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. Author manuscript; available in PMC: 2016 Sep 17.
Published in final edited form as: Mol Cell. 2015 Sep 10;59(6):998–1010. doi: 10.1016/j.molcel.2015.07.030

The replication checkpoint prevents two types of fork collapse without regulating replisome stability

Huzefa Dungrawala 1, Kristie L Rose 1,2, Kamakoti P Bhat 1, Kareem N Mohni 1, Gloria G Glick 1, Frank B Couch 1, David Cortez 1,*
PMCID: PMC4575883  NIHMSID: NIHMS712738  PMID: 26365379

Summary

The ATR replication checkpoint ensures that stalled forks remain stable when replisome movement is impeded. Using an improved iPOND protocol combined with SILAC mass spectrometry, we characterized human replisome dynamics in response to fork stalling. Our data provide a quantitative picture of the replisome and replication stress response proteomes in 32 experimental conditions. Importantly, rather than stabilize the replisome, the checkpoint prevents two distinct types of fork collapse. Unsupervised hierarchical clustering of protein abundance on nascent DNA is sufficient to identify protein complexes and place newly identified replisome-associated proteins into functional pathways. As an example, we demonstrate that ZNF644 complexes with the G9a/GLP methyltransferase at replication forks and is needed to prevent replication-associated DNA damage. Our data reveal how the replication checkpoint preserves genome integrity, provide insights into the mechanism of action of ATR inhibitors, and will be a useful resource for replication, DNA repair, and chromatin investigators.

Introduction

Successful DNA replication requires duplicating billions of base pairs of DNA in each cell division cycle rapidly, accurately and completely. In addition, the epigenetic state of the DNA and chromatin must be re-established. These processes are executed by a large protein machine called the replisome consisting of polymerases, helicases, nucleases, and ligases that copy the DNA as well as accessory factors that assemble chromatin on the newly synthesized DNA strands.

Genetic inheritance is challenged by damaged template DNA, collisions with transcription complexes, difficult to replicate DNA sequences, and other sources of replication stress including oncogene activation (Branzei and Foiani, 2010; Zeman and Cimprich, 2014). A primary response to replication stress is recruitment and activation of the ATR (ATM- and Rad-3 related) replication checkpoint kinase (Cimprich and Cortez, 2008). ATR is essential for DNA replication and is activated every S-phase to regulate origin firing, stabilize replication forks, and promote repair and restart of damaged forks, thus ensuring complete DNA synthesis prior to mitosis.

There are three ways by which ATR is thought to prevent fork collapse. First, in cells experiencing extensive replication stress, the replication checkpoint is needed to prevent new origin firing and RPA exhaustion (Toledo et al., 2013). Uncoupling of helicase and polymerase activities at stalled forks exposes single-stranded DNA (ssDNA) (Byun et al., 2005). RPA is needed in these circumstances to protect the ssDNA (Fanning et al., 2006) and to direct repair protein activities (Betous et al., 2013; Bhat et al., 2015). In the absence of ATR-dependent origin suppression, RPA becomes limiting leading to fork breakage (Toledo et al., 2013). Secondly, ATR directly regulates the activity of fork repair proteins including the WRN helicase and SMARCAL1 DNA translocase to prevent fork collapse (Ammazzalorso et al., 2010; Couch et al., 2013). ATR phosphorylation of SMARCAL1 restrains its fork remodeling activities (Couch et al., 2013). In the absence of that phosphorylation, SMARCAL1 generates DNA structures that are cleaved by an SLX4-dependent nuclease to generate double-strand breaks (Couch et al., 2013; Ragland et al., 2013). Finally, ATR may stabilize the replisome itself. In fact, fork collapse has previously been defined as replisome dissociation from a stalled fork (Carr et al., 2011).

The evidence for ATR-dependent replisome stabilization comes primarily from S. cerevisiae where highly efficient, sequence-defined origins of replication permit a chromatin-immunoprecipitation (ChIP) approach to studying replisome stability in synchronized cells. ChIP of helicase and polymerase subunits near early origins of replication indicated that individual replisome subunits dissociate from stalled forks in replication checkpoint-deficient yeast (Cobb et al., 2003; Cobb et al., 2005; Lucca et al., 2004). However, a recent genome-wide analysis of replication forks found replisome stability is independent of the checkpoint suggesting the specific chromosomal location examined in earlier studies may have been misleading (De Piccoli et al., 2012).

Studies in vertebrates support a checkpoint-dependent replisome stability model, although the data is indirect. For example, PCNA and polymerases were reported to be less abundant in chromatin fractions upon genetic loss of ATR in mouse cells (Ragland et al., 2013). Decreased chromatin association of selected replisome subunits was also reported during in vitro replication in checkpoint-deficient Xenopus extracts (Hashimoto et al., 2012; Trenz et al., 2006). In addition, imaging of replication factories in human cells (as well as S. pombe) suggested that the replisome is destabilized in response to replication stress when the replication checkpoint is inactivated (Dimitrova and Gilbert, 2000; Meister et al., 2005). However, in all of these studies, only a few replication proteins were monitored so a complete picture of what happens to the replisome is lacking. In addition, although some replication proteins like the MCM helicase are phosphorylation targets of ATR (Cortez et al., 2004; Yoo et al., 2004), there is little mechanistic information for how the ATR pathway would promote replisome stabilization.

Using iPOND (isolation of proteins on nascent DNA) to purify replication-associated proteins provides an opportunity to directly test the checkpoint-dependent replisome stability model in human cells (Sirbu et al., 2011). Here we improved the iPOND protocol and combined it with SILAC (stable isotope labeling of amino acids in cell culture) mass spectrometry to analyze the replisome at stalled forks in checkpoint-proficient and deficient cells. Critically, by comparing 32 experimental perturbations, we provide a comprehensive description of the active and stalled replication fork-associated proteomes. Hierarchical clustering of proteins with similar fork abundance profiles across experimental conditions is sufficient to identify protein complexes. Importantly, our data indicate that replisome stabilization is not a major function of the replication checkpoint to prevent fork collapse. Furthermore, the collapse of stalled forks that initially trigger checkpoint activation is different from the collapse of forks that start from aberrantly fired origins in checkpoint-deficient cells.

RESULTS

iPOND-SILAC-MS identifies replication and replication stress response protein complexes

iPOND provides a direct method to examine all proteins at active and stalled replication forks using unbiased mass spectrometry detection (Lopez-Contreras et al., 2013; Sirbu et al., 2011; Sirbu et al., 2013). iPOND utilizes click chemistry to conjugate biotin to newly synthesized DNA labeled with the nucleoside analog EdU. Biotin conjugation permits a single-step purification of proteins associated with the labeled DNA. To enable better detection and quantitation, we optimized the biotin conjugation and purification steps, and coupled iPOND with SILAC mass spectrometry (SILAC-MS).

Two controls were included. First, we compared cells with and without a 10-minute incubation in EdU to distinguish proteins bound to chromatin in S-phase versus those that were non-specifically purified (Figure S1A). 864 proteins were at least two-fold enriched on chromatin (Table S1). Second, we compared cells treated with EdU for 10 minutes with cells treated and then incubated without EdU for an additional hour as a chase sample (Figure S1A). This procedure identified 218 proteins that are enriched near replication forks compared to bulk chromatin (Table S2). These proteins include the CMG helicase, DNA polymerases, PCNA interacting proteins, chromatin remodeling and histone modifying complexes, nucleases and ligases required for lagging strand synthesis, as well as many replication stress response proteins.

To monitor how these replication fork-associated proteins change in response to added replication stress, we compared cells treated with 3mM hydroxyurea (HU) for increasing times (5 minutes to 24 hours) to untreated cells (Figure 1A). Also, to test whether the replication checkpoint stabilizes the replisome at the stalled fork, we compared samples treated with HU in the presence or absence of a selective ATR kinase inhibitor (ATRi). Each HU+ATRi sample was compared to the same time point with HU alone (Figure 1B). The ATR inhibitor, VE821, does not target ATM, mTOR, or DNA-PKcs (Charrier et al., 2011), but is sufficient to completely block ATR signaling to substrates like CHK1 at least for the first couple of hours after HU addition (Figure S1B). Eventually a small amount of CHK1 phosphorylation returns but this is independent of ATR since re-addition of fresh ATRi has no effect.

Figure 1.

Figure 1

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iPOND-SILAC-MS identifies replisome and replication stress response protein complexes. (A) Each HU-treated sample was compared to an untreated sample. EdU remained in the growth media during the HU time course. (B) Cells were treated with HU or HU and ATRi for between 15 minutes and eight hours. Each HU/ATRi-treated sample was compared to a HU-treated reference sample. (C and D) Unsupervised hierarchical clustering of proteins. The first column (Ctl) is a control comparing EdU-labeled vs. unlabeled cells. Green in this column indicates the protein is enriched on chromatin. Green in the second column (chase) indicates proteins that are enriched at replication forks compared to bulk chromatin. The other columns indicate proteins that are either increased (red) or decreased (green) in abundance upon HU-treatment or ATR inhibition. (Grey = not observed) See also Figures S1 and S2, Tables S1-S6.

An average of 1250 proteins were quantitated in each experimental condition. Biological replicates for most samples were prepared by swapping the isotope labels and triplicate samples were prepared for the 2-hour time points. The median Pearson correlation coefficient for pairwise replicates is 0.7 with samples containing larger protein abundance changes having larger correlation coefficients (Figure S2).

Average enrichment ratios were determined from biological replicates. 68 proteins had statistically significant increases in abundance at HU-stalled forks in at least two time points (Table S3). The proteins most enriched include the 9-1-1 checkpoint clamp, BRCA1-BARD1, SMARCAL1, WRN, BLM, and ATR. Only a small percentage of proteins are further affected by ATR inhibition with ATM showing the largest increase and other DNA damage response proteins like MDC1 showing the largest decrease (Tables S4 and S5). The complete iPONDSILAC-MS dataset is presented in Table S6.

We used unsupervised hierarchical clustering to generate a heat map of protein abundance across all experimental perturbations (Figure 1C). This method revealed a strong correlation of abundance for subunits of known protein complexes. For example, the clustering algorithm identified the MMS22L-TONSL, FANCD2-I, RFC1-5, and MCM2-7 complexes (Figure 1D). Even subunits of the cohesin and PRPF19 complexes that change abundance only modestly in any single experimental sample cluster together strongly. Thus, these correlations can generate hypotheses about the functions of unstudied proteins in the dataset.

To illustrate, the iPOND-SILAC-MS data indicates that ZNF644 is highly enriched at unperturbed replication forks. The proteins most highly correlated with ZNF644 include the G9a/GLP methyltransferase complex (Figures 2A and 2B). Thus, we hypothesized that ZNF644 travels with the fork as a subunit of this complex. Indeed, ZNF644 co-immunoprecipitates with G9a (Figure 2C) and has a high degree of sequence identity with WIZ (Figure 2D), a known G9a/GLP interacting protein (Ueda et al., 2006). ZNF644 colocalizes with PCNA at replication foci (Figure 2E). Furthermore, ZNF644 knockdown resulted in decreased proliferation, hypersensitivity to replication stress, and an increase in the amount of DNA damage in replicating cells (Figure 2F-H). Thus, ZNF644 is a WIZ1 paralog that functions in a complex with G9a/GLP at replication forks. This conclusion is consistent with a report identifying ZNF644 as a G9a/GLP-interacting protein published while this manuscript was in review (Bian et al., 2015). These data illustrate the power of combining iPOND with SILAC-MS and highlight the ability of these datasets to predict functional protein complexes

Figure 2.

Figure 2

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ZNF644 forms a complex with G9A at active replication forks. (A) Unsupervised hierarchical clustering of the iPOND-SILAC-MS data identified ZNF644 as a protein that clusters with the G9a/GLP. (B) A profile plot of all the proteins in the dataset indicates that ZNF644 (red) abundance on nascent DNA is highly correlated with G9a, GLP, and WIZ (blue). (C) Coimmunoprecipitation of Flag-ZNF644 with G9a from HEK293T cells. (D) ZNF644 is a paralog of WIZ. Locations of zinc finger domains (ZnF) are indicated along with the percent amino acid sequence identity and location of the G9a/GLP binding motif in WIZ. (E) ZNF644 co-localizes with PCNA at sites of DNA replication. (F-H) U2OS cells were transfected with the indicated siRNAs (NT, non-targeting; si644, ZNF644). (F) Cell proliferation was measured using alamar blue. (*p<0.001) (G) The viability of cells treated with 0.2mM hydroxyurea (HU) for 72 hours was compared to untreated cells. (*p<0.05) (H) Cells were labeled with 10μM EdU for 30 minutes and then left untreated or treated with 2mM HU for 1 hour. The graph depicts the γH2AX intensity in each EdU positive nucleus. (*p<0.001) Data are presented as mean +/− SE.

Replisome stability is ATR-independent but fork stability is ATR-dependent

If a major function of ATR is to stabilize the replisome, then replisome proteins should dissociate from the fork when ATR is inhibited. However, while we do observe a slow decrease in replisome subunit association with the nascent DNA during the HU time course, these replisome proteins are almost completely unaffected by ATR inhibition. In fact, proteins that are directly involved in DNA synthesis including leading and lagging strand polymerases, the CMG helicase, RFC complex, and PCNA all accumulate slightly more on nascent DNA in HU-treated cells when ATR is inactivated (Figures 3A and 3B). Proteins involved in chromatin maturation like CAF1, ATP-dependent chromatin remodelers, the DNA methyltransferase DNMT1, and G9a/GLP behave similarly to PCNA in replication stress conditions and also show no ATR-dependency (Figure 3C). Thus, the iPOND data indicates that ATR signaling has no effect on replisome stability at stalled replication forks.

Figure 3.

Figure 3

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ATR signaling does not regulate replisome stability but does control fork stability. (A-E) Each HU sample was compared to an untreated reference sample while each HU/ATRi sample was compared to HU alone. The Log2 of the average abundance ratio of all subunits in selected complexes is depicted for simplicity when the subunits behave similarly. (GINS = GINS1-4; CAF1 = CAF1A and CAF1B; POLε = POLE and POLE2; POLδ = POLD1-3; POLα-PRIM = POLA1, POLA2, PRIM1, PRIM2; RNAseH = RNASEHA, B, & C; FACT = SUPT6H and SSRP1; (RPA = RPA1-3; MRN = MRE11-RAD50-NBS1). See also Figures S3-S5.

Previously published chromatin fractionation experiments suggested some replisome subunits dissociated from chromatin in ATR-deficient mouse cells (Ragland et al., 2013). However, consistent with the iPOND data, we did not observe a loss of PCNA, helicase, or polymerase subunits after ATR inhibition using standard chromatin fractionation procedures (Figure S3). Chromatin fractionation does show that HU-treatment alone causes a small decrease in the amount of PCNA and CLASPIN on chromatin, and the amount of CLASPIN on chromatin is increased after ATR inhibition. The difference with previously reported results could reflect a difference in genetic deletion versus kinase inactivation. Nonetheless, these data indicate that examining proteins by chromatin fractionation yields results that are consistent with examining their abundance by iPOND, although iPOND provides greater accuracy and quantifies the proteins directly associated with the fork.

We also confirmed that ATR does not stabilize the replisome in response to replication stress induced by aphidicolin (Figure S4A, Table S6), indicating the results are not specific to HU-induced stress. In addition, we repeated the experiment in the presence of a higher dose of HU and aphidicolin along with a CDC7 inhibitor (CDC7i) to eliminate any possible contributions of continued fork movement or new origin firing to the abundance of proteins captured by iPOND (see below for further explanation). By DNA fiber labeling, combining high dose HU and aphidicolin completely prevented DNA synthesis (Figure S4B). Again, ATR inhibition had no effect on the abundance of replisome subunits at these stalled forks (Figure S4C, Table S6). Thus, replisome stabilization at stalled forks is not a major a function of the ATR checkpoint.

In both the HU and aphidicolin-treated cells, we observed a modest enrichment of the replisome components at replication forks when ATR is inhibited. This enrichment could be explained simply by an increased number of replication forks due to new origin firing when the ATR checkpoint is inactivated. To test this hypothesis, we repeated the HU/ATRi experiments in the presence of CDC7i to prevent new origin firing. Indeed, CDC7 inhibition largely eliminated the increase in replisome subunits captured with nascent DNA in the checkpoint-deficient cells (Figure S5A, Table S6). Similar results were obtained with the CDK2 inhibitor roscovitine, which also blocks new origin firing (Figure S5B, Table S6).

Although replisome stability is independent of ATR activity, ATR inactivation causes changes to the fork proteome that are consistent with fork collapse. RPA and its interacting proteins SMARCAL1, BLM, WRN, and FANCJ are recruited rapidly upon fork stalling and maintain their association throughout the 24-hour time course (Figure 3D). The amount of these proteins captured with nascent DNA all increased further in response to ATR inactivation (Figure 3D) consistent with the increased ssDNA generated by ATR inactivation (Couch et al., 2013).

DSB response proteins including ATM, MRN, and DNA-PK all increase in abundance at the collapsing forks as well (Figure 3E). Of these, only the MRN complex travels with elongating forks. The ATM response to fork collapse appears to be faster than the DNA-PK response. This ATM recruitment is also observed at late time points in cells treated with only HU perhaps due to fork breakage in these circumstances (Petermann et al., 2010). The hyper-recruitment of ATM to forks in ATR inhibited cells is consistent with the synthetic lethality relationship between these kinases (Mohni et al., 2014; Reaper et al., 2011), and it also may explain the small increase in CHK1 phosphorylation at later times in the ATRi-treated cells (Figure S1B).

Replication stress response proteins exhibit varying recruitment patterns to stalled replication forks

ATR, ATRIP and proteins needed to activate ATR including RAD9, HUS1, RAD1, and TOPBP1 are already enriched at stalled forks with 5 minutes of adding HU (Figure 4A). More surprisingly, these proteins are also enriched at elongating replication forks compared to bulk chromatin even in the absence of added replication stress which could be due to endogenous sources of replication stress or other functions at elongating forks (Oliveria et al., 2015). The ATR signaling mediators CLASPIN and TIMELESS have essential replication functions and behave more like replisome proteins. Both dissociate slowly during the HU time course with CLASPIN dissociation preceding TIMELESS by several hours (Figure 4A). Similar results were obtained with aphidicolin (Figure S4A, Table S6).

Figure 4.

Figure 4

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The replication stress response includes changes in ATR, USP1 and PARG that regulate post-translational modifications needed for protein recruitment. (A-D) The abundance changes of select replication stress and DNA damage response proteins and protein complexes are diagrammed.

Other proteins that are recruited to stalled forks rapidly include FANCI-D2, MDC1, 53BP1, and RNF169 (Figure 4B). MDC1, 53BP1, and RNF169 recruitment to sites of double-strand breaks is regulated by H2AX phosphorylation (γH2AX). Their recruitment to stalled forks is also likely regulated by γH2AX since these proteins are not present at normal elongating forks, and their recruitment to stalled forks is at least partly ATR-dependent which phosphorylates H2AX at stalled forks (Figure 4B).

FANCI-D2 localization to damage sites requires ubiquitination (Garcia-Higuera et al., 2001). Total ubiquitin levels do increase rapidly at stalled forks (Figure 4C). The rise in ubiquitin corresponds to a decrease in the abundance of the de-ubiquitinating complex USP1-WDR48, which targets FANCD2 (Nijman et al., 2005). The iPOND data indicates that USP1-WDR48 also travels with elongating forks. Thus, USP1 may be constantly removing ubiquitin from proteins during replication, and its displacement from stalled forks may permit an increase in ubiquitin levels during the replication stress response.

A second post-translational modification at stalled forks that may also be regulated by the localization of an antagonizing enzyme is poly-ADP-ribosylation (PAR). PARP1 catalyzes PARylation at DNA damage sites and has essential functions at stalled replication forks (Bryant et al., 2009); however, we did not observe an increase in PARP1 protein at stalled forks (Figure 4D). Instead, the PARG enzyme that antagonizes PARylation disappears while the macrohistones H2A.1 and H2A.2 that are recruited via PAR (Khurana et al., 2014) increase in abundance (Figure 4D). Their increase is not simply due to more capture of EdU-labeled DNA since other histones remain relatively unchanged. PARG also travels with undamaged forks, which is consistent with the observation that it is needed to prevent the accumulation of abnormal replication structures in an unperturbed S phase (Mortusewicz et al., 2011; Ray Chaudhuri et al., 2015). Thus, a net increase in PAR at stalled forks may be mediated by the decrease in PARG activity. The PAR and checkpoint-dependent phosphorylation regulatory circuits are independent since ATR inhibition had no effect on these proteins.

Replisome protein abundance indicates replicon termination during the long HU time-course

To better understand why we observe a slow decrease in abundance of all replisome proteins during the HU time-course when the checkpoint is active, we considered the possibility that the 3mM HU treatment did not fully stop DNA replication. If true, then perhaps the slow loss of the core replisome components is not due to their dissociation from stalled forks, but rather dissociation due to normal termination after completion of DNA synthesis within a replicon. To test this idea, we performed DNA fiber labeling using the same conditions as the iPOND experiments (Figure 5A). Cells were labeled with IdU for 15 minutes prior to the addition of CldU and 3mM HU for between 15 minutes and 24 hours prior to examining DNA fibers. The very earliest time points of 15 minutes to one hour revealed green fibers with a single red dot at the end indicating a very small amount of CldU incorporation after HU addition. (Figure 5B). The CldU label increased in length at a constant rate between one and 16 hours, with a small amount of additional elongation observed at 24 hours (Figure 5A-C). There was no change in the length of the IdU tracks.

Figure 5.

Figure 5

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HU-treated cell populations exhibit slow replication elongation and minimal new replication initiation. (A-C) Cells were labeled with IdU for 15 minutes, then CldU in the presence of 3mM HU for increasing times between 15 minutes and 24 hours. (A) Representative DNA fiber images. There was no significant change in green fiber length in any sample. (B) Quantitation of CldU fiber length. At least 200 DNA fibers were measured per time point. (C) Mean fiber length for each time point is depicted. A rate of elongation in the HU-treated samples was calculated from the slope of the fitted line and converted to base pairs per minute. (D) iPOND-SILAC-MS was utilized to compare cell populations treated with CDC7i to untreated cells at the two and eight hour HU-time points. (MCM = MCM2-7; GINS = GINS1-4; CAF1 = CAF1A and CAF1B; POLε = POLE and POLE2; POLδ = POLD1-3; POLα-PRIM = POLA1, POLA2, PRIM1, PRIM2) (E) The percentage of CldU only fibers as a measure of new origin firing is depicted as a percentage of total fibers. See also Figure S6.

The rate of fork movement in HU-treated HEK293T cells was approximately 8 base pairs/min compared to 540 base pairs/min in untreated cells (Figure 5C). This untreated fork movement rate is 3-fold slower than what we observe in other cell types like hTERT-immortilized RPE cells (data not shown) perhaps due to more replicative stress or a higher density of replication origins. In any case, the approximately 70-fold decrease in fork elongation rates in the HU-treated cells still would allow fork convergence and normal termination over the extended time course analyzed. Thus, replicon termination likely explains most of the slow unloading of core replisome proteins from the nascent DNA in checkpoint-proficient HU-treated cells.

To rule out the possibility that new origins firing either due to cells transitioning from G1 to S phase or as a response to added replication stress are masking dissociation of the replisome components, we carried out iPOND analyses with HU for 2 and 8 hours with and without CDC7i. Abundance of the core replisome proteins were unchanged at two hours and only changed minimally at the 8-hour time point (Figure 5D, Table S6). These small changes were not statistically significant. These data suggest that new origin firing when cells transition from G1 to S in the presence of HU makes minimal contributions to the iPOND data. Indeed, DNA fiber labeling indicates that there is less than a 5% increase in new origin firing in the cell population during the HU treatment through the 8-hour time point (Figure 5E). Thus, new origin firing in cells transitioning from G1 to S is not large enough to substantially affect the amount of replisome proteins we detect by iPOND. At time points later than sixteen hours, protein abundance measurements will increasingly be affected by new origin firing and fork breakage events (Petermann et al., 2010).

Slow termination of completed replicons may explain most of the decreased abundance of helicase and polymerase subunits, but it cannot account for the rapid loss of PCNA and PCNA-associated proteins like CAF1. PCNA and CAF1 are already approximately 2- and 4-fold less abundant after only 15 minutes in HU compared to normal elongating forks (Figure 3). We previously observed a similar rapid loss of PCNA and CAF1 by immunoblotting iPOND experiments (Sirbu et al., 2011). Furthermore, quantitative immunofluorescence imaging also confirms that PCNA is less abundant at replication foci in cells treated with HU for short time periods (Figure S6A).

To better understand this effect, we converted the abundance ratios generated by the SILAC data to percent remaining values by setting the amount of PCNA and CAF1 proteins observed in the 60 minute “chase” control sample at zero abundance and the amount in the 10 minute EdU reference sample at 100%. This conversion yielded curves for PCNA and CAF1 abundance at forks that are best described by two-state decay equations (Figure S6B). A rapid initial loss in the first 30 minutes of HU-treatment is followed by a slow decrease in the next 16 hours. The slow decrease is exactly what would be predicted by normal termination events as replicons are completed due to slow fork movement. We suspect the fast decay before 30 minutes is a consequence of a rapid decrease in PCNA loading on the lagging strand without a corresponding decrease in the unloading rates (Figure S6C and D).

Suppression of new origin firing in replication checkpoint-deficient cells reveals two separable types of collapsed forks

Blocking new origin firing with the CDC7 inhibitor was reported to prevent RPA exhaustion and fork collapse in ATR-inactivated cells (Toledo et al., 2013). However, we noticed that the ssDNA sensing machinery including RPA and its binding partners appear to be insensitive to new origin firing—exhibiting hyper-accumulation in the presence of the ATR inhibitor whether or not new origin firing is blocked (Figure 6A). Some, but not all of the DSB sensing machinery and recombination factors like ATM, MRN, BRCA1, EXO1, and the MMS22L-TONSL complex are also either insensitive to CDC7 inhibition or become even more enriched in iPOND purifications (Figure 6A and 6B). In contrast, the recruitment of DNA-PKcs, KU70, and KU80 to forks is completely dependent on new origin firing (Figure 6C and S5C).

Figure 6.

Figure 6

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Blocking origin firing in ATR-inhibited cells reveals two distinct populations of collapsed forks. (A-C) ATRi+HU vs. HU abundance ratios (blue) are compared to ATRi+HU+CDC7i vs. HU+CDC7i abundance ratios (red). (D) Cells were labeled with IdU for 20 minutes, washed, then treated for 80, 120, or 240 minutes with HU, HU+ATRi, or HU+ATRi+CDC7i. These drugs were removed prior to the addition of CldU for 30 minutes. DNA fibers were analyzed and the percentage of fibers with both IdU and CldU staining indicating fork restart were quantitated. At least 300 fibers were analyzed. Data is presented as mean +/−SD. (E) A neutral comet assay was performed on cells treated with the indicated drugs for four hours. At least 100 tail moments were quantitated with CometScore software. One-way ANOVA analysis for the samples (p<0.0001) was followed up by Bonferroni’s multiple comparison tests. (n.s = not significant; *p<0.0001). See also Figure S7.

These data can be explained if there are two types of collapsed forks. Forks starting from new origins that fire when ATR is inhibited preferentially recruit NHEJ factors while preexisting forks preferentially recruit HR proteins. Consistent with this explanation, we also found that new origin firing is needed for the recruitment of PNKP and LIG3 to the nascent DNA when ATR is inhibited (Figure 6C). PNKP and LIG3 function in NHEJ but not HR (Goodarzi and Jeggo, 2013). In contrast, EXO1, which promotes end resection for recombination, is actually more enriched on nascent DNA when new origin firing is inhibited (Figures 6B and S5C), perhaps explaining the continued increase in RPA and its binding factors.

This explanation suggests that ATR inhibition continues to cause fork collapse even when new origin firing is inhibited. However, inhibition of new origin firing was reported to prevent fork collapse in HU/ATRi-treated cells when measured by H2AX phosphorylation (Toledo et al., 2013). In the course of examining γH2AX in ATRi/HU treated cells, we found that it was pan-nuclear instead of in foci, and also largely dependent on DNA-PKcs (Figure S7A and B). Thus, the CDC7-dependency for DNA-PKcs recruitment to the nascent DNA in HU/ATRi-cells may explain why CDC7 inhibition decreases H2AX phosphorylation. The continued hyper-accumulation of RPA, RPA-binding proteins, MRN, and ATM suggests fork processing continues even when new origin firing is prevented.

To determine if this fork processing is sufficient to cause fork collapse even when new origin firing is blocked, we examined whether DNA synthesis could resume from the stalled forks in these conditions. While 80% of the forks recover after removing HU, only 20% are able to recover after ATR is inactivated (Figure 6D). This is consistent with minimal DNA synthesis with increased fork collapse in the presence of the ATRi (Figure S7C and D). Blocking new origin firing with CDC7i delays, but does not rescue, fork collapse (Figure 6D). We also found that DSBs continue to form based on neutral comet assays (Figure 6E). These observations are consistent with the inability of CDC7i to rescue the lethality associated with HU/ATRi treatment (Couch et al., 2013). Thus, ATM and MRN complex enrichment is explained by continual collapse of pre-existing, stalled forks. However, DNA-PK recruitment is restricted to newly fired origins in checkpoint-deficient cells indicating two distinct fork collapse proteomes.

RPA exhaustion explains at least part of the fork collapse in checkpoint-deficient cells (Toledo et al., 2013). Indeed RPA overexpression delays fork collapse when ATR is inhibited and can largely suppress fork collapse if combined with CDC7 inhibition (Figure 7A and B). However, when examined by iPOND-SILAC-MS, RPA overexpression makes very little difference to the abundance of DNA damage response proteins recruited to the stalled and collapsing forks (Figure 7C; Table S6). Thus, the amount of RPA at stalled forks may be critical to regulate the activity of enzymes that contribute to break formation instead of only acting as a protein recruitment platform.

Figure 7.

Figure 7

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RPA overexpression delays fork collapse but does not alter the abundance of DNA damage response proteins on nascent DNA in checkpoint-deficient cells. (A) Immunoblot showing RPA32 overexpression compared to untransfected (Unt.) cells. (B) Fork recovery assays were performed as in Figure 6D in cells overexpressing RPA. (C) Abundance values of proteins from iPOND-SILAC-MS analysis comparing fork proteomes in RPA overexpressing to untransfected cell populations that were treated with HU and ATRi for the indicated times.

Discussion

The ATR checkpoint kinase ensures accurate and complete DNA replication in the context of replication stress. When ATR is inactivated, forks collapse and are unable to resume DNA synthesis. Since cancer cells have elevated levels of replication stress, ATR inhibitors are currently in clinical trials as a potential anti-cancer therapy (Lecona and Fernandez-Capetillo, 2014; Macheret and Halazonetis, 2015).

At least three, non-exclusive models have been proposed for how ATR prevents replication fork collapse. First, ATR may directly regulate replisome stability (Branzei and Foiani, 2010; Carr et al., 2011). Second, ATR prevents aberrant origin firing to prevent exhaustion of RPA (Toledo et al., 2013). Third, ATR directly regulates the activity of multiple fork remodeling enzymes to prevent fork cleavage by structure-specific nucleases (Couch et al., 2013; Ragland et al., 2013).

We investigated these models utilizing iPOND-SILAC-MS. Our data indicates that replisome stability is not regulated by ATR signaling in human cells. ATR-dependent inhibition of origin firing does reduce fork collapse. However, existing replication forks continue to be processed into DSBs and many do not recover DNA synthesis even when new origin firing is prevented. In contrast to forks that start from newly fired origins when the checkpoint is inactivated, the existing forks collapse into DSBs that do not recruit the NHEJ factors. Thus, there are at least two types of collapsed forks with distinct proteomes. RPA overexpression delays fork collapse without dramatically altering the stalled fork proteome. Thus, in addition to acting as a protein recruitment platform, RPA may regulate enzyme activities or otherwise protect the ssDNA from cleavage by structure specific nucleases.

Our conclusion that ATR does not regulate replisome stability in human cells is consistent with the genome-wide ChIP analysis in S. cerevisiae (De Piccoli et al., 2012) Differences with other approaches like chromatin fractionation and imaging data may be due to the inability of these methods to directly monitor the association of proteins with the fork. It is also possible that there is a requirement for ATR that is separable from its kinase activity. Finally, we cannot exclude the possibility that the proteome at some stalled forks differ from the average; however, replisome stabilization is not a major function of the ATR checkpoint to prevent fork collapse.

ATR checkpoint kinase prevents two types of fork collapse

Lukas and colleagues showed that fork collapse can be caused by RPA exhaustion (Toledo et al., 2013). ATR prevents the accumulation of excessive ssDNA and RPA exhaustion in part by inhibiting new origin firing in replication stress conditions (Couch et al., 2013; Toledo et al., 2013). Our data supports this model, but indicates that fork collapse can occur even if new origin firing is prevented. This conclusion is consistent with observations from yeast systems where the essential function for yeast ATR in regulating fork stability is separable from its function in regulating origin firing (Tercero et al., 2003).

RPA exhaustion is likely delayed but not prevented by inhibiting new origin firing. Indeed, we verified that RPA overexpression delays fork collapse and this delay is even stronger if combined with CDC7 inhibition. RPA overexpression does not substantially change the abundance of replisome or DNA damage proteins at collapsing forks, suggesting that in addition to being a scaffold for the recruitment of many proteins, RPA may also prevent fork collapse by regulating enzyme activities. A candidate for this regulation is SMARCAL1 since its activity is regulated by RPA and unregulated SMARCAL1 activity causes fork collapse (Bansbach et al., 2009; Betous et al., 2013; Bhat et al., 2015; Couch et al., 2013).

Unexpectedly, we observed differences in the fork proteomes depending on whether the fork was already active at the time of replication stress addition or whether it originated from new replication initiation events due to checkpoint inactivation. HR factors are recruited to existing forks, but end-joining proteins including DNA-PKcs are only recruited to the forks generated by new origin firing. This idea is supported by our observations that the pan-nuclear γH2AX in ATR-deficient cells is largely dependent on DNA-PKcs, and CDC7 inhibition substantially reduces and confines γH2AX to foci. The mechanism by which DNA-PK is excluded from some collapsing forks may involve end processing. We observe a large increase in EXO1 enrichment at collapsed forks in the HU+ATRi+CDC7i-treated cells. EXO1 may resect the breaks at the collapsed forks, thereby preventing the association of DNA-PKcs/KU with free DSB ends. This would be advantageous to the cell since it could promote recombination-based mechanisms of fork restart and avoid end joining.

Identification of new replication and repair proteins using iPOND-SILAC-MS

iPOND-SILAC-MS provides an unbiased discovery tool that not only identifies proteins that associate with active or damaged replication forks, but also can provide insights into the protein complexes at forks. Subunits of multi-protein complexes are quantitated with such precision that unsupervised hierarchical clustering of their enrichment ratios across multiple experimental perturbations is sufficient to identify them as interacting partners. Other studies also utilized iPOND or related technologies to identify replication proteins (Alabert et al., 2014; Lopez-Contreras et al., 2013). However, the strong complex clustering property of the iPONDSILAC-MS data provides added value.

As an example, we identified the protein ZNF644 as a subunit of the G9a/GLP methyltransferase complex consistent with another study published while this manuscript was under review (Bian et al., 2015). Our data indicates that the ZNF644/G9a/GLP complex travels with the replication fork, and ZNF644 knockdown causes phenotypes consistent with a replication or replication stress function. It may act to methylate and regulate non-histone fork proteins. Alternatively, the knockdown phenotype could be due to the lack of proper reestablishment of epigenetic marks on newly synthesized chromatin. In either case, the results illustrate the power of the iPOND-SILAC-MS data to not only identify replisome and replication stress proteins, but also to predict how they may function in protein complexes.

Dynamic changes in replisome composition upon HU stress

While fork stalling recruits replication stress response proteins, it also causes a rapid decrease in the abundance of PCNA and PCNA interacting proteins within minutes of adding HU followed by a slow reduction that parallels other replisome components. This observation is consistent with immunoblotting experiments (Sirbu et al., 2011), and a recent report of PCNA unloading from the lagging strand at stalled forks in budding yeast (Yu et al., 2014). PCNA is a processivity factor for replicative polymerases and also functions as a platform for recruitment of chromatin deposition and modifying enzymes (Moldovan et al., 2007). We suggest that at unperturbed replication forks, there is an equilibrium between PCNA loading and unloading on the lagging strand (Figure S6). Upon fork stalling, PCNA loading is reduced while unloading continues. As a result, the overall abundance of PCNA rapidly declines upon fork stalling.

Interestingly, we find the histone chaperone CAF1 is lost more rapidly than PCNA even though CAF1 is recruited to forks via an interaction with PCNA (Shibahara and Stillman, 1999). One possible explanation is that CAF1 is not equally distributed on all PCNA molecules, but is preferentially associated with the PCNA that is unloaded. This model would be consistent with the finding that changes in the post-translational modifications of newly deposited histones continue even at HU-stalled replication forks (Sirbu et al., 2011). Thus, DNA synthesis can be uncoupled from chromatin maturation and as maturation completes, histone chaperones and other chromatin modifiers are unloaded with PCNA.

We did not observe enrichment of phosphatases on nascent DNA. In contrast, PARG and the deubiquitinating enzyme USP1 both travel with elongating replication forks and are displaced from stalled forks. These changes correspond to an increase in poly-ADP-ribose and ubiquitin binding proteins like the macro-histones and RNF169. Thus, In contrast to phosphorylation, the increase in these post-translational modifications at stalled forks may be primarily regulated by a reduction in the enzymes that antagonize them.

Many additional replisome changes are apparent in the dataset and will provide a useful resource for hypothesis generation. Combining iPOND with other methods will be essential to understand these observations since iPOND only reports the protein composition associated with newly synthesized, EdU-labeled DNA. It will also be useful to repeat the iPOND experiments with varying doses of HU or other replication stress agents. Such experiments would better differentiate normal replicon termination from other effects and may reveal differences in the replication stress response proteomes at low and high stress levels. However, very high doses of replication inhibitors sufficient to fully stop DNA synthesis may cause undesired affects since continued primer synthesis at stalled forks contributes to checkpoint activation (Van et al., 2010).

Summary

By measuring the quantitative localization behavior of hundreds of proteins to replication forks, the iPOND-SILAC-MS data provides unexpected insights into the replication stress response. In some cases, the tight correlation between proteins provides evidence they are in a complex or act in the same pathway. In other cases, their disparate behaviors suggest unexpected complexity in the response. In contrast to prevailing models of checkpoint function in human cells, replisome stabilization is not a major function of ATR signaling. Instead, ATR promotes fork stability by preventing two separable types of fork collapse. These data also provide insights into the mechanism of action of ATR inhibitors, which are currently in clinical trials as cancer therapeutics.

Experimental Procedures

iPOND-SILAC mass spectrometry

iPOND was performed as described previously (Dungrawala and Cortez, 2015; Sirbu et al., 2012) with the following modifications. The click reaction was completed in 2 hours. Capture of DNA-protein complexes utilized streptavidin-coupled C1 magnabeads for one hour. Beads were washed with lysis buffer (1% SDS in 50mM Tris pH 8.0), low salt buffer (1% Triton X-100, 20mM Tris pH 8.0, 2mM EDTA, 150mM NaCl), high salt buffer (1% Triton X-100, 20mM Tris pH 8.0, 2mM EDTA, 500mM NaCl), lithium chloride wash buffer (100mM Tris pH 8.0, 500mM LiCl, 1% Igepal) and twice in lysis buffer. For most experimental samples, 4×108 asynchronous HEK293T cells were used for both reference and experimental samples, and light and heavy labeled cells were mixed 1:1 prior to the click reaction.

iPOND samples were separated by SDS-PAGE. Gel regions above and below the streptavidin band were excised, treated with 45mM DTT for 30 minutes, and available cysteine residues were carbamidomethylated with 100mM iodoacetamide for 45 minutes. After destaining the gel pieces with 50% MeCN in 25mM ammonium bicarbonate, proteins were digested with trypsin (Promega) in 25mM ammonium bicarbonate at 37°C. Peptides were extracted by gel dehydration (60% MeCN, 0.1% TFA), vacuum dried, and reconstituted in 0.1% formic acid.

MudPIT analysis was performed with an 8-step salt gradient. Peptides were introduced via nano-electrospray into a Q Exactive mass spectrometer (Thermo Scientific) operating in the data-dependent mode acquiring HCD MS/MS scans (R = 17,500) after each MS1 scan (R = 70,000) on the 20 most abundant ions using an MS1 ion target of 1 × 106 ions and an MS2 target of 1 × 105 ions. The maximum ion time for MS/MS scans was set to 100 ms, the HCD-normalized collision energy was set to 28, dynamic exclusion was set to 30s, and peptide match and isotope exclusion were enabled.

Mass spectrometry data analysis

For peptide and protein identification, data were analyzed using the Maxquant software package, version 1.3.0.5 (Cox and Mann, 2008; Cox et al., 2011). MS/MS spectra were searched against a human subset database created from the UniprotKB protein database (www.uniprot.org). Precursor mass tolerance was set to 20ppm for the first search, and for the main search, a 10ppm precursor mass tolerance was used. The maximum precursor charge state was set to 7. Variable modifications included carbamidomethylation of cysteines (+57.0214) and oxidation of methionines (+15.9949). Enzyme specificity was set to Trypsin/P, and a maximum of 2 missed cleavages were allowed. The target-decoy false discovery rate (FDR) for peptide and protein identification was set to 1% for peptides and 2% for proteins. A multiplicity of 2 was used, and Arg10 and Lys8 heavy labels were selected. For SILAC protein ratios, a minimum of 2 unique peptides and a minimum ratio count of 1 were required, and the requantify option was enabled. Protein groups identified as reverse hits were removed from the datasets. All reported protein groups were identified with two or more distinct peptides and were quantified with one or more ratio counts. SILAC protein ratios for repeat samples were averaged for analysis within the Perseus software package. Average ratios were utilized to create profile plots. Heat maps were generated using hierarchical clustering with Euclidean distance. All datasets after averaging repeats and removing rows with less than 20 valid values were included in the clustering analysis.

Drugs and Antibodies

The following compounds were used: hydroxyurea (HU, 3mM), ATR inhibitor VE821 (ATRi, 3μM), aphidicolin (2.95μM), CDC7 inhibitor (CDC7i, 20μM) and roscovitine (20μM). HU and aphidicolin were used at 5mM and 14.75μM respectively for high dose replication stress. Antibodies used were CHK1 total (Santa Cruz), CHK1 pS317 (Cell Signaling), MCM2 (BD Biosciences), POLD3 (Bethyl), RPA2 (Abcam), PCNA (Santa Cruz), CDC45 (Santa Cruz), CLASPIN (Bethyl) and ORC2 (BD Biosciences).

Chromatin fractionation and Immunofluorescence

Chromatin fractionation, as described previously (Mendez and Stillman, 2000), was performed in 293T cells that were synchronized using a double thymidine block and then released into S-phase for three hours prior to treating with drugs. Immunofluorescence was carried out as described previously (Couch et al., 2013)

Neutral comet assay and DNA fiber labeling

Neutral comet assays to detect changes in DSBs were carried out as per the instructions provided by Trevigen. Comets were scored using CometScore software. Fiber labeling to measure DNA replication rates was completed essentially as described (Couch et al., 2013).

Supplementary Material

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Acknowledgements

We thank Jiri Lukas for providing the RPA over-expressing plasmid. The research was supported by R01CA102729 and Breast Cancer Research Foundation grants to D.C. Support for processing samples in the Vanderbilt Mass Spectrometry core came from the Vanderbilt-Ingram Cancer Center (P30 CA068485), the SPORE in Breast Cancer (P50 CA098131), and a CTSA award UL1TR000445 from the National Center for Advancing Translational Sciences. The biotin-azide, EdU, and ATR inhibitor were synthesized by the Chemical Synthesis Core of the Vanderbilt-Institute for Chemical Biology. We thank Salisha Hill and Amanda Hachey for processing of the mass spectrometry samples.

Footnotes

Author Contributions

Conceptualization, H.D., K.P.B., K.N.M. and F.B.C; Methodology, H.D.; Investigation, H.D., K.L.R. K.P.B., K.N.M. G.G.G. F.B.C.; Writing – Original Draft, H.D. and D.C.; Writing – Review and Editing, H.D., K.L.R., K.P.B, K.N.M, D.C.; Funding Acquisition, D.C.; Supervision, D.C.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS712738-supplement-1.pdf (6.3MB, pdf)
2
NIHMS712738-supplement-2.xlsx (91.3KB, xlsx)
3
NIHMS712738-supplement-3.xlsx (42.5KB, xlsx)
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NIHMS712738-supplement-4.xlsx (55.6KB, xlsx)
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NIHMS712738-supplement-5.xlsx (33.4KB, xlsx)
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NIHMS712738-supplement-6.xlsx (28.9KB, xlsx)
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NIHMS712738-supplement-7.xlsx (1.1MB, xlsx)

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