A biofertilizing fungal endophyte of cranberry plants suppresses the plant pathogen Diaporthe
Bibliographic record
Abstract
Fungi colonizing plants are gaining attention because of their ability to promote plant growth and suppress pathogens. While most studies focus on endosymbionts from grasses and legumes, the large and diverse group of ericaceous plants has been much neglected. We recently described one of the very few fungal endophytes promoting the growth of the Ericaceae Vaccinium macrocarpon (American cranberry), notably the Codinaeella isolate EC4. Here, we show that EC4 also suppresses fungal pathogens, which makes it a promising endophyte for sustainable cranberry cultivation. By dual-culture assays on agar plates, we tested the potential growth suppression (or biocontrol) of EC4 on other microbes, notably 12 pathogenic fungi and one oomycete reported to infect not only cranberry but also blueberry, strawberry, tomato plants, rose bushes and olive trees. Under greenhouse conditions, EC4 protects cranberry plantlets infected with one of the most notorious cranberry-plant pathogens, Diaporthe vaccinii, known to cause upright dieback and berry rot. The nuclear genome sequence of EC4 revealed a large arsenal of genes potentially involved in biocontrol. About ∼60 distinct clusters of genes are homologs of secondary metabolite gene clusters, some of which were shown in other fungi to synthesize nonribosomal peptides and polyketides, but in most cases, the exact compounds these clusters may produce are unknown. The EC4 genome also encodes numerous homologs of hydrolytic enzymes known to degrade fungal cell walls. About half of the nearly 250 distinct glucanases and chitinases are likely involved in biocontrol because they are predicted to be secreted outside the cell. Transcriptome analysis shows that the expression of about a quarter of the predicted secondary-metabolite gene clusters and glucan and chitin-degrading genes of EC4 is stimulated when it is co-cultured with D. vaccinii. Some of the differentially expressed EC4 genes are alternatively spliced exclusively in the presence of the pathogen, altering the proteins’ domain content and subcellular localization signal, thus adding a second level of proteome adaptation in response to habitat competition. To our knowledge, this is the first report of Diaporthe-induced alternative splicing of biocontrol genes.
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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".