Carbon acquisition and metabolism changes during fungal biotrophic plant pathogenesis: insights from <i>Ustilago maydis</i>
Bibliographic record
Abstract
Throughout the plant disease cycle, biotrophic fungal pathogens must obtain host-derived carbon molecules to act as building blocks and sources of energy. Gaining access to these resources requires biotrophic fungi to breach plant cell walls without eliciting substantial plant defences. The plant cell wall is composed mostly of glucose- and xylose-based polysaccharides, which can support fungal growth. Thus, fungi can acquire carbon compounds through the targeted depolymerization of specific wall components. When the plant cell wall is breached, biotrophs redirect photoassimilates, increase sink strengths, and express invertases and transporters to acquire carbon compounds. Transitions in enzyme and transporter expression during pathogenesis must be tightly controlled to ensure a continued supply of carbon compounds. This review describes carbon acquisition and metabolism, including regulation of available carbon source utilization mechanisms such as carbon catabolite repression. While carbon acquisition has been extensively studied in the ascomycetes, the mechanisms used by biotrophic fungi to acquire carbon during pathogenesis are poorly understood. Furthermore, the relationship between plant cell wall-degrading enzymes and carbon acquisition in biotrophic fungal pathogens is not well characterized. As such, this review summarizes the current knowledge of carbon source utilization by fungal pathogens, with an emphasis on research involving the corn smut pathogen Ustilago maydis, and provides a basis from which to extend our knowledge in this key area of fungal plant pathogenesis.
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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 | 0.001 |
| 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".