To understand how pluripotency is achieved we compared somat

To understand how pluripotency is achieved, we compared somatic cell reprogramming with the acquisition of pluripotency in PGCs. Although these two phenomena are different, the obtained pluripotent stem Nanaomycin A have similar characteristics. Analysis of how pluripotent stem cells are generated from different cell types might help to clarify the mechanism of reprogramming. Of note, PGCs already have many similarities with pluripotent stem cells. Both processes showed two distinct waves of gene expression changes, in the early and late phases (Polo et al., 2012). During somatic cell reprogramming, both waves showed similar patterns of upregulated and downregulated genes. In contrast, during the acquisition of pluripotency in PGCs, the first and second waves were mainly composed of upregulated and downregulated genes, respectively (Figure 1E). These oppositely regulated genes are associated with the GO terms cell cycle, development, and metabolism (Table S1). In the early phase of somatic cell reprogramming, genes associated with “gain of proliferation”, “transient activation of developmental regulators”, and “metabolic changes” are regulated (Polo et al., 2012). Genes that are oppositely regulated during the conversion of PGCs to EGCs might have an important role in somatic cell reprogramming. Furthermore, in both cases, cells lost their original characteristics during the early phase, after which genes in the pluripotency-associated network were upregulated. Comparison of these two types of pluripotency induction would improve our understanding of the reprogramming mechanisms and characteristics of PGCs. Taken together with the results of our previous study (Nagamatsu et al., 2012a), these findings summarize the process of acquisition of pluripotency in PGCs (Figure 7I).
Experimental Procedures
Author Contributions
Acknowledgments We thank Dr. A. Tarakhovsky (Rockefeller University) for providing the Blimp-1flox/flox mice and Dr. T. Nakano and Dr. T. Kimura (Osaka University) for providing the AKT-Mer mice. We also thank Dr. K. Hosokawa (Kyushu University) for providing the recombinant CRE protein and Dr. K. Hayashi (Kyushu University) for a critical reading of this manuscript. This study was supported in part by a grant from the Project for Realization of Regenerative Medicine. Support for the Core Institutes for iPS Cell Research was provided by MEXT and the Keio University Medical Science Fund. G.N. was supported by a PRESTO grant of the Japan Science and Technology Agency and by Funds for the Development of Human Resources in Science and Technology of the Program to Disseminate a Tenure Tracking System for the Tenure-Track Program at the Sakaguchi Laboratory.
Introduction In the hematopoietic stem cell (HSC) field, the identification of regulators of HSC physiology has relied mainly on candidate gene approaches with genetically modified mouse models (Rossi et al., 2012). However, these studies have not necessarily provided a complete picture of the complex network of signals that govern HSC proliferation, differentiation, and function. Therefore non-biased strategies, such as transcriptome analyses, have been used, but these approaches also sometimes are restricted by the screen itself (Hope et al., 2010; Karlsson et al., 2013). For example, many genes have been shown to be expressed at specific differentiation stages, including the most primitive HSCs (Chambers et al., 2007), but they do not provide information as to the key regulators of HSC physiology. An alternative unbiased strategy to elucidate the determinants of HSC frequency/function is to leverage naturally occurring variation in a forward genetics approach. In this regard, prior studies have shown that hematopoietic stem/progenitor cell (HSPC) frequency in mice differs as a function of genetic background (de Haan et al., 1997), and attempts to identify the underlying genes have employed linkage analysis in crosses between inbred mouse strains. For example, using the long-term culture-initiating cell (LTC-IC; Müller-Sieburg and Riblet, 1996) or cobblestone area-forming cell (CAFC; de Haan and Van Zant, 1997) assays, quantitative trait loci (QTLs) for HSPC frequency have been identified on chromosomes 1 and 18 among a panel of recombinant inbred (RI) strains derived from C57Bl/6 × DBA/2 (BXD). This strategy also has been used to map loci for dynamic changes in CAFC frequency associated with aging (de Haan and Van Zant, 1999; Geiger et al., 2001). With the development of immunophenotypic markers that could identify functional HSPCs (Morrison and Weissman, 1994), flow cytometric analysis has been used to identify QTLs for HSPC frequency in the BXD RI panel, but this approach has only been successful in aged mice (Henckaerts et al., 2002, 2004). In addition, QTLs for HSPCs distinct from those mapped in the BXD RI panel have been identified using different inbred mouse strains (Morrison et al., 2002; van Os et al., 2006; Jawad et al., 2008), suggesting that the limited variation in crosses between two strains restricts the identification of other genetic factors that play a role in HSPC physiology. Furthermore, the classical QTL approach has inherently low mapping resolution, since the regions of interest typically span large chromosomal intervals and can contain hundreds to thousands of genes. To overcome these obstacles, a genetical genomics approach has been proposed, where transcriptomics analysis is combined with QTL mapping (de Haan et al., 2002; Bystrykh et al., 2005; Gerrits et al., 2008). However, to date, the incorporation of intermediate molecular traits into linkage analysis has led to the identification of only one gene, latexin, where differential levels of expression influence the pool of HSPCs in the bone marrow (BM) (Liang et al., 2007).