Tunable DNMT1 degradation reveals cooperation of DNMT1 and DNMT3B in regulating DNA methylation dynamics and genome organization

  1. Andrea Scelfo
  2. Viviana Barra
  3. Nezar Abdennur
  4. George Spracklin
  5. Florence Busato
  6. Catalina Salinas-Luypaert
  7. Elena Bonaiti
  8. Guillaume Velasco
  9. Anna Chipont
  10. Coralie Guérin
  11. Andréa E. Tijhuis
  12. Diana C.J. Spierings
  13. Claire Francastel
  14. Floris Foijer
  15. Jorg Tost
  16. Leonid Mirny
  17. D. Fachinetti

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Abstract

DNA methylation (DNAme) is a key epigenetic mark that regulates critical biological processes maintaining overall genome stability. Given its pleiotropic function, studies of DNAme dynamics are crucial, but currently available tools to interfere with DNAme have limitations and major cytotoxic side effects. Here, we present untransformed and cancer cell models that allow inducible and reversible global modulation of DNAme through DNMT1 depletion. By dynamically assessing the effects of induced passive demethylation through cell divisions at both the whole genome and locus-specific level, we reveal a cooperative activity between DNMT1 and DNMT3B to maintain and control DNAme. Moreover, we show that gradual loss of DNAme is accompanied by progressive and reversible changes in heterochromatin abundance, compartmentalization, and peripheral localization. DNA methylation loss coincided with a gradual reduction of cell fitness due to G1 arrest, but with minor level of mitotic failure. Altogether, this powerful system allows DNMT and DNA methylation studies with fine temporal resolution, which may help to reveal the etiologic link between DNA methylation dysfunction and human disease.

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    Referee #4

    Evidence, reproducibility and clarity

    Summary

    The authors in this manuscript create in vitro degron models of DNMT1 as tools to investigate the roles and functions of DNA methylation in molecular and cellular processes. Degron models can directly target the tagged protein of interest leading to its degradation. When it comes to DNMT1, this system can bypass the use DNMT inhibitors, like DAC and GSK3685032 that can have secondary cytotoxic effects. More specifically, the authors create DNMT1 degron tagged models of two cell lines (DLD-1 and RPE1), as well as a DNMT1 degron tagged model of a DNMT3BKO DLD-1 cell line. These systems allowed the authors to investigate the passive demethylation occurring over consecutive cell divisions, and particularly the role of DNMT1 and DNMT3B and their cooperativity in maintaining DNA methylation levels and how this differs among different genomic regions. The authors characterise the cell fitness of the models they established when DNMT1 is degraded, and methylation levels are lost, and observe a reduction of fitness due to G1 arrest. Finally, the authors show that the loss of DNA methylation observed in these cells leads to reduced levels of heterochromatin (H3K9me3) as well as changes in chromatin and nuclear compartmentalization. Overall, the authors, show an appealing in vitro model that can directly target DNMT1, allowing for more delicate experiments that address the impact of DNA methylation levels in somatic cells, to de-convolute their exact roles from other epigenetic marks and cellular processes that are often correlated with.

    Major comments

    • The auxin degron system relies on the ectopic expression of OsTir1, which is described in materials and methods under 'Plasmids and Cell line generation'. However, OsTir1 expression is never addressed during the manuscript. Quantification of OsTir1 expression levels across the different cell lines is very important in order to more comprehensively characterise this system. This is especially when considering one of the key points of the authors is to establish these new in vitro models as a new tool to study DNA methylation dynamics in the field.
    • The degron system requires an endogenous tag of the protein of interest. Specifically in this work, a tag including the mNGreen and the AID sequence are incorporated at the N-terminus of DNMT1. It is unlikely that there is major interference of the tag to protein function as the tagged cells for DLD-1 and RPE1 are both viable and demonstrate high methylation levels. However, the authors do not consider or discuss that the tag might interfere with the function of the protein at all. It would be useful if the authors compared the tagged cell lines (untreated) with wildtype controls for their methylation levels and/or DNMT1 expression and/or DNMT1 localisation with imaging. These experiments would better substantiate the use of untreated cells as 'wildtype' equivalents and contribute to the better characterisation of these systems as in vitro models.

    Furthermore, DNMT1 can have different transcripts that begin from different sites. Do the authors consider whether the tag is included in all/most isoforms of DNMT1, or if there are any expressed without it?

    • The authors observe that DNMT1 is important for maintaining methylation levels as well as proper cell proliferation. They also observe that DNMT1 depletion does not lead to complete lethality as previously observed (Rhee et al., 2000 Nature, Chen et al., 2007 Nature Genetics). They hypothesise that this might be due to non-specific toxic effects (from CRE) and suggest that the degron system is better suited to bypass such toxicity effects. While this might be true and degron systems do provide a direct and acute protein depletion without non-specific toxicity, the authors do not discuss the implications p53 activity might have on the lack of lethality they observe. Omitting the role of p53 in hypomethylation models and drawing conclusions about toxicity effects between different systems can be misleading and should be corrected. Specifically, it has been shown that hypomethylation triggers p53 dependent apoptosis (Jackson-Grusby et al., 2001 Nature Genetics). The authors do acknowledge the difference in p53 activity when comparing between DLD-1 and RPE-1 DNMT1 depleted cells. The reduced proliferation of RPE-1 cells would suggest that irrespective to the degron system, viability depends on tolerance of each cell line to hypomethylation (whether this is p53 dependent or not). DLD-1 cells seem to have a single nucleotide variant in p53 (p.Ser241Phe (c.722C>T)) (Liu et al., 2006 PNAS), that could potentially explain their viability upon hypomethylation, although further work is required to conclusively suggest such interaction. Furthermore, DNA methylation levels and chromatin organisation of RPE-1 NADNMT1 cells are not characterised in the manuscript and is unclear why.
    • Figure 1D, 1E: The authors provide a Western blot of DNMT (1/3A/3B) across the established cell lines. While some effects like the degradation of DNMT1 based on the degron system or the KO of DNMT3B are convincing (and work well to validate the cell lines), the observation about upregulation of DNMT3B when DNMT1 is degraded, or levels of DNMT1 after wash out, are not as convincing when only showing one blot. This is especially when considering that the DNMTs might have cell cycle expression differences. Additional replicas of the western blot and quantification of bands across replicas, or qPCR to show upregulation of DNMT transcripts, or imaging (like figure S1E), would help make the claim of DNMT3B upregulation and DNMT1 recovery more convincing.
    • The authors show that during wash out (after stopping the IAA treatment), DNMT1 levels can recover slightly and show the methylation levels of specific sites (figure 2B). However, the authors do not make any characterisation of the global levels of methylation levels and their potential recovery (?) after wash out. This could be either done by imaging (like in figure 1F and 1G) or dot blot (like figure S2A) or mass-spec.

    The authors note that recovery of DNMT1 after wash out is to a lesser extent in the NADNMT1/DNMT3B-/- background. The authors do not speculate why would this be. Past reports of degron tagged proteins show that after treatment endogenous protein levels can recover. Does this hint towards a viability issue of the line due to excessive hypomethylation? While difficult to prove it would be useful to speculate why this effect occurs.

    • The authors employ DNAme arrays to assess the DNA methylation loss after degradation of DNMT1 and study where in the genome this occurs. Specifically, the authors look on differentially methylated probes between treated/non treated samples and demonstrate their abundance over different genomic regions (figure 2E and S2 H, I, J, K). However, this way of visualising the data is a bit difficult to interpret as differences can be small. Furthermore, number of probes across the genome is not uniformly distributed, so it would be useful to include these numbers. It would be helpful if authors can provide genome browser snapshots with methylation levels and accompanying histone marks (from available data, Rokavec et al., 2017?) like done in figure 4F, S4B and S5C to show representative regions that showcase their observations. Coverage of the EPIC array will mean that these tracks will not have high coverage and thus gaps, and ideally one would need whole genome bisulfite data, however hopefully some snapshots can demonstrate locus specific changes better.

    Considering the function of DNMT1 in remethylating the DNA after replication, one would assume that methylation is lost equally across the genome as a simplistic model. Of course, there are many reasons like secondary functions of DNMT1, DNMT3A/3B and TET activity etc that could alter this and provide biases over regions of the genome. The authors discuss this and note most probes show such loss (106,647 of 178,529). It would be useful for the authors to better describe where the rest of the probes (that do not lose the expected methylation, annotated as 'late') are located and speculate what mechanisms might be involved. This is partly addressed in figures S2H and J, but it is not immediately clear what distinguishes late regions from early. Genome tracks with methylation levels and histone tracks as mentioned above could provide examples of regions.

    The authors briefly discuss the role of DNMT1 and DNMT3B in methylating specific regions and their cooperativity as well as the underexplored de novo activity of DNMT1. Based on their findings, can the authors draw any new mechanistic conclusions/observations about the activity of DNMT1 and/or DNMT3B and how it is directed? Are there any sequence signatures or histone mark profiles that could explain the hypomethylation or remethylation (after wash out) of specific loci?

    • The authors observe that 70% of DMPs display an increased methylation in the DNMT3BKO cell line compares to NADNMT1. The authors speculate that this is due to an 'uncontrolled activity' of DNMT1 in the absence of DNMT3B. The increased levels observed could be a clonal effect when generating the KO line. While including additional clonal lines can be a significant amount of work, the authors should acknowledge the effects of clonality in their findings when comparing between the cell lines used (that do not relate to the IAA treatments).
    • In figures 3D and S3D, the authors compare the viability between IAA treated cells as well as DAC and GSK3685032 and observe increased toxicity/lethality in the case of DAC and GSK3685032. It would be helpful for the authors to discuss the dosage and concentration they used for each drug and why. In order to compare the viability of cells between treatment of different drugs, one would expect dosages that lead to equivalent extents of hypomethylation.
    • The authors show in figure 3 that the cell lines used have major cell cycle defects, with pronounce G1 arrest, when treated with IAA. Then the authors proceed to perform HiC in treated and untreated sample in figure 4. Can cell cycle differences be cofounding in chromatin compartments and thus affect the data observed in HiC?
    • For figure 4F and G the authors note a global reduction of H3K9me3 levels after treatment. It would be helpful if the authors include assessment of global levels of H3K9me3 (for e.g. by WB) or ChIP qPCR on loci of interest or specify the use of spike-in in methods, as alterations in global levels of a mark can lead to skewed normalisation/quantifications between samples. Alternatively, comparing the peaks/domains of a mark (and whether they are conserved across cell lines) but not directly compare levels can provide a safer interpretation of the data.
    • For figures 4F and S5C different days of treatment are provided, with HiC and H3K9me3 being done after 10d of IAA and CpG methylation after 4d of IAA. It is not explained why this discrepancy in days of treatment has occurred, which can be misleading as 10d treated cells should have lower methylation levels from 4d treated cells.

    Minor comments:

    • Typo in introduction: germiline
    • Introduction has some sentences that might need rewording. For example: 'Somatic DNAme domains are erased right after fertilization to establish a totipotent germiline epigenotype, deposited de novo during early development and undergo massive re-shaping during differentiation, lineage specification, and in response to external cues; then, they are maintained and inherited through cell divisions'. It would be good if this is broken into smaller sections as it is hard to follow.
    • Introduction does not include the degron technologies and their advancement in the last couple of years. Considering the main point of the paper is to establish an in vitro tool to study DNA methylation based on degrons, it would be helpful to include some information about the technology in the introduction.
    • Introduction does not include HiC technologies and the different compartments (A/B, and further subcategories) that the genome can be divided in by them. As the authors then proceed to use HiC data and perform such genome compartmentalisation, it would be helpful if this is addressed briefly at the introduction.
    • The authors do not mention the DNMT3BKO strategy they employed. Specifically, the exact strategy should be listed under 'Plasmids and Cell line generation'. A genotyping PCR at supplementary (like figure S1B) could be added. A schematic like Supplementary Figure S1A would also be helpful, but not necessary.
    • The duration and concentration of DAC and GSK368503 are not always indicated in figure legends.
    • Figure 1C. Homozygous intensity of GFP is much more heterogeneous than the heterozygous levels. It would be interesting if authors could speculate why this is.
    • Figure S1D, S1E: Quantification of imaging experiments is shown, however there is no representative images of the staining performed. Incorporate an example image of each staining would be helpful to accompany the quantifications.
    • Typo: 106,647 ("early") of 178,529 probes
    • Figure 2D: DNA methylation levels in somatic cell lines usually have a bimodal distribution with highly and lowly methylated regions, thus the representation of the data with a boxplot can be misleading.
    • Figure 3E: The no. of colonies after IAA removal (from figure 3D) is not included, as suggested from the text.
    • Figure S3E: Aneuploidy will be dependent on number of cell divisions so it would be helpful if authors specified how long after treatment the experiment was performed.
    • Figure S4B typo: On top track blue compartment is annotated as DLD1-H, while I think it should be DLD1-B2/B3?
    • It would be helpful if the authors include an example image of how the segmentation and quantifications for figure 4A and 4B-C were performed as a supplementary figure, demonstrating the area they consider as periphery.
    • Figure 3B-C have no error bars and figure legend mentions N>15643 cells per condition. It would be helpful if the number of cells per condition is included in the legend and error bars are included in the figure.
    • The authors note that there must be a cooperative effect of DNMT1 and DNMT3B in maintaining DNA methylation and that they observe a strong additive effect in cell survival in double DNMT1/3B depleted cells. These observations have already been observed in the past in HCT116 cells, so it would be useful to cite these papers along with their observations. For e.g. Rhee et al., 2002 Nature, Cai et al., 2017 Genome Research
    • A degron tagged DNMT1 in HCT116 cells has already been shown at Onoda et al 2022 bioRxiv that would be good to reference. While the authors in this preprint do not perform any characterisation of methylation levels of the tagged line as in this work, it provides a similar in vitro model that is helpful to include.
    • The effects of extensive hypomethylation due to the lack of DNMT activity and its effect in 3D genome integrity has also been shown in the best and would be helpful to mention. For e.g. Du et al., 2021 Cell Reports

    Significance

    The authors in this work generate and characterise an untransformed (DLD-1) and cancer (RPE-1) cell line model of DNMT1 with a degron tag, as well as DNMT3BKO line of DLD-1 with the degron tagged DNMT1. These in vitro degron models allow for acute deletion of DNMT1 and induced hypomethylation and can be valuable tools to study the effect of DNA methylation in other epigenetic marks and cellular processes. The authors demonstrate the role of DNMT1 and DNMT3B and their cooperativity in maintaining DNA methylation levels in these cells, as previously demonstrated in similar somatic cell models. They also characterise the fitness of these cell lines after DNMT1 degradation and note their viability over DAC and GSK3685032 treatments that can have secondary cytotoxic effects. However, the viability of the cells and the reasons of observed lethality in some systems is underexplored, with the extent of hypomethylation in each system not specified. Finally, the authors show that DNMT1 and DNMT3B impact heterochromatin and the loss of DNA methylation leads to changes in chromatin compartmentalization (with HiC), which have been observed before. While the DNA methylation levels and chromatin organisation of DLD-1 cells was investigated, the authors do not provide any characterisation of these in RPE-1 cells. Furthermore, it appears that RPE-1 cells show more pronounced cell cycle defects and reduced viability hinting towards p53 dependent apoptosis due to loss of methylation, something which is not extensively explored. These observations suggest that the viability of the DLD-1 cells is 'DLD-1 specific'/p53 dependent and not due to the degron system overall. Nevertheless, these in vitro tools will be highly valuable in the epigenetics and specifically DNA methylation fields and their more comprehensive characterisation and will be of high significance.

    My field of expertise lies within DNA methylation mechanisms and have limited expertise in HiC experiments.

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    Referee #3

    Evidence, reproducibility and clarity

    The manuscript by Scelfo et al. describes the establishment of an auxin-inducible degron system for depleting DNMT1 in human cancer and immortalized cell lines. Using this system, the authors show that lack of DNMT1 leads to a profound passive loss in 5mC that is enhanced in the context of DNMT3B knock-out. The decrease in 5mC is further associated with cell-cycle arrest in G1. In addition, they demonstrate through microscopy that the peripheral distribution of heterochromatin in the nucleus depends on DNA methylation. By running Hi-C analysis, the authors further show that specific chromatin domain interactions depend on DNMT1 levels while others depend on DNMT3B. Finally, restoring DNMT1 levels through auxin wash out, although with different kinetics, partially alleviates the above-mentioned effects of DNMT1 loss.

    This is a well-designed study and in general the data support the conclusions that are drawn by the authors. I have one concern though regarding the description of the genomic distribution of DMPs (Page 11 of the text and Fig. S2H and S2I). Indeed, the authors indicate that "DNAme at active promoters was unaffected" but Fig. S2H and S2I show that, if I understood correctly how data are represented, 2.5 % (S2H) to 5 % (S2I) of DMPs are falling within the active promoter category. I agree that these are under-represented when compared to the % of CpGs falling in this category in the Epic array but one cannot say that no DNAme change is targeting these regions. A similar concern applies to the description of Fig. S2J,K. In addition, I found the authors could describe and justify in a more detailed way their choice of the two cell lines used in the present study (DLD1 and RPE cells). Also, it would be useful to have information on the cell cycle duration in these cells in order to be able to fully interpret the impact of DNMT1 loss on cell cycle in the time frame used here. Finally, information regarding the cell lines used to acquire data is missing in a number of figure legends. This is the case for instance for Fig. 1B,D,E,F,G, Fig. 3A and Fig. S3E in which it is not specified whether the authors used RPE or DLD1 cells.

    Significance

    The novel system described here will certainly be of interest to researcher in the field of DNA methylation and chromatin organization. This article presents convincing and original data showing that DNMT1 levels can be reversibly down-regulated through the auxin-inducible degron thus providing opportunities to study the effects of DNA methylation loss on chromatin organization without the drawbacks usually observed in long-term KO experiments or treatment with toxic DNMT inhibitors. Example of such data obtained with the degron system are convincingly showing that peripheral heterochromatin relies on DNA methylation by DNMT1 and that interaction of heterochromatic domains also depend on DNMT1 activity. Another original finding is that the spatial organization of different silent chromatin domains can also depend on DNMT3B activity, independently of DNMT1. The fact that DNMT1 levels can be restored after wash out of auxin medium is probably one of the most interesting aspects of this study since it allows to run assays that are not possible in the context of DNMT KO experiments. Using this strategy, the authors demonstrated that, concomitant to an increase in DNA methylation, heterochromatin relocates to the periphery of the nucleus and that DLD1-BA compartmentalization is restored. In this respect, the authors observed that compartmentalization of B4 is rapid and near complete at a time when DNA methylation recovery is still partial, suggesting that DNMT1 could have catalytic-independent roles in this process. Although this is possible, another explanation could be that a partial re-methylation of DNA is sufficient for recovering homotypic interactions.

    Regarding Hi-C data, similar results obtained with DNMT1/DNMT3B DKO and 5-aza-deoxycytidine-treated HCT116 cells were already described in a previous study from the authors (Spracklin et al. Nat Struct Mol Biol, 2023). However, differences in the reorganization of chromatin contacts after auxin treatment of DLD1 cells compared to 5-aza-dC or DKO HCT116 cells can be evidenced and are possibly linked to a difference in the organization of heterochromatin between DLD1 cells and HCT116, highlighting the usefulness of running these analyses in different cell types.

    Although not really crucial in the context of the present study, information on the transcriptomic changes induced by DNMT1 loss could add some insights into the cellular state induced by auxin treatment. Indeed, cells are arrested in G1 and peripheral heterochromatin seems to undergo spatial rearrangement. This is reminiscent of senescence-associated processes and a loss of DNA methylation during replicative aging has already been documented. Especially, knock-down of DNMT1 is known to trigger premature senescence entry (Cruickshanks et al., Nat Cell Biol, 2013). Hence, a further characterization of the G1-arrested cells upon auxin treatment would clearly add some value to the manuscript.

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    Referee #2

    Evidence, reproducibility and clarity

    Summary:

    In this paper, Scelfo, A. et al. investigated the mechanisms underlying the cooperative maintenance of DNA methylation by DNMT1 and DNMT3B. Using a rapid degradation of DNMT1 by the auxin-inducible Degron system, which allows the assessment of reversible and time-dependent effects of DNMT1 loss with low cytotoxicity, the authors revealed a cooperative activity between DNMT1 and DNMT3B to maintain DNA methylation. Furthermore, they showed that gradual loss of DNA methylation is accompanied by progressive and reversible changes in heterochromatin abundance, compartmentalization, and peripheral localization. Collectively, this study provides a new cellular model to investigate the fundamental and biological role of DNA methylation and the molecular mechanism underlying its establishment and maintenance.

    Important comments:

    1. Are there any data showing no change in DNA methylation level of WT DLD-1 and DNMT NA DLD-1 cells?
    2. In Fig. S2A and S2D, differences in global DNA methylation between NA-DNMT1-IAA-Day4 and DAC-treated cannot be determined from these images alone because the blot intensities appear similar. It is better to present the results in a more quantitative manner with appropriate statistical analysis.
    3. In Figure 2D, it is better to present the results in a more quantitative manner with appropriate statistical analysis.
    4. In Figures 3A and S3B, the authors show that DNMT1 depletion decreases the percentage of cells in S phase and increases the percentage of cells in G1 phase and sub-G1 phase in NA-DNMT1/DNMT3B KO. The authors postulate that the decrease in cell proliferation after DNMT1 depletion is due to activation of p53. Data demonstrating activation of the p53 pathway are needed.

    Minor comments:

    1. Regarding IAA-induced DNMT1 degradation, the authors should provide complete DNMT1 blots to show that no additional isoforms are present.
    2. In Fig. S1C. Molecular weight was not labeled in the immunoblot.
    3. In Fig. S2B. NeonGreen-IAA 10days images appear to be exposed for different lengths of time.
    4. In Fig. 4B-C, S1E, S2E. It is better to present the results in a more quantitative manner with appropriate statistical analysis.
    5. Contrary to Fig. S2A, the PCA analysis does not seem to show any difference between NA-DNMT1-IAA Day2 and Day4.

    Significance

    Collectively, this study provides a new cellular model to investigate the fundamental and biological role of DNA methylation and the molecular mechanism underlying its establishment and maintenance.

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    Referee #1

    Evidence, reproducibility and clarity

    In this manuscript, Scelfo et al. describe how different DNMTs cooperate to maintain DNA methylation and the impact of decreased DNA methylation on chromatin structure. Toward this, they established an inducible degradation system for DNMT1 in untransformed and cancer cell models. The experiments revealed that DNMT1 and DNMT3B are required to maintain and control DNA methylation patterns throughout the genome. The authors also demonstrate that heterochromatic regions are highly susceptible to DNA demethylation, with loss of their localization to the nuclear periphery and disappearance of their compartmentalization patterns. Together, this work will allow better temporal resolution analysis of DNA methylation abnormalities and will be useful for clarifying the role of DNA methylation and its regulatory mechanism.

    Major comments:

    1. In Figure 2G, the authors report increased DNA methylation at selected loci in the absence of DNMT3B and suggest a compensatory role for DNMT1 in de novo methylation, as this increased DNA methylation is lost upon DNMT1 depletion. However, how do the authors rule out the possibility that this methylation is catalyzed by DNMT3A and maintained by DNMT1?
    2. In Figure 3, the authors demonstrate that DNMT1 depletion leads to cell cycle arrest at G1. Since p53-proficient RPE-1 cells showed faster G1 arrest (Figure S3B), the authors suggest that DNMT1 depletion activates the cell cycle checkpoint. The authors might want to check p53, p16, and p21 levels in line with their suggestion.
    3. Figure 4F and 4G demonstrate a global reduction of H3K9me3 levels upon DNMT1 depletion. Is a similar effect seen with H3K27me3?

    Significance

    Understanding how DNMTs regulate chromatin structure and cell fitness is critical to understand better how DNA methylation impact cell fate and function.

    Strength: This work established an inducible DNMT1-degradation system with reversibility, temporal control, and low toxicity.

    Weakness: this work is merely descriptive and preliminary to understand the observed phenotypes clearly.

    Advance: Although the presented experiments are accurate and well-designed, it has already been reported that DNMT1 and DNMT3A/3B cooperate to maintain DNA methylation patterns and that reduced DNA methylation leads to the disruption of H3K9me3/HP1-enriched heterochromatin structure. In addition, the molecular understanding underlying these phonotypes remains unexplored. In its current form, the contributions of this study to the field will be limited.

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    In this study the authors analyse the impact of removal of the maintenance DNA methyltransferase DNMT1 in human cells using a degron system. This follows on from previous work on the role of DNMT1 in maintaining mammalian DNA methylation patterns and the effect that its removal has on cellular phenotypes. While degron tagged DNMT1 has been reported in the literature, this study did not examine phenotypes in detail (Kikuchi et al 2022, https://doi.org/10.1038/s41467-022-34779-4). The study focuses on characterisation of the system, before examining how degradation of DNMT1 affects DNA methylation patterns, cellular phenotypes and genome organisation.

    Based on their study, the authors conclude that:

    - Degradation of tagged DNMT1 results in demethylation of the genome to a greater extent than 5-aza-2'-deoxycytidine

    - Removal of DNMT3B results in greater hypomethylation upon DNMT1 degradation

    - DNMT1 degradation has minor effects on cellular fitness compared to previous reports using alternative approaches

    - Changes in heterochromatin organisation are observed upon DNMT1 degradation

    Overall, this is a very interesting study making the first use of a degron approach to understand DNMT1 function. However, the study suffers from a lack of discussion about how the main cell line model used might be responsible for differences in cellular phenotypes compared to previous work. In addition, some of the analyses could be conducted in a more statistically robust fashion and to greater depth.

    Specific comments:

    - The authors engineer the degron tag into both DLD1 and RPE1 cells. They highlight that they use two cell lines in the abstract but the vast majority of studies are undertaken on DLD1 cells with very little work done in RPE1 cells. The study would benefit from greater replication and comparison between the two cell lines.

    - The blot showing increased DNMT3B upon degradation of DNMT1 in the DLD1 cells do not appear to be quantified or replicated (Fig 1D).

    - There are no replicates for the Infinium array experiment. It is also unclear how generalisable these results are to cell lines/types.

    - The losses of DNA methylation observed upon depletion of DNMT1 are reported to be biased towards certain chromatin contexts. Given that DNMT1 is thought to act on the whole genome, we wondered why losses of methylation might show this bias?

    - DNMT3B loss is reported to be associated with decreased methylation at heterochromatin. This is puzzling given that DNMT3B is thought to be predominantly recruited to H3K36me3 in gene bodies. Can the authors discuss why this might be the case?

    - No quantification or statistics are presented when describing the results shown in heatmaps shown in figure 1F-I. Note that the beta value threshold applied to DMPs displayed in the heatmaps differs from that in other analyses due to the high numbers of DMPs. These results might be better displayed in an alternative fashion to show all DMPs.

    - The chromatin states used to characterise the differentially methylated probes come from HEPG2 liver carcinoma cells. It is unclear these data were used as ChIP-seq data are available for colorectal cancer cell lines in ENCODE. It would have also been nice to see verification of major conclusions using ChIP-seq data from DLD1 cells.

    - The authors observe some regions that gain methylation in the DNMT3B KO cells. They suggest that this could result from uncontrolled DNMT1 activity but do not provide evidence that this is the case and their appear to be other potential reasons. For example, the DNMT3B KO cells have been through multiple rounds of clonal selection. Could selection for clones that survive due to a higher level of methylation at some sites explain the gains of methylation observed?

    - One of the major claims of the manuscript is that DNMT1 depletion by degron is less toxic to cells than deletion of the gene or pharmacological inhibition. However, the authors state that the DLD-1 cell line used is p53 deficient. Previous studies have shown that DNMT1 KO causes p53-dependent apoptosis (Jackson-Grusby et al 2001 https://doi.org/10.1038/83730). In addition, upregulation of p53 was noted upon inducible deletion of DNMT1 in HCT116 cells in the Chen et al (https://doi.org/10.1038/ng1982) that the authors directly compare to ('In sharp contrast to previous reports using a inducible DNMT1 KO in HCT116 colorectal cancer cells…'). The study would be improved by work to disentangle whether the degron approach p53 deficiency underpin the differences compared to previous work (for example through greater exploration of the differences observed in RPE1 cells).

    - The authors include a comparison with GSK3685032 but do not show how the dose used affects DNA methylation levels. A recent study also suggests that a related compound, GSK3484862, results in DNMT1 degradation (Chen et al 2023 https://doi.org/10.1093/narcan/zcad022). It would be interesting to know whether the doses of GSK3685032 used by the authors also cause DNMT1 degradation and whether this is to a level similar that observed with the degron.

    - The authors suggest that high-numbers of mitotic errors are unlikely to explain the reduced proliferation potential observed in their experiments because they observed low levels of aneuploidy using single cell sequencing. However, this is an indirect assay and the study would be strengthened by a direct examination of mitosis. In addition, we were unclear how the level of aneuploidy observed in single cell sequencing relates to the rate of mitotic error and note that the percentage of aneuploidy observed increased from 12.2 to 20% in the treated cells. This result does not appear to be analysed by statistical testing.

    - The authors observe the inactive genomic compartment associated with H3K9me3 occupies a very small proportion of the genome. How does this compare to the staining in Figure 4A? Also was the loss of H3K9me3 reported upon DNMT1 degradation/ DNMT3B deletion seen in this staining. Can robust conclusions be drawn from the ChIP-seq as it does not appear to have been conducted using a spike-in control?

    - How do the changes in genome organisation observed by HiC correspond to changes in DNA methylation. Could there be off target effects of the degron that might affect other proteins required for nuclear organisation?

    Minor comments:

    - Figure 2D. The distribution of Beta values derived from Infinium arrays is generally bimodal. As such representing the data as a boxplot in Fig 2D obscures the true distribution and an alternative type of plot would be preferable for this figure.

    - It was unclear whether hypomethylated probes and hypermethylated probes in the Infinium array analysis were considered together when analysing enrichments in genomic annotations and chromatin annotations (Fig 2E and SH to K).

    - On Page 9 the authors suggest that in comparison to DAC 'The total demethylation achieved using this system is approximately 4 times higher…'. This statement appears to be based on the number of significant probes. However, given that Infinium array data only measure a small percentage of CpG sites in the genome making it difficult to conclude anything about the total level of demethylation. The previous analyses in Figure S2A and D also contradict this statement. This sentence would be clearer if it was rephrased.

    - Figure 2E it is unclear if only selected significances are shown or if all were tested and others were not significant. Several of the bars look very similar to each other but only some are starred indicating they are significant.

    - Figure S2J,K. We were unsure how this analysis was conducted. The graph appears to imply that 100% of probes on the EPIC array are located within repeat elements.

    - The authors cite Lehnertz et al 2003 in support of the statement 'it has been proposed that DNAme can influence H3K9me3 deposition'. However, that study states that deletion of DNMTs did not alter H3K9me3: 'In contrast, H3-K9 trimethylation at pericentric heterochromatin is not impaired in Dnmt1 single- or Dnmt3a/Dnmt3b double-deficient ES cells.'.

    - It is unclear what the dots represent in figure 4B and C? Are these means from replicates? It would be useful to see the overall distribution of the data from these analyses.

    Written by Duncan Sproul on behalf of the members of the Sproul group following a journal club discussion during labmeeting.

    Competing interests

    The author declares that they have no competing interests.

  7. Version published to 10.1101/2023.05.04.539406v1 on bioRxiv