Identification of Novel Pathogenicity Genes by PCR Signature-Tagged Mutagenesis and Related Technologies
Bibliographic record
Abstract
Microbial pathogens possess a repertoire of virulence determinants that make unique contributions to bacterial fitness during infection. In this chapter, we focus on the recent progress and adaptations of signature-tagged mutagenesis (STM) by PCR instead of hybridization. This is a PCR-based STM mutation-based screening method using a population of bacterial mutants for the simultaneous identification of multiple virulence genes in microbial pathogens by negative selection. Modifications of STM developed in our laboratory have been applied to Pseudomonas aeruginosa PAO1. Screening of a collection of 6912 STM mutants in the rat chronic lung model of infection identified 214 P. aeruginosa STM mutants defective in virulence. For further studies, and to illustrate better the strategies that need to be utilized, we present detailed analysis of nine selected STM mutants. The data obtained indicate that in vivo, defects in virulence give a wide variety of phenotypes: defects in known virulence factors have been found, thereby validating the method; defects have also been found in orthologs with predicted functions, and in some genes whose functions cannot be predicted from databases. A general strategy and a simple scenario is discussed using the nine STM mutants selected for further characterization. PCR-based STM represent a genomics-based method for in vivo high-throughput screening of new virulence factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".