Publication:
Genome-wide prediction of topoisomerase IIβ binding by architectural factors and chromatin accessibility

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Date

2021-01-19

Authors

Martínez-García, Pedro Manuel
García-Torres, Miguel
Divina, Federico
Terrón-Bautista, José
Delgado-Sainz, Irene
Gómez-Vela, Francisco
Cortés-Ledesma, Felipe

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Public Library of Science
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Abstract

DNA topoisomerase II-β (TOP2B) is fundamental to remove topological problems linked to DNA metabolism and 3D chromatin architecture, but its cut-and-reseal catalytic mechanism can accidentally cause DNA double-strand breaks (DSBs) that can seriously compromise genome integrity. Understanding the factors that determine the genome-wide distribution of TOP2B is therefore not only essential for a complete knowledge of genome dynamics and organization, but also for the implications of TOP2-induced DSBs in the origin of oncogenic translocations and other types of chromosomal rearrangements. Here, we conduct a machine-learning approach for the prediction of TOP2B binding using publicly available sequencing data. We achieve highly accurate predictions, with accessible chromatin and architectural factors being the most informative features. Strikingly, TOP2B is sufficiently explained by only three features: DNase I hypersensitivity, CTCF and cohesin binding, for which genome-wide data are widely available. Based on this, we develop a predictive model for TOP2B genome-wide binding that can be used across cell lines and species, and generate virtual probability tracks that accurately mirror experimental ChIP-seq data. Our results deepen our knowledge on how the accessibility and 3D organization of chromatin determine TOP2B function, and constitute a proof of principle regarding the in silico prediction of sequence-independent chromatin-binding factors.

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Medical Subject Headings::Organisms::Eukaryota::Animals
Medical Subject Headings::Anatomy::Cells::Cells, Cultured
Medical Subject Headings::Phenomena and Processes::Genetic Phenomena::Genetic Structures::Genome
Medical Subject Headings::Disciplines and Occupations::Natural Science Disciplines::Biological Science Disciplines::Biology::Genetics::Genomics
Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans
Medical Subject Headings::Anatomy::Cells::Cells, Cultured::Cell Line::Cell Line, Tumor::MCF-7 Cells
Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Rodentia::Muridae::Murinae::Mice
Medical Subject Headings::Phenomena and Processes::Chemical Phenomena::Biochemical Phenomena::Biochemical Processes::Protein Binding
Medical Subject Headings::Anatomy::Cells::Thymocytes
Medical Subject Headings::Anatomy::Cells::Cellular Structures::Intracellular Space::Cell Nucleus::Cell Nucleus Structures::Intranuclear Space::Chromosomes::Chromosome Structures::Chromatin
Medical Subject Headings::Chemicals and Drugs::Enzymes and Coenzymes::Enzymes::Isomerases::DNA Topoisomerases::DNA Topoisomerases, Type II
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Models, Theoretical::Models, Biological::Models, Genetic
Medical Subject Headings::Phenomena and Processes::Genetic Phenomena::Genetic Processes::DNA Damage::DNA Breaks::DNA Breaks, Double-Stranded
Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Probability
Medical Subject Headings::Anatomy::Cells::Cells, Cultured::Cell Line
Medical Subject Headings::Chemicals and Drugs::Enzymes and Coenzymes::Enzymes::Hydrolases::Esterases::Deoxyribonucleases::Endodeoxyribonucleases::Deoxyribonuclease I

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Keywords

Machine learning, Chromatin, Type II DNA topoisomerases, Thymocytes, Genome, MCF7 cells, Aprendizaje automático, Cromatina, ADN-Topoisomerasas de tipo II, Timocitos, Genoma, Células MCF-7

Citation

Martínez-García PM, García-Torres M, Divina F, Terrón-Bautista J, Delgado-Sainz I, Gómez-Vela F, et al. Genome-wide prediction of topoisomerase IIβ binding by architectural factors and chromatin accessibility. PLoS Comput Biol. 2021 Jan 19;17(1):e1007814.