Polymerase Chain Reaction-Based Signature-Tagged Mutagenesis
Bibliographic record
Abstract
The study of bacterial pathogenicity in vitro has identified many signals, at the molecular and cellular levels that affect expression of virulence and other factors in causing disease. Because these pathways are not necessarily reproduced in vitro, their implication and their regulation in pathogenesis in vivo remains circumstantial. Genomics-based technologies can now be used to study pathogenesis in vivo (1). Signature-tagged mutagenesis (STM) (2) is an elegant method, based on negative selection, to identify mutations in a gene, which is essential during the infection process. In STM, transposon (Tn) mutants are generated, and each unique cell clone is tagged with a specific DNA sequence (2). Compared to traditional pathogenicity assays, STM minimizes the number of animals to be utilized, and eliminates false-positive and false-negative results. The strategy of STM depends on tagged Tn mutants, defective in virulence, which cannot be maintained in vivo. Attenuated mutants are selected and retested to confirm attenuation; disrupted genes are cloned via the Tn marker, and the inactivated gene confirmed by DNA sequencing. Modifications of STM, allowing rapid and easy identification of attenuated mutants, have recently been described and called “polymerase chain reaction (PCR)based STM” (3).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".