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Record W3014605631 · doi:10.1038/s41467-020-15449-9

The translational landscape of ground state pluripotency

2020· article· en· W3014605631 on OpenAlexafffund
Yaser Atlasi, Seyed Mehdi Jafarnejad, Christos G. Gkogkas, Michiel Vermeulen, Nahum Sonenberg, Hendrik G. Stunnenberg

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersKWF KankerbestrijdingOncode InstituteQueen's UniversityQueen's University BelfastEuropean Commission
KeywordsMessenger RNATranslational regulationBiologyProteomeGene expressionEmbryonic stem cellCell biologyRegulation of gene expressionTranslation (biology)Translational efficiencyGenePost-transcriptional regulationRibosome profilingProtein biosynthesisRibosomeGeneticsRNA

Abstract

fetched live from OpenAlex

Translational control plays a central role in regulation of gene expression and can lead to significant divergence between mRNA- and protein-abundance. Here, we used genome-wide approaches combined with time-course analysis to measure the mRNA-abundance, mRNA-translation rate and protein expression during the transition of naïve-to-primed mouse embryonic stem cells (ESCs). We find that the ground state ESCs cultured with GSK3-, MEK-inhibitors and LIF (2iL) display higher ribosome density on a selective set of mRNAs. This set of mRNAs undergo strong translational buffering to maintain stable protein expression levels in 2iL-ESCs. Importantly, we show that the global alteration of cellular proteome during the transition of naïve-to-primed pluripotency is largely accompanied by transcriptional rewiring. Thus, we provide a comprehensive and detailed overview of the global changes in gene expression in different states of ESCs and dissect the relative contributions of mRNA-transcription, translation and regulation of protein stability in controlling protein abundance.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.146

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 head0.011
GPT teacher head0.299
Teacher spread0.288 · 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 designNot applicable
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

Citations36
Published2020
Admission routes2
Has abstractyes

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