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Combinatorial control of ATF4‐dependent gene transcription in osteoblasts

2011· review· en· W1906416839 on OpenAlexafffund
René St‐Arnaud, Bahareh Hekmatnejad

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill UniversityShriners Hospitals for Children - Canada
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthRéseau de Recherche en Santé Buccodentaire et Osseuse
KeywordsATF3ATF4Leucine zipperTranscription factorBasic helix-loop-helix leucine zipper transcription factorsCell biologyCoactivatorSp3 transcription factorActivating transcription factor 2bZIP domainTAF2BiologyActivating transcription factorE-boxChemistryDNA-binding proteinGenePromoterGeneticsGene expressionEnhancer

Abstract

fetched live from OpenAlex

Osteoblast-specific gene transcription requires interaction between bone cell-specific transcription factors and more widely expressed transcriptional regulators. This is particularly evident for the basic domain-leucine zipper factor activating transcription factor 4 (ATF4), whose activity can be enhanced or inhibited through interaction with other leucine zipper proteins, intermediate filament proteins, components of the basic transcriptional machinery, nuclear matrix attachment molecules, or ubiquitously expressed transcription factors. We discuss the results supporting the relevance of these interactions and present the first evidence of a functional interaction between ATF4, FIAT (factor-inhibiting ATF4-mediated transcription), and αNAC (nascent polypeptide-associated complex and coactivator alpha), three proteins that have been previously shown to associate using various protein-protein interaction assays.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.111
GPT teacher head0.369
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2011
Admission routes2
Has abstractyes

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