A Study and Modeling of Bifidobacterium and Bacillus Coculture Continuous Fermentation under Distal Intestine Simulated Conditions

• Main Authors: Evdokimova, S.A (Author), Gordienko, M.G (Author), Gradova, N.B (Author), Guseva, E.V (Author), Karetkin, B.A (Author), Khabibulina, N.V (Author), Menshutina, N.V (Author), Panfilov, V.I (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Bacillus cereus; Bifidobacterium adolescentis; computational biology; continuous coculture fermentation; foodborne pathogens; growth inhibition model; oligofructose; prebiotics; probiotics
• Online Access: View Fulltext in Publisher

 The diversity and the stability of the microbial community are associated with microeco-logical interactions between its members. Antagonism is one type of interaction, which particularly determines the benefits that probiotics bring to host health by suppressing opportunistic pathogens and microbial contaminants in food. Mathematical models allow for quantitatively predicting in-trapopulation relationships. The aim of this study was to create predictive models for bacterial con-tamination outcomes depending on the probiotic antagonism and prebiotic concentration. This should allow an improvement in the screening of synbiotic composition for preventing gut microbial infections. The functional model (fermentation) was based on a three-stage continuous system, and the distal colon section (N2, pH 6.8, flow rate 0.04 h) was simulated. The strains Bifidobacterium adolescentis ATCC 15703 and Bacillus cereus ATCC 9634 were chosen as the model probiotic and pathogen. Oligofructose Orafti P95 (OF) was used as the prebiotic at concentrations of 2, 5, 7, 10, 12, and 15 g/L of the medium. In the first stage, the system was inoculated with Bifidobacterium, and a dynamic equilibrium (Bifidobacterium count, lactic, and acetic acids) was achieved. Then, the system was contaminated with a 3-day Bacillus suspension (spores). The microbial count, as well as the concentration of acids and residual carbohydrates, was measured. A Bacillus monoculture was stud-ied as a control. The stationary count of Bacillus in monoculture was markedly higher. An increase (up to 8 h) in the lag phase was observed for higher prebiotic concentrations. The specific growth rate in the exponential phase varied at different OF concentrations. Thus, the OF concentration in-fluenced two key events of bacterial infection, which together determine when the maximal pathogen count will be reached. The mathematical models were developed, and their accuracies were acceptable for Bifidobacterium (relative errors ranging from 1.00% to 2.58%) and Bacillus (relative errors ranging from 0.74% to 2.78%) count prediction. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.