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Record W3191112330 · doi:10.1242/dmm.049175

A mouse model of brittle cornea syndrome caused by mutation in <i>Zfp469</i>

2021· article· en· W3191112330 on OpenAlexfundno aff
Chloë M. Stanton, Amy S. Findlay, Camilla Drake, Mohammad Z. Mustafa, Philippe Gautier, Lisa McKie, Ian J. Jackson, Véronique Vitart

Bibliographic record

VenueDisease Models & Mechanisms · 2021
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsnot available
FundersMedical Research CouncilInstitute of GeneticsWellcome Trust
KeywordsCorneaMutationGeneticsBiologyOphthalmologyMedicineGene

Abstract

fetched live from OpenAlex

Brittle cornea syndrome (BCS) is a rare recessive condition characterised by extreme thinning of the cornea and sclera. BCS results from loss-of-function mutations in the poorly understood genes ZNF469 or PRDM5. In order to determine the function of ZNF469 and to elucidate pathogenic mechanisms, we used genome editing to recapitulate a human ZNF469 BCS mutation in the orthologous mouse gene Zfp469. Ophthalmic phenotyping showed that homozygous Zfp469 mutation causes significant central and peripheral corneal thinning arising from reduced stromal thickness. Expression of key components of the corneal stroma in primary keratocytes from Zfp469BCS/BCS mice is affected, including decreased Col1a1 and Col1a2 expression. This alters the collagen type I/collagen type V ratio and results in collagen fibrils with smaller diameter and increased fibril density in homozygous mutant corneas, correlating with decreased biomechanical strength in the cornea. Cell-derived matrices generated by primary keratocytes show reduced deposition of collagen type I, offering an in vitro model for stromal dysfunction. Work remains to determine whether modulating ZNF469 activity will have therapeutic benefit in BCS or in conditions such as keratoconus in which the cornea thins progressively. This article has an associated First Person interview with the first author of the paper.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.809

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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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 designSimulation or modeling
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

Citations19
Published2021
Admission routes1
Has abstractyes

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