Crosstalk Regulation Between Bacterial Chromosome Replication and Chromosome Partitioning
Bibliographic record
Abstract
Despite much effort, the bacterial cell cycle has proved difficult to study and understand. Bacteria do not conform to the standard eukaryotic model of sequential cell cycle phases. Instead for example, bacteria overlap their phases of chromosome replication and chromosome partitioning. In “eukaryotic terms,” bacteria simultaneously perform “S-phase” and “mitosis” whose coordination is absolutely required for rapid growth and survival. In this review we focus on the signaling “crosstalk”, meaning the signalling mechanisms that advantageously commit bacteria to start both chromosome replication and chromosome partitioning. After briefly reviewing the molecular mechanisms of replication and partitioning, we highlight the crosstalk research from Bacillus subtilis, Vibrio cholerae and Caulobacter crescentus. As the initiator of chromosome replication, DnaA also mediates crosstalk in each of these model bacteria but not always in the same way. We next focus on the C. crescentus cell cycle and describe how it is revealing novel crosstalk mechanisms. Recent experiments show that the novel nucleoid associated protein GapR has a special role(s) in starting and separating the replicating chromosomes so that upon asymmetric cell division the new chromosomes acquire different fates in C. crescentus’s distinct replicating and non-replicating cell types. The C. crescentus PopZ protein forms a special cell-pole organizing matrix that anchors the chromosomes through their centromere-like DNA sequences near the origin of replication. We also describe how PopZ anchors and interacts with serval key cell cycle regulators thereby providing an organized subcellular environment for more novel crosstalk mechanisms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".