Abstract
There are approximately 500 known origins of replication in the yeast genome, and the process by which DNA replication initiates at these locations is well understood. In particular, these sites are made competent to initiate replication by loading of the Mcm replicative helicase prior to the start of S phase; thus, “a site to which MCM is bound in G1” might be considered to provide an operational definition of a replication origin. By fusing a subunit of Mcm to micrococcal nuclease, a technique referred to as “Chromatin Endogenous Cleavage”, we previously showed that known origins are typically bound by a single Mcm double hexamer, loaded adjacent to the ARS consensus sequence (ACS). Here we extend this analysis from known origins to the entire genome, identifying candidate Mcm binding sites whose signal intensity varies over at least 3 orders of magnitude. Published data quantifying the production of ssDNA during S phase showed clear evidence of replication initiation among the most abundant 1600 of these sites, with replication activity decreasing in concert with Mcm abundance and disappearing at the limit of detection of ssDNA. Three other hallmarks of replication origins were apparent among the most abundant 5,500 sites. Specifically, these sites (1) appeared in intergenic nucleosome-free regions that were flanked on one or both sides by well-positioned nucleosomes; (2) were flanked by ACSs; and (3) exhibited a pattern of GC skew characteristic of replication initiation. Furthermore, the high resolution of this technique allowed us to demonstrate in vivo that, as is the case in vitro, the Mcm complex is loaded directionally downstream of the ACS. We conclude that DNA replication origins are at least 3-fold more abundant than previously assumed, and we suggest that replication may occasionally initiate in essentially every intergenic region. These results shed light on recent reports that as many as 15% of replication events initiate outside of known origins, and they reveal S phase in yeast to be surprisingly similar to that in humans.
Introduction
DNA replication in Saccharomyces cerevisiae initiates at multiple sites across the genome known as replication origins. Fragments of DNA containing these sites were initially isolated in the late 1970s on the basis of their ability to support replication of plasmids (Beach et al., 1980; Broach et al., 1983; Chan and Tye, 1980; Stinchcomb et al., 1979), and in this context they acquired the designation of “autonomously replicating sequences” or “ARSs”. In subsequent years, additional origins were discovered using a variety of techniques to demonstrate their function (Brewer and Fangman, 1987; Liachko et al., 2013; Saffer and Miller, 1986). Origins vary widely in their efficiency, with some being used in almost every cell cycle while others may be used in only one in one thousand S phases (Boos and Ferreira, 2019). The total number of reported yeast origins of replication is between 350 and 830 (Nieduszynski et al., 2007; Siow et al., 2012; Wyrick et al., 2001; Xu et al., 2006).
The mechanism by which these sequences function as replication origins has been elucidated in great detail (Bell and Labib, 2016; Costa and Diffley, 2022; Fragkos et al., 2015; Hu and Stillman, 2023): During the G1 phase of the cell cycle, a 6 protein “Origin Recognition Complex” (ORC) binds to the origin at a loosely defined “ARS Consensus Sequence” (ACS) (Bell and Stillman, 1992) and loads a replicative helicase complex called MCM (for “Mini-Chromosome Maintenance”). MCM exists as two rings that encircle the DNA double helix, with each ring composed of six subunits encoded by MCM2-7 (Bell and Botchan, 2013; Deegan and Diffley, 2016). Mcm complexes are activated at the beginning of S phase through a combination of CDK and DDK kinase activities, and the two single hexamers then move in opposite directions to separate the two strands of DNA in front of the replication apparatus (Deegan et al., 2016; Douglas et al., 2018; Francis et al., 2009; Lewis et al., 2022). MCM is not loaded at every origin during every cell cycle, and nor does every MCM complex that is loaded become activated (Rhind and Gilbert, 2013). This variation in both loading and firing leads to the variation in origin efficiency mentioned above.
Replication origins exhibit several characteristics that distinguish them from the surrounding DNA. First, they appear in nucleosome-free regions or NFRs (Eaton et al., 2010; Lipford and Bell, 2001). While nucleosomes are typically spaced at 165 base pairs, leaving approximately 18 base pairs of unbound DNA between, NFRs consist of longer (100-200 base pair) stretches of DNA that are not only devoid or depleted of nucleosomes, but are also flanked on one or both sides by well-positioned nucleosomes (Eaton et al., 2010; Jansen and Verstrepen, 2011; Rando and Chang, 2009). Second, these NFRs occur predominantly between rather than within genic regions (Wang and Gao, 2019). Finally, an evolutionary footprint marking the point of divergence of leading and lagging strand replication, which manifests itself as a skewing in the numbers of Gs versus Cs along a single strand of DNA, has been reported at replication origins. This is thought to reflect increased susceptibility of Cs to deamination when they are replicated in the more single-stranded context of the lagging strand (Grigoriev, 1998; Lobry, 1996). The association of patterns of GC skew with replication origins is more firmly established in prokaryotes than in eukaryotes, because the presence of a single replication origin and terminus makes this subtle signature easier to discern.
Until recently, limitations of population-based measurements of replication initiation precluded identification of very low activity origins. However, the development of single-molecule nanopore-based techniques such as Fork-seq and D-Nascent have overcome these constraints, and studies employing these techniques have revealed many initiation events between known origins (Hennion et al., 2020; Muller et al., 2019; Theulot et al., 2022). Indeed, the fraction of initiations that occur outside of known replication origins has been reported to be as high as 20% (Hennion et al., 2020). Given that the vast majority of these “extra-origin” initiation events are detected as unique events in single-molecule studies, whether they reflect initiation from origins that are qualitatively similar to better characterized origins but are simply less active or, instead, reflect stochastic initiation events remains unclear. Thus, identification of further origins would benefit from a technique that has higher throughput, to distinguish repeated use from stochastic events, while retaining the sensitivity required to distinguish very low activity origins from background noise.
We have previously shown that “Chromatin Endogenous Cleavage” or “ChEC” (Schmid et al., 2004; Zentner et al., 2015) allows one to identify sites at which Mcm double-hexamers have been loaded with both high resolution, revealing that Mcm complexes are loaded immediately on or adjacent to the ACS, and also high sensitivity, with Mcm complexes identified at almost all known origins (Foss et al., 2021). The sensitivity of this technique is due, at least in part, to the fact that it creates relatively short fragments of interest within a background of much longer PCR-recalcitrant fragments: When Mcm subunits are tagged on their C-terminal ends with MNase, cleavage of the DNA underneath complexes loaded at origins will release small (50-100 base pair) fragments of DNA in a background of much larger (tens of kb) inter-origin fragments. Because these small fragments are more amenable to PCR amplification steps, one can create sequencing libraries that are composed almost exclusively of the fragments of interest without size-based purification.
Here we apply the ChEC technique to identify candidate MCM binding sites, and we then ask how deeply we can proceed into the lower intensity ChEC signals while still finding evidence that these sites serve as replication origins. Using replication itself as a readout, by making use of previously published data measuring the generation of single-stranded DNA in S phase, we find evidence that the 1600 sites with the most intense ChEC signals are used as replication origins, with the magnitude of the ChEC signal corresponding to the level of single-stranded DNA generated all the way down to the limits of detection of ssDNA. The next approximately 4000 peaks of ChEC signal continue to exhibit the hallmarks of replication origins described above, namely appearance in NFRs located in intergenic regions that are flanked by well-positioned nucleosomes and ACSs, and also a skewing of the numbers of Gs versus Cs. We conclude that low abundance sites of Mcm binding represent qualitatively similar, but less active, counterparts to better characterized origins of replication. This relaxation of the notion of what constitutes an origin of replication makes genome duplication in yeast resemble that in mammals more closely than has been previously thought.
Results
Identification of candidate Mcm binding sites that vary in abundance across three orders of magnitude
Our first goal was to determine, as comprehensively as possible, the genome-wide distribution of Mcm binding. To this end, we used MCM-ChEC, as previously described (Foss et al., 2019; Foss et al., 2021). Briefly, we fused the C-terminus of either MCM2, MCM4 or MCM6 to micrococcal nuclease (MNase), permeabilized cells, activated the MNase with calcium, and prepared sequencing libraries without size fractionation (Figure 1A). As previously reported, the size distribution of the DNA fragments produced by MCM-MNase cleavage peaked at 61 base pairs, which corresponds to the length of DNA covered by the MCM double-hexamer in cryo-EM studies (Figure 1B). In order to capture the most robust peaks, we analyzed a total of 12 samples, collected under a variety of conditions, including G1 arrest, HU arrest, and log phase. Measurements were highly reproducible, with mean and median values for r2 of 0.98 and 0.99, respectively (Figure S1). Combining all 12 samples yielded a single data set with average coverage of 650x.
We next compared our results with previously published data derived from chromatin immunoprecipitation with anti-MCM antibodies, with or without treatment of the precipitated chromatin with exonucleases to trim the ends and thereby sharpen the signal (ChIP-seq and ChIP-exo-seq, respectively; Figure 1C) (Belsky et al., 2015; Das et al., 2015; Rossi et al., 2021). Datasets derived from digestion with free MNase in G1-arrested cells are shown as controls (bottom rows within each panel) (Foss et al., 2021). In ChIP-exo, the signal on the Watson and Crick strands are shown separately, because each strand serves to delineate just one edge of the protein binding site: Specifically, the right and left sides of the binding site correspond to the steep declines on the right side of the Watson (blue) strand and the left side Crick (red) strand, respectively, and computational algorithms that recognize these patterns in the two strands are used to integrate the signals and thereby define binding site midpoints. Figure 1C shows the distribution of read depths on chromosome IV centered at ARS1 at 4 different scales. Both ChEC- and ChIP-based techniques identified a common set of sites (top left panel), which correspond largely to known origins. The higher sensitivity of ChEC over ChIP derives from the lower background signal, which is evident at all four scales. The advantage in resolution of ChEC over ChIP is most clear at the 3 kb scale: While both ChEC and ChIP reveal a footprint at the midpoint of Mcm binding (yellow shaded area in bottom right panel), the fraction of the total signal at ARS1 (red rectangle) that is derived from this midpoint is much higher in the former case. Our ability to determine the directionality of loading of Mcm relative to the ACS or detect evidence for DNA replication in the form of GC skew (described below), would not be possible without this high level of resolution. In summary, we conclude that ChEC reveals known sites of Mcm binding with a high degree of both sensitivity and resolution.
We next used a peak identification algorithm to convert our continuous genome-wide measurements of Mcm binding activity into a discrete list of candidate Mcm binding sites (CMBSs). Because a central goal of this study was to explore the behavior of low abundance Mcm binding sites, we wanted to avoid arbitrarily specifying an abundance cutoff to distinguish meaningful low abundance binding sites from background noise. Instead, we chose to compile an exhaustive list of CMBSs, assigning approximately one per kb for the entire genome, and then retrospectively determine how far down this list we could go while still discerning known characteristics replication origins.
We used a simple iterative algorithm to generate a list of CMBSs, first creating a genome-wide per-base pair read depth matrix based on our 12 MCM-ChEC data sets and smoothing these values with a 200 base pair moving window. The highest peak in this data set was assigned a rank of 1. To avoid assigning multiple IDs to essentially the same peak, once a CMBS was assigned, we eliminated the region within 500 base pairs of the midpoint of that peak from consideration for the definition of further peaks before repeating the process to assign a CMBS with a rank of 2, etc., ultimately arriving at a list of 17,618 candidate sites (for details, see Materials and Methods). CMBS signal magnitude exhibited a rapid decline over the course of the first 500 CMBSs, followed by a relatively gradual decline, punctuated by a slight inflection around 5500 (Figure 1D). As discussed below, this inflection point coincides approximately with the point at which CMBSs no longer exhibit characteristics of replication origins. There were high abundance CMBSs (ranks <= 500) within 100 base pairs of 82% of origins reported in SGD (282 out of 343 unambiguously mapping origins). We note, however, that the regions defined as origins of replication in SGD tend to be significantly larger (mean size 290 base pairs) than the 61 base pairs occupied by an Mcm double hexamer. More precise origins’ midpoint coordinates can be assigned for that subset of origins that have a defined ACS sequence (187 of 343), and in these cases, there were CMBSs within 100 base pairs of 78% (146 out of 187) of these ACS sequences. These 146 CMBSs, marked by blue vertical line in Figure 1D, were all ranked among the most abundant 500. Thus, whether we use relaxed or stringent criteria for the coordinates of reported origins, we conclude that the vast majority of known origins are associated with the 500 most abundant CMBSs.
Assessment of replication initiation at CMBSs
To determine how CMBSs correspond to sites where DNA replication initiates, irrespective of what has previously been defined as a replication origin, we used published data measuring the appearance of single-stranded DNA (ssDNA) during S phase in cells released from an alpha factor G1 block into medium containing hydroxyurea (HU), which impedes replication fork progression by inhibiting ribonucleotide reductase (Feng et al., 2006). To establish a benchmark for replication initiation, we first analyzed ssDNA accumulation at 181 well-characterized origins centered on their known ACSs and, as expected, we observed a peak of ssDNA accumulation (Figure 2A). Furthermore, this peak was similar in both shape and magnitude to an analogous peak generated by juxtaposing the midpoints of the 200 most abundant CMBSs (Figure 2B, black line), which is expected, because the two groups are largely overlapping. Extending this line of analysis, we asked how far down in abundance we could go, in groups of 200, and still discern a signal of replication initiation (Figure 2B, colored lines). Two aspects of these results are notable: First, clear peaks of ssDNA signal extend down to the eighth cohort (brown line), which corresponds to CMBSs ranked 1401-1600. Of course, this does not imply that all of these sites function as replication origins, and nor does it imply that no sites below that rank do so, since we have reached the limits of detection of this ssDNA-based assay. Nonetheless, it suggests that replication activity is common among sites extending at least down to rank 1600. Second, there is a rapid drop in ssDNA after the second cohort, demonstrating that the sharp drop in Mcm abundance shown in Figure 1D is recapitulated in a drop in replication activity. Based on our previous analysis of MCM occupancy (Foss et al., 2021), which showed that approximately 90% cells have an MCM complex loaded at one of the most active known replication origins, we estimate that ∼1-2 % cells have an Mcm complex loaded at the Mcm binding sites in the eighth cohort (ranks 1401-1600). rad53 mutants are used here because they do not suppress firing of late origins, and therefore allow a more comprehensive assessment of origin activity. ssDNA measurements for both wild type and rad53 genotypes at other time points (Figure S2A), as well as BrdU-based measurements of replication activity in rad53 mutants (Yoshida et al., 2014) (Figure S2B), yielded comparable results. We conclude that replication activity parallels Mcm abundance, and that this activity continues to be prevalent at least through the top 1600 CMBSs.
Mcm binding sites are enriched in nucleosome-free regions
The abundance of CMBSs that rank below the limit of detection of ssDNA is only marginally lower than that in the cohort that still exhibits ssDNA accumulation (e.g. 1.5% vs 1.4% for cohorts 1401-1600 vs 1601-1800). To determine whether these lower abundance sites, whose potential origin activity was below the limits of detection by ssDNA, might, nonetheless, function as replication origins, we assessed them for another hallmark of replication origins, namely their presence in intergenic NFRs. Nucleosome positions were determined based on published data that exploit site-directed mutagenesis to confer chemical cleavage activity to a histone subunit (Chereji et al., 2018). Specifically, residue 85 of histone H3 was changed from a glutamine to a cysteine. With two copies of H3 per histone octamer, each nucleosome ends up containing two of these engineered cysteines, with the two cysteines separated by a 51 base pair stretch of the DNA backbone centered on the nucleosome dyad. Ex vivo coupling of these residues to phenanthroline in the presence of copper ions and peroxide endows these cysteines with endonuclease activity, thereby releasing a 51 base pair fragment of DNA centered at the midpoint of the nucleosome, where the DNA is most tightly wrapped. High throughput sequencing, followed by read depth profiling of library fragments in the 51 base pair range (46-56 base pairs), thus provides a high resolution map of nucleosome positioning that is relatively robust to variable cleavage of the DNA due to unwrapping at nucleosome entry and exit sites. Heatmaps of these data centered at the midpoints of the CMBSs reveal an approximately 150 base pair NFR, consistent with the size of NFRs at the known origins (Eaton et al., 2010), for the first 5000 CMBSs and their flanking nucleosomes (Figure 3A). In contrast, we observed a clear nucleosomal signal at the midpoints of most CMBSs that rank >5000. To better characterize this transition from NFR to nucleosome, we analyzed cumulative chemical cleavage signal in cohorts of 500 successively ranking CMBSs centered at their midpoint (Figure 3B). This analysis revealed a central valley of ChEC signal corresponding to the NFR in the first 10 cohorts (through 4,501-5000), at which point we observe a gradual transition from central NFR to a predominantly nucleosomal signal at the midpoint, which is complete by the 14th cohort (ranks 6501-7000). These findings indicate that most of the low abundance (rank >5000) CMBSs correspond to nucleosomes, presumably due to low level non-specific MNase activity. Mapping of nucleosome locations by enzymatic digestion of chromatin with MNase (Foss et al., 2021) confirmed these results (Figure S3). It is notable that the Mcm-ChEC panel of Figure 3A shows no obvious change in Mcm stoichiometry across the entire range, from low abundance, at the bottom, to high abundance, at the top. This argues against models in which higher replication activity at more active origins reflect the loading of more Mcm double-hexamers at those origins within a single cell. We conclude that nucleosome mapping studies using two orthogonal methods demonstrate a fundamental difference between the first 5000 CMBSs, which are located in the NFR and are flanked by adjacent nucleosomes, from those that rank >5000, which largely correspond to nucleosomes.
Mcm binding sites localize to intergenic regions or within poorly transcribed genes
We next evaluated CMBSs for another feature of replication origins, namely their localization to intergenic regions. As expected, the most abundant sites, which contain most of the known origins of replication, were located predominantly between rather than within genes (89% of sites ranked <= 500 were intergenic). Furthermore, in those instances when high abundance sites were located within genes, those genes were frequently non-transcribed. For example, 15 of the 56 genes that contained a high abundance site have been implicated in meiosis and sporulation and are not expressed during vegetative growth (∼5 out of 56 expected from random sampling), consistent with previous observations (Mori and Shirahige, 2007). CMBSs remained largely intergenic with decreasing abundance until approximately rank 5,000, at which point they switched rather abruptly to genic localization (Figure 4A). Moreover, the level of transcription of genes that contained CMBSs showed an abrupt increase at this same point. This coincides approximately with the point at which sites transition from nucleosome-free to nucleosome-occupied regions. Thus, the first 5000 ranking CMBSs, in addition to being located in the NFR are also largely intergenic, or located withing poorly transcribed genes, whereas the CMBSs that rank > 5000 correspond largely to nucleosomal signal within transcribed genes. Such a distribution of ChEC signal can be appreciated throughout the genome, with most intergenic fragments featuring a clear high-ranking ChEC signal within their NFR (black arrows in Figure 1D), whereas the low abundance signal within gene correspond to nucleosomes (red arrows in Figure 1D).
Inspection of the orientation of genes that flank CMBSs supports the simple view that Mcm complexes are loaded at these sites rather than, for example, being loaded within the body of adjacent genes and then pushed out into the intergenic region by RNA polymerase. Lower abundance intergenic MCM signals (ranks between 500 and 4000) showed a distinct preference for divergent over convergent transcription of flanking genes (Figure 4B). This is the opposite of what would be expected if these helicase complexes were loaded within gene bodies and then pushed out by transcription. Furthermore, this is consistent with our observation that these sites are flanked by ACSs (see below). In contrast to the low abundance sites, the most abundant 500 sites showed a preference for convergent over divergent transcription (left of vertical dotted line in Figure 4B), in agreement with a previous report (Li et al., 2014). While this might be interpreted to suggest displacement of helicase complexes by transcription, our published ChEC analysis of Mcm locations at origins with defined ACSs, all of which fall within the most abundant 500 sites, demonstrates that these helicases are found almost exclusively immediately adjacent to the ACS. In summary, we favor the parsimonious view that Mcm complexes are initially loaded within the same NFRs in which we have localized them through ChEC.
Mcm binding sites are flanked by ACS sequences, and Mcm is loaded downstream of known ACSs
Another characteristic of known origins that we could use as a criterion to assess the nature of Mcm binding sites is the presence of an ACS. The ACS is a loosely defined AT-rich 11-17 base pair sequence whose directionality is determined on the basis of the relative numbers of As versus Ts in the two strands (Breier et al., 2004). This sequence is recognized by the Origin Recognition Complex (Orc), a 6-protein complex that loads MCM (Broach et al., 1983; Deshpande and Newlon, 1992; Eaton et al., 2010; Kearsey, 1984; Newlon and Theis, 1993; Singh and Krishnamachari, 2016; Srienc et al., 1985). Although the characteristics that allow a sequence to serve as an Orc binding site remain poorly understood, with, for example, only approximately half of the origins listed in SGD reporting associated ACS signals, researchers have calculated a probability weight matrix to evaluate the fidelity of the correspondence of any 17 base pair sequence with a hypothetically “perfect” ACS (Coster and Diffley, 2017). Because the ACS sequence is asymmetric, these scores are calculated separately for the Watson and Crick strands (shown in blue and red, respectively, in Figure 5A). Using this metric, we found that the most abundant 5,500 CMBSs are enriched for flanking ACS sequences, approximately 35 base pairs from the midpoint of Mcm binding. Furthermore, these ACSs are oriented in a direction consistent with that expected from in vitro loading of Mcm complexes by Orc. Finally, the degree to which these sequences match a hypothetically perfect ACS decreases with increasing rank (i.e. with decreasing abundance), largely disappearing by rank 6000 (Figure 5B). Our results allowed us to explore the question of whether individual Mcm binding sites contain two Orc binding sites or whether, instead, the cumulative high scores on both sides of CMBSs in Figure 5A reflect the juxtaposition of multiple unoriented sites, each of which contains just one Orc binding site. This issue has implications for the mechanism by which Orc loads two hexameric helicases and for the design of biochemical experiments aimed at elucidating that mechanism (Coster and Diffley, 2017; Gupta et al., 2021). To address the question, we extracted 594 CMBSs that had a particularly strong (PWM >= 10.8) ACS on the Watson strand and then asked whether there was still an enrichment for a corresponding ACS sequence on the Crick strand, as shown in Figure 5C. We found that 594 loci with a strong ACS on the Watson strand were still enriched for ACS on the Crick strand, thus supporting models in which origins have two binding sites for Orc (Coster and Diffley, 2017).
The high resolution of our data also allowed a more demanding test of the directionality of loading of MCM relative to the ACS: The results described above demonstrate that sequences centered on MCM binding sites will, in aggregate, contain adjacent ACS sequences that are oriented in the direction predicted by biochemical experiments. Conversely, and more stringently, we could ask whether, when centering and orienting sequences according to previously reported ACSs, the peak of MCM signal appeared on the expected side of the ACS. Consistent with directional MCM loading downstream of the ACS, cumulative abundance of Mcm-ChEC signal at 187 origins centered at their ACS peaked downstream of the ACS (Figure 5D). There were CMBSs within 100 base pairs of 146 of the 187 ACS sequences reported in SGD, and in 112 out of 146 cases (77%), the CMBS was downstream of the ACS. This in vivo confirmation of the in vitro prediction for directionality has not been previously possible (Belsky et al., 2015; Dukaj and Rhind, 2021), as it requires a level of resolution not attainable by ChIP-based techniques (Figure 1B). In summary, our results not only provide in vivo confirmation of in vitro predictions of the directionality of Mcm loading by Orc, but they are also consistent with the notion that essentially all of the most abundant 5,500 Mcm binding sites can function, at least on rare occasions, as replication origins.
GC skew as evolutionary footprint of replication initiation
GC skew, which is a measure of the deviation from the expected 1:1 ratio of Gs to Cs along a single strand of DNA suggests another potential metric to determine whether sites have been used as replication origins. GC skew is calculated as (G-C)/(G+C), with G and C representing the numbers of Gs and Cs, respectively, within a specified window sliding along a single strand of DNA (Grigoriev, 1998; Lobry, 1996). Consistent use of particular sequences as replication origins over long time scales can leave evolutionary “footprints” in which an excess of Gs over Cs reverses to become an excess of Cs over Gs as one traverses the site of initiation of replication. Such reversals of GC skew reflect the combination of (1) the transition between leading and lagging strand synthesis precisely where DNA replication initiates; (2) the fact that the template for lagging strand synthesis exists in a single-stranded state more than does the template for leading strand synthesis; (3) the increased vulnerability of cytosines to deaminases when in a single-stranded state; and (4) replication-induced conversion of deaminated cytosine to thymine. This phenomenon has been best characterized in bacteria, where the presence of a single origin and terminus of replication on each circular chromosome ensures that, at any particular location in the genome, each strand of DNA consistently serves as a template for exclusively either the leading or the lagging strand polymerase (Lu and Salzberg, 2020). A change in GC skew across origins has also been reported in yeast (Li et al., 2014), but the magnitude of the effect is small, and the direction of the effect is contrary to both the observation in bacteria and the theoretical expectation (Figure S4A). To determine whether sites identified by MCM-ChEC exhibited patterns of GC skew consistent with their use as replication origins, we aligned 400 base pair sequences centered on the 5,500 most abundant CMBSs and calculated GC skew using a 51 base pair sliding window. This analysis revealed a general rise in GC skew as one follows either strand in the 5’ to 3’ direction across the CMBS, a result that is consistent with both the theoretical expectation and the empirical observation in bacteria (Figure 6A). Subdividing CMBSs into groups of 1100 and arranging these groups according to decreasing abundance revealed that GC-skew-based evidence for DNA replication initiation continued until approximately rank 6000 (Figure 6B). Our ability to detect this subtle effect requires precise alignment at the midpoint of Mcm double-hexamer binding, as alignment of identical sequences according to the ACS does not recapitulate the result (Figure S4A). A short inversion of this pattern at the midpoint is consistent with biochemical results showing that the two Mcm hexamers cross over each other before they begin diverging (Douglas et al., 2018) (see Discussion). We conclude that the distortion of the expected 1:1 ratio of Gs to Cs on a single strand of DNA is consistent with the hypotheses that the most abundant 6000 CMBSs have served as replication origins.
Discussion
The first yeast origin of replication, ARS1, was reported in 1979 as a sequence that could sustain plasmid replication (Stinchcomb et al., 1979). Not surprisingly, given the assay by which it was discovered, ARS1 is among the most active origins in the genome. Over the subsequent 25 years, this simple assay for origin activity led to numerous other genomic sequences being designated as origins, with the number stabilizing at around 500 (Nieduszynski et al., 2007; Siow et al., 2012). While few of these origins are as efficient as ARS1, all must exhibit replication activity sufficient to support colony formation. For example, origins that fire in only one in ten S phases will not produce even a microcolony in a plasmid transformation assay, and therefore will not be detected. Thus, the set of known origins is largely restricted to those with relatively high activity. This conclusion is consistent with Figure 1D, in which the known (light blue) origins are strikingly concentrated on the left (high abundance) side of the curve.
We used MCM-ChEC to identify a more comprehensive set of replication origins. In this approach, we first identify sites at which Mcm helicases are loaded and then subsequently examine those sites for characteristics of replication origins. These characteristics include, but are not limited to, replication itself. There are four advantages to this approach: First, the ChEC assay is exceptionally sensitive because, as noted in the introduction, the short DNA fragments of interest enjoy a tremendous selective advantage for amplification during PCR steps involved in library construction. Thus, we can detect Mcm complexes that are loaded in as few as 1 in 500 cells (Foss et al., 2021). Second, by averaging signals of replication from multiple Mcm binding sites, we were able to extract weak signals of replication. This is due to the fact that noise, which is randomly distributed, will tend to cancel itself out, while signals of replication will consistently augment the signal at the midpoint of the origin (Figure 2). Third, even beyond the level of detection possible through direct measurements of replication, such as generation of ssDNA, we were able to infer past replication activity from the subtle evolutionary signatures of replication that manifest themselves in GC skew (Figure 6). The ability to detect these evolutionary footprints is exquisitely sensitive to the precision with which DNA sequences can be aligned to the site of replication initiation, where the bias in Gs to Cs reverses (Figure S4). Finally, we were able to use the power of averaging signals from multiple Mcm binding sites to extract not only direct (ssDNA) and indirect (GC skew) metrics of replication, but also to discern other characteristics of replication origins, namely their appearance in intergenic NFRs (Figures 3 and 4) with flanking ACS signals (Figure 5).
It is notable that multiple criteria converge on the conclusion that the total number of replication origins is approximately 5,500 (Table 1): For example, in Figure 3A (left heat map), one can see the chemical cleavage nucleosomes signature transitioning from relatively sharp to fuzzy at rank 5000. This transition is recapitulated in the MNase-seq-based nucleosome assay (Figure S3) and in the MCM-ChEC signal itself (Figure 3A, right heat map). Furthermore, it is in this same general region, i.e. from ranks 4000 to 6000, that (1) CMBSs transition from being mostly intergenic to mostly genic (Figure 4A); (2) transcription levels for genes that contain CMBSs rise sharply (Figure 4A); (3) position weight matrix scores for ACSs decrease sharply (Figure 5B); and (3) transitions in GC skew across peak midpoints disappear (Figure 6B). Of course, we do not conclude that all CMBSs with ranks lower than 5500 function as replication origins, nor that none with ranks above 5500 do so, but only that the number of replication origins is likely to be approximately an order of magnitude higher than widely believed. With approximately 6000 genes in the yeast genome, this would suggest that replication can initiate, if only rarely, in essentially every intergenic region. Cumulatively, 500 most abundant peaks of ChEC signal, which contain most of the known replication origins, comprise 85% of the abundance of total 5500 peaks, which we propose serve as likely replication origins. This distribution of MCM abundance is consistent with that of replication initiation from two single-molecule nanopore-based studies, which found that 9% (Hennion et al., 2020) and 20% (Muller et al., 2019) of replication initiates outside the known replication origins. Our study also suggests that ∼15% of replication initiation is distributed among 5000 sites, which can explain why most of these initiation events appear unique in single molecule studies.
In addition to their ramifications about the number of sites from which replication initiates, our results have implications regarding the mechanism by which this occurs at individual sites. First, the high resolution of our approach allowed us to demonstrate that Mcm is typically loaded downstream of the ACS, something that has been demonstrated in vitro, but that researchers have thus far been unable to confirm in vivo (Belsky et al., 2015; Dukaj and Rhind, 2021). Specifically, in 112 out of 146 instances in which a peak of Mcm signal was within 100 base pairs of a known ACS, that peak was downstream of the ACS. The 34 exceptions may reflect (1) incorrect identification of the ACS; (2) incorrect inference of the directionality of the site; or (3) sliding of the Mcm complex after it has been loaded. In interpreting these results, it is important to keep in mind that the relationship between the theoretical consensus sequence for Orc binding (i.e. the ACS) and the actual sequence to which Orc binds is so weak that approximately half of the origins reported in SGD do not have a corresponding ACS specified. Second, while we observed a general increase in GC skew across Mcm binding sites, there was a short but obvious reversal in the trend precisely at the midpoint (Figure 6A). This feature is consistent with the in vitro observation that Mcm single hexamer are loaded such that they must cross over each other before diverging in bidirectional replication (Douglas et al., 2018). Given that both the location and the size of the inflection are consistent with this hypothesis, we favor this explanation. Finally, as we have noted previously, peaks of Mcm binding typically reflect the binding of just one double-hexamer, with no trend toward higher stoichiometries when moving toward higher activity origins (Figure 3A, Mcm-ChEC heat map). This is inconsistent with models in which higher origin activity is achieved by loading of more Mcm complexes (Das et al., 2015; Das and Rhind, 2016; Dukaj and Rhind, 2021). In summary, we conclude that (1) replication at individual origins occurs as predicted in vitro, with Mcm complexes loaded downstream of the ACS in a configuration such that they must cross over each other; and (2) that part of control of origin activity that operates at the level of origin licensing is exerted by modulation of the fraction of cells in a population that have a single double-hexamer bound to an origin, rather than the through variation of the number of double-hexamers that are bound to individual sites in a single cell.
One potentially unexpected aspect of the change in GC skew that we observed across Mcm binding sites was the fact that the pattern was visible not just among the most abundant 1100 member cohort CMBSs, which included the most active origins, but continued to be evident, though to a lesser degree, all the way down to the fifth cohort (ranks 4401-5500), comprising the least active origins. We suggest that either or both of the following possibilities may cause even very low activity origins to leave an evolutionary footprint of replication initiation: (1) The depletion of cytosines through deamination-mediated conversion to thymines may function as a unidirectional “ratchet”, fixing changes to permanence even if they occur only rarely; and (2) the relative activity of different yeast origins may be in continual flux over evolutionary time frames, such that origins that are currently barely active were not always so.
Over the last 40 years, the field of eukaryotic DNA replication has been built largely on a foundation of discoveries made in budding yeast. Yeast’s strength as a model organism came in large part because it made available short DNA sequences capable of serving as efficient replication origins. On the other hand, the sharply focused nature of its replication origins made S phase in yeast appear distinct from that in other organisms. Our discovery that sites of replication initiation in yeast are much more widely dispersed than previously believed, with at least 1600 and possibly as many as 5500 origins, emphasizes its continued relevance to understanding genome duplication in humans.
Materials and Methods
Key resources table
strains:
16535: Mat A, his3, leu2, met15, ura3, hmla:: HYG
16747: Mat A, his3, leu2, met15, ura3, hmla:: HYG, with MCM2 MNase 3xFlag Tag with KanMx
16753: Mat A, his3, leu2, met15, ura3, hmla:: HYG, with MCM6 MNase 3xFlag Tag with KanMx
16754: Mat A, his3, leu2, met15, ura3, hmla:: HYG, with MCM6 MNase 3xFlag Tag with KanMx
16749: Mat A, his3, leu2, met15, ura3, hmla:: HYG, with MCM4 MNase 3xFlag Tag with KanMx
16964: Mat A, his3, leu2, ura3, met15, hmla::NAT, MCM2 3x Flag Mnase tag with KanMX
Strain and plasmid construction and growth conditions
HU arrest in budding yeast was carried out by adding 200 mM hydroxyurea to logarithmically growing cultures for 50 minutes.
Sequencing
Sequencing was performed using an Illumina HiSeq 2500 in Rapid mode employing a paired-end, 50 base read length (PE50) sequencing strategy. Image analysis and base calling was per-formed using Illumina’s Real Time Analysis v1.18 software, followed by ‘demultiplexing’ of indexed reads and generation of FASTQ files, using Illumina’s bcl2fastq Conversion Software v1.8.4.
Sequence alignment and quantitation
Sequence analysis fastq files were aligned to the sacCer3 genome assembly with bwa using the -n 1 option, which causes reads that map to more than one location to be randomly assigned to one of those locations. bam files were then processed with Picard’s CleanSam, SortSam, FixMateInformation, AddOrReplaceReadGroups, ValidateSamFile tools, and MergeSamFiles (version 2.21.6; http://broadinstitute.github.io/picard/). Per-base pair read depths were determined with BedTools’ genomecov tool, Version 2.29.1, using the -d and -split options. Quantitation of library fragments within specific size ranges was done by using the map locations of paired-end fragments to infer the insert size, and then summing per-base pair read depths across the entire inferred fragment or summing midpoints of inferred fragments, as described. This was done with the following three Perl scripts, available in the Supplemental Online Material:
step_1_in_custom_quantification_of_bwa_aligned_reads_121222_1.pl
step_2_if_quantifying_entire_lengths_of_library_inserts_121222_1.pl;
step_2_if_quantifying_midpoints_of_library_inserts_121222_1.pl.
Chromatin Endogenous Cleavage (ChEC)
ChEC-seq was carried out as previously described (Foss, et al., 2019; Foss et al., 2021). Briefly, cells were centrifuged at 1,500 x g for 2 mins, and washed twice in cold Buffer A (15 mM Tris pH 7.5, 80 mM KCl, 0.1 mM EGTA) without additives. Washed cells were carefully resuspended in 570 μL Buffer A with additives (0.2 mM spermidine, 0.5 mM spermine, 1 mM PMSF, ½ cOmplete ULTRA protease inhibitors tablet, Roche, per 5 mL Buffer A) and permeabilized with 0.1% digitonin in 30 ° C water bath for 5 min. Permeabilized cells were cooled at room temperature for 1 min and 1/5th of cells were transferred in a tube with freshly made 2x stop buffer (400 mM NaCl, 20 mM EDTA, 4 mM EGTA)/1% SDS solution for undigested control. Micrococcal nuclease was activated with 5.5 μL of 200 mM CaCl2 at various times (30 sec, 1 min, 5 mins, and 10 mins) and the reaction stopped with 2x stop buffer/1% SDS. Once all time points were collected, proteinase K was added to each collected time points and incubated at 55 ° C water bath for 30 mins. DNA was extracted using phenol/chloroform and precipitated with ethanol. Micrococcal nuclease digestion was analyzed via gel electrophoresis prior to proceeding to library preparation. Library was prepared as previously described used using total DNA, without any fragment size selection (Foss et al., 2019; Foss et al., 2021).
Peak identification and quantitation
Peaks were identified by applying the following algorithm to the bam/sam alignment file that was generated by merging 12 individual bam/sam files, as described in “Sequence alignment and quantitation”: (1) Quantify 51-100 base pair library inserts both across entire length and also using only the fragment midpoints, as described above; (2) smooth both data sets with a 10 base pair sliding window; (3) identify the genomic coordinate with the highest smoothed signal quantified across entire library insert; (4) choose the coordinate with the highest signal for smoothed data quantified only at midpoints that is within 60 base pairs of coordinate chosen in step 3 and assign this peak rank 1; (5) assign all coordinates in both data sets (i.e. the data set quantified along entire insert length and that quantified only by midpoint of library insert) that are within 500 base pairs of the coordinate chosen in step 4 to 0; (6) repeat the process to assign peak ranks 2, 3, etc., continuing until sharp drop-off (Figure 1D) indicates entire genome covered; (7) eliminate all but a single peak, which corresponds to the known rDNA origin ARS1200-1, in the highly repetitive rDNA; (8) eliminate peaks that are within 100 base pairs of Ty elements listed in SGD; and (9) renumber ranks to range consecutively from 1 (highest abundance) to 17,618 (lowest abundance). Steps 3 and 4 were motivated by a desire to choose peaks initially based on wider and presumably more robust peaks (step 3), and then to identify precise coordinates based on sharper but less robust peaks (step 4). In practice, the mean and median absolute distances that peak midpoints were shifted from step 3 to step 4 were 17.2 and 16 base pairs, respectively. Step 5 was included to avoid redundant peak assignments.
ssDNA- and BrdU-based assessment of replication activity
Relative S/G1 ratios of ssDNA and BrdU incorporation data were taken from processed files associated with previous reports (Feng, et al., 2006; Yoshida et al., 2014).
MNase seq
We carried out MNase-Seq as previously described (Foss, et al., 2017). Briefly, cells grown to log phase in rich medium, Yeast Peptone Agar with 2% glucose (YEPD), from an overnight 25 mL culture were synchronized with 3 μM alpha-factor for 1.5 hrs. at 30 ° C. Arrested cells were crosslinked with 1% formaldehyde for 30 min at room temperature water bath with shaking. Formaldehyde was quenched with 125 mM glycine and cells were centrifuged at 3000 rpm for 5 min. Cells were washed twice with water and resuspended in 1.5 mL Buffer Z (1 M sorbitol, 50 mM Tris-HCl pH 7.4) with 1 mM beta-mercaptoethanol (1.1 μL of 14.3 M beta-mercaptoethanol diluted 1:10 in Buffer Z) per 25 mL culture. Cells were treated with 100 μL 20 mg/ mL zymolyase at 30 ° C for 20–30 min depending on cell density. Spheroplasts were centrifuged at 5000 rpm for 10 min and resuspended in 5 mL NP buffer (1 M sorbitol, 50 mM NaCl, 10 mM Tris pH 7.4, 5 mM MgCl2, 1 mM CaCl2) supplemented with 500 μM spermidine, 1 mM beta-mercaptoethanol and 0.075% NP-40. Nuclei were aliquoted in tubes with varying concentrations of micrococcal nuclease (Worthington), mixed via tube inversion, and incubated at room temperature for 20 mins. Chromatin digested with 1.9 U–7.5 U micrococcal nuclease per 1/5th of spheroplasts from a 25 mL culture yielded appropriate mono-, di-, tri- nucleosome protected fragments for next-generation sequencing. Digestion was stopped with freshly made 5x stop buffer (5% SDS, 50 mM EDTA) and proteinase K was added (0.2 mg/ml final concentration) for an overnight incubation at 65 ° C to reverse crosslinking. DNA was extracted with phenol/chloroform and precipitated with ethanol. Micrococcal nuclease digestion was analyzed via gel electrophoresis prior to proceeding to library preparation. Sequencing libraries were prepared as described above for ChEC.
Analysis of nucleosome chemical cleavage data
Numbers were derived from 46-56 base pair library fragments from bwa-aligned SRR5399542 fastq files (Chereji et al., 2018).
Analysis of genic versus intergenic regions
Coordinates for gene bodies were based on the “SGD_features.tab” file, available from https://www.yeastgenome.org.
EACS-PWM analysis
EACS-PWM scores were based on the following position-weight matrix (from (Coster and Diffley, 2017)):
Position A C G T
1 0.288 0.097 0.052 0.563
2 0.451 0.021 0.052 0.476
3 0.236 0.028 0.028 0.708
4 0.253 0.007 0.097 0.642
5 0.052 0.000 0.000 0.948
6 0.038 0.007 0.003 0.951
7 0.160 0.073 0.007 0.760
8 0.969 0.000 0.000 0.031
9 0.087 0.177 0.000 0.736
10 0.288 0.056 0.576 0.080
11 0.066 0.000 0.017 0.917
12 0.000 0.000 0.000 1.000
13 0.000 0.007 0.000 0.993
14 0.517 0.000 0.014 0.469
15 0.021 0.056 0.708 0.215
16 0.076 0.076 0.295 0.552
17 0.104 0.125 0.045 0.726
Analysis of GC skew
GC skew was calculated according to the numbers of Gs and Cs in the Watson strand along a 51 base pair moving window, as (G-C)/(G+C).
References
- Isolation of chromosomal origins of replication in yeastNature 284:185–187https://doi.org/10.1038/284185a0
- The minichromosome maintenance replicative helicaseCold Spring Harb Perspect Biol 5https://doi.org/10.1101/cshperspect.a012807
- Chromosome Duplication in Saccharomyces cerevisiaeGenetics 203:1027–1067https://doi.org/10.1534/genetics.115.186452
- ATP-dependent recognition of eukaryotic origins of DNA replication by a multiprotein complexNature 357:128–134https://doi.org/10.1038/357128a0
- Genome-wide chromatin footprinting reveals changes in replication origin architecture induced by pre-RC assemblyGenes Dev 29:212–224https://doi.org/10.1101/gad.247924.114
- Origin Firing Regulations to Control Genome Replication TimingGenes (Basel) 10https://doi.org/10.3390/genes10030199
- Prediction of Saccharomyces cerevisiae replication originsGenome Biol 5https://doi.org/10.1186/gb-2004-5-4-r22
- The localization of replication origins on ARS plasmids in S. cerevisiaeCell 51:463–471https://doi.org/10.1016/0092-8674(87)90642-8
- Localization and sequence analysis of yeast origins of DNA replicationCold Spring Harb Symp Quant Biol 47:1165–1173https://doi.org/10.1101/sqb.1983.047.01.132
- Autonomously replicating sequences in Saccharomyces cerevisiaeProc Natl Acad Sci U S A 77:6329–6333https://doi.org/10.1073/pnas.77.11.6329
- Precise genome-wide mapping of single nucleosomes and linkers in vivoGenome Biol 19https://doi.org/10.1186/s13059-018-1398-0
- The Initiation of Eukaryotic DNA ReplicationAnnu Rev Biochem 91:107–131https://doi.org/10.1146/annurev-biochem-072321-110228
- Bidirectional eukaryotic DNA replication is established by quasi-symmetrical helicase loadingScience 357:314–318https://doi.org/10.1126/science.aan0063
- Replication timing is regulated by the number of MCMs loaded at originsGenome Res 25:1886–1892https://doi.org/10.1101/gr.195305.115
- How and why multiple MCMs are loaded at origins of DNA replicationBioessays 38:613–617https://doi.org/10.1002/bies.201600012
- MCM: one ring to rule them allCurr Opin Struct Biol 37:145–151https://doi.org/10.1016/j.sbi.2016.01.014
- Phosphopeptide binding by Sld3 links Dbf4-dependent kinase to MCM replicative helicase activationEMBO J 35:961–973https://doi.org/10.15252/embj.201593552
- The ARS consensus sequence is required for chromosomal origin function in Saccharomyces cerevisiaeMol Cell Biol 12:4305–4313https://doi.org/10.1128/mcb.12.10.4305-4313.1992
- The mechanism of eukaryotic CMG helicase activationNature 555:265–268https://doi.org/10.1038/nature25787
- The capacity of origins to load MCM establishes replication timing patternsPLoS Genet 17https://doi.org/10.1371/journal.pgen.1009467
- Conserved nucleosome positioning defines replication originsGenes Dev 24:748–753https://doi.org/10.1101/gad.1913210
- Genomic mapping of single-stranded DNA in hydroxyurea-challenged yeasts identifies origins of replicationNat Cell Biol 8:148–155https://doi.org/10.1038/ncb1358
- Sir2 suppresses transcription-mediated displacement of Mcm2-7 replicative helicases at the ribosomal DNA repeatsPLoS Genet 15https://doi.org/10.1371/journal.pgen.1008138
- SIR2 suppresses replication gaps and genome instability by balancing replication between repetitive and unique sequencesProc Natl Acad Sci U S A 114:552–557https://doi.org/10.1073/pnas.1614781114
- Chromosomal Mcm2-7 distribution and the genome replication program in species from yeast to humansPLoS Genet 17https://doi.org/10.1371/journal.pgen.1009714
- DNA replication origin activation in space and timeNat Rev Mol Cell Biol 16:360–374https://doi.org/10.1038/nrm4002
- Incorporation into the prereplicative complex activates the Mcm2-7 helicase for Cdc7-Dbf4 phosphorylationGenes Dev 23:643–654https://doi.org/10.1101/gad.1759609
- Analyzing genomes with cumulative skew diagramsNucleic Acids Res 26:2286–2290https://doi.org/10.1093/nar/26.10.2286
- A helicase-tethered ORC flip enables bidirectional helicase loadingElife 10https://doi.org/10.7554/eLife.74282
- FORK-seq: replication landscape of the Saccharomyces cerevisiae genome by nanopore sequencingGenome Biol 21https://doi.org/10.1186/s13059-020-02013-3
- Origins of DNA replication in eukaryotesMol Cell 83:352–372https://doi.org/10.1016/j.molcel.2022.12.024
- Nucleosome positioning in Saccharomyces cerevisiaeMicrobiol Mol Biol Rev 75:301–320https://doi.org/10.1128/MMBR.00046-10
- Structural requirements for the function of a yeast chromosomal replicatorCell 37:299–307https://doi.org/10.1016/0092-8674(84)90326-x
- Mechanism of replication origin melting nucleated by CMG helicase assemblyNature 606:1007–1014https://doi.org/10.1038/s41586-022-04829-4
- Sequence analysis of origins of replication in the Saccharomyces cerevisiae genomesFront Microbiol 5https://doi.org/10.3389/fmicb.2014.00574
- High-resolution mapping, characterization, and optimization of autonomously replicating sequences in yeastGenome Res 23:698–704https://doi.org/10.1101/gr.144659.112
- Nucleosomes positioned by ORC facilitate the initiation of DNA replicationMol Cell 7:21–30https://doi.org/10.1016/s1097-2765(01)00151-4
- Asymmetric substitution patterns in the two DNA strands of bacteriaMol Biol Evol 13:660–665https://doi.org/10.1093/oxfordjournals.molbev.a025626
- SkewIT: The Skew Index Test for large-scale GC Skew analysis of bacterial genomesPLoS Comput Biol 16https://doi.org/10.1371/journal.pcbi.1008439
- Perturbation of the activity of replication origin by meiosis-specific transcriptionJ Biol Chem 282:4447–4452https://doi.org/10.1074/jbc.M609671200
- Capturing the dynamics of genome replication on individual ultra-long nanopore sequence readsNat Methods 16:429–436https://doi.org/10.1038/s41592-019-0394-y
- The structure and function of yeast ARS elementsCurr Opin Genet Dev 3:752–758https://doi.org/10.1016/s0959-437x(05)80094-2
- OriDB: a DNA replication origin databaseNucleic Acids Res 35:D40–46https://doi.org/10.1093/nar/gkl758
- Genome-wide views of chromatin structureAnnu Rev Biochem 78:245–271https://doi.org/10.1146/annurev.biochem.78.071107.134639
- DNA replication timingCold Spring Harb Perspect Biol 5https://doi.org/10.1101/cshperspect.a010132
- A high-resolution protein architecture of the budding yeast genomeNature 592:309–314https://doi.org/10.1038/s41586-021-03314-8
- Electron microscopic study of Saccharomyces cerevisiae rDNA chromatin replicationMol Cell Biol 6:1148–1157https://doi.org/10.1128/mcb.6.4.1148-1157.1986
- ChIC and ChEC; genomic mapping of chromatin proteinsMol Cell 16:147–157https://doi.org/10.1016/j.molcel.2004.09.007
- Context based computational analysis and characterization of ARS consensus sequences (ACS) of Saccharomyces cerevisiae genomeGenom Data 9:130–136https://doi.org/10.1016/j.gdata.2016.07.005
- OriDB, the DNA replication origin database updated and extendedNucleic Acids Res 40:D682–686https://doi.org/10.1093/nar/gkr1091
- Effect of ARS1 mutations on chromosome stability in Saccharomyces cerevisiaeMol Cell Biol 5:1676–1684https://doi.org/10.1128/mcb.5.7.1676-1684.1985
- Isolation and characterisation of a yeast chromosomal replicatorNature 282:39–43https://doi.org/10.1038/282039a0
- Genome-wide mapping of individual replication fork velocities using nanopore sequencingNat Commun 13https://doi.org/10.1038/s41467-022-31012-0
- Comprehensive Analysis of Replication Origins in Saccharomyces cerevisiae GenomesFront Microbiol 10https://doi.org/10.3389/fmicb.2019.02122
- Genome-wide distribution of ORC and MCM proteins in S. cerevisiae: high-resolution mapping of replication originsScience 294:2357–2360https://doi.org/10.1126/science.1066101
- Genome-wide mapping of ORC and Mcm2p binding sites on tiling arrays and identification of essential ARS consensus sequences in S. cerevisiaeBMC Genomics 7https://doi.org/10.1186/1471-2164-7-276
- The histone deacetylases sir2 and rpd3 act on ribosomal DNA to control the replication program in budding yeastMol Cell 54:691–697https://doi.org/10.1016/j.molcel.2014.04.032
- ChEC-seq kinetics discriminates transcription factor binding sites by DNA sequence and shape in vivoNat Commun 6https://doi.org/10.1038/ncomms9733
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