A ProQ/FinO family protein involved in plasmid copy number control favours fitness of bacteria carrying<i>mcr-1</i>-bearing IncI2 plasmids
Bibliographic record
Abstract
The plasmid-encoded colistin resistance gene mcr-1 challenges the use of polymyxins and poses a threat to public health. Although IncI2-type plasmids are the most common vector for spreading the mcr-1 gene, the mechanisms by which these plasmids adapt to host bacteria and maintain resistance genes remain unclear. Herein, we investigated the regulatory mechanism for controlling the fitness cost of an IncI2 plasmid carrying mcr-1. A putative ProQ/FinO family protein encoded by the IncI2 plasmid, designated as PcnR (plasmid copy number repressor), balances the mcr-1 expression and bacteria fitness by repressing the plasmid copy number. It binds to the first stem-loop structure of the repR mRNA to repress RepA expression, which differs from any other previously reported plasmid replication control mechanism. Plasmid invasion experiments revealed that pcnR is essential for the persistence of the mcr-1-bearing IncI2 plasmid in the bacterial populations. Additionally, single-copy mcr-1 gene still exerted a fitness cost to host bacteria, and negatively affected the persistence of the IncI2 plasmid in competitive co-cultures. These findings demonstrate that maintaining mcr-1 plasmid at a single copy is essential for its persistence, and explain the significantly reduced prevalence of mcr-1 following the ban of colistin as a growth promoter in China.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".