Development of droplet digital PCR assays to quantify genes involved in nitrification and denitrification, comparison with quantitative real-time PCR and validation of assays in vineyard soil
Bibliographic record
Abstract
Quantifying genes in soil is important to relate the abundance of soil bacteria to biogeochemical cycles. Quantitative real-time PCR is widely used for quantification, but its use with environmental samples is limited by poor reaction efficiencies or by PCR inhibition through co-purified soil substances. Droplet digital PCR (ddPCR) is a technology for absolute, sensitive quantification of genes. This study optimized eight ddPCR assays to quantify total bacteria and archaea as well as the nitrification (bacterial and archaeal amoA) and denitrification (nirS, nirK, nosZI, nosZII) genes involved in the generation or reduction of the greenhouse gas nitrous oxide. Detection and quantification thresholds were compared with those of quantitative real-time PCR and were equal to, or improved, in ddPCR. To validate the assays using environmental samples, soil DNA was isolated from two vineyards in the Okanagan valley in British Columbia, Canada, over the 2017 growing season. Soil properties related to the observed gene abundances were determined. Total bacteria, nirK, and nosZII increased with time and the soil C/N ratio and NH4+-N concentration affected total archaea and archaeal amoA negatively. The results, compared with those of other studies, showed that ddPCR is a valid alternative to qPCR to quantify genes involved in nitrification or denitrification.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".