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

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.

[1]  Fangting Li,et al.  Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise , 2014, PloS one.

[2]  R. Young,et al.  Stem Cells, the Molecular Circuitry of Pluripotency and Nuclear Reprogramming , 2008, Cell.

[3]  Mary Anne Wheeler,et al.  Stem , 1985 .

[4]  Periklis Pantazis,et al.  Oct4 kinetics predict cell lineage patterning in the early mammalian embryo , 2011, Nature Cell Biology.

[5]  Janet Rossant,et al.  Blastocyst lineage formation, early embryonic asymmetries and axis patterning in the mouse , 2009, Development.

[6]  T. Enver,et al.  Forcing cells to change lineages , 2009, Nature.

[7]  Isabel Guerrero,et al.  Cytoneme-mediated cell-to-cell signaling during development , 2013, Cell and Tissue Research.

[8]  H. Kramers Brownian motion in a field of force and the diffusion model of chemical reactions , 1940 .

[9]  Luigi Biancone,et al.  Exosomes/microvesicles as a mechanism of cell-to-cell communication. , 2010, Kidney international.

[10]  Cedric E. Ginestet ggplot2: Elegant Graphics for Data Analysis , 2011 .

[11]  J. Rossant,et al.  Making the blastocyst: lessons from the mouse. , 2010, The Journal of clinical investigation.

[12]  Janet Rossant,et al.  Krüppel-like factor 5 is essential for blastocyst development and the normal self-renewal of mouse ESCs. , 2008, Cell stem cell.

[13]  Kuniya Abe,et al.  Development and Stem Cells Research Article , 2022 .

[14]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[15]  J. Hubbard,et al.  Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model , 2012, Proceedings of the National Academy of Sciences.

[16]  Richard A Young,et al.  Control of the Embryonic Stem Cell State , 2011, Cell.

[17]  Ian Chambers,et al.  The transcriptional foundation of pluripotency , 2009, Development.

[18]  Ling Xu,et al.  MicroRNA transport: A new way in cell communication , 2013, Journal of cellular physiology.

[19]  S. Counce The Strategy of the Genes , 1958, The Yale Journal of Biology and Medicine.

[20]  Albert Goldbeter,et al.  STEM CELLS AND REGENERATION Gata 6 , Nanog and Erk signaling control cell fate in the inner cell mass through a tristable regulatory network , 2014 .

[21]  Janet Rossant,et al.  FGF signal-dependent segregation of primitive endoderm and epiblast in the mouse blastocyst , 2010, Development.

[22]  Samantha A. Morris,et al.  Making a firm decision: multifaceted regulation of cell fate in the early mouse embryo , 2009, Nature Reviews Genetics.

[23]  Hiroshi Ohta,et al.  Autonomic neurocristopathy-associated mutations in PHOX2B dysregulate Sox10 expression. , 2012, The Journal of clinical investigation.

[24]  Sui Huang,et al.  Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. , 2007, Developmental biology.

[25]  T. Sharpee,et al.  Mathematical approaches to modeling development and reprogramming , 2014, Proceedings of the National Academy of Sciences.

[26]  Anna-Katerina Hadjantonakis,et al.  Understanding the Molecular Circuitry of Cell Lineage Specification in the Early Mouse Embryo , 2011, Genes.

[27]  H. Schöler,et al.  Formation of Pluripotent Stem Cells in the Mammalian Embryo Depends on the POU Transcription Factor Oct4 , 1998, Cell.

[28]  J. Ferrell Bistability, Bifurcations, and Waddington's Epigenetic Landscape , 2012, Current Biology.

[29]  Dekun Wang,et al.  Molecular basis of the first cell fate determination in mouse embryogenesis , 2010, Cell Research.

[30]  Tony Pawson,et al.  Early lineage segregation between epiblast and primitive endoderm in mouse blastocysts through the Grb2-MAPK pathway. , 2006, Developmental cell.

[31]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[32]  E. Marco,et al.  Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape , 2014, Proceedings of the National Academy of Sciences.

[33]  Nickolay V. Bukoreshtliev,et al.  Mechanical cues in cellular signalling and communication , 2012, Cell and Tissue Research.

[34]  V. Papaioannou,et al.  Paracrine action of FGF4 during periimplantation development maintains trophectoderm and primitive endoderm , 2003, Genesis.

[35]  J. Royston An Extension of Shapiro and Wilk's W Test for Normality to Large Samples , 1982 .

[36]  Jin Wang,et al.  Quantifying the Waddington landscape and biological paths for development and differentiation , 2011, Proceedings of the National Academy of Sciences.

[37]  N. Stanietsky,et al.  The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.

[38]  Mikael Huss,et al.  Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. , 2010, Developmental cell.

[39]  Sui Huang,et al.  Understanding gene circuits at cell-fate branch points for rational cell reprogramming. , 2011, Trends in genetics : TIG.

[40]  R. de Hertogh,et al.  Control of trophectoderm differentiation by inner cell mass‐derived fibroblast growth factor‐4 in mouse blastocysts and corrective effect of fgf‐4 on high glucose‐induced trophoblast disruption , 2001, Molecular reproduction and development.

[41]  Kim Sneppen,et al.  Theory for the stability and regulation of epigenetic landscapes , 2010, Physical biology.

[42]  Lutz Brusch,et al.  Predicting Pancreas Cell Fate Decisions and Reprogramming with a Hierarchical Multi-Attractor Model , 2011, PloS one.

[43]  Qiang Zhang,et al.  A deterministic map of Waddington's epigenetic landscape for cell fate specification , 2011, BMC Systems Biology.

[44]  Jacob G Foster,et al.  A model of sequential branching in hierarchical cell fate determination. , 2009, Journal of theoretical biology.

[45]  Supratim Choudhuri,et al.  From Waddington’s epigenetic landscape to small noncoding RNA: some important milestones in the history of epigenetics research , 2011, Toxicology mechanisms and methods.

[46]  A. Goldbeter,et al.  Gata6, Nanog and Erk signaling control cell fate in the inner cell mass through a tristable regulatory network , 2014, Development.

[47]  R. Lovell-Badge,et al.  Multipotent cell lineages in early mouse development depend on SOX2 function. , 2003, Genes & development.

[48]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[49]  Matt Thomson,et al.  Pluripotency Factors in Embryonic Stem Cells Regulate Differentiation into Germ Layers , 2011, Cell.

[50]  Sui Huang Reprogramming cell fates: reconciling rarity with robustness , 2009, BioEssays : news and reviews in molecular, cellular and developmental biology.

[51]  Jin Wang,et al.  Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation , 2013, Journal of The Royal Society Interface.

[52]  Jean-Claude Hervé,et al.  Gap-junction-mediated cell-to-cell communication , 2012, Cell and Tissue Research.

[53]  Julianne D. Halley,et al.  A General Model for Binary Cell Fate Decision Gene Circuits with Degeneracy: Indeterminacy and Switch Behavior in the Absence of Cooperativity , 2011, PloS one.

[54]  S. Severini,et al.  Cellular network entropy as the energy potential in Waddington's differentiation landscape , 2013, Scientific Reports.

[55]  Hossein Baharvand,et al.  Concise Review: Alchemy of Biology: Generating Desired Cell Types from Abundant and Accessible Cells , 2011, Stem cells.

[56]  Hernán A Makse,et al.  Modularity map of the network of human cell differentiation , 2010, Proceedings of the National Academy of Sciences.

[57]  Hans R. Schöler,et al.  Establishment of totipotency does not depend on Oct4A , 2013, Nature Cell Biology.

[58]  Jin Wang,et al.  Quantifying Cell Fate Decisions for Differentiation and Reprogramming of a Human Stem Cell Network: Landscape and Biological Paths , 2013, PLoS Comput. Biol..