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Record W2980054850 · doi:10.1128/aem.01828-19

Glucose-Mediated Repression of Plant Biomass Utilization in the White-Rot Fungus <i>Dichomitus squalens</i>

2019· article· en· W2980054850 on OpenAlexafffund
Paul Daly, Mao Peng, Marcos Di Falco, Anna Lipzen, Mei Wang, Vivian Ng, Igor V. Grigoriev, Adrian Tsang, Miia Mäkelä, Ronald P. de Vries

Bibliographic record

VenueApplied and Environmental Microbiology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsConcordia University
FundersOffice of ScienceJoint Genome InstituteNederlandse Organisatie voor Wetenschappelijk OnderzoekAcademy of FinlandGenome CanadaU.S. Department of Energy
KeywordsCatabolite repressionBiologyFungusPsychological repressionBiomass (ecology)BotanyProteasesMicrobiologyEnzymeGeneAgronomyBiochemistryGene expression

Abstract

fetched live from OpenAlex

White-rot fungi are critical to the carbon cycle because they can mineralize all wood components using enzymes that also have biotechnological potential. The occurrence of carbon catabolite repression (CCR) in white-rot fungi is poorly understood. Previously, CCR in wood-rotting fungi has only been demonstrated for a small number of genes. We demonstrated widespread glucose-mediated CCR of plant biomass utilization in the white-rot fungus Dichomitus squalens . This indicates that the CCR mechanism has been largely retained even though wood-rotting fungi rarely experience commonly considered CCR conditions in their woody environment. The general lack of repression of genes encoding proteases along with the reduction in secreted CAZymes during CCR suggested that the retention of CCR may be connected with the need to conserve nitrogen use during growth on nitrogen-scarce wood. The widespread repression indicates that derepressed strains could be beneficial for enzyme production.

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 score0.692
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.174
Teacher spread0.164 · 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

Citations38
Published2019
Admission routes2
Has abstractyes

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