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

Nature Genetics volume 54, pages 295–305 (2022)Cite this article

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Abstract

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.

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Fig. 1: dHIT imputes histone modifications using nascent transcription.
Fig. 2: dHIT identifies bivalent H3K4me3-/H3K27me3-marked genes.
Fig. 3: Inference of chromatin states defined by ChromHMM using transcription.
Fig. 4: ChIP-seq measures changes in histone modifications following transcription inhibition by Trp.
Fig. 5: Chromatin accessibility is not sufficient for transcription initiation.
Fig. 6: Transcription is required for chromatin landscaping.

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Data Availability

Publicly available data used in this study can be found in the Supplementary Tables 1 and 2. Tables in CSV format can be downloaded from https://github.com/alexachivu/dHITpaper_2021. Data generated in this study can be found in the Gene Expression Omnibus under accession code GSE163043.

Code Availability

dHIT software and scripts can be found on GitHub (https://github.com/Danko-Lab/histone-mark-imputation). Custom code for analyzing sequencing data can be found on GitHub (https://github.com/alexachivu/dHITpaper_2021/blob/main/Git.code_dHIT.uploa)

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Acknowledgements

We thank the Extreme Science and Engineering Discovery Environment (allocation number TG-MCB160061) and Nvida (GPU grant) for providing the computational resources required in this study. We thank J. Lewis, H. Yu, A. Vihervaara, M. DeBerardine and all members of the Danko and Lis laboratories for valuable discussions and suggestions. Work in this publication was supported by the National Human Genome Research Institute (grant R01-HG009309 to C.G.D.), the Zweig Memorial Fund for Equine Research (grant to D.F.A. and C.G.D.), the LiaoNing Revitalization Talents Program (grant XLYC2002010 to Z.W.) and the National Institutes of Health (grant RM1GM139738 to J.T.L.). D.F.A. is an investigator of the Dorothy Russell Havemeyer Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Some of the figures in this article were created using BioRender.

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Author notes

  1. These authors contributed equally: Zhong Wang, Alexandra G. Chivu.

Authors and Affiliations

  1. Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Zhong Wang, Alexandra G. Chivu, Lauren A. Choate, Edward J. Rice, Donald C. Miller, Tinyi Chu, Shao-Pei Chou, Douglas F. Antczak & Charles G. Danko

  2. School of Software Technology, Dalian University of Technology, Dalian, China

    Zhong Wang

  3. Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA

    Alexandra G. Chivu & John T. Lis

  4. Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California, Davis, Davis, CA, USA

    Nicole B. Kingsley & Rebecca R. Bellone

  5. Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, USA

    Jessica L. Petersen

  6. Department of Population Health and Reproduction, University of California, Davis, Davis, CA, USA

    Carrie J. Finno

  7. Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Charles G. Danko

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Contributions

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 were involved in revisions and approved the final manuscript.

Corresponding author

Correspondence to Charles G. Danko.

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Extended data

Extended Data Fig. 1 Imputation of histone marks using nascent transcription.

Scatterplots show predicted (Y-axis) as a function of experimental ChIP-seq signal (X-axis) for ten different histone modifications in K562 and GM12878. Plots show correlations in a holdout chromosome (chr22) at three distinct length scales.

Extended Data Fig. 2 Evaluating dHIT predictions.

a. ROC and PRC plots describe the relationship between imputed and ENCODE ChIP-seq data within ENCODE peaks on chr21, holdout during dHIT training. b. Quantification of area under precision curves for both ROP and PRC plots in A. (c–l) Heatmaps show the experimental and imputed abundance of active, punctate histone marks in K562 (C-G) or GM12878 (H-L). Heatmaps show all peaks calls based on experimental ChIP-seq data ordered by the highest total signal intensity. m. Scatterplots depict imputed H3K9me3 (Y-axis) as a function of CUT&TAG experimental (X-axis) for H3K9me3 in K562. Spearman correlations were computed on the holdout chromosome chr21 (A) and chr22 (B). n. Mean-squared error (MSE) quantification at different subsets of genomic sites in GM12878.

Extended Data Fig. 3 Comparison between experimental and imputed MNase ChIP-seq.

(a,b).Heatmaps show the Pearson (A) and Spearman (B) correlations between predicted and experimental MNase ChIP-seq in 10 kb windows on a holdout chromosome (chr22). c. Genome browser plots show the distribution of PRO-seq, DNase-I hypersensitivity signal, and the signal for H3K4me3, H3K4me2, and H3K4me1 derived from MNase ChIP-seq and imputation near 9 transcribed regions in K562 cells. d. Heatmaps show MNase ChIP-seq and imputed signal intensity for H3K36me3, a gene body mark, deposited in the body of annotated genes. Genes are sorted by gene length. e. Heatmaps show the distribution of transcription (left) and histone modifications (right) predicted using transcription. Rows represent transcription initiation domains in GM12878 cells defined using GRO-cap data by Core, Martins, et. al. (2014) Nat. Gen. Heatmaps were ordered by the distance between the most frequently used TSS in each transcription initiation domain on the plus and minus strand.

Extended Data Fig. 4 Evaluation of cross-cell line imputation by different metrics.

(a-c) Heatmaps show Pearson’s correlation (A), Spearman’s rank correlation (B), Jensen-Shannon divergence. (c) between predicted and ChIP-seq measurements of nine histone modifications. Values are computed in 10 kb windows on the holdout chromosome (chr22) in humans, chr1 in horse, and chr1 in mice. Empty cells indicate that no experimental data is available for comparison in the cell type shown. (d) Heatmap shows Pearson’s correlation between the training dataset in K562 cells and experimental data collected in the indicated human cell line. Values are computed in 1 kb windows on the holdout chromosome (chr22) in humans. (e) Heatmap shows Pearson’s correlation between the ENCODE experimental data and either the imputed data or the average signal of the other human cell lines investigated. Values are computed in 1 kb windows on the holdout chromosome (chr22) in GM12878.

Extended Data Fig. 5 Comparison between imputation and multiple ChIP-seq experiments.

Box and whiskers plot shows the Pearson correlation between different experimental datasets for six histone marks in K562 and GM12878.The correlation between data imputed in K562 and GM12878 and the ENCODE experimental data in the same cell line is shown respectively by red and blue squares. All values are computed on a holdout chromosome (chr22) not used during training and are presented as mean values + /- standard deviation.

Extended Data Fig. 6 Comparing between imputed and experimental Chip-seq.

a. Browser shot shows the ENCODE, imputed, and experimental ChIP-seq signals at the CERK locus. b. Meta plots compare the H3K27ac content of two different sets of H3K27ac annotated peaks: peak high in ENCODE signal and depleted in imputed ChIP (top) or vice-versa (bottom). c. Genome browser compares experimental and predicted H3K27me3 signals at all four Hox gene clusters in relation to PRO-seq signal. d. Principal component analysis of 86 H3K27me3 ChIP-seq datasets from the Epigenome Roadmap project. e. Genome browser shows the distribution of H3K27me3 in the 8 of the Epigenome Roadmap cell lines. f. Quantification of PC1 H3K27me3 signal in 5 classes of cells. An unpaired Wilcox test was used to compare the Primary/Adult to the Pluripotent classes.

Extended Data Fig. 7 Chromatin annotations with dHIT.

Supplementary Fig. 7a. Enrichment of 18 chromatin states near RefSeq annotated transcription start sites for histone abundance predicted by dHIT (thick solid line), ChIP-seq from Broad (thin solid line), or using an alternative source of ChIP-seq data (thin dashed line). (b,c). Confusion matrix shows the Jaccard distance between dHIT and ChIP-seq data in 18 chromatin states (B) or between two separate sources of ChIP-seq data (C). Color scales are shown beside the plot, and are identical between panels (B) and (C). d. Genome browser shows the distribution of transcription, H3K27ac, H3K4me3, H3K4me1 and H3K27me3 in equine liver. E Genome browser shows the distri-bution of eight histone marks in mouse brain (top) and H3K27ac across nine murine tissues (bottom).

Extended Data Fig. 8 Data validation.

a. PCA shows the first two principal components of nine histone modifications in nine murine tissues (81 total datasets) in 100 bp bins on mm10 chr1. b. PCA of active, punctate marks (H3K4me3, H3K4me2, H3K9ac, and H3K27ac) shows that active punctate marks cluster by tissue. c. Genome browser shows the distribution of H3K36me3, H3K4me3, and H3K4me1 observed using ChIP-seq experiments or predicted using either PRO-seq or H3K4me2. Data is shown in two loci covering several transcribed genes (top) and near the transcription start site of ZNF74 (bottom). d. Correlations between PRO-seq 0 h and H3K4me3 and H3K27ac at TSSs. e. Heatmaps centered on transcription initiation domains show loss in transcription measured by PRO-seq after Trp treatment. f. Genome browser shows loss in transcription measured by PRO-seq after Trp treatment. Loss in PRO-seq signal at both enhancers and gene promoters. g. Spearman correlations between ChIP-seq replicates (left), each ChIP-seq replicate and ENCODE data (middle) genome-wide at 10 kb resolution, and at ENCODE peaks between merged Reps and ENCODE. h. H3 CUT&RUN 10 kb resolution Spearman correlation between replicates. i. Genome-wide, 10 kb resolution PCA of all ChIP-seq samples.

Extended Data Fig. 9 Changes in histone marks during Triptolide time course.

a. Heatmaps compare the level of H3K36me3 ChIP-seq after Triptolide inhibition. b. Meta plots show the H3K4me1 levels in a 4 kb window centered on transcription start sites in K562 cells. c. Meta plots show the level in H3K27me3 in a 40 kb window centered in EZH2 binding sites. d. Meta plots show transcription content of EZH2 binding sites during the Triptolide time course. e. Heatmaps shows H3K27me3 signal within gene bodies during the Triptolide treatment. Genes are sorted by gene length. f. Schematics of western blot experimental design. (g,h). Each western blot depicts the abundance of chromatin-bound histone mark or Pol II during the indicated Triptolide incubation time point. Each blot represents a different experiment. A dilution series of the untreated samples was used as standard curve to quantify changes in signal. Experiments were repeated at least twice and a minimum of 2 replicates per histone mark are provided. MM defined the Molecular marker depicted in [kDa]. i. Each western blot depicts the abundance of chromatin-bound H3K27ac or H3K27me3 during the indicated incubation time point of Triptolide, or Triptolide and Trichostatin dual treatment. Each blot represents a different experiment. A dilution series of the untreated samples was used as standard curve to quantify changes in signal. Ponceau staining of membranes imaged are also depicted as total protein loading control. j. Quantification of H3K27ac/H3K27me3 signals of the western blot in I. H3K27me3 was used as loading control. All values are depicted as mean values + /− SD.

Extended Data Fig. 10 Studying transcription activators and repressors.

a. Bar plots display absorbance quantified at 590 nm for AlmarBlue dye incubated with K562 cells during Triptolide, or Triptolide and Trichostatin A treatments. Two technical replicates were averaged for each time point. R1 and R2 define separate biological replication of the experiment. b. Scatterplots display the loss in H3K4me3 (left) and H3K27ac (right) as a function of Pol II transcription (top) or change in transcription (bottom). Changes in histone marks and transcription were calculated as log2 fold changes between 4 h of Triptolide treatment and untreated cells. Plots show Spearman’s Rho between conditions. (c-h) Scatterplots show experimental DNase-I hypersensitivity (x axis) as a function of predicted DNase-I hypersensitivity (y axis) in 100 bp windows intersected with transcriptional repressors (C-E) or transcriptional activators (F-H). (i-j) Meta (I) and Violin (J) plots display TBP (TATA-binding protein) CUT&RUN signal at gene promoters and enhancers in a short 30 min Triptolide time course.

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Supplementary Notes 1–7.

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Supplementary Table 1

Excel document describing the source of all PRO-seq and ChIP-seq data used in our study.

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Wang, Z., Chivu, A.G., Choate, L.A. et al. Prediction of histone post-translational modification patterns based on nascent transcription data. Nat Genet 54, 295–305 (2022). https://doi.org/10.1038/s41588-022-01026-x

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