Quantification of Viable but Nonculturable <i>Escherichia coli</i> O157:H7 by Targeting the <i>rpoS</i> mRNA
Bibliographic record
Abstract
Escherichia coli O157:H7 easily becomes viable but nonculturable (VBNC) under environmental stresses and escapes detection by current methods. Here, we report a unique method enabling the quantification of VBNC E. coli O157:H7 using a selective marker within the rpoS gene. A nucleotide at position +543 within the rpoS gene open reading frame was identified to be unique to E. coli O157:H7. Specifically designed primers and probe combinations were able to differentiate E. coli O157:H7 from closely related bacteria and other common bacteria. The application of this strategy correctly identified 36 clinical and bovine isolates of E. coli O157:H7. A one-step quantification method combining reverse transcription (RT) and real-time quantitative polymerase chain reaction (qPCR) was developed to provide a linear relationship (R(2) > 0.99) of copies of RNA with threshold cycles (Ct) and the capability of detecting a single copy of rpoS RNA standards. This technique was used to determine the copies of the rpoS mRNA in culturable cells at different growth phases (mid-log, late-log, and stationary phase) to be 1.57, 0.56, and 0.41 copies/CFU, respectively. VBNC E. coli O157:H7 was determined to have one copy of the rpoS mRNA for every 10 cells, and no rpoS mRNA was detected in 10(6) dead cells and negative controls. This technique had a linear dynamic range over 6 orders of magnitude and >90% amplification efficiency for tap and river water samples. It was able to selectively quantify as few as 7 E. coli O157:H7 cells in pure culture, 9 culturable cells in tap water and river water, and 23 VBNC cells in river water, demonstrating the best quantification limits for culturable and VBNC E. coli O157:H7 in environmental water.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".