Interactions between Co-Habitating fungi Elicit Synthesis of Taxol from an Endophytic Fungus in Host Taxus Plants
Bibliographic record
Abstract
Within a plant, there can exist an ecosystem of pathogens and endophytes, the latter described as bacterial and fungal inhabitants that thrive without causing disease to the host. Interactions between microbial inhabitants represent a novel area of study for natural products research. Here we analyzed the interactions between the fungal endophytes of Taxus (yew) trees. Fungal endophytes of Taxus have been proposed to produce the terpenoid secondary metabolite, Taxol, an anti-cancer drug. It is widely reported that plant extracts stimulate endophytic fungal Taxol production, but the underlying mechanism is not understood. Here, Taxus bark extracts stimulated fungal Taxol production 30-fold compared to a 10-fold induction with wood extracts. However, candidate plant-derived defense compounds (i.e., salicylic acid, benzoic acid) were found to act only as modest elicitors of fungal Taxol production from the endophytic fungus Paraconiothyrium SSM001, consistent with previous studies. We hypothesized the Taxus plant extracts may contain elicitors derived from other microbes inhabiting these tissues. We investigated the effects of co-culturing SSM001 with other fungi observed to inhabit Taxus bark, but not wood. Surprisingly, co-culture of SSM001 with a bark fungus (Alternaria) caused a ∼threefold increase in Taxol production. When SSM001 was pyramided with both the Alternaria endophyte along with another fungus (Phomopsis) observed to inhabit Taxus, there was an ∼eightfold increase in fungal Taxol production from SSM001. These results suggest that resident fungi within a host plant interact with one another to stimulate Taxol biosynthesis, either directly or through their metabolites. More generally, our results suggest that endophyte secondary metabolism should be studied in the context of its native ecosystem.
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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".