Dataset Information

Regulation of embryonic stem cell self-renewal and pluripotency by Foxd3.

ABSTRACT: The Foxd3 forkhead transcription factor is required for maintaining pluripotent cells in the early mouse embryo and for the establishment of murine embryonic stem cell (ESC) lines. To begin to understand the role of Foxd3 in ESC maintenance, we derived ESC lines from blastocysts that carried two conditional Foxd3 alleles and a tamoxifen-inducible Cre transgene. Tamoxifen treatment produced a rapid and near complete loss of Foxd3 mRNA and protein. Foxd3-deficient ESCs maintained a normal proliferation rate but displayed increased apoptosis, and clonally dispersed ESCs showed a decreased ability to self-renew. Under either self-renewal or differentiation-promoting culture conditions we observed a strong, precocious differentiation of Foxd3 mutant ESCs along multiple lineages, including trophectoderm, endoderm, and mesendoderm. This profound alteration in biological behavior occurred in the face of continued expression of factors known to induce pluripotency, including Oct4, Sox2, and Nanog. We present a model for the role of Foxd3 in repressing differentiation, promoting self-renewal, and maintaining survival of mouse ESCs. Disclosure of potential conflicts of interest is found at the end of this article.

SUBMITTER: Liu Y

PROVIDER: S-EPMC2658636 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Regulation of embryonic stem cell self-renewal and pluripotency by Foxd3.

Liu Ying Y Labosky Patricia A PA

Stem cells (Dayton, Ohio) 20080724 10

The Foxd3 forkhead transcription factor is required for maintaining pluripotent cells in the early mouse embryo and for the establishment of murine embryonic stem cell (ESC) lines. To begin to understand the role of Foxd3 in ESC maintenance, we derived ESC lines from blastocysts that carried two conditional Foxd3 alleles and a tamoxifen-inducible Cre transgene. Tamoxifen treatment produced a rapid and near complete loss of Foxd3 mRNA and protein. Foxd3-deficient ESCs maintained a normal prolifer ...[more]

PMID: 18653770

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Project description:ObjectiveTo identify novel genes associated with self-renewal and pluripotency of mouse embryonic stem cells(mESCs)by integrating multiomics data based on machine learning methods.MethodsWe integrated multiomics information of mESCs involving transcriptome, histone modifications, chromatin accessibility, transcription factor binding and architectural protein binding, and compared the signal differences between known stem cell self-renewal and pluripotency genes and other genes.By integrating these multiomics data, we established prediction models based on several machine learning classifiers including random forests and performed 5-fold cross validations.The model was trained using the training dataset containing two thirds of the input samples, and the remaining one third of the input samples were used as the test dataset to assess the performance of the model in independent tests.Finally, the results predicted by the model were validated through gene function annotation and cell function experiments including cell viability assay, colony formation assay and cell cycle analysis.ResultsCompared with the random genes, the genes known to be associated with self-renewal and pluripotency of mESCs in the multiomics data showed significantly different features.Random forest outperformed the other machine learning algorithms tested on these multiomics data, with an area under the curve (AUC) of 0.883±0.018 for cross validation and an AUC of 0.880±0.028 for independent test.Based on this model, we identified 893 potential regulatory genes associated wwith self-renewal and pluripotency of mESCs, which were similar to the known genes in functional annotation.Known-down of the predicted novel regulator gene Cct6a resulted in significant decreases in the cell viability of mESCs (P < 0.0001) and the number of cell clones (P < 0.01), significantly increased the number of cells in G1 phase (P < 0.01) and decreasedthe number of S phase cells (P < 0.05).Knockdown of Cct6a also led to failure of positive alkaline phosphatase staining of the mESCs.ConclusionMachine learning model based on multiomics data can be used to predict potential self-renewal and pluripotency regulators with high performance.By using this model, we predicted potential self-renewal and pluripotency regulatory genes including Cct6a and applied experimental validation.This model provides new insights into the regulatory mechanism of mESCs and contribute to stem cell research.

Dataset Information

Regulation of embryonic stem cell self-renewal and pluripotency by Foxd3.

Publications

Regulation of embryonic stem cell self-renewal and pluripotency by Foxd3.

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