Overexpression of Ribosomal Proteins Leads to Zn Resistance in <i>Escherichia coli</i>
Bibliographic record
Abstract
Abstract Knockout of ribosomal protein bL36 (RpmJ) leads to Zn resistance in Escherichia coli , and the expression of ribosomal protein genes other than RpmJ increase in the rpmJ -knockout strain. In this study, we examined whether the overexpression of ribosomal proteins causes Zn resistance using an E. coli overexpression gene library (ASKA clone library). The overexpression of 48 of the 54 ribosomal proteins led to Zn resistance. However, the overexpression of proteins other than ribosomal proteins did not lead to Zn resistance, suggesting that Zn resistance is a phenomenon specific to the overexpression of ribosomal proteins. In addition, the overexpression of ribosomal proteins did not lead to resistance to metal ions other than Zn (Cu 2+ , Ni 2+ , Mn 2+ , and Ag + ), suggesting a Zn-specific resistance mechanism. Deletion of ZntA, a Zn efflux pump, resulted in the loss of Zn resistance in a ribosomal protein-overexpressing strain. Deletion of Lon protease, which is responsible for degrading misfolded proteins, in a ribosomal protein-overexpressing strain resulted in the accumulation of overexpressed ribosomal proteins and loss of Zn resistance. These results suggest that the overexpression of ribosomal proteins leads to Zn resistance in E. coli via ZntA and Lon protease. Importance The ribosome is a complex comprising ribosomal RNAs and more than 50 types of ribosomal proteins. Ribosomal proteins play an important role in ribosomal function responsible for protein translation; however, their involvement in other cellular processes is not fully understood. Based on the finding that ribosomal protein expression increases in a Zn-resistant E. coli mutant, we analyzed 54 ribosomal proteins and found that the overexpression of 48 ribosomal proteins led to Zn resistance. This finding suggests a role for ribosomal proteins in resistance to zinc stress.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".