1
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Recio PS, Mitra NJ, Shively CA, Song D, Jaramillo G, Lewis KS, Chen X, Mitra R. Zinc cluster transcription factors frequently activate target genes using a non-canonical half-site binding mode. Nucleic Acids Res 2023; 51:5006-5021. [PMID: 37125648 PMCID: PMC10250231 DOI: 10.1093/nar/gkad320] [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: 12/09/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 05/02/2023] Open
Abstract
Gene expression changes are orchestrated by transcription factors (TFs), which bind to DNA to regulate gene expression. It remains surprisingly difficult to predict basic features of the transcriptional process, including in vivo TF occupancy. Existing thermodynamic models of TF function are often not concordant with experimental measurements, suggesting undiscovered biology. Here, we analyzed one of the most well-studied TFs, the yeast zinc cluster Gal4, constructed a Shea-Ackers thermodynamic model to describe its binding, and compared the results of this model to experimentally measured Gal4p binding in vivo. We found that at many promoters, the model predicted no Gal4p binding, yet substantial binding was observed. These outlier promoters lacked canonical binding motifs, and subsequent investigation revealed Gal4p binds unexpectedly to DNA sequences with high densities of its half site (CGG). We confirmed this novel mode of binding through multiple experimental and computational paradigms; we also found most other zinc cluster TFs we tested frequently utilize this binding mode, at 27% of their targets on average. Together, these results demonstrate a novel mode of binding where zinc clusters, the largest class of TFs in yeast, bind DNA sequences with high densities of half sites.
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Affiliation(s)
- Pamela S Recio
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Nikhil J Mitra
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Christian A Shively
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - David Song
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Grace Jaramillo
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Kristine Shady Lewis
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Xuhua Chen
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Robi D Mitra
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- McDonnell Genome Institute, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
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2
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Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis. MATHEMATICS 2022. [DOI: 10.3390/math10132169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Modelling is a tool used to decipher the biochemical mechanisms involved in transcriptional control. Experimental evidence in genetics is usually supported by theoretical models in order to evaluate the effects of all the possible interactions that can occur in these complicated processes. Models derived from the thermodynamic method are critical in this labour because they are able to take into account multiple mechanisms operating simultaneously at the molecular micro-scale and relate them to transcriptional initiation at the tissular macro-scale. This work is devoted to adapting computational techniques to this context in order to theoretically evaluate the role played by several biochemical mechanisms. The interest of this theoretical analysis relies on the fact that it can be contrasted against those biological experiments where the response to perturbations in the transcriptional machinery environment is evaluated in terms of genetically activated/repressed regions. The theoretical reproduction of these experiments leads to a sensitivity analysis whose results are expressed in terms of the elasticity of a threshold function determining those activated/repressed regions. The study of this elasticity function in thermodynamic models already proposed in the literature reveals that certain modelling approaches can alter the balance between the biochemical mechanisms considered, and this can cause false/misleading outcomes. The reevaluation of classical thermodynamic models gives us a more accurate and complete picture of the interactions involved in gene regulation and transcriptional control, which enables more specific predictions. This sensitivity approach provides a definite advantage in the interpretation of a wide range of genetic experimental results.
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3
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Villaluenga JP, Cao-García FJ. Cooperative kinetics of ligand binding to linear polymers. Comput Struct Biotechnol J 2022; 20:521-533. [PMID: 35495112 PMCID: PMC9019704 DOI: 10.1016/j.csbj.2021.12.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/27/2021] [Accepted: 12/30/2021] [Indexed: 11/29/2022] Open
Abstract
Cooperative kinetic equation for large ligands binding to long polymers. Cooperativity in general affects binding and release rates. Appropriate counting of the available binding sites for a ligand to a linear polymer. Positive cooperativity increases polymer coverage by the ligand. Large ligand size reduces cooperativity effects.
Ligands change the chemical and mechanical properties of polymers. In particular, single strand binding protein (SSB) non-specifically bounds to single-stranded DNA (ssDNA), modifying the ssDNA stiffness and the DNA replication rate, as recently measured with single-molecule techniques. SSB is a large ligand presenting cooperativity in some of its binding modes. We aim to develop an accurate kinetic model for the cooperative binding kinetics of large ligands. Cooperativity accounts for the changes in the affinity of a ligand to the polymer due to the presence of another bound ligand. Large ligands, attaching to several binding sites, require a detailed counting of the available binding possibilities. This counting has been done by McGhee and von Hippel to obtain the equilibrium state of the ligands-polymer complex. The same procedure allows to obtain the kinetic equations for the cooperative binding of ligands to long polymers, for all ligand sizes. Here, we also derive approximate cooperative kinetic equations in the large ligand limit, at the leading and next-to-leading orders. We found cooperativity is negligible at the leading-order, and appears at the next-to-leading order. Positive cooperativity (increased affinity) can be originated by increased binding affinity or by decreased release affinity, implying different kinetics. Nevertheless, the equilibrium state is independent of the origin of cooperativity and only depends on the overall increase in affinity. Next-to-leading approximation is found to be accurate, particularly for small cooperativity. These results allow to understand and characterize relevant ligand binding processes, as the binding kinetics of SSB to ssDNA, which has been reported to affect the DNA replication rate for several SSB-polymerase pairs.
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Affiliation(s)
- Juan P.G. Villaluenga
- Departamento de Estructura de la Materia, Física Térmica y Electrónica, Universidad Complutense de Madrid, Plaza de Ciencias, 1, 28040 Madrid, Spain
- Corresponding author.
| | - Francisco Javier Cao-García
- Departamento de Estructura de la Materia, Física Térmica y Electrónica, Universidad Complutense de Madrid, Plaza de Ciencias, 1, 28040 Madrid, Spain
- Instituto Madrileño de Estudios Avanzados en Nanociencia, IMDEA Nanociencia, Calle Faraday, 9, 28049 Madrid, Spain
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4
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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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5
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Mitra S, Zhong J, Tran TQ, MacAlpine DM, Hartemink AJ. RoboCOP: jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data. Nucleic Acids Res 2021; 49:7925-7938. [PMID: 34255854 PMCID: PMC8373080 DOI: 10.1093/nar/gkab553] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 05/28/2021] [Accepted: 07/08/2021] [Indexed: 01/25/2023] Open
Abstract
Chromatin is a tightly packaged structure of DNA and protein within the nucleus of a cell. The arrangement of different protein complexes along the DNA modulates and is modulated by gene expression. Measuring the binding locations and occupancy levels of different transcription factors (TFs) and nucleosomes is therefore crucial to understanding gene regulation. Antibody-based methods for assaying chromatin occupancy are capable of identifying the binding sites of specific DNA binding factors, but only one factor at a time. In contrast, epigenomic accessibility data like MNase-seq, DNase-seq, and ATAC-seq provide insight into the chromatin landscape of all factors bound along the genome, but with little insight into the identities of those factors. Here, we present RoboCOP, a multivariate state space model that integrates chromatin accessibility data with nucleotide sequence to jointly compute genome-wide probabilistic scores of nucleosome and TF occupancy, for hundreds of different factors. We apply RoboCOP to MNase-seq and ATAC-seq data to elucidate the protein-binding landscape of nucleosomes and 150 TFs across the yeast genome, and show that our model makes better predictions than existing methods. We also compute a chromatin occupancy profile of the yeast genome under cadmium stress, revealing chromatin dynamics associated with transcriptional regulation.
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Affiliation(s)
- Sneha Mitra
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Jianling Zhong
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA
| | - Trung Q Tran
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - David M MacAlpine
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA.,Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA
| | - Alexander J Hartemink
- Department of Computer Science, Duke University, Durham, NC 27708, USA.,Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA
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6
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Loop competition and extrusion model predicts CTCF interaction specificity. Nat Commun 2021; 12:1046. [PMID: 33594051 PMCID: PMC7886907 DOI: 10.1038/s41467-021-21368-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 01/22/2021] [Indexed: 12/20/2022] Open
Abstract
Three-dimensional chromatin looping interactions play an important role in constraining enhancer–promoter interactions and mediating transcriptional gene regulation. CTCF is thought to play a critical role in the formation of these loops, but the specificity of which CTCF binding events form loops and which do not is difficult to predict. Loops often have convergent CTCF binding site motif orientation, but this constraint alone is only weakly predictive of genome-wide interaction data. Here we present an easily interpretable and simple mathematical model of CTCF mediated loop formation which is consistent with Cohesin extrusion and can predict ChIA-PET CTCF looping interaction measurements with high accuracy. Competition between overlapping loops is a critical determinant of loop specificity. We show that this model is consistent with observed chromatin interaction frequency changes induced by CTCF binding site deletion, inversion, and mutation, and is also consistent with observed constraints on validated enhancer–promoter interactions. Boundaries of topologically associated domains in genomes are marked by CTCF and cohesin binding. Here the authors predict CTCF interaction specificity by building a simple mathematical model with features including loop competition and extrusion.
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7
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Abstract
Determining whether and how a gene is transcribed are two of the central processes of life. The conceptual basis for understanding such gene regulation arose from pioneering biophysical studies in eubacteria. However, eukaryotic genomes exhibit vastly greater complexity, which raises questions not addressed by this bacterial paradigm. First, how is information integrated from many widely separated binding sites to determine how a gene is transcribed? Second, does the presence of multiple energy-expending mechanisms, which are absent from eubacterial genomes, indicate that eukaryotes are capable of improved forms of genetic information processing? An updated biophysical foundation is needed to answer such questions. We describe the linear framework, a graph-based approach to Markov processes, and show that it can accommodate many previous studies in the field. Under the assumption of thermodynamic equilibrium, we introduce a language of higher-order cooperativities and show how it can rigorously quantify gene regulatory properties suggested by experiment. We point out that fundamental limits to information processing arise at thermodynamic equilibrium and can only be bypassed through energy expenditure. Finally, we outline some of the mathematical challenges that must be overcome to construct an improved biophysical understanding of gene regulation.
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Affiliation(s)
- Felix Wong
- Institute for Medical Engineering & Science, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Jeremy Gunawardena
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA;
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8
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Shokri L, Inukai S, Hafner A, Weinand K, Hens K, Vedenko A, Gisselbrecht SS, Dainese R, Bischof J, Furger E, Feuz JD, Basler K, Deplancke B, Bulyk ML. A Comprehensive Drosophila melanogaster Transcription Factor Interactome. Cell Rep 2020; 27:955-970.e7. [PMID: 30995488 PMCID: PMC6485956 DOI: 10.1016/j.celrep.2019.03.071] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/04/2019] [Accepted: 03/18/2019] [Indexed: 12/14/2022] Open
Abstract
Combinatorial interactions among transcription factors (TFs) play essential roles in generating gene expression specificity and diversity in metazoans. Using yeast 2-hybrid (Y2H) assays on nearly all sequence-specific Drosophila TFs, we identified 1,983 protein-protein interactions (PPIs), more than doubling the number of currently known PPIs among Drosophila TFs. For quality assessment, we validated a subset of our interactions using MITOMI and bimolecular fluorescence complementation assays. We combined our interactome with prior PPI data to generate an integrated Drosophila TF-TF binary interaction network. Our analysis of ChIP-seq data, integrating PPI and gene expression information, uncovered different modes by which interacting TFs are recruited to DNA. We further demonstrate the utility of our Drosophila interactome in shedding light on human TF-TF interactions. This study reveals how TFs interact to bind regulatory elements in vivo and serves as a resource of Drosophila TF-TF binary PPIs for understanding tissue-specific gene regulation. Combinatorial regulation by transcription factors (TFs) is one mechanism for achieving condition and tissue-specific gene regulation. Shokri et al. mapped TF-TF interactions between most Drosophila TFs, reporting a comprehensive TF-TF network integrated with previously known interactions. They used this network to discern distinct TF-DNA binding modes.
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Affiliation(s)
- Leila Shokri
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Sachi Inukai
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Antonina Hafner
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Systems Biology Graduate Program, Harvard University, Cambridge, MA 02138, USA; Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Kathryn Weinand
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Bioinformatics and Integrative Genomics Ph.D. Program, Harvard University, Cambridge, MA 02138, USA
| | - Korneel Hens
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anastasia Vedenko
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Stephen S Gisselbrecht
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Riccardo Dainese
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Johannes Bischof
- Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Edy Furger
- Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Jean-Daniel Feuz
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Konrad Basler
- Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Martha L Bulyk
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Systems Biology Graduate Program, Harvard University, Cambridge, MA 02138, USA; Bioinformatics and Integrative Genomics Ph.D. Program, Harvard University, Cambridge, MA 02138, USA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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9
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Ray JP, de Boer CG, Fulco CP, Lareau CA, Kanai M, Ulirsch JC, Tewhey R, Ludwig LS, Reilly SK, Bergman DT, Engreitz JM, Issner R, Finucane HK, Lander ES, Regev A, Hacohen N. Prioritizing disease and trait causal variants at the TNFAIP3 locus using functional and genomic features. Nat Commun 2020; 11:1237. [PMID: 32144282 PMCID: PMC7060350 DOI: 10.1038/s41467-020-15022-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 02/17/2020] [Indexed: 12/19/2022] Open
Abstract
Genome-wide association studies have associated thousands of genetic variants with complex traits and diseases, but pinpointing the causal variant(s) among those in tight linkage disequilibrium with each associated variant remains a major challenge. Here, we use seven experimental assays to characterize all common variants at the multiple disease-associated TNFAIP3 locus in five disease-relevant immune cell lines, based on a set of features related to regulatory potential. Trait/disease-associated variants are enriched among SNPs prioritized based on either: (1) residing within CRISPRi-sensitive regulatory regions, or (2) localizing in a chromatin accessible region while displaying allele-specific reporter activity. Of the 15 trait/disease-associated haplotypes at TNFAIP3, 9 have at least one variant meeting one or both of these criteria, 5 of which are further supported by genetic fine-mapping. Our work provides a comprehensive strategy to characterize genetic variation at important disease-associated loci, and aids in the effort to identify trait causal genetic variants.
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Affiliation(s)
- John P Ray
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Carl G de Boer
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Caleb A Lareau
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, 02115, USA
| | - Masahiro Kanai
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, 02115, USA
| | - Jacob C Ulirsch
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, 02115, USA
| | - Ryan Tewhey
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Leif S Ludwig
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Steven K Reilly
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Drew T Bergman
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Harvard Society of Fellows, Harvard University, Cambridge, MA, 02138, USA
| | - Robbyn Issner
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.
- Howard Hughes Medical Institute, Cambridge, MA, 02142, USA.
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, 02114, USA.
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10
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Mann M, Mehta A, de Boer CG, Kowalczyk MS, Lee K, Haldeman P, Rogel N, Knecht AR, Farouq D, Regev A, Baltimore D. Heterogeneous Responses of Hematopoietic Stem Cells to Inflammatory Stimuli Are Altered with Age. Cell Rep 2019; 25:2992-3005.e5. [PMID: 30540934 DOI: 10.1016/j.celrep.2018.11.056] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 10/05/2018] [Accepted: 11/13/2018] [Indexed: 12/30/2022] Open
Abstract
Long-term hematopoietic stem cells (LT-HSCs) maintain hematopoietic output throughout an animal's lifespan. However, with age, the balance is disrupted, and LT-HSCs produce a myeloid-biased output, resulting in poor immune responses to infectious challenge and the development of myeloid leukemias. Here, we show that young and aged LT-HSCs respond differently to inflammatory stress, such that aged LT-HSCs produce a cell-intrinsic, myeloid-biased expression program. Using single-cell RNA sequencing (scRNA-seq), we identify a myeloid-biased subset within the LT-HSC population (mLT-HSCs) that is prevalent among aged LT-HSCs. We identify CD61 as a marker of mLT-HSCs and show that CD61-high LT-HSCs are uniquely primed to respond to acute inflammatory challenge. We predict that several transcription factors regulate the mLT-HSCs gene program and show that Klf5, Ikzf1, and Stat3 play an important role in age-related inflammatory myeloid bias. We have therefore identified and isolated an LT-HSC subset that regulates myeloid versus lymphoid balance under inflammatory challenge and with age.
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Affiliation(s)
- Mati Mann
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Arnav Mehta
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; David Geffen School of Medicine, UCLA, Los Angeles, CA 90025, USA
| | - Carl G de Boer
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | | | - Kevin Lee
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Pearce Haldeman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Noga Rogel
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | - Abigail R Knecht
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | - Daneyal Farouq
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Koch Institute of Integrative Cancer Biology, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02140, USA.
| | - David Baltimore
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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11
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Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat Biotechnol 2019; 38:56-65. [PMID: 31792407 PMCID: PMC6954276 DOI: 10.1038/s41587-019-0315-8] [Citation(s) in RCA: 131] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 10/16/2019] [Indexed: 11/26/2022]
Abstract
How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity, and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation. Gene expression levels in yeast are predicted using a massive dataset on promoters with random sequences.
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12
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Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG, Jenkins RW, Lieb DJ, Chen JH, Frederick DT, Barzily-Rokni M, Freeman SS, Reuben A, Hoover PJ, Villani AC, Ivanova E, Portell A, Lizotte PH, Aref AR, Eliane JP, Hammond MR, Vitzthum H, Blackmon SM, Li B, Gopalakrishnan V, Reddy SM, Cooper ZA, Paweletz CP, Barbie DA, Stemmer-Rachamimov A, Flaherty KT, Wargo JA, Boland GM, Sullivan RJ, Getz G, Hacohen N. Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma. Cell 2018; 175:998-1013.e20. [PMID: 30388456 PMCID: PMC6641984 DOI: 10.1016/j.cell.2018.10.038] [Citation(s) in RCA: 1059] [Impact Index Per Article: 176.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 07/07/2018] [Accepted: 10/15/2018] [Indexed: 02/07/2023]
Abstract
Treatment of cancer has been revolutionized by immune checkpoint blockade therapies. Despite the high rate of response in advanced melanoma, the majority of patients succumb to disease. To identify factors associated with success or failure of checkpoint therapy, we profiled transcriptomes of 16,291 individual immune cells from 48 tumor samples of melanoma patients treated with checkpoint inhibitors. Two distinct states of CD8+ T cells were defined by clustering and associated with patient tumor regression or progression. A single transcription factor, TCF7, was visualized within CD8+ T cells in fixed tumor samples and predicted positive clinical outcome in an independent cohort of checkpoint-treated patients. We delineated the epigenetic landscape and clonality of these T cell states and demonstrated enhanced antitumor immunity by targeting novel combinations of factors in exhausted cells. Our study of immune cell transcriptomes from tumors demonstrates a strategy for identifying predictors, mechanisms, and targets for enhancing checkpoint immunotherapy.
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MESH Headings
- Animals
- Antibodies, Monoclonal, Humanized/immunology
- Antibodies, Monoclonal, Humanized/pharmacology
- Antigens, CD/immunology
- Antineoplastic Agents, Immunological/immunology
- Antineoplastic Agents, Immunological/pharmacology
- Apyrase/antagonists & inhibitors
- Apyrase/immunology
- CD8-Positive T-Lymphocytes/immunology
- Cell Line, Tumor
- Humans
- Immunotherapy/methods
- Leukocyte Common Antigens/antagonists & inhibitors
- Leukocyte Common Antigens/immunology
- Melanoma/immunology
- Melanoma/therapy
- Mice
- Mice, Inbred BALB C
- Mice, Inbred C57BL
- T Cell Transcription Factor 1/metabolism
- Transcriptome
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Affiliation(s)
- Moshe Sade-Feldman
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Keren Yizhak
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
| | - Stacey L Bjorgaard
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
| | - John P Ray
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
| | - Carl G de Boer
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
| | - Russell W Jenkins
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA; Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - David J Lieb
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
| | - Jonathan H Chen
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Department of Pathology, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Dennie T Frederick
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA
| | - Michal Barzily-Rokni
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA
| | - Samuel S Freeman
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Department of Biomedical Informatics, HMS, Boston, MA, USA
| | - Alexandre Reuben
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul J Hoover
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Brigham & Women's Hospital, Division of Rheumatology, Immunology and Allergy, Boston, MA, USA
| | - Alexandra-Chloé Villani
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Elena Ivanova
- Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA; Belfer Center for Applied Cancer Science, Dana Farber Cancer Institute, Boston, MA, USA
| | - Andrew Portell
- Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA; Belfer Center for Applied Cancer Science, Dana Farber Cancer Institute, Boston, MA, USA
| | - Patrick H Lizotte
- Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA; Belfer Center for Applied Cancer Science, Dana Farber Cancer Institute, Boston, MA, USA
| | - Amir R Aref
- Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA; Belfer Center for Applied Cancer Science, Dana Farber Cancer Institute, Boston, MA, USA
| | - Jean-Pierre Eliane
- Department of Pathology, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Marc R Hammond
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA
| | - Hans Vitzthum
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA
| | - Shauna M Blackmon
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA
| | - Bo Li
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Department of Virology, Harvard Medical School, Boston, MA, USA
| | | | - Sangeetha M Reddy
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zachary A Cooper
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cloud P Paweletz
- Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA; Belfer Center for Applied Cancer Science, Dana Farber Cancer Institute, Boston, MA, USA
| | - David A Barbie
- Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA
| | | | - Keith T Flaherty
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Jennifer A Wargo
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Genevieve M Boland
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Department of Surgery, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Ryan J Sullivan
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Gad Getz
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Department of Pathology, Massachusetts General Hospital, HMS, Boston, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA.
| | - Nir Hacohen
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA; Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA.
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13
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Alazi E, Ram AFJ. Modulating Transcriptional Regulation of Plant Biomass Degrading Enzyme Networks for Rational Design of Industrial Fungal Strains. Front Bioeng Biotechnol 2018; 6:133. [PMID: 30320082 PMCID: PMC6167437 DOI: 10.3389/fbioe.2018.00133] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 09/05/2018] [Indexed: 01/08/2023] Open
Abstract
Filamentous fungi are the most important microorganisms for the industrial production of plant polysaccharide degrading enzymes due to their unique ability to secrete these proteins efficiently. These carbohydrate active enzymes (CAZymes) are utilized industrially for the hydrolysis of plant biomass for the subsequent production of biofuels and high-value biochemicals. The expression of the genes encoding plant biomass degrading enzymes is tightly controlled. Naturally, large amounts of CAZymes are produced and secreted only in the presence of the plant polysaccharide they specifically act on. The signal to produce is conveyed via so-called inducer molecules which are di- or mono-saccharides (or derivatives thereof) released from the specific plant polysaccharides. The presence of the inducer results in the activation of a substrate-specific transcription factor (TF), which is required not only for the controlled expression of the genes encoding the CAZymes, but often also for the regulation of the expression of the genes encoding sugar transporters and catabolic pathway enzymes needed to utilize the released monosaccharide. Over the years, several substrate-specific TFs involved in the degradation of cellulose, hemicellulose, pectin, starch and inulin have been identified in several fungal species and systems biology approaches have made it possible to uncover the enzyme networks controlled by these TFs. The requirement for specific inducers for TF activation and subsequently the expression of particular enzyme networks determines the choice of feedstock to produce enzyme cocktails for industrial use. It also results in batch-to-batch variation in the composition and amounts of enzymes due to variations in sugar composition and polysaccharide decorations of the feedstock which hampers the use of cheap feedstocks for constant quality of enzyme cocktails. It is therefore of industrial interest to produce specific enzyme cocktails constitutively and independently of inducers. In this review, we focus on the methods to modulate TF activities for inducer-independent production of CAZymes and highlight various approaches that are used to construct strains displaying constitutive expression of plant biomass degrading enzyme networks. These approaches and combinations thereof are also used to construct strains displaying increased expression of CAZymes under inducing conditions, and make it possible to design strains in which different enzyme mixtures are simultaneously produced independently of the carbon source.
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Affiliation(s)
| | - Arthur F. J. Ram
- Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, Leiden, Netherlands
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14
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de Boer CG, Regev A. BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization. BMC Bioinformatics 2018; 19:253. [PMID: 29970004 PMCID: PMC6029352 DOI: 10.1186/s12859-018-2255-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 06/20/2018] [Indexed: 12/31/2022] Open
Abstract
Background Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark. Results BROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in the k-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding – and any associated epigenomic mark – is inherently more variable than non-cooperative binding. Conclusions BROCKMAN and related approaches will help gain a mechanistic understanding of the trans determinants of chromatin variability between cells, treatments, and individuals. Electronic supplementary material The online version of this article (10.1186/s12859-018-2255-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Carl G de Boer
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. .,Department of Biology, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02140, USA. .,Howard Hughes Medical Institute, Chevy Chase, MD, 20815, USA.
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15
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Zhang Q, Fan X, Wang Y, Sun MA, Shao J, Guo D. BPP: a sequence-based algorithm for branch point prediction. Bioinformatics 2018. [PMID: 28633445 DOI: 10.1093/bioinformatics/btx401] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Motivation Although high-throughput sequencing methods have been proposed to identify splicing branch points in the human genome, these methods can only detect a small fraction of the branch points subject to the sequencing depth, experimental cost and the expression level of the mRNA. An accurate computational model for branch point prediction is therefore an ongoing objective in human genome research. Results We here propose a novel branch point prediction algorithm that utilizes information on the branch point sequence and the polypyrimidine tract. Using experimentally validated data, we demonstrate that our proposed method outperforms existing methods. Availability and implementation: https://github.com/zhqingit/BPP. Contact djguo@cuhk.edu.hk. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qing Zhang
- School of Life Sciences and the State Key Laboratory of Agrobiotechnology
| | - Xiaodan Fan
- Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Yejun Wang
- Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen 518060, China
| | - Ming-An Sun
- School of Life Sciences and the State Key Laboratory of Agrobiotechnology
| | - Jianlin Shao
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Dianjing Guo
- School of Life Sciences and the State Key Laboratory of Agrobiotechnology
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16
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Inherent limitations of probabilistic models for protein-DNA binding specificity. PLoS Comput Biol 2017; 13:e1005638. [PMID: 28686588 PMCID: PMC5521849 DOI: 10.1371/journal.pcbi.1005638] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 07/21/2017] [Accepted: 06/21/2017] [Indexed: 01/10/2023] Open
Abstract
The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible. Transcription factors (TFs), a class of DNA-binding proteins, play a central role in the regulation of gene expression. TFs control the rate of transcription by binding to the genome in a sequence-specific manner. Thus, one important aspect in the study of gene regulation mechanism is to model the binding specificities of TFs, namely the features of the DNA sequences that a TF prefers to bind. Multiple models have been proposed to characterize the binding specificities of TFs, among which the class of probabilistic models is the most popular. In this study, we point out several major limitations of the well-established probabilistic model by comparing it with the biophysical model. Through simulations we demonstrate that the probabilistic model is only an approximation of the biophysical model. The latter has most of the advantages of the former, and is a more accurate representation of binding specificities. We propose a shift from the probabilistic model to the biophysical model in future studies of protein-DNA interactions.
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17
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Alazi E, Khosravi C, Homan TG, du Pré S, Arentshorst M, Di Falco M, Pham TTM, Peng M, Aguilar-Pontes MV, Visser J, Tsang A, de Vries RP, Ram AFJ. The pathway intermediate 2-keto-3-deoxy-L-galactonate mediates the induction of genes involved in D-galacturonic acid utilization in Aspergillus niger. FEBS Lett 2017; 591:1408-1418. [PMID: 28417461 PMCID: PMC5488244 DOI: 10.1002/1873-3468.12654] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 04/10/2017] [Indexed: 01/21/2023]
Abstract
In Aspergillus niger, the enzymes encoded by gaaA, gaaB, and gaaC catabolize d‐galacturonic acid (GA) consecutively into l‐galactonate, 2‐keto‐3‐deoxy‐l‐galactonate, pyruvate, and l‐glyceraldehyde, while GaaD converts l‐glyceraldehyde to glycerol. Deletion of gaaB or gaaC results in severely impaired growth on GA and accumulation of l‐galactonate and 2‐keto‐3‐deoxy‐l‐galactonate, respectively. Expression levels of GA‐responsive genes are specifically elevated in the ∆gaaC mutant on GA as compared to the reference strain and other GA catabolic pathway deletion mutants. This indicates that 2‐keto‐3‐deoxy‐l‐galactonate is the inducer of genes required for GA utilization.
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Affiliation(s)
- Ebru Alazi
- Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, The Netherlands
| | - Claire Khosravi
- Fungal Physiology, Westerdijk Fungal Biodiversity Institute, Utrecht University, The Netherlands
| | - Tim G Homan
- Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, The Netherlands
| | - Saskia du Pré
- Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, The Netherlands
| | - Mark Arentshorst
- Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, The Netherlands
| | - Marcos Di Falco
- Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada
| | - Thi T M Pham
- Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada
| | - Mao Peng
- Fungal Physiology, Westerdijk Fungal Biodiversity Institute, Utrecht University, The Netherlands
| | | | - Jaap Visser
- Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, The Netherlands.,Fungal Physiology, Westerdijk Fungal Biodiversity Institute, Utrecht University, The Netherlands
| | - Adrian Tsang
- Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada
| | - Ronald P de Vries
- Fungal Physiology, Westerdijk Fungal Biodiversity Institute, Utrecht University, The Netherlands
| | - Arthur F J Ram
- Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, The Netherlands
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18
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Zhao J, Li D, Seo J, Allen AS, Gordân R. Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY : ... ANNUAL INTERNATIONAL CONFERENCE, RECOMB ... : PROCEEDINGS. RECOMB (CONFERENCE : 2005- ) 2017; 10229:336-352. [PMID: 28691125 DOI: 10.1007/978-3-319-56970-3_21] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Many recent studies have emphasized the importance of genetic variants and mutations in cancer and other complex human diseases. The overwhelming majority of these variants occur in non-coding portions of the genome, where they can have a functional impact by disrupting regulatory interactions between transcription factors (TFs) and DNA. Here, we present a method for assessing the impact of non-coding mutations on TF-DNA interactions, based on regression models of DNA-binding specificity trained on high-throughput in vitro data. We use ordinary least squares (OLS) to estimate the parameters of the binding model for each TF, and we show that our predictions of TF-binding changes due to DNA mutations correlate well with measured changes in gene expression. In addition, by leveraging distributional results associated with OLS estimation, for each predicted change in TF binding we also compute a normalized score (z-score) and a significance value (p-value) reflecting our confidence that the mutation affects TF binding. We use this approach to analyze a large set of pathogenic non-coding variants, and we show that these variants lead to significant differences in TF binding between alleles, compared to a control set of common variants. Thus, our results indicate that there is a strong regulatory component to the pathogenic non-coding variants identified thus far.
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Affiliation(s)
- Jingkang Zhao
- Center for Genomic and Computational Biology, Duke University, Durham NC 27708, USA.,Program in Computational Biology and Bioinformatics, Duke University, Durham NC 27708, USA
| | - Dongshunyi Li
- Department of Biostatistics and Bioinformatics, Duke University, Durham NC 27708, USA
| | - Jungkyun Seo
- Program in Computational Biology and Bioinformatics, Duke University, Durham NC 27708, USA
| | - Andrew S Allen
- Center for Genomic and Computational Biology, Duke University, Durham NC 27708, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham NC 27708, USA
| | - Raluca Gordân
- Center for Genomic and Computational Biology, Duke University, Durham NC 27708, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham NC 27708, USA.,Department of Computer Science, Duke University, Durham NC 27708, USA
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19
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Uhl M, Houwaart T, Corrado G, Wright PR, Backofen R. Computational analysis of CLIP-seq data. Methods 2017; 118-119:60-72. [PMID: 28254606 DOI: 10.1016/j.ymeth.2017.02.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 02/17/2017] [Accepted: 02/20/2017] [Indexed: 01/01/2023] Open
Abstract
CLIP-seq experiments are currently the most important means for determining the binding sites of RNA binding proteins on a genome-wide level. The computational analysis can be divided into three steps. In the first pre-processing stage, raw reads have to be trimmed and mapped to the genome. This step has to be specifically adapted for each CLIP-seq protocol. The next step is peak calling, which is required to remove unspecific signals and to determine bona fide protein binding sites on target RNAs. Here, both protocol-specific approaches as well as generic peak callers are available. Despite some peak callers being more widely used, each peak caller has its specific assets and drawbacks, and it might be advantageous to compare the results of several methods. Although peak calling is often the final step in many CLIP-seq publications, an important follow-up task is the determination of binding models from CLIP-seq data. This is central because CLIP-seq experiments are highly dependent on the transcriptional state of the cell in which the experiment was performed. Thus, relying solely on binding sites determined by CLIP-seq from different cells or conditions can lead to a high false negative rate. This shortcoming can, however, be circumvented by applying models that predict additional putative binding sites.
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Affiliation(s)
- Michael Uhl
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Germany
| | - Torsten Houwaart
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Germany
| | - Gianluca Corrado
- Department of Information Engineering and Computer Science, University of Trento, Italy
| | - Patrick R Wright
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Germany; Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany
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20
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Natural variation in non-coding regions underlying phenotypic diversity in budding yeast. Sci Rep 2016; 6:21849. [PMID: 26898953 PMCID: PMC4761897 DOI: 10.1038/srep21849] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 02/01/2016] [Indexed: 01/27/2023] Open
Abstract
Linkage mapping studies in model organisms have typically focused their efforts in polymorphisms within coding regions, ignoring those within regulatory regions that may contribute to gene expression variation. In this context, differences in transcript abundance are frequently proposed as a source of phenotypic diversity between individuals, however, until now, little molecular evidence has been provided. Here, we examined Allele Specific Expression (ASE) in six F1 hybrids from Saccharomyces cerevisiae derived from crosses between representative strains of the four main lineages described in yeast. ASE varied between crosses with levels ranging between 28% and 60%. Part of the variation in expression levels could be explained by differences in transcription factors binding to polymorphic cis-regulations and to differences in trans-activation depending on the allelic form of the TF. Analysis on highly expressed alleles on each background suggested ASN1 as a candidate transcript underlying nitrogen consumption differences between two strains. Further promoter allele swap analysis under fermentation conditions confirmed that coding and non-coding regions explained aspartic and glutamic acid consumption differences, likely due to a polymorphism affecting Uga3 binding. Together, we provide a new catalogue of variants to bridge the gap between genotype and phenotype.
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21
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Samee MAH, Lim B, Samper N, Lu H, Rushlow CA, Jiménez G, Shvartsman SY, Sinha S. A Systematic Ensemble Approach to Thermodynamic Modeling of Gene Expression from Sequence Data. Cell Syst 2015; 1:396-407. [PMID: 27136354 DOI: 10.1016/j.cels.2015.12.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 10/19/2015] [Accepted: 12/02/2015] [Indexed: 11/17/2022]
Abstract
To understand the relationship between an enhancer DNA sequence and quantitative gene expression, thermodynamics-driven mathematical models of transcription are often employed. These "sequence-to-expression" models can describe an incomplete or even incorrect set of regulatory relationships if the parameter space is not searched systematically. Here, we focus on an enhancer of the Drosophila gene ind and demonstrate how a systematic search of parameter space can reveal a more comprehensive picture of a gene's regulatory mechanisms, resolve outstanding ambiguities, and suggest testable hypotheses. We describe an approach that generates an ensemble of ind models; all of these models are technically acceptable solutions to the sequence-to-expression problem in light of wild-type data, and some represent mechanistically distinct hypotheses about the regulation of ind. This ensemble can be restricted to biologically plausible models using requirements gleaned from in vivo perturbation experiments. Biologically plausible models make unique predictions about how specific ind enhancer sequences affect ind expression; we validate these predictions in vivo through site mutagenesis in transgenic Drosophila embryos.
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Affiliation(s)
- Md Abul Hassan Samee
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Bomyi Lim
- Department of Chemical and Biological Engineering and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Núria Samper
- Department of Developmental Biology, Instituto de Biología Molecular de Barcelona, Consejo Superior de Investigaciones Científicas (CSIC), Barcelona 08208, Spain
| | - Hang Lu
- School of Chemical and Biomolecular Engineering and Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | | | - Gerardo Jiménez
- Department of Developmental Biology, Instituto de Biología Molecular de Barcelona, Consejo Superior de Investigaciones Científicas (CSIC), Barcelona 08208, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
| | - Stanislav Y Shvartsman
- Department of Chemical and Biological Engineering and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Saurabh Sinha
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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22
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Kibet CK, Machanick P. Transcription factor motif quality assessment requires systematic comparative analysis. F1000Res 2015; 4:ISCB Comm J-1429. [PMID: 27092243 PMCID: PMC4821295 DOI: 10.12688/f1000research.7408.2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/29/2016] [Indexed: 11/22/2022] Open
Abstract
Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.
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Affiliation(s)
- Caleb Kipkurui Kibet
- Department of Computer Science and Research Unit in Bioinformatics (RUBi), Rhodes University, Grahamstown, South Africa
| | - Philip Machanick
- Department of Computer Science and Research Unit in Bioinformatics (RUBi), Rhodes University, Grahamstown, South Africa
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Kibet CK, Machanick P. Transcription factor motif quality assessment requires systematic comparative analysis. F1000Res 2015; 4:ISCB Comm J-1429. [PMID: 27092243 DOI: 10.12688/f1000research.7408.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/19/2015] [Indexed: 03/26/2024] Open
Abstract
Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. Finally, we demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.
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Affiliation(s)
- Caleb Kipkurui Kibet
- Department of Computer Science and Research Unit in Bioinformatics (RUBi), Rhodes University, Grahamstown, South Africa
| | - Philip Machanick
- Department of Computer Science and Research Unit in Bioinformatics (RUBi), Rhodes University, Grahamstown, South Africa
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24
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Grassi E, Zapparoli E, Molineris I, Provero P. Total Binding Affinity Profiles of Regulatory Regions Predict Transcription Factor Binding and Gene Expression in Human Cells. PLoS One 2015; 10:e0143627. [PMID: 26599758 PMCID: PMC4658012 DOI: 10.1371/journal.pone.0143627] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Accepted: 11/07/2015] [Indexed: 11/29/2022] Open
Abstract
Transcription factors regulate gene expression by binding regulatory DNA. Understanding the rules governing such binding is an essential step in describing the network of regulatory interactions, and its pathological alterations. We show that describing regulatory regions in terms of their profile of total binding affinities for transcription factors leads to increased predictive power compared to methods based on the identification of discrete binding sites. This applies both to the prediction of transcription factor binding as revealed by ChIP-seq experiments and to the prediction of gene expression through RNA-seq. Further significant improvements in predictive power are obtained when regulatory regions are defined based on chromatin states inferred from histone modification data.
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Affiliation(s)
- Elena Grassi
- Dept. of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Ettore Zapparoli
- Dept. of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Ivan Molineris
- Dept. of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Paolo Provero
- Dept. of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Center for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, Milan, Italy
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25
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MORPHEUS, a Webtool for Transcription Factor Binding Analysis Using Position Weight Matrices with Dependency. PLoS One 2015; 10:e0135586. [PMID: 26285209 PMCID: PMC4540572 DOI: 10.1371/journal.pone.0135586] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Accepted: 07/24/2015] [Indexed: 12/21/2022] Open
Abstract
Transcriptional networks are central to any biological process and changes affecting transcription factors or their binding sites in the genome are a key factor driving evolution. As more organisms are being sequenced, tools are needed to easily predict transcription factor binding sites (TFBS) presence and affinity from mere inspection of genomic sequences. Although many TFBS discovery algorithms exist, tools for using the DNA binding models they generate are relatively scarce and their use is limited among the biologist community by the lack of flexible and user-friendly tools. We have developed a suite of web tools (called Morpheus) based on the proven Position Weight Matrices (PWM) formalism that can be used without any programing skills and incorporates some unique features such as the presence of dependencies between nucleotides positions or the possibility to compute the predicted occupancy of a large regulatory region using a biophysical model. To illustrate the possibilities and simplicity of Morpheus tools in functional and evolutionary analysis, we have analysed the regulatory link between LEAFY, a key plant transcription factor involved in flower development, and its direct target gene APETALA1 during the divergence of Brassicales clade.
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26
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Wang J. Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence. BMC Bioinformatics 2014; 15:289. [PMID: 25158938 PMCID: PMC4161872 DOI: 10.1186/1471-2105-15-289] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 08/18/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computational modeling transcription factor (TF) sequence specificity is an important research topic in regulatory genomics. A systematic comparison of 26 algorithms to learn TF-DNA binding specificity in in vitro protein-binding microarray (PBM) data was published recently, but the quality of those examined PBMs was not evaluated completely. RESULTS Here, new quality-control parameters such as principal component analysis (PCA) ellipse is proposed to assess the data quality for either single or paired PBMs. Additionally, a biophysical model of TF-DNA interactions including adjacent dinucleotide interdependence was implemented in a new program - BayesPI2, where sparse Bayesian learning and relevance vector machine are used to predict unknown model parameters. Then, 66 mouse TFs from the DREAM5 challenge were classified into two groups (i.e. good vs. bad) based on the paired PBM quality-control parameters. Subsequently, computational methods to model TF sequence specificity were evaluated between the two groups. CONCLUSION Results indicate that both the algorithm performance and the predicted TF-binding energy-level of a motif are significantly influenced by PBM data quality, where poor PBM data quality is linked to specific protein domains (e.g. C2H2 DNA-binding domain). Especially, the new dinucleotide energy-dependent model (BayesPI2) offers great improvement in testing prediction accuracy over the simple energy-independent model, for at least 21% of analyzed the TFs.
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Affiliation(s)
- Junbai Wang
- Pathology Department, Oslo University Hospital - Norwegian Radium Hospital, Montebello, Oslo, 0310, Norway.
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27
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Mayhew D, Mitra RD. Transcription factor regulation and chromosome dynamics during pseudohyphal growth. Mol Biol Cell 2014; 25:2669-76. [PMID: 25009286 PMCID: PMC4148256 DOI: 10.1091/mbc.e14-04-0871] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
A multiplexed analysis of the transcriptional regulation of yeast pseudohyphal growth recorded the binding of 28 different transcription factors with barcoded transposons. A core set of target genes is identified, and a process of DNA looping at the FLO11 locus that provides transcriptional memory for expression of the gene is described. Pseudohyphal growth is a developmental pathway seen in some strains of yeast in which cells form multicellular filaments in response to environmental stresses. We used multiplexed transposon “Calling Cards” to record the genome-wide binding patterns of 28 transcription factors (TFs) in nitrogen-starved yeast. We identified TF targets relevant for pseudohyphal growth, producing a detailed map of its regulatory network. Using tools from graph theory, we identified 14 TFs that lie at the center of this network, including Flo8, Mss11, and Mfg1, which bind as a complex. Surprisingly, the DNA-binding preferences for these key TFs were unknown. Using Calling Card data, we predicted the in vivo DNA-binding motif for the Flo8-Mss11-Mfg1 complex and validated it using a reporter assay. We found that this complex binds several important targets, including FLO11, at both their promoter and termination sequences. We demonstrated that this binding pattern is the result of DNA looping, which regulates the transcription of these targets and is stabilized by an interaction with the nuclear pore complex. This looping provides yeast cells with a transcriptional memory, enabling them more rapidly to execute the filamentous growth program when nitrogen starved if they had been previously exposed to this condition.
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Affiliation(s)
- David Mayhew
- Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108
| | - Robi D Mitra
- Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108
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Zhong J, Wasson T, Hartemink AJ. Learning protein-DNA interaction landscapes by integrating experimental data through computational models. ACTA ACUST UNITED AC 2014; 30:2868-74. [PMID: 24974204 DOI: 10.1093/bioinformatics/btu408] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
MOTIVATION Transcriptional regulation is directly enacted by the interactions between DNA and many proteins, including transcription factors (TFs), nucleosomes and polymerases. A critical step in deciphering transcriptional regulation is to infer, and eventually predict, the precise locations of these interactions, along with their strength and frequency. While recent datasets yield great insight into these interactions, individual data sources often provide only partial information regarding one aspect of the complete interaction landscape. For example, chromatin immunoprecipitation (ChIP) reveals the binding positions of a protein, but only for one protein at a time. In contrast, nucleases like MNase and DNase can be used to reveal binding positions for many different proteins at once, but cannot easily determine the identities of those proteins. Currently, few statistical frameworks jointly model these different data sources to reveal an accurate, holistic view of the in vivo protein-DNA interaction landscape. RESULTS Here, we develop a novel statistical framework that integrates different sources of experimental information within a thermodynamic model of competitive binding to jointly learn a holistic view of the in vivo protein-DNA interaction landscape. We show that our framework learns an interaction landscape with increased accuracy, explaining multiple sets of data in accordance with thermodynamic principles of competitive DNA binding. The resulting model of genomic occupancy provides a precise mechanistic vantage point from which to explore the role of protein-DNA interactions in transcriptional regulation. AVAILABILITY AND IMPLEMENTATION The C source code for compete and Python source code for MCMC-based inference are available at http://www.cs.duke.edu/∼amink. CONTACT amink@cs.duke.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jianling Zhong
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Todd Wasson
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Alexander J Hartemink
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA
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29
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Mahony S, Edwards MD, Mazzoni EO, Sherwood RI, Kakumanu A, Morrison CA, Wichterle H, Gifford DK. An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding. PLoS Comput Biol 2014; 10:e1003501. [PMID: 24675637 PMCID: PMC3967921 DOI: 10.1371/journal.pcbi.1003501] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2013] [Accepted: 01/16/2014] [Indexed: 11/24/2022] Open
Abstract
Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS's multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5. Many proteins that regulate the activity of other genes do so by attaching to the genome at specific binding sites. The locations that a given regulatory protein will bind, and the strength or frequency of such binding at an individual location, can vary depending on the cell type. We can profile the locations that a protein binds in a particular cell type using an experimental method called ChIP-seq, followed by computational interpretation of the data. However, since the experimental data are typically noisy, it is often difficult to compare the computational analyses of ChIP-seq data across multiple experiments in order to understand any differences in binding that may occur in different cell types. In this paper, we present a new computational method named MultiGPS for simultaneously analyzing multiple related ChIP-seq experiments in an integrated manner. By analyzing all the data together in an appropriate way, we can gain a more accurate picture of where the profiled protein is binding to the genome, and we can more easily and reliably detect differences in protein binding across cell types. We demonstrate the MultiGPS software using a new analysis of the regulatory protein Cdx2 in three different developmental cell types.
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Affiliation(s)
- Shaun Mahony
- Center for Eukaryotic Gene Regulation, Department of Biochemistry & Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail: (SM); (DKG)
| | - Matthew D. Edwards
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Esteban O. Mazzoni
- Department of Biology, New York University, New York, New York, United States of America
| | - Richard I. Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Akshay Kakumanu
- Center for Eukaryotic Gene Regulation, Department of Biochemistry & Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Carolyn A. Morrison
- Departments of Pathology and Cell Biology, Neurology, and Neuroscience, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Medical Center, New York, New York, United States of America
| | - Hynek Wichterle
- Departments of Pathology and Cell Biology, Neurology, and Neuroscience, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Medical Center, New York, New York, United States of America
| | - David K. Gifford
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- * E-mail: (SM); (DKG)
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30
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Zeigler RD, Cohen BA. Discrimination between thermodynamic models of cis-regulation using transcription factor occupancy data. Nucleic Acids Res 2013; 42:2224-34. [PMID: 24288374 PMCID: PMC3936720 DOI: 10.1093/nar/gkt1230] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Many studies have identified binding preferences for transcription factors (TFs), but few have yielded predictive models of how combinations of transcription factor binding sites generate specific levels of gene expression. Synthetic promoters have emerged as powerful tools for generating quantitative data to parameterize models of combinatorial cis-regulation. We sought to improve the accuracy of such models by quantifying the occupancy of TFs on synthetic promoters in vivo and incorporating these data into statistical thermodynamic models of cis-regulation. Using chromatin immunoprecipitation-seq, we measured the occupancy of Gcn4 and Cbf1 in synthetic promoter libraries composed of binding sites for Gcn4, Cbf1, Met31/Met32 and Nrg1. We measured the occupancy of these two TFs and the expression levels of all promoters in two growth conditions. Models parameterized using only expression data predicted expression but failed to identify several interactions between TFs. In contrast, models parameterized with occupancy and expression data predicted expression data, and also revealed Gcn4 self-cooperativity and a negative interaction between Gcn4 and Nrg1. Occupancy data also allowed us to distinguish between competing regulatory mechanisms for the factor Gcn4. Our framework for combining occupancy and expression data produces predictive models that better reflect the mechanisms underlying combinatorial cis-regulation of gene expression.
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Affiliation(s)
- Robert D Zeigler
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine in St. Louis, MO 63108, USA
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31
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Mathelier A, Wasserman WW. The next generation of transcription factor binding site prediction. PLoS Comput Biol 2013; 9:e1003214. [PMID: 24039567 PMCID: PMC3764009 DOI: 10.1371/journal.pcbi.1003214] [Citation(s) in RCA: 124] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Accepted: 07/22/2013] [Indexed: 12/29/2022] Open
Abstract
Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.
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Affiliation(s)
- Anthony Mathelier
- Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Wyeth W. Wasserman
- Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
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32
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Mapping the fine structure of a eukaryotic promoter input-output function. Nat Genet 2013; 45:1207-15. [PMID: 23955598 DOI: 10.1038/ng.2729] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2013] [Accepted: 07/22/2013] [Indexed: 12/16/2022]
Abstract
The precise tuning of gene expression levels is essential for the optimal performance of transcriptional regulatory networks. We created 209 variants of the Saccharomyces cerevisiae PHO5 promoter to quantify how different binding sites for the transcription factor Pho4 affect its output. We found that transcription-factor binding affinities determined in vitro could quantitatively predict the output of a complex yeast promoter. Promoter output was precisely tunable by subtle changes in binding-site affinity of less than 3 kcal mol(-1), which are accessible by modifying 1-2 bases. Our results provide insights into how transcription-factor binding sites regulate gene expression, their possible evolution and how they can be used to precisely tune gene expression. More generally, we show that in vitro binding-energy landscapes of transcription factors can precisely predict the output of a native yeast promoter, indicating that quantitative models of transcriptional regulatory networks are feasible.
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33
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Dresch JM, Richards M, Ay A. A primer on thermodynamic-based models for deciphering transcriptional regulatory logic. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2013; 1829:946-53. [PMID: 23643643 DOI: 10.1016/j.bbagrm.2013.04.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2012] [Revised: 04/24/2013] [Accepted: 04/25/2013] [Indexed: 11/27/2022]
Abstract
A rigorous analysis of transcriptional regulation at the DNA level is crucial to the understanding of many biological systems. Mathematical modeling has offered researchers a new approach to understanding this central process. In particular, thermodynamic-based modeling represents the most biophysically informed approach aimed at connecting DNA level regulatory sequences to the expression of specific genes. The goal of this review is to give biologists a thorough description of the steps involved in building, analyzing, and implementing a thermodynamic-based model of transcriptional regulation. The data requirements for this modeling approach are described, the derivation for a specific regulatory region is shown, and the challenges and future directions for the quantitative modeling of gene regulation are discussed.
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34
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Aow JSZ, Xue X, Run JQ, Lim GFS, Goh WS, Clarke ND. Differential binding of the related transcription factors Pho4 and Cbf1 can tune the sensitivity of promoters to different levels of an induction signal. Nucleic Acids Res 2013; 41:4877-87. [PMID: 23558744 PMCID: PMC3643608 DOI: 10.1093/nar/gkt210] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Transcription factors that belong to the same family typically have similar, but not identical, binding specificities. As such, they can be expected to compete differentially for binding to different variants of their binding sites. Pho4 is a yeast factor whose nuclear concentration is up-regulated in low phosphate, while the related factor, Cbf1, is constitutively expressed. We constructed 16 GFP-reporter genes containing all palindromic variants of the motif NNCACGTGNN, and determined their activities at a range of phosphate concentrations. Pho4 affinity did not explain expression data well except under fully induced conditions. However, reporter activity was quantitatively well explained under all conditions by a model in which Cbf1 itself has modest activating activity, and Pho4 and Cbf1 compete with one another. Chromatin immunoprecipitation and computational analyses of natural Pho4 target genes, along with the activities of the reporter constructs, indicates that genes differ in their sensitivity to intermediate induction signals in part because of differences in their affinity for Cbf1. The induction sensitivity of both natural Pho4 target genes and reporter genes was well explained only by a model that assumes a role for Cbf1 in remodeling chromatin. Our analyses highlight the importance of taking into account the activities of related transcription factors in explaining system-wide gene expression data.
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Affiliation(s)
- Jonathan S Z Aow
- Computational and Systems Biology, Genome Institute of Singapore, 60 Biopolis St., Singapore 138672, Singapore
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35
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Teif VB, Erdel F, Beshnova DA, Vainshtein Y, Mallm JP, Rippe K. Taking into account nucleosomes for predicting gene expression. Methods 2013; 62:26-38. [PMID: 23523656 DOI: 10.1016/j.ymeth.2013.03.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2012] [Accepted: 03/10/2013] [Indexed: 01/10/2023] Open
Abstract
The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147 bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes. The main theoretical problems are (i) the computation speed that is becoming a bottleneck when partial unwrapping of DNA from the nucleosome is considered, (ii) the perturbation of the binding equilibrium by the activity of ATP-dependent chromatin remodelers, which translocate nucleosomes along the DNA, and (iii) the model parameterization from high-throughput sequencing data and fluorescence microscopy experiments in living cells. We discuss strategies that address these issues to efficiently compute transcription factor binding in chromatin.
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Affiliation(s)
- Vladimir B Teif
- Research Group Genome Organization & Function, Deutsches Krebsforschungszentrum-DKFZ & BioQuant, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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36
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Arbab M, Mahony S, Cho H, Chick JM, Rolfe PA, van Hoff JP, Morris VW, Gygi SP, Maas RL, Gifford DK, Sherwood RI. A multi-parametric flow cytometric assay to analyze DNA-protein interactions. Nucleic Acids Res 2012; 41:e38. [PMID: 23143268 PMCID: PMC3554230 DOI: 10.1093/nar/gks1034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Interactions between DNA and transcription factors (TFs) guide cellular function and
development, yet the complexities of gene regulation are still far from being understood.
Such understanding is limited by a paucity of techniques with which to probe
DNA–protein interactions. We have devised magnetic protein immobilization on
enhancer DNA (MagPIE), a simple, rapid, multi-parametric assay using flow cytometric
immunofluorescence to reveal interactions among TFs, chromatin structure and DNA. In
MagPIE, synthesized DNA is bound to magnetic beads, which are then incubated with nuclear
lysate, permitting sequence-specific binding by TFs, histones and methylation by native
lysate factors that can be optionally inhibited with small molecules. Lysate
protein–DNA binding is monitored by flow cytometric immunofluorescence, which allows
for accurate comparative measurement of TF-DNA affinity. Combinatorial fluorescent
staining allows simultaneous analysis of sequence-specific TF-DNA interaction and
chromatin modification. MagPIE provides a simple and robust method to analyze complex
epigenetic interactions in vitro.
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Affiliation(s)
- Mandana Arbab
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - Shaun Mahony
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - Hyunjii Cho
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - Joel M. Chick
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - P. Alexander Rolfe
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - John Peter van Hoff
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - Viveca W.S. Morris
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - Steven P. Gygi
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - Richard L. Maas
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - David K. Gifford
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
| | - Richard I. Sherwood
- Division of Genetics, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA
02115, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139 and Department of Cell Biology,
Harvard Medical School, Boston, MA 02115, USA
- *To whom correspondence should be addressed. Tel:
+1 617 525 4772; Fax: +1 617 525 4751;
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37
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Rajkumar AS, Maerkl SJ. Rapid synthesis of defined eukaryotic promoter libraries. ACS Synth Biol 2012; 1:483-90. [PMID: 23656186 DOI: 10.1021/sb300045j] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Current gene synthesis methods allow the generation of long segments of dsDNA. We show that these techniques can be used to create synthetic regulatory elements and describe a method for the creation of completely defined, synthetic variants of the PHO5 promoter from the budding yeast Saccharomyces cerevisae. Overall, 128 promoters were assembled by high-temperature ligation, cloned into plasmids by isothermal assembly, maintained in E. coli, and consequently transformed into yeast by homologous recombination. Synthesis errors occurred at frequencies comparable to or lower than those achieved with current gene synthesis methods. The promoter synthesis method reported here is robust, fast, and readily accessible. Synthetically engineered promoter libraries will be useful tools for dissecting the intricacies of promoter input-output functions and may serve as tunable components for synthetic genetic networks.
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Affiliation(s)
- Arun S. Rajkumar
- School of Engineering,
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Sebastian J. Maerkl
- School of Engineering,
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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38
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Ihuegbu NE, Stormo GD, Buhler J. Fast, sensitive discovery of conserved genome-wide motifs. J Comput Biol 2012; 19:139-47. [PMID: 22300316 DOI: 10.1089/cmb.2011.0249] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Regulatory sites that control gene expression are essential to the proper functioning of cells, and identifying them is critical for modeling regulatory networks. We have developed Magma (Multiple Aligner of Genomic Multiple Alignments), a software tool for multiple species, multiple gene motif discovery. Magma identifies putative regulatory sites that are conserved across multiple species and occur near multiple genes throughout a reference genome. Magma takes as input multiple alignments that can include gaps. It uses efficient clustering methods that make it about 70 times faster than PhyloNet, a previous program for this task, with slightly greater sensitivity. We ran Magma on all non-coding DNA conserved between Caenorhabditis elegans and five additional species, about 70 Mbp in total, in <4 h. We obtained 2,309 motifs with lengths of 6-20 bp, each occurring at least 10 times throughout the genome, which collectively covered about 566 kbp of the genomes, approximately 0.8% of the input. Predicted sites occurred in all types of non-coding sequence but were especially enriched in the promoter regions. Comparisons to several experimental datasets show that Magma motifs correspond to a variety of known regulatory motifs.
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Affiliation(s)
- Nnamdi E Ihuegbu
- Department of Genetics, Washington University School of Medicine, Saint Louis, Missouri 63108, USA
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39
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Conserved Motifs and Prediction of Regulatory Modules in Caenorhabditis elegans. G3-GENES GENOMES GENETICS 2012; 2:469-81. [PMID: 22540038 PMCID: PMC3337475 DOI: 10.1534/g3.111.001081] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2011] [Accepted: 02/06/2012] [Indexed: 01/30/2023]
Abstract
Transcriptional regulation, a primary mechanism for controlling the development of multicellular organisms, is carried out by transcription factors (TFs) that recognize and bind to their cognate binding sites. In Caenorhabditis elegans, our knowledge of which genes are regulated by which TFs, through binding to specific sites, is still very limited. To expand our knowledge about the C. elegans regulatory network, we performed a comprehensive analysis of the C. elegans, Caenorhabditis briggsae, and Caenorhabditis remanei genomes to identify regulatory elements that are conserved in all genomes. Our analysis identified 4959 elements that are significantly conserved across the genomes and that each occur multiple times within each genome, both hallmarks of functional regulatory sites. Our motifs show significant matches to known core promoter elements, TF binding sites, splice sites, and poly-A signals as well as many putative regulatory sites. Many of the motifs are significantly correlated with various types of experimental data, including gene expression patterns, tissue-specific expression patterns, and binding site location analysis as well as enrichment in specific functional classes of genes. Many can also be significantly associated with specific TFs. Combinations of motif occurrences allow us to predict the location of cis-regulatory modules and we show that many of them significantly overlap experimentally determined enhancers. We provide access to the predicted binding sites, their associated motifs, and the predicted cis-regulatory modules across the whole genome through a web-accessible database and as tracks for genome browsers.
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40
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GORDÂN RALUCA, PYNE SAUMYADIPTA, BULYK MARTHAL. Identification of cell cycle-regulated, putative hyphal genes in Candida albicans. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2012:299-310. [PMID: 22174285 PMCID: PMC3331874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Candida albicans, a major fungal pathogen in human, can grow in a variety of morphological forms ranging from budding yeast to pseudohyphae and hyphae, and its ability to transition to true hyphae is critical for virulence in various types of C. albicans infections. Here, we identify 17 putative hyphal genes whose expression peaks during the S/G2 transition of the cell cycle in C. albicans . These genes are Candida-specific (i.e., they do not have orthologs in S.cerevisiae, a related fungal species that does not exhibit hyphal growth and is primarily non-pathogenic), and their promoters are enriched for the DNA binding site motifs of Tec1 and Rfg1, two transcription factors (TFs) known to play important roles in hyphal growth and virulence. For 5 of the 17 genes we found strong evidence in the literature that confirms our hypothesis that these genes are involved in hyphal growth and/or virulence, for 5 additional genes we found suggestive (albeit weak) evidence, while the other genes remain to be tested. It will be interesting to determine in future studies whether these 17 putative hyphal genes, whose expression peaks during the S/G2 transition, are part of a mechanism for this pathogenic fungus to 'turn on' hyphal growth late during the cell cycle, or if these genes are used to sustain hyphal growth and ensure that the cell does not transition back to yeast growth. In either case, the involvement of these genes in hyphal growth makes them putative targets for new antifungal drugs aimed at inhibiting hyphae formation in C. albicans.
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Affiliation(s)
- RALUCA GORDÂN
- Division of Genetics, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - SAUMYADIPTA PYNE
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,
| | - MARTHA L. BULYK
- Division of Genetics, Department of Medicine, Department of Pathology, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA, Harvard-MIT Division of Health Sciences & Technology (HST), Harvard Medical School, Boston, MA 02115, USA,
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41
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Kaplan T, Biggin MD. Quantitative models of the mechanisms that control genome-wide patterns of animal transcription factor binding. Methods Cell Biol 2012; 110:263-83. [PMID: 22482953 DOI: 10.1016/b978-0-12-388403-9.00011-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Animal transcription factors drive complex spatial and temporal patterns of gene expression during development by binding to a wide array of genomic regions. While the in vivo DNA binding landscape and in vitro DNA binding affinities of many such proteins have been characterized, our understanding of the forces that determine where, when, and the extent to which these transcription factors bind DNA in cells remains primitive. In this chapter, we describe computational thermodynamic models that predict the genome-wide DNA binding landscape of transcription factors in vivo and evaluate the contribution of biophysical determinants, such as protein-protein interactions and chromatin accessibility, on DNA occupancy. We show that predictions based only on DNA sequence and in vitro DNA affinity data achieve a mild correlation (r=0.4) with experimental measurements of in vivo DNA binding. However, by incorporating direct measurements of DNA accessibility in chromatin, it is possible to obtain much higher accuracy (r=0.6-0.9) for various transcription factors across known target genes. Thus, a combination of experimental DNA accessibility data and computational modeling of transcription factor DNA binding may be sufficient to predict the binding landscape of any animal transcription factor with reasonable accuracy.
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Affiliation(s)
- Tommy Kaplan
- Department of Molecular and Cell Biology, California Institute of Quantitative Biosciences, University of California, Berkeley, California, USA; School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel
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42
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Gordân R, Murphy KF, McCord RP, Zhu C, Vedenko A, Bulyk ML. Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights. Genome Biol 2011; 12:R125. [PMID: 22189060 PMCID: PMC3334620 DOI: 10.1186/gb-2011-12-12-r125] [Citation(s) in RCA: 84] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2011] [Revised: 12/09/2011] [Accepted: 12/21/2011] [Indexed: 11/24/2022] Open
Abstract
Background Transcription factors (TFs) play a central role in regulating gene expression by interacting with cis-regulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae TFs, but a comprehensive evaluation of their data has been lacking. Results We analyzed in vitro and in vivo TF-DNA binding data reported in previous large-scale studies to generate a comprehensive, curated resource of DNA binding specificity data for all characterized S. cerevisiae TFs. Our collection comprises DNA binding site motifs and comprehensive in vitro DNA binding specificity data for all possible 8-bp sequences. Investigation of the DNA binding specificities within the basic leucine zipper (bZIP) and VHT1 regulator (VHR) TF families revealed unexpected plasticity in TF-DNA recognition: intriguingly, the VHR TFs, newly characterized by protein binding microarrays in this study, recognize bZIP-like DNA motifs, while the bZIP TF Hac1 recognizes a motif highly similar to the canonical E-box motif of basic helix-loop-helix (bHLH) TFs. We identified several TFs with distinct primary and secondary motifs, which might be associated with different regulatory functions. Finally, integrated analysis of in vivo TF binding data with protein binding microarray data lends further support for indirect DNA binding in vivo by sequence-specific TFs. Conclusions The comprehensive data in this curated collection allow for more accurate analyses of regulatory TF-DNA interactions, in-depth structural studies of TF-DNA specificity determinants, and future experimental investigations of the TFs' predicted target genes and regulatory roles.
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Affiliation(s)
- Raluca Gordân
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
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43
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Thomas-Chollier M, Hufton A, Heinig M, O'Keeffe S, Masri NE, Roider HG, Manke T, Vingron M. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat Protoc 2011; 6:1860-9. [PMID: 22051799 DOI: 10.1038/nprot.2011.409] [Citation(s) in RCA: 168] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The transcription factor affinity prediction (TRAP) method calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation-sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism. The protocol describes how to use the TRAP web tools to address these questions, and it also presents a way to run TRAP on random control sequences to better estimate the significance of the results. All of the tools are fully available online and do not need any additional installation. The complete protocol takes about 45 min, but each individual tool runs in a few minutes.
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Affiliation(s)
- Morgane Thomas-Chollier
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
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44
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Pollock DD, de Koning APJ, Kim H, Castoe TA, Churchill MEA, Kechris KJ. Bayesian analysis of high-throughput quantitative measurement of protein-DNA interactions. PLoS One 2011; 6:e26105. [PMID: 22069446 PMCID: PMC3206046 DOI: 10.1371/journal.pone.0026105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2011] [Accepted: 09/19/2011] [Indexed: 11/19/2022] Open
Abstract
Transcriptional regulation depends upon the binding of transcription factor (TF) proteins to DNA in a sequence-dependent manner. Although many experimental methods address the interaction between DNA and proteins, they generally do not comprehensively and accurately assess the full binding repertoire (the complete set of sequences that might be bound with at least moderate strength). Here, we develop and evaluate through simulation an experimental approach that allows simultaneous high-throughput quantitative analysis of TF binding affinity to thousands of potential DNA ligands. Tens of thousands of putative binding targets can be mixed with a TF, and both the pre-bound and bound target pools sequenced. A hierarchical Bayesian Markov chain Monte Carlo approach determines posterior estimates for the dissociation constants, sequence-specific binding energies, and free TF concentrations. A unique feature of our approach is that dissociation constants are jointly estimated from their inferred degree of binding and from a model of binding energetics, depending on how many sequence reads are available and the explanatory power of the energy model. Careful experimental design is necessary to obtain accurate results over a wide range of dissociation constants. This approach, which we call Simultaneous Ultra high-throughput Ligand Dissociation EXperiment (SULDEX), is theoretically capable of rapid and accurate elucidation of an entire TF-binding repertoire.
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Affiliation(s)
- David D Pollock
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
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45
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Reece-Hoyes JS, Barutcu AR, McCord RP, Jeong JS, Jiang L, MacWilliams A, Yang X, Salehi-Ashtiani K, Hill DE, Blackshaw S, Zhu H, Dekker J, Walhout AJM. Yeast one-hybrid assays for gene-centered human gene regulatory network mapping. Nat Methods 2011; 8:1050-2. [PMID: 22037702 PMCID: PMC3263363 DOI: 10.1038/nmeth.1764] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Accepted: 08/22/2011] [Indexed: 12/23/2022]
Abstract
Gateway-compatible yeast one-hybrid (Y1H) assays provide a convenient gene-centered (DNA to protein) approach to identify transcription factors that can bind a DNA sequence of interest. We present Y1H resources, including clones for 988 of 1,434 (69%) predicted human transcription factors, that can be used to detect both known and new interactions between human DNA regions and transcription factors.
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Affiliation(s)
- John S Reece-Hoyes
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - A Rasim Barutcu
- Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Rachel Patton McCord
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Jun Seop Jeong
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Center for High-Throughput Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Lizhi Jiang
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Center for High-Throughput Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Andrew MacWilliams
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Kourosh Salehi-Ashtiani
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Seth Blackshaw
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Center for High-Throughput Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Heng Zhu
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Center for High-Throughput Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Job Dekker
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Albertha J M Walhout
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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Assessing the effects of symmetry on motif discovery and modeling. PLoS One 2011; 6:e24908. [PMID: 21949783 PMCID: PMC3176789 DOI: 10.1371/journal.pone.0024908] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Accepted: 08/19/2011] [Indexed: 11/23/2022] Open
Abstract
Background Identifying the DNA binding sites for transcription factors is a key task in modeling the gene regulatory network of a cell. Predicting DNA binding sites computationally suffers from high false positives and false negatives due to various contributing factors, including the inaccurate models for transcription factor specificity. One source of inaccuracy in the specificity models is the assumption of asymmetry for symmetric models. Methodology/Principal Findings Using simulation studies, so that the correct binding site model is known and various parameters of the process can be systematically controlled, we test different motif finding algorithms on both symmetric and asymmetric binding site data. We show that if the true binding site is asymmetric the results are unambiguous and the asymmetric model is clearly superior to the symmetric model. But if the true binding specificity is symmetric commonly used methods can infer, incorrectly, that the motif is asymmetric. The resulting inaccurate motifs lead to lower sensitivity and specificity than would the correct, symmetric models. We also show how the correct model can be obtained by the use of appropriate measures of statistical significance. Conclusions/Significance This study demonstrates that the most commonly used motif-finding approaches usually model symmetric motifs incorrectly, which leads to higher than necessary false prediction errors. It also demonstrates how alternative motif-finding methods can correct the problem, providing more accurate motif models and reducing the errors. Furthermore, it provides criteria for determining whether a symmetric or asymmetric model is the most appropriate for any experimental dataset.
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Abstract
Gene regulatory networks allow the control of gene expression patterns in living cells. The study of network topology has revealed that certain subgraphs of interactions or "motifs" appear at anomalously high frequencies. We ask here whether this phenomenon may emerge because of the functions carried out by these networks. Given a framework for describing regulatory interactions and dynamics, we consider in the space of all regulatory networks those that have prescribed functional capabilities. Markov Chain Monte Carlo sampling is then used to determine how these functional networks lead to specific motif statistics in the interactions. In the case where the regulatory networks are constrained to exhibit multistability, we find a high frequency of gene pairs that are mutually inhibitory and self-activating. In contrast, networks constrained to have periodic gene expression patterns (mimicking for instance the cell cycle) have a high frequency of bifan-like motifs involving four genes with at least one activating and one inhibitory interaction.
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Zhou X, O'Shea EK. Integrated approaches reveal determinants of genome-wide binding and function of the transcription factor Pho4. Mol Cell 2011; 42:826-36. [PMID: 21700227 DOI: 10.1016/j.molcel.2011.05.025] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2010] [Revised: 01/08/2011] [Accepted: 05/18/2011] [Indexed: 11/15/2022]
Abstract
DNA sequences with high affinity for transcription factors occur more frequently in the genome than instances of genes bound or regulated by these factors. It is not clear what factors determine the genome-wide pattern of binding or regulation for a given transcription factor. We used an integrated approach to study how trans influences shape the binding and regulatory landscape of Pho4, a budding yeast transcription factor activated in response to phosphate limitation. We find that nucleosomes significantly restrict Pho4 binding. At nucleosome-depleted sites, competition from another transcription factor, Cbf1, determines Pho4 occupancy, raising the threshold for transcriptional activation in phosphate replete conditions and preventing Pho4 activation of genes outside the phosphate regulon during phosphate starvation. Pho4 binding is not sufficient for transcriptional activation-a cooperative interaction between Pho2 and Pho4 specifies genes that are activated. Combining these experimental observations, we are able to globally predict Pho4 binding and its functionality.
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Affiliation(s)
- Xu Zhou
- Howard Hughes Medical Institute, Harvard University Faculty of Arts and Sciences Center for Systems Biology, Northwest Labs, 52 Oxford Street, Cambridge, MA 02138, USA
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49
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Voss TC, Schiltz RL, Sung MH, Yen PM, Stamatoyannopoulos JA, Biddie SC, Johnson TA, Miranda TB, John S, Hager GL. Dynamic exchange at regulatory elements during chromatin remodeling underlies assisted loading mechanism. Cell 2011; 146:544-54. [PMID: 21835447 DOI: 10.1016/j.cell.2011.07.006] [Citation(s) in RCA: 243] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2010] [Revised: 04/12/2011] [Accepted: 07/07/2011] [Indexed: 10/17/2022]
Abstract
The glucocorticoid receptor (GR), like other eukaryotic transcription factors, regulates gene expression by interacting with chromatinized DNA response elements. Photobleaching experiments in living cells indicate that receptors transiently interact with DNA on the time scale of seconds and predict that the response elements may be sparsely occupied on average. Here, we show that the binding of one receptor at the glucocorticoid response element (GRE) does not reduce the steady-state binding of another receptor variant to the same GRE. Mathematical simulations reproduce this noncompetitive state using short GR/GRE residency times and relatively long times between DNA binding events. At many genomic sites where GR binding causes increased chromatin accessibility, concurrent steady-state binding levels for the variant receptor are actually increased, a phenomenon termed assisted loading. Temporally sparse transcription factor-DNA interactions induce local chromatin reorganization, resulting in transient access for binding of secondary regulatory factors.
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Affiliation(s)
- Ty C Voss
- Laboratory of Receptor Biology and Gene Expression, Building 41, B602, 41 Library Drive, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Ay A, Arnosti DN. Mathematical modeling of gene expression: a guide for the perplexed biologist. Crit Rev Biochem Mol Biol 2011; 46:137-51. [PMID: 21417596 DOI: 10.3109/10409238.2011.556597] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.
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Affiliation(s)
- Ahmet Ay
- Department of Biology, Colgate University, Hamilton, NY, USA
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