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Record W1999508252 · doi:10.1002/jcb.21263

Dissociation of growth arrest and CYP24 induction by VDR ligands in mammary tumor cells

2007· article· en· W1999508252 on OpenAlexaff
Meggan Valrance, Andrea H. Brunet, Anna M. Acosta, JoEllen Welsh

Bibliographic record

VenueJournal of Cellular Biochemistry · 2007
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsQueen's University
FundersNational Cancer InstituteHenry Luce FoundationNational Institutes of HealthNational Science Foundation
KeywordsCalcitriol receptorApoptosisVitamin D and neurologyChemistryCell biologyDownregulation and upregulationCancer researchCell growthBiologyEndocrinologyBiochemistryGene

Abstract

fetched live from OpenAlex

Murine mammary tumor cells with differential vitamin D receptor (VDR) expression were used to study the mechanisms of growth inhibition by vitamin D steroids. In VDR-expressing WT145 cells, 1,25D and its synthetic analog EB1089 induce growth arrest and transcriptionally upregulate the well-characterized VDR target gene CYP24. 1,25D also induces apoptosis in WT145 cells through activation of initiator and executioner caspases and the calcium-dependent protease calpain. We also demonstrate that WT145 cells express CYP27B1, the enzyme that converts 25-hydroxyvitamin D(3) (25D) to 1,25D, and that 25D inhibits growth of these cells but does not trigger apoptosis or induce CYP24 expression. Comparative studies were conducted in KO240 cells, which were derived from VDR knockout mice and found to retain expression of CYP27B1. KO240 cells were not growth inhibited nor rendered apoptotic by any of the tested vitamin D compounds. These data conclusively demonstrate that VDR mediates the anti-proliferative and pro-apoptotic effects of vitamin D metabolites and analogs, but that the potency of a vitamin D compound to induce the VDR target gene CYP24 does not accurately predict its potency in mediating growth regulation.

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 score0.005
Threshold uncertainty score0.333

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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

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