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Record W2766232114 · doi:10.1242/dev.161018

The DEAD-box RNA helicase Ddx39ab is essential for myocyte and lens development in zebrafish

2018· article· en· W2766232114 on OpenAlexafffund
Linlin Zhang, Yuxi Yang, Beibei Li, Ian C. Scott, Xin Lou

Bibliographic record

VenueDevelopment · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNanjing UniversityUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBiologyZebrafishRNA Helicase AHelicaseLens (geology)Cell biologyDEAD boxMyocyteRNAGeneticsGene

Abstract

fetched live from OpenAlex

ABSTRACT RNA helicases from the DEAD-box family are found in almost all organisms and have important roles in RNA metabolism, including RNA synthesis, processing and degradation. The function and mechanism of action of most of these helicases in animal development and human disease remain largely unexplored. In a zebrafish mutagenesis screen to identify genes essential for heart development we identified a mutant that disrupts the gene encoding the RNA helicase DEAD-box 39ab (ddx39ab). Homozygous ddx39ab mutant embryos exhibit profound cardiac and trunk muscle dystrophy, along with lens abnormalities, caused by abrupt terminal differentiation of cardiomyocyte, myoblast and lens fiber cells. Loss of ddx39ab hindered splicing of mRNAs encoding epigenetic regulatory factors, including members of the KMT2 gene family, leading to misregulation of structural gene expression in cardiomyocyte, myoblast and lens fiber cells. Taken together, these results show that Ddx39ab plays an essential role in establishment of the proper epigenetic status during differentiation of multiple cell lineages.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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 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

Citations18
Published2018
Admission routes2
Has abstractyes

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