Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water
Bibliographic record
Abstract
The limitation of 16S rRNA gene sequencing (DNA-based) for microbial community analyses in water is the inability to differentiate live (dormant cells as well as growing or non-growing metabolically active cells) and dead cells, which can lead to false positive results in the absence of live microbes. Propidium-monoazide (PMA) has been used to selectively remove DNA from dead cells during downstream sequencing process. In comparison, 16S rRNA sequencing (RNA-based) can target live microbial cells in water as both dormant and metabolically active cells produce rRNA. The objective of this study was to compare the efficiency and sensitivity of DNA-based, PMA-based and RNA-based 16S rRNA Illumina sequencing methodologies for live bacteria detection in water samples experimentally spiked with different combination of bacteria (2 gram-negative and 2 gram-positive/acid fast species either all live, all dead, or combinations of live and dead species) or obtained from different sources (First Nation community drinking water; city of Winnipeg tap water; water from Red River, Manitoba, Canada). The RNA-based method, while was superior for detection of live bacterial cells still identified a number of 16S rRNA targets in samples spiked with dead cells. In environmental water samples, the DNA- and PMA-based approaches perhaps overestimated the richness of microbial community compared to RNA-based method. Our results suggest that the RNA-based sequencing was superior to DNA- and PMA-based methods in detecting live bacterial cells in water.
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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 | 0.001 | 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) | 0.000 | 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".