Prediction of histone post-translational modification patterns based on nascent transcription data

The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histo...

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Bibliographic Details
Published inNature genetics Vol. 54; no. 3; pp. 295 - 305
Main Authors Wang, Zhong, Chivu, Alexandra G., Choate, Lauren A., Rice, Edward J., Miller, Donald C., Chu, Tinyi, Chou, Shao-Pei, Kingsley, Nicole B., Petersen, Jessica L., Finno, Carrie J., Bellone, Rebecca R., Antczak, Douglas F., Lis, John T., Danko, Charles G.
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.03.2022
Nature Publishing Group
Subjects
38
38/91
631/114
631/208/177
631/337/2019
631/553
Accuracy
Agriculture
Animal Genetics and Genomics
Biomedical and Life Sciences
Biomedicine
Cancer Research
Chromatin
Chromatin - genetics
Chromosomes
Datasets
Deoxyribonuclease
Gene Function
Genes
Genomes
Histone Code - genetics
Histones
Histones - genetics
Histones - metabolism
Human Genetics
Machine learning
Nucleosomes - genetics
Perturbation
Post-translation
Protein Processing, Post-Translational - genetics
RNA polymerase
Transcription
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Summary:The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3, consistent with a role for RNA in recruiting PRC2. A subset of DNase-I-hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than an essential regulatory, role in transcription. A machine-learning tool can predict the distribution of histone post-translational modifications using nascent transcription data. Inhibiting transcription impacts H3K4me3, H3K27ac and H3K27me3 dynamics.
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Author Contributions Statement
Z.W., A.G.C. and C.G.D. designed the study. Z.W., T.C., and C.G.D. developed the support-vector regression method. A.G.C., E.J.R., and L.A.C. performed experimental research. A.G.C., Z.W., S.P.C., J.T.L., and C.G.D. analyzed and interpreted sequencing data. A.G.C. performed and analyzed Trp experiments. D.C.M., N.B.K., J.L.P., C.J.F., R.R.B., D.F.A., E.J.R., and Z.W. prepared and analyzed data from FAANG horse liver tissue. Z.W., A.G.C., J.T.L., and C.G.D. wrote the manuscript. All authors have been involved in revisions and approved the final manuscript.
Denotes equal contribution and interchangeable ordering.
ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/s41588-022-01026-x