Jasmonic and salicylic acid response in the fern <scp><i>Azolla filiculoides</i></scp> and its cyanobiont
Bibliographic record
Abstract
Plants sense and respond to microbes utilizing a multilayered signalling cascade. In seed plants, the phytohormones jasmonic and salicylic acid (JA and SA) are key denominators of how plants respond to certain microbes. Their interplay is especially well-known for tipping the scales in plants' strategies of dealing with phytopathogens. In non-angiosperm lineages, the interplay is less well understood, but current data indicate that it is intertwined to a lesser extent and the canonical JA/SA antagonism appears to be absent. Here, we used the water fern Azolla filiculoides to gain insights into the fern's JA/SA signalling and the molecular communication with its unique nitrogen fixing cyanobiont Nostoc azollae, which the fern inherits both during sexual and vegetative reproduction. By mining large-scale sequencing data, we demonstrate that Azolla has most of the genetic repertoire to produce and sense JA and SA. Using qRT-PCR on the identified biosynthesis and signalling marker genes, we show that Azolla is responsive to exogenously applied SA. Furthermore, exogenous SA application influenced the abundance and gene expression of Azolla's cyanobiont. Our data provide a framework for JA/SA signalling in ferns and suggest that SA might be involved in Azolla's communication with its vertically inherited cyanobiont.
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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".