Relative and Absolute Quantitative Real-Time PCR-Based Quantifications of <i>hcnC</i> and <i>phlD</i> Gene Transcripts in Natural Soil Spiked with <i>Pseudomonas</i> sp. Strain LBUM300
Bibliographic record
Abstract
Transcriptional analysis of microbial gene expression using relative quantitative real-time PCR (qRT-PCR) has been hampered by various technical problems. One such problem is the unavailability of an exogenous standard robust enough for use in a complex matrix like soil. To circumvent this technical issue, we made use of a recently developed artificial RNA (myIC) as an exogenous "spike-in" control. Nonsterile field soil was inoculated with various concentrations of the test bacterium Pseudomonas sp. strain LBUM300, ranging from 4.3- to 8.3-log bacterial cells per gram of soil. Total soil RNA was extracted at days 0, 7, and 14 postinoculation, and using two-step TaqMan assays, phlD (encoding the production of 2,4-diacetylphloroglucinol) and hcnC (encoding the production of hydrogen cyanide) gene expression was monitored. For relative quantification, a defined quantity of in vitro-synthesized myIC RNA was spiked during the RNA extraction procedure. Absolute qRT-PCR was also performed in parallel. Both the absolute and relative quantifications showed similar transcriptional trends. Overall, the transcriptional activity of phlD and hcnC changed over time and with respect to the bacterial concentrations used. Transcripts of the phlD and hcnC genes were detected for all five bacterial concentrations, but the phlD transcript copy numbers detected were lower than those detected for hcnC, regardless of the initial bacterial concentration or sampling date. For quantifying a low number of transcripts, the relative method was more reliable than the absolute method. This study demonstrates for the first time the use of a relative quantification approach to quantifying microbial gene transcripts from field soil using an exogenous spike-in control.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".