Identification of toluene degraders in a methanogenic enrichment culture
Bibliographic record
Abstract
Methanogenic biodegradation involves the cooperative metabolism of syntrophic bacteria that catalyse the initial attack and subsequent degradation of hydrocarbons, and methanogens that convert intermediates such as hydrogen and carbon dioxide, formate, and/or acetate to methane. The identity of syntrophic microbes and the nature of their interactions with other syntrophs and methanogens are not well understood. Furthermore, it is difficult to isolate the organisms responsible for the initial activation and subsequent degradation of hydrocarbon substrates under methanogenic conditions due to the thermodynamic relationships that exist among microbes in methanogenic communities. We used time-resolved RNA stable isotope probing and RT-qPCR to identify the organisms involved in the initial attack on toluene and subsequent degradation reactions in a highly enriched toluene-degrading methanogenic culture. Our results reveal the importance of a Desulfosporosinus sp. in anaerobic toluene activation in the culture. Other organisms that appear to play roles in toluene degradation include Syntrophaceae, Desulfovibrionales and Chloroflexi. The high bacterial diversity observed in this culture and the extensive labelling of different phylogenetic groups over the course of the stable isotope probing experiment highlight the complexity of the relationships that exist in methanogenic ecosystems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".