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Record W4401616940 · doi:10.1038/s42003-024-06647-y

Role of the circadian nuclear receptor REV-ERBα in dorsal raphe serotonin synthesis in mood regulation

2024· article· en· W4401616940 on OpenAlexaff
Inah Park, Mijung Choi, Jeongah Kim, Sangwon Jang, Doyeon Kim, Jihoon Kim, Youngshik Choe, Dongho Geum, Seong-Woon Yu, Ji‐Woong Choi, Cheil Moon, Han Kyoung Choe, Gi Hoon Son, Kyungjin Kim

Bibliographic record

VenueCommunications Biology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsHospital for Sick Children
FundersMinistry of Science and ICT, South KoreaNational Research FoundationKorea Brain Research InstituteNational Science Foundation
KeywordsDorsal raphe nucleusCircadian rhythmSerotoninRaphe nucleiNeuroscienceMoodBiologyMelatonin receptorPsychologyReceptorSerotonergicInternal medicineEndocrinologyMedicineClinical psychologyGenetics

Abstract

fetched live from OpenAlex

Affective disorders are frequently associated with disrupted circadian rhythms. The existence of rhythmic secretion of central serotonin (5-hydroxytryptamine, 5-HT) pattern has been reported; however, the functional mechanism underlying the circadian control of 5-HTergic mood regulation remains largely unknown. Here, we investigate the role of the circadian nuclear receptor REV-ERBα in regulating tryptophan hydroxylase 2 (Tph2), the rate-limiting enzyme of 5-HT synthesis. We demonstrate that the REV-ERBα expressed in dorsal raphe (DR) 5-HTergic neurons functionally competes with PET-1-a nuclear activator crucial for 5-HTergic neuron development. In mice, genetic ablation of DR 5-HTergic REV-ERBα increases Tph2 expression, leading to elevated DR 5-HT levels and reduced depression-like behaviors at dusk. Further, pharmacological manipulation of the mice DR REV-ERBα activity increases DR 5-HT levels and affects despair-related behaviors. Our findings provide valuable insights into the molecular and cellular link between the circadian rhythm and the mood-controlling DR 5-HTergic systems.

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 score0.447
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.274
Teacher spread0.248 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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