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Record W1970369203 · doi:10.1083/jcb.200412139

FIAT represses ATF4-mediated transcription to regulate bone mass in transgenic mice

2005· article· en· W1970369203 on OpenAlexaff
Vionnie W.C. Yu, Gourgen Ambartsoumian, Lieve Verlinden, Janet M. Moir, Josée Prud’homme, Claude Gauthier, Peter J. Roughley, René St‐Arnaud

Bibliographic record

VenueThe Journal of Cell Biology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill UniversityShriners Hospitals for Children - Canada
Fundersnot available
KeywordsATF4OsteoblastOsteocalcinBiologyCell biologyTranscription factorOsteoclastInternal medicineTransgeneEndocrinologyAlkaline phosphataseIn vitroGeneBiochemistry

Abstract

fetched live from OpenAlex

We report the characterization of factor inhibiting activating transcription factor 4 (ATF4)-mediated transcription (FIAT), a leucine zipper nuclear protein. FIAT interacted with ATF4 to inhibit binding of ATF4 to DNA and block ATF4-mediated transcription of the osteocalcin gene in vitro. Transgenic mice overexpressing FIAT in osteoblasts also had reduced osteocalcin gene expression and decreased bone mineral density, bone volume, mineralized volume, trabecular thickness, trabecular number, and decreased rigidity of long bones. Mineral homeostasis, osteoclast number and activity, and osteoblast proliferation and apoptosis were unchanged in transgenics. Expression of osteoblastic differentiation markers was largely unaffected and type I collagen synthesis was unchanged. Mineral apposition rate was reduced in transgenic mice, suggesting that the lowered bone mass was due to a decline in osteoblast activity. This cell-autonomous decrease in osteoblast activity was confirmed by measuring reduced alkaline phosphatase activity and mineralization in primary osteoblast cultures. These results show that FIAT regulates bone mass accrual and establish FIAT as a novel transcriptional regulator of osteoblastic function.

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)0.000
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 score0.025
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations59
Published2005
Admission routes1
Has abstractyes

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