Targeted genetic screening in bacteria with a Cas12k-guided transposase
Bibliographic record
Abstract
Microbes employ sophisticated cellular networks encoded by complex genomes to rapidly adapt to changing environments. High-throughput genome engineering methods are valuable tools for functionally profiling genotype-phenotype relationships and understanding the complexity of cellular networks. However, current methods either rely on special homologous recombination systems and are thus applicable in only limited bacterial species or can generate only nonspecific mutations and thus require extensive subsequent screening. Here, we report a site-specific transposon-assisted genome engineering (STAGE) method that allows high-throughput Cas12k-guided mutagenesis in various microorganisms, such as Pseudomonas aeruginosa and Klebsiella pneumoniae. Exploiting the powerful STAGE technique, we construct a site-specific transposon mutant library that focuses on all possible transcription factors (TFs) in P. aeruginosa, enabling the comprehensive identification of essential genes and antibiotic-resistance-related factors. Given its broad host range activity and easy programmability, this method can be widely adapted to diverse microbial species for rapid genome engineering and strain evolution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".