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    • Among six pluripotency regulators examined only NANOG and PO

    2018-10-26

    Among six pluripotency regulators examined, only NANOG and POU5F1 were downregulated by 2days of LY364947 treatment, which impaired DE competence. NANOG is a direct target of Nodal/Activin signaling, and the maintenance of its expression requires active Nodal/Activin signaling (Xu et al., 2008; Chambers et al., 2007; Vallier et al., 2009; Yu et al., 2011). Furthermore, NANOG is essential to maintain DE competence in hESCs, because the knockdown of NANOG expression by RNAi compromises the ability of hESCs to become DE in response to enhanced Nodal/Activin signaling (Vallier et al., 2009). Nonetheless, the loss of the DE competence by LY364947 treatment is unlikely caused by downregulation of NANOG for the following two reasons. First, either 1day or 2days of LY364947 treatment caused substantial downregulation of NANOG, while only 2days of treatment abrogated DE competence. Second, the expression of NANOG was almost immediately restored by Activin A treatment even after 2days of LY364947 treatment. Thus, downregulation of NANOG does not always correlate with the loss of DE competence. On the other hand, however, the downregulation of POU5F1 correlates with the abrogation of DE competence. Specifically, both the level of POU5F1 and the DE competence were significantly reduced only after 2days, but not 1day, of LY364947 treatment. In addition, the expression of POU5F1 remained low after 2days of LY364947 treatment even when Nodal/Activin signaling was activated afterward with Activin A. Repression of POU5F1 in human and mouse ESCs results in differentiation to trophectoderm (Nichols et al., 1998; Matin et al., 2004; Zaehres et al., 2005; Babaie et al., 2007), while overexpression of POU5F1 causes expression of mesendoderm genes, including Sox17, Hex, and T (Niwa et al., 2000; Stefanovic et al., 2009). Recent studies with mouse ESCs and P19 embryonal carcinoma THZ2 suggested that Pou5f1 is also required for mesendoderm differentiation, as knockdown of Pou5f1 impaired the upregulation of Brachyury in response to Wnt/β-catenin signaling activation (Thomson et al., 2011; Marikawa et al., 2011). Thus, downregulation of POU5F1 may be contributing to the loss of DE competence, although other factors may also be involved. Future studies should be designed to identify molecular alterations at the transcriptional, protein, and epigenetic levels that occur in response to 2days, but not 1day, of Nodal/Activin signaling inhibition, which lead to a loss of competence to the DE lineage.
    Materials and methods
    Author contributions
    Conflict of interest
    Acknowledgments This work was supported by grants from the George F. Straub Trust Fund of the Hawaii Community Foundation Medical Research Grant (11ADVC-49271) and Johns Hopkins Center for Alternatives to Animal Testing Grant (#2012-11) to Y.M. FACS analysis was conducted at the Institute for Biogenesis Research FACS Core Facility, which was supported by the COBRE grants from the National Center for Research Resources (5P20RR024206-05) and the National Institute of General Medical Sciences (8P20GM103457-05) from the National Institutes of Health. The authors thank Dr. Phillip Davy and Natsumi Takahashi for their assistance with our FACS analysis. The authors also thank Dr. Vernadeth B. Alarcon and Dana Ann A. Tamashiro at the Institute for Biogenesis Research for technical assistance and encouragement throughout the study. K.L.J. thanks the Cell and Molecular Biology Graduate Program at the University of Hawaii for support.
    Introduction Gene expression signatures have yielded a wealth of information for benchmarking stem cell phenotypes (for example, (Muller et al., 2008, 2011; Pelekanos et al., 2012)), identifying core signalling networks (Novershtern et al., 2011) and mining novel gene products with roles in the maintenance and differentiation of various stem cell lines. It is increasingly common for researchers to include gene expression profiling as a metric of cell quality, particularly in the derivation of iPSC from various tissues or disease cohorts (Bock et al., 2011; Nayler et al., 2012). As a result, a large number of gene expression datasets generated on various stem cell models, using microarray or sequencing platforms, are publicly available, in gene expression repositories such as ArrayExpress (Parkinson et al., 2011) or GEO (Barrett et al., 2011). However, assessment of gene expression patterns across different stem cell datasets remains difficult, and as such, there is a demand for tools that allow researchers to determine the reproducibility of genes and gene signatures that correlate with particular stem cell phenotypes.