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Record W4400113442 · doi:10.1126/sciadv.adl3199

Twilight length alters growth and flowering time in Arabidopsis via <i>LHY</i> / <i>CCA1</i>

2024· article· en· W4400113442 on OpenAlexafffund
Devang Mehta, Sabine Scandola, Curtis Kennedy, Christina Lummer, M. C. Rodriguez Gallo, Lauren E. Grubb, Maryalle Tan, Enrico Scarpella, R. Glen Uhrig

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversity of Alberta
FundersUniversity of California, San DiegoUniversity of California, DavisSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAlberta InnovatesMitacsKU LeuvenNational Science Foundation
KeywordsTwilightphotoperiodismBiologyArabidopsisAdaptation (eye)BotanyGeneGeneticsMutant

Abstract

fetched live from OpenAlex

Decades of research have uncovered how plants respond to two environmental variables that change across latitudes and over seasons: photoperiod and temperature. However, a third such variable, twilight length, has so far gone unstudied. Here, using controlled growth setups, we show that the duration of twilight affects growth and flowering time via the LHY/CCA1 clock genes in the model plant Arabidopsis. Using a series of progressively truncated no-twilight photoperiods, we also found that plants are more sensitive to twilight length compared to equivalent changes in solely photoperiods. Transcriptome and proteome analyses showed that twilight length affects reactive oxygen species metabolism, photosynthesis, and carbon metabolism. Genetic analyses suggested a twilight sensing pathway from the photoreceptors PHY E , PHY B , PHY D , and CRY2 through LHY/CCA1 to flowering modulation through the GI-FT pathway. Overall, our findings call for more nuanced models of day-length perception in plants and posit that twilight is an important determinant of plant growth and development.

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 score0.014
Threshold uncertainty score0.400

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 head0.003
GPT teacher head0.232
Teacher spread0.228 · 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

Citations26
Published2024
Admission routes2
Has abstractyes

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