Blocking utilization of major plant biomass polysaccharides leads <i>Aspergillus</i> <i>niger</i> towards utilization of minor components
Bibliographic record
Abstract
Fungi produce a wide range of enzymes that allow them to grow on diverse plant biomass. Wheat bran is a low-cost substrate with high potential for biotechnological applications. It mainly contains cellulose and (arabino)xylan, as well as starch, proteins, lipids and lignin to a lesser extent. In this study, we dissected the regulatory network governing wheat bran degradation in Aspergillus niger to assess the relative contribution of the regulators to the utilization of this plant biomass substrate. Deletion of genes encoding transcription factors involved in (hemi-)cellulose utilization (XlnR, AraR, ClrA and ClrB) individually and in combination significantly reduced production of polysaccharide-degrading enzymes, but retained substantial growth on wheat bran. Proteomic analysis suggested the ability of A. niger to grow on other carbon components, such as starch, which was confirmed by the additional deletion of the amylolytic regulator AmyR. Growth was further reduced but not impaired, indicating that other minor components provide sufficient energy for residual growth, displaying the flexibility of A. niger, and likely other fungi, in carbon utilization. Better understanding of the complexity and flexibility of fungal regulatory networks will facilitate the generation of more efficient fungal cell factories that use plant biomass as a substrate.
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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 | 0.000 |
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".