Understanding the acid tolerance response of bifidobacteria
Bibliographic record
Abstract
AIMS: To investigate the effect of pH on the viability and the acid tolerance response (ATR) of bifidobacteria. METHODS AND RESULTS: The impact of low pH on the viability of five species of bifidobacteria was examined under conditions of strict anaerobiosis. Although differences in the ability to resist the lethal effects of low pH were apparent among the species, cell viability could be improved by the provision of fermentable substrate during an acidic pH stress or through the use of stationary phase cells. While a stationary phase ATR was found to occur in two species of bifidobacteria, there was no adaptive response in exponential phase cells. Proteomic analysis of exponential phase Bifidobacterium longum subjected to a mild acid pre-exposure (pH 4.5, 2 h) prior to an acid challenge revealed a substantial loss in the total number of cellular proteins. In contrast, proteomic analysis of stationary phase cells revealed an increased abundance of proteins associated with the general stress response as well as the beta-subunit of the F(0)F(1)-ATPase, known to be important in bifidobacteria acid tolerance. CONCLUSION: Neither Bif. longum or Bifidobacterium breve possesses an inducible exponential phase ATR. SIGNIFICANCE AND IMPACT OF THE STUDY: These findings provide further insights into the impact of pH on the viability of bifidobacteria and may partially explain the loss in viability associated with their storage in acid foods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".