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Record W2895889093 · doi:10.7554/elife.39399

The role of APETALA1 in petal number robustness

2018· article· en· W2895889093 on OpenAlexfundno aff
Marie Monniaux, Bjorn Pieper, Sarah M. McKim, Anne‐Lise Routier‐Kierzkowska, Daniel Kierzkowski, Richard S. Smith, Angela Hay

Bibliographic record

VenueeLife · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaMax-Planck-GesellschaftDeutsche ForschungsgemeinschaftHuman Frontier Science ProgramDirectorate for Biological SciencesRoyal SocietyEuropean Molecular Biology Organization
KeywordsBiologyPetalBotanyRobustness (evolution)GeneticsEvolutionary biologyGene

Abstract

fetched live from OpenAlex

Invariant floral forms are important for reproductive success and robust to natural perturbations. Petal number, for example, is invariant in Arabidopsis thaliana flowers. However, petal number varies in the closely related species Cardamine hirsuta, and the genetic basis for this difference between species is unknown. Here we show that divergence in the pleiotropic floral regulator APETALA1 (AP1) can account for the species-specific difference in petal number robustness. This large effect of AP1 is explained by epistatic interactions: A. thaliana AP1 confers robustness by masking the phenotypic expression of quantitative trait loci controlling petal number in C. hirsuta. We show that C. hirsuta AP1 fails to complement this function of A. thaliana AP1, conferring variable petal number, and that upstream regulatory regions of AP1 contribute to this divergence. Moreover, variable petal number is maintained in C. hirsuta despite sufficient standing genetic variation in natural accessions to produce plants with four-petalled flowers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.181

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 head0.020
GPT teacher head0.207
Teacher spread0.187 · 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 designObservational
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

Citations38
Published2018
Admission routes1
Has abstractyes

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