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Record W2167619166 · doi:10.1152/ajpcell.00448.2002

Autoregulation of glucocorticoid receptor by cortisol in rainbow trout hepatocytes

2003· article· en· W2167619166 on OpenAlexaff
Ramesh Sathiyaa, Mathilakath M. Vijayan

Bibliographic record

VenueAmerican Journal of Physiology-Cell Physiology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGlucocorticoid receptorInternal medicineGlucocorticoidEndocrinologyTroutBiologyReceptorMessenger RNAChemistryGeneMedicineBiochemistry

Abstract

fetched live from OpenAlex

We used primary cultures of trout hepatocytes and a physiological dose of cortisol (100 ng/ml), mimicking stressed levels in salmonid fish, to address the impact of glucocorticoid stimulation on glucocorticoid receptor (GR) mRNA abundance and protein content. Cortisol significantly elevated GR mRNA content over a 24-h period; this increase was abolished by actinomycin D, suggesting transcriptional control of GR. However, cortisol significantly decreased GR protein content, leading us to hypothesize that lower GR protein content may be regulating GR mRNA abundance. Indeed, treatment of hepatocytes with MG-132, a proteasomal inhibitor shown to prevent GR degradation by cortisol, abolished cortisol-mediated GR mRNA upregulation. Also, geldanamycin, a heat shock protein 90-specific inhibitor, abolished the GR mRNA increase evident with cortisol but did not modify cortisol-induced increases in abundance of mRNA for phosphoenolpyruvate carboxykinase, a glucocorticoid-responsive gene, or hepatocyte glucose release. Together, our results suggest a negative feedback loop for GR gene regulation by cortisol in trout hepatocytes. The autoregulation of GR may be a crucial step in the physiological stress response process, especially in modulating energy-dependent processes that are glucocorticoid dependent, including gluconeogenesis.

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 categoriesInsufficient payload (model declined to judge)
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 score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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.

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

Citations159
Published2003
Admission routes1
Has abstractyes

Explore more

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