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Catta-Preta R, Lindtner S, Ypsilanti A, Price J, Abnousi A, Su-Feher L, Wang Y, Juric I, Jones IR, Akiyama JA, Hu M, Shen Y, Visel A, Pennacchio LA, Dickel D, Rubenstein JLR, Nord AS. Combinatorial transcription factor binding encodes cis-regulatory wiring of forebrain GABAergic neurogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.28.546894. [PMID: 37425940 PMCID: PMC10327028 DOI: 10.1101/2023.06.28.546894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Transcription factors (TFs) bind combinatorially to genomic cis-regulatory elements (cREs), orchestrating transcription programs. While studies of chromatin state and chromosomal interactions have revealed dynamic neurodevelopmental cRE landscapes, parallel understanding of the underlying TF binding lags. To elucidate the combinatorial TF-cRE interactions driving mouse basal ganglia development, we integrated ChIP-seq for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and transcriptional state, and transgenic enhancer assays. We identified TF-cREs modules with distinct chromatin features and enhancer activity that have complementary roles driving GABAergic neurogenesis and suppressing other developmental fates. While the majority of distal cREs were bound by one or two TFs, a small proportion were extensively bound, and these enhancers also exhibited exceptional evolutionary conservation, motif density, and complex chromosomal interactions. Our results provide new insights into how modules of combinatorial TF-cRE interactions activate and repress developmental expression programs and demonstrate the value of TF binding data in modeling gene regulatory wiring.
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Affiliation(s)
- Rinaldo Catta-Preta
- Department of Neurobiology, Physiology and Behavior, and Department of Psychiatry and Behavioral Sciences, University of California, Davis, Davis, CA 95618, USA
- Current Address: Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Susan Lindtner
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Athena Ypsilanti
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - James Price
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Armen Abnousi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44106, USA
- Current Address: NovaSignal, Los Angeles, CA 90064, USA
| | - Linda Su-Feher
- Department of Neurobiology, Physiology and Behavior, and Department of Psychiatry and Behavioral Sciences, University of California, Davis, Davis, CA 95618, USA
| | - Yurong Wang
- Department of Neurobiology, Physiology and Behavior, and Department of Psychiatry and Behavioral Sciences, University of California, Davis, Davis, CA 95618, USA
| | - Ivan Juric
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44106, USA
| | - Ian R Jones
- Institute for Human Genetics, Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, CA 94143, USA
| | - Jennifer A Akiyama
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44106, USA
| | - Yin Shen
- Institute for Human Genetics, Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, CA 94143, USA
| | - Axel Visel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA
- School of Natural Sciences, University of California, Merced, Merced, CA 95343, USA
| | - Len A Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA
- Comparative Biochemistry Program, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Diane Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - John L R Rubenstein
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Alex S Nord
- Department of Neurobiology, Physiology and Behavior, and Department of Psychiatry and Behavioral Sciences, University of California, Davis, Davis, CA 95618, USA
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Lim AL, Moos P, Pond CD, Larson EC, Martins LJ, Szaniawski MA, Planelles V, Barrows LR. HIV-1 provirus transcription and translation in macrophages differs from pre-integrated cDNA complexes and requires E2F transcriptional programs. Virulence 2022; 13:386-413. [PMID: 35166645 PMCID: PMC8855869 DOI: 10.1080/21505594.2022.2031583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
HIV-1 cDNA pre-integration complexes persist for weeks in macrophages and remain transcriptionally active. While previous work has focused on the transcription of HIV-1 genes; our understanding of the cellular milieu that accompanies viral production is incomplete. We have used an in vitro system to model HIV-1 infection of macrophages, and single-cell RNA sequencing (scRNA-seq) to compare the transcriptomes of uninfected cells, cells harboring pre-integration complexes (PIC), and those containing integrated provirus and making late HIV proteins. scRNA-seq can distinguish between provirus and PIC cells because their background transcriptomes vary dramatically. PIC cell transcriptomes are characterized by NFkB and AP-1 promoted transcription, while transcriptomes of cells transcribing from provirus are characterized by E2F family transcription products. We also find that the transcriptomes of PIC cells and Bystander cells (defined as cells not producing any HIV transcript and thus presumably not infected) are indistinguishable except for the presence of HIV-1 transcripts. Furthermore, the presence of pathogen alters the transcriptome of the uninfected Bystander cells, so that they are distinguishable from true control cells (cells not exposed to any pathogen). Therefore, a single cell comparison of transcriptomes from provirus and PIC cells provides a new understanding of the transcriptional changes that accompany HIV-1 integration.
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Affiliation(s)
- Albebson L Lim
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah, USA.,Marine Science Institute, University of the Philippines Diliman, Quezon City, Philippines
| | - Philip Moos
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah, USA
| | - Christopher D Pond
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah, USA
| | - Erica C Larson
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah, USA.,Department of Microbiology & Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Laura J Martins
- Department of Pathology, University of Utah, Salt Lake City, Utah, USA
| | | | - Vicente Planelles
- Department of Pathology, University of Utah, Salt Lake City, Utah, USA
| | - Louis R Barrows
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah, USA
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3
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Lim H, Xie L. A New Weighted Imputed Neighborhood-Regularized Tri-Factorization One-Class Collaborative Filtering Algorithm: Application to Target Gene Prediction of Transcription Factors. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:126-137. [PMID: 31995498 PMCID: PMC7382975 DOI: 10.1109/tcbb.2020.2968442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Identifying target genes of transcription factors (TFs) is crucial to understand transcriptional regulation. However, our understanding of genome-wide TF targeting profile is limited due to the cost of large-scale experiments and intrinsic complexity of gene regulation. Thus, computational prediction methods are useful to predict unobserved TF-gene associations. Here, we develop a new Weighted Imputed Neighborhood-regularized Tri-Factorization one-class collaborative filtering algorithm, WINTF. It predicts unobserved target genes for TFs using known but noisy, incomplete, and biased TF-gene associations and protein-protein interaction networks. Our benchmark study shows that WINTF significantly outperforms its counterpart matrix factorization-based algorithms and tri-factorization methods that do not include weight, imputation, and neighbor-regularization, for TF-gene association prediction. When evaluated by independent datasets, accuracy is 37.8 percent on the top 495 predicted associations, an enrichment factor of 4.19 compared with random guess. Furthermore, many predicted novel associations are supported by literature evidence. Although we only use canonical TF-gene interaction data, WINTF can directly be applied to tissue-specific data when available. Thus, WINTF provides a potentially useful framework to integrate multiple omics data for further improvement of TF-gene prediction and applications to other sparse and noisy biological data. The benchmark dataset and source code are freely available at https://github.com/XieResearchGroup/WINTF.
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Gurska D, Vargas Jentzsch IM, Panfilio KA. Unexpected mutual regulation underlies paralogue functional diversification and promotes epithelial tissue maturation in Tribolium. Commun Biol 2020; 3:552. [PMID: 33020571 PMCID: PMC7536231 DOI: 10.1038/s42003-020-01250-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 08/21/2020] [Indexed: 02/03/2023] Open
Abstract
Insect Hox3/zen genes represent an evolutionary hotspot for changes in function and copy number. Single orthologues are required either for early specification or late morphogenesis of the extraembryonic tissues, which protect the embryo. The tandemly duplicated zen paralogues of the beetle Tribolium castaneum present a unique opportunity to investigate both functions in a single species. We dissect the paralogues' expression dynamics (transcript and protein) and transcriptional targets (RNA-seq after RNAi) throughout embryogenesis. We identify an unexpected role of Tc-Zen2 in repression of Tc-zen1, generating a negative feedback loop that promotes developmental progression. Tc-Zen2 regulation is dynamic, including within co-expressed multigene loci. We also show that extraembryonic development is the major event within the transcriptional landscape of late embryogenesis and provide a global molecular characterization of the extraembryonic serosal tissue. Altogether, we propose that paralogue mutual regulation arose through multiple instances of zen subfunctionalization, leading to their complementary extant roles.
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Affiliation(s)
- Daniela Gurska
- Institute of Zoology: Developmental Biology, University of Cologne, 50674, Cologne, Germany
| | - Iris M Vargas Jentzsch
- Institute of Zoology: Developmental Biology, University of Cologne, 50674, Cologne, Germany
| | - Kristen A Panfilio
- Institute of Zoology: Developmental Biology, University of Cologne, 50674, Cologne, Germany.
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK.
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Springer N, de León N, Grotewold E. Challenges of Translating Gene Regulatory Information into Agronomic Improvements. TRENDS IN PLANT SCIENCE 2019; 24:1075-1082. [PMID: 31377174 DOI: 10.1016/j.tplants.2019.07.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/26/2019] [Accepted: 07/05/2019] [Indexed: 06/10/2023]
Abstract
Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.
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Affiliation(s)
- Nathan Springer
- Department of Plant and Microbial Biology, University of Minnesota, St Paul, MN 55108, USA.
| | - Natalia de León
- Department of Agronomy, University of Wisconsin, Madison, WI 56706, USA
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
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Lim H, Xie L. Target Gene Prediction of Transcription Factor Using a New Neighborhood-regularized Tri-factorization One-class Collaborative Filtering Algorithm. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2019; 2018:1-10. [PMID: 31061989 DOI: 10.1145/3233547.3233551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Identifying the target genes of transcription factors (TFs) is one of the key factors to understand transcriptional regulation. However, our understanding of genome-wide TF targeting profile is limited due to the cost of large scale experiments and intrinsic complexity. Thus, computational prediction methods are useful to predict the unobserved associations. Here, we developed a new one-class collaborative filtering algorithm tREMAP that is based on regularized, weighted nonnegative matrix tri-factorization. The algorithm predicts unobserved target genes for TFs using known gene-TF associations and protein-protein interaction network. Our benchmark study shows that tREMAP significantly outperforms its counterpart REMAP, a bi-factorization-based algorithm, for transcription factor target gene prediction in all four performance metrics AUC, MAP, MPR, and HLU. When evaluated by independent data sets, the prediction accuracy is 37.8% on the top 495 predicted associations, an enrichment factor of 4.19 compared with the random guess. Furthermore, many of the predicted novel associations by tREMAP are supported by evidence from literature. Although we only use canonical TF-target gene interaction data in this study, tREMAP can be directly applied to tissue-specific data sets. tREMAP provides a framework to integrate multiple omics data for the further improvement of TF target gene prediction. Thus, tREMAP is a potentially useful tool in studying gene regulatory networks. The benchmark data set and the source code of tREMAP are freely available at https://github.com/hansaimlim/REMAP/tree/master/TriFacREMAP.
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Affiliation(s)
- Hansaim Lim
- PhD program in Biochemistry, Graduate Center of the City University of New York NY 10016 United States
| | - Lei Xie
- Department of Computer Science, Hunter College and Graduate Center, the City University of New York NY 10065 United States
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Kabir MH, O'Connor MD. Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks. Biophys Rev 2019; 11:41-50. [PMID: 30684132 DOI: 10.1007/s12551-018-0486-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 11/15/2018] [Indexed: 01/31/2023] Open
Abstract
Identification of new drug and cell therapy targets for disease treatment will be facilitated by a detailed molecular understanding of normal and disease development. Human pluripotent stem cells can provide a large in vitro source of human cell types and, in a growing number of instances, also three-dimensional multicellular tissues called organoids. The application of stem cell technology to discovery and development of new therapies will be aided by detailed molecular characterisation of cell identity, cell signalling pathways and target gene networks. Big data or 'omics' techniques-particularly transcriptomics and proteomics-facilitate cell and tissue characterisation using thousands to tens-of-thousands of genes or proteins. These gene and protein profiles are analysed using existing and/or emergent bioinformatics methods, including a growing number of methods that compare sample profiles against compendia of reference samples. This review assesses how compendium-based analyses can aid the application of stem cell technology for new therapy development. This includes via robust definition of differentiated stem cell identity, as well as elucidation of complex signalling pathways and target gene networks involved in normal and diseased states.
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Affiliation(s)
- Md Humayun Kabir
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia.,Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Michael D O'Connor
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia. .,Medical Sciences Research Group, Western Sydney University, Campbelltown, NSW, Australia.
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Lu R, Rogan PK. Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations. F1000Res 2018; 7:1933. [PMID: 31001412 PMCID: PMC6464064 DOI: 10.12688/f1000research.17363.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/05/2018] [Indexed: 10/12/2023] Open
Abstract
Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets. Methods: Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. Corresponding promoter sequences were reduced to DNase I-accessible intervals; TFBSs were then identified within these intervals using information theory-based position weight matrices for each TF (iPWMs) and clustered. Features from information-dense TFBS clusters predicted these genes with machine learning classifiers, which were evaluated for accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed to in silico examine their impact on cluster densities and the regulatory states of target genes. Results: We initially chose the glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, to test this approach. SLC25A32 and TANK were found to exhibit the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the largest area under the Receiver Operating Characteristic (ROC) curve in detecting such genes. Target gene prediction was confirmed using siRNA knockdown of TFs, which was found to be more accurate than those predicted after CRISPR/CAS9 inactivation. In-silico mutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.
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Affiliation(s)
- Ruipeng Lu
- Computer Science, University of Western Ontario, London, Ontario, N6A 5B7, Canada
| | - Peter K. Rogan
- Computer Science, University of Western Ontario, London, Ontario, N6A 5B7, Canada
- Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada
- Cytognomix, London, Ontario, N5X 3X5, Canada
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Lu R, Rogan PK. Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations. F1000Res 2018; 7:1933. [PMID: 31001412 PMCID: PMC6464064 DOI: 10.12688/f1000research.17363.2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/28/2019] [Indexed: 12/20/2022] Open
Abstract
Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results: The glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.
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Affiliation(s)
- Ruipeng Lu
- Computer Science, University of Western Ontario, London, Ontario, N6A 5B7, Canada
| | - Peter K. Rogan
- Computer Science, University of Western Ontario, London, Ontario, N6A 5B7, Canada
- Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada
- Cytognomix, London, Ontario, N5X 3X5, Canada
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Trott AJ, Menet JS. Regulation of circadian clock transcriptional output by CLOCK:BMAL1. PLoS Genet 2018; 14:e1007156. [PMID: 29300726 PMCID: PMC5771620 DOI: 10.1371/journal.pgen.1007156] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 01/17/2018] [Accepted: 12/14/2017] [Indexed: 01/20/2023] Open
Abstract
The mammalian circadian clock relies on the transcription factor CLOCK:BMAL1 to coordinate the rhythmic expression of 15% of the transcriptome and control the daily regulation of biological functions. The recent characterization of CLOCK:BMAL1 cistrome revealed that although CLOCK:BMAL1 binds synchronously to all of its target genes, its transcriptional output is highly heterogeneous. By performing a meta-analysis of several independent genome-wide datasets, we found that the binding of other transcription factors at CLOCK:BMAL1 enhancers likely contribute to the heterogeneity of CLOCK:BMAL1 transcriptional output. While CLOCK:BMAL1 rhythmic DNA binding promotes rhythmic nucleosome removal, it is not sufficient to generate transcriptionally active enhancers as assessed by H3K27ac signal, RNA Polymerase II recruitment, and eRNA expression. Instead, the transcriptional activity of CLOCK:BMAL1 enhancers appears to rely on the activity of ubiquitously expressed transcription factors, and not tissue-specific transcription factors, recruited at nearby binding sites. The contribution of other transcription factors is exemplified by how fasting, which effects several transcription factors but not CLOCK:BMAL1, either decreases or increases the amplitude of many rhythmically expressed CLOCK:BMAL1 target genes. Together, our analysis suggests that CLOCK:BMAL1 promotes a transcriptionally permissive chromatin landscape that primes its target genes for transcription activation rather than directly activating transcription, and provides a new framework to explain how environmental or pathological conditions can reprogram the rhythmic expression of clock-controlled genes.
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Affiliation(s)
- Alexandra J. Trott
- Department of Biology, Program of Genetics and Center for Biological Clocks Research, Texas A&M University, College Station, TX, United States of America
| | - Jerome S. Menet
- Department of Biology, Program of Genetics and Center for Biological Clocks Research, Texas A&M University, College Station, TX, United States of America
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Chidambaran V, Zhang X, Martin LJ, Ding L, Weirauch MT, Geisler K, Stubbeman BL, Sadhasivam S, Ji H. DNA methylation at the mu-1 opioid receptor gene ( OPRM1) promoter predicts preoperative, acute, and chronic postsurgical pain after spine fusion. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2017; 10:157-168. [PMID: 28533693 PMCID: PMC5432115 DOI: 10.2147/pgpm.s132691] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction The perioperative pain experience shows great interindividual variability and is difficult to predict. The mu-1 opioid receptor gene (OPRM1) is known to play an important role in opioid-pain pathways. Since deoxyribonucleic acid (DNA) methylation is a potent repressor of gene expression, DNA methylation was evaluated at the OPRM1 promoter, as a predictor of preoperative, acute, and chronic postsurgical pain (CPSP). Methods A prospective observational cohort study was conducted in 133 adolescents with idiopathic scoliosis undergoing spine fusion under standard protocols. Data regarding pain, opioid consumption, anxiety, and catastrophizing (using validated questionnaires) were collected before and 2–3 months postsurgery. Outcomes evaluated were preoperative pain, acute postoperative pain (area under curve [AUC] for pain scores over 48 hours), and CPSP (numerical rating scale >3/10 at 2–3 months postsurgery). Blood samples collected preoperatively were analyzed for DNA methylation by pyrosequencing of 22 CpG sites at the OPRM1 gene promoter. The association of each pain outcome with the methylation percentage of each CpG site was assessed using multivariable regression, adjusting for significant (P<0.05) nongenetic variables. Results Majority (83%) of the patients reported no pain preoperatively, while CPSP occurred in 36% of the subjects (44/121). Regression on dichotomized preoperative pain outcome showed association with methylation at six CpG sites (1, 3, 4, 9, 11, and 17) (P<0.05). Methylation at CpG sites 4, 17, and 18 was associated with higher AUC after adjusting for opioid consumption and preoperative pain score (P<0.05). After adjusting for postoperative opioid consumption and preoperative pain score, methylation at CpG sites 13 and 22 was associated with CPSP (P<0.05). Discussion Novel CPSP biomarkers were identified in an active regulatory region of the OPRM1 gene that binds multiple transcription factors. Inhibition of binding by DNA methylation potentially decreases the OPRM1 gene expression, leading to a decreased response to endogenous and exogenous opioids, and an increased pain experience.
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Affiliation(s)
| | - Xue Zhang
- Division of Human Genetics.,Pyrosequencing Core for Genomic and Epigenomic Research
| | - Lisa J Martin
- Department of Pediatrics.,Division of Human Genetics
| | - Lili Ding
- Division of Biostatistics and Epidemiology
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology.,Division of Biomedical Informatics.,Division of Developmental Biology
| | | | | | | | - Hong Ji
- Pyrosequencing Core for Genomic and Epigenomic Research.,Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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