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Transcriptional Networks Regulating Embryonic Stem Cell Fate Decisions Emily Walker and William L. Stanford

Abstract A comprehensive understanding of the transcriptional regulation of embryonic stem cell (ESC) fate decisions will provide the key to their successful manipulation for therapeutic purposes as well as provide insight into the process of early embryogenesis. Traditional molecular and genetic approaches have been successful in identifying several essential regulators of pluripotency, notably Oct4, Nanog, and Sox2. However, these approaches will not be sufficient to understand the global regulatory control of transcriptional networks. Genome-wide work in model organisms such as Escherichia coli, yeast, and sea urchin reveal that transcriptional networks can be broken down into a small set of evolutionarily conserved network motifs, each with its own biological function. Initial genome-wide studies in ESCs reveal the presence of these same network motifs, providing mechanistic explanations of cell fate decisions. Thus, as is being performed in lower organisms, the drafting of a comprehensive transcriptional network controlling ESC fate will require systematic characterisation of the functional targets of each ESC-expressed transcription factor. Keywords Embryonic stem cells (ESCs) · Transcription factor · Network motif · Transcriptional network · Microarray · Promoter occupancy

1 Introduction Embryonic stem cells (ESCs) are unspecialized cells that have the ability to self-renew, producing daughter cells with equivalent developmental potential, or to differentiate into more specialized cells. They are derived from the inner cell

W.L. Stanford (B) Institute of Biomaterials and Biomedical Engineering; Institute of Medical Science; Department of Chemical Engineering and Applied Chemistry, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3G9 e-mail: [email protected]

mass of the pre-implantation embryo and are pluripotent, as they are able to differentiate in vivo into all cell types of the adult organism, but not into extraembryonic tissue. Exogenous control of the pluripotent state can be achieved by a limited number of factors. When grown in fetal bovine serum (FBS)-containing medium and in the presence of murine embryonic fibroblast feeder cells [1, 2] or the cytokine leukemia inhibitory factor (LIF) [3–6], mouse ESCs remain undifferentiated. LIF activates gp130 signalling through binding to LIFRβ. LIFRβ dimerizes with gp130 and transduces the signal through the JAK-STAT pathway. While STAT3 plays an important role in self-renewal of ESCs, Stat3−/− embryos can undergo gastrulation, suggesting the existence of a STAT3-independent pathway for ESC selfrenewal [7]. Another factor, BMP4, provided by the serum, functions in the presence of LIF to maintain pluripotency by inducing phosphorylation and nuclear localization of Smad1, followed by upregulation of Id proteins that block neural differentiation [8]. Despite similarities to mouse ESCs, human ESCs do not require LIF to maintain their undifferentiated state [9] and BMP4 initiates differentiation towards the trophoblast lineage [10]. Instead it has been shown that activin/nodal [11], basic FGF [12], and IGF [13] signalling are required to maintain human ESC pluripotency. Recent reports have shown that pluripotent mouse cell lines can also be derived from the post-implantation epliblast [14, 15]. These cells, termed epiblast stem cells (EpiSCs), can be maintained in activin and FGF, have a similar morphology, gene expression, and epigenetic profile to human ESCs. This suggests that the disparity between signalling responsiveness may not be due to species differences but rather to the developmental origin (early or late epiblast) of the cells. Despite differences in extrinsic factors and possibly the developmental origins of mouse and human ESCs, self-renewal of both human and mouse ESCs is controlled by the key transcription factors Oct4 [16], Nanog [17, 18], and Sox2 [19]. These factors form a core transcriptional network module. Because ESCs can be expanded indefinitely in their pluripotent state and are also able to differentiate into cell

V.K. Rajasekhar, M.C. Vemuri (eds.), Regulatory Networks in Stem Cells, Stem Cell Biology and Regenerative Medicine, c Humana Press, a part of Springer Science+Business Media, LLC 2009 DOI 10.1007/978-1-60327-227-8 8,

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types of all three germ layers in vitro, they are an ideal system for transcriptional network analysis. Of additional value for analysis, they can be genetically manipulated to stably or inducibly express shRNAs, transgenes, or reporter systems. In general, it is important to understand transcriptional regulation in ESCs because they represent both a tractable model of early embryogenesis and somatic stem cells and a potential source of cells for therapeutic applications via tissue engineering and regenerative medicine [20]. In this chapter, we will first define a transcriptional network, its constituent motifs and their predicted biological functions. We will discuss the current understanding of individual transcriptional regulators in ESCs and the initial genome-wide studies in ESCs. We will describe an integrative approach that could extend our understanding of the complex transcriptional cascades responsible for defining ESC fate. Finally, we will describe the model of ESC maintenance that has emerged from these works.

2 Transcriptional Networks Cells respond to external stimuli and carry out cellular processes by regulating gene expression. Transcription factors are proteins that bind to the promoter region of their target gene through interaction with DNA binding domains and function to regulate expression by either increasing (activation) or decreasing (repression) the rate of gene transcription. More than 2000 human genes are classified as transcription factors, mainly through characterization of common structural elements known to be involved in DNA binding [21]. Transcription factors regulate complex processes through transcriptional regulatory networks. It has been shown in many organisms, including the bacteria E. coli [22], the budding yeast Saccharomyces cerevisiae [23], the plant Arabidopsis [24], mouse hematopoietic stem cells [25], and human pancreatic cells [26], that these networks are made up of network motifs. Network motifs are the simplest recurring patterns of interactions between transcription factors and their gene targets. They have been more rigorously defined as patterns of interconnections that recur in many different parts of a network at a frequency much higher than those found in randomized networks [22]. The recurrence of the same patterns amongst widely varying organisms suggests that these motifs comprise the basic building blocks of any transcriptional regulatory network. It is proposed that each network motif confers a specific regulatory capacity to the network and it is hoped that the dynamics of an entire transcriptional network can be understood, at least in part, through an understanding of the dynamics of each component network motif. Although not discussed here, many other proteins play important roles in transcriptional regulation, such as coactivators or corepressors or through the epigenetic effects

E. Walker and W.L. Stanford

of chromatin remodelling proteins, histone acetylases, histone deacetylases and histone and DNA methyltransferases.

2.1 Transcriptional Motifs and Their Predicted Biological Functions 2.1.1 Negative Autoregulation and Negative Feedback Autoregulation occurs when a transcription factor binds to the promoter of its own gene [27, 28]. It has been reported that in E. coli, a large percentage of genes are autoregulated (52–74%) [22, 29], whereas in S. cerevisiae, about 10% of genes are autoregulated [28]. There are two instances of autoregulation, positive and negative, each with a unique function. Negative autoregulation occurs when a transcription factor represses the expression of its own gene [27]. The goal of this motif is to rapidly produce a steady-state protein concentration. The three advantages of the negative autoregulatory motif over an unregulated system (Fig. 1A) are increased stability of gene expression levels [30], reduced noise [31], and reduced response time [32]. The response time or rise-time is defined as the delay from the initiation of transcript production until half maximal product concentration is reached [32]. To understand how this motif confers stability, consider the example shown in Fig. 1B. Transcription factor “B” is initially produced until it reaches a repression threshold. It is then able to repress transcription of its own gene, “gene b,” and the production of transcription factor “B” is then reduced. Once it is reduced below the repression threshold, transcription of “gene b” resumes. This cycle continues, fixing the concentration of “B” into a steady-state level, hovering around its repression threshold. In comparison, an unregulated system, in which a transcription factor activates the expression of a gene other than its own (Fig. 1A), must rely on alternative methods of controlling concentration of the transcription factor, such as promoter binding affinities or transcription factor degradation. Such a system is slow to achieve steady-state and thus results in large cell-cell variability. An unregulated motif can only reach a steadystate equal to the negative autoregulatory motif when the unregulated gene is controlled by a weak promoter and the autoregulated gene is controlled by a strong promoter. The use of the strong promoter is advantageous because it dramatically increases the rise-time of the system. It has been experimentally demonstrated in E. coli that the rise-time of an unregulated system is approximately one cell-cycle compared to the negative autoregulatory system with a rise-time of 1/5th a cell cycle [32]. Additional factors influencing the response of these motifs include protein degradation

Embryonic Stem Cell Transcriptional Networks

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Fig. 1 Six basic network motifs. For all figures, an oval represents a protein product and in all cases shown it is a transcription factor (TF). A rectangle represents the promoter of the gene. An arrowhead represents an activating interaction and a perpendicular line represents a repressive interaction. (A) In unregulated transcription, transcription factor (TF) A binds to “gene x,” causing activation or repression. (B) In negative autoregulation, “TF B” represses the expression of its own gene, “gene b.” (C) In positive autoregulation, “TF C” activates the expression of

its own gene, “gene c.” (D) In the general feedforward motif, “TF D” regulates the expression of “gene e” and both “TF D” and “TF E” regulate the expression of “gene f.” (E) In the single-input motif, “TF F” regulates a group of target genes. (F) In the multi-input motif, a set of transcription factors (TF F, G, and H) all control a set of target genes (w, x, y, z). (G) In a transcriptional cascade, “TF I” regulates “gene j.” “TF J” regulates “gene k.” ‘TF K’ regulates “gene m” and so on

rates, mRNA production and degradation rates, delays in the formation of protein from mRNA, strength of promoter activation and/or repression and cooperativity with other regulators. Mathematical models to account for these factors have been developed and outside of extreme inputs, correlate well with the theory and experimental evidence. Details of these models can be found in Becskei and Serrano [30] and Rosenfeld et al. [32]. Negative feedback is a variation of negative autoregulation and occurs when the gene target of a transcription factor represses the activating transcription factor. It has the same effects on response-time, noise reduction, and gene expression stability. In addition, there is evidence to suggest that it significantly increases the production of the downstream gene target [31]. A mathematical simulation of a negative feedback motif can be found in Dublanche et al. [31].

stability of gene expression levels [34, 35]. To understand how this motif slows response times, consider the example in Fig. 1C. Transcription factor “C” is initially produced, but while levels of “C” remain low, transcription rates are also low. Once “C” reaches an activation threshold, it is able to increase the rate of transcription of its own gene, “gene c.” This motif results in an increase in cell-cell variability when in some cells, the activation threshold of “gene c” has not been reached and the concentration of “C” remains low, while in other cells, the activation threshold is surpassed and the concentration of “C” becomes dramatically higher [34]. Once the concentration of “C” becomes high, it tends to stay high, even for a time after the loss of the activating signal. It has been suggested that this mechanism confers “memory” to the cell since the response delay can last longer than one cell cycle [33, 35]. In some systems, positive autoregulation can result in a bimodal distribution in which there are essentially two populations of cells – those in which “C” is off (low expression) and those is which “C” is on (activated expression) [34]. Positive feedback is a variation of positive autoregulation and occurs when the gene target of a transcription factor activates the activating transcription factor. It has similar effects

2.1.2 Positive Autoregulation and Positive Feedback Positive autoregulation occurs when a transcription factor increases the expression of its own gene. The effects of this motif are to slow response time [33] and to decrease the

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Fig. 2 Feed-forward motifs. Since each regulatory element in a feed-forward motif can be either an activator or a repressor, there are eight possible motif structures. (A–D) In coherent feed-forward motifs, the sign of the direct path from transcription “TF X” to “gene z” is the same as the overall sign of the indirect path through “TF Y.” (E–H) In incoherent feed-forward motifs, the two paths have opposite signs

on response-time, gene expression stability and bimodal distribution [33]. A mathematical simulation of a positive feedback motif can be found in Maeda et al. [33].

2.1.3 Feed-Forward The feed-forward motif consists of three elements, two transcription factors and one effector gene. As depicted in Fig. 1D, the first transcription factor (“D”) regulates the expression of the second (“E”) and both “D” and “E” regulate the expression of the effector gene “gene f.” Since each regulatory element can be acting as either an activator or a repressor, there are eight possible feed-forward motif structures (Fig. 2) [27]. As such, this is diverse class of motifs and has been shown to have multiple functions [36]. It has been broken down into two subclasses, coherent feed-forward motifs (Fig. 2A–2D) in which the sign of the direct path from transcription factor “X” to “gene z” is the same as the overall sign of the indirect path through transcription factor “Y,” and incoherent feed-forward motifs (Fig. 2E–2H) in which the two paths have opposite signs [27]. It has been proposed that coherent feed-forward loops can provide a delayed response to the appearance/disappearance of an input [27, 37, 38]. The biological function of these motifs is to act as a filter that can protect the target gene from fluctuations in input. Consider the motif in Fig. 2A in which transcription factor “X” initially induces the expression of transcription factor “Y.” If expression of “gene z” requires both “X” and “Y,” there will be a delay in “gene z” expression until “Y” accumulates to its activation threshold. Thus, its expression is initially resistant to fluctuations in either “X” or

“Y,” as it will require constant activation of both for a defined period before it becomes activated. However, once the signal causing activation of either “X” or “Y” is removed, expression of “gene z” will be rapidly deactivated, as it requires both inputs. This same motif can also work in the opposite way. Consider that only one of either “X” or “Y” is required for activation of “gene z.” In this case, upon expression of “X,” “gene z” will be quickly expressed. At the same time, “X” will cause production of “Y” which will eventually also enhance “gene z.” When stimulation by either “X” or “Y” is removed, there will be a delay in the deactivation of “gene z” because if “X” is shut down, the presence of “Y” will continue until it is degraded and even if “Y” is shut down, “X” can still be active. Thus, shutting off this motif requires absence of both inputs for a defined length of time but will otherwise be able to filter out brief fluctuations in “X” and “Y” and maintain stable levels of “gene z.” In contrast, the incoherent feed-forward motif has been shown to accelerate the response time of the target [39]. Consider the motif in Fig. 2E in which transcription factor “X” initially induces the expression of “gene z.” At the same time, transcription factor “X” induces the expression of transcription factor “Y.” Once “Y” reaches the repressive threshold of “gene z,” it will begin to repress “gene z.” In the case that “Y” does not entirely repress its target, “gene z” will become expressed at a steady-state level, balanced by activation through “X” and repression through “Y.” Mathematical and experimental models demonstrate that this will occur approximately three times more rapidly than in an unregulated system [39]. Note that the function of this motif is similar to the autoregulatory motif except that the effects are felt upon the “gene z” target that is not necessarily a transcription factor itself.


Mathematical models of various feed-forward motifs can be found in Mangan et al. [37], Wall et al. [36], and Mangan et al. [39].

2.1.4 Single-Input Motif

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stabilization acquired at each stage, the initial signal is no longer required to propagate the cascade [40].

3 Core Transcriptional Regulators in ES Cells

A single-input motif occurs when a transcription factor regulates a group of target genes. These target genes are not regulated by any other transcription factor. The predicted function of this motif is to allow coordinated expression of a group of genes with a common function [27]. A more refined function of this motif is to create a temporal cascade of target gene expression, defined by the activation threshold for each target gene. Considering the motif in Fig. 1E, as the concentration of “F” rises over time, it will cross the activation threshold of each target gene – x, y, and z, turning on its targets in a defined order. Alternatively, if “F” is a repressor, as “F” rises over time, the repressive threshold of each target will be crossed, turning off its targets in a defined order. This system is important to prevent protein production before it is necessary and also to coordinate developmental processes, for example controlling the activation of the endomesodermal zygotic control apparatus in sea urchin [40].

Considerable work has been performed to identify and characterize transcription factors crucial for ESC fate decisions. Because most of this work has been on targeted, singlegene studies, it has not been possible to recognize network motifs in the context of all possible network connections, as they have been defined in lower organisms. However, each observed transcriptional interaction can be placed into one of these predefined motifs. Assuming that these motifs are conserved across species and are the building blocks of all transcriptional networks, it is possible to infer the biological function of these interactions in maintaining the identity of the ESC. Studies on a genome-wide scale are required to confirm that the above described motifs are in fact conserved in ESC transcriptional networks. Genome-wide studies supporting this will be discussed in the next section, but first we will describe the detailed understanding of key transcriptional interactions maintaining ESC pluripotency.

2.1.5 Multi-Input Motif

3.1 Oct4 and Sox2

A multi-input motif, shown in Fig. 1F, occurs when a set of transcription factors all control a set of target genes. Generally, the targets will all co-ordinate an essential cellular function [27]. It has been proposed that this motif allows for the regulation of a set of genes under different biological conditions, depending on which transcription factors are present [28].

Oct4 (Pou5f1) is a POU-domain transcription factor expressed exclusively in totipotent and pluripotent ES cells and germ cells [41]. It has a highly conserved role in maintaining pluripotent cell populations [42, 43] and its expression level dictates ESC fate [16]. If Oct4 expression remains within ±50% of normal diploid expression, the ESC will maintain the undifferentiated phenotype. If Oct4 expression is increased above 150%, ESCs will differentiate into primitive endoderm and mesoderm and finally, if Oct4 expression is reduced below 50%, cells will dedifferentiate into the trophectoderm lineage. Oct4-null embryos die at the blastocyst stage because although they form the trophoblast lineage, they cannot form the inner cell mass [42]. OCT4 is known to bind an octomeric sequence, ATGCAAAT, and associates with HMG-containing transcription factor, SOX2, which binds to a neighboring sox element [41]. It is proposed that the OCT4-SOX2 complex is necessary to co-operatively activate target genes which contain a sox-oct regulatory element [44, 45]. Genes with regulatory regions containing sox-oct elements include Fgf4, Utf1, Lefty1, Fbxo15, and Nanog. In addition, OCT4 regulates the transcription of Sox2 and the OCT4-SOX2 complex activates both Oct4 and Sox2 expression [46–48]. This autoregulatory interaction is shown in Fig. 3A.

2.1.6 Transcriptional Cascades A transcriptional cascade, such as shown in Fig. 1G, occurs when one transcription factor binds to the promoter of a second transcription factor, which in turn binds a third transcription factor and so on. This cascade passes a signal on a slow timescale, perhaps one cell generation at each cascade step making it an appropriate system for executing a developmental program. Developmental programs often use repressor cascades which can be more robust to noise in protein-production rates than those of activator cascades. For example, in sea urchin development, a cascade of transcription factors is activated. Each is stabilized by association with an autoregulatory or feedback loop and then proceeds to activate the next gene in the cascade. Eventually, through the

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Fig. 3 ESC-specific transcriptional networks. (A) OCT4 and SOX2 function in a complex to activate many targets genes. Activation of Oct4 and Sox2 themselves constitute one important ESC autoregulatory motif. (B) SALL4 and NANOG function in a complex to activate many targets genes while SALL4 alone activates Oct4. Activation of Nanog and Sall4 themselves constitutes another important ESC autoregulatory motif. (C) An integrated pluripotency network showing the intricate assembly of autoregulatory motifs controlling Oct4, Nanog, Sox2, and Sall4 expression. Black dots represent binding of a complex (either the NANOG/SALL4 or OCT4/SOX2 complex) to the indicated promoter. Gata6 and Cdx2 are repressed by NANOG and OCT4/SOX2, respectively, while also participating in autoregulatory loops to enhance their own expression once activated

In more recent work, Sox2-null ESCs were used to show that although Sox2 is essential for the maintenance of pluripotency, it was not essential for the activation of the sox-oct elements in several predicted targets, as its presence could be compensated for by other Sox factors: SOX4, SOX11, or SOX15. Microarray analysis following induction of Sox2 loss showed up-regulation of negative regulators of Oct4 (Nr2f2) and differentiation inducers (Eomes and Esx1), and down-regulation of positive regulators (Nr5a2) suggesting that the essential function of Sox2 may be to regulate the expression of Oct4 through regulation of these other transcription factors [49].

3.2 Nanog Nanog is a homeobox-containing transcription factor expressed prior to blastocyst formation in the inner cells of the morula and then restricted to the inner cell mass in the blastocyst. Nanog expression is not detectable at implantation but reappears in the proximal epiblast at E6 and then remains restricted to the epiblast [50]. Forced overexpression of Nanog maintains pluripotency and OCT4 levels in ESCs, even in the absence of LIF [17, 18]. Nanog expression is regulated through a sox-oct element in its proximal promoter [50, 51]. Conflicting results report that this element is either

occupied by OCT4 and SOX2 [50] or OCT4 and some other sox-element binding transcription factor [51]. The Nanog promoter also contains a NANOG-bound region, but it has not been shown that this association is functional [52].

3.3 Sall4 Sall4 is a spalt-like zinc finger transcription factor identified through mass spectrometry analysis as interacting with NANOG [53]. SALL4 functionally binds to an upstream enhancer of Nanog. NANOG and SALL4 bind to an intronic enhancer of Sall4 [52]. In addition, SALL4 binds to many genomic sites that are also known to be bound by NANOG [53]. This suggests that there exists a NANOG-SALL4 complex that functions to activate transcription, similar to the OCT4-SOX2 complex. Sall4 is essential for ESC self-renewal as well as being involved in many developmental processes. In human, SALL4 mutations have been linked to Okihiro syndrome, characterized by limb deformities and eye movement abnormalities and Sall4-null mice are embryonic lethal following a failure to properly form the epiblast [54]. Outgrowths of Sall4-deficient embryos show that Sall4 is essential for the expansion of the inner cell mass, but is dispensable for trophectoderm development. Despite attempts, it has not been


possible to generate Sall4-null ESCs [54] but in one study, Sall4 shRNA knockdown experiments in ESCs resulted in reduced proliferation due to decreased in S-phase and increased G-1 phase, but no altered colony morphology, Oct4 expression or up-regulation of differentiation markers. In another study [55], Sall4 was targeted because of the similarity between its expression and Oct4 expression during early embryonic development [56]. Contrary to the previous study, shRNA knockdown ofSall4 led to reductions in the expression of Oct4, Nanog, Sox2, increased colony differentiation and up-regulation of trophectodermal markers. Overexpression of Sall4 led to the specific up-regulation of Oct4, Nanog, and Sox2. It is important to note that the second shRNA study was performed in feeder-free conditions, while the first study was performed in the presence of feeders, suggesting that factors produced by the feeder population were compensating for the loss of Sall4, masking the effect on differentiation. Finally, there is a direct interaction between SALL4 and the Oct4 promoter that is responsible for the activation of Oct4 in ESCs [55]. These data suggest that Sall4 functions to both regulate the expression of Oct4, preventing the aberrant development of trophectoderm and to regulate the expression of Nanog, while also forming a complex with NANOG to activate additional downstream targets. These interactions are summarized in Fig. 3B.

3.4 Zfp206 Zfp206 is a SCAN-zinc finger transcription factor originally identified because of its highly specific expression in undifferentiated ESCs [57]. It is rapidly down-regulated upon treatment with differentiation agent retinoic acid (RA) and overexpression and shRNA knockdown studies show that it has a positive influence on the ability of the ESC to remain undifferentiated [57], even following treatment with RA [58]. In in vitro experiments, ZFP206 is both able to enhance its own transcription and also enhance the transcription of Oct4 and Nanog [58], although it has not been established that ZFP206 acts directly on the promoters of either Oct4 or Nanog. It has also been shown that the first intron of Zfp206 is directly bound by both OCT4 and SOX2 through a sox-oct element that confers transcriptional activation of Zfp206 [52, 59, 60].

3.5 Zic3 Zic3 is a Gli-family zinc finger transcription factor first studied in ESCs because its promoter region was found to be occupied by OCT4, NANOG, and SOX2 in both mouse and

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human ESCs [52, 60]. It is highly expressed in undifferentiated ESCs and its expression is down-regulated following induction of retinoic acid (RA) differentiation [61]. Zic3 is dynamically expressed during the embryonic development of mouse [62], Xenopus [63], chick [64], and zebrafish [65]. In mouse it is primarily involved in the development of ectoderm and mesoderm during gastrulation [66], during neurulation and is exclusively expressed in the cerebellum of the adult [62]. In Zic3 mutant mice, left-right patterning is disrupted [67], 50% of null animals die in utero and 30% die perinatally due to congenital heart defects, pulmonary isomerism, and CNS defects [68]. These data suggest that Zic3 is an important mediator of mesoderm and ectoderm development. In ESCs, shRNA knockdown of Oct4 or Sox2 reduced Zic3 transcripts to 25% of control and knockdown of Nanog reduced Zic3 to 70% of the control. Likewise, overexpression of Nanog sustained Zic3 expression during RA differentiation, together suggesting that observed OCT4, SOX2, and NANOG promoter occupancy may result in functional regulation [61]. Colony-forming assays with Zic3 shRNA and overexpression cells show that it has a positive influence on pluripotency, however, Oct4, Sox2, and Nanog show very little change in expression in the Zic3 knockdowns, suggesting that the effect of Zic3 is downstream of these key pluripotency factors [61]. Finally, Zic3 knockdown, both transient and stable, induced expression of a panel of endodermal markers (Sox17, PDGFRA, Gata6, Gata4, Foxa2) while having no effect on markers for mesoderm, ectoderm, or trophectoderm [61]. This suggests that the specific role of Zic3 in undifferentiated ESCs is to repress expression of endodermal genes, thus positively influencing the development of mesoderm and ectoderm in the developing embryo.

3.6 Induced Pluripotent Stem (iPS) Cells Three groups reported that it is possible to induce mouse fibroblasts, either embryonic or adult tail-tip, to acquire pluripotent characteristics including chimera formation and, in one case, germline transmission through the overexpression of only four transcription factors: Oct4, Sox2, Klf4, and c-Myc [69–71]. Given the importance of Oct4 and Sox2 in maintaining pluripotency, their involvement is not surprising, but Klf4 and c-Myc must also play crucial roles in either setting the stage for pluripotency or for maintaining it. Interestingly, ectopic expression of Nanog was not required as Nanog expression was induced by reprogramming. c-Myc is a basic helix-loop-helix transcription factor that is a well-known accelerator of cell cycle [72]. It is a direct target of STAT3 and overexpression can maintain self-renewal of ESCs in the absence of LIF [73]. Its role in

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reprogramming may be due to its association with histone acetyltransferase (HAT) complexes [74]. By inducing global histone acetylation, the chromatin structure may be opened so that Oct4 and Sox2 can access necessary targets. Klf4 is a zinc-finger containing transcription factor that has been characterized as an oncogene [75] and has many cell cycle targets [76]. Klf4 has been shown to repress p53 [77], which may be beneficial for reprogramming considering evidence that p53 suppresses Nanog [78]. It has also been shown that KLF4 cooperates with OCT4 and SOX2 to activate at least one target gene, Lefty1, and may similarly activate other Oct4-target genes [79]. Mice derived from iPS cells developed tumors with age, likely due to the overexpression of the oncogenes c-Myc and Klf4 [80].

3.7 Transcription Factors Regulating Commitment to Specific Lineages Since down-regulation of Oct4 leads to trophectoderm specification and down-regulation of Nanog leads to extra-embryonic or primitive endoderm specification, it has been proposed that one mechanism by which ESCs prevent commitment is through the repression of transcription factors that drive lineage specific differentiation. Thus far, very few direct transcriptional interactions have been identified. Cdx2 is a caudal-type homeodomain transcription factor that is specifically expressed in the trophectoderm at the blastocyst stage [81]. Cdx2 homozygous mutant mice die around the time of implantation due to a failure to maintain the blastocoel [82, 83]. While it is possible to generate ESCs from Cdx2−/− embryos [84], indicating that the ICM is not affected, it is not possible to form trophectoderm stem (TS) cells [85]. Activation of Cdx2 results in differentiation towards the trophectoderm lineage [86] and loss of CDX2 leads to aberrant expression of Oct4 and Nanog outside the cells of the ICM. These data indicate that CDX2 is required for the formation of the trophoblast lineage and is also required for the restriction of Oct4 and Nanog to the ICM. It has also been reported that Cdx2 is autoregulated and that OCT4 and CDX2 interact with one another to form a repressive complex that acts to shut down Cdx2 expression is ESCs and Oct4 expression in trophectoderm [86]. Gata6 is a zinc-finger transcription factor expressed in extraembryonic and primitive endoderm lineages [87]. Gata6 null homozygous mice die between embryonic day 5.5 and 7.5 due to defects in visceral endoderm formation [88]. The activation of Gata6 is sufficient for the induction of ESCs to the extraembryonic endoderm lineage [89]. This is similar to the phenotype observed following downregulation of Nanog, leading to the suggestion that ESCs prevent endodermal specification through Nanog-mediated


repression of Gata6 [90]. There is currently no direct evidence to support this suggestion.

4 Genome-Wide Analyses to Extend Transcriptional Networks In general, traditional molecular, genetic and developmental biology approaches are indispensable for confirming, defining and fully characterizing specific interactions but will not be sufficient to reveal the global architecture of the pluripotency network. One problem so far is that all the associated pluripotency transcription factors have been identified through their interaction with one of either Oct4 or Nanog. Remaining questions that must be addressed on a genomewide scale include: (1) What is the nature of the targets of OCT4, NANOG and SOX2 in ESCs? (2) Are there be additional regulators of pluripotency working outside the Oct4, Nanog, Sox2 network? (3) What are the overall mechanisms of pluripotency in ESCs? Genome-wide strategies currently being employed in ESC research include gene expression studies, with both microarray and high-throughput sequencing, protein-protein interaction studies such as mass spectrometry, and promoter occupancy studies using chromatin immunoprecipitation (ChIP) combined with either promoter array analysis (ChIP-chip) or high-throughput sequencing (ChIP-PET). Each of these techniques can yield valuable information about the overall structure of ESC transcriptional networks.

4.1 Microarray Expression Studies Expression analysis can be studied either along the time course of an important biological event, such as ESC commitment to differentiation [91, 92], or following perturbation of a specific transcription factor of interest by shRNA knockdown or overexpression [92, 93]. The idea is to identify genes that are up or down-regulated in response to the changing conditions. Time course studies are more likely to identify new transcriptional targets involved in the process under study, while genome-wide expression analysis following genetic perturbation of known transcription factors will identify downstream targets of that transcription factor. 4.1.1 Common Stem Cell Genes Initial microarray studies tested the hypothesis that genes commonly expressed between different stem cell populations (ESCs, hematopoietic stem cells, and neural stem cells) would constitute a set of genes that defined stem cell identity [94, 95]. Despite testing the same three types of stem cells


and using controls to eliminate the identification of housekeeping genes, there was an almost nonexistent correlation between the lists of genes generated by these two groups, demonstrating that it is not possible to characterize all stem cells with a common list of genes.

4.1.2 Undifferentiated Vs. Differentiated Instead, it may be more useful to identify the differences in expression between undifferentiated and differentiated populations of ESCs. This has been studied in mouse and human ESCs using large-scale EST sequencing [96, 97]. In this strategy, ESTs over-represented or under-represented in undifferentiated ESCs compared to differentiated populations were identified. The differentiated populations in one study were: (1) day 8 embryoid bodies (EBs), (2) day 5 under DMSO differentiation (known to differentiate towards hepatocytes), and (3) 7 days under RA differentiation (known to differentiate towards neuroectoderm). The second study compared undifferentiated hESCs and mESCs to (1) day 4 mouse EBs, and (2) day 12 human EBs. The major contribution of these studies was to identify differences between signalling pathways essential to mESC and hESC growth. In terms of using this data to extend transcriptional networks, one study, for example, successfully identified, Oct4, Sox2, Znf206 (Zfp206 in mouse), and Zic3 but not other important pluripotency factors, such as Nanog [96]. This suggests that the cell populations under study were still too divergent to be able to identify the critical factors influencing pluripotency. Forming traditional EBs introduces large gene expression changes due to cell-cell interactions and microenvironmental stimuli within the EB. In addition, the time points studied were too far along their respective differentiation pathways. Given the large number of genes that would have shown differential expression between these populations, it was impossible to identify the subtle changes occurring during the initial commitment decision. Construction of network connections requires observation of step-by-step changes in gene expression that can be directly associated with the change in the regulating transcription factor.

4.1.3 Time Courses of Differentiation Thus, a third strategy is to identify the genes that are dynamically regulated during differentiation of ESCs by performing microarrays at incremental time points throughout the early stages of differentiation. Each time point represents a snapshot of gene expression that can be used to construct the temporal profile of transcriptional repression or activation of each transcription factor in the system. This strategy has been attempted in mouse ESCs [91, 92].

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One study employed RA induced differentiation of mouse ESCs in monolayer culture for six days with a microarray performed at one-day intervals [92]. Genes whose expression changed by at least 2-fold were hypothesized to be regulators of ESC fate. This study revealed three genes, Tbx3, Esrrb, and Tcl1, which were shown to have previously unknown effects on ESC pluripotency. This group also performed microarrays on shRNA knockdowns and overexpressors for Oct4, Nanog, Sox2 as well as Tbx3, Esrrb, and Tcl1. Analysis of genes regulated following these manipulations reveal that OCT4 represses trophectodermal genes, NANOG represses genes of endoderm, trophectoderm and epliblast origin, SOX2 represses trophectoderm and epiblast genes, ESRRB and TBX3 repress mesoderm, endoderm, and neural crest genes, and TCL1 represses a subset of neural crest genes. A second study employed two modes of differentiation, one following the simple withdrawal of LIF and the other following the withdrawal of LIF supplemented by RA. Mouse ESCs were cultured in monolayer for five days with microarrays performed at two-day intervals [91]. Genes whose expression changed gradually and consistently under both differentiation scenarios were hypothesized to be the regulators of ESC fate. Importantly for the construction of transcriptional networks, a step-wise reduction in levels of Oct4, Nanog, and Sox2 expression in the LIF time course was observed, although in the RA time course these genes were already dramatically depleted by the second day of culture. These step-wise changes facilitated the construction of a temporal cascade of transcription factors, outlining the order in which these factors are activated or repressed. In general, down-regulated genes were enriched for transcription factors and chromatin remodeling proteins while the up-regulated genes were enriched for transcription factors responsible for differentiation to all germ layers.

4.1.4 shRNA Knockdown and Overexpression A strategy to more directly assess targets of key transcription factors is to perform expression microarray studies on shRNA and overexpression constructs of those key transcription factors. In general, if a gene is up-regulated in the knockdown, it is predicted that the transcription factor was responsible for its repression in the undifferentiated state. Conversely, if a gene is down-regulated, it is predicted that the transcription factor was responsible for its activation. Likewise, if a gene is up-regulated or down-regulated in the overexpressor, it will be predicted that the transcription factor was responsible for its activation or repression, respectively. Knockdown experiments have been done for Oct4, Nanog, and Sox2 as well as other predicted pluripotency genes [91,

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92, 98–102]. Knockdown of these key genes results in expression changes in over 1000 genes. In general, it has been confirmed that Oct4 knockdown results in up-regulation of a trophectodermal markers while Nanog knockdown results in up-regulation of endodermal markers [98, 99]. In addition, many regulated genes are involved in epigenetics and chromatin remodelling, further suggesting this as important mediator of stem cell pluripotency [100]. This gives important clues as to the nature of the downstream targets of the pluripotency genes. An improvement on this technique is to combine expression analysis of knockdowns and overexpressors to determine genes that are oppositely regulated, as was done to predict the targets of OCT4 [93] and ZFP206 [57]. The limitation of this approach is that microarray expression analysis cannot distinguish between direct and indirect regulation.

4.2 Promoter Occupancy Studies Another strategy to identify new regulators of ESC fate decisions is to study the promoter occupancy of known regulators of pluripotency. Two major studies have taken advantage of the established importance of the transcription factors OCT4, NANOG, and SOX2. The hypothesis is that genes bound by any of these three factors, and especially in combination, would be the additional key regulators in ESCs [52, 60]. In human ESCs, chromatin immunoprecipitation experiments for OCT4, NANOG and SOX2 were combined with promoter array analysis [60]. The arrays used for this study contained oligonucleotide probes ranging from 8 kb upstream to 2 kb downstream of the start site of 17,917 annotated human genes, at a resolution of 60 bases [60]. OCT4, NANOG, and SOX2 co-occupied many target genes. This may be expected considering the evidence that OCT4 and SOX2 co-operate to activate target genes, but interestingly, NANOG was also present on the promoters of over 90% of target genes co-occupied by OCT4 and SOX2. In addition, these three factors were often found in close proximity to one another, suggesting that they may function together. OCT4, NANOG, and SOX2 occupied the promoters of both transcriptionally active and inactive genes, suggesting that they can act as activators or repressors. Active genes occupied by all three included several known pluripotency regulators, including Oct4, Nanog, Sox2, Stat3, and Zic3. Gene ontology (GO) analysis revealed that many of the inactive genes were transcription factors involved in development of all three germ layers. In mouse ESCs, chromatin immunoprecipitation for OCT4 and NANOG was combined with high throughput sequencing of DNA fragments, thus achieving an unbiased, genome-wide survey of OCT4 and NANOG binding [52]. In general, binding sites frequently occurred much further


from the start site of genes than was studied in the human experiments, suggesting that the human dataset only constitutes a portion of the total number of binding sites. There were a large number of genes co-occupied by OCT4 and NANOG, including Oct4, Sox2, and Nanog. Additionally, the predominant transcription factor binding motif occupied by OCT4 included the sox element (approximately 70%), supporting evidence that OCT4 targets genomic sequences in co-operation with SOX2, through the sox-oct element. Rif1 and Essrb were identified as two downstream targets of OCT4 and NANOG that were reduced following shRNA knockdown of Oct4 and Nanog and also displayed altered colony phenotypes when knocked down in ESCs.

4.3 Future Directions: Systematic Integration of Expression and Promoter Occupancy Data Expression analysis and promoter occupancy studies are the two prominent genome-wide methods for determining transcriptional interactions. Despite the enormous amount of data generated from the studies discussed above, there has been little progress towards constructing a comprehensive transcriptional network controlling ESC fate decisions. Part of the difficulty lies in the limitations of using either of these experimental techniques in isolation. The limitation of expression analysis of either a time course or following perturbation of a specific gene is that it is impossible to determine which genes are direct targets and which are downstream or indirect targets. In addition, there will likely be a large number of false positive predictions of genes that are changing due to secondary effects such as culture conditions. Promoter occupancy studies do demonstrate a direct biochemical interaction between a transcription factor and the promoters of all genes available on the microarray chip. One major complication is that even if a gene can be bound by a transcription factor, it may not necessarily be functionally regulated. In addition, it may not be functional under the studied conditions. This could be due, for example, to low affinity of the binding site, absence of critical co-factors or unfavorable spatial orientation of the chromatin. Another issue is that the regulatory regions controlling genes in the mammalian system are very large and can be 5 , 3 , or even intergenic [52]. Thus, to capture the complete profile of binding sites for a given transcription factor, a truly genome-wide experimental technique must be employed. For some transcription factors, for which the transcription factor binding site is known, it is possible to use a purely bioinformatic approach to determine predicted binding sites by searching


the genome for these conserved binding site motifs, but this approach will unavoidably identify many more sites than are regulated, or even accessible. The next logical step in extending ESC transcriptional networks is to combine expression data following perturbations in a system of interest, with genome-wide promoter occupancy studies, as has been done in yeast [101, 102]. The strictest criteria, designed to eliminate false positive predictions, would be to only accept interactions that included both binding data and expression correlation. Ensuring both direct biochemical interaction and regulation of expression provides more confidence in the identification of a functional transcriptional interaction. To achieve complete characterization, the functional targets of each expressed transcription factor must be defined. In this way, the basic transcriptional control of each gene can be appreciated. Two studies have employed this strategy to narrow down the list of OCT4, NANOG, and SOX2 targets. The first used expression analysis of Oct4 and Nanog shRNA knockdown cell lines to determine which targets found by ChIP-PET were regulated [52]. A total of 776 genes were found to be bound by OCT4, and 4711 genes were regulated followed by Oct4 knockdown but only 394 were both bound and regulated. In the same study, 1802 genes were bound by NANOG, 2264 were regulated following knockdown of Nanog but only 475 were both bound and regulated. A second study combined a time course of ESC differentiation with ChIP-chip and ChIP-PET data for OCT4, NANOG, and SOX2 from published sources to reveal the

Fig. 4 A general model of ESC maintenance. OCT4, NANOG, and SOX2 function to both activate and repress a large set of target genes, including themselves. Activated genes are transcription factors and chromatin remodeling genes that co-operate with pluripotency genes to maintain repression of transcription factors controlling development of tissues derived from all three germ layers

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temporal architecture of gene regulation during early commitment [91]. Many of the regulated genes were directly bound by OCT4, NANOG, and SOX2, but many more were not, revealing that they were either independently regulated or indirectly regulated by OCT4, NANOG, or SOX2. Further, microarray analysis of Oct4 and Sox2 knockdowns was used to confirm that those genes predicted to be both bound and regulated during commitment were regulated following enforced down-regulation of Oct4 or Sox2. Finally, additional ChIP-chip data for two genes that were both bound by OCT4 and NANOG and down-regulated in the time course was incorporated to build two new nodes in the network. Both of these genes were polycomb group (PcG) proteins, known to repress developmental targets through trimethylation of lysine 27 on histone 3. These genes had a set of targets that were commonly bound by OCT4 and NANOG and also upregulated during differentiation. Together, these connections resulted in the prediction of 43 targets of coherent type 3 feed-forward motifs (Fig. 2C), many of which were transcription factors controlling development of specific germ layers. One of these targets was Gata6, whose importance in defining endoderm has been described above. Thus, this integrative approach predicted many new targets that could play similar roles to Cdx2 and Gata6. A third study combined microarray analysis of under and over-expression of Oct4 to determine a list of OCT4 targets [93]. Their data was then combined with published ChIPchip and ChIP-PET studies of OCT4 to distinguish primary targets from secondary or tertiary targets.

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In these studies, the intersection of these two experiments represents a higher confidence dataset of true OCT4 and NANOG targets to construct the first level of a transcriptional network controlling ESCs. The second study reveals how performing analysis on new nodes will expand the transcriptional network and also provide new insights into the overall mechanism of ESC maintenance. These data reveal that the action of OCT4 and NANOG is widespread and acts to both activate pluripotency genes and repress developmental genes.

4.4 A General Model of ESC Maintenance Together, the genome-wide studies surveying both the regulation of genes during commitment and the influences of Oct4, Nanog, and Sox2 have suggested a general model of stem cell maintenance in which OCT4, NANOG, and SOX2 function to both activate and repress a large set of target genes. Activated genes are transcription factors and chromatin remodeling genes while repressed genes are transcription factors controlling development of tissues derived from all three germ layers. Thus, a stem cell remains undifferentiated while expression of Oct4, Nanog, and Sox2 as well as many other transcription factors and chromatin remodeling proteins remain high. The elevated expression of these genes results in the strict repression of developmental regulators. Once perturbed by the signal to differentiate, these factors are down-regulated, gradually releasing their repression and allowing the expression of key developmental regulators (Fig. 4).

5 Conclusions Drafting of a comprehensive transcriptional network controlling ESC fate will require systematic characterisation of the functional targets of each ESC-expressed transcription factor by integration of promoter occupancy data and gene expression changes following targeted perturbations of these transcription factors. Considering that, despite the wealth of data already generated, a complete evaluation of the yeast transcriptional network is still not complete, this undertaking in ESCs in monumental. Fortunately, the emergence of improved technologies such as high-throughput sequencing and whole-genome tiling arrays will allow researchers to capture the high-resolution, genome-wide information needed to build accurate network connections. The structure of the emerging network and the identification of relevant network motifs will help identify the transcriptional mechanisms by which ESCs maintain pluripotency and control fate decisions. Understanding the


transcriptional control of pluripotency will enable deliberate and intelligent manipulation of ESCs for therapeutic uses.

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