Filter-Based Assay for Escherichia coli in Aqueous Samples Using Bacteriophage-Based Amplification
Bibliographic record
Abstract
This paper describes a method to detect the presence of bacteria in aqueous samples, based on the capture of bacteria on a syringe filter, and the infection of targeted bacterial species with a bacteriophage (phage). The use of phage as a reagent provides two opportunities for signal amplification: (i) the replication of phage inside a live bacterial host and (ii) the delivery and expression of the complementing gene that turns on enzymatic activity and produces a colored or fluorescent product. Here we demonstrate a phage-based amplification scheme with an M13KE phage that delivers a small peptide motif to an F(+), α-complementing strain of Escherichia coli K12, which expresses the ω-domain of β-galactosidase (β-gal). The result of this complementation-an active form of β-gal-was detected colorimetrically, and the high level of expression of the ω-domain of β-gal in the model K12 strains allowed us to detect, on average, five colony-forming units (CFUs) of this strain in 1 L of water with an overnight culture-based assay. We also detected 50 CFUs of the model K12 strain in 1 L of water (or 10 mL of orange juice, or 10 mL of skim milk) in less than 4 h with a solution-based assay with visual readout. The solution-based assay does not require specialized equipment or access to a laboratory, and is more rapid than existing tests that are suitable for use at the point of access. This method could potentially be extended to detect many different bacteria with bacteriophages that deliver genes encoding a full-length enzyme that is not natively expressed in the target bacteria.
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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) | 0.009 | 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".