| Autor: |
Evdokimova SA; Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia., Karetkin BA; Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia., Guseva EV; Department of Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia., Gordienko MG; Department of Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia., Khabibulina NV; Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia., Panfilov VI; Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia., Menshutina NV; Department of Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia., Gradova NB; Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia. |
| Jazyk: |
angličtina |
| Zdroj: |
Microorganisms [Microorganisms] 2022 Apr 28; Vol. 10 (5). Date of Electronic Publication: 2022 Apr 28. |
| DOI: |
10.3390/microorganisms10050929 |
| Abstrakt: |
The diversity and the stability of the microbial community are associated with microecological 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 intrapopulation relationships. The aim of this study was to create predictive models for bacterial contamination 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 (N 2 , pH 6.8, flow rate 0.04 h -1 ) 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 studied 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 influenced 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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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