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• W4306399518 abstract "Article17 October 2022Open Access Transparent process Dynamic chromosomal interactions and control of heterochromatin positioning by Ki-67 Tom van Schaik Tom van Schaik orcid.org/0000-0001-7850-5074 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: Conceptualization, Data curation, Software, Formal analysis, ​Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Stefano G Manzo Stefano G Manzo orcid.org/0000-0002-6911-3527 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: ​Investigation, Methodology Search for more papers by this author Athanasios E Vouzas Athanasios E Vouzas Department of Biological Science, The Florida State University, Tallahassee, FL, USA San Diego Biomedical Research Institute, San Diego, CA, USA Contribution: ​Investigation, Methodology Search for more papers by this author Ning Qing Liu Ning Qing Liu orcid.org/0000-0002-3151-638X Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: ​Investigation, Methodology Search for more papers by this author Hans Teunissen Hans Teunissen Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: ​Investigation, Methodology Search for more papers by this author Elzo de Wit Elzo de Wit orcid.org/0000-0003-2883-1415 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: Formal analysis, Supervision, Funding acquisition Search for more papers by this author David M Gilbert David M Gilbert orcid.org/0000-0001-8087-9737 Department of Biological Science, The Florida State University, Tallahassee, FL, USA San Diego Biomedical Research Institute, San Diego, CA, USA Contribution: Supervision, Funding acquisition Search for more papers by this author Bas van Steensel Corresponding Author Bas van Steensel [email protected] orcid.org/0000-0002-0284-0404 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Department of Cell Biology, Erasmus University Medical Centre, Rotterdam, The Netherlands Contribution: Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Tom van Schaik Tom van Schaik orcid.org/0000-0001-7850-5074 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: Conceptualization, Data curation, Software, Formal analysis, ​Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Stefano G Manzo Stefano G Manzo orcid.org/0000-0002-6911-3527 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: ​Investigation, Methodology Search for more papers by this author Athanasios E Vouzas Athanasios E Vouzas Department of Biological Science, The Florida State University, Tallahassee, FL, USA San Diego Biomedical Research Institute, San Diego, CA, USA Contribution: ​Investigation, Methodology Search for more papers by this author Ning Qing Liu Ning Qing Liu orcid.org/0000-0002-3151-638X Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: ​Investigation, Methodology Search for more papers by this author Hans Teunissen Hans Teunissen Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: ​Investigation, Methodology Search for more papers by this author Elzo de Wit Elzo de Wit orcid.org/0000-0003-2883-1415 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: Formal analysis, Supervision, Funding acquisition Search for more papers by this author David M Gilbert David M Gilbert orcid.org/0000-0001-8087-9737 Department of Biological Science, The Florida State University, Tallahassee, FL, USA San Diego Biomedical Research Institute, San Diego, CA, USA Contribution: Supervision, Funding acquisition Search for more papers by this author Bas van Steensel Corresponding Author Bas van Steensel [email protected] orcid.org/0000-0002-0284-0404 Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands Department of Cell Biology, Erasmus University Medical Centre, Rotterdam, The Netherlands Contribution: Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Author Information Tom Schaik1, Stefano G Manzo1, Athanasios E Vouzas2,3, Ning Qing Liu1, Hans Teunissen1, Elzo Wit1, David M Gilbert2,3 and Bas Steensel *,1,4 1Division of Gene Regulation and Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands 2Department of Biological Science, The Florida State University, Tallahassee, FL, USA 3San Diego Biomedical Research Institute, San Diego, CA, USA 4Department of Cell Biology, Erasmus University Medical Centre, Rotterdam, The Netherlands *Corresponding author. Tel: +31 20 5122040; E-mail: [email protected] EMBO Reports (2022)23:e55782https://doi.org/10.15252/embr.202255782 PDFDownload PDF of article text and main figures.PDF PLUSDownload PDF of article text, main figures, expanded view figures and appendix. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Ki-67 is a chromatin-associated protein with a dynamic distribution pattern throughout the cell cycle and is thought to be involved in chromatin organization. The lack of genomic interaction maps has hampered a detailed understanding of its roles, particularly during interphase. By pA-DamID mapping in human cell lines, we find that Ki-67 associates with large genomic domains that overlap mostly with late-replicating regions. Early in interphase, when Ki-67 is present in pre-nucleolar bodies, it interacts with these domains on all chromosomes. However, later in interphase, when Ki-67 is confined to nucleoli, it shows a striking shift toward small chromosomes. Nucleolar perturbations indicate that these cell cycle dynamics correspond to nucleolar maturation during interphase, and suggest that nucleolar sequestration of Ki-67 limits its interactions with larger chromosomes. Furthermore, we demonstrate that Ki-67 does not detectably control chromatin-chromatin interactions during interphase, but it competes with the nuclear lamina for interaction with late-replicating DNA, and it controls replication timing of (peri)centromeric regions. Together, these results reveal a highly dynamic choreography of genome interactions and roles for Ki-67 in heterochromatin organization. Synopsis pA-DamID mapping and perturbation experiments reveal that Ki-67 interacts in interphase with late-replicating genomic regions in pre-nucleolar bodies and in nucleoli, in competition with the nuclear lamina. The results uncover roles for Ki-67 in nuclear organisation and control of replication timing. pA-DamID maps reveal genome-wide interaction patterns of Ki-67 in interphase. Ki-67 interacts with late-replicating genomic regions in pre-nucleolar bodies and in nucleoli. Ki-67 controls replication timing of centromeric regions. Introduction Ki-67 is a chromosomal, nuclear, and nucleolar protein that is widely used as a marker for cellular proliferation (reviewed in Scholzen & Gerdes, 2000; Sun & Kaufman, 2018; Remnant et al, 2021). It has been implicated in chromatin biology in various stages of the cell cycle. During mitosis, Ki-67 is a key component of the peri-chromosomal layer (PCL) (Verheijen et al, 1989; Booth et al, 2014), where it acts as a surfactant to prevent chromosomal intermingling (Cuylen et al, 2016; Takagi et al, 2016). Following anaphase, Ki-67 changes from a repelling into an attracting behavior to exclude cytoplasmic proteins and compact chromosomes (Cuylen-Haering et al, 2020). Early in interphase Ki-67 accumulates in pre-nucleolar bodies (PNBs), which are punctate structures containing rRNA precursors and various proteins (Ochs et al, 1985). These PNBs gradually fade away as several mature nucleoli are formed (Dundr et al, 2000; Savino et al, 2001; Carron et al, 2012). In these mature nucleoli, Ki-67 is positioned specifically at the nucleolar rim. Together with the nuclear lamina (NL), the nucleolus is a major hub for heterochromatin, as illustrated by both microscopy (Ohno et al, 1959; Lima-De-Faria & Reitalu, 1963) and genomics observations (Guelen et al, 2008; van Koningsbruggen et al, 2010; Dillinger et al, 2017; Vertii et al, 2019). Often, individual heterochromatic genomic loci are stochastically distributed between the NL and nucleoli, with a variable preference for one or the other (Kind et al, 2013; Ragoczy et al, 2014; Vertii et al, 2019). Additionally, disruption of one of the two structures may enhance interactions with the other (Solovei et al, 2013; Ragoczy et al, 2014), which may indicate a competitive mechanism. Interestingly, depletion of Ki-67 has been shown to lead to a loss of heterochromatin around the nucleolus (Sobecki et al, 2016), suggesting that it may tether heterochromatin to the nucleolus. So far, most studies of the interplay between Ki-67 and chromatin have relied on microscopy observations (e.g., Booth et al, 2014; Sobecki et al, 2016; Matheson & Kaufman, 2017). While these experiments have been highly informative, it has remained unclear how exactly Ki-67 interacts with the genome throughout the cell cycle. Genome-wide interaction data would greatly enhance this understanding and permit comparisons with other nuclear positioning data, the epigenetic landscape, and functional readouts of the genome such as transcription and replication timing. Here, we provide such data using our recently developed pA-DamID technology, which allows for simultaneous in situ visualization of protein-DNA interactions and generation of genome-wide interaction maps (van Schaik et al, 2020). Our results uncover remarkably dynamic interactions of Ki-67 with the genome of human cells and provide insights into its roles in heterochromatin organization and replication timing. Results pA-DamID captures genome–Ki-67 interactions We used our recently developed pA-DamID method (van Schaik et al, 2020, 2022) to profile Ki-67 interactions with the genome. pA-DamID allows us to both create maps of genome-wide protein-DNA interactions, and visualize these interactions in situ with the m6A-Tracer protein (Fig 1A; Kind et al, 2013; van Schaik et al, 2020). Following pA-DamID with a Ki-67 antibody in hTERT-RPE, HCT116, and K562 human cell lines, we indeed observe that m6A-Tracer binding (and hence the interaction of Ki-67 with the genome) is enriched at nucleoli stained by Ki-67, compared to a free Dam control (Fig 1B and C). m6A-Tracer binding occurs mostly at the edges of nucleoli, indicating that Ki-67 preferentially contacts DNA at the nucleolar periphery. However, Ki-67 is not exclusively localized at nucleoli and may be locally enriched elsewhere, such as the nuclear periphery (Fig 1B, orange arrows). This overlaps with m6A-Tracer staining, indicating that Ki-67 at these sites can also engage in genome interactions. Despite these individual foci, on average m6A-Tracer binding is not enriched at the nuclear periphery compared to the Dam control in hTERT-RPE and HCT116 cells (Appendix Fig S1A). In addition, a moderate homogeneous m6A-Tracer signal throughout the nucleus may be caused by low concentrations of DNA-interacting Ki-67 in the nuclear interior, but also by non-specific antibody binding. We then processed these m6A-tagged DNA samples for high-throughput sequencing to identify the genomic regions that interact with Ki-67. We first describe results in unsynchronized cells; below we discuss the dynamics throughout the cell cycle. As described previously (van Schaik et al, 2020), pA-DamID utilizes a free Dam control to normalize DNA accessibility and amplification biases (Greil et al, 2006). After this Dam normalization, we observed a striking domain-like pattern of Ki-67 binding to the genome (Appendix Fig S1B). We balanced data resolution and reproducibility by using 50 kb averaging bins that yield Pearson correlation coefficients between independent biological replicate experiments in the range of 0.40–0.80 (Appendix Fig S1C and D); at smaller bin sizes the data were too noisy to be informative. To validate these interaction maps, we used an HCT116 cell line with mClover- and AID-tagged Ki-67, which allow for protein visualization and rapid protein depletion upon the addition of auxin (Takagi et al, 2016). Incubation of these cells with auxin for 24 h resulted in a near-complete depletion of mClover fluorescence, but only a partial decrease in Ki-67 immunostaining signal (Appendix Fig S1E and F). This difference may be caused by the higher sensitivity of indirect immunofluorescence (Takagi et al, 2016). In accordance with previous RNAi depletion experiments (Booth et al, 2014), the residual Ki-67 signal appeared to localize in fewer and more interior positioned nucleoli (Appendix Fig S1E). Genome-wide mapping of Ki-67 interactions with pA-DamID resulted in a strong signal loss at the Ki-67 interaction domains upon the addition of auxin (Appendix Fig S1G and H). We assume that the remaining signals (e.g., on chr22) result from the residual Ki-67 protein. These data verify that Ki-67 interaction domains are specific for Ki-67 and not caused by technical artifacts. Because Ki-67 is a very large protein (~ 350 kDa), we reasoned that the location of the antibody epitope could affect the observed interaction patterns. Ki-67 contains protein binding domains and a DNA-binding domain, positioned at the N and C-terminus, respectively (reviewed in Sun & Kaufman, 2018). The initial antibody we used was generated against a peptide sequence roughly in the middle of the protein (~ 1150/3256 amino acids), so we chose two additional antibodies to target each protein end. As before, immunostaining following auxin-mediated Ki-67 depletion confirms antibody specificity to Ki-67 (Fig EV1A). Following pA-DamID, only the C-terminus antibody results in m6A-Tracer enrichment around Ki-67 domains (Fig EV1B) and yields a genome-wide domain pattern that is similar to that of the initially used antibody (Fig EV1C). With the N-terminus antibody, some of this domain pattern can also be observed, but the data quality is rather poor (Fig EV1D and E), possibly because the antibody epitope is too far from the DNA. These results thus show that Ki-67 profiles can be reproduced with different antibodies, and are in accordance with C-terminal location of the DNA binding domain. Click here to expand this figure. Figure EV1. Reproducibility of pA-DamID maps with different Ki-67 antibodies A. Quantification of Ki-67 levels as described in (Fig S1E and F) based on mClover and Ki-67 immunostaining signals in HCT116 Ki-67-AID cells using different Ki-67 antibodies. For this experiment, single confocal microscopy sections from the middle of cell nuclei were used rather than maximum projections from entire nuclei. Results are combined from two biological replicates (r1 and r2, marked in different colors), with in each case at least 60 cells scored. Triangles mark a few extreme values that were clipped. Boxplots: horizontal lines represent 25th, 50th, and 75th percentiles; whiskers extend to 5th and 95th percentiles. B. Quantification of m6A-Tracer enrichment around Ki-67-marked nucleoli in HCT116 cells, as described in (Fig 1B and C). Results are combined from one (middle-targeting antibody) or two (N- and C-terminus antibodies) biological replicates, with a total of 27, 13, 20, and 31 cells for free Dam, Ki-67 middle, Ki-67 N-terminus, and Ki-67 C-terminus, respectively. Data from Fig 1C are not included here. C. Ki-67 interactions in HCT116 cells on two representative chromosomes as determined by pA-DamID using three different Ki-67 antibodies. Data profiles are averages of n biological replicates and smoothed with a running mean across nine 50 kb bins. Centromeres are highlighted by black bars. D, E. Correlation of all 50 kb genomic bins between Ki-67 interactions profiled with the middle-targeting antibody and the N- (D) and C-terminus antibodies (E). Download figure Download PowerPoint Finally, we sought to confirm these results by chromatin immunoprecipitation followed by sequencing (ChIP-seq). We focused on the Ki-67-AID cells, where depletion of Ki-67 by auxin treatment should cause a substantial loss of specific ChIP-seq signal. Reassuringly, when normalized over this control, cells not treated with auxin showed a domain-like ChIP-seq pattern that was very similar to the pA-DamID pattern (Appendix Fig S2A–F). However, the dynamic range of this ChIP-seq pattern was low compared to that of pA-DamID, and the ChIP-seq pattern was obscured when a conventional normalization over input-DNA was applied (Appendix Fig S2C and E), presumably because this normalization incompletely corrects for technical biases. Thus, the pA-DamID map can be recapitulated by ChIP-seq, but the signals obtained with the latter are of borderline quality. This may explain why no ChIP-seq maps have been published for Ki-67 so far. Combined, we conclude that the application of pA-DamID results in robust genome-wide Ki-67 interaction maps, although the data resolution remains limited to about 50 kb. The m6A-Tracer staining indicates that interactions of Ki-67 with the genome are enriched near nucleoli, but may also occur elsewhere. Ki-67 binding varies between cell types but is consistently enriched near centromeres and at small chromosomes Nucleoli are formed around rDNA repeats that are positioned on the p-arm of several human chromosomes, next to the centromere. We therefore expected to find binding of the nucleolar protein Ki-67 near these regions. Indeed, Ki-67 interactions are enriched near centromeres of rDNA-containing chromosomes for all cell types (Fig 1D and E). However, Ki-67 may lack affinity for rDNA sequences themselves, as these show no enrichment in our data (Fig EV2A). Rather, Ki-67 may interact with peri-centromeric heterochromatin (see below). Together with the m6A-Tracer visualization (Fig 1B), these results support a model where Ki-67 interacts with chromatin at the nucleolar surface rather than in the nucleolar interior where rDNA is located (Nemeth & Grummt, 2018). Figure 1. Visualization and genome-wide profiling of DNA-Ki-67 interactions using pA-DamID Schematic overview of pA-DamID (van Schaik et al, 2020). Permeabilized cells are incubated with a primary antibody (e.g., against Ki-67), followed by a fusion of protein A and Dam (pA-Dam). After removal of unbound pA-Dam, the Dam enzyme is activated by the addition of S-adenosylmethionine (SAM), resulting in local deposition of m6A marks. m6A-marked DNA can be processed for high-throughput sequencing, or alternatively cells can be fixed and m6A marks visualized using the m6A-Tracer protein. Representative confocal microscopy sections of HCT116 cells following pA-DamID with free Dam (top panel) or Ki-67 antibody (bottom panel), labeled with m6A-Tracer protein and stained for Ki-67. Scale bar: 5 μm. Quantification of the enrichment of Ki-67 antibody and m6A-Tracer signals relative to segmented Ki-67 domains (that we interpret as nucleoli) in different cell lines. For every cell, the enrichment is calculated by pixel-distances (pixel: 80 nm) relative to the mean signal of that cell and represented as a log2-ratio. Negative distances are outside of Ki-67 domains, a distance of zero marks the domain boundary and positive distances are inside of Ki-67 domains. Every thin line corresponds to an individual cell and the thick line is the mean of all cells. Results are combined from three (hTERT-RPE) or one (HCT116 and K562) biological replicates. The number of analyzed cells is included in the bottom right of each m6A-Tracer panel. P-values are according to Wilcoxon tests comparing m6A-Tracer signals of free Dam control and Ki-67 pA-DamID samples, within the Ki-67 domains. Comparison of Ki-67 pA-DamID profiles (log2-ratios over the free Dam control) across two chromosomes in hTERT-RPE, HCT116, and K562 cells. Sequenced reads are counted and normalized in 50 kb bins. Data are averages of n biological replicates and smoothed with a running mean across nine 50 kb bins for visualization purposes. Centromeres are highlighted by black bars. The mean Ki-67 interaction score within 2 Mb of centromeres for each chromosome, ordered by size. rDNA-containing chromosomes are highlighted in red. P-values (Wilcoxon test) indicate statistical significance of the centromeric log2 signals being > 0. Distributions of Ki-67 interactions nearby centromeres. Boxplots are drawn for every 0.5 Mb, summarizing values of 10 overlapping 50 kb genomic bins from all chromosomes. Boxplots: horizontal lines represent 25th, 50th (highlighted in red), and 75th percentiles; whiskers extend to 5th and 95th percentiles. Analysis is based on averaged data from (D). Representative confocal maximum projection of hTERT-RPE cells stained for CENPA and Ki-67. Cells were treated with 0.05% dimethyl sulfoxide (DMSO). Scale bar: 5 μm. Quantification of the Ki-67 enrichment at centromeres, relative to the mean Ki-67 intensity of a cell. Every point represents one centromere. Results are combined from two biological replicates. In total, 44 cells were analyzed. A Wilcoxon test was used to test for a statistically significant difference from zero (P-value < 2.2e-16). Boxplot: horizontal lines represent 25th, 50th, and 75th percentiles; whiskers extend to 5th and 95th percentiles. Average enrichment of CENPA and Ki-67 from centromeres. For every cell, the enrichment is calculated by pixel-distances (pixel: 80 nm) relative to the mean signal of that cell and represented as a log2-ratio. Every thin line corresponds to an individual cell, and the thick line is the mean of all cells. The blue dashed line represents the distance up to which the log2-enrichment is higher than zero. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Ki-67 interactions are enriched on small chromosomes, but not enriched at rDNA repeats Enrichment of rDNA sequences detected by Ki-67 pA-DamID compared to the Dam control. Every point represents a biological replicate. Pearson correlations between cell lines along all 50 kb genomic bins of individual chromosomes. The panel title indicates the first cell line and the color indicates the second cell line between which the correlation was calculated. For all six cell line comparisons, all Wilcoxon tests between chromosome sizes and the Pearson correlations between cell lines were significant (P-value < 2.4e-13). Distributions of Ki-67 interactions for all chromosomes, ordered by decreasing chromosome size. rDNA-containing chromosomes are highlighted by black borders. Boxplots show the distribution across all 50 kb genomic bins in a chromosome. Boxplots: horizontal lines represent 25th, 50th, and 75th percentiles; whiskers extend to 5th and 95th percentiles. A Wilcoxon test between chromosome sizes and standard deviations of the Ki-67 interactions was significant (P-value < 2.4e-13) for all three cell lines. Analyses in (B, C) are based on replicate-averaged data as shown in Fig 1D. Download figure Download PowerPoint Our data indicate that Ki-67 also interacts with peri-centromeric regions of nearly all other chromosomes (Fig 1E and F). We speculated that these interactions would correspond to the Ki-67 foci outside of the apparent nucleoli (Fig 1B). Indeed, co-immunostaining showed that centromeres (marked by CENPA) often overlap with Ki-67, even when these are not at nucleoli (Fig 1G and H). Moreover, the Ki-67 signal extends beyond CENPA foci (Fig 1I), which is in agreement with the observed binding at peri-centromeric DNA. Differences in Ki-67 interactions among cell types are most apparent on the arms of large chromosomes (Figs 1D and EV2B). In contrast, on small chromosomes, the interactions are more consistent (Fig EV2B), and more frequent as illustrated by a higher dynamic range compared to larger chromosomes (Fig EV2C). Small chromosomes are typically positioned in the nuclear interior and in the vicinity of nucleoli (Bolzer et al, 2005; Su et al, 2020), suggesting that these interactions mostly involve nucleolar Ki-67. Release of Ki-67 from nucleoli drastically changes its DNA interactions To test the importance of nucleolar Ki-67 for the observed genomic interactions, we released Ki-67 from nucleoli by adding a low dose of actinomycin D (ActD; 50 ng/ml) for 3 h. ActD at this concentration specifically inhibits PolI transcription and results in nucleolar breakdown (Perry & Kelley, 1970; Ragoczy et al, 2014). As a result, Ki-67 is no longer restricted to nucleoli and shows patterns that are distinct from other nucleolar markers (i.e. MKI67IP; Figs 2A, and EV3A, and B). Ki-67 localization to mitotic chromosomes is not affected by ActD (Fig 2A, orange arrow). Figure 2. Ki-67 binding is constrained by nucleolar positioning and variable between cell types Representative confocal microscopy sections of hTERT-RPE cells following 3 h of a low dose of actinomycin D (ActD, 50 ng/ml in 0.05% DMSO) to disrupt nucleoli, and of cells treated with a similar quantity of DMSO as control. Cells were stained for MKI-67IP and Ki-67 proteins to visualize nucleolar disruption and new Ki-67 localization patterns. Orange arrows highlight mitotic chromosomes, which show unaltered Ki-67 localization following ActD treatment. Scale bar: 10 μm. Comparison of Ki-67 interactions in the three cell lines after ActD-induced nucleolar disruption for two representative chromosomes. Log2-ratios were converted to z-scores to correct for differences in dynamic range between conditions and replicates. Data are averages of two biological replicates and smoothed with a running mean across nine 50 kb bins for visualization. Centromeres are highlighted by black bars. Binned scatterplots between Ki-67 interactions in DMSO and ActD conditions. Pearson correlation scores were calculated with the ‘cor’ function in R. Overlay between Ki-67 interactions between the cell types in DMSO and ActD conditions for a representative locus, as described in panel B. Overview of Pearson correlations between Ki-67 interactions in different cell types, in DMSO and ActD conditions. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Actinomycin D reduces Ki-67 binding to centromeres A, B. Representative confocal microscopy sections showing the effect of ActD on nucleolar morphology in HCT116 (A) and K562 cells (B), as described in (Fig 2A). Scale bar: 10 μm. C. The differences between ActD and control conditions in mean chromosomal Ki-67 signal are plotted for chromosomes sorted by size. rDNA-containing chromosomes are highlighted with triangles. D. The mean Ki-67 interactions scores near centromeres are plotted for each chromosome (within 2 Mb of centromeres, overlapping the enrichment in (Fig 1F)) in control and ActD conditions. rDNA-containing chromosomes are highlighted in red. E, F. Similar plots showing Ki-67 enrichment at centromeres as (Fig 1H and I), but including cells treated with ActD (43 cells in total from two biological replicates). DMSO samples are the same as before. Boxplots: horizontal lines represent 25th, 50th, and 75th percentiles; whiskers extend to 5th and 95th percentiles. In panel E, the difference between DMSO and ActD-treated cells is statistically significant (P < 2.2e-16, Wilcoxon test). Download figure Download PowerPoint We then generated pA-DamID maps of Ki-67 (Fig 2B). For these quantitative analyses of Ki-67 interactions, we first converted the log2-ratios to z-scores. This equalizes any variable dynamic ranges between conditions and replicates without affecting the data distribution (van Schaik et al, 2020). The results show that nucleolar breakdown affects Ki-67 binding to the genome in different ways depending on the cell type (Fig 2B). In hTERT-RPE and HCT116 cells, the genomic pattern of Ki-67 seems mostly maintained but interactions show strong quantitative differences (Fig 2C–E). This includes an overall balance shift of Ki-67 from small chromosomes to large chromosomes (Fig EV3C). In contrast, the interaction pattern in K562 cells is more broadly altered (Fig 2C–E), although a reduction of Ki-67 interactions with rDNA-containing chromosomes is shared among all three cell types (Fig EV3C). All three cell types also exhibit a consistent loss of Ki-67 interactions near centromeres, again most clearly for rDNA-containing chromosomes (Fig EV3D). We verified this result by immunostaining, which showed a reduced overlap of Ki-67 with centromeres upon the addition of ActD (Fig EV3E and F). These results illustrate that nucleolar integrity is required for a normal Ki-67 interaction pattern across the genome, in particular for the preference at centromeres and small and rDNA-containing chromosomes. Cell cycle dynamics of Ki-67 interactions So far, we performed these pA-DamID experiments in unsynchronized cells. However, microscopy studies have shown that Ki-67 coats chromosomes during" @default.
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• W4306399518 title "Dynamic chromosomal interactions and control of heterochromatin positioning by Ki‐67" @default.
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• W4306399518 doi "https://doi.org/10.15252/embr.202255782" @default.
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