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Loell KJ, Friedman RZ, Myers CA, Corbo JC, Cohen BA, White MA. Transcription factor interactions explain the context-dependent activity of CRX binding sites. PLoS Comput Biol 2024; 20:e1011802. [PMID: 38227575 PMCID: PMC10817189 DOI: 10.1371/journal.pcbi.1011802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 01/26/2024] [Accepted: 01/06/2024] [Indexed: 01/18/2024] Open
Abstract
The effects of transcription factor binding sites (TFBSs) on the activity of a cis-regulatory element (CRE) depend on the local sequence context. In rod photoreceptors, binding sites for the transcription factor (TF) Cone-rod homeobox (CRX) occur in both enhancers and silencers, but the sequence context that determines whether CRX binding sites contribute to activation or repression of transcription is not understood. To investigate the context-dependent activity of CRX sites, we fit neural network-based models to the activities of synthetic CREs composed of photoreceptor TFBSs. The models revealed that CRX binding sites consistently make positive, independent contributions to CRE activity, while negative homotypic interactions between sites cause CREs composed of multiple CRX sites to function as silencers. The effects of negative homotypic interactions can be overcome by the presence of other TFBSs that either interact cooperatively with CRX sites or make independent positive contributions to activity. The context-dependent activity of CRX sites is thus determined by the balance between positive heterotypic interactions, independent contributions of TFBSs, and negative homotypic interactions. Our findings explain observed patterns of activity among genomic CRX-bound enhancers and silencers, and suggest that enhancers may require diverse TFBSs to overcome negative homotypic interactions between TFBSs.
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Affiliation(s)
- Kaiser J. Loell
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
| | - Ryan Z. Friedman
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
| | - Connie A. Myers
- Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
| | - Joseph C. Corbo
- Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
| | - Barak A. Cohen
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
| | - Michael A. White
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
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2
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Harden TT, Vincent BJ, DePace AH. Transcriptional activators in the early Drosophila embryo perform different kinetic roles. Cell Syst 2023; 14:258-272.e4. [PMID: 37080162 PMCID: PMC10473017 DOI: 10.1016/j.cels.2023.03.006] [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: 03/08/2021] [Revised: 06/26/2022] [Accepted: 03/21/2023] [Indexed: 04/22/2023]
Abstract
Combinatorial regulation of gene expression by transcription factors (TFs) may in part arise from kinetic synergy-wherein TFs regulate different steps in the transcription cycle. Kinetic synergy requires that TFs play distinguishable kinetic roles. Here, we used live imaging to determine the kinetic roles of three TFs that activate transcription in the Drosophila embryo-Zelda, Bicoid, and Stat92E-by introducing their binding sites into the even-skipped stripe 2 enhancer. These TFs influence different sets of kinetic parameters, and their influence can change over time. All three TFs increased the fraction of transcriptionally active nuclei; Zelda also shortened the first-passage time into transcription and regulated the interval between transcription events. Stat92E also increased the lifetimes of active transcription. Different TFs can therefore play distinct kinetic roles in activating the transcription. This has consequences for understanding the composition and flexibility of regulatory DNA sequences and the biochemical function of TFs. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Timothy T Harden
- 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
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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3
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Fisher WW, Hammonds AS, Weiszmann R, Booth BW, Gevirtzman L, Patton JEJ, Kubo CA, Waterston RH, Celniker SE. A modERN resource: identification of Drosophila transcription factor candidate target genes using RNAi. Genetics 2023; 223:iyad004. [PMID: 36652461 PMCID: PMC10078917 DOI: 10.1093/genetics/iyad004] [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: 11/18/2022] [Revised: 11/18/2022] [Accepted: 12/22/2022] [Indexed: 01/19/2023] Open
Abstract
Transcription factors (TFs) play a key role in development and in cellular responses to the environment by activating or repressing the transcription of target genes in precise spatial and temporal patterns. In order to develop a catalog of target genes of Drosophila melanogaster TFs, the modERN consortium systematically knocked down the expression of TFs using RNAi in whole embryos followed by RNA-seq. We generated data for 45 TFs which have 18 different DNA-binding domains and are expressed in 15 of the 16 organ systems. The range of inactivation of the targeted TFs by RNAi ranged from log2fold change -3.52 to +0.49. The TFs also showed remarkable heterogeneity in the numbers of candidate target genes identified, with some generating thousands of candidates and others only tens. We present detailed analysis from five experiments, including those for three TFs that have been the focus of previous functional studies (ERR, sens, and zfh2) and two previously uncharacterized TFs (sens-2 and CG32006), as well as short vignettes for selected additional experiments to illustrate the utility of this resource. The RNA-seq datasets are available through the ENCODE DCC (http://encodeproject.org) and the Sequence Read Archive (SRA). TF and target gene expression patterns can be found here: https://insitu.fruitfly.org. These studies provide data that facilitate scientific inquiries into the functions of individual TFs in key developmental, metabolic, defensive, and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks during embryogenesis.
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Affiliation(s)
- William W Fisher
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ann S Hammonds
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Richard Weiszmann
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Benjamin W Booth
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Louis Gevirtzman
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Jaeda E J Patton
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Connor A Kubo
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Robert H Waterston
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Susan E Celniker
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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4
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Kim YJ, Rhee K, Liu J, Jeammet S, Turner MA, Small SJ, Garcia HG. Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer. eLife 2022; 11:73395. [PMID: 36503705 PMCID: PMC9836395 DOI: 10.7554/elife.73395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity captures the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature.
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Affiliation(s)
- Yang Joon Kim
- Chan Zuckerberg Biohub, San Francisco, United States
| | - Kaitlin Rhee
- Department of Chemical Biology, University of California, Berkeley, Berkeley, United States
| | - Jonathan Liu
- Department of Physics, University of California, Berkeley, Berkeley, United States
| | - Selene Jeammet
- Department of Biology, Ecole Polytechnique, Paris, France
| | - Meghan A Turner
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States
| | - Stephen J Small
- Department of Biology, New York University, New York, United States
| | - Hernan G Garcia
- Chan Zuckerberg Biohub, San Francisco, United States.,Department of Physics, University of California, Berkeley, Berkeley, United States.,Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, United States
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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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Binary Expression Enhances Reliability of Messaging in Gene Networks. ENTROPY 2020; 22:e22040479. [PMID: 33286254 PMCID: PMC7516962 DOI: 10.3390/e22040479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 04/20/2020] [Accepted: 04/20/2020] [Indexed: 01/31/2023]
Abstract
The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by Shannon's entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter with long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a binary gene expression regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon's theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.
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7
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Barr K, Reinitz J, Radulescu O. An in silico analysis of robust but fragile gene regulation links enhancer length to robustness. PLoS Comput Biol 2019; 15:e1007497. [PMID: 31730659 PMCID: PMC6881076 DOI: 10.1371/journal.pcbi.1007497] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 11/27/2019] [Accepted: 10/22/2019] [Indexed: 12/31/2022] Open
Abstract
Organisms must ensure that expression of genes is directed to the appropriate tissues at the correct times, while simultaneously ensuring that these gene regulatory systems are robust to perturbation. This idea is captured by a mathematical concept called r-robustness, which says that a system is robust to a perturbation in up to r - 1 randomly chosen parameters. r-robustness implies that the biological system has a small number of sensitive parameters and that this number can be used as a robustness measure. In this work we use this idea to investigate the robustness of gene regulation using a sequence level model of the Drosophila melanogaster gene even-skipped. We consider robustness with respect to mutations of the enhancer sequence and with respect to changes of the transcription factor concentrations. We find that gene regulation is r-robust with respect to mutations in the enhancer sequence and identify a number of sensitive nucleotides. In both natural and in silico predicted enhancers, the number of nucleotides that are sensitive to mutation correlates negatively with the length of the sequence, meaning that longer sequences are more robust. The exact degree of robustness obtained is dependent not only on DNA sequence, but also on the local concentration of regulatory factors. We find that gene regulation can be remarkably sensitive to changes in transcription factor concentrations at the boundaries of expression features, while it is robust to perturbation elsewhere.
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Affiliation(s)
- Kenneth Barr
- Department of Genetic Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - John Reinitz
- Departments of Statistics, Ecology & Evolution, Molecular Genetics & Cell Biology, University of Chicago, Chicago, Illinois, United States of America
| | - Ovidiu Radulescu
- LPHI UMR CNRS 5235, University of Montpellier, Montpellier, France
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8
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Abstract
We consider evolution of a large population, where fitness of each organism is defined by many phenotypical traits. These traits result from expression of many genes. Under some assumptions on fitness we prove that such model organisms are capable, to some extent, to recognize the fitness landscape. That fitness landscape learning sharply reduces the number of mutations needed for adaptation. Moreover, this learning increases phenotype robustness with respect to mutations, i.e., canalizes the phenotype. We show that learning and canalization work only when evolution is gradual. Organisms can be adapted to many constraints associated with a hard environment, if that environment becomes harder step by step. Our results explain why evolution can involve genetic changes of a relatively large effect and why the total number of changes are surprisingly small.
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Affiliation(s)
- John Reinitz
- Departments of Statistics, Ecology and Evolution, Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, USA
| | - Sergey Vakulenko
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russian Federation
| | - Dmitri Grigoriev
- CNRS, Mathématiques, Université de Lille, Villeneuve d'Ascq, France
| | - Andreas Weber
- Department of Computer Science, University of Bonn, Bonn, Germany
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9
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Abstract
We consider evolution of a large population, where fitness of each organism is defined by many phenotypical traits. These traits result from expression of many genes. Under some assumptions on fitness we prove that such model organisms are capable, to some extent, to recognize the fitness landscape. That fitness landscape learning sharply reduces the number of mutations needed for adaptation. Moreover, this learning increases phenotype robustness with respect to mutations, i.e., canalizes the phenotype. We show that learning and canalization work only when evolution is gradual. Organisms can be adapted to many constraints associated with a hard environment, if that environment becomes harder step by step. Our results explain why evolution can involve genetic changes of a relatively large effect and why the total number of changes are surprisingly small.
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Affiliation(s)
- John Reinitz
- Departments of Statistics, Ecology and Evolution, Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, USA
| | - Sergey Vakulenko
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russian Federation
| | - Dmitri Grigoriev
- CNRS, Mathématiques, Université de Lille, Villeneuve d'Ascq, France
| | - Andreas Weber
- Department of Computer Science, University of Bonn, Bonn, Germany
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10
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Sabino AU, Vasconcelos MFS, Sittoni MY, Lautenschlager WW, Queiroga AS, Morais MCC, Ramos AF. Lessons and perspectives for applications of stochastic models in biological and cancer research. Clinics (Sao Paulo) 2018; 73:e536s. [PMID: 30281699 PMCID: PMC6131223 DOI: 10.6061/clinics/2018/e536s] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 06/14/2018] [Indexed: 11/18/2022] Open
Abstract
The effects of randomness, an unavoidable feature of intracellular environments, are observed at higher hierarchical levels of living matter organization, such as cells, tissues, and organisms. Additionally, the many compounds interacting as a well-orchestrated network of reactions increase the difficulties of assessing these systems using only experiments. This limitation indicates that elucidation of the dynamics of biological systems is a complex task that will benefit from the establishment of principles to help describe, categorize, and predict the behavior of these systems. The theoretical machinery already available, or ones to be discovered to help solve biological problems, might play an important role in these processes. Here, we demonstrate the application of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that have been employed to provide the reader with a biologically motivated phenomenological perspective of the use of theoretical methods. Finally, we end this review with a discussion of new research perspectives motivated by our results.
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Affiliation(s)
- Alan U Sabino
- Escola de Artes Ciências e Humanidades (EACH), Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Miguel FS Vasconcelos
- Escola de Artes Ciências e Humanidades (EACH), Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Misaki Yamada Sittoni
- Escola de Artes Ciências e Humanidades (EACH), Universidade de Sao Paulo, Sao Paulo, SP, BR
- Departamento de Radiologia e Oncologia, Instituto do Cancer do Estado de Sao Paulo (ICESP), Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | | | - Alexandre S Queiroga
- Escola de Artes Ciências e Humanidades (EACH), Universidade de Sao Paulo, Sao Paulo, SP, BR
- Departamento de Radiologia e Oncologia, Instituto do Cancer do Estado de Sao Paulo (ICESP), Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Mauro CC Morais
- Escola de Artes Ciências e Humanidades (EACH), Universidade de Sao Paulo, Sao Paulo, SP, BR
- Departamento de Radiologia e Oncologia, Instituto do Cancer do Estado de Sao Paulo (ICESP), Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Alexandre F Ramos
- Escola de Artes Ciências e Humanidades (EACH), Universidade de Sao Paulo, Sao Paulo, SP, BR
- Departamento de Radiologia e Oncologia, Instituto do Cancer do Estado de Sao Paulo (ICESP), Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, BR
- *Corresponding author. E-mail:
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11
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Datta RR, Ling J, Kurland J, Ren X, Xu Z, Yucel G, Moore J, Shokri L, Baker I, Bishop T, Struffi P, Levina R, Bulyk ML, Johnston RJ, Small S. A feed-forward relay integrates the regulatory activities of Bicoid and Orthodenticle via sequential binding to suboptimal sites. Genes Dev 2018; 32:723-736. [PMID: 29764918 PMCID: PMC6004077 DOI: 10.1101/gad.311985.118] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/17/2018] [Indexed: 11/25/2022]
Abstract
Datta et al. define three major classes of enhancers that are differentially sensitive to binding and transcriptional activation by Bicoid (Bcd) and Orthodenticle (Otd). The specific activities of enhancers in each class are mediated by DNA motif variants preferentially bound by Bcd or Otd and the presence or absence of sites for cofactors that interact with these proteins. The K50 (lysine at amino acid position 50) homeodomain (HD) protein Orthodenticle (Otd) is critical for anterior patterning and brain and eye development in most metazoans. In Drosophila melanogaster, another K50HD protein, Bicoid (Bcd), has evolved to replace Otd's ancestral function in embryo patterning. Bcd is distributed as a long-range maternal gradient and activates transcription of a large number of target genes, including otd. Otd and Bcd bind similar DNA sequences in vitro, but how their transcriptional activities are integrated to pattern anterior regions of the embryo is unknown. Here we define three major classes of enhancers that are differentially sensitive to binding and transcriptional activation by Bcd and Otd. Class 1 enhancers are initially activated by Bcd, and activation is transferred to Otd via a feed-forward relay (FFR) that involves sequential binding of the two proteins to the same DNA motif. Class 2 enhancers are activated by Bcd and maintained by an Otd-independent mechanism. Class 3 enhancers are never bound by Bcd, but Otd binds and activates them in a second wave of zygotic transcription. The specific activities of enhancers in each class are mediated by DNA motif variants preferentially bound by Bcd or Otd and the presence or absence of sites for cofactors that interact with these proteins. Our results define specific patterning roles for Bcd and Otd and provide mechanisms for coordinating the precise timing of gene expression patterns during embryonic development.
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Affiliation(s)
- Rhea R Datta
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Jia Ling
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Jesse Kurland
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Xiaotong Ren
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Zhe Xu
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Gozde Yucel
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Jackie Moore
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Leila Shokri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Isabel Baker
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Timothy Bishop
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Paolo Struffi
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Rimma Levina
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Robert J Johnston
- Department of Biology, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Stephen Small
- Center for Developmental Genetics, Department of Biology, New York University, New York, New York 10003, USA
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