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• W2950557054 abstract "Resource27 November 2018Open Access Transparent process A common molecular logic determines embryonic stem cell self-renewal and reprogramming Sara-Jane Dunn Sara-Jane Dunn orcid.org/0000-0002-5964-1043 Microsoft Research, Cambridge, UK Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK Search for more papers by this author Meng Amy Li Meng Amy Li orcid.org/0000-0002-6619-9919 Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK Search for more papers by this author Elena Carbognin Elena Carbognin orcid.org/0000-0001-6591-6915 Department of Molecular Medicine, University of Padua, Padua, Italy Search for more papers by this author Austin Smith Corresponding Author Austin Smith [email protected] orcid.org/0000-0002-3029-4682 Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK Department of Biochemistry, University of Cambridge, Cambridge, UK Search for more papers by this author Graziano Martello Corresponding Author Graziano Martello [email protected] orcid.org/0000-0001-5520-085X Department of Molecular Medicine, University of Padua, Padua, Italy Search for more papers by this author Sara-Jane Dunn Sara-Jane Dunn orcid.org/0000-0002-5964-1043 Microsoft Research, Cambridge, UK Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK Search for more papers by this author Meng Amy Li Meng Amy Li orcid.org/0000-0002-6619-9919 Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK Search for more papers by this author Elena Carbognin Elena Carbognin orcid.org/0000-0001-6591-6915 Department of Molecular Medicine, University of Padua, Padua, Italy Search for more papers by this author Austin Smith Corresponding Author Austin Smith [email protected] orcid.org/0000-0002-3029-4682 Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK Department of Biochemistry, University of Cambridge, Cambridge, UK Search for more papers by this author Graziano Martello Corresponding Author Graziano Martello [email protected] orcid.org/0000-0001-5520-085X Department of Molecular Medicine, University of Padua, Padua, Italy Search for more papers by this author Author Information Sara-Jane Dunn1,2,‡, Meng Amy Li2,‡, Elena Carbognin3, Austin Smith *,2,4 and Graziano Martello *,3 1Microsoft Research, Cambridge, UK 2Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK 3Department of Molecular Medicine, University of Padua, Padua, Italy 4Department of Biochemistry, University of Cambridge, Cambridge, UK ‡These authors contributed equally to this work *Corresponding author. Tel: +44 1223 760233; E-mail: [email protected] *Corresponding author. Tel: +39 049 8276088; E-mail: [email protected] The EMBO Journal (2019)38:e100003https://doi.org/10.15252/embj.2018100003 See also: OJL Rackham & JM Polo (January 2019) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract During differentiation and reprogramming, new cell identities are generated by reconfiguration of gene regulatory networks. Here, we combined automated formal reasoning with experimentation to expose the logic of network activation during induction of naïve pluripotency. We find that a Boolean network architecture defined for maintenance of naïve state embryonic stem cells (ESC) also explains transcription factor behaviour and potency during resetting from primed pluripotency. Computationally identified gene activation trajectories were experimentally substantiated at single-cell resolution by RT–qPCR. Contingency of factor availability explains the counterintuitive observation that Klf2, which is dispensable for ESC maintenance, is required during resetting. We tested 124 predictions formulated by the dynamic network, finding a predictive accuracy of 77.4%. Finally, we show that this network explains and predicts experimental observations of somatic cell reprogramming. We conclude that a common deterministic program of gene regulation is sufficient to govern maintenance and induction of naïve pluripotency. The tools exemplified here could be broadly applied to delineate dynamic networks underlying cell fate transitions. Synopsis While a minimal interaction network of transcription factors recapitulates naïve state maintenance of embryonic stem cells, our understanding of the logic controlling acquisition of pluripotency remains fragmentary. In this study, an iterative, computational-experimental approach reveals that a common network governs both maintenance and installation of naïve pluripotency. Formal verification of Boolean network dynamics uncovers the network logic governing induction of naïve pluripotency from murine primed epiblast stem cells (EpiSC) and somatic cells. Model predictions identify factors to enhance or be required for reprogramming. Dual combinations of factors act synergistically and sequentially to enhance EpiSC resetting. Network architecture and update rules predict gene activation dynamics substantiated by single-cell gene expression analysis. Individual cells follow a deterministic trajectory in the final stage of productive reprogramming. Introduction Over the last 10 years, a multitude of protocols have been developed that allow the conversion of one cell type into another (Graf & Enver, 2009). Most of these strategies rely on the forced expression of transcription factors (TFs) highly expressed by the target cell type that have either been chosen empirically or, recently, with the aid of computational tools such as CellNet or Mogrify (Cahan et al, 2014; Rackham et al, 2016; Radley et al, 2017). Despite the large amount of transcriptomic data available for such conversions, our understanding of the dynamics and logic followed by cells during reprogramming and transdifferentiation remains fragmentary. The most studied cell fate transition is the generation of murine-induced pluripotent stem cells (iPSCs) from somatic cells (Takahashi & Yamanaka, 2006). Bona fide iPSCs are, like murine embryonic stem cells (ESCs), competent to form blastocyst chimaeras and are considered to occupy a state of naïve pluripotency similar to that in the pre-implantation embryo (Nichols & Smith, 2009; Boroviak et al, 2015). This unique identity is determined by a self-reinforcing interaction network of TFs. Experimental and computational efforts have led to circuitry mapping of the core TF program that maintains ESC self-renewal under defined conditions (Chen et al, 2008; Niwa et al, 2009; MacArthur et al, 2012; Dunn et al, 2014; Herberg & Roeder, 2015; Rue & Martinez Arias, 2015; Yachie-Kinoshita et al, 2018). We previously applied a mathematical and computational modelling approach based on automated formal reasoning to elucidate the dynamic regulatory network architecture for self-renewing mouse ESCs (Dunn et al, 2014; Yordanov et al, 2016). A minimal interaction network of 12 components was found to recapitulate a large number of observations concerning naïve state maintenance and successfully predicted non-intuitive responses to compound genetic perturbations (Dunn et al, 2014). Forced expression of several components of this core TF network in various cell types leads to a state of induced pluripotency (Takahashi & Yamanaka, 2006; Nakagawa et al, 2007; Silva et al, 2008; Feng et al, 2009; Hanna et al, 2009; Han et al, 2010a; Buganim et al, 2012; Tang et al, 2012; O'Malley et al, 2013; Stuart et al, 2014; Sone et al, 2017). Accumulating evidence suggests that cells progress through defined stages, with a final transition entailing the hierarchical activation and stabilisation of the naïve pluripotency TF network (Mikkelsen et al, 2008; Silva et al, 2009; Han et al, 2010b; Samavarchi-Tehrani et al, 2010; Buganim et al, 2012; Golipour et al, 2012; Di Stefano et al, 2013, 2016; O'Malley et al, 2013; Tanabe et al, 2013). However, it is not clear if cells undergoing successful conversion follow a deterministic trajectory of gene activation, defined by the naïve pluripotency TF network architecture, or if genes are activated in random sequence. A tractable experimental system with which to investigate activation of naïve pluripotency is the resetting of post-implantation epiblast stem cells (EpiSCs; Guo et al, 2009). EpiSCs are related to gastrulation stage epiblast (Kojima et al, 2014; Tsakiridis et al, 2014). They represent a primed state of pluripotency, developmentally downstream of the naïve state and unable to contribute substantially to blastocyst chimaeras (Nichols & Smith, 2009). EpiSCs exhibit distinct growth factor dependency, transcriptional and epigenetic regulation compared to ESCs. They self-renew when cultured in defined media containing FGF2 and ActivinA (F/A) and lack significant expression of most functionally defined naïve pluripotency factors (Brons et al, 2007; Tesar et al, 2007; Guo et al, 2009). EpiSC resetting proceeds over 6–8 days, much faster than somatic cell reprogramming, and entails primarily the activation and consolidation of the naïve pluripotency identity (Hall et al, 2009; Festuccia et al, 2012; Gillich et al, 2012; Martello et al, 2013). In addition, EpiSC resetting does not require a complex reprogramming cocktail. The activation of Jak/Stat3 signalling (Han et al, 2010a; Yang et al, 2010; Bernemann et al, 2011a) or forced expression of a single naïve TF factor (Guo et al, 2009; Silva et al, 2009; Han et al, 2010a) is sufficient to mediate reprogramming in combination with dual inhibition (2i) of the Erk pathway and glycogen synthase kinase-3 (GSK3; Ying et al, 2008). In this study, we undertook an iterative computational and experimental approach to test the hypothesis that a common network is sufficient to govern both naïve state maintenance and induction. Focusing on EpiSC resetting, we investigated whether naïve state induction follows an ordered sequence of network component activation. By refining our understanding of the network governing this process, we sought to delineate transcription factors crucial for the execution of EpiSC resetting, and identify synergistic combinations that accelerate resetting kinetics. Finally, we extended the approach to investigate whether the same network architecture is operative in somatic cell reprogramming. Results Deriving a set of network models consistent with EpiSC resetting We previously studied the TF network controlling maintenance of naïve pluripotency through a combined computational and experimental approach (Dunn et al, 2014). Our methodology is based on the definition of relevant network components derived from functional studies in the literature, and the identification of “possible” interactions between these components (Fig 1A). Possible interactions are inferred based on gene expression correlation using the Pearson coefficient as a metric (Materials and Methods) and are used to define a set of alternative concrete Boolean network models, each with unique topology. We refer to this set of concrete models as an Abstract Boolean Network (ABN). This formalism allows us to navigate some of the uncertainty in the interactions that may exist between network components, which can arise due to noisy or conflicting data. We then define a set of experimental results, such as the effect of genetic perturbations, which serve as constraints to identify those models from the ABN that recapitulate expected behaviour. The Reasoning Engine for Interaction Networks (RE:IN, www.research.microsoft.com/rein) is software based on automated formal reasoning, developed to synthesise only those concrete models that are provably consistent with the experimental constraints (Dunn et al, 2014; Yordanov et al, 2016). The set of consistent models is defined as a constrained Abstract Boolean Network (cABN), which is subsequently used to generate predictions of untested molecular and cellular behaviour. Our approach differs from typical modelling strategies in that we do not generate a single network model, but rather a set of models, which individually are consistent with known behaviours. We formulate predictions of untested behaviour only when all models agree, such that predictions are consistent with the limits of current understanding. This is important because different network models can recapitulate the same experimental observations, and one should not be prioritised over another. Whenever predictions are falsified by new experimental results, it is possible to refine the cABN by incorporating the new findings as additional constraints (Fig 1A). The refined cABN is then used to generate further predictions. Figure 1. Network models consistent with naïve state maintenance predict the effect of TF forced expression in resetting from primed pluripotency. See also Appendix Figs S1 and S2 Flowchart describing the methodology. Network components were identified based on functional studies from the literature, and possible interactions between components defined based on pairwise gene expression correlation. A set of experimental results served as constraints. The software RE:IN synthesises all possible interaction networks consistent with the constraints, which is termed the cABN. The cABN is used to formulate predictions to be tested experimentally. Importantly, predictions do not overlap with imposed constraints. If predictions are falsified, the cABN can be further refined by incorporating new experimental results as constraints. The refined cABN is used to generate further predictions. cABN derived from a Pearson coefficient threshold of 0.832, consistent with constraints previously defined for ESC self-renewal (Dunn et al, 2014). Solid arrow, required interaction; dashed arrow, possible interaction; black arrow, activation; red arrow, inhibition. There is no regulation hierarchy associated with component positioning. Illustration of EpiSC resetting constraints. See Appendix Fig S1F. Predicted number of regulation steps required for all models to stabilise in the naïve state under forced expression of a single network component. The red dashed line indicates the number of steps required under empty vector control. Fold increase of Oct4-GFP+ colony number over control under forced expression of individual factors. n ≥ 5, where each dot indicates an independent experiment. Box plots show median, 1st and 3rd quartile values. One-sample Wilcoxon test P-values are as indicated, with P < 0.05 shown in red. cABN derived from a Pearson coefficient threshold of 0.782. Predictions from the 0.782 cABN. Light green regions indicate where some, but not all, concrete networks allow stable conversion to the naïve state. Sall4 is indicated in green, as this was imposed as a constraint and therefore is not a model prediction. Download figure Download PowerPoint For the present study, we first refined the cABN describing maintenance of naïve pluripotency by adding further expression profiles generated using RNA sequencing and RT–qPCR to the five datasets used previously to infer possible interactions (Dunn et al, 2014) and by using an updated version of RE:IN (Yordanov et al, 2016; Materials and Methods). A Pearson correlation threshold of 0.832 was sufficient to define an ABN consistent with observations of maintenance of naïve pluripotency (Appendix Fig S1A–C). We identified required and disallowed interactions from this ABN to define the 0.832 cABN (Fig 1B), which we subsequently tested against new gene perturbation experiments in mouse ESCs (Appendix Fig S1D) and observed a significant increase in prediction accuracy over the previous version (Dunn et al, 2014). We therefore used the 0.832 cABN as the starting point for analysis of EpiSC resetting. We asked whether the naïve state maintenance cABN is consistent with experimental observations of EpiSC resetting. To this end, we exploited GOF18 EpiSCs, which are susceptible to resetting in 2i+LIF in the absence of transgenes (Han et al, 2010a). In accordance with the Boolean modelling formalism, we discretised gene expression patterns of the network components for the initial (GOF18 EpiSC) and final (naïve state ESC) states, such that each gene is High/Low in each case (Appendix Fig S1E and Materials and Methods). We defined a set of six constraints based on experimental observations of when EpiSC resetting can or cannot be achieved (Fig 1C, Appendix Fig S1F and Materials and Methods). For example, one constraint specifies that if a given cell has none of the naïve pluripotency factors initially expressed, then 2i+LIF alone is not sufficient to induce the naïve state (Fig 1C, top arrow). In contrast, resetting can be achieved if the initial state is equivalent to GOF18 EpiSCs, which express Oct4, Sox2 and Sall4 (Fig 1C, third arrow from the top). We found that these additional constraints were satisfied by the naïve state maintenance cABN, which suggests that a common network may control both maintenance and induction of naïve pluripotency. The number of concrete models in the 0.832 cABN is in the order of 105. As a control, we randomly generated 10,000 models with the same number of components and possible interactions. None of these models could satisfy the entire set of constraints. Indeed, if interactions with a Pearson correlation of at least 0.5 are chosen randomly, the probability of generating the 0.832 ABN is of the order 10−31. This indicates that the data-driven approach facilitated identification of meaningful interactions between network components, and in practical terms substantially reduced the compute time for subsequent analyses. To test the requirement for each component in the cABN, we explored the consequence of deleting individual TFs from the network and constraints (Materials and Methods). Deleting 8 of the TFs made the initial constraints unsatisfiable. Only removal of Esrrb could be tolerated, but with substantially reduced number and accuracy of predictions. Therefore, the models are highly sensitive to all components of the cABN. Prediction of resetting potency for individual network components The dynamics of the concrete networks in the cABN were determined by a synchronous update scheme: from a given initial state, each and every component updates its state in response to its upstream regulators at each step (see Materials and Methods). Accordingly, we could examine the sequence of activation of each component along the trajectory towards the naïve state. RE:IN can be used to determine the number of regulation steps required by all models to reach the naïve state. This can be used as a metric to study the resetting process (Materials and Methods). Spontaneous GOF18 EpiSC resetting can be enhanced by expression of naïve network factors such as Klf2 (Hall et al, 2009; Gillich et al, 2012; Qiu et al, 2015), and such resetting events, measured by reporter activation, often possess faster activation kinetics than control (Gillich et al, 2012). The GOF18 EpiSC line contains a transgenic GFP reporter driven by the upstream regulatory region of Pou5f1 (commonly known as Oct4). This transgene does not behave as endogenous Oct4. It is active in ESCs but only in a rare subpopulation of EpiSCs. Therefore, it serendipitously allows the live monitoring of EpiSC to ESC conversion (Han et al, 2010a). We hypothesised that enhanced EpiSC resetting upon naïve factor expression may be due to accelerated network activation. We sought to test this computationally by determining the number of regulation steps required for all concrete models of the cABN to stabilise in the naïve state in 2i+LIF, with or without Klf2 transgene expression. The 0.832 cABN predicted that forced expression of Klf2 in GOF18 EpiSCs results in the network stabilising in the naïve state in only three steps, compared with five steps for transgene-free control (Appendix Fig S2A). Experimentally, we confirmed that transient Klf2 expression induced Oct4-GFP+ colony formation earlier than empty vector control and led to higher colony number throughout 10 days of EpiSC resetting time course (Appendix Fig S2B; Gillich et al, 2012). Thereafter, we assumed that the number of Oct4-GFP+ colonies obtained reflected EpiSC resetting dynamics and used this as an experimental output to compare with computational predictions. We predicted the effect of forced expression of each network component using the 0.832 cABN (Fig 1D). The predictions indicated that expression of all factors except Tbx3 and Sox2 would lead to stabilisation in the naïve state in fewer steps than control, indicating that most network components could enhance EpiSC resetting. For example, when Esrrb is introduced, all concrete models predicted full activation of the naïve network by Step 2, compared to Step 5 for control. To test these predictions experimentally, we generated expression constructs for each factor by cloning the cDNA into an identical vector backbone and transiently transfected GOF18 EpiSCs 1 day prior to initiating resetting in 2i+LIF. We measured the relative efficiency between different components by the fold increase of Oct4-GFP+ colonies formed at Day 7 over empty vector control (Fig 1E, Appendix Fig S2C). While some factors, such as Sall4 and Oct4, had no significant effect over control, others, notably Esrrb, Klf2 and Klf4, showed a robust enhancement. The computational predictions showed a similar trend to the experimental results, with seven out of eleven cases correctly predicted (Fig 1D, Appendix Fig S2D). Predictions for Tbx3, Stat3 and Oct4 transgene expression were incorrect. Most strikingly, Sall4 was predicted to be one of the most efficient factors, but was found to be the least efficient experimentally. The iterative nature of our approach (Fig 1A) allows the refinement of the cABN in the light of new experimental results that were predicted incorrectly. We encoded the experimental observation that Sall4 expression was no more efficient than control as an additional constraint (Materials and Methods). Satisfying the new constraint together with the original set required additional possible interactions, which were identified by lowering the Pearson coefficient threshold (Fig 1F). The new threshold, 0.782, was the highest to define a cABN that satisfied the updated experimental constraints. We then generated a new set of predictions for single factor forced expression (note that forced expression of Sall4 is encoded as a constraint therefore is not used to make a prediction). In each case, we observed a range of steps for which some concrete models predicted stabilisation in the naïve state, while others did not (Fig 1G, light green). However, predictions can only be formulated when all concrete models are in agreement (Fig 1G, dark green). Therefore, forced expression of Esrrb, Klf4, Gbx2, Klf2 or Tfcp2l1 was predicted to be more efficient than control, in agreement with the experimental results shown in Fig 1E (see also Appendix Fig S2D). For forced expression of Nanog, Tbx3, Stat3 and Sox2, overlap of the light green regions with control prevented definitive predictions (Fig 1G). To resolve this uncertainty, we formally tested in silico whether expressing a given factor would be more efficient than control for every concrete model. This resulted in the correct predictions that Nanog was always at least, or more efficient than control, while Stat3, Sox2 and Oct4 were not (Appendix Fig S2D). The strategy did not generate a prediction for Tbx3 because some concrete models generated different kinetics to others. We extended the test to perform a pairwise comparison of all genes to delineate the relative efficiency of individual factors (Appendix Fig S2E). Predictions could be formulated for 37 out of 55 possible comparisons. Of these, 22 were supported experimentally, while 9 were incorrect. For the remaining 6, the experimental results showed a trend in agreement with the predictions, although without reaching statistical significance due to variability in the naïve colony number between independent experiments. Appendix Fig S2F summarises all significant pairwise comparisons with experimental support. Delineating the sequence of network activation The 0.782 cABN accurately predicted the effect of forced expression of naïve components on EpiSC resetting, which suggests that resetting is not a random process. We therefore asked if resetting occurs via a precise sequence of gene activation, and whether this could also be identified using the cABN. We investigated whether a defined sequence of gene activation was common to all concrete models, or whether individual models transition through unique trajectories. We focussed on those genes expressed at low levels in GOF18 EpiSCs, to enable unequivocal detection of activation over time in population-based measurements. To predict the sequence of gene activation during EpiSC resetting, we examined the number of regulation steps required for each gene to be permanently activated in 2i+LIF without transgene expression (Fig 2A). The 0.782 cABN predicts that Stat3 and Tfcp2l1 are the first to be activated, at Steps 1 and 2, respectively, while Gbx2, Klf4 and Esrrb are activated last, at Steps 6 and 7. The wide range of step values for permanent Tbx3 activation predicted by different concrete models within the cABN (Fig 2A, light blue region) prevented a definitive prediction in this case. Figure 2. Models predict the sequence of gene activation during resetting to naïve pluripotency Model predictions of the number of regulation steps required for permanent activation of each network component. Light blue regions indicate where only some, while dark blue regions indicate that all concrete networks predict that the given gene has permanently activated. Heatmap of average gene expression normalised to β-actin over an EpiSC resetting time course in 2i+LIF. Each row is coloured according to the unique minimum and maximum for that gene. The values shown are average expression of four independent experiments. Gene expression for Stat3, Klf2, Esrrb and Tfcp2l1 during EpiSC resetting relative to established mouse ESCs. β-actin serves as an internal control. Mean ± SEM, n = 4 independent experiments. *P < 0.05 Student's t-test. Left: Local network topology for Tfcp2l1 and Esrrb. Right: Summary of regulation conditions required by Tfcp2l1 and Esrrb in the 0.782 cABN. Download figure Download PowerPoint To test these predictions, we measured the expression of each gene over the EpiSC resetting time course in 2i+LIF for up to 4 days (Fig 2B and C). We defined gene activation to be an upregulated expression level that is statistically significant over EpiSCs. As predicted, Stat3 was significantly induced as early as 2 h after 2i+LIF induction, Tfcp2l1 after 8 h, while Klf4, Esrrb and Tbx3 only became detectable between 48 and 96 h. In contrast to the predictions, Klf2 was significantly increased after only 1 h of 2i+LIF treatment. Tfcp2l1 and Esrrb are direct targets of the LIF/Stat3 and CH/Tcf3 axes (Martello et al, 2012, 2013; Ye et al, 2013; Qiu et al, 2015). However, even though CH and LIF were applied simultaneously to initiate resetting, Tfcp2l1 and Esrrb displayed distinct activation kinetics. We hypothesised that the local regulation topology of these two components may affect the timing of their activation. We therefore examined all immediate upstream regulators of Tfcp2l1 and Esrrb, and the logical update rules that define the conditions under which each component becomes active (Fig 2D). Tfcp2l1 had six upstream activators, of which Stat3 and Esrrb were definite, and one inhibitor, Tcf3. Esrrb had three definite activators, Sall4, Nanog and Tfcp2l1, as well as a definite and an optional inhibitor. The computational methodology defines a set of alternative update rules, referred to as regulation conditions, that span the possible scenarios under which a target can be activated (Materials and Methods; Yordanov et al, 2016). In the same manner in which some possible interactions were found to be required or disallowed when experimental constraints were applied to the ABN, certain regulations conditions were also found to be used or unused in order to satisfy the constraints. We compared the subset of regulation conditions assigned to Tfcp2l1 and Esrrb across all concrete models in the cABN, and one key difference emerged. While Tfcp2l1 required only one of its potential activators (Stat3, Esrrb, Tbx3, Gbx2, Klf2 or Klf4) to activate expression, Esrrb required the presence of all activators (Nanog, Tfcp2l1, Sall4; Fig 2D). Since Stat3 was activated after 1 h in response to 2i+LIF, early activation of Tfcp2l1 could therefore be attributed to Stat3. Esrrb would necessarily only be activated after activation of Tfcp2l1. This local topology analysis therefore provides a network explanation accounting for the rapid activation of Tfcp2l1 (8 h, Fig 2B) and the delayed activation of Esrrb (48 h). Combinations of factors can enhance EpiSC resetting Earlier studies have shown that forced expression of a combination of factors can synergistically enhance resetting efficiency (Yang et al, 2010; Gillich et al, 2012; Qiu et al, 2015). Our computational approach enabled us to investigate the effect of factor combinations in a systematic manner. We focused on those factors found to be potent inducers when expressed individually: Klf4, Klf2, Esrrb, Tbx3 and Tfcp2l1 (Fig 1E). Six out of seven combinations were predicted to reduce the number of regulation steps required to" @default.
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• W2950557054 title "A common molecular logic determines embryonic stem cell self‐renewal and reprogramming" @default.
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