Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes

  1. Dario D’Asaro
  2. Jean-Michel Arbona
  3. Vinciane Piveteau
  4. Aurèle Piazza
  5. Cédric Vaillant
  6. Daniel Jost

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Abstract

Although significant progress has been made on our understanding of DNA replication and spatial chromosome organization in eukaryotes, how they both interplay remains elusive. In particular, from the local structure of two diverging sister-forks to the higher-level organization of the replication machinery into nuclear domains, the mechanistic details of chromatin duplication in the 3D nuclear space remain debated. In this study, we use a computational model of the Saccharomyces cerevisiae genome to explore how replication influences chromatin folding. By integrating both a realistic description of the genome 3D architecture and 1D replication timing, simulations reveal that the colocalization of sister-forks produce a characteristic “fountain” pattern around early origins of replication. We confirm the presence of similar features in vivo in early S-phase with new Hi-C data in various conditions, showing that it is replication-dependent and cohesin-independent. At a larger scale, we show that the 3D genome leads to forks being highly enriched at one pole of the nucleus in early S-phase, before later redistributing more homogeneously, and may favor the higher-order clustering of forks into Replication Foci, as observed in earlier microscopy experiments. Additionally, replication causes temporary chromatin slowdown and reduced mobility due to fork passage and sister chromatid intertwining. Overall, our model offers new insights into the spatial and dynamic organization of chromatin during replication in eukaryotes.

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    Reply to the reviewers

    Below is a point-by-point response to reviewers concerns.

    Main changes are colored in red in the revised manuscript.

    Reviewer #1 (Significance (Required)):

    General assessment:

    This study provides a valuable computational framework for investigating the dynamic interplay between DNA replication and 3D genome architecture. While the current implementation focuses on Saccharomyces cerevisiae, whose genome organization differs significantly from mammalian systems.

    Advance: providing the first in vivo experimental evidence in investigating the role(s) of Cohesin and Ctf4 in the coupling of sister replication forks.

    Audience: broad interests; including DNA replication, 3D genome structure, and basic research

    Expertise: DNA replication and DNA damage repair within the chromatin environment.

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    By developing a new genome-wide 3D polymer simulation framework, D'Asaro et al. investigated the spatiotemporal interplay between DNA replication and chromatin organization in budding yeast: (1) The simulations recapitulate fountain-like chromatin patterns around early replication origins, driven by colocalized sister replication forks. These findings align with Repli-HiC observations in human and mouse cells, yet the authors advance the field by demonstrating that these patterns are independent of Cohesin and Ctf4, underscoring replication itself as the primary driver. (2) Simulations reveal a replication "wave" where forks initially cluster near the spindle pole body (SPB) and redistribute during S-phase. While this spatial reorganization mirrors microscopy-derived replication foci (RFis), discrepancies in cluster sizes compared to super-resolution data suggest unresolved mechanistic nuances. (3) Replication transiently reduces chromatin mobility, attributed to sister chromatid intertwining rather than active forks.

    This work bridges replication timing, 3D genome architecture, and chromatin dynamics, offering a quantitative framework to dissect replication-driven structural changes. This work provides additional insights into how replication shapes nuclear organization and vice versa, with implications for genome stability and regulation.

    We thank Reviewer 1 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

    However, the following revisions could strengthen the manuscript:

    Major:

    Generalizability to Other Species While the model successfully recapitulates yeast replication, its applicability to larger genomes (e.g., mammals) remains unclear. Testing the model against (Repli-HiC/ in situ HiC, and Repli-seq) data from other eukaryotes (particularly in mammalian cells) could enhance its broader relevance.

    We agree with the reviewer that testing the model in higher eukaryotes would be highly informative. The availability of Repli-HiC on one hand and higher resolution microscopy on the other could enable insightful quantitative analyses. With our formalism, it is in principle already possible to capture realistic 1D replication dynamics as the integrated mathematical formalism (by Arbona et al. ref. [63]) was already used to model human genome S-phase. In addition, the formalism developed for chain duplication is generic and can be contextualized to any species. However, when addressing the problem in 3D, we would likely require including other crucial structural features such as TADs or compartments. Such a model would require an extensive characterization worthy of its own publication. These considerations are now mentioned in the Discussion as exciting future perspectives (Page 17).

    On the other hand, we would like to highlight that, while very minimal in many aspects, our model includes many layers of complexity (explicit replication, different forks interactions, stochastic 1D replication dynamics, physical constraints at the nuclear level). In addition, addressing this problem in budding yeast offers the great advantage of simultaneously capturing at the same time both the local and global spatio-temporal properties of DNA replication and to focus first only on those aspects and not on the interplay with other mechanisms like A/B compartmentalization (absent in yeast) that may add confusions in the data analysis and comparison with experimental data . Studying such an interplay is a very important and challenging question that, we believe, goes beyond the scope of the present work.

    Validation with Repli-HiC or Time-Resolved Techniques

    The Hi-C data in early S-phase supports the model, but the intensity of replication-specific chromatin interactions is faint, which could be further validated using Repli-HiC, which captures interactions around replication forks. Alternatively, ChIA-PET or HiChIP targeting core component(s) (eg. PCNA or GINS) of replisomes may also solidify the coupling of sister replication forks.

    We thank the reviewer for the suggestion. Unfortunately, corroborating our HiC results using Repli-HiC or HiChIP would require developing and adapting the protocols to budding yeast which is well beyond the scope of this work mainly focused on computational modelling. In addition, we believe that the signature found in our Hi-C data is clear and significant enough to demonstrate the effect.

    However, we included in the Discussion (Page 15) a more detailed description on how our work compares with the Repli-HiC study in mammals. In particular, we added a new supplementary figure (new Fig. S23) where we discuss our prediction on how Repli-HiC maps would appear in yeast in both scenarios of sister-forks interaction. Interestingly, we find that:

    1. Fountain signals are strongly enhanced when sister forks interact.

    2. Only mild replication dependent enrichment is detected when diverging forks do not interact.

    These two results imply that disrupting putative sister-forks interaction would have a drastic effect on Repli-HiC if compared to HiC.

    Interactions Between Convergent Forks

    The study focuses on sister-forks but overlooks convergent forks (forks moving toward each other from adjacent origins), whose coupling has been observed in Repli-HiC. Could the simulation detect the coupling of convergent fork dynamics?

    We thank the reviewer for this suggestion. We included in our Hi-C analysis aggregate plots around termination sites. Interestingly, no clear signature of coupling between convergent forks was detected (such as type II fountains in mammals) in vivo and in silico. Similarly, from visual inspection of individual termination sites, no fountains were clearly observed. These results can be found in the new Fig. S24 and possible mechanistic explanations are described more in detail in the Discussion (Page 15).

    Unexpected Increase in Fountain Intensity in Cohesin/Ctf4 Knockouts.

    In Fig.3A, a schematic illustrating the cell treatment would improve clarity. In Sccl- and Ctf4-depleted cells, fountain signals persist or even intensify (Fig. 3A). This counterintuitive result warrants deeper investigation. Could the authors provide any suggestions or discussions? Potential explanations may include:

    Compensatory mechanisms (e.g., other replisome proteins stabilizing sister-forks).

    Altered chromatin mobility in mutants, enhancing Hi-C signal resolution.

    Artifacts from incomplete depletion (western blots for Sccl/Ctf4 levels should be included).

    A scheme illustrating the experimental protocol for degron systems (CDC45-miniAID & SCC1-V5-AID) with the corresponding western blots and cell-cycle progression are shown in Fig. S26. Note that for Ctf4, we are using a KO cell line where the gene was deleted.

    We do agree with the reviewer that there exist several possible explanations explaining the differences between WT fountains and those observed in mutants. In the revised manuscript, we discussed some of them in Section 2 II B (Page 8):

    (1) As already suggested in the paper, asynchronization of cells may impact the intensity of the fountains due a dilution effect mediated by the cells still in G1. Therefore, possible differences in the fractions of replicating/non-relicating cells between the different experiments (new Fig. S7C) would also result in differences in the signal. Moreover, it is important to highlight that aggregate plots are normalized (Observed/Expected) by the average signal (P(s)). Therefore, as Scc1-depleted cells do not exhibit cohesin-mediated loop-extrusion (see aggregate plots around CARs in new Fig. S7B), we may expect an enhancement of signal at origins due to dividing each pixel by a lower contact frequency with respect to the one found in WT.

    (2) In the new Fig. S10, we plotted the relative enrichment of Hi-C reads around origins. While we already used the same approach to compare replicon sizes between simulations and experiments (see Fig S7A and response to comment n°9 of Reviewer 3), this analysis is instructive also when comparing different experimental conditions. While we find that the experiment in WT and Scc1-depleted cells show very similar replicon sizes, we do observe a small increase in the peak height for the cohesin mutant. This may also partially motivate differences in the intensity of the fountain. For ctf4Δ, we observe significantly smaller replicons. We speculate that such a mutant might exhibit slower replication and consequently might be enriched in sister-forks contacts.

    (3) Compensatory mechanisms: we now briefly discussed this in the Discussion (Page 15).

    Inconsistent Figure References

    Several figure citations are mismatched. For instance, Fig. S1A has not been cited in the manuscript. Moreover, there is no Fig.1E in figure 1, while it has been cited in the text. All figure/panel references must be cross-checked and corrected.

    We thank the reviewer for this observation. We have now corrected the mismatches.

    Minor:

    Page2: "While G1 chromosomes lack of structural features such as TADs or loops [3]" However, Micro-C captures chromatin loops, although much smaller than those in mammalian cells, within budding yeast.

    Loops of approx 20-40 kb are found in interphase in budding yeast but only after the onset of S-phase ( ref. [52-61]). For this reason, our G1 model of yeast without loops well captures the experimental P(s) curves (Fig. S2). See also answer to point 12 of reviewer 2 .

    In figure 2E, chromatin fountain signals can be readily observed in the fork coupling situation and movement can also be observed. However, the authors should indicate the location of DNA replication termination sites and show some examples at certain loci but not only the aggregated analysis.

    The initial use of aggregate plots was motivated by the fact that fountains are quite difficult to observe at the single origin level in the experimental Hi-C due to the strong intensity of surrounding contacts (along the diagonal). However, when dividing early-S phase maps by the corresponding G1 map, we can now observe clear correlation between origin and fountain positions on such normalized maps. We now added an example for chromosome 7 in Fig.3 indicating early/late origins.

    In Fig. S8 and S9 (where we also included termination sites), we show that fountains are prominently found at origins during S-phase and are lost in G2/M.

    Reviewer #2 (Significance (Required)):

    The topic is relevant and the problem being addressed is very interesting. While there has been some earlier work in this area, the polymer simulation approach used here is novel. The simulation methodology is technically sound and appropriate for the problem. Results are novel. The authors compare their simulations with experimental data and explore both interacting and non-interacting replication forks. Most conclusions are supported by the data presented. Reviewer #2 (Evidence, reproducibility and clarity (Required)):

    The manuscript by D'Asaro et al. investigates the relationship between DNA replication and chromatin organization using polymer simulations. While this is primarily a simulation-based study, the authors also present relevant comparisons with experimental data and explore mechanistic aspects of replication fork interactions.

    We thank Reviewer 2 for her/his positive evaluation of our work and her/his suggestions that help us to clarify many aspects in our manuscript.

    The primary weakness is that many aspects are not clear from the manuscript. Below is a list of questions that the authors must clarify:

    In the Model and Methods section, it is written "Arbitrarily, we choose the backbone to be divided into two equally long arms, in random directions." It is unclear what is meant by "backbone to be divided" and "two equally long arms." Does this refer to replication?

    We agree with the reviewer that the term backbone may be ambiguous. In the context of the initialization of the polymer, it refers to the L/4 initial bonds used to recursively build an unknotted polymer chain of final size L using the Hedgehog algorithm (see refs [101,109]). As shown in the Fig S1A, these initial L/4 bonds define the initial backbone of each chromosome before they are recursively grown to their final size. We chose to divide them into two branches (called “arms” in the old version of the manuscript) of equal length (L/8) and with random orientations. To avoid any ambiguity between the term arm used in that context and the chromosome arms in a biological sense (sequences on the left and right with respect to centromeres), we changed it to “linear branches” to improve clarity. We highlighted in Fig. S1A two examples of such a “V-shaped” backbone.

    As stated in the text, these initial configurations are artificial and just aim to generate unknotted, random structures. After initiating the structures, we then added the geometrical constraints to the centromeric, telomeric and rDNA beads. This, combined with the tendency of the polymer to explore and fill the spherical volume, determine the relaxed G1-like state (see Fig. S2) obtained after an equilibration stage (corresponding to 10^7 MCS). Only after that initialization protocol, DNA replication is activated.

    In chromosome 12, since the length inside the nucleolus (rDNA) is finite, the entry and exit points should be constrained. Have the authors applied any relevant constraint in the model?

    Indeed, we did not introduce any specific constraint on the relative distance between rDNA boundary monomers in our model. They can therefore freely diffuse, independently from each other, on the nucleolus surface. This point is now clarified in the text. Note that, in this paper, we did not aim to finely describe the rDNA organization and its interactions with the rest of the genome, that is why we did not explicitly model rDNA. Moreover, to the best of our knowledge, there is not available experimental data to potentially tune such additional restraints.

    Previous models such as Tjong et al. (ref. [66]) and Di Stefano et al. (ref [67]) have used very similar approximations than us. In the works of Wong et al. (ref.[61]) and Arbona et al. (ref.[63]), rDNA is explicitly modelled via larger/thicker beads/segments, and thus accounts for some generic polymer-based constraints between rDNA boundary elements.

    However, note that all these different models, including ours, still correctly predict the strong depletion of contacts between rDNA boundaries, indicating that there exists a spatial separation between the two boundary elements that is qualitatively well captured by our model (See Fig. S1 D and Fig. 1B).

    What is the rationale for normalizing the experimental and simulation results by dividing by the respective P_intra(s = 10 kb)?

    This normalization was used in Fig. 1 to obtain a rescaling between experiments and simulations. This approach assumes that simulated and experimental Hi-C maps are proportional by a factor that, in Fig 1B, was set to P_exp(s=16kb)/P_sim(s=16kb). Similar strategies are used in a number of modeling studies (for example ref. [103,106]).

    We use the average contact frequency (P_intra) at this genomic scale (s in the order of 10s of kb) because our polymer simulations well capture the experimental P(s) decay above this scale. This method allows to plot the two signals with the same color scale and to give a qualitative, visual intuition on the quality of the modeling. Note that normalization has no impact on the Pearson correlation given in text. More generally, it allows to semi-quantitatively compare predicted and experimental Hi-C data.

    In Fig 1D, we instead normalize the average signal between pairs of centromeres (inter-chromosomal aggregate plot off-diagonal) by the average P_intra(s=10kb). This method allows estimating how frequently centromeres of different chromosomes are in contact relative to intra-chromosomal contacts at the chosen scale (10 kb). In the new paragraph “Comparison with in vivo HiC maps in G1” (Page 22) , we describe more in detail the quantitative insights that can be recovered from such analysis.

    As a comparison, such normalization is not required when computing Observed/Expected maps (Fig. 1C or aggregate plots in Fig. 2 and Fig. 3) as simulation and experimental maps are normalized by their own P(s) curves. We now clarify this aspect in the Materials in Methods under the paragraph “Comparison between on diagonal aggregate plots” (Page 22).

    In the sentence "For instance, chromosomes are strictly bound by the strong potential to localize between 250 and 320 nm from the SPB," is it 320 or 325 nm? Is there a typo?

    We confirm that the upper bound is indeed 325 nm as stated in Eq.2 and not 320 nm.

    Please list the number of beads in each chromosome and the location of the centromere beads.

    A new table (Table S2) was included to highlight beads number and centromere positions.

    In Eq. 7, when the Euclidean distance between the sister forks d_ij > 50 nm, the energy becomes more and more negative. This implies that the preferred state of sister forks is at distances much greater than 50 nm. Then how is "co-localization of sister forks" maintained?

    We corrected the typo sign in Eq.7. The corrected equation without the minus sign - consistently with what simulated - implies that sister forks tend to minimize their 3D distance. The term goes to zero when their distance is within 40 nm (2 nearest-neighbouring sites).

    The section on "non-specific fork interactions" is unclear. You state that the interaction is between "all the replication forks in the system," but f_ij is non-zero only for second nearest-neighbors. The whole subsection needs clarification.

    We corrected the text, specifying that the energy is non-zero for both first and second neighbours. In practice, two given forks do not experience any attractive energy unless their 3D distance is less than 2 nearest-neighbours. To clarify this aspect, we articulated more in the methods how non-specific fork interactions are implemented in the lattice during the KMC algorithm. We also included a new supplementary image (Fig. S15), where we schematize how forks move in 3D and how changes in their position update the table that tracks the number of forks around each lattice site.

    Eq. 6 has no H_{sister-forks}. Is this a typo?

    We confirm that it is a typo and the formula was corrected to H_{sister-forks}.

    While discussing the published work, the authors may cite the recent paper [https://doi.org/10.1103/PhysRevE.111.054413].

    The reference is now included when discussing previous polymer models of DNA replication.

    It is not clear how the authors actually increase the length of new DNA in a time-dependent manner. For example, when a new monomer is added near the replication origin (green bead in Fig. 3C), what happens to the red and blue polymer segments? Do they get shifted? How do the authors take into account self-avoidance while adding a new monomer? These details are not clear.

    The detailed description of the chain duplication algorithm and its systematic analysis was performed in our previous study (ref. [25]).

    However, we agree with the reviewer that to improve self-consistency more details must be included in the present manuscript (see also answer to comment 1 of Reviewer 3). In particular, we now highlight in Materials and Methods that self-avoidance is indeed temporarily broken when we add a newly replicated monomer on top of the site where the fork is. Such double occupancy in the lattice rapidly vanishes due to 3D local moves. We refer to our PRX work (ref [25] and in particular to the following figure (extracted from FIG. S1 in ref.[25]) which illustrates how the bonds/segments of the two sister chromatids are consistently maintained.

    How do the authors ensure that monomers get added at a rate corresponding to velocity v? The manuscript mentions "1 MCS = 0.075 msec," but in how many MC steps is a new monomer added? How is it decided?

    Similarly to origin firing, replication by fork movement along the genome occurs stochastically, with a rate which we derive by converting the physiological fork speed in yeast 2.2 kb/min (ref. [41]) into a rate in (number of monomer/MCS) units. In practice, we generate a random number that, if smaller than such a rate, leads to forks duplication. We clarify this aspect in the Materials and Methods, also referring to our previous work for a more detailed summary.

    The authors stress the relevance of loop extrusion. However, in their polymer simulation, the newly replicated chromatin does not form any loops. Is this consistent with what is known?

    Indeed, our simulations do not have any concurrent extrusion mechanism such as cohesin-mediated loops. This choice was purposely made to isolate and characterize replication-dependent effects.

    That is why we compare our predictions on chromatin fountain patterns (Fig. 3) with data obtained for the Scc1 mutant strain where cohesin is absent in order to disentangle the possible interference with loop-extruding cohesin. For subsection C where microscopy data are available only in WT condition, we cannot rule out that the observed discrepancies between experiments and predictions cannot be due to missing mechanisms including loop extrusion. It was already mentioned in the Discussion (Page 16). It is however unclear whether sparse and small loops between CARs (see Fig. S7B) in S-phase, could be sufficient to recapitulate the microscopy estimates on the sizes of replication foci and no clear signature of inter-origin loops (possibly mediated by loop extrusion) are observed in Hi-C data in WT and Scc1 deficient conditions.

    Moreover, as mentioned in the Discussion, the poorly characterized mechanisms behind forks/extruding-cohesin encounters does not allow for a straightforward modelling of such processes whose accurate description/simulation would require its own study.

    Please add a color bar to Fig. 4B.

    The color bar was included.

    In the MSD plot (Fig. 6), even though it appears to be a log-log plot, the exponents are not computed. Typically, exponents define the dynamics.

    We plot the expected 0.5 exponent at smaller time-scales as mentioned in the main text in Fig. 6, previously included only in new Fig. S19A.

    The dynamics will depend on the precise nature of interactions, such as the presence or absence of loop extrusion. If the authors present dynamics without extrusion, is it likely to be correct?

    The reviewer is correct in highlighting how our model does not capture the potential decrease in dynamics due to cohesin mediated loop extrusion. However, our model does capture the expected Rouse regime (see Fig. 6A, S19A and ref [83]), which justify our timemapping strategy. In comment 16 of reviewer 3, we discuss more in detail the robustness of our results with respect to variation in such a mapping. In the specific context of Fig. 6A, we predict the gradual decrease in dynamics due to sister chromatids intertwining independently of any cohesin-associated activity (both loop-extruding and cohesive). As loop extrusion is also decreasing chromatin mobility overall (ref. [87]), if such a decrease in mobility is observed in WT in vivo, it may be indeed difficult to assign such a decrease to replication rather than loop extrusion. That is why in the Discussion (Page 16), we propose to compare our prediction to experiments in cohesin-depleted cells. In the context of Fig.6B&C, we don’t expect loop extrusion to be a confounding effect as the predicted decrease in dynamics is specific to forks.

    Reviewer #3 (Significance (Required)):

    The work has been conducted thoroughly, and in general the paper is well written with good attention to detail. As far as I am aware, this is the first study where replication is simulated in a whole nucleus context, and the scale of the simulations is impressive. This allows the authors to address questions on replication foci and the spatiotemporal organisation of replication which would not be possible with more limited simulations, and to compare the model with previous experimental work. This, together with the new HiC data, I think this makes this a strong paper which will be of interest to biophysics and molecular biology researchers; the manuscript is written such that it would suit an interdisciplinary basic research audience.

    We thank Reviewer 3 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

    Reviewer #3 (Evidence, reproducibility and clarity (Required)):

    The paper "Genome-wide modelling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes" by D'Asaro et. al presents new computational and experimental results on the dynamics of genome replication in yeast. The authors present whole-nucleus scale simulations using a kinetic Monte Carlo polymer physics model. New HiC data for synchronised yeast samples with different protein knock-downs are also presented.

    The main questions which the paper addresses are whether sister forks remain associated during replication, whether there is more general clustering of replication forks, and whether replication occurs in a 'spatial wave' through the nucleus. While the authors' model data are not able to conclusively show whether sister forks remain co-localised, the work provides some important insights which will be of high interest to the field.

    I have no major issues with the paper, only some minor comments and suggestions to improve the readability of the manuscript or provide additional detail which will be of interest to readers. I list these here in the order in which they appear in the paper. There are also a number of typos and grammatical issues through the text, so I recommend thorough proofreading.

    The paper seems to be aimed at a broad interdisciplinary audience of biophysicists and molecular biologists. For this reason, the introduction could be expanded slightly to include some more background on DNA replication, the key players and terminology. Also, it seems that this work builds on previous modelling work (Ref. 19), so a bit more detail of what was done there, and what is new here would be helpful. The final paragraph the introduction mentions chromosome features such as TADs and loops, which should be explained in more detail.

    We now have expanded the introduction to address some of these aspects. In particular, also as a response to comment 1 of Reviewer 4, we included additional background on the eukaryotic replication time program. We address in more detail its known interplay and correlation with crucial 3D structural features such as compartments and TADs. Finally, we add a sentence to clarify how the current work is distinct from the prior implementation and the novelty introduced here.

    In the first results section, end of p2, the "typical brush-like architecture" is mentioned. This is not well explained, some additional detail or a diagram might help.

    As very briefly summarized in the mentioned paragraph, the yeast genome is organized in the so-called Rabl organization where chromosome arms are all connected via the centromeres at the Spindle Pole Body (SPB). This is analogous to the definition of a polymer brush where several branches (the arms in this case), are grafted to a surface or to another polymer (see new Inset panel in Fig S1B). We refer in the main text to the scheme in Fig. S1B where we also include the snapshot of a single chromosome and the physical constraints that characterize this large-scale organization and extend the caption to clarify the analogy. A typical emerging feature at the single chromosome level is described in Fig. 1 B and C.

    On p3-4, some previous work is described, with Pearson correlations of 0.86 and 0.94 are mentioned. What cases these two different values correspond to is not clear.

    These Pearson correlations are obtained for our own modeling. We correct the values in the main text and more clearly indicate the specific correspondence with the maps used. We describe now in the Materials and Methods (new paragraph “Comparison with in vivo HiC maps in G1” and Table S2) how these values were obtained.

    In section II-A-2, on the modelling details, it should be made clearer that the nucleus volume is kept constant, and that this is an approximation since typically the nucleus grows during S-phase. This is discussed in the Methods section, but it would be useful to also mention it here (and give some justification why it will not likely change the results).

    We now state more clearly in the main text the limitation of our model regarding the doubling of DNA content without any increase of nuclear size. As mentioned in the Discussion, we do not expect this approximation to strongly impact our results, which mainly focus on early S-phase.

    We now also included in the Discussion how the detection of the “replication wave” should be qualitatively independent of the density regime. In fact, even in the case of growing nuclei and constant density, the polarity induced by the Rabl organization and replication timing are the main drivers of such fork redistribution.

    Regarding the slowdowning in diffusion due to sister chromatids intertwinings (see response to comment 13), we instead verified that the effect is indeed density independent (new Fig S21).

    Fig 2. The text in Fig 2B is much smaller than other panels and difficult to read. Also Fig 3B, Fig 6.

    This is now corrected.

    In 2E, are the times given above each map the range which is averaged over? This could be clearer in the caption. In the caption it stated that these are 'observed over expected'; what the 'expected' is could be clearer.

    We reformulate the description in the caption to make clearer that the time indicated above the plots indicate the time window used for the computation. As mentioned more in detail in the response to comment 17 below (and comment 3 of Reviewer 2), we included in the Material and Methods a more precise description on the normalization used in the case of on-diagonal aggregate plots (observed-over-expected).

    In section II-B-2, the authors state that the cells are fixed 20 mins after release from S-phase. Can they comment on the rationale behind this choice, since from Fig 2 their simulations predict that the fountain pattern will no-longer be visible by that time.

    In the experimental setup, cells are arrested in G1 with alpha-factor and then released in S-phase (see Fig S26 with corresponding scheme). The release from G1 synchronisation is not immediate, and staging of cells by flow-cytometry every 5 minutes for 30 minutes after release (data not shown in the main text but provided below) proved 20 minutes to be an adequate early S-phase timepoint (Page 17 in the Materials and Methods). As a consequence, the times indicated when describing the in vivo experiment, do not correspond to the ones indicated in our in silico system, for which the onset of replication is well defined. For these reasons, we have to determine which time window among the ones used in Fig 2E, is the most appropriate to compare with the experiment (see response to comment 9 for more details).

    Fig.R1: Cell cycle progression monitored by flow cytometry after the release. For the first 15 minutes, cells are still mainly in G1 and only start replicating ~20 minutes after the release.

    Section II-B-2(b) could be clearer. I don't understand what the conclusion the authors take from the metaphase arrest maps is. I'm not sure why they discuss again the Cdc45-depleted cells here, since this was already covered in the previous section.

    Taken together, the G1, Cdc20 (metaphase-arrested cells), and Cdc45-depleted (early S cells but not replicated) conditions suggest that fountains reflect ongoing replication. Namely, G1-arrest shows that fountains require S-phase entry; Cdc45-depletion shows that fountains require origin firing and is not due to another S-phase event; and metaphase-arrested cells show that fountains are not permanent structures established by replication, but a transient replication-dependent structure.

    This demonstrates that the emerging signal is not trivially dependent on (1) the presence of the second sister chromatids; or on (2) potential overlaps between origin positions and barriers (CARs) to loop extrusion (see also comment 12 of Reviewer 2). A sentence at the end of II-a was added to clarify the different information gained with the two strains.

    We discuss again the cdc20 and cdc45 mutants in II-b to highlight how the results in II-a do not exclude potential interplay between cohesin-mediated loop-extrusion in presence forks progression. These considerations motivated our experiment in Scc1-depleted cells during early S-phase.

    At the start of p8 (II-B-3) there is a discussion of the mapping to times to the early-S stage experiments. This could have more explanation. I don't follow what the issue is, or the process which has been used to do the mapping. From Fig 2B, it seems that the simulation time is already mapped well to real time.

    As mentioned above in comment 7, we cannot clearly define a “t=0” when replication starts in vivo as the release from the G1-arrest is not immediate and perfectly synchronous. On the other hand, the times indicated within the text are those following the onset of polymer self-duplication in our simulations. Note that the mean replication time (MRT) shown in Fig.2B does not represent an absolute time, but rather an average relative timing along S-phase (signal rescaled between 0 and 1).

    For all these considerations, we think that the most reliable strategy to compare fountains in vivo and in silico is to look at the replicon size via the enrichment in raw contacts around early origins, as illustrated in Fig S7A. In practice, looking at the relative counts of contacts around early origins we have a proxy for the average replicon size that we can match by computing the same analysis on simulated signals (Fig S7A). As a result, we find that the best simulated time window is between 5 and 7.5 minutes, compatible with early-S phase and with an approximate duration of G1 after release of 15 minutes as observed in other studies (ref. [61]).

    Note that our conclusions are robust with respect to modulating this mapping method. In particular in Fig. S7, we thoroughly investigated how several confounding factors (such as time window used or partial synchronization) may impact the quantitative nature of our prediction without affecting the qualitative insights.

    We included a more precise reference to the Supplementary Materials, where the approach is described and clarified.

    In Fig 4A above each plot there is a cartoon showing the fork scenario. The left-hand cartoon is rendered properly, but the right-hand one has overlapping black boxes which I don't think should be there. These black boxes are present in many other figures (4B, 3B, 2E etc).

    This issue seems to appear using the default PDF viewer on Mac OS. We have corrected the problem and no more black boxes should appear in the main text and in the Supplementary Material.

    In II-C-2(b) it is mentioned that the number of forks within RFis is always assumed to be even. This discussion could be clearer. In particular, the authors state that under both fork scenarios, in the simulations they can detect odd numbers of forks within RFis - how can this happen in the case where sister forks are held together?

    We included a more accurate description in the main text about why Saner et al. (ref [20]) make these assumptions in their estimates. We highlight possible inconsistencies such as the presence of termination events which, in our formalism, break sister forks interactions and lead to single forks to be detected. We also clarify the latter point when describing Fig 5B and describe in more detail replication bubbles merging events in the Materials and Methods.

    Fig 6B and C, it would be useful if the same scale was used on both plots.

    We now use the same scale when plotting Fig 6B and C.

    Section II-D-1. There is a discussion on the presence of catenated chains; I did not understand how the replicated DNA becomes catenated, and what this actually means in this context. The way the process is described and the snapshots in Fig2C do not suggest that the chains are catenated. Some further discussion or a diagram would be useful here.

    We included a small paragraph to better explain how intertwining of sister chromatids occurs, and more clearly refer to a snapshot in supplementary figure S19D (Page 14). As correctly mentioned by the reviewer, replication bubbles by construction are always unknotted during their growth (see example in Fig. 2C). As we thoroughly characterize in our previous work (ref. [25]), when several replication bubbles merge, the random orientation of sister chromatids potentially lead to catenation points and intertwined structures. We show below a scheme from our previous work (ref [25]). While in this past work, we demonstrated that the center of mass of the two sister chromatids show subdiffusive behaviour due to the additional topological constraints of their intertwining, this new analysis in the present work suggests that possible effects may also be observed when tracking the MSD (mean square displacement at the locus level) in a more realistic scenario where we included correct replication timing, chromosome sizes and Rabl-organization.

    On p14 (section III) there is a section discussing possible mechanisms for sister fork interactions, and that result that Ctf4 might not play a role in this, as previously suggested. Are there any other candidate proteins which could be tested in the future?

    To the best of our knowledge, there is no other candidate protein of the replisome that has been directly associated to sister-fork pairing in previous studies (as Ctf4). However, components of the replisome such as Cdt1, that have the capacity to oligomerize/self-interact, could be good candidates. We now mention this possibility in the Discussion (Page 15).

    As on p14, second paragraph: there is a sentence "replication wave [51] cannot be easily visualised at the single cell level.", which seems to contradict the discussion on p9 "such a "wave" can also be observed at the level of an individual trajectory (Video S3,4) even if much more stochastic." I think more explanation is needed here.

    We rephrased the mentioned passages to clarify the differences in detecting such “replication wave” at the population vs single cell level. In video S3 and S4, we can still observe an enrichment of forks at the SPB and later in S-phase a shift towards the equatorial plane. However, the stochasticity of polymer dynamics and 1D replication strongly hinder the ability to clearly visualize such redistribution.

    In the methods section, p18, it is mentioned that the volume fraction is 3%. I assume this is before replication, and so after replication is complete this will increase to 6%. This should be stated more explicitly, with also a comment on the 5% volume fraction used in the time-scale mapping discussed on p17.

    Indeed, we choose to map the experimental MSD measured in ref [83] by simulating a homopolymer 5% volume fraction and in periodic boundary conditions for consistency to previous work in the group (ref. [102-106]) and our previous replication model (ref.[25]). Moreover, this intermediate density regime also lies in between the minimal (3%) and maximal (6%) densities present in our system. When redoing the time mapping with the G1 MSD plotted in Fig 6A and new Fig S19A, we obtain a very similar value of approx. 1MC=0.6ms. Note that the time mapping aims to obtain a rough estimation of real times as several factors, such as active processes, non-constant density, cell-cycle progression may all contribute to chromatin diffusion *in vivo *(see also comment 15 to Reviewer 2). In the context of our formalism, differences in time mapping do not affect the 1D replication dynamics as all the parameters to model the 1D process are rescaled by the same factor. Moreover, as we characterized in more depth in our previous work (ref [25]), a crucial aspect that defines self-replicating polymers is the relationship between fork progression and the polymer relaxation dynamics. In physiological conditions, we remain in the regime where forks progress almost quasi-statically to allow the bubbles to re-equilibrate. Therefore, small discrepancies in the time mapping will not modify this regime and our results should remain robust.

    On p20, processing of simulated HiC using cooltools is discussed. For readers unfamiliar with this software, a bit more detail should be given. Specifically, how does the normalisation account for having some segments which have been replicated and some which have not. Later on the same page (IV-C-2) two different strategies for comparing HiC maps are given; why are two different methods required, and what is the reasoning in each case?

    In the raw - unbalanced - data, we observe an artificial increase in contacts around origins in S-phase for both simulation and experiments. This is simply due to the presence of the second Sister chromatids and the fact that contacts between distinct DNA segments are mapped to a single bin.

    In the new Fig. S25, we illustrate this effect by computing aggregate plots around early origins using single-chromosome simulations. We demonstrate that the ICE normalization corrects for the variations in copy number due to replication and thus for such artificial increases in contacts during S-phase. We show that such a normalization is equivalent to explicitly divide each bin by the average copy-number of the corresponding segments.

    We have now included a sentence in the Materials and Methods to clarify this. Moreover, a detailed description of the other alternative strategies used to compare experiments and simulations were presented in response to comment 3 to Reviewer 2 and two new paragraphs were added in the Materials and Methods.

    The references section has an unusual formatting with journal names underlined.

    We updated the formatting.

    Reviewer #4 (Significance (Required)):

    D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condensin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

    Reviewer #4 (Evidence, reproducibility and clarity (Required)):

    In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

    We thank Reviewer 4 for her/his positive evaluation of our work and her/his comments that help us to greatly improve the manuscript.

    Reviewer Comments / Significance

    In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors D’Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

    D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

    Major Comments

    There is a tremendous amount of work coupling RT domains to 3D genome architecture, especially deriving from the ENCODE and 4D Nucleome consortia. These studies are not adequately highlighted in the introduction and discussion of this manuscript, and this treatment of the literature would ideally be amended in any revised manuscript.

    We include new sentences in the introduction to discuss more in detail the correlation between 3D genome architecture and replication timing program, and advancement in this field in the last decades. We also included additional citations to reviews and publications (ref [8-16]). These references were also included at the end of the Discussion where we address the exciting perspective of employing our model in higher eukaryotes and potentially tackle the complex interplay between 3D nuclear compartmentalization and replication dynamics (see also response 1 to Reviewer 1).

    S. cerevisiae origins of replication differ from metazoan origins of replication in that they are sequence-defined and are known to fire in a largely deterministic pattern (see classic study PMID11588253). From the methods of the authors it is not clear that the known deterministic firing pattern is being used here, but instead a stochastic sampling method? Please clarify in the manuscript. Specifically, it would be good to understand how the Initiation Probability Landscape Signal correlates with what is already known about origin firing timing.

    In our model, the positions of origins are stochastically sampled proportionally to the IPLS which was inferred directly from experimental MRT (ref. [63]) and RFD (ref. [44]). This modeling approach allows reproducing with a very high accuracy the known replication timing data (correlation of 0.96) and Fork directionality data (correlation of 0.91) (see ref. [71]). Origins were defined as the peaks in the IPLS signal. In Fig S3, we extensively compare these origins and the known ARS positions from the Oridb database. For example, most of our early origins (96%) are located close to known, confirmed ARS. Moreover, even if our algorithm is stochastic for origin firing, we remark that each early origin will fire in 90 % of the simulations, coherent with the quasi-deterministic pattern of origin firing and experimental MRT and RFD data. We now have added such statistics of firing in the revised manuscript (Page 4).

    It seems possible that experimental sister chromatid Hi-C data (PMID32968250) and nanopore replicon data (PMID35240057) could be used to further ascertain the validity of some of the findings of this paper. Specifically, could the authors demonstrate evidence in sister chromatid Hi-C data that the replisome is in fact extruding sister chromatids? Moreover, are the interactions being measured specifically in cis (as opposed to trans sister contacts)? For the nanopore replicon data, how do replicon length, replication timing, and position along the replication 'wave' correlate?

    We thank the reviewer for the suggestions.

    Hopelessly there is currently no Sister-C data available during S-phase. In the seminal study (PMID32968250), cells were arrested in G2/M via nocodazole treatment. For a different unpublished work, we already analysed in detail the SisterC dataset and we did not observe clear fountain-like signature, consistent with our own G2/M Hi-C maps (cdc20) where fountains were absent. Note that, in the present work, in order to compare our predictions with standard HiC data, we included all contacts (cis and trans chromatids), mapping pairwise contacts from distinct replicated sequences/monomers to a single bin (see also response to comment 17 to Reviewer 3 and new Fig. S25).

    We now mention in the Discussion that Sister-C data during S-phase could help monitoring the role of replisomes on relative sister-chromatids organization (Page 15).

    Main results from the nanopore replicon data study include the observed high symmetry between sister forks and their linear progression, as the density of replicons appears to be uniform with respect to their length. Since these two specific constraints are already present in the framework of Arbona et al. (ref. [63]), our model is able to reproduce these features of DNA replication captured by the nanopore data.

    Moreover, as we model with very high accuracy replication timing data (see response to comment 2) and forks positioning, we can assume that our formalism well captures replicon positioning and lengths observed in vivo.

    As this study does not include any additional exploration or variation of the parameters inferred by Arbona et al. (ref. [63]), we consider a quantitative comparison with the nanopore replicon data to be beyond the scope of this paper.

    Minor Comments:

    The paper is in most places easy to follow. However, Section C bucked this trend and in general was quite difficult to follow. We would recommend that the authors try to revise this section to make clearer the actual physical parameters that govern a 'replication wave' and the formation of replication foci - how many forks, the extent to which the sisters are coordinated, etc for early vs. late replicating regions.

    We now state more clearly with a sentence in the main text the driving forces behind the formation of such a “replication wave”. We believe that the several additions and clarifications following the various comments, improved the clarity of the manuscri

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    Referee #4

    Evidence, reproducibility and clarity

    In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

    Reviewer Comments / Significance

    In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

    Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

    Major Comments

    • There is a tremendous amount of work coupling RT domains to 3D genome architecture, especially deriving from the ENCODE and 4D Nucleome consortia. These studies are not adequately highlighted in the introduction and discussion of this manuscript, and this treatment of the literature would ideally be amended in any revised manuscript.
    • S. cerevisiae origins of replication differ from metazoan origins of replication in that they are sequence-defined and are known to fire in a largely deterministic pattern (see classic study PMID11588253). From the methods of the authors it is not clear that the known deterministic firing pattern is being used here, but instead a stochastic sampling method? Please clarify in the manuscript. Specifically, it would be good to understand how the Initiation Probability Landscape Signal correlates with what is already known about origin firing timing.
    • It seems possible that experimental sister chromatid Hi-C data (PMID32968250) and nanopore replicon data (PMID35240057) could be used to further ascertain the validity of some of the findings of this paper. Specifically, could the authors demonstrate evidence in sister chromatid Hi-C data that the replisome is in fact extruding sister chromatids? Moreover, are the interactions being measured specifically in cis (as opposed to trans sister contacts)? For the nanopore replicon data, how do replicon length, replication timing, and position along the replication 'wave' correlate?

    Minor Comments:

    • The paper is in most places easy to follow. However, Section C bucked this trend and in general was quite difficult to follow. We would recommend that the authors try to revise this section to make clearer the actual physical parameters that govern a 'replication wave' and the formation of replication foci - how many forks, the extent to which the sisters are coordinated, etc for early vs. late replicating regions.

    Significance

    Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

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    Referee #3

    Evidence, reproducibility and clarity

    The paper "Genome-wide modelling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes" by D'Asaro et. al presents new computational and experimental results on the dynamics of genome replication in yeast. The authors present whole-nucleus scale simulations using a kinetic Monte Carlo polymer physics model. New HiC data for synchronised yeast samples with different protein knock-downs are also presented.

    The main questions which the paper addresses are whether sister forks remain associated during replication, whether there is more general clustering of replication forks, and whether replication occurs in a 'spatial wave' through the nucleus. While the authors' model data are not able to conclusively show whether sister forks remain co-localised, the work provides some important insights which will be of high interest to the field.

    I have no major issues with the paper, only some minor comments and suggestions to improve the readability of the manuscript or provide additional detail which will be of interest to readers. I list these here in the order in which they appear in the paper. There are also a number of typos and grammatical issues through the text, so I recommend thorough proofreading.

    1. The paper seems to be aimed at a broad interdisciplinary audience of biophysicists and molecular biologists. For this reason, the introduction could be expanded slightly to include some more background on DNA replication, the key players and terminology. Also, it seems that this work builds on previous modelling work (Ref. 19), so a bit more detail of what was done there, and what is new here would be helpful. The final paragraph the introduction mentions chromosome features such as TADs and loops, which should be explained in more detail.
    2. In the first results section, end of p2, the "typical brush-like architecture" is mentioned. This is not well explained, some additional detail or a diagram might help.
    3. On p3-4, some previous work is described, with Pearson correlations of 0.86 and 0.94 are mentioned. What cases these two different values correspond to is not clear.
    4. In section II-A-2, on the modelling details, it should be made clearer that the nucleus volume is kept constant, and that this is an approximation since typically the nucleus grows during S-phase. This is discussed in the Methods section, but it would be useful to also mention it here (and give some justification was to why it will not likely change the results).
    5. Fig 2. The text in Fig 2B is much smaller than other panels and difficult to read. Also Fig 3B, Fig 6.
    6. In 2E, are the times given above each map the range which is averaged over? This could be clearer in the caption. In the caption it stated that these are 'observed over expected'; what the 'expected' is could be clearer.
    7. In section II-B-2, the authors state that the cells are fixed 20 mins after release from S-phase. Can they comment on the rational behind this choice, since from Fig 2 their simulations predict that the fountain pattern will no-longer be visible by that time.
    8. Section II-B-2(b) could be clearer. I don't understand what the conclusion the authors take from the metaphase arrest maps is. I'm not sure why they discuss again the Cdc45-depleted cells here, since this was already covered in the previous section.
    9. At the start of p8 (II-B-3) there is a discussion of the mapping to times to the early-S stage experiments. This could have more explanation. I don't follow what the issue is, or the process which has been used to do the mapping. From Fig 2B, it seems that the simulation time is already mapped well to real time.
    10. In Fig 4A above each plot there is a cartoon showing the fork scenario. The left-hand cartoon is rendered properly, but the right-hand one has overlapping black boxes which I don't think should be there. These black boxes are present in many other figures (4B, 3B, 2E etc).
    11. In II-C-2(b) it is mentioned that the number of forks within RFis is always assumed to be even. This discussion could be clearer. In particular, the authors state that under both fork scenarios, in the simulations they can detect odd numbers of forks within RFis - how can this happen in the case where sister forks are held together?
    12. Fig 6B and C, it would be useful if the same scale was used on both plots.
    13. Section II-D-1. There is a discussion on the presence of catenated chains; I did not understand how the replicated DNA becomes catenated, and what this actually means in this context. The way the process is described and the snapshots in Fig2C do not suggest that the chains are catenated. Some further discussion or a diagram would be useful here.
    14. On p14 (section III) there is a section discussing possible mechanisms for sister fork interactions, and that result that Ctf4 might not play a role in this, as previously suggested. Are there any other candidate proteins which could be tested in the future?
    15. As on p14, second paragraph: there is a sentence "replication wave [51] cannot be easily visualised at the single cell level.", which seems to contradict the discussion on p9 "such a "wave" can also be observed at the level of an individual trajectory (Video S3,4) even if much more stochastic." I think more explanation is needed here.
    16. In the methods section, p18, it is mentioned that the volume fraction is 3%. I assume this is before replication, and so after replication is complete this will increase to 6%. This should be stated more explicitly, with also a comment on the 5% volume fraction used in the time-scale mapping discussed on p17.
    17. On p20, processing of simulated HiC using cooltools is discussed. For readers unfamiliar with this software, a bit more detail should be given. Specifically, how does the normalisation account for having some segments which have been replicated and some which have not. Later on the same page (IV-C-2) two different strategies for comparing HiC maps are given; why are two different methods required, and what is the reasoning in each case?
    18. The references section has an unusual formatting with journal names underlined.

    Significance

    The work has been conducted thoroughly, and in general the paper is well written with good attention to detail. As far as I am aware, this is the first study where replication is simulated in a whole nucleus context, and the scale of the simulations is impressive. This allows the authors to address questions on replication foci and the spatiotemporal organisation of replication which would not be possible with more limited simulations, and to compare the model with previous experimental work. This, together with the new HiC data, I think this makes this a strong paper which will be of interested to biophysics and molecular biology researchers; the manuscript is written such that it would suit a interdisciplinary basic research audience.

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    Referee #2

    Evidence, reproducibility and clarity

    The manuscript by D'Asaro et al. investigates the relationship between DNA replication and chromatin organization using polymer simulations. While this is primarily a simulation-based study, the authors also present relevant comparisons with experimental data and explore mechanistic aspects of replication fork interactions.

    The primary weakness is that many aspects are not clear from the manuscript. Below is a list of questions that the authors must clarify:

    1. In the Model and Methods section, it is written "Arbitrarily, we choose the backbone to be divided into two equally long arms, in random directions." It is unclear what is meant by "backbone to be divided" and "two equally long arms." Does this refer to replication?
    2. In chromosome 12, since the length inside the nucleolus (rDNA) is finite, the entry and exit points should be constrained. Have the authors applied any relevant constraint in the model?
    3. What is the rationale for normalizing the experimental and simulation results by dividing by the respective P_intra(s = 10 kb)?
    4. In the sentence "For instance, chromosomes are strictly bound by the strong potential to localize between 250 and 320 nm from the SPB," is it 320 or 325 nm? Is there a typo?
    5. Please list the number of beads in each chromosome and the location of the centromere beads.
    6. In Eq. 7, when the Euclidean distance between the sister forks d_ij > 50 nm, the energy becomes more and more negative. This implies that the preferred state of sister forks is at distances much greater than 50 nm. Then how is "co-localization of sister forks" maintained?
    7. The section on "non-specific fork interactions" is unclear. You state that the interaction is between "all the replication forks in the system," but f_ij is non-zero only for second nearest-neighbors. The whole subsection needs clarification.
    8. Eq. 6 has no H_{sister-forks}. Is this a typo?
    9. While discussing the published work, the authors may cite the recent paper [https://doi.org/10.1103/PhysRevE.111.054413].
    10. It is not clear how the authors actually increase the length of new DNA in a time-dependent manner. For example, when a new monomer is added near the replication origin (green bead in Fig. 3C), what happens to the red and blue polymer segments? Do they get shifted? How do the authors take into account self-avoidance while adding a new monomer? These details are not clear.
    11. How do the authors ensure that monomers get added at a rate corresponding to velocity v? The manuscript mentions "1 MCS = 0.075 msec," but in how many MC steps is a new monomer added? How is it decided?
    12. The authors stress the relevance of loop extrusion. However, in their polymer simulation, the newly replicated chromatin does not form any loops. Is this consistent with what is known?
    13. Please add a color bar to Fig. 4B.
    14. In the MSD plot (Fig. 6), even though it appears to be a log-log plot, the exponents are not computed. Typically, exponents define the dynamics.
    15. The dynamics will depend on the precise nature of interactions, such as the presence or absence of loop extrusion. If the authors present dynamics without extrusion, is it likely to be correct?

    Significance

    1. The topic is relevant and the problem being addressed is very interesting. While there has been some earlier work in this area, the polymer simulation approach used here is novel.
    2. The simulation methodology is technically sound and appropriate for the problem. Results are novel.
    3. The authors compare their simulations with experimental data and explore both interacting and non-interacting replication forks.
    4. Most conclusions are supported by the data presented.
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    Referee #1

    Evidence, reproducibility and clarity

    By developing a new genome-wide 3D polymer simulation framework, D'Asaro et al. investigated the spatiotemporal interplay between DNA replication and chromatin organization in budding yeast: (1) T The simulations recapitulate fountain-like chromatin patterns around early replication origins, driven by colocalized sister replication forks. These findings align with Repli-HiC observations in human and mouse cells, yet the authors advance the field by demonstrating that these patterns are independent of Cohesin and Ctf4, underscoring replication itself as the primary driver. (2) Simulations reveal a replication "wave" where forks initially cluster near the spindle pole body (SPB) and redistribute during S-phase. While this spatial reorganization mirrors microscopy-derived replication foci (RFis), discrepancies in cluster sizes compared to super-resolution data suggest unresolved mechanistic nuances. (3) Replication transiently reduces chromatin mobility, attributed to sister chromatid intertwining rather than active forks. This work bridges replication timing, 3D genome architecture, and chromatin dynamics, offering a quantitative framework to dissect replication-driven structural changes. This work provides additional insights into how replication shapes nuclear organization and vice versa, with implications for genome stability and regulation. However, the following revisions could strengthen the manuscript:

    Major:

    1. Generalizability to Other Species While the model successfully recapitulates yeast replication, its applicability to larger genomes (e.g., mammals) remains unclear. Testing the model against (Repli-HiC/ in situ HiC, and Repli-seq) data from other eukaryotes (particularly in mammalian cells) could enhance its broader relevance.
    2. Validation with Repli-HiC or Time-Resolved Techniques The Hi-C data in early S-phase supports the model, but the intensity of replication-specific chromatin interactions is faint, which could be further validated using Repli-HiC, which captures interactions around replication forks. Alternatively, ChIA-PET or HiChIP targeting core component(s) (eg. PCNA or GINS) of replisomes may also solidify the coupling of sister replication forks.
    3. Interactions Between Convergent Forks The study focuses on sister-forks but overlooks convergent forks (forks moving toward each other from adjacent origins), whose coupling has been observed in Repli-HiC. Could the simulation detect the coupling of convergent fork dynamics?
    4. Unexpected Increase in Fountain Intensity in Cohesin/Ctf4 Knockouts In Fig.3A, a schematic illustrating the cell treatment would improve clarity.

    In Sccl- and Ctf4-depleted cells, fountain signals persist or even intensify (Fig. 3A). This counterintuitive result warrants deeper investigation. Could the authors provide any suggestions or discussions? Potential explanations may include: Compensatory mechanisms (e.g., other replisome proteins stabilizing sister-forks). Altered chromatin mobility in mutants, enhancing Hi-C signal resolution. Artifacts from incomplete depletion (western blots for Sccl/Ctf4 levels should be included).

    1. Inconsistent Figure References Several figure citations are mismatched. For instance, Fig. S1A has not been cited in the manuscript. Moreover, there is no Fig.1E in figure 1, while it has been cited in the text. All figure/panel references must be cross-checked and corrected.

    Minor:

    1. Page2: "While G1 chromosomes lack of structural features such as TADs or loops [3]" However, Micro-C captures chromatin loops, although much smaller than those in mammalian cells, within budding yeast.
    2. In figure 2E, chromatin fountain signals can be readily observed in the fork coupling situation and movement can also be observed. However, the authors should indicate the location of DNA replication termination sites and show some examples at certain loci but not only the aggregated analysis.

    Significance

    General assessment:

    This study provides a valuable computational framework for investigating the dynamic interplay between DNA replication and 3D genome architecture. While the current implementation focuses on Saccharomyces cerevisiae, whose genome organization differs significantly from mammalian systems.

    Advance: providing the first in vivo experimental evidence in investigating the role(s) of Cohesin and Ctf4 in the coupling of sister replication forks.

    Audience: broad interests; including DNA replication, 3D genome structure, and basic research

    Expertise: DNA replication and DNA damage repair within the chromatin environment.

  6. Version published to 10.1101/2025.04.23.650322 on bioRxiv