Roles of Ring-Hydroxylating Dioxygenases in Styrene and Benzene Catabolism in<i>Rhodococcus jostii</i>RHA1
Bibliographic record
Abstract
Proteomics and targeted gene disruption were used to investigate the catabolism of benzene, styrene, biphenyl, and ethylbenzene in Rhodococcus jostii RHA1, a well-studied soil bacterium whose potent polychlorinated biphenyl (PCB)-transforming properties are partly due to the presence of the related Bph and Etb pathways. Of 151 identified proteins, 22 Bph/Etb proteins were among the most abundant in biphenyl-, ethylbenzene-, benzene-, and styrene-grown cells. Cells grown on biphenyl, ethylbenzene, or benzene contained both Bph and Etb enzymes and at least two sets of lower Bph pathway enzymes. By contrast, styrene-grown cells contained no Etb enzymes and only one set of lower Bph pathway enzymes. Gene disruption established that biphenyl dioxygenase (BPDO) was essential for growth of RHA1 on benzene or styrene but that ethylbenzene dioxygenase (EBDO) was not required for growth on any of the tested substrates. Moreover, whole-cell assays of the delta bphAa and etbAa1::cmrA etbAa2::aphII mutants demonstrated that while both dioxygenases preferentially transformed biphenyl, only BPDO transformed styrene. Deletion of pcaL of the beta-ketoadipate pathway disrupted growth on benzene but not other substrates. Thus, styrene and benzene are degraded via meta- and ortho-cleavage, respectively. Finally, catalases were more abundant during growth on nonpolar aromatic compounds than on aromatic acids. This suggests that the relaxed specificities of BPDO and EBDO that enable RHA1 to grow on a range of compounds come at the cost of increased uncoupling during the latter's initial transformation. The stress response may augment RHA1's ability to degrade PCBs and other pollutants that induce similar uncoupling.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".