Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation

Flahaut, N.A.L.; Wiersma, Anne; Bunt, Bert van de; Martens, Dirk E.; Schaap, Peter J.; Sijtsma, L.; Santos, Vitor Ap Martins dos; Vos, Willem M. de

doi:10.1007/s00253-013-5140-2

Cited by 106 publications

(105 citation statements)

References 48 publications

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“…The estimated coefficients are similar to the values as previously reported for the genome-scale models of L. plantarum and L. lactis (7,8).…”

Section: Resultssupporting

confidence: 77%

“…We used an implemented task in PySCeS CBMPy to guarantee mass and charge balance. We further tested the genome-scale model for the presence of loops, as reported for the genome-scale model of L. lactis (8). The model was annotated and exported as SBML level 3 (22) and will be publicly available in BIOMODLES and SysMO SEEK and is additionally provided as a spread sheet in the appendix (see Table SA1 in the supplemental material).…”

Section: Methodsmentioning

confidence: 99%

“…The model was used to define a minimal growth medium, determine energy parameters for growth and maintenance, and revealed the presence of futile cycles under rich growth conditions. A recently published genome-scale model of the lactic acid bacterium Lactococcus lactis focused in detail on flavor-forming pathways that might have important implications in dairy industry, where this organism is frequently used (8). Outside the group of lactic acid bacteria, it is worth mentioning the genome-scale model of the human pathogen Listeria monocytogenes that was studied in a novel combination with transcriptome data and revealed a strong link between changes in the metabolism and induction of virulence (9).…”

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confidence: 99%

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Using a Genome-Scale Metabolic Model of Enterococcus faecalis V583 To Assess Amino Acid Uptake and Its Impact on Central Metabolism

Veith

Solheim

Grinsven

et al. 2015

Appl Environ Microbiol

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dIncreasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially L-glutamine and L-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets. Enterococcus faecalis plays an important role in both biotechnology and medicine (1, 2). Some strains are used in the dairy industry for food fermentation and flavor production. At the same time, other strains play an increasingly important role as pathogens, especially in hospital-acquired infections (1). E. faecalis strains show multiple antibiotic resistances and are therefore the cause of serious complications. Alternative strategies for combating multiple resistant bacteria such as E. faecalis are urgently needed. Targeting vulnerable points in the bacterial metabolism could offer such an alternative route and has been discussed as a promising strategy (3, 4). However, surprisingly little is known about the detailed metabolism of E. faecalis, and it is therefore mandatory to explore this in a more elaborate fashion than previously reported.E. faecalis shows a high stress tolerance and is adapted to a variety of different native environments ranging from soil up to human or animal digestive tracts (1, 2). This environmental variability requires a highly flexible metabolic system to quickly adapt to diverse and changing environmental conditions. Therefore, strategies...

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“…The estimated coefficients are similar to the values as previously reported for the genome-scale models of L. plantarum and L. lactis (7,8).…”

Section: Resultssupporting

confidence: 77%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Using a Genome-Scale Metabolic Model of Enterococcus faecalis V583 To Assess Amino Acid Uptake and Its Impact on Central Metabolism

Veith

Solheim

Grinsven

et al. 2015

Appl Environ Microbiol

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“…Construction of host-microbe models We retrieved 11 manually constructed and validated reconstructions of human gut microbes 13,[43][44][45][46][47][48][49][50] and an extensive, highquality reconstruction of human metabolism.…”

Section: Methodsmentioning

confidence: 99%

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

2015

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Keywords: human gut microbiome, in silico, metabolome, metabolic modelingThe gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates "germfree" and "exgermfree" human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial cometabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism.

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“…We used 11 manually curated, published gut microbe reconstructions (23,(26)(27)(28)(29)(30)(31)(32)(33) that we had refined previously (24). The reconstructed strains are listed in Table S1 in the supplemental material, and the reconstructions are shown in spreadsheet format in Table S2 in the supplemental material.…”

Section: Definition Of Scenariosmentioning

confidence: 99%

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

Heinken

Thiele

2015

Appl Environ Microbiol

113

100

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bThe human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other's presence as well as to the host and engage in complex cross feeding. Constraint-based modeling has been successfully applied to predicting microbe-microbe interactions, such as commensalism, mutualism, and competition. Here, we apply a constraint-based approach to model pairwise interactions between 11 representative gut microbes. Microbe-microbe interactions were computationally modeled in conjunction with human small intestinal enterocytes, and the microbe pairs were subjected to three diets with various levels of carbohydrate, fat, and protein in normoxic or anoxic environments. Each microbe engaged in species-specific commensal, parasitic, mutualistic, or competitive interactions. For instance, Streptococcus thermophilus efficiently outcompeted microbes with which it was paired, in agreement with the domination of streptococci in the small intestinal microbiota. Under anoxic conditions, the probiotic organism Lactobacillus plantarum displayed mutualistic behavior toward six other species, which, surprisingly, were almost entirely abolished under normoxic conditions. This finding suggests that the anoxic conditions in the large intestine drive mutualistic cross feeding, leading to the evolvement of an ecosystem more complex than that of the small intestinal microbiota. Moreover, we predict that the presence of the small intestinal enterocyte induces competition over host-derived nutrients. The presented framework can readily be expanded to a larger gut microbial community. This modeling approach will be of great value for subsequent studies aiming to predict conditions favoring desirable microbes or suppressing pathogens.T he human intestine is inhabited by a complex ecosystem consisting of thousands of microbial species. Its collective genome (the microbiome) contains more than 150 times as many genes as our own genome (1). The community of gut microbes has coevolved with humans and has adapted to the competitive conditions in the intestine. Gut microbes differ in their metabolic potential to exploit various environmental conditions and to persist in the intestine (2). They respond differently to the availability of dietary nutrients (3). Moreover, the gut microbiota has to contend with a steep oxygen gradient, which ranges from a partial O 2 pressure of approximately 80 mm Hg to nearly anoxic conditions (4). The diverse physiology of the small intestine creates a wide range of local oxygen microenvironments that favor particular groups of bacteria (4). Furthermore, metabolic interaction patterns between microbes affect microbial growth. Generally, six types of speciesspecies interactions can be distinguished (5) (Fig. 1a). In the case of neutralism, two organisms do not depend on each other for growth. If shared resources become scarce, this condition leads to competition (5). In the gut, competition over fermentable carbohydrates typically arises (6). If only one...

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Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation

Cited by 106 publications

References 48 publications

Using a Genome-Scale Metabolic Model of Enterococcus faecalis V583 To Assess Amino Acid Uptake and Its Impact on Central Metabolism

Using a Genome-Scale Metabolic Model of Enterococcus faecalis V583 To Assess Amino Acid Uptake and Its Impact on Central Metabolism

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

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