A defined transposon mutant library and its use in identifying motility genes in <i>Vibrio cholerae</i>
Bibliographic record
Abstract
Defined mutant libraries allow for efficient genome-scale screening and provide a convenient collection of mutations in almost any nonessential gene of interest. Here, we present a near-saturating transposon insertion library in Vibrio cholerae strain C6706, a clinical isolate belonging to the O1 El Tor biotype responsible for the current cholera pandemic. Automated sequencing analysis of 23,312 mutants allowed us to build a 3,156-member subset library containing a representative insertion in every disrupted ORF. Because uncharacterized mutations that affect motility have shown utility in attenuating V. cholerae live vaccines, we used this genome-wide subset library to define all genes required for motility and to further assess the accuracy and purity of the library. In this screen, we identified the hypothetical gene VC2208 (flgT) as essential for motility. Flagellated cells were very rare in a flgT mutant, and transcriptional analysis showed it was specifically stalled at the class III/IV assembly checkpoint of the V. cholerae flagellar regulatory system. Because FlgT is predicted to have structural homology to TolB, a protein involved in determining outer membrane architecture, and the sheath of the V. cholerae flagellum appears to be derived from the cell's outer membrane, FlgT may play a direct role in flagellar sheath formation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".