Plants promote mating and dispersal of the human pathogenic fungus Cryptococcus
Bibliographic record
Abstract
Infections due to Cryptococcus are a leading cause of fungal infections worldwide and are acquired as a result of environmental exposure to desiccated yeast or spores. The ability of Cryptococcus to grow, mate, and produce infectious propagules in association with plants is important for the maintenance of the genetic diversity and virulence factors important for infection of animals and humans. In the Western United States and Canada, Cryptococcus has been associated with conifers and tree species other than Eucalyptus; however, to date Cryptococcus has only been studied on live Arabidopsis thaliana, Eucalyptus sp., and Terminalia catappa (almond) seedlings. Previous research has demonstrated the ability of Cryptococcus to colonize live plants, leaves, and vasculature. We investigated the ability of Cryptococcus to grow on live seedlings of the angiosperms, A. thaliana, Eucalyptus camaldulensis, Colophospermum mopane, and the gymnosperms, Pseudotsuga menziesii (Douglas fir), and Tsuga heterophylla (Western hemlock). We observed a broad-range ability of Cryptococcus to colonize both traditional infection models as well as newly tested conifer species. Furthermore, C. neoformans, C. deneoformans, C. gattii (VGI), C. deuterogattii (VGII) and C. bacillisporus (VGIII) were able to colonize live plant leaves and needles but also undergo filamentation and mating on agar seeded with plant materials or in saprobic association with dead plant materials. The ability of Cryptococcus to grow and undergo filamentation and reproduction in saprobic association with both angiosperms and gymnosperms highlights an important role of plant debris in the sexual cycle and exposure to infectious propagules. This study highlights the broad importance of plants (and plant debris) as the ecological niche and reservoirs of infectious propagules of Cryptococcus in the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".