Knockout of ribosomal protein RpmJ leads to zinc resistance in Escherichia coli
Bibliographic record
Abstract
Zinc is an essential metal for cells, but excess amounts are toxic. Other than by regulating the intracellular zinc concentration by zinc uptake or efflux, the mechanisms underlying bacterial resistance to excess zinc are unknown. In the present study, we searched for zinc-resistant mutant strains from the Keio collection, a gene knockout library of Escherichia coli, a model gram-negative bacteria. We found that knockout mutant of RpmJ (L36), a 50S ribosomal protein, exhibited zinc resistance. The rpmJ mutant was sensitive to protein synthesis inhibitors and had altered translation fidelity, indicating ribosomal dysfunction. In the rpmJ mutant, the intracellular zinc concentration was decreased under excess zinc conditions. Knockout of ZntA, a zinc efflux pump, abolished the zinc-resistant phenotype of the rpmJ mutant. RNA sequence analysis revealed that the rpmJ mutant exhibited altered gene expression of diverse functional categories, including translation, energy metabolism, and stress response. These findings suggest that knocking out RpmJ alters gene expression patterns and causes zinc resistance by lowering the intracellular zinc concentration. Knockouts of other ribosomal proteins, including RplA, RpmE, RpmI, and RpsT, also led to a zinc-resistant phenotype, suggesting that deletion of ribosomal proteins is closely related to zinc resistance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".