Contrasts and similarities in the transcriptomic response to antimicrobial coinage metals in <i>Escherichia coli</i>
Bibliographic record
Abstract
ABSTRACT With the rise of resistance to last-resort antibiotics, metal-based antimicrobials have re-emerged as an alternative to prevent and manage infections. The group 11 metals (copper, silver, gold), historically known for their usage in coins, have demonstrated promising bactericidal activity. Despite their efficiency, we do not have a complete understanding of how bacteria are eradicated by metal ions and how they respond to metal-induced stress. Additionally, most studies in the field focus on the physiological response to acute toxicity, often overlooking longer exposure models. We used RNA-seq profiling to understand the Escherichia coli physiological response to sublethal inhibitory antimicrobial coinage metal stress after 10 hours of incubation. Gene expression patterns of the adaptive and intrinsic response elicited by each metal were identified, including increased essential metal uptake (Ag, Cu, Au), cysteine biosynthesis (Cu, Au), change of the metal ion oxidation state (Cu, Au), efflux of metal stressor (Cu), protein translation, and ribosome biogenesis (Au), and cell envelope stress response (Ag). We highlight the remarkable differences and similarities in the transcriptomic response profile of E. coli to these antimicrobial metal elements. IMPORTANCE Dogma existed in the past, stating that all antimicrobial metals kill bacteria the same way. Thus, the assumption was that bacteria respond the same way to metal toxicity. Nowadays, we understand better why some metal elements are more toxic than others, but questions remain in relation to how bacteria adapt to survive and thrive when challenged by different metal-based antimicrobials. Our study advances the field by characterizing the type of bacterial response needed to acclimate and grow in the presence of silver, copper, and gold—metallic elements known for their antimicrobial activity. Taking advantage of well-characterized Escherichia coli , we propose a model that summarizes our findings after comparing the shared and unique responses to each of these metals. This information enhances our understanding of bacterial tolerance to metal-based antimicrobials, which can lead to improved drug development strategies as society continues to search for alternatives against antibiotic-resistant pathogens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".