Using a chemical genetic screen to enhance our understanding of the antimicrobial properties of copper
Bibliographic record
Abstract
The competitive toxic and stress-inducing nature of copper necessitates systems that sequester and export this metal from the cytoplasm of bacterial cells. Several predicted mechanisms of toxicity include the production of reactive oxygen species, thiol depletion, DNA, and iron-sulfur cluster disruption. Accompanying these mechanisms include pathways of homeostasis such as chelation, oxidation, and transport. Still, the mechanisms of copper resistance and sensitivity are not fully understood. Furthermore, studies fail to recognize that the response to copper is likely a result of numerous mechanisms, as in the case for homeostasis, in which proteins and enzymes work as a collective to maintain appropriate copper concentrations. In this study, we used the Keio collection, an array of 3985 Escherichia coli mutants, each with a deleted non-essential gene, to gain a better understanding of the effects of prolonged exposure to copper. In short, we recovered two copper homeostatic genes involved in transporting and assembling that are required in mediating prolonged copper stress under the conditions assessed. The gene coding for the protein TolC was uncovered as a sensitive hit, and we demonstrated that tolC, an outer membrane efflux channel, is key in mitigating copper sensitivity. Additionally, the activity of tRNA processing was enriched along with the deletion of several proteins involved in importing generated copper tolerance. Lastly, key genes belonging to central carbon metabolism and nicotinamide adenine dinucleotide biosynthesis were uncovered as tolerant hits. Overall, this study shows that copper sensitivity and tolerance are a result of numerous mechanisms acting in combination within the cell.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".