Article

More than 40,000 transcripts, including novel and noncoding transcripts, in mouse embryonic stem cells.

Transcriptome Research Center, National Institute of Radiological Sciences, Chiba, Japan.
Stem Cells (impact factor: 7.78). 12/2006; 24(11):2522-8. DOI:10.1634/stemcells.2006-0005 pp.2522-8
Source: PubMed

ABSTRACT To study the transcriptome of embryonic stem cells, we used a new gene expression profiling method that can measure the expression levels of unknown and rarely expressed transcripts precisely. We detected a total of 33,136 signal peaks representing transcripts in mouse embryonic stem cells, E14. Subsequent random cloning of the peaks suggests that mouse embryonic stem cells express at least 40,000 transcripts, of which about 2,000 are still unknown. In addition, we identified 1,022 noncoding transcripts, several of which change depending on differentiation in gene expression. Our database provides a high-resolution expression profile of E14 cells and is applicable to other mouse embryonic stem cell analyses. It includes most transcription regulation factor-encoding genes and a significant number of unknown and noncoding transcripts.

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    Article: On theoretical models of gene expression evolution with random genetic drift and natural selection.
    Osamu Ogasawara, Kousaku Okubo
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    ABSTRACT: The relative contributions of natural selection and random genetic drift are a major source of debate in the study of gene expression evolution, which is hypothesized to serve as a bridge from molecular to phenotypic evolution. It has been suggested that the conflict between views is caused by the lack of a definite model of the neutral hypothesis, which can describe the long-run behavior of evolutionary change in mRNA abundance. Therefore previous studies have used inadequate analogies with the neutral prediction of other phenomena, such as amino acid or nucleotide sequence evolution, as the null hypothesis of their statistical inference. In this study, we introduced two novel theoretical models, one based on neutral drift and the other assuming natural selection, by focusing on a common property of the distribution of mRNA abundance among a variety of eukaryotic cells, which reflects the result of long-term evolution. Our results demonstrated that (1) our models can reproduce two independently found phenomena simultaneously: the time development of gene expression divergence and Zipf's law of the transcriptome; (2) cytological constraints can be explicitly formulated to describe long-term evolution; (3) the model assuming that natural selection optimized relative mRNA abundance was more consistent with previously published observations than the model of optimized absolute mRNA abundances. The models introduced in this study give a formulation of evolutionary change in the mRNA abundance of each gene as a stochastic process, on the basis of previously published observations. This model provides a foundation for interpreting observed data in studies of gene expression evolution, including identifying an adequate time scale for discriminating the effect of natural selection from that of random genetic drift of selectively neutral variations.
    PLoS ONE 01/2009; 4(11):e7943. · 4.09 Impact Factor
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Keywords

1,022 noncoding transcripts
 
cell analyses
 
E14 cells
 
embryonic
 
expression levels
 
gene expression
 
high-resolution expression profile
 
mouse embryonic
 
new gene expression profiling method
 
noncoding transcripts
 
Subsequent random cloning
 
transcription regulation factor-encoding genes
 
transcripts
 
unknown
 

Ryoko Araki