Genome-Scale Metabolic Modeling Combined with Transcriptome Profiling Provides Mechanistic Understanding of Streptococcus thermophilus CH8 Metabolism

Rau, Martin Holm; Gaspar, Paula; Jensen, Maiken Lund; Geppel, Asger; Neves, Ana Rute; Zeidan, Ahmad A.

doi:10.1128/aem.00780-22

Cited by 8 publications

(9 citation statements)

References 63 publications

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Section: Resultsmentioning

confidence: 99%

“…To check the validity of reconstructed GSMMs, FBA with proteome allocation constraints was performed for ST and LB on complete CDM (Rau et al, 2022) and milieu proche du lait (MPL) medium (Chervaux et al, 2000) (Figure 4a,b). For ST, the growth rate reported by Rau et al (2022) on complete CDM was 0.98 h −1 (Rau et al, 2022), and proteome‐constrained FBA, with a fixed upper bound of amino acid uptake rate from Özcan et al (2021), predicted a close value, 1.2 h −1 . Due to lack of similar amino acid uptake rate data measured or estimated for LB, the upper bound was estimated in this work by fixing LB's growth rate on MPL medium, reported to be 0.7 h −1 (Chervaux et al, 2000) (see Supporting Information, Section 3 and Figure S3A).…”

Section: Resultsmentioning

confidence: 99%

“…For refinement of GSMMs, the growth function (biomass synthesis,

{v}_{{Growth}}

) should be set as species‐specific (see Supporting Information, Section 1). The stoichiometric coefficients in ST's growth function were adjusted based on measured biomass composition (Pastink et al, 2009), and the growth‐associated adenosine triphosphate (ATP) requirement was adopted from the GSMM of S. thermophilus CH8 (Rau et al, 2022). Due to data limitation, the biomass composition in LB's growth function was set according to the default of Gram‐positive bacteria (Magnúsdóttir et al, 2017), and the growth‐associated ATP requirement was adopted from the GSMM of Lactobacillus plantarum (Teusink et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

“…To check the validity of reconstructed GSMMs, FBA with proteome allocation constraints was performed for ST and LB on complete CDM (Rau et al, 2022) and Figure S3A). The predicted lactic acid yield ratio from lactose consumed (mol lactate/mol lactose) was 1.6919 for ST and 1.9305 for LB, which was consistent with the experimental measurement (Table S1) as well as with previous studies (Ghasemi et al, 2009;Özcan et al, 2021;Rau et al, 2022).…”

Section: Reconstruction Of Gsmms With Proteome Allocation Constraints...mentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…For refinement of GSMMs, the growth function (biomass synthesis,

{v}_{{Growth}}

Section: Methodsmentioning

confidence: 99%

Section: Reconstruction Of Gsmms With Proteome Allocation Constraints...mentioning

confidence: 99%

See 2 more Smart Citations

Dynamic metagenome‐scale metabolic modeling of a yogurt bacterial community

Qiu

Zeng

Yang

et al. 2023

Biotech & Bioengineering

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“…They are predominantly composed of mesophilic strains of Lactococcus, including the species L. lactis and L. cremoris as well as thermophilic strains of Streptococcus thermophilus. A rich literature exists on the physiology of both species in mono-cultures [4,5,6], and genome-scale metabolic models have been used to model their metabolism [7,8]. In contrast, the interactions between the two taxa is still largely unknown from a mechanistic perspective despite indications of strong inter-dependencies [9].…”

Section: Introductionmentioning

confidence: 99%

Inter-species metabolic interactions in cheese flavour formation

Melkonian

Zorrilla

Kjærbølling

et al. 2023

Preprint

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Cheese fermentation and flavour formation are governed by complex biochemical reactions driven by polymicrobial activity. While the compositional dynamics of cheese microbiomes is relatively well mapped, the mechanistic role of microbial interactions in flavour formation is yet unknown. We microbially and metabolically characterised a year-long Cheddar cheese process using a commonly used starter culture containing Streptococcus thermophilus and Lactococcus strains. By using an experimental strategy whereby certain strains were left out from the starting mixture, we identified the critical role of S. thermophilus in boosting Lactococcus growth and in shaping flavour compound profile. Controlled milk fermentations with systematic exclusion of single Lactococcus strains, combined with genomics, genome-scale metabolic modelling, and metatranscriptomics, indicated that proteolytic activity of S. thermophilus relieves nitrogen limitation for Lactococcus and boosts de novo nucleotide biosynthesis. While S. thermophilus had large contribution to the flavour profile, L. cremoris also played a role by limiting diacetyl and acetoin formation which leads to off-flavour when in excess. This off-flavour control could be attributed to different metabolic re-routing of citrate between L. cremoris and other L. lactis strains. Further, closely related L. lactis strains exhibited different interaction patterns with S. thermophilus highlighting the importance of strain-specificity in cheese-making. Overall, our results bring forward the critical role of competitive and cooperative microbial interactions shaping cheese flavour profile.

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Genome‐scale reconstruction of the metabolic network in Streptococcus thermophilus S‐3 and assess urea metabolism

Hou,

Song,

Xiong

et al. 2023

J Sci Food Agric

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BACKGROUNDStreptococcus thermophilus is an important strain widely used in dairy fermentation, with distinct urea metabolism characteristics compared to other lactic acid bacteria. The conversion of urea by S. thermophilus has been shown to affect the flavor and acidification characteristics of milk. Additionally, urea metabolism has been found to significantly increase the number of cells and reduce cell damage under acidic pH conditions, resulting in higher activity. However, the physiological role of urea metabolism in S. thermophilus has not been fully evaluated. A deep understanding of this metabolic feature is of great significance for its production and application. Genome‐scale metabolic network models (GEMs) are effective tools for investigating the metabolic network of organisms using computational biology methods. Constructing an organism‐specific GEM can assist us in comprehending its characteristic metabolism at a systemic level.RESULTSIn the present study, we reconstructed a high‐quality GEM of S. thermophilus S‐3 (iCH492), which contains 492 genes, 608 metabolites and 642 reactions. Growth phenotyping experiments were employed to validate the model both qualitatively and quantitatively, yielding satisfactory predictive accuracy (95.83%), sensitivity (93.33%) and specificity (100%). Subsequently, a systematic evaluation of urea metabolism in S. thermophilus was performed using iCH492. The results showed that urea metabolism reduces intracellular hydrogen ions and creates membrane potential by producing and transporting ammonium ions. This activation of glycolytic fluxes and ATP synthase produces more ATP for biomass synthesis. The regulation of fluxes of reactions involving NAD(P)H by urea metabolism improves redox balance.CONCLUSIONModel iCH492 represents the most comprehensive knowledge‐base of S. thermophilus to date, serving as a potent tool. The evaluation of urea metabolism led to novel insights regarding the role of urease. © 2023 Society of Chemical Industry.

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Genome-Scale Metabolic Modeling Combined with Transcriptome Profiling Provides Mechanistic Understanding of Streptococcus thermophilus CH8 Metabolism

Cited by 8 publications

References 63 publications

Dynamic metagenome‐scale metabolic modeling of a yogurt bacterial community

Dynamic metagenome‐scale metabolic modeling of a yogurt bacterial community

Inter-species metabolic interactions in cheese flavour formation

Genome‐scale reconstruction of the metabolic network in Streptococcus thermophilus S‐3 and assess urea metabolism

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