Analysis on Gene Modular Network Reveals Morphogen-Directed Development Robustness in Drosophila

Total Page:16

File Type:pdf, Size:1020Kb

Analysis on Gene Modular Network Reveals Morphogen-Directed Development Robustness in Drosophila Zhang et al. Cell Discovery (2020) 6:43 Cell Discovery https://doi.org/10.1038/s41421-020-0173-z www.nature.com/celldisc ARTICLE Open Access Analysis on gene modular network reveals morphogen-directed development robustness in Drosophila Shuo Zhang1,2,JuanZhao1,3, Xiangdong Lv1,2, Jialin Fan1,2,YiLu1,TaoZeng 1,3, Hailong Wu1, Luonan Chen 1,3,4,5 and Yun Zhao1,4,6 Abstract Genetic robustness is an important characteristic to tolerate genetic or nongenetic perturbations and ensure phenotypic stability. Morphogens, a type of evolutionarily conserved diffusible molecules, govern tissue patterns in a direction-dependent or concentration-dependent manner by differentially regulating downstream gene expression. However, whether the morphogen-directed gene regulatory network possesses genetic robustness remains elusive. In the present study, we collected 4217 morphogen-responsive genes along A-P axis of Drosophila wing discs from the RNA-seq data, and clustered them into 12 modules. By applying mathematical model to the measured data, we constructed a gene modular network (GMN) to decipher the module regulatory interactions and robustness in morphogen-directed development. The computational analyses on asymptotical dynamics of this GMN demonstrated that this morphogen-directed GMN is robust to tolerate a majority of genetic perturbations, which has been further validated by biological experiments. Furthermore, besides the genetic alterations, we further demonstrated that this morphogen-directed GMN can well tolerate nongenetic perturbations (Hh production changes) via computational fi 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; analyses and experimental validation. Therefore, these ndings clearly indicate that the morphogen-directed GMN is robust in response to perturbations and is important for Drosophila to ensure the proper tissue patterning in wing disc. Introduction further subdivide the tissue into different regions to fulfill – All of the multicellular organisms display specific body the specific function1 3. patterns, which is a result of the systemic incorporation of Morphogens are evolutionarily conserved, diffusible and individual tissues or organs. Therefore, correct tissue long-range signaling molecules that govern the tissue patterns are essential for body pattern formation. Tissue pattern formation by regulating the expression of down- patterns are well organized in early development process stream genes in a distance-dependent and/or – in both vertebrates and invertebrates, and are mainly concentration-dependent manner1 4. Because of the dif- determined by the morphogen gradients, which could fusible property, different concentrations of morphogens thus form the morphogen gradient, which results in dif- ferential gene expression profiles carrying tissue posi- – tional information5 10. The morphogen gradient plays an Correspondence: Yun Zhao ([email protected])or indispensable role in tissue patterning and is widely Luonan Chen ([email protected]) investigated in the mouse neural tube, the limb bud, the 1State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy blastoderm, the anterior–posterior (A–P) and of Sciences, University of Chinese Academy of Sciences, 200031 Shanghai, dorsal–ventral (D–V) axes of Drosophila imaginal discs, China especially in the wing imaginal disc2,7,11. 2University of Chinese Academy of Sciences, 100049 Beijing, China Full list of author information is available at the end of the article. These authors contributed equally: Shuo Zhang, Juan Zhao © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Zhang et al. Cell Discovery (2020) 6:43 Page 2 of 14 The Drosophila wing imaginal disc is one of the most morphogen-directed gene modular network (GMN) typical models to investigate the morphogen-mediated based on the Boolean model, which is an effective – tissue patterning in development4,7,12 14. In the wing approach for inferring and analyzing biological net- imaginal disc, there exist two axes, the A–P and D–V axes works24,25. Through in silico and in vivo experiments, we (Fig. 1a)7,11,12,15. The tissue patterning of wing disc in both demonstrated that the morphogen-directed GMN is axes is precisely guided by different morphogen gradients robust in response to various genetic perturbations. and is critical for the wing growth and proper morphol- Moreover, by combining computational analyses and ogy2,16,17 (Fig. 1a, c). Generally, tissue patterning in the experimental validations, we demonstrated that this D–V direction is determined by the Wingless signaling morphogen-directed GMN can also tolerate Hh produc- whereas the Hedgehog (Hh) and Decapentaplegic (Dpp) tion changes. Interestingly, Hh production increase seems gradients play important roles in tissue patterning along more likely to be tolerated by this GMN compared to Hh the A–P direction9,13,18,19. production decrease. In wing discs, Hh is exclusively produced and post- translationally modified by adding the cholesterol and Results palmitic acid in P compartment cells7,11,12, indicating the In vivo samples acquired by Geo-seq fit the morphogen help of lipoprotein or extracellular structures in Hh gradients transport8. Previous reports have shown that Hh patterns To simplify our investigation on the morphogen- the central part of the wing7,8, and acts as a morphogen to mediated genetic robustness, we chose to analyze the A compartment cells11. When Hh diffuses into A com- PGE profiles exclusively along A–P direction. For this partment, it binds to Patched (Ptc), the receptor of Hh, purpose, we developed a GFP reporter fly(ptcGal4-uas- and alleviates the inhibition of Smoothened (Smo), and GFP) in which GFP expression is driven by patched (ptc) then Ci is changed into active form, which could activate a promoter that responds to Hh gradient. As a result, the series of downstream target genes, including dpp and ptc. cells in A/P boundary is marked with GFP (Fig. 1d–d‴). Therefore, there exists a secondary effect of Hh gradient We then collected the PGE information by adopting Geo- in wing disc patterning, which is achieved by activating seq approach26, a method combining laser capture the expression of Dpp in cells in A compartment near the microdissection27,28 with tiny-sample RNA-seq, and per- A/P boundary11. In addition, the graded upregulation of formed a series of bioinformatic analyses (Fig. 1q). To Ptc could further restrict the spread of Hh via endocy- precisely collect tissue samples along A–P direction, the tosis1. Moreover, neither the dysfunction nor over- GFP-labeled wing imaginal discs were first consecutively expression of Hh could result in normal adult wing, cut into 4 μm-thick sections via frozen section along the indicating that Hh gradient is critical for the proper wing A–P axis (Fig. 1b), and these 4 μm microdissections were disc patterning in Drosophila7,20. then subjected to precise laser capture to collect tiny In addition, organisms are always facing various genetic tissue masses from the defined section areas (Fig. 1e–k′). and/or nongenetic perturbations/alterations during the This sample collection procedure greatly ensures the PGE development and long-time evolution, which have the profiles derived from those samples faithfully reflecting potential to cause abnormality of tissue patterning and morphogen gradients along the A–P axis but not the even organism lethality21,22. To avoid those detrimental Wingless gradient along the D–V axis. Following this changes, organisms usually possess genetic robustness to sample collection procedure, we collected a set of frozen tolerate those variations to maintain the phenotypic and sections in the A/P boundary region in the direction of A functional stability21,23. Although it is well known that to P compartment and conducted the laser microdissec- those morphogen-mediated differential position-related tion to acquire the defined areas (Fig. 1e–k′). The samples gene expression (PGE) profiles form a tissue-specific gene are then named with B1–B7 sequentially according to the regulatory network to determine proper tissue pattern, position of frozen sections from A to P compartment whether morphogen-directed gene expression network (sample set B). The following qPCR assays demonstrated possesses genetic robustness remains an open question that hh was exclusively expressed in position B7 and ci without systematic investigations. expression was restricted in the region from positions B1 In the current study, we used the Drosophila wing disc to B6, indicating that position B7 is in the P compartment as a model system, in which cells established their spa- while the region
Recommended publications
  • 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.
    [Show full text]
  • Functional Analysis of the Homeobox Gene Tur-2 During Mouse Embryogenesis
    Functional Analysis of The Homeobox Gene Tur-2 During Mouse Embryogenesis Shao Jun Tang A thesis submitted in conformity with the requirements for the Degree of Doctor of Philosophy Graduate Department of Molecular and Medical Genetics University of Toronto March, 1998 Copyright by Shao Jun Tang (1998) National Library Bibriothèque nationale du Canada Acquisitions and Acquisitions et Bibiiographic Services seMces bibliographiques 395 Wellington Street 395, rue Weifington OtbawaON K1AW OttawaON KYAON4 Canada Canada The author has granted a non- L'auteur a accordé une licence non exclusive licence alIowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distri%uteor sell reproduire, prêter' distribuer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fkom it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Functional Analysis of The Homeobox Gene TLr-2 During Mouse Embryogenesis Doctor of Philosophy (1998) Shao Jun Tang Graduate Department of Moiecular and Medicd Genetics University of Toronto Abstract This thesis describes the clonhg of the TLx-2 homeobox gene, the determination of its developmental expression, the characterization of its fiuiction in mouse mesodem and penpheral nervous system (PNS) developrnent, the regulation of nx-2 expression in the early mouse embryo by BMP signalling, and the modulation of the function of nX-2 protein by the 14-3-3 signalling protein during neural development.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Products for Morphogen Research
    R&D Systems Tools for Cell Biology Research™ Products for Morphogen Research BMP-4 NEURAL PLATE BMP-7 PROSPECTIVE NEURAL CREST NON-NEURAL ECTODERM Noggin Shh Noggin PRESOMITIC MESODERM NOTOCHORD NON-NEURAL ECTODERM FUTURE FLOOR PLATE BMP-4 Shh Noggin DORSAL AORTA ROOF PLATE Products for Morphogen Research for Products Noggin Wnt-1 Wnt-3a Wnt-4 NT-3 Wnt-6 Wnt-7a EARLY SOMITE Myf5 NEURAL TUBE Pax3 Sim1 BMP-4 INTERMEDIATE MESODERM Shh ShhShh NogginNoggin BMP-4 DORSAL AORTA Ihh NOTOCHORD MORPHOGENS Morphogens are molecules that regulate cell fate during development. Formation of morphogen concentration gradients directs the biological responses of surrounding cells. Graded responses occur as a result of morphogens binding to specific cell surface receptors that subsequently activate intracellular signaling pathways and promote or repress gene expression at specific threshold concentrations. Activation or inactivation of these signaling pathways provides positional information that ultimately determines tissue organization and morphology. Research in model organisms has revealed that morphogens are involved in many aspects of development. For example, morphogens are required in Drosophila for patterning of the dorso-ventral and anterior-posterior axes, segment patterning, and positional signaling in the leg and wing imaginal discs. Proteins belonging to the Wingless/Wnt, Notch, Hedgehog, and TGF-b families have been identified as morphogens that direct a number of these processes. Research in higher organisms has demonstrated that homologues of these same signaling molecules regulate vertebrate axis formation, anterior/posterior polarity during limb development, mesoderm patterning, and numerous other processes that establish an organism’s basic body structure. R&D Systems offers a wide selection of proteins, antibodies, and ELISAs for morphogen-related developmental research.
    [Show full text]
  • Rapid Changes in Morphogen Concentration Control Self-Organized
    RESEARCH COMMUNICATION Rapid changes in morphogen concentration control self-organized patterning in human embryonic stem cells Idse Heemskerk1†, Kari Burt1, Matthew Miller1, Sapna Chhabra2, M Cecilia Guerra1, Lizhong Liu1, Aryeh Warmflash1,3* 1Department of Biosciences, Rice University, Houston, United States; 2Systems, Synthetic and Physical Biology Program, Rice University, Houston, United States; 3Department of Bioengineering, Rice University, Houston, United States Abstract During embryonic development, diffusible signaling molecules called morphogens are thought to determine cell fates in a concentration-dependent way. Yet, in mammalian embryos, concentrations change rapidly compared to the time for making cell fate decisions. Here, we use human embryonic stem cells (hESCs) to address how changing morphogen levels influence differentiation, focusing on how BMP4 and Nodal signaling govern the cell-fate decisions associated with gastrulation. We show that BMP4 response is concentration dependent, but that expression of many Nodal targets depends on rate of concentration change. Moreover, in a self- organized stem cell model for human gastrulation, expression of these genes follows rapid changes in endogenous Nodal signaling. Our study shows a striking contrast between the specific ways ligand dynamics are interpreted by two closely related signaling pathways, highlighting both the *For correspondence: subtlety and importance of morphogen dynamics for understanding mammalian embryogenesis [email protected] and designing optimized protocols for directed stem cell differentiation. Editorial note: This article has been through an editorial process in which the authors decide how † Present address: Department to respond to the issues raised during peer review. The Reviewing Editor’s assessment is that all of Cell and Developmental the issues have been addressed (see decision letter).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Annominer Is a New Web-Tool to Integrate Epigenetics, Transcription
    www.nature.com/scientificreports OPEN AnnoMiner is a new web‑tool to integrate epigenetics, transcription factor occupancy and transcriptomics data to predict transcriptional regulators Arno Meiler1,3, Fabio Marchiano2,3, Margaux Haering2, Manuela Weitkunat1, Frank Schnorrer1,2 & Bianca H. Habermann1,2* Gene expression regulation requires precise transcriptional programs, led by transcription factors in combination with epigenetic events. Recent advances in epigenomic and transcriptomic techniques provided insight into diferent gene regulation mechanisms. However, to date it remains challenging to understand how combinations of transcription factors together with epigenetic events control cell‑type specifc gene expression. We have developed the AnnoMiner web‑server, an innovative and fexible tool to annotate and integrate epigenetic, and transcription factor occupancy data. First, AnnoMiner annotates user‑provided peaks with gene features. Second, AnnoMiner can integrate genome binding data from two diferent transcriptional regulators together with gene features. Third, AnnoMiner ofers to explore the transcriptional deregulation of genes nearby, or within a specifed genomic region surrounding a user‑provided peak. AnnoMiner’s fourth function performs transcription factor or histone modifcation enrichment analysis for user‑provided gene lists by utilizing hundreds of public, high‑quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • '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.
    [Show full text]