Escherichia coli growing under antimicrobial gallium nitrate stress reveals new processes of tolerance and toxicity
Bibliographic record
Abstract
Metals have been used throughout history to manage disease. With the rising incidence of antibiotic-resistant bacterial strains, metal-based antimicrobials (MBAs) have re-emerged as an alternative to combat infections. Gallium nitrate has shown promising efficacy against several pathogens. Although its main toxicity mechanisms have focused on oxidative stress and its "trojan horse" iron mimetic strategy, there are still many knowledge gaps in the full-systems response elicited to counteract its toxic effects, especially in non-acute toxicity models that evaluate longer exposure times. In this study, we explore the transcriptomic response profile of Escherichia coli K12 BW25113 when challenged to grow planktonically for 10 h in the presence of a sublethal inhibitory concentration of gallium nitrate. 581 genes were significantly up-regulated, and 791 down-regulated. Some of the affected biological systems identified in our analysis include iron homeostasis, sulfate metabolism, oxidative and nitrosative stress response, cysteine biosynthesis, anaerobic respiration, toxin-antitoxin interactions, and DNA repair. Altogether, this work provides a valuable snapshot of how E. coli acclimates to this MBA and expands the current knowledge of mechanisms of sensitivity and tolerance. This is a significant step in understanding how bacteria can adjust their physiology to coexist with sublethal concentrations of toxic metals.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".