Characterization of <i><scp>A</scp>spergillus nidulans</i> α‐glucan synthesis: roles for two synthases and two amylases
Bibliographic record
Abstract
Cell walls are essential for fungal survival and growth. Fungal walls are ∼ 90% carbohydrate, mostly types not found in humans, making them promising targets for anti-fungal drug development. Echinocandins, which inhibit the essential β-glucan synthase, are already clinically available. In contrast, α-glucan, another abundant fungal cell wall component has attracted relatively little research attention because it is not essential for most fungi. Aspergillus nidulans has two α-glucan synthases (AgsA and AgsB) and two α-amylases (AmyD and AmyG), all of which affect α-glucan synthesis. Gene deletion showed that AgsB was the major synthase. In addition, AmyG promoted α-glucan synthesis whereas AmyD had a repressive effect. The lack of α-glucan had no phenotypic impact on solid medium, but reduced conidial adhesion during germination in shaken liquid. Moreover, α-glucan level correlated with resistance to Calcofluor White. Intriguingly, overexpression of agsA could compensate for the loss of agsB at the α-glucan level, but not for phenotypic defects. Thus, products of AgsA and AgsB have different roles in the cell wall, consistent with agsA being mainly expressed at conidiation. These results suggest that α-glucan contributes to drug sensitivity and conidia adhesion in A. nidulans, and is differentially regulated by two synthases and two amylases.
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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) | 0.000 | 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) | 0.001 | 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".