How Criticality of Gene Regulatory Networks Affects the Resulting
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Dynamical Gene Regulatory Networks Are Tuned by Transcriptional Autoregulation with Microrna Feedback Thomas G
www.nature.com/scientificreports OPEN Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback Thomas G. Minchington1, Sam Grifths‑Jones2* & Nancy Papalopulu1* Concepts from dynamical systems theory, including multi‑stability, oscillations, robustness and stochasticity, are critical for understanding gene regulation during cell fate decisions, infammation and stem cell heterogeneity. However, the prevalence of the structures within gene networks that drive these dynamical behaviours, such as autoregulation or feedback by microRNAs, is unknown. We integrate transcription factor binding site (TFBS) and microRNA target data to generate a gene interaction network across 28 human tissues. This network was analysed for motifs capable of driving dynamical gene expression, including oscillations. Identifed autoregulatory motifs involve 56% of transcription factors (TFs) studied. TFs that autoregulate have more interactions with microRNAs than non‑autoregulatory genes and 89% of autoregulatory TFs were found in dual feedback motifs with a microRNA. Both autoregulatory and dual feedback motifs were enriched in the network. TFs that autoregulate were highly conserved between tissues. Dual feedback motifs with microRNAs were also conserved between tissues, but less so, and TFs regulate diferent combinations of microRNAs in a tissue‑dependent manner. The study of these motifs highlights ever more genes that have complex regulatory dynamics. These data provide a resource for the identifcation of TFs which regulate the dynamical properties of human gene expression. Cell fate changes are a key feature of development, regeneration and cancer, and are ofen thought of as a “land- scape” that cells move through1,2. Cell fate changes are driven by changes in gene expression: turning genes on or of, or changing their levels above or below a threshold where a cell fate change occurs. -
Self-Organization Principles of Intracellular Pattern Formation
Self-organization principles of intracellular pattern formation J. Halatek. F. Brauns, and E. Frey∗ Arnold{Sommerfeld{Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilians-Universit¨atM¨unchen, Theresienstraße 37, D-80333 M¨unchen,Germany (Dated: February 21, 2018) Abstract Dynamic patterning of specific proteins is essential for the spatiotemporal regulation of many important intracellular processes in procaryotes, eucaryotes, and multicellular organisms. The emergence of patterns generated by interactions of diffusing proteins is a paradigmatic example for self-organization. In this article we review quantitative models for intracellular Min protein patterns in E. coli, Cdc42 polarization in S. cerevisiae, and the bipolar PAR protein patterns found in C. elegans. By analyzing the molecular processes driving these systems we derive a theoretical perspective on general principles underlying self-organized pattern formation. We argue that intracellular pattern formation is not captured by concepts such as \activators", \inhibitors", or \substrate-depletion". Instead, intracellular pattern formation is based on the redistribution of proteins by cytosolic diffusion, and the cycling of proteins between distinct conformational states. Therefore, mass-conserving reaction-diffusion equations provide the most appropriate framework to study intracellular pattern formation. We conclude that directed transport, e.g. cytosolic diffusion along an actively maintained cytosolic gradient, is the key process underlying pattern formation. Thus the basic principle of self-organization is the establishment and maintenance of directed transport by intracellular protein dynamics. Keywords: self-organization; pattern formation; intracellular patterns; reaction-diffusion; cell polarity; arXiv:1802.07169v1 [physics.bio-ph] 20 Feb 2018 NTPases 1 INTRODUCTION In biological systems self-organization refers to the emergence of spatial and temporal structure. -
Visualizing Morphogenesis with the Processing Programming Language 15
Visualizing Morphogenesis with the Processing Programming Language 15 Visualizing Morphogenesis with the Processing Programming Language Avik Patel, Amar Bains, Richard Millet, and Tamira Elul changing tissue shapes. A powerful technique for elucidating the We used Processing, a visual artists’ programming dynamic cellular mechanisms of morphogenesis is time-lapse video- language developed at MIT Media Lab, to simulate microscopic imaging of cells in embryonic tissues undergoing cellular mechanisms of morphogenesis – the generation morphogenesis (Elul and Keller 2000; Elul et al., 1997; Harris et al., of form and shape in embryonic tissues. Based on 1987). The mechanisms underlying morphogenetic processes can be clarified by observation of cell dynamics from these time-lapse video observations of in vivo time-lapse image sequences, we sequences. Morphometric measurement further defines the created animations of neural cell motility responsible for quantitative and statistical relationships between the cell dynamics that elongating the spinal cord, and of optic axon branching underlie morphogenesis (Kim and Davidson 2011; Marshak et al., dynamics that establish primary visual connectivity. 2007; Elul et al., 1997; Witte et al., 1996). Mathematical These visual models underscore the significance of the decomposition of cell dynamics additionally facilitates the computational decomposition of cellular dynamics computational modeling of cellular mechanisms that drive underlying morphogenesis. morphogenesis (Satulovsky et al., 2008). Methods In this paper, we use the Processing programming language to visualize dynamic cell behaviors driving morphogenesis in the Introduction developing nervous system. Based on in vivo time-lapse image sequences, we created models of cell dynamics underlying two Processing is a Java based software language and environment morphogenetic processes in the developing nervous system. -
Innovation and Robustness in Complex Regulatory Gene Networks
Innovation and robustness in complex regulatory gene networks S. Ciliberti*, O. C. Martin*†, and A. Wagner‡§ *Unite´Mixte Recherche 8565, Laboratoire de Physique The´orique et Mode`les Statistiques, Universite´Paris-Sud and Centre National de la Recherche Scientifique, F-91405 Orsay, France; †Unite´Mixte de Recherche 820, Laboratoire de Ge´ne´ tique Ve´ge´ tale, L’Institut National de la Recherche Agronomique, Ferme du Moulon, F-91190 Gif-sur-Yvette, France; and ‡Department of Biochemistry, University of Zurich, Y27-J-54, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland Communicated by Giorgio Parisi, University of Rome, Rome, Italy, June 20, 2007 (received for review November 24, 2006) The history of life involves countless evolutionary innovations, a environmental variation. Mutational robustness means that a steady stream of ingenuity that has been flowing for more than 3 system produces little phenotypic variation when subjected to billion years. Very little is known about the principles of biological genotypic variation caused by mutations. At first sight, such organization that allow such innovation. Here, we examine these robustness might pose a problem for evolutionary innovation, principles for evolutionary innovation in gene expression patterns. because a robust system cannot produce much of the variation To this end, we study a model for the transcriptional regulation that can become the basis for evolutionary innovation. networks that are at the heart of embryonic development. A As we shall see, there is some truth to this appearance, but it genotype corresponds to a regulatory network of a given topol- is in other respects flawed. Robustness and the ability to innovate ogy, and a phenotype corresponds to a steady-state gene expres- cannot only coexist, but the first may be a precondition for the sion pattern. -
Systematic Comparison of Sea Urchin and Sea Star Developmental Gene Regulatory Networks Explains How Novelty Is Incorporated in Early Development
ARTICLE https://doi.org/10.1038/s41467-020-20023-4 OPEN Systematic comparison of sea urchin and sea star developmental gene regulatory networks explains how novelty is incorporated in early development Gregory A. Cary 1,3,5, Brenna S. McCauley1,4,5, Olga Zueva1, Joseph Pattinato1, William Longabaugh2 & ✉ Veronica F. Hinman 1 1234567890():,; The extensive array of morphological diversity among animal taxa represents the product of millions of years of evolution. Morphology is the output of development, therefore phenotypic evolution arises from changes to the topology of the gene regulatory networks (GRNs) that control the highly coordinated process of embryogenesis. A particular challenge in under- standing the origins of animal diversity lies in determining how GRNs incorporate novelty while preserving the overall stability of the network, and hence, embryonic viability. Here we assemble a comprehensive GRN for endomesoderm specification in the sea star from zygote through gastrulation that corresponds to the GRN for sea urchin development of equivalent territories and stages. Comparison of the GRNs identifies how novelty is incorporated in early development. We show how the GRN is resilient to the introduction of a transcription factor, pmar1, the inclusion of which leads to a switch between two stable modes of Delta-Notch signaling. Signaling pathways can function in multiple modes and we propose that GRN changes that lead to switches between modes may be a common evolutionary mechanism for changes in embryogenesis. Our data additionally proposes a model in which evolutionarily conserved network motifs, or kernels, may function throughout development to stabilize these signaling transitions. 1 Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA. -
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B. M ü ller, G ü nter P. Wagner, and Werner Callebaut, editors The Evolution of Cognition , edited by Cecilia Heyes and Ludwig Huber, 2000 Origination of Organismal Form: Beyond the Gene in Development and Evolutionary Biology , edited by Gerd B. M ü ller and Stuart A. Newman, 2003 Environment, Development, and Evolution: Toward a Synthesis , edited by Brian K. Hall, Roy D. Pearson, and Gerd B. M ü ller, 2004 Evolution of Communication Systems: A Comparative Approach , edited by D. Kimbrough Oller and Ulrike Griebel, 2004 Modularity: Understanding the Development and Evolution of Natural Complex Systems , edited by Werner Callebaut and Diego Rasskin-Gutman, 2005 Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution , by Richard A. Watson, 2006 Biological Emergences: Evolution by Natural Experiment , by Robert G. B. Reid, 2007 Modeling Biology: Structure, Behaviors, Evolution , edited by Manfred D. Laubichler and Gerd B. M ü ller, 2007 Evolution of Communicative Flexibility: Complexity, Creativity, and Adaptability in Human and Animal Communication , edited by Kimbrough D. Oller and Ulrike Griebel, 2008 Functions in Biological and Artifi cial Worlds: Comparative Philosophical Perspectives , edited by Ulrich Krohs and Peter Kroes, 2009 Cognitive Biology: Evolutionary and Developmental Perspectives on Mind, Brain, and Behavior , edited by Luca Tommasi, Mary A. Peterson, and Lynn Nadel, 2009 Innovation in Cultural Systems: Contributions from Evolutionary Anthropology , edited by Michael J. O ’ Brien and Stephen J. Shennan, 2010 The Major Transitions in Evolution Revisited , edited by Brett Calcott and Kim Sterelny, 2011 Transformations of Lamarckism: From Subtle Fluids to Molecular Biology , edited by Snait B. -
Guiding Self-Organized Pattern Formation in Cell Polarity Establishment
ARTICLES https://doi.org/10.1038/s41567-018-0358-7 Guiding self-organized pattern formation in cell polarity establishment Peter Gross1,2,3,8, K. Vijay Kumar" "3,4,8, Nathan W. Goehring" "5,6, Justin S. Bois" "7, Carsten Hoege2, Frank Jülicher3 and Stephan W. Grill" "1,2,3* Spontaneous pattern formation in Turing systems relies on feedback. But patterns in cells and tissues seldom form spontane- ously—instead they are controlled by regulatory biochemical interactions that provide molecular guiding cues. The relationship between these guiding cues and feedback in controlled biological pattern formation remains unclear. Here, we explore this relationship during cell-polarity establishment in the one-cell-stage Caenorhabditis elegans embryo. We quantify the strength of two feedback systems that operate during polarity establishment: feedback between polarity proteins and the actomyosin cortex, and mutual antagonism among polarity proteins. We characterize how these feedback systems are modulated by guid- ing cues from the centrosome, an organelle regulating cell cycle progression. By coupling a mass-conserved Turing-like reac- tion–diffusion system for polarity proteins to an active-gel description of the actomyosin cortex, we reveal a transition point beyond which feedback ensures self-organized polarization, even when cues are removed. Notably, the system switches from a guide-dominated to a feedback-dominated regime well beyond this transition point, which ensures robustness. Together, these results reveal a general criterion for controlling biological pattern-forming systems: feedback remains subcritical to avoid unstable behaviour, and molecular guiding cues drive the system beyond a transition point for pattern formation. ell polarity establishment in Caenorhabditis elegans zygotes phase where myosin appears to reach a steady state (Supplementary is a prototypical example for cellular pattern formation that Fig. -
An Automatic Design Framework of Swarm Pattern Formation Based on Multi-Objective Genetic Programming
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming Zhun Fan, Senior Member, IEEE, Zhaojun Wang, Xiaomin Zhu, Bingliang Hu, Anmin Zou, and Dongwei Bao Abstract—Most existing swarm pattern formation methods predetermined trajectory and maintain a specific swarm pattern depend on a predefined gene regulatory network (GRN) structure in the execution of tasks. For example, Jin [11] proposed a that requires designers’ priori knowledge, which is difficult to hierarchical gene regulatory network for adaptive multi-robot adapt to complex and changeable environments. To dynamically adapt to the complex and changeable environments, we propose pattern formation. In this work, the swarm pattern is designed an automatic design framework of swarm pattern formation as a band of circle, which encircles targets in a dynamic based on multi-objective genetic programming. The proposed environments. In the latter case of using adaptive formation, framework does not need to define the structure of the GRN- swarm robots follow an adaptive pattern to encircle targets in based model in advance, and it applies some basic network motifs the environment. For example, Oh et al. [12] have introduced to automatically structure the GRN-based model. In addition, a multi-objective genetic programming (MOGP) combines with an evolving hierarchical gene regulatory network for morpho- NSGA-II, namely MOGP-NSGA-II, to balance the complexity genetic pattern formation in order to generate adaptive patterns and accuracy of the GRN-based model. In evolutionary process, which are adaptable to dynamic environments. an MOGP-NSGA-II and differential evolution (DE) are applied to In addition, swarm pattern formation has been widely ex- optimize the structures and parameters of the GRN-based model plored in recent years, which can be divided into four cate- in parallel. -
Synergism Between Chromatin Dynamics and Gene Transcription
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.17.444405; this version posted May 17, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Synergism between Chromatin Dynamics and Gene Transcription 2 Enhances Robustness and Stability of Epigenetic Cell Memory 3 Zihao Wang1,2, Songhao Luo1,2, Meiling Chen1,2, Tianshou Zhou1,2,*, and Jiajun Zhang1,2,† 4 1 Key Laboratory of Computational Mathematics, Guangdong Province 5 2 School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, P. R. China 6 *[email protected], †[email protected] 7 8 Abstract 9 Apart from carrying hereditary information inherited from their ancestors and being able to pass on 10 the information to their descendants, cells can also inherit and transmit information that is not stored 11 as changes in their genome sequence. Such epigenetic cell memory, which is particularly important 12 in multicellular organisms, involves multiple biochemical modules mainly including chromatin 13 organization, epigenetic modification and gene transcription. The synergetic mechanism among 14 these three modules remains poorly understood and how they collaboratively affect the robustness 15 and stability of epigenetic memory is unclear either. Here we developed a multiscale model to 16 address these questions. We found that the chromatin organization driven by long-range epigenetic 17 modifications can significantly enhance epigenetic cell memory and its stability in contrast to that 18 driven by local interaction and that chromatin topology and gene activity can promptly and 19 simultaneously respond to changes in nucleosome modifications while maintaining the robustness 20 and stability of epigenetic cell memory over several cell cycles. -
Temporal Flexibility of Gene Regulatory Network Underlies a Novel Wing Pattern in Flies
Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies Héloïse D. Dufoura,b, Shigeyuki Koshikawa (越川滋行)a,b,1,2,3, and Cédric Fineta,b,3,4 aHoward Hughes Medical Institute, University of Wisconsin, Madison, WI 53706; and bLaboratory of Molecular Biology, University of Wisconsin, Madison, WI 53706 Edited by Denis Duboule, University of Geneva, Geneva 4, Switzerland, and approved April 6, 2020 (received for review February 3, 2020) Organisms have evolved endless morphological, physiological, and Nevertheless, it does not explain how the new expression of behavioral novel traits during the course of evolution. Novel traits the coopted toolkit genes does not interfere with the develop- were proposed to evolve mainly by orchestration of preexisting ment of the tissue. Some authors have suggested that the reuse genes. Over the past two decades, biologists have shown that of toolkit genes might only happen during late development after cooption of gene regulatory networks (GRNs) indeed underlies completion of the early function of the redeployed genes (21, 22, numerous evolutionary novelties. However, very little is known 29). However, little is known about the properties of a GRN that about the actual GRN properties that allow such redeployment. allow the cooption of one or several of its components/genes Here we have investigated the generation and evolution of the without impairing the development of the tissue. complex wing pattern of the fly Samoaia leonensis. We show that In this study, we use the complex wing pigmentation pattern of the transcription factor Engrailed is recruited independently from the fly species Samoaia leonensis as a model to address how the the other players of the anterior–posterior specification network to generate a new wing pattern. -
Gene Regulatory Networks
Gene Regulatory Networks 02-710 Computaonal Genomics Seyoung Kim Transcrip6on Factor Binding Transcrip6on Control • Gene transcrip.on is influenced by – Transcrip.on factor binding affinity for the regulatory regions of target genes – Transcrip.on factor concentraon – Nucleosome posi.oning and chroman states – Enhancer ac.vity Gene Transcrip6onal Regulatory Network • The expression of a gene is controlled by cis and trans regulatory elements – Cis regulatory elements: DNA sequences in the regulatory region of the gene (e.g., TF binding sites) – Trans regulatory elements: RNAs and proteins that interact with the cis regulatory elements Gene Transcrip6onal Regulatory Network • Consider the following regulatory relaonships: Target gene1 Target TF gene2 Target gene3 Cis/Trans Regulatory Elements Binding site: cis Target TF regulatory element TF binding affinity gene1 can influence the TF target gene Target gene2 expression Target TF gene3 TF: trans regulatory element TF concentra6on TF can influence the target gene TF expression Gene Transcrip6onal Regulatory Network • Cis and trans regulatory elements form a complex transcrip.onal regulatory network – Each trans regulatory element (proteins/RNAs) can regulate mul.ple target genes – Cis regulatory modules (CRMs) • Mul.ple different regulators need to be recruited to ini.ate the transcrip.on of a gene • The DNA binding sites of those regulators are clustered in the regulatory region of a gene and form a CRM How Can We Learn Transcriponal Networks? • Leverage allele specific expressions – In diploid organisms, the transcript levels from the two copies of the genes may be different – RNA-seq can capture allele- specific transcript levels How Can We Learn Transcriponal Networks? • Leverage allele specific gene expressions – Teasing out cis/trans regulatory divergence between two species (WiZkopp et al. -
Nicotinic Receptor Alpha7 Expression During Tooth Morphogenesis Reveals Functional Pleiotropy
Nicotinic Receptor Alpha7 Expression during Tooth Morphogenesis Reveals Functional Pleiotropy Scott W. Rogers1,2*, Lorise C. Gahring1,3 1 Geriatric Research, Education and Clinical Center, Veteran’s Administration, Salt Lake City, Utah, United States of America, 2 Department of Neurobiology and Anatomy, University of Utah School of Medicine, Salt Lake City, Utah, United States of America, 3 Division of Geriatrics, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America Abstract The expression of nicotinic acetylcholine receptor (nAChR) subtype, alpha7, was investigated in the developing teeth of mice that were modified through homologous recombination to express a bi-cistronic IRES-driven tau-enhanced green fluorescent protein (GFP); alpha7GFP) or IRES-Cre (alpha7Cre). The expression of alpha7GFP was detected first in cells of the condensing mesenchyme at embryonic (E) day E13.5 where it intensifies through E14.5. This expression ends abruptly at E15.5, but was again observed in ameloblasts of incisors at E16.5 or molar ameloblasts by E17.5–E18.5. This expression remains detectable until molar enamel deposition is completed or throughout life as in the constantly erupting mouse incisors. The expression of alpha7GFP also identifies all stages of innervation of the tooth organ. Ablation of the alpha7-cell lineage using a conditional alpha7Cre6ROSA26-LoxP(diphtheria toxin A) strategy substantially reduced the mesenchyme and this corresponded with excessive epithelium overgrowth consistent with an instructive role by these cells during ectoderm patterning. However, alpha7knock-out (KO) mice exhibited normal tooth size and shape indicating that under normal conditions alpha7 expression is dispensable to this process.