From Understanding the Development Landscape of the Canonical Fate-Switch Pair to Constructing a Dynamic Landscape for Two-Step Neural Differentiation

Abstract Recent progress in stem cell biology, notably cell fate conversion, calls for novel theoretical understanding for cell differentiation. The existing qualitative concept of Waddington’s “epigenetic landscape” has attracted particular attention because it captures subsequent fate decision points, thus manifesting the hierarchical (“tree-like”) nature of cell fate diversification. Here, we generalized a recent work and explored such a developmental landscape for a two-gene fate decision circuit by integrating the underlying probability landscapes with different parameters (corresponding to distinct developmental stages). The change of entropy production rate along the parameter changes indicates which parameter changes can represent a normal developmental process while other parameters’ change can not. The transdifferentiation paths over the landscape under certain conditions reveal the possibility of a direct and reversible phenotypic conversion. As the intensity of noise increases, we found that the landscape becomes flatter and the dominant paths more straight, implying the importance of biological noise processing mechanism in development and reprogramming. We further extended the landscape of the one-step fate decision to that for two-step decisions in central nervous system (CNS) differentiation. A minimal network and dynamic model for CNS differentiation was firstly constructed where two three-gene motifs are coupled. We then implemented the SDEs (Stochastic Differentiation Equations) simulation for the validity of the network and model. By integrating the two landscapes for the two switch gene pairs, we constructed the two-step development landscape for CNS differentiation. Our work provides new insights into cellular differentiation and important clues for better reprogramming strategies.

[1]  R. Thom.  Quelques propriétés globales des variétés différentiables , 1954 .

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

[3]  S. Kauffman.  Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[4]  S. Kauffman.  Homeostasis and Differentiation in Random Genetic Control Networks , 1969, Nature.

[5]  H. Weintraub,et al.  Expression of a single transfected cDNA converts fibroblasts to myoblasts , 1987, Cell.

[6]  Michael T. Turvey,et al.  Thermodynamic Reasons for Perception--Action Cycles , 1991 .

[7]  W. Ebeling.  Stochastic Processes in Physics and Chemistry , 1995 .

[8]  François Guillemot,et al.  Proneural genes and the specification of neural cell types , 2002, Nature Reviews Neuroscience.

[9]  E. Jablonka,et al.  The Changing Concept of Epigenetics , 2002, Annals of the New York Academy of Sciences.

[10]  P. Ao.  Stochastic Force Defined Evolution in Dynamical Systems , 2003 .

[11]  Arjen Van Ooyen,et al.  Modeling neural development , 2003 .

[12]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[13]  P Ao,et al.  LETTER TO THE EDITOR: Potential in stochastic differential equations: novel construction , 2004 .

[14]  T. Graf,et al.  Stepwise Reprogramming of B Cells into Macrophages , 2004, Cell.

[15]  C.J.H. Mann,et al.  Modeling Neural Development , 2004 .

[16]  D. Thouless,et al.  Structure of stochastic dynamics near fixed points. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Alfonso Martinez Arias,et al.  Filtering transcriptional noise during development: concepts and mechanisms , 2006, Nature Reviews Genetics.

[18]  P. Ao,et al.  Laws in Darwinian evolutionary theory , 2005, q-bio/0605020.

[19]  L. Yin,et al.  Existence and construction of dynamical potential in nonequilibrium processes without detailed balance , 2006 .

[20]  J. Barker,et al.  Identification of a Novel Oligodendrocyte Cell Adhesion Protein Using Gene Expression Profiling , 2006, The Journal of Neuroscience.

[21]  S. Yamanaka,et al.  Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors , 2006, Cell.

[22]  R. Jaenisch,et al.  In vitro reprogramming of fibroblasts into a pluripotent ES-cell-like state , 2007, Nature.

[23]  P. Ao,et al.  On the existence of potential landscape in the evolution of complex systems , 2007, Complex..

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

[25]  Jin Wang,et al.  Potential landscape and flux framework of nonequilibrium networks: Robustness, dissipation, and coherence of biochemical oscillations , 2008, Proceedings of the National Academy of Sciences.

[26]  C. Please,et al.  Stochasticity and the Molecular Mechanisms of Induced Pluripotency , 2008, PloS one.

[27]  J. Kohyama,et al.  Identification of genes that restrict astrocyte differentiation of midgestational neural precursor cells , 2008, Neuroscience.

[28]  Joachim Klose,et al.  Transcriptome and proteome analysis of early embryonic mouse brain development , 2008, Proteomics.

[29]  Hannah H. Chang,et al.  Transcriptome-wide noise controls lineage choice in mammalian progenitor cells , 2008, Nature.

[30]  Erkang Wang,et al.  Robustness, dissipations and coherence of the oscillation of circadian clock: potential landscape and flux perspectives , 2008, PMC biophysics.

[31]  Jiuhong Kang,et al.  Enhanced efficiency of generating induced pluripotent stem (iPS) cells from human somatic cells by a combination of six transcription factors , 2008, Cell Research.

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

[33]  Carsten Peterson,et al.  Computational Modeling of the Hematopoietic Erythroid-Myeloid Switch Reveals Insights into Cooperativity, Priming, and Irreversibility , 2009, PLoS Comput. Biol..

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

[35]  Jianhua Xing,et al.  Mapping between dissipative and Hamiltonian systems , 2009, 0908.4526.

[36]  Hannah H. Chang,et al.  Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours , 2009, Nature Reviews Genetics.

[37]  P. Ao.  Global view of bionetwork dynamics: adaptive landscape. , 2009, Journal of genetics and genomics = Yi chuan xue bao.

[38]  Jin Wang,et al.  Kinetic paths, time scale, and underlying landscapes: a path integral framework to study global natures of nonequilibrium systems and networks. , 2010, The Journal of chemical physics.

[39]  Thomas Vierbuchen,et al.  Direct conversion of fibroblasts to functional neurons by defined factors , 2010, Nature.

[40]  Ping Ao,et al.  Constructive Proof of Global Lyapunov Function as Potential Function , 2010, 1012.2721.

[41]  Sui Huang,et al.  The potential landscape of genetic circuits imposes the arrow of time in stem cell differentiation. , 2010, Biophysical journal.

[42]  James Sharpe,et al.  An atlas of gene regulatory networks reveals multiple three-gene mechanisms for interpreting morphogen gradients , 2010, Molecular systems biology.

[43]  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.

[44]  B. Lim,et al.  A novel strategy to derive iPS cells from porcine fibroblasts , 2011, Science China Life Sciences.

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

[46]  Y. Liu,et al.  A brief review on current progress in neuroscience in China , 2011, Science China Life Sciences.

[47]  Stéphanie Boué,et al.  Methods for making induced pluripotent stem cells: reprogramming à la carte , 2011, Nature Reviews Genetics.

[48]  Sui Huang,et al.  On the intrinsic inevitability of cancer: from foetal to fatal attraction. , 2011, Seminars in cancer biology.

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

[50]  S. Lipton,et al.  Direct reprogramming of mouse fibroblasts to neural progenitors , 2011, Proceedings of the National Academy of Sciences.

[51]  Fabian J Theis,et al.  Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network , 2011, PloS one.

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

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

[54]  J. C. Belmonte,et al.  Dedifferentiation, transdifferentiation and reprogramming: three routes to regeneration , 2011, Nature Reviews Molecular Cell Biology.

[55]  Sui Huang.  The molecular and mathematical basis of Waddington's epigenetic landscape: A framework for post‐Darwinian biology? , 2012, BioEssays : news and reviews in molecular, cellular and developmental biology.