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Record W2999763841 · doi:10.1038/s41467-019-13830-x

Genetic aberrations in iPSCs are introduced by a transient G1/S cell cycle checkpoint deficiency

2020· article· en· W2999763841 on OpenAlexaff
Ryoko Araki, Yuko Hoki, Tomo Suga, Chizuka Obara, Misato Sunayama, Kaori Imadome, Mayumi Fujita, Satoshi Kamimura, Miki Nakamura, Sayaka Wakayama, András Nagy, Teruhiko Wakayama, Masumi Abe

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersJapan Society for the Promotion of ScienceMitsubishi FoundationUehara Memorial FoundationTakeda Science Foundation
KeywordsInduced pluripotent stem cellGene deletionCell cycle checkpointCell cycleTransient (computer programming)BiologyCellCell biologyComputational biologyGeneticsComputer scienceGeneEmbryonic stem cell

Abstract

fetched live from OpenAlex

A number of point mutations have been identified in reprogrammed pluripotent stem cells such as iPSCs and ntESCs. The molecular basis for these mutations has remained elusive however, which is a considerable impediment to their potential medical application. Here we report a specific stage at which iPSC generation is not reduced in response to ionizing radiation, i.e. radio-resistance. Quite intriguingly, a G1/S cell cycle checkpoint deficiency occurs in a transient fashion at the initial stage of the genome reprogramming process. These cancer-like phenomena, i.e. a cell cycle checkpoint deficiency resulting in the accumulation of point mutations, suggest a common developmental pathway between iPSC generation and tumorigenesis. This notion is supported by the identification of specific cancer mutational signatures in these cells. We describe efficient generation of human integration-free iPSCs using erythroblast cells, which have only a small number of point mutations and INDELs, none of which are in coding regions.

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 score0.236
Threshold uncertainty score0.635

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 science0.0010.000
Research integrity0.0000.001
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 head0.247
Teacher spread0.237 · 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

Citations48
Published2020
Admission routes1
Has abstractyes

Explore more

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