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Record W2044288080 · doi:10.1371/journal.pbio.1001645

The CK2 Kinase Stabilizes CLOCK and Represses Its Activity in the Drosophila Circadian Oscillator

2013· article· en· W2044288080 on OpenAlexfundno aff
Áron Szabó, Christian Papin, Daniela Zorn, Prishila Ponien, Frank Weber, Thomas Raabe, François Rouyer

Bibliographic record

VenuePLoS Biology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersInstitute of GeneticsAgence Nationale de la RechercheSixth Framework ProgrammeDeutsche ForschungsgemeinschaftEuropean CommissionAssociation pour la Recherche sur le Cancer
KeywordsBiologyCircadian clockTimelessProtein subunitCell biologyPost-translational regulationCasein kinase 1PhosphorylationProtein kinase ACircadian rhythmGeneticsGene

Abstract

fetched live from OpenAlex

Phosphorylation is a pivotal regulatory mechanism for protein stability and activity in circadian clocks regardless of their evolutionary origin. It determines the speed and strength of molecular oscillations by acting on transcriptional activators and their repressors, which form negative feedback loops. In Drosophila, the CK2 kinase phosphorylates and destabilizes the PERIOD (PER) and TIMELESS (TIM) proteins, which inhibit CLOCK (CLK) transcriptional activity. Here we show that CK2 also targets the CLK activator directly. Downregulating the activity of the catalytic α subunit of CK2 induces CLK degradation, even in the absence of PER and TIM. Unexpectedly, the regulatory β subunit of the CK2 holoenzyme is not required for the regulation of CLK stability. In addition, downregulation of CK2α activity decreases CLK phosphorylation and increases per and tim transcription. These results indicate that CK2 inhibits CLK degradation while reducing its activity. Since the CK1 kinase promotes CLK degradation, we suggest that CLK stability and transcriptional activity result from counteracting effects of CK1 and CK2.

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)0.001
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 score0.288
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.038
GPT teacher head0.262
Teacher spread0.225 · 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

Citations52
Published2013
Admission routes1
Has abstractyes

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