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Hayase T, Sugino S, Moriya H, Yamakage M. TACR1gene polymorphism and sex differences in postoperative nausea and vomiting. Anaesthesia 2015; 70:1148-59. [DOI: 10.1111/anae.13082] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2015] [Indexed: 12/17/2022]
Affiliation(s)
- T. Hayase
- Department of Anesthesiology; Sapporo Medical University School of Medicine; Sapporo Japan
| | - S. Sugino
- Department of Anesthesiology; Sapporo Medical University School of Medicine; Sapporo Japan
| | - H. Moriya
- Department of Pharmacy; Hokkaido Pharmaceutical University School of Pharmacy; Otaru Japan
| | - M. Yamakage
- Department of Anesthesiology; Sapporo Medical University School of Medicine; Sapporo Japan
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102
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Pundhir S, Poirazi P, Gorodkin J. Emerging applications of read profiles towards the functional annotation of the genome. Front Genet 2015; 6:188. [PMID: 26042150 PMCID: PMC4437211 DOI: 10.3389/fgene.2015.00188] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 05/06/2015] [Indexed: 12/21/2022] Open
Abstract
Functional annotation of the genome is important to understand the phenotypic complexity of various species. The road toward functional annotation involves several challenges ranging from experiments on individual molecules to large-scale analysis of high-throughput sequencing (HTS) data. HTS data is typically a result of the protocol designed to address specific research questions. The sequencing results in reads, which when mapped to a reference genome often leads to the formation of distinct patterns (read profiles). Interpretation of these read profiles is essential for their analysis in relation to the research question addressed. Several strategies have been employed at varying levels of abstraction ranging from a somewhat ad hoc to a more systematic analysis of read profiles. These include methods which can compare read profiles, e.g., from direct (non-sequence based) alignments to classification of patterns into functional groups. In this review, we highlight the emerging applications of read profiles for the annotation of non-coding RNA and cis-regulatory elements (CREs) such as enhancers and promoters. We also discuss the biological rationale behind their formation.
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Affiliation(s)
- Sachin Pundhir
- Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences (IKVH), University of Copenhagen Frederiksberg C, Denmark
| | - Panayiota Poirazi
- Computational Biology Lab, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas Heraklion, Greece
| | - Jan Gorodkin
- Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences (IKVH), University of Copenhagen Frederiksberg C, Denmark
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Thomas D, Finan C, Newport MJ, Jones S. DNA entropy reveals a significant difference in complexity between housekeeping and tissue specific gene promoters. Comput Biol Chem 2015; 58:19-24. [PMID: 25988219 DOI: 10.1016/j.compbiolchem.2015.05.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 05/01/2015] [Accepted: 05/01/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND The complexity of DNA can be quantified using estimates of entropy. Variation in DNA complexity is expected between the promoters of genes with different transcriptional mechanisms; namely housekeeping (HK) and tissue specific (TS). The former are transcribed constitutively to maintain general cellular functions, and the latter are transcribed in restricted tissue and cells types for specific molecular events. It is known that promoter features in the human genome are related to tissue specificity, but this has been difficult to quantify on a genomic scale. If entropy effectively quantifies DNA complexity, calculating the entropies of HK and TS gene promoters as profiles may reveal significant differences. RESULTS Entropy profiles were calculated for a total dataset of 12,003 human gene promoters and for 501 housekeeping (HK) and 587 tissue specific (TS) human gene promoters. The mean profiles show the TS promoters have a significantly lower entropy (p<2.2e-16) than HK gene promoters. The entropy distributions for the 3 datasets show that promoter entropies could be used to identify novel HK genes. CONCLUSION Functional features comprise DNA sequence patterns that are non-random and hence they have lower entropies. The lower entropy of TS gene promoters can be explained by a higher density of positive and negative regulatory elements, required for genes with complex spatial and temporary expression.
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Affiliation(s)
- David Thomas
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - Chris Finan
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - Melanie J Newport
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - Susan Jones
- The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK
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104
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Dogan N, Wu W, Morrissey CS, Chen KB, Stonestrom A, Long M, Keller CA, Cheng Y, Jain D, Visel A, Pennacchio LA, Weiss MJ, Blobel GA, Hardison RC. Occupancy by key transcription factors is a more accurate predictor of enhancer activity than histone modifications or chromatin accessibility. Epigenetics Chromatin 2015; 8:16. [PMID: 25984238 PMCID: PMC4432502 DOI: 10.1186/s13072-015-0009-5] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Accepted: 04/02/2015] [Indexed: 12/12/2022] Open
Abstract
Background Regulated gene expression controls organismal development, and variation in regulatory patterns has been implicated in complex traits. Thus accurate prediction of enhancers is important for further understanding of these processes. Genome-wide measurement of epigenetic features, such as histone modifications and occupancy by transcription factors, is improving enhancer predictions, but the contribution of these features to prediction accuracy is not known. Given the importance of the hematopoietic transcription factor TAL1 for erythroid gene activation, we predicted candidate enhancers based on genomic occupancy by TAL1 and measured their activity. Contributions of multiple features to enhancer prediction were evaluated based on the results of these and other studies. Results TAL1-bound DNA segments were active enhancers at a high rate both in transient transfections of cultured cells (39 of 79, or 56%) and transgenic mice (43 of 66, or 65%). The level of binding signal for TAL1 or GATA1 did not help distinguish TAL1-bound DNA segments as active versus inactive enhancers, nor did the density of regulation-related histone modifications. A meta-analysis of results from this and other studies (273 tested predicted enhancers) showed that the presence of TAL1, GATA1, EP300, SMAD1, H3K4 methylation, H3K27ac, and CAGE tags at DNase hypersensitive sites gave the most accurate predictors of enhancer activity, with a success rate over 80% and a median threefold increase in activity. Chromatin accessibility assays and the histone modifications H3K4me1 and H3K27ac were sensitive for finding enhancers, but they have high false positive rates unless transcription factor occupancy is also included. Conclusions Occupancy by key transcription factors such as TAL1, GATA1, SMAD1, and EP300, along with evidence of transcription, improves the accuracy of enhancer predictions based on epigenetic features. Electronic supplementary material The online version of this article (doi:10.1186/s13072-015-0009-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nergiz Dogan
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA
| | - Weisheng Wu
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA ; Bioinformatics Core, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218 USA
| | - Christapher S Morrissey
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA
| | - Kuan-Bei Chen
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA
| | - Aaron Stonestrom
- Division of Hematology, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104 USA ; Perelman School of Medicine at the University of Pennsylvania, 415 Curie Boulevard, Philadelphia, PA 19104 USA
| | - Maria Long
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA
| | - Cheryl A Keller
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA
| | - Yong Cheng
- Department of Genetics, Mail Stop-5120, Stanford University, Stanford, CA 94305 USA
| | - Deepti Jain
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA
| | - Axel Visel
- Genomics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mailstop 84-171, Berkeley, CA 94720 USA ; DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598 USA
| | - Len A Pennacchio
- Genomics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mailstop 84-171, Berkeley, CA 94720 USA ; DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598 USA
| | - Mitchell J Weiss
- Department of Hematology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105 USA
| | - Gerd A Blobel
- Division of Hematology, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104 USA ; Perelman School of Medicine at the University of Pennsylvania, 415 Curie Boulevard, Philadelphia, PA 19104 USA
| | - Ross C Hardison
- Center for Comparative Genomics and Bioinformatics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA 16802 USA
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105
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Wang D, Yan KK, Sisu C, Cheng C, Rozowsky J, Meyerson W, Gerstein MB. Loregic: a method to characterize the cooperative logic of regulatory factors. PLoS Comput Biol 2015; 11:e1004132. [PMID: 25884877 PMCID: PMC4401777 DOI: 10.1371/journal.pcbi.1004132] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Accepted: 01/12/2015] [Indexed: 12/24/2022] Open
Abstract
The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic’s gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy. Gene expression is controlled by various gene regulatory factors. Those factors work cooperatively forming a complex regulatory circuit genome wide. Corruptions of regulatory cooperativity may lead to abnormal gene expression activity such as cancer. Traditional experimental methods, however, can only identify small-scale regulatory activity. Thus, to systematically understand the cooperativity between and among different types of regulatory factors, we need the efficient and systematic computational methods. Regulatory circuits have been suggested to behave analogously to the electronic circuits in which a wide variety of electronic elements work coordinately to function correctly. Recently, an increasing amount of next generation sequencing data provides a great resource to study regulatory activity. Thus, we developed a general-purpose computational method using logic-circuit models from electronics and applied it to a human leukemia dataset, identifying the genome-wide cooperativity of transcription factors and microRNAs.
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Affiliation(s)
- Daifeng Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Koon-Kiu Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Cristina Sisu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
| | - Joel Rozowsky
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - William Meyerson
- School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Mark B. Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Department of Computer Science, Yale University, New Haven, Connecticut, United States of America
- * E-mail:
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106
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High-throughput and quantitative assessment of enhancer activity in mammals by CapStarr-seq. Nat Commun 2015; 6:6905. [PMID: 25872643 DOI: 10.1038/ncomms7905] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2014] [Accepted: 03/12/2015] [Indexed: 01/19/2023] Open
Abstract
Cell-type specific regulation of gene expression requires the activation of promoters by distal genomic elements defined as enhancers. The identification and the characterization of enhancers are challenging in mammals due to their genome complexity. Here we develop CapStarr-Seq, a novel high-throughput strategy to quantitatively assess enhancer activity in mammals. This approach couples capture of regions of interest to previously developed Starr-seq technique. Extensive assessment of CapStarr-seq demonstrates accurate quantification of enhancer activity. Furthermore, we find that enhancer strength is associated with binding complexity of tissue-specific transcription factors and super-enhancers, while additive enhancer activity isolates key genes involved in cell identity and function. The CapStarr-Seq thus provides a fast and cost-effective approach to assess the activity of potential enhancers for a given cell type and will be helpful in decrypting transcription regulation mechanisms.
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107
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Oppenheim SJ, Baker RH, Simon S, DeSalle R. We can't all be supermodels: the value of comparative transcriptomics to the study of non-model insects. INSECT MOLECULAR BIOLOGY 2015; 24:139-54. [PMID: 25524309 PMCID: PMC4383654 DOI: 10.1111/imb.12154] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Insects are the most diverse group of organisms on the planet. Variation in gene expression lies at the heart of this biodiversity and recent advances in sequencing technology have spawned a revolution in researchers' ability to survey tissue-specific transcriptional complexity across a wide range of insect taxa. Increasingly, studies are using a comparative approach (across species, sexes and life stages) that examines the transcriptional basis of phenotypic diversity within an evolutionary context. In the present review, we summarize much of this research, focusing in particular on three critical aspects of insect biology: morphological development and plasticity; physiological response to the environment; and sexual dimorphism. A common feature that is emerging from these investigations concerns the dynamic nature of transcriptome evolution as indicated by rapid changes in the overall pattern of gene expression, the differential expression of numerous genes with unknown function, and the incorporation of novel, lineage-specific genes into the transcriptional profile.
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Affiliation(s)
- Sara J Oppenheim
- Department of Entomology, Division of Invertebrates, Sackler Institute for Comparative Genomics, American Museum of Natural HistoryNew York, NY, USA
| | - Richard H Baker
- Department of Entomology, Division of Invertebrates, Sackler Institute for Comparative Genomics, American Museum of Natural HistoryNew York, NY, USA
| | - Sabrina Simon
- Biosystematics Group, Wageningen UniversityWageningen, The Netherlands
| | - Rob DeSalle
- Department of Entomology, Division of Invertebrates, Sackler Institute for Comparative Genomics, American Museum of Natural HistoryNew York, NY, USA
- Correspondence: Dr. Robert DeSalle, Sackler Institute for Comparative Genomics, American Museum of Natural History, 79th Street at Central Park West, New York, NY 10024, USA. Tel.: 212-769-5670; e-mail:
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108
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Pazin MJ. Using the ENCODE Resource for Functional Annotation of Genetic Variants. Cold Spring Harb Protoc 2015; 2015:522-36. [PMID: 25762420 DOI: 10.1101/pdb.top084988] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This article illustrates the use of the Encyclopedia of DNA Elements (ENCODE) resource to generate or refine hypotheses from genomic data on disease and other phenotypic traits. First, the goals and history of ENCODE and related epigenomics projects are reviewed. Second, the rationale for ENCODE and the major data types used by ENCODE are briefly described, as are some standard heuristics for their interpretation. Third, the use of the ENCODE resource is examined. Standard use cases for ENCODE, accessing the ENCODE resource, and accessing data from related projects are discussed. Although the focus of this article is the use of ENCODE data, some of the same approaches can be used with data from other projects.
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Affiliation(s)
- Michael J Pazin
- Division of Genome Sciences, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892
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109
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Suryamohan K, Halfon MS. Identifying transcriptional cis-regulatory modules in animal genomes. WILEY INTERDISCIPLINARY REVIEWS. DEVELOPMENTAL BIOLOGY 2015; 4:59-84. [PMID: 25704908 PMCID: PMC4339228 DOI: 10.1002/wdev.168] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 11/04/2014] [Accepted: 11/16/2014] [Indexed: 11/08/2022]
Abstract
UNLABELLED Gene expression is regulated through the activity of transcription factors (TFs) and chromatin-modifying proteins acting on specific DNA sequences, referred to as cis-regulatory elements. These include promoters, located at the transcription initiation sites of genes, and a variety of distal cis-regulatory modules (CRMs), the most common of which are transcriptional enhancers. Because regulated gene expression is fundamental to cell differentiation and acquisition of new cell fates, identifying, characterizing, and understanding the mechanisms of action of CRMs is critical for understanding development. CRM discovery has historically been challenging, as CRMs can be located far from the genes they regulate, have few readily identifiable sequence characteristics, and for many years were not amenable to high-throughput discovery methods. However, the recent availability of complete genome sequences and the development of next-generation sequencing methods have led to an explosion of both computational and empirical methods for CRM discovery in model and nonmodel organisms alike. Experimentally, CRMs can be identified through chromatin immunoprecipitation directed against TFs or histone post-translational modifications, identification of nucleosome-depleted 'open' chromatin regions, or sequencing-based high-throughput functional screening. Computational methods include comparative genomics, clustering of known or predicted TF-binding sites, and supervised machine-learning approaches trained on known CRMs. All of these methods have proven effective for CRM discovery, but each has its own considerations and limitations, and each is subject to a greater or lesser number of false-positive identifications. Experimental confirmation of predictions is essential, although shortcomings in current methods suggest that additional means of validation need to be developed. For further resources related to this article, please visit the WIREs website. CONFLICT OF INTEREST The authors have declared no conflicts of interest for this article.
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Affiliation(s)
- Kushal Suryamohan
- Department of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- NY State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, NY 14203, USA
| | - Marc S. Halfon
- Department of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- Department of Biological Sciences, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- Department of Biomedical Informatics, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- NY State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, NY 14203, USA
- Molecular and Cellular Biology Department and Program in Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
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Wilken MS, Brzezinski JA, La Torre A, Siebenthall K, Thurman R, Sabo P, Sandstrom RS, Vierstra J, Canfield TK, Hansen RS, Bender MA, Stamatoyannopoulos J, Reh TA. DNase I hypersensitivity analysis of the mouse brain and retina identifies region-specific regulatory elements. Epigenetics Chromatin 2015; 8:8. [PMID: 25972927 PMCID: PMC4429822 DOI: 10.1186/1756-8935-8-8] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 01/27/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The brain, spinal cord, and neural retina comprise the central nervous system (CNS) of vertebrates. Understanding the regulatory mechanisms that underlie the enormous cell-type diversity of the CNS is a significant challenge. Whole-genome mapping of DNase I-hypersensitive sites (DHSs) has been used to identify cis-regulatory elements in many tissues. We have applied this approach to the mouse CNS, including developing and mature neural retina, whole brain, and two well-characterized brain regions, the cerebellum and the cerebral cortex. RESULTS For the various regions and developmental stages of the CNS that we analyzed, there were approximately the same number of DHSs; however, there were many DHSs unique to each CNS region and developmental stage. Many of the DHSs are likely to mark enhancers that are specific to the specific CNS region and developmental stage. We validated the DNase I mapping approach for identification of CNS enhancers using the existing VISTA Browser database and with in vivo and in vitro electroporation of the retina. Analysis of transcription factor consensus sites within the DHSs shows distinct region-specific profiles of transcriptional regulators particular to each region. Clustering developmentally dynamic DHSs in the retina revealed enrichment of developmental stage-specific transcriptional regulators. Additionally, we found reporter gene activity in the retina driven from several previously uncharacterized regulatory elements surrounding the neurodevelopmental gene Otx2. Identification of DHSs shared between mouse and human showed region-specific differences in the evolution of cis-regulatory elements. CONCLUSIONS Overall, our results demonstrate the potential of genome-wide DNase I mapping to cis-regulatory questions regarding the regional diversity within the CNS. These data represent an extensive catalogue of potential cis-regulatory elements within the CNS that display region and temporal specificity, as well as a set of DHSs common to CNS tissues. Further examination of evolutionary conservation of DHSs between CNS regions and different species may reveal important cis-regulatory elements in the evolution of the mammalian CNS.
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Affiliation(s)
- Matthew S Wilken
- Department of Biological Structure, University of Washington, 1959 NE Pacific Street, Box 357420, Seattle, WA 98195 USA ; Molecular and Cellular Biology Program, University of Washington, MCB Program Office, T-466 Health Sciences Building, Box 357275, Seattle, WA 98195 USA
| | - Joseph A Brzezinski
- Department of Biological Structure, University of Washington, 1959 NE Pacific Street, Box 357420, Seattle, WA 98195 USA ; Department of Ophthalmology, University of Colorado School of Medicine, 1675 Aurora Court, Aurora, CO 80045 USA
| | - Anna La Torre
- Department of Biological Structure, University of Washington, 1959 NE Pacific Street, Box 357420, Seattle, WA 98195 USA
| | - Kyle Siebenthall
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - Robert Thurman
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - Peter Sabo
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - Richard S Sandstrom
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - Jeff Vierstra
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - Theresa K Canfield
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - R Scott Hansen
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - Michael A Bender
- Department of Pediatrics, University of Washington, 1959 NE Pacific St, Health Sciences Building, Seattle, WA Box 356320, 98195 USA ; Clinical Research Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109 USA
| | - John Stamatoyannopoulos
- Department of Genome Sciences, University of Washington, Foege Building S-250, 3720 15th Ave NE, Box 355065, Seattle, WA 98195 USA
| | - Thomas A Reh
- Department of Biological Structure, University of Washington, 1959 NE Pacific Street, Box 357420, Seattle, WA 98195 USA
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111
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Genome-wide comparative analysis reveals human-mouse regulatory landscape and evolution. BMC Genomics 2015; 16:87. [PMID: 25765714 PMCID: PMC4333152 DOI: 10.1186/s12864-015-1245-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Accepted: 01/15/2015] [Indexed: 11/29/2022] Open
Abstract
Background Because species-specific gene expression is driven by species-specific regulation, understanding the relationship between sequence and function of the regulatory regions in different species will help elucidate how differences among species arise. Despite active experimental and computational research, relationships among sequence, conservation, and function are still poorly understood. Results We compared transcription factor occupied segments (TFos) for 116 human and 35 mouse TFs in 546 human and 125 mouse cell types and tissues from the Human and the Mouse ENCODE projects. We based the map between human and mouse TFos on a one-to-one nucleotide cross-species mapper, bnMapper, that utilizes whole genome alignments (WGA). Our analysis shows that TFos are under evolutionary constraint, but a substantial portion (25.1% of mouse and 25.85% of human on average) of the TFos does not have a homologous sequence on the other species; this portion varies among cell types and TFs. Furthermore, 47.67% and 57.01% of the homologous TFos sequence shows binding activity on the other species for human and mouse respectively. However, 79.87% and 69.22% is repurposed such that it binds the same TF in different cells or different TFs in the same cells. Remarkably, within the set of repurposed TFos, the corresponding genome regions in the other species are preferred locations of novel TFos. These events suggest exaptation of some functional regulatory sequences into new function. Despite TFos repurposing, we did not find substantial changes in their predicted target genes, suggesting that CRMs buffer evolutionary events allowing little or no change in the TFos – target gene associations. Thus, the small portion of TFos with strictly conserved occupancy underestimates the degree of conservation of regulatory interactions. Conclusion We mapped regulatory sequences from an extensive number of TFs and cell types between human and mouse using WGA. A comparative analysis of this correspondence unveiled the extent of the shared regulatory sequence across TFs and cell types under study. Importantly, a large part of the shared regulatory sequence is repurposed on the other species. This sequence, fueled by turnover events, provides a strong case for exaptation in regulatory elements. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1245-6) contains supplementary material, which is available to authorized users.
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112
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Shibata M, Gulden FO, Sestan N. From trans to cis: transcriptional regulatory networks in neocortical development. Trends Genet 2015; 31:77-87. [PMID: 25624274 DOI: 10.1016/j.tig.2014.12.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 12/19/2014] [Accepted: 12/19/2014] [Indexed: 01/25/2023]
Abstract
Transcriptional mechanisms mediated by the binding of transcription factors (TFs) to cis-acting regulatory elements (CREs) in DNA play crucial roles in directing gene expression. While TFs have been extensively studied, less effort has gone towards the identification and functional characterization of CREs and associated epigenetic modulation. However, owing to methodological and analytical advances, more comprehensive studies of regulatory elements and mechanisms are now possible. We summarize recent progress in integrative analyses of these regulatory components in the development of the cerebral neocortex, the part of the brain involved in cognition and complex behavior. These studies are uncovering not only the underlying transcriptional regulatory networks, but also how these networks are altered across species and in neurological and psychiatric disorders.
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Affiliation(s)
- Mikihito Shibata
- Department of Neurobiology and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Forrest O Gulden
- Department of Neurobiology and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Nenad Sestan
- Department of Neurobiology and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Psychiatry and Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale School of Medicine, New Haven, CT 06510, USA.
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Yue F, Cheng Y, Breschi A, Vierstra J, Wu W, Ryba T, Sandstrom R, Ma Z, Davis C, Pope BD, Shen Y, Pervouchine DD, Djebali S, Thurman RE, Kaul R, Rynes E, Kirilusha A, Marinov GK, Williams BA, Trout D, Amrhein H, Fisher-Aylor K, Antoshechkin I, DeSalvo G, See LH, Fastuca M, Drenkow J, Zaleski C, Dobin A, Prieto P, Lagarde J, Bussotti G, Tanzer A, Denas O, Li K, Bender MA, Zhang M, Byron R, Groudine MT, McCleary D, Pham L, Ye Z, Kuan S, Edsall L, Wu YC, Rasmussen MD, Bansal MS, Kellis M, Keller CA, Morrissey CS, Mishra T, Jain D, Dogan N, Harris RS, Cayting P, Kawli T, Boyle AP, Euskirchen G, Kundaje A, Lin S, Lin Y, Jansen C, Malladi VS, Cline MS, Erickson DT, Kirkup VM, Learned K, Sloan CA, Rosenbloom KR, Lacerda de Sousa B, Beal K, Pignatelli M, Flicek P, Lian J, Kahveci T, Lee D, Kent WJ, Ramalho Santos M, Herrero J, Notredame C, Johnson A, Vong S, Lee K, Bates D, Neri F, Diegel M, Canfield T, Sabo PJ, Wilken MS, Reh TA, Giste E, Shafer A, Kutyavin T, Haugen E, Dunn D, Reynolds AP, Neph S, Humbert R, Hansen RS, De Bruijn M, Selleri L, Rudensky A, Josefowicz S, Samstein R, Eichler EE, Orkin SH, Levasseur D, Papayannopoulou T, Chang KH, Skoultchi A, Gosh S, Disteche C, Treuting P, Wang Y, Weiss MJ, Blobel GA, Cao X, Zhong S, Wang T, Good PJ, Lowdon RF, Adams LB, Zhou XQ, Pazin MJ, Feingold EA, Wold B, Taylor J, Mortazavi A, Weissman SM, Stamatoyannopoulos JA, Snyder MP, Guigo R, Gingeras TR, Gilbert DM, Hardison RC, Beer MA, Ren B. A comparative encyclopedia of DNA elements in the mouse genome. Nature 2015; 515:355-64. [PMID: 25409824 PMCID: PMC4266106 DOI: 10.1038/nature13992] [Citation(s) in RCA: 1166] [Impact Index Per Article: 129.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2014] [Accepted: 10/24/2014] [Indexed: 12/11/2022]
Abstract
The laboratory mouse shares the majority of its protein-coding genes with humans, making it the premier model organism in biomedical research, yet the two mammals differ in significant ways. To gain greater insights into both shared and species-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium has mapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications and replication domains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we not only confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degree of divergence of sequences involved in transcriptional regulation, chromatin state and higher order chromatin organization. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, and provide a general resource for research into mammalian biology and mechanisms of human diseases.
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Affiliation(s)
- Feng Yue
- 1] Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA. [2] Department of Biochemistry and Molecular Biology, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania 17033, USA
| | - Yong Cheng
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Alessandra Breschi
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Jeff Vierstra
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Weisheng Wu
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Tyrone Ryba
- Department of Biological Science, 319 Stadium Drive, Florida State University, Tallahassee, Florida 32306-4295, USA
| | - Richard Sandstrom
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Zhihai Ma
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Carrie Davis
- Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA
| | - Benjamin D Pope
- Department of Biological Science, 319 Stadium Drive, Florida State University, Tallahassee, Florida 32306-4295, USA
| | - Yin Shen
- Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Dmitri D Pervouchine
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Sarah Djebali
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Robert E Thurman
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Rajinder Kaul
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Eric Rynes
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Anthony Kirilusha
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Georgi K Marinov
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Brian A Williams
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Diane Trout
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Henry Amrhein
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Katherine Fisher-Aylor
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Igor Antoshechkin
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Gilberto DeSalvo
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - Lei-Hoon See
- Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA
| | - Meagan Fastuca
- Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA
| | - Jorg Drenkow
- Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA
| | - Chris Zaleski
- Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA
| | - Alex Dobin
- Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA
| | - Pablo Prieto
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Julien Lagarde
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Giovanni Bussotti
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Andrea Tanzer
- 1] Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain. [2] Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Waehringerstrasse 17/3/303, A-1090 Vienna, Austria
| | - Olgert Denas
- Departments of Biology and Mathematics and Computer Science, Emory University, O. Wayne Rollins Research Center, 1510 Clifton Road NE, Atlanta, Georgia 30322, USA
| | - Kanwei Li
- Departments of Biology and Mathematics and Computer Science, Emory University, O. Wayne Rollins Research Center, 1510 Clifton Road NE, Atlanta, Georgia 30322, USA
| | - M A Bender
- 1] Department of Pediatrics, University of Washington, Seattle, Washington 98195, USA. [2] Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Miaohua Zhang
- Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Rachel Byron
- Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Mark T Groudine
- 1] Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. [2] Department of Radiation Oncology, University of Washington, Seattle, Washington 98195, USA
| | - David McCleary
- Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Long Pham
- Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Zhen Ye
- Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Samantha Kuan
- Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Lee Edsall
- Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Yi-Chieh Wu
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - Matthew D Rasmussen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - Mukul S Bansal
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - Manolis Kellis
- 1] Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Cheryl A Keller
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Christapher S Morrissey
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Tejaswini Mishra
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Deepti Jain
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Nergiz Dogan
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Robert S Harris
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Philip Cayting
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Trupti Kawli
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Alan P Boyle
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Ghia Euskirchen
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Shin Lin
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Yiing Lin
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Camden Jansen
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, California 92697, USA
| | - Venkat S Malladi
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Melissa S Cline
- Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, California 95064, USA
| | - Drew T Erickson
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Vanessa M Kirkup
- Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, California 95064, USA
| | - Katrina Learned
- Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, California 95064, USA
| | - Cricket A Sloan
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Kate R Rosenbloom
- Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, California 95064, USA
| | - Beatriz Lacerda de Sousa
- Departments of Obstetrics/Gynecology and Pathology, and Center for Reproductive Sciences, University of California San Francisco, San Francisco, California 94143, USA
| | - Kathryn Beal
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Miguel Pignatelli
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jin Lian
- Yale University, Department of Genetics, PO Box 208005, 333 Cedar Street, New Haven, Connecticut 06520-8005, USA
| | - Tamer Kahveci
- Computer &Information Sciences &Engineering, University of Florida, Gainesville, Florida 32611, USA
| | - Dongwon Lee
- McKusick-Nathans Institute of Genetic Medicine and Department of Biomedical Engineering, Johns Hopkins University, 733 N. Broadway, BRB 573 Baltimore, Maryland 21205, USA
| | - W James Kent
- Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, California 95064, USA
| | - Miguel Ramalho Santos
- Departments of Obstetrics/Gynecology and Pathology, and Center for Reproductive Sciences, University of California San Francisco, San Francisco, California 94143, USA
| | - Javier Herrero
- 1] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. [2] Bill Lyons Informatics Centre, UCL Cancer Institute, University College London, London WC1E 6DD, UK
| | - Cedric Notredame
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Audra Johnson
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Shinny Vong
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Kristen Lee
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Daniel Bates
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Fidencio Neri
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Morgan Diegel
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Theresa Canfield
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Peter J Sabo
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Matthew S Wilken
- Department of Biological Structure, University of Washington, HSB I-516, 1959 NE Pacific Street, Seattle, Washington 98195, USA
| | - Thomas A Reh
- Department of Biological Structure, University of Washington, HSB I-516, 1959 NE Pacific Street, Seattle, Washington 98195, USA
| | - Erika Giste
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Anthony Shafer
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Tanya Kutyavin
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Eric Haugen
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Douglas Dunn
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Alex P Reynolds
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Shane Neph
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Richard Humbert
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - R Scott Hansen
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Marella De Bruijn
- MRC Molecular Haemotology Unit, University of Oxford, Oxford OX3 9DS, UK
| | - Licia Selleri
- Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, New York 10065, USA
| | - Alexander Rudensky
- HHMI and Ludwig Center at Memorial Sloan Kettering Cancer Center, Immunology Program, Memorial Sloan Kettering Cancer Canter, New York, New York 10065, USA
| | - Steven Josefowicz
- HHMI and Ludwig Center at Memorial Sloan Kettering Cancer Center, Immunology Program, Memorial Sloan Kettering Cancer Canter, New York, New York 10065, USA
| | - Robert Samstein
- HHMI and Ludwig Center at Memorial Sloan Kettering Cancer Center, Immunology Program, Memorial Sloan Kettering Cancer Canter, New York, New York 10065, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Stuart H Orkin
- Dana Farber Cancer Institute, Harvard Medical School, Cambridge, Massachusetts 02138, USA
| | - Dana Levasseur
- University of Iowa Carver College of Medicine, Department of Internal Medicine, Iowa City, Iowa 52242, USA
| | - Thalia Papayannopoulou
- Division of Hematology, Department of Medicine, University of Washington, Seattle, Washington 98195, USA
| | - Kai-Hsin Chang
- University of Iowa Carver College of Medicine, Department of Internal Medicine, Iowa City, Iowa 52242, USA
| | - Arthur Skoultchi
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York 10461, USA
| | - Srikanta Gosh
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York 10461, USA
| | - Christine Disteche
- Department of Pathology, University of Washington, Seattle, Washington 98195, USA
| | - Piper Treuting
- Department of Comparative Medicine, University of Washington, Seattle, Washington 98195, USA
| | - Yanli Wang
- Bioinformatics and Genomics program, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Mitchell J Weiss
- Department of Hematology, St Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Gerd A Blobel
- 1] Division of Hematology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA. [2] Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Xiaoyi Cao
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Ting Wang
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Peter J Good
- NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Rebecca F Lowdon
- NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Leslie B Adams
- NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Xiao-Qiao Zhou
- NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Michael J Pazin
- NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Elise A Feingold
- NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Barbara Wold
- Division of Biology, California Institute of Technology, Pasadena, California 91125, USA
| | - James Taylor
- Departments of Biology and Mathematics and Computer Science, Emory University, O. Wayne Rollins Research Center, 1510 Clifton Road NE, Atlanta, Georgia 30322, USA
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, California 92697, USA
| | - Sherman M Weissman
- Yale University, Department of Genetics, PO Box 208005, 333 Cedar Street, New Haven, Connecticut 06520-8005, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA
| | - Roderic Guigo
- Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain
| | - Thomas R Gingeras
- Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA
| | - David M Gilbert
- Department of Biological Science, 319 Stadium Drive, Florida State University, Tallahassee, Florida 32306-4295, USA
| | - Ross C Hardison
- Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Michael A Beer
- McKusick-Nathans Institute of Genetic Medicine and Department of Biomedical Engineering, Johns Hopkins University, 733 N. Broadway, BRB 573 Baltimore, Maryland 21205, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
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Chen Z, Lan X, Thomas-Ahner JM, Wu D, Liu X, Ye Z, Wang L, Sunkel B, Grenade C, Chen J, Zynger DL, Yan PS, Huang J, Nephew KP, Huang THM, Lin S, Clinton SK, Li W, Jin VX, Wang Q. Agonist and antagonist switch DNA motifs recognized by human androgen receptor in prostate cancer. EMBO J 2014; 34:502-16. [PMID: 25535248 DOI: 10.15252/embj.201490306] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Human transcription factors recognize specific DNA sequence motifs to regulate transcription. It is unknown whether a single transcription factor is able to bind to distinctly different motifs on chromatin, and if so, what determines the usage of specific motifs. By using a motif-resolution chromatin immunoprecipitation-exonuclease (ChIP-exo) approach, we find that agonist-liganded human androgen receptor (AR) and antagonist-liganded AR bind to two distinctly different motifs, leading to distinct transcriptional outcomes in prostate cancer cells. Further analysis on clinical prostate tissues reveals that the binding of AR to these two distinct motifs is involved in prostate carcinogenesis. Together, these results suggest that unique ligands may switch DNA motifs recognized by ligand-dependent transcription factors in vivo. Our findings also provide a broad mechanistic foundation for understanding ligand-specific induction of gene expression profiles.
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Affiliation(s)
- Zhong Chen
- Department of Molecular Virology, Immunology and Medical Genetics and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Xun Lan
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Jennifer M Thomas-Ahner
- Division of Medical Oncology, Department of Internal Medicine and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Dayong Wu
- Department of Molecular Virology, Immunology and Medical Genetics and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Xiangtao Liu
- Department of Molecular Virology, Immunology and Medical Genetics and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Zhenqing Ye
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA Department of Molecular Medicine, Cancer Therapy and Research Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Liguo Wang
- Division of Biostatistics, Dan L. Duncan Cancer Center Baylor College of Medicine, Houston, TX, USA Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Benjamin Sunkel
- Department of Molecular Virology, Immunology and Medical Genetics and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Cassandra Grenade
- Department of Molecular Virology, Immunology and Medical Genetics and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Junsheng Chen
- Division of Biostatistics, Dan L. Duncan Cancer Center Baylor College of Medicine, Houston, TX, USA Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Debra L Zynger
- Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Pearlly S Yan
- Department of Molecular Virology, Immunology and Medical Genetics and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Jiaoti Huang
- Departments of Pathology and Urology, Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Broad Center for Regenerative Medicine and Stem Cell Research, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Kenneth P Nephew
- Medical Sciences Program, Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Bloomington, IN, USA
| | - Tim H-M Huang
- Department of Molecular Medicine, Cancer Therapy and Research Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, OH, USA
| | - Steven K Clinton
- Division of Medical Oncology, Department of Internal Medicine and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Wei Li
- Division of Biostatistics, Dan L. Duncan Cancer Center Baylor College of Medicine, Houston, TX, USA Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Victor X Jin
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA Department of Molecular Medicine, Cancer Therapy and Research Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Qianben Wang
- Department of Molecular Virology, Immunology and Medical Genetics and the Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, OH, USA
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115
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Yang TH, Wang CC, Hung PC, Wu WS. cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 4:S8. [PMID: 25521507 PMCID: PMC4290730 DOI: 10.1186/1752-0509-8-s4-s8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. DESCRIPTION It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. CONCLUSIONS cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cisMEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes.
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Niu M, Tabari ES, Su Z. De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets. BMC Genomics 2014; 15:1047. [PMID: 25442502 PMCID: PMC4265420 DOI: 10.1186/1471-2164-15-1047] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 11/19/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In eukaryotes, transcriptional regulation is usually mediated by interactions of multiple transcription factors (TFs) with their respective specific cis-regulatory elements (CREs) in the so-called cis-regulatory modules (CRMs) in DNA. Although the knowledge of CREs and CRMs in a genome is crucial to elucidate gene regulatory networks and understand many important biological phenomena, little is known about the CREs and CRMs in most eukaryotic genomes due to the difficulty to characterize them by either computational or traditional experimental methods. However, the exponentially increasing number of TF binding location data produced by the recent wide adaptation of chromatin immunoprecipitation coupled with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) technologies has provided an unprecedented opportunity to identify CRMs and CREs in genomes. Nonetheless, how to effectively mine these large volumes of ChIP data to identify CREs and CRMs at nucleotide resolution is a highly challenging task. RESULTS We have developed a novel graph-theoretic based algorithm DePCRM for genome-wide de novo predictions of CREs and CRMs using a large number of ChIP datasets. DePCRM predicts CREs and CRMs by identifying overrepresented combinatorial CRE motif patterns in multiple ChIP datasets in an effective way. When applied to 168 ChIP datasets of 56 TFs from D. melanogaster, DePCRM identified 184 and 746 overrepresented CRE motifs and their combinatorial patterns, respectively, and predicted a total of 115,932 CRMs in the genome. The predictions recover 77.9% of known CRMs in the datasets and 89.3% of known CRMs containing at least one predicted CRE. We found that the putative CRMs as well as CREs as a whole in a CRM are more conserved than randomly selected sequences. CONCLUSION Our results suggest that the CRMs predicted by DePCRM are highly likely to be functional. Our algorithm is the first of its kind for de novo genome-wide prediction of CREs and CRMs using larger number of transcription factor ChIP datasets. The algorithm and predictions will hopefully facilitate the elucidation of gene regulatory networks in eukaryotes. All the predicted CREs, CRMs, and their target genes are available at http://bioinfo.uncc.edu/mniu/pcrms/www/.
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Affiliation(s)
| | | | - Zhengchang Su
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA.
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117
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Tough DF, Lewis HD, Rioja I, Lindon MJ, Prinjha RK. Epigenetic pathway targets for the treatment of disease: accelerating progress in the development of pharmacological tools: IUPHAR Review 11. Br J Pharmacol 2014; 171:4981-5010. [PMID: 25060293 PMCID: PMC4253452 DOI: 10.1111/bph.12848] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Revised: 05/22/2014] [Accepted: 06/13/2014] [Indexed: 02/06/2023] Open
Abstract
The properties of a cell are determined both genetically by the DNA sequence of its genes and epigenetically through processes that regulate the pattern, timing and magnitude of expression of its genes. While the genetic basis of disease has been a topic of intense study for decades, recent years have seen a dramatic increase in the understanding of epigenetic regulatory mechanisms and a growing appreciation that epigenetic misregulation makes a significant contribution to human disease. Several large protein families have been identified that act in different ways to control the expression of genes through epigenetic mechanisms. Many of these protein families are finally proving tractable for the development of small molecules that modulate their function and represent new target classes for drug discovery. Here, we provide an overview of some of the key epigenetic regulatory proteins and discuss progress towards the development of pharmacological tools for use in research and therapy.
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Affiliation(s)
- David F Tough
- Immuno-Inflammation Therapy Area, GlaxoSmithKline R&D, Medicines Research Centre, Epinova DPU, Stevenage, UK
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Ballester B, Medina-Rivera A, Schmidt D, Gonzàlez-Porta M, Carlucci M, Chen X, Chessman K, Faure AJ, Funnell APW, Goncalves A, Kutter C, Lukk M, Menon S, McLaren WM, Stefflova K, Watt S, Weirauch MT, Crossley M, Marioni JC, Odom DT, Flicek P, Wilson MD. Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways. eLife 2014; 3:e02626. [PMID: 25279814 PMCID: PMC4359374 DOI: 10.7554/elife.02626] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2014] [Accepted: 09/02/2014] [Indexed: 12/20/2022] Open
Abstract
As exome sequencing gives way to genome sequencing, the need to interpret the function of regulatory DNA becomes increasingly important. To test whether evolutionary conservation of cis-regulatory modules (CRMs) gives insight into human gene regulation, we determined transcription factor (TF) binding locations of four liver-essential TFs in liver tissue from human, macaque, mouse, rat, and dog. Approximately, two thirds of the TF-bound regions fell into CRMs. Less than half of the human CRMs were found as a CRM in the orthologous region of a second species. Shared CRMs were associated with liver pathways and disease loci identified by genome-wide association studies. Recurrent rare human disease causing mutations at the promoters of several blood coagulation and lipid metabolism genes were also identified within CRMs shared in multiple species. This suggests that multi-species analyses of experimentally determined combinatorial TF binding will help identify genomic regions critical for tissue-specific gene control.
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Affiliation(s)
- Benoit Ballester
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
- Aix-Marseille Université, UMR1090 TAGC, Marseille, France
- INSERM, UMR1090 TAGC, Marseille, France
| | | | - Dominic Schmidt
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
| | - Mar Gonzàlez-Porta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Matthew Carlucci
- Genetics and Genome Biology Program, SickKids Research Institute, Toronto, Canada
| | - Xiaoting Chen
- School of Electronic and Computing Systems, University of Cincinnati, Cincinnati, United States
| | - Kyle Chessman
- Genetics and Genome Biology Program, SickKids Research Institute, Toronto, Canada
| | - Andre J Faure
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Alister PW Funnell
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, Australia
| | - Angela Goncalves
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Claudia Kutter
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
| | - Margus Lukk
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
| | - Suraj Menon
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
| | - William M McLaren
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Klara Stefflova
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
| | - Stephen Watt
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, United States
- Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, United States
| | - Merlin Crossley
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, Australia
| | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Duncan T Odom
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Michael D Wilson
- Genetics and Genome Biology Program, SickKids Research Institute, Toronto, Canada
- Cancer Research UK–Cambridge InstituteUniversity of Cambridge, Cambridge, United Kingdom
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
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119
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Pike JW, Meyer MB. Fundamentals of vitamin D hormone-regulated gene expression. J Steroid Biochem Mol Biol 2014; 144 Pt A:5-11. [PMID: 24239506 PMCID: PMC4144817 DOI: 10.1016/j.jsbmb.2013.11.004] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Revised: 10/24/2013] [Accepted: 11/05/2013] [Indexed: 12/20/2022]
Abstract
Initial research focused upon several known genetic targets provided early insight into the mechanism of action of the vitamin D hormone (1,25-dihydroxyvitamin D3 (1,25(OH)2D3)). Recently, however, a series of technical advances involving the coupling of chromatin immunoprecipitation (ChIP) to unbiased methodologies that initially involved tiled DNA microarrays (ChIP-chip analysis) and now Next Generation DNA Sequencing techniques (ChIP-seq analysis) has opened new avenues of research into the mechanisms through which 1,25(OH)2D3 regulates gene expression. In this review, we summarize briefly the results of this early work and then focus on more recent studies in which ChIP-chip and ChIP-seq analyses have been used to explore the mechanisms of 1,25(OH)2D3 action on a genome-wide scale providing specific target genes as examples. The results of this work have advanced our understanding of the mechanisms involved at both genetic and epigenetic levels and have revealed a series of new principles through which the vitamin D hormone functions to control the expression of genes. This article is part of a Special Issue entitled '16th Vitamin D Workshop'.
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Affiliation(s)
- J Wesley Pike
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States.
| | - Mark B Meyer
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
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120
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Abstract
Combined with TCR stimuli, extracellular cytokine signals initiate the differentiation of naive CD4(+) T cells into specialized effector T-helper (Th) and regulatory T (Treg) cell subsets. The lineage specification and commitment process occurs through the combinatorial action of multiple transcription factors (TFs) and epigenetic mechanisms that drive lineage-specific gene expression programs. In this article, we review recent studies on the transcriptional and epigenetic regulation of distinct Th cell lineages. Moreover, we review current study linking immune disease-associated single-nucleotide polymorphisms with distal regulatory elements and their potential role in the disease etiology.
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Affiliation(s)
- Subhash K Tripathi
- Turku Centre for Biotechnology, University of Turku and
Åbo Akademi UniversityTurku, Finland
- National Doctoral Programme in Informational and
Structural BiologyTurku, Finland
- Turku Doctoral Programme of Molecular Medicine (TuDMM),
University of TurkuTurku, Finland
| | - Riitta Lahesmaa
- Turku Centre for Biotechnology, University of Turku and
Åbo Akademi UniversityTurku, Finland
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121
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Dippold RP, Fisher SA. A bioinformatic and computational study of myosin phosphatase subunit diversity. Am J Physiol Regul Integr Comp Physiol 2014; 307:R256-70. [PMID: 24898838 PMCID: PMC4121627 DOI: 10.1152/ajpregu.00145.2014] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 05/25/2014] [Indexed: 01/01/2023]
Abstract
Variability in myosin phosphatase (MP) subunits may provide specificity in signaling pathways that regulate muscle tone. We utilized public databases and computational algorithms to investigate the phylogenetic diversity of MP regulatory (PPP1R12A-C) and inhibitory (PPP1R14A-D) subunits. The comparison of exonic coding sequences and expression data confirmed or refuted the existence of isoforms and their tissue-specific expression in different model organisms. The comparison of intronic and exonic sequences identified potential expressional regulatory elements. As examples, smooth muscle MP regulatory subunit (PPP1R12A) is highly conserved through evolution. Its alternative exon E24 is present in fish through mammals with two invariant features: 1) a reading frame shift generating a premature termination codon and 2) a hexanucleotide sequence adjacent to the 3' splice site hypothesized to be a novel suppressor of exon splicing. A characteristic of the striated muscle MP regulatory subunit (PPP1R12B) locus is numerous and phylogenetically variable transcriptional start sites. In fish this locus only codes for the small (M21) subunit, suggesting the primordial function of this gene. Inhibitory subunits show little intragenic variability; their diversity is thought to have arisen by expansion and tissue-specific expression of different gene family members. We demonstrate differences in the regulatory landscape between smooth muscle enriched (PPP1R14A) and more ubiquitously expressed (PPP1R14B) family members and identify deeply conserved intronic sequence and predicted transcriptional cis-regulatory elements. This bioinformatic and computational study has uncovered a number of attributes of MP subunits that supports selection of ideal model organisms and testing of hypotheses regarding their physiological significance and regulated expression.
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Affiliation(s)
- Rachael P Dippold
- Department of Medicine, Cardiology, University of Maryland Baltimore, Baltimore, Maryland
| | - Steven A Fisher
- Department of Medicine, Cardiology, University of Maryland Baltimore, Baltimore, Maryland
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122
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Zernant J, Xie YA, Ayuso C, Riveiro-Alvarez R, Lopez-Martinez MA, Simonelli F, Testa F, Gorin MB, Strom SP, Bertelsen M, Rosenberg T, Boone PM, Yuan B, Ayyagari R, Nagy PL, Tsang SH, Gouras P, Collison FT, Lupski JR, Fishman GA, Allikmets R. Analysis of the ABCA4 genomic locus in Stargardt disease. Hum Mol Genet 2014; 23:6797-806. [PMID: 25082829 DOI: 10.1093/hmg/ddu396] [Citation(s) in RCA: 101] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Autosomal recessive Stargardt disease (STGD1, MIM 248200) is caused by mutations in the ABCA4 gene. Complete sequencing of ABCA4 in STGD patients identifies compound heterozygous or homozygous disease-associated alleles in 65-70% of patients and only one mutation in 15-20% of patients. This study was designed to find the missing disease-causing ABCA4 variation by a combination of next-generation sequencing (NGS), array-Comparative Genome Hybridization (aCGH) screening, familial segregation and in silico analyses. The entire 140 kb ABCA4 genomic locus was sequenced in 114 STGD patients with one known ABCA4 exonic mutation revealing, on average, 200 intronic variants per sample. Filtering of these data resulted in 141 candidates for new mutations. Two variants were detected in four samples, two in three samples, and 20 variants in two samples, the remaining 117 new variants were detected only once. Multimodal analysis suggested 12 new likely pathogenic intronic ABCA4 variants, some of which were specific to (isolated) ethnic groups. No copy number variation (large deletions and insertions) was detected in any patient suggesting that it is a very rare event in the ABCA4 locus. Many variants were excluded since they were not conserved in non-human primates, were frequent in African populations and, therefore, represented ancestral, and not disease-associated, variants. The sequence variability in the ABCA4 locus is extensive and the non-coding sequences do not harbor frequent mutations in STGD patients of European-American descent. Defining disease-associated alleles in the ABCA4 locus requires exceptionally well characterized large cohorts and extensive analyses by a combination of various approaches.
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Affiliation(s)
| | | | - Carmen Ayuso
- Department of Genetics, Instituto de Investigacion Sanitaria-University Hospital Fundacion Jimenez Diaz, UAM (IIS-FJD), Madrid, Spain Centro de Investigacion Biomedica en Red (CIBER) de Enfermedades Raras, ISCIII, Madrid, Spain
| | - Rosa Riveiro-Alvarez
- Department of Genetics, Instituto de Investigacion Sanitaria-University Hospital Fundacion Jimenez Diaz, UAM (IIS-FJD), Madrid, Spain Centro de Investigacion Biomedica en Red (CIBER) de Enfermedades Raras, ISCIII, Madrid, Spain
| | - Miguel-Angel Lopez-Martinez
- Department of Genetics, Instituto de Investigacion Sanitaria-University Hospital Fundacion Jimenez Diaz, UAM (IIS-FJD), Madrid, Spain Centro de Investigacion Biomedica en Red (CIBER) de Enfermedades Raras, ISCIII, Madrid, Spain
| | - Francesca Simonelli
- Eye Clinic, Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy
| | - Francesco Testa
- Eye Clinic, Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy
| | - Michael B Gorin
- Department of Ophthalmology Department of Human Genetics, Jules Stein Eye Institute and
| | - Samuel P Strom
- Department of Ophthalmology Department of Human Genetics, Jules Stein Eye Institute and Department of Pathology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Mette Bertelsen
- Kennedy Center Eye Clinic, Glostrup Hospital, Glostrup, Denmark
| | | | - Philip M Boone
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Bo Yuan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Radha Ayyagari
- Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA and
| | - Peter L Nagy
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Stephen H Tsang
- Department of Ophthalmology and Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | | | - Frederick T Collison
- The Pangere Center for Hereditary Retinal Diseases, The Chicago Lighthouse for People Who are Blind or Visually Impaired, Chicago, IL, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Gerald A Fishman
- The Pangere Center for Hereditary Retinal Diseases, The Chicago Lighthouse for People Who are Blind or Visually Impaired, Chicago, IL, USA
| | - Rando Allikmets
- Department of Ophthalmology and Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
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123
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Affiliation(s)
- Ross C Hardison
- Center for Comparative Genomics and Bioinformatics, Huck Institutes for Life Sciences and Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, USA
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124
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Cha M, Zhou Q. Detecting clustering and ordering binding patterns among transcription factors via point process models. Bioinformatics 2014; 30:2263-71. [PMID: 24790155 DOI: 10.1093/bioinformatics/btu303] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Recent development in ChIP-Seq technology has generated binding data for many transcription factors (TFs) in various cell types and cellular conditions. This opens great opportunities for studying combinatorial binding patterns among a set of TFs active in a particular cellular condition, which is a key component for understanding the interaction between TFs in gene regulation. RESULTS As a first step to the identification of combinatorial binding patterns, we develop statistical methods to detect clustering and ordering patterns among binding sites (BSs) of a pair of TFs. Testing procedures based on Ripley's K-function and its generalizations are developed to identify binding patterns from large collections of BSs in ChIP-Seq data. We have applied our methods to the ChIP-Seq data of 91 pairs of TFs in mouse embryonic stem cells. Our methods have detected clustering binding patterns between most TF pairs, which is consistent with the findings in the literature, and have identified significant ordering preferences, relative to the direction of target gene transcription, among the BSs of seven TFs. More interestingly, our results demonstrate that the identified clustering and ordering binding patterns between TFs are associated with the expression of the target genes. These findings provide new insights into co-regulation between TFs. AVAILABILITY AND IMPLEMENTATION See 'www.stat.ucla.edu/∼zhou/TFKFunctions/' for source code.
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Affiliation(s)
- Maria Cha
- Department of Statistics, University of California, Los Angeles, CA 90095, USA
| | - Qing Zhou
- Department of Statistics, University of California, Los Angeles, CA 90095, USA
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125
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Dissection of thousands of cell type-specific enhancers identifies dinucleotide repeat motifs as general enhancer features. Genome Res 2014; 24:1147-56. [PMID: 24714811 PMCID: PMC4079970 DOI: 10.1101/gr.169243.113] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Gene expression is determined by genomic elements called enhancers, which contain short motifs bound by different transcription factors (TFs). However, how enhancer sequences and TF motifs relate to enhancer activity is unknown, and general sequence requirements for enhancers or comprehensive sets of important enhancer sequence elements have remained elusive. Here, we computationally dissect thousands of functional enhancer sequences from three different Drosophila cell lines. We find that the enhancers display distinct cis-regulatory sequence signatures, which are predictive of the enhancers’ cell type-specific or broad activities. These signatures contain transcription factor motifs and a novel class of enhancer sequence elements, dinucleotide repeat motifs (DRMs). DRMs are highly enriched in enhancers, particularly in enhancers that are broadly active across different cell types. We experimentally validate the importance of the identified TF motifs and DRMs for enhancer function and show that they can be sufficient to create an active enhancer de novo from a nonfunctional sequence. The function of DRMs as a novel class of general enhancer features that are also enriched in human regulatory regions might explain their implication in several diseases and provides important insights into gene regulation.
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126
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Rouault H, Santolini M, Schweisguth F, Hakim V. Imogene: identification of motifs and cis-regulatory modules underlying gene co-regulation. Nucleic Acids Res 2014; 42:6128-45. [PMID: 24682824 PMCID: PMC4041412 DOI: 10.1093/nar/gku209] [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: 11/23/2022] Open
Abstract
Cis-regulatory modules (CRMs) and motifs play a central role in tissue and condition-specific gene expression. Here we present Imogene, an ensemble of statistical tools that we have developed to facilitate their identification and implemented in a publicly available software. Starting from a small training set of mammalian or fly CRMs that drive similar gene expression profiles, Imogene determines de novocis-regulatory motifs that underlie this co-expression. It can then predict on a genome-wide scale other CRMs with a regulatory potential similar to the training set. Imogene bypasses the need of large datasets for statistical analyses by making central use of the information provided by the sequenced genomes of multiple species, based on the developed statistical tools and explicit models for transcription factor binding site evolution. We test Imogene on characterized tissue-specific mouse developmental CRMs. Its ability to identify CRMs with the same specificity based on its de novo created motifs is comparable to that of previously evaluated ‘motif-blind’ methods. We further show, both in flies and in mammals, that Imogene de novo generated motifs are sufficient to discriminate CRMs related to different developmental programs. Notably, purely relying on sequence data, Imogene performs as well in this discrimination task as a previously reported learning algorithm based on Chromatin Immunoprecipitation (ChIP) data for multiple transcription factors at multiple developmental stages.
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Affiliation(s)
- Hervé Rouault
- Developmental and Stem Cell Biology Department, Institut Pasteur, F-75015 Paris, France CNRS, URA2578, F-75015 Paris, France
| | - Marc Santolini
- Laboratoire de Physique Statistique, CNRS, École Normale Supérieure, Université P. et M. Curie, Université Paris-Diderot
| | - François Schweisguth
- Developmental and Stem Cell Biology Department, Institut Pasteur, F-75015 Paris, France CNRS, URA2578, F-75015 Paris, France
| | - Vincent Hakim
- Laboratoire de Physique Statistique, CNRS, École Normale Supérieure, Université P. et M. Curie, Université Paris-Diderot
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127
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FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells. Nat Methods 2014; 11:559-65. [PMID: 24658142 PMCID: PMC4020622 DOI: 10.1038/nmeth.2885] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Accepted: 01/28/2014] [Indexed: 11/08/2022]
Abstract
Promoters and enhancers establish precise gene transcription patterns. The development of functional approaches for their identification in mammalian cells has been complicated by the size of these genomes. Here we report a high-throughput functional assay for directly identifying active promoter and enhancer elements called FIREWACh (Functional Identification of Regulatory Elements Within Accessible Chromatin), which we used to simultaneously assess over 80,000 DNA fragments derived from nucleosome-free regions within the chromatin of embryonic stem cells (ESCs) and identify 6,364 active regulatory elements. Many of these represent newly discovered ESC-specific enhancers, showing enriched binding-site motifs for ESC-specific transcription factors including SOX2, POU5F1 (OCT4) and KLF4. The application of FIREWACh to additional cultured cell types will facilitate functional annotation of the genome and expand our view of transcriptional network dynamics.
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128
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Transcriptional enhancers: from properties to genome-wide predictions. Nat Rev Genet 2014; 15:272-86. [PMID: 24614317 DOI: 10.1038/nrg3682] [Citation(s) in RCA: 912] [Impact Index Per Article: 91.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Cellular development, morphology and function are governed by precise patterns of gene expression. These are established by the coordinated action of genomic regulatory elements known as enhancers or cis-regulatory modules. More than 30 years after the initial discovery of enhancers, many of their properties have been elucidated; however, despite major efforts, we only have an incomplete picture of enhancers in animal genomes. In this Review, we discuss how properties of enhancer sequences and chromatin are used to predict enhancers in genome-wide studies. We also cover recently developed high-throughput methods that allow the direct testing and identification of enhancers on the basis of their activity. Finally, we discuss recent technological advances and current challenges in the field of regulatory genomics.
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129
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Jing Z, Gangalum RK, Mock DC, Bhat SP. A gene-specific non-enhancer sequence is critical for expression from the promoter of the small heat shock protein gene αB-crystallin. Hum Genomics 2014; 8:5. [PMID: 24589182 PMCID: PMC3975602 DOI: 10.1186/1479-7364-8-5] [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: 12/14/2013] [Accepted: 02/10/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deciphering of the information content of eukaryotic promoters has remained confined to universal landmarks and conserved sequence elements such as enhancers and transcription factor binding motifs, which are considered sufficient for gene activation and regulation. Gene-specific sequences, interspersed between the canonical transacting factor binding sites or adjoining them within a promoter, are generally taken to be devoid of any regulatory information and have therefore been largely ignored. An unanswered question therefore is, do gene-specific sequences within a eukaryotic promoter have a role in gene activation? Here, we present an exhaustive experimental analysis of a gene-specific sequence adjoining the heat shock element (HSE) in the proximal promoter of the small heat shock protein gene, αB-crystallin (cryab). These sequences are highly conserved between the rodents and the humans. RESULTS Using human retinal pigment epithelial cells in culture as the host, we have identified a 10-bp gene-specific promoter sequence (GPS), which, unlike an enhancer, controls expression from the promoter of this gene, only when in appropriate position and orientation. Notably, the data suggests that GPS in comparison with the HSE works in a context-independent fashion. Additionally, when moved upstream, about a nucleosome length of DNA (-154 bp) from the transcription start site (TSS), the activity of the promoter is markedly inhibited, suggesting its involvement in local promoter access. Importantly, we demonstrate that deletion of the GPS results in complete loss of cryab promoter activity in transgenic mice. CONCLUSIONS These data suggest that gene-specific sequences such as the GPS, identified here, may have critical roles in regulating gene-specific activity from eukaryotic promoters.
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Affiliation(s)
| | | | | | - Suraj P Bhat
- Jules Stein Eye Institute, University of California, Los Angeles, CA 90095, USA.
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130
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Hernandez-Garcia CM, Finer JJ. Identification and validation of promoters and cis-acting regulatory elements. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2014; 217-218:109-19. [PMID: 24467902 DOI: 10.1016/j.plantsci.2013.12.007] [Citation(s) in RCA: 310] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 12/03/2013] [Accepted: 12/05/2013] [Indexed: 05/18/2023]
Abstract
Studies of promoters that largely regulate gene expression at the transcriptional level are crucial for improving our basic understanding of gene regulation and will expand the toolbox of available promoters for use in plant biotechnology. In this review, we present a comprehensive analysis of promoters and their underlying mechanisms in transcriptional regulation, including epigenetic marks and chromatin-based regulation. Large-scale prediction of promoter sequences and their contributing cis-acting elements has become routine due to recent advances in transcriptomic technologies and genome sequencing of several plants. However, predicted regulatory sequences may or may not be functional and demonstration of the contribution of the element to promoter activity is essential for confirmation of regulatory sequences. Synthetic promoters and introns provide useful approaches for functional validation of promoter sequences. The development and improvement of gene expression tools for rapid, efficient, predictable, and high-throughput analysis of promoter components will be critical for confirmation of the functional regulatory element sequences identified through transcriptomic and genomic analyses.
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Affiliation(s)
- Carlos M Hernandez-Garcia
- Department of Horticulture and Crop Science, OARDC/The Ohio State University, 1680 Madison Avenue, Wooster, OH 44691, USA
| | - John J Finer
- Department of Horticulture and Crop Science, OARDC/The Ohio State University, 1680 Madison Avenue, Wooster, OH 44691, USA.
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131
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Tuteja G, Moreira KB, Chung T, Chen J, Wenger AM, Bejerano G. Automated discovery of tissue-targeting enhancers and transcription factors from binding motif and gene function data. PLoS Comput Biol 2014; 10:e1003449. [PMID: 24499934 PMCID: PMC3907286 DOI: 10.1371/journal.pcbi.1003449] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Accepted: 12/09/2013] [Indexed: 12/01/2022] Open
Abstract
Identifying enhancers regulating gene expression remains an important and challenging task. While recent sequencing-based methods provide epigenomic characteristics that correlate well with enhancer activity, it remains onerous to comprehensively identify all enhancers across development. Here we introduce a computational framework to identify tissue-specific enhancers evolving under purifying selection. First, we incorporate high-confidence binding site predictions with target gene functional enrichment analysis to identify transcription factors (TFs) likely functioning in a particular context. We then search the genome for clusters of binding sites for these TFs, overcoming previous constraints associated with biased manual curation of TFs or enhancers. Applying our method to the placenta, we find 33 known and implicate 17 novel TFs in placental function, and discover 2,216 putative placenta enhancers. Using luciferase reporter assays, 31/36 (86%) tested candidates drive activity in placental cells. Our predictions agree well with recent epigenomic data in human and mouse, yet over half our loci, including 7/8 (87%) tested regions, are novel. Finally, we establish that our method is generalizable by applying it to 5 additional tissues: heart, pancreas, blood vessel, bone marrow, and liver. Enhancers are distal gene regulatory elements that can activate tissue- and time-point specific gene expression. Identification of active enhancers is challenging, and is the subject of intense investigation. We developed an automated computational framework to predict transcription factors (TFs) and enhancers that target a tissue of interest by combining two growing resources: TF binding motifs and target gene function annotations. We applied our framework to the placenta, and confirmed our enhancer predictions are more active in placental cell types than others. To demonstrate generalizability, we applied our approach to 5 additional tissues. The combination of experimental sampling with computational prediction approaches will aid in the identification of those enhancers that are most likely active in a particular tissue, as well as the characterization of groups of TFs associated with these enhancers.
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Affiliation(s)
- Geetu Tuteja
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
| | - Karen Betancourt Moreira
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
| | - Tisha Chung
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
| | - Jenny Chen
- Biomedical Informatics Program, Stanford University, Stanford, California, United States of America
| | - Aaron M. Wenger
- Department of Computer Science, Stanford University, Stanford, California, United States of America
| | - Gill Bejerano
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
- Department of Computer Science, Stanford University, Stanford, California, United States of America
- * E-mail:
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132
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Kurokawa D, Ohmura T, Sakurai Y, Inoue K, Suda Y, Aizawa S. Otx2 expression in anterior neuroectoderm and forebrain/midbrain is directed by more than six enhancers. Dev Biol 2014; 387:203-13. [PMID: 24457099 DOI: 10.1016/j.ydbio.2014.01.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2013] [Revised: 01/12/2014] [Accepted: 01/14/2014] [Indexed: 11/25/2022]
Abstract
Otx2 plays essential roles in each site at each step of head development. We previously identified the AN1 enhancer at 91kb 5' upstream for the Otx2 expressions in anterior neuroectoderm (AN) at neural plate stage before E8.5, and the FM1 enhancer at 75kb 5' upstream and the FM2 enhancer at 122kb 3' downstream for the expression in forebrain/midbrain (FM) at brain vesicle stage after E8.5. The present study identified a second AN enhancer (AN2) at 88kb 5' upstream; the AN2 enhancer also recapitulates the endogenous Otx2 expression in choroid plexus, cortical hem and choroidal roof. However, the enhancer mutants indicated the presence of another AN enhancer. The study also identified a third FM enhancer (FM3) at 153kb 5' upstream. Thus, the Otx2 expressions in anterior neuroectoderm and forebrain/midbrain are regulated by more than six enhancers located far from the coding region. The enhancers identified are differentially conserved among vertebrates; none of the AN enhancers has activities in caudal forebrain and midbrain at brain vesicle stage after E8.5, nor do any of the FM enhancers in anterior neuroectoderm at neural plate stage before E8.5.
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Affiliation(s)
- Daisuke Kurokawa
- Laboratory for Vertebrate Body Plan, Center for Developmental Biology (CDB), RIKEN Kobe, 2-2-3 Minatojima Minami-machi, Chuo-Ku, Kobe, Hyogo 650-0047, Japan; Misaki Marine Biological Station, Graduate School of Science, The University of Tokyo, 1024 Koajiro, Misaki, Miura, Kanagawa 238-0225, Japan
| | - Tomomi Ohmura
- Laboratory for Vertebrate Body Plan, Center for Developmental Biology (CDB), RIKEN Kobe, 2-2-3 Minatojima Minami-machi, Chuo-Ku, Kobe, Hyogo 650-0047, Japan
| | - Yusuke Sakurai
- Laboratory for Vertebrate Body Plan, Center for Developmental Biology (CDB), RIKEN Kobe, 2-2-3 Minatojima Minami-machi, Chuo-Ku, Kobe, Hyogo 650-0047, Japan
| | - Kenichi Inoue
- Laboratory for Animal Resources and Genetic Engineering, Center for Developmental Biology (CDB), RIKEN Kobe, 2-2-3 Minatojima Minami-machi, Chuo-Ku, Kobe, Hyogo 650-0047, Japan
| | - Yoko Suda
- Laboratory for Vertebrate Body Plan, Center for Developmental Biology (CDB), RIKEN Kobe, 2-2-3 Minatojima Minami-machi, Chuo-Ku, Kobe, Hyogo 650-0047, Japan
| | - Shinichi Aizawa
- Laboratory for Vertebrate Body Plan, Center for Developmental Biology (CDB), RIKEN Kobe, 2-2-3 Minatojima Minami-machi, Chuo-Ku, Kobe, Hyogo 650-0047, Japan; Laboratory for Animal Resources and Genetic Engineering, Center for Developmental Biology (CDB), RIKEN Kobe, 2-2-3 Minatojima Minami-machi, Chuo-Ku, Kobe, Hyogo 650-0047, Japan.
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133
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Dynamic regulation of transcriptional states by chromatin and transcription factors. Nat Rev Genet 2013; 15:69-81. [PMID: 24342920 DOI: 10.1038/nrg3623] [Citation(s) in RCA: 333] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The interaction of regulatory proteins with the complex nucleoprotein structures that are found in mammalian cells involves chromatin reorganization at multiple levels. Mechanisms that support these transitions are complex on many timescales, which range from milliseconds to minutes or hours. In this Review, we discuss emerging concepts regarding the function of regulatory elements in living cells. We also explore the involvement of these dynamic and stochastic processes in the evolution of fluctuating transcriptional activity states that are now commonly reported in eukaryotic systems.
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134
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de Laat W, Duboule D. Topology of mammalian developmental enhancers and their regulatory landscapes. Nature 2013; 502:499-506. [PMID: 24153303 DOI: 10.1038/nature12753] [Citation(s) in RCA: 353] [Impact Index Per Article: 32.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Accepted: 09/02/2013] [Indexed: 12/26/2022]
Abstract
How a complex animal can arise from a fertilized egg is one of the oldest and most fascinating questions of biology, the answer to which is encoded in the genome. Body shape and organ development, and their integration into a functional organism all depend on the precise expression of genes in space and time. The orchestration of transcription relies mostly on surrounding control sequences such as enhancers, millions of which form complex regulatory landscapes in the non-coding genome. Recent research shows that high-order chromosome structures make an important contribution to enhancer functionality by triggering their physical interactions with target genes.
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Affiliation(s)
- Wouter de Laat
- Hubrecht Institute-KNAW and University Medical Center Utrecht, Uppsalalaan 8, 3584 CT, Utrecht, the Netherlands
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135
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Han Y, Garcia BA. Combining genomic and proteomic approaches for epigenetics research. Epigenomics 2013; 5:439-52. [PMID: 23895656 DOI: 10.2217/epi.13.37] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Epigenetics is the study of changes in gene expression or cellular phenotype that do not change the DNA sequence. In this review, current methods, both genomic and proteomic, associated with epigenetics research are discussed. Among them, chromatin immunoprecipitation (ChIP) followed by sequencing and other ChIP-based techniques are powerful techniques for genome-wide profiling of DNA-binding proteins, histone post-translational modifications or nucleosome positions. However, mass spectrometry-based proteomics is increasingly being used in functional biological studies and has proved to be an indispensable tool to characterize histone modifications, as well as DNA-protein and protein-protein interactions. With the development of genomic and proteomic approaches, combination of ChIP and mass spectrometry has the potential to expand our knowledge of epigenetics research to a higher level.
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Affiliation(s)
- Yumiao Han
- Epigenetics Program, Department of Biochemistry & Biophysics, Perelman School of Medicine, University of Pennsylvania, 1009C Stellar-Chance Laboratories, 422 Curie Boulevard, Philadelphia, PA 19104, USA
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136
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Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 2013; 504:306-310. [PMID: 24213634 DOI: 10.1038/nature12716] [Citation(s) in RCA: 331] [Impact Index Per Article: 30.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2012] [Accepted: 09/24/2013] [Indexed: 12/22/2022]
Abstract
In multicellular organisms, transcription regulation is one of the central mechanisms modelling lineage differentiation and cell-fate determination. Transcription requires dynamic chromatin configurations between promoters and their corresponding distal regulatory elements. It is believed that their communication occurs within large discrete foci of aggregated RNA polymerases termed transcription factories in three-dimensional nuclear space. However, the dynamic nature of chromatin connectivity has not been characterized at the genome-wide level. Here, through a chromatin interaction analysis with paired-end tagging approach using an antibody that primarily recognizes the pre-initiation complexes of RNA polymerase II, we explore the transcriptional interactomes of three mouse cells of progressive lineage commitment, including pluripotent embryonic stem cells, neural stem cells and neurosphere stem/progenitor cells. Our global chromatin connectivity maps reveal approximately 40,000 long-range interactions, suggest precise enhancer-promoter associations and delineate cell-type-specific chromatin structures. Analysis of the complex regulatory repertoire shows that there are extensive colocalizations among promoters and distal-acting enhancers. Most of the enhancers associate with promoters located beyond their nearest active genes, indicating that the linear juxtaposition is not the only guiding principle driving enhancer target selection. Although promoter-enhancer interactions exhibit high cell-type specificity, promoters involved in interactions are found to be generally common and mostly active among different cells. Chromatin connectivity networks reveal that the pivotal genes of reprogramming functions are transcribed within physical proximity to each other in embryonic stem cells, linking chromatin architecture to coordinated gene expression. Our study sets the stage for the full-scale dissection of spatial and temporal genome structures and their roles in orchestrating development.
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137
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Khan MR, Ali GM. Functional evolution of cis-regulatory modules of STMADS11 superclade MADS-box genes. PLANT MOLECULAR BIOLOGY 2013; 83:489-506. [PMID: 23860795 DOI: 10.1007/s11103-013-0105-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Accepted: 07/03/2013] [Indexed: 06/02/2023]
Abstract
Evolution of phenotypic morphologies is closely associated with modular organization of cis-regulatory elements underlying expression divergence. The MADS-box gene family is the subject of extensive studies that try to unscramble the structural complexity of flowering plants. This study is envisaged to explore the potential of CRMs in highly constrained non-coding elements of STMADS11superclade MADS-box genes in expression divergence. Phylogenetic reconstruction differentiated the STMADS11 genes into SVP-like, ZMM19-like, MPF1-like and MPF2-like clades. Differential gene expression in vegetative and floral organs was evident within the clades as well as at inter-clade level. The genomic DNA search for clusters of short motifs and sequence conservation of the -2 kb promoter region of particularly, MPF2-like clade permitted to establish three well defined CRMs where transcription factors bind, being CRM1 the activator, CRM2 the repressor, and CRM3 the enhancer element. Similar clusters were also mapped in the large 1st introns in the coding region. Within these CRMs many transcription factor-binding sites, particularly the hotspots for MADS-domain TF binding elements--CArG-boxes, directing sepal specific expression in Arabidopsis--were accrued in the CRM1 of MPF2-like promoters. Site-directed mutagenesis and motif swapping through reporter assays allude towards their implication as functionally active elements. In terms of directional evolution of MPF2-like promoters, CRMs are significantly more conserved than flanking regions, hence, bearing the signatures for purifying selection. Thus, CRMs are the pervasive feature of STMADS11 genes and mutations and/or appearance of new transcription factor binding sites and position of the CRMs are responsible for the divergence in expression patterns in this clade. These results have implications in understanding functional evolution of cis-regulatory modules in plants.
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Affiliation(s)
- Muhammad Ramzan Khan
- National Institute for Genomics and Advanced Biotechnology (NIGAB), National Agricultural Research Centre, Park Road, Islamabad, Pakistan,
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138
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Roccaro M, Ahmadinejad N, Colby T, Somssich IE. Identification of functional cis-regulatory elements by sequential enrichment from a randomized synthetic DNA library. BMC PLANT BIOLOGY 2013; 13:164. [PMID: 24138055 PMCID: PMC3923269 DOI: 10.1186/1471-2229-13-164] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 10/08/2013] [Indexed: 06/01/2023]
Abstract
BACKGROUND The identification of endogenous cis-regulatory DNA elements (CREs) responsive to endogenous and environmental cues is important for studying gene regulation and for biotechnological applications but is labor and time intensive. Alternatively, by taking a synthetic biology approach small specific DNA binding sites tailored to the needs of the scientist can be generated and rapidly identified. RESULTS Here we report a novel approach to identify stimulus-responsive synthetic CREs (SynCREs) from an unbiased random synthetic element (SynE) library. Functional SynCREs were isolated by screening the SynE libray for elements mediating transcriptional activity in plant protoplasts. Responsive elements were chromatin immunoprecipitated by targeting the active Ser-5 phosphorylated RNA polymerase II CTD (Pol II ChIP). Using sequential enrichment, deep sequencing and a bioinformatics pipeline, candidate responsive SynCREs were identified within a pool of constitutively active DNA elements and further validated. These included bonafide biotic/abiotic stress-responsive motifs along with novel SynCREs. We tested several SynCREs in Arabidopsis and confirmed their response to biotic stimuli. CONCLUSIONS Successful isolation of synthetic stress-responsive elements from our screen illustrates the power of the described methodology. This approach can be applied to any transfectable eukaryotic system since it exploits a universal feature of the eukaryotic Pol II.
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Affiliation(s)
- Mario Roccaro
- Department of Plant Microbe Interaction, Max Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, Cologne 50829, Germany
| | - Nahal Ahmadinejad
- Department of Plant Microbe Interaction, Max Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, Cologne 50829, Germany
- Current address: INRES - Crop Bioinformatics, Universität Bonn, Katzenburgweg 2, Bonn 53115, Germany
| | - Thomas Colby
- Mass Spectrometry Group, Max Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, Cologne 50829, Germany
| | - Imre E Somssich
- Department of Plant Microbe Interaction, Max Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, Cologne 50829, Germany
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139
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Brand LH, Henneges C, Schüssler A, Kolukisaoglu HÜ, Koch G, Wallmeroth N, Hecker A, Thurow K, Zell A, Harter K, Wanke D. Screening for protein-DNA interactions by automatable DNA-protein interaction ELISA. PLoS One 2013; 8:e75177. [PMID: 24146751 PMCID: PMC3795721 DOI: 10.1371/journal.pone.0075177] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 08/12/2013] [Indexed: 12/22/2022] Open
Abstract
DNA-binding proteins (DBPs), such as transcription factors, constitute about 10% of the protein-coding genes in eukaryotic genomes and play pivotal roles in the regulation of chromatin structure and gene expression by binding to short stretches of DNA. Despite their number and importance, only for a minor portion of DBPs the binding sequence had been disclosed. Methods that allow the de novo identification of DNA-binding motifs of known DBPs, such as protein binding microarray technology or SELEX, are not yet suited for high-throughput and automation. To close this gap, we report an automatable DNA-protein-interaction (DPI)-ELISA screen of an optimized double-stranded DNA (dsDNA) probe library that allows the high-throughput identification of hexanucleotide DNA-binding motifs. In contrast to other methods, this DPI-ELISA screen can be performed manually or with standard laboratory automation. Furthermore, output evaluation does not require extensive computational analysis to derive a binding consensus. We could show that the DPI-ELISA screen disclosed the full spectrum of binding preferences for a given DBP. As an example, AtWRKY11 was used to demonstrate that the automated DPI-ELISA screen revealed the entire range of in vitro binding preferences. In addition, protein extracts of AtbZIP63 and the DNA-binding domain of AtWRKY33 were analyzed, which led to a refinement of their known DNA-binding consensi. Finally, we performed a DPI-ELISA screen to disclose the DNA-binding consensus of a yet uncharacterized putative DBP, AtTIFY1. A palindromic TGATCA-consensus was uncovered and we could show that the GATC-core is compulsory for AtTIFY1 binding. This specific interaction between AtTIFY1 and its DNA-binding motif was confirmed by in vivo plant one-hybrid assays in protoplasts. Thus, the value and applicability of the DPI-ELISA screen for de novo binding site identification of DBPs, also under automatized conditions, is a promising approach for a deeper understanding of gene regulation in any organism of choice.
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Affiliation(s)
- Luise H. Brand
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | - Carsten Henneges
- Cognitive Systems, Center for Bioinformatics, University of Tuebingen, Tuebingen, Germany
| | - Axel Schüssler
- Cognitive Systems, Center for Bioinformatics, University of Tuebingen, Tuebingen, Germany
| | - H. Üner Kolukisaoglu
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
- Center for Life Science Automation, Rostock, Germany
| | - Grit Koch
- Center for Life Science Automation, Rostock, Germany
| | - Niklas Wallmeroth
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | - Andreas Hecker
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | | | - Andreas Zell
- Cognitive Systems, Center for Bioinformatics, University of Tuebingen, Tuebingen, Germany
| | - Klaus Harter
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | - Dierk Wanke
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
- * E-mail:
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140
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Ren J, Song K, Sun F, Deng M, Reinert G. Multiple alignment-free sequence comparison. Bioinformatics 2013; 29:2690-8. [PMID: 23990418 DOI: 10.1093/bioinformatics/btt462] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Recently, a range of new statistics have become available for the alignment-free comparison of two sequences based on k-tuple word content. Here, we extend these statistics to the simultaneous comparison of more than two sequences. Our suite of statistics contains, first, C(*)1 and C(S)1, extensions of statistics for pairwise comparison of the joint k-tuple content of all the sequences, and second, C(*)2, C(S)2 and C(geo)2, averages of sums of pairwise comparison statistics. The two tasks we consider are, first, to identify sequences that are similar to a set of target sequences, and, second, to measure the similarity within a set of sequences. RESULTS Our investigation uses both simulated data as well as cis-regulatory module data where the task is to identify cis-regulatory modules with similar transcription factor binding sites. We find that although for real data, all of our statistics show a similar performance, on simulated data the Shepp-type statistics are in some instances outperformed by star-type statistics. The multiple alignment-free statistics are more sensitive to contamination in the data than the pairwise average statistics. AVAILABILITY Our implementation of the five statistics is available as R package named 'multiAlignFree' at be http://www-rcf.usc.edu/∼fsun/Programs/multiAlignFree/multiAlignFreemain.html. CONTACT reinert@stats.ox.ac.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jie Ren
- School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
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Alonso-Perez E, Fernandez-Poceiro R, Lalonde E, Kwan T, Calaza M, Gomez-Reino JJ, Majewski J, Gonzalez A. Identification of three new cis-regulatory IRF5 polymorphisms: in vitro studies. Arthritis Res Ther 2013; 15:R82. [PMID: 23941291 PMCID: PMC3978921 DOI: 10.1186/ar4262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2012] [Accepted: 08/13/2013] [Indexed: 01/18/2023] Open
Abstract
Background Polymorphisms in the interferon regulatory factor 5 (IRF5) gene are associated with susceptibility to systemic lupus erythematosus, rheumatoid arthritis and other diseases through independent risk and protective haplotypes. Several functional polymorphisms are already known, but they do not account for the protective haplotypes that are tagged by the minor allele of rs729302. Methods Polymorphisms in linkage disequilibrium (LD) with rs729302 or particularly associated with IRF5 expression were selected for functional screening, which involved electrophoretic mobility shift assays (EMSAs) and reporter gene assays. Results A total of 54 single-nucleotide polymorphisms in the 5' region of IRF5 were genotyped. Twenty-four of them were selected for functional screening because of their high LD with rs729302 or protective haplotypes. In addition, two polymorphisms were selected for their prominent association with IRF5 expression. Seven of these twenty-six polymorphisms showed reproducible allele differences in EMSA. The seven were subsequently analyzed in gene reporter assays, and three of them showed significant differences between their two alleles: rs729302, rs13245639 and rs11269962. Haplotypes including the cis-regulatory polymorphisms correlated very well with IRF5 mRNA expression in an analysis based on previous data. Conclusion We have found that three polymorphisms in LD with the protective haplotypes of IRF5 have differential allele effects in EMSA and in reporter gene assays. Identification of these cis-regulatory polymorphisms will allow more accurate analysis of transcriptional regulation of IRF5 expression, more powerful genetic association studies and deeper insight into the role of IRF5 in disease susceptibility.
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142
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Boulling A, Wicht L, Schorderet DF. Identification of HMX1 target genes: a predictive promoter model approach. Mol Vis 2013; 19:1779-94. [PMID: 23946633 PMCID: PMC3742133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Accepted: 08/02/2013] [Indexed: 11/19/2022] Open
Abstract
PURPOSE A homozygous mutation in the H6 family homeobox 1 (HMX1) gene is responsible for a new oculoauricular defect leading to eye and auricular developmental abnormalities as well as early retinal degeneration (MIM 612109). However, the HMX1 pathway remains poorly understood, and in the first approach to better understand the pathway's function, we sought to identify the target genes. METHODS We developed a predictive promoter model (PPM) approach using a comparative transcriptomic analysis in the retina at P15 of a mouse model lacking functional Hmx1 (dmbo mouse) and its respective wild-type. This PPM was based on the hypothesis that HMX1 binding site (HMX1-BS) clusters should be more represented in promoters of HMX1 target genes. The most differentially expressed genes in the microarray experiment that contained HMX1-BS clusters were used to generate the PPM, which was then statistically validated. Finally, we developed two genome-wide target prediction methods: one that focused on conserving PPM features in human and mouse and one that was based on the co-occurrence of HMX1-BS pairs fitting the PPM, in human or in mouse, independently. RESULTS The PPM construction revealed that sarcoglycan, gamma (35kDa dystrophin-associated glycoprotein) (Sgcg), teashirt zinc finger homeobox 2 (Tshz2), and solute carrier family 6 (neurotransmitter transporter, glycine) (Slc6a9) genes represented Hmx1 targets in the mouse retina at P15. Moreover, the genome-wide target prediction revealed that mouse genes belonging to the retinal axon guidance pathway were targeted by Hmx1. Expression of these three genes was experimentally validated using a quantitative reverse transcription PCR approach. The inhibitory activity of Hmx1 on Sgcg, as well as protein tyrosine phosphatase, receptor type, O (Ptpro) and Sema3f, two targets identified by the PPM, were validated with luciferase assay. CONCLUSIONS Gene expression analysis between wild-type and dmbo mice allowed us to develop a PPM that identified the first target genes of Hmx1.
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Affiliation(s)
| | - Linda Wicht
- Institute for Research in Ophthalmology, Sion, Switzerland,School of Life Sciences, Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Daniel F. Schorderet
- Institute for Research in Ophthalmology, Sion, Switzerland,School of Life Sciences, Federal Institute of Technology (EPFL), Lausanne, Switzerland,Department of Ophthalmology, University of Lausanne, Lausanne, Switzerland
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143
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Rothenberg EV. Epigenetic mechanisms and developmental choice hierarchies in T-lymphocyte development. Brief Funct Genomics 2013; 12:512-24. [PMID: 23922132 DOI: 10.1093/bfgp/elt027] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Three interlocking problems in gene regulation are: how to explain genome-wide targeting of transcription factors in different cell types, how prior transcription factor action can establish an 'epigenetic state' that changes the options for future transcription factor action, and how directly a sequence of developmental decisions can be memorialized in a hierarchy of repression structures applied to key genes of the 'paths not taken'. This review uses the finely staged process of T-cell lineage commitment as a test case in which to examine how changes in developmental status are reflected in changes in transcription factor expression, transcription factor binding distribution across genomic sites, and chromatin modification. These are evaluated in a framework of reciprocal effects of previous chromatin structure features on transcription factor access and of transcription factor binding on other factors and on future chromatin structure.
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Affiliation(s)
- Ellen V Rothenberg
- Division of Biology 156-29, California Institute of Technology, Pasadena, CA 91125, USA. Tel.: +1 626 395 4992; Fax: +1 626 449 0756;
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Chen YC, Cheng JH, Tsai ZTY, Tsai HK, Chuang TJ. The impact of trans-regulation on the evolutionary rates of metazoan proteins. Nucleic Acids Res 2013; 41:6371-80. [PMID: 23658220 PMCID: PMC3711421 DOI: 10.1093/nar/gkt349] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Revised: 04/10/2013] [Accepted: 04/14/2013] [Indexed: 11/13/2022] Open
Abstract
Transcription factor (TF) and microRNA (miRNA) are two crucial trans-regulatory factors that coordinately control gene expression. Understanding the impacts of these two factors on the rate of protein sequence evolution is of great importance in evolutionary biology. While many biological factors associated with evolutionary rate variations have been studied, evolutionary analysis of simultaneously accounting for TF and miRNA regulations across metazoans is still uninvestigated. Here, we provide a series of statistical analyses to assess the influences of TF and miRNA regulations on evolutionary rates across metazoans (human, mouse and fruit fly). Our results reveal that the negative correlations between trans-regulation and evolutionary rates hold well across metazoans, but the strength of TF regulation as a rate indicator becomes weak when the other confounding factors that may affect evolutionary rates are controlled. We show that miRNA regulation tends to be a more essential indicator of evolutionary rates than TF regulation, and the combination of TF and miRNA regulations has a significant dependent effect on protein evolutionary rates. We also show that trans-regulation (especially miRNA regulation) is much more important in human/mouse than in fruit fly in determining protein evolutionary rates, suggesting a considerable variation in rate determinants between vertebrates and invertebrates.
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Affiliation(s)
- Yi-Ching Chen
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan and Genomic Research Center, Academia Sinica, Taipei 115, Taiwan
| | - Jen-Hao Cheng
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan and Genomic Research Center, Academia Sinica, Taipei 115, Taiwan
| | - Zing Tsung-Yeh Tsai
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan and Genomic Research Center, Academia Sinica, Taipei 115, Taiwan
| | - Huai-Kuang Tsai
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan and Genomic Research Center, Academia Sinica, Taipei 115, Taiwan
| | - Trees-Juen Chuang
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan and Genomic Research Center, Academia Sinica, Taipei 115, Taiwan
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145
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Karagodin DA, Omelina ES, Fedorova EV, Baricheva EM. Identification of functionally significant elements in the second intron of the Drosophila melanogaster Trithorax-like gene. Gene 2013; 520:178-84. [PMID: 23481306 DOI: 10.1016/j.gene.2013.02.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Revised: 01/14/2013] [Accepted: 02/07/2013] [Indexed: 10/27/2022]
Abstract
It is known that a lot of genes having a distinct expression pattern require the complex system of transcription regulation. The regulatory regions of such genes can include not only the 5'-flanking regions, but also other regions, particularly their intron sequences. The Drosophila melanogaster Trithorax-like (Trl) gene, encoding the GAGA protein, is one of the genes with complex expression pattern. GAGA is one of a few transcription factors that can regulate gene expression at multiple levels. The GAGA-mediated modulation of expression seems to be linked with modifications of the chromatin structure. Nowadays, the regulatory potential of the Trl 5'-flanking region that contains multiple GAGA binding sites has been analyzed, but the presence of the functionally significant elements in other Trl regions has not been examined. We found DNase I hypersensitive sites, evolutionary-conserved sequences and numerous GAGA binding sites in the second intron of the Trl gene. Interestingly, these sequences localize in two main regions of the intron in immediate proximity to preferred regions of transposon insertions. Additionally, we revealed that deletion of the intron fragment in the Trl(1-72) mutants caused an alteration of the Trl expression pattern. These results allow us to conclude that the second intron of the Trl gene contains functionally significant elements.
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Affiliation(s)
- D A Karagodin
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, 10 Lavrentieva Street, Novosibirsk 630090, Russian Federation
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146
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Symmons O, Spitz F. From remote enhancers to gene regulation: charting the genome's regulatory landscapes. Philos Trans R Soc Lond B Biol Sci 2013; 368:20120358. [PMID: 23650632 DOI: 10.1098/rstb.2012.0358] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Vertebrate genes are characterized by the presence of cis-regulatory elements located at great distances from the genes they control. Alterations of these elements have been implicated in human diseases and evolution, yet little is known about how these elements interact with their surrounding sequences. A recent survey of the mouse genome with a regulatory sensor showed that the regulatory activities of these elements are not organized in a gene-centric manner, but instead are broadly distributed along chromosomes, forming large regulatory landscapes with distinct tissue-specific activities. A large genome-wide collection of expression data from this regulatory sensor revealed some basic principles of this complex genome regulatory architecture, including a substantial interplay between enhancers and other types of activities to modulate gene expression. We discuss the implications of these findings for our understanding of non-coding transcription, and of the possible consequences of structural genomic variations in disease and evolution.
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Affiliation(s)
- Orsolya Symmons
- Developmental Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
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147
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Spielmann M, Mundlos S. Structural variations, the regulatory landscape of the genome and their alteration in human disease. Bioessays 2013; 35:533-43. [PMID: 23625790 DOI: 10.1002/bies.201200178] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
High-throughput genomic technologies are revolutionizing human genetics. So far the focus has been on the 1.5% of the genome, which is coding, in spite of the fact that the great majority of genomic variants fall outside the coding regions. Recent efforts to annotate the non-coding sequence show that over 80% of the genome is biochemically active. The genome is divided into regulatory domains consisting of sequence regions that enhance and/or silence the expression of nearby genes and are, in some cases, separated by boundaries with insulator activity. In this paper, we review the recent advances in the identification of variations that influence gene regulation and their consequences for human disease. We hypothesize that structural variations outside of the coding genome can interfere with normal gene regulation by disrupting the regulatory landscape. Therefore, the regulatory landscape of the genome has also to be taken into consideration when investigating the pathology of human disease.
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Affiliation(s)
- Malte Spielmann
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
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148
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Cutter AD, Jovelin R, Dey A. Molecular hyperdiversity and evolution in very large populations. Mol Ecol 2013; 22:2074-95. [PMID: 23506466 PMCID: PMC4065115 DOI: 10.1111/mec.12281] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2012] [Revised: 01/24/2013] [Accepted: 01/29/2013] [Indexed: 02/06/2023]
Abstract
The genomic density of sequence polymorphisms critically affects the sensitivity of inferences about ongoing sequence evolution, function and demographic history. Most animal and plant genomes have relatively low densities of polymorphisms, but some species are hyperdiverse with neutral nucleotide heterozygosity exceeding 5%. Eukaryotes with extremely large populations, mimicking bacterial and viral populations, present novel opportunities for studying molecular evolution in sexually reproducing taxa with complex development. In particular, hyperdiverse species can help answer controversial questions about the evolution of genome complexity, the limits of natural selection, modes of adaptation and subtleties of the mutation process. However, such systems have some inherent complications and here we identify topics in need of theoretical developments. Close relatives of the model organisms Caenorhabditis elegans and Drosophila melanogaster provide known examples of hyperdiverse eukaryotes, encouraging functional dissection of resulting molecular evolutionary patterns. We recommend how best to exploit hyperdiverse populations for analysis, for example, in quantifying the impact of noncrossover recombination in genomes and for determining the identity and micro-evolutionary selective pressures on noncoding regulatory elements.
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Affiliation(s)
- Asher D Cutter
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.
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149
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Su MY, Steiner LA, Bogardus H, Mishra T, Schulz VP, Hardison RC, Gallagher PG. Identification of biologically relevant enhancers in human erythroid cells. J Biol Chem 2013; 288:8433-8444. [PMID: 23341446 DOI: 10.1074/jbc.m112.413260] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Identification of cell type-specific enhancers is important for understanding the regulation of programs controlling cellular development and differentiation. Enhancers are typically marked by the co-transcriptional activator protein p300 or by groups of cell-expressed transcription factors. We hypothesized that a unique set of enhancers regulates gene expression in human erythroid cells, a highly specialized cell type evolved to provide adequate amounts of oxygen throughout the body. Using chromatin immunoprecipitation followed by massively parallel sequencing, genome-wide maps of candidate enhancers were constructed for p300 and four transcription factors, GATA1, NF-E2, KLF1, and SCL, using primary human erythroid cells. These data were combined with gene expression analyses, and candidate enhancers were identified. Consistent with their predicted function as candidate enhancers, there was statistically significant enrichment of p300 and combinations of co-localizing erythroid transcription factors within 1-50 kb of the transcriptional start site (TSS) of genes highly expressed in erythroid cells. Candidate enhancers were also enriched near genes with known erythroid cell function or phenotype. Candidate enhancers exhibited moderate conservation with mouse and minimal conservation with nonplacental vertebrates. Candidate enhancers were mapped to a set of erythroid-associated, biologically relevant, SNPs from the genome-wide association studies (GWAS) catalogue of NHGRI, National Institutes of Health. Fourteen candidate enhancers, representing 10 genetic loci, mapped to sites associated with biologically relevant erythroid traits. Fragments from these loci directed statistically significant expression in reporter gene assays. Identification of enhancers in human erythroid cells will allow a better understanding of erythroid cell development, differentiation, structure, and function and provide insights into inherited and acquired hematologic disease.
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Affiliation(s)
- Mack Y Su
- Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Laurie A Steiner
- Department of Pediatrics, University of Rochester, Rochester, New York 14642
| | - Hannah Bogardus
- Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Tejaswini Mishra
- Department of Biochemistry and Molecular Biology, Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, University Park, Pennsylvania 16802
| | - Vincent P Schulz
- Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Ross C Hardison
- Department of Biochemistry and Molecular Biology, Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, University Park, Pennsylvania 16802
| | - Patrick G Gallagher
- Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut 06520; Departments of Pathology and Genetics, Yale University School of Medicine, New Haven, Connecticut 06520.
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150
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Taher L, Smith RP, Kim MJ, Ahituv N, Ovcharenko I. Sequence signatures extracted from proximal promoters can be used to predict distal enhancers. Genome Biol 2013; 14:R117. [PMID: 24156763 PMCID: PMC3983659 DOI: 10.1186/gb-2013-14-10-r117] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2013] [Accepted: 10/24/2013] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Gene expression is controlled by proximal promoters and distal regulatory elements such as enhancers. While the activity of some promoters can be invariant across tissues, enhancers tend to be highly tissue-specific. RESULTS We compiled sets of tissue-specific promoters based on gene expression profiles of 79 human tissues and cell types. Putative transcription factor binding sites within each set of sequences were used to train a support vector machine classifier capable of distinguishing tissue-specific promoters from control sequences. We obtained reliable classifiers for 92% of the tissues, with an area under the receiver operating characteristic curve between 60% (for subthalamic nucleus promoters) and 98% (for heart promoters). We next used these classifiers to identify tissue-specific enhancers, scanning distal non-coding sequences in the loci of the 200 most highly and lowly expressed genes. Thirty percent of reliable classifiers produced consistent enhancer predictions, with significantly higher densities in the loci of the most highly expressed compared to lowly expressed genes. Liver enhancer predictions were assessed in vivo using the hydrodynamic tail vein injection assay. Fifty-eight percent of the predictions yielded significant enhancer activity in the mouse liver, whereas a control set of five sequences was completely negative. CONCLUSIONS We conclude that promoters of tissue-specific genes often contain unambiguous tissue-specific signatures that can be learned and used for the de novo prediction of enhancers.
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Affiliation(s)
- Leila Taher
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, 18057, Germany
| | - Robin P Smith
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Mee J Kim
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ivan Ovcharenko
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
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