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Record W2913684698 · doi:10.1096/fj.201801385rr

A miR‐29a‐driven negative feedback loop regulates peripheral glucocorticoid receptor signaling

2019· article· en· W2913684698 on OpenAlexaff
Christina Glantschnig, Mascha Koenen, Manuel Gil‐Lozano, Michael Karbiener, Ines Pickrahn, Jasmine Williams‐Dautovich, Rucha Patel, Carolyn L. Cummins, Maude Giroud, Götz Hartleben, Elena Sophie Vogl, Matthias Blüher, Jan Tuckermann, N. Henriette Uhlenhaut, Stephan Herzig, Marcel Scheideler

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Toronto
FundersDementia Collaborative Research Centres, AustraliaÖsterreichischen Akademie der WissenschaftenDeutsche ForschungsgemeinschaftAustrian Science FundSeventh Framework ProgrammeUniversity of California, Santa CruzAlexander von Humboldt-Stiftung
KeywordsNegative feedbackPositive feedbackGlucocorticoid receptorFeedback loopGlucocorticoidSignal transductionPeripheralCell biologyFeedback regulationControl theory (sociology)ReceptorBiologyComputer sciencePhysicsEndocrinologyInternal medicineMedicinePsychologyGeneticsEngineeringVoltageControl (management)Computer security

Abstract

fetched live from OpenAlex

The glucocorticoid receptor (GR) represents the crucial molecular mediator of key endocrine, glucocorticoid hormone-dependent regulatory circuits, including control of glucose, protein, and lipid homeostasis. Consequently, aberrant glucocorticoid signaling is linked to severe metabolic disorders, including insulin resistance, obesity, and hyperglycemia, all of which also appear upon chronic glucocorticoid therapy for the treatment of inflammatory conditions. Of note, long-term glucocorticoid exposure under these therapeutic conditions typically induces glucocorticoid resistance, requiring higher doses and consequently triggering more severe metabolic phenotypes. However, the molecular basis of acquired glucocorticoid resistance remains unknown. In a screen of differential microRNA expression during glucocorticoid-dependent adipogenic differentiation of human multipotent adipose stem cells, we identified microRNA 29a (miR-29a) as one of the most down-regulated transcripts. Overexpression of miR-29a impaired adipogenesis. We found that miR-29a represses GR in human adipogenesis by directly targeting its mRNA, and downstream analyses revealed that GR mediates most of miR-29a's anti-adipogenic effects. Conversely, miR-29a expression depends on GR activation, creating a novel miR-29-driven feedback loop. miR-29a and GR expression were inversely correlated both in murine adipose tissue and in adipose tissue samples obtained from human patients. In the latter, miR-29a levels were additionally strongly negatively correlated with body mass index and adipocyte size. Importantly, inhibition of miR-29 in mice partially rescued the down-regulation of GR during dexamethasone treatment. We discovered that, in addition to modulating GR function under physiologic conditions, pharmacologic glucocorticoid application in inflammatory disease also induced miR-29a expression, correlating with reduced GR levels. This effect was abolished in mice with impaired GR function. In summary, we uncovered a novel GR-miR-29a negative feedback loop conserved between mice and humans, in health and disease. For the first time, we elucidate a microRNA-related mechanism that might contribute to GR dysregulation and resistance in peripheral tissues.-Glantschnig, C., Koenen, M., Gil-Lozano, M., Karbiener, M., Pickrahn, I., Williams-Dautovich, J., Patel, R., Cummins, C. L., Giroud, M., Hartleben, G., Vogl, E., Blüher, M., Tuckermann, J., Uhlenhaut, H., Herzig, S., Scheideler, M. A miR-29a-driven negative feedback loop regulates peripheral glucocorticoid receptor signaling.

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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.229
Teacher spread0.220 · 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

Citations35
Published2019
Admission routes1
Has abstractyes

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