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Record W1993308342 · doi:10.4161/psb.25372

MOS2 has redundant function with its homolog MOS2H and is required for proper splicing of<i>SNC1</i>

2013· article· en· W1993308342 on OpenAlexaff
Charles Copeland, Shaohua Xu, Yijun Qi, Xin Li

Bibliographic record

VenuePlant Signaling & Behavior · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsBiologyRNA splicingMutantGeneticsGeneGenetic screenMutationArabidopsisAlternative splicingWild typeFunction (biology)Positional cloningCell biologyRNAMessenger RNA

Abstract

fetched live from OpenAlex

Plant immunity is essential for plant survival and resistance (R) proteins serve essential roles in pathogen detection and defense signal initiation. A gain-of-function mutation in SNC1, a TIR-type R gene, results in a characteristic autoimmune phenotype in Arabidopsis. From a forward genetic suppressor screen using snc1, MOS2 (MODIFIER of snc1), which encodes an RNA-binding protein, was identified. When MOS2 function is lost, the autoimmunity caused by snc1 is abolished and basal resistance against virulent pathogens is attenuated. Recently, it was shown that mos2 mutants also have defects in miRNA processing. However, it is not known how the role of MOS2 in miRNA production is related to the suppression of snc1-mediated autoimmunity. Here, we show that MOS2 contributes to proper splicing of SNC1 transcript, agreeing with its potential association with the MOS4-associated complex (MAC). In addition, although mutant plants carrying a mutation in the MOS2 homolog MOS2H are wild-type like, the double mutant mos2 mos2h is lethal. These data suggest that MOS2 and MOS2H have unequally redundant functions. Overall, MOS2 and MOS2H probably have diverse functions in both alternative splicing and miRNA processing.

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 score0.126
Threshold uncertainty score0.253

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 head0.045
GPT teacher head0.221
Teacher spread0.176 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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