Evidence map›Paper›PMID 21542696›Full record

ArticleStem cells and development2011

Functional modules distinguish human induced pluripotent stem cells from embryonic stem cells.

Anyou Wang, Kevin Huang, Yin Shen, Zhigang Xue, Chaochao Cai, Steve Horvath, Guoping Fan

Abstract read
In one paragraph

Article in Stem cells and development, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Functional Annotation ofBiomolecules · 2018
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  16. A web-server of cell type discrimination system.TheScientificWorldJournal · 2014
    Article
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  18. Review
  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anyou WangDepartment of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California 90095, USA.
Kevin Huang
Yin Shen
Zhigang Xue
Chaochao Cai
Steve Horvath
Guoping Fan

Funding

Human Embryonic Stem Cell CoreP01GM081621 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ZACK, JEROME A. · 2008 to 2012
$9.3M
NIGMS NIH HHS P01 GM 081621
6 · The paper itself

Abstract

It has been debated whether human induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs) express distinctive transcriptomes. By using the method of weighted gene co-expression network analysis, we showed here that iPSCs exhibit altered functional modules compared with ESCs. Notably, iPSCs and ESCs differentially express 17 modules that primarily function in transcription, metabolism, development, and immune response. These module activations (up- and downregulation) are highly conserved in a variety of iPSCs, and genes in each module are coherently co-expressed. Furthermore, the activation levels of these modular genes can be used as quantitative variables to discriminate iPSCs and ESCs with high accuracy (96%). Thus, differential activations of these functional modules are the conserved features distinguishing iPSCs from ESCs. Strikingly, the overall activation level of these modules is inversely correlated with the DNA methylation level, suggesting that DNA methylation may be one mechanism regulating the module differences. Overall, we conclude that human iPSCs and ESCs exhibit distinct gene expression networks, which are likely associated with different epigenetic reprogramming events during the derivation of iPSCs and ESCs.

Indexed as

AlgorithmsAnimalsCluster AnalysisDNA MethylationEmbryonic Stem CellsEpistasis, GeneticGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansInduced Pluripotent Stem CellsMiceModels, GeneticOligonucleotide Array Sequence AnalysisSupport Vector MachineTranscription, Genetic

Identifiers

PMID21542696
PMCPMC3202894

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.