Quantitative PCR Targeting 16S rRNA and Reductive Dehalogenase Genes Simultaneously Monitors Multiple<i>Dehalococcoides</i>Strains
Bibliographic record
Abstract
The 16S rRNA gene provides insufficient information to infer the range of chloroorganic electron acceptors used by different Dehalococcoides organisms. To overcome this limitation and provide enhanced diagnostic tools for growth measurements, site assessment, and bioremediation monitoring, a quantitative real-time PCR (qPCR) approach targeting 16S rRNA genes and three Dehalococcoides reductive dehalogenase (RDase) genes with assigned function (i.e., tceA, bvcA, and vcrA) was designed and evaluated. qPCR standard curves generated for the RDase genes by use of genomic DNA from Dehalococcoides pure cultures correlated with standard curves obtained for both Bacteria- and Dehalococcoides-targeted 16S rRNA genes, suggesting that the RDase genes are useful targets for quantitative assessment of Dehalococcoides organisms. RDase gene probe/primer pairs were specific for the Dehalococcoides strains known to carry the diagnostic RDase gene sequences, and the qPCR method allowed the detection of as few as 1 to 20 and quantification of as few as 50 to 100 tceA, bvcA, or vcrA gene targets per PCR volume. The qPCR approach was applied to dechlorinating enrichment cultures, microcosms, and samples from a contaminated site. In characterized enrichment cultures where known Dehalococcoides strains were enumerated, the sum of the three RDase genes equaled the total Dehalococcoides cell numbers. In site samples and chloroethane-dechlorinating microcosms, the sum of the three RDase genes was much less than that predicted by Dehalococcoides-targeted qPCR, totaling 10 to 30% of the total Dehalococcoides cell numbers. Hence, a large number of Dehalococcoides spp. contain as-yet-unidentified RDase genes, indicating that our current understanding of the dechlorinating Dehalococcoides community is incomplete.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 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) | 0.001 | 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".