Increased robustness of early embryogenesis through collective decision-making by key transcription factors

Abstract

BackgroundUnderstanding the mechanisms by which hundreds of diverse cell types develop from a single mammalian zygote has been a central challenge of developmental biology. Conrad H. Waddington, in his metaphoric “epigenetic landscape” visualized the early embryogenesis as a hierarchy of lineage bifurcations. In each bifurcation, a single progenitor cell type produces two different cell lineages. The tristable dynamical systems are used to model the lineage bifurcations. It is also shown that a genetic circuit consisting of two auto-activating transcription factors (TFs) with cross inhibitions can form a tristable dynamical system.ResultsWe used gene expression profiles of pre-implantation mouse embryos at the single cell resolution to visualize the Waddington landscape of the early embryogenesis. For each lineage bifurcation we identified two clusters of TFs – rather than two single TFs as previously proposed – that had opposite expression patterns between the pair of bifurcated cell types. The regulatory circuitry among each pair of TF clusters resembled a genetic circuit of a pair of single TFs; it consisted of positive feedbacks among the TFs of the same cluster, and negative interactions among the members of the opposite clusters. Our analyses indicated that the tristable dynamical system of the two-cluster regulatory circuitry is more robust than the genetic circuit of two single TFs.ConclusionsWe propose that a modular hierarchy of regulatory circuits, each consisting of two mutually inhibiting and auto-activating TF clusters, can form hierarchical lineage bifurcations with improved safeguarding of critical early embryogenesis against biological perturbations. Furthermore, our computationally fast framework for modeling and visualizing the epigenetic landscape can be used to obtain insights from experimental data of development at the single cell resolution.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0169-8) contains supplementary material, which is available to authorized users.

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“…This type of regulatory switches (Figure 1.a) are so sensitive to mutations and perturbations that directly affect the cell fate in the population. It suggests designing more complex regulatory networks consisting of a pair of clusters, with multiple elements in each, to determine the final cell fate [36]. It is revealed that these hypothesized clusters existed in biological regulatory circuitries through a literature review [65][66][67][68][69][70][71][72][73][74][75][76][77][78].…”

“…The extended switch results in more robustness against perturbations. The buffering effect is achieved by presence of more elements and the positive feed-backs in each cluster [36]. It is expected that this effect would be even stronger in more complex switches, which is in agreement with the Waddington's idea of "canalisation" in [79]: "canalisations are more likely to appear when there are many cross links between the various processes, that is to say when the rate of change of any one variable is affected by the concentrations of many of the other variables".…”

“…Cellular regulatory networks are known to play a crucial role in adjusting the decision-making mechanism by considering the effects from permanent intrinsic noise associated with living cells.Such regulatory networks have been studied extensively in a variety of organisms spanning from viruses to mammals [20]. These networks are known to control decision making from viruses [26][27][28] to bacteria [29][30][31][32], yeast [33] and human embryonic stem cells [34][35][36][37][38].…”

“…iv) the decision bias in the internal switch is determined by model parameters representing interactions between the switch elements [10]. v) a switch with more contributing components would be more stable against environmental fluctuations [10,36].…”

“…Inspired by previous studies that revealed the impact of regulatory networks on the stability of biological systems [11,14,27,33,41,[55][56][57][58], here, we introduce a tristable switch described by a set of ordinary differential equations (ODEs) which is a formal framework to study the regulatory circuitries [36,59]. The evolution of our inherently stochastic system is simulated by the Gillespie algorithm.…”