Lysis of <i>Escherichia coli</i> by colicin Ib contributes to bacterial cross-feeding by releasing active β-galactosidase
Bibliographic record
Abstract
The diffusible toxin ColIb produced by Salmonella enterica serovar Typhimurium SL1344 is a potent inhibitor of Escherichia coli growth. To identify and parameterize metabolic cross-feeding in states of competition, we established defined communities in which E. coli was the only species able to access a sole carbon source, lactose. Although ColIb was predicted to undermine cross-feeding by killing the lactose-converting E. coli, S. enterica populations thrived in co-culture. We discovered that ColIb caused the release of active β-galactosidase from E. coli cells, which induced galactose uptake by S. enterica. Although iron limitation stimulates ColIb production and makes E. coli more sensitive to the toxin, ColIb killing in iron-limited conditions did not enhance iron acquisition or siderophore scavenging by S. enterica. Also unexpected was the rapid rate at which resistance to ColIb evolved in E. coli through spontaneous mutation of the ColIb receptor gene cirA or horizontal acquisition of the S. enterica colicin immunity gene imm. Mathematical modelling effectively predicted the growth kinetics of E. coli and S. enterica populations, revealing a tractable system in which ColIb can shrink a competitor population while simultaneously amplifying the metabolic contributions of the suppressed population.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".