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Record W2166715567 · doi:10.1242/jcs.001362

Targeted alterations in iron homeostasis underlie plant defense responses

2007· article· en· W2166715567 on OpenAlexaff
Guosheng Liu, David L. Greenshields, Ramaswami Sammynaiken, Rozina Hirji, Gopalan Selvaraj, Yangdou Wei

Bibliographic record

VenueJournal of Cell Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsPlant Biotechnology InstituteUniversity of Saskatchewan
Fundersnot available
KeywordsBiologyIntracellularCell biologyTranscription factorMediatorSecretionHomeostasisOxidative phosphorylationRespiratory burstPathogenesisOxidative stressPathogenIron homeostasisMetabolismBiochemistryImmunologyGene

Abstract

fetched live from OpenAlex

Iron (Fe) is a ubiquitous redox-active element essential for most life. The formation of localized cell wall appositions, the oxidative burst and the production of pathogenesis-related proteins are hallmarks of plant defense responses. Here, we report that iron is a central mediator linking these three phenomena. We show that in response to pathogen attack, reactive Fe3+, but not Fe2+, is deposited at cell wall appositions where it accumulates and mediates the oxidative burst. We provide evidence that the bulk secretion of Fe3+ provoked by pathogen attack leads to intracellular iron depletion, and that H2O2 itself induces ATP-dependent intracellular iron efflux. Finally, we demonstrate that this intracellular iron depletion promotes the transcription of pathogenesis-related genes in concert with H2O2. This work identifies iron as an underlying factor associated with the oxidative burst and regulating cereal defenses, and establishes links between disease-related iron homeostasis in plants and animals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations180
Published2007
Admission routes1
Has abstractyes

Explore more

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