Metagenomic scaffolds enable combinatorial lignin transformation
Bibliographic record
Abstract
Engineering the microbial transformation of lignocellulosic biomass is essential to developing modern biorefining processes that alleviate reliance on petroleum-derived energy and chemicals. Many current bioprocess streams depend on the genetic tractability of Escherichia coli with a primary emphasis on engineering cellulose/hemicellulose catabolism, small molecule production, and resistance to product inhibition. Conversely, bioprocess streams for lignin transformation remain embryonic, with relatively few environmental strains or enzymes implicated. Here we develop a biosensor responsive to monoaromatic lignin transformation products compatible with functional screening in E. coli. We use this biosensor to retrieve metagenomic scaffolds sourced from coal bed bacterial communities conferring an array of lignin transformation phenotypes that synergize in combination. Transposon mutagenesis and comparative sequence analysis of active clones identified genes encoding six functional classes mediating lignin transformation phenotypes that appear to be rearrayed in nature via horizontal gene transfer. Lignin transformation activity was then demonstrated for one of the predicted gene products encoding a multicopper oxidase to validate the screen. These results illuminate cellular and community-wide networks acting on aromatic polymers and expand the toolkit for engineering recombinant lignin transformation based on ecological design principles.
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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 | 0.001 | 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".