Exploring benzene mineralization by anaerobes isolated from denitrifying enrichment cultures
Bibliographic record
Abstract
Objectives The study aimed to isolate and characterize benzene-mineralizing anaerobes from a nitrate-reducing community. The goal was to evaluate their potential for benzene degradation under anoxic conditions and to compare composition and metabolic activity in media with and without ammonium. Methods Two putative isolates (Bz4 and Bz7) were obtained using classical isolation techniques under nitrate-reducing conditions with either acetate or benzene as the sole carbon and energy source. Enrichment media differed by the presence (Bz4) or absence (Bz7) of ammonium. The ability of the isolates to mineralize [ 13 C]-labeled acetate and [ 13 C 6 ]-labeled benzene was assessed by monitoring 13 CO 2 production under anoxic conditions over 184 days. Nitrite production was also measured. Community composition was determined via 16S rRNA gene amplicon sequencing. Results Both Bz4 and Bz7 consortia mineralized [ 13 C]-acetate anoxically, with mineralization rates of 3.3 μM day -1 and 2.7 μM day -1 , respectively. Benzene mineralization was observed only in Bz4, which degraded [ 13 C 6 ]-benzene at a rate of 0.298 μM day -1 , resulting in 13 CO 2 production with δ 13 C values reaching 960.2 ± 0.3 ‰. Bz4 also produced nitrite (4.60 ± 0.004 μM), while Bz7 showed no benzene mineralization with a smaller quantity of nitrite production. Microbial community analysis revealed that Bz4 was dominated by Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium (51.4%), Rhizobiaceae (21.0%), Pseudomonas (18.2%), and Nocardioides (8.9%). In contrast, Bz7 was almost exclusively composed of Simplicispira (96.7%). Conclusions The Bz4 consortium demonstrated effective anaerobic benzene mineralization under nitrate-reducing conditions, highlighting its potential for further purification and study in the context of anaerobic benzene biodegradation.
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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) | 0.001 | 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) | 0.000 | 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".