Using a Toxicogenomic Approach to Understand How Silver Poisons the Bacterial Cell
Bibliographic record
Abstract
Introduction The emergence of the post‐antibiotic era has left us desperate for alternative strategies to combat infectious disease. Historically, silver has been used to prevent and control microbial infections. Silver is now seeing widespread use in a variety of medical devices including bandages, catheters, and endotracheal tubes; as well as numerous household items. Yet, we still don't mechanistically understand how silver poisons the microbial cell. Objectives Recently, our research group demonstrated that the higher oxidation states of silver have an enhanced capacity to eradicate laboratory and antibiotic‐resistant strains of bacteria – both planktonic bacteria as well as biofilms. Our current research objective is to understand the mechanistic details of how silver exerts its toxicity on the bacterial cell. Methodology To address the mechanisms of how silver kills bacterial cells, we used a high‐throughput, toxicogenomic approach ‐ screening a knockout collection of E.coli K12 (the Keio collection) for Ag‐sensitive/resistant mutants. Following our toxicogenomic screen, markerless deletion mutants were generated from statistically significant hits using the Red Recombinase system. The markerless genetic mutants were then subjected to phenotypic and biochemical assays to verify their resistance or sensitivity profile to silver treatment. Results Although silver is thought to exert its toxicity by disrupting thiol metabolism and cellular redox‐status, our results demonstrate that silver poisons E.coli through a variety of previously unestablished mechanisms. These include interfering with genes involved in maintaining cell wall integrity, toxin/antitoxin systems, nutrient catabolism, and quorum sensing. Conclusion Here we demonstrate how a toxicogenomic approach was used to determine the mechanistic manner by which silver poisons the bacterial cell. To that end, the robust nature of this methodology can be used to screen other metals, toxins, and antimicrobials to determine their mechanisms of action. Mechanisms that are desperately needed to develop novel antimicrobial strategies. Support or Funding Information This work has been supported by the Natural Science and Engineering Council of Canada. J.L. is supported by a Banting Postdoctoral Fellowship and an Alberta Innovates Health Solutions Postdoctoral Fellowship Award.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".