Bacterial catabolism of acetovanillone, a lignin-derived compound
Bibliographic record
Abstract
Bacterial catabolic pathways have considerable potential as industrial biocatalysts for the valorization of lignin, a major component of plant-derived biomass. Here, we describe a pathway responsible for the catabolism of acetovanillone, a major component of several industrial lignin streams. Rhodococcus rhodochrous GD02 was previously isolated for growth on acetovanillone. A high-quality genome sequence of GD02 was generated. Transcriptomic analyses revealed a cluster of eight genes up-regulated during growth on acetovanillone and 4-hydroxyacetophenone, as well as a two-gene cluster up-regulated during growth on acetophenone. Bioinformatic analyses predicted that the hydroxyphenylethanone (Hpe) pathway proceeds via phosphorylation and carboxylation, before β-elimination yields vanillate from acetovanillone or 4-hydroxybenzoate from 4-hydroxyacetophenone. Consistent with this prediction, the kinase, HpeHI, phosphorylated acetovanillone and 4-hydroxyacetophenone. Furthermore, HpeCBA, a biotin-dependent enzyme, catalyzed the ATP-dependent carboxylation of 4-phospho-acetovanillone but not acetovanillone. The carboxylase’s specificity for 4-phospho-acetophenone ( k cat / K M = 34 ± 2 mM −1 s −1 ) was approximately an order of magnitude higher than for 4-phospho-acetovanillone. HpeD catalyzed the efficient dephosphorylation of the carboxylated products. GD02 grew on a preparation of pine lignin produced by oxidative catalytic fractionation, depleting all of the acetovanillone, vanillin, and vanillate. Genomic and metagenomic searches indicated that the Hpe pathway occurs in a relatively small number of bacteria. This study facilitates the design of bacterial strains for biocatalytic applications by identifying a pathway for the degradation of acetovanillone.
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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) | 0.000 | 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 | 0.000 |
| 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".