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Sun J, Zhang C, Gao F, Stathopoulos A. Single-cell transcriptomics illuminates regulatory steps driving anterior-posterior patterning of Drosophila embryonic mesoderm. Cell Rep 2023; 42:113289. [PMID: 37858470 DOI: 10.1016/j.celrep.2023.113289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 08/29/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023] Open
Abstract
Single-cell technologies promise to uncover how transcriptional programs orchestrate complex processes during embryogenesis. Here, we apply a combination of single-cell technology and genetic analysis to investigate the dynamic transcriptional changes associated with Drosophila embryo morphogenesis at gastrulation. Our dataset encompassing the blastoderm-to-gastrula transition provides a comprehensive single-cell map of gene expression across cell lineages validated by genetic analysis. Subclustering and trajectory analyses revealed a surprising stepwise progression in patterning to transition zygotic gene expression and specify germ layers as well as uncovered an early role for ecdysone signaling in epithelial-to-mesenchymal transition in the mesoderm. We also show multipotent progenitors arise prior to gastrulation by analyzing the transcription trajectory of caudal mesoderm cells, including a derivative that ultimately incorporates into visceral muscles of the midgut and hindgut. This study provides a rich resource of gastrulation and elucidates spatially regulated temporal transitions of transcription states during the process.
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Affiliation(s)
- Jingjing Sun
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Chen Zhang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Fan Gao
- Bioinformatics Resource Center, Beckman Institute, California Institute of Technology, Pasadena, CA 91125, USA
| | - Angelike Stathopoulos
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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2
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Argyris GA, Lluch Lafuente A, Tribastone M, Tschaikowski M, Vandin A. Reducing Boolean networks with backward equivalence. BMC Bioinformatics 2023; 24:212. [PMID: 37221494 DOI: 10.1186/s12859-023-05326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis. RESULTS We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance. CONCLUSIONS BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.
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Affiliation(s)
- Georgios A Argyris
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Alberto Lluch Lafuente
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | | | - Max Tschaikowski
- Department of Computer Science, University of Aalborg, Aalborg, Denmark
| | - Andrea Vandin
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
- Department of Excellence EMbeDS and Institute of Economics, Sant'Anna School for Advanced Studies, Pisa, Italy.
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3
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Bhogale S, Sinha S. Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding. iScience 2022; 25:104152. [PMID: 35465052 PMCID: PMC9018382 DOI: 10.1016/j.isci.2022.104152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/28/2021] [Accepted: 03/21/2022] [Indexed: 11/30/2022] Open
Abstract
Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach to test if such indirect binding by TFs plays a significant role in gene regulation. We first incorporated regulatory function of indirectly bound TFs into a thermodynamics-based model for predicting enhancer-driven expression from its sequence. We then fit the new model to a rich data set comprising hundreds of enhancers and their regulatory activities during mesoderm specification in Drosophila embryogenesis and showed that the newly incorporated mechanism results in significantly better agreement with data. In the process, we derived the first sequence-level model of this extensively characterized regulatory program. We further showed that allowing indirect binding of a TF explains its localization at enhancers more accurately than with direct binding only. Our model also provided a simple explanation of how a TF may switch between activating and repressive roles depending on context. Inclusion of indirect DNA binding of transcription factor improves enhancer function prediction Context specific activating or repressive roles of TFs Indirect binding improves fits to experimental TF-DNA binding data Role of Tinman depends on its DNA-binding mode (direct or indirect)
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Junion G, Jagla K. Diversification of muscle types in Drosophila embryos. Exp Cell Res 2022; 410:112950. [PMID: 34838813 DOI: 10.1016/j.yexcr.2021.112950] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/22/2021] [Accepted: 11/24/2021] [Indexed: 12/31/2022]
Abstract
Drosophila embryonic somatic muscles represent a simple and tractable model system to study the gene regulatory networks that control diversification of cell types. Somatic myogenesis in Drosophila is initiated by intrinsic action of the mesodermal master gene twist, which activates a cascade of transcriptional outputs including myogenic differentiation factor Mef2, which triggers all aspects of the myogenic differentiation program. In parallel, the expression of a combinatorial code of identity transcription factors (iTFs) defines discrete particular features of each muscle fiber, such as number of fusion events, and specific attachment to tendon cells or innervation, thus ensuring diversification of muscle types. Here, we take the example of a subset of lateral transverse (LT) muscles and discuss how the iTF code and downstream effector genes progressively define individual LT properties such as fusion program, attachment and innervation. We discuss new challenges in the field including the contribution of posttranscriptional and epitranscriptomic regulation of gene expression in the diversification of cell types.
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Affiliation(s)
- Guillaume Junion
- Genetics Reproduction and Development Institute (iGReD), CNRS UMR6293, INSERM U1103, University of Clermont Auvergne, Clermont-Ferrand, France
| | - Krzysztof Jagla
- Genetics Reproduction and Development Institute (iGReD), CNRS UMR6293, INSERM U1103, University of Clermont Auvergne, Clermont-Ferrand, France.
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5
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Floc'hlay S, Molina MD, Hernandez C, Haillot E, Thomas-Chollier M, Lepage T, Thieffry D. Deciphering and modelling the TGF-β signalling interplays specifying the dorsal-ventral axis of the sea urchin embryo. Development 2021; 148:dev.189944. [PMID: 33298464 DOI: 10.1242/dev.189944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 11/16/2020] [Indexed: 11/20/2022]
Abstract
During sea urchin development, secretion of Nodal and BMP2/4 ligands and their antagonists Lefty and Chordin from a ventral organiser region specifies the ventral and dorsal territories. This process relies on a complex interplay between the Nodal and BMP pathways through numerous regulatory circuits. To decipher the interplay between these pathways, we used a combination of treatments with recombinant Nodal and BMP2/4 proteins and a computational modelling approach. We assembled a logical model focusing on cell responses to signalling inputs along the dorsal-ventral axis, which was extended to cover ligand diffusion and enable multicellular simulations. Our model simulations accurately recapitulate gene expression in wild-type embryos, accounting for the specification of ventral ectoderm, ciliary band and dorsal ectoderm. Our model simulations further recapitulate various morphant phenotypes, reveal a dominance of the BMP pathway over the Nodal pathway and stress the crucial impact of the rate of Smad activation in dorsal-ventral patterning. These results emphasise the key role of the mutual antagonism between the Nodal and BMP2/4 pathways in driving early dorsal-ventral patterning of the sea urchin embryo.
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Affiliation(s)
- Swann Floc'hlay
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | | | - Céline Hernandez
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | - Emmanuel Haillot
- Institut Biologie Valrose, Université Côte d'Azur, 06108 Nice, France
| | - Morgane Thomas-Chollier
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France.,Institut Universitaire de France (IUF), 75005 Paris, France
| | - Thierry Lepage
- Institut Biologie Valrose, Université Côte d'Azur, 06108 Nice, France
| | - Denis Thieffry
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
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6
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Sinha S, Jones BM, Traniello IM, Bukhari SA, Halfon MS, Hofmann HA, Huang S, Katz PS, Keagy J, Lynch VJ, Sokolowski MB, Stubbs LJ, Tabe-Bordbar S, Wolfner MF, Robinson GE. Behavior-related gene regulatory networks: A new level of organization in the brain. Proc Natl Acad Sci U S A 2020; 117:23270-23279. [PMID: 32661177 PMCID: PMC7519311 DOI: 10.1073/pnas.1921625117] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Neuronal networks are the standard heuristic model today for describing brain activity associated with animal behavior. Recent studies have revealed an extensive role for a completely distinct layer of networked activities in the brain-the gene regulatory network (GRN)-that orchestrates expression levels of hundreds to thousands of genes in a behavior-related manner. We examine emerging insights into the relationships between these two types of networks and discuss their interplay in spatial as well as temporal dimensions, across multiple scales of organization. We discuss properties expected of behavior-related GRNs by drawing inspiration from the rich literature on GRNs related to animal development, comparing and contrasting these two broad classes of GRNs as they relate to their respective phenotypic manifestations. Developmental GRNs also represent a third layer of network biology, playing out over a third timescale, which is believed to play a crucial mediatory role between neuronal networks and behavioral GRNs. We end with a special emphasis on social behavior, discuss whether unique GRN organization and cis-regulatory architecture underlies this special class of behavior, and review literature that suggests an affirmative answer.
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Affiliation(s)
- Saurabh Sinha
- Department of Computer Science, University of Illinois, Urbana-Champaign, IL 61801;
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, IL 61801
| | - Beryl M Jones
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, IL 61801
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
| | - Ian M Traniello
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, IL 61801
- Neuroscience Program, University of Illinois, Urbana-Champaign, IL 61801
| | - Syed A Bukhari
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, IL 61801
- Informatics Program, University of Illinois, Urbana-Champaign, IL 61820
| | - Marc S Halfon
- Department of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY 14203
| | - Hans A Hofmann
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712
- Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712
| | - Sui Huang
- Institute for Systems Biology, Seattle, WA 98109
| | - Paul S Katz
- Department of Biology, University of Massachusetts, Amherst, MA 01003
| | - Jason Keagy
- Department of Evolution, Ecology, and Behavior, School of Integrative Biology, University of Illinois, Urbana-Champaign, IL 61801
| | - Vincent J Lynch
- Department of Biological Sciences, University at Buffalo-State University of New York, Buffalo, NY 14260
| | - Marla B Sokolowski
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3B2, Canada
- Program in Child and Brain Development, Canadian Institute for Advanced Research, Toronto, ON M5G 1M1, Canada
| | - Lisa J Stubbs
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, IL 61801
- Department of Cell and Developmental Biology, University of Illinois, Urbana-Champaign, IL 61801
| | - Shayan Tabe-Bordbar
- Department of Computer Science, University of Illinois, Urbana-Champaign, IL 61801
| | - Mariana F Wolfner
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14850
| | - Gene E Robinson
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, IL 61801;
- Neuroscience Program, University of Illinois, Urbana-Champaign, IL 61801
- Department of Entomology, University of Illinois, Urbana-Champaign, IL 61801
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Rothenberg EV. Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges. J Comput Biol 2019; 26:703-718. [PMID: 31063008 DOI: 10.1089/cmb.2019.0098] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Gene regulatory network modeling has played a major role in advancing the understanding of developmental systems, by crystallizing structures of relevant extant information, by formally posing hypothetical functional relationships between network elements, and by offering clear predictive tests to improve understanding of the mechanisms driving developmental progression. Both ordinary differential equation (ODE)-based and Boolean models have also been highly successful in explaining dynamics within subcircuits of more complex processes. In a very small number of cases, gene regulatory network models of much more global scope have been proposed that successfully predict the dynamics of the processes establishing most of an embryonic body plan. Can such successes be expanded to very different developmental systems, including post-embryonic mammalian systems? This perspective discusses several problems that must be solved in more quantitative and predictive theoretical terms, to make this possible. These problems include: the effects of cellular history on chromatin state and how these affect gene accessibility; the dose dependence of activities of many transcription factors (a problem for Boolean models); stochasticity of some transcriptional outputs (a problem for simple ODE models); response timing delays due to epigenetic remodeling requirements; functionally different kinds of repression; and the regulatory syntax that governs responses of genes with multiple enhancers.
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Affiliation(s)
- Ellen V Rothenberg
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
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8
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Naldi A, Hernandez C, Abou-Jaoudé W, Monteiro PT, Chaouiya C, Thieffry D. Logical Modeling and Analysis of Cellular Regulatory Networks With GINsim 3.0. Front Physiol 2018; 9:646. [PMID: 29971008 PMCID: PMC6018412 DOI: 10.3389/fphys.2018.00646] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 05/11/2018] [Indexed: 11/13/2022] Open
Abstract
The logical formalism is well adapted to model large cellular networks, in particular when detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step toward the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network.
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Affiliation(s)
- Aurélien Naldi
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
| | - Céline Hernandez
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
| | - Wassim Abou-Jaoudé
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
| | - Pedro T. Monteiro
- INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | | | - Denis Thieffry
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
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9
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Espinosa-Soto C. On the role of sparseness in the evolution of modularity in gene regulatory networks. PLoS Comput Biol 2018; 14:e1006172. [PMID: 29775459 PMCID: PMC5979046 DOI: 10.1371/journal.pcbi.1006172] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 05/31/2018] [Accepted: 05/01/2018] [Indexed: 12/13/2022] Open
Abstract
Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases. Modular systems have performance and design advantages over non-modular systems. Thus, modularity is very important for the development of a wide range of new technological or clinical applications. Moreover, modularity is paramount to evolutionary biology since it allows adjusting one organismal function without disturbing other previously evolved functions. But how does modularity itself evolve? Here I analyse the structure of regulatory networks and follow simulations of network evolution to study two hypotheses for the origin of modules in gene regulatory networks. The first hypothesis considers that sparseness, a low number of interactions among the network genes, could be responsible for the evolution of modular networks. The second, that modules evolve when selection favours the production of additional gene activity patterns. I found that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, it enhances the effects of selection for multiple gene activity patterns. While selection for multiple patterns may be decisive in the evolution of modularity, that sparseness is widespread across gene regulatory networks suggests that its contributions should not be neglected.
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Affiliation(s)
- Carlos Espinosa-Soto
- Instituto de Física, Universidad Autónoma de San Luis Potosí, Manuel Nava 6, Zona Universitaria, San Luis Potosí, Mexico
- * E-mail:
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