Chromosomal Complementation Using Tn <i>7</i> Transposon Vectors in Enterobacteriaceae
Bibliographic record
Abstract
Genetic complementation in many bacteria is commonly achieved by reintroducing functional copies of the mutated or deleted genes on a recombinant plasmid. Chromosomal integration systems using the Tn7 transposon have the advantage of providing a stable single-copy integration that does not require selective pressure. Previous Tn7 systems have been developed, although none have been shown to work effectively in a variety of enterobacteria. We have developed several mini-Tn7 and transposase vectors to provide a more versatile system. Transposition of Tn7 at the chromosomal attTn7 site was achieved by a classical conjugation approach, wherein the donor strain harbored the mini-Tn7 vector and the recipient strain possessed the transposase vector. This approach was efficient for five different pathogenic enterobacterial species. Thus, this system provides a useful tool for single-copy complementation at an episomal site for research in bacterial genetics and microbial pathogenesis. Furthermore, these vectors could also be used for the introduction of foreign genes for use in biotechnology applications, vaccine development, or gene expression and gene fusion constructs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".