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Record W2487603429 · doi:10.1016/j.celrep.2016.06.089

mRNA Cap Methylation in Pluripotency and Differentiation

2016· article· en· W2487603429 on OpenAlexaff
Laura Grasso, Olga Suska, L. S. P. Davidson, Thomas Gonatopoulos-Pournatzis, Ritchie Williamson, Lize Wasmus, Simone Wiedlich, Mark Peggie, Marios P. Stavridis, Victoria H. Cowling

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Toronto
FundersLister Institute of Preventive MedicineWellcome TrustGlaxoSmithKlineMedical Research CouncilMerck KGaAAstraZenecaUniversity of DundeePfizer
KeywordsReprogrammingBiologyCellular differentiationEmbryonic stem cellCell biologyGene expressionMethylationMolecular biologyGeneGenetics

Abstract

fetched live from OpenAlex

The mRNA cap recruits factors essential for transcript processing and translation initiation. We report that regulated mRNA cap methylation is a feature of embryonic stem cell (ESC) differentiation. Expression of the mRNA cap methyltransferase activating subunit RAM is elevated in ESCs, resulting in high levels of mRNA cap methylation and expression of a cohort of pluripotency-associated genes. During neural differentiation, RAM is suppressed, resulting in repression of pluripotency-associated factors and expression of a cohort of neural-associated genes. An established requirement of differentiation is increased ERK1/2 activity, which suppresses pluripotency-associated genes. During differentiation, ERK1/2 phosphorylates RAM serine-36, targeting it for ubiquitination and proteasomal degradation, ultimately resulting in changes in gene expression associated with loss of pluripotency. Elevated RAM expression also increases the efficiency of fibroblast reprogramming. Thus, the mRNA cap emerges as a dynamic mark that instructs change in gene expression profiles during differentiation and reprogramming.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.248

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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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