Bacteriophages as antimicrobial agents against bacterial contaminants in yeast fermentation processes
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Bibliographic record
Abstract
BACKGROUND: The emergence of biofuels produced through yeast fermentation represents an important avenue for sustainable energy production. Despite all its advantages, this process is vulnerable to contamination by other organisms - most commonly lactic acid bacteria - that are present in raw feedstocks and/or in production lines. These contaminants compete with the yeast for nutrients, reducing the overall biofuel yield, and release substances that inhibit yeast growth. Here, we investigated the application of bacteriophages as potential antibacterial agents in yeast fermentation. RESULTS: Experiments conducted to understand the impact of pH on yeast, bacterial, and phage development showed that the yeast Saccharomyces cerevisiae Superstart™ grew in a similar fashion at pH levels ranging from 3 to 6. Growth of Lactobacillus plantarum ATCC® 8014™ was inhibited by pH levels of less than 4, and phages ATCC® 8014-B1™ (phage B1) and ATCC® 8014-B2™ (phage B2) displayed different infectivities within the pH range tested (pH 3.5 to 7). Phage B1 showed the best infectivity at pH 6, while phage B2 was most virulent at pH levels ranging from 4 to 5, and the cocktail of these phages showed highest infectivity in the range from pH 4 to 6. Population dynamics studies in MRS medium at pH 6 showed that, in the presence of bacteria inoculated at 10(7) cells/ml, yeast cultures were impeded under aerobic and anaerobic conditions, showing major decreases in both cell yield and ethanol production. The addition of the phage cocktail at a low initial multiplicity of infection was sufficient to reduce contamination by over 99%, and to allow yeast and ethanol yields to reach values equivalent to those of axenic cultures. CONCLUSIONS: From the results observed, phages are good candidates as antimicrobial agents, to be used in place of or in conjunction with antibiotics, in yeast fermentation processes. Their implementation with other common contamination abatement/prevention methods could further increase their efficacy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it