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Record W2121349454 · doi:10.1093/pcp/pcq130

Programmed Cell Death of the Megagametophyte during Post-germinative Growth of White Spruce (Picea glauca) Seeds is Regulated by Reactive Oxygen Species and the Ubiquitin-mediated Proteolytic System

2010· article· en· W2121349454 on OpenAlexafffund
Xue‐Ying He, Allison R. Kermode

Bibliographic record

VenuePlant and Cell Physiology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaArizona State University
KeywordsProgrammed cell deathCatalaseBiologyReactive oxygen speciesProteasomeProteasesMG132DNA fragmentationBiochemistryCell biologyBotanyApoptosisEnzymeProteasome inhibitor

Abstract

fetched live from OpenAlex

The megagametophyte of white spruce (Picea glauca) seeds undergoes programmed cell death following seed germination. This process is characterized by distinct morphological and biochemical features, such as DNA fragmentation and the induction of proteases. Biphasic production of hydrogen peroxide was detected in the megagametophyte following seed germination. ROS scavengers or inhibitors of ROS production decreased caspase-like protease activity and slowed the progression of cell death. One catalase (CAT) of white spruce reacted with antibodies directed against cotton-seed CAT. The corresponding CAT gene was cloned and compared with the catalase genes of other plant species. The activity of the white spruce CAT enzyme was stimulated by tyrosine phosphorylation. The phosphorylated CAT was subjected to ubiquitination and degraded by the proteasome. Furthermore, the proteasome inhibitor MG132 inhibited the degradation of CAT and delayed cell death. These results suggest that the interplay of CAT and the ubiquitin-mediated proteolytic system is critical in the control of ROS production and subsequent cell death.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.362

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 head0.004
GPT teacher head0.170
Teacher spread0.167 · 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

Citations29
Published2010
Admission routes2
Has abstractyes

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