Unravelling the myths and mysteries of the antimicrobial agent, silver
Bibliographic record
Abstract
Background Silver is being widely‐deployed in the clinic – in wound dressing, catheters, and endotracheal tubes ‐ to combat and control infectious disease. Despite this widespread use, we still don't know the precise way that silver kills the bacterial cell. Recently, our research group demonstrated that silver formulations with novel chemistries – silver oxysalts – have an enhanced antibacterial activity. Yet, we still don't know why different silver formulations show variable antimicrobial efficacy. Methods To test the efficacy of various silver compounds, numerous strains of E.coli , P.aeruginosa , and S.aureus were grown as single and multispecies biofilms in the Calgary Biofilm Device. The capacity of different silver compounds to prevent the formation of and eradicate planktonic and biofilm populations of bacteria was performed using the minimal biofilm eradication concentration assay, confocal microscopy and crystal violet staining. To enhance our understanding of how silver poisons bacteria, we undertook a robotic chemical genetic screen of an ordered mutant library of E.coli bacteria – the Keio collection. To confirm the genetic linkage of our silver responsive genes identified in our chemical genetic screen, we used Scarless Cas‐9 Assisted Recombineering to generate unmarked mutants. Our genes of interest were then linked to silver sensitivity or resistance phenotypes using minimal inhibition concentration assays and transmission electron microscopy (TEM). Results We observed that higher oxidation states of silver have enhanced activity for preventing the formation of, and eradicating single and multispecies, planktonic and biofilm, populations of bacteria – suggesting that the chemistry of silver formulations dictates its antimicrobial efficacy. Our chemical genetic screen suggests that silver poisoning has previously unanticipated effects on the bacterial cell including disrupting the bacterial cell envelope, altering indole metabolism, and controlling bacterial cell population. Moreover, using a Recombineering workflow and TEM we confirmed the involvement of a gene involved in the production of a cell wall protein, and a monovalent cation transporter, in silver sensitivity and resistance, respectively. The latter finding is relevant because the protein responsible for the entrance of silver into the bacterial cell has yet to be identified. Conclusions Altogether, our findings established novel mechanisms regarding the mode‐of‐action of silver in the bacterial cell. We've established that the specific chemistries of silver compounds determine antimicrobial capacity. Additionally, we've identified genes that may lead to silver resistance. These findings are ever‐important as we aim to maintain the utility of this valuable antimicrobial agent. Support or Funding Information We graciously acknowledge funding from the Natural Science and Engineering Research Council of Canada and the Canadian Institutes of Health Research. JL was funded by a Banting Postdoctoral Fellowship and an Alberta Innovates Health Solutions Postdoctoral award. We would also like to thank the University of Calgary for providing an Eyes High Graduate/Postgraduate Fellowship to NG and HA, respectively and research funding for JL.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".