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PRC1 drives Polycomb-mediated gene repression by controlling transcription initiation and burst frequency

Nature Structural & Molecular Biology volume 28, pages 811–824 (2021)Cite this article

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Abstract

The Polycomb repressive system plays a fundamental role in controlling gene expression during mammalian development. To achieve this, Polycomb repressive complexes 1 and 2 (PRC1 and PRC2) bind target genes and use histone modification-dependent feedback mechanisms to form Polycomb chromatin domains and repress transcription. The inter-relatedness of PRC1 and PRC2 activity at these sites has made it difficult to discover the specific components of Polycomb chromatin domains that drive gene repression and to understand mechanistically how this is achieved. Here, by exploiting rapid degron-based approaches and time-resolved genomics, we kinetically dissect Polycomb-mediated repression and discover that PRC1 functions independently of PRC2 to counteract RNA polymerase II binding and transcription initiation. Using single-cell gene expression analysis, we reveal that PRC1 acts uniformly within the cell population and that repression is achieved by controlling transcriptional burst frequency. These important new discoveries provide a mechanistic and conceptual framework for Polycomb-dependent transcriptional control.

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Fig. 1: Acute depletion of PRC1 reveals a rapid turnover of H2AK119ub1.
Fig. 2: Perturbing PRC1 has immediate effects on gene expression.
Fig. 3: PRC1-mediated gene repression does not rely directly on PRC2 or H3K27me3.
Fig. 4: Removal of PRC1 causes rapid new binding of Pol II and accumulation of H3K4me3.
Fig. 5: New initiation is required for Polycomb target gene derepression.
Fig. 6: Single-cell analysis of Polycomb target gene expression.
Fig. 7: The Polycomb system represses gene expression by limiting transcription burst frequency.

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Data availability

High-throughput sequencing datasets generated for this study are available in the GEO database under the accession number GSE159400. Published data used in this study include BioCAP-seq data (GEO, GSE43512) from ref. 118, cnRNA-seq of Ring1a−/−;Ring1bfl/fl (PRC1CKO) (GEO, GSE119619) samples from ref. 29 and cChIP–seq data for JARID2, AEBP2, PCL2 and EPOP in untreated (wild-type) Ring1aI50A/D53K;Ring1b(WT->I53A/D56K)fl/fl ES cells (GEO, GSE132754) from ref. 30. For cnRNA-seq processing, we used mm10 (GenBank, BK000964.3) and dm6 (GenBank, M21017.1) rDNA genomic datasets. Source data are provided with this paper.

Code availability

All R and Perl scripts used for genomic data analysis in this study are available at https://github.com/pauladobrinic/PRC1degron-2021. A custom-made ImageJ script for preprocessing 3D images (ThunderFISH) is publicly available with a detailed manual for sample preparation and script use at https://github.com/aleks-szczure/ThunderFISH.

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Acknowledgements

Work in the Klose laboratory is supported by the Wellcome Trust (209400/Z/17/Z) (R.J.K.), the European Research Council (681440) (R.J.K.) and the Lister Institute of Preventive Medicine (R.J.K.). We are grateful to A. Williams at the Department of Zoology, University of Oxford, for sequencing support on the NextSeq 500. We express our gratitude to D. Ennis and I. Davis for their advice on RNA-FISH probe design. We thank N. Fursova for her valuable advice on computational analysis. We are grateful to N. Fursova, N. Blackledge and E. Dimitrova for critical reading of the manuscript.

Author information

Authors and Affiliations

  1. Department of Biochemistry, University of Oxford, Oxford, UK

    Paula Dobrinić, Aleksander T. Szczurek & Robert J. Klose

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  1. Paula Dobrinić
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  2. Aleksander T. Szczurek
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  3. Robert J. Klose
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Contributions

Conceptualization, P.D., A.T.S. and R.J.K.; methodology, investigation and formal analysis, P.D. and A.T.S.; writing (original draft), P.D., A.T.S. and R.J.K.; writing (review and editing), P.D., A.T.S. and R.J.K.; funding acquisition and supervision, R.J.K.

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Correspondence to Robert J. Klose.

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The authors declare no competing interests.

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Peer review information Nature Structural and Molecular Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Editor recognition statement Anke Sparmann and Carolina Perdigoto were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Detailed characterisation of the PRC1deg.

a, Schematic of the PRC1deg system. Both endogenous Ring1B alleles are N-terminally fused to the full-length AID tag in a Ring1A-/- background, while OsTIR1 is inserted in Rosa26 locus. b, Western blot analysis of RING1A and RING1B in the control cell line (TIR1) and PRC1deg cells (left panel) with a quantification of RING1B levels (right panel). Shown are values and mean from n = 2 independent experiments. c, Genomic snapshots of typical Polycomb target genes showing cChIP-seq signal for RING1B in wild-type (TIR1) and PRC1deg cells before auxin treatment. d, Metaplot analysis of RING1B cChIP-seq at RING1B-bound sites in wild-type (TIR1) and PRC1deg cells before auxin treatment. Maximal read density in PRC1deg cells was set to e, A scatterplot showing the relationship between RING1B cChIP-seq signal at RING1B-bound sites in wild-type (TIR1) and PRC1deg cells before auxin treatment. R2 is the coefficient of determination for linear regression and cor is Pearson correlation coefficient. f, Immunoprecipitation of RING1B from TIR1 and PRC1deg nuclear extracts followed by western blot analysis of PRC1 components, and SUZ12 (PRC2 component) as a negative control. Shown are representative results from the two independent experiments. vPRC1 – variant PRC1 components, cPRC1 – canonical PRC1 components. g, Chromatin occupancy of PCGF2 (a canonical PRC1 complex component) at Polycomb target gene promoters and control loci in wild-type (TIR1) and PRC1deg cells before auxin treatment assessed by ChIP-qPCR. Individual biological replicates (n = 3) are shown as dots along with mean and SEM. h, As in (G), but for RYBP (a variant PRC1 complex component). (i) Western blot analysis of PRC1 components and SUZ12 in the nuclear extracts of the PRC1deg cells following IAA treatment. CFP1 serves as a loading control. Shown are representative results from the two independent experiments. j, Boxplots comparing H2AK119ub1 cChIP-seq signal before and after IAA treatment at RING1B-bound sites (n = 7240) and over 100 kb genomic windows (n = 27282). Read coverages represent merged spike-in normalised values from n = 3 biologically independent experiments. All signal is normalised to the median RPKM value of RING1B-bound sites in untreated cells. Boxes show interquartile range, center line represents median, whiskers extend by 1.5x IQR, while notches extend by 1.58x IQR/sqrt(n), giving a roughly 95% confidence interval for comparing medians.

Source data

Extended Data Fig. 2 Expression and chromatin features of genes derepressed after PRC1 removal.

a, Scatterplots comparing the log2 fold changes in gene expression (cnRNA-seq) at different time points after auxin treatment in PRC1deg cells with gene expression changes in PRC1CKO (72 h OHT) cells. R2 is the coefficient of determination for linear regression and cor is Pearson correlation coefficient. b, An UpSet plot for genes with significantly increased expression (p-adj < 0.05, fold change > 1.5) following auxin treatment of PRC1deg cells at the indicated time points, or a 72 h OHT treatment of PRC1CKO cells. The total number of genes in each set is shown in the bar chart on the left. Non-empty intersections were sorted by size, while excluding any intersection with less than 10 members for clarity. Intersections not included in the PRC1CKO (72 h OHT) set are highlighted in green and may imply compensatory effects on expression, while the blue column denotes gene changes exclusive to the PRC1CKO and could correspond to secondary effects on expression. c, Boxplots comparing the log2 fold changes in expression of genes split into three groups based on the earliest time point when they became derepressed. Log2 fold changes are derived from n = 3 biologically independent experiments. Dotted line represents 1.5-fold change. Boxes show interquartile range, center line represents median, whiskers extend by 1.5x IQR, while notches extend by 1.58x IQR/sqrt(n), giving a roughly 95% confidence interval for comparing medians. d, Boxplots comparing the gene expression levels in the untreated PRC1deg cells for the three groups of derepressed genes defined in (B). RPKM values represent mean of n = 3 biologically independent experiments. Boxplots are defined as in (C). e, Boxplots comparing the promoter (TSS ± 2.5 kb) cChIP-seq signal for RING1B, H2AK119ub1, SUZ12 and H3K27me3 in the untreated PRC1deg cells for the three groups of derepressed genes defined in (B). Read coverages represent merged spike-in normalised values from n = 3 biologically independent experiments. Boxplots are defined as in (C). f, Boxplots comparing the promoter (TSS ± 2.5 kb) cChIP-seq signal for total Pol II and H3K4me3 in the untreated PRC1deg cells for the three groups of derepressed genes defined in (B). Read coverages represent merged spike-in normalised values from n = 3 biologically independent experiments. Boxplots are defined as in (C).

Extended Data Fig. 3 Characterisation of PRC2 and H3K27me3 after PRC1 removal.

a, Heatmap analysis of SUZ12 cChIP-seq at TSSs for the three groups of genes defined by the earliest time of derepression in PRC1deg cells treated with IAA for the indicated times. Heatmaps are sorted by RING1B cChIP-seq signal in untreated cells. b, Boxplots illustrating log2 fold changes in expression (cnRNA-seq, left panel), SUZ12 cChIP-seq signal (middle) or H3K27me3 cChIP-seq signal (right) at promoters (TSS ± 2.5 kb) of Polycomb target genes showing a significant reduction in SUZ12 levels following 2 hours of IAA treatment. Log2 fold changes are derived from n = 3 biologically independent experiments. Genes are divided into Polycomb target genes that become derepressed (UP, n = 955) and those that do not change in expression (n.s., n = 3739) by 2 hours. SUZ12 binding was reduced in both groups, while H3K27me3 levels were only modestly affected. Boxes show interquartile range, center line represents median, whiskers extend by 1.5x IQR, while notches extend by 1.58x IQR/sqrt(n), giving a roughly 95% confidence interval for comparing medians. c, Boxplots comparing the promoter (TSS ± 2.5 kb) cChIP-seq signal of PRC2 components for the two groups of genes defined in (B), in the wild-type state. Polycomb target genes derepressed after 2 hours of PRC1 depletion are characterised by high levels of PRC2 components, in agreement with this group of genes being highly enriched in Polycomb chromatin domain features. SUZ12 cChIP-seq data is from this study (PRC1deg cells), while JARID2, AEBP2, PCL2 and EPOP are from30. Read coverages represent merged spike-in normalised values from n = 3 biologically independent experiments. Boxplots are defined as in (B). d, As in (A) but for H3K27me3 cChIP-seq. e,The dynamics of reduction in H3K27me3 cChIP-seq signal at RING1B-bound sites which show a significant reduction in H3K27me3 levels by 24 hours of IAA treatment (n = 5926). Central line represents median, with interquartile range shown as shaded area. A theoretical exponential decay function is shown as dashed line, assuming that H3K27me3 levels are halved with every cell cycle if maintenance is completely disrupted. The doubling time of mouse ES cells is approximately 12 hours. f, Genomic snapshots of typical Polycomb target genes showing cChIP-seq signal for H3K27me3 in wild-type (E14) and PRC2deg cells before dTAG-13 treatment. g, Metaplot analysis of H3K27me3 cChIP-seq at RING1B-bound sites in wild-type (E14) and PRC2deg cells before dTAG-13 treatment. Maximal read density in E14 cells was set to 1. h, A scatterplot showing the relationship between H3K27me3 cChIP-seq signal at RING1B-bound sites in wild-type (E14) and PRC2deg cells before dTAG-13 treatment. R2 is the coefficient of determination for linear regression and cor is Pearson correlation coefficient. i, Metaplot analysis of H3K27me3 cChIP-seq at RING1B-bound sites in PRC2deg cells before and after 2 h dTAG-13 treatment. Maximal read density in untreated cells was set to 1.

Extended Data Fig. 4 Effects on total Pol II and H3K4me3 at genes after PRC1 removal.

a, Heatmaps illustrating RING1B binding in untreated cells, and total Pol II and H3K4me3 cChIP-seq signal at TSSs of the three groups of genes defined by the earliest time of derepression in PRC1deg cells treated with IAA for the indicated times. Heatmaps are sorted by RING1B signal in untreated cells. b, Boxplots comparing the log2 fold changes in total Pol II cChIP-seq signal following IAA treatment for genes split into three groups defined by the earliest time of derepression (2 hours, n = 1044; 4 hours, n = 1822; 8 hours, n = 2017). Log2 fold changes are derived from n = 3 biologically independent experiments. The analysis was done at promoters (TSS ± 2.5 kb) and over gene bodies (TSS to TES). Boxes show interquartile range, center line represents median, whiskers extend by 1.5x IQR, while notches extend by 1.58x IQR/sqrt(n), giving a roughly 95% confidence interval for comparing medians. c, Boxplots comparing the log2 fold changes in H3K4me3 cChIP-seq signal following IAA treatment at promoter regions (TSS ± 2.5 kb) for genes split into three groups defined by the earliest time of derepression (2 hours, n = 1044; 4 hours, n = 1822; 8 hours, n = 2017). Log2 fold changes are derived from n = 3 biologically independent experiments. Boxplots are defined as in (B).

Extended Data Fig. 5 Effects on Pol II phosphorylation state after PRC1 removal.

a, Genomic snapshot of Hoxd locus showing cChIP-seq signal for RING1B, total Pol II, Ser5P-Pol II and Ser2P-Pol II in untreated PRC1deg cells and 2 hours after IAA addition. b, Heatmaps illustrating RING1B, total Pol II, Ser5P-Pol II and Ser2P-Pol II levels and changes in polymerase occupancy following 2 h of IAA treatment for three groups of genes defined by the earliest time of derepression. Heatmaps are sorted by RING1B signal in untreated cells. c, Metaplot analysis of Ser5P-Pol II cChIP-seq at promoters of the three groups of genes defined by the earliest time of derepression in untreated PRC1deg cells and 2 hours after IAA addition. The profiles represent the median signal over the shown genomic region. d, As in (C) but for Ser2P-Pol II cChIP-seq over gene bodies.

Extended Data Fig. 6 Detailed characterisation of the smRNA-FISH transcript counting approach.

a, A schematic illustrating our automated approach to analyse smRNA-FISH in a high throughput manner. This approach enables effective single-cell segmentation, conversion of the field of view into single-cell smRNA-FISH images, in which individual transcripts can then be counted using ThunderSTORM119. b, An illustration of the effectiveness of ThunderSTORM threshold factor (TF) value identification for spot (transcript) detection in cells. An Hspg2 dataset is shown as an example. The TF unit is set at a standard deviation of the image background. Spots per cell (mean ± SD; top) and heterogeneity of detected transcript levels expressed as coefficient of variation (middle) are shown for a range of TF values. Vertical red lines indicate the range of TF values yielding similar spot-counting outcomes and which were ultimately employed in this study. Density plots (bottom) demonstrate transcript per cell distributions depending on the TF used. TFs from 6 to 10 yield comparable spot-counting values. Very large or very low TF values lead either to overcounting or undercounting of transcript signals. c, An illustration of the reproducibility between technical and biological replicates for our smRNA-FISH transcript counting approach. Mean transcripts per cell (top), coefficient of variation (middle), and mean transcription burst size inferred from the 2-state model (bottom) are shown. Error bars in the top panel represent 10% of standard deviation of transcripts per cell distributions around the mean. d, To ensure the robustness of our transcript counting approach, we compared it to other spot (transcript) counting methods and manual counting of transcripts in 50 cells. The methods compared are: 3D-FISH QUANT125 (left), 3D Objects Counter120 (middle), and technique used in this study (right). The right panel indicates that our technique can be prone to a slight undercounting when number of transcripts per cell exceeds 60-70, but otherwise performs comparably to other approaches. Pearson correlation coefficient (r) and slope derived from linear regression are presented. e, Transcript counting using 3D-FISH Quant and 3D Objects Counter correlate well with the transcripts counting using our approach.

Source data

Extended Data Fig. 7 Effects on absolute transcript numbers in single cells after PRC1 removal.

a, An MA plot depicting gene expression (cnRNA-seq) changes following 4 h IAA treatment in PRC1deg cells with candidate genes for smRNA-FISH highlighted. The genes chosen span a wide range of initial transcript levels and transcript increase upon RING1B removal. The control genes are highlighted in blue and Polycomb target genes in black. b, Correlation of log2 fold changes between cnRNA-seq and smRNA-FISH after 4 hours of IAA treatment. Hollow dots represent genes excluded from the linear fit. Dots represent mean values while error bars represent standard error of 3 biological replicates. c, Normalised histograms illustrating the distribution of transcripts per cell over the time course of RING1B removal for all genes studied with smRNA-FISH. Shown is a representative biological replicate of 3 independent experiments. n indicates minimum number of cells measured at any given time-point per gene dataset. d, A heatmap of mean fold changes in transcripts per cell over the time course of RING1B removal. Numbers represent the mean of 3 biological replicates. e, As in (D) but representing the mean number of transcripts per cell.

Source data

Extended Data Fig. 8 Analysis of transcription burst size and frequency after PRC1 removal.

a, Examples of model fits to cellular transcripts distributions for all the genes examined in untreated state. The negative binomial fit is in red and density in blue. The Poisson distribution fit is indicated for comparison as a dashed purple line. b, A heatmap of the goodness of fit p-value (one-sided Chi-square test) throughout the time course of RING1B depletion. High p-value (light yellow) represents a good negative binomial fit to the data. c, Barplots representing mean inferred transcription burst sizes for Polycomb target genes and control genes. Error bars correspond to standard error of 3 biological replicates. d, The relationship between nascent spot intensity (active transcription site, measured using nascent smRNA-FISH targeting intronic sequences) and transcription burst size inferred from mRNA-FISH (targeting exonic sequences) reveals that genes with higher predicted burst size values have greater nascent spot intensity. Dots represent mean value while error bars correspond to standard deviation of 3 biological replicates. e, Heatmaps illustrating the fold change in inferred transcription burst size (left) and burst frequency (right) over the time course of RING1B depletion for the panel of genes studied. f, Examples of the relationship between square coefficient of variation (CV) and mean number of transcripts per cell demonstrate that Polycomb target genes derepressed upon PRC1 removal experience increase in transcript number while simultaneously retaining constant transcription burst size. Dashed lines represent theoretical relation between CV2 and the mean number of transcripts at constant burst size values (CV2 = b/mean#mRNA) with changing burst frequencies. Dots represent mean values while error bars correspond to the standard deviation of 3 biological replicates.

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Extended Data Fig. 9 Active allele distribution in the cell population and transcription noise decomposition.

(a) Frequency of nascent spots representing active alleles in cell population (mean percentage of cells with 0, 1, or >1 nascent spot per cell obtained in 3 biological replicates). For comparison a simulated negative binomial distribution of active alleles per cell is shown assuming their expression is independent and random. The simulated distribution assumes the same frequency of spots as in the experimental data and 3 alleles per cell because a large proportion of ES cells will exist in S-phase at any given point. Importantly, the independence of allele expression is maintained 4 h after removal of PRC1 by the addition of IAA. A minimum of 887 cells was measured per biological replicate. (b) Pearson’s correlation coefficient between number of transcripts per cell and either DAPI intensity per cell (a proxy for the cell cycle stage as G2 cells have generally 2x the genetic material of the cells in G1), or cell area (representing cell volume) – the two primary sources of extrinsic noise. (c) i – iii) Examples of area or DAPI signal per cell plotted against transcripts per cell for a control (Hspg2) or Polycomb target genes (Zic2, E2f6). Each data-point represents a single cell measurement. Shading around regression line represents 95% confidence interval. iv) Correlation between cell area and DAPI signal intensity (Pearson’s r = 0.74) suggests that volume of ES cells is strongly related to their cell cycle phase. We note that the cell nucleus in ES cells occupies a significant portion of the total cell volume. (d) Specific heterogeneity (noise) in transcripts per cell expressed as % of the total heterogeneity measured as square coefficient of variation of the transcripts per cell distributions. Cell area and DAPI signal intensity contribute very little to the overall variability in number of transcripts per cell in a population for Polycomb target genes. We note, that this measure of extrinsic variability likely suffers from overestimation as DAPI intensity and cell area are moderately correlated, and hence to some extent represent the same source of variability (see C).

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Dobrinić, P., Szczurek, A.T. & Klose, R.J. PRC1 drives Polycomb-mediated gene repression by controlling transcription initiation and burst frequency. Nat Struct Mol Biol 28, 811–824 (2021). https://doi.org/10.1038/s41594-021-00661-y

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