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Record W2596496863 · doi:10.1093/biolreprod/87.s1.11

Activation of AMPK Actively Represses Steroid Hormone Synthesis in MA-10 Leydig Cells.

2012· article· en· W2596496863 on OpenAlexaff
Houssein S. Abdou, Nicholas M. Robert, Jacques Tremblay

Bibliographic record

VenueBiology of Reproduction · 2012
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBiologySteroidAMPKEndocrinologySteroid hormoneHormoneInternal medicineLeydig cellCell biologyLuteinizing hormoneProtein kinase APhosphorylation

Abstract

fetched live from OpenAlex

Steroid hormones are required for several physiological processes and inadequate steroid hormone levels are associated with various pathological conditions. Thus, steroid hormone synthesis must be tightly regulated. In testosterone-producing Leydig cells, steroidogenesis is strongly stimulated by LH that acts through a G protein-coupled receptor leading to increased cAMP production from ATP. In Leydig cells, LH/cAMP increase expression of the steroidogenic acute regulatory (STAR) protein, which is essential for the initiation of steroidogenesis. The cAMP pathway activates de novo production of the NUR77 transcription factor, which contributes to increased Star expression and androgen production. When proper testosterone levels are reached, Leydig cell steroidogenesis must return to basal levels to prevent excess testosterone production. This is achieved by the negative feedback loop where testosterone acts on the pituitary to suppress LH production. Without LH, Leydig cell steroidogenesis is passively decreased and this is accompanied by the rapid degradation of cAMP into AMP by phosphodiesterases (PDE). Inhibition of PDEs was recently shown to increase Star expression and testosterone production in primary mouse Leydig cells and in the MA-10 Leydig cell line. In other tissues, high AMP levels activate the AMP-activated protein kinase (AMPK), which acts a cellular energy sensor and downregulates ATP-consuming processes. Since AMP levels are dramatically increased in Leydig cells following cAMP degradation, we hypothesized that AMP could actively repress steroidogenesis by activating AMPK. Using Western blot and PCR, we detected AMPK in MA-10 Leydig cells. We next assessed progesterone levels in MA-10 Leydig cells following activation of AMPK by AICAR, an AMP analog and AMPK agonist. In the presence of AICAR, forskolin (FSK)-induced progesterone synthesis was reduced by 50%. Basal progesterone production was not affected by AICAR. To identify genes targeted by AMPK, a microarray analysis was performed using mRNA from MA-10 Leydig cells treated or not with FSK in the presence or absence of AICAR. Activation of AMPK by AICAR inhibited expression of several genes including transcription factors involved in hormone-stimulated steroidogenesis (NUR77 and cJUN) and proteins involved in cholesterol shuttling from extracellular environment to the cytoplasm (Scavenger receptor B1) and from the cytoplasm to the mitochondria (STAR). These results were validated by qPCR. FSK-induced Star promoter activity was also repressed by 60% as determined by transient transfections in MA-10 Leydig cells. Since NUR77 and cJUN contribute to cAMP-induced Star expression, we tested whether AMPK could repress Star transcription by modulating the expression/activity of these transcription factors. Mutations of the NUR77 or AP1 elements in the Star promoter partially relieved AMPK repressive effects. Additionally, overexpression of either NUR77 or cJUN rescued the AMPK repressive effects on the Star promoter. Activation of AMPK also impaired steroidogenesis in the adrenal cell line Y-1 by repressing NUR77 and STAR expression. Altogether our data identify AMPK as an active repressor of steroid hormone biosynthesis in both Leydig and adrenal cells. AMPK thus acts to preserve cellular energy and prevent excess steroid production. Supported by CIHR and NSERC.

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)0.001
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 score0.175
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.037
GPT teacher head0.319
Teacher spread0.282 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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