Molecular Profile of Phage Infection: A Novel Approach for the Characterization of Erwinia Phages through qPCR
Bibliographic record
Abstract
Due to the emergence of antibiotic resistance, phage-mediated biocontrol has become an attractive alternative for pathogen management in agriculture. While the infection characteristics of many phages can be adequately described using plaque assays and optical density, the results from phages of the apple pathogen Erwinia amylovora have low reproducibility with these techniques. Using quantitative real-time PCR (qPCR), the stage of the lytic cycle was determined through a combination of chloroform-based sampling, centrifugation, and DNase treatment. Monitoring the transition of phage genomes through the lytic cycle generates a molecular profile from which phage infection characteristics such as adsorption rate and burst size can be determined. To our knowledge, this is the first report of qPCR being used to determine these infection parameters. The characteristics of four different genera of Erwinia phages were determined. The phage ΦEa46-1-A1 was able to adsorb at a rate up to 6.6 times faster than ΦEa35-70 and ΦEa9-2. The low enrichment titer of ΦEa9-2 was shown to be due to the absence of lysis. The ΦEa46-1-A1 and ΦEa21-4 phages had the highest productivity, with burst sizes of 57 virions in 38 min and 185 virions in 98 min, respectively, suggesting these genera would make stronger candidates for the phage-mediated biocontrol of E. amylovora.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".