Discovery of lignin-transforming bacteria and enzymes in thermophilic environments using stable isotope probing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Characterizing microorganisms and enzymes involved in lignin biodegradation in thermal ecosystems can identify thermostable biocatalysts. We integrated stable isotope probing (SIP), genome-resolved metagenomics, and enzyme characterization to investigate the degradation of high-molecular weight, 13C-ring-labeled synthetic lignin by microbial communities from moderately thermophilic hot spring sediment (52 °C) and a woody “hog fuel” pile (53 and 62 °C zones). 13C-Lignin degradation was monitored using IR-GCMS of 13CO2, and isotopic enrichment of DNA was measured with UHLPC-MS/MS. Assembly of 42 metagenomic libraries (72 Gb) yielded 344 contig bins, from which 125 draft genomes were produced. Fourteen genomes were significantly enriched with 13C from lignin, including genomes of Actinomycetes (Thermoleophilaceae, Solirubrobacteraceae, Rubrobacter sp.), Firmicutes (Kyrpidia sp., Alicyclobacillus sp.) and Gammaproteobacteria (Steroidobacteraceae). We employed multiple approaches to screen genomes for genes encoding putative ligninases and pathways for aromatic compound degradation. Our analysis identified several novel laccase-like multi-copper oxidase (LMCO) genes in 13C-enriched genomes. One of these LMCOs was heterologously expressed and shown to oxidize lignin model compounds and minimally transformed lignin. This study elucidated bacterial lignin depolymerization and mineralization in thermal ecosystems, establishing new possibilities for the efficient valorization of lignin at elevated temperature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 it