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Record W3158463597 · doi:10.1210/jendso/bvab048.474

Follicle-Stimulating Hormone Does Not Impact RANKL-Induced Osteoclastogenesis

2021· article· en· W3158463597 on OpenAlexaff
Ziyue Zhou, Luisina Ongaro Gambino, Daniel J. Bernard

Bibliographic record

VenueJournal of the Endocrine Society · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsRANKLEndocrinologyInternal medicineOsteoclastCathepsin KFollicle-stimulating hormoneMacrophage colony-stimulating factorChemistryRANK LigandFollicle-stimulating hormone receptorAcid phosphataseReceptorBiologyHormoneMedicineActivator (genetics)MacrophageIn vitroLuteinizing hormone

Abstract

fetched live from OpenAlex

Abstract Postmenopausal osteoporosis has been attributed to decreased estradiol levels. In the hypothalamic-pituitary-gonadal axis, estradiol synthesis is stimulated by follicle-stimulating hormone (FSH). FSH is secreted from the anterior pituitary gland and estradiol feeds back to the hypothalamus and pituitary to suppress FSH production. In postmenopausal women, the loss of estradiol (and inhibin) negative feedback leads to elevated serum FSH levels. It was recently proposed that this increase in FSH also contributes to postmenopausal osteoporosis by stimulating differentiation and activation of bone-resorbing osteoclast cells. Our objectives were to determine whether FSH has direct actions on osteoclast differentiation in vitro and, if so, its mechanism(s) of action. First, a murine leukemic monocyte/macrophage cell line, RAW264.7, was differentiated into osteoclasts by treatment with receptor activator of nuclear factor kappa-B ligand (RANKL, 50 ng/mL) for seven days. As expected, we observed the appearance of osteoclasts, characterized as tartrate-resistant acid phosphatase (TRAP)-positive multinucleated cells. RANKL also induced expression of established osteoclast differentiation markers, including Trap, cathepsin K (Ctsk), and matrix metalloproteinase-9 (Mmp9). The mRNA expression of FSH receptor (Fshr), however, was low to undetectable both before and after osteoclast differentiation. Co-treatment of RAW264.7 cells with FSH (35, 70 and 140 IU/L) and RANKL did not further impact the expression of Rank, Trap, Ctsk, Mmp-9, or Fshr. Second, primary murine monocytes were differentiated into osteoclasts by treatment with RANKL (50 ng/mL) and macrophage colony-stimulating factor (M-CSF, 25 ng/mL) for five days. FSH co-treatment (70 and 140 IU/L) had no impact on the expression of osteoclast markers, and Fshr expression was low to undetectable both before and after osteoclast differentiation, consistent with the results from RAW264.7 cells. In conclusion, in our hands, FSH does not impact RANKL-induced osteoclast differentiation in immortalized or primary murine monocytes.

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 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 score0.031
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.012
GPT teacher head0.277
Teacher spread0.265 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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