High-copy bacterial plasmids diffuse in the nucleoid-free space, replicate stochastically and are randomly partitioned at cell division
Bibliographic record
Abstract
Bacterial plasmids play important roles in the metabolism, pathogenesis and bacterial evolution and are highly versatile biotechnological tools. Stable inheritance of plasmids depends on their autonomous replication and efficient partition to daughter cells at cell division. Active partition systems have not been identified for high-copy number plasmids, and it has been generally believed that they are partitioned randomly at cell division. Nevertheless, direct evidence for the cellular location of replicating and nonreplicating plasmids, and the partition mechanism has been lacking. We used as model pJHCMW1, a plasmid isolated from Klebsiella pneumoniae that includes two β-lactamase and two aminoglycoside resistance genes. Here we report that individual ColE1-type plasmid molecules are mobile and tend to be excluded from the nucleoid, mainly localizing at the cell poles but occasionally moving between poles along the long axis of the cell. As a consequence, at the moment of cell division, most plasmid molecules are located at the poles, resulting in efficient random partition to the daughter cells. Complete replication of individual molecules occurred stochastically and independently in the nucleoid-free space throughout the cell cycle, with a constant probability of initiation per plasmid.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".