Bacterial Transformation of Aromatic Monomers in Softwood Black Liquor
Bibliographic record
Abstract
The valorization of lignin, a major component of plant-derived biomass, is essential to sustainable biorefining. We identified the major monoaromatic compounds present in black liquor, a lignin-rich stream generated in the kraft pulping process, and investigated their bacterial transformation. Among tested solvents, acetone extracted the greatest amount of monoaromatic compounds from softwood black liquor, with guaiacol, vanillin, and acetovanillone, in an approximately 4:3:2 ratio, constituting ~90% of the total extracted monoaromatic content. 4-Ethanol guaiacol, vanillate, and 4-propanol guaiacol were also present. Bacterial strains that grew on minimal media supplemented with the BL extracts at 1mM total aromatic compounds included Pseudomonas putida KT2442, Sphingobium sp. SYK-6, and Rhodococcus rhodochrous EP4. By contrast, the extracts inhibited the growth of Rhodococcus jostii RHA1 and Rhodococcus opacus PD630, strains extensively studied for lignin valorization. Of the strains that grew on the extracts, only R. rhodochrous GD01 and GD02, isolated for their ability to grow on acetovanillone, depleted the major extracted monoaromatics. Genomic analyses revealed that EP4, GD01, and GD02 share an average nucleotide identity (ANI) of 98% and that GD01 and GD02 harbor a predicted three-component carboxylase not present in EP4. A representative carboxylase gene was upregulated ~100-fold during growth of GD02 on a mixture of the BL monoaromatics, consistent with the involvement of the enzyme in acetovanillone catabolism. More generally, quantitative RT-PCR indicated that GD02 catabolizes the BL compounds in a convergent manner via the β-ketoadipate pathway. Overall, these studies help define the catabolic capabilities of potential biocatalytic strains, describe new isolates able to catabolize the major monoaromatic components of BL, including acetovanillone, and facilitate the design of biocatalysts to valorize under-utilized components of industrial lignin streams.
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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 | 0.001 |
| 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".