Enrichment and characterization of MTBE-degrading cultures under iron and sulfate reducing conditions
Bibliographic record
Abstract
The aim of this study was to enrich cultures capable of anaerobic methyl tert-butyl ether (MTBE) biodegradation and to determine their compositions using biomolecular tools. MTBE biodegradation as the sole carbon source was observed under Fe(III) and SO42–reducing conditions and with both electron acceptors present together. The estimated MTBE biodegradation rates ranged from 3.80 to 9.20 mg·L–1·d–1when Fe(III) was the sole electron acceptor, from 1.46 to 1.70 mg·L–1·d–1when Fe(III) and SO42–were present together, and from 1.13 to 1.71 mg·L–1·d–1when SO42–was the sole electron acceptor. Five to eight members were identified in the three consortia, and their characteristics were congruent with the electron acceptor conditions. A clone 99% similar to a recently described MTBE degrader, Ochrobactrum cytisi, was detected in both cultures containing Fe(III). Other 16S rRNA gene sequences detected were highly similar at the species or genus level to additional known MTBE degraders, including Pseudomonas spp. (cometabolic), Sphinogomonas, Achromobacter, and Rhodococcus. Results suggest that the buildup of intermediates and (or) the presence of sulfides had an inhibitory effect on biodegradation. Anaerobic MTBE biodegradation remains poorly understood, and degrading strains still have not been identified to date. This work represents the first detailed 16S rRNA gene profiling of highly enriched iron- and sulfate-reducing MTBE-degrading consortia. These results may provide useful biomarkers to support current efforts for confirming anaerobic MTBE biodegradation in the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".