The Sonic Hedgehog Signaling System As a Bistable Genetic Switch
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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. -
Role of Sonic Hedgehog Signaling Pathway in Neuroblastoma Development
Review Article Biology and Medicine, 1 (4): Rev2, 2009 eISSN: 09748369, www.biolmedonline.com Role of Sonic hedgehog signaling pathway in neuroblastoma development Mehdi Hayat Shahi1,2,3,§, Subrata Sinha2, *Mohammad Afzal3, *Javier S Castresana1 1Unidad de Biologia de Tumoures Cerebrales, Universidad de Navarra, 31008 Pamplona, Spain. 2Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), New Delhi-110029, India. 3Section of Genetics, Department of Zoology, Aligarh Muslim University, Aligarh-202002, India. §Present address: Department of Genetics and Pathology, Rudbeck Laboratory, University of Uppsala, Uppsala- 75185, Sweden. *Corresponding Authors: Javier S Castresana, [email protected] Mohammad Afzal, [email protected] Abstract Malignant transformation of normal cells is a complex and accumulative process. Understanding this event gives insight into mechanisms of developmental biology and physical interaction of cellular machinery with surrounding ambient factors. However, the trend of embryonic malignancy is not interactive with ambient factors, rather a cause of deregulations of internal developmental process. In this review, we have attempted to explore the possibility of Sonic hedgehog role (Shh) in the development of neuroblastoma tumour. It is the major extra cranial tumour and develops in very early stage of childhood. Sonic hedgehog signaling is very well studied in another major childhood tumour i.e. medulloblastoma that contributes 20-25% of childhood tumours, and one-fourth of medulloblastoma is due to abnormality in the Shh signaling pathway. Therefore, we would consider whether Shh could also contribute to the development of neuroblastoma. Although scientists are coming up with the role of Shh in the neuroblastoma, the Sonic hedgehog signaling is very much one of the promising pathways because of its multi-dimensional role not only in CNS development but also in organogenesis and other major tumour development. -
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. -
Sonic Hedgehog Signaling Limits Atopic Dermatitis Via Gli2-Driven Immune Regulation
Sonic Hedgehog signaling limits atopic dermatitis via Gli2-driven immune regulation Eleftheria Papaioannou, … , Ryan F. L. O’Shaughnessy, Tessa Crompton J Clin Invest. 2019. https://doi.org/10.1172/JCI125170. Research Article Immunology Inflammation Hedgehog (Hh) proteins regulate development and tissue homeostasis, but their role in atopic dermatitis (AD) remains unknown. We found that on induction of mouse AD, Sonic Hedgehog (Shh) expression in skin, and Hh pathway action in skin T cells were increased. Shh signaling reduced AD pathology and the levels of Shh expression determined disease severity. Hh-mediated transcription in skin T cells in AD-induced mice increased Treg populations and their suppressive function through increased active transforming growth factor–b (TGF-b) in Tregs signaling to skin T effector populations to reduce disease progression and pathology. RNA sequencing of skin CD4+ T cells from AD-induced mice demonstrated that Hh signaling increased expression of immunoregulatory genes and reduced expression of inflammatory and chemokine genes. Addition of recombinant Shh to cultures of naive human CD4+ T cells in iTreg culture conditions increased FOXP3 expression. Our findings establish an important role for Shh upregulation in preventing AD, by increased Gli-driven Treg cell–mediated immune suppression, paving the way for a potential new therapeutic strategy. Find the latest version: http://jci.me/125170/pdf The Journal of Clinical Investigation RESEARCH ARTICLE Sonic Hedgehog signaling limits atopic dermatitis via Gli2-driven immune regulation Eleftheria Papaioannou,1 Diana C. Yánez,1,2 Susan Ross,1 Ching-In Lau,1 Anisha Solanki,1 Mira Manilal Chawda,1 Alex Virasami,1 Ismael Ranz,3 Masahiro Ono,1,4 Ryan F. -
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. -
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. -
A Computational Model Inspired by Gene Regulatory Networks
Rui Miguel Lourenço Lopes A Computational Model Inspired by Gene Regulatory Networks Doctoral thesis submitted to the Doctoral Program in Information Science and Technology, supervised by Full Professor Doutor Ernesto Jorge Fernandes Costa, and presented to the Department of Informatics Engineering of the Faculty of Sciences and Technology of the University of Coimbra. January 2015 A Computational Model Inspired by Gene Regulatory Networks A thesis submitted to the University of Coimbra in partial fulfillment of the requirements for the Doctoral Program in Information Science and Technology by Rui Miguel Lourenço Lopes [email protected] Department of Informatics Engineering Faculty of Sciences and Technology University of Coimbra Coimbra, January 2015 Financial support by Fundação para a Ciência e a Tecnologia, through the PhD grant SFRH/BD/69106/2010. A Computational Model Inspired by Gene Regulatory Networks ©2015 Rui L. Lopes ISBN 978-989-20-5460-5 Cover image: Composition of a Santa Fe trail program evolved by ReNCoDe overlaid on the ancestors, with a 3D model of the DNA crystal structure (by Paul Hakimata). This dissertation was prepared under the supervision of Ernesto J. F. Costa Full Professor of the Department of Informatics Engineering of the Faculty of Sciences and Tecnology of the University of Coimbra In loving memory of my father. Acknowledgements Thank you very much to Prof. Ernesto Costa, for his vision on research and education, for all the support and contributions to my ideas, and for the many life stories that always lighten the mood. Thank you also to Nuno Lourenço for the many discussions, shared books, and his constant helpfulness. -
Gene Regulatory Network Reconstruction Using Bayesian
1 Gene regulatory network reconstruction using Bayesian networks, the Dantzig selector, the Lasso and their meta-analysis Matthieu Vignes1∗, Jimmy Vandel1, David Allouche1, Nidal Ramadan-Alban1, Christine Cierco-Ayrolles1, Thomas Schiex1, Brigitte Mangin1, Simon de Givry1 1 SaAB Team/BIA Unit, INRA Toulouse, Castanet-Tolosan, France ∗ E-mail: [email protected] Abstract Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth \Dialogue for Reverse Engineering Assessments and Methods" (DREAM5) challenges are aimed at assessing methods and as- sociated algorithms devoted to the inference of biological networks. Challenge 3 on \Systems Genetics" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We in- vestigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics. Introduction Inferring gene regulatory networks. Gene regulatory networks (GRN) are simplified representa- tions of mechanisms that make up the functioning of an organism under given conditions. A node in such a network stands for a gene i.e. -
'Non-Genetic' Inheritance: Insights from Molecular-Evolutionary Crosstalk
Adrian-Kalchhauser et al / Understanding NGI: Insights from Molecular-Evolutionary Crosstalk / EcoEvoRxiv 20200501 Understanding 'Non-genetic' Inheritance: Insights from Molecular-Evolutionary Crosstalk Irene Adrian-Kalchhauser1, Sonia E. Sultan2, Lisa Shama3, Helen Spence-Jones4, Stefano Tiso5, Claudia Isabelle Keller Valsecchi6, Franz J. Weissing5 Addresses 1Centre for Fish and Wildlife Health, Department for Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3012 Bern, Switzerland 2Biology Department, Wesleyan University, Middletown CT 06459 USA 3Coastal Ecology Section, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Wadden Sea Station Sylt, Hafenstrasse 43, 25992 List, Germany 4Centre for Biological Diversity, School of Biology, University of St Andrews, UK 5Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands 6Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany Corresponding author: Adrian-Kalchhauser, I. ([email protected]) Keywords epigenetics, epiallelic variation, methylation, transgenerational effects, inherited gene regulation, gene regulatory network model Abstract Understanding the evolutionary and ecological roles of 'non-genetic' inheritance is daunting due to the complexity and diversity of epigenetic mechanisms. We draw on precise insights from molecular structures and events to identify three general features of 'non-genetic' inheritance systems that are central to broader investigations: (i) they are functionally interdependent with, rather than separate from, DNA sequence; (ii) each of these mechanisms is not uniform but instead varies phylogenetically and operationally; and (iii) epigenetic elements are probabilistic, interactive regulatory factors and not deterministic 'epi-alleles' with defined genomic locations and effects. We explain each feature and offer research recommendations. -
Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation
Utah State University DigitalCommons@USU All Graduate Theses and Dissertations Graduate Studies 5-2015 Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation Hamid Mohamadlou Utah State University Follow this and additional works at: https://digitalcommons.usu.edu/etd Part of the Computer Sciences Commons Recommended Citation Mohamadlou, Hamid, "Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation" (2015). All Graduate Theses and Dissertations. 4539. https://digitalcommons.usu.edu/etd/4539 This Dissertation is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. ALGORITHMIC INFORMATION THEORY APPLICATIONS IN BRIGHT FIELD MICROSCOPY AND EPITHELIAL PATTERN FORMATION by Hamid Mohamadlou A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science Approved: Dr. Nicholas Flann Dr. Gregory Podgorski Major Professor Committee Member Dr. Xiaojun Qi Dr. Minghui Jiang Committee Member Committee Member Dr. Haitao Wang Dr. Mark R. McLellan Committee Member Vice President for Research and Dean of the School of Graduate Studies UTAH STATE UNIVERSITY Logan, Utah 2015 ii Copyright c Hamid Mohamadlou 2015 All Rights Reserved iii ABSTRACT Algorithmic Information Theory applications in Bright Field Microscopy and Epithelial Pattern Formation by Hamid Mohamadlou, Doctor of Philosophy Utah State University, 2015 Major Professor: Dr. Nicholas Flann Department: Computer Science Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information. -
Computational Representation of Developmental Genetic Regulatory Networks
Developmental Biology 283 (2005) 1 – 16 www.elsevier.com/locate/ydbio Review Computational representation of developmental genetic regulatory networks William J.R. Longabaugha, Eric H. Davidsonb,1, Hamid Bolouria,b,* aInstitute for Systems Biology, Seattle, WA 98103-8904, USA bDivision of Biology, California Institute of Technology, Pasadena, CA 91125, USA Received for publication 12 February 2005, revised 30 March 2005, accepted 16 April 2005 Available online 23 May 2005 Abstract Developmental genetic regulatory networks (GRNs) have unique architectural characteristics. They are typically large-scale, multi- layered, and organized in a nested, modular hierarchy of regulatory network kernels, function-specific building blocks, and structural gene batteries. They are also inherently multicellular and involve changing topological relationships among a growing number of cells. Reconstruction of developmental GRNs requires unique computational tools that support the above representational requirements. In addition, we argue that DNA-centered network modeling, separate descriptions of network organization and network behavior, and support for network documentation and annotation are essential requirements for computational modeling of developmental GRNs. Based on these observations, we have developed a freely available, platform-independent, open source software package (BioTapestry) which supports both the process of model construction and also model visualization, analysis, documentation, and dissemination. We provide an overview of the main features of BioTapestry. The BioTapestry software and additional documents are available from http://www.biotapestry.org.We recommend BioTapestry as the substrate for further co-development for and by the developmental biology community. D 2005 Elsevier Inc. All rights reserved. Keywords: Genetic regulatory networks; Computational modeling; Visualization; Documentation Introduction can be fully and unambiguously verified by well-established experimental procedures.