Pluripotency maintenance mechanism of embryonic stem cells and reprogramming
ABSTRACT Embryonic stem (ES) cells are derived from blastocysts and are pluripotent. This pluripotency has attracted the interest of numerous researchers, both to expand our fundamental understanding of developmental biology and also because of potential applications in regenerative medicine. Systems biological studies have demonstrated that the pivotal transcription factors form a network. There they activate pluripotency-associated genes, including themselves, while repressing the developmentally regulated genes through co-occupation with various protein complexes. The chromatin structure characteristic of ES cells also contributes to the maintenance of the network. In this review, I focus on recent advances in our understanding of the transcriptional network that maintains pluripotency in mouse ES cells.
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ABSTRACT: Embryonic stem cells (ESC) have the capacity to self-renew and remain pluripotent, while continuously providing a source of a variety of differentiated cell types. Understanding what governs these properties at the molecular level is crucial for stem cell biology and its application to regenerative medicine. Of particular relevance is to elucidate those molecular interactions which govern the reprogramming of somatic cells into ESC. A computational approach can be used as a framework to explore the dynamics of a simplified network of the ESC with the aim to understand how stem cells differentiate and also how they can be reprogrammed from somatic cells. We propose a computational model of the embryonic stem cell network, in which a core set of transcription factors (TFs) interact with each other and are induced by external factors. A stochastic treatment of the network dynamics suggests that NANOG heterogeneity is the deciding factor for the stem cell fate. In particular, our results show that the decision of staying in the ground state or commitment to a differentiated state is fundamentally stochastic, and can be modulated by the addition of external factors (2i/3i media), which have the effect of reducing fluctuations in NANOG expression. Our model also hosts reprogramming of a committed cell into an ESC by over-expressing OCT4. In this context, we recapitulate the important experimental result that reprogramming efficiency peaks when OCT4 is over-expressed within a specific range of values. We have demonstrated how a stochastic computational model based upon a simplified network of TFs in ESCs can elucidate several key observed dynamical features. It accounts for (i) the observed heterogeneity of key regulators, (ii) characterizes the ESC under certain external stimuli conditions and (iii) describes the occurrence of transitions from the ESC to the differentiated state. Furthermore, the model (iv) provides a framework for reprogramming from somatic cells and conveys an understanding of reprogramming efficiency as a function of OCT4 over-expression.BMC Systems Biology 08/2012; 6:98. DOI:10.1186/1752-0509-6-98 · 2.85 Impact Factor
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ABSTRACT: To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.PLoS ONE 11/2012; 7(11):e49086. DOI:10.1371/journal.pone.0049086 · 3.53 Impact Factor
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ABSTRACT: In the last few years recent evidence has revealed that inside an apparently homogeneous cell population there indeed appears to be heterogeneity. This is particularly true for embryonic stem (ES) cells where markers of pluripotency are dynamically expressed within the single cells. In this work we have designed and tested a new set of primers for multiplex PCR detection of pluripotency markers expression, and have applied it to perform single cell analysis in murine ES cells cultured on three different substrates that could play an important role in controlling cell behaviour and fate: i) mouse embryonic fibroblast (MEF) feeder layer, as the standard method for ES cells culture; ii) Matrigel coating; iii) micropatterned hydrogel. Compared to population analyses, using a single cell approach we were able to evaluate not only the number of cells that maintained the expression of a specific gene but, most importantly, how many cells co-expressed different markers. We found that micropatterned hydrogel seems to represent a good alternative to MEF, since the expression of stemness markers is better preserved than in Matrigel culture. This single cell assay allows for the assessment of the stemness maintenance at single cell level in terms of gene expression profile, and can be applied in stem cell research to characterize freshly isolated and cultured cells, or to standardize, for instance, the method of culture closely linked to the transcriptional activity and the differentiation potential. This article is protected by copyright. All rights reserved.Biology of the Cell 09/2013; DOI:10.1111/boc.201300034 · 3.87 Impact Factor