Cohesins and condensins orchestrate the 4D dynamics of yeast chromosomes during the cell cycle

Abstract Duplication and segregation of chromosomes involves dynamic reorganization of their internal structure by conserved architectural proteins, including the structural maintenance of chromosomes (SMC) complexes cohesin and condensin. Despite active investigation of the roles of these factors, a genome‐wide view of dynamic chromosome architecture at both small and large scale during cell division is still missing. Here, we report the first comprehensive 4D analysis of the higher‐order organization of the Saccharomyces cerevisiae genome throughout the cell cycle and investigate the roles of SMC complexes in controlling structural transitions. During replication, cohesion establishment promotes numerous long‐range intra‐chromosomal contacts and correlates with the individualization of chromosomes, which culminates at metaphase. In anaphase, mitotic chromosomes are abruptly reorganized depending on mechanical forces exerted by the mitotic spindle. Formation of a condensin‐dependent loop bridging the centromere cluster with the rDNA loci suggests that condensin‐mediated forces may also directly facilitate segregation. This work therefore comprehensively recapitulates cell cycle‐dependent chromosome dynamics in a unicellular eukaryote, but also unveils new features of chromosome structural reorganization during highly conserved stages of cell division.

Thank you for submitting your manuscript to The EMBO Journal, along with the referee reports and your response from the previous round of peer review at another journal. We have now consulted with an arbitrating advisor who had access to your manuscript, the referee reports, and your response -and this person supports publication here following minor revision as outlined below.
As you will see, our advisor highlights the quality and quantity of your data but at the same time points out that you should discuss the validity in depicting 3D maps of chromome organization/interactions based on data averaging numerous cells. In addition, the advisor finds that cross-validation using a separate technical approach such as FISH would significantly strengthen the conclusiveness of the HiC-derived interactions. I realise that doing this at a broader scale would be outside the scope of the current study but in case you have data available for a few contact points (as a proof of principle) I would suggest that you include it in the final version of the manuscript. Finally, you will see that our advisor would like to see an extended discussion section.
Based on the overall positive recommendation from our advisor I would invite you to submit a revised version of the manuscript in which you address the points outlined above.
Thank you again for giving us the chance to consider your manuscript for The EMBO Journal, I look forward to your revision.
The study is a Hi-C tour the force and is of good technical quality. The results are somewhat descriptive and largely expected, but will be of interested to the specialists in the field.
Specific points to be addressed: 1. Although widely used in the community, the use of the 3D representations based on 2D maps is misleading. The datasets represent ensemble information from millions of cells and the 3D representations give the impression that the population is homogenous, i.e. all cells in the population have the same 3D organization. We know from single cell hi-C that this is not the case. The authors should 1) provide some indication as to how many interactions are on average detected per analyzed cell, 2) should, if possible, provide a sense of heterogeneity in the population and 3) should, at least, clearly indicate in the text that what they observe are averaged ensemble representations. 2. The study lacks orthogonal validation of many of the conclusions. Ideally the authors should perform FISH experiments to test the validity of some of the key conclusions such as the formation of the condensin-dependent chromosome 12 rDNA loop and the effect of condensin on anaphase organization by localizing specific interaction sites and demonstrating altered spatial relationships. 3. As a minor point, the Conclusion session seems to have been written in haste. It is very superficial and should be re-written with a little more care. Response to referees: Referee #1: Lazar-Stefanita et al. use hi-C methods to describe the changes in higher order genome organization through the yeast cell cycle. Not surprisingly, they find considerable re-organization of genome interactions at various stages of the cell cycle, including cell division. Using mutants they provide evidence for a role of cohesins in S-phase re-organization and condensin in anaphase reorganization.
The study is a Hi-C tour the force and is of good technical quality. The results are somewhat descriptive and largely expected, but will be of interested to the specialists in the field.
Specific points to be addressed: 1. Although widely used in the community, the use of the 3D representations based on 2D maps is misleading. The datasets represent ensemble information from millions of cells and the 3D representations give the impression that the population is homogenous, i.e. all cells in the population have the same 3D organization. We know from single cell hi-C that this is not the case. The authors should 1) provide some indication as to how many interactions are on average detected per analyzed cell, 2) should, if possible, provide a sense of heterogeneity in the population and 3) should, at least, clearly indicate in the text that what they observe are averaged ensemble representations.
The methods was actually very explicit about the fact that the Hi-C 2D and 3D maps are average representations (as highlighted below). We have moved part of these explanations in the main text, to be even more explicit. As for the number of contacts per cell, our sequencing depth is far from being exhaustive (we have only a few % of duplicates in our libraries). Therefore, the ratio corresponding to 20M reads (on average) generated over a population of ~1bn cells is not informative (and is not a metric used in any Hi-C, ChIP or RNA-seq paper).

3D Representation of contact maps.
Here is the new main text paragraph explaining how to interpret the 3D structures: These 2D maps were translated into 3D representations to visualize the main folding features (Lesne et al, 2014) (e.g. centromeres and telomeres clustering in G1, Fig 1B; Fig EV1). These 3D structures are average representations of the contact frequencies quantified over a population of cells, and therefore do not represent the exact structure found in individual cells. For instance, on these 3D representations all the telomeres loosely cluster together. In a single nucleus, telomeres rather form small groups scattered all around the nuclear membrane (Taddei & Gasser, 2012). Since in different cells the composition of these clusters differ, all telomeres end up being regrouped together in the average 3D structure that reflects the population average of contacts. In addition, they are not polymer models, and cannot be interpreted as such. Nevertheless, these representations conveniently highlight important structural features not readily apparent in the 2D maps.
2. The study lacks orthogonal validation of many of the conclusions. Ideally the authors should perform FISH experiments to test the validity of some of the key conclusions such as the formation of the condensin-dependent chromosome 12 rDNA loop and the effect of condensin on anaphase organization by localizing specific interaction sites and demonstrating altered spatial relationships. We agree that backing several of our observations with FISH analysis would be ideal. However, we think this is a future work that needs to be also tacked on the basis of the present study. We have added sentences in the discussion that clearly state that the interpretation we propose for some of our data will require further investigation, notably through single cell approaches, to be fully validated.
In addition, we also show that the two main regions of condensin deposition, i.e. the centromeres and the rDNA locus, are bridged during anaphase through a condensin-dependent mechanism resulting in a loop-like structure on the right arm of chromosome 12. Whether this structure is systematically found in all cells, or only in a subset of the population, remains to be determined through single cell imaging approaches such as FISH analysis. Although the precise mechanisms of formation remains unknown as well as its functional importance, we show that the setting up of the loop depends on condensin.
3. As a minor point, the Conclusion session seems to have been written in haste. It is very superficial and should be re-written with a little more care.
We have converted the conclusion section into a discussion.
2nd Editorial Decision 26 June 2017 Thank you for submitting a revised version of your manuscript, I am pleased to inform you that it is now in principle ready for acceptance here. However, before we can go ahead and transfer your manuscript files for production there are still a few formatting issues that need to be resolved. In the pink boxes below, please ensure that the answers to the following questions are reported in the manuscript itself. Every question should be answered. If the question is not relevant to your research, please write NA (non applicable). We encourage you to include a specific subsection in the methods section for statistics, reagents, animal models and human subjects.

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Each of the Hi--C experiment was performed on synchronized cell populations of about 2 x 10^9 cells. The size of the population allows to obtain high amounts of genomic DNA so to maximize the diversity of captured contacts in the Illumina libraries. For each Hi--C data we evaluated the normal distribution of the contacts and the s.d. was used to generate the corrected contact map. We also computed the non--parametric Wilcoxon test (P values) to evaluate the significance of the different distributions.
Estimations of datasets variability have been performed within dataset (distribution of Hi--C matrix) and between different datasets (replicates and non--replicates).
The variance is similar between all replicates. Significant changes are observed between replicates of different biological condition.