1
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Coetzee SG, Hazelett DJ. MotifbreakR v2: extended capability and database integration. ARXIV 2024:arXiv:2407.03441v1. [PMID: 39010878 PMCID: PMC11247919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
MotifbreakR is a software tool that scans genetic variants against position weight matrices of transcription factors (TF) to determine the potential for the disruption of TF binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to operate across a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single nucleotide variants (common and rare SNVs) on potential TF binding sites, in motifbreakR v2, we have updated the functionality. New features include the ability to query other types of more complex genetic variants, such as short insertions and deletions (indels). This function allows modeling a more extensive array of variants that may have more significant effects on TF binding. Additionally, while TF binding is based partly on sequence preference, predictions of TF binding based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR implements querying the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.
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Affiliation(s)
- Simon G Coetzee
- Department of Computational Biomedicine at Cedars-Sinai Medical Center
| | - Dennis J Hazelett
- Department of Computational Biomedicine at Cedars-Sinai Medical Center
- Cancer Prevention and Control - Samuel Oschin Cancer Center, Cedars-Sinai
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2
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Stasevich EM, Simonova AV, Bogomolova EA, Murashko MM, Uvarova AN, Zheremyan EA, Korneev KV, Schwartz AM, Kuprash DV, Demin DE. Cut from the same cloth: RNAs transcribed from regulatory elements. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2024; 1867:195049. [PMID: 38964653 DOI: 10.1016/j.bbagrm.2024.195049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/06/2024]
Abstract
A certain degree of chromatin openness is necessary for the activity of transcription-regulating regions within the genome, facilitating accessibility to RNA polymerases and subsequent synthesis of regulatory element RNAs (regRNAs) from these regions. The rapidly increasing number of studies underscores the significance of regRNAs across diverse cellular processes and diseases, challenging the paradigm that these transcripts are non-functional transcriptional noise. This review explores the multifaceted roles of regRNAs in human cells, encompassing rather well-studied entities such as promoter RNAs and enhancer RNAs (eRNAs), while also providing insights into overshadowed silencer RNAs and insulator RNAs. Furthermore, we assess notable examples of shorter regRNAs, like miRNAs, snRNAs, and snoRNAs, playing important roles. Expanding our discourse, we deliberate on the potential usage of regRNAs as biomarkers and novel targets for cancer and other human diseases.
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Affiliation(s)
- E M Stasevich
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - A V Simonova
- Laboratory of Intracellular Signaling in Health and Disease, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - E A Bogomolova
- Laboratory of Intracellular Signaling in Health and Disease, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia; Moscow Center for Advanced Studies, Moscow, Russia
| | - M M Murashko
- Laboratory of Intracellular Signaling in Health and Disease, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia; Moscow Center for Advanced Studies, Moscow, Russia
| | - A N Uvarova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - E A Zheremyan
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - K V Korneev
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - A M Schwartz
- Department of Human Biology, University of Haifa, Haifa, Israel
| | - D V Kuprash
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - D E Demin
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
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3
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Duttke SH, Guzman C, Chang M, Delos Santos NP, McDonald BR, Xie J, Carlin AF, Heinz S, Benner C. Position-dependent function of human sequence-specific transcription factors. Nature 2024; 631:891-898. [PMID: 39020164 PMCID: PMC11269187 DOI: 10.1038/s41586-024-07662-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/04/2024] [Indexed: 07/19/2024]
Abstract
Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites1-3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.
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Affiliation(s)
- Sascha H Duttke
- School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, WA, USA.
| | - Carlos Guzman
- Department of Medicine, Division of Endocrinology, U.C. San Diego School of Medicine, La Jolla, CA, USA
| | - Max Chang
- Department of Medicine, Division of Endocrinology, U.C. San Diego School of Medicine, La Jolla, CA, USA
| | - Nathaniel P Delos Santos
- Department of Medicine, Division of Endocrinology, U.C. San Diego School of Medicine, La Jolla, CA, USA
| | - Bayley R McDonald
- School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, WA, USA
| | - Jialei Xie
- Department of Pathology and Medicine, U.C. San Diego School of Medicine, La Jolla, CA, USA
| | - Aaron F Carlin
- Department of Pathology and Medicine, U.C. San Diego School of Medicine, La Jolla, CA, USA
| | - Sven Heinz
- Department of Medicine, Division of Endocrinology, U.C. San Diego School of Medicine, La Jolla, CA, USA.
| | - Christopher Benner
- Department of Medicine, Division of Endocrinology, U.C. San Diego School of Medicine, La Jolla, CA, USA.
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4
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He AY, Danko CG. Dissection of core promoter syntax through single nucleotide resolution modeling of transcription initiation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.13.583868. [PMID: 38559255 PMCID: PMC10979970 DOI: 10.1101/2024.03.13.583868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Our understanding of how the DNA sequences of cis-regulatory elements encode transcription initiation patterns remains limited. Here we introduce CLIPNET, a deep learning model trained on population-scale PRO-cap data that accurately predicts the position and quantity of transcription initiation with single nucleotide resolution from DNA sequence. Interpretation of CLIPNET revealed a complex regulatory syntax consisting of DNA-protein interactions in five major positions between -200 and +50 bp relative to the transcription start site, as well as more subtle positional preferences among different transcriptional activators. Transcriptional activator and core promoter motifs occupy different positions and play distinct roles in regulating initiation, with the former driving initiation quantity and the latter initiation position. We identified core promoter motifs that explain initiation patterns in the majority of promoters and enhancers, including DPR motifs and AT-rich TBP binding sequences in TATA-less promoters. Our results provide insights into the sequence architecture governing transcription initiation.
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Affiliation(s)
- Adam Y. He
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University
- Graduate Field of Computational Biology, Cornell University
| | - Charles G. Danko
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University
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5
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Jones T, Sigauke RF, Sanford L, Taatjes DJ, Allen MA, Dowell RD. A transcription factor (TF) inference method that broadly measures TF activity and identifies mechanistically distinct TF networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585303. [PMID: 38559193 PMCID: PMC10980006 DOI: 10.1101/2024.03.15.585303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
TF profiler is a method of inferring transcription factor regulatory activity, i.e. when a TF is present and actively regulating transcription, directly directly from nascent sequencing assays such as PRO-seq and GRO-seq. Transcription factors orchestrate transcription and play a critical role in cellular maintenance, identity and response to external stimuli. While ChIP assays have measured DNA localization, they fall short of identifying when and where transcription factors are actively regulating transcription. Our method, on the other hand, uses RNA polymerase activity to infer TF activity across hundreds of data sets and transcription factors. Based on these classifications we identify three distinct classes of transcription factors: ubiquitous factors that play roles in cellular homeostasis, driving basal gene programs across tissues and cell types, tissue specific factors that act almost exclusively at enhancers and are themselves regulated at transcription, and stimulus responsive TFs which are regulated post-transcriptionally but act predominantly at enhancers. TF profiler is broadly applicable, providing regulatory insights on any PRO-seq sample for any transcription factor with a known binding motif.
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Song C, Zhang G, Mu X, Feng C, Zhang Q, Song S, Zhang Y, Yin M, Zhang H, Tang H, Li C. eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse. Nucleic Acids Res 2024; 52:D81-D91. [PMID: 37889077 PMCID: PMC10767853 DOI: 10.1093/nar/gkad925] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/26/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Enhancer RNAs (eRNAs) transcribed from distal active enhancers serve as key regulators in gene transcriptional regulation. The accumulation of eRNAs from multiple sequencing assays has led to an urgent need to comprehensively collect and process these data to illustrate the regulatory landscape of eRNAs. To address this need, we developed the eRNAbase (http://bio.liclab.net/eRNAbase/index.php) to store the massive available resources of human and mouse eRNAs and provide comprehensive annotation and analyses for eRNAs. The current version of eRNAbase cataloged 10 399 928 eRNAs from 1012 samples, including 858 human samples and 154 mouse samples. These eRNAs were first identified and uniformly processed from 14 eRNA-related experiment types manually collected from GEO/SRA and ENCODE. Importantly, the eRNAbase provides detailed and abundant (epi)genetic annotations in eRNA regions, such as super enhancers, enhancers, common single nucleotide polymorphisms, expression quantitative trait loci, transcription factor binding sites, CRISPR/Cas9 target sites, DNase I hypersensitivity sites, chromatin accessibility regions, methylation sites, chromatin interactions regions, topologically associating domains and RNA spatial interactions. Furthermore, the eRNAbase provides users with three novel analyses including eRNA-mediated pathway regulatory analysis, eRNA-based variation interpretation analysis and eRNA-mediated TF-target gene analysis. Hence, eRNAbase is a powerful platform to query, browse and visualize regulatory cues associated with eRNAs.
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Affiliation(s)
- Chao Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Guorui Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Xinxin Mu
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chenchen Feng
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qinyi Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Shuang Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hang Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Huifang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan, 421001, China
| | - Chunquan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
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7
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Sigauke RF, Sanford L, Maas ZL, Jones T, Stanley JT, Townsend HA, Allen MA, Dowell RD. Atlas of nascent RNA transcripts reveals enhancer to gene linkages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570626. [PMID: 38105978 PMCID: PMC10723487 DOI: 10.1101/2023.12.07.570626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in unstable, non-coding bidirectional transcription. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as an SQL database which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.
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Affiliation(s)
- Rutendo F. Sigauke
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Lynn Sanford
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Zachary L. Maas
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Computer Science, University of Colorado Boulder, 1111 Engineering Drive, UCB 430, Boulder, 80309, CO, USA
| | - Taylor Jones
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Jacob T. Stanley
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Hope A. Townsend
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Molecular, Cellular and Developmental Biology, University of Colorado Boulder, 1945 Colorado Ave, UCB 347, Boulder, 80309, CO, USA
| | - Mary A. Allen
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Robin D. Dowell
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Computer Science, University of Colorado Boulder, 1111 Engineering Drive, UCB 430, Boulder, 80309, CO, USA
- Molecular, Cellular and Developmental Biology, University of Colorado Boulder, 1945 Colorado Ave, UCB 347, Boulder, 80309, CO, USA
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8
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Lee SA, Kristjánsdóttir K, Kwak H. eRNA co-expression network uncovers TF dependency and convergent cooperativity. Sci Rep 2023; 13:19085. [PMID: 37925545 PMCID: PMC10625640 DOI: 10.1038/s41598-023-46415-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 10/31/2023] [Indexed: 11/06/2023] Open
Abstract
Enhancer RNAs (eRNAs) are non-coding RNAs produced by transcriptional enhancers that are highly correlated with their activity. Using a capped nascent RNA sequencing (PRO-cap) dataset in human lymphoblastoid cell lines across 67 individuals, we identified inter-individual variation in the expression of over 80 thousand transcribed transcriptional regulatory elements (tTREs), in both enhancers and promoters. Co-expression analysis of eRNAs from tTREs across individuals revealed how enhancers are associated with each other and with promoters. Mid- to long-range co-expression showed a distance-dependent decay that was modified by TF occupancy. In particular, we found a class of "bivalent" TFs, including Cohesin, that both facilitate and isolate the interaction between enhancers and/or promoters, depending on their topology. At short distances, we observed strand-specific correlations between nearby eRNAs in both convergent and divergent orientations. Our results support a cooperative model of convergent eRNAs, consistent with eRNAs facilitating adjacent enhancers rather than interfering with each other. Therefore, our approach to infer functional interactions from co-expression analyses provided novel insights into the principles of enhancer interactions as a function of distance, orientation, and binding landscapes of TFs.
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Affiliation(s)
- Seungha Alisa Lee
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA
| | - Katla Kristjánsdóttir
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA
| | - Hojoong Kwak
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA.
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9
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Lee SA, Kristjánsdóttir K, Kwak H. Revealing eRNA interactions: TF dependency and convergent cooperativity. RESEARCH SQUARE 2023:rs.3.rs-2592357. [PMID: 36909657 PMCID: PMC10002804 DOI: 10.21203/rs.3.rs-2592357/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Enhancer RNAs (eRNAs) are non-coding RNAs produced from transcriptional enhancers that are highly correlated with their activities. Using capped nascent RNA sequencing (PRO-cap) dataset in human lymphoblastoid cell lines across individuals, we identified inter-individual variation of expression in over 80 thousand transcribed transcriptional regulatory elements (tTREs), in both enhancers and promoters. Co-expression analysis of eRNAs from tTREs across individuals revealed how enhancers interact with each other and with promoters. Mid-to-long range interactions showed distance-dependent decay, which was modified by TF occupancy. In particular, we found a class of 'bivalent' TFs, including Cohesin, which both facilitates and insulates the interaction between enhancers and/or promoters depending on the topology. In short ranges, we observed strand specific interactions between nearby eRNAs in both convergent or divergent orientations. Our finding supports a cooperative convergent eRNA model, which is compatible with eRNA remodeling neighboring enhancers rather than interfering with each other. Therefore, our approach to infer functional interactions from co-expression analyses provided novel insights into the principles of enhancer interactions depending on the distance, orientation, and the binding landscapes of TFs.
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10
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ATM suppresses c-Myc overexpression in the mammary epithelium in response to estrogen. Cell Rep 2023; 42:111909. [PMID: 36640339 PMCID: PMC10023214 DOI: 10.1016/j.celrep.2022.111909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/27/2022] [Accepted: 12/12/2022] [Indexed: 12/31/2022] Open
Abstract
ATM gene mutation carriers are predisposed to estrogen-receptor-positive breast cancer (BC). ATM prevents BC oncogenesis by activating p53 in every cell; however, much remains unknown about tissue-specific oncogenesis after ATM loss. Here, we report that ATM controls the early transcriptional response to estrogens. This response depends on topoisomerase II (TOP2), which generates TOP2-DNA double-strand break (DSB) complexes and rejoins the breaks. When TOP2-mediated ligation fails, ATM facilitates DSB repair. After estrogen exposure, TOP2-dependent DSBs arise at the c-MYC enhancer in human BC cells, and their defective repair changes the activation profile of enhancers and induces the overexpression of many genes, including the c-MYC oncogene. CRISPR/Cas9 cleavage at the enhancer also causes c-MYC overexpression, indicating that this DSB causes c-MYC overexpression. Estrogen treatment induced c-Myc protein overexpression in mammary epithelial cells of ATM-deficient mice. In conclusion, ATM suppresses the c-Myc-driven proliferative effects of estrogens, possibly explaining such tissue-specific oncogenesis.
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11
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Watts JA, Grunseich C, Rodriguez Y, Liu Y, Li D, Burdick J, Bruzel A, Crouch RJ, Mahley RW, Wilson S, Cheung V. A common transcriptional mechanism involving R-loop and RNA abasic site regulates an enhancer RNA of APOE. Nucleic Acids Res 2022; 50:12497-12514. [PMID: 36453989 PMCID: PMC9757052 DOI: 10.1093/nar/gkac1107] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/30/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022] Open
Abstract
RNA is modified by hundreds of chemical reactions and folds into innumerable shapes. However, the regulatory role of RNA sequence and structure and how dysregulation leads to diseases remain largely unknown. Here, we uncovered a mechanism where RNA abasic sites in R-loops regulate transcription by pausing RNA polymerase II. We found an enhancer RNA, AANCR, that regulates the transcription and expression of apolipoprotein E (APOE). In some human cells such as fibroblasts, AANCR is folded into an R-loop and modified by N-glycosidic cleavage; in this form, AANCR is a partially transcribed nonfunctional enhancer and APOE is not expressed. In contrast, in other cell types including hepatocytes and under stress, AANCR does not form a stable R-loop as its sequence is not modified, so it is transcribed into a full-length enhancer that promotes APOE expression. DNA sequence variants in AANCR are associated significantly with APOE expression and Alzheimer's Disease, thus AANCR is a modifier of Alzheimer's Disease. Besides AANCR, thousands of noncoding RNAs are regulated by abasic sites in R-loops. Together our data reveal the essentiality of the folding and modification of RNA in cellular regulation and demonstrate that dysregulation underlies common complex diseases such as Alzheimer's disease.
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Affiliation(s)
- Jason A Watts
- Department of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Epigenetics and Stem Cell Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Christopher Grunseich
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yesenia Rodriguez
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Yaojuan Liu
- Department of Pediatrics and Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dongjun Li
- Department of Pediatrics and Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Joshua T Burdick
- Department of Pediatrics and Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alan Bruzel
- Department of Pediatrics and Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert J Crouch
- Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Robert W Mahley
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Departments of Pathology and Medicine, University of California, San Francisco, CA, USA
| | - Samuel H Wilson
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Vivian G Cheung
- Department of Pediatrics and Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA
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12
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Koido M, Hon CC, Koyama S, Kawaji H, Murakawa Y, Ishigaki K, Ito K, Sese J, Parrish NF, Kamatani Y, Carninci P, Terao C. Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning. Nat Biomed Eng 2022:10.1038/s41551-022-00961-8. [PMID: 36411359 DOI: 10.1038/s41551-022-00961-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 10/12/2022] [Indexed: 11/22/2022]
Abstract
Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn's disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits.
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Affiliation(s)
- Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.,Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chung-Chau Hon
- Laboratory for Genome Information Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Satoshi Koyama
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Hideya Kawaji
- Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy.,Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Jun Sese
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi, Koto-ku, Tokyo, Japan.,Humanome Lab Inc., Tokyo, Japan
| | - Nicholas F Parrish
- Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research and RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Piero Carninci
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory for Single Cell Technologies, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Human Technopole, Milan, Italy
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan. .,The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
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13
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Hu X, Wu L, Yao Y, Ma J, Li X, Shen H, Liu L, Dai H, Wang W, Chu X, Sheng C, Yang M, Zheng H, Song F, Chen K, Liu B. The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance. iScience 2022; 25:105075. [PMID: 36157578 PMCID: PMC9490034 DOI: 10.1016/j.isci.2022.105075] [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] [Received: 01/18/2022] [Revised: 06/09/2022] [Accepted: 08/31/2022] [Indexed: 12/24/2022] Open
Abstract
The comprehensive regulation effect of eRNA on tumor immune cell infiltration and the outcome remains obscure. We comprehensively identify the eRNA-mediated immune infiltration patterns of gastric cancer (GC) samples. We creatively proposed a random forest machine-learning (ML) algorithm to map eRNA to mRNA expression patterns. The eRNA score was constructed using principal component analysis algorithms and validated in an independent cohort. Three subtypes with distinct eRNA expression patterns were determined in GC. There were significant differences between the three subtypes in the overall survival rate, immune cell infiltration characteristics, and immunotherapy response indicators. The patients in the high eRNA score group have a higher overall survival rate and might benefit from immunotherapy. This work revealed that eRNA regulation might be a new prognostic index and might offer a potential biomarker in the response of immunotherapy. Evaluating the eRNA regulation manner of GC will contribute to guiding more effective immunotherapy strategies.
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Affiliation(s)
- Xin Hu
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Liuxing Wu
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Yanxin Yao
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Junfu Ma
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Xiangchun Li
- Tianjin Cancer Institute, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Hongru Shen
- Tianjin Cancer Institute, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Luyang Liu
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Wei Wang
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Xinlei Chu
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Chao Sheng
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Meng Yang
- Tianjin Cancer Institute, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Hong Zheng
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
| | - Ben Liu
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology, Tianjin 300060, China
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14
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Abstract
Enhancers confer precise spatiotemporal patterns of gene expression in response to developmental and environmental stimuli. Over the last decade, the transcription of enhancer RNAs (eRNAs) – nascent RNAs transcribed from active enhancers – has emerged as a key factor regulating enhancer activity. eRNAs are relatively short-lived RNA species that are transcribed at very high rates but also quickly degraded. Nevertheless, eRNAs are deeply intertwined within enhancer regulatory networks and are implicated in a number of transcriptional control mechanisms. Enhancers show changes in function and sequence over evolutionary time, raising questions about the relationship between enhancer sequences and eRNA function. Moreover, the vast majority of single nucleotide polymorphisms associated with human complex diseases map to the non-coding genome, with causal disease variants enriched within enhancers. In this Primer, we survey the diverse roles played by eRNAs in enhancer-dependent gene expression, evaluating different models for eRNA function. We also explore questions surrounding the genetic conservation of enhancers and how this relates to eRNA function and dysfunction. Summary: This Primer evaluates the ideas that underpin developing models for eRNA function, exploring cases in which perturbed eRNA function contributes to disease.
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Affiliation(s)
- Laura J. Harrison
- Molecular and Cellular Biology, School of Biosciences, Sheffield Institute For Nucleic Acids, The University of Sheffield, Firth Court, Western Bank , Sheffield S10 2TN , UK
| | - Daniel Bose
- Molecular and Cellular Biology, School of Biosciences, Sheffield Institute For Nucleic Acids, The University of Sheffield, Firth Court, Western Bank , Sheffield S10 2TN , UK
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15
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Chou SP, Alexander AK, Rice EJ, Choate LA, Danko CG. Genetic dissection of the RNA polymerase II transcription cycle. eLife 2022; 11:78458. [PMID: 35775732 PMCID: PMC9286732 DOI: 10.7554/elife.78458] [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] [Received: 03/08/2022] [Accepted: 06/30/2022] [Indexed: 11/20/2022] Open
Abstract
How DNA sequence affects the dynamics and position of RNA Polymerase II (Pol II) during transcription remains poorly understood. Here, we used naturally occurring genetic variation in F1 hybrid mice to explore how DNA sequence differences affect the genome-wide distribution of Pol II. We measured the position and orientation of Pol II in eight organs collected from heterozygous F1 hybrid mice using ChRO-seq. Our data revealed a strong genetic basis for the precise coordinates of transcription initiation and promoter proximal pause, allowing us to redefine molecular models of core transcriptional processes. Our results implicate DNA sequence, including both known and novel DNA sequence motifs, as key determinants of the position of Pol II initiation and pause. We report evidence that initiation site selection follows a stochastic process similar to Brownian motion along the DNA template. We found widespread differences in the position of transcription termination, which impact the primary structure and stability of mature mRNA. Finally, we report evidence that allelic changes in transcription often affect mRNA and ncRNA expression across broad genomic domains. Collectively, we reveal how DNA sequences shape core transcriptional processes at single nucleotide resolution in mammals.
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Affiliation(s)
- Shao-Pei Chou
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Adriana K Alexander
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Edward J Rice
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Lauren A Choate
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
| | - Charles G Danko
- Baker Institute for Animal Health, Cornell University, Ithaca, United States
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16
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Laiker I, Frankel N. Pleiotropic enhancers are ubiquitous regulatory elements in the human genome. Genome Biol Evol 2022; 14:6585219. [PMID: 35552697 PMCID: PMC9156028 DOI: 10.1093/gbe/evac071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Enhancers are regulatory elements of genomes that determine spatio-temporal patterns of gene expression. The human genome contains a vast number of enhancers, which largely outnumber protein-coding genes. Historically, enhancers have been regarded as highly tissue-specific. However, recent evidence has demonstrated that many enhancers are pleiotropic, with activity in multiple developmental contexts. Yet, the extent and impact of pleiotropy remain largely unexplored. In this study we analyzed active enhancers across human organs based on the analysis of both eRNA transcription (FANTOM5 consortium data sets) and chromatin architecture (ENCODE consortium data sets). We show that pleiotropic enhancers are pervasive in the human genome and that most enhancers active in a particular organ are also active in other organs. In addition, our analysis suggests that the proportion of context-specific enhancers of a given organ is explained, at least in part, by the proportion of context-specific genes in that same organ. The notion that such a high proportion of human enhancers can be pleiotropic suggests that small regions of regulatory DNA contain abundant regulatory information and that these regions evolve under important evolutionary constraints.
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Affiliation(s)
- Ian Laiker
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) y Universidad de Buenos Aires (UBA), Buenos Aires 1428, Argentina
| | - Nicolas Frankel
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) y Universidad de Buenos Aires (UBA), Buenos Aires 1428, Argentina.,Departamento de Ecología, Genética y Evolución, Facultad de Ciencias Exactas y Naturales (FCEN), Universidad de Buenos Aires (UBA), Buenos Aires 1428, Argentina
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17
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Enhancer RNA AL928768.3 from the IGH Locus Regulates MYC Expression and Controls the Proliferation and Chemoresistance of Burkitt Lymphoma Cells with IGH/MYC Translocation. Int J Mol Sci 2022; 23:ijms23094624. [PMID: 35563017 PMCID: PMC9103539 DOI: 10.3390/ijms23094624] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 12/10/2022] Open
Abstract
Chromosomal rearrangements leading to the relocation of proto-oncogenes into transcription-active regions are found in various types of tumors. In particular, the transfer of proto-oncogenes to the locus of heavy chains of immunoglobulins (IGH) is frequently observed in B-lymphomas. The increased expression of the MYC proto-oncogene due to IGH/MYC translocation is detected in approximately 85% of Burkitt lymphoma cases. The regulatory mechanisms affecting the oncogenes upon translocation include non-coding enhancer RNAs (eRNAs). We conducted a search for the eRNAs that may affect MYC transcription in the case of IGH/MYC translocation in Burkitt lymphoma, looking for potentially oncogenic eRNAs located at the IGH locus and predominantly expressed in B cells. Overexpression and knockdown of our primary candidate eRNA AL928768.3 led to the corresponding changes in the expression of MYC proto-oncogene in Burkitt lymphoma cells. Furthermore, we demonstrated that AL928768.3 knockdown decreased lymphoma cell proliferation and resistance to chemotherapy. Significant effects were observed only in cell lines bearing IGH/MYC abnormality but not in B-cell lines without this translocation nor primary B-cells. Our results indicate that AL928768.3 plays an important role in the development of Burkitt’s lymphoma and suggest it and similar, yet undiscovered eRNAs as potential tissue-specific targets for cancer treatment.
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18
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Wan L, Li W, Meng Y, Hou Y, Chen M, Xu B. Inflammatory Immune-Associated eRNA: Mechanisms, Functions and Therapeutic Prospects. Front Immunol 2022; 13:849451. [PMID: 35514959 PMCID: PMC9063412 DOI: 10.3389/fimmu.2022.849451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
The rapid development of multiple high-throughput sequencing technologies has made it possible to explore the critical roles and mechanisms of functional enhancers and enhancer RNAs (eRNAs). The inflammatory immune response, as a fundamental pathological process in infectious diseases, cancers and immune disorders, coordinates the balance between the internal and external environment of the organism. It has been shown that both active enhancers and intranuclear eRNAs are preferentially expressed over inflammation-related genes in response to inflammatory stimuli, suggesting that enhancer transcription events and their products influence the expression and function of inflammatory genes. Therefore, in this review, we summarize and discuss the relevant inflammatory roles and regulatory mechanisms of eRNAs in inflammatory immune cells, non-inflammatory immune cells, inflammatory immune diseases and tumors, and explore the potential therapeutic effects of enhancer inhibitors affecting eRNA production for diseases with inflammatory immune responses.
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Affiliation(s)
- Lilin Wan
- Medical School, Southeast University, Nanjing, China
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| | - Wenchao Li
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| | - Yuan Meng
- Department of Urology, Nanjing Lishui District People’s Hospital, Zhongda Hospital, Southeast University, Nanjing, China
| | - Yue Hou
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Ming Chen
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
- Department of Urology, Nanjing Lishui District People’s Hospital, Zhongda Hospital, Southeast University, Nanjing, China
| | - Bin Xu
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
- Department of Urology, Nanjing Lishui District People’s Hospital, Zhongda Hospital, Southeast University, Nanjing, China
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19
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Zhang Y, Wu KM, Yang L, Dong Q, Yu JT. Tauopathies: new perspectives and challenges. Mol Neurodegener 2022; 17:28. [PMID: 35392986 PMCID: PMC8991707 DOI: 10.1186/s13024-022-00533-z] [Citation(s) in RCA: 94] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Tauopathies are a class of neurodegenerative disorders characterized by neuronal and/or glial tau-positive inclusions. MAIN BODY Clinically, tauopathies can present with a range of phenotypes that include cognitive/behavioral-disorders, movement disorders, language disorders and non-specific amnestic symptoms in advanced age. Pathologically, tauopathies can be classified based on the predominant tau isoforms that are present in the inclusion bodies (i.e., 3R, 4R or equal 3R:4R ratio). Imaging, cerebrospinal fluid (CSF) and blood-based tau biomarkers have the potential to be used as a routine diagnostic strategy and in the evaluation of patients with tauopathies. As tauopathies are strongly linked neuropathologically and genetically to tau protein abnormalities, there is a growing interest in pursuing of tau-directed therapeutics for the disorders. Here we synthesize emerging lessons on tauopathies from clinical, pathological, genetic, and experimental studies toward a unified concept of these disorders that may accelerate the therapeutics. CONCLUSIONS Since tauopathies are still untreatable diseases, efforts have been made to depict clinical and pathological characteristics, identify biomarkers, elucidate underlying pathogenesis to achieve early diagnosis and develop disease-modifying therapies.
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Affiliation(s)
- Yi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Kai-Min Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
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20
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Towards the Genetic Architecture of Complex Gene Expression Traits: Challenges and Prospects for eQTL Mapping in Humans. Genes (Basel) 2022; 13:genes13020235. [PMID: 35205280 PMCID: PMC8871770 DOI: 10.3390/genes13020235] [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: 12/20/2021] [Revised: 01/21/2022] [Accepted: 01/25/2022] [Indexed: 12/10/2022] Open
Abstract
The discovery of expression quantitative trait loci (eQTLs) and their target genes (eGenes) has not only compensated for the limitations of genome-wide association studies for complex phenotypes but has also provided a basis for predicting gene expression. Efforts have been made to develop analytical methods in statistical genetics, a key discipline in eQTL analysis. In particular, mixed model– and deep learning–based analytical methods have been extremely beneficial in mapping eQTLs and predicting gene expression. Nevertheless, we still face many challenges associated with eQTL discovery. Here, we discuss two key aspects of these challenges: 1, the complexity of eTraits with various factors such as polygenicity and epistasis and 2, the voluminous work required for various types of eQTL profiles. The properties and prospects of statistical methods, including the mixed model method, Bayesian inference, the deep learning method, and the integration method, are presented as future directions for eQTL discovery. This review will help expedite the design and use of efficient methods for eQTL discovery and eTrait prediction.
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21
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Kyzar EJ, Bohnsack JP, Pandey SC. Current and Future Perspectives of Noncoding RNAs in Brain Function and Neuropsychiatric Disease. Biol Psychiatry 2022; 91:183-193. [PMID: 34742545 PMCID: PMC8959010 DOI: 10.1016/j.biopsych.2021.08.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 08/05/2021] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
Noncoding RNAs (ncRNAs) represent the majority of the transcriptome and play important roles in regulating neuronal functions. ncRNAs are exceptionally diverse in both structure and function and include enhancer RNAs, long ncRNAs, and microRNAs, all of which demonstrate specific temporal and regional expression in the brain. Here, we review recent studies demonstrating that ncRNAs modulate chromatin structure, act as chaperone molecules, and contribute to synaptic remodeling and behavior. In addition, we discuss ncRNA function within the context of neuropsychiatric diseases, particularly focusing on addiction and schizophrenia, and the recent methodological developments that allow for better understanding of ncRNA function in the brain. Overall, ncRNAs represent an underrecognized molecular contributor to complex neuronal processes underlying neuropsychiatric disorders.
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Affiliation(s)
- Evan J Kyzar
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois; Department of Psychiatry, Columbia University Irving Medical Center, New York State Psychiatric Institute, New York, New York
| | - John Peyton Bohnsack
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois
| | - Subhash C Pandey
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown Veterans Affairs Medical Center, University of Illinois at Chicago, Chicago, Illinois; Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, Illinois.
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22
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Yan T, Wang K, Zhao Q, Zhuang J, Shen H, Ma G, Cong L, Du J. Gender specific eRNA TBX5-AS1 as the immunological biomarker for male patients with lung squamous cell carcinoma in pan-cancer screening. PeerJ 2021; 9:e12536. [PMID: 34900441 PMCID: PMC8627656 DOI: 10.7717/peerj.12536] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/03/2021] [Indexed: 11/27/2022] Open
Abstract
As an innate feature of human beings, gender differences have an influence on various biological phenotypes, yet it does not attract enough attention in genomics studies. The prognosis of multiple carcinomas usually exhibits a favorable ending for female patients, but the neglect of gender differences can cause serious bias in survival analysis. Enhancer RNAs (eRNAs) are mostly downstream of androgens or estrogen. The present study was aimed to screen eRNAs in patients with non-small-cell lung cancer. The findings revealed that eRNA TBX5-AS1 was expressed differently between female and male patients. Meanwhile, its prognostic significance appeared only in male patients with squamous cell carcinoma (SCC) type. The Gene Set Enrichment Analysis proved that the expression level of TBX5-AS1 increased following the activation of the androgen signaling pathway. In pan-cancer analysis, the prognostic prediction based on gender grouping obtained more meaningful results, and the synergy between TBX5-AS1 and its homologous target was more consistent. Furthermore, immunity variations between sexes prompted us to explore the role that TBX5-AS1 played in tumor microenvironment and immunotherapy. The robust evidence proved that male patients with high expression of TBX5-AS1 possessed a malignant immune microenvironment and urgently needed immune checkpoint inhibitor treatment. In conclusion, TBX5-AS1 may be one of the strongest candidates to predict prognosis for male patients with SCC and provide a reference for immunotherapy.
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Affiliation(s)
- Tao Yan
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Kai Wang
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Healthcare Respiratory Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qidi Zhao
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Junjie Zhuang
- Institute of Oncology, Shandong Provincial Hospital affiliated to Shandong First Medicine University, Jinan, China
| | - Hongchang Shen
- Department of Oncology, Shandong Provincial Hospital affiliated to Shandong First Medicine University, Jinan, China
| | - Guoyuan Ma
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lei Cong
- Department of Oncology, Shandong Provincial Hospital affiliated to Shandong First Medicine University, Jinan, China.,Department of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jiajun Du
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
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23
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Ishigaki K. Beyond GWAS: from simple associations to functional insights. Semin Immunopathol 2021; 44:3-14. [PMID: 34605948 DOI: 10.1007/s00281-021-00894-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/08/2021] [Indexed: 12/31/2022]
Abstract
Each human, when born, has slightly different DNA sequences, which make each of us unique. The variations in DNA sequences are called genetic variants. The primary aim of genome-wide association study (GWAS) is to detect associations between genetic variants and human phenotypes. Since GWAS focuses on germ-line variants, there is no reverse causation. Therefore, GWAS is one of the few tools that can assess the causality of human diseases. In the past 10 years, many large-scale GWAS have been conducted. Although the primary outputs of GWAS are just a series of statistics, its downstream analyses provided many insights beyond simple associations: the causal mechanisms for autoimmune diseases and shared etiology between diseases. Moreover, GWAS downstream analyses generated scores potentially helpful in predicting clinical outcomes of each patient. This review focuses on GWAS for autoimmune diseases and introduces significant achievements of its downstream analyses. We also provide future directions that potentially overcome current limitations. We restrict our discussion to common autoimmune diseases (e.g., rheumatoid arthritis) since rare Mendelian diseases possess distinct genetic etiologies and are not tested by GWAS.
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Affiliation(s)
- Kazuyoshi Ishigaki
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan.
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24
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Guerrini MM, Oguchi A, Suzuki A, Murakawa Y. Cap analysis of gene expression (CAGE) and noncoding regulatory elements. Semin Immunopathol 2021; 44:127-136. [PMID: 34468849 DOI: 10.1007/s00281-021-00886-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/13/2021] [Indexed: 01/06/2023]
Abstract
Cap analysis of gene expression (CAGE) was developed to detect the 5' end of RNA. Trapping of the RNA 5'-cap structure enables the enrichment and selective sequencing of complete transcripts. Upscaled high-throughput versions of CAGE have enabled the genome-wide identification of transcription start sites, including transcriptionally active promoters and enhancers. CAGE sequencing can be exploited to draw comprehensive maps of active genomic regulatory elements in a cell type- and activation-specific manner. The cells of the immune system are among the best candidates to be analyzed in humans, since they are easily accessible. In this review, we discuss how CAGE data are instrumental for integrative analyses with quantitative trait loci and omics data, and their usefulness in the mechanistic interpretation of the effects of genetic variations over the entire human genome. Integrating CAGE data with the currently available omics information will contribute to better understanding of the genome-wide association study variants that lie outside of annotated genes, deepening our knowledge on human diseases, and enabling the targeted design of more specific therapeutic interventions.
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Affiliation(s)
- Matteo Maurizio Guerrini
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
| | - Akiko Oguchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- IFOM-the FIRC Institute of Molecular Oncology, Milan, Italy
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25
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Abstract
The world of long non-coding RNAs (lncRNAs) has opened up massive new prospects in understanding the regulation of gene expression. Not only are there seemingly almost infinite numbers of lncRNAs in the mammalian cell, but they have highly diverse mechanisms of action. In the nucleus, some are chromatin-associated, transcribed from transcriptional enhancers (eRNAs) and/or direct changes in the epigenetic landscape with profound effects on gene expression. The pituitary gonadotrope is responsible for activation of reproduction through production and secretion of appropriate levels of the gonadotropic hormones. As such, it exemplifies a cell whose function is defined through changes in developmental and temporal patterns of gene expression, including those that are hormonally induced. Roles for diverse distal regulatory elements and eRNAs in gonadotrope biology have only just begun to emerge. Here, we will present an overview of the different kinds of lncRNAs that alter gene expression, and what is known about their roles in regulating some of the key gonadotrope genes. We will also review various screens that have detected differentially expressed pituitary lncRNAs associated with changes in reproductive state and those whose expression is found to play a role in gonadotrope-derived nonfunctioning pituitary adenomas. We hope to shed light on this exciting new field, emphasize the open questions, and encourage research to illuminate the roles of lncRNAs in various endocrine systems.
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Affiliation(s)
- Tal Refael
- Faculty of Biology, Technion Israel Institute of Technology, Haifa 32000, Israel
| | - Philippa Melamed
- Faculty of Biology, Technion Israel Institute of Technology, Haifa 32000, Israel
- Correspondence: Philippa Melamed, PhD, Faculty of Biology, Technion - Israel Institute of Technology, Haifa 32000, Israel.
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26
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Kosmicki JA, Horowitz JE, Banerjee N, Lanche R, Marcketta A, Maxwell E, Bai X, Sun D, Backman JD, Sharma D, Kang HM, O'Dushlaine C, Yadav A, Mansfield AJ, Li AH, Watanabe K, Gurski L, McCarthy SE, Locke AE, Khalid S, O'Keeffe S, Mbatchou J, Chazara O, Huang Y, Kvikstad E, O'Neill A, Nioi P, Parker MM, Petrovski S, Runz H, Szustakowski JD, Wang Q, Wong E, Cordova-Palomera A, Smith EN, Szalma S, Zheng X, Esmaeeli S, Davis JW, Lai YP, Chen X, Justice AE, Leader JB, Mirshahi T, Carey DJ, Verma A, Sirugo G, Ritchie MD, Rader DJ, Povysil G, Goldstein DB, Kiryluk K, Pairo-Castineira E, Rawlik K, Pasko D, Walker S, Meynert A, Kousathanas A, Moutsianas L, Tenesa A, Caulfield M, Scott R, Wilson JF, Baillie JK, Butler-Laporte G, Nakanishi T, Lathrop M, Richards JB, Jones M, Balasubramanian S, Salerno W, Shuldiner AR, Marchini J, Overton JD, Habegger L, Cantor MN, Reid JG, Baras A, Abecasis GR, Ferreira MA. A catalog of associations between rare coding variants and COVID-19 outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2020.10.28.20221804. [PMID: 33655273 PMCID: PMC7924298 DOI: 10.1101/2020.10.28.20221804] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We investigated associations between rare genetic variants and seven COVID-19 outcomes in 543,213 individuals, including 8,248 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 14 interferon pathway genes in which rare deleterious variants have been reported in severe COVID-19 patients; (ii) 167 genes located in COVID-19 GWAS risk loci; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly browsable at https://rgc-covid19.regeneron.com.
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Affiliation(s)
- J A Kosmicki
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - J E Horowitz
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - N Banerjee
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - R Lanche
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - A Marcketta
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - E Maxwell
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - X Bai
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - D Sun
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - J D Backman
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - D Sharma
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - H M Kang
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - C O'Dushlaine
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - A Yadav
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - A J Mansfield
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - A H Li
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - K Watanabe
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - L Gurski
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - S E McCarthy
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - A E Locke
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - S Khalid
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - S O'Keeffe
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - J Mbatchou
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - O Chazara
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Y Huang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - E Kvikstad
- Bristol Myers Squibb, Route 206 and Province Line Road, Princeton, NJ 08543, USA
| | - A O'Neill
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - P Nioi
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - M M Parker
- Alnylam Pharmaceuticals, 675 West Kendall St, Cambridge, MA 02142, USA
| | - S Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - H Runz
- Biogen, 300 Binney St, Cambridge, MA 02142, USA
| | - J D Szustakowski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Q Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - E Wong
- Biogen, 300 Binney St, Cambridge, MA 02142, USA
| | | | - E N Smith
- Takeda California Inc., 9625 Towne Centre Dr, San Diego, CA 92121, USA
| | - S Szalma
- Takeda California Inc., 9625 Towne Centre Dr, San Diego, CA 92121, USA
| | - X Zheng
- AbbVie, Inc., 1 N. Waukegan Rd, North Chicago, IL 60064, USA
| | - S Esmaeeli
- AbbVie, Inc., 1 N. Waukegan Rd, North Chicago, IL 60064, USA
| | - J W Davis
- AbbVie, Inc., 1 N. Waukegan Rd, North Chicago, IL 60064, USA
| | - Y-P Lai
- Pfizer, Inc., 1 Portland Street, Cambridge MA 02139, USA
| | - X Chen
- Pfizer, Inc., 1 Portland Street, Cambridge MA 02139, USA
| | | | | | | | | | - A Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - G Sirugo
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - M D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - D J Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - G Povysil
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - D B Goldstein
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Genetics & Development, Columbia University, New York, NY 10032, USA
| | - K Kiryluk
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY 10032, USA
| | - E Pairo-Castineira
- Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, EH25 9RG, UK
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
| | - K Rawlik
- Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, EH25 9RG, UK
| | - D Pasko
- Genomics England, London EC1M 6BQ, UK
| | - S Walker
- Genomics England, London EC1M 6BQ, UK
| | - A Meynert
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
| | | | | | - A Tenesa
- Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, EH25 9RG, UK
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, Teviot Place, Edinburgh EH8 9AG, UK
| | - M Caulfield
- Genomics England, London EC1M 6BQ, UK
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - R Scott
- Genomics England, London EC1M 6BQ, UK
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - J F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, Teviot Place, Edinburgh EH8 9AG, UK
| | - J K Baillie
- Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, EH25 9RG, UK
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
- Intensive Care Unit, Royal Infirmary of Edinburgh, 54 Little France Drive, Edinburgh, EH16 5SA, UK
| | - G Butler-Laporte
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec H3A 0G4, Canada
| | - T Nakanishi
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montréal, Québec H3A 0G4, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan
- Research Fellow, Japan Society for the Promotion of Science
| | - M Lathrop
- Department of Human Genetics, McGill University, Montréal, Québec H3A 0G4, Canada
- Canadian Centre for Computational Genomics, McGill University, Montréal, Québec H3A 0G4, Canada
| | - J B Richards
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec H3A 0G4, Canada
- Department of Human Genetics, McGill University, Montréal, Québec H3A 0G4, Canada
- Department of Twins Research, King's College London, London WC2R 2LS, UK
| | - M Jones
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - S Balasubramanian
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - W Salerno
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - A R Shuldiner
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - J Marchini
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - J D Overton
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - L Habegger
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - M N Cantor
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - J G Reid
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - A Baras
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - G R Abecasis
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
| | - M A Ferreira
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY 10591, USA
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27
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Cardiello JF, Sanchez GJ, Allen MA, Dowell RD. Lessons from eRNAs: understanding transcriptional regulation through the lens of nascent RNAs. Transcription 2019; 11:3-18. [PMID: 31856658 DOI: 10.1080/21541264.2019.1704128] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Nascent transcription assays, such as global run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), have uncovered a myriad of unstable RNAs being actively produced from numerous sites genome-wide. These transcripts provide a more complete and immediate picture of the impact of regulatory events. Transcription factors recruit RNA polymerase II, effectively initiating the process of transcription; repressors inhibit polymerase recruitment. Efficiency of recruitment is dictated by sequence elements in and around the RNA polymerase loading zone. A combination of sequence elements and RNA binding proteins subsequently influence the ultimate stability of the resulting transcript. Some of these transcripts are capable of providing feedback on the process, influencing subsequent transcription. By monitoring RNA polymerase activity, nascent assays provide insights into every step of the regulated process of transcription.
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Affiliation(s)
| | - Gilson J Sanchez
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Mary A Allen
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Robin D Dowell
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA.,Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, CO, USA
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