Zymoseptoria tritici white-collar complex integrates light, temperature and plant cues to initiate dimorphism and pathogenesis
Bibliographic record
Abstract
Transitioning from spores to hyphae is pivotal to host invasion by the plant pathogenic fungus Zymoseptoria tritici. This dimorphic switch can be initiated by high temperature in vitro (~27 °C); however, such a condition may induce cellular heat stress, questioning its relevance to field infections. Here, we study the regulation of the dimorphic switch by temperature and other factors. Climate data from wheat-growing areas indicate that the pathogen sporadically experiences high temperatures such as 27 °C during summer months. However, using a fluorescent dimorphic switch reporter (FDR1) in four wild-type strains, we show that dimorphic switching already initiates at 15-18 °C, and is enhanced by wheat leaf surface compounds. Transcriptomics reveals 1261 genes that are up- or down-regulated in hyphae of all strains. These pan-strain core dimorphism genes (PCDGs) encode known effectors, dimorphism and transcription factors, and light-responsive proteins (velvet factors, opsins, putative blue light receptors). An FDR1-based genetic screen reveals a crucial role for the white-collar complex (WCC) in dimorphism and virulence, mediated by control of PCDG expression. Thus, WCC integrates light with biotic and abiotic cues to orchestrate Z. tritici infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".