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Record W2907428629 · doi:10.1016/j.isci.2018.12.031

Genome-wide Screen for Culture Adaptation and Tumorigenicity-Related Genes in Human Pluripotent Stem Cells

2019· article· en· W2907428629 on OpenAlexfundno aff
Uri Weissbein, Mordecai Peretz, Omer Plotnik, Ofra Yanuka, Ido Sagi, Tamar Golan‐Lev, Nissim Benvenisty

Bibliographic record

VenueiScience · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Academy of Sciences and HumanitiesUnited States-Israel Binational Science FoundationRosetrees Trust
KeywordsInduced pluripotent stem cellBiologyGeneRegenerative medicinePI3K/AKT/mTOR pathwayAdaptation (eye)PhenotypeGenome editingStem cellGenomeGeneticsCell biologyComputational biologyEmbryonic stem cellSignal transductionNeuroscience

Abstract

fetched live from OpenAlex

Human pluripotent stem cells (hPSCs) acquire genetic changes during their propagation in culture that can affect their use in research and future therapies. To identify the key genes involved in selective advantage during culture adaptation and tumorigenicity of hPSCs, we generated a genome-wide screening system for genes and pathways that provide a growth advantage either in vitro or in vivo. We found that hyperactivation of the RAS pathway confers resistance to selection with the hPSC-specific drug PluriSIn-1. We also identified that inactivation of the RHO-ROCK pathway gives growth advantage during culture adaptation. Last, we demonstrated the importance of the PI3K-AKT and HIPPO pathways for the teratoma formation process. Our screen revealed key genes and pathways relevant to the tumorigenicity and survival of hPSCs and should thus assist in understanding and confronting their tumorigenic potential.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.259
Teacher spread0.241 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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