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Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells.

ABSTRACT: A 30-node signed and directed network responsible for self-renewal and pluripotency of mouse embryonic stem cells (mESCs) was extracted from several ChIP-Seq and knockdown followed by expression prior studies. The underlying regulatory logic among network components was then learned using the initial network topology and single cell gene expression measurements from mESCs cultured in serum/LIF or serum-free 2i/LIF conditions. Comparing the learned network regulatory logic derived from cells cultured in serum/LIF vs. 2i/LIF revealed differential roles for Nanog, Oct4/Pou5f1, Sox2, Esrrb and Tcf3. Overall, gene expression in the serum/LIF condition was more variable than in the 2i/LIF but mostly consistent across the two conditions. Expression levels for most genes in single cells were bimodal across the entire population and this motivated a Boolean modeling approach. In silico predictions derived from removal of nodes from the Boolean dynamical model were validated with experimental single and combinatorial RNA interference (RNAi) knockdowns of selected network components. Quantitative post-RNAi expression level measurements of remaining network components showed good agreement with the in silico predictions. Computational removal of nodes from the Boolean network model was also used to predict lineage specification outcomes. In summary, data integration, modeling, and targeted experiments were used to improve our understanding of the regulatory topology that controls mESC fate decisions as well as to develop robust directed lineage specification protocols.

SUBMITTER: Xu H

PROVIDER: S-EPMC4133156 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells.

Xu Huilei H Ang Yen-Sin YS Sevilla Ana A Lemischka Ihor R IR Ma'ayan Avi A

PLoS computational biology 20140814 8

A 30-node signed and directed network responsible for self-renewal and pluripotency of mouse embryonic stem cells (mESCs) was extracted from several ChIP-Seq and knockdown followed by expression prior studies. The underlying regulatory logic among network components was then learned using the initial network topology and single cell gene expression measurements from mESCs cultured in serum/LIF or serum-free 2i/LIF conditions. Comparing the learned network regulatory logic derived from cells cult ...[more]

PMID: 25122140

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Project description:BackgroundSelf-renewal and pluripotency are considered as unwavering features of embryonic stem cells (ESCs). How ESCs regulate the self-renewal and differentiation is a central question in development and regenerative medicine research. Epidermal growth factor receptor (EGFR) was identified as a critical regulator in embryonic development, but its role in the maintenance of ESCs is poorly understood.MethodsHere, EGFR was disrupted by its specific inhibitor AG1478 in mouse ESCs (mESCs), and its self-renewal and pluripotency were characterized according to their proliferation, expression of pluripotency markers, embryoid body (EB) formation, and mRNA expression patterns. We also used another EGFR inhibitor (gefitinib) and RNA interference assay to rule out the possibility of non-specific effects of AG1478.ResultsEGFR inhibition by AG1478 treatment in mESCs markedly reduced cell proliferation, caused cell cycle arrest at G0/G1 phase, and altered protein expressions of the cell cycle regulatory genes (CDK2 (decreased 11.3%) and proliferating cell nuclear antigen (decreased 25.2%)). The immunoreactivities and protein expression of pluripotency factors (OCT4 (decreased 26.9%)) also dramatically decreased, while the differentiation related genes (GATA4 (increased 1.6-fold)) were up-regulated in mESCs after EGFR inhibition. Meanwhile, EGFR inhibition in mESCs disrupted EB formation, indicating its impaired pluripotency. Additionally, the effects observed by EGFR inhibition with another inhibitor gefitinib and siRNA were consistent with those observed by AG1478 treatment in mESCs. These effects were manifested in the decreased expression of proliferative and pluripotency-related genes and the increased expression of genes involved in differentiation. Moreover, RNA-seq analysis displayed that transcript profiling was changed significantly after EGFR inhibition by AG1478. A large number of differentially expressed genes were involved in cell cycle, apoptotic process, epigenetic modification, and metabolic process, which were related to self-renewal and pluripotency, confirming that EGFR deficiency impaired self-renewal and pluripotency in mESCs.ConclusionsTaken together, our results demonstrated the importance of EGFR in guarding the stemness of mESCs.

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.

Project description:Mouse embryonic stem cells (mESCs) are derived from the inner cell mass of a developing blastocyst and can be cultured indefinitely in-vitro. Their distinct features are their ability to self-renew and to differentiate to all adult cell types. Genes that maintain mESCs self-renewal and pluripotency identity are of interest to stem cell biologists. Although significant steps have been made toward the identification and characterization of such genes, the list is still incomplete and controversial. For example, the overlap among candidate self-renewal and pluripotency genes across different RNAi screens is surprisingly small. Meanwhile, machine learning approaches have been used to analyze multi-dimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self-renewal and pluripotency gene membership.For this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG) using support vector machines (SVM). The data used to train the classifier was derived from mESCs-related studies using mRNA microarrays, measuring gene expression in various stages of early differentiation, as well as ChIP-seq studies applied to mESCs profiling genome-wide binding of key transcription factors, such as Nanog, Oct4, and Sox2, to the regulatory regions of other genes. Comparison to other classification methods using the leave-one-out cross-validation method was employed to evaluate the accuracy and generality of the classification. Finally, two sets of candidate genes from genome-wide RNA interference screens are used to test the generality and potential application of the classifier.Our results reveal that an SVM approach can be useful for prioritizing genes for functional validation experiments and complement the analyses of high-throughput profiling experimental data in stem cell research.

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Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells.

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Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells.

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