Discovery of lignin-transforming bacteria and enzymes in thermophilic environments using stable isotope probing
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".