Metabolic Influence of S. boulardii and S. cerevisiae in Cross-Kingdom Models of S. mutans and C. albicans

Abstract

Recent studies highlight the potential of Saccharomyces species as probiotics due to their ability to modulate microbial interactions and reduce cariogenic activity, yet the underlying metabolic mechanisms remain unclear. This study investigates the cross-kingdom metabolic effects of Saccharomyces boulardii and Saccharomyces cerevisiae on the metabolic processes of Streptococcus mutans and Candida albicans using a metabolomics-based approach. Untargeted LC-MS/MS analysis was conducted to assess metabolites in a planktonic model, followed by metabolomic profiling and pathway analysis to identify key metabolic alterations. The results revealed that S. boulardii and S. cerevisiae demonstrated metabolic regulatory effects on S. mutans and C. albicans. Specifically, S. boulardii down-regulated 262 metabolites and up-regulated 168, while S. cerevisiae down-regulated 265 metabolites and up-regulated 168. Both yeast species down-regulated carbohydrate and amino acid metabolism in S. mutans and C. albicans, resulting in reduced biomolecule synthesis and a less acidic environment. S. boulardii and S. cerevisiae also up-regulated certain metabolic processes, including purine metabolism, suggesting a compensatory mechanism for nucleotide synthesis. Notably, dual regulatory effects were observed, where specific metabolites were simultaneously up-regulated and down-regulated, indicating complex metabolic crosstalk. These findings suggest that both S. boulardii and S. cerevisiae modulate microbial metabolism through a shared mechanism, offering potentials for dental caries prevention.

Keywords: Candida albicans; Saccharomyces boulardii; Saccharomyces cerevisiae; Streptococcus mutans; metabolic influence; microbial interactions.

Figure 1

The untargeted metabolomics analysis of the effects of Saccharomyces on S. mutans–C. albicans. (a) A schematic representation of the process of a planktonic model (created with BioRender.com). Dual-species and multi-species conditions of Streptoccocus mutans (10⁵ CFU/mL), Candida albicans (10³ CFU/mL), and Saccharomyces (S. boulardii or S. cerevisiae, 10⁷ CFU/mL) in 10 mL of TSBYE broth supplemented with 1% glucose for 20 h. (b) Principal component analysis (PCA) two-dimensional scores plot from the untargeted metabolomics analysis, with each dot representing a biological sample.

Figure 2

The functional analysis of S. mutans metabolites regulated by S. boulardii. (a) A volcano plot showing 168 up-regulated metabolites (adjusted p < 0.05, log2 FC > 1) and 262 down-regulated metabolites (adjusted p < 0.05, log2 FC < −1) in the multi-species model with S. boulardii added. (b) A clustering heatmap illustrating the classification of metabolites regulated by S. boulardii in planktonic models. The rows (metabolites) and columns (samples) are clustered separately, with raw data normalized to Z-scores. The mapping grids are color-coded according to their Z-scores. (c) The analysis of down-regulated metabolic pathways using the web-based MetaboAnalyst 6.0, based on S. mutans pathway libraries. (d) Down-regulated metabolic pathway networks in S. mutans. (e) The analysis of up-regulated metabolic pathways based on S. mutans pathway libraries. (f) Up-regulated metabolic pathway networks in S. mutans.

Figure 3

Functional analysis of C. albicans metabolites regulated by S. boulardii. (a) Analysis of down-regulated metabolic pathways using web-based MetaboAnalyst 6.0, based on C. albicans pathway libraries. (b) Down-regulated metabolic pathway networks in C. albicans. (c) Analysis of up-regulated metabolic pathways based on C. albicans pathway libraries. (d) Up-regulated metabolic pathway networks in C. albicans.

Figure 4

The functional analysis of S. mutans metabolites regulated by S. cerevisiae. (a) A volcano plot showing 168 up-regulated metabolites (adjusted p < 0.05, log2 FC > 1) and 265 down-regulated metabolites (adjusted p < 0.05, log2 FC < −1) in the multi-species model with S. cerevisiae added. (b) A clustering heatmap illustrating the classification of metabolites regulated by S. cerevisiae in planktonic models. The rows (metabolites) and columns (samples) are clustered separately, with raw data normalized to Z-scores. The mapping grids are color-coded according to their Z-scores. (c) The analysis of down-regulated metabolic pathways using the web-based MetaboAnalyst 6.0, based on S. mutans pathway libraries. (d) Down-regulated metabolic pathway networks in S. mutans. (e) The analysis of up-regulated metabolic pathways based on S. mutans pathway libraries. (f) Up-regulated metabolic pathway networks in S. mutans.

Figure 5

Functional analysis of C. albicans metabolites regulated by S. cerevisiae. (a) Analysis of down-regulated metabolic pathways using web-based MetaboAnalyst 6.0, based on C. albicans pathway libraries. (b) Down-regulated metabolic pathway networks in C. albicans. (c) Analysis of up-regulated metabolic pathways based on C. albicans pathway libraries. (d) Up-regulated metabolic pathway networks in C. albicans.

Figure 6

Crosstalk between S. boulardii and S. cerevisiae on metabolic network of S. mutans and C. albicans. (a) Down-regulated metabolic pathway networks in S. mutans caused by Saccharomyces. (b) Up-regulated pathway networks in S. mutans caused by Saccharomyces. (c) Down-regulated pathway networks in C. albicans caused by Saccharomyces. (d) Up-regulated pathway networks in C. albicans caused by Saccharomyces.