Investigating the Role of β-Disodium Glycerophosphate and Urea in Promoting Growth of Streptococcus thermophilus from Omics-Integrated Genome-Scale Models

This study investigates the impact of urea and β-GP on the growth of Streptococcus thermophilus S-3, a bacterium commonly used in industrial fermentation processes. Through a series of growth experiments, transcriptome, metabolome, and omics-based analyses, the research demonstrates that both urea and β-GP can enhance the biomass of S. thermophilus, with urea showing a more significant effect. The optimal urea concentration for growth was determined to be 3 g/L in M17 medium. The study also highlights the metabolic pathways influenced by urea and β-GP, particularly the galactose metabolism pathway, which is crucial for cell growth when lactose is the substrate. The integration of omics data into the genome-scale metabolic model of S. thermophilus, iCH502, allowed for a more accurate prediction of metabolic fluxes and growth rates. The study concludes that urea can serve as a viable substitute for β-GP in the cultivation of S. thermophilus, offering potential cost and efficiency benefits in industrial fermentation processes. The findings are supported by validation experiments with 11 additional strains of S. thermophilus, which showed increased biomass in UM17 medium.

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

BACKGROUND

Streptococcus 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.

RESULTS

In 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.

CONCLUSION

Model 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.

Software-based screening for efficient sgRNAs in Lactococcus lactis

BACKGROUND

The two essential editing elements in the clustered regularly interspaced short palindromic repeats (CRISPR) editing system are promoter and single-guide RNA (sgRNA), the latter of which determines whether Cas protein can precisely target a specific location to edit the targeted gene. Therefore, the selection of sgRNA is crucial to the efficiency of the CRISPR editing system. Various online prediction tools for sgRNA are currently available. These tools can predict all possible sgRNAs of the targeted gene and rank sgRNAs according to certain scoring criteria according to the demands of the user.

RESULTS

We designed sgRNAs for Lactococcus lactis NZ9000 LLNZ_RS02020 (ldh) and LLNZ_RS10925 (upp) individually using online prediction software - CRISPOR - and successfully constructed a series of knockout strains to allow comparison of the knockout efficiency of each sgRNA and analyze the differences between software predictions and actual experimental results.

CONCLUSION

Our experimental results showed that the actual editing efficiency of the screened sgRNAs did not match the predicted results - a phenomenon that suggests that established findings from eukaryotic studies are not universally applicable to prokaryotes. Software prediction can still be used as a tool for the initial screening of sgRNAs before further selection of suitable sgRNAs through experimental experience. © 2023 Society of Chemical Industry.