Characterization of <i>p</i> -Hydroxycinnamate Catabolism in a Soil Actinobacterium
Bibliographic record
Abstract
p-Hydroxycinnamates, such as ferulate and p-coumarate, are components of plant cell walls and have a number of commercial applications. Rhodococcus jostii RHA1 (RHA1) catabolizes ferulate via vanillate and the β-ketoadipate pathway. Here, we used transcriptomics to identify genes in RHA1 that are upregulated during growth on ferulate versus benzoate. The upregulated genes included three transcriptional units predicted to encode the uptake and β-oxidative deacetylation of p-hydroxycinnamates: couHTL, couNOM, and couR. Neither ΔcouL mutants nor ΔcouO mutants grew on p-hydroxycinnamates, but they did grow on vanillate. Among several p-hydroxycinnamates, CouL catalyzed the thioesterification of p-coumarate and caffeate most efficiently (k(cat)/K(m) = ∼ 400 mM(-1) s(-1)). p-Coumarate was also RHA1's preferred growth substrate, suggesting that CouL is a determinant of the pathway's specificity. CouL did not catalyze the activation of sinapate, in similarity to two p-coumaric acid:coenzyme A (CoA) ligases from plants, and contains the same bulged loop that helps determine substrate specificity in the plant homologues. The couO mutant accumulated 4-hydroxy-3-methoxyphenyl-β-ketopropionate in the culture supernatant when incubated with ferulate, supporting β-oxidative deacetylation. This phenotype was not complemented with a D257N variant of CouO, consistent with the predicted role of Asp257 as a metal ligand in this amidohydrolase superfamily member. These data suggest that CouO functionally replaces the β-ketothiolase and acyl-CoA thioesterase that occur in canonical β-oxidative pathways. Finally, the transcriptomics data suggest the involvement of two distinct formaldehyde detoxification pathways in vanillate catabolism and identify a eugenol catabolic pathway. The results of this study augment our understanding of the bacterial catabolism of aromatics from renewable feedstocks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".