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Record W2268619077 · doi:10.1016/j.celrep.2015.12.101

Nac1 Coordinates a Sub-network of Pluripotency Factors to Regulate Embryonic Stem Cell Differentiation

2016· article· en· W2268619077 on OpenAlexfundno aff
Mohan Malleshaiah, Megha Padi, Pau Rué, John Quackenbush, Alfonso Martínez-Arias, Jeremy Gunawardena

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsSOX2Embryonic stem cellBiologyInduced pluripotent stem cellCell biologyProgenitor cellCellular differentiationStem cellCell fate determinationGeneticsTranscription factorGene

Abstract

fetched live from OpenAlex

Pluripotent cells give rise to distinct cell types during development and are regulated by often self-reinforcing molecular networks. How such networks allow cells to differentiate is less well understood. Here, we use integrative methods to show that external signals induce reorganization of the mouse embryonic stem cell pluripotency network and that a sub-network of four factors, Nac1, Oct4, Tcf3, and Sox2, regulates their differentiation into the alternative mesendodermal and neuroectodermal fates. In the mesendodermal fate, Nac1 and Oct4 were constrained within quantitative windows, whereas Sox2 and Tcf3 were repressed. In contrast, in the neuroectodermal fate, Sox2 and Tcf3 were constrained while Nac1 and Oct4 were repressed. In addition, we show that Nac1 coordinates differentiation by activating Oct4 and inhibiting both Sox2 and Tcf3. Reorganization of progenitor cell networks around shared factors might be a common differentiation strategy and our integrative approach provides a general methodology for delineating such networks.

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 score0.016
Threshold uncertainty score0.696

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 head0.009
GPT teacher head0.222
Teacher spread0.214 · 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

Citations41
Published2016
Admission routes1
Has abstractyes

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