Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/32167

TítuloFootprinting microbial metabolites in nature and medicine
Autor(es)Carneiro, S.
Correia, D. M.
Ferreira, Eugénio C.
Rocha, I.
Palavras-chaveSystems Medicine
Data2012
CitaçãoCarneiro, S.; Correia, Daniela M.; Ferreira, E. C.; Rocha, I., Footprinting microbial metabolites in nature and medicine. SYSMED 2012 - International Conference on Systems Medicine. Dublin, Irlande, Sep 10-13, 63-65, 2012.
Resumo(s)The study of metabolic alterations in response to genetic and environmental perturbations has been a central topic in microbial metabolomics (Fiehn, 2002; Kol et al., 2010; Villas-Boas et al., 2008). Some of these alterations can be readily detected by changes in their surroundings, normally associated with metabolites that are released by cells as by-products of the metabolism or as extracellular signalling molecules to mediate cross-talk within microbial communities. The analysis of these metabolites, also known as metabolic footprinting, has been widely applied with different purposes: discriminating between metabolic phenotypes in order to classify and identify mutant strains (Villas-Boas et al., 2008); monitoring bioprocesses with the aim to detect specific metabolites that indicate alterations in the culture performance (Carneiro et al., 2011; Sue et al., 2011); and identifying quorum-sensing metabolites that indicate potential targets to annihilate pathogens (Birkenstock et al., 2012). These metabolic readouts have been also useful to give insights into intracellular metabolic activities and provide a straightforward way to analyse simultaneously multiple metabolic activities, since no extraction procedures are required to analyse the endometabolome (i.e., intracellular metabolites). Thus, through metabolic footprint analysis we can assess central metabolic activities that characterize the reproduction and survival of organisms. We have developed a methodology to evaluate the metabolic state of microbial cultures by analysing the footprints of two microbial systems: the bacterium Escherichia coli and the human pathogen Helicobacter pylori. Strategies for sampling and sample preparation were developed, as well as the analytical procedures based on gas chromatography with mass spectrometry (GC-MS). A wide variety of metabolites was detected, including fatty, amino and organic acids, which allowed us to address changes in most central metabolic pathways, such as the tricarboxylic acid cycle (TCA cycle), the biosynthesis of amino and fatty acids, as well as other energy generating metabolic reactions. The analysis of extracellular metabolites of E. coli cultures at different growth conditions were first performed to discriminate the physiological state of cultures and to evaluate the metabolic alterations produced at different growth conditions. According to our results in these experiments, metabolic footprints are good indicators of alterations in the intracellular metabolism. Next, the metabolic footprints of H. pylori cultures were investigated to get insights on the catabolism of this human pathogen. Overall, fifteen amino acids were detected in extracellular medium; six of them were confirmed as essentials for H. pylori growth, four amino acids were identified as non-essentials and can be used as carbon source, whilst five amino acids were identified as non-essentials and non-carbon source. In addition, some organic acids were also identified as carbon sources for H. pylori. This metabolic footprint analysis of H. pylori cultures allowed us to uncover key metabolic activities, mainly related with amino acids catabolism and to get insight on the metabolic behaviour of this organism. The characterization of catabolic pathways, as well as of possible metabolic constraints, is of major importance to understand the dynamic basis of the interactions host–microbe in the human gut, and in particular to discover potential ‘diagnostic’ biomarkers. It is well-known that pathogen's metabolism can influence the host health and may affect drug metabolism, toxicity and the efficacy of therapies (Holmes et al., 2011). However, little is known about their metabolic structure and behaviour. Our methodology allows uncovering part of the metabolic structure of H. pylori metabolism and undisclosed catabolic activities. Acknowledgments This work was partially supported by the MIT-Portugal Program in Bioengineering (MIT-Pt/BS-BB/0082/2008), the research project HeliSysBio-Molecular Systems Biology Helicobacter pylori (FCT PTDC/EBB-EBI/104235/2008) and a Post-doc grant from Portuguese FCT (Fundação para a Ciência e Tecnologia) (ref. SFRH/BPD/73951/2010). 1. Fiehn O. 2002. Metabolomics - the link between genotypes and phenotypes. Plant Molecular Biology 48: 155-71. 2. Kol S, Merlo ME, Scheltema RA, de VM, Vonk RJ, Kikkert NA, Dijkhuizen L, Breitling R, Takano E. 2010. Metabolomic characterization of the salt stress response in Streptomyces coelicolor. Applied and Environmental Microbiology 76: 2574-81. 3. Villas-Boas SG, Moon CD, Noel S, Hussein H, Kelly WJ, Cao M, Lane GA, Cookson AL, Attwood GT. 2008. Phenotypic characterization of transposon-inserted mutants of Clostridium proteoclasticum B316(T) using extracellular metabolomics. Journal of Biotechnology 134: 55-63. 4. Sue T, Obolonkin V, Griffiths H, Villas-Boas SG. 2011. An exometabolomics approach to monitoring microbial contamination in microalgal fermentation processes by using metabolic footprint analysis. Appl Environ Microbiol 77: 7605-10. 5. Carneiro S, Villas-Boas SG, Ferreira EC, Rocha I. 2011. Metabolic footprint analysis of recombinant Escherichia coli strains during fed-batch fermentations. Molecular Biosystems 7: 899-910. 6. Birkenstock T, Liebeke M, Winstel V, Krismer B, Gekeler C, Niemiec MJ, Bisswanger H, Lalk M, Peschel A. 2012. Exometabolome analysis identifies pyruvate dehydrogenase as a target for the antibiotic triphenylbismuthdichloride in multiresistant bacterial pathogens. J Biol Chem 287: 2887-95. 7. Holmes E, Li JV, Athanasiou T, Ashrafian H, Nicholson JK. 2011. Understanding the role of gut microbiome-host metabolic signal disruption in health and disease. Trends Microbiol 19: 349-59.
TipoResumo em ata de conferência
URIhttps://hdl.handle.net/1822/32167
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CEB - Resumos em Livros de Atas / Abstracts in Proceedings

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