The effect of host‐encoded nucleoid proteins on transposition: H‐NS influences targeting of both IS<i>903</i> and Tn<i>10</i>
Bibliographic record
Abstract
Nucleoid proteins are small, abundant, DNA-binding proteins that profoundly affect the local and global structure of the chromosome, and play a major role in gene regulation. Although several of these proteins have been shown to enhance assembly of transpososomes before initiating transposition, no systematic survey has been carried out examining the in vivo role(s) of these proteins in transposition. We have examined the requirement of the six most abundant nucleoid proteins in transposition for three different transposons, IS903, Tn10 and Tn552. Most notably, H-NS was required for efficient transposition of all three elements in a papillation assay, suggesting a general role for H-NS in bacterial transposition. Further studies indicated that H-NS was exerting its effect on target capture. Targeting preferences for IS903 into the Escherichia coli chromosome were dramatically altered in the absence of H-NS. In addition, the alterations observed in the IS903 target profile emphasized the important role that H-NS plays in chromosome organization. A defect in target capture was also inferred for Tn10, as an excised transposon fragment, a precursor to target capture, accumulated in in vivo induction assays. Furthermore, a transposase mutant that is known to increase target DNA bending and to relax target specificity eliminated this block to target capture. Together, these results imply a role for H-NS in target capture, either by providing regions of DNA more accessible to transposition or by stabilizing transpososome binding to captured targets immediately before strand transfer.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".