1
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Lin S, Lim B. Multifaceted effects on even-skipped transcriptional dynamics upon Krüppel dosage changes. Development 2024; 151:dev202132. [PMID: 38345298 PMCID: PMC10948998 DOI: 10.1242/dev.202132] [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: 06/27/2023] [Accepted: 02/08/2024] [Indexed: 03/05/2024]
Abstract
Although fluctuations in transcription factor (TF) dosage are often well tolerated, TF dosage modulation can change the target gene expression dynamics and result in significant non-lethal developmental phenotypes. Using MS2/MCP-mediated quantitative live imaging in early Drosophila embryos, we analyzed how changing levels of the gap gene Krüppel (Kr) affects transcriptional dynamics of the pair-rule gene even-skipped (eve). Halving the Kr dosage leads to a transient posterior expansion of the eve stripe 2 and an anterior shift of stripe 5. Surprisingly, the most significant changes are observed in eve stripes 3 and 4, the enhancers of which do not contain Kr-binding sites. In Kr heterozygous embryos, both stripes 3 and 4 display narrower widths, anteriorly shifted boundaries and reduced mRNA production levels. We show that Kr dosage indirectly affects stripe 3 and 4 dynamics by modulating other gap gene dynamics. We quantitatively correlate moderate body segment phenotypes of Kr heterozygotes with spatiotemporal changes in eve expression. Our results indicate that nonlinear relationships between TF dosage and phenotypes underlie direct TF-DNA and indirect TF-TF interactions.
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Affiliation(s)
- Shufan Lin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bomyi Lim
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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2
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Gaiewski MJ, Drewell RA, Dresch JM. Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm. Math Biosci 2021; 342:108716. [PMID: 34687735 DOI: 10.1016/j.mbs.2021.108716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 09/10/2021] [Accepted: 09/17/2021] [Indexed: 11/30/2022]
Abstract
A detailed comprehension of transcriptional regulation is critical to understanding the genetic control of development and disease across many different organisms. To more fully investigate the complex molecular interactions controlling the precise expression of genes, many groups have constructed mathematical models to complement their experimental approaches. A critical step in such studies is choosing the most appropriate parameter estimation algorithm to enable detailed analysis of the parameters that contribute to the models. In this study, we develop a novel set of evolutionary algorithms that use a pseudo-random Sobol Set to construct the initial population and incorporate parameter sensitivities into the adaptation of mutation rates, using local, global, and hybrid strategies. Comparison of the performance of these new algorithms to a number of current state-of-the-art global parameter estimation algorithms on a range of continuous test functions, as well as synthetic biological data representing models of gene regulatory systems, reveals improved performance of the new algorithms in terms of runtime, error and reproducibility. In addition, by analyzing the ability of these algorithms to fit datasets of varying quality, we provide the experimentalist with a guide to how the algorithms perform across a range of noisy data. These results demonstrate the improved performance of the new set of parameter estimation algorithms and facilitate meaningful integration of model parameters and predictions in our understanding of the molecular mechanisms of gene regulation.
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Affiliation(s)
- Michael J Gaiewski
- Department of Mathematics and Computer Science, Clark University, Worcester, MA, USA; Department of Mathematics, University of Connecticut, Storrs, CT, USA.
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3
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Dibaeinia P, Sinha S. Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks. Nucleic Acids Res 2021; 49:10309-10327. [PMID: 34508359 PMCID: PMC8501998 DOI: 10.1093/nar/gkab765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/18/2021] [Accepted: 08/25/2021] [Indexed: 11/18/2022] Open
Abstract
Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.
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Affiliation(s)
- Payam Dibaeinia
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Saurabh Sinha
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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4
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Jindal GA, Farley EK. Enhancer grammar in development, evolution, and disease: dependencies and interplay. Dev Cell 2021; 56:575-587. [PMID: 33689769 PMCID: PMC8462829 DOI: 10.1016/j.devcel.2021.02.016] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 12/19/2022]
Abstract
Each language has standard books describing that language's grammatical rules. Biologists have searched for similar, albeit more complex, principles relating enhancer sequence to gene expression. Here, we review the literature on enhancer grammar. We introduce dependency grammar, a model where enhancers encode information based on dependencies between enhancer features shaped by mechanistic, evolutionary, and biological constraints. Classifying enhancers based on the types of dependencies may identify unifying principles relating enhancer sequence to gene expression. Such rules would allow us to read the instructions for development within genomes and pinpoint causal enhancer variants underlying disease and evolutionary changes.
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Affiliation(s)
- Granton A Jindal
- Division of Cardiology, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Division of Biological Sciences, Section of Molecular Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Emma K Farley
- Division of Cardiology, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Division of Biological Sciences, Section of Molecular Biology, University of California San Diego, La Jolla, CA 92093, USA.
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5
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Abstract
Motivation The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typically, such investigations have taken a ‘black box’ approach in which the internal structure of themodel used is set purely by machine learning considerations with little consideration of representing the internalstructure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to thedetailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcriptionfactors to DNA, in part because such models are in part formulated in terms of specific chemical equationsthat appear different in form from those used in neural networks. Results In this paper, we give an example of a DNN whichcan model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretableand is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from asystems biology model that was not previously recognized as having a DNN structure. Although we apply our DNNto data from the early embryo of the fruit fly Drosophila, this system serves as a test bed for analysis of much larger datasets obtained by systems biology studies on a genomic scale. . Availability and implementation The implementation and data for the models used in this paper are in a zip file in the supplementary material. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi Liu
- Department of Statistics, Ecology and Evolution, Molecular Genetics & Cell Biology, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Kenneth Barr
- Department of Human Genetics, Ecology and Evolution, Molecular Genetics & Cell Biology, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - John Reinitz
- Departments of Statistics, Ecology and Evolution, Molecular Genetics & Cell Biology, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
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6
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Lammers NC, Galstyan V, Reimer A, Medin SA, Wiggins CH, Garcia HG. Multimodal transcriptional control of pattern formation in embryonic development. Proc Natl Acad Sci U S A 2020; 117:836-847. [PMID: 31882445 PMCID: PMC6969519 DOI: 10.1073/pnas.1912500117] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.
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Affiliation(s)
| | - Vahe Galstyan
- Biochemistry and Molecular Biophysics Option, California Institute of Technology, Pasadena, CA 91126
- Department of Physics, Columbia University, New York, NY 10027
| | - Armando Reimer
- Biophysics Graduate Group, University of California, Berkeley, CA 94720
| | - Sean A Medin
- Department of Physics, University of California, Berkeley, CA 94720
| | - Chris H Wiggins
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027;
- Data Science Institute, Columbia University, New York, NY 10027
- Department of Systems Biology, Columbia University, New York, NY 10027
- Department of Statistics, Columbia University, New York, NY 10027
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California, Berkeley, CA 94720;
- Department of Physics, University of California, Berkeley, CA 94720
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, CA 94720
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7
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Merle M, Messio L, Mozziconacci J. Turing-like patterns in an asymmetric dynamic Ising model. Phys Rev E 2019; 100:042111. [PMID: 31770944 DOI: 10.1103/physreve.100.042111] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Indexed: 11/07/2022]
Abstract
To investigate novel aspects of pattern formation in spin systems, we use a mapping between reactive concentrations in a reaction-diffusion system and spin orientations in a dynamic multiple-spin Ising model. While pattern formation in Ising models always relies on infinite-range interactions, this mapping allows us to design a finite-range-interactions Ising model that can produce patterns observed in reaction-diffusion systems including Turing patterns with a tunable typical length scale. This model has asymmetric interactions and several spin types coexisting at a site. While we use the example of genetic regulation during embryogenesis to build our model, it can be used to study the behavior of other complex systems of interacting agents.
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Affiliation(s)
- Mélody Merle
- Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, F-75005 Paris, France
| | - Laura Messio
- Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, F-75005 Paris, France
| | - Julien Mozziconacci
- Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, F-75005 Paris, France
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8
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Wunderlich Z, Fowlkes CC, Eckenrode KB, Bragdon MDJ, Abiri A, DePace AH. Quantitative Comparison of the Anterior-Posterior Patterning System in the Embryos of Five Drosophila Species. G3 (BETHESDA, MD.) 2019; 9:2171-2182. [PMID: 31048401 PMCID: PMC6643877 DOI: 10.1534/g3.118.200953] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 05/01/2019] [Indexed: 11/18/2022]
Abstract
Complex spatiotemporal gene expression patterns direct the development of the fertilized egg into an adult animal. Comparisons across species show that, in spite of changes in the underlying regulatory DNA sequence, developmental programs can be maintained across millions of years of evolution. Reciprocally, changes in gene expression can be used to generate morphological novelty. Distinguishing between changes in regulatory DNA that lead to changes in gene expression and those that do not is therefore a central goal of evolutionary developmental biology. Quantitative, spatially-resolved measurements of developmental gene expression patterns play a crucial role in this goal, enabling the detection of subtle phenotypic differences between species and the development of computations models that link the sequence of regulatory DNA to expression patterns. Here we report the generation of two atlases of cellular resolution gene expression measurements for the primary anterior-posterior patterning genes in Drosophila simulans and Drosophila virilis By combining these data sets with existing atlases for three other Drosophila species, we detect subtle differences in the gene expression patterns and dynamics driving the highly conserved axis patterning system and delineate inter-species differences in the embryonic morphology. These data sets will be a resource for future modeling studies of the evolution of developmental gene regulatory networks.
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Affiliation(s)
- Zeba Wunderlich
- Department of Developmental and Cell Biology, University of California, Irvine, CA, 92697
| | - Charless C Fowlkes
- Department of Computer Science, University of California, Irvine, CA, 92697
| | - Kelly B Eckenrode
- Department of Systems Biology, Harvard Medical School, Boston, MA, 20115
| | - Meghan D J Bragdon
- Department of Systems Biology, Harvard Medical School, Boston, MA, 20115
| | - Arash Abiri
- Department of Developmental and Cell Biology, University of California, Irvine, CA, 92697
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA, 20115
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9
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Combs PA, Fraser HB. Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic. PLoS Genet 2018; 14:e1007631. [PMID: 30383747 PMCID: PMC6211617 DOI: 10.1371/journal.pgen.1007631] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 08/14/2018] [Indexed: 12/30/2022] Open
Abstract
Spatial patterning of gene expression is a key process in development, yet how it evolves is still poorly understood. Both cis- and trans-acting changes could participate in complex interactions, so to isolate the cis-regulatory component of patterning evolution, we measured allele-specific spatial gene expression patterns in D. melanogaster × simulans hybrid embryos. RNA-seq of cryo-sectioned slices revealed 66 genes with strong spatially varying allele-specific expression. We found that hunchback, a major regulator of developmental patterning, had reduced expression of the D. simulans allele specifically in the anterior tip of hybrid embryos. Mathematical modeling of hunchback cis-regulation suggested a candidate transcription factor binding site variant, which we verified as causal using CRISPR-Cas9 genome editing. In sum, even comparing morphologically near-identical species we identified surprisingly extensive spatial variation in gene expression, suggesting not only that development is robust to many such changes, but also that natural selection may have ample raw material for evolving new body plans via changes in spatial patterning.
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Affiliation(s)
- Peter A. Combs
- Department of Biology, Stanford University, Stanford, California, United States of America
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, California, United States of America
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10
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Affiliation(s)
- Sofie Y. N. Delbare
- Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America
| | - Andrew G. Clark
- Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America
- * E-mail:
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11
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Vincent BJ, Staller MV, Lopez-Rivera F, Bragdon MDJ, Pym ECG, Biette KM, Wunderlich Z, Harden TT, Estrada J, DePace AH. Hunchback is counter-repressed to regulate even-skipped stripe 2 expression in Drosophila embryos. PLoS Genet 2018; 14:e1007644. [PMID: 30192762 PMCID: PMC6145585 DOI: 10.1371/journal.pgen.1007644] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 09/19/2018] [Accepted: 08/17/2018] [Indexed: 01/18/2023] Open
Abstract
Hunchback is a bifunctional transcription factor that can activate and repress gene expression in Drosophila development. We investigated the regulatory DNA sequence features that control Hunchback function by perturbing enhancers for one of its target genes, even-skipped (eve). While Hunchback directly represses the eve stripe 3+7 enhancer, we found that in the eve stripe 2+7 enhancer, Hunchback repression is prevented by nearby sequences-this phenomenon is called counter-repression. We also found evidence that Caudal binding sites are responsible for counter-repression, and that this interaction may be a conserved feature of eve stripe 2 enhancers. Our results alter the textbook view of eve stripe 2 regulation wherein Hb is described as a direct activator. Instead, to generate stripe 2, Hunchback repression must be counteracted. We discuss how counter-repression may influence eve stripe 2 regulation and evolution.
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Affiliation(s)
- Ben J. Vincent
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Max V. Staller
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Francheska Lopez-Rivera
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Meghan D. J. Bragdon
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Edward C. G. Pym
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kelly M. Biette
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Zeba Wunderlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Timothy T. Harden
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Javier Estrada
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Angela H. DePace
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
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12
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Samee MAH, Lydiard-Martin T, Biette KM, Vincent BJ, Bragdon MD, Eckenrode KB, Wunderlich Z, Estrada J, Sinha S, DePace AH. Quantitative Measurement and Thermodynamic Modeling of Fused Enhancers Support a Two-Tiered Mechanism for Interpreting Regulatory DNA. Cell Rep 2018; 21:236-245. [PMID: 28978476 DOI: 10.1016/j.celrep.2017.09.033] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 07/30/2017] [Accepted: 09/08/2017] [Indexed: 02/07/2023] Open
Abstract
Computational models of enhancer function generally assume that transcription factors (TFs) exert their regulatory effects independently, modeling an enhancer as a "bag of sites." These models fail on endogenous loci that harbor multiple enhancers, and a "two-tier" model appears better suited: in each enhancer TFs work independently, and the total expression is a weighted sum of their expression readouts. Here, we test these two opposing views on how cis-regulatory information is integrated. We fused two Drosophila blastoderm enhancers, measured their readouts, and applied the above two models to these data. The two-tier mechanism better fits these readouts, suggesting that these fused enhancers comprise multiple independent modules, despite having sequence characteristics typical of single enhancers. We show that short-range TF-TF interactions are not sufficient to designate such modules, suggesting unknown underlying mechanisms. Our results underscore that mechanisms of how modules are defined and how their outputs are combined remain to be elucidated.
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Affiliation(s)
- Md Abul Hassan Samee
- Gladstone Institutes, University of California San Francisco, San Francisco, CA 94158, USA
| | - Tara Lydiard-Martin
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Kelly M Biette
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ben J Vincent
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Meghan D Bragdon
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Kelly B Eckenrode
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Zeba Wunderlich
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA
| | - Javier Estrada
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Saurabh Sinha
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute of Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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13
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Crocker J, Ilsley GR. Using synthetic biology to study gene regulatory evolution. Curr Opin Genet Dev 2017; 47:91-101. [DOI: 10.1016/j.gde.2017.09.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 09/06/2017] [Accepted: 09/11/2017] [Indexed: 12/21/2022]
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14
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Sharpe J. Computer modeling in developmental biology: growing today, essential tomorrow. Development 2017; 144:4214-4225. [DOI: 10.1242/dev.151274] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
D'Arcy Thompson was a true pioneer, applying mathematical concepts and analyses to the question of morphogenesis over 100 years ago. The centenary of his famous book, On Growth and Form, is therefore a great occasion on which to review the types of computer modeling now being pursued to understand the development of organs and organisms. Here, I present some of the latest modeling projects in the field, covering a wide range of developmental biology concepts, from molecular patterning to tissue morphogenesis. Rather than classifying them according to scientific question, or scale of problem, I focus instead on the different ways that modeling contributes to the scientific process and discuss the likely future of modeling in developmental biology.
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Affiliation(s)
- James Sharpe
- Systems Biology Unit, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
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15
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Crocker J, Stern DL. Functional regulatory evolution outside of the minimal even-skipped stripe 2 enhancer. Development 2017; 144:3095-3101. [PMID: 28760812 DOI: 10.1242/dev.149427] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 07/19/2017] [Indexed: 12/27/2022]
Abstract
Transcriptional enhancers are regions of DNA that drive precise patterns of gene expression. Although many studies have elucidated how individual enhancers can evolve, most of this work has focused on what are called 'minimal' enhancers, the smallest DNA regions that drive expression that approximates an aspect of native gene expression. Here, we explore how the Drosophila erecta even-skipped (eve) locus has evolved by testing its activity in the divergent D. melanogaster genome. We found, as has been reported previously, that the D. erecta eve stripe 2 enhancer (eveS2) fails to drive appreciable expression in D. melanogaster However, we found that a large transgene carrying the entire D. erecta eve locus drives normal eve expression, including in stripe 2. We performed a functional dissection of the region upstream of the D. erecta eveS2 region and found multiple Zelda motifs that are required for normal expression. Our results illustrate how sequences outside of minimal enhancer regions can evolve functionally through mechanisms other than changes in transcription factor-binding sites that drive patterning.
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Affiliation(s)
- Justin Crocker
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - David L Stern
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
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16
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Lobo D, Levin M. Computing a Worm: Reverse-Engineering Planarian Regeneration. EMERGENCE, COMPLEXITY AND COMPUTATION 2017. [DOI: 10.1007/978-3-319-33921-4_24] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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17
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Crocker J, Tsai A, Stern DL. A Fully Synthetic Transcriptional Platform for a Multicellular Eukaryote. Cell Rep 2017; 18:287-296. [DOI: 10.1016/j.celrep.2016.12.025] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 12/14/2015] [Accepted: 12/07/2016] [Indexed: 01/12/2023] Open
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18
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Iacobas DA. The Genomic Fabric Perspective on the Transcriptome Between Universal Quantifiers and Personalized Genomic Medicine. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s13752-016-0245-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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19
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Vincent BJ, Estrada J, DePace AH. The appeasement of Doug: a synthetic approach to enhancer biology. Integr Biol (Camb) 2016; 8:475-84. [DOI: 10.1039/c5ib00321k] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Ben J. Vincent
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Javier Estrada
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Angela H. DePace
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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20
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Hoermann A, Cicin-Sain D, Jaeger J. A quantitative validated model reveals two phases of transcriptional regulation for the gap gene giant in Drosophila. Dev Biol 2016; 411:325-338. [DOI: 10.1016/j.ydbio.2016.01.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Revised: 12/22/2015] [Accepted: 01/08/2016] [Indexed: 01/05/2023]
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21
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Quantitatively predictable control of Drosophila transcriptional enhancers in vivo with engineered transcription factors. Nat Genet 2016; 48:292-8. [DOI: 10.1038/ng.3509] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2015] [Accepted: 01/15/2016] [Indexed: 12/13/2022]
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22
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McCarthy GD, Drewell RA, Dresch JM. Global sensitivity analysis of a dynamic model for gene expression in Drosophila embryos. PeerJ 2015; 3:e1022. [PMID: 26157608 PMCID: PMC4476099 DOI: 10.7717/peerj.1022] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 05/25/2015] [Indexed: 11/20/2022] Open
Abstract
It is well known that gene regulation is a tightly controlled process in early organismal development. However, the roles of key processes involved in this regulation, such as transcription and translation, are less well understood, and mathematical modeling approaches in this field are still in their infancy. In recent studies, biologists have taken precise measurements of protein and mRNA abundance to determine the relative contributions of key factors involved in regulating protein levels in mammalian cells. We now approach this question from a mathematical modeling perspective. In this study, we use a simple dynamic mathematical model that incorporates terms representing transcription, translation, mRNA and protein decay, and diffusion in an early Drosophila embryo. We perform global sensitivity analyses on this model using various different initial conditions and spatial and temporal outputs. Our results indicate that transcription and translation are often the key parameters to determine protein abundance. This observation is in close agreement with the experimental results from mammalian cells for various initial conditions at particular time points, suggesting that a simple dynamic model can capture the qualitative behavior of a gene. Additionally, we find that parameter sensitivites are temporally dynamic, illustrating the importance of conducting a thorough global sensitivity analysis across multiple time points when analyzing mathematical models of gene regulation.
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Affiliation(s)
| | | | - Jacqueline M Dresch
- Department of Mathematics, Amherst College , Amherst, MA , USA ; Department of Mathematics and Computer Science, Clark University , Worcester, MA , USA
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23
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Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration. PLoS Comput Biol 2015; 11:e1004295. [PMID: 26042810 PMCID: PMC4456145 DOI: 10.1371/journal.pcbi.1004295] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 04/21/2015] [Indexed: 01/18/2023] Open
Abstract
Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form. Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments. However, existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form, shape, and pattern. We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes, producing network models as testable hypotheses explaining regeneration data. Our system integrates a formalization of the published results in planarian regeneration, an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay, and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results. We applied our method to explain the key experiments in planarian regeneration, and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical, pharmacological, and genetic manipulations. Beyond the planarian data, our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine, and represents the first developmental model discovered de novo from morphological outcomes by an automated system.
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24
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Combs PA, Eisen MB. Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols. PeerJ 2015; 3:e869. [PMID: 25834775 PMCID: PMC4380159 DOI: 10.7717/peerj.869] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Accepted: 03/11/2015] [Indexed: 01/23/2023] Open
Abstract
Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in per-gene linearity of response, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.
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Affiliation(s)
- Peter A Combs
- Graduate Program in Biophysics, University of California , Berkeley, CA , USA
| | - Michael B Eisen
- Department of Molecular and Cell Biology, University of California , Berkeley, CA , USA ; Howard Hughes Medical Institute, University of California , Berkeley, CA , USA
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25
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Holloway DM, Spirov AV. Mid-embryo patterning and precision in Drosophila segmentation: Krüppel dual regulation of hunchback. PLoS One 2015; 10:e0118450. [PMID: 25793381 PMCID: PMC4368514 DOI: 10.1371/journal.pone.0118450] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Accepted: 12/15/2014] [Indexed: 12/26/2022] Open
Abstract
In early development, genes are expressed in spatial patterns which later define cellular identities and tissue locations. The mechanisms of such pattern formation have been studied extensively in early Drosophila (fruit fly) embryos. The gap gene hunchback (hb) is one of the earliest genes to be expressed in anterior-posterior (AP) body segmentation. As a transcriptional regulator for a number of downstream genes, the spatial precision of hb expression can have significant effects in the development of the body plan. To investigate the factors contributing to hb precision, we used fine spatial and temporal resolution data to develop a quantitative model for the regulation of hb expression in the mid-embryo. In particular, modelling hb pattern refinement in mid nuclear cleavage cycle 14 (NC14) reveals some of the regulatory contributions of simultaneously-expressed gap genes. Matching the model to recent data from wild-type (WT) embryos and mutants of the gap gene Krüppel (Kr) indicates that a mid-embryo Hb concentration peak important in thoracic development (at parasegment 4, PS4) is regulated in a dual manner by Kr, with low Kr concentration activating hb and high Kr concentration repressing hb. The processes of gene expression (transcription, translation, transport) are intrinsically random. We used stochastic simulations to characterize the noise generated in hb expression. We find that Kr regulation can limit the positional variability of the Hb mid-embryo border. This has been recently corroborated in experimental comparisons of WT and Kr- mutant embryos. Further, Kr regulation can decrease uncertainty in mid-embryo hb expression (i.e. contribute to a smooth Hb boundary) and decrease between-copy transcriptional variability within nuclei. Since many tissue boundaries are first established by interactions between neighbouring gene expression domains, these properties of Hb-Kr dynamics to diminish the effects of intrinsic expression noise may represent a general mechanism contributing to robustness in early development.
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Affiliation(s)
- David M. Holloway
- Mathematics Department, British Columbia Institute of Technology, Burnaby, B.C., V5G 3H2, Canada
- * E-mail:
| | - Alexander V. Spirov
- Computer Science, and Center of Excellence in Wireless and Information Technology, State University of New York, Stony Brook, Stony Brook, New York, United States of America
- The Sechenov Institute of Evolutionary Physiology and Biochemistry, St. Petersburg, Russia
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26
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Niklas KJ, Bondos SE, Dunker AK, Newman SA. Rethinking gene regulatory networks in light of alternative splicing, intrinsically disordered protein domains, and post-translational modifications. Front Cell Dev Biol 2015; 3:8. [PMID: 25767796 PMCID: PMC4341551 DOI: 10.3389/fcell.2015.00008] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 01/26/2015] [Indexed: 11/16/2022] Open
Abstract
Models for genetic regulation and cell fate specification characteristically assume that gene regulatory networks (GRNs) are essentially deterministic and exhibit multiple stable states specifying alternative, but pre-figured cell fates. Mounting evidence shows, however, that most eukaryotic precursor RNAs undergo alternative splicing (AS) and that the majority of transcription factors contain intrinsically disordered protein (IDP) domains whose functionalities are context dependent as well as subject to post-translational modification (PTM). Consequently, many transcription factors do not have fixed cis-acting regulatory targets, and developmental determination by GRNs alone is untenable. Modeling these phenomena requires a multi-scale approach to explain how GRNs operationally interact with the intra- and intercellular environments. Evidence shows that AS, IDP, and PTM complicate gene expression and act synergistically to facilitate and promote time- and cell-specific protein modifications involved in cell signaling and cell fate specification and thereby disrupt a strict deterministic GRN-phenotype mapping. The combined effects of AS, IDP, and PTM give proteomes physiological plasticity, adaptive responsiveness, and developmental versatility without inefficiently expanding genome size. They also help us understand how protein functionalities can undergo major evolutionary changes by buffering mutational consequences.
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Affiliation(s)
- Karl J Niklas
- Plant Biology Section, School of Integrative Plant Science, Cornell University Ithaca, NY, USA
| | - Sarah E Bondos
- Department of Molecular and Cellular Medicine, Texas A&M Health Science Center College Station, TX, USA
| | - A Keith Dunker
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, IN, USA
| | - Stuart A Newman
- Department of Cell Biology and Anatomy, New York Medical College Valhalla, NY, USA
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27
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Staller MV, Fowlkes CC, Bragdon MDJ, Wunderlich Z, Estrada J, DePace AH. A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate. Development 2015; 142:587-96. [PMID: 25605785 PMCID: PMC4302997 DOI: 10.1242/dev.117796] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 12/01/2014] [Indexed: 01/31/2023]
Abstract
In developing embryos, gene regulatory networks drive cells towards discrete terminal fates, a process called canalization. We studied the behavior of the anterior-posterior segmentation network in Drosophila melanogaster embryos by depleting a key maternal input, bicoid (bcd), and measuring gene expression patterns of the network at cellular resolution. This method results in a gene expression atlas containing the levels of mRNA or protein expression of 13 core patterning genes over six time points for every cell of the blastoderm embryo. This is the first cellular resolution dataset of a genetically perturbed Drosophila embryo that captures all cells in 3D. We describe the technical developments required to build this atlas and how the method can be employed and extended by others. We also analyze this novel dataset to characterize the degree and timing of cell fate canalization in the segmentation network. We find that in two layers of this gene regulatory network, following depletion of bcd, individual cells rapidly canalize towards normal cell fates. This result supports the hypothesis that the segmentation network directly canalizes cell fate, rather than an alternative hypothesis whereby cells are initially mis-specified and later eliminated by apoptosis. Our gene expression atlas provides a high resolution picture of a classic perturbation and will enable further computational modeling of canalization and gene regulation in this transcriptional network.
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Affiliation(s)
- Max V Staller
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Charless C Fowlkes
- Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA
| | - Meghan D J Bragdon
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Zeba Wunderlich
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Javier Estrada
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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28
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Shadow enhancers enable Hunchback bifunctionality in the Drosophila embryo. Proc Natl Acad Sci U S A 2015; 112:785-90. [PMID: 25564665 DOI: 10.1073/pnas.1413877112] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Hunchback (Hb) is a bifunctional transcription factor that activates and represses distinct enhancers. Here, we investigate the hypothesis that Hb can activate and repress the same enhancer. Computational models predicted that Hb bifunctionally regulates the even-skipped (eve) stripe 3+7 enhancer (eve3+7) in Drosophila blastoderm embryos. We measured and modeled eve expression at cellular resolution under multiple genetic perturbations and found that the eve3+7 enhancer could not explain endogenous eve stripe 7 behavior. Instead, we found that eve stripe 7 is controlled by two enhancers: the canonical eve3+7 and a sequence encompassing the minimal eve stripe 2 enhancer (eve2+7). Hb bifunctionally regulates eve stripe 7, but it executes these two activities on different pieces of regulatory DNA--it activates the eve2+7 enhancer and represses the eve3+7 enhancer. These two "shadow enhancers" use different regulatory logic to create the same pattern.
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29
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Zabet NR, Adryan B. Estimating binding properties of transcription factors from genome-wide binding profiles. Nucleic Acids Res 2015; 43:84-94. [PMID: 25432957 PMCID: PMC4288167 DOI: 10.1093/nar/gku1269] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Revised: 10/22/2014] [Accepted: 11/19/2014] [Indexed: 12/20/2022] Open
Abstract
The binding of transcription factors (TFs) is essential for gene expression. One important characteristic is the actual occupancy of a putative binding site in the genome. In this study, we propose an analytical model to predict genomic occupancy that incorporates the preferred target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in the case of eukaryotes), the number of TF molecules expected to be bound specifically to the DNA and a parameter that modulates the specificity of the TF. Given actual occupancy data in the form of ChIP-seq profiles, we backwards inferred copy number and specificity for five Drosophila TFs during early embryonic development: Bicoid, Caudal, Giant, Hunchback and Kruppel. Our results suggest that these TFs display thousands of molecules that are specifically bound to the DNA and that whilst Bicoid and Caudal display a higher specificity, the other three TFs (Giant, Hunchback and Kruppel) display lower specificity in their binding (despite having PWMs with higher information content). This study gives further weight to earlier investigations into TF copy numbers that suggest a significant proportion of molecules are not bound specifically to the DNA.
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Affiliation(s)
- Nicolae Radu Zabet
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK
| | - Boris Adryan
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK
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30
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Drewell RA, Nevarez MJ, Kurata JS, Winkler LN, Li L, Dresch JM. Deciphering the combinatorial architecture of a Drosophila homeotic gene enhancer. Mech Dev 2014; 131:68-77. [PMID: 24514265 DOI: 10.1016/j.mod.2013.10.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Revised: 10/07/2013] [Accepted: 10/08/2013] [Indexed: 01/20/2023]
Abstract
In Drosophila, the 330 kb bithorax complex regulates cellular differentiation along the anterior–posterior axis during development in the thorax and abdomen and is comprised of three homeotic genes: Ultrabithorax, abdominal-A, and Abdominal-B. The expression of each of these genes is in turn controlled through interactions between transcription factors and a number of cis-regulatory modules in the neighboring intergenic regions. In this study, we examine how the sequence architecture of transcription factor binding sites mediates the functional activity of one of these cis-regulatory modules. Using computational, mathematical modeling and experimental molecular genetic approaches we investigate the IAB7b enhancer, which regulates Abdominal-B expression specifically in the presumptive seventh and ninth abdominal segments of the early embryo. A cross-species comparison of the IAB7b enhancer reveals an evolutionarily conserved signature motif containing two FUSHI-TARAZU activator transcription factor binding sites. We find that the transcriptional repressors KNIRPS, KRUPPEL and GIANT are able to restrict reporter gene expression to the posterior abdominal segments, using different molecular mechanisms including short-range repression and competitive binding. Additionally, we show the functional importance of the spacing between the two FUSHI-TARAZU binding sites and discuss the potential importance of cooperativity for transcriptional activation. Our results demonstrate that the transcriptional output of the IAB7b cis-regulatory module relies on a complex set of combinatorial inputs mediated by specific transcription factor binding and that the sequence architecture at this enhancer is critical to maintain robust regulatory function.
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31
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Ezer D, Zabet NR, Adryan B. Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression. Comput Struct Biotechnol J 2014; 10:63-9. [PMID: 25349675 PMCID: PMC4204428 DOI: 10.1016/j.csbj.2014.07.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The organization of binding sites in cis-regulatory elements (CREs) can influence gene expression through a combination of physical mechanisms, ranging from direct interactions between TF molecules to DNA looping and transient chromatin interactions. The study of simple and common building blocks in promoters and other CREs allows us to dissect how all of these mechanisms work together. Many adjacent TF binding sites for the same TF species form homotypic clusters, and these CRE architecture building blocks serve as a prime candidate for understanding interacting transcriptional mechanisms. Homotypic clusters are prevalent in both bacterial and eukaryotic genomes, and are present in both promoters as well as more distal enhancer/silencer elements. Here, we review previous theoretical and experimental studies that show how the complexity (number of binding sites) and spatial organization (distance between sites and overall distance from transcription start sites) of homotypic clusters influence gene expression. In particular, we describe how homotypic clusters modulate the temporal dynamics of TF binding, a mechanism that can affect gene expression, but which has not yet been sufficiently characterized. We propose further experiments on homotypic clusters that would be useful in developing mechanistic models of gene expression.
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Affiliation(s)
- Daphne Ezer
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - Nicolae Radu Zabet
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - Boris Adryan
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
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32
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Jug F, Pietzsch T, Preibisch S, Tomancak P. Bioimage Informatics in the context of Drosophila research. Methods 2014; 68:60-73. [PMID: 24732429 DOI: 10.1016/j.ymeth.2014.04.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 04/02/2014] [Accepted: 04/04/2014] [Indexed: 01/05/2023] Open
Abstract
Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development.
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Affiliation(s)
- Florian Jug
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
| | - Tobias Pietzsch
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
| | - Stephan Preibisch
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Anatomy and Structural Biology, Gruss Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Pavel Tomancak
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.
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33
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Martinez C, Rest JS, Kim AR, Ludwig M, Kreitman M, White K, Reinitz J. Ancestral resurrection of the Drosophila S2E enhancer reveals accessible evolutionary paths through compensatory change. Mol Biol Evol 2014; 31:903-16. [PMID: 24408913 DOI: 10.1093/molbev/msu042] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Upstream regulatory sequences that control gene expression evolve rapidly, yet the expression patterns and functions of most genes are typically conserved. To address this paradox, we have reconstructed computationally and resurrected in vivo the cis-regulatory regions of the ancestral Drosophila eve stripe 2 element and evaluated its evolution using a mathematical model of promoter function. Our feed-forward transcriptional model predicts gene expression patterns directly from enhancer sequence. We used this functional model along with phylogenetics to generate a set of possible ancestral eve stripe 2 sequences for the common ancestors of 1) D. simulans and D. sechellia; 2) D. melanogaster, D. simulans, and D. sechellia; and 3) D. erecta and D. yakuba. These ancestral sequences were synthesized and resurrected in vivo. Using a combination of quantitative and computational analysis, we find clear support for functional compensation between the binding sites for Bicoid, Giant, and Krüppel over the course of 40-60 My of Drosophila evolution. We show that this compensation is driven by a coupling interaction between Bicoid activation and repression at the anterior and posterior border necessary for proper placement of the anterior stripe 2 border. A multiplicity of mechanisms for binding site turnover exemplified by Bicoid, Giant, and Krüppel sites, explains how rapid sequence change may occur while maintaining the function of the cis-regulatory element.
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Affiliation(s)
- Carlos Martinez
- Institute for Genomics and Systems Biology, University of Chicago
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34
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Chakravarti A, Clark AG, Mootha VK. Distilling pathophysiology from complex disease genetics. Cell 2013; 155:21-6. [PMID: 24074858 DOI: 10.1016/j.cell.2013.09.001] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2013] [Indexed: 01/14/2023]
Abstract
Technologies for genome-wide sequence interrogation have dramatically improved our ability to identify loci associated with complex human disease. However, a chasm remains between correlations and causality that stems, in part, from a limiting theoretical framework derived from Mendelian genetics and an incomplete understanding of disease physiology. Here we propose a set of criteria, akin to Koch's postulates for infectious disease, for assigning causality between genetic variants and human disease phenotypes.
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35
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Abstract
Computer simulations and quantitative imaging of Drosophila embryos have been used to recreate the dynamic activities of a complex transcriptional enhancer.
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Affiliation(s)
- Michael Levine
- is at the Center for Integrative Genomics, Division of Genetics, Genomics, and Development, Department of Molecular and Cell Biology , University of California, Berkeley , Berkeley , United States
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