ABSTRACT
The organisation of chromatin is closely intertwined with biological activities of chromosome domains, including transcription and DNA replication status. Scaffold-attachment factor A (SAF-A), also known as heterogeneous nuclear ribonucleoprotein U (HNRNPU), contributes to the formation of open chromatin structure. Here, we demonstrate that SAF-A promotes the normal progression of DNA replication and enables resumption of replication after inhibition. We report that cells depleted of SAF-A show reduced origin licensing in G1 phase and, consequently, reduced origin activation frequency in S phase. Replication forks also progress less consistently in cells depleted of SAF-A, contributing to reduced DNA synthesis rate. Single-cell replication timing analysis revealed two distinct effects of SAF-A depletion: first, the boundaries between early- and late-replicating domains become more blurred; and second, SAF-A depletion causes replication timing changes that tend to bring regions of discordant domain compartmentalisation and replication timing into concordance. Associated with these defects, SAF-A-depleted cells show elevated formation of phosphorylated histone H2AX (γ-H2AX) and tend to enter quiescence. Overall, we find that SAF-A protein promotes robust DNA replication to ensure continuing cell proliferation.
INTRODUCTION
DNA replication in eukaryotic genomes initiates from discrete sites termed DNA replication origins. Potential replication origin sites are defined by stepwise assembly of a protein complex, the pre-replication complex (pre-RC), during G1 phase of the cell cycle (Fragkos et al., 2015). During pre-RC formation, the origin recognition complex (ORC) and CDT1 cooperate to load the heterohexameric minichromosome maintenance complex (MCM), leading to ‘origin licensing’ (McIntosh and Blow, 2012). MCM plays a critical role when DNA replication initiates at each origin, forming the central component of the replicative helicase (Fragkos et al., 2015). Cells monitor the level of replication licensing and prevent cell cycle progression if an insufficient number of sites are licensed (Feng et al., 2003; Lau et al., 2009; Nevis et al., 2009; Shreeram et al., 2002; Zimmerman et al., 2013). This ‘licensing checkpoint’ mechanism appears to be compromised in cancer cells (Feng et al., 2003; Lau et al., 2009; Nevis et al., 2009; Shreeram et al., 2002; Zimmerman et al., 2013).
A recent study has demonstrated that replication licensing is impacted by the state of chromatin packaging. The histone methyltransferase SET8 (also known as PR-SET7 or KMT5A) can stimulate origin licensing at specific sites (Tardat et al., 2010), but also prevents over-licensing by enhancing chromatin compaction as cells exit mitosis (Shoaib et al., 2018). SET8 is responsible for the methylation of histone H4 at lysine 20 (H4K20) and for maintaining chromatin compaction at the M-G1 boundary (Shoaib et al., 2018). Replication licensing is therefore impacted both by local chromatin changes and broader changes occurring at the chromosome domain level. How chromatin packaging status affects origin licensing and the subsequent steps in DNA replication is still not fully understood, but there is a well-established connection between chromatin packaging and the temporal programme of replication (Fu et al., 2018; Gilbert, 2010; Gilbert et al., 2010), in which euchromatin domains containing active genes generally replicate early in S phase, whereas heterochromatic, highly packaged domains containing mainly inactive genes replicate late. Replication timing of some domains is modulated during development, often reflecting changes in gene activity (Hiratani et al., 2008). The replication timing programme is established at early G1 phase, which coincides with chromatin decompaction and chromatin remodelling as cells exit M phase (Shoaib et al., 2018), suggesting that the dynamic controls over chromatin structure imposed as cells exit mitosis determine the replication potential and subsequent replication timing of local chromatin domains (Dimitrova and Gilbert, 1999; Dimitrova et al., 2002).
Chromatin packaging also impacts on replication progression, with processive replication of heterochromatin regions requiring local decompaction (Chagin et al., 2019). Recent studies also highlight numerous ‘difficult-to-replicate’ regions (Cortez, 2015; Gadaleta and Noguchi, 2017), including DNA–protein complexes, repetitive DNA such as centromeres and telomeres, and secondary DNA structures. Replicating such regions requires support by specific proteins, without which replication tends to fail, leading to genome instability (Cortez, 2015; Gadaleta and Noguchi, 2017) and the formation of fragile sites (Boteva et al., 2020). These observations highlight the importance of modulating chromatin structure during the replication process.
Scaffold-attachment factor A (SAF-A; also known as heterogeneous nuclear ribonucleoprotein U, HNRNPU) is an RNA- and DNA-binding protein that modulates chromatin structure by tethering chromatin-associated RNA (caRNA) to chromatin (Creamer et al., 2021; Fackelmayer et al., 1994; Kiledjian and Dreyfuss, 1992; Nozawa et al., 2017; Sharp et al., 2020). SAF-A oligomerisation contributes to decompacted chromatin (Creamer et al., 2021; Nozawa et al., 2017), and depletion of SAF-A causes global chromatin condensation (Fan et al., 2018). A super-resolution microscopy study also implicates SAF-A in the establishment of correct chromatin structure, and SAF-A has been shown to regulate both active chromatin and X-chromosome inactivation (Hasegawa et al., 2010; Lu et al., 2020; Smeets et al., 2014). SAF-A interacts and colocalises with proteins that define chromatin domain boundaries, namely CTCF and cohesin, and plays a role in defining boundaries of topologically associated domains that form smaller units of chromosome organisation (Fan et al., 2018; Zhang et al., 2019). Although SAF-A promotes open chromatin, depletion of SAF-A has fairly minor effects on transcription (Nozawa et al., 2017). SAF-A is, however, reported to be implicated in alternative splicing (Xiao et al., 2012; Ye et al., 2015), mRNA stability (Yugami et al., 2007) and nuclear retention of mRNA (Huang et al., 2021).
Association of SAF-A with chromatin is cell cycle-regulated: SAF-A is associated with chromatin throughout interphase but is removed from chromatin in M phase (Sharp et al., 2020). Regulated dissociation of SAF-A from the mitotic chromosome, triggered by phosphorylation of SAF-A by Aurora kinase B, is essential for proper progression of mitosis (Douglas et al., 2015; Sharp et al., 2020). SAF-A reassociates with chromatin as cells exit from mitosis, implicating SAF-A in chromatin decompaction at this cell cycle stage.
SAF-A also localises to DNA damage sites swiftly after γ-ray irradiation (perhaps to modulate chromatin structure), and then at a later stage appears to be excluded from damage sites (Hegde et al., 2016). Interestingly, the expression of the SAF-A gene tends to increase in a wide range of cancers, particularly breast invasive carcinoma (The Cancer Genome Atlas; https://www.cancer.gov/tcga). This increased expression suggests that SAF-A contributes to the formation or survival of cancer cells in a dose-dependent manner. Conversely, SAF-A loss-of-function alleles are linked to developmental disorders, including microcephaly (Durkin et al., 2020; Leduc et al., 2017; Yates et al., 2017). Overall, these observations suggest a positive role for SAF-A in promoting cell proliferation. While roles of SAF-A in mitosis have been investigated (Sharp et al., 2020), its contribution to cell proliferation during interphase, particularly to DNA replication, has not been studied.
Here, we investigate the effects of SAF-A on DNA replication. We show that SAF-A protein is required for full replication licensing in the G1 phase of the cell cycle, and depleting SAF-A leads to increased spacing between replication origins. We find, moreover, that replication fork progression is compromised in cells depleted for SAF-A, and that SAF-A protein plays a role in defining the boundaries of early and late replication domains in the genome-wide replication programme. Loss of these functions leads to spontaneous replication stress and increases cellular entry to quiescence, explaining the need for SAF-A for normal cell proliferation.
RESULTS
SAF-A is required for robust DNA replication
To assess the general impact of SAF-A on subnuclear organisation of chromatin, we examined the distribution of DNA within nuclei of hTERT-RPE1 cells treated with siRNA targeting SAF-A (siSAF-A) (Fig. 1A). hTERT-RPE1 is a non-cancer cell line derived from retinal pigment epithelial cells immortalised by expression of human telomerase (hTERT; Bodnar et al., 1998). Using super-resolution microscopy to examine very thin sections, we found that control nuclei showed relatively homogeneous DNA density distribution (i.e. mostly intermediate DAPI signal intensity; green in Fig. 1A), with smaller areas of higher (yellow and red in Fig. 1A) or lower (cyan and blue in Fig. 1A) DNA density. In siSAF-A cells, the DNA density distribution revealed larger areas with low DNA density (blue in Fig. 1A) interspersed with high DNA density areas (red in Fig. 1A), indicative of a more polarised distribution with sections of genomic DNA densely packed in abnormally compact domains. Unbiased classification of ‘DAPI high’ and ‘DAPI low’ areas in each nucleus (Fig. 1B) confirmed the formation of larger ‘DAPI low’ areas in siSAF-A nuclei, with chromatin confined into smaller areas. These data suggest that SAF-A promotes proper dispersal of chromatin distribution within nuclei and prevents the formation of over-compacted chromatin. This microscopic observation is consistent with SAF-A function in maintaining correct chromatin architecture as revealed by microscopy (Creamer et al., 2021; Nozawa et al., 2017) and Hi-C chromatin conformation capture methods (Fan et al., 2018).
SAF-A is required for robust DNA replication. (A) Specimen images showing the distribution of DNA within nuclei of hTERT-RPE1 cells treated with siControl and siSAF-A. Super-resolution images of DAPI-stained DNA were identically processed and are displayed in pseudo-colour scale as shown below the images. Scale bar: 5 µm. (B) Quantification of ‘DAPI-high’ areas. Images show the nuclei depicted in A with DAPI intensity in greyscale and identified ‘DAPI-high’ areas overlaid in red. Plots below show the proportion of nuclear area that is ‘DAPI high’ in two independent experiments (shown in red and blue), with mean±s.d. indicated. Triangles represent the mean values for each separate experiment, and the number of nuclei quantified is shown on the graph. Statistical significance was calculated for each biological replicate by two-tailed unpaired t-test. (C) Growth of hTERT-RPE1 cells treated with siControl or siSAF-A in DMEM medium, measured by counting cell number at each passage. Mean±s.d. values of three biological replicates are shown. The y-axis is shown in log2 scale. (D) Cells depleted of SAF-A are defective in recovery from replication stress. siControl and siSAF-A cells were arrested with 4 mM HU for 24 h, then released into fresh medium. Cells were sampled at the indicated time points, and DNA content analysed by flow cytometry. Results from one of five biological replicates are shown. (E) Percentages of EdU-positive cells after removal of HU. After removal of HU, cells were pulse labelled for 20 min with EdU at the indicated time points, and EdU-positive cells were identified by flow cytometry. See Fig. S1B for gating strategy. Data are from one of two biological replicates. (F) Cells depleted of SAF-A show reduced DNA synthesis rate. Asynchronously growing cells were pulse labelled with 20 µM EdU for 1 h and collected. DNA content and the amount of EdU was measured by flow cytometry. Contour intervals are set with 5% of cells falling between successive contour lines. Representative results from one of four biological replicates are shown, with the number of cells analysed for each cell line indicated. Dashed line marks maximum EdU incorporation level in siControl cells and boxes indicate gates for EdU-positive cells. The percentage of EdU-positive cells is indicated for each cell line. (G) Incorporation of EdU in S-phase cells. EdU incorporation per cell was measured in EdU-positive (S-phase) cells. Violin plots show the median (solid line) and quartiles (dotted lines). The number of cells analysed for each cell line is shown below the graph. Results from one of two biological replicates are shown. Statistical significance was calculated by Mann–Whitney–Wilcoxon test. **P<0.01; ***P<0.001; ****P<0.0001.
SAF-A is required for robust DNA replication. (A) Specimen images showing the distribution of DNA within nuclei of hTERT-RPE1 cells treated with siControl and siSAF-A. Super-resolution images of DAPI-stained DNA were identically processed and are displayed in pseudo-colour scale as shown below the images. Scale bar: 5 µm. (B) Quantification of ‘DAPI-high’ areas. Images show the nuclei depicted in A with DAPI intensity in greyscale and identified ‘DAPI-high’ areas overlaid in red. Plots below show the proportion of nuclear area that is ‘DAPI high’ in two independent experiments (shown in red and blue), with mean±s.d. indicated. Triangles represent the mean values for each separate experiment, and the number of nuclei quantified is shown on the graph. Statistical significance was calculated for each biological replicate by two-tailed unpaired t-test. (C) Growth of hTERT-RPE1 cells treated with siControl or siSAF-A in DMEM medium, measured by counting cell number at each passage. Mean±s.d. values of three biological replicates are shown. The y-axis is shown in log2 scale. (D) Cells depleted of SAF-A are defective in recovery from replication stress. siControl and siSAF-A cells were arrested with 4 mM HU for 24 h, then released into fresh medium. Cells were sampled at the indicated time points, and DNA content analysed by flow cytometry. Results from one of five biological replicates are shown. (E) Percentages of EdU-positive cells after removal of HU. After removal of HU, cells were pulse labelled for 20 min with EdU at the indicated time points, and EdU-positive cells were identified by flow cytometry. See Fig. S1B for gating strategy. Data are from one of two biological replicates. (F) Cells depleted of SAF-A show reduced DNA synthesis rate. Asynchronously growing cells were pulse labelled with 20 µM EdU for 1 h and collected. DNA content and the amount of EdU was measured by flow cytometry. Contour intervals are set with 5% of cells falling between successive contour lines. Representative results from one of four biological replicates are shown, with the number of cells analysed for each cell line indicated. Dashed line marks maximum EdU incorporation level in siControl cells and boxes indicate gates for EdU-positive cells. The percentage of EdU-positive cells is indicated for each cell line. (G) Incorporation of EdU in S-phase cells. EdU incorporation per cell was measured in EdU-positive (S-phase) cells. Violin plots show the median (solid line) and quartiles (dotted lines). The number of cells analysed for each cell line is shown below the graph. Results from one of two biological replicates are shown. Statistical significance was calculated by Mann–Whitney–Wilcoxon test. **P<0.01; ***P<0.001; ****P<0.0001.
Cells depleted of SAF-A have been reported to show proliferation defects (Nozawa et al., 2017), but the exact nature of the defect has not been studied in detail. We examined the cell proliferation and DNA replication profiles of cells depleted of SAF-A. siSAF-A cells showed a significant and reproducible reduction in cell proliferation rates compared with those of hTERT-RPE1 cells treated with control siRNA (siControl; Fig. 1C), which is consistent with a previous report (Nozawa et al., 2017). Flow cytometry analysis of DNA content in asynchronous cultures (Fig. S1A), however, showed no specific cell cycle arrest point but did reveal a slight reduction of the S phase population, suggesting that loss of SAF-A may cause problems with DNA replication.
Cells depleted of SAF-A have been reported to be defective in recovery from replication inhibition by the DNA polymerase inhibitor aphidicolin (Nozawa et al., 2017). We therefore tested whether SAF-A-depleted cells also fail to recover from the DNA replication inhibitor hydroxyurea (HU), an inhibitor of ribonucleotide reductases that causes stalled replication forks. After treating siControl and siSAF-A cells with 4 mM HU for 24 h to cause early S phase arrest (Fig. 1D, 0 h), we examined recovery by monitoring DNA content and incorporation of a thymidine analogue ethynyl deoxyuridine (EdU). Control cells recovered from arrest efficiently and reached mid-S phase by 4 h after release from HU (Fig. 1D, siControl). In contrast, very few siSAF-A cells recovered to reach a similar stage by 6 h (Fig. 1D, siSAF-A). Assessment of EdU-positive cells further indicated that a reduced number of siSAF-A cells were able to resume DNA synthesis compared with siControl cells (Fig. 1E), and that the rate of DNA synthesis in EdU-positive siSAF-A cells was lower than that in siControl cells until 6 h after release (Fig. S1B,C). By 8 h after the release, the majority of siControl cells had finished DNA replication, whereas a notable fraction of siSAF-A cells were still synthesising DNA (Fig. S1B,C). These observations indicate that cells depleted of SAF-A have difficulty in recovering from HU treatment. Taken together, our results show that deficiency of SAF-A causes cells to be severely impaired in recovery from replication stress.
We tested whether depletion of SAF-A impacts DNA replication in the absence of exogenous stress by measuring DNA synthesis rate based on pulse labelling nascent DNA with EdU followed by flow cytometry analysis. Cells depleted of SAF-A showed a significantly reduced percentage of EdU-positive cells (Fig. 1F; 37.2% in siControl and 24.7% in siSAF-A). Moreover, the EdU-positive population of siSAF-A cells showed a reduced DNA synthesis rate compared to that of siControl cells (Fig. 1G). This DNA synthesis defect of siSAF-A cells is not confined to a specific stage of S phase (Fig. S1D), suggesting that SAF-A function is required throughout DNA replication. Taken together, these results indicate that SAF-A is required for robust DNA replication without exogenous replication stress, and also supports the recovery of cells after replication stress.
SAF-A is important for replication licensing
Changes in chromatin due to SAF-A depletion could potentially affect multiple steps of DNA replication, including origin licensing, replication fork progression and fork restart. We decided to assess the requirement for SAF-A for each of these steps in a series of experiments.
Since SAF-A plays a positive role in open chromatin structure and prevents over-compaction (Fig. 1A,B) we hypothesised that SAF-A may play a positive role in stimulating origin licensing by promoting open chromatin. To test this hypothesis, we used a flow cytometry ‘3D licensing assay’ (Moreno et al., 2016) (Fig. 2A,B), which simultaneously measures MCM loading on chromatin and EdU incorporation (to assess cell cycle stage). In this assay (Fig. 2A), cells in G1 (red box), S (cyan box) and G2/M (orange box) phase can be clearly distinguished, and the amount of chromatin-associated MCM3 can be assessed in each cell cycle population (Fig. 2B). As clearly seen in Fig. 2B, siSAF-A-treated hTERT-RPE1 cells showed reduced levels of chromatin-associated MCM3 in individual cells both in G1 phase (red) and in cells entering S phase (left-hand part of cyan population). This observation indicates that siSAF-A cells show compromised levels of MCM loading in G1-phase cells that persist into S phase, suggestive of a defect in origin licensing.
SAF-A is important for replication licensing. (A) Flow cytometry analysis of hTERT-RPE1 cell cycle phases by DNA content and EdU incorporation. Gates used in B are indicated by coloured dotted parallelograms. Contour intervals are set with 5% of cells falling between successive contour lines. (B) 3D licensing assay in hTERT-RPE1 cells. G1-phase (red), S-phase (cyan) and G2/M-phase (orange) cell populations were distinguished as in A. Chromatin-associated MCM3 was measured as previously described (Hiraga et al., 2017). (C) SAF-A promotes chromatin association of CDT1 protein in G1 phase. Chromatin association of CDT1 protein in control and SAF-A-depleted cells was tested in hTERT-RPE1 cells. Contour line intervals are set to 5%. (D) SAF-A is required for full chromatin association of ORC1 and CDT1 proteins in G1 phase. Chromatin association of FLAG-tagged ORC1 protein in a HEK293-derived cell line was tested using anti-FLAG antibody (top panels). Chromatin association of CDT1 (middle panels) and MCM3 (bottom panels) proteins was tested in the same batch of cells. Contour line intervals are set to 5%. For the number of replicates, please refer to Fig. S2B–D.
SAF-A is important for replication licensing. (A) Flow cytometry analysis of hTERT-RPE1 cell cycle phases by DNA content and EdU incorporation. Gates used in B are indicated by coloured dotted parallelograms. Contour intervals are set with 5% of cells falling between successive contour lines. (B) 3D licensing assay in hTERT-RPE1 cells. G1-phase (red), S-phase (cyan) and G2/M-phase (orange) cell populations were distinguished as in A. Chromatin-associated MCM3 was measured as previously described (Hiraga et al., 2017). (C) SAF-A promotes chromatin association of CDT1 protein in G1 phase. Chromatin association of CDT1 protein in control and SAF-A-depleted cells was tested in hTERT-RPE1 cells. Contour line intervals are set to 5%. (D) SAF-A is required for full chromatin association of ORC1 and CDT1 proteins in G1 phase. Chromatin association of FLAG-tagged ORC1 protein in a HEK293-derived cell line was tested using anti-FLAG antibody (top panels). Chromatin association of CDT1 (middle panels) and MCM3 (bottom panels) proteins was tested in the same batch of cells. Contour line intervals are set to 5%. For the number of replicates, please refer to Fig. S2B–D.
We next tested whether SAF-A affects other licensing proteins. CDT1 interacts with MCM and assists its loading onto chromatin in G1 phase (Frigola et al., 2017; Zhai et al., 2017a): outside of G1 phase CDT1 is negatively regulated by geminin (Blow and Tanaka, 2005). In vitro studies show that CDT1 dissociates from MCM after the assembly of the double MCM hexamer on DNA (Zhai et al., 2017b). Consistently, flow cytometry analysis showed that CDT1 associates with chromatin predominantly in G1 phase (vertical spikes in Fig. 2C). We found that depletion of SAF-A in hTERT-RPE1 cells caused reduced chromatin association of CDT1 (Fig. 2C, siSAF-A), consistent with the reduced MCM licensing in G1 phase (Fig. 2B).
We next tested the chromatin association of ORC1 protein. ORC1 protein is a subunit of the ORC protein complex that initially defines MCM loading sites. ORC1 protein expression and stability is cell cycle-regulated so that it is present predominantly in G1 phase, helping to confine MCM loading to G1 phase of the cell cycle (Méndez et al., 2002; Ohta et al., 2003; Tatsumi et al., 2003). In the absence of an ORC1 antibody suitable for flow cytometry analysis, we made use of a HEK293-based cell line expressing FLAG-tagged ORC1 protein (Tatsumi et al., 2003) to analyse chromatin association of ORC1 (Fig. 2D). As expected, and consistent with previous reports (Hiraga et al., 2017), ORC1 chromatin association was detected predominantly in the G1 phase of the cell cycle (Fig. 2D, top panels). Depletion of SAF-A resulted in a reduction of chromatin-associated ORC1 (Fig. 2D, top-right panel). We also observed that CDT1 and MCM licensing were reduced when SAF-A was depleted in the HEK293 FLAG–ORC1 cell line (Fig. 2D, middle and bottom panels, respectively), similar to the effects in hTERT-RPE1 cells (Fig. 2B). In contrast, chromatin association of ORC2, which does not fluctuate during the cell cycle (Méndez and Stillman, 2000; Méndez et al., 2002), was not affected by SAF-A depletion (Fig. S2A). We confirmed the reproducibility of these effects in multiple independent experiments in both HEK293-derived cells and hTERT-RPE1 cells (Fig. S2B–D).
Reduced association of licensing factors in SAF-A-depleted hTERT-RPE1 cells was confirmed by western blot analysis of chromatin-associated proteins. Western blotting of chromatin-enriched fractions confirmed the reduced association of CDT1 protein (Fig. S2E, lanes 3 and 4) and ORC1 protein (Fig. S2E, lanes 7 and 8, top panel) after SAF-A depletion. CDC6, another protein required for replication licensing (Blow and Tanaka, 2005), however, did not show such a reduction in chromatin association (Fig. S2E, lanes 7 and 8, middle panel). Chromatin association of ORC2 was not affected by SAF-A depletion (Fig. S2E, lanes 11 and 12), consistent with the flow cytometry result (Fig. S2A).
The effects of SAF-A depletion on chromatin association of FLAG–ORC1 and CDT1 appeared very similar in HEK293 FLAG–ORC1 cells extracted with CSK buffer (which contains 100 mM NaCl, as compared to standard conditions for flow cytometry that involve extraction with only 10 mM NaCl) (Fig. S3). CSK buffer is widely used for biochemical preparation of chromatin-associated protein fractions in HEK293-derived cells. In hTERT-RPE1 cells, we noticed that CDT1 chromatin association appeared more salt sensitive (data not shown), suggesting that chromatin association may differ between cell types.
Since the western blot analysis reported above suggests some reduction in total CDT1 and ORC1 levels (Fig. S2E, lanes 1 and 2 for CDT1, lanes 5 and 6 for ORC1), we investigated expression of these proteins per cell using flow cytometry. CDT1 expression was not affected by SAF-A depletion in hTERT-RPE1 cells. In FLAG–ORC1 cells depleted of SAF-A, we found that levels of FLAG–ORC1 and CDT1 were somewhat reduced (Fig. S4), although results were variable between experimental repeats (compare top two rows in Fig. S4). Overall, we conclude that reduced ORC1 and CDT1 expression may contribute to phenotypes of SAF-A depletion but cannot fully account for the reduced licensing levels, particularly in hTERT-RPE1 cells.
Overall, the data presented show that SAF-A promotes the G1 phase chromatin association of several origin licensing components, including loading of MCM itself.
SAF-A depletion results in reduced origin activation
Impaired replication licensing in cells depleted of SAF-A suggests there will be a reduced number of potential replication origins available for activation. Therefore, we next tested whether a reduced origin frequency is observed on chromosomes in SAF-A-depleted cells, by measuring inter-origin distances (IODs) using single-molecule DNA fibre analysis.
To detect origin activation on single DNA molecules, nascent DNA was sequentially labelled with thymidine analogues 5-chloro-2′-deoxyuridine (CldU) and 5-iodo-2′-deoxyuridine (IdU), as illustrated in Fig. 3A. Analogue incorporation was analysed by immunostaining of DNA fibres stretched by molecular DNA combing (Bianco et al., 2012). Replication origins can be identified as illustrated in the top panel of Fig. 3B, with the mid-point between divergent replication forks assigned as a replication origin. In these experiments, increased distance between replication origins is indicative of fewer active origins. We found that depletion of SAF-A caused an increase in IOD compared with that of the control (Fig. 3B), suggesting that the number of active origins is indeed reduced by SAF-A depletion. This reduction in origin activation frequency probably reflects inefficient origin licensing.
Cells depleted of SAF-A have a reduced origin activation potential and are defective in the activation of dormant origins. (A) Scheme of the experiment. hTERT-RPE1 cells were treated with either siControl or siSAF-A for 72 h then pulse labelled sequentially with CldU then IdU for 20 min each. Cells were collected, and genomic DNA subjected to DNA combing. Specimen image shows visualised CldU (red) and IdU (green). Scale bar: 10 µm (B) IOD was measured as illustrated in A in hTERT-RPE1 cells treated with siControl or siSAF-A, and with or without HU treatment. Schematic shows CldU (red) and IdU (green) labelling patterns used to identify origins. Superplots below show results from three independent experiments (blue, orange and grey). The mean with 95% confidence interval is indicated, and triangles show mean values for each experiment. For HU-treated samples, CldU and IdU labellings were done at the end of 4 h HU treatment at 0.1 mM. Treatment with 0.1 mM HU does not stop DNA synthesis completely (Fig. 4A). Mean values from each experiment were statistically tested by one-tailed pairwise t-test. (C) IOD was measured as described in A. The results from one representative experiment (the dataset shown in grey in B) are shown in two-sided bean plots, with the number of IODs analysed for each cell line and condition indicated. Short bars indicate data points and long bars indicate means. The blue dashed line shows the 20-kb reference point and the dotted line marks the average of all the data points shown. Note that the y-axis is in log scale. Statistical significance was calculated by Mann–Whitney–Wilcoxon test. *P<0.05; **P<0.01; ns, not significant.
Cells depleted of SAF-A have a reduced origin activation potential and are defective in the activation of dormant origins. (A) Scheme of the experiment. hTERT-RPE1 cells were treated with either siControl or siSAF-A for 72 h then pulse labelled sequentially with CldU then IdU for 20 min each. Cells were collected, and genomic DNA subjected to DNA combing. Specimen image shows visualised CldU (red) and IdU (green). Scale bar: 10 µm (B) IOD was measured as illustrated in A in hTERT-RPE1 cells treated with siControl or siSAF-A, and with or without HU treatment. Schematic shows CldU (red) and IdU (green) labelling patterns used to identify origins. Superplots below show results from three independent experiments (blue, orange and grey). The mean with 95% confidence interval is indicated, and triangles show mean values for each experiment. For HU-treated samples, CldU and IdU labellings were done at the end of 4 h HU treatment at 0.1 mM. Treatment with 0.1 mM HU does not stop DNA synthesis completely (Fig. 4A). Mean values from each experiment were statistically tested by one-tailed pairwise t-test. (C) IOD was measured as described in A. The results from one representative experiment (the dataset shown in grey in B) are shown in two-sided bean plots, with the number of IODs analysed for each cell line and condition indicated. Short bars indicate data points and long bars indicate means. The blue dashed line shows the 20-kb reference point and the dotted line marks the average of all the data points shown. Note that the y-axis is in log scale. Statistical significance was calculated by Mann–Whitney–Wilcoxon test. *P<0.05; **P<0.01; ns, not significant.
SAF-A supports replication fork processivity. (A) SAF-A depletion does not affect replication fork speed. Nascent DNA was labelled as in Fig. 3A, and replication fork speed measured based on IdU tract length. SuperPlots showing replication fork speed from biological replicates (four experiments in red, blue, orange and grey for untreated, and two experiments in red and blue for HU-treated conditions). The mean with 95% confidence interval is indicated, and triangles show mean values for each experiment. Mean values from each experiment were statistically tested by two-tailed t-test. For comparisons between two conditions with a equal number of replicates (e.g. siControl vs siSAF-A), a pairwise t-test was used. Otherwise, an unpaired t-test was used. Fork speeds under HU-treated conditions in each experiment were also tested by two-tailed F-test to compare the variances. (B) SAF-A is required for fork processivity. IdU:CldU tract length ratios were measured where the two tracts were consecutive, and log2 values are plotted. SuperPlots from multiple biological replicates (four experiments for untreated, and two experiments for HU-treated conditions) are shown. The mean with 95% confidence interval is indicated, and triangles show mean values for each experiment. Variances under HU-treated conditions were tested by two-tailed F-test for each experiment. *P<0.05; **P<0.01; ****P<0.0001; ns, not significant.
SAF-A supports replication fork processivity. (A) SAF-A depletion does not affect replication fork speed. Nascent DNA was labelled as in Fig. 3A, and replication fork speed measured based on IdU tract length. SuperPlots showing replication fork speed from biological replicates (four experiments in red, blue, orange and grey for untreated, and two experiments in red and blue for HU-treated conditions). The mean with 95% confidence interval is indicated, and triangles show mean values for each experiment. Mean values from each experiment were statistically tested by two-tailed t-test. For comparisons between two conditions with a equal number of replicates (e.g. siControl vs siSAF-A), a pairwise t-test was used. Otherwise, an unpaired t-test was used. Fork speeds under HU-treated conditions in each experiment were also tested by two-tailed F-test to compare the variances. (B) SAF-A is required for fork processivity. IdU:CldU tract length ratios were measured where the two tracts were consecutive, and log2 values are plotted. SuperPlots from multiple biological replicates (four experiments for untreated, and two experiments for HU-treated conditions) are shown. The mean with 95% confidence interval is indicated, and triangles show mean values for each experiment. Variances under HU-treated conditions were tested by two-tailed F-test for each experiment. *P<0.05; **P<0.01; ****P<0.0001; ns, not significant.
Stalling of DNA replication forks due to replication stress causes activation of nearby dormant origins (Ge et al., 2007), believed to protect cells from replication stress by guarding against the formation of unreplicated stretches between two stalled or collapsed replication forks (Blow and Ge, 2009; Kawabata et al., 2011). Since the licensing defect of SAF-A-depleted cells might affect the number of available dormant origins, we assessed whether cells depleted of SAF-A activate dormant origins normally (Fig. 3B,C). siRNA-treated cells were incubated for 4 h with 0.1 mM HU to slow replication forks. At the end of the HU treatment, nascent DNA was labelled with CldU and IdU as shown in Fig. 3A. Under this condition, replication forks continued to progress but with significantly reduced speed (Fig. 4A). As expected, HU treatment induced the activation of dormant origins near stalled forks, evidenced by a reduction in IOD and appearance of very short IODs below 20 μm (below blue dashed line in Fig. 3C), both in siControl and siSAF-A cells (Fig. 3C). In cells depleted of SAF-A, however, short IODs (in the range 0–30 kb) occurred at reduced frequency compared to siControl cells (Fig. 3C), suggesting impaired dormant origin activation. Overall, these data confirm that the number of active DNA replication origins is reduced in cells depleted of SAF-A, and that SAF-A is required for activation of dormant origins at normal frequency under replication stress.
SAF-A supports DNA replication fork progression
SAF-A depletion leads to decreased cellular DNA synthesis rate in unperturbed S phase (Fig. 1F,G), as well as reduced origin licensing (Fig. 2) and activation (Fig. 3). However, it was unclear whether reduced origin activation fully accounts for the decreased cellular DNA synthesis. To explore whether altered DNA replication fork speed also affects DNA synthesis when SAF-A is deficient, we investigated replication fork speed using the same DNA combing technique as depicted in Fig. 3A. The lengths of IdU tracts in stretched DNA molecules were taken as a proxy for DNA synthesis rate. The average replication fork speed was not affected by SAF-A depletion (Fig. 4A, compare siControl and siSAF-A). However, we repeatedly detected wider variance of replication fork speed in siSAF-A cells treated with HU, compared with that in siControl cells treated with HU (Fig. 4A, compare siControl+HU and siSAF-A+HU), suggesting that in SAF-A-depleted cells replication fork speed is less tightly regulated during replication stress.
We next tested whether SAF-A depletion affects the processivity of DNA replication forks. If processivity is high, the rate of DNA synthesis will stay consistent through the CldU and IdU labelling periods, and the log2 value of the ratio of IdU tract length to CldU tract length [log2(IdU/CldU)] is expected to be close to 0 (for example, Fig. 4B siControl). Frequent pause or collapse of forks will lead to a wider spread in log2(IdU/CldU) values. We found that cells depleted of SAF-A show wider spreading of the log2(IdU/CldU) values under HU-treated conditions, indicating increased probability of fork slowing, pause or collapse in cells depleted of SAF-A (Fig. 4B; note that forks pausing or collapsing in the CldU labelling period were not counted – because they produce only CldU labelling, they are indistinguishable from termination sites). This result suggests that SAF-A is required to support processive DNA synthesis under replication stress. The nascent DNA labelling experiments demonstrate that SAF-A is required for robust replication fork progression, as well as to support origin licensing.
SAF-A affects replication timing at domain boundaries, and at regions of discordance between compartmentalisation and replication timing
Given its effect on replication origin activation and fork progression, we examined whether SAF-A is important for DNA replication timing. To enable detection of changes that might not be evident in a population analysis (such as increased variability that does not affect the average replication time of a locus), we examined the replication timing programme in single cells. Briefly, we used a recently described method (Miura et al., 2020; Takahashi et al., 2019) in which single mid-S-phase cells are collected by cell sorting based on their DNA content, then next-generation sequencing (NGS) library preparation and copy number sequencing are carried out for each individual single cell. As a control, we carried out similar analysis using a pool of 100 mid-S-phase cells. The relative copy number of 200-kb segments was calculated based on the number of sequencing reads, normalised against reads obtained from G1-phase cells.
We compared the replication timing profiles of 33 single mid-S-phase hTERT-RPE1 cells for siControl, and 25 single mid-S-phase cells for siSAF-A (Fig. 5A). We found that, as previously proposed, replication timing of single siControl cells generally reflects A/B compartment distribution as determined by hTERT-RPE1 cell Hi-C analysis (Darrow et al., 2016; Miura et al., 2018). While in siSAF-A cells the overall replication timing profiles are largely similar to those of siControl cells, we found that the boundaries of the replication timing domains are less uniform. For example, in the regions shown magnified at the bottom of Fig. 5A, siControl cells show clear, fairly uniform boundaries between unreplicated (blue) and replicated (red) domains across the 33 analysed cells. In siSAF-A cells, in contrast, the boundary position shows more variation between single cells, resulting in a lack of clear boundaries when viewed across the population. The ‘RT changes’ plot in Fig. 5A shows the difference between single siControl and siSAF-A cells in average replication timing. Statistical comparison of single-cell replication timing between siControl and siSAF-A cells confirms the impression that boundaries are blurred (see ‘−log10P’ plot in Fig. 5A), with peaks indicating regions showing significant difference between the siControl and siSAF-A profiles, generally coinciding with timing domain boundaries. We identified 420 ‘−log10P’ peaks across the genome, of which 173 overlap with replication timing domain boundaries (RT boundaries). A one-tailed Fisher's exact test found that the co-occurrence of ‘−log10P’ peaks and RT boundaries is statistically significant (P=2.22×10−18), whereas no statistical significance was found if genomic locations of ‘−log10P’ peaks were randomly shuffled (P=0.99; 76 co-occurrences out of 420).
Replication timing is affected by SAF-A. (A) A 60 Mb region of chromosome 8 illustrating the impact of depleting SAF-A on single-cell replication timing profiles. Heat maps show replication in single mid-S-phase hTERT-RPE1 cells (red, early replicating; blue, late replicating). Each row represents the replication profile of a single cell (33 siControl cells and 25 siSAF-A cells) in 200-kb windows. The ‘−log10P’ plot (green, above 0; grey, equal to or below 0) shows statistical significance of the difference between single-cell replication timing of siControl and siSAF-A cells. The RT changes plot shows the impact of deleting SAF-A on replication timing (orange, replication timing earlier in siSAF-A than in siControl; green, replication timing later in siSAF-A). A/B compartment plot shows A or B compartment distribution of genomic segments (red, A compartment; blue, B compartment). RT domain plots indicate the distribution of genomic segments (red, early; blue, late) and RT boundary plots show RT boundaries defined by transition of RTs. Two example regions showing differences between siControl and siSAF-A cells are magnified at the bottom. (B) Replication timing changes caused by SAF-A depletion. Violin plots show changes in (i) the whole genome, (ii) all replication timing domain boundaries, (iii) replication timing domain boundaries overlapping with −log10P peaks, (iv) −log10P peak genomic loci and (v) the same set of −log10P peaks scrambled to random genomic loci. Note that (iii) is an intersection of (ii) and (iv). The replication timing shift is expressed as changes in binarised replication timing of 100 single cells at genomic segments as shown in the bottom. White circles mark the medians, black box limits indicate the 25th and 75th percentiles and whiskers extend to 1.5 times the interquartile range from the 25th and 75th percentiles. N values indicate the number of genomic segments depicted by each plot. (C) Distribution of NGS tag density in 100 cells of siControl and siSAF-A. A total of 100 mid-S-phase cells were collected by a cell sorter, and NGS libraries were prepared. Tag densities were calculated for 200 kb sliding windows at 40 kb intervals across the genome. (D) t-SNE clustering analysis of replication timing in siControl and siSAF-A cells. Each dot represents a single cell.
Replication timing is affected by SAF-A. (A) A 60 Mb region of chromosome 8 illustrating the impact of depleting SAF-A on single-cell replication timing profiles. Heat maps show replication in single mid-S-phase hTERT-RPE1 cells (red, early replicating; blue, late replicating). Each row represents the replication profile of a single cell (33 siControl cells and 25 siSAF-A cells) in 200-kb windows. The ‘−log10P’ plot (green, above 0; grey, equal to or below 0) shows statistical significance of the difference between single-cell replication timing of siControl and siSAF-A cells. The RT changes plot shows the impact of deleting SAF-A on replication timing (orange, replication timing earlier in siSAF-A than in siControl; green, replication timing later in siSAF-A). A/B compartment plot shows A or B compartment distribution of genomic segments (red, A compartment; blue, B compartment). RT domain plots indicate the distribution of genomic segments (red, early; blue, late) and RT boundary plots show RT boundaries defined by transition of RTs. Two example regions showing differences between siControl and siSAF-A cells are magnified at the bottom. (B) Replication timing changes caused by SAF-A depletion. Violin plots show changes in (i) the whole genome, (ii) all replication timing domain boundaries, (iii) replication timing domain boundaries overlapping with −log10P peaks, (iv) −log10P peak genomic loci and (v) the same set of −log10P peaks scrambled to random genomic loci. Note that (iii) is an intersection of (ii) and (iv). The replication timing shift is expressed as changes in binarised replication timing of 100 single cells at genomic segments as shown in the bottom. White circles mark the medians, black box limits indicate the 25th and 75th percentiles and whiskers extend to 1.5 times the interquartile range from the 25th and 75th percentiles. N values indicate the number of genomic segments depicted by each plot. (C) Distribution of NGS tag density in 100 cells of siControl and siSAF-A. A total of 100 mid-S-phase cells were collected by a cell sorter, and NGS libraries were prepared. Tag densities were calculated for 200 kb sliding windows at 40 kb intervals across the genome. (D) t-SNE clustering analysis of replication timing in siControl and siSAF-A cells. Each dot represents a single cell.
Depletion of SAF-A does not lead to any clear trend in replication timing changes genome-wide (with similar numbers of regions becoming earlier or later; Fig. 5B, i), nor does it cause a consistent shift in timing at all RT domain boundaries (Fig. 5B, ii). If, however, we consider all regions showing significant difference in replication time between siControl and siSAF-A cells (defined by −log10P>3), then SAF-A depletion results in some tendency towards earlier replication timing (Fig. 5B, iv), although the changes vary substantially in direction and magnitude with some sequences replicating later than normal. Furthermore, at RT boundaries where siControl and siSAF-A cells show substantial differences, the effect of SAF-A depletion is noticeably bimodal, with the majority of such boundary regions replicating earlier but some replicating later (Fig. 5B, iii). Our finding that many RT boundaries are sensitive to SAF-A depletion is consistent with the proposed function of SAF-A in defining chromatin domain boundaries (Fan et al., 2018).
We also noticed that chromosomal locations that show replication timing shift in siSAF-A cells tend to coincide with genomic locations where A/B compartment and replication timing patterns are discordant or ‘disagree’ (see Fig. S5A for specimen loci). For statistical comparison, we picked the top 10% of genomic segments showing later replication timing in siSAF-A cells compared to siControl cells (‘EtoL’ sites), and the top 10% of segments showing earlier replication in siSAF-A cells (‘LtoE’ sites). These EtoL and LtoE sites show a significantly higher proportion of discordance between compartment and replication timing than the genomic average (Table 1). Moreover, at these loci, the observed replication timing shift in siSAF-A cells tends to bring replication timing into alignment with the A/B compartment pattern (Fig. S5B; Table 1).
To quantitatively confirm the variability in replication timing between individual siSAF-A cells, we compared the number of NGS reads per 200 kb sliding window (i.e. tag density) at 40-kb intervals (Miura et al., 2020). In the pool of 100 mid-S-phase siControl cells, distribution of the tag density forms two overlapping peaks (Fig. 5C, left), representing unreplicated (left peak) and replicated (right peak) portions of the genome. The separation of these peaks in the 100 pooled cells means that (1) unreplicated and replicated domains are distinct in each cell and (2) this distinct pattern is essentially conserved in the 100 cells. In other words, the replication timing programme is well-conserved in these 100 siControl cells. In contrast, tag density from the 100 mid-S-phase siSAF-A cells does not show clear peak separation (Fig. 5C, right). Note, however, that we do see clear separation of two peaks when analysing single siSAF-A cells at mid-S phase (Fig. S5B), similar to single siControl cells, indicating that our single-cell analysis does effectively distinguish unreplicated and replicated domains. Therefore, the poor peak separation of tag density in the pool of 100 mid-S-phase siSAF-A cells is due to compromised conservation of the replication timing programme between single cells.
Although the overall replication timing profiles appear to be fairly similar (Fig. 5A) and only a limited number of loci show altered replication timing (Fig. 5B, Table 1; Fig. S5A), t-SNE clustering analysis (van der Maaten, 2014; van der Maaten and Hinton, 2008) of the distribution of early and late domains in single cells showed a clear separation of the siControl and siSAF-A populations (Fig. 5D), indicating that the genome-wide replication timing programme is indeed altered in siSAF-A cells.
We conclude that in cells depleted of SAF-A, the genome-wide DNA replication timing programme is less well-defined, becoming more ‘blurred’ and unstable, particularly at replication timing domain boundaries and in regions where A/B compartment and replication timing are normally discordant.
SAF-A prevents spontaneous quiescence
Our data suggest that DNA replication is aberrant at various stages in cells depleted of SAF-A, even without exogenous replication stress (Fig. 1F,G; Fig. S1D). Recent studies suggest that cells with incomplete DNA replication and/or DNA damage can progress through mitosis but may activate the p53-mediated G1 checkpoint in the subsequent cell cycle, leading to a transient quiescence of daughter cells (Arora et al., 2017; Barr et al., 2017). Such delayed progression can be monitored by examining expression of the CDK inhibitor p21 (also known as p21WAF1 and CDKN1A). We tested the possibility that replication problems in siSAF-A cells lead to spontaneous quiescence by looking at the expression of p21. Cells depleted of SAF-A showed clear expression of p21 without any exogenous damage (Fig. 6A), whereas the expression of p21 was barely detectable in siControl cells. Flow cytometry (Fig. 6B; Fig. S6A) revealed that a significant proportion of siSAF-A cells with a ‘G1 phase’ DNA content showed p21 expression, suggesting that these cells are in quiescence (G0 phase). Interestingly, a fraction of G2-phase siSAF-A cells already expressed p21. Expression of p21 in G2-phase cells has been reported for cells that have undergone DNA damage and are destined to enter quiescence (Arora et al., 2017; Barr et al., 2017). The tendency of SAF-A-depleted cells to enter quiescence was also confirmed by measuring the phosphorylation of retinoblastoma (Rb, also known as RB1) protein. Rb is a negative regulator of the cell cycle and is phosphorylated in proliferating cells but remains unphosphorylated in quiescent cells (Giacinti and Giordano, 2006). Measurements of the cellular levels of Rb phosphorylation at Ser-807 and/or Ser-811 demonstrated that a significantly higher proportion of siSAF-A cells were in quiescence, as evidenced by dominance of cells with unphosphorylated Rb (Fig. 6C,D; Fig. S6B,C).
Loss of SAF-A leads to spontaneous replication stress and quiescence. (A) Depletion of SAF-A leads to p21 expression. Whole-cell extracts were prepared from hTERT-RPE1 cells treated with control siRNA (−) and SAF-A siRNA (+), then the abundance of SAF-A and p21 were examined using western blotting. The stain-free gel image serves as a loading control. Results from one of two biological replicates are shown. (B) Cell cycle analysis of p21 expression. hTERT-RPE1 cells treated with siControl or siSAF-A were analysed for DNA content and p21 expression by flow cytometry. Gates used to designate p21-positive cells are shown. The total number of cells analysed and the percentage of p21-positive cells are indicated on the plots. Results from one of two biological replicates are shown. (C) Analysis of phosphorylated Rb protein in SAF-A-depleted cells. Cells were treated with siRNA as in B and fixed, and the abundance of Rb protein phosphorylated at Ser-807 and/or Ser-811 (Phospho Rb) was analysed by flow cytometry. Results from one of two biological replicates are shown. (D) Quantification of phosphorylated Rb protein. Cells with 2N DNA content were classified as having either high Phospho Rb levels (High-P) or low Phospho Rb levels (Low-P) as depicted in Fig. S6B. Results from one of two biological replicates are shown. ****P<0.0001 (two-tailed Fisher's exact test).
Loss of SAF-A leads to spontaneous replication stress and quiescence. (A) Depletion of SAF-A leads to p21 expression. Whole-cell extracts were prepared from hTERT-RPE1 cells treated with control siRNA (−) and SAF-A siRNA (+), then the abundance of SAF-A and p21 were examined using western blotting. The stain-free gel image serves as a loading control. Results from one of two biological replicates are shown. (B) Cell cycle analysis of p21 expression. hTERT-RPE1 cells treated with siControl or siSAF-A were analysed for DNA content and p21 expression by flow cytometry. Gates used to designate p21-positive cells are shown. The total number of cells analysed and the percentage of p21-positive cells are indicated on the plots. Results from one of two biological replicates are shown. (C) Analysis of phosphorylated Rb protein in SAF-A-depleted cells. Cells were treated with siRNA as in B and fixed, and the abundance of Rb protein phosphorylated at Ser-807 and/or Ser-811 (Phospho Rb) was analysed by flow cytometry. Results from one of two biological replicates are shown. (D) Quantification of phosphorylated Rb protein. Cells with 2N DNA content were classified as having either high Phospho Rb levels (High-P) or low Phospho Rb levels (Low-P) as depicted in Fig. S6B. Results from one of two biological replicates are shown. ****P<0.0001 (two-tailed Fisher's exact test).
Depletion of SAF-A leads to replication stress
A previous study has demonstrated that depletion of SAF-A increases the proportion of cells showing diffuse localisation of the histone variant H2AX phosphorylated at its C terminus (referred to as γ-H2AX) (Nozawa et al., 2017). γ-H2AX has commonly been used as a DNA damage marker, but recent studies suggest that diffuse localisation of γ-H2AX within the nucleus is indicative of replication stress rather than DNA damage, whereas a more focal γ-H2AX localisation pattern represents DNA damage (Dhuppar et al., 2020; Moeglin et al., 2019). We assessed the impact of depletion of SAF-A with or without replication stress based on γ-H2AX localisation pattern. Fig. 7A shows a specimen image with ‘diffuse’ and ‘focal’ γ-H2AX localisation patterns. Without replication stress, few siControl cells exhibited either γ-H2AX pattern, with most cells showing no apparent γ-H2AX signal (Fig. 7B, siCont). Replication stress (induced by 3 h HU treatment) significantly increased the proportion of cells with ‘diffuse’ γ-H2AX (29%; Fig. S7A), consistent with the suggestion that diffuse γ-H2AX signal is indicative of DNA replication stress. In contrast, 29% of cells depleted of SAF-A had diffuse γ-H2AX even without HU treatment (Fig. 7B, siSAF-A), suggesting that depletion of SAF-A imposes replication stress on cells. In a separate experiment, we confirmed that virtually all cells with diffuse γ-H2AX signal were in S phase (Fig. S7B), and that a large fraction of S-phase cells had diffuse γ-H2AX signal when SAF-A was depleted (Fig. S7C).
SAF-A depletion causes replication stress. (A) Specimen image showing different γ-H2AX localisation patterns. White arrowheads indicate a cell with ‘diffuse’ γ-H2AX localisation, and amber arrows indicate cells with γ-H2AX foci. In the merged image, γ-H2AX is shown in red and DNA (DAPI) is shown in blue. Scale bar: 10 µm. (B) Depletion of SAF-A leads to spontaneous replication stress. hTERT-RPE1 cells treated with siControl (siCont) and siSAF-A were analysed for the localisation of γ-H2AX by immunofluorescence. Percentage of cells with either γ-H2AX foci or diffuse γ-H2AX localisations were scored. Data are presented as the mean±s.e.m. of four independent experiments. At least 50 cells were analysed for each condition. The P-values were calculated by two-tailed paired t-test. (C) Effects of inhibiting checkpoint kinases on γ-H2AX signals. Cells were treated with siControl or siSAF-A for 3 days. Protein kinase inhibitors and EdU were added 24 h and 20 min before the cell fixation, respectively. Inhibitors were ATR inhibitor (ATRi; 1 µM VE-821), ATM inhibitor (ATMi; 2 µM KU-60019), and DNA-PK inhibitor (DNA-PKi; 1 µM NU-7441). The distributions of integrated intensities of nuclear γ-H2AX signals in EdU-positive cells are shown in two-sided bean plots, with the mean indicated as a black line. Short black and white bars indicate individual data points and the dotted line shows the population average of all the data points analysed. At least 50 EdU-positive cells were analysed for each condition. Results from one of two independent experiments are shown. Note that the y-axis is log10 scale. The P-values were calculated by Mann–Whitney–Wilcoxon test. *P<0.05; ****<P<0.0001; ns, not significant.
SAF-A depletion causes replication stress. (A) Specimen image showing different γ-H2AX localisation patterns. White arrowheads indicate a cell with ‘diffuse’ γ-H2AX localisation, and amber arrows indicate cells with γ-H2AX foci. In the merged image, γ-H2AX is shown in red and DNA (DAPI) is shown in blue. Scale bar: 10 µm. (B) Depletion of SAF-A leads to spontaneous replication stress. hTERT-RPE1 cells treated with siControl (siCont) and siSAF-A were analysed for the localisation of γ-H2AX by immunofluorescence. Percentage of cells with either γ-H2AX foci or diffuse γ-H2AX localisations were scored. Data are presented as the mean±s.e.m. of four independent experiments. At least 50 cells were analysed for each condition. The P-values were calculated by two-tailed paired t-test. (C) Effects of inhibiting checkpoint kinases on γ-H2AX signals. Cells were treated with siControl or siSAF-A for 3 days. Protein kinase inhibitors and EdU were added 24 h and 20 min before the cell fixation, respectively. Inhibitors were ATR inhibitor (ATRi; 1 µM VE-821), ATM inhibitor (ATMi; 2 µM KU-60019), and DNA-PK inhibitor (DNA-PKi; 1 µM NU-7441). The distributions of integrated intensities of nuclear γ-H2AX signals in EdU-positive cells are shown in two-sided bean plots, with the mean indicated as a black line. Short black and white bars indicate individual data points and the dotted line shows the population average of all the data points analysed. At least 50 EdU-positive cells were analysed for each condition. Results from one of two independent experiments are shown. Note that the y-axis is log10 scale. The P-values were calculated by Mann–Whitney–Wilcoxon test. *P<0.05; ****<P<0.0001; ns, not significant.
Multiple protein kinases, including ATM, ATR and DNA-PK, have been implicated in γ-H2AX formation upon DNA damage (Rogakou et al., 1998; Wang et al., 2005). However, during replication stress, ATR is generally believed to be the kinase responsible for formation of γ-H2AX (Ward and Chen, 2001), although some reports implicate other protein kinases (Buisson et al., 2015; Chanoux et al., 2009; Serrano et al., 2013). To investigate which protein kinase is responsible for increased γ-H2AX in siSAF-A cells, we tested the impact of inhibiting ATM (using KU-60019), ATR (using VE-821) and DNA-PK (using NU-7441) on nuclear γ-H2AX signal in S-phase cells (Fig. 7C; Fig. S7D). Inhibition of ATR almost completely suppressed γ-H2AX induction upon SAF-A depletion (Fig. 7C, compare right halves of untreated and ATRi plots), consistent with the increased γ-H2AX signal in siSAF-A cells being caused by replication stress. Unexpectedly, ATM inhibition caused an increase in basal γ-H2AX signal even in control cells (Fig. 7C, compare left halves of untreated and ATMi plots), but did not notably impact the γ-H2AX levels in siSAF-A cells. Inhibition of DNA-PK had a slight effect on γ-H2AX levels. In conclusion, the increased γ-H2AX in SAF-depleted cells appears to be mediated mainly by ATR.
These data demonstrate that cells depleted of SAF-A suffer from constant replication stress, leading to more frequent (or more extended) quiescence than in control cells, which can at least partly explain the slower cell proliferation (Fig. 1C) and the reduced fraction of S-phase cells (Fig. 1F).
Taking all these observations together, our results demonstrate that SAF-A supports DNA replication by promoting origin licensing, fork progression speed and fork processivity, probably by modulating chromatin compaction to ensure the optimal structure for robust DNA replication.
DISCUSSION
Our investigation of the effects of SAF-A on DNA replication establishes that SAF-A promotes replication licensing (Fig. 2). Consistent with this effect, cells depleted of SAF-A showed increased origin spacing when compared to control cells, as well as reduced ability to activate dormant origins under replication stress (Fig. 3B,C). It has recently been demonstrated that the histone methyltransferase SET8, in contrast, limits replication licensing (Shoaib et al., 2018), presumably through its activity in histone H4K20 methylation. Given that SAF-A mediates the establishment of open chromatin structure (Creamer et al., 2021; Nozawa et al., 2017; Sharp et al., 2020), it therefore appears that the correct level of origin licensing requires an appropriate balance between oppositely acting cellular mechanisms that specify chromatin compaction.
In addition to its implication in chromatin structure, SAF-A is known to affect gene expression by modulating mRNA splicing, mRNA stability and mRNA localisation (Huang et al., 2021; Xiao et al., 2012; Ye et al., 2015; Yugami et al., 2007). We found a slight reduction in protein levels of FLAG–ORC1 and CDT1 (Fig. S4) that may contribute to the reduced licensing in cells depleted of SAF-A. SAF-A depletion appears to compromise replication licensing more severely than it does licensing factor expression, so we suspect that altered chromatin structure is the major determinant of reduced licensing in siSAF-A cells, with reduced protein expression an additional contributing factor.
Once origins have initiated, replication fork progression is also affected by SAF-A depletion, with fork speed less tightly regulated (Fig. 4A) and higher variance in fork processivity under replication stress (Fig. 4B). These changes may reflect an increased incidence of ‘chromatin obstacles’ in the absence of SAF-A, corresponding to hard-to-replicate sites that challenge the replication machinery (Gadaleta and Noguchi, 2017). It has been demonstrated that processive replication through heterochromatin regions is coupled with local chromatin decompaction (Chagin et al., 2019), so that chromatin over-compaction in the absence of SAF-A may increase the number of replication fork impediments, causing the observed inconsistent fork processivity (Fig. 4B). Interestingly, it is proposed that unactivated (‘non-CMG-assembled’) MCM proteins reduce replication fork speed to prevent DNA damage during DNA replication (Sedlackova et al., 2020). Reduced chromatin association of MCM in siSAF-A cells could be envisaged to increase the variance in fork processivity in this manner as well.
Despite having a moderate effect on origin licensing, depletion of SAF-A had a fairly mild effect on EdU incorporation levels without additional replication stress (Fig. 1F,G; Fig. S1D). This probably reflects the fact that under normal circumstances, MCM is loaded at a larger number of sites than will be utilised, so that a modest reduction in origin licensing has only a slight impact on cellular DNA replication dynamics under unperturbed conditions (Ge et al., 2007; Woodward et al., 2006). We find, however, that there is a stronger requirement for SAF-A in enabling cells to recover from replication stress (Fig. 1D,E; Fig. S1B,C). One possible explanation, based on the origin licensing defect of SAF-A-depleted cells, is that insufficient ‘dormant’ origins are available for activation to enable proper recovery from stress (Fig. 3B,C). Inadequate licensing that fails to provide enough dormant origins may lead to chromosome segments remaining unreplicated if incoming replication forks from both directions collapse under replication stress conditions (McIntosh and Blow, 2012).
Depletion of SAF-A led to increased γ-H2AX signal without exogenous damage (Fig. 7A,B). Aiming to identify the protein kinase responsible for the increased γ-H2AX, we treated cells with inhibitors against three candidate kinases (ATM, ATR and DNA-PK; Fig. 7C). The result confirmed that the increased phosphorylation of H2AX is mainly mediated by ATR, consistent with our inference based on the diffuse γ-H2AX localisation pattern that depletion of SAF-A causes replication stress.
Although depletion of SAF-A led to reduced overall licensing and increased origin spacing, we found that the genome-wide replication timing programme was not severely affected (Fig. 5A). This is perhaps consistent with the fact that each replication timing domain contains multiple replication origins whose activation is concomitantly regulated. Therefore, even if some licensed origins are lost from a replication timing domain, the domain can still retain its programmed replication timing, enabled by correctly regulated initiation at the remaining origins. We did, however, observe that the sharp boundaries that normally delineate replication timing domains tend to be blurred, showing considerably increased cell-to-cell heterogeneity (Fig. 5A,B). Such increased heterogeneity in timing domain boundaries will contribute to the poor separation of ‘early’ and ‘late’ replication peaks in the analysis of genome-wide tag density distribution (Fig. 5C), and is likely to be an important parameter in the separation of siControl cells and siSAF-A cells in t-SNE analysis (Fig. 5D). Interestingly, SAF-A protein has been reported to interact with chromatin domain boundary proteins, including CTCF and cohesion subunit RAD21 (Fan et al., 2018; Zhang et al., 2019), and to be involved in defining chromatin domain boundaries (Fan et al., 2018). We observed that depletion of SAF-A also tends to cause replication timing shift at loci where A/B compartment and early/late replication timing disagree (Table 1; Fig. S5A). Although in general the A/B compartment pattern corresponds well to the early/late replication pattern, there are some exceptional loci are where replication timing and A/B compartment patterns disagree. At some such loci, SAF-A is required for the establishment or maintenance of the replication timing pattern discordance with chromatin compartmentalisation.
SAF-A has been implicated in inactivation of X chromosomes (Hasegawa et al., 2010; Lu et al., 2020; Smeets et al., 2014). The hTERT-RPE1 cells used for our timing analysis are female, but we did not observe any obvious impact of depleting SAF-A on X-chromosome replication timing (data not shown). However, subtle changes in the inactive X-chromosome replication timing might not be detected, given that our methodology did not separately analyse the two X chromosomes.
RNA appears to be a functional component of chromatin (Brockdorff, 2019; Michieletto and Gilbert, 2019; Rodriguez-Campos and Azorin, 2007). One recent study proposes that chromatin-associated RNA promotes open chromatin structures by neutralising the positive charges on histone tails (Dueva et al., 2019). The ‘polarised’ chromatin distribution we observed (Fig. 1A,B) may reflect a role for SAF-A in tethering RNA to decompact chromatin (Creamer et al., 2021).
In summary, we have demonstrated that SAF-A is required for robust DNA replication, both in unperturbed conditions and in the recovery from replication stress. Moreover, we show that depletion of SAF-A leads to spontaneous replication stress and increased quiescence. Elevated expression of SAF-A in a wide range of cancers (The Cancer Genome Atlas; https://www.cancer.gov/tcga) suggests that SAF-A is important for cancer cell survival, possibly through the management of replication stress in cancer cells (Gaillard et al., 2015; Macheret and Halazonetis, 2015). Overall, our findings reported here show that the promotion of robust DNA replication by SAF-A is crucial for its role in supporting cellular capacity for proliferation.
MATERIALS AND METHODS
Cell lines and cell culture
Cell lines hTERT-RPE1 (Bodnar et al., 1998) and HEK293 FLAG–ORC1 were as previously described (Tatsumi et al., 2003). Cells were checked for mycoplasma contamination at regular intervals.
All human cell lines were cultivated in synthetic defined media (described below) supplemented with 10% foetal bovine serum (tetracycline-free; Biosera), 100 U/ml penicillin and 100 μg/ml streptomycin in 5% CO2, ambient O2 and at 37°C. hTERT-RPE1 cells were generally cultivated in DMEM/F12 (Gibco), except for experiments involving dNTP analogue (EdU, CldU and IdU) labelling where DMEM (Gibco) was used. Other cell lines were cultivated in DMEM.
siRNA
siRNAs used were: SAF-A siRNA, human HNRNPU (3192) ON-TARGETplus SMARTpool (Dharmacon Cat# L-013501-00-0005; Horizon Discovery); and control siRNA, luciferase (GL2) (Dharmacon Cat# D-001100–01; Horizon Discovery). Cells were transfected with 10 nM siRNA using Lipofectamine RNAiMAX reagent (Invitrogen, Thermo Fisher Scientific).
Antibodies
Primary antibodies used were: anti-SAF-A (mouse monoclonal, 3G6; Abcam, ab10297; 0.5 µg/106 cells for flow cytometry and 1/10,000 for western blotting); anti-FLAG (mouse monoclonal, M2; Sigma-Aldrich, F-1804; 1/200 for flow cytometry); anti-MCM3 (goat polyclonal, N-19 IgG; Santa Cruz Biotechnology, sc-9850; 1/200 for flow cytometry); anti-CDT1 (rabbit monoclonal, EPR17891; Abcam, ab202067; 1/200 for flow cytometry); anti-ORC1 (mouse monoclonal, F-10 IgG1; Santa Cruz Biotechnology, sc-398734; 1/200 for western blotting); anti-ORC2 (rabbit polyclonal; Bethyl, A302-734A; 1/100 for flow cytometry); anti-p21 (rabbit polyclonal, C-19; Santa Cruz Biotechnology, sc-397; 1/200 for flow cytometry and western blotting); anti-histone H3 (rabbit polyclonal; Abcam, ab1791; 1/5000 for western blotting); anti-CldU [rat monoclonal anti-BrdU, BU1/75 (ICR1); Abcam, ab6326; 1/100 for DNA combing]; anti-IdU (mouse monoclonal anti-BrdU; BD Biosciences, cat# 347580; 1/100 for DNA combing); anti-ssDNA (mouse monoclonal IgG3, 16-19; Millipore, MAB3868; 1/100 for DNA combing); anti-γ-H2AX (rabbit monoclonal, 20E3; Cell Signaling Technology, #9718; used at 1/400 for immunofluorescence); Alexa Fluor 647-conjugated anti-γ-H2AX (mouse monoclonal, N1-431; BD Pharmingen, 560447; used at 5 µl per tube); phospho-Rb (Ser807/811) (rabbit monoclonal, D20B12 IgG; Cell Signaling Technology, #8516; 1/800 for flow cytometry).
Secondary antibodies used were: Alexa Fluor 647-conjugated donkey anti-rabbit IgG (H+L) (Abcam, ab150063); Alexa Fluor 488-conjugated donkey anti-rabbit IgG (H+L) (Abcam, ab150065); Alexa Fluor 488-conjugated donkey anti-mouse IgG (H+L) (Abcam, ab150109); Alexa Fluor 647-conjugated donkey anti-goat IgG (H+L) (Abcam, ab150135); Alexa Fluor 488-conjugated goat anti-mouse IgG (H+L) (Abcam, ab150117); Alexa Fluor 594-conjugated goat anti-rat IgG (H+L) (Molecular Probes, A-11007, Thermo Fisher Scientific); Alexa Fluor 350-conjugated goat anti-mouse IgG (H+L) (Molecular Probes, A-11045, Thermo Fisher Scientific); Alexa Fluor 488-conjugated goat anti-mouse IgG1 (Molecular Probes, A-21121, Thermo Fisher Scientific). They were all used at 1/2000 dilution.
Chromatin fractionation
To prepare chromatin-enriched fractions (Fig. S2E), cells were lysed in cytoskeleton (CSK) buffer [10 mM HEPES-KOH (pH 7.4), 100 mM NaCl, 3 mM MgCl2, 300 mM sucrose] containing 0.2% Triton X-100, 1× cOmplete protease inhibitor cocktail EDTA-free (Roche, 04693159001), and 1× HALT protease and phosphatase inhibitor (Thermo Fisher Scientific, 78446) for 10 min on ice. Lysed cells were then centrifuged for 3 min at 2000 g. The pellet was washed once with CSK buffer, centrifuged for 4 min at 2000 g and resuspended in CSK buffer containing 10 µl/ml Benzonase (Millipore) for 30 min on ice. Samples were boiled in 1× Laemmli sample buffer for 10 min, and 5% β-mercaptoethanol was added.
To prepare chromatin-enriched fractions for analysis of p21 (Fig. 7A), CDT1 (Fig. S2C, left), CDC6 and ORC1 (Fig. S2C, middle), cells were lysed in low-salt extraction (LSE) buffer [10 mM potassium phosphate buffer (pH 7.4), 10 mM NaCl, 5 mM MgCl2] containing 0.1% Igepal CA-630 and 1 mM PMSF for 5 min on ice. Lysed cells were then centrifuged (10 min at 12,000 g), and the pellet was washed once with LSE buffer. The pellet was resuspended and boiled in 1× Laemmli sample buffer for 10 min, and 5% β-mercaptoethanol was added.
Protein concentrations in whole-cell extracts (WCEs) were determined using the Bio-Rad RC DC Protein assay kit. For western blots, an equal amount of total protein was loaded on each WCE lane, and loading for the corresponding chromatin fractions was calculated based on cell equivalency. Equal loading was further confirmed by examining total protein using Mini-PROTEAN stain-free gels (Bio-Rad).
DNA combing
For analysis of nascent DNA on DNA fibres, cells were pulse labelled sequentially with CldU (50 µM; Sigma-Aldrich, C6891) and IdU (250 µM; Sigma-Aldrich, I7125) for 20 min each. Cells were then collected, and DNA combing carried using a FiberComb instrument (Genomic Vision, Bagneux, France) according to the manufacturer's instructions. Detection of CldU and IdU was as previously described (Garzon et al., 2019). Images were acquired on a Zeiss Axio Imager M2 microscope and 63×/NA1.4 objective equipped with ORCA-Flash 4.0LT CMOS camera (Hamamatsu Photonics). Images were analysed as previously described (Garzon et al., 2019). For inter-origin distance measurements, 1 µm was converted to 2 kb based on a predetermined value (Bensimon et al., 1994, 1995).
Flow cytometry
Cell cycle analysis of cells stained with DAPI was performed as described previously (Hiraga et al., 2017; Watts et al., 2020). EdU labelling and its detection by flow cytometry have been previously described (Hiraga et al., 2017). Cells were extracted before fixation with low-salt extraction buffer [0.1% Igepal CA-630, 10 mM NaCl, 5 mM MgCl2, 0.1 mM PMSF, 10 mM potassium phosphate buffer (pH 7.4)] unless otherwise noted. Detection and analysis of chromatin-bound proteins by flow cytometry were performed as previously described (Hiraga et al., 2017) with multiplexing as described below. For analysis of total proteins, cells were fixed with formaldehyde prior to permeabilisation. Data were acquired on Becton Dickinson LSRII or Fortessa flow cytometers with FACSDiva software (Becton Dickinson) and were analysed using FlowJo software version 10.4.2 (FlowJo LLC., Ashland, OR, USA).
We found that apparent MCM levels per cell are very sensitive to the number of cells analysed (i.e. the ratio of cells to antibody during immunostaining), causing tube-to-tube variations. To avoid this issue, we adopted a ‘multiplexing’ strategy. In brief, before immunostaining, samples were differentially labelled with CellTrace Yellow (Molecular Probes, Thermo Fisher Scientific) at a concentration unique to each sample (between 0 µM and 0.5 µM final concentration). Differentially stained samples were then mixed and were immunostained in a single tube to eliminate tube-to-tube variations. After data acquisition by flow cytometry, cell populations were separated based on their CellTrace Yellow signal levels. We confirmed that the CellTrace Yellow signal does not affect the quantification of Alexa Fluor 488 and Alexa Fluor 647 signals.
Microscopy
For visualisation of chromatin DNA within the nucleus, cells were grown on Ibidi chambered slides (ibi-treated; obtained from Thistle Scientific Ltd., Glasgow, UK). Cells were washed with phosphate-buffered saline (PBS), and fixed with neutral buffered 4% formaldehyde (Sigma) for 15 min at room temperature, then permeabilised with 0.1% Triton X-100 in PBS for 15 min at room temperature. After washing cells three times with PBS containing 0.1% Igepal CA-630, DNA was stained with 0.25 µg/ml DAPI for 30 min. Cells were finally washed and mounted in Ibidi mounting medium. Eleven z-section images were acquired at 170 nm intervals on a Zeiss LSM-880/AiryScan microscope with 63×/NA 1.3 objective (with Zen Black software; Zeiss). The middle section of the z-stacks was assigned as the plane where each nucleus has the largest x–y projection. After AiryScan processing (with the automatic 3D AiryScan processing condition), the middle section was used for analysis. The areas of DAPI-high and DAPI-low regions were determined in an unbiased manner by a custom pipeline utilising Minimal Cross-Entropy on CellProfiler 3.19 (McQuin et al., 2018).
For visualisation of EdU incorporation and immunofluorescence detection of γ-H2AX, cells were grown and fixed as above, and kept in 70% ethanol. Cells were then washed with PBS, permeabilised with 0.5% Triton X-100 in PBS, and incorporated EdU was visualised using an Alexa Fluor 488 EdU imaging kit (Molecular Probes C10337) according to the manufacturer's instructions, followed by indirect immunofluorescence staining of γ-H2AX. Antibodies used were p-histone H2A.X S139 (20E3) rabbit monoclonal antibody (Cell Signaling Technology, #9718) and Alexa Fluor 647-conjugated anti-rabbit IgG (Abcam, ab150063). Z-stack images were acquired at 250 nm intervals to cover entire nuclei in the field. After AiryScan processing, maximum intensity z-projection images were created for downstream analysis using ImageJ (NIH, Bethesda, MD, USA). Detection of cells with diffuse γ-H2AX or γ-H2AX foci were carried out by using a custom CellProfiler pipeline. For the data presented in Fig. 7C, a Zeiss Axio Observer Z1 with Plan-Apochromat 63x/1.40 objective was used. Images were taken with ORCA-Flash4.0 V3 CMOS camera (Hamamatsu Photonics).
Single-cell replication timing analysis and bioinformatics
Homo sapiens (human) genome assembly GRCh38 (hg38) from Genome Reference Consortium was used throughout the analysis. Single-cell replication timing of siControl and siSAF-A hTERT-RPE1 cells were analysed as described previously (Miura et al., 2020; Takahashi et al., 2019). In brief, single cells were sorted into a 96-well plate by flow cytometry (Sony SH800), and an NGS library was prepared from each single cell as previously described (Miura et al., 2020; Takahashi et al., 2019). Copy number analysis of human genomic segments were performed as described (Miura et al., 2020; Takahashi et al., 2019). Replication timing boundaries were defined as corresponding to the transition point for binarised replication values (from −1 to 1 or from 1 to −1) of 100 siControl mid-S-phase cells. Replication timing changes (RT changes) between siControl and siSAF-A cells were calculated by comparing the average replication timing of single siControl and siSAF-A cells. The ‘−log10P’ values were calculated by comparing the distribution of single-cell replication timing of 100-kb segments between siControl and siSAF-A cells using a two-tailed unpaired t-test. The ‘−log10P’ peaks were defined as those with ‘−log10P’ values above 3, which corresponds to P-values below 0.0001. Bedtools (version 2.30.0) was used for additional data analysis (Quinlan and Hall, 2010). Bedtools intersect was used to identify intersections between different data tracks (e.g. −log10P peaks and RT boundaries).
Bedtools fisher was used to perform Fisher's exact test to evaluate the co-occurrence between −log10P peaks and RT boundaries (Quinlan and Hall, 2010). A one-tailed test was performed, with the null hypothesis that occurrences of −log10P peaks have no correlation with RT boundaries, and the alternative hypothesis that occurrences of −log10P peaks have positive correlation with RT boundaries.
t-SNE clustering analysis of replication timing profile data was performed using Rtsne R library (see https://github.com/jkrijthe/Rtsne), with R version 3.6.1. For t-SNE analysis, all the 40-kb chromosome segments with measurements from all the single cells (i.e. no missing values) were used. The number of 40-kb chromosome segments used was 71,979, which covers 93% of the genome. The following parameters were supplied to Rtsne; perplexity=19, PCA-scaling=T.
Chromatin A/B compartments in hTERT-RPE1 were determined as described previously (Miura et al., 2018), using a previously published Hi-C dataset in 100-kb bins (Darrow et al., 2016). The original genome coordinates in genome assembly GRCh37 (hg19) to GRch38 (hg38) were converted using UCSC liftOver tool (Hinrichs et al., 2006) and remapped using BEDOPS bedmap (with a weighted average option) (Neph et al., 2012), so that both datasets could be compared in a common genomic segmentation system. For the analysis presented in Table 1, A/B compartment and replication timing in siControl cells was compared at each 100-kb segment of the genome.
Other software and statistical analysis
GraphPad Prism (version 7; GraphPad Software, San Diego, CA, USA) and R Studio (version 1.4.1717 with R 4.1.0) were used for statistical analysis of experimental data and creating graphs. Student's t-test was used for data with normal distributions as indicated in figure legends, and Mann–Whitney–Wilcoxon test was used for non-normal data (in Fig. 1G, Fig. 3C and Fig. 7C). Two-tailed Fisher's exact test was used in Fig. 6D and Table 1. Specific conditions used are stated either in the main text or in the figure legends when necessary. Beanplot R package (version 1.2) was used for creating two-sided bean plots (Kampstra, 2008).
Acknowledgements
Information for SAF-A expression was obtained at The Cancer Genome Atlas (TCGA) Research Network (https://www.cancer.gov/tcga). We thank Dr Ryu-suke Nozawa for help in the early stage of the project, and Professor Julian Blow for advice on the 3D licensing assay. Thanks to the staff of the Iain Fraser Cytometry Centre, and Microscopy and Histology facility at the University of Aberdeen.
Footnotes
Author contributions
Conceptualization: C.C., A.D.D., S.-I.H.; Methodology: C.C., S.T., H.M., I.H., S.-I.H.; Software: H.M.; Formal analysis: S.-I.H.; Investigation: C.C., S.T., S.-I.H.; Resources: I.H., N.G.; Data curation: C.C., H.M., S.-I.H.; Writing - original draft: C.C., S.-I.H.; Writing - review & editing: C.C., I.H., N.G., A.D.D., S.-I.H.; Visualization: C.C., H.M., S.-I.H.; Supervision: I.H., N.G., A.D.D., S.-I.H.; Project administration: N.G., A.D.D., S.-I.H.; Funding acquisition: N.G., A.D.D., S.-I.H.
Funding
C.C. was supported by a Biotechnology and Biological Sciences Research Council EASTBIO Doctoral Training programme PhD studentship. S.-I.H. was supported by the Daiwa Anglo-Japanese Foundation (12928/13746). Work in the Hiraga–Donaldson lab is supported by Cancer Research UK awards C1445/A19059 and DRCPGM\100013. N.G. is supported by Medical Research Council (MC_UU_00007/13).
Data availability
The Illumina sequence dataset has been deposited to ArrayExpress (accession number E-MTAB-10234).
Peer review history
The peer review history is available online at https://journals.biologists.com/jcs/article-lookup/doi/10.1242/jcs.258991.
References
Competing interests
The authors declare no competing or financial interests.