<i><scp>B</scp>urkholderia cenocepacia</i> conditional growth mutant library created by random promoter replacement of essential genes
Bibliographic record
Abstract
Identification of essential genes by construction of conditional knockouts with inducible promoters allows the identification of essential genes and creation of conditional growth (CG) mutants that are then available as genetic tools for further studies. We used large-scale transposon delivery of the rhamnose-inducible promoter, PrhaB followed by robotic screening of rhamnose-dependent growth to construct a genomic library of 106 Burkholderia cenocepacia CG mutants. Transposon insertions were found where PrhaB was in the same orientation of widely conserved, well-characterized essential genes as well as genes with no previous records of essentiality in other microorganisms. Using previously reported global gene-expression analyses, we demonstrate that PrhaB can achieve the wide dynamic range of expression levels required for essential genes when the promoter is delivered randomly and mutants with rhamnose-dependent growth are selected. We also show specific detection of the target of an antibiotic, novobiocin, by enhanced sensitivity of the corresponding CG mutant (PrhaB controlling gyrB expression) within the library. Modulation of gene expression to achieve 30-60% of wild-type growth created conditions for specific hypersensitivity demonstrating the value of the CG mutant library for chemogenomic experiments. In summary, CG mutants can be obtained on a large scale by random delivery of a tightly regulated inducible promoter into the bacterial chromosome followed by a simple screening for the CG phenotype, without previous information on gene essentiality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".