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Record W2949032338 · doi:10.3389/fpls.2019.00764

Enhancement of Jasmonate-Mediated Antiherbivore Defense Responses in Tomato by Acetic Acid, a Potent Inducer for Plant Protection

2019· article· en· W2949032338 on OpenAlexfundno aff
Daoqian Chen, Min Shao, Shaozhi Sun, Tingting Liu, Hao Zhang, Ningning Qin, Rensen Zeng, Yuanyuan Song

Bibliographic record

VenueFrontiers in Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationChinese Academy of SciencesInstitute of GeneticsNational Natural Science Foundation of China
KeywordsPlant defense against herbivoryInducerJasmonateMethyl jasmonateBiologyBotanyJasmonic acidOxylipinSalicylic acidChemistryBiochemistryGeneArabidopsisMutant

Abstract

fetched live from OpenAlex

Acetic acid has been proved as a chemical that could prime the jasmonic acid (JA) signaling pathway for plant drought tolerance. In this study, the capability of acetic acid (AA) for priming of tomato defense against a chewing caterpillar Spodoptera litura and its underlying molecular mechanism were evaluated. AA pretreatment significantly increased tomato resistance against S. litura larvae. Upon larval attack, tomato plants pretreated with AA exhibited increased transcript levels of defense-related genes and elevated activities of polyphenol oxidase and peroxidase, and accumulation of protease inhibitor. Moreover, AA pretreatment resulted in upregulated transcription of JA biosynthesis genes and elevated JA accumulation in tomato seedlings upon insect attack. Furthermore, an apparent loss of AA-induced resistance was observed in a JA pathway-impaired mutant suppressor of prosystemin-mediated responses8 (spr8). These results indicate that AA enhances jasmonate-mediated antiherbivore defense responses in tomato. This raises the possibility of use of acetic acid, a basic and simple biochemical compound, as a promising inducer for management of agricultural pests.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.206
Teacher spread0.195 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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