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Record W2782412737 · doi:10.1111/pce.13131

Jasmonic and salicylic acid response in the fern <scp><i>Azolla filiculoides</i></scp> and its cyanobiont

2018· article· en· W2782412737 on OpenAlexafffund
Sophie de Vries, Jan de Vries, Hendrik Teschke, Janina K. von Dahlen, Laura Rose, Sven B. Gould

Bibliographic record

VenuePlant Cell & Environment · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsDalhousie University
FundersKillam TrustsHeinrich-Heine-Universität DüsseldorfDeutsche Forschungsgemeinschaft
KeywordsFernAzollaJasmonic acidBiologyBotanySalicylic acidGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.180
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2018
Admission routes2
Has abstractyes

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