Induction of <i>lcc</i> 2 expression and activity by <scp> <i>A</i> </scp> <i>garicus bisporus</i> provides defence against <scp> <i>T</i> </scp> <i>richoderma aggressivum</i> toxic extracts
Bibliographic record
Abstract
Laccases are used by fungi for several functions including defence responses to stresses associated with attack by other fungi. Laccase activity changes and the induction of two laccase genes, lcc1 and lcc2, in Agaricus bisporus were measured in response to toxic extracts of medium in which Trichoderma aggressivum, the cause of green mould disease, was grown. A strain of A. bisporus that shows resistance to the extracts showed higher basal levels and greater enzymatic activity after extract exposure than did a sensitive strain. Furthermore, pre-incubation of T. aggressivum extract with laccases reduced toxicity. Faster induction and greater numbers of lcc2 transcripts in response to the extract were noted in the resistant strain than in the sensitive strain. The timing and increase in lcc2 transcript abundance mirrored changes in total laccase activity. No correlation between resistance and lcc1 transcription was apparent. Transcript abundance in transformants with a siRNA construct homologous to both genes varied widely. A strong negative correlation between transcript abundance and sensitivity of the transformant to toxic extract was observed in plate assays. These results indicated that laccase activity and in particular that encoded by lcc2 contributes to toxin metabolism and by extension green mould disease resistance.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".