MétaCan
Menu
Back to cohort
Record W2520375473 · doi:10.1016/j.stemcr.2016.08.003

Molecular Characterization of Down Syndrome Embryonic Stem Cells Reveals a Role for RUNX1 in Neural Differentiation

2016· article· en· W2520375473 on OpenAlexfundno aff
Tomer Halevy, Juan-Carlos Biancotti, Ofra Yanuka, Tamar Golan‐Lev, Nissim Benvenisty

Bibliographic record

VenueStem Cell Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationHebrew University of JerusalemRosetrees Trust
KeywordsBiologyRUNX1Embryonic stem cellChromosome 21Neural stem cellDownregulation and upregulationCell biologyGeneticsPhenotypeProgenitor cellNeural developmentStem cellGeneTranscription factorChromosome

Abstract

fetched live from OpenAlex

Down syndrome (DS) is the leading genetic cause of mental retardation and is caused by a third copy of human chromosome 21. The different pathologies of DS involve many tissues with a distinct array of neural phenotypes. Here we characterize embryonic stem cell lines with DS (DS-ESCs), and focus on the neural aspects of the disease. Our results show that neural progenitor cells (NPCs) differentiated from five independent DS-ESC lines display increased apoptosis and downregulation of forehead developmental genes. Analysis of differentially expressed genes suggested RUNX1 as a key transcription regulator in DS-NPCs. Using genome editing we were able to disrupt all three copies of RUNX1 in DS-ESCs, leading to downregulation of several RUNX1 target developmental genes accompanied by reduced apoptosis and neuron migration. Our work sheds light on the role of RUNX1 and the importance of dosage balance in the development of neural phenotypes in DS.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueStem Cell ReportsSame topicDown syndrome and intellectual disability researchFrench-language works237,207