Escherichia coli: Physiological Clues Which Turn On the Synthesis of Antimicrobial Molecules
Bibliographic record
Abstract
Zoonotic pathogens, like Shiga toxin-producing Escherichia coli (STEC) are a food safety and health risk. To battle the increasing emergence of virulent microbes, novel mitigation strategies are needed. One strategy being considered to combat pathogens is antimicrobial compounds produced by microbes, coined microcins. However, effectors for microcin production are poorly understood, particularly in the context of complex physiological responses along the gastro-intestinal tract (GIT). Previously, we identified an E. coli competitor capable of producing a strong diffusible antimicrobial with microcin-associated characteristics. Our objective was to examine how molecule production of this competitor is affected by physiological properties associated with the GIT, namely the effects of carbon source, bile salt concentration and growth phase. Using previously described liquid- and agar-based assays determined that carbon sources do not affect antimicrobial production of E. coli O103F. However, bile salt concentrations affected production significantly, suggesting that E. coli O103F uses cues along the GIT to modulate the expression of antimicrobial production. Furthermore, E. coli O103F produces the molecule during the exponential phase, contrary to most microcins identified to date. The results underscored the importance of experimental design to identify producers of antimicrobials. To detect antimicrobials, conventional microbiological methods can be a starting point, but not the gold standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".