Characterization of a phylogenetically distinct extradiol dioxygenase involved in the bacterial catabolism of lignin-derived aromatic compounds
Bibliographic record
Abstract
The actinobacterium Rhodococcus jostii RHA1 grows on a remarkable variety of aromatic compounds and has been studied for applications ranging from the degradation of polychlorinated biphenyls to the valorization of lignin, an underutilized component of biomass. In RHA1, the catabolism of two classes of lignin-derived compounds, alkylphenols and alkylguaiacols, involves a phylogenetically distinct extradiol dioxygenase, AphC, previously misannotated as BphC, an enzyme involved in biphenyl catabolism. To better understand the role of AphC in RHA1 catabolism, we first showed that purified AphC had highest apparent specificity for 4-propylcatechol ( k cat / K M ∼10 6 M −1 s −1 ), and its apparent specificity for 4-alkylated substrates followed the trend for alkylguaiacols: propyl > ethyl > methyl > phenyl > unsubstituted. We also show AphC only poorly cleaved 3-phenylcatechol, the preferred substrate of BphC. Moreover, AphC and BphC cleaved 3-phenylcatechol and 4-phenylcatechol with different regiospecificities, likely due to the substrates' binding mode. A crystallographic structure of the AphC·4-ethylcatechol binary complex to 1.59 Å resolution revealed that the catechol is bound to the active site iron in a bidentate manner and that the substrate's alkyl side chain is accommodated by a hydrophobic pocket. Finally, we show RHA1 grows on a mixture of 4-ethylguaiacol and guaiacol, simultaneously catabolizing these substrates through meta- cleavage and ortho- cleavage pathways, respectively, suggesting that the specificity of AphC helps to prevent the routing of catechol through the Aph pathway. Overall, this study contributes to our understanding of the bacterial catabolism of aromatic compounds derived from lignin, and the determinants of specificity in extradiol dioxygenases.
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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".