1. Cell Biology
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Alpha-satellite RNA transcripts are repressed by centromere–nucleolus associations

  1. Leah Bury
  2. Brittania Moodie
  3. Jimmy Ly
  4. Liliana S McKay
  5. Karen HH Miga
  6. Iain M Cheeseman  Is a corresponding author
  1. Whitehead Institute for Biomedical Research, United States
  2. Department of Biology, Massachusetts Institute of Technology, United States
  3. UC Santa Cruz Genomics Institute, University of California, Santa Cruz, United States
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Cite this article as: eLife 2020;9:e59770 doi: 10.7554/eLife.59770

Abstract

Although originally thought to be silent chromosomal regions, centromeres are instead actively transcribed. However, the behavior and contributions of centromere-derived RNAs have remained unclear. Here, we used single-molecule fluorescence in-situ hybridization (smFISH) to detect alpha-satellite RNA transcripts in intact human cells. We find that alpha-satellite RNA-smFISH foci levels vary across cell lines and over the cell cycle, but do not remain associated with centromeres, displaying localization consistent with other long non-coding RNAs. Alpha-satellite expression occurs through RNA polymerase II-dependent transcription, but does not require established centromere or cell division components. Instead, our work implicates centromere–nucleolar interactions as repressing alpha-satellite expression. The fraction of nucleolar-localized centromeres inversely correlates with alpha-satellite transcripts levels across cell lines and transcript levels increase substantially when the nucleolus is disrupted. The control of alpha-satellite transcripts by centromere-nucleolar contacts provides a mechanism to modulate centromere transcription and chromatin dynamics across diverse cell states and conditions.

Introduction

Chromosome segregation requires the function of a macromolecular kinetochore structure to connect chromosomal DNA and spindle microtubule polymers. Kinetochores assemble at the centromere region of each chromosome. The position of centromeres is specified epigenetically by the presence of the histone H3-variant, CENP-A, such that specific DNA sequences are neither necessary nor sufficient for centromere function. (McKinley and Cheeseman, 2016). However, despite the lack of strict sequence requirements, centromere regions are typically characterized by repetitive DNA sequences, such as the alpha-satellite repeats found at human centromeres. Understanding centromere function requires knowledge of the centromere-localized protein components, as well as a clear understanding of the nature and dynamics of centromere chromatin. Although originally thought to be silent chromosome regions, centromeres are actively transcribed (Perea-Resa and Blower, 2018). Prior work has detected α-satellite transcription at centromere and pericentromere regions based on the localization of RNA polymerase II (Bergmann et al., 2012; Chan et al., 2012) and the production of centromere RNA transcripts (Chan et al., 2012; Saffery et al., 2003; Wong et al., 2007). Centromere transcription and the resulting RNA transcripts have been proposed to play diverse roles in kinetochore assembly and function (Biscotti et al., 2015; Blower, 2016; Fachinetti et al., 2013; Ferri et al., 2009; Grenfell et al., 2016; Ideue et al., 2014; McNulty et al., 2017; Quénet and Dalal, 2014; Rošić and Erhardt, 2016; Wong et al., 2007). However, due to limitations for analyses of centromere transcripts that average behaviors across populations of cells and based on varying results between different studies, the nature, behavior, and contributions of centromere-derived RNAs have remained incompletely understood.

Here, we used single-molecule fluorescence in-situ hybridization (smFISH) to detect alpha-satellite RNA transcripts in individual, intact human cells. Our results define the parameters for the expression and localization of centromere and pericentromere-derived transcripts across a range of conditions. We find that the predominant factor controlling alpha-satellite transcription is the presence of centromere–nucleolar contacts, providing a mechanism to modulate centromere transcription and the underlying chromatin dynamics across diverse cell states and conditions.

Results and discussion

Quantitative detection of alpha-satellite RNAs by smFISH

Prior work analyzed centromere RNA transcripts primarily using population-based assays, such as RT-qPCR and RNA-seq, or detected centromere RNAs in spreads of mitotic chromosomes. To visualize alpha-satellite RNA transcripts in individual intact human cells, we utilized single-molecule fluorescence in-situ hybridization (smFISH), a strategy that has been used to detect mRNAs and cellular long non-coding RNAs (lncRNAs) (Raj et al., 2008). The high sensitivity of smFISH allows for the accurate characterization of number and spatial distribution of RNA transcripts.

Alpha-satellite DNA is degenerate such that it can vary substantially between different chromosomes with the presence of higher-order repeats of alpha-satellite variants (Waye and Willard, 1987; Willard and Waye, 1987b). Thus, we first designed targeted probe sets to detect RNAs derived from centromere regions across multiple chromosomes: (1) Sequences complementary to a pan-chromosomal consensus alpha-satellite sequence (labeled as ‘ASAT’), (2) sequences that target supra-chromosomal family 1 (SF1) higher-order arrays, present on chromosomes 1, 3, 5, 6, 7, 10, 12, 16, and 19 (labeled as ‘SF1’) (Alexandrov et al., 2001; Uralsky et al., 2019), and (3) sequences that are enriched for transcripts from the Supra-Chromosomal family three higher-order arrays present on chromosomes 1, 11, 17 and X (labeled as ‘SF3’), with an increased number of targets on chromosome 17 (D17Z1) (Willard and Waye, 1987a). Second, we designed probes that detect sequences enriched on specific chromosomes including the X chromosome (DXZ1, labeled ‘X’; Miga et al., 2014; Willard et al., 1983) and chromosome 7 (D7Z2, labeled as ‘7.2’; Waye et al., 1987). For complete sequence information and an analysis of sequence matches to different chromosomes, see Supplementary files 1 and 2. Alpha-satellite DNA can span megabases of DNA on a chromosome, whereas the active centromere region is predicted to be as small as 100 kb in many cases (McKinley and Cheeseman, 2016). Thus, these smFISH probes will detect RNA transcripts from both the active centromere region and flanking pericentric alpha-satellite DNA.

In asynchronously cycling HeLa cells, we detected clear foci using smFISH probe sets for ASAT, SF1, and SF3 (Figure 1A). To ensure that this signal was not due to non-specific hybridization of the RNA probes to genomic DNA, we treated cells with RNase A prior to hybridization. The RNA- FISH signal was diminished substantially after RNase A treatment (Figure 1A,B), confirming the ribonucleic source of the observed signal. As an additional validation of these probes to confirm that they are recognizing alpha-satellite-derived sequences, we used them in a modified procedure to conduct DNA FISH. DNA FISH revealed multiple DNA-associated puncta that were distributed throughout the nucleus in interphase and aligned along the spindle axis on metaphase/anaphase chromosomes (Figure 1—figure supplement 1A), consistent with the behavior of centromere regions. In contrast to the ASAT, SF1, and SF3 probes, we did not detect smFISH foci using oligos designed to recognize transcripts derived from the centromere regions of chromosome seven or the X chromosome (Figure 1—figure supplement 1B). As the absence of signal could reflect a variety of technical features of probe design or a detection limit for the expression level or length of these sequences, we chose not to pursue these probes further. To quantify the number of distinct RNA-FISH foci, we used CellProfiler (Carpenter et al., 2006) to measure the number of foci per nucleus systematically using z-projections of the acquired images. The number of smFISH foci varied between individual cells, but averaged approximately four foci/cell for the ASAT, SF1, and SF3 probe sets in HeLa cells (Figure 1C).

Figure 1 with 1 supplement see all
Quantitative detection of centromere RNAs using smFISH.

(A) Detection of alpha-satellite RNA transcripts by smFISH in asynchronous HeLa cells. Designed probes detected RNAs derived from centromeres across subsets of multiple chromosomes, but with distinct specificity (see Supplementary file 2; ASAT, SF1, and SF3 repeats). Treatment of cells with RNase A prior to hybridization diminished RNA-smFISH signals. (B) Quantification of smFISH foci in the presence or absence of RNase A treatment indicates that the signal observed is due to a ribonucleic source. Points represent the number of foci per cell for each cell test. Error bars represent the mean and standard deviation of at least 100 cells. (C) Detection of anti-sense alpha-satellite transcripts in HeLa cells for the ASAT smFISH probe sequences. Error bars represent the mean and standard deviation of at least 100 cells. (D) Images showing varying abundance of alpha-satellite RNA across cell lines (based on smFISH foci), with RPE-1 cells displaying overall lower levels of centromere smFISH foci. For the RPE-1 + p53 KO condition, p53 was eliminated using an established TP53 iKO cell line (McKinley and Cheeseman, 2017). (E) Left, quantification indicating the variation of smFISH foci across selected cell lines. Error bars represent the mean and standard deviation of at least 100 cells. Right, average smFISH foci/cell for multiple independent replicates to enable statistical comparisons. p-values represent T-tests conducted on replicates of smFISH foci numbers for each selected cell line. (F) Graph showing quantification of RT-qPCR for alpha-satellite transcripts from chromosome 21. Levels of chromosome 21 alpha-satellite RNAs was not detected in Rpe1 cells and was therefore set to 0 in the figure. The levels of alpha-satellite transcripts in RPE-1 cells are reduced compared to HeLa cells. A semi-quantitative assessment of the RT-PCR data (with no standard curve interpolation, see Figure 1—figure supplement 1D) indicated a ~ 20-fold reduction in alpha-satellite transcripts in RPE-1 cells relative to HeLa. We performed three biological replicates of the RT-qPCR. Scale bars, 25 µm.

Transcription of non-coding RNAs often occurs from both strands of DNA at a given locus. We therefore tested whether we could detect antisense (relative to the ‘sense’ probes used above) alpha-satellite transcripts in Hela cells using smFISH. Indeed, for the ASAT probe sequences, we were able to visualize ~3 foci/cell using antisense smFISH probes, similar to numbers using the sense probe set (four foci/cell) (Figure 1C). Antisense transcription at the centromere has also been previously reported across a variety of species (Carone et al., 2009; Choi et al., 2011; Chueh et al., 2009; Ideue et al., 2014; Koo et al., 2016; Li et al., 2008; May et al., 2005).

The level of transcription for centromeric and pericentric satellite DNA has been proposed to vary between developmental stages and tissue types (Maison et al., 2010; Pezer and Ugarković, 2008). In addition, changes in centromere and pericentromere transcription have been observed in cancers (Ting et al., 2011). Therefore, we next sought to analyze differences in smFISH foci across different cell lines using the ASAT and SF1 probe sets. We selected the chromosomally-unstable osteosarcoma cell line U2OS, the breast cancer cell line MCF7, and the immortalized, but non-transformed hTERT-RPE-1 cell line. We found that the levels of alpha-satellite transcripts varied modestly across cell lines (Figure 1D,E; Figure 1—figure supplement 1C), with RPE-1 cells displaying overall lower levels of smFISH foci. As an additional confirmation of these behaviors, we tested the presence of alpha-satellite transcripts by RT-qPCR. Using a previously validated RT-qPCR primer pair against the alpha-satellite array on chromosome 21 (Molina et al., 2016; Nakano et al., 2003), we observed dramatically reduced levels of alpha-satellite transcripts in RPE-1 cells compared to HeLa cells (Figure 1—figure supplement 1D; Figure 1F). To test whether the transformation status of the cell line correlated with the level of smFISH foci, we eliminated the tumor suppressor p53 in RPE-1 cells using our previously-established inducible knockout strategy (McKinley and Cheeseman, 2017). Eliminating p53 did not substantially alter the levels of alpha-satellite smFISH foci in Rpe1 cells (Figure 1D,E) indicating that other factors likely contribute to the observed cellular levels of alpha-satellite RNA transcripts. Together, this strategy provides the ability to quantitatively detect centromere and pericentromere-derived alpha-satellite RNA transcripts using smFISH probes against alpha-satellite sequences and demonstrates that human cell lines display varying levels of alpha-satellite transcripts.

Analysis of alpha-satellite transcript localization and cell- cycle control

We next sought to assess the localization of alpha-satellite RNA transcripts within a cell. Prior work suggested that non-coding centromere transcripts are produced in cis and remain associated with the centromere from which they are derived, including through associations with centromere proteins (McNulty et al., 2017). Other studies support the action of centromere-derived RNAs in trans (Blower, 2016), but again acting at centromeres. To investigate the distribution of the centromere transcripts, we performed combined immunofluorescence and smFISH to visualize alpha-satellite transcripts relative to centromeres and microtubules. In interphase cells, smFISH foci localized within the nucleus (Figure 2A). Thus, unlike many mRNAs, alpha-satellite-derived RNAs are not exported to the cytoplasm. Although we detected colocalization of alpha-satellite RNAs with a subset of centromeres in HeLa cells, only ~10% of smFISH foci overlapped with centromeres (Figure 2A,B). In mitotic cells, smFISH foci did not associate with chromatin (Figure 2C). Instead, during all stages of mitosis, alpha-satellite RNA transcripts appeared broadly distributed within the cytoplasm. Finally, as the cells exited mitosis into G1, the smFISH foci remained distinct from the chromosomal DNA and were thus excluded from the nucleus when the nuclear envelope reformed (Figure 2D). Similar patterns of cell-cycle dependent localization changes with mitotic exclusion from chromatin have been reported for other cellular long non-coding RNAs (Cabili et al., 2015; Clemson et al., 1996). In contrast to our findings that alpha-satellite transcripts are primarily separable from centromere loci, prior work from others found close associations between alpha-satellite transcripts and centromeres (Blower, 2016; Bobkov et al., 2018; McNulty et al., 2017; Rošić et al., 2014). Based on these different behaviors, we hypothesize that the smFISH approach using the native fixation conditions detects mature alpha-satellite transcripts, but is unable to detect nascent RNAs in the process of transcription. Thus, once transcribed, alpha-satellite non-coding RNAs visualized by smFISH display nuclear localization, but are not tightly associated with the centromere regions from which they are derived.

Analysis of centromere RNA foci across the cell cycle.

(A) Immunofluorescence images (using anti-tubulin antibodies in green and anti-centromere antibodies (ACA) in red) showing alpha-satellite derived transcripts (smFISH; ASAT probe sets) localized to the nucleus during interphase in HeLa cells. The majority of detected transcripts do not co-localize with centromeres. (B) Graph showing the fraction of ASAT smFISH foci that overlap with centromeres by immunofluorescence. Each point represents one cell. n = 36 cells. (C) Immunofluorescence of HeLa cells (as in A) throughout the cell cycle reveals smFISH foci are separable from chromatin in mitosis. (D) Immunofluorescence-smFISH analysis indicates that progression of cells into G1 (defined by cells with a mid-body) results in the nuclear exclusion of smFISH foci. Left: Foci are located in the cytoplasm after the nuclear envelope reforms. Right: Foci are absent, possibly reflecting the degradation of cytoplasmic RNA. (E) Quantification of smFISH foci throughout the cell cycle (for either ASAT or SF1 probe sets) reveals that transcripts levels are high in S/G2 and mitotic cells, but reduced as cells exit mitosis into G1. A T-test was conducted on independent replicates of the ASAT smFISH data for each selected cell-cycle state. Error bars represent the mean and standard deviation of at least 8 cells/replicate. Scale bars, 10 µm.

We next analyzed the temporal changes in alpha-satellite transcript numbers during the cell cycle. In contrast to other genomic loci, RNA Polymerase II is present at human and murine centromeres during mitosis (Chan and Wong, 2012; Perea-Resa et al., 2020). In addition, centromere transcription during G1 has been proposed to play a role in CENP-A loading (Bobkov et al., 2018; Chen et al., 2015; Quénet and Dalal, 2014). Recent work measuring the levels of satellite transcripts originating from specific centromeres in human cells suggested the presence of stable RNA levels during the entire cell cycle (McNulty et al., 2017). smFISH provides the capacity to measure the levels of alpha-satellite transcripts in individual cells over the course of the cell cycle. We utilized combined immunofluorescence-smFISH to simultaneously label alpha-satellite RNA transcripts and microtubules, allowing us to distinguish between G1 cells (due to the presence of a mid-body), an S/G2 interphase population, and mitotic cells. In contrast to previous observations, our analysis revealed that the transcripts detected by our smFISH method increased in S/G2 and remained stable throughout mitosis (Figure 2E). We note that a G2/M peak of transcript levels has been reported for murine Minor Satellite transcripts (Ferri et al., 2009). However, as cells exited mitosis into G1, transcripts detected by smFISH were reduced (Figure 2E). We speculate that this may result from the nuclear exclusion of the existing alpha-satellite transcripts, which would make this more susceptible to degradation by cytoplasmic RNAses. Thus, alpha-satellite transcript levels fluctuate over the cell cycle with G1 as a period of low transcript numbers, either indicating reduced transcription during this cell-cycle stage or the increased elimination of alpha-satellite-derived RNA transcripts.

Alpha-satellite RNAs are products of Pol II-mediated transcription

Previous studies have suggested that centromeres are actively transcribed by RNA polymerase II. RNA polymerase II localizes to centromeres in S. pombe, Drosophila melanogaster, and human cells, including at centromeric chromatin on human artificial chromosomes (HACs) and at neocentromeres (Bergmann et al., 2011; Catania et al., 2015; Chan and Wong, 2012; Chueh et al., 2009; Ferri et al., 2009; Li et al., 2008; Ohkuni and Kitagawa, 2011; Perea-Resa et al., 2020; Quénet and Dalal, 2014; Rošić et al., 2014; Wong et al., 2007). However, it remains possible that additional polymerases contribute to the transcription of alpha-satellite regions. To determine the polymerases that are responsible for generating the alpha-satellite transcripts detected by our smFISH assay, we treated Hela cells with small-molecule inhibitors against all three RNA polymerases. We found a significant reduction in alpha-satellite smFISH foci following inhibition of RNA Polymerase II activity using the small-molecule THZ1 (Figure 3A–C; Figure 3—figure supplement 1A,B), which targets the RNA Pol II activator Cdk7 (Kwiatkowski et al., 2014). In contrast, we did not detect a reduction in smFISH foci following treatment with inhibitors against RNA polymerase I (small-molecule inhibitor BHM-21; Colis et al., 2014) or RNA polymerase III (ML-60218; Wu et al., 2003; Figure 3A–C; Figure 3—figure supplement 1A,B). Instead, as discussed below, we found dramatically increased alpha-satellite smFISH foci following RNA polymerase I inhibition. Consistent with the effects of RNA polymerase I and II inhibition on alpha-satellite transcript levels as detected by smFISH, RT-qPCR analyses indicated substantially decreased chromosome 21 alpha-satellite transcripts following CDK7 inhibition, but increased levels following RNA polymerase I inhibition (Figure 3D). This indicates that the alpha-satellite RNA transcripts detected by smFISH are products of RNA Pol II-mediated transcription.

Figure 3 with 1 supplement see all
Alpha-satellite RNAs are products of Pol II-mediated transcription.

(A) Treatment of HeLa cells with small-molecule inhibitors reveals that alpha-satellite transcripts are mediated by RNA polymerase II. Cells were treated with the RNA Polymerase I inhibitor BMH-21 (24 hr), the RNA Polymerase III inhibitor ML-60218 (24 hr), or the Cdk7 inhibitor THZ1 (5 hr), which inhibits RNA Polymerase II initiation. Transcripts were identified using the ASAT smFISH probe set. (B) Quantification of smFISH foci from (A) after treatment of HeLa cells with small-molecule inhibitors against Cdk7, RNA Pol I, and RNA Pol III. smFISH foci were substantially reduced after inhibition of RNA Pol II activator, Cdk7, but increased by RNA Pol I inhibition. Error bars represent the mean and standard deviation of at least 240 cells. (C) Graph showing independent replicates of ASAT smFISH foci for each small-molecule inhibitor treatment (Cdk7, RNA Pol I, and RNA Pol III). P-values represent T-tests for the indicated comparisons. (D) RT-qPCR quantification reveals significantly reduced levels of chromosome 21 alpha-satellite transcripts of cells treated by the Cdk7 inhibitor THZ1 for 5 hr, but increased levels following RNA polymerase I inhibition (24 hr treatment) when compared to control HeLa cells. The levels of alpha-satellite RNA from chromosome 21 detected was outside of our quantifiable range in cells treated with CDK7 inhibitor and thus was set to 0. The mean of 3 biological replicates was plotted and error bars represent the standard deviation. P-value represents the results of a T-test.

Functional analysis of the protein requirements for alpha-satellite transcripts

We next sought to determine the requirements for the production of alpha-satellite transcripts. Centromere DNA functions as a platform for assembly of the kinetochore structure (McKinley and Cheeseman, 2016), an integrated scaffold of protein interactions that mediates the connection between the DNA and microtubules of the mitotic spindle. One possibility to explain the observed transcription of centromere regions, including at neocentromere loci lacking alpha-satellite sequences, is that centromere and kinetochore components act to recruit the RNA Polymerase machinery. To test this, we selectively eliminated diverse centromere and kinetochore components using a panel of CRISPR inducible knockout cell lines expressing dox-inducible Cas9 and guide RNAs (McKinley and Cheeseman, 2017; McKinley et al., 2015). We targeted the centromere-specific H3 variant CENP-A, the CENP-A chaperone HJURP (to block new CENP-A incorporation), the centromere alpha-satellite DNA binding protein CENP-B, the constitutive centromere components CENP-C, CENP-N, and CENP–W, and the outer kinetochore microtubule-binding protein Ndc80. Our prior work has documented the efficacy of each of these inducible knockout cell lines (McKinley and Cheeseman, 2017; McKinley et al., 2015). Consistently, we found that the gene targets were effectively eliminated from centromeres throughout the population for the CENP-A, CENP-B, and CENP-C inducible knockout cell lines (Figure 4—figure supplement 1A; also see McKinley et al., 2015). Eliminating these centromere and kinetochore components did not prevent the presence of alpha-satellite RNA-smFISH foci (Figure 4A). In contrast, the number of foci/cell increased in many of these inducible knockout cell lines, from moderate increases in most knockout cell lines to a substantial increase in CENP-C inducible knockout cells (Figure 4A). This suggests that centromere components are not required for the specific recruitment of RNA Polymerase II to centromere regions, although active centromeres may act to retain RNA Polymerase II during mitosis due to the persistence of sister chromatid cohesion (Perea-Resa et al., 2020).

Figure 4 with 1 supplement see all
Eliminating CENP-C results in substantially increased alpha-satellite transcript numbers.

(A) Quantification of smFISH foci (ASAT probe set) after elimination of selected centromere and kinetochore components reveals that centromere components are not required for the production of alpha-satellite transcripts. Inducible knockouts were generated using Cas9 using previously described cell lines (McKinley and Cheeseman, 2017; McKinley et al., 2015). Notably, inducible knockout of CENP-C results in a substantial increase in smFISH foci. Error bars represent the mean and standard deviation of at least 240 cells. (B) Representative images showing the substantial increase in smFISH foci after elimination of the centromere component CENP-C. (C) Quantification of ASAT smFISH foci under the indicated conditions. The increase in alpha-satellite transcripts in cells depleted for CENP-C depends on RNA Polymerase II, as THZ1 treatment (Cdk7 inhibition; 5 hr) resulted in a substantial reduction in smFISH foci in both control cells and CENP-C inducible knockout cells. (D) Quantification of smFISH foci in CENP-C inducible knockout RPE-1 cells reveals that the increase in alpha-satellite transcripts following CENP-C knockout is not specific to HeLa cells. Error bars represent the mean and standard deviation of at least 170 cells. (E) RT-qPCR for alpha-satellite transcripts from chromosome 21 indicates a substantial increase in steady state alpha-satellite RNA levels in HeLa CENP-C inducible knockout cells. The mean of three biological replicates for control and four biological replicates for the CENP-C inducible knockouts was plotted. Error bars represent the standard deviation. P-value represents the results of a T-test. (F) Quantification of smFISH foci number in CENP-C inducible KO cells and Pol I-inhibited (24 hr treatment) cells compared to HeLa cell controls. (G) Quantification of the intensity of individual smFISH foci from the same experiment tested in F showing similar intensities despite the increase in foci number. (H) The half-life of alpha-satellite RNAs derived from chromosome 21 was determined in HeLa and CENP-C inducible knockout cells by RT-qPCR various times following RNA polymerase II inhibition (THZ1 treatment). The level of chromosome 21 alpha-satellite RNA was normalized to GAPDH, a stable mRNA. The half-life of these centromeric transcripts is 78 and 72 min in HeLa and CENP-C inducible knockout cells, respectively. Graph shows mean and standard deviation for two biological replicates. Scale bars, 25 µm.

We also tested the contribution of non-centromere-localized cell division components to alpha-satellite transcription. Because of its DNA-based nature, the centromere is subject to cell-cycle-specific challenges that include chromatin condensation, cohesion, and DNA replication. We thus sought to assess whether disruption of any of these complexes would influence alpha-satellite RNA transcript levels. To do this, we targeted proteins involved in centromere regulation (Sgo1 and BubR1), DNA replication (Mcm6, Gins1, Orc1, and Cdt1), sister chromatid cohesion (ESCO2, Scc1), chromosome condensation (Smc2, CAPG, CAPG2, TOP2A), and nucleosome remodeling (SSRP1). Strikingly, despite the diverse roles of these proteins in different aspects of centromere function, none of these inducible knockouts resulted in reduced levels of ASAT alpha-satellite transcripts as detected by smFISH analysis (Figure 4—figure supplement 1B). Instead, in many cases we detected a modest increase in alpha-satellite smFISH foci in the inducible knockout cells. Overall, our results indicate alpha-satellite transcription does not require the presence of specific DNA binding proteins, DNA structures, or cell division components, and instead that multiple factors act to restrict transcription at centromeres.

CENP-C acts to repress alpha-satellite RNA levels

Of proteins that we tested, eliminating CENP-C had a particularly substantial effect on the number of smFISH foci (Figure 4A). To confirm this behavior following the loss of CENP-C, we repeated these experiments for both the ASAT and SF1 smFISH probes (Figure 4B,C; Figure 4—figure supplement 1C). In both cases, we observed a strong increase in smFISH foci. To test whether this behavior was specific to HeLa cells, we analyzed the CENP-C inducible knockout in RPE-1 cells. Although there are fewer ASAT smFISH foci in the parental RPE-1 cells, eliminating CENP-C resulted in a strong increase in the number of ASAT smFISH foci (Figure 4D). Moreover, we observed a substantial increase in steady state alpha-satellite RNA levels in HeLa CENP-C inducible knockout cells based on RT-qPCR (Figure 4E). We also note that recent work found that CENP-C overexpression resulted in decreased RNA Polymerase II occupancy at centromere regions (Melters et al., 2019). This increase in alpha-satellite transcripts in cells depleted for CENP-C depends on RNA Polymerase II, as THZ1 treatment resulted in a clear reduction in smFISH foci for the ASAT and SF1 probe sets in both control cells and CENP-C inducible knockout cells (Figure 4C; Figure 4—figure supplement 1C). The changes in smFISH foci that we observe in the CENP-C and RNA Polymerase I-inhibited cells likely reflects the number of independent and diffusible transcripts, as we did not detect a corresponding change in smFISH focus intensity (Figure 4F,G). Importantly, despite the increased numbers of smFISH foci in CENP-C inducible knockout cells, we found that alpha-satellite RNA transcripts displayed a similar half-life for their turnover based on RT-qPCR in control HeLa cells and CENP-C inducible knockout cells based on their loss following RNA Polymerase II inhibition (THZ1 treatment; Figure 4H). As the half-life of the alpha-satellite smFISH foci is similar in each case, this suggests that increased numbers of smFISH foci reflects increased transcription of alpha-satellite DNA instead of the increased stability of alpha-satellite transcripts. As eliminating CENP-C potently disrupts the localization of all centromere proteins (McKinley et al., 2015), this suggests that centromere and kinetochore formation could act as a physical block to restrict the passage of RNA polymerase through the centromere, downregulating alpha-satellite transcript levels. Alternatively, kinetochore proteins could act to create a repressive environment for transcription (see below).

Centromere–nucleolar associations act to repress alpha-satellite transcript levels

In the functional analysis described above, we were surprised that most perturbations resulted in increased centromere smFISH RNA foci instead of a loss of signal. The largest increases were observed for the depletion of CENP-C (Figure 4A–C) and the inhibition of RNA Polymerase I (Figure 3B). RNA Polymerase I transcribes rDNA, but also has an important role in assembling the nucleolus, which creates a repressive transcriptional environment. Given reported connections between the centromere and nucleolus in prior work (Ochs and Press, 1992; Padeken et al., 2013; Wong et al., 2007), we hypothesized that alpha-satellite transcription occurs at a basal level, but that this transcription is repressed by associations between the centromere and the nucleolus. To test this model, we first visualized centromeres and nucleoli in human cells. In HeLa and RPE-1 cells, a subset of centromeres overlap with the nucleolus, as marked with antibodies against Ki-67 (Figure 5A,B) or Fibrillarin (Figure 5—figure supplement 1A). However, other centromeres are present outside of the nucleoli within the rest of the nucleus. This contrasts with work in Drosophila cells, where centromeres from all four chromosomes are found in close proximity surrounding the nucleolus (Padeken et al., 2013). Importantly, we observed an inverse relationship between the fraction nucleoli-localized centromeres and the numbers of alpha-satellite smFISH foci. First, we observed an increased fraction of nucleoli-localized centromeres in RPE-1 cells compared to HeLa cells (Figure 5A,B), correlating with the reduced numbers of alpha-satellite smFISH foci in RPE-1 cells (Figure 1F). Similarly, we found that CENP-C inducible knockout cells displayed a reduced fraction of nucleoli-localized centromeres (Figure 5C,D), again correlating with the increased alpha-satellite smFISH foci in these cells (Figure 4C). In contrast, we did not detect a change in centromere-nucleolar associations in the CENP-B inducible knockout (Figure 5—figure supplement 1B), which does not substantially alter smFISH foci numbers (Figure 4A). When the different conditions affecting the nucleolus are compared, there is a clear inverse relationship between nucleolar-localized centromeres and the number of ASAT smFISH foci per cell (Figure 5E).

Figure 5 with 1 supplement see all
The nucleolus represses centromere RNA production.

(A) Immunofluorescence of HeLa (Top) and RPE1 (Bottom) cells showing the colocalization of centromeres with the nucleolus, as marked with antibodies against Ki-67 and anti-centromere antibodies (ACA). Scale bars, 10 µm. (B) Quantification reveals RPE1 cells have a greater fraction of centromeres that overlap with nucleoli (57%) compared to HeLa cells (44.6%). Error bars represent the mean and standard deviation of 25 cells. (C) Immunofluorescence of HeLa control (top) and HeLa CENP-C iKO (bottom) cells showing the colocalization of centromeres with the nucleolus, as marked with antibodies against Ki-67 and CENP-A. Scale bar, 10 µm. (D) Quantification reveals that depletion of CENP-C results in a reduced fraction of nucleoli-localized centromeres (32.8%) compared to control cells (44.6%). The asterisk indicates that the data from control cells is repeated from (B). Error bars represent the mean and standard deviation of 25 cells. (E) Graph showing the relationship between the number of ASAT smFISH foci (summarized from data in Figures 14) and the fraction of nucleolar-localized centromeres in the indicated conditions. RNA Polymerase I inhibition should eliminate nucleolar function, and so is listed as ‘0’ for nucleolar centromeres. Dashed line shows a linear fit trendline. (F) smFISH analysis reveals an increase of alpha-satellite transcripts in Ki67 knockout cells (right) when compared to control (left). Scale bar, 25 µm. (G) Quantification reveals a 2–3 fold increase in alpha-satellite transcript levels for both the ASAT and SF1 smFISH probes in Ki67 stable knockout cells. Error bars represent the mean and standard deviation of at least 100 cells. Right, graph showing replicates of the indicated data. P-values indicate T-tests for ASAT and SF1 replicates for Ki67 knockout cells compared to the corresponding control.

To assess the functional relationship between the nucleolus and alpha-satellite transcription, we generated inducible knockout cell lines for the nucleolar components Fibrillarin and Ki-67 using our established inducible Cas9 knockout system (McKinley and Cheeseman, 2017). Induction of these knockouts resulted in increased levels of alpha-satellite transcripts, particularly for Ki-67 (Figure 5—figure supplement 1C). Although Ki-67 plays important roles in nucleogenesis, mitotic chromosome structure, and transcription of cell-cycle targets (Booth et al., 2014; Cuylen et al., 2016; Sobecki et al., 2016; Sun et al., 2017), deletion of Ki-67 is not lethal (Sobecki et al., 2016). Therefore, we additionally generated a stable Ki67 knockout cell line in HeLa cells (Figure 5—figure supplement 1D). Ki-67 knockout cells proliferated normally, but displayed a 2–3 fold increase in alpha-satellite transcript levels for both the ASAT and SF1 smFISH probes (Figure 5F,G). However, we note that we did not detect a significant change in alpha-satellite transcript levels based on RT-qPCR (Figure 5—figure supplement 1E). The discrepancy between the smFISH and qPCR may represent differences between single molecule and bulk assays, or technical considerations as the smFISH probes recognize alpha-satellite RNAs derived from multiple chromosomes, whereas the RT-qPCR experiments detect alpha-satellite transcripts from only chromosome 21. Despite the increased alpha-satellite transcript levels detected by smFISH, we did not detect notable consequences to centromere protein levels (based on the localization of CENP-A; Figure 5—figure supplement 1D,F) or chromosome mis-segregation (not shown). The combination of these data supports a model in which a properly functioning nucleolus and nucleolar-centromere connections act to limit centromere and pericentromere transcript levels.

Roles for alpha-satellite transcripts and centromere transcription

Together, this work defines the parameters for the production of alpha-satellite RNA transcripts and demonstrates that centromere-nucleolar connections act to restrict alpha-satellite transcription. The nature of the behavior that we observed for alpha-satellite smFISH foci, including the lack of persistent localization to centromeres or mitotic structures, is inconsistent with a direct, physical role for these transcripts in cell division processes. Instead, we propose that the process of ongoing transcription at centromeres itself, rather than the presence of alpha-satellite-derived RNAs, is an important feature of centromere biology. Active transcription could act to promote the dynamics of centromeric chromatin, resulting in the gradual turnover of DNA-bound proteins, including nucleosomes. For example, prior work using artificial tethering of chromatin and transcription factors to chromosome regions has suggested that centromere function requires an intermediate level of transcription with strongly repressive or activating states incompatible with centromere function (Molina et al., 2017; Molina et al., 2016; Nakano et al., 2008). In addition, our recent work found that transcription was required to promote the gradual turnover of CENP-A nucleosomes in non-dividing cells, resulting in continued ‘rejuvenation’ of centromere proteins (Swartz et al., 2019). As part of that work, we also found a similar behavior for non-centromere chromatin, with transcription acting to drive the turnover of histone H3 on the chromosome arms (Swartz et al., 2019), suggesting that this may be a general feature of non-coding regions. Together, we propose that basal centromere transcription acts to promote the turnover of DNA-bound proteins, providing a mechanism to ensure refresh CENP-A chromatin. Importantly, changes in nuclear and nucleolar organization and in centromere-nucleolar associations across cell types, between cell states (including both dividing and quiescent cells), and in disease states has the potential to create consequential changes to centromere transcription and centromere protein dynamics.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
Cell line (H. sapiens)HeLaDon Cleveland lab (UCSD)HeLaCell line maintained in the Cheeseman Lab
Cell line (H. sapiens)U20SDon Cleveland lab (UCSD)U20SCell line maintained in the Cheeseman Lab initially received from the Cleveland lab
Cell line (H. sapiens)MCF7American Type Culture CollectionMCF7Cell line maintained in the Cheeseman Lab initially received from American Type Culture Collection
Cell line (H. sapiens)RPE1Prasad Jallepalli Lab (MSKCC)RPE1Cell line maintained in the Cheeseman Lab initially received from Dr. Prasad Jallepalli
Cell line (H. sapiens)cTT20 (inducible Cas9 in HeLa)PMID:26698661cTT20Cell line maintained in the Cheeseman Lab initially generated by Tonia Tsinman
Cell line (H. sapiens)cTT33 (inducible Cas9 in RPE1)PMID:28216383cTT33.1Cell line maintained in the Cheeseman Lab initially generated by Tonia Tsinman
Cell line (H. sapiens)CENP-C iKO (in HeLa/cTT20)PMID:26698661cKM153Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)CENP-B iKO (in HeLa/cTT20)PMID:28216383cKMKO C1.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)CENP-A iKO (in HeLa/cTT20)PMID:28216383cKMKO B12.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)CENPN iKO (in HeLa/cTT20)PMID:26698661cKMKOCell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)HJURP iKO (in HeLa/cTT20)PMID:28216383cKMKO E4.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)CENPW iKO (in HeLa/cTT20)PMID:28216383cKMKO H3.3Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Ndc80 iKO (in HeLa/cTT20)PMID:28216383cKMKO F11.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Fibrillarin iKO (in HeLa/cTT20)This PapercBM002Cell line maintained in the Cheeseman Lab initially generated by Brittania Moodie
Cell line (H. sapiens)Ki67 iKO (in HeLa/cTT20)This PapercBM3.10Cell line maintained in the Cheeseman Lab initially generated by Brittania Moodie
Cell line (H. sapiens)Sgo1 iKO (in HeLa/cTT20)PMID:28216383cKMKO H1.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)BubR1 iKO (in HeLa/cTT20)PMID:28216383cKMKO A8.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Mcm6 iKO (in HeLa/cTT20)PMID:28216383cKMKO E2.2Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Gins1 iKO (in HeLa/cTT20)PMID:28216383cKMKO D11.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Orc1 iKO (in HeLa/cTT20)PMID:28216383cKMKO G2.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Cdt1 iKO (in HeLa/cTT20)PMID:28216383cKMKO B9.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)ESCO2 iKO (in HeLa/cTT20)PMID:28216383cKMKO C9.2Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Scc1 iKO (in HeLa/cTT20)PMID:28216383cKMKO G11.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)Smc2 iKO (in HeLa/cTT20)PMID:28216383cKMKO H5.1Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)CAPG iKO (in HeLa/cTT20)PMID:28216383cKMKO E9.2Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)CAPG2 iKO (in HeLa/cTT20)PMID:28216383cKMKO E11.2Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)TOP2A iKO (in HeLa/cTT20)PMID:28216383cKMKO G11.2Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
Cell line (H. sapiens)SSRP1 iKO (in HeLa/cTT20)PMID:28216383cKMKO G10.2Cell line maintained in the Cheeseman Lab initially generated by Dr. Kara McKinley
AntibodyDM1a (anti-tubulin); mouse monoclonalSigma AldrichT9026-.2ML(1:10000)
AntibodyACA (anti-centromere antibodies; human
auto-immune serum)
Antibodies, Inc15-234-0001(1:1000)
AntibodyAnti-CENP-A (mouse monoclonal)Abcamab13939(1:1000)
AntibodyAnti-Ki67 (rabbit polyclonal)Abcamab15580(1:100)
AntibodyAnti-Fibrillarin (rabbit polyclonal)Abcamab5821(1:300)
AntibodyAnti-CENP-C (rabbit polyclonal)Cheeseman Lab (Whitehead Institute)N/A(1:1000)
AntibodyAnti-CENP-B (rabbit polyclonal)Abcamab25734(1:1000)
Commercial assay or kitCustom Stellaris RNA FISH probes (Quasar570 or Quasar670)Biosearch Technologies; PixelBiotech GmbHN/AProbe sequences may be found in Supplementary file 2
Commercial assay or kitCustom HuluFISH probes (Atto565)PixelBiotech GmbHN/AProbe sequences may be found in Supplementary file 2
Commercial assay or kitMaxima First Strand cDNA Synthesis Kit for RT-qPCRLife Technologies (Thermo Scientific)K1671
Commercial assay or kitSYBR Green PCR Master MixThermo Fisher ScientifcA25742
Chemical compound, drugTRIzol Reagent (Tri Reagent solution)Life TechnologiesAM9738
Chemical compound, drugRNase AQiagen191011:1000
Chemical compound, drugBMH-21; Pol I InhMillipore; Sigma Aldrich509911; SML11831 µM
Chemical compound, drugML-60218; Pol III InhFisher Scientific (Thermo Fisher Scientific)55740320 µM
Chemical compound, drugTHZ1; Cdk7 InhFisher Scientific (Thermo Fisher Scientific)53237200011 µM
Chemical compound, drugFormamide (Deionized)Life TechnologiesAM9342
Chemical compound, drugRibonucleoside vanadyl complexesSigma AldrichR3380-5ML

Cell culture

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All cells were grown in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% Fetal Bovine Serum, 100 units/mL penicillin, 100 units/mL streptomycin, and 2 mM L-glutamine (Complete Media) at 37°C with 5% CO2. Cell lines represent established and ongoing cell lines used by the Cheeseman lab. They are validated based on their behavior and properties. All cell lines are tested for mycoplasma contamination on a regular and ongoing basis. For experiments using inducible knockout cell lines, cells were seeded onto uncoated glass coverslips and doxycycline (DOX, Sigma) was added at 1 mg/L for 48 hr. Cells were fixed and stained at 4 or 5 days following DOX addition. For inhibitor experiments, RNA Polymerase inhibitors were added to cells at the following concentrations: BMH-21 (RNAPI inhibitor; Millipore) at 1 µM; ML-60218 (RNAP III inhibitor; Fisher) at 20 µM; THZ1 (Cdk7 Inhibitor; Fisher Scientific) at 1 µM. Treatment times are indicated in the figure legends.

Inducible knockouts for nucleolar components in HeLa cells were created as described previously (McKinley and Cheeseman, 2017). Briefly, sgRNA sequences were cloned into pLenti-sgRNA (Wang et al., 2015), and used to generate lentiviruses for stable infection in cells harboring inducible Cas9 (HeLa cells – cTT20; RPE-1 cell - cTT33). Cells were then selected with puromycin as described previously (McKinley and Cheeseman, 2017). Additional cell cycle and chromosome inducible knockouts were from McKinley and Cheeseman, 2017. For the Ki67 stable knockout cell line, the HeLa cell inducible knockout version was induced with Dox and subsequently sorted by FACS to create clonal cell lines, which were screened using immunofluorescence against Ki67.

Single-molecule RNA fluorescence in-situ hybridization (smFISH)

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Custom Stellaris RNA-FISH probes labeled with Quasar dyes (i.e., Quasar570 or Quasar670) were designed against specific centromere RNAs and purchased from Biosearch Technologies (Petaluma, CA) and PixelBiotech GmbH (Schriesheim, Germany). To conduct single-molecule FISH, cells were grown on poly-L lysine coverslip in 12-well plates were washed with PBS and fixed with 4% paraformaldehyde in 1X PBS containing RVC (Ribonucleoside Vanadyl Complex) for 10 min at room temperature (RT). After washing cells twice with 1X PBS, cells were permeabilized in 70% ethanol for at least 20 min at 4°C. Cells were pre-incubated with 2X SSC; 10% deionized formamide for 5 min, and incubated with hybridization mix (0.1 μM RNA-FISH, 10% deionized formamide, in Hybridization Buffer (Biosearch Technologies)) overnight at 37°C in the dark. Finally, cells were washed twice with 10% deionized formamide in 2X SSC for 30 min at 37°C and once with Wash B (Biosearch Technologies) for 5 min at RT. For experiments with immunofluorescence coupled to smFISH, HeLa cells grown on poly-L lysine coverslip in 12-well plates were washed with PBS and fixed with 4% paraformaldehyde in 1X PBS containing RVC (Ribonucleoside Vanadyl Complex) for 10 min at RT. After washing cells with 1X PBS, cells were permeabilized for 5 min at RT with 0.1% Triton-X in PBS with RVC. After washing, primary and secondary antibody incubation was performed at RT for 1 hr in PBS and RVC. Antibody concentrations: DM1a (anti-tubulin; Sigma): 1:10000, ACA (anti-centromere antibodies – human auto-immune serum; Antibodies, Inc): 1:1000, CENP-A (Abcam, ab13939) at 1:1000, Fibrillarin (Abcam, ab5821) at 1:300, and Ki67 (Abcam, ab15580) at 1:100. For smFISH, cells were fixed again for 10 min with 4% PFA in 1x PBS. Hybridization was performed as above. Coverslips were mounted on cells with Vectashield containing Hoechst.

For imaging, slides were imaged using a DeltaVision Core microscope (Applied Precision/GE Healthsciences) with a CoolSnap HQ2 CCD camera and 60x and 100 × 1.40 NA Olympus U- PlanApo objective. smFISH foci were counted per nucleus from z-projected images using CellProfiler (Carpenter et al., 2006). Prior to projection, each z-section was examined and the appropriate z-slices were projected (max intensity projection). For the analysis of smFISH foci, we used a projection of the entire cell volume. For the analysis of the overlap between smFISH foci in the nucleus and the presence of centromeres in the nucleolus, we analyzed individual z sections. Image files were processed using Deltavision software or Fiji (Schindelin et al., 2012).

DNA FISH

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Custom multiplexing single-molecule FISH (smFISH) HuluFISH probes labeled with Atto565 were designed against specific centromere RNAs and purchased from PixelBiotech GmbH (Schriesheim, Germany). To conduct DNA FISH, cells were grown on poly-L lysine coverslip in 12-well plates were washed with PBS and fixed with 4% paraformaldehyde in 1X PBS for 10 min at room temperature (RT). After washing the cells with 1X PBS for 10 min, cells were permeabilized in 70% ethanol at −20°C overnight. Cells were treated with RNase A (Qiagen) at 1:1000 for 30 min at 37°C followed by denaturation in 70% deionized formamide; 2X SSC buffer at 75°C. The samples were then dehydrated in series of cold ethanol washes (70%, 90% and 100%) for 2 min each and air dried. Cells were washed with Hulu Wash (PixelBiotech) twice for 10 min at room temperature and incubated with hybridization mix (0.5 µL HuluFISH probes in 1X HuluHyb solution) overnight at 30°C in the dark. Finally, cells were washed twice with Hulu Wash for 30 min at room temperature.

Reverse transcription and quantitative real-time PCR

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Cells were harvested 24 hr after BMH-21 addition, 5 hr after THZ1 treatment, or 4 days after doxycycline addition to induce the CENP-C knockout. RNA was purified using TRIzol reagent (Life Technologies) according to manufacturer’s instructions. 2 µg of total RNA was used in the cDNA synthesis reaction with the Maxima First Strand cDNA Synthesis kit for RT-qPCR (Thermo Scientific). We used twice the recommended volume of dsDNase and allowed the DNase treatment to proceed for 30 min at 37°C. The increased concentration of dsDNase and length of reaction was critical for the complete removal of genomic DNA. The cDNA was subjected to quantitative real-time PCR using the SYBR green PCR mastermix (Thermo Scientific) according to manufacturer’s protocol. Standard curves were used for quantitative assessment of RNA levels and centromeric RNA levels were normalized to GAPDH mRNA. If the levels of centromeric RNA fall far below of the linear range in the standard curve, we noted that the RNA was not detectable and was set to 0 in the figures. After normalizing centromeric RNA levels to GAPDH, all of the data was normalized to HeLa. For RNA half-life experiments, HeLa or CENP-C inducible knockout cells (induced for 4 days) were treated with THZ1 and RNA was isolated the indicated times after THZ1 inhibition for RT-qPCR analysis as described above. The chromosome 21 alpha-satellite RNA levels were normalized to a stable mRNA, GAPDH. To calculate half-life the data was fit to an initial plateau followed by single exponential decay using GraphPad Prism. GAPDH primers: 5’-TCGGAGTCAACGGATTTGGT-3’ and 5’-TTCCCGTTCTCAGCCTTGAC-3’. chromosome 21 (CH21) primers: 5'-GTCTACCTTTTATTTGAATTCCCG-3' and 5'-AGGGAATGTCTTCCCATAAAAACT-3' (Nakano et al., 2003; Molina et al., 2016).

References

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Decision letter

  1. Silke Hauf
    Reviewing Editor; Virginia Tech, United States
  2. Anna Akhmanova
    Senior Editor; Utrecht University, Netherlands

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

[Editors' note: this paper was reviewed by Review Commons.]

Acceptance summary:

Centromeres are needed for the proper inheritance of chromosomes and – although centromeres typically do not contain genes – they are being transcribed. These transcripts are important for centromere function, but their regulation and role is still poorly understood. Centromeres are often, but not always, located next to nucleoli, with which they have an intricate, but also incompletely understood, relationship. This paper now adds one more facet to this picture by showing that contacts between centromeres and the nucleolus influence the abundance of centromeric transcripts. This reinforces the relevance of the centromere-nucleolus interaction and raises the question how physiologic or pathologic disruptions of this interaction influence centromeres and chromosome segregation.

Decision letter after peer review:

Thank you for submitting your article "Alpha-satellite RNA transcripts are repressed by centromere-nucleolus associations" for consideration by eLife. We have considered the reviews from Review Commons as well as your response. Since one of the reviewers had a conflict of interest, we solicited additional advice. The comments below reflect the discussion between all involved. The evaluation has been overseen by Silke Hauf as Reviewing Editor and Anna Akhmanova as the Senior Editor.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option.

Summary:

Transcription of centromeric DNA is still poorly understood. It remains unclear or controversial how, where and when centromeric transcripts are produced, how long they are, how they are modified, where they are located, how their levels are controlled, and what their functions are. Here, you used single-cell smFISH to localize and quantify centromeric transcripts in human cell lines. You confirm previous reports that RNA Polymerase II is required for the production of centromeric transcripts. In contrast, you find that a large number of centromere proteins, kinetochore proteins or chromatin regulators are not required for centromere transcription. On the contrary, depletion of the inner kinetochore protein CENP-C increased the number of transcripts. (The reason for this remains unclear.)

Unlike prior reports, your data suggest that centromeric transcripts do not remain associated with centromeres, although they remain in the nucleus until nuclear envelope breakdown in mitosis. Furthermore, you find a negative correlation between the number of centromere transcript foci and the fraction of centromeres that co-localize with nucleoli, and you interpret this as evidence that centromere-nucleolus interactions may limit centromeric transcription.

Overall, your data adds to the diverse picture of findings on centromeric transcripts, and will surely inspire future investigations into this topic.

Several conclusions that you want to make require additional experimental support:

1) All reviewers indicated that controls are needed for the iKO experiments. Although you have validated these cell lines previously, the controls are required to demonstrate that knock-outs/knock-downs occurred in your current experiments, ideally in the cells that you are analyzing. If the antibodies do not work in the smFISH experiment, you could provide immunoblots. Minimally, this should be shown for the particularly relevant CENP-C iKO cells.

If you do not have antibodies available, or in order to get an estimate for the fraction of cells with efficient knock-down, a quantification of cellular phenotypes in the population could also be useful. According to your reply, you have already performed the latter. Please provide these data.

2) In all conditions where you observe a larger number of smFISH foci, you interpret this as an increase in transcription. Before making this conclusion, alternative reasons need to be excluded:

i) It needs to be checked whether the increased number of transcripts could reflect stabilization (increased half-life) rather than increased transcription. Two experiments seem important: Is the drastic drop in transcript levels during G1 still observed in the knock-outs/inhibitions that increase transcript levels? Is the half-life during S/G2 increased? (Although prior reports have described centromeric transcripts as very stable, the fact that you see drastic drops after 5 hours of RNA Pol II inhibition, suggests that the transcripts that you are assaying are turning-over at a detectable rate.)

ii) For the CENP-C iKO cells that show an increased number of ASAT transcripts, it needs to be addressed whether this can be attributed to failed cytokinesis. Is it possible that the cells have become larger / diploid, and that this is the sole reason for the higher number of transcripts? Similar concerns could apply to some of the other knock-outs tested. A quantification of transcripts and centromeres in the same cells could be useful.

3) The conclusiveness of your results would greatly profit from confirmation by a different approach. Quantitative RT-PCR was suggested, and you already indicated it should be feasible to test for centromeric transcripts by qPCR in your key experimental conditions (CENP-C iKO, Ki67 iKO, fibrillarin iKO, RNA polymerase I inhibition, RNA polymerase II inhibition).

4) The conclusiveness of the results would also profit from showing that the FISH probes indeed detect alpha satellite RNA. We suggest that you perform a DNA-FISH experiment using the same probes.

https://doi.org/10.7554/eLife.59770.sa1

Author response

Reviewer #1:

In this manuscript the authors explore the requirements for centromere transcription using single-molecule FISH. Previous studies have found that centromeres are transcriptionally active in a wide variety of organisms. Centromere transcription has been proposed to facilitate Cenp-A deposition through chromatin remodeling and to directly contribute to centromere/kinetochore function by producing a functional ncRNA. However, we currently know almost nothing about how transcription is initiated at the centromere or how levels of centromere transcripts are controlled. This manuscript makes several major findings that are potentially of importance to groups studying centromere transcription. 1. Centromere RNAs are produced by RNA Polymerase II and are localized in the nucleus of a wide-range of cell types. 2. Centromere RNAs do not localize to the centromere, which is in contrast to several recent studies. 3. Centromere proteins are not required for transcription of alpha-satellite sequences. 4. Localization of centromeres to the nucleolus represses centromere transcription. Overall, this is a solid manuscript and has the potential to make a significant impact in the field. Below I suggest a couple of experiments and modification to the data presentation that could improve the manuscript.

We thank this reviewer for their interest in this paper and agree with their clear articulation of the key points.

1) All of the experiments in this manuscript rely on detection of centromere RNAs using single molecule FISH probes. These probes are validated by showing the RNase treatment removes the FISH signal. A strength of this approach is that the authors use multiple different probe sets and achieve comparable results. However, there is no orthogonal validation that the probes detect alpha satellite RNA. All of the experiments in this manuscript would be significantly improved by showing that the results presented here can be confirmed by a different approach. I suggest that the authors use Q-RT-PCR to validate the smFISH results.

The smFISH probes provide a powerful and unique strategy to detect alpha-satellite transcripts. To ensure that these experiments are carefully controlled, we analyzed multiple distinct probe sequences that recognize alpha-satellite transcripts derived from different chromosomes, as this reviewer highlights. We also conducted an in-depth computational analysis to ensure that these probes do not match genomic sequences outside of alpha-satellite regions. However, we recognize and agree that a complementary method to detect these transcripts would be a useful addition to this paper. We are currently highly constrained in our ability to conduct these experiments due to COVID-19-related laboratory closures, but if feasible our goal for a revised manuscript would be to conduct qPCR experiments for a subset of the conditions that are the most central to the key results in this paper (focusing particularly on HeLa and Rpe1 control cell lines, CENP-C iKO, Ki67 KO, and RNA Polymerase I and RNA Pol II inhibitors).

2) Several results in this manuscript directly contradict results in published studies, but these discrepancies are not discussed. I believe the authors need to discuss the following discrepancies between their results and those in the literature:

a) McNulty et al., 2017. Show that alpha-satellite RNA is transcribed from all centromeres and remains localized to the site of transcription. The different results and possible explanations for the differences should be discussed.

b) Additionally, Rosic et al., 2014, Blower, 2016 and Bobkov et al., 2018 all show that centromere RNAs localize to centromere regions. The differences between these studies and the authors results should be discussed.

c) The authors show that satellite RNA cannot be detected on mitotic chromosomes. However, Johnson et al., 2017, Bobkov et al., 2018, and Perea-Resa et al., 2020 show that EU-labeled RNA can be detected at the centromere during mitosis. The authors should discuss the discrepancy between their results and these studies. Is it possible that their smFISH probes do not detect nascent, chromatin-bound transcripts?

We believe that a strength of our paper is that it assesses alpha-satellite transcripts in individual intact cells using fixation conditions that preserve the native behaviors without disruptive and harsh extraction. As our results differ from those of other laboratories in some cases, we agree that it would be helpful to comment more directly on these differences with prior work. Points a, b, and c above all relate to the presence of alpha-satellite transcripts at centromeres. For the revised paper, we will include a discussion of these prior observations and some possible reasons for the differing results. In particular, we think that these discrepancies reflect two key differences:

1) Other strategies with harsh extraction conditions likely eliminate soluble alpha-satellite transcripts that are not tightly associated with centromeres, whereas our work preserves these.

2) It is possible that we are unable to detect nascent transcripts by smFISH as these are embedded within the RNA polymerase.

Extraction conditions: An advantage of the smFISH probes used in our paper is that these require mild fixation conditions without prior extraction to better preserve cellular structures allowing us to analyzed intact cells, rather than chromosome spreads. Thus, our approach maintains the diverse alpha-satellite transcripts that are not bound to centromeres, and which may have been washed away in other studies. In contrast, some prior studies used stringent extraction conditions and primarily conducted experiments in chromosome spreads (not intact cells). Although it is not feasible to precisely determine the basis for differences without repeating this work the precise approaches and conditions from each paper and working closely with each group, we believe that these substantial technical differences explain our differing observations that reveal that the majority of alpha-satellite transcripts do not remain at centromeres.

Nascent transcripts: As suggested by this reviewer, we agree that our differing conditions may mean that we are unable to detect nascent transcripts that are closely associated with the RNA polymerase, inaccessible due to their chromatin proximity, or that are not sufficiently elongated such that they are present to hybridize to multiple copies of the smFISH probes to be detectable. The alpha-satellite transcripts must be derived from centromeric and pericentromeric regions and so must exist there at some point (as also attested to the EU signals that this reviewer mentions in the work from our collaborative the Blower lab; we have also detected EU signal at centromeres). However, our work suggests that alpha-satellite transcripts do not persist at centromeres indefinitely once generated, with mature transcripts in the nucleoplasm and liberated from chromosomes during mitosis. We believe that the combination of the relative inability of our smFISH probes to detect nascent transcripts, but stringent conditions disrupting non-centromere bound transcripts for prior work likely explain these distinctions.

d) The authors show nicely that deletion of Ki-67 reduces centromere localization to the nucleolus and increases centromere transcription. However, this has no effect on centromere function. Studies from the Earnshaw lab (e.g. Nakano et al., 2008 and Bergmann et al., 2011) show that increasing or decreasing centromere transcription results in loss of kinetochore function on a human artificial chromosome. The authors should discuss the differences between their results and these studies. Is it possible that the small size of the HAC exaggerates the importance of the correct levels of centromere transcription?

We are big fans of the Earnshaw lab work. In this case, there are a couple of possibilities to explain the strong effect that the Earnshaw lab observed on kinetochore function by perturbing centromere transcription. First, the degree of the change in centromere transcription may make a big difference. The Ki-67 results in an approximately 2-fold increase in alpha-satellite smFISH foci, which may still be within a permissive range for normal kinetochore function. Second, the experiments from the Earnshaw lab rely on targeting activating or silencing proteins to the centromere region, and it is possible that changes in centromere chromatin downstream of these factors contribute to the observed phenotypes in addition to altering the amount of centromere transcription. We will include a brief discussion of the Earnshaw work in a revised paper.

3) The authors treat cells with transcriptional inhibitors for 24 hours. I am concerned that this may result in massive cell death. It would be helpful to include cell viability data from these experiments.

We appreciate this point and agree that cell lethality is an important consideration given the essential role of the RNA polymerases. For the inhibitors, we first treated the cells for a variety of different time points to evaluate these behaviors. For example, we found that we could treat cells with RNA Polymerase II inhibitors for as much 48-72 hours without detecting noticeable cell death. Thus, at the 24 hour time point, the cells remain viable and intact, as is also visible in the images showing DNA staining for these treatments in Figure 3. We also note that this timing is consistent with prior studies that block transcription or translation. However, we did additionally conduct these experiments at earlier time points (5 hours and 12 hours post-drug addition) and obtained similar results. For example, for the Cdk7 inhibitor using the ASAT probe, we observed the following smFISH foci/cell: Control (3.4 foci/cell), 5 h (1.5 foci/cell), 12 h (1.2 foci/cell), 24 h (0.9 foci/cell). There is a clear effect even at 5 hours of treatment and a continued downward trend. Both for simplicity and because the replicates and number of cells that were quantified were lower for these conditions, we chose not to include these in the paper. We will include a statement regarding these earlier time points in the revised version.

4) In Figure 3C the authors examine the effects of centromere protein knock outs on centromere transcription. To me this is the most important experiment in the manuscript and is a major step forward for the field. The authors use inducible CRISPR knock out cell lines that are not 100% penetrant. It would be helpful if the authors could describe how they ensured that cells included in the image quantification were knock out cells.

Based on this comment and the other questions from the other reviewers, we recognize that we need to provide a much better description of the CRISPR knockout strategy, the prior validation of these cell lines, and the strategies that allow us to use these cell lines in a robust manner to ensure that we are effectively eliminating the target genes. We have systematically tested this strategy in multiple cases and find that this strategy is superior to RNAi for its efficacy and the potency of the phenotype, particularly for this type of cell biological assay.

The Cas9-based strategy is a highly effective way to conditionally eliminate essential genes. In this case, the efficiency of the Cas9 nuclease ensures that the genomic locus is cleaved in essentially 100% of cases. As this is repaired in an error prone manner and typically using non-homologous end joining, 66% of individual events result in frame shifts mutations that disrupt the coding sequence of a target gene, with ~50% of cells resulting in frame shifts in both copies of a gene. In addition, if a sgRNA targets a region of a gene that cannot tolerate mis-sense mutations, this will result in an even greater fraction of mutant cells. Thus, these inducible knockout cell lines result in robust and irreversible gene knockout, with a large fraction of cells (50% or more) displaying a clear phenotype. However, it is also true that there are a subset of cells within the population that will repair the DNA damage following Cas9 cleavage in a way that preserves protein function such that they behave similarly to control cells. Importantly, this means that there will be two classes of cells within a population – those that are unaffected, and those that are strongly affected. As we are analyzing each cell individually instead of creating a population average, this will capture this phenotypic diversity to reveal two populations of behaviors in cases where eliminating a gene results in a substantial change in smFISH foci. For example, the smFISH foci/cell data for the CENP-C inducible knockout (Figure 3C and 3E) indicates that many cells have smFISH foci numbers that are comparable to control cells, but others that display substantial differences and highly increased numbers. An ideal control in these experiments would be to additionally analyze the levels of the target protein together with the smFISH analysis. Unfortunately, many of the antibodies are not compatible with the conditions needed for the smFISH. For CENP-C, the antibody that we have is not compatible with the conditions that we are using for the smFISH, so it is not feasible to co-stain these cells as suggested. Instead, for our analysis of the centromere-nucleoli localization (for example), we used the presence of a clear CENP-C interphase phenotype (“bag of grapes” resulting from chromosome mis-segregation) as an indication that the cells had been knocked out for CENP-C.

The majority of the Cas9-based inducible knockouts that we used for this paper were generated previously in the lab (McKinley et al., 2015; McKinley et al., 2017). For the centromere protein knockouts (McKinley et al., 2015), these were analyzed previously with respect to phenotype and monitored for the depletion of each gene target over time. For the larger collection of cell cycle and cell division inducible knockouts, for our prior work we systematically validated each of these with respect to their phenotype (see http://cellcycleknockouts.wi.mit.edu). Thus, we are confident that each of these cell lines is functional and effective for eliminating the target gene.

For conducting the experiments using the inducible Cas9 cell lines in this paper, we used the presence of these previously-defined phenotypes within the population as a validation that the strategy is working. Again, in general we find these knockouts are both penetrant and severe in their phenotypes. Importantly, for this diverse set of genes, we note that our goal was to broadly survey diverse factors to identify changes in alpha-satellite transcript levels. We intended this analysis as a “screen” where we would identify factors that resulted in a substantial change in the number of smFISH foci. As with any larger analysis, it is possible that there are false negatives where we did not detect a strong effect on transcript levels (such that they may contribute to centromere transcription). We have tried to use caution not to indicate that this data excludes any possible role for these factors in transcript levels, although in general the majority of the tested factors did not show a substantial change in smFISH foci. For the revised paper, we will make an explicit statement to this effect.

5) The authors cite Quenet and Dalal., 2014 for the idea that transcription during G1 is important for new Cenp-A loading. They should also cite Chen et al., 2015 and Bobkov et al., 2018.

Thank you for these helpful suggestions. We will update the text to incorporate these references.

Reviewer #2:

The study by Bury et al. investigates the formation of two different types of alpha-satellite transcripts (ASAT, SF1 and 3) in different human cell lines. Using smFISH they find that during the cell cycle these centromeric transcripts don’t stay at the centromere and are found in the cytoplasm after mitosis. Using specific inhibitors, they find that transcription is dependent on RNAPII, but not on various centromere and kinetochore proteins taking advantage of an inducible CRISPR-depletion system that the lab had previously developed. Interestingly, they find that CENP-C, a major component of the centromere and previously characterised as an RNA-binding protein, negatively regulates alpha-satellite transcript levels. Another regulator for transcript levels appears to be centromere-nucleolus interactions (as also indicated in the title) acting to suppress expression of these non-coding RNAs.

This is overall a really interesting study and indeed, transcription at the centromere is little understood at this point. Given the importance of the centromere the findings in this manuscript will be of high interest to both researchers in the field and a general audience. There are novel and interesting insights into centromeric transcripts but the study still requires some controls.

We appreciate this reviewer’s kind words and their clear description of our work.

1) The authors state that the majority of smFISH foci do not colocalise with centromeres in a combined IF/FISH experiment (some quantification and a % of that subpopulation should be given somewhere). This is a bit concerning but of course could also be true. It either means that alpha-satellite transcripts leave the centromere as suggested by the authors (although some should be visible at the centromeres during the act of transcription). Alternatively, a trivial explanation would be that there is a lot of unspecific staining, which can occur in FISH-experiments to varying degrees. The RNase treatment to control for the absence of potential DNA hybridization is convincing, but the FISH probe could also interact with non-centromeric cellular RNA. With the centromere localisation as a reference point gone, some control is needed to validate that the RNA-FISH signals are indeed recognising alpha-satellite RNA that emerged from centromeres. The authors could try competition experiments titrating unlabelled specific or unspecific DNA probes alongside their labelled specific FISH probe into their FISH experiment to see if they lose or maintain the signal and the number of foci. The specific RNA FISH probes could also be used in DNA FISH, to demonstrate they are working and recognising specific centromeres.

For understanding this behavior, we believe that an important feature of alpha-satellite transcripts is that they are relatively stable (protected from nucleases within the nucleus), but that their overall number is low, consistent with transcription of other non-coding regions across the genome. Thus, if a transcript were produced at centromeres, but subsequently diffuses away, only a small subset would be detectable at centromeres. In addition to our validation these probes using RNAse, we would like to highlight that we have analyzed multiple distinct sequences that recognize different subsets of alpha-satellite repeats. In each case, the observed behaviors are very similar. In addition, the nature of the oligo FISH method requires multiple individual probes to anneal to the same transcript such that a signal is only detected if a sufficient number of oligos bind to the same transcript. This makes nonspecific binding unlikely to contribute to a false signal. Finally, a subset of the perturbations that we tested that are relevant to centromere function (including the CENP-C inducible knockout) clearly affect the levels of these transcripts, supporting a centromere origin. The additional control experiments suggested by the reviewer could be useful, but are technically complex with their own caveats in interpretation and we do not feel that they would add substantially to the existing paper. Instead, as discussed in response to reviewer #1, point #1, we plan to validate key results described in the paper using qRT-PCR (if possible based on current experimental constraints in the lab associated with COVD-19).

As described above in response to reviewer #1, point #2, we also believe that some differences with prior work suggesting that alpha-satellite transcripts localize to centromeres may be due to stringent extraction conditions that eliminated non-centromere bound transcripts, while at the same time reflecting our inability to detect nascent transcripts. Quantifying “colocalization” within the nucleus is limited by the resolution in light microscopy, and we would prefer to use caution in defining which transcripts in our smFISH analysis overlap with centromeres. However, we believe that our work clearly highlights the fact that a general feature of mature alpha-satellite transcripts is that they localize throughout the nucleoplasm and are not strongly associated with mitotic chromosomes.

2) Apart from Figure 4, there is no analysis shown for statistical significance. This should be done for most if not all quantifications. Are indeed ASAT and antisense RNA Foci number not significantly different? The authors say that the levels of alpha-sat RNA in Rpe1 cells are not substantially different from other cell lines, but is it also not significant (Figure 1F)? In Figure 2D it is concluded that transcripts foci number are increased in S/G2 (from G1) and remain stable in mitosis, but it looks like there is an increase in mitosis. Again, it looks like the higher number of smFISH foci/Cell is significantly higher for both ASAT and SF1, so some statistical analysis would be required here.

For this paper, we quantified hundreds of cells for each condition, measuring the number of foci/cell in each case. Because of these large n’s, even relatively small differences between samples become statistically significant when tested using standard statistical comparisons (unpaired T test and one-way ANOVA test amongst others). For our experiments, every sample condition included an analysis of control cells, allowing us to compare the control condition to any perturbations on the same day. However, there is some variability between these different replicates, with the average number of ASAT smFISH foci/cell in HeLa cells ranging from 3.4 to 5.6. When compared relative to each other, a subset of these control samples will appear to be statistically different from each other despite the fact that this is not a substantial difference between replicates. Similarly, the majority of the tested inducible knockout cell lines are statistically different from control cells, even when the differences are relatively minor. Therefore, we have tried to use caution when applying the double-edged sword of statistics to these analyses. Instead, we have tried to consider differences with a “substantial magnitude” instead of “statistically significant” differences that may make modest, but statistically significant differences seem artificially more important. We believe that the graphs in which every data point is represented, together with listing the average number of foci/cell in each condition allow the reader to evaluate this data for themselves. Many of the trends that this reviewer highlights are indeed interesting comparisons to consider for future work.

3) Starting with the description of Figure 1E in the main text the paper equates foci count of smFISH per cell with RNA transcript levels. I'm not convinced that these are necessarily the same. You could have many weak foci or few very bright with the same amount of overall transcripts in both. The authors start out introducing smFISH as highly sensitive "for accurate characterisation of number.…of RNA transcripts". This suggests that foci intensity could be used as a read-out for transcript levels. It should be possible to measure individual intensity of the foci for a subset of images. Do foci intensity correlate or anti-correlate with foci numbers? Is the sum of the intensities of all the foci less variable than the foci number for an individual cell type?

Due to the repetitive nature of alpha-satellite sequences, an increased intensity of a smFISH foci could reflect either the close proximity of multiple separable transcripts, or a longer transcript with multiple binding sites for the smFISH probes. Because of this, throughout the paper, we have referred to these as “foci” instead of stating a specific transcript number. As part of the automated computational analysis of the smFISH images, we additional analyzed foci intensity. In general, these values were similar across a cell population and between various perturbations with the key results and findings consistent whether we measured foci number or overall foci intensity per cell. However, foci intensity can vary slightly across a coverslip (technical constraints, not biological differences), and thus we have focused on foci number as a more consistent metric that correlates with the production of alpha-satellite transcripts.

4) I really like the use of the inducible CRISPR system to remove various centromere factors. However, some validation would be required to show that the system is effective in removing the proteins of interest in these experiments. For instance it would be helpful to show in Figure 3D an additional panel with CENP-C staining. Also for a subset of factors, some antibody staining co-staining with the smFISH could be provided in the supplemental material.

We appreciate this point. However, we feel that the existing experiments appropriately consider the nature of the knockout. First, we primarily used Cas9-based inducible knockouts that were generated previously in the lab (McKinley et al., 2015 and McKinley et al., 2017). As these knockouts have been described previously and extensively validated with respect to phenotype (in every case; see http://cellcycleknockouts.wi.mit.edu for example) and antibody staining (in selected cases), we have not repeated this here for the diverse cell cycle knockouts used. In general, we find these knockouts are both penetrant and severe in their phenotypes. Given the broad number of knockouts that we tested, this is not feasible in every case. We also intended this analysis as a type of “screen” where we could validate any “hits” that were observed, and will use caution in our wording not to imply that a negative result is decisive.

The important exceptions to this are CENP-C (which we analyzed more closely) and Ki67 (for which both the inducible and stable knockouts were generated for this paper). For Ki67, the antibody staining is shown and we believe that this is clear. For CENP-C, the antibody that we have is unfortunately not compatible with the conditions that we are using for the smFISH, so it is not feasible to co-stain these cells as suggested. For the smFISH analysis in the inducible CENP-C knockout, we analyzed every single cell, including some cells that are likely to have intact CENP-C levels. Thus, if anything, the potent increase in smFISH foci underrepresents the dramatic effect of CENPC depletion. Based on our prior work (McKinley et al., 2015) we found that the CENP-C knockout results in a pervasive “bag of grapes” phenotype in which chromosomes mis-segregate during mitosis and are packaged into separable interphase nuclei. For the analysis of the nucleoli, we selected cells that displayed this clear phenotype (as shown in the figures).

5) Since none of the CRISPR iKO has a particular inhibiting phenotype it would be useful to include some positive control in the CRISPR experiment. Would it be possible to use a CRISPR iKO target that affect some factor of the transcription machinery (RNA Pol II or similar) to reduce transcript levels?

Generating additional Cas9 iKO cell lines is feasible, but would be time consuming. In this case, we are not convinced of the value of generating and validating these additional cell lines (particularly with the additional current constraints due to COVID-19). For evaluating the role of the RNA polymerases, we believe that the effect of the drug treatment is clear. For creating a positive control to assess whether the CRISPR iKO strategy is a feasible way to conduct these experiments, we would like to highlight the CENP-C iKO cell line, which has a potent effect in this assay.

6) The authors find a negative correlation between the nucleolus-centromere association and the number of alpha sat foci. This is really interesting and they suggest that the nucleolus association could negatively regulate centromere transcription. However, this correlation is rather indirect in the sense that cells with a higher-degree of nucleolus-centromere localisation have fewer smFISH foci and the inverse, disruption of the nucleolus increases smFISH foci number as a whole. A model based on physical association would suggest that a nucleolus associated centromere produces less or no transcripts. Given that this is not a population-based assay, it should be possible to address this directly by analysing the location of individual centromeres and corresponding transcripts to strengthen the hypothesis. This could be done by either analysing the smaller subset of centromere-associated foci that colocalise with the smFISH signal and test whether the majority of these signals are proximal or distal to the nucleolus (this would not work or be less meaningful if the subpopulation is very small). Or doing a combined DNA/RNA FISH experiment. The expectation would be that DNA FISH signals of centromeres close to the nucleolus would not produce an RNA FISH signal somewhere else, and vice versa.

We predict that centromere-nucleolar associations are dynamic. Thus, we anticipate that centromeres would be associated transiently with the nucleolus (perhaps for a few hours), and that a given centromere would not be associated with the nucleolus in every cell at a specific time point. Thus, we believe that analyzing these behaviors across a diverse range of cells, as we did for this paper, is appropriate. In addition, technical considerations make these suggested experiments prohibitive. Defining the relationship between a centromere RNA and its originating centromere would require combined DNA and RNA FISH. The repetitive nature of alpha-satellite repeats and the strong similarity of these sequences between chromosomes makes it highly complex to visualize an individual centromere. Even if we were able to do this, the conditions required to simultaneously detect nucleoli (immunofluorescence), RNA (smFISH), and DNA (requires denaturation and hybridization) make this such that it would be complex to correlate the localization of an individual centromere with the levels of the corresponding alpha-satellite transcripts. In addition, these RNAs are likely to persist for an extended duration (possibly throughout the course of an entire cell cycle), such that they would not necessarily correlate with the current localization behavior of the centromere from which they are derived. For future work (beyond the scope of this paper), we plan to create cell lines expressing both centromere (CENP-A) and nucleolar markers (for example, Ki67) to conduct time lapse imaging to assess the dynamic associations between these structures.

7) At the end of the Abstract, the authors conclude that the control of centromere transcription might be regulated by the centromere-nucleolar contacts to modulate chromatin dynamics. What does that really mean? One possibility they give in the discussion is rejuvenating centromeric chromatin. It would be nice if they could show some effect along those lines at the centromere in one of the manipulations they did (either through inhibiting or increase transcription). At least as discussed in the paper (Figure 3—figure supplement 1) it appears that overall levels of CENP-A are not affected. Is this different for newly loaded CENP-A? Or some other aspect of chromatin dynamics that is modulated? I realise that this might have been difficult to detect and therefore missing in the current study.

In a separate study from our lab as part of our recent work (Swartz et al., 2018), we found that CENP-A is gradually incorporated at centromeres in non-dividing quiescent cells, including non-transformed human Rpe1 cells and starfish oocytes. In the case of oocytes, which contain a substantial pool of mRNAs such that they do not require ongoing transcription for viability, we found that inhibiting RNA Polymerase II and preventing ongoing transcription blocked the incorporation of newly synthesized histones, including both canonical histone H3 and CENP-A. We realize that our description of this prior work was not sufficient to understand our integrated model, which relies on information from both papers. For the revised paper, we will update our discussion to better describe this data and present our model.

8) The authors state that as cells entered mitosis, dissociation of smFISH foci from chromatin was observed. While the absence of co-localisation of DAPI and smFISH signals is obvious in mitotic cells, what evidence is there that smFISH foci are chromatin associated in interphase nuclei? Rephrasing this bit might avoid confusion here.

We appreciate this point. We did not mean to imply that the smFISH foci are bound to (or associate with) chromatin in the interphase nucleus. We will reword this as suggested.

Reviewer #3:

The manuscript of Bury et al. addresses how alpha-satellite transcription around centromeres is regulated. Using smFISH to detect alpha-satellite RNA transcripts, the authors find that alpha satellites are transcribed by RNA pol II, but their transcription is independent of centromeric proteins. In addition, they present evidence that nucleolar association represses alpha-satellite transcription. The data is convincing, solid and generally supports the conclusions. The manuscript includes appropriate control experiments, such as test for the validity of the RNA FISH probes. The manuscript is well-written and easy to follow, also for someone who is not directly an expert in the field.

The authors use a single-cell technique (smFISH) to look at the localization and transcription of alpha-satellite transcription from centromeres. The technical advance of this paper is limited, as smFISH is a well-established technique by now. Nevertheless, applying this single-cell approach to these repetitive regions has resulted in new insights regarding the regulation of alpha-satellite transcription, especially their localization of centromeres to nucleoli. Regarding the significance of these insights in the context of centromere biology/regulation and its literature is hard to evaluate for me, because this is not my field of expertise (my background is in single-cell transcription regulation). As a researcher from a related research field, I think the findings of this manuscript are mostly relevant for the direct research community of centromere and alpha-satellite biology, but not for researchers outside the field.

We appreciate these comments regarding the carefully controlled nature of our paper and the value of the advances for understanding alpha-satellite transcription. We also agree that smFISH is an established technique, although it has not been applied to these repetitive alpha-satellite sequences in prior work, allowing us to make important new observations using the studies in this paper.

1) The description of the inducible knock out cell lines is very limited. My main concern is how is checked that the gene is actually knocked out. I went back to the referenced paper, but it is still is not clear to me whether the new knockouts are sufficiently checked. It would be more convincing if the authors could show western blots or other evidence that their knockouts are working. In any case, the description of the knockout generation should be more elaborate.

This important point was also noted by the other reviewers. Please see our responses to reviewer #1 point 4 and reviewer #2 point 4. As described above, for a revised paper, we will provide an improved description of these knockout cell lines, our validation of these tools, and how we conducted the experiments in this paper.

2) The authors nicely show that there is an inverse correlation between nucleolar association of the centromere and alpha-satellite transcription. The data supports this claim, but given the many knockouts and cell lines that were tested, with many intermediate phenotypes (such as CENP-B), I find the correlation based on 4 points a bit sparse. I would recommend filling up Figure 4C with a few more mutants, to show that the inverse correlation holds for all mutants. These experiments would be straightforward for the authors, as the knockout/cell lines and techniques are already available.

We see a compelling general correlation between the fraction of nucleolar-localized centromeres and alpha-satellite transcript levels. Our goal for Figure 4C was to highlight this correlation for a selected subset of conditions. However, we do not believe that there will be a precise linear correlation between transcript levels and nucleolar centromeres under every condition. Indeed, it is quite possible that some perturbations would affect transcript levels without altering nucleolar associations. This is particularly true for perturbations that cause subtle phenotypes. Systematically analyzing centromere-nucleolar co-localization for each of the knockouts represents a substantial undertaking that we do not feel would contribute substantially to this existing paper.

3) The nucleolar repression is also supported by the Fibrillarin and Ki67 knockout. These are nice experiments which support their findings. What I am missing is whether these data quantitatively agree with the inverse correlation. Are these mutants completely lacking nucleoli, and if so, would you not expect both mutants to show the same upregulation? Similar to my point above, where do these mutants fall in the graph of Figure 4C?

For the perturbations described in this paper, we believe that inhibiting RNA Polymerase I most closely approximates the condition where nucleolar function is eliminated. Although Ki67 is a nucleolar protein in interphase, loss of Ki67 does not cause lethality indicating that nucleolar function is largely intact. We agree that it would be a good experiment to assess nucleolar-centromere associations in the Ki67 knockout. In fact, we have tried these experiments several times. However, due to the absence of Ki67 (for which we have the best localization tools), we instead needed to use Fibrillarin to monitor nucleoli. We have found this antibody to be much more finicky and not as readily compatible with the fixation conditions needed to detect centromeres. Thus far, we have not been able to generate clear data for this behavior.

4) Related to this, since their imaging techniques have single-cell resolution, I wonder if cells that contain many centromeres in the nucleolus have less alpha satellite transcripts than cell with few centromeres.

The correlation between centromere-nucleolar associations and alpha-satellite transcript numbers is strongly supported by our data across a population. However, analyzing this in individual cells is additionally complicated by the fact that we found that transcript levels vary over the cell cycle (low in G1, higher in S/G2). In addition, monitoring each of these markers in individual cells is technically complicated. Thus, while we appreciate this suggestion, we believe that our data stands on its own.

5) One claim that is a bit speculative is the suggestion that transcription itself and not the RNA may be required for the function of the alpha-satellites. This is indeed supported by the fact that most transcripts are not localized at the centromeres. However, this contrasts to the findings of the papers that increasing alpha-satellite transcription in different mutants does not appear to result in any phenotype on centromere function. For a non-expert, the function of these transcripts/transcription itself is not clear from the current manuscript, so I would recommend discussing the nuances of its functions in more detail in the discussion.

We agree that our model is speculative, but have chosen to include this to provide our perspective on the possible roles for centromere transcription based on this paper and our other recent work (Swartz et al., 2018). We believe that our data provide a context and set of constraints for potential roles of centromere transcription, but also agree that future work is needed to resolve these. Based on this comment and those from the other reviewers, we will also provide a better description of the data in the Swartz et al. paper, which analyzed different features of centromere transcription.

6) To quantify the smFISH data, the authors count the number of foci. From the images, it looks like the different foci have very different intensities. This may occur if the transcripts are different length when transcribed from different genomic regions. However, this may also occur if several RNA co-localize to the same spot, i.e. if one spot contains several RNAs. Can the authors verify that the distribution of spot intensities matches the expected intensities based on the different transcribed alpha-satellite regions?

Please see our response to reviewer #2, point #3.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Several conclusions that you want to make require additional experimental support:

1) All reviewers indicated that controls are needed for the iKO experiments. Although you have validated these cell lines previously, the controls are required to demonstrate that knock-outs/knock-downs occurred in your current experiments, ideally in the cells that you are analyzing. If the antibodies do not work in the smFISH experiment, you could provide immunoblots. Minimally, this should be shown for the particularly relevant CENP-C iKO cells.

If you do not have antibodies available, or in order to get an estimate for the fraction of cells with efficient knock-down, a quantification of cellular phenotypes in the population could also be useful. According to your reply, you have already performed the latter. Please provide these data.

We have now performed immunofluorescence with antibodies specific to the gene target for the CENP-A, CENP-B, and CENP-C iKO cell lines (as representative knockouts that are particularly relevant to the conclusions in the paper). These data are included in Figure 4—figure supplement 1A. Our results indicate that, after induction with doxycycline, the localization of the target protein is substantially diminished or undetectable in the vast majority of cells in the population. In addition, the substantial chromosome mis-segregation that occurs following CENP-C knockout is clearly visible based on the misshapen interphase nuclei. We have also already included immunofluorescence data for the stable Ki67 knockout (Figure 5—figure supplement 1D). In combination with our previous validation of the inducible knockout cell lines (McKinley et al., 2017), we believe that this strongly validates this approach.

We also note that the majority of the Cas9-based inducible knockouts that we used for this paper were generated and validated previously (McKinley et al., 2015; McKinley et al., 2017). For the centromere protein knockouts (McKinley et al., 2015), these were analyzed previously with respect to phenotype and monitored for the depletion of each gene target over time. For the larger collection of cell cycle and cell division inducible knockouts, for our prior work we systematically validated each of these with respect to their phenotype (see http://cellcycleknockouts.wi.mit.edu). Thus, we are confident that each of these cell lines is functional and effective for eliminating the target gene. However, as these other knockouts are not a focus of the paper and we do not have specific antibodies in each case, we have refrained from reanalyzing these for the broad collection of gene targets tested in Figure 4A and Figure 4—figure supplement 1B. Based on our extended prior work, we find this Cas9 inducible knockout strategy to be both penetrant and severe in their phenotypes.

2) In all conditions where you observe a larger number of smFISH foci, you interpret this as an increase in transcription. Before making this conclusion, alternative reasons need to be excluded:

i) It needs to be checked whether the increased number of transcripts could reflect stabilization (increased half-life) rather than increased transcription. Two experiments seem important: Is the drastic drop in transcript levels during G1 still observed in the knock-outs/inhibitions that increase transcript levels? Is the half-life during S/G2 increased? (Although prior reports have described centromeric transcripts as very stable, the fact that you see drastic drops after 5 hours of RNA Pol II inhibition, suggests that the transcripts that you are assaying are turning-over at a detectable rate.)

We agree that it is important to consider differences in RNA half-life in addition to changes in transcription to account for differences in transcript levels. For the revised paper, we have now measured the alpha-satellite RNA half-life in control HeLa cells and in the CENP-C inducible knockout. For these experiments, we inhibited the synthesis of new RNA by RNA Pol II inhibition (THZ1 treatment to inhibit Cdk7) and measured alpha-satellite RNA levels over time by RT-qPCR. We found that in both control cells and CENP-C depleted cell, the half-life of centromeric RNAs is ~70 minutes (Figure 4H) with the transcripts substantially reduced by 5 hours of treatment. We believe that these data provide a strong addition to the conclusions of the paper and suggest that the changes in transcript number reflect changes to the level of transcription instead of altered transcript stability. Although we believe that the CENP-C iKO data provides a valuable addition, we chose not to test RNA half-life as part of the cell cycle analysis due to technical complications and the fact that these inhibitor treatments may alter cell cycle state or progression. Together, these results combined with the smFISH data strongly suggest that the observed changes occur through a transcriptional, rather than post-transcriptional, effect on alpha-satellite RNA levels.

ii) For the CENP-C iKO cells that show an increased number of ASAT transcripts, it needs to be addressed whether this can be attributed to failed cytokinesis. Is it possible that the cells have become larger / diploid, and that this is the sole reason for the higher number of transcripts? Similar concerns could apply to some of the other knock-outs tested. A quantification of transcripts and centromeres in the same cells could be useful.

For this revised paper, we have now assessed key conditions, such as the CENP-C inducible KO, using RT-qPCR for an alpha-satellite array associated with chromosome 21. We have normalized alpha-satellite transcript levels to that of GAPDH, which allows us to assess changes in transcription relative to cell size and other features. Importantly, our qPCR analysis reveals very similar behaviors as our smFISH analysis. In addition, we note that, based on our analysis of the CENP-C knockout phenotype (for both prior work and this paper), we do not detect a substantial increase in nuclear size, DNA intensity, or centromere numbers that would indicate the presence of a failure in cytokinesis. Instead, we detect misshapen nuclei that are consistent with cell division occurring with substantial chromosome mis-segregation.

3) The conclusiveness of your results would greatly profit from confirmation by a different approach. Quantitative RT-PCR was suggested, and you already indicated it should be feasible to test for centromeric transcripts by qPCR in your key experimental conditions (CENP-C iKO, Ki67 iKO, fibrillarin iKO, RNA polymerase I inhibition, RNA polymerase II inhibition).

For this revised paper, we have invested substantial effort to optimize conditions for RT-qPCR to detect centromere transcripts, as well as test key conditions that are relevant to the paper. We now present data for the qPCR for a previously published primer pair that is specific to Chromosome 21 (Nakano et al., 2003). This data is included in Figures 1F, 3D, 4E, and Figure 5—figure supplement 1E for HeLa cells, Rpe1 cells, RNA polymerase I inhibition, RNA polymerase II inhibition, CENP-C iKO, and Ki67 KO. We are excited for this data, which we believe substantially add to the paper and the strength of the conclusions.

Overall, our data indicate highly similar findings for the qPCR data as compared to our previous smFISH data. We find that Rpe1 cells display reduced transcript levels as compared to HeLa cells (Figure 1F), that the abundance of these transcripts depends upon RNA Polymerase II activity (Figure 3D and 4H), and that transcript levels are substantially increased following the inhibition of RNA Polymerase I (Figure 3D), and that transcript levels increase when CENP-C is eliminated (Figure 4E). For these conditions that display substantial changes in transcript levels, the results are striking. In contrast, we do not detect a dramatic change in alpha satellite transcript levels by qPCR for the Ki67 knockout (Figure 5—figure supplement 1E). We have noted this difference in the text, which may reflect the degree of sensitivity of each assay, technical differences, single molecule vs bulk assays, or a less potent role for Ki67.

4) The conclusiveness of the results would also profit from showing that the FISH probes indeed detect alpha satellite RNA. We suggest that you perform a DNA-FISH experiment using the same probes.

For our previous smFISH probes, the versions that we obtained from the manufacturer (Biosearch Technologies) were not suitable for DNA-FISH experiments. We have now obtained an additional set of probes using identical sequences, but from a second manufacturer (PixelBiotech GmbH). Using the RNA FISH protocol, we validated these probes and found that they displayed identical behavior for the detecting alpha satellite transcript levels (including number of smFISH foci, the sensitivity to RNAse A treatment, and the localization of the smFISH foci). We then used these probes in a modified protocol to conduct DNA-FISH (requires harsher conditions to denature the DNA). Under these conditions, these probes now display multiple clear foci that are consistent with centromeres – i.e., they localize throughout the nucleus in interphase and align during mitosis in the same way as a centromere marker would (similar to the staining of centromere components that we have been conducting for years). Although we are unable to co-localize these foci due to the fixation and extraction conditions, we believe that these images provide additional support for the specificity of these probes. These data are included in Figure 1—figure supplement 1A.

https://doi.org/10.7554/eLife.59770.sa2

Article and author information

Author details

  1. Leah Bury

    Whitehead Institute for Biomedical Research, Cambridge, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation, Visualization, Methodology, Writing - review and editing
    Contributed equally with
    Brittania Moodie
    Competing interests
    No competing interests declared
  2. Brittania Moodie

    Whitehead Institute for Biomedical Research, Cambridge, United States
    Contribution
    Investigation, Visualization, Methodology, Writing - review and editing
    Contributed equally with
    Leah Bury
    Competing interests
    No competing interests declared
  3. Jimmy Ly

    1. Whitehead Institute for Biomedical Research, Cambridge, United States
    2. Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    Contribution
    Conceptualization, Validation, Investigation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Liliana S McKay

    Whitehead Institute for Biomedical Research, Cambridge, United States
    Contribution
    Investigation, Visualization
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5426-5539
  5. Karen HH Miga

    UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, United States
    Contribution
    Formal analysis, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Iain M Cheeseman

    1. Whitehead Institute for Biomedical Research, Cambridge, United States
    2. Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Visualization, Writing - original draft, Writing - review and editing
    For correspondence
    icheese@wi.mit.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3829-5612

Funding

National Institute of General Medical Sciences (R35GM126930)

  • Iain M Cheeseman

American Cancer Society

  • Leah Bury

G. Harold and Leila Y. Mathers Foundation

  • Iain M Cheeseman

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the members of the Cheeseman lab and Gayathri Muthukumar for their support and input. This work was supported by grants from The Harold G and Leila Y Mathers Charitable Foundation, the NIH/National Institute of General Medical Sciences (R35GM126930) to IMC, and an American Cancer Society post-doctoral fellowship to LB. The authors declare that they have no conflict of interest.

Senior Editor

  1. Anna Akhmanova, Utrecht University, Netherlands

Reviewing Editor

  1. Silke Hauf, Virginia Tech, United States

Publication history

  1. Received: June 8, 2020
  2. Accepted: November 9, 2020
  3. Accepted Manuscript published: November 11, 2020 (version 1)
  4. Version of Record published: November 20, 2020 (version 2)

Copyright

© 2020, Bury et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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