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Record W4361271943 · doi:10.1016/j.stemcr.2023.03.004

Differentiation of uniparental human embryonic stem cells into granulosa cells reveals a paternal contribution to gonadal development

2023· article· en· W4361271943 on OpenAlexfundno aff
Gal Keshet, Shiran Bar, Roni Sarel-Gallily, Ofra Yanuka, Nissim Benvenisty, Talia Eldar‐Geva

Bibliographic record

VenueStem Cell Reports · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationRosetrees Trust
KeywordsBiologyGenomic imprintingParthenogenesisSexual differentiationInduced pluripotent stem cellEmbryonic stem cellImprinting (psychology)GeneticsGonadGenomeGeneStem cellCellular differentiationEmbryoGene expressionDNA methylationEndocrinology

Abstract

fetched live from OpenAlex

Genomic imprinting underlies the mammalian requirement for sexual reproduction. Nonetheless, the relative contribution of the two parental genomes during human development is not fully understood. Specifically, a fascinating question is whether the formation of the gonad, which holds the ability to reproduce, depends on equal contribution from both parental genomes. Here, we differentiated androgenetic and parthenogenetic human pluripotent stem cells (hPSCs) into ovarian granulosa-like cells (GLCs). We show that in contrast to biparental and androgenetic cells, parthenogenetic hPSCs present a reduced capacity to differentiate into GLCs. We further identify the paternally expressed gene IGF2 as the most upregulated imprinted gene upon differentiation. Remarkably, while IGF2 knockout androgenetic cells fail to differentiate into GLCs, the differentiation of parthenogenetic cells supplemented with IGF2 is partly rescued. Thus, our findings unravel a surprising essentiality of genes that are only expressed from the paternal genome to the development of the female reproductive system.

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 score0.034
Threshold uncertainty score0.791

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 head0.231
Teacher spread0.221 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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