Characterization of Cell Wall α-1,3-Glucan–Deficient Mutants in <i>Aspergillus oryzae</i> Isolated by a Screening Method Based on Their Sensitivities to Congo Red or Lysing Enzymes
Bibliographic record
Abstract
We previously reported that sensitivity to Congo Red (CR) or Lysing Enzymes (LE) is affected by the loss of cell-wall α-1,3-glucan (AG) in Aspergillus nidulans. We found that the amount of CR adsorbed to AG was significantly less than the amount adsorbed to β-1,3-glucan (BG) or chitin, suggesting that loss of cell-wall AG would increase exposure of BG on the cell surface, and thereby increase the sensitivity to CR. Generally, fungal BGs are known as biological response modifiers because of their recognition by Dectin-1 receptors in human immune systems. Therefore, isolation of AG-deficient mutants in Aspergillus oryzae has been used in the Japanese fermentation industry to create strains with increased ability to promote immune responses. Here, we aimed to isolate AG-deficient strains by mutagenizing A. oryzae conidia with chemical mutagens. Based on the increased sensitivity to CR in AG-deficient strains of A. nidulans and A. oryzae, we established a screening method for isolation of AG-deficient strains. Several candidate AG-deficient mutants of A. oryzae were isolated using the screening method; these strains showed increased sensitivity to CR and/or LE. Cytokine production was increased in the dendritic cells co-incubated with germinated conidia of the AG-deficient mutants. Furthermore, according to a Dectin-1 NFAT (nuclear factor of activator T cells)-GFP (green fluorescent protein) reporter assay, Dectin-1 response levels in the AG-deficient mutants were higher than those in wild-type A. oryzae. These results suggest that we successfully isolated AG-deficient mutants of A. oryzae with immunostimulatory effects.
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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) | 0.001 | 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".