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Record W2538626350 · doi:10.1016/j.stemcr.2016.09.011

Marked Differences in C9orf72 Methylation Status and Isoform Expression between C9/ALS Human Embryonic and Induced Pluripotent Stem Cells

2016· article· en· W2538626350 on OpenAlexafffund
Yaara Cohen‐Hadad, Gheona Altarescu, Talia Eldar‐Geva, Ephrat Levi-Lahad, Ming Zhang, Ekaterina Rogaeva, Marc Gotkine, Osnat Bartok, Reut Ashwal-Fluss, Sebastián Kadener, Silvina Epsztejn‐Litman, Rachel Eiges

Bibliographic record

VenueStem Cell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersIsrael Science FoundationWeston Brain InstituteConsortium canadien en neurodégénérescence associée au vieillissementALS Association
KeywordsBiologyInduced pluripotent stem cellEmbryonic stem cellCell biologyDNA methylationEpigeneticsMethylationGene isoformStem cellCellular differentiationNeural stem cellMolecular biologyGeneticsGene expressionGene

Abstract

fetched live from OpenAlex

We established two human embryonic stem cell (hESC) lines with a GGGGCC expansion in the C9orf72 gene (C9), and compared them with haploidentical and unrelated C9 induced pluripotent stem cells (iPSCs). We found a marked difference in C9 methylation between the cells. hESCs and parental fibroblasts are entirely unmethylated while the iPSCs are hypermethylated. In addition, we show that the expansion alters promoter usage and interferes with the proper splicing of intron 1, eventually leading to the accumulation of repeat-containing mRNA following neural differentiation. These changes are attenuated in C9 iPSCs, presumably owing to hypermethylation. Altogether, this study highlights the importance of neural differentiation in the pathogenesis of disease and points to the potential role of hypermethylation as a neuroprotective mechanism against pathogenic mRNAs, envisaging a milder phenotype in C9 iPSCs.

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 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 score0.026
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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

Citations21
Published2016
Admission routes2
Has abstractyes

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