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Record W2996990088 · doi:10.1002/mgg3.1100

A novel de novo nonsense mutation in <i>ZC4H2</i> causes Wieacker‐Wolff Syndrome

2019· article· en· W2996990088 on OpenAlexafffund
Dan Wang, Dongjie Hu, Zhichao Guo, Rong Hu, Qunxian Wang, Yannan Liu, Mingjing Liu, Zijun Meng, Huan Yang, Yun Zhang, Fang Cai, Weihui Zhou, Weihong Song

Bibliographic record

VenueMolecular Genetics & Genomic Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of British Columbia
FundersCanada Research Chairs
KeywordsNonsense mutationMutationProbandGeneticsPathogenesisNonsenseZinc fingerBiologyExome sequencingMutantGeneMolecular biologyMissense mutationImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Wieacker-Wolff syndrome (WWS) is a congenital X-linked neuromuscular disorder, which was firstly reported in 1985. Zinc finger C4H2-type containing (ZC4H2) gene has been found to be associated with the disease pathogenesis. However, the underlying mechanism remains elusive. METHODS: Whole-exome sequencing was performed to identify the mutations. Expression plasmids were constructed and cell culture and immune-biochemical assays were used to examine the effects of the mutation. RESULTS: We reported a female patient with classical symptoms of WWS and discovered a novel nonsense heterozygous mutation (p.R67X; c.199C>T) in ZC4H2 gene in the patient but not in her parents. The mutation resulted in a 66 amino-acid truncated ZC4H2 protein. The mutation is located in the key helix domain and it altered the subcellular locations of the mutant ZC4H2 protein. X-chromosome inactivation (XCI) pattern analysis revealed that the XCI ratio of the proband was 22:78. CONCLUSION: Female heterozygous carriers with nonsense mutation with a truncated ZC4H2 protein could lead to the pathogenesis of Wieacker-Wolff syndrome and our study provides a potential new target for the disease treatment.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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 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

Citations15
Published2019
Admission routes2
Has abstractyes

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