Modeling Lactococcus lactis using a genome-scale flux model

Oliveira, Ana Paula; Nielsen, Jens; Förster, Jochen

doi:10.1186/1471-2180-5-39

Cited by 221 publications

(103 citation statements)

References 46 publications

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“…Samples for mass spectrometry analysis were taken from the in vitro cultures grown in CDM2 described above for the entire length of the experiment (before inoculation and at 4,8,12,16,20, and 24 h). The results represented the average of three biological replicates.…”

Section: Methodsmentioning

confidence: 99%

“…For instance, constraint-based modeling was used for minimal medium design for the lactic acid bacterium Lactococcus lactis (20), for the eukaryotic pathogen Leishmania major, for which improvements to gene annotations were also proposed (21), and for the pathogenic bacterium Neisseria meningitidis (22). However, the described approaches targeted moderately well studied to well-studied organisms.…”

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confidence: 99%

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Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

et al. 2014

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eThe human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii, which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

et al. 2014

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“…Their relatively simple metabolism makes them also favourable objects for metabolic studies and for the development of tools for modelling (Oliveira et al 2005). Lactococcus lactis IL1403 was the first LAB to be sequenced and it is also the most studied strain, thus providing a good case for understanding physiology of the lactic acid bacteria in both natural and laboratory environments (Bolotin et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Quasi steady state growth of Lactococcus lactis in glucose-limited acceleration stat (A-stat) cultures

Adamberg

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Valgepea

et al. 2009

Antonie van Leeuwenhoek

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Quasi steady state growth of Lactococcus lactis IL 1403 was studied in glucose-limited A-stat cultivation experiments with acceleration rates (a) from 0.003 to 0.06 h(-2) after initial stabilization of the cultures in chemostat at D = 0.2-0.3 h(-1). It was shown that the high limit of quasi steady state growth rate depended on the acceleration rate used-at an acceleration rate 0.003 h(-2) the quasi steady state growth was observed until mu (crit) = 0.59 h(-1), which is also the mu (max) value for the culture. Lower values of mu (crit) were observed at higher acceleration rates. The steady state growth of bacteria stabilized at dilution rate 0.2 h(-1) was immediately disrupted after initiating acceleration at the highest acceleration rate studied-0.06 h(-2). Observation was made that differences [Delta(mu - D)] of the specific growth rates from pre-programmed dilution rates were the lowest using an acceleration rate of 0.003 h(-2) (< 4% of preset changing growth rate). The adaptability of cells to follow preprogrammed growth rate was found to decrease with increasing dilution rate-it was shown that lower acceleration rates should be applied at higher growth rates to maintain the culture in the quasi steady state. The critical specific growth rate and the biomass yields based on glucose consumption were higher if the medium contained S (0) = 5 g L(-1) glucose instead of S (0) = 10 g L(-1). It was assumed that this was due to the inhibitory effect of lactate accumulating at higher concentrations in the latter cultures. Parallel A-stat experiments at the same acceleration and dilution rates showed good reproducibility-Delta(mu - D) was less than 5%, standard deviations of biomass yields per ATP produced (Y (ATP)), and biomass yields per glucose consumed (Y (XS)) were less than 15%.

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“…GEMs have primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions (McCloskey et al, 2013). Initially, these models only considered well -characterized organisms; nevertheless, the interest in the generation of metabolic models of less characterized and complex biological systems has progressively increased, including the GEMs of several lactic acid bacteria, such as Lactococcus lactis (Oliveira et al, 2005; Oddone et al, 2009; Verouden et al, 2009; Flahaut et al, 2013), Lactobacillus plantarum (Teusink et al, 2006) and Streptococcus thermophilus (Pastink et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Genome-Scale Reconstruction of the Metabolic Network in Oenococcus oeni to Assess Wine Malolactic Fermentation

et al. 2017

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Oenococcus oeni is the main responsible agent for malolactic fermentation in wine, an unpredictable and erratic process in winemaking. To address this, we have constructed and exhaustively curated the first genome-scale metabolic model of Oenococcus oeni, comprising 660 reactions, 536 metabolites and 454 genes. In silico experiments revealed that nutritional requirements are predicted with an accuracy of 93%, while 14 amino acids were found to be essential for the growth of this bacterial species. When the model was applied to determine the non-growth associated maintenance, results showed that O. oeni grown at 12% ethanol concentration spent 30 times more ATP to stay alive than in the absence of ethanol. Most of this ATP is employed for extruding protons outside of the cell. A positive relationship was also found between specific consumption rates of fructose, amino acids, oxygen, and malic acid and the specific production rates of erythritol, lactate, and acetate, according to the ethanol content of the medium. The metabolic model reconstructed here represents a unique tool to predict the successful completion of wine malolactic fermentation carried out either by different strains of Oenococcus oeni, as well as at any particular physico-chemical composition of wine. It will also allow the development of consortium metabolic models that could be applied to winemaking to simulate and understand the interactions between O. oeni and other microorganisms that share this ecological niche.

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Modeling Lactococcus lactis using a genome-scale flux model

Cited by 221 publications

References 46 publications

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

Quasi steady state growth of Lactococcus lactis in glucose-limited acceleration stat (A-stat) cultures

Genome-Scale Reconstruction of the Metabolic Network in Oenococcus oeni to Assess Wine Malolactic Fermentation

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