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Record W4378071400 · doi:10.1126/sciadv.adg1258

Molecular insights into the evolution of woody plant decay in the gut of termites

2023· article· en· W4378071400 on OpenAlexaff
Hongjie Li, Xue Kang, Mengyi Yang, Boris Dodji Kasseney, Xuguo Zhou, Shiyou Liang, Xiaojie Zhang, Jia‐Long Wen, Bao-Ting Yu, Ning Liu, Yufen Zhao, Jianchu Mo, Cameron R. Currie, John Ralph, Daniel J. Yelle

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLigninDepolymerizationBiologyPolysaccharideHerbivoreBotanyChemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Plant cell walls represent the most abundant pool of organic carbon in terrestrial ecosystems but are highly recalcitrant to utilization by microbes and herbivores owing to the physical and chemical barrier provided by lignin biopolymers. Termites are a paradigmatic example of an organism’s having evolved the ability to substantially degrade lignified woody plants, yet atomic-scale characterization of lignin depolymerization by termites remains elusive. We report that the phylogenetically derived termite Nasutitermes sp. efficiently degrades lignin via substantial depletion of major interunit linkages and methoxyls by combining isotope-labeled feeding experiments and solution-state and solid-state nuclear magnetic resonance spectroscopy. Exploring the evolutionary origin of lignin depolymerization in termites, we reveal that the early-diverging woodroach Cryptocercus darwini has limited capability in degrading lignocellulose, leaving most polysaccharides intact. Conversely, the phylogenetically basal lineages of “lower” termites are able to disrupt the lignin-polysaccharide inter- and intramolecular bonding while leaving lignin largely intact. These findings advance knowledge on the elusive but efficient delignification in natural systems with implications for next-generation ligninolytic agents.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.236
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2023
Admission routes1
Has abstractyes

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