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Record W4409191092 · doi:10.1016/j.stemcr.2025.102471

Large-scale analysis of loss of chromosome Y in human pluripotent stem cells: Implications for Turner syndrome and ribosomopathies

2025· article· en· W4409191092 on OpenAlexfundno aff
Roni Sarel-Gallily, Keith M. Gunapala, Nissim Benvenisty

Bibliographic record

VenueStem Cell Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsnot available
FundersUnited States-Israel Binational Science FoundationRosetrees TrustEuropean CommissionHORIZON EUROPE European Research CouncilAzrieli FoundationIsrael Science FoundationUnited States - Israel Binational Science Foundation
KeywordsBiologyInduced pluripotent stem cellTurner syndromeScale (ratio)GeneticsStem cellKaryotypeChromosomeEvolutionary biologyEmbryonic stem cellGene

Abstract

fetched live from OpenAlex

Loss of chromosome Y (LOY) occurs in aging and cancers, but its extent and implications in human embryonic stem cells (hESCs) have not been studied. Here, we analyzed over 2,650 RNA sequencing (RNA-seq) samples from hESCs and their differentiated derivatives to detect LOY. We found that 12% of hESC samples have lost their chromosome Y and identified LOY in all three germ layers. Differential expression analysis revealed that LOY samples showed a decrease in expression of pluripotency markers and in ribosomal protein (RP) genes. Strikingly, significant RP transcription downregulation was observed in most RP genes, although there is only one expressed Y-linked RP gene. We further analyzed RP expression in Turner syndrome and Diamond-Blackfan anemia samples and observed overall downregulation of RP transcription. This broad analysis sheds light on the scope and effects of LOY in hESCs, suggesting a novel dosage-sensitive mechanism regulating RP gene transcription in LOY and autosomal ribosomopathies.

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 score0.019
Threshold uncertainty score0.437

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 head0.262
Teacher spread0.253 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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