Detection of filamentous genus Gordonia in foam samples using genus-specific primers combined with PCR – denaturing gradient gel electrophoresis analysis
Bibliographic record
Abstract
A nested-PCR amplification combined with denaturing gradient gel electrophoresis (PCR-DGGE) approach was used to detect and identify Gordonia populations from wastewater treatment plant foam samples. The PCR-amplified region (position 722-1119) by specifically designed primers G699F and G1096R covered the hypervariable region of the Gordonia 16S rRNA gene sequence. This approach successfully distinguished Gordonia species to the interspecies level. The differential ability of PCR-DGGE analysis was effectively used to separate 12 Gordonia species belonging to different 16S rRNA gene-based phylogenetic lineages into 8 groups. Based on this method, the minimum limit of Gordonia detection was 5 x 10(4) CFU.g -1 in the seeded soil samples. The PCR-DGGE bands obtained were excised and identified by sequence analysis. Gordonia polyisoprenivorans, Gordonia amicalis, DGGE type II Gordonia species, and an uncertain Gordonia species dominated the activated sludge foam samples. Results of this study indicate that the detection and analyses of genus Gordonia within a complex microbial community could be accomplished using the PCR-DGGE approach to a larger extent, with certain limitations. Detection of diverse Gordonia populations in foam samples from wastewater treatment plants revealed the significant role of Gordonia in biological foaming during wastewater treatment. The nested-PCR amplification and DGGE can be used as a diagnostic tool for the early detection of foaming incidents in wastewater treatment plants using Gordonia as indicator organism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".