Reproduction response of <i>Colletotrichum</i> fungi under the fungicide stress reveals new aspects of chemical control of fungal diseases
Bibliographic record
Abstract
Systemic fungicides and antifungals are used as frontline treatments for fungal diseases in plants and humans. It is generally accepted that fungicides will bring significant negative side-effects to the environment and result in fungicide resistance in the pathogenic fungi. Although previous research has focused on fungicide application rates and fungal resistance for a long time, little attention has been paid to fungicide residues after treatment, especially their potential role in fungal growth and sporulation. Here we investigated the effect of fungicides at sublethal concentrations on fungal sporulation. The results showed that two kinds of 14α-demethylase inhibitors (DMIs) fungicides increased the number of isolates of Colletotrichum spp. to sporulate on PDA. Both on PDA medium and plant tissue, low concentration of DMI fungicides could promote spore production of Colletotrichum spp., whereas pyraclostrobin, a quinone outside inhibitor (QoIs), had no significant effects on sporulation of Colletotrichum spp. Transcriptomic analysis suggested that the DMIs fungicide stress signal may be transmitted to the central regulatory pathway through the FluG-mediated signalling pathway, and further confirmed the morphological effect of DMI fungicide on promoting sporulation of Colletotrichum. To our knowledge, this is the first study to provide insights into the reproductive response of fungi in response to fungicide stress. Our findings indicate that fungicides have two-way effects on the growth and reproduction of pathogenic fungi and provide a new basis for the scientific and rational use of fungicides.
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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".