Optimization, validation, and application of a real-time PCR protocol for quantification of viable bacterial cells in municipal sewage sludge and biosolids using reporter genes and<i>Escherichia coli</i>
Bibliographic record
Abstract
Biosolids result from treatment of sewage sludge to meet jurisdictional standards, including pathogen reduction. Once government regulations are met, materials can be applied to agricultural lands. Culture-based methods are used to enumerate pathogen indicator microorganisms but may underestimate cell densities, which is partly due to bacteria existing in a viable but non-culturable physiological state. Viable indicators can also be quantified by realtime polymerase chain reaction (qPCR) used with propidium monoazide (PMA), a dye that inhibits amplification of DNA found extracellularly or in dead cells. The objectives of this study were to test an optimized PMA-qPCR method for viable pathogen detection in wastewater solids and to validate it by comparing results to data obtained by conventional plating. Reporter genes from genetically marked Pseudomonas sp. UG14Lr and Agrobacterium tumefaciens 542 cells were spiked into samples of primary sludge, and anaerobically digested and Lystek-treated biosolids as cell-free DNA, dead cells, viable cells, and mixtures of live and dead cells, followed by DNA extraction with and without PMA, and qPCR. The protocol was then used for Escherichia coli quantification in the three matrices, and results compared to plate counts. PMA-qPCR selectively detected viable cells, while inhibiting signals from cell-free DNA and DNA found in membrane-compromised cells. PMA-qPCR detected 0.5-1 log unit more viable E. coli cells in both primary solids and dewatered biosolids than plate counts. No viable E. coli was found in Lystek-treated biosolids. These data suggest PMA-qPCR may more accurately estimate pathogen cell numbers than traditional culture methods.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".