The dark septate endophyte <i>Phialocephala sphaeroides</i> suppresses conifer pathogen transcripts and promotes root growth of Norway spruce
Bibliographic record
Abstract
Plant-associated microbes including dark septate endophytes (DSEs) of forest trees play diverse functional roles in host fitness including growth promotion and increased defence. However, little is known about the impact on the fungal transcriptome and metabolites during tripartite interaction involving plant host, endophyte and pathogen. To understand the transcriptional regulation of endophyte and pathogen during co-infection, Norway spruce (Picea abies) seedlings were infected with DSE Phialocephala sphaeroides, or conifer root-rot pathogen Heterobasidion parviporum, or both. Phialocephala sphaeroides showed low but stable transcripts abundance (a decrease of 40%) during interaction with Norway spruce and conifer pathogen. By contrast, H. parviporum transcripts were significantly reduced (92%) during co-infection. With RNA sequencing analysis, P. sphaeroides experienced a shift from cell growth to anti-stress and antagonistic responses, while it repressed the ability of H. parviporum to access carbohydrate nutrients by suppressing its carbohydrate/polysaccharide-degrading enzyme machinery. The pathogen on the other hand secreted cysteine peptidase to restrict free growth of P. sphaeroides. The expression of both DSE P. sphaeroides and pathogen H. parviporum genes encoding plant growth promotion products were equally detected in both dual and tripartite interaction systems. This was further supported by the presence of tryptophan-dependent indolic compound in liquid culture of P. sphaeroides. Norway spruce and Arabidopsis seedlings treated with P. sphaeroides culture filtrate exhibited auxin-like phenotypes, such as enhanced root hairs, and primary root elongation at low concentration but shortened primary root at high concentration. The results suggested that the presence of the endophyte had strong repressive or suppressive effect on H. parviporum transcripts encoding genes involved in nutrient acquisition.
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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".