen, I. T., Banerjei, L., Myers, G. S. A.,  et al. 2003. Role of Mobile DNA in the Evolution of Vancomycin-Resistant Enterococcus faecalis. s.l. : American Association for the Advancement of Science. 2. Palsson, Bernhard O., and  Edwards , Jeremy S., 1999 Systems Properties of the Haemophilus influenzae Rd. 18.   AUTHORS’ BIOGRAPHIES CARLA PORTELA was born in Porto, Portugal and went to the University of Minho, where she studied biological engineering and obtained her degree in 2006. After her degree she has worked in Lyngby, Denmark and Lisbon, Portugal as a research assistant before enrolling in her PhD. Since 2009 she is working in her PhD project in Chemical and Biological Engineering under the Supervision of Eugénio C. Ferreira (IBB - UM), Isabel Rocha (IBB - UM) and Silas Villas-Bôas (UoA – New Zealand). Her e-mail address is: portela.carla@deb.uminho.pt.   

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Genome scale metabolic network reconstruction of pathogen – Enterococcus faecalis

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Universidade do Minho: RepositoriUM

Universidade do Minho: RepositoriUM

Universidade do Minho: RepositoriUM

Universidade do Minho: RepositoriUM