A genome-scale metabolic network of the aroma bacterium Leuconostoc mesenteroides subsp. cremoris

Özcan, Emrah; Selvi, Sinem Selvin; Nikerel, Emrah; Teusink, Bas; Öner, Ebru Toksoy; Çakır, Tunahan

doi:10.1007/s00253-019-09630-4

Cited by 29 publications

(31 citation statements)

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“…Glucose concentration was determined by reducing sugar analysis using DNS method (Miller, 1959). Organic acids and amino acids concentrations were determined by high‐performance liquid chromatography as described previously (Özcan et al, 2019). CO 2 production profiles of L. lactis and S. thermophilus strains showing homolactic fermentation patterns were assumed to be negligibly small compared to the total carbon outflow under anaerobic conditions as also stated in the literature (Jensen et al, 2001).…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Molar concentration of ethanol and CO 2 produced by Leu. mesenteroides were estimated based on the glucose and citrate consumption rates as described by our previous study (Özcan et al, 2019).…”

Section: Materials and Methodsmentioning

confidence: 99%

“…lactis IL1403 is known to be unable to synthesize these amino acids (Aller et al, 2014;Cocaign-Bousquet et al, 1995;van Niel & Hahn-Hägerdal, 1999). The GSMM of S. thermophilus LMG 18311 (Pastink et al, 2009) was revised via the following steps: (i) the draft GSMM of S. thermophilus LMG 18311 was reconstructed using genome sequence of S. thermsophilus LMG 18311 (Bolotin et al, 2004 (Flahaut et al, 2013;Oliveira et al, 2005;Özcan et al, 2019;Pastink et al, 2009). For the simulation of the cultures towards the end of the batch, the low glucose uptake rate might not support the original ATP maintenance rate (m ATP ) constraints, which results in an infeasible solution.…”

Section: Genome-scale Metabolic Models (Gsmms) Used In This Studymentioning

confidence: 99%

“…In this study, we have investigated dairy origin LAB in cheese starter cultures by a dynamic metabolic modelling approach based on genome-scale metabolic networks of involved organisms. There are several genome-scale metabolic modelling studies of dairy-origin LAB at single-species level (Flahaut et al, 2013;Oliveira et al, 2005;Özcan et al, 2019;Pastink et al, 2009). Yet, this study is the first metabolic modelling study that models the different LAB composing a microbial consortium using genome-scale dynamic metabolic modelling approach.…”

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Dynamic co‐culture metabolic models reveal the fermentation dynamics, metabolic capacities and interplays of cheese starter cultures

Özcan

Seven

Şirin

et al. 2020

Biotech & Bioengineering

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In this study, we have investigated the cheese starter culture as a microbial community through a question: can the metabolic behaviour of a co-culture be explained by the characterized individual organism that constituted the co-culture? To address this question, the dairy-origin lactic acid bacteria Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. lactis, Streptococcus thermophilus and Leuconostoc mesenteroides, commonly used in cheese starter cultures, were grown in pure and four different cocultures. We used a dynamic metabolic modelling approach based on the integration of the genome-scale metabolic networks of the involved organisms to simulate the cocultures. The strain-specific kinetic parameters of dynamic models were estimated using the pure culture experiments and they were subsequently applied to co-culture models. Biomass, carbon source, lactic acid and most of the amino acid concentration profiles simulated by the co-culture models fit closely to the experimental results and the coculture models explained the mechanisms behind the dynamic microbial abundance. We then applied the co-culture models to estimate further information on the co-cultures that could not be obtained by the experimental method used. This includes estimation of the profile of various metabolites in the co-culture medium such as flavour compounds produced and the individual organism level metabolic exchange flux profiles, which revealed the potential metabolic interactions between organisms in the co-cultures. K E Y W O R D S co-culture metabolic modelling, genome-scale metabolic network, lactic acid bacteria, starter cultures 1 | INTRODUCTION Milk has been processed by humankind for millennia and cheese is one of the oldest fermented dairy food (Salque et al., 2013). Cheese is traditionally made either by the lactic acid bacteria (LAB) naturally present in milk or by the back-slopping technique, which is adding a small portion of a previous batch of cheese to milk. On the other hand, in industrial cheese production, defined mixtures of purified and characterized LAB, known as starter cultures, are used to standardize the bulk production (Cogan & Hill, 1993; Leroy &

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Genome-scale Metabolic Models (Gsmms) Used In This Studymentioning

confidence: 99%

mentioning

confidence: 99%

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Dynamic co‐culture metabolic models reveal the fermentation dynamics, metabolic capacities and interplays of cheese starter cultures

Özcan

Seven

Şirin

et al. 2020

Biotech & Bioengineering

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“…These LAB are homofermentative or facultatively heterofermentative organisms and have substantial differences in metabolism compared to strict heterofermenters such as L. reuteri [3]. Metabolic models for the heterofermenters Leuconostoc mesenteroides [29, 39] are available (Table 1), but this is only distantly related to L. reuteri [3] and shows different metabolic features such as malolactic fermentation and a limited ability to use amino acids as energy source [29]. Models for two probiotic strains of L. reuteri have been previously published [52] (Table 1).…”

Section: Introductionmentioning

confidence: 99%

A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

et al. 2019

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BackgroundLactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.ResultsA genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.ConclusionWe have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

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Dynamic metagenome‐scale metabolic modeling of a yogurt bacterial community

Qiu

Zeng

Yang

et al. 2023

Biotech & Bioengineering

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Genome-scale metabolic models and flux balance analysis (FBA) have been extensively used for modeling and designing bacterial fermentation. However, FBA-based metabolic models that accurately simulate the dynamics of coculture are still rare, especially for lactic acid bacteria used in yogurt fermentation. To investigate metabolic interactions in yogurt starter culture of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus, this study built a dynamic metagenome-scale metabolic model which integrated constrained proteome allocation. The accuracy of the model was evaluated by comparing predicted bacterial growth, consumption of lactose and production of lactic acid with reference experimental data. The model was then used to predict the impact of different initial bacterial inoculation ratios on acidification. The dynamic simulation demonstrated the mutual dependence of S. thermophilus and L. d. bulgaricus during the yogurt fermentation process. As the first dynamic metabolic model of the yogurt bacterial community, it provided a foundation for the computer-aided process design and control of the production of fermented dairy products.

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A genome-scale metabolic network of the aroma bacterium Leuconostoc mesenteroides subsp. cremoris

Cited by 29 publications

References 58 publications

Dynamic co‐culture metabolic models reveal the fermentation dynamics, metabolic capacities and interplays of cheese starter cultures

Dynamic co‐culture metabolic models reveal the fermentation dynamics, metabolic capacities and interplays of cheese starter cultures

A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

Dynamic metagenome‐scale metabolic modeling of a yogurt bacterial community

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