In Vitro Suppression of the Swiss Needle Cast Pathogen <i>Nothophaeocryptopus gaeumannii</i> by Metabolite Extracts from Endophytes of Douglas-Fir
Bibliographic record
Abstract
Douglas-fir ( Pseudotsuga menziesii) is one of the most economically important softwood trees grown worldwide. However, the past several decades has seen a decline in its productivity due to increased infection by the fungus Nothophaeocryptopus gaeumannii, the causal agent of Swiss needle cast disease, for which there are currently no feasible control methods. We studied Douglas-fir endophyte extracts to direct future investigations profiling their biologically active natural products. Endophytes were isolated from asymptomatic Douglas-fir needles, and 63 strains representing 32 unique species across 20 families in 14 orders were selected for investigation. A bioassay was developed to assess the antifungal properties of culture filtrate extracts from the endophyte collection against the three distinct N. gaeumannii lineages. Depending on the P value stringency, 26 ( P ≤ 0.05) or 8 ( P ≤ 0.01) strains significantly inhibited the growth of N. gaeumannii, most notably Lachnum virgineum and a putatively novel species of Biscogniauxia. In addition, eight metabolites from the Douglas-fir endophytes Xylaria hypoxylon and Coleophoma sp. were purified and characterized, and an additional 10 metabolites were identified by high-resolution mass spectrometry. Endophytes that inhibited the N. gaeumannii lineages will be prioritized for future studies to determine their potential to modify foliar microbiomes and assist forest disease management. [Formula: see text] Copyright © 2025 His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources Canada. This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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) | 0.000 | 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 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".