Identification and Characterisation of an Iron-Responsive Candidate Probiotic
Bibliographic record
Abstract
BACKGROUND: Iron is an essential cofactor in almost all biological systems. The lactic acid bacteria (LAB), frequently employed as probiotics, are unusual in having little or no requirement for iron. Iron in the human body is sequestered by transferrins and lactoferrin, limiting bacterial growth. An increase in the availability of iron in the intestine by bleeding, surgery, or under stress leads to an increase in the growth and virulence of many pathogens. Under these high iron conditions, LAB are rapidly out-competed; for the levels of probiotic bacteria to be maintained under high iron conditions they must be able to respond by increasing growth rate to compete with the normal flora. Despite this, iron-responsive genera are poorly characterised as probiotics. METHODOLOGY/PRINCIPAL FINDINGS: Here, we show that a panel of probiotics are not able to respond to increased iron availability, and identify an isolate of Streptococcus thermophilus that can increase growth rate in response to increased iron availability. The isolate of S. thermophilus selected was able to reduce epithelial cell death as well as NF-κB signalling and IL-8 production triggered by pathogens. It was capable of crossing an epithelial cell barrier in conjunction with E. coli and downregulating Th1 and Th17 responses in primary human intestinal leukocytes. CONCLUSIONS/SIGNIFICANCE: We propose that an inability to compete with potential pathogens under conditions of high iron availability such as stress and trauma may contribute to the lack of efficacy of many LAB-based probiotics in treating disease. Therefore, we offer an alternative paradigm which considers that probiotics should be able to be competitive during periods of intestinal bleeding, trauma or stress.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".