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Record W1862118948 · doi:10.1016/j.celrep.2015.07.037

GRK2 Fine-Tunes Circadian Clock Speed and Entrainment via Transcriptional and Post-translational Control of PERIOD Proteins

2015· article· en· W1862118948 on OpenAlexafffund
Nandita Mehta, Arthur H. Cheng, Cheng‐Kang Chiang, Lucía Mendoza-Viveros, Harrod H. Ling, Abhilasha Patel, Bo Xu, Daniel Figeys, Hai‐Ying Mary Cheng

Bibliographic record

VenueCell Reports · 2015
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of OttawaUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsSuprachiasmatic nucleusCircadian clockCircadian rhythmEntrainment (biomusicology)Cell biologyCLOCKPeriod (music)BiologyChemistryNeuroscienceInternal medicineRhythmPhysicsMedicine

Abstract

fetched live from OpenAlex

The pacemaker properties of the suprachiasmatic nucleus (SCN) circadian clock are shaped by mechanisms that influence the expression and behavior of clock proteins. Here, we reveal that G-protein-coupled receptor kinase 2 (GRK2) modulates the period, amplitude, and entrainment characteristics of the SCN. Grk2-deficient mice show phase-dependent alterations in light-induced entrainment, slower recovery from jetlag, and longer behavioral rhythms. Grk2 ablation perturbs intrinsic rhythmic properties of the SCN, increasing amplitude and decreasing period. At the cellular level, GRK2 suppresses the transcription of the mPeriod1 gene and the trafficking of PERIOD1 and PERIOD2 proteins to the nucleus. Moreover, GRK2 can physically interact with PERIOD1/2 and promote PERIOD2 phosphorylation at Ser545, effects that may underlie its ability to regulate PERIOD1/2 trafficking. Together, our findings identify GRK2 as an important modulator of circadian clock speed, amplitude, and entrainment by controlling PERIOD at the transcriptional and post-translational levels.

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 score0.245
Threshold uncertainty score0.555

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 head0.018
GPT teacher head0.221
Teacher spread0.204 · 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

Citations18
Published2015
Admission routes2
Has abstractyes

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