Biphenyl and ethylbenzene dioxygenases of <i>Rhodococcus jostii</i> RHA1 transform PBDEs
Bibliographic record
Abstract
Polybrominated diphenyl ethers (PBDEs) are a class of flame retardants that have been widely used in consumer products, but that are problematic because of their environmental persistence and endocrine-disrupting properties. To date, very little is known about PBDE degradation by aerobic microorganisms and the enzymes involved in PBDE transformation. Resting cells of the polychlorinated biphenyl-degrading actinomycete, Rhodococcus jostii RHA1, depleted nine mono- through penta-BDEs in separate assays. Extensive depletion of PBDEs occurred with cells grown on biphenyl, ethylbenzene, propane, or styrene, whereas very limited depletion occurred with cells grown on pyruvate or benzoate. In RHA1, expression of bphAa encoding biphenyl dioxygenase (BPDO) and etbAa1 and etbAc encoding ethylbenzene dioxygenase (EBDO) was induced 30- to 3,000-fold during growth on the substrates that supported PBDE depletion. The BPDO and EBDO enzymes had gene expression profiles that matched the PBDE-depletion profiles exhibited by RHA1 grown on different substrates. Using the non-PBDE-degrading bacterium Rhodococcus erythropolis as a host, two recombinant strains were developed by inserting the eth and bph genes of RHA1, respectively. The resultant EBDO extensively depleted mono- through penta-BDEs, while the BPDO depleted only mono-, di-, and one tetra-BDE. A dihydroxylated-BDE was detected as the primary metabolite of 4-bromodiphenyl ether in both recombinant strains. These results indicate that although both dioxygenases are capable of transforming PBDEs, EBDO more potently transforms the highly brominated congeners. The availability of substrates or inducing compounds can markedly affect total PBDE removal as well as patterns of removal of individual congeners.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".