Isolation of estrogen-degrading bacteria from an activated sludge bioreactor treating swine waste, including a strain that converts estrone to β-estradiol
Bibliographic record
Abstract
An estrogen-degrading bacterial consortium from a swine wastewater biotreatment was enriched in the presence of low concentrations (1 mg/L) of estrone (E1), 17β-estradiol (βE2), and equol (EQO) as sole carbon sources. The consortium removed 99% ± 1% of these three estrogens in 48 h. Estrogen removal occurred even in the presence of an ammonia monooxygenase inhibitor, suggesting that nitrifiers are not involved. Five strains showing estrogen-metabolizing activity were isolated from the consortium on mineral agar medium with estrogens as sole carbon source. They are related to four genera ( Methylobacterium (strain MI6.1R), Ochrobactrum (strains MI6.1B and MI9.3), Pseudomonas (strain MI14.1), and Mycobacterium (strain MI21.2)) distributed among three classes (Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria). Depending on the culture medium, strains MI6.1B, MI9.3, MI14.1, and MI21.2 partially transform βE2 into E1, whereas Methylobacterium sp. strain MI6.1R reduces E1 into βE2 under aerobic conditions, in contrast with the usually observed conversion of βE2 into E1. Since βE2 is a more potent endocrine disruptor than E1, it means that the presence of Methylobacterium sp. strain MI6.1R (or other bacteria with the same E1-reducing activity) in a treatment could transiently increase the estrogenicity of the effluent. MI6.1R can also reduce the ketone group of 16-ketoestradiol, a hydroxylated analog of E1. All βE2 and E1 transformation activities were constitutive, and many of them are favoured in a rich medium than a medium containing no other carbon source. None of the isolated strains could degrade EQO.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".