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Record W2104244397 · doi:10.1242/jcs.078071

PERP regulates enamel formation via effects on cell–cell adhesion and gene expression

2011· article· en· W2104244397 on OpenAlexaff
Andrew H. Jheon, Pasha Mostowfi, Malcolm L. Snead, Rebecca A. Ihrie, Eli D. Sone, Tiziano Pramparo, Laura D. Attardi, Ophir D. Klein

Bibliographic record

VenueJournal of Cell Science · 2011
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsUniversity of Toronto
FundersNational Center for Research ResourcesNational Institute of Dental and Craniofacial ResearchNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsAmeloblastBiologyGene knockdownCell biologyEnamel paintAmelogenesisGene expressionGeneMolecular biologyGeneticsMaterials science

Abstract

fetched live from OpenAlex

Little is known about the role of cell-cell adhesion in the development of mineralized tissues. Here we report that PERP, a tetraspan membrane protein essential for epithelial integrity, regulates enamel formation. PERP is necessary for proper cell attachment and gene expression during tooth development, and its expression is controlled by P63, a master regulator of stratified epithelial development. During enamel formation, PERP is localized to the interface between the enamel-producing ameloblasts and the stratum intermedium (SI), a layer of cells subjacent to the ameloblasts. Perp-null mice display dramatic enamel defects, which are caused, in part, by the detachment of ameloblasts from the SI. Microarray analysis comparing gene expression in teeth of wild-type and Perp-null mice identified several differentially expressed genes during enamel formation. Analysis of these genes in ameloblast-derived LS8 cells upon knockdown of PERP confirmed the role for PERP in the regulation of gene expression. Together, our data show that PERP is necessary for the integrity of the ameloblast-SI interface and that a lack of Perp causes downregulation of genes that are required for proper enamel formation.

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 score0.012
Threshold uncertainty score0.232

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)0.0000.000

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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

Citations35
Published2011
Admission routes1
Has abstractyes

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