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Record W2045212225 · doi:10.1002/dvdy.21002

<i>Zac1</i> promotes a Müller glial cell fate and interferes with retinal ganglion cell differentiation in <i>Xenopus</i> retina

2006· article· en· W2045212225 on OpenAlexafffund
Lin Ma, Jennifer C. Hocking, Carrie L. Hehr, Carol Schuurmans, Sarah McFarlane

Bibliographic record

VenueDevelopmental Dynamics · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of Calgary
FundersAlberta Heritage Foundation for Medical ResearchFondation pour la Recherche MédicaleCanadian Institutes of Health ResearchMarch of Dimes Foundation
KeywordsBiologyCell fate determinationCell biologyRetinaXenopusCell cycleCellular differentiationProgenitor cellCell growthTranscription factorMuller gliaRetinal ganglion cellGliogenesisCellNeuroscienceStem cellGeneticsGene

Abstract

fetched live from OpenAlex

The timing of cell cycle exit is tightly linked to cell fate specification in the developing retina. Accordingly, several tumor suppressor genes, which are key regulators of cell cycle exit in cancer cells, play critical roles in retinogenesis. Here we investigated the role of Zac1, a tumor suppressor gene encoding a zinc finger transcription factor, in retinal development. Strikingly, in gain-of-function assays in Xenopus, mouse Zac1 promotes proliferation and apoptosis at an intermediate stage of retinogenesis. Zac1 also influences cell fate decisions, preferentially promoting the differentiation of tumor-like clusters of abnormal neuronal cells in the ganglion cell layer, as well as inducing the formation of supernumerary Müller glial cells at the expense of other cell types. Thus Zac1 has the capacity to influence cell cycle exit, and cell fate specification and differentiation decisions by retinal progenitors, suggesting that further functional studies will uncover new insights into how retinogenesis is regulated.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

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 head0.002
GPT teacher head0.172
Teacher spread0.170 · 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.

Study designObservational
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

Citations18
Published2006
Admission routes2
Has abstractyes

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