1
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Oh JW, Beer MA. Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals. Nat Commun 2024; 15:6464. [PMID: 39085231 PMCID: PMC11291912 DOI: 10.1038/s41467-024-50708-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: 10/27/2023] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
Gene regulatory elements drive complex biological phenomena and their mutations are associated with common human diseases. The impacts of human regulatory variants are often tested using model organisms such as mice. However, mapping human enhancers to conserved elements in mice remains a challenge, due to both rapid enhancer evolution and limitations of current computational methods. We analyze distal enhancers across 45 matched human/mouse cell/tissue pairs from a comprehensive dataset of DNase-seq experiments, and show that while cell-specific regulatory vocabulary is conserved, enhancers evolve more rapidly than promoters and CTCF binding sites. Enhancer conservation rates vary across cell types, in part explainable by tissue specific transposable element activity. We present an improved genome alignment algorithm using gapped-kmer features, called gkm-align, and make genome wide predictions for 1,401,803 orthologous regulatory elements. We show that gkm-align discovers 23,660 novel human/mouse conserved enhancers missed by previous algorithms, with strong evidence of conserved functional activity.
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Affiliation(s)
- Jin Woo Oh
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michael A Beer
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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2
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Yang TH, Yu YH, Wu SH, Chang FY, Tsai HC, Yang YC. DMLS: an automated pipeline to extract the Drosophila modular transcription regulators and targets from massive literature articles. Database (Oxford) 2024; 2024:0. [PMID: 38900628 PMCID: PMC11188685 DOI: 10.1093/database/baae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 05/10/2024] [Accepted: 05/31/2024] [Indexed: 06/22/2024]
Abstract
Transcription regulation in multicellular species is mediated by modular transcription factor (TF) binding site combinations termed cis-regulatory modules (CRMs). Such CRM-mediated transcription regulation determines the gene expression patterns during development. Biologists frequently investigate CRM transcription regulation on gene expressions. However, the knowledge of the target genes and regulatory TFs participating in the CRMs under study is mostly fragmentary throughout the literature. Researchers need to afford tremendous human resources to fully surf through the articles deposited in biomedical literature databases in order to obtain the information. Although several novel text-mining systems are now available for literature triaging, these tools do not specifically focus on CRM-related literature prescreening, failing to correctly extract the information of the CRM target genes and regulatory TFs from the literature. For this reason, we constructed a supportive auto-literature prescreener called Drosophila Modular transcription-regulation Literature Screener (DMLS) that achieves the following: (i) prescreens articles describing experiments on modular transcription regulation, (ii) identifies the described target genes and TFs of the CRMs under study for each modular transcription-regulation-describing article and (iii) features an automated and extendable pipeline to perform the task. We demonstrated that the final performance of DMLS in extracting the described target gene and regulatory TF lists of CRMs under study for given articles achieved test macro area under the ROC curve (auROC) = 89.7% and area under the precision-recall curve (auPRC) = 77.6%, outperforming the intuitive gene name-occurrence-counting method by at least 19.9% in auROC and 30.5% in auPRC. The web service and the command line versions of DMLS are available at https://cobis.bme.ncku.edu.tw/DMLS/ and https://github.com/cobisLab/DMLS/, respectively. Database Tool URL: https://cobis.bme.ncku.edu.tw/DMLS/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
- Medical Device Innovation Center, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
| | - Yu-Huai Yu
- Department of Biomedical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
| | - Sheng-Hang Wu
- Department of Information Management, National Central University, No. 300, Zhongda RD., Zhongli District, Taoyuan 320, Taiwan
| | - Fang-Yuan Chang
- Department of Information Management, National Central University, No. 300, Zhongda RD., Zhongli District, Taoyuan 320, Taiwan
| | - Hsiu-Chun Tsai
- Department of Information Management, National Central University, No. 300, Zhongda RD., Zhongli District, Taoyuan 320, Taiwan
| | - Ya-Chiao Yang
- Institute of Information Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu 300093,Taiwan
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3
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Mowery CT, Freimer JW, Chen Z, Casaní-Galdón S, Umhoefer JM, Arce MM, Gjoni K, Daniel B, Sandor K, Gowen BG, Nguyen V, Simeonov DR, Garrido CM, Curie GL, Schmidt R, Steinhart Z, Satpathy AT, Pollard KS, Corn JE, Bernstein BE, Ye CJ, Marson A. Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells. Nat Genet 2024; 56:1156-1167. [PMID: 38811842 PMCID: PMC11176074 DOI: 10.1038/s41588-024-01743-5] [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: 12/20/2022] [Accepted: 04/04/2024] [Indexed: 05/31/2024]
Abstract
Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis.
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Grants
- P30 DK063720 NIDDK NIH HHS
- R01 HG008140 NHGRI NIH HHS
- T32 GM007618 NIGMS NIH HHS
- S10 OD028511 NIH HHS
- F99 CA234842 NCI NIH HHS
- S10 OD021822 NIH HHS
- K00 CA234842 NCI NIH HHS
- P01 AI138962 NIAID NIH HHS
- U01 HL157989 NHLBI NIH HHS
- R01 DK129364 NIDDK NIH HHS
- T32 DK007418 NIDDK NIH HHS
- R01 AI136972 NIAID NIH HHS
- F30 AI157167 NIAID NIH HHS
- R01 HG011239 NHGRI NIH HHS
- NIH grants 1R01DK129364-01A1, P01AI138962, and R01HG008140; the Larry L. Hillblom Foundation (grant no. 2020-D-002-NET); and Northern California JDRF Center of Excellence. A.M. is a member of the Parker Institute for Cancer Immunotherapy (PICI), and has received funding from the Arc Institute, Chan Zuckerberg Biohub, Innovative Genomics Institute (IGI), Cancer Research Institute (CRI) Lloyd J. Old STAR award, a gift from the Jordan Family, a gift from the Byers family and a gift from B. Bakar.
- UCSF ImmunoX Computational Immunology Fellow, is supported by NIH grant F30AI157167, and has received support from NIH grants T32DK007418 and T32GM007618
- NIH grant R01HG008140
- Career Award for Medical Scientists from the Burroughs Wellcome Fund, a Lloyd J. Old STAR Award from the Cancer Research Institute, and the Parker Institute for Cancer Immunotherapy
- NIH grant U01HL157989
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Affiliation(s)
- Cody T Mowery
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Jacob W Freimer
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Zeyu Chen
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA
| | - Salvador Casaní-Galdón
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA
| | - Jennifer M Umhoefer
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Maya M Arce
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Ketrin Gjoni
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Bence Daniel
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech, South San Francisco, CA, USA
| | - Katalin Sandor
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Benjamin G Gowen
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Vinh Nguyen
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
- Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- UCSF CoLabs, University of California, San Francisco, San Francisco, CA, USA
| | - Dimitre R Simeonov
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Christian M Garrido
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Gemma L Curie
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Ralf Schmidt
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Zachary Steinhart
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Ansuman T Satpathy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Program in Immunology, Stanford University, Stanford, CA, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub SF, San Francisco, CA, USA
| | - Jacob E Corn
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Bradley E Bernstein
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA
| | - Chun Jimmie Ye
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA.
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
- Chan Zuckerberg Biohub SF, San Francisco, CA, USA.
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA.
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA.
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA.
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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4
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Chen Y, Paramo MI, Zhang Y, Yao L, Shah SR, Jin Y, Zhang J, Pan X, Yu H. Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants. Annu Rev Genet 2023; 57:201-222. [PMID: 37562413 DOI: 10.1146/annurev-genet-030723-120717] [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] [Indexed: 08/12/2023]
Abstract
Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.
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Affiliation(s)
- You Chen
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Mauricio I Paramo
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yingying Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Li Yao
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Sagar R Shah
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yiyang Jin
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Junke Zhang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Xiuqi Pan
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
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5
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Gonçalves TM, Stewart CL, Baxley SD, Xu J, Li D, Gabel HW, Wang T, Avraham O, Zhao G. Towards a comprehensive regulatory map of Mammalian Genomes. RESEARCH SQUARE 2023:rs.3.rs-3294408. [PMID: 37841836 PMCID: PMC10571623 DOI: 10.21203/rs.3.rs-3294408/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Genome mapping studies have generated a nearly complete collection of genes for the human genome, but we still lack an equivalently vetted inventory of human regulatory sequences. Cis-regulatory modules (CRMs) play important roles in controlling when, where, and how much a gene is expressed. We developed a training data-free CRM-prediction algorithm, the Mammalian Regulatory MOdule Detector (MrMOD) for accurate CRM prediction in mammalian genomes. MrMOD provides genome position-fixed CRM models similar to the fixed gene models for the mouse and human genomes using only genomic sequences as the inputs with one adjustable parameter - the significance p-value. Importantly, MrMOD predicts a comprehensive set of high-resolution CRMs in the mouse and human genomes including all types of regulatory modules not limited to any tissue, cell type, developmental stage, or condition. We computationally validated MrMOD predictions used a compendium of 21 orthogonal experimental data sets including thousands of experimentally defined CRMs and millions of putative regulatory elements derived from hundreds of different tissues, cell types, and stimulus conditions obtained from multiple databases. In ovo transgenic reporter assay demonstrates the power of our prediction in guiding experimental design. We analyzed CRMs located in the chromosome 17 using unsupervised machine learning and identified groups of CRMs with multiple lines of evidence supporting their functionality, linking CRMs with upstream binding transcription factors and downstream target genes. Our work provides a comprehensive base pair resolution annotation of the functional regulatory elements and non-functional regions in the mammalian genomes.
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Affiliation(s)
| | | | | | - Jason Xu
- Missouri University of Science & Technology
| | - Daofeng Li
- Washington University School of Medicine
| | | | - Ting Wang
- Washington University School of Medicine
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6
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Gosai SJ, Castro RI, Fuentes N, Butts JC, Kales S, Noche RR, Mouri K, Sabeti PC, Reilly SK, Tewhey R. Machine-guided design of synthetic cell type-specific cis-regulatory elements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552077. [PMID: 37609287 PMCID: PMC10441439 DOI: 10.1101/2023.08.08.552077] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.
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Affiliation(s)
- SJ Gosai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Graduate Program in Biological and Biomedical Science, Boston MA
- Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - RI Castro
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - N Fuentes
- The Jackson Laboratory, Bar Harbor, ME, USA
- Harvard College, Harvard University, Cambridge, MA, USA
| | - JC Butts
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
| | - S Kales
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - RR Noche
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA
- Yale Zebrafish Research Core, Yale School of Medicine, New Haven, CT, USA
| | - K Mouri
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - PC Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - SK Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - R Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
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7
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Tan J, Shenker-Tauris N, Rodriguez-Hernaez J, Wang E, Sakellaropoulos T, Boccalatte F, Thandapani P, Skok J, Aifantis I, Fenyö D, Xia B, Tsirigos A. Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening. Nat Biotechnol 2023; 41:1140-1150. [PMID: 36624151 PMCID: PMC10329734 DOI: 10.1038/s41587-022-01612-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/14/2022] [Indexed: 01/11/2023]
Abstract
Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features-CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific trans-acting regulators that collectively determine chromatin architecture. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.
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Affiliation(s)
- Jimin Tan
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY, USA
| | - Nina Shenker-Tauris
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
- Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, NY, USA
| | - Javier Rodriguez-Hernaez
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
- Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, NY, USA
| | - Eric Wang
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
- The Jackson Laboratory for Genomics Medicine, Farmington, CT, USA
| | | | - Francesco Boccalatte
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
- Department of Women's and Children's Health, University of Padua, Padua, Italy
| | - Palaniraja Thandapani
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Jane Skok
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Iannis Aifantis
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - David Fenyö
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY, USA
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY, USA
| | - Bo Xia
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY, USA.
- Society of Fellows, Harvard University, Cambridge, MA, USA.
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Aristotelis Tsirigos
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA.
- Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, NY, USA.
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.
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8
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Yang TH, Yu YH, Wu SH, Zhang FY. CFA: An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes. Comput Biol Med 2023; 152:106375. [PMID: 36502693 DOI: 10.1016/j.compbiomed.2022.106375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/07/2022] [Accepted: 11/27/2022] [Indexed: 11/30/2022]
Abstract
Metazoa gene expression is controlled by modular DNA segments called cis-regulatory modules (CRMs). CRMs can convey promoter/enhancer/insulator roles, generating additional regulation layers in transcription. Experiments for understanding CRM roles are low-throughput and costly. Large-scale CRM function investigation still depends on computational methods. However, existing in silico tools only recognize enhancers or promoters exclusively, thus accumulating errors when considering CRM promoter/enhancer/insulator roles altogether. Currently, no algorithm can concurrently consider these CRM roles. In this research, we developed the CRM Function Annotator (CFA) model. CFA provides complete CRM transcriptional role labeling based on epigenetic profiling interpretation. We demonstrated that CFA achieves high performance (test macro auROC/auPRC = 94.1%/90.3%) and outperforms existing tools in promoter/enhancer/insulator identification. CFA is also inspected to recognize explainable epigenetic codes consistent with previous findings when labeling CRM roles. By considering the higher-order combinations of the epigenetic codes, CFA significantly reduces false-positive rates in CRM transcriptional role annotation. CFA is available at https://github.com/cobisLab/CFA/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan.
| | - Yu-Huai Yu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Sheng-Hang Wu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Fang-Yuan Zhang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
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9
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Limited conservation in cross-species comparison of GLK transcription factor binding suggested wide-spread cistrome divergence. Nat Commun 2022; 13:7632. [PMID: 36494366 PMCID: PMC9734178 DOI: 10.1038/s41467-022-35438-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 12/02/2022] [Indexed: 12/13/2022] Open
Abstract
Non-coding cis-regulatory variants in animal genomes are an important driving force in the evolution of transcription regulation and phenotype diversity. However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites' regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.
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10
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Wu X, Liu S, Liang G. Detecting clusters of transcription factors based on a nonhomogeneous poisson process model. BMC Bioinformatics 2022; 23:535. [PMID: 36494794 PMCID: PMC9738027 DOI: 10.1186/s12859-022-05090-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind. RESULTS In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells (ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules. CONCLUSIONS Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes.
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Affiliation(s)
- Xiaowei Wu
- grid.438526.e0000 0001 0694 4940Department of Statistics, Virginia Tech, 250 Drillfield Drive, Blacksburg, VA 24061 USA
| | - Shicheng Liu
- grid.438526.e0000 0001 0694 4940Department of Mathematics, Virginia Tech, 225 Stanger Street, Blacksburg, VA 24061 USA
| | - Guanying Liang
- grid.438526.e0000 0001 0694 4940Department of Mathematics, Virginia Tech, 225 Stanger Street, Blacksburg, VA 24061 USA
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11
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Rivière Q, Corso M, Ciortan M, Noël G, Verbruggen N, Defrance M. Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants. PLANT & CELL PHYSIOLOGY 2022; 63:1457-1473. [PMID: 35799371 DOI: 10.1093/pcp/pcac095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 06/07/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays.
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Affiliation(s)
- Quentin Rivière
- Brussels Bioengineering School, Laboratory of Plant Physiology and molecular Genetics, Université Libre de Bruxelles, Brussels 1050, Belgium
| | - Massimiliano Corso
- Brussels Bioengineering School, Laboratory of Plant Physiology and molecular Genetics, Université Libre de Bruxelles, Brussels 1050, Belgium
- INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), Université Paris-Saclay, Versailles 78000, France
| | - Madalina Ciortan
- Interuniversity Institute of Bioinformatics in Brussels, Machine Learning Group, Université Libre de Bruxelles, Brussels 1050, Belgium
| | - Grégoire Noël
- Functional and Evolutionary Entomology, Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés 2, Gembloux 5030, Belgium
| | - Nathalie Verbruggen
- Brussels Bioengineering School, Laboratory of Plant Physiology and molecular Genetics, Université Libre de Bruxelles, Brussels 1050, Belgium
| | - Matthieu Defrance
- Interuniversity Institute of Bioinformatics in Brussels, Machine Learning Group, Université Libre de Bruxelles, Brussels 1050, Belgium
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12
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Structural and developmental dynamics of Matrix associated regions in Drosophila melanogaster genome. BMC Genomics 2022; 23:725. [PMID: 36284304 PMCID: PMC9597980 DOI: 10.1186/s12864-022-08944-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 10/10/2022] [Indexed: 11/18/2022] Open
Abstract
Background Eukaryotic genome is compartmentalized into structural and functional domains. One of the concepts of higher order organization of chromatin posits that the DNA is organized in constrained loops that behave as independent functional domains. Nuclear Matrix (NuMat), a ribo-proteinaceous nucleoskeleton, provides the structural basis for this organization. DNA sequences located at base of the loops are known as the Matrix Attachment Regions (MARs). NuMat relates to multiple nuclear processes and is partly cell type specific in composition. It is a biochemically defined structure and several protocols have been used to isolate the NuMat where some of the steps have been critically evaluated. These sequences play an important role in genomic organization it is imperative to know their dynamics during development and differentiation. Results Here we look into the dynamics of MARs when the preparation process is varied and during embryonic development of D. melanogaster. A subset of MARs termed as “Core-MARs” present abundantly in pericentromeric heterochromatin, are constant unalterable anchor points as they associate with NuMat through embryonic development and are independent of the isolation procedure. Euchromatic MARs are dynamic and reflect the transcriptomic profile of the cell. New MARs are generated by nuclear stabilization, and during development, mostly at paused RNA polymerase II promoters. Paused Pol II MARs depend on RNA transcripts for NuMat association. Conclusions Our data reveals the role of MARs in functionally dynamic nucleus and contributes to the current understanding of nuclear architecture in genomic context. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08944-4.
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13
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Blume R, Yemets A, Korkhovyi V, Radchuk V, Rakhmetov D, Blume Y. Genome-wide identification and analysis of the cytokinin oxidase/dehydrogenase ( ckx) gene family in finger millet ( Eleusine coracana). Front Genet 2022; 13:963789. [PMID: 36299586 PMCID: PMC9589517 DOI: 10.3389/fgene.2022.963789] [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: 06/07/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
Cytokinin dehydrogenase/oxidase (CKX) enzymes play a key role in regulating cytokinin (CK) levels in plants by degrading the excess of this phytohormone. CKX genes have proven an attractive target for genetic engineering, as their silencing boosts cytokinin accumulation in various tissues, thereby contributing to a rapid increase in biomass and overall plant productivity. We previously reported a similar effect in finger millet (Eleusine coracana) somaclonal lines, caused by downregulation of EcCKX1 and EcCKX2. However, the CKX gene family has numerous representatives, especially in allopolyploid crop species, such as E. coracana. To date, the entire CKX gene family of E. coracana and its related species has not been characterized. We offer here, for the first time, a comprehensive genome-wide identification and analysis of a panel of CKX genes in finger millet. The functional genes identified in the E. coracana genome are compared with the previously-identified genes, EcCKX1 and EcCKX2. Exon-intron structural analysis and motif analysis of FAD- and CK-binding domains are performed. The phylogeny of the EcCKX genes suggests that CKX genes are divided into several distinct groups, corresponding to certain isotypes. Finally, the phenotypic effect of EcCKX1 and EcCKX2 in partially silencing the SE7 somaclonal line is investigated, showing that lines deficient in CKX-expression demonstrate increased grain yield and greater bushiness, enhanced biomass accumulation, and a shorter vegetation cycle.
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Affiliation(s)
- Rostyslav Blume
- Department of Population Genetics, Institute of Food Biotechnology and Genomics, National Academy of Sciences of Ukraine, Kyiv, Ukraine,*Correspondence: Rostyslav Blume,
| | - Alla Yemets
- Department of Cell Biology and Biotechnology, Institute of Food Biotechnology and Genomics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Vitaliy Korkhovyi
- Department of Cell Biology and Biotechnology, Institute of Food Biotechnology and Genomics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Volodymyr Radchuk
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany
| | - Dzhamal Rakhmetov
- M. M. Gryshko National Botanic Garden of National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Yaroslav Blume
- Department of Genomics and Molecular Biotechnology, Institute of Food Biotechnology and Genomics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
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14
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Single-Cell Gene Expression Analysis Revealed Immune Cell Signatures of Delta COVID-19. Cells 2022; 11:cells11192950. [PMID: 36230912 PMCID: PMC9563974 DOI: 10.3390/cells11192950] [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: 08/03/2022] [Revised: 09/04/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) is accompanied by a cytokine storm with the release of many proinflammatory factors and development of respiratory syndrome. Several SARS-CoV-2 lineages have been identified, and the Delta variant (B.1.617), linked with high mortality risk, has become dominant in many countries. Understanding the immune responses associated with COVID-19 lineages may therefore aid the development of therapeutic and diagnostic strategies. Multiple single-cell gene expression studies revealed innate and adaptive immunological factors and pathways correlated with COVID-19 severity. Additional investigations covering host–pathogen response characteristics for infection caused by different lineages are required. Here, we performed single-cell transcriptome profiling of blood mononuclear cells from the individuals with different severity of the COVID-19 and virus lineages to uncover variant specific molecular factors associated with immunity. We identified significant changes in lymphoid and myeloid cells. Our study highlights that an abundant population of monocytes with specific gene expression signatures accompanies Delta lineage of SARS-CoV-2 and contributes to COVID-19 pathogenesis inferring immune components for targeted therapy.
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15
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Gnocis: An integrated system for interactive and reproducible analysis and modelling of cis-regulatory elements in Python 3. PLoS One 2022; 17:e0274338. [PMID: 36084008 PMCID: PMC9462789 DOI: 10.1371/journal.pone.0274338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 08/25/2022] [Indexed: 11/23/2022] Open
Abstract
Gene expression is regulated through cis-regulatory elements (CREs), among which are promoters, enhancers, Polycomb/Trithorax Response Elements (PREs), silencers and insulators. Computational prediction of CREs can be achieved using a variety of statistical and machine learning methods combined with different feature space formulations. Although Python packages for DNA sequence feature sets and for machine learning are available, no existing package facilitates the combination of DNA sequence feature sets with machine learning methods for the genome-wide prediction of candidate CREs. We here present Gnocis, a Python package that streamlines the analysis and the modelling of CRE sequences by providing extensible APIs and implementing the glue required for combining feature sets and models for genome-wide prediction. Gnocis implements a variety of base feature sets, including motif pair occurrence frequencies and the k-spectrum mismatch kernel. It integrates with Scikit-learn and TensorFlow for state-of-the-art machine learning. Gnocis additionally implements a broad suite of tools for the handling and preparation of sequence, region and curve data, which can be useful for general DNA bioinformatics in Python. We also present Deep-MOCCA, a neural network architecture inspired by SVM-MOCCA that achieves moderate to high generalization without prior motif knowledge. To demonstrate the use of Gnocis, we applied multiple machine learning methods to the modelling of D. melanogaster PREs, including a Convolutional Neural Network (CNN), making this the first study to model PREs with CNNs. The models are readily adapted to new CRE modelling problems and to other organisms. In order to produce a high-performance, compiled package for Python 3, we implemented Gnocis in Cython. Gnocis can be installed using the PyPI package manager by running ‘pip install gnocis’. The source code is available on GitHub, at https://github.com/bjornbredesen/gnocis.
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16
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Fan Y, Fan H, Li P, Liu Q, Huang L, Zhou Y. Mitogen-activating protein kinase kinase kinase kinase-3, inhibited by Astragaloside IV through H3 lysine 4 monomethylation, promotes the progression of diabetic nephropathy by inducing apoptosis. Bioengineered 2022; 13:11517-11529. [PMID: 35510516 PMCID: PMC9275872 DOI: 10.1080/21655979.2022.2068822] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 11/16/2022] Open
Abstract
Astragaloside IV (AS-IV) is a bioactive saponin extracted from the Astragalus root and has been reported to exert a protective effect on diabetic nephropathy (DN). However, the underlying mechanism remains unclear. Herein, we found that AS-IV treatment alleviated DN symptoms in DN mice accompanied by reduced metabolic parameters (body weight, urine microalbumin and creatinine, creatinine clearance, and serum urea nitrogen and creatinine), pathological changes, and apoptosis. Epigenetic histone modifications are closely related to diabetes and its complications, including H3 lysine 4 monomethylation (H3K4me1, a promoter of gene transcription). A ChIP-seq assay was conducted to identify the genes regulated by H3K4me1 in DN mice after AS-IV treatment and followed by a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The results showed that there were 16 common genes targeted by H3K4me1 in normal and AS-IV-treated DN mice, 1148 genes were targeted by H3K4me1 only in DN mice. From the 1148 genes, we screened mitogen-activating protein kinase kinase kinase kinase-3 (MAP4K3) for the verification of gene expression and functional study. The results showed that MAP4K3 was significantly increased in DN mice and high glucose (HG)-treated NRK-52E cells, which was reversed by AS-IV. MAP4K3 silencing reduced the apoptosis of NRK-52E cells under HG condition, as evidenced by decreased cleaved caspase 3 and Bax (pro-apoptotic factors), and increased Bcl-2 and Bcl-xl (anti-apoptotic factors). Collectively, AS-IV may downregulate MAP4K3 expression by regulating H3K4me1 binding and further reducing apoptosis, which may be one of the potential mechanisms that AS-IV plays a protective effect on DN.
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Affiliation(s)
- Yuyan Fan
- Department of Traditional Chinese Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Hongyu Fan
- Remote Consultation Center, Liaoyang Central Hospital, Liaoyang, Liaoning, People’s Republic of China
| | - Ping Li
- Department of Pharmacy and Pharmacology, Institute of Clinical Medicine, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Qingshan Liu
- IKey Laboratory of Ethnic Medicine of Ministry of Education, Minzu University of China, Beijing, People’s Republic of China
| | - Lixia Huang
- Department of Traditional Chinese Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yilun Zhou
- Department of Nephrology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
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17
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Johari YB, Scarrott JM, Pohle TH, Liu P, Mayer A, Brown AJ, James DC. Engineering of the CMV promoter for controlled expression of recombinant genes in HEK293 cells. Biotechnol J 2022; 17:e2200062. [PMID: 35482470 DOI: 10.1002/biot.202200062] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/07/2022] [Accepted: 04/23/2022] [Indexed: 11/08/2022]
Abstract
Expression of recombinant genes in HEK293 cells is frequently utilized for production of recombinant proteins and viral vectors. These systems frequently employ the cytomegalovirus (CMV) promoter to drive recombinant gene transcription. However, the mechanistic basis of CMV-mediated transcriptional activation in HEK293 cells is unknown and consequently there are no strategies to engineer CMV for controlled expression of recombinant genes. Extensive bioinformatic analyses of transcription factor regulatory elements (TFREs) within the human CMV sequence and transcription factor mRNAs within the HEK293 transcriptome revealed 80 possible regulatory interactions. Through in vitro functional testing using reporter constructs harboring discrete TFREs or CMV deletion variants we identified key TFRE components and clusters of TFREs (cis-regulatory modules) within the CMV sequence. Our data reveal that CMV activity in HEK293 cells is a function of the promoters various constituent TFREs including AhR:ARNT, CREB, E4F, Sp1, ZBED1, JunB, c-Rel, and NF-κB. We also identified critical Sp1-dependent upstream activator elements near the transcriptional start site that were required for efficient transcription and YY1 and RBP-Jκ binding sites that mediate transrepression. Our study shows for the first time that novel, compact CMV-derived promoters can be engineered that exhibit up to 50% higher transcriptional efficiency (activity per unit DNA sequence) or 14% increase in total activity compared to the wild-type counterpart.
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Affiliation(s)
- Yusuf B Johari
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Joseph M Scarrott
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Thilo H Pohle
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Ping Liu
- Cell Line Development, REGENXBIO Inc., Rockville, Maryland, USA
| | - Ayda Mayer
- Cell Line Development, REGENXBIO Inc., Rockville, Maryland, USA
| | - Adam J Brown
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK.,Syngensys Ltd., Sheffield, UK
| | - David C James
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK.,Syngensys Ltd., Sheffield, UK
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18
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Lynch TR, Xue M, Czerniak CW, Lee C, Kimble J. Notch-dependent DNA cis-regulatory elements and their dose-dependent control of C. elegans stem cell self-renewal. Development 2022; 149:274985. [PMID: 35394007 PMCID: PMC9058496 DOI: 10.1242/dev.200332] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/16/2022] [Indexed: 11/20/2022]
Abstract
A long-standing biological question is how DNA cis-regulatory elements shape transcriptional patterns during metazoan development. Reporter constructs, cell culture assays and computational modeling have made major contributions to answering this question, but analysis of elements in their natural context is an important complement. Here, we mutate Notch-dependent LAG-1 binding sites (LBSs) in the endogenous Caenorhabditis elegans sygl-1 gene, which encodes a key stem cell regulator, and analyze the consequences on sygl-1 expression (nascent transcripts, mRNA, protein) and stem cell maintenance. Mutation of one LBS in a three-element cluster approximately halved both expression and stem cell pool size, whereas mutation of two LBSs essentially abolished them. Heterozygous LBS mutant clusters provided intermediate values. Our results lead to two major conclusions. First, both LBS number and configuration impact cluster activity: LBSs act additively in trans and synergistically in cis. Second, the SYGL-1 gradient promotes self-renewal above its functional threshold and triggers differentiation below the threshold. Our approach of coupling CRISPR/Cas9 LBS mutations with effects on both molecular and biological readouts establishes a powerful model for in vivo analyses of DNA cis-regulatory elements. Summary: Notch-dependent DNA cis-regulatory elements work together in their developmental context in C. elegans to shape a transcriptional gradient, control stem cell pool size, and govern differentiation onset.
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Affiliation(s)
- Tina R. Lynch
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Integrated Program in Biochemistry, Madison, WI 53706, USA
| | - Mingyu Xue
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Life Sciences, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Cazza W. Czerniak
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - ChangHwan Lee
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Biological Sciences, University at Albany, State University of New York, Albany, NY 12222, USA
| | - Judith Kimble
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Integrated Program in Biochemistry, Madison, WI 53706, USA
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19
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Functional annotation of breast cancer risk loci: current progress and future directions. Br J Cancer 2022; 126:981-993. [PMID: 34741135 PMCID: PMC8980003 DOI: 10.1038/s41416-021-01612-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 11/20/2022] Open
Abstract
Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.
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20
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Yang TH, Yang YC, Tu KC. regCNN: identifying Drosophila genome-wide cis-regulatory modules via integrating the local patterns in epigenetic marks and transcription factor binding motifs. Comput Struct Biotechnol J 2022; 20:296-308. [PMID: 35035784 PMCID: PMC8724954 DOI: 10.1016/j.csbj.2021.12.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/10/2021] [Accepted: 12/10/2021] [Indexed: 11/20/2022] Open
Abstract
Transcription regulation in metazoa is controlled by the binding events of transcription factors (TFs) or regulatory proteins on specific modular DNA regulatory sequences called cis-regulatory modules (CRMs). Understanding the distributions of CRMs on a genomic scale is essential for constructing the metazoan transcriptional regulatory networks that help diagnose genetic disorders. While traditional reporter-assay CRM identification approaches can provide an in-depth understanding of functions of some CRM, these methods are usually cost-inefficient and low-throughput. It is generally believed that by integrating diverse genomic data, reliable CRM predictions can be made. Hence, researchers often first resort to computational algorithms for genome-wide CRM screening before specific experiments. However, current existing in silico methods for searching potential CRMs were restricted by low sensitivity, poor prediction accuracy, or high computation time from TFBS composition combinatorial complexity. To overcome these obstacles, we designed a novel CRM identification pipeline called regCNN by considering the base-by-base local patterns in TF binding motifs and epigenetic profiles. On the test set, regCNN shows an accuracy/auROC of 84.5%/92.5% in CRM identification. And by further considering local patterns in epigenetic profiles and TF binding motifs, it can accomplish 4.7% (92.5%–87.8%) improvement in the auROC value over the average value-based pure multi-layer perceptron model. We also demonstrated that regCNN outperforms all currently available tools by at least 11.3% in auROC values. Finally, regCNN is verified to be robust against its resizing window hyperparameter in dealing with the variable lengths of CRMs. The model of regCNN can be downloaded athttp://cobisHSS0.im.nuk.edu.tw/regCNN/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Ya-Chiao Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Kai-Chi Tu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
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21
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OUP accepted manuscript. Hum Mol Genet 2022; 31:2223-2235. [DOI: 10.1093/hmg/ddac023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/11/2022] [Accepted: 02/03/2022] [Indexed: 11/13/2022] Open
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22
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Yang TH, Wang CY, Tsai HC, Yang YC, Liu CT. YTLR: Extracting yeast transcription factor-gene associations from the literature using automated literature readers. Comput Struct Biotechnol J 2022; 20:4636-4644. [PMID: 36090812 PMCID: PMC9449546 DOI: 10.1016/j.csbj.2022.08.041] [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: 05/06/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022] Open
Abstract
Cells adapt to environmental stresses mainly via transcription reprogramming. Correct transcription control is mediated by the interactions between transcription factors (TF) and their target genes. These TF-gene associations can be probed by chromatin immunoprecipitation techniques and knockout experiments, revealing TF binding (TFB) and regulatory (TFR) evidence, respectively. Nevertheless, most evidence is still fragmentary in the literature and requires tremendous human resources to curate. We developed the first pipeline called YTLR (Yeast Transcription-regulation Literature Reader) to automate TF-gene relation extraction from the literature. YTLR first identifies articles with TFB and TFR information. Then TF-gene binding pairs are extracted from the TFB articles, and TF-gene regulatory associations are recognized from the TFR papers. On gathered test sets, YTLR achieves an AUC value of 98.8% in identifying articles with TFB evidence and AUC = 83.4% in extracting the detailed TF-gene binding pairs. And similarly, YTLR also obtains an AUC value of 98.2% in identifying TFR articles and AUC = 80.4% in extracting the detailed TF-gene regulatory associations. Furthermore, YTLR outperforms previous methods in both tasks. To facilitate researchers in extracting TF-gene transcriptional relations from large-scale queried articles, an automated and easy-to-use software tool based on the YTLR pipeline is constructed. In summary, YTLR aims to provide easier literature pre-screening for curators and help researchers gather yeast TF-gene transcriptional relation conclusions from articles in a high-throughput fashion. The YTLR pipeline software tool can be downloaded at https://github.com/cobisLab/YTLR/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
- Corresponding author.
| | - Chung-Yu Wang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Hsiu-Chun Tsai
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Ya-Chiao Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Cheng-Tse Liu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
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23
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Raza H, Khan MR, Zafar SA, Kirch HH, Bartles D. Aldehyde dehydrogenase 3I1 gene is recruited in conferring multiple abiotic stress tolerance in plants. PLANT BIOLOGY (STUTTGART, GERMANY) 2022; 24:85-94. [PMID: 34670007 DOI: 10.1111/plb.13337] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
Plant growth and productivity is restricted by a multitude of abiotic stresses. These stresses negatively affect physiological and metabolic pathways, leading to the production of many harmful substances like ROS, lipid peroxides and aldehydes. This study was conducted to investigate the role of Arabidopsis ALDH3I1 gene in multiple abiotic stress tolerance. Transgenic tobacco plants were generated that overexpress the ALDH3I1 gene driven by the CaMV35S promoter and evaluated under different abiotic stresses, namely salt, drought, cold and oxidative stress. Tolerance to stress was evaluated based on responses of various growth and physiological traits under stress condition. Transgenic plants displayed elevated ALDH3I1 transcript levels compared to WT plants. The constitutive ectopic expression of ALDH3I1 conferred increased tolerance to salt, drought, cold and oxidative stresses in transgenic plants, along with improved plant growth. Transgenic plants overexpressing ALDH3I1 had higher chlorophyll content, photosynthesis rate and proline, and less accumulation of ROS and malondialdehyde compared to the WT, which contributed to stress tolerance in transgenic plants. Our results further revealed that ALDH3I1 had a positive effect on CO2 assimilation rate in plants under abiotic stress conditions. Overall, this study revealed that ALDH3I1 positively regulates abiotic stress tolerance in plants, and has future implications in producing transgenic cereal and horticultural plants tolerant to abiotic stresses.
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Affiliation(s)
- H Raza
- Institute for Molecular Physiology & Biotechnology of Plants (IMBIO), University of Bonn, Bonn, Germany
| | - M R Khan
- National Institute for Genomics and Advanced Biotechnology, National Agricultural Research Centre, Islamabad, Pakistan
| | - S A Zafar
- National Institute for Genomics and Advanced Biotechnology, National Agricultural Research Centre, Islamabad, Pakistan
| | - H H Kirch
- Institute for Molecular Physiology & Biotechnology of Plants (IMBIO), University of Bonn, Bonn, Germany
| | - D Bartles
- Institute for Molecular Physiology & Biotechnology of Plants (IMBIO), University of Bonn, Bonn, Germany
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24
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Yi X, Zheng Z, Xu H, Zhou Y, Huang D, Wang J, Feng X, Zhao K, Fan X, Zhang S, Dong X, Wang Z, Shen Y, Cheng H, Shi L, Li MJ. Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin. iScience 2021; 24:103468. [PMID: 34888502 PMCID: PMC8634045 DOI: 10.1016/j.isci.2021.103468] [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: 08/02/2021] [Revised: 09/23/2021] [Accepted: 11/12/2021] [Indexed: 12/14/2022] Open
Abstract
Context-specific activities of transcription regulators (TRs) in the nucleus modulate spatiotemporal gene expression precisely. Using the largest ChIP-seq data and chromatin loops in the human K562 cell line, we initially interrogated TR cooperation in 3D chromatin via a graphical model and revealed many known and novel TRs manipulating context-specific pathways. To explore TR cooperation across broad tissue/cell types, we systematically leveraged large-scale open chromatin profiles, computational footprinting, and high-resolution chromatin interactions to investigate tissue/cell type-specific TR cooperation. We first delineated a landscape of TR cooperation across 40 human tissue/cell types. Network modularity analyses uncovered the commonality and specificity of TR cooperation in different conditions. We also demonstrated that TR cooperation information can better interpret the disease-causal variants identified by genome-wide association studies and recapitulate cell states during neural development. Our study characterizes shared and unique patterns of TR cooperation associated with the cell type specificity of gene regulation in 3D chromatin. Computational inference of transcriptional regulator (TR) cooperation in 3D chromatin A landscape of 3D TR cooperation across 40 human tissue/cell types TR cooperation can better interpret the disease-causal variants identified by GWAS Cooperation of certain TRs shapes context-specific gene regulation in cell development
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Affiliation(s)
- Xianfu Yi
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China.,Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China
| | - Zhanye Zheng
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hang Xu
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Dandan Huang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jianhua Wang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xiangling Feng
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Ke Zhao
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Shijie Zhang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xiaobao Dong
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Zhao Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yujun Shen
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hui Cheng
- State Key Laboratory of Experimental Hematology, Chinese Academy of Medical Sciences, Tianjin 300070, China
| | - Lei Shi
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Mulin Jun Li
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
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25
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Pavlova A, Harrisson KA, Turakulov R, Lee YP, Ingram BA, Gilligan D, Sunnucks P, Gan HM. Labile sex chromosomes in the Australian freshwater fish family Percichthyidae. Mol Ecol Resour 2021; 22:1639-1655. [PMID: 34863023 DOI: 10.1111/1755-0998.13569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 11/24/2021] [Accepted: 11/29/2021] [Indexed: 12/01/2022]
Abstract
Sex-specific ecology has management implications, but rapid sex-chromosome turnover in fishes hinders sex-marker development for monomorphic species. We used annotated genomes and reduced-representation sequencing data for two Australian percichthyids, Macquarie perch Macquaria australasica and golden perch M. ambigua, and whole genome resequencing for 50 Macquarie perch of each sex, to identify sex-linked loci and develop an affordable sexing assay. In silico pool-seq tests of 1,492,004 Macquarie perch SNPs revealed that a 275-kb scaffold was enriched for gametologous loci. Within this scaffold, 22 loci were sex-linked in a predominantly XY system, with females being homozygous for the X-linked allele at all 22, and males having the Y-linked allele at >7. Seven XY-gametologous loci (all males, but no females, are heterozygous or homozygous for the male-specific allele) were within a 146-bp region. A PCR-RFLP sexing assay targeting one Y-linked SNP, tested in 66 known-sex Macquarie perch and two of each sex of three confamilial species, plus amplicon sequencing of 400 bp encompassing the 146-bp region, revealed that the few sex-linked positions differ between species and between Macquarie perch populations. This indicates sex-chromosome lability in Percichthyidae, supported by nonhomologous scaffolds containing sex-linked loci for Macquarie- and golden perches. The present resources facilitate genomic research in Percichthyidae, including formulation of hypotheses about candidate genes of interest such as transcription factor SOX1b that occurs in the 275-kb scaffold ~38 kb downstream of the 146-bp region containing seven XY-gametologous loci. Sex-linked markers will be useful for determining genetic sex in some populations and studying sex chromosome turnover.
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Affiliation(s)
- Alexandra Pavlova
- School of Biological Sciences, Monash University, Clayton, Vic., Australia
| | - Katherine A Harrisson
- Department of Ecology, Environment & Evolution, La Trobe University, Bundoora, Vic., Australia.,Department of Environment, Land, Water and Planning, Arthur Rylah Institute for Environmental Research, Heidelberg, Vic., Australia
| | - Rustam Turakulov
- Division of Ecology and Evolution, RSB, Australian National University, Acton, ACT, Australia
| | - Yin Peng Lee
- School of Life and Environmental Sciences, Deakin University, Geelong, Vic., Australia.,Deakin Genomics Centre, Deakin University, Geelong, Vic., Australia
| | | | - Dean Gilligan
- Freshwater Ecosystems Research, New South Wales Department of Primary Industries - Fisheries, Batemans Bay, NSW, Australia
| | - Paul Sunnucks
- School of Biological Sciences, Monash University, Clayton, Vic., Australia
| | - Han Ming Gan
- School of Life and Environmental Sciences, Deakin University, Geelong, Vic., Australia.,Deakin Genomics Centre, Deakin University, Geelong, Vic., Australia.,GeneSEQ Sdn Bhd, Rawang, Malaysia
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26
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Murad R, Macias-Muñoz A, Wong A, Ma X, Mortazavi A. Coordinated Gene Expression and Chromatin Regulation during Hydra Head Regeneration. Genome Biol Evol 2021; 13:evab221. [PMID: 34877597 PMCID: PMC8651858 DOI: 10.1093/gbe/evab221] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2021] [Indexed: 12/13/2022] Open
Abstract
The cnidarian model organism Hydra has long been studied for its remarkable ability to regenerate its head, which is controlled by a head organizer located near the hypostome. The canonical Wnt pathway plays a central role in head organizer function during regeneration and during bud formation, which is the asexual mode of reproduction in Hydra. However, it is unclear how shared the developmental programs of head organizer genesis are in budding and regeneration. Time-series analysis of gene expression changes during head regeneration and budding revealed a set of 298 differentially expressed genes during the 48-h head regeneration and 72-h budding time courses. In order to understand the regulatory elements controlling Hydra head regeneration, we first identified 27,137 open-chromatin elements that are open in one or more sections of the organism body or regenerating tissue. We used histone modification ChIP-seq to identify 9,998 candidate proximal promoter and 3,018 candidate enhancer-like regions respectively. We show that a subset of these regulatory elements is dynamically remodeled during head regeneration and identify a set of transcription factor motifs that are enriched in the enhancer regions activated during head regeneration. Our results show that Hydra displays complex gene regulatory structures of developmentally dynamic enhancers, which suggests that the evolution of complex developmental enhancers predates the split of cnidarians and bilaterians.
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Affiliation(s)
- Rabi Murad
- Department of Developmental and Cell Biology, University of California Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, USA
| | - Aide Macias-Muñoz
- Department of Developmental and Cell Biology, University of California Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, USA
| | - Ashley Wong
- Department of Developmental and Cell Biology, University of California Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, USA
| | - Xinyi Ma
- Department of Developmental and Cell Biology, University of California Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, USA
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, USA
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27
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Tonnessen BW, Bossa-Castro AM, Martin F, Leach JE. Intergenic spaces: a new frontier to improving plant health. THE NEW PHYTOLOGIST 2021; 232:1540-1548. [PMID: 34478160 DOI: 10.1111/nph.17706] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
To more sustainably mitigate the impact of crop diseases on plant health and productivity, there is a need for broader spectrum, long-lasting resistance traits. Defense response (DR) genes, located throughout the genome, participate in cellular and system-wide defense mechanisms to stave off infection by diverse pathogens. This multigenic resistance avoids rapid evolution of a pathogen to overcome host resistance. DR genes reside within resistance-associated quantitative trait loci (QTL), and alleles of DR genes in resistant varieties are more active during pathogen attack relative to susceptible haplotypes. Differential expression of DR genes results from polymorphisms in their regulatory regions, that includes cis-regulatory elements such as transcription factor binding sites as well as features that influence epigenetic structural changes to modulate chromatin accessibility during infection. Many of these elements are found in clusters, known as cis-regulatory modules (CRMs), which are distributed throughout the host genome. Regulatory regions involved in plant-pathogen interactions may also contain pathogen effector binding elements that regulate DR gene expression, and that, when mutated, result in a change in the plants' response. We posit that CRMs and the multiple regulatory elements that comprise them are potential targets for marker-assisted breeding for broad-spectrum, durable disease resistance.
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Affiliation(s)
- Bradley W Tonnessen
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
- Western Colorado Research Center, Colorado State University, 30624 Hwy 92, Hotchkiss, CO, 81419, USA
| | - Ana M Bossa-Castro
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
- Universidad de los Andes, Bogotá, 111711, Colombia
| | - Federico Martin
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Jan E Leach
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
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28
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Mushimiyimana I, Niskanen H, Beter M, Laakkonen JP, Kaikkonen MU, Ylä-Herttuala S, Laham-Karam N. Characterization of a functional endothelial super-enhancer that regulates ADAMTS18 and angiogenesis. Nucleic Acids Res 2021; 49:8078-8096. [PMID: 34320216 PMCID: PMC8373076 DOI: 10.1093/nar/gkab633] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 05/28/2021] [Accepted: 07/12/2021] [Indexed: 12/20/2022] Open
Abstract
Super-enhancers are clusters of enhancers associated with cell lineage. They can be powerful gene-regulators and may be useful in cell-type specific viral-vector development. Here, we have screened for endothelial super-enhancers and identified an enhancer from within a cluster that conferred 5–70-fold increase in transgene expression. Importantly, CRISPR/Cas9 deletion of enhancers demonstrated regulation of ADAMTS18, corresponding to evidence of chromatin contacts between these genomic regions. Cell division-related pathways were primarily affected by the enhancer deletions, which correlated with significant reduction in cell proliferation. Furthermore, we observed changes in angiogenesis-related genes consistent with the endothelial specificity of this SE. Indeed, deletion of the enhancers affected tube formation, resulting in reduced or shortened sprouts. The super-enhancer angiogenic role is at least partly due to its regulation of ADAMTS18, as siRNA knockdown of ADAMTS18 resulted in significantly shortened endothelial sprouts. Hence, functional characterization of a novel endothelial super-enhancer has revealed substantial downstream effects from single enhancer deletions and led to the discovery of the cis-target gene ADAMTS18 and its role in endothelial function.
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Affiliation(s)
- Isidore Mushimiyimana
- A. I. Virtanen Institute for Molecular Sciences; University of Eastern Finland; Kuopio 70211, Finland
| | - Henri Niskanen
- A. I. Virtanen Institute for Molecular Sciences; University of Eastern Finland; Kuopio 70211, Finland
| | - Mustafa Beter
- A. I. Virtanen Institute for Molecular Sciences; University of Eastern Finland; Kuopio 70211, Finland
| | - Johanna P Laakkonen
- A. I. Virtanen Institute for Molecular Sciences; University of Eastern Finland; Kuopio 70211, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences; University of Eastern Finland; Kuopio 70211, Finland
| | - Seppo Ylä-Herttuala
- A. I. Virtanen Institute for Molecular Sciences; University of Eastern Finland; Kuopio 70211, Finland.,Heart Center and Gene Therapy Unit; Kuopio University Hospital; Kuopio 70029, Finland
| | - Nihay Laham-Karam
- A. I. Virtanen Institute for Molecular Sciences; University of Eastern Finland; Kuopio 70211, Finland
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29
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ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements. PLoS Comput Biol 2021; 17:e1009203. [PMID: 34292930 PMCID: PMC8330942 DOI: 10.1371/journal.pcbi.1009203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 08/03/2021] [Accepted: 06/20/2021] [Indexed: 11/19/2022] Open
Abstract
Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIP-seq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on co-localization of ChIP-seq peaks, often many weak binding events are missed, especially for mediators, resulting in incomplete identification of modules. To address this problem, we develop a ChIP-seq data-driven Gibbs Sampler to infer Modules (ChIP-GSM) using a Bayesian framework that integrates ChIP-seq profiles of multiple TFs. ChIP-GSM samples read counts of module TFs iteratively to estimate the binding potential of a module to each region and, across all regions, estimates the module abundance. Using inferred module-region probabilistic bindings as feature units, ChIP-GSM then employs logistic regression to predict active regulatory elements. Validation of ChIP-GSM predicted regulatory regions on multiple independent datasets sharing the same context confirms the advantage of using TF modules for predicting regulatory activity. In a case study of K562 cells, we demonstrate that the ChIP-GSM inferred modules form as groups, activate gene expression at different time points, and mediate diverse functional cellular processes. Hence, ChIP-GSM infers biologically meaningful TF modules and improves the prediction accuracy of regulatory region activities.
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30
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Barreira SN, Nguyen AD, Fredriksen MT, Wolfsberg TG, Moreland RT, Baxevanis AD. AniProtDB: A Collection of Consistently Generated Metazoan Proteomes for Comparative Genomics Studies. Mol Biol Evol 2021; 38:4628-4633. [PMID: 34048573 DOI: 10.1093/molbev/msab165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
To address the void in the availability of high-quality proteomic data traversing the animal tree, we have implemented a pipeline for generating de novo assemblies based on publicly available data from the NCBI Sequence Read Archive, yielding a comprehensive collection of proteomes from 100 species spanning 21 animal phyla. We have also created the Animal Proteome Database (AniProtDB), a resource providing open access to this collection of high-quality metazoan proteomes, along with information on predicted proteins and protein domains for each taxonomic classification and the ability to perform sequence similarity searches against all proteomes generated using this pipeline. This solution vastly increases the utility of these data by removing the barrier to access for research groups who do not have the expertise or resources to generate these data themselves and enables the use of data from non-traditional research organisms that have the potential to address key questions in biomedicine.
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Affiliation(s)
- Sofia N Barreira
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Anh-Dao Nguyen
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Mark T Fredriksen
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Tyra G Wolfsberg
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - R Travis Moreland
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Andreas D Baxevanis
- Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
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31
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Bredesen BA, Rehmsmeier M. MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics. BMC Bioinformatics 2021; 22:234. [PMID: 33962556 PMCID: PMC8105988 DOI: 10.1186/s12859-021-04143-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cis-regulatory elements (CREs) are DNA sequence segments that regulate gene expression. Among CREs are promoters, enhancers, Boundary Elements (BEs) and Polycomb Response Elements (PREs), all of which are enriched in specific sequence motifs that form particular occurrence landscapes. We have recently introduced a hierarchical machine learning approach (SVM-MOCCA) in which Support Vector Machines (SVMs) are applied on the level of individual motif occurrences, modelling local sequence composition, and then combined for the prediction of whole regulatory elements. We used SVM-MOCCA to predict PREs in Drosophila and found that it was superior to other methods. However, we did not publish a polished implementation of SVM-MOCCA, which can be useful for other researchers, and we only tested SVM-MOCCA with IUPAC motifs and PREs. RESULTS We here present an expanded suite for modelling CRE sequences in terms of motif occurrence combinatorics-Motif Occurrence Combinatorics Classification Algorithms (MOCCA). MOCCA contains efficient implementations of several modelling methods, including SVM-MOCCA, and a new method, RF-MOCCA, a Random Forest-derivative of SVM-MOCCA. We used SVM-MOCCA and RF-MOCCA to model Drosophila PREs and BEs in cross-validation experiments, making this the first study to model PREs with Random Forests and the first study that applies the hierarchical MOCCA approach to the prediction of BEs. Both models significantly improve generalization to PREs and boundary elements beyond that of previous methods-including 4-spectrum and motif occurrence frequency Support Vector Machines and Random Forests-, with RF-MOCCA yielding the best results. CONCLUSION MOCCA is a flexible and powerful suite of tools for the motif-based modelling of CRE sequences in terms of motif composition. MOCCA can be applied to any new CRE modelling problems where motifs have been identified. MOCCA supports IUPAC and Position Weight Matrix (PWM) motifs. For ease of use, MOCCA implements generation of negative training data, and additionally a mode that requires only that the user specifies positives, motifs and a genome. MOCCA is licensed under the MIT license and is available on Github at https://github.com/bjornbredesen/MOCCA .
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Affiliation(s)
- Bjørn André Bredesen
- Computational Biology Unit, Department of Informatics, University of Bergen, P.O. Box 7803, 5020, Bergen, Norway.
| | - Marc Rehmsmeier
- Department of Biology, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
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32
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Wan R, Guo C, Hou X, Zhu Y, Gao M, Hu X, Zhang S, Jiao C, Guo R, Li Z, Wang X. Comparative transcriptomic analysis highlights contrasting levels of resistance of Vitis vinifera and Vitis amurensis to Botrytis cinerea. HORTICULTURE RESEARCH 2021; 8:103. [PMID: 33931625 PMCID: PMC8087793 DOI: 10.1038/s41438-021-00537-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 02/23/2021] [Accepted: 03/08/2021] [Indexed: 05/08/2023]
Abstract
Botrytis cinerea is a major grapevine (Vitis spp.) pathogen, but some genotypes differ in their degree of resistance. For example, the Vitis vinifera cultivar Red Globe (RG) is highly susceptible, but V. amurensis Rupr Shuangyou (SY) is highly resistant. Here, we used RNA sequencing analysis to characterize the transcriptome responses of these two genotypes to B. cinerea inoculation at an early infection stage. Approximately a quarter of the genes in RG presented significant changes in transcript levels during infection, the number of which was greater than that in the SY leaves. The genes differentially expressed between infected leaves of SY and RG included those associated with cell surface structure, oxidation, cell death and C/N metabolism. We found evidence that an imbalance in the levels of reactive oxygen species (ROS) and redox homeostasis probably contributed to the susceptibility of RG to B. cinerea. SY leaves had strong antioxidant capacities and improved ROS homeostasis following infection. Regulatory network prediction suggested that WRKY and MYB transcription factors are associated with the abscisic acid pathway. Weighted gene correlation network analysis highlighted preinfection features of SY that might contribute to its increased resistance. Moreover, overexpression of VaWRKY10 in Arabidopsis thaliana and V. vinifera Thompson Seedless enhanced resistance to B. cinerea. Collectively, our study provides a high-resolution view of the transcriptional changes of grapevine in response to B. cinerea infection and novel insights into the underlying resistance mechanisms.
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Affiliation(s)
- Ran Wan
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- College of Horticulture, Henan Agricultural University, 450002, Zhengzhou, Henan, China
| | - Chunlei Guo
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, 066004, Qinhuangdao, Hebei, China
| | - Xiaoqing Hou
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- Key Laboratory of Horticultural Plant Biology and Germplasm Innovation in Northwest China, Ministry of Agriculture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
| | - Yanxun Zhu
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- Key Laboratory of Horticultural Plant Biology and Germplasm Innovation in Northwest China, Ministry of Agriculture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
| | - Min Gao
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- Key Laboratory of Horticultural Plant Biology and Germplasm Innovation in Northwest China, Ministry of Agriculture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
| | - Xiaoyan Hu
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, 066004, Qinhuangdao, Hebei, China
| | - Songlin Zhang
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- Key Laboratory of Horticultural Plant Biology and Germplasm Innovation in Northwest China, Ministry of Agriculture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
| | - Chen Jiao
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY, 14853, USA
| | - Rongrong Guo
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- Grape and Wine Research Institute, Guangxi Academy of Agricultural Sciences, 53000, Nanning, Guangxi, China
| | - Zhi Li
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
- Key Laboratory of Horticultural Plant Biology and Germplasm Innovation in Northwest China, Ministry of Agriculture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China
| | - Xiping Wang
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Horticulture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China.
- Key Laboratory of Horticultural Plant Biology and Germplasm Innovation in Northwest China, Ministry of Agriculture, Northwest A&F University, 712100, Yangling, Xianyang, Shaanxi, China.
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Froehlich JJ, Uyar B, Herzog M, Theil K, Glažar P, Akalin A, Rajewsky N. Parallel genetics of regulatory sequences using scalable genome editing in vivo. Cell Rep 2021; 35:108988. [PMID: 33852857 DOI: 10.1016/j.celrep.2021.108988] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/13/2021] [Accepted: 03/23/2021] [Indexed: 12/27/2022] Open
Abstract
How regulatory sequences control gene expression is fundamental for explaining phenotypes in health and disease. Regulatory elements must ultimately be understood within their genomic environment and development- or tissue-specific contexts. Because this is technically challenging, few regulatory elements have been characterized in vivo. Here, we use inducible Cas9 and multiplexed guide RNAs to create hundreds of mutations in enhancers/promoters and 3' UTRs of 16 genes in C. elegans. Our software crispr-DART analyzes indel mutations in targeted DNA sequencing. We quantify the impact of mutations on expression and fitness by targeted RNA sequencing and DNA sampling. When applying our approach to the lin-41 3' UTR, generating hundreds of mutants, we find that the two adjacent binding sites for the miRNA let-7 can regulate lin-41 expression independently of each other. Finally, we map regulatory genotypes to phenotypic traits for several genes. Our approach enables parallel analysis of regulatory sequences directly in animals.
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Affiliation(s)
- Jonathan J Froehlich
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany
| | - Bora Uyar
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany
| | - Margareta Herzog
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany
| | - Kathrin Theil
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany
| | - Petar Glažar
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany
| | - Altuna Akalin
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany.
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34
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Gu H, Zhang P, Xu M, Liang D. Amplicon genome fishing (AGF): a rapid and efficient method for sequencing target cis-regulatory regions in nonmodel organisms. Mol Genet Genomics 2021; 296:527-539. [PMID: 33797587 DOI: 10.1007/s00438-021-01775-0] [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: 01/27/2021] [Accepted: 03/15/2021] [Indexed: 10/21/2022]
Abstract
Cis-regulatory sequences play a crucial role in regulating gene expression and are evolutionary hot spots that drive phenotypic divergence among organisms. Sequencing some cis-regulatory regions of interest in many different species is common in comparative genetic studies. For nonmodel organisms lacking genomic data, genome walking is often the preferred method for this type of application. However, applying genome walking will be laborious and time-consuming when the number of cis-regulatory regions and species to be analyzed is large. In this study, we propose a novel method called amplicon genome fishing (AGF), which can isolate and sequence cis-regulatory regions of interest for any organism. The main idea of the AGF method is to use fragments amplified from the target cis-regulatory regions as enrichment baits to capture and sequence the whole target cis-regulatory regions from genomic library pools. Unlike genome walking, the AGF method is based on hybridization capture and high-throughput sequencing, which makes this method rapid and efficient for projects where some cis-regulatory regions have to be sequenced for many species. We used human amplicons as capture baits and successfully sequenced five target enhancer regions of Homo sapiens, Mus musculus, Gallus gallus, and Xenopus tropicalis, proving the feasibility and repeatability of AGF. To show the utility of the AGF method in real studies, we used it to sequence the ZRS enhancer, a cis-regulatory region associated with the limb loss of snakes, for twenty-three vertebrate species (includes many limbless species never sequenced before). The newly obtained ZRS sequences provide new perspectives into the relationship between the ZRS enhancer's evolution and limb loss in major tetrapod lineages.
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Affiliation(s)
- HanMei Gu
- State Key Laboratory of Biocontrol, Higher Education Mega Center, School of Life Sciences, College of Ecology and Evolution, Sun Yat-Sen University, #434, Guangzhou, 510006, China
| | - Peng Zhang
- State Key Laboratory of Biocontrol, Higher Education Mega Center, School of Life Sciences, College of Ecology and Evolution, Sun Yat-Sen University, #434, Guangzhou, 510006, China
| | - ManHao Xu
- State Key Laboratory of Biocontrol, Higher Education Mega Center, School of Life Sciences, College of Ecology and Evolution, Sun Yat-Sen University, #434, Guangzhou, 510006, China
| | - Dan Liang
- State Key Laboratory of Biocontrol, Higher Education Mega Center, School of Life Sciences, College of Ecology and Evolution, Sun Yat-Sen University, #434, Guangzhou, 510006, China.
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35
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Li J, Lee YK, Fu W, Whalen AM, Estable MC, Raftery LA, White K, Weiner L, Brissette JL. Modeling by disruption and a selected-for partner for the nude locus. EMBO Rep 2020; 22:e49804. [PMID: 33369874 PMCID: PMC7926259 DOI: 10.15252/embr.201949804] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 11/03/2020] [Accepted: 11/20/2020] [Indexed: 12/25/2022] Open
Abstract
A long‐standing problem in biology is how to dissect traits for which no tractable model exists. Here, we screen for genes like the nude locus (Foxn1)—genes central to mammalian hair and thymus development—using animals that never evolved hair, thymi, or Foxn1. Fruit flies are morphologically disrupted by the FOXN1 transcription factor and rescued by weak reductions in fly gene function, revealing molecules that potently synergize with FOXN1 to effect dramatic, chaotic change. Strong synergy/effectivity in flies is expected to reflect strong selection/functionality (purpose) in mammals; the more disruptive a molecular interaction is in alien contexts (flies), the more beneficial it will be in its natural, formative contexts (mammals). The approach identifies Aff4 as the first nude‐like locus, as murine AFF4 and FOXN1 cooperatively induce similar cutaneous/thymic phenotypes, similar gene expression programs, and the same step of transcription, pre‐initiation complex formation. These AFF4 functions are unexpected, as AFF4 also serves as a scaffold in common transcriptional‐elongation complexes. Most likely, the approach works because an interaction's power to disrupt is the inevitable consequence of its selected‐for power to benefit.
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Affiliation(s)
- Jian Li
- Department of Cell Biology, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA.,Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Yun-Kyoung Lee
- Department of Cell Biology, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA
| | - Wenyu Fu
- Department of Cell Biology, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA.,Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Anne M Whalen
- Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Mario C Estable
- Department of Chemistry and Biology, Ryerson University, Toronto, ON, Canada
| | - Laurel A Raftery
- Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Kristin White
- Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Lorin Weiner
- Department of Cell Biology, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA.,Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Janice L Brissette
- Department of Cell Biology, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA.,Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
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36
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Galli M, Feng F, Gallavotti A. Mapping Regulatory Determinants in Plants. Front Genet 2020; 11:591194. [PMID: 33193733 PMCID: PMC7655918 DOI: 10.3389/fgene.2020.591194] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/28/2020] [Indexed: 12/24/2022] Open
Abstract
The domestication and improvement of many plant species have frequently involved modulation of transcriptional outputs and continue to offer much promise for targeted trait engineering. The cis-regulatory elements (CREs) controlling these trait-associated transcriptional variants however reside within non-coding regions that are currently poorly annotated in most plant species. This is particularly true in large crop genomes where regulatory regions constitute only a small fraction of the total genomic space. Furthermore, relatively little is known about how CREs function to modulate transcription in plants. Therefore understanding where regulatory regions are located within a genome, what genes they control, and how they are structured are important factors that could be used to guide both traditional and synthetic plant breeding efforts. Here, we describe classic examples of regulatory instances as well as recent advances in plant regulatory genomics. We highlight valuable molecular tools that are enabling large-scale identification of CREs and offering unprecedented insight into how genes are regulated in diverse plant species. We focus on chromatin environment, transcription factor (TF) binding, the role of transposable elements, and the association between regulatory regions and target genes.
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Affiliation(s)
- Mary Galli
- Waksman Institute of Microbiology, Rutgers University, Piscataway, NJ, United States
| | - Fan Feng
- Waksman Institute of Microbiology, Rutgers University, Piscataway, NJ, United States
| | - Andrea Gallavotti
- Waksman Institute of Microbiology, Rutgers University, Piscataway, NJ, United States.,Department of Plant Biology, Rutgers University, New Brunswick, NJ, United States
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37
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Osmala M, Lähdesmäki H. Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns. BMC Bioinformatics 2020; 21:317. [PMID: 32689977 PMCID: PMC7370432 DOI: 10.1186/s12859-020-03621-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 06/19/2020] [Indexed: 12/11/2022] Open
Abstract
Background The binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently. Results In this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods. Conclusion PREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.
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Affiliation(s)
- Maria Osmala
- Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland.
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland
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Abstract
Deep neural networks have been revolutionizing the field of machine learning for the past several years. They have been applied with great success in many domains of the biomedical data sciences and are outperforming extant methods by a large margin. The ability of deep neural networks to pick up local image features and model the interactions between them makes them highly applicable to regulatory genomics. Instead of an image, the networks analyze DNA and RNA sequences and additional epigenomic data. In this review, we survey the successes of deep learning in the field of regulatory genomics. We first describe the fundamental building blocks of deep neural networks, popular architectures used in regulatory genomics, and their training process on molecular sequence data. We then review several key methods in different gene regulation domains. We start with the pioneering method DeepBind and its successors, which were developed to predict protein–DNA binding. We then review methods developed to predict and model epigenetic information, such as histone marks and nucleosome occupancy. Following epigenomics, we review methods to predict protein–RNA binding with its unique challenge of incorporating RNA structure information. Finally, we provide our overall view of the strengths and weaknesses of deep neural networks and prospects for future developments.
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Affiliation(s)
- Mira Barshai
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Eitamar Tripto
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Yaron Orenstein
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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39
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Xiang G, Keller CA, Heuston E, Giardine BM, An L, Wixom AQ, Miller A, Cockburn A, Sauria MEG, Weaver K, Lichtenberg J, Göttgens B, Li Q, Bodine D, Mahony S, Taylor J, Blobel GA, Weiss MJ, Cheng Y, Yue F, Hughes J, Higgs DR, Zhang Y, Hardison RC. An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis. Genome Res 2020; 30:472-484. [PMID: 32132109 PMCID: PMC7111515 DOI: 10.1101/gr.255760.119] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 02/21/2020] [Indexed: 01/29/2023]
Abstract
Thousands of epigenomic data sets have been generated in the past decade, but it is difficult for researchers to effectively use all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for validated systematic integration of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By using IDEAS as our integrative and discriminative epigenome annotation system, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of more than 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website to aid research in genomics and hematopoiesis.
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Affiliation(s)
- Guanjue Xiang
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Cheryl A Keller
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Elisabeth Heuston
- NHGRI Hematopoiesis Section, Genetics and Molecular Biology Branch, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Belinda M Giardine
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Lin An
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Alexander Q Wixom
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Amber Miller
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - April Cockburn
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Michael E G Sauria
- Departments of Biology and Computer Science, Johns Hopkins University, Baltimore, Maryland 20218, USA
| | - Kathryn Weaver
- Departments of Biology and Computer Science, Johns Hopkins University, Baltimore, Maryland 20218, USA
| | - Jens Lichtenberg
- NHGRI Hematopoiesis Section, Genetics and Molecular Biology Branch, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Berthold Göttgens
- Welcome and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 1TN, United Kingdom
| | - Qunhua Li
- Department of Statistics, Program in Bioinformatics and Genomics, Center for Computational Biology and Bioinformatics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - David Bodine
- NHGRI Hematopoiesis Section, Genetics and Molecular Biology Branch, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Shaun Mahony
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - James Taylor
- Departments of Biology and Computer Science, Johns Hopkins University, Baltimore, Maryland 20218, USA
| | - Gerd A Blobel
- Department of Pediatrics, Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - Mitchell J Weiss
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Yong Cheng
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Feng Yue
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania 17033, USA
| | - Jim Hughes
- MRC Weatherall Institute of Molecular Medicine, Oxford University, Oxford OX3 9DS, United Kingdom
| | - Douglas R Higgs
- MRC Weatherall Institute of Molecular Medicine, Oxford University, Oxford OX3 9DS, United Kingdom
| | - Yu Zhang
- Department of Statistics, Program in Bioinformatics and Genomics, Center for Computational Biology and Bioinformatics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Ross C Hardison
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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Hardison RC, Zhang Y, Keller CA, Xiang G, Heuston EF, An L, Lichtenberg J, Giardine BM, Bodine D, Mahony S, Li Q, Yue F, Weiss MJ, Blobel GA, Taylor J, Hughes J, Higgs DR, Göttgens B. Systematic integration of GATA transcription factors and epigenomes via IDEAS paints the regulatory landscape of hematopoietic cells. IUBMB Life 2020; 72:27-38. [PMID: 31769130 PMCID: PMC6972633 DOI: 10.1002/iub.2195] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 10/17/2019] [Indexed: 01/15/2023]
Abstract
Members of the GATA family of transcription factors play key roles in the differentiation of specific cell lineages by regulating the expression of target genes. Three GATA factors play distinct roles in hematopoietic differentiation. In order to better understand how these GATA factors function to regulate genes throughout the genome, we are studying the epigenomic and transcriptional landscapes of hematopoietic cells in a model-driven, integrative fashion. We have formed the collaborative multi-lab VISION project to conduct ValIdated Systematic IntegratiON of epigenomic data in mouse and human hematopoiesis. The epigenomic data included nuclease accessibility in chromatin, CTCF occupancy, and histone H3 modifications for 20 cell types covering hematopoietic stem cells, multilineage progenitor cells, and mature cells across the blood cell lineages of mouse. The analysis used the Integrative and Discriminative Epigenome Annotation System (IDEAS), which learns all common combinations of features (epigenetic states) simultaneously in two dimensions-along chromosomes and across cell types. The result is a segmentation that effectively paints the regulatory landscape in readily interpretable views, revealing constitutively active or silent loci as well as the loci specifically induced or repressed in each stage and lineage. Nuclease accessible DNA segments in active chromatin states were designated candidate cis-regulatory elements in each cell type, providing one of the most comprehensive registries of candidate hematopoietic regulatory elements to date. Applications of VISION resources are illustrated for the regulation of genes encoding GATA1, GATA2, GATA3, and Ikaros. VISION resources are freely available from our website http://usevision.org.
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Affiliation(s)
- Ross C. Hardison
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - Yu Zhang
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - Cheryl A. Keller
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - Guanjue Xiang
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - Elisabeth F. Heuston
- Genetics and Molecular Biology Branch, Hematopoiesis SectionNational Institutes of Health, NHGRIBethesdaMD
| | - Lin An
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - Jens Lichtenberg
- Genetics and Molecular Biology Branch, Hematopoiesis SectionNational Institutes of Health, NHGRIBethesdaMD
| | - Belinda M. Giardine
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - David Bodine
- Genetics and Molecular Biology Branch, Hematopoiesis SectionNational Institutes of Health, NHGRIBethesdaMD
| | - Shaun Mahony
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - Qunhua Li
- Departments of Biochemistry and Molecular Biology and of StatisticsThe Pennsylvania State University, University ParkPA
| | - Feng Yue
- Department of Biochemistry and Molecular BiologyThe Pennsylvania State University College of MedicineHershey, PA
| | - Mitchell J. Weiss
- Hematology DepartmentSt. Jude Children's Research HospitalMemphis, TN
| | | | - James Taylor
- Departments of Biology and of Computer ScienceJohns Hopkins UniversityBaltimore, MD
| | - Jim Hughes
- Laboratory of Gene RegulationWeatherall Institute of Molecular Medicine, Oxford UniversityOxfordUK
| | - Douglas R. Higgs
- Laboratory of Gene RegulationWeatherall Institute of Molecular Medicine, Oxford UniversityOxfordUK
| | - Berthold Göttgens
- Department of Hematology, Cambridge Institute for Medical ResearchUniversity of CambridgeCambridgeUK
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Identification and Characterization of Cis-Regulatory Elements for Photoreceptor-Type-Specific Transcription in ZebraFish. Methods Mol Biol 2020; 2092:123-145. [PMID: 31786786 DOI: 10.1007/978-1-0716-0175-4_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2022]
Abstract
Tissue-specific or cell-type-specific transcription of protein-coding genes is controlled by both trans-regulatory elements (TREs) and cis-regulatory elements (CREs). However, it is challenging to identify TREs and CREs, which are unknown for most genes. Here, we describe a protocol for identifying two types of transcription-activating CREs-core promoters and enhancers-of zebrafish photoreceptor type-specific genes. This protocol is composed of three phases: bioinformatic prediction, experimental validation, and characterization of the CREs. To better illustrate the principles and logic of this protocol, we exemplify it with the discovery of the core promoter and enhancer of the mpp5b apical polarity gene (also known as ponli), whose red, green, and blue (RGB) cone-specific transcription requires its enhancer, a member of the rainbow enhancer family. While exemplified with an RGB-cone-specific gene, this protocol is general and can be used to identify the core promoters and enhancers of other protein-coding genes.
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Abstract
Monkeys are a premier model organism for neuroscience research. Activity in the central nervous systems of monkeys can be recorded and manipulated while they perform complex perceptual, motor, or cognitive tasks. Conventional techniques for manipulating neural activity in monkeys are too coarse to address many of the outstanding questions in primate neuroscience, but optogenetics holds the promise to overcome this hurdle. In this article, we review the progress that has been made in primate optogenetics over the past 5 years. We emphasize the use of gene regulatory sequences in viral vectors to target specific neuronal types, and we present data on vectors that we engineered to target parvalbumin-expressing neurons. We conclude with a discussion of the utility of optogenetics for treating sensorimotor hearing loss and Parkinson's disease, areas of translational neuroscience in which monkeys provide unique leverage for basic science and medicine.
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Mahmud AKMF, Yang D, Stenberg P, Ioshikhes I, Nandi S. Exploring a Drosophila Transcription Factor Interaction Network to Identify Cis-Regulatory Modules. J Comput Biol 2019; 27:1313-1328. [PMID: 31855461 DOI: 10.1089/cmb.2018.0160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Multiple transcription factors (TFs) bind to specific sites in the genome and interact among themselves to form the cis-regulatory modules (CRMs). They are essential in modulating the expression of genes, and it is important to study this interplay to understand gene regulation. In the present study, we integrated experimentally identified TF binding sites collected from published studies with computationally predicted TF binding sites to identify Drosophila CRMs. Along with the detection of the previously known CRMs, this approach identified novel protein combinations. We determined high-occupancy target sites, where a large number of TFs bind. Investigating these sites revealed that Giant, Dichaete, and Knirp are highly enriched in these locations. A common TAG team motif was observed at these sites, which might play a role in recruiting other TFs. While comparing the binding sites at distal and proximal promoters, we found that certain regulatory TFs, such as Zelda, were highly enriched in enhancers. Our study has shown that, from the information available concerning the TF binding sites, the real CRMs could be predicted accurately and efficiently. Although we only may claim co-occurrence of these proteins in this study, it may actually point to their interaction (as known interaction proteins typically co-occur together). Such an integrative approach can, therefore, help us to provide a better understanding of the interplay among the factors, even though further experimental verification is required.
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Affiliation(s)
| | - Doo Yang
- Ottawa Institute of Computational Biology and Bioinformatics (OICBB) and Ottawa Institute of Systems Biology (OISB) and Department of Biochemistry, Microbiology and Immunology (BMI), Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Per Stenberg
- Department of Molecular Biology, Umeå University, Umeå, Sweden
| | - Ilya Ioshikhes
- Ottawa Institute of Computational Biology and Bioinformatics (OICBB) and Ottawa Institute of Systems Biology (OISB) and Department of Biochemistry, Microbiology and Immunology (BMI), Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Soumyadeep Nandi
- Life Sciences Division, Institute of Advanced Study in Science and Technology, Vigyan Path, Paschim Boragaon, Guwahati, India; Amity University Haryana, Gurugram, India
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Vijayabaskar MS, Goode DK, Obier N, Lichtinger M, Emmett AML, Abidin FNZ, Shar N, Hannah R, Assi SA, Lie-A-Ling M, Gottgens B, Lacaud G, Kouskoff V, Bonifer C, Westhead DR. Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets. PLoS Comput Biol 2019; 15:e1007337. [PMID: 31682597 PMCID: PMC6855567 DOI: 10.1371/journal.pcbi.1007337] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 11/14/2019] [Accepted: 08/15/2019] [Indexed: 01/22/2023] Open
Abstract
Gene expression governs cell fate, and is regulated via a complex interplay of transcription factors and molecules that change chromatin structure. Advances in sequencing-based assays have enabled investigation of these processes genome-wide, leading to large datasets that combine information on the dynamics of gene expression, transcription factor binding and chromatin structure as cells differentiate. While numerous studies focus on the effects of these features on broader gene regulation, less work has been done on the mechanisms of gene-specific transcriptional control. In this study, we have focussed on the latter by integrating gene expression data for the in vitro differentiation of murine ES cells to macrophages and cardiomyocytes, with dynamic data on chromatin structure, epigenetics and transcription factor binding. Combining a novel strategy to identify communities of related control elements with a penalized regression approach, we developed individual models to identify the potential control elements predictive of the expression of each gene. Our models were compared to an existing method and evaluated using the existing literature and new experimental data from embryonic stem cell differentiation reporter assays. Our method is able to identify transcriptional control elements in a gene specific manner that reflect known regulatory relationships and to generate useful hypotheses for further testing.
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Affiliation(s)
- M. S. Vijayabaskar
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
| | - Debbie K. Goode
- Wellcome Trust & MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
| | - Nadine Obier
- Institute for Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham. Birmingham, United Kingdom
| | - Monika Lichtinger
- Institute for Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham. Birmingham, United Kingdom
| | - Amber M. L. Emmett
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
| | - Fatin N. Zainul Abidin
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
| | - Nisar Shar
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
| | - Rebecca Hannah
- Wellcome Trust & MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
| | - Salam A. Assi
- Institute for Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham. Birmingham, United Kingdom
| | - Michael Lie-A-Ling
- CRUK Manchester Institute, University of Manchester, Manchester, United Kingdom
| | - Berthold Gottgens
- Wellcome Trust & MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
| | - Georges Lacaud
- CRUK Manchester Institute, University of Manchester, Manchester, United Kingdom
| | - Valerie Kouskoff
- Division of Developmental Biology and Medicine, The University of Manchester, Manchester, United Kingdom
| | - Constanze Bonifer
- Institute for Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham. Birmingham, United Kingdom
| | - David R. Westhead
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
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Rothenberg EV. Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges. J Comput Biol 2019; 26:703-718. [PMID: 31063008 DOI: 10.1089/cmb.2019.0098] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Gene regulatory network modeling has played a major role in advancing the understanding of developmental systems, by crystallizing structures of relevant extant information, by formally posing hypothetical functional relationships between network elements, and by offering clear predictive tests to improve understanding of the mechanisms driving developmental progression. Both ordinary differential equation (ODE)-based and Boolean models have also been highly successful in explaining dynamics within subcircuits of more complex processes. In a very small number of cases, gene regulatory network models of much more global scope have been proposed that successfully predict the dynamics of the processes establishing most of an embryonic body plan. Can such successes be expanded to very different developmental systems, including post-embryonic mammalian systems? This perspective discusses several problems that must be solved in more quantitative and predictive theoretical terms, to make this possible. These problems include: the effects of cellular history on chromatin state and how these affect gene accessibility; the dose dependence of activities of many transcription factors (a problem for Boolean models); stochasticity of some transcriptional outputs (a problem for simple ODE models); response timing delays due to epigenetic remodeling requirements; functionally different kinds of repression; and the regulatory syntax that governs responses of genes with multiple enhancers.
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Affiliation(s)
- Ellen V Rothenberg
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
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Zhang N, McHale LK, Finer JJ. Changes to the core and flanking sequences of G-box elements lead to increases and decreases in gene expression in both native and synthetic soybean promoters. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:724-735. [PMID: 30191675 PMCID: PMC6419578 DOI: 10.1111/pbi.13010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 08/05/2018] [Accepted: 08/31/2018] [Indexed: 05/22/2023]
Abstract
Cis-regulatory elements in promoters are major determinants of binding specificity of transcription factors (TFs) for transcriptional regulation. To improve our understanding of how these short DNA sequences regulate gene expression, synthetic promoters consisting of both classical (CACGTG) and variant G-box core sequences along with different flanking sequences derived from the promoters of three different highly expressing soybean genes, were constructed and used to regulate a green fluorescent protein (gfp) gene. Use of the classical 6-bp G-box provided information on the base level of GFP expression while modifications to the 2-4 flanking bases on either side of the G-box influenced the intensity of gene expression in both transiently transformed lima bean cotyledons and stably transformed soybean hairy roots. The proximal 2-bp sequences on either flank of the G-box significantly affected G-box activity, while the distal 2-bp flanking nucleotides also influenced gene expression albeit with a decreasing effect. Manipulation of the upstream 2- to 4-bp flanking sequence of a G-box variant (GACGTG), found in the proximal region of a relatively weak soybean glycinin promoter, significantly enhanced promoter activity using both transient and stable expression assays, if the G-box variant was first converted into a classical G-box (CACGTG). In addition to increasing our understanding of regulatory element composition and structure, this study shows that minimal targeted changes in native promoter sequences can lead to enhanced gene expression, and suggests that genome editing of the promoter region can result in useful and predictable changes in native gene expression.
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Affiliation(s)
- Ning Zhang
- Department of Horticulture and Crop ScienceThe Ohio State UniversityWoosterOHUSA
- Present address:
Boyce Thompson InstituteCornell UniversityIthacaNYUSA
| | - Leah K. McHale
- Department of Horticulture and Crop ScienceThe Ohio State UniversityColumbusOHUSA
| | - John J. Finer
- Department of Horticulture and Crop ScienceThe Ohio State UniversityWoosterOHUSA
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Vega WHO, Quirino CR, Bartholazzi-Junior A, Rua MAS, Serapião RV, Oliveira CS. Variants in the CYP19A1 gene can affect in vitro embryo production traits in cattle. J Assist Reprod Genet 2018; 35:2233-2241. [PMID: 30232641 DOI: 10.1007/s10815-018-1320-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 09/13/2018] [Indexed: 11/30/2022] Open
Abstract
PURPOSE This study aimed to associate DNA variants in promoter and exon flanking regions of the CYP19A1 gene with in vitro embryo production traits in cattle. The role of transcription factor binding sites created or lost due to DNA sequence variation and their possible effect on gene expression was also evaluated. METHODS We collected date from Gyr dairy oocyte donor cows (Bos taurus indicus) at a commercial in vitro embryo production farm and analyzed the genotype-phenotype association with in vitro production traits. Using Sanger sequencing and web-based software, we assessed important CYP19A1 gene regions in oocyte donor cows and analyzed the effects of variants on the transcription factor binding sites. RESULTS Two SNP mutations significantly associated with oocyte production, oocyte viability, embryo development, and pregnancies were found (T > C in the untranslated exon 1 flanking region ([GenBank: AJ250379.1]: rs718446508 T > C), and a T > C in the 5'-upstream region (1.1 promoter) ([GenBank: AC_000167.1]: rs41651668 T > C). Six new transcription factor binding sites were created. A binding site for transcription factors associated with the development of the placenta and embryo implantation was eliminated due to variations in the DNA sequence identified. CONCLUSIONS The CYP19A1 gene contributes to genetic variation of in vitro embryo production traits in cattle. The complexity of the physiological phenomena related to estrogen pathways and their influence on reproduction in cattle allow indication of the mutations evaluated here as possible genetic markers for embryo production traits, which should be validated in the next steps of marker-assisted selection.
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Affiliation(s)
- Wilder Hernando Ortiz Vega
- Laboratory for Animal Breeding and Genetic Improvement, Norte Fluminense State University, Av. Alberto Lamego, 2000, Campos dos Goytacazes, Rio de Janeiro, 28013-602, Brazil.
| | - Celia Raquel Quirino
- Laboratory for Animal Breeding and Genetic Improvement, Norte Fluminense State University, Av. Alberto Lamego, 2000, Campos dos Goytacazes, Rio de Janeiro, 28013-602, Brazil
| | - Aylton Bartholazzi-Junior
- Laboratory for Animal Breeding and Genetic Improvement, Norte Fluminense State University, Av. Alberto Lamego, 2000, Campos dos Goytacazes, Rio de Janeiro, 28013-602, Brazil
| | - Miguel Alejandro Silva Rua
- Laboratory for Animal Breeding and Genetic Improvement, Norte Fluminense State University, Av. Alberto Lamego, 2000, Campos dos Goytacazes, Rio de Janeiro, 28013-602, Brazil
| | - Raquel Varella Serapião
- PESAGRO-RIO, Laboratory for Animal Reproduction, Santa Mônica Experimental Farm (CESM), Valença, Rio de Janeiro, Brazil
| | - Clara Slade Oliveira
- Embrapa Dairy Cattle Research Unit, Laboratory for Animal Reproduction, Santa Mônica Experimental Farm (CESM), Valença, Rio de Janeiro, Brazil
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Genome-wide identification of enhancer elements in the placenta. Placenta 2018; 79:72-77. [PMID: 30268337 DOI: 10.1016/j.placenta.2018.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 09/03/2018] [Accepted: 09/10/2018] [Indexed: 12/25/2022]
Abstract
Normal placental development is essential for a healthy pregnancy, and is contingent upon tight spatiotemporal regulation of gene expression. One level of transcriptional control is via enhancer elements in the genome. Enhancers are distal cis-regulatory elements that can impact gene expression regardless of their position or orientation. The study of enhancers in the placenta is usually focused on one or two at a time, and the simultaneous identification of all enhancers has been limited. However, such a holistic approach is necessary if we are to gain a systems-level understanding of gene expression regulation in the placenta. Here, we review current methods for genome-scale enhancer identification, as well as studies that have applied those techniques in the placenta, with the aim of guiding future research.
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Niu M, Tabari E, Ni P, Su Z. Towards a map of cis-regulatory sequences in the human genome. Nucleic Acids Res 2018; 46:5395-5409. [PMID: 29733395 PMCID: PMC6009671 DOI: 10.1093/nar/gky338] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 04/14/2018] [Accepted: 04/19/2018] [Indexed: 01/10/2023] Open
Abstract
Accumulating evidence indicates that transcription factor (TF) binding sites, or cis-regulatory elements (CREs), and their clusters termed cis-regulatory modules (CRMs) play a more important role than do gene-coding sequences in specifying complex traits in humans, including the susceptibility to common complex diseases. To fully characterize their roles in deriving the complex traits/diseases, it is necessary to annotate all CREs and CRMs encoded in the human genome. However, the current annotations of CREs and CRMs in the human genome are still very limited and mostly coarse-grained, as they often lack the detailed information of CREs in CRMs. Here, we integrated 620 TF ChIP-seq datasets produced by the ENCODE project for 168 TFs in 79 different cell/tissue types and predicted an unprecedentedly completely map of CREs in CRMs in the human genome at single nucleotide resolution. The map includes 305 912 CRMs containing a total of 1 178 913 CREs belonging to 736 unique TF binding motifs. The predicted CREs and CRMs tend to be subject to either purifying selection or positive selection, thus are likely to be functional. Based on the results, we also examined the status of available ChIP-seq datasets for predicting the entire regulatory genome of humans.
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Affiliation(s)
- Meng Niu
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
| | - Ehsan Tabari
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
| | - Pengyu Ni
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
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50
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Medina-Rivera A, Santiago-Algarra D, Puthier D, Spicuglia S. Widespread Enhancer Activity from Core Promoters. Trends Biochem Sci 2018; 43:452-468. [PMID: 29673772 DOI: 10.1016/j.tibs.2018.03.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/09/2018] [Accepted: 03/12/2018] [Indexed: 01/04/2023]
Abstract
Gene expression in higher eukaryotes is precisely regulated in time and space through the interplay between promoters and gene-distal regulatory regions, known as enhancers. The original definition of enhancers implies the ability to activate gene expression remotely, while promoters entail the capability to locally induce gene expression. Despite the conventional distinction between them, promoters and enhancers share many genomic and epigenomic features. One intriguing finding in the gene regulation field comes from the observation that many core promoter regions display enhancer activity. Recent high-throughput reporter assays along with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-related approaches have indicated that this phenomenon is common and might have a strong impact on our global understanding of genome organisation and gene expression regulation.
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Affiliation(s)
- Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Mexico
| | - David Santiago-Algarra
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labéllisée, Ligue Contre le Cancer, Paris, France
| | - Denis Puthier
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labéllisée, Ligue Contre le Cancer, Paris, France
| | - Salvatore Spicuglia
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labéllisée, Ligue Contre le Cancer, Paris, France.
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