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Reply to the reviewers

We sincerely thank all three reviewers for their professional and constructive feedback. We appreciate the thorough evaluation of our manuscript and are committed to revising both the manuscript and supplemental materials based on the suggestions. We have carefully considered each comment and have addressed most of them in the initial revised version, which has been transferred. Additionally, we are currently conducting new experiments to provide the requested data to address a few comments. We are confident that these revision experiments will be completed in a couple of months or so, which will significantly enhance the quality of our study.

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

In the manuscript by Agarwal and Ghosh, the authors examine yeast Scm3 function in the DNA damage response. They show that Scm3 loss results in DNA damage sensitivity and more Rad52 foci. Importantly, Scm3 is recruited to DSB sites using an HO-cut site and its loss results in an attenuated DNA damage checkpoint as measured by Rad53 phosphorylation. The authors demonstrate convincingly that Scm3, like its human counterpart HJURP, plays a role in the DNA damage response through altered Rad53 activation. However, what its specific role in DNA repair is remains ambiguous.

Major comments:

1. It is unusual to see multiple DNA repair foci as those observed in Figure 2B. What is the distribution of cells with 1, 2, 3, 4, or more foci? Are more observed in SCM3-AID cells perhaps suggesting that the DSB ends are not being clustered as would be expected in WT cells exposed to DNA damage?

Response: As per the reviewer’s comment, we have included a graph (Figure R2) showing the distribution of cells with 1, 2, 3, 4, or more Rad52-GFP foci when they are treated with MMS. There are more cells with 4 or more foci when Scm3 is depleted (SCM3-AID + Auxin) compared to the wild type (SCM3-AID). The average number of Rad52-GFP foci per cell presented in Figure 2B (2.8 in the mutant vs. 1.9 in the wild type) is well in accord with the previous report (Conde and San-Segundo, 2008), where the same was reported as ~2.5 in the cells lacking a methyl transferase Dot1, vs ~ 1.5 in the wild type. More Rad52-GFP foci in MMS-treated cells lacking Scm3 may arise due to the creation of too many damaged sites to be accommodated in 1-2 foci and/or due to the inability of the cells to cluster the DSB ends.

This result has been incorporated as a new supplementary Figure S4C and new text has been added in the revised manuscript as: “We further quantified the distribution of cells with 1, 2, 3, or >4 Rad52-GFP foci in wild type (SCM3-AID) or Scm3 depleted (SCM3-AID + auxin) cells treated with MMS. Scm3 depleted cells showed a significantly higher number of cells with more than >4 Rad52-GFP foci, suggesting the possibility of the creation of too many damaged sites to be accommodated in 1-2 foci or the inability of such cells to cluster the DSB ends.” in page 7, lines: 237-241.

2. The peaks with increased Scm3 recruitment by ChIP-seq upon MMS is confusing as MMS does not induce specific damage at genomic locations. Is Scm3 being recruited at other genomic sites that might be more susceptible to DNA damage? Is Scm3 recruited to Pol2 sites for example? Or fragile sites?

__Response: __We believe that the MMS induced increase in association of Scm3 with the non-centromeric chromatin loci depends on MMS sensitive vulnerable chromosomal sites. We agree with the reviewer that MMS might cause DNA damage at these sites, leading to Scm3 occupancy at those sites. Therefore, we compared the sites of Scm3 occupancy with possible such sites available from the literature that include fragile sites, RNA Pol II binding sites, double strand break hotspots, and coldspots. Based on our analysis, we have included the following lines in the ‘discussion’ section in page 16-17, lines 566-594 as follows:

“Moreover, an overall increase in the chromatin association of Scm3 in response to MMS also suggests that Scm3 might be recruited to several repair centers or sites that are susceptible to DNA damage, for example, the fragile sites (Figure 3B, C, E, S6). These sites in yeast are DNA regions prone to breakage under replication stress, often corresponding to replication-slow zones (RSZs) (Lemoine et al., 2005). These regions include replication termination (TER) sequences, tRNA genes, long-terminal repeats (LTRs), highly transcribed genes, inverted repeats/palindromes, centromeres, autonomously replicating sequences (ARS), telomeres, and rDNA (Song et al., 2014). Since the helicase Rrm3 is often associated with these fragile regions (Song et al., 2014), we compared Scm3 binding sites with the top 25 Rrm3 binding sites from the literature (Azvolinsky et al., 2009). In untreated cells, Scm3 sites overlapped with three Rrm3 sites on chromosomes X, XII, and XIV. Whereas in MMS treated cells, overlapping was found with four Rrm3 sites, with two (on chromosomes XII and XIV) shared with untreated cells and two new sites were observed on chromosomes II and XII (Table R1). Mapping of the Scm3 sites with the tRNA genes and LTRs revealed that these sites from the untreated cells did not overlap with the LTRs (Raveendranathan et al., 2006). However, the same from the treated cells showed overlap with two LTRs on chromosome XVI. No overlap with tRNA genes was observed in the treated cells (Table R1). We next examined Scm3 occupancy at 71 TERs documented in the literature (Fachinetti et al., 2010). Scm3 was found to bind to 6 TERs in both untreated and MMS-treated cells. Notably, MMS treatment resulted in three new peaks, while three peaks were shared with untreated samples (Table R1). Lastly, we compared Scm3 sites with top 25 RNA Pol II sites obtained from the literature (Azvolinsky et al., 2009). In untreated cells, Scm3 was found at only one of these Pol II sites, whereas after MMS treatment, Scm3 sites overlapped with four such sites (Table R1). We further checked the occupancy of Scm3 at a few DSB hotspots (BUD23, ECM3, and CCT6) and DSB coldspot (YCR093W) as mentioned in the literature (Dash et al., 2024; Nandanan et al., 2021). However, we did not find Scm3 binding to these sites. Overall, in-silico analysis of the binding sites indicates that the non-centromeric enrichment of Scm3 occurs at sites that are amenable to DNA damage.”

Table R1: The table summarising the occupancy of Scm3 in untreated or MMS treated conditions at the indicated regions

Region

Chromosome

Scm3 occupancy

Untreated

MMS treated

Rrm3 binding sites

Chr II

YES

Chr X

YES

Chr XII

YES

Chr XII

YES

YES

Chr XIV

YES

YES

LTRs

Chr XVI

YES

Chr XVI

YES

tRNA

Chr XV

YES

TERs

Chr IV

YES

Chr V

YES

Chr VI

YES

YES

Chr VII

YES

Chr X

YES

Chr X

YES

Chr XIV

YES

YES

Chr XV

YES

YES

Chr XVI

YES

Pol II binding sites

Chr II

YES

Chr X

YES

Chr XII

YES

Chr XII

YES

Chr XV

YES

The Table R1 has been incorporated as Table S1 in the revised manuscript.

3. The phosphorylation aspect of Scm3 is intriguing and the authors show that Mec1 is not responsible for mediating its phosphorylation. Tel1 is another kinase that should be examined.

__Response: __We thank the reviewer for the suggestion. We are in the process of examining the role of Tel1 kinase on Scm3 phosphorylation. The results from the experiment will be incorporated in the manuscript.

Minor comment: 1. It is hard to see what MMS resistance the authors state is observed in Mif2-depleted cells in Figure S3. Perhaps this could be better explained or the claim removed.

__Response: __We agree with the comment and have removed the claim from the manuscript.

2. Protein levels of Scm3 or any of the other factors depleted with AID were never assessed.

__Response: __We have assessed the protein level of Scm3 and a control protein, tubulin using western blotting as per the reviewer’s suggestion (Figure R3). We did not observe any significant change in the protein levels in SCM3-HA or SCM3-HA-AID cells, suggesting that the AID tagging of Scm3 per se did not make the cells non-functional and the protein was degraded as expected upon addition of auxin. Moreover, the SCM3-AID cells were used previously to examine the effect of Scm3 on kinetochore assembly (Lang et al., 2018).

This result has been incorporated as Figure S2C, and new text has been added in the revised manuscript as: “The depletion of Scm3 was verified by observing a higher percentage of G2/M arrested cells and by western blot analysis verifying degradation of Scm3-AID after auxin treatment (Figure S2B, C).” in page 5, lines: 150-152.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

This manuscript describes a study implicating the Scm3 protein from budding yeast in the DNA damage response (DDR). Scm3 is a chaperone protein, whose main role is considered to be the loading of CENP-A(Cse4) at centromeres to facilitate chromosome segregation. However, the human ortholog of Scm3, HJURP, is known to have a role in DDR and in this study the authors provide evidence that Scm3 is also involved in the DDR in yeast. For the most part, the results presented support the conclusions made.

Main Points

1. Figure 1 Could depletion of Scm3 arrest cells in late G2/M and it is this delay that causes increased sensitivity to DNA damaging agents? A control with nocodazole or other means - that also arrests cells at this point - might provide a nice control for this. Perhaps the other kinetochore mutants, used therein, achieve this control - but cell cycle phase would need to be assessed.

__Response: __ We thank the reviewer for pointing out to a probable effect of the cell cycle stage on the observed MMS sensitivity. In fact, we were also concerned that the observed DNA damage sensitivity in Scm3 depleted cells might be due to G2/M arrest. To rule out this possibility, we monitored Rad52-GFP foci as a marker for DNA damage in the wild type and Scm3 depleted cells both arrested at G2/M using nocodazole (Figure S4). While Scm3 depleted condition exhibited >20% Rad52-GFP positive cells, less than 10% wild type cells showed the same in the absence of any DNA damaging agents (Figure S4E, no MMS, 60 mins). Upon challenging these cells with MMS in the presence of nocodazole, Scm3 depleted condition exhibited over 40% Rad52-GFP positive cells, whereas less than 20% wild-type cells harboured Rad52-GFP. This significant increase in Rad52-GFP positive cells when Scm3 is depleted clearly indicates that the observed MMS sensitivity in these cells is due to the absence of Scm3 rather than due to an effect of a cell cycle stage. Furthermore, we have also used Cdc20 depleted G2/M arrested cells as a wild type control to test the activation of the DNA damage checkpoint by Rad53 phosphorylation. These cells showed robust Rad53 activation in response to MMS, in contrast to poor activation in Scm3 depleted cells (Figure 6), suggesting that G2/M arrest is not the reason for the DNA damage sensitivity observed in the latter cells.

However, as per the reviewer's suggestion, we examined the MMS sensitivity of the wild type cells arrested at G2/M by nocodazole. As expected, these cells did not show increased sensitivity which further confirms that the DNA damage sensitivity observed in the scm3 mutant is not due to G2/M arrest (Figure R4B). This result has been incorporated within Figure S3, replacing the earlier Figure S3.

To include this result, we have included new text, and revised the result section in page 5-6, lines 160-181 as follows:

“The increased sensitivity of scm3-depleted cells to DNA-damaging agents could be due to the weakening of the kinetochores as Scm3-mediated deposition of Cse4 promotes kinetochore assembly or due to the delay in cell cycle, as Scm3 depleted cells arrest in late G2/M phase (Camahort et al., 2007; Cho and Harrison, 2011). If either of these holds true, perturbation of the kinetochore by degradation of other kinetochore proteins or wild type cells arrested at metaphase must show a similar sensitivity to MMS. In budding yeast, Ndc10 is recruited to the centromeres upstream of Scm3 (Lang et al., 2018), whereas the centromeric localization of Mif2, another essential inner kinetochore protein, depends on Scm3 and Cse4 (Xiao et al., 2017). We constructed NDC10-AID and MIF2-AID strains and used them for our assay to represent the proteins independent or dependent on Scm3 for centromeric localization, respectively. We also included one non-essential kinetochore protein, Ctf19, a protein of the COMA complex, to remove any possible mis-judgement in distinguishing cell-growth-arrest phenotype occurring due to drug-sensitivity vs. auxin-mediated degradation of essential proteins. The COMA complex is directly recruited to the centromeres through interaction with the N terminal tail of Cse4, hence dependent on Scm3 (Chen et al., 2000; Fischböck-Halwachs et al., 2019). Mid-log phase cells were harvested and spotted on the indicated plates, however, we did not observe any increased sensitivity of such cells to MMS (Figure S3). Further, wild type cells, when challenged in the presence of nocodazole and MMS, also did not show any increased sensitivity to MMS. Therefore, the increased sensitivity to MMS in scm3 mutant but not in other kinetochore mutant or metaphase arrested cells indicates that Scm3 possesses an additional function in genome stability besides its role in kinetochore assembly.”

Further we have also revised the discussion section to include the observed results in page 15, lines 502-510 as follows:

“However, since the primary function of Scm3 is to promote kinetochore formation by depositing Cse4 at the centromeres, it is important to address if the observed sensitivity is due to perturbation in kinetochores or due to cell cycle delay imposed in the absence of Scm3. Therefore, we similarly partially depleted two essential kinetochore proteins, Ndc10 and Mif2, and deleted one non-essential kinetochore protein, Ctf19, in separate cells and also challenged wild type cells to metaphase block but failed to detect any increased sensitivity to DNA damage stress (Figure S3). These results indicate that the drug sensitivity phenotype of Scm3 depleted cells is not due to weakly formed kinetochores or cell cycle delay.”

2. Mutants of the HR pathway in yeast (e.g. rad52∆ with mre11∆ for example) are typically epistatic. The observation that Scm3 depletion is not epistatic with rad52∆ (Figure 1C) suggests the Scm3 acts via another pathway than the classic Rad52 HR pathway. This should be pointed out and discussed.

__Response: __We have now included the discussion “In yeast, although HR is the preferred repair pathway, in the case of perturbed HR, an alternate pathway named non-homologous end joining (NHEJ) can occur. The absence of epistatic interaction between SCM3 and RAD52 (Figure 1C) suggests that Scm3 may function in ways other than the Rad52-mediated classical HR pathway. In this context, it would be interesting to test how Scm3 might interact with the key proteins of the NHEJ pathway, such as Ku70/Ku80 and Lig4 (Gao et al., 2016). It is possible that Scm3 may promote a certain chromatin architecture facilitating the DSB ends to stay together to be accessible for NHEJ-mediated end joining.” in page 16, lines 541-548.

3. Figure 2 should include auxin treatment of RAD52-GFP cells (without the Scm3 degron) to show that the auxin treatment alone does not increase Rad52 foci.

__Response: __ We performed the suggested experiment and did not observe any significant increase in Rad52-GFP positive cells when treated the cells with auxin+DMSO as compared to only DMSO (Figure R5).

This result has been incorporated as a new supplementary Figure S4A,B and new text has been added in the revised manuscript as “To rule out the possibility that auxin treatment alone can cause increased Rad52-GFP foci formation, we challenged the wild type (RAD52-GFP) cells with auxin or DMSO and counted the number of cells with Rad52-GFP foci. We did not observe any increase in Rad52-GFP positive cells when treated with auxin+DMSO as compared to only DMSO (Figure S4A, B).” in page 7, lines: 233-236.

4. Line 246-247 For the data presented, it seems to me possible that Scm3 depleted cells may indicate a defective DDR pathway (as stated) or may indicate defects in DNA replication or an increase in some other form of DNA damage?

__Response: __We agree with the reviewer’s comment that the depletion of Scm3 can cause replication error or other form of DNA damage in addition to the defect in DDR pathway. To include this, we have modified the sentence as “Taken together, Scm3 depleted cells exhibit more Rad52 foci, indicating a compromised DDR pathway in these cells. Although, defects in DNA replication or creation of other DNA lesions producing more foci also cannot be ruled out.” in page 8, lines 255-257.

5. In Figure 1 and throughout, please describe in the figure legends how error bars and p values are derived, and the number of experiments involved.

__Response: __We have now verified all the figure legends and described how error bars and p values are derived and have mentioned the number of experiments involved.

Minor points Line 35 replace 'cell survival' with 'cell division' - non-dividing cells can survive fine without chromosome segregation. See also line 62.

__Response: __We have now changed ‘cell survival’ with ‘cell division’ in lines 35 and 62.

Line 52 and throughout, I suggest replacing CenH3 with CENP-A or Cse4. The term CenH3 is confusing since regional centromeres contain both CENP-A nucleosomes and H3 nucleosomes - the latter of which can also be called CenH3 nucleosomes.

__Response: __We have replaced CenH3 with CENP-A or Cse4 at the appropriate locations.

Lines 69-79 specific references are needed for the sentences starting "HJURP was so named...", "In addition,...", "As a corollary,..." and "Finally,..." The final sentence of this paragraph, starting "Perhaps due to..." is unclear.

__Response: __We have included the reference as mentioned by the reviewer. Also, we have changed the last line as “Notably, HJURP has been visualized to be diffusely present throughout the nucleus (Dunleavy et al., 2009; Kato et al., 2007), which may be due to its global chromatin binding and involvement in DDR.” in page 3, lines 77-79.

Line 96 "gross chromatin" is unclear; also line 476.

__Response: __We have changed gross chromatin to “bulk of the chromatin.” and incorporated it into the main text.

Line 103 "dimerize"

__Response: __We have replaced ‘dimerizes’ with ‘dimerize’

Line 109 "most" and "highly" don't work together - perhaps better to say "the functions appear conserved from humans to yeast".

__Response: __We have changed the wording as the reviewer suggested.

Line 175 "grown" to "phase", see also line 223.

__Response: __We have changed the wording as the reviewer suggested.

Line 293 delete "besides"

__Response: __We have deleted the word ‘besides’.

Figure 5 - panels C & D, please make x axis labels clearer - they are directly underneath the 2kb ChIP. They should include a horizontal bar to indicate that all 5 ChIP experiments are included in each time point.

__Response: __We have now included a horizontal bar in both Figure 5 and the corresponding supplementary Figure S8, to better represent the ChIP experiments. We thank the reviewer for pointing this out.

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

Summary This manuscript studies the role of the Cse4 histone chaperone Scm3 in the S. cerevisiae DNA damage response. The authors show that decreased Scm3 levels exhibit genetic interactions with mutations in the Rad52 gene and sensitivity to MMS. They go on to show that some Scm3 co-localizes with sites of DNA damage using spreads and ChIP-seq techniques and that decreased levels of Scm3 have a reduced DNA damage checkpoint response. The Scm3 protein is also phosphorylated in response to DNA damage. Taken together, the authors propose a model whereby DNA damage recruits the Scm3 protein and Scm3 then helps mediate the checkpoint response. Overall, the data make a case that Scm3 has a relationship to the DNA damage checkpoint but the authors should be careful not to over-conclude that it has a precise role in checkpoint activation based on the data.

Major Comments 1. The western blots in the paper are not always entirely convincing. In addition, they are not described in enough detail to understand if a membrane was cut or if multiple gels were run. For example, the tubulin loading in Figure 6D is interrupted toward the end of the blot and the bands in Figure 7D go in different directions for the two blots for the MMS treated cells. In figure 6B, there are no detectable phospho-forms of Rad53 detected on the upper blot for the WT and scm3 lanes even though quantification is given on the right. It would be good to present better examples of the westerns or at least better describe what the reader is visualizing so the quantification and conclusions can be understood. How were the blots quantified? How were the westerns run and processed?

Response: We have now included a separate paragraph in materials and methods regarding the gel run, processing and quantification of the western blots in the revised manuscript for better understanding of the readers:

“To detect Scm3-6HA, Rad53, and g-H2A, the total proteins isolated from the appropriate cells were run on 12%, 8%, and 15% SDS gels, respectively. The proteins were transferred to the membranes, which were cut to detect the above proteins and the control protein tubulin separately. For the quantification of the bands on the western blots, a region of interest (ROI) was made around the band of interest, and the intensity of the band was calculated using ImageJ. A same ROI from a no-band area of the blot was used to calculate the background intensity. The background intensity was subtracted from the band intensity. The same process was done for the tubulin bands. The intensity of the target bands (Scm3-6HA, Rad53, and g-H2A) was divided by control tubulin band intensity to get the normalized values for the target bands, which were plotted using GraphPad Prism 9.0 (Version 9.4.1) software.” This has been added in page 25, lines: 881-890.

Furthermore, we will again perform the experiments for a better representation of the western blots in figures 6B, D, and 7D.

2. The argument that scm3 depletion leads to a defect in DNA damage checkpoint activation is not strongly supported. Monitoring exit from the cell cycle by multibudding is not the most rigorous assay, especially since the image shows one cell with 5 nuclei. The authors should release cells from G1 into auxin and MMS and monitor cell cycle progression at least one other cell cycle marker, such as anaphase onset, DNA replication and/or Pds1 levels.

Response: As per the reviewer’s suggestion, in order to support our argument that the absence of Scm3 causes a defect in DNA damage checkpoint activation, we will examine if these cells abrogate G2/M arrest and show an early anaphase onset. For this, we will monitor the levels of Pds1, as a marker of anaphase onset, along the cell cycle in wild type and Scm3-depleted cells both deleted for Mad2 to remove any inadvertent effect of spindle assembly checkpoint. The schematics of the experimental workflow is given in Figure R1. Typically, the cells will be released from alpha factor arrest in the absence or presence of auxin (for the depletion of Scm3) and in the absence or presence of MMS. The samples will be harvested at the indicated time points and will be analyzed for:

1. Western blot: Pds1-Myc (to detect anaphase onset)
2. Western blot: Rad53 and p-Rad53 (to detect DNA damage activation)
3. Immunofluorescence: Tubulin (to detect cell cycle stages) The results of the above experiment will be incorporated in the revised manuscript.

3. The quantification in Figure 3B is not clear. Is it done on a per/nuclei basis? What pools of Scm3 and Ndc10 are being normalized?

Response: The intensity was calculated as done before (Mittal et al., 2020, Shah et al., 2023). Typically, the intensity was first measured from the total signal of Scm3/Ndc10 from each chromatin mass or spread (DAPI) by making a polygon (ROI) around the Scm3/Ndc10+DAPI signal. The same ROI was dragged to the background area, from where two separate intensities were calculated. The average of the background intensities was then subtracted from the Scm3/Ndc10 intensity obtained from the same spread to get the normalized intensity depicting each dot in the box plot of Figure 3B. At least 30 spreads were quantified in a similar manner.

We have mentioned this in the materials and methods section under “Microscopic image analysis.” section in page 22, lines 770-777 as follows: “For intensity calculation, a Region of Interest (ROI) was drawn around the Scm3/Ndc10/g-H2A+DAPI signal, and the intensity of Scm3/Ndc10/g-H2A was measured from each chromatin mass or spread (DAPI). An ROI of the same size was put elsewhere in the background area, from where two separate intensities were calculated. The average of the background intensities was then subtracted from the Scm3/Ndc10/g-H2A intensity obtained from the same spread to get the normalized intensity depicting each dot in the box plot of the respective figures as mentioned previously (Mittal et al., 2020; Shah et al., 2023).”

Minor Comments 1. The model is elegant but there are chromatin pools (beyond the kinetochore pool) of Scm3 that do not contain Rad52 and/or gamma-H2X and vice versa. It would be helpful if the authors could speculate on how to reconcile these different pools. It might be premature to suggest such a detailed model at this point since the function of Scm3 in the checkpoint is still very unclear so I would encourage the authors to make a less detailed model.

Response: By showing the green hallow, we have depicted the nuclear pool of Scm3, and we have not shown that the pool contains DDR proteins viz., Rad52 or g-H2A. Rather, we have shown the recruitment of these proteins at the DNA damage sites. Since the focus of this manuscript is on the non-centromeric functions of Scm3, we have not shown the kinetochore pool of Scm3. Although the model is a detailed one, the contribution from this work has been mentioned legitimately at every stage so that the readers can judge the merit of this work. We believe that a detailed model would provide a better perspective to the readers to correlate the revealed as well as yet-to-reveal functions of Scm3 in a spatiotemporal manner with the other players of the DDR pathway. Therefore, we prefer to keep the model in a detailed form.

2. The chip-seq data is not publicly accessible. There is no reference to the data being available to review.

__Response: __The data will be uploaded to the public domain.

3. Line 103: not clear what "both the proteins dimerize" means...probably should be "both proteins dimerize"

__Response: __We have changed the wording to “both proteins dimerize”.

4. The argument that Ndc10 does not have a growth defect on MMS is a weak conclusion given that almost no control cells grow on auxin in the absence of MMS.

__Response: __We have now repeated the spotting assay with a lesser concentration of auxin and replaced Figure S3 with a new Figure S3 (Figure R4) to better represent and conclude that the loss of Ndc10 does not cause MMS sensitivity.

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Referee #3

Evidence, reproducibility and clarity

Summary

This manuscript studies the role of the Cse4 histone chaperone Scm3 in the S. cerevisiae DNA damage response. The authors show that decreased Scm3 levels exhibit genetic interactions with mutations in the Rad52 gene and sensitivity to MMS. They go on to show that some Scm3 co-localizes with sites of DNA damage using spreads and ChIP-seq techniques and that decreased levels of Scm3 have a reduced DNA damage checkpoint response. The Scm3 protein is also phosphorylated in response to DNA damage. Taken together, the authors propose a model whereby DNA damage recruits the Scm3 protein and Scm3 then helps mediate the checkpoint response. Overall, the data make a case that Scm3 has a relationship to the DNA damage checkpoint but the authors should be careful not to over-conclude that it has a precise role in checkpoint activation based on the data.

Major Comments

1. The western blots in the paper are not always entirely convincing. In addition, they are not described in enough detail to understand if a membrane was cut or if multiple gels were run. For example, the tubulin loading in Figure 6D is interrupted toward the end of the blot and the bands in Figure 7D go in different directions for the two blots for the MMS treated cells. In figure 6B, there are no detectable phospho-forms of Rad53 detected on the upper blot for the WT and scm3 lanes even though quantification is given on the right. It would be good to present better examples of the westerns or at least better describe what the reader is visualizing so the quantification and conclusions can be understood. How were the blots quantified? How were the westerns run and processed?

2. The argument that scm3 depletion leads to a defect in DNA damage checkpoint activation is not strongly supported. Monitoring exit from the cell cycle by multibudding is not the most rigorous assay, especially since the image shows one cell with 5 nuclei. The authors should release cells from G1 into auxin and MMS and monitor cell cycle progression at least one other cell cycle marker, such as anaphase onset, DNA replication and/or Pds1 levels.

3. The quantification in Figure 3B is not clear. Is it done on a per/nuclei basis? What pools of Scm3 and Ndc10 are being normalized?

Minor Comments

1. The model is elegant but there are chromatin pools (beyond the kinetochore pool) of Scm3 that do not contain Rad52 and/or gamma-H2X and vice versa. It would be helpful if the authors could speculate on how to reconcile these different pools. It might be premature to suggest such a detailed model at this point since the function of Scm3 in the checkpoint is still very unclear so I would encourage the authors to make a less detailed model.

2. The chip-seq data is not publicly accessible. There is no reference to the data being available to review.

3. Line 103: not clear what "both the proteins dimerize" means...probably should be "both proteins dimerize"

4. The argument that Ndc10 does not have a growth defect on MMS is a weak conclusion given that almost no control cells grow on auxin in the absence of MMS.

Significance

Significance

This is the first examination of the role of Scm3 in the DNA damage response in S. cerevisiae. My expertise is in the chromatin and segregation fields, but I believe this work will be of interest to the DNA damage field as well. While the homologs of Scm3 are known to have a role in DNA damage, it was unclear if this was conserved in budding yeast. The data in this manuscript are consistent with findings in other organisms but the precise role of the chaperone in the DNA damage response is still unclear.

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Referee #2

Evidence, reproducibility and clarity

This manuscript describes a study implicating the Scm3 protein from budding yeast in the DNA damage response (DDR). Scm3 is a chaperone protein, whose main role is considered to be the loading of CENP-A(Cse4) at centromeres to facilitate chromosome segregation. However, the human ortholog of Scm3, HJURP, is known to have a role in DDR and in this study the authors provide evidence that Scm3 is also involved in the DDR in yeast. For the most part, the results presented support the conclusions made.

Main Points:

1. Figure 1 Could depletion of Scm3 arrest cells in late G2/M and it is this delay that causes increased sensitivity to DNA damaging agents? A control with nocodazole or other means - that also arrests cells at this point - might provide a nice control for this. Perhaps the other kinetochore mutants, used therein, achieve this control - but cell cycle phase would need to be assessed.

2. Mutants of the HR pathway in yeast (e.g. rad52∆ with mre11∆ for example) are typically epistatic. The observation that Scm3 depletion is not epistatic with rad52∆ (Figure 1C) suggests the Scm3 acts via another pathway than the classic Rad52 HR pathway. This should be pointed out and discussed.

3. Figure 2 should include auxin treatment of RAD52-GFP cells (without the Scm3 degron) to show that the auxin treatment alone does not increase Rad52 foci.

4. Line 246-247 For the data presented, it seems to me possible that Scm3 depleted cells may indicate a defective DDR pathway (as stated) or may indicate defects in DNA replication or an increase in some other form of DNA damage?

5. In Figure 1 and throughout, please describe in the figure legends how error bars and p values are derived, and the number of experiments involved.

Minor points:

1. Line 35 replace 'cell survival' with 'cell division' - non-dividing cells can survive fine without chromosome segregation. See also line 62.

2. Line 52 and throughout, I suggest replacing CenH3 with CENP-A or Cse4. The term CenH3 is confusing since regional centromeres contain both CENP-A nucleosomes and H3 nucleosomes - the latter of which can also be called CenH3 nucleosomes.

3. Lines 69-79 specific references are needed for the sentences starting "HJURP was so named...", "In addition,...", "As a corollary,..." and "Finally,..." The final sentence of this paragraph, starting "Perhaps due to..." is unclear.

4. Line 96 "gross chromatin" is unclear; also line 476.

5. Line 103 "dimerize"

6. Line 109 "most" and "highly" don't work together - perhaps better to say "the functions appear conserved from humans to yeast".

7. Line 175 "grown" to "phase", see also line 223.

8. Line 293 delete "besides"

9. Figure 5 - panels C & D, please make x axis labels clearer - they are directly underneath the 2kb ChIP. They should include a horizontal bar to indicate that all 5 ChIP experiments are included in each time point.

Significance

This is a nice complement to the human work on HJURP and provides convincing evidence that Scm3 can be used to model the function of HJURP. Since yeast is such a tractable model, this work provides a route to study the role of this chaperone in DNA damage repair, which may also be true for human HJURP. The work itself is perhaps not too surprising, but is a solid advance in our understanding of the role of Scm3.

My own expertise is in yeast DNA repair and chromosome segregation.

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Referee #1

Evidence, reproducibility and clarity

In the manuscript by Agarwal and Ghosh, the authors examine yeast Scm3 function in the DNA damage response. They show that Scm3 loss results in DNA damage sensitivity and more Rad52 foci. Importantly, Scm3 is recruited to DSB sites using an HO-cut site and its loss results in an attenuated DNA damage checkpoint as measured by Rad53 phosphorylation. The authors demonstrate convincingly that Scm3, like its human counterpart HJURP, plays a role in the DNA damage response through altered Rad53 activation. However, what its specific role in DNA repair is remains ambiguous.

Major comment:

1. It is unusual to see multiple DNA repair foci as those observed in Figure 2B. What is the distribution of cells with 1, 2, 3, 4, or more foci? Are more observed in SCM3-AID cells perhaps suggesting that the DSB ends are not being clustered as would be expected in WT cells exposed to DNA damage?

2. The peaks with increased Scm3 recruitment by ChIP-seq upon MMS is confusing as MMS does not induce specific damage at genomic locations. Is Scm3 being recruited at other genomic sites that might be more susceptible to DNA damage? Is Scm3 recruited to Pol2 sites for example? Or fragile sites?

3. The phosphorylation aspect of Scm3 is intriguing and the authors show that Mec1 is not responsible for mediating its phosphorylation. Tel1 is another kinase that should be examined.

Minor comment:

1. It is hard to see what MMS resistance the authors state is observed in Mif2-depleted cells in Figure S3. Perhaps this could be better explained or the claim removed.

2. Protein levels of Scm3 or any of the other factors depleted with AID were never assessed.

Significance

As mentioned above, a clear link for Scm3 in DNA damage repair has now been established in this work but its function in this process is descriptive.

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Reply to the reviewers

Manuscript number: RC-2024-02825

Corresponding author(s): Padinjat, Raghu

Key to revision plan document:

Black: reviewer comments

Red: response to reviewer comment-authors

Blue: specific changes that will be done in a revision-authors

1. General Statements [optional]

We thank the reviewers for their detailed comments on our manuscript and appreciating the novelty, quality and thoroughness of the work. Detailed responses to individual queries and revision plans are indicated below.

2. Description of the planned revisions

Reviewer 1:

Summary The study by Sharma et al uses iPSC and neural differentiation in 2D and 3D to investigate how mutation in the OCRL gene affects neural differentiation and neurons. Mutation in the OCRL gene the cause of Lowe Syndrome (LS), a neurodevelopmental disorder. Neural cultures derived from LS patient iPSCs exhibited reduced excitability and increased glial markers expression. Additional data show increased levels of DLK1, cleaved Notch protein, and HES5 indicate upregulated Notch signaling in OCRL mutated neural cells. Treatment of brain organoids with a PIP5K inhibitor restored calcium signalling in neurons. These findings describe new dysregulated phenotypes in neural cultures of OCRL mutated cell shedding light on the underlaying caus of Lowe Syndrome.

Major comments

1. In general, I think the use of iNeurons usually means direct reprogramming from a somatic cell to neurons without the iPSC stage. Could be confusing to use this term for iPSC derived neurons. Thank you for pointing this out. We agree and will remove this term and replace it with a more suitable one in the revised manuscript.

Please add at least one more replicate of WP cell line to the single nuclei RNAseq.

There is no cell line called WP1 in the manuscript. We believe the reviewer was likely referring to WT1 (wild-type 1).

10xgenomics guidelines highlight that the statistical power of a multiome experiment relies on several factors including sequencing depth, total number of cells per sample, sample size and number of cells per cell type of interest (10xgenomics). In this study, we performed a multiome experiment and obtained high-quality reads from 20,000 nuclei for each sample for both the modalities: snRNA seq and snATAC seq. The multiome kit recommends a lower limit is 10,000 nuclei per sample. Thus the number of cells sampled per cell line is double the suggested minimum. Therefore, and consistent with other single-cell seq studies already published, our study followed the approach where biological replicates were not included ( for e.g see PMID: 39487141, GSE238206; PMID: 31651061; PMID: 32109367, GSE144477; PMID: 40056913, GSE279894; PMID: 38280846 GSE250386; PMID: 36430334, GSE213798; PMID: 33333020, GSE123722; PMID: 32989314, GSE145122; PMID: 38711218, GSE243015, PMID: 38652563, GSE236197). Furthermore, single-cell RNA-seq inherently treats each individual cell as as a replicate (Satija lab guidelines, PMID: 29567991; Wellcome Sanger Institute), reducing the necessity for additional biological replicates. Overall this appears to be the current standard in the field which we have followed.

Importantly, we took additional steps to validate the predictions our single-nuclei RNA-seq findings experimentally. For this we used a 3D brain organoid system. We confirmed key observations noted initially in 2D neural stem cells using a brain organoid model. This approach allowed us to confirm key predictions from the single cell sequencing data set. For example, in Lowe Syndrome patient derived organoids and OCRL-KO organoids, we noted increased DLK1 levels (Fig5.C-D, H-I) as well as increased GFAP+ cells and gene expression in brain organoids (Fig.S4E,F). These complementary approaches strengthen our confidence in the biological relevance of our findings from the single nuclei sequencing experiments.

The WT1 and the patient lines are rarely analysed together with the WT2 and KO lines, thus it is tricky to understand if the KO line is mimicking the patient lines? Please, add more merged analyses. Co-analysing all lines:

(i)would show if the KO line is more similar to the patient lines or to the WT1 or somewhere in between.

1. ii) Could answer questions about the variation in phenotypes between the genetic backgrounds. iii) Elucidate how much variability there is between the two WT lines in your assays. If the two WT lines vary much then conclusions about phenotypes in the patients and KO lines might need to be rethought? The reviewer is right is noting that throughout the manuscript we have analysed the patient lines with WT1 and the KO line with WT2. This was a conscious decision which we believe is the correct one for the following reasons:

It is well recognized and discussed in the literature that genetic background can be a key factor contributing to phenotypes observed in cells differentiated from iPSC (Anderson et al., 2021, PMID: 33861989; Brunner et al., 2023, PMID: 36385170; Hockemeyer and Jaenisch, 2016, PMID: 27152442; Soldner and Jaenisch, 2012, PMID: 30340033; Volpato and Webber, 2020, PMID: 31953356). Therefore, as a matter of abundant precaution, in this study we have tried to use the closest possible genetically matched control lines for analysis.

The patient lines used in this study for Lowe syndrome were all derived from a family in India of Indian ethnic origin. Therefore, in order to reduce the potential impact of genetic background contributing to potential phenotypes, we have used a control line derived from an individual of Indian ethnic background; this line has previously been developed and published by our group (PMID: 29778976 DOI: 10.1016/j.scr.2018.05.001). By contrast, the OCRLKO line was generated using the control line NCRM5 (WT2); this line is derived from a Caucasian male (RRID: CVCL_1E75). Therefore, whenever we have analyzed OCRLKO, we have used NCRM5 as the control; throughout the manuscript, NCRM5 is referred to as WT2.

However, in deference to the reviewer’s concerns we have performed a few analyses to compare the extent of variability between the two control lines.

Figure Legend: Replotted [Ca2+]i transients data from LS patient lines, OCRLKO and two control cell lines WT1 and WT2. (A) There is no statistical difference in the frequency of [Ca2+]i transients between WT 1 and WT2. Test used-Mann Whitney test. (B) Plot with WT1 and WT2 data combined versus all three LS lines and OCRLKO combined. Test used-Mann Whitney test. (C) WT1 and WT2 combined plotted against three individual patient lines and OCRLKO. Statistical test used One-way ANOVA. (total neurons analysed: WT1:808; WT2:267; LSP2:150; LSP3:462; LSP4:463; OCRLKO:411)

(i) We compared the frequency of calcium transients between neurons of age 30 DIV between WT1 and WT2 (Panel A above). We found no significant difference between these.

Additionally, as suggest we combined the data from both control lines into a single set and that from all the LSP patient lines and OCRLKO into another one (Panel B above). At the end of the analysis the difference between control and OCRL depleted cells remains. Please note the large number of cells studied in each genotype.

We also combined both control lines into a single control data set and compared it to each patient line and OCRLKO. We find that each patient line and OCRLKO is still significantly different from the control set (panel C above).

We did not find that OCRLKO to be significantly different from LSP2 or LSP4, indicating that the OCRLKO line closely aligns with the patient-derived lines, supporting the idea that the observed phenotype is primarily disease-driven rather than background-dependent. However, we did observe a significant difference between LSP3 and OCRLKO, highlighting some degree of inter-patient variability. Therefore, the key point is that the disease phenotype remains stable across different backgrounds, reinforcing the idea that the observed differences are driven by OCRL loss rather than background variability. This will be discussed in the revision.

(ii) In our RTPCR assay for HES5, when WT1 and WT2 are plotted together, there is no significant difference observed (panel A below). Similarly, western blotting data for cNotch (panel C) and DLK1 (panel B) of pooled WT1 and WT2 together on one plot shows no significant difference (Unpaired t-test, Welch’s correction). Overall, based on the above data, WT1 and WT2 are not statistically different.

Figure legend: Comparison of control lines WT1 and WT2. (A) comparison of HES5 transcripts. (B) Western blot for DLK1 levels. (C) Western blot for cleaved notch protein levels. Statistical test: Unpaired t-test, Welch’s correction.

Please include more discussion and rational around the link between the expression pattern of OCRL and the various phenotypes shown. From the RNAseq data performed at the NSC state where the expression of OCRL is lower than in neurons there are considerable differences in cell type distribution between lines. How can this skew cell type distribution affect downstream differentiation and neuronal function?

We would like to highlight that we did not perform bulk RNAseq in NSC and neurons; rather, we performed snRNA seq in NSCs (Fig3). The data in Fig.1E is mined from a publicly available resource dataset (Sidhaye et.al., 2023, PMID: 36989136) as mentioned in line 155, which is an integrated proteomics and transcriptomics generated from iPSC-derived human brain organoids at different stages of development in-vitro.

Fig 1D and 1E do indeed show lower levels of OCRL expression in NSC compared to neurons. However, it is important to bear in mind that even though OCRL may be expressed at relatively low levels during the NSC stage, its enzymatic activity could still have a substantial impact. Therefore, even at low expression levels, OCRL could be modulating the PI(4,5)P2 pool in ways that significantly influence cellular functions, especially during early stages of neurodevelopment that alter cell-fate decisions thereby affecting neuronal excitability.

Our working model posits that loss of OCRL leads to increased levels of PI(4,5)P2 which upregulates Notch pathway thereby leading to an increase in its downstream effector HES5. HES5 is a known transcription factor influencing gliogenesis and thus leading to a precocious glial shift in OCRL deficient NSCs as seen in our multiome dataset. This temporal perturbation in differentiation affects maturation of LS/OCRL-KO neurons and/or astrocytes leading to a defective neuronal excitability.

Also, OCRL is expressed also at the iPSC state as shown in Figure 1I, do you see any phenotypes in iPSC? If not, explain how that could be.

Yes, OCRL is indeed expressed in iPSCs as shown in Figure 1I. In an earlier paper from our lab that described the generation of these patient derived iPSC from Lowe syndrome patients (Akhtar et.al 2022 PMID: 35023542), we have reported that PIP2 levels are elevated at the iPSC stage as well as NSC stage in OCRL patient lines. We have not performed a detailed analysis of the iPSC stage for these lines as the focus of our investigation was primarily on the later stages of differentiation, particularly in neural progenitors and differentiated neurons. However, in response to the reviewer’s questions on why there are no obvious phenotypes at the iPSC we would suggest that this is due to compensation from the activity of other genes of the 5-phosphatse family. In support of this, we would cite our previous study (Akhtar et.al 2022 PMID: 35023542), in which we show that in LS patient derived lines, at the iPSC Stage, at least six other 5-phosphatases are upregulated.

There is not enough data in the manuscript to show mechanistic links between OCRL, DLK1 and Notch so be aware not to overstate the conclusions.

We appreciate the reviewer’s constructive comment regarding the mechanistic links between OCRL, DLK1, and Notch. Treatment of organoids and neurons with UNC-3230 PIP5K1C inhibitor rescues the observed phenotypes suggesting a role for a PIP2 dependent process, this process itself remains to be identified. We will adjust the wording in the manuscript during the revision to ensure that this comes through and the conclusions do not appear overstated.

Line 173, please describe what mutation in the OCRL these patients have, is it a biallelic deletion? Is the protein totally absents? Please show western blot analyses of the protein in the patient lines.

The patients from whom these LS lines were generated, the nature of the OCRL allele in them and the status of OCRL protein have all been previously been described in detail in a paper from our lab. This paper (Akhtar et.al 2022 PMID: 35023542) has been cited in the present manuscript at the very first occasion that the lines are described (Line 174, references 26 and 27). In addition, in the present manuscript, the protein status of OCRL in all the three patient lines is shown with a Western blot in Figure 3C.

Would be good with a bit of clinical explanation of these patients? Do they have the same level of severity? Are there any differences between their clinical symptoms? This could be interesting to link to differences in cellular phenotypes.

The clinical details of each patient are described in a preprint from our lab (Pallikonda et.al., 2021 bioRxiv 2021.06.22.449382).The potential reasons for the difference in severity, a very interesting scientific question, is also addressed in this preprint. Currently experimental analysis to support the proposed likely reasons is ongoing in our lab. We feel those analysis are beyond the scope of this manuscript and will be published later this year as a separate study.

As described in in ref 26 and 27, LSP patients have a mutation in exon 8 leading to a stop codon. We mimicked this by CRISPR based genome editing to introduce a stop codon and protein truncation in exon 8 to generate of WT2 to OCRLKO. This is also described in supplementary Fig 1 of the present manuscript and the technical details of line generation are fully described in the materials and methods.

Like the patient lines OCRLKO is a protein null allele-this is shown by Western blot in Fig 2D. Also in OCRLKO, the PIP2 levels are elevated (Fig 2E) recapitulating what has been described by us in (Akhtar et.al 2022 PMID: 35023542). We will explicitly state this detail around line 185.

Figure 1I, could the protein levels at the different stages be quantified?

Yes, we can and will do it in the revision

Figure 3A, there seem to be much more cells in LSP2, making it tricky to compare with the other cell lines. Density during differentiation can affect the cell fate. Please, provide images from the different lines that are comparable with similar density.

We controlled for cell density by seeding equal number of cells 50,000 cells/cm2 for all the genotypes, as mentioned in the material and methods. However, heterogeneity between lines during terminal differentiation is well-established, leading to crowding in some genotypes while not in others. Additionally, different growth rates during terminal differentiation also leads to crowded neural cultures as a function of genotype. Therefore, to complement our immunostaining data, we have provided western blot analyses showing increased GFAP protein levels in LS patient lines compared to controls. We will provide images from different lines that are comparable in density during the revision.

Please provide quantification to the statement that there is fewer number of S100B cells in the LSP lines.

As we haven’t quantified the number of S100B cells, we will remove that statement.

Figure 3B, the images show cells very different, and it is tricky to compare similarities and differences, please provide images that look more similar to each other. Avoid images with clusters of cells or make sure to select representative images with clusters from each cell line. If the clustering is a phenotype explain and quantify that. Make sure the density is similar in all pictures.

We will provide images of matched density during the revision. Also see response to comment above.

Line 2018, the statement "In the same cultures, there was no change in the staining pattern of the neuronal markers MAP2 and CTIP2 (Fig 3B)" is not strengthened by the figure. Please provide new pictures or data to prove the statement.

As CTIP2 staining is inherently observed in either clumps or sparsely distributed regions across WT1 and LSP genotypes, we will replace the CTIP2 marker with TBR1, which is also a deep layer cortical marker (layer VI-V), as shown below. Using this additional marker for neurons, we continue to see no change in staining pattern of neuronal markers MAP2 and TBR1. Corresponding images for each genotype are optically zoomed-in images of individual neurons positive for MAP2 and TBR1. Scale bar=50µm, 20µm.

Figure 3E, please describe all markers in the picture, thus also MAP2, S100B, CTIP2 and draw conclusions. Try to show comparable pictures.

This will be attended in the revision

Fig 3D and G, what are the replicates? please explain.

Each point represents a single neural induction done on iPSCs to generate NSCs and then terminally differentiated 30DIV cultures. Experiments were done across 3-6 independent neural inductions. This detail will be included in the revised figure legend.

Figure 4 A, C, there is a large difference in the ratio of different cell types between the different cell lines, also between the LSP2 and LSP3. This would indicate either that the genetic background affects the phenotype to a large extent or that there is large variability between rounds of differentiation. To understand how much variability that comes from the differentiation and culturing: another replicate of WP cell from another donor (WT2) should be included (single nuclei RNAseq). Confirm that three independed rounds of differentiation of the WT1, WT2, LSP2, LSP3, LSP4, and OCRL-KO result in similar outcome when it comes to cell type distribution. Could be done with qPCR marker.

For scientific reasons explained in response to the reviewer’s comment #2 we feel it is not necessary to perform replicates of the single nucleus multiome seq. However to allay the reviewer’s concern of variability between differentiations leading to a conclusion of altered cell state we present the following three suggestions for a revised manuscript:

• We will perform multiple differentiations from iPSC to NSC and test the altered cell state using Q-PCR for transcripts of glial lineage markers.

• Shown below are western blot analyses for WT1, LSP2, LSP3 and LSP4 NSCs (left). Analyses were done from 4 independent rounds of neural inductions and exhibit a significant increase in the levels of a astrocytic fate-determinant marker NF1A in LSP NSCs wrt to WT1 (Mann Whitney test used to measure statistical significance). Each point represents sample from an independent neural differentiation.

• We would also like to highlight that we have already demonstrated increased GFAP levels in LS patient derived differentiated cultures and OCRLKO. These data, quantified in Fig 3D are done using samples derived from multiple differentiations of iPSC to NSC and then terminally differentiated. Thus the phenotype of enhanced glial cells in LS derived cultures, is most likely a consequence of the increased number of glial precursor cells is seen across multiple differentiations.

Line 309, "astrocytic transcripts NF1A and GFAP was elevated" It is unclear from this sentence in which cell lines NF1A and GFAP is elevated? Please explain.

We acknowledge the incompleteness in the statement. We will add the complete statement explaining the graphs. The levels of astrocytic transcripts NF1A and GFAP were elevated in LSP3 and LSP4 compared to WT1.

Figure 5C, E, G, there is a large variation of Notch and Hes5 expression between the different

This comment is incomplete.

Figure 5H, unclear which of the bands that is DLK1 and how the bands relate to the quantification. The band at 50 kDa seems to be stronger in the WT2 than in the OCRL-KO but in the quantification in Figure 5I, it shows 2x more in the KO. Thus, the other way around.

The datasheet of DLK1 antibody used (Abcam ab21682; RRID_AB731965) describes bands seen at 50,48, 45 and 15kDa. We have quantified the bands at 50kDa and 48-45kDa for all the genotypes. This will be explicitly stated in the revised figure legend.

Figure 6, please show that the inhibitor is inhibiting PIP5KC.

Have you titered the added concentration of the inhibitor?

Figure legend: Fields of view from WT1 derived NSC expressing the plasma membrane PIP2 reporter. Plasma membrane distribution of the probe indicating PIP2 levels is shown in (A) untreated cells (B) treatment with 10mM and (C) 50mM UNC-3230 PIP5K1C inhibitor. Scale bar=50µm (D) Quantification of plasma membrane PIP2 levels using this reporter. Y-axis shows probe levels at PM; X-axis shows treatment conditions.

Yes, we used a previously generated plasma membrane PH-PLC::mCherry reporter WT1-NSCs (Akhtar et.al., 2021) and carried out a dose-response experiment using 10mM and 50mM of the UNC-3230 PIP5K1C inhibitor as shown above. We quantified intensity of PI(4,5)P2::mCherry at the plasma membrane and plotted the mean intensity. We observed a significant decrease in plasma PI(4,5)P2 levels at 50mM (Statistical used: Mann Whitney test) but not 10mM and therefore we selected that concentration for our experiments.

Figure 6B, why do the calcium data for the WT2+1Ci look so different to the other, the dots are much more spread and seem to fewer replicates that for the other sample, please explain.

We had only analysed a few replicates for WT2+1Ci genotype. We analysed the remaining replicates and have updated the data as shown below. The revised data set resolves the reviewer’s concern. The revised data set will be included in the revision.

Figure 6F, there is no significant differences between the bars but the statement in the text (sentence starts on line 332) indicate it is, please update the figure or remove the statement.

We added more replicates (now total is 7-10 biological replicates each with 15-20 organoids) and updated the figure (panel B) is shown below. The differences between treated and untreated of OCRLKO are significant whereas there is no significant difference between wild type, treated and untreated (statistical test: Mann Whitney test).

Revised figure will be included in the revision

Figure 6G, the HES5 expression seem to behave very similar in both WT2 and OCRL-KO cells when the inhibitor is used. What does this mean? Seems to not be linked to OCRL. Explain.

Thank you for your comment. In our initial experiment (shown in original version of manuscript), we observed a reduction in HES5 expression upon inhibitor treatment in both WT2 and OCRL-KO cells. However, to ensure robustness of our findings, we repeated the experiment across multiple, additional independent organoid differentiation batches. In this redone experiment, we no longer observe the previous trend. Instead, we see no significant changes in WT2 on inhibitor treatment, while OCRLKO cells show a reduction in HES5 expression upon inhibitor treatment (Panel A). Similarly, the protein levels of cNotch and DLK1 are not different between WT2 and WT2+1Ci (panel B and C). This strongly suggests loss of OCRL leading to elevated levels of PIP2 perturbs Notch pathway, resulting in higher cNotch and thereby increased effector expression of HES5. New data set will be included in the revision.

Minor comments

The panels in Figure 6 are not completely referred to correctly in the text, please check. Double check that all figure panels are referred to properly in the text

Yes, we will correct it in the revised manuscript.

Reviewer #1 (Significance (Required)): The manuscript is an interesting addition to the in vitro iPSC derived cellular modelling of neurodevelopmental disorder. Strengths: The use of both patient iPSC lines and CRISPR edited lines The use of both monolayer and 3D cultures We thanks the reviewer for their detailed critique. Addressing these has helped improve the manuscript. We thank the reviewer for appreciating the strengths of the manuscript. Weaknesses: the significance decrease a bit due too few replicates (only 1 WT line in each experiment) and the variability between the patients' cell lines. We thank the reviewer for this comment. As explained above we have added substantially more data and revised the analysis which should remove this concern.

Reviewer 2:

This paper describes the effects of loss of OCRL (the Lowe syndrome protein) upon the function and differentiation of neurones, using an in vitro iPSC model system. Cells derived from three related Lowe syndrome patients and an OCRL knockout, generated using CRISPR, were used for these experiments. The results show that upon loss of OCRL, differentiation of stem cells into neurones is reduced, with an increased number of cells adopting glial and astrocytic fates. The neurones that are generated have reduced calcium transients and electrical activity. Gene expression data combined with biochemical analysis indicate altered Notch activity, which may account for the altered cell fate data seen in the in vitro differentiation model. Finally, rescue of cell fate and neuronal activity is seen upon knockdown of a PIP5K, which indicates that these phenotypes are due to the elevated PIP2 levels seen on the OCRL-deficient cells.

The results provide new insights into the pathogenesis of Lowe syndrome. I found the paper to be well done, and the data supports the conclusions of the authors. I have a few comments below that may improve the manuscript:

We thank the reviewer for summarizing the comprehensive nature of our study and appreciating the value of our study in providing new insights into the pathogenesis of Lowe syndrome with respect to the brain. Thank you for appreciating that our study is well done, and that the data supports the conclusions of the authors.

Major points

1. The UMAP and ATAC-Seq data indicate different maps for the two different Lowe syndrome patient-derived cells (Fig 4 and Fig S3). This suggests that the cells are quite different, and therefore that changes seen in one Lowe syndrome patient may not be applicable to the others. I think this heterogeneity has important implications for the paper i.e. how general are findings obtained? Several different glioblast types are described (numbered 1-5)- how different or similar are these? We are unclear what the reviewer means by “ the UMAP and ATAC seq data indicate different maps…….”.

UMAP is a technique for visually representing data generated by single cell analysis methods be it RNAseq or ATAC seq. Perhaps what the reviewer means is that the UMAP generated from RNA seq and ATAC seq data looks different from each other.

We would like to reiterate that the UMAP generated from single cell RNA seq data is based on the complement of transcripts in each cell of the analysis compared to an existing single cell RNAseq data set, whereas the UMAP generated from ATACseq is generated from regions of open chromatin detected in and around genes and therefore presumably also reflecting ongoing gene expression. In principle the two analyses for any set of cells should indicate overall clustering into similar groups on UMAPs generated using both data sets, if the ATACseq based read out of transcription largely maps the RNAseq based read out of differences in transcription. However, it may not be reasonable to expect them to be identical. This is indeed what we see for our data set, and this has been represented in Fig 4E. The cell clusters detected based on GEX (gene expression i.e single cell RNA seq) analysis are plotted against the cells clusters detected from ATACseq data using a confusion matrix. As can be seen from this panel (Fig 4E), a very large fraction of cells falls on the diagonal indicated a large degree of similarity between clusters detected by both methods (GEX and ATACseq) of analysis. This can be reiterated more strongly during the revision by strengthening this statement.

The PIP5K inhibitor seems to have a very strong effect on both WT and KO cells in terms of Notch activity (Fig 5G). This strongly suggests the effects of this inhibitor are not through OCRL and that changes in PIP2 induced by the inhibitor override those of OCRL. Thus, the experiments shown in Fig 5 seem not to be due to a rescue of OCRL activity as such.

We think reviewer means Fig 6G and our response is as follows:

In our initial experiment (shown in the current version of manuscript), we observed a reduction in HES5 expression upon inhibitor treatment in both WT2 and OCRLKO cells. However, to ensure robustness of our findings, we repeated the experiment across multiple, additional independent organoid differentiation batches. In this redone experiment, we no longer observe the previous trend. Instead, we see no significant changes in WT2 on inhibitor treatment, while OCRLKO cells show a reduction in HES5 expression upon inhibitor treatment (Panel A). Similarly, the protein levels of cNotch and DLK1 are not different between WT2 and WT2+1Ci (panel B and C). This strongly suggests loss of OCRL leading to elevated levels of PIP2 perturbs Notch pathway, resulting in higher cNotch and thereby increased effector expression of HES5. The figures updated with the new data will be included in the revision.

Minor points

1. The main text needs to say what synapsin is and why it was analysed. In Fig 1I, synapsin abundance declines at 90 days. This appears quite strange. The authors should comment on it in the text. We will add a line about use of synapsin in the western. Synapsin is only used qualitatively to highlight mature neuronal culture age, as was done in Sidhaye et.al PMID: 36989136.

In the revised main text, we will add the following explanation: "We also analyzed the expression of synapsin-1, a synaptic vesicle protein that serves as a marker for mature synapses and functional neuronal networks. The presence of synapsin-1 indicates the development of synaptic connections in our cultures, providing evidence of neuronal maturation."

.

The decline and thereby variability in synapsin-1 protein levels has been reported before. Regarding the decline in synapsin-1 at 90 days, we can add the following discussion:

"We observed a decline in synapsin-1 levels at 90 days in vitro (DIV) compared to earlier time points. This pattern has been previously reported in iPSC-derived neuronal models (Togo et.al PMID: 34629097 and Nazir et.al PMID: 30342961). Such variability in synapsin-1 expression over extended culture periods may reflect the dynamic nature of synaptic remodeling and maturation processes in vitro. It's important to note that synapsin-1 levels can fluctuate due to various factors, including culture conditions and the heterogeneity of neuronal populations present at different time points."

In Fig 2A and 3B there are clumps of green cells (CTIP2 positive). I am concerned that the lack of uniformity in the cell distribution could impact other analysis performed, where certain fields of view have been analysed e.g. by imaging or electrophysiology e.g. calcium measurements.

To address the reviewers concern about uniformity, in the revised manuscript, we will provide/replace the representative images of deep layer markers along with MAP2 from all genotypes showing the areas selected for analysis to demonstrate that data collection was performed in comparable regions across all experimental conditions. As answered in the response to reviewer 1, comment 11.

The clumps of neurons (as seen in Fig2A) poses challenges for obtaining high-quality seals during patch-clamp recordings. To address this, we primarily selected areas with sparsely distributed neurons for electrophysiology experiments. This approach ensured robust recordings. To address this, we can provide a clarification in the Methods section to explicitly state that neurons used for all patch-clamp recordings were chosen from regions where cells were sparsely distributed.

In case of calcium imaging experiments, we focused on both crowded and sparse fields of views across genotypes to avoid potential biases introduced by clumped cells. However, it is to be noted that during the stages of terminal differentiation there are NSCs undergoing proliferation, which makes the neuronal culture denser. We can provide video files as a supplementary material to demonstrate the types of areas used for calcium imaging experiments. Additionally, we will include a statement in the Methods section specifying that regions with uniform neuronal distribution were selected for calcium imaging to ensure consistency in our analysis.

In Fig 2J and 2K are the differences between sampels significant? The error bars are huge.

From line 204-209, we have not used the word “significantly different”. We acknowledge that the error bars in Figures 2J and 2K are indeed large, which is not uncommon in electrophysiological recordings from iPSC-derived neurons due to their inherent variability. We have intentionally refrained from claiming statistical significance for these specific comparisons. Instead, we describe the data as showing a pattern or trend of reduced currents in OCRLKO neurons compared to WT2. To improve clarity, we propose to add a statement in the results section acknowledging the variability in these measurements and explaining our interpretation of the data as a trend rather than a statistically significant difference.

In Fig S4- it would be good to show gene expression analysis and GFAP staining

We are not completely sure what this comment means. However the present figure shows double staining with GFAP and S100beta. These will be split and shown separately to enhance clarity.

Fig 5A needs more annotation- fold change comparing what to what?

We will add the annotation “fold change wrt to WT1”.

There should be more information provided in the main text relating to DLK1. For example, it is shown to be secreted, but no information is provided on whether this is expected. Secreted? The DLK1 blot in Fig 5F is not convincing.

We will add more information relating to DLK1 and secretion status.

DLK1 is a non-canonical notch ligand that is indeed known to be secreted by neighboring cells to either activate/inhibit notch pathway. While we acknowledge the blot could have been better, however, variability in the blot could arise due to differences in secretion efficiency, or protein stability in the cell culture media that could have led to inconsistencies across LSP genotypes. However, as shown in the blot, the OCRLKO shows a clear enrichment of secreted-DLK1 compared to WT2.

We have performed the western blot analyses across two independent differentiations of organoids from WT1, LSP2, LSP3, LSP4, WT2, OCRL-KO iPSCs in phenol-free neurobasal-A medium, and quantified secreted protein. We then loaded 40mg of protein per genotype. Shown below is the quantification. The quantification of mean intensity of DLK1 band shows a moderate increase in LSP2, and substantial increase in LSP3 and LSP4 organoids as compared to WT1. While OCRL-KO a substantial increase compared to its control, WT2. A revised figure will be used in the revision.

Rationale for choosing PIP5K1C

PIP5K1C is one of the major regulators maintaining appropriate levels of the synaptic pool of PI(4,5)P2, synaptic transmission and synaptic vesicle trafficking (Hara et al., 2013 PMID: 23802628; Morleo et al., 2023 PMID: 37451268; Wenk et al., 2001 PMID: 11604140). Therefore, we were interested in rescuing the physiological phenotype, we chose PIP5K1C. Additionally, in initial experiments we found that inhibiting PIP5K1B using ISA-2011B killed the organoids or lead to detachment of 2D neuronal cultures.

Fig 6D is confusing. I suspect the figure labelling is not correct- it does not correlate with the graphs.

We apologise for the error and will correct this.

Reviewer #2 (Significance (Required)):

This paper is significant because it provides important new information on the neurological features of Lowe syndrome. The approach is novel in terms of studying this condition. The findings are likely to be of interest to clinicians, cell biologists, neurobiologists and those studying human development. My expertise is in membrane traffic and OCRL/Lowe syndrome. I am not a neurobiologist.

We thank the reviewer for appreciating the importance of our study, novelty of findings and newof our approach we have used. We would light to highlight that while extensive work has been done with respect to the renal phenotype of Lowe syndrome, the brain phenotypes have remained largely a black box. This is in part because mouse knockouts of OCRL have failed to recapitulate the brain related clinical phenotypes displayed by Lowe syndrome patients (for e.g. PMID: 30590522; PMCID: PMC6548226; DOI: 10.1093/hmg/ddy449). Our study of brain development defects in Lowe syndrome depleted cells provides the first insight into the cellular and developmental changes in this disorder.

Reviewer 3:

This paper by Sharma et al describes findings in an iPSC model of Lowe Syndrome. This is an important line of research because no mouse models phenocopy the neurodevelopmental aspects of the condition. They identified a potential role of Notch signaling in pathogenesis, a potentially druggable target. However, several issues need to be addressed.

We thank the reviewer for appreciating the importance of our study in covering the basis of the neurodevelopmental phenotype of Lowe syndrome. Due to a lack of a mouse model, there was previously no understanding of how the clinical features related to the brain arise.

Major issues

1. The sample size is very small, which is understandable to some extent given the expense and difficulty doing research using iPSCs. However, there are a couple of opportunities to improve the sample size. For example, in the analysis of DLK1 and other proteins shown in Figure 5, the analysis amounts to a single control vs the 3 patient lines, and a single control vs the KO line. The separation is justified because a complete KO of the gene might result in differences compared to hypomorphic mutation that apparently affects the 3 cases. However, there is no reason why WT1 and WT2 shouldn't be combined. They are both random controls. This might not affect the results of the other proteins analyzed, NOTCH and HES5, but the significance of DLK1 could change. Nature of the allele in LS patient lines

There is a misconception in the reviewer comment that the OCRL allele in the three Lowe syndrome lines is a hypomorph. This is not correct. In the patients from whom these LS lines were generated, the nature of the OCRL allele and the status of OCRL protein in cells have been previously described in detail in a peer-reviewed, published paper from our lab. This paper (Akhtar et.al 2022 PMID: 35023542) has been cited in the present manuscript at the very first occasion that the LS patient lines are described (Line 174, references 26 and 27). As described in in ref 26 and 27, LSP patients have a mutation in exon 8 leading to a stop codon. This results in a protein null allele of OCRL in all three patient lines. This has been shown in Fig 1B of Akhtar et.al 2022 by immunofluorescence using an OCRL specific antibody (PMID: 35023542). It has also been demonstrated by Western blot using an OCRL specific antibody for all three LS patient lines in Fig 3C and 5C of the present manuscript. The nature of the allele will be highlighted more clearly in the revision.

*Combining WT1 and WT2 *

We are not in favour of combining WT1 and WT2. The reason for this is as follows.

It is well recognized and discussed that genetic background can be a key factor contributing to phenotypes observed in cells differentiated from iPSC (Anderson et al., 2021, PMID: 33861989; Brunner et al., 2023, PMID: 36385170; Hockemeyer and Jaenisch, 2016, PMID: 27152442; Soldner and Jaenisch, 2012, PMID: 30340033; Volpato and Webber, 2020, PMID: 31953356). As a result, it is recommended that a line closely matched for genetic background be used when assessing the validity of observed phenotypes. The patient lines used in this study for Lowe syndrome were all derived from a family in India of Indian ethnic origin. Therefore, in order to reduce the impact of genetic background contributing to potential phenotypes, we have used a control line (referred to in this manuscript as WT1) derived from an individual of Indian ethnic background; this line has previously been developed and published by our group (PMID: 29778976 DOI: 10.1016/j.scr.2018.05.001).”

In the case of OCRLKO we have genome edited NCRM5 (a white Caucasian male control line) to introduce a stop codon in exon 8 to mimic the truncation seen in our LS patient lines. This allele is also protein null as shown by Western blot using an OCRL specific antibody. The data is shown in Fig 2D of the present manuscript. Therefore, we reiterate that all the LS patient lines in this study and OCRLKO are protein null alleles.

Status of DLK1 levels

We have performed a combined analysis of DLK1 levels in the two control lines and all the patient lines as well as OCRLKO. As shown below the result remains unchanged, namely that DLK1 levels are elevated in OCRL depleted cells in this model system.

Figure legend: Quantification of DLK1 protein levels in control, LS patient and OCRLKO iPSC lines. Western blot intensities for each patient line and OCRLKO were normalized to GAPDH and then to the respective internal WT control (WT1 or WT2) resulting in fold-change values. For statistical analysis across genotypes, normalized fold-change values from different gels were pooled post hoc. All statistical testing was performed on fold-change values. Statistical test used: Mann Whitney test. (A) Values for WT1 and WT2 have been combined and plotted against individual values for three patient lines and OCRLKO (B) Values for WT1 and WT2 have been combined and plotted against combined values for all three LSP lines and OCRLKO.

Reviewer comment: DLK1 expression brings up another point. This, along with MEG3 and MEG8 are imprinted genes, two of the top differentially expressed genes in this study. However, these findings can be questioned by the well-known phenomenon that the expression of some imprinted genes may not be properly maintained during iPSC reprogramming. Thus, the differential expression of these imprinted genes might be due to a reprogramming artifact rather than the effects of OCRL per se. Analyzing both controls together could mitigate this objection. However, even if it does, the potential dysregulation of imprinted genes in the development of iPSCs should be acknowledged and addressed.

We are aware that the DLK1 locus is imprinted. However, we feel that reprogramming artifacts are very unlikely to explain the observed changes in DLK1 levels.

It is important to note that the patient lines and WT1 were not directly re-programmed from White blood cells to iPSC and then used for differentiation and analysis. As detailed in our previous peer-reviewed publications WT1 (PMID: 29778976) and the patient LSP lines (PMID: 35023542) were first converted to lymphoblastoid cell lines and subsequently reprogrammed into iPSC.

We think that re-programming induced imprinting changes are unlikely to be responsible for the elevated levels of DLK1 seen in LS patient lines. The reason is as follows:

We compared DLK1 levels in WT2 and OCRLKO which is a CRISPR edited line that introduces a stop codon in exon 8. NCRM-5/WT2 was derived from CD34+ cord blood cells. What we found is that levels of DLK1 are elevated in OCRLKO compared to WT2. Since OCRLKO was generated by genome editing WT2, it must be the case that the level of imprinting of the DLK-DIO3 locus is comparable if not identical between the two lines. Therefore, the difference in DLK1 levels between WT2 and OCRLKO cannot be a consequence of different imprinting status of the DLK1 locus between these two lines. Rather, it strongly suggests a causal link to OCRL deficiency. Following on from this, the DLK1 levels are elevated in patient lines compared to the OCRLKO. We will highlight and discuss and explain this in the revised version.

Similarly, in the calcium signaling experiment shown in fig.2, the KO and patient lines are justifiably separated. However, again, why not combine both controls in the comparison with the patient samples?

The data has been reanalyzed and presented as requested by the reviewer. There is no change in the conclusion.

For the reasons described above, it remains our preference to present each set of lines with the appropriate control; i.e WT1 and the three LS patient lines and WT2 with OCRLKO. However, as the reviewer has asked for it, we also present below analysis in which WT1 and WT2 and combined and LS patient lines and OCRLKO are combined. The replotted data is shown below. The essential conclusion shown in the main manuscript remains, namely that [Ca2+]i transients in LS depleted developing neurons is lower than in wild type.

Figure Legend: Replotted [Ca2+]i transients from LS patient lines, OCRLKO and two control cell lines WT1 and WT2 (A) There is no statistical difference in the frequency of [Ca2+]i transients between WT 1 and WT2. Test used-Mann Whitney test. (B) Plot with WT1 and WT2 data combined v all three LS lines and OCRLKO combined. Test used-Mann Whitney test. (C) WT1 and WT2 combined plotted against three individual patient lines and OCRLKO. Statistical test used One-way ANOVA. (total neurons analyzed: WT1:808; WT2:267; LSP2:150; LSP3:462; LSP4:463; OCRLKO:411)

Regarding the hypomorphic nature of the patient-specific iPSC, I do not see the OCRL variant that was found in the family. Please correct me if I missed that, and if it was omitted, it should be included. I suspect that the variant generates a hypomorphic OCRL protein because the authors show expression in Figure 1D. Hypomorphic OCRL mutations compared with complete KO could show differences in molecular phenotypes, as found in Barnes et al. (PMID: 30147856) in an analysis of F-actin and WAVE-1 expression.

Nature of the allele in LS patient lines

There is a misconception in the reviewer’s comment that the OCRL allele in the three Lowe syndrome lines is a hypomorph. This is incorrect. In the patients from whom these LS lines were generated, the nature of the OCRL allele in them and the status of OCRL protein have all previously been described in detail in a peer-reviewed, published paper from our lab. This paper (Akhtar et.al 2022 PMID: 35023542) has been cited in the present manuscript at the very first occasion that the LS patient lines are described (Line 174, references 26 and 27). As described in in ref 26 and 27, LSP patients have a mutation in exon 8 leading to a stop codon. This results in a protein null allele of OCRL in all three patient lines. This has been shown in Fig 1B of Akhtar et.al 2022 by immunofluorescence using an OCRL specific antibody (PMID: 35023542). It has also been demonstrated by Western blot using an OCRL specific antibody for all three LS patient lines in Fig 3C and 5C of the present manuscript.

The data presented in Fig.1D, E is a publicly available resource data PMID: 36989136 as mentioned in line 155, which is an integrated proteomics and transcriptomics generated from control iPSC-derived human brain organoids at different stages of development in-vitro.

Minor issue

The authors use the term mental retardation on line 102 to describe the cognitive phenotype in Lowe Syndrome. Medical, legal, and advocacy groups have abandoned this term because it is viewed as offensive. It is being replaced by intellectual disability, although this term also is problematic. In any event, many conferences on autism and intellectual disabilities are attended by families, and high-functioning cases sometimes address an audience of scientists. They would object to the use of this term if presented in a talk by one of the co-authors.

Thank you. We will rephrase this line.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Not applicable at this stage. The above is a revision plan.

4. Description of analyses that authors prefer not to carry out

We prefer to not carry out replicates of the single cell multiome analysis. As explained above the state of the art in the single cell analysis field is to not do so. The scientific reasons as to why such replicates are not required have been explained in the response to the reviewer comment.

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Referee #3

Evidence, reproducibility and clarity

This paper by Sharma et al describes findings in an iPSC model of Lowe Syndrome. This is an important line of research because no mouse models phenocopy the neurodevelopmental aspects of the condition. They identified a potential role of Notch signaling in pathogenesis, a potentially druggable target. However, several issues need to be addressed.

Major issues

1. The sample size is very small, which is understandable to some extent given the expense and difficulty doing research using iPSCs. However, there are a couple of opportunities to improve the sample size. For example, in the analysis of DLK1 and other proteins shown in Figure 5, the analysis amounts to a single control vs the 3 patient lines, and a single control vs the KO line. The separation is justified because a complete KO of the gene might result in differences compared to hypomorphic mutation that apparently affects the 3 cases. However, there is no reason why WT1 and WT2 shouldn't be combined. They are both random controls. This might not affect the results of the other proteins analyzed, NOTCH and HES5, but the significance of DLK1 could change. DLK1 expression brings up another point. This, along with MEG3 and MEG8 are imprinted genes, two of the top differentially expressed genes in this study. However, these findings can be questioned by the well-known phenomenon that the expression of some imprinted genes may not be properly maintained during iPSC reprogramming. Thus, the differential expression of these imprinted genes might be due to a reprogramming artifact rather than the effects of OCRL per se. Analyzing both controls together could mitigate this objection. However, even if it does, the potential dysregulation of imprinted genes in the development of iPSCs should be acknowledged and addressed.
2. Similarly, in the calcium signaling experiment shown in fig.2, the KO and patient lines are justifiably separated. However, again, why not combine both controls in the comparison with the patient samples?
3. Regarding the hypomorphic nature of the patient-specific iPSC, I do not see the OCRL variant that was found in the family. Please correct me if I missed that, and if it was omitted, it should be included. I suspect that the variant generates a hypomorphic OCRL protein because the authors show expression in Figure 1D. Hypomorphic OCRL mutations compared with complete KO could show differences in molecular phenotypes, as found in Barnes et al. (PMID: 30147856) in an analysis of F-actin and WAVE-1 expression.

Minor issue

The authors use the term mental retardation on line 102 to describe the cognitive phenotype in Lowe Syndrome. Medical, legal, and advocacy groups have abandoned this term because it is viewed as offensive. It is being replaced by intellectual disability, although this term also is problematic. In any event, many conferences on autism and intellectual disabilities are attended by families, and high-functioning cases sometimes address an audience of scientists. They would object to the use of this term if presented in a talk by one of the co-authors.

Significance

as noted above, this study is significant because there are no mammalian models available to address the neurodevelopmental aspects of Lowe Syndrome

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Referee #2

Evidence, reproducibility and clarity

This paper describes the effects of loss of OCRL (the Lowe syndrome protein) upon the function and differentiation of neurones, using an in vitro iPSC model system. Cells derived from three related Lowe syndrome patients and an OCRL knockout, generated using CRISPR, were used for these experiments. The results show that upon loss of OCRL, differentiation of stem cells into neurones is reduced, with an increased number of cells adopting glial and astrocytic fates. The neurones that are generated have reduced calcium transients and electrical activity. Gene expression data combined with biochemical analysis indicate altered Notch activity, which may account for the altered cell fate data seen in the in vitro differentiation model. Finally, rescue of cell fate and neuronal activity is seen upon knockdown of a PIP5K, which indicates that these phenotypes are due to the elevated PIP2 levels seen on the OCRL-deficient cells.

The results provide new insights into the pathogenesis of Lowe syndrome. I found the paper to be well done, and the data supports the conclusions of the authors. I have a few comments below that may improve the manuscript:

Major points

1. The UMAP and ATAC-Seq data indicate different maps for the two different Lowe syndrome patient-derived cells (Fig 4 and Fig S3). This suggests that the cells are quite different, and therefore that changes seen in one Lowe syndrome patient may not be applicable to the others. I think this heterogeneity has important implications for the paper i.e. how general are findings obtained? Several different glioblast types are described (numbered 1-5 )- how different or similar are these?
2. The PIP5K inhibitor seems to have a very strong effect on both WT and KO cells in terms of Notch activity (Fig 5G). This strongly suggests the effects of this inhibitor are not through OCRL and that changes in PIP2 induced by the inhibitor override those of OCRL. Thus, the experiments shown in Fig 5 seem not to be due to a rescue of OCRL activity as such.

Minor points

1. The main text needs to say what synapsin is and why it was analysed. In Fig 1I, synapsin abundance declines at 90 days. This appears quite strange. The authors should comment on it in the text.
2. In Fig 2A and 3B there are clumps of green cells (CTIP2 positive). I am concerned that the lack of uniformity in the cell distribution could impact other analysis performed, where certain fields of view have been analysed e.g. by imaging or electrophysiology e.g. calcium measurements.
3. In Fig 2J and 2K are the differences between sampels significant? The error bars are huge.
4. In Fig S4- it would be good to show gene expression analysis and GFAP staining for organoids made using the OCRL KO cells
5. Fig 5A needs more annotation- fold change comparing what to what?
6. There should be more information provided in the main text relating to DLK1. For example, it is shown to be secreted, but no information is provided on whether this is expected. Secreted? The DLK1 blot in Fig 5F is not convincing.
7. Of the 3 PIP5Ks, only PIP5Kc was targeted. The rationale for picking only this one needs to be provided.
8. Fig 6D is confusing. I suspect the figure labelling is not correct- it does not correlate with the graphs.

Significance

This paper is significant because it provides important new information on the neurological features of Lowe syndrome. The approach is novel in terms of studying this condition. The findings are likely to be of interest to clinicians, cell biologists, neurobiologists and those studying human development. My expertise is in membrane traffic and OCRL/Lowe syndrome. I am not a neurobiologist.

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Referee #1

Evidence, reproducibility and clarity

Summary

The study by Sharma et al uses iPSC and neural differentiation in 2D and 3D to investigate how mutation in the OCRL gene affects neural differentiation and neurons. Mutation in the OCRL gene the cause of Lowe Syndrome (LS), a neurodevelopmental disorder. Neural cultures derived from LS patient iPSCs exhibited reduced excitability and increased glial markers expression. Additional data show increased levels of DLK1, cleaved Notch protein, and HES5 indicate upregulated Notch signaling in OCRL mutated neural cells. Treatment of brain organoids with a PIP5K inhibitor restored calcium signalling in neurons. These findings describe new dysregulated phenotypes in neural cultures of OCRL mutated cell shedding light on the underlaying caus of Lowe Syndrome.

Major comments

In general, I think the use of iNeurons usually means direct reprogramming from a somatic cell to neurons without the iPSC stage. Could be confusing to use this term for iPSC derived neurons.

Please add at least one more replicate of WP cell line to the single nuclei RNAseq.

The WT1 and the patient lines are rarely analysed together with the WT2 and KO lines, thus it is tricky to understand if the KO line is mimicking the patient lines? Please, add more merged analyses. Co-analysing all lines:

i) would show if the KO line is more similar to the patient lines or to the WT1 or somewhere inbetween.

ii) Could answer questions about the variation in phenotypes between the genetic backgrounds.

iii) Elucidate how much variability there is between the the two WT lines in your assays. If the two WT lines vary much then conclusions about phenotypes in the patients and KO lines might need to be rethought?

Please include more discussion and rational around the link between the expression patten of OCRL and the various phenotypes shown. From the RNAseq data performed at the NSC state where the expression of OCRL is lower than in neurons there are considerable differences in cell type distribution between lines. How can this skew cell type distribution affect downstream differentiation and neuronal function? Also, OCRL is expressed also at the iPSC state as shown in Figure 1I, do you see any phenotypes in iPSC? If not, explain how that could be.

There is not enough data in the manuscript to show mechanistic links between OCRL, DLK1 and Notch so be aware not to overstate the conclusions.

Line 173, please describe what mutation in the OCRL these patients have, is it a biallelic deletion? Is the protein totally absents? Please show western blot analyses of the protein in the patient lines. Would be good with a bit of clinical explanation of these patients? Do they have the same level of severity? Are there any differences between their clinical symptoms? This could be interesting to link to differences in cellular phenotypes. Line 185, please be clear on how the CRISPR KO line genetically mimics the patients' lines.

Figure 1I, could the protein levels at the different stages be quantified?

Figure 3A, there seem to be much more cells in LSP2, making it tricky to compare with the other cell lines. Density during differentiation can affect the cell fate. Please, provide images from the different lines that are comparable with similar density. Please provide quantification to the statement that there is fewer number of S100B cells in the LSP lines. Figure 3B, the images show cells very different, and it is tricky to compare similarities and differences, please provide images that look more similar to each other. Avoid images with clusters of cells or make sure to select representative images with clusters from each cell line. If the clustering is a phenotype explain and quantify that. Make sure the density is similar in all pictures.

Line 2018, the statement "In the same cultures, there was no change in the staining pattern of the neuronal markers MAP2 and CTIP2 (Fig 3B)" is not strengthened by the figure. Please provide new pictures or data to prove the statement.

Figure 3E, please describe all markers in the picture, thus also MAP2, S100B, CTIP2 and draw conclusions. Try to show comparable pictures.

Fig 3D and G, what are the replicates? please explain.

Figure 4 A, C, there is a large difference in the ratio of different cell types between the different cell lines, also between the LSP2 and LSP3. This would indicate either that the genetic background affects the phenotype to a large extent or that there is large variability between rounds of differentiation. To understand how much variability that comes from the differentiation and culturing:

i) another replicate of WP cell from another donor (WT2) should be included (single nuclei RNAseq)

ii) Confirm that three independed rounds of differentiation of the WT1, WT2, LSP2, LSP3, LSP4, and OCRL-KO result in similar outcome when it comes to cell type distribution. Could be done with qPCR marker.

Line 309, "astrocytic transcripts NF1A and GFAP was elevated" It is unclear from this sentece in which cell lines NF1A and GFAP is elevenated? Please explain.

Figure 5C, E, G, there is a large variation of Cnotch and Hes5 experession between the different

Figure 5H, unclear which of the bands that is DLK1 and how the bands relate to the quantification. The band at 50 kDa seems to be stronger in the WT2 than in the OCRL-KO but in the quantification in Figure 5I, it shows 2x more in the KO. Thus, the other way around.

Figure 6, please show that the inhibitor is inhibiting PIP5KC. Have you titered the added concentration of the inhibitor?

Figure 6B, why do the calcium data for the WT2+1Ci look so different to the other, the dots are much more spread and seem to fewer replicates that for the other sample, please explain.

Figure 6F, there is no significant differences between the bars but the statement in the text (sentence starts on line 332) indicate it is, please update the figure or remove the statement.

Figure 6G, the HES5 expression seem to behave very similar in both WT2 and OCRL-KO cells when the inhibitor is used. What does this mean? Seems to not be linked to OCRL. Explain.

Minor comments

The panels in Figure 6 are not completely referred to correctly in the text, please check. Double check that all figure panels are referred to properly in the text

Significance

The manuscript is an interesting addition to the in vitro iPSC derived cellular modelling of neurodevelopmental disorder.

Strengths:

The use of both patient iPSC lines and CRISPR edited lines The use of both monolayer and 3D cultures

Weaknesses: the significance decrease a bit due too few replicates (only 1 WT line in each experiment) and the variability between the patients' cell lines.

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Reply to the reviewers

1 Summary of changes There were several points that were raised by multiple reviewers, which we respond to as follows. 1. The reviewers pointed to a lack of clear comparison with experimental data. Perhaps this was insufficiently clear in the first submission, but the analysis of ECT-2 localization during cytokinesis was intended as a validation of the model, parameterized based on polarization and applied without further modification to cytokinesis. These situations differ in numerous respects: centrosome number, centrosome size, and we used several experimental conditions to control centrosome positioning. To address this more extensively, in the revised submission we analyzed our data further (Longhini and Glotzer, 2022) to extract profiles of ECT-2 and myosin. We used these profiles both to constrain model parameters (Appendix B.3) and to compare with model predictions for both polarization and cytokinesis (Figs. 3 and 5).

1. All of the reviewers pointed to our assumption that myosin indirectly recruits ECT-2. We apologize for a lack of clarity in the original draft about this. We had intended to convey the hypothesis that ECT-2 is recruited by a species that is advected with myosin, but for the sake of the minimal model we do not introduce any extra equations for this species and instead assume it colocalizes with myosin. In the revised manuscript, we address this by clearly listing the assumption (#2 on p. 7), and by comparing to an alternative model (Eq. (S4) and Fig. S7) that accounts directly for a third advected species. We also document specifically (second panel from left in Fig. 4) why the short residence time of ECT-2 makes patterning by pure advection impossible. That said, we still do not know the identity of this factor.

2. The reviewers pointed out that our use of the M4 term to limit contractility was dubious. This was a (probably misguided) attempt to use previously-published models to constrain our model. In the revised submission, we replaced this term with a more general nonlinear term Mk, where we first demonstrate that k = 1 is insufficient to match the data (p. 32), then consider k = 2,3. We present results in the main text for k = 2, while Fig. S5 shows that the corresponding results for k = 3 are not very different. Put another way, we empirically demonstrate that the specific form of this nonlinear term is not important, as long as it prevents contractile instabilities (as pointed out by one of the reviewers).

3. Apparently, our extension of the model to cytokinesis, and the evidence for validation of the model, was not clear in the original draft. Because of this, we reformulated the section (3.4) and figure (5) on cytokinesis. We identified four representative examples of centrosome positions, then compared the experimental profile of ECT-2 accumulation to the model result. For simplicity, we also eliminated the simulations of the non-phosphorylatable inactive copy of ECT-2 (“ECT-2 6A”). A more detailed analysis of that data revealed that the pattern of accumulation of ECT-2 6A at cleavage furrowing was more similar to the end of polarization, indicating that this copy of ECT-2 appears to have much slower turnover than the endogenous copy (as would expected from phosphorylation-dependent membrane displacement).

4. Fundamentally, our study addresses a similar question to (Illukkumbura et al., 2023), in the sense that we seek to understand how cortical flows could pattern ECT-2 and myosin, even though the residence time of ECT-2 is very low. Despite the similarities, it differs from the cited study in that ECT-2 is not an inert component that is asymmetrically distributed, but rather a component which regulates myosin levels and cortical flows, ultimately feeding back on its own accumulation. Due to these similarities and differences, we added an expository section in the discussion (p. 18) comparing our results to those of that study.

Point-by-point description of the revisions Reviewer 1 In this article, Maxian et al. propose a model combining 1-d simulations of ECT-2 and Myosin concentration at the cortex through binding/unbinding and advection at the cortex, with an input for AIR-1 cortical concentration based on the spatial localisation of the centrosomes in the cytoplasm. The objective of the authors is to recapitulate the role of (1) AIR-1, (2) its effector ECT-2 and (3) the downstream effector, driver of cortical flows, the molecular motors Myosin, in two key physiological processes, polarization and cell division. This is important as work over the last 10 years have emphasized the role of AIR-1 in embryo polarization. Previous biochemical-mechanical models have focused on RhoA/Myosin interactions (Nishikawa et al, 2017), the importance of a negative feedback and excitable RhoA dynamics (Michaux et al, 2018), or anterior PARs/posterior PARs/Myosin (Gross et al, 2019). The authors thus attempt to provide a new descriptive model in which RhoA is implicit, instead focusing on the role of centrosome localization on AIR-1 localization, and providing a framework to explore polarity establishment and cell division based on these 3 simple players. The first part of the model is very reminiscent of previously published models, while the second instead provides a link between the initial polarizing cue AIR-1 and polarization. Based on this description, the model is precisely tuned to achieve polarization while matching experimental observations of flow speed and ECT-2 A/P enrichment shape. The results are therefore certainly new and interesting.

Thank you for the positive assessment!

Major comments: 1. The authors use the position of the centrosomes as a static entry, resulting in a static AIR-1 input. Is this true, or are the positions of the centrosomes dynamically modulated over the course of the different processes simulated here (for example as a consequence of cortical flows?), and if so, is the assumption of immobile position?

We assume that the centrosomes are fixed on the timescale of the cortical dynamics, and study how the cortex responds to a static AIR-1 signal (see clarifying comment on p. 4). In Fig. S4, we show that the cortex responds rapidly to changes in the existence or position of the AIR-1 signal. As such, slower dynamics might be the result of slowly moving centrosomes, as we show in supplementary simulations (Fig. S8).

1. While in its principle the model is quite simple and elegant, the detailed form of the equations describing the interactions between the players is more complex. Are all these required? If they are crucially important for the behavior of the model, these should be described more thoroughly, and if possible rooted more directly in experimental results:

Thank you for this comment. We agree that there were several non-trivial terms in our “minimal” model. Our guiding principle for the revision was to reduce complexity and better justify the terms that are included.

(a) kMEMEc (Linear enhancement term): why would myosin impact E concentration? The authors state, p.7, ”There is a modest increase in the recruitment rate of ECT-2 due to cortical myosin (directly or indirectly), in a myosin concentration-dependent manner (Longhini and Glotzer, 2022).” I could not find the data supporting this assumption Longhini and Glotzer apparently rather point to a modulation of cortical flows. (”During anaphase, asymmetric ECT-2 accumulation is also myosin-dependent, presumably due to its role in generating cortical flows.”). Embedding this effect in the recruitment rate instead of expecting it from the model thus appears awkward. Could the authors specify how they came to this conclusion, which the authors might have derived from observations made in their previous work, but maybe did not fully document there?

This is an important issue. Since it was raised by all of the reviewers, we addressed it on page 1 above. Throughout the manuscript (Figs. 4 and S4), we tried to highlight that cortical flows are insufficient to localize ECT-2, while the recruitment hypothesis provides a better match to the experimental data. The recruitment by an advected species was speculated upon in Longhini and Glotzer: ”Rather, we favor a model in which the association of ECT-2 with the cortex involves interactions with cortical component(s) that are concentrated by cortical flows.”

(b) kEME2Mc (ECT-2 non-linear impact on Myosin): does the specific form of the value to convey the enhancement (square form) have an impact on the results?

The specific form does not have an impact. In fact, in the revised version, our experimental data shows an asymmetry in myosin that is actually lower than ECT-2. As such, a nonlinear term here lacks justification, and we switched to a linear term of the form kEMEMc (see model equations on p. 6).

(c) KfbM4 ”The form of this term is a coarse-grained version of previously-published work (Michaux et al., 2018).” Myosin feedback on myosin localization proportionally to M4 does not seem to directly derive from Michaux et al. Please detail this points more extensively and detail the derivation, in the supplements if not in the main text.

Based on this comment and that of reviewer 2, we decided to switch to a more general term for nonlinear negative feedback, as discussed in point 3 on page 1.

(d) P23. Parameter values: ”This is 1.5 times longer than the estimate for single molecules (Nishikawa et al., 2017; Gross et al., 2019) to reflect the more long-lived nature of myosin foci during establishment phase (Munro et al., 2004).” Not sure what the authors mean by more long-lived duration of foci during establishment phase. Seems rather arbitrary.

This was a misstatement on our part. A closer look at Gross et al. revealed that, under conditions similar to those we simulate (initial polarity establishment), the residence time of myosin is about 15 s (off rate 0.06 s−1). We modified our justification (p. 30) to include this. We also looked at the effect of longer myosin residence time on polarity establishment (Fig. S8).

1. It would be very helpful (and indeed more convincing) to include a direct comparison between modeling results and experimental counterpart whenever possible. This might not be possible for some data (e.g. Fig. 3d from Cowan et al), but should be possible for other, in particular Fig. 3c and Fig. 5b, for the flow speed and ECT-2 profiles. In Fig. 5b in particular, previously published experimental data could be produced to give the reader to compare model with experiments (possibly provided as an inset, at least for the wild type conditions).

We tried to bring in more data based on what was available from previous work (Longhini and Glotzer, 2022). Frame intervals of 10 s prohibited a PIV analysis for flow speeds, and punctate myosin profiles often made it difficult to measure myosin concentration. We were, however, able to extract the ECT-2 concentration from our previous movies and compare it to the model results. We included these comparisons in Figs. 3 and 5, with accompanying discussion in the text.

Minor comments: 1. Fig. 5b: ECT-2 C 6A(dhc-1) do not seem to be referenced or discussed in the main text. Also, why present the results for the flow for 2 conditions and the ECT-2 localisation for 4? Or does the variation of ECT-2 not impact the flow profile?

As discussed above on p. 2 (#4), we decided to reformulate the cytokinesis figure to incorporate more experimental data. Since we have detailed data on ECT-2 localization, we presented these in Fig. 5 for four experimental conditions, comparing each to the model.

1. p.6: Given that the non-normalized data is used in the main text, and the normalized only appears in the supplemental, maybe star the dimensionless and remove all hats from the main for greater legibility?

We changed the notation to make the main text variables (dimensional) unadorned, while the dimensionless variables in the SI now have hats.

1. p.6: Eqn 1a: carrot missing on 3rd E? This is now a moot point because of the previous comment.

2. p.14: replace “embryo treatment” with ”experimental conditions”? We changed “embryo treatment” to “experimental conditions” globally.

3. p.21, S4a: add ˆA=A/A(Tot) We added it in the last display on p. 28.

4. p.22: ”L = 134.6 µm” - please write 134 µm to retain the precision of original measurements We made this change.

5. p.22: Please provide formula for all dimensionless values as a table at the end of the supplemental for the eager but less-mathematically proficient reader. We added Table 1 to list the relationship between dimensional and dimensionless parameters.

Reviewer 2 The manuscript by Maxian, Longhini and Glotzer presents purely modeling work performed by the first author in conjunction with the already published experimental work by Longhini and Glotzer (eLife, 2022). The aim of the manuscript is to provide a mathematical model that connects the actomyosin contractility of the cell cortex in C. elegans zygote with the activity of the centrosomal kinase AurA (AIR-1 in C. elegans). The major claim of the authors is that their model, fitted to the experimental data pertaining to the zygote polarization, also describes dynamics during the zygote cytokinesis. In the model, the authors provide a heuristic approach to the biochemical dynamics, reducing their treatment to two variables: myosin and Ect2 Rho GEF. The biochemical model is integrated with a simple 1D active gel-type model for the cortical flow. The model uses static diffusive field of activity of AurA kinase in the cytoplasm as an input to their chemo-mechanical model. Major concerns: 1. The biochemical model is highly heuristic and several major assumptions are poorly justified. Thus, the authors explicitly introduce recruitment of Ect2 by myosin, something apparently based on the experimental observations by Longhini and Glotzer in 2022, which had not been biochemically confirmed since with a clear molecular mechanism. This is an important issue, and we appreciate your concern which was shared by the other reviewers. As discussed above on p. 1, we tried to justify this assumption better by (a) clearly stating it on p. 7, and (b) demonstrating that the dynamics we observe in live embryos are impossible without it. The model confirms what was pointed out by Longhini and Glotzer, that the short residence time of ECT-2, combined with in vivo flow speeds on the order of 10 µm/min, make it impossible for cortical flows alone to redistribute ECT-2. 2. The contribution of AurA is introduced highly schematically as a term based on enzyme inhibition biochemistry that increases the off rate of Ect2. The major assumption of the model is that AurA phosphorylates Ect2 strictly on the membrane (cortex) of the cell. Why? No molecular justification is given. If the authors cannot provide clear justification, this major assumption has to be clearly declared as such. The phosphorylation/dephosphorylation dynamics of Ect2 is not considered at all.

We clarified that the species we consider in the model (E) is unphosphorylated ECT-2, so that the negative flux comes from either unbinding or phosphorylation. Of course, AIR-1 phosphorylates ECT-2 in the cytoplasm as well, but our model only tracks the binding of unphosphorylated ECT-2 to the cortex. We clarified this on p. 6.

1. In the equation for myosin, the authors introduce disassembly/ inactivation term proportional to the fourth order of concentration of myosin. Why? This is a major assumption, which appears to be derived from the work by Michaux et al. 2018. There the authors (Michaux et al.) postulated that the rate of inactivation of RhoA GTPase was somehow proportional to the fourth power of RhoA concentration. It appears that Maxian et al. further assume that the myosin concentration is fast variable enslaved by Rho, so that M∼ [RhoA]. They then presumably assume that if the rate of degradation/ inactivation of Rho is proportional to the fourth power of Rho concentration, so is true for myosin (M). This is a logical error and is not justified. An important question, why do the current authors need this unusual assumption with such a high power of M disassembly/inactivation? Perhaps, this is because without this rather dubious term the cortex flow produces a blow-up of myosin concentration? This would be expected in their mechanical model - the continuous flow of actomyosin not compensated by cortex disassembly generally causes blow-up of biochemical concentrations transported by the flow, this is a known problem of the “simple” active gel model used by the authors. Maxian et al. have to provide clear derivation of the term−KfbM4 and also demonstrate why they need this exotic assumption.

As mentioned above on p. 1, this was a misguided attempt on our part to use previous literature to directly assign values to model parameters. In the revised manuscript, we considered a more general term for the nonlinear feedback. The fitting occurs in Fig. S3, where we impose the ECT-2 profile during pseudo-cleavage and try to fit the myosin profile. k = 1 is eliminated because the ECT-2 and myosin have different asymmetries. Higher order nonlinearities (k= 2,3) are successful in fitting the experimental data. In the main text, we present results from k= 2, then use Fig. S5 to present results on the k= 3 case.

1. The equation for myosin M has a membrane-binding term, which is second order in concentration of Ect2∼ E2, without which the model will not show the instability that the authors need. The only justification given is that ”some nonlinearity is required”. A proper derivation should be given here.

Our experimental data shows an asymmetry in myosin that is actually lower than ECT-2. As such, a nonlinear term in the binding rate lacks justification, and we switched to a linear term of the form kEMEMc (see model equations on p. 6).

1. The diffusion coefficients for Ect2 and myosin are chosen to be the same. Why? Clearly these molecules so different in size myosin being a gigantic cluster monster of∼ 300nm believed to be bound to actin, should have a much smaller diffusion coefficient?

Thank you for raising this point. We used the same diffusion coefficient for simplicity; because its dimensionless value is less than 10−4, diffusion is relatively unimportant in shaping the concentration fields. If we assume instead, for instance, that myosin cannot diffuse in the membrane, while ECT-2 has a ten-fold larger diffusion coefficient, the steady state profiles of ECT-2 and myosin are changed by at most 5% (see Fig. S6).

1. There are confusing statements regarding the role of actomyosin flows. In the beginning of the manuscript, the authors seem to state that since Ect2 has a high off rate, the effect of the flow on Ect2 localization is negligible in comparison with direct binding to myosin. Later, the authors state that flows are absolutely essential for the patterning. The authors need to clearly explain where and how the flows are important or not.

Thank you for pointing out this confusion. In the revised manuscript, we tried to be explicit that the combination of recruitment and flows is essential for patterning ECT-2. We did this in Figs. 4 and 5 by showing the results of simulations without recruitment (Fig. 4) and without recruitment and flows (Fig. 5).

Minor points: 1. page 9. Why is the rate of dephosphorylation of AurA is named Koff? We changed the notation to ¯kinac to reflect inactivation.

1. page 10. “Note that the model is calibrated to predict... which matches experimental observations” - this sentence needs changing. You want to say that you fit the model to experiments in the Longhini and Glotzer paper. There is no prediction here. We removed this sentence.

2. page 14. “A plot of Ect-2 accumulation as a function of distance from the nearest cortex...” - clearly the word ”centrosome” is meant here instead of ”cortex”. What was meant by this sentence was the distance from the centrosome to the nearest cortex pole (anterior or posterior). We modified it to make this more clear (p. 15).

3. page 16. ”Inactive, non-phosphorylatable version of Ect-2...” - non-phosphorylatable is clear, but why inactive? As discussed on p. 2 (#4), we decided to simplify the cytokinesis figure and remove the simulations with non-phosphorylatable ECT-2. While it is not relevant, the ECT-2 6A variant represents a fragment of the protein that lacks the catalytic domain. Our original goal was to use these data to track the ECT-2 localization without perturbing the system biochemistry, but the data gave the hint of longer exchange kinetics, which confounded our analysis.

Reviewer 3 Maxian et al. developed a mathematical model to explain the essential elements and interactions necessary and sufficient for the polarisation of the C. elegans zygote. The initiation of zygote polarisation has been extensively studied in recent years, highlighting the role of the centrosomal kinase Aurora-A (AIR-1) in controlling the cortical distribution of RhoGEF (ECT-2) and actomyosin contractility during polarisation. Although genetic experiments have demonstrated their function in this process, it remains to be tested whether these factors and their interactions are sufficient to induce polarisation. This work has provided a theoretical framework to predict the activity of AIR-1 in the cytoplasm and at the cell cortex, and the cortical distribution of ECT-2 and myosin-II (NMY-2). This framework can recapitulate the dynamic rearrangement of ECT-2 and myosin-II during polarisation, with centrosomes positioned at the posterior pole of the zygote. This model can explain, at least in part, the asymmetric distribution of ECT-2 and myosin-II in the zygote undergoing cytokinesis, suggesting that the mechanism of AIR-1-mediated control of ECT-2 and myosin-II would regulate patterning during polarisation and cytokinesis. This theoretical framework is developed with reasonable assumptions based on previous genetic experiments (except for the myosin-dependent regulation of ECT-2; see comments below).

Thank you for the positive assessment!

Major issues: 1. The authors insist that this model correctly predicts the spatio-temporal dynamics of ECT-2 and myosin-II during polarisation and cytokinesis. However, the predicted results do not reproduce the in vivo pattern of ECT-2 in both phases. ECT-2 is cleared from the posterior cortex and establishes a graded pattern across the antero-posterior axis during polarisation (see their previous publication in eLife 2022, 11, e83992, Fig1A -480s) and cytokinesis (see eLife 2022, 11, e83992, Fig1C 60s and 120s). During both stages, ECT-2 does not show local enrichment at the boundary between the anterior and posterior cortical domains in vivo. In fact, when comparing the predicted results with the in vivo pattern of ECT-2 and cortical flow, the authors used non-quantitative descriptions such as ’in good agreement’, ’a realistic magnitude’, ’resemble’. These vague descriptions should be revised and a quantitative assessment of ECT-2 distribution between in silico and in vivo should be included in a revised manuscript.

As mentioned on p. 1, in the revised manuscript we interacted with the data in a much stronger way. We first used data during pseudo-cleavage to infer the ECT-2/myosin relationship. We then examined (Fig. 3) quantitatively how the ECT-2 accumulation during polarization matches the experimental data (it matches early but not later stages). We repeated this for cytokinesis in Fig. 5, where we compared the ECT-2 profile across four experimental conditions to the model prediction.

1. I assume that the strange local enrichment of ECT-2 at the anteroposterior boundary is due to their assumption that the binding rate of ECT-2 is increased by a linear increase via cortical myosin-II (page 6). This assumption is not directly supported by experimental evidence. A previous study by the same group (eLife 2022, 11, e83992) showed that a progressive increase in ECT-2 concentration at the anterior cortex is partially accompanied by an increase in cortical flow and transport of myosin-II from the posterior pole to the anterior cortex. This observation supports the idea that ECT-2 may associate with cortical components transported by myosin-II based cortical flow. This unrealistic assumption makes the predicted distribution pattern of ECT-2 almost identical to that of cortical myosin-II, resulting in an increase in the concentration of ECT-2 at the anteroposterior boundary where myosin-II forms pseudocleavages and cleavage furrows. The authors should clarify why their mathematical model used this assumption and provide a comprehensive analysis and evaluation of the parameter value for an ECT-2-myosin-II interaction.

In the revised manuscript, we outlined the justification for this assumption after presenting the model equations. In Appendix B.3, we were able to constrain all parameters except the recruitment term. Then, in Appendix B.3.3, we provided an analysis of how polarization changes when the recruitment term is increased. We show that the ECT-2 asymmetries with myosin flows are the same as those simply due to AIR-1 inhibition (since the lifetime of ECT-2 is small). Adding indirect recruitment gives asymmetries that resemble experimental data from early establishment of polarity. We showed this both by assuming “myosin” (a species which colocalizes with myosin) recruits ECT-2 (Fig. S4) and by simulating an alternative model (Eq. (S4)) where an explicit species that is advected with cortical flows recruits myosin (Fig. S7).

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Referee #3

Evidence, reproducibility and clarity

Maxian et al. developed a mathematical model to explain the essential elements and interactions necessary and sufficient for the polarisation of the C. elegans zygote. The initiation of zygote polarisation has been extensively studied in recent years, highlighting the role of the centrosomal kinase Aurora-A (AIR-1) in controlling the cortical distribution of RhoGEF (ECT-2) and actomyosin contractility during polarisation. Although genetic experiments have demonstrated their function in this process, it remains to be tested whether these factors and their interactions are sufficient to induce polarisation.

This work has provided a theoretical framework to predict the activity of AIR-1 in the cytoplasm and at the cell cortex, and the cortical distribution of ECT-2 and myosin-II (NMY-2). This framework can recapitulate the dynamic rearrangement of ECT-2 and myosin-II during polarisation, with centrosomes positioned at the posterior pole of the zygote. This model can explain, at least in part, the asymmetric distribution of ECT-2 and myosin-II in the zygote undergoing cytokinesis, suggesting that the mechanism of AIR-1-mediated control of ECT-2 and myosin-II would regulate patterning during polarisation and cytokinesis. This theoretical framework is developed with reasonable assumptions based on previous genetic experiments (except for the myosin-dependent regulation of ECT-2; see comments below).

Issue #1

The authors insist that this model correctly predicts the spatio-temporal dynamics of ECT-2 and myosin-II during polarisation and cytokinesis. However, the predicted results do not reproduce the in vivo pattern of ECT-2 in both phases. ECT-2 is cleared from the posterior cortex and establishes a graded pattern across the antero-posterior axis during polarisation (see their previous publication in eLife 2022, 11, e83992, Fig1A -480s) and cytokinesis (see eLife 2022, 11, e83992, Fig1C 60s and 120s). During both stages, ECT-2 does not show local enrichment at the boundary between the anterior and posterior cortical domains in vivo. In fact, when comparing the predicted results with the in vivo pattern of ECT-2 and cortical flow, the authors used non-quantitative descriptions such as 'in good agreement', 'a realistic magnitude', 'resemble'. These vague descriptions should be revised and a quantitative assessment of ECT-2 distribution between in silico and in vivo should be included in a revised manuscript.

Issue #2

I assume that the strange local enrichment of ECT-2 at the anteroposterior boundary is due to their assumption that the binding rate of ECT-2 is increased by a linear increase via cortical myosin-II (page 6). This assumption is not directly supported by experimental evidence. A previous study by the same group (eLife 2022, 11, e83992) showed that a progressive increase in ECT-2 concentration at the anterior cortex is partially accompanied by an increase in cortical flow and transport of myosin-II from the posterior pole to the anterior cortex. This observation supports the idea that ECT-2 may associate with cortical components transported by myosin-II based cortical flow. This unrealistic assumption makes the predicted distribution pattern of ECT-2 almost identical to that of cortical myosin-II, resulting in an increase in the concentration of ECT-2 at the anteroposterior boundary where myosin-II forms pheudo-cleavages and cleavage furrows. The authors should clarify why their mathematical model used this assumption and provide a comprehensive analysis and evaluation of the parameter value for an ECT-2-myosin-II interaction.

Significance

This work includes a valuable tool that can be used to explain other actomyosin-mediated polarisation processes. Although the paper provides useful insights in principle, the weakness of this work is that the model is designed with parameter sets that only recapitulate previously published phenotypes. Therefore, this paper confirms previous findings and provides less/no new mechanistic insights into cell polarisation. As such, this work would be of interest to specialised cell biologists and biophysicists working on the cytoskeleton and cell division, but will not be of general interest to biologists and biochemists.

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Referee #2

Evidence, reproducibility and clarity

The manuscript by Maxian, Longhini and Glotzer presents purely modeling work performed by the first author in conjunction with the already published experimental work by Longhini and Glotzer (eLife, 2022). The aim of the manuscript is to provide a mathematical model that connects the actomyosin contractility of the cell cortex in C. elegans zygote with the activity of the centrosomal kinase AurA (AIR-1 in C. elegans). The major claim of the authors is that their model, fitted to the experimental data pertaining to the zygote polarization, also describes dynamics during the zygote cytokinesis. In the model, the authors provide a heuristic approach to the biochemical dynamics, reducing their treatment to two variables: myosin and Ect2 Rho GEF. The biochemical model is integrated with a simple 1D active gel-type model for the cortical flow. The model uses static diffusive field of activity of AurA kinase in the cytoplasm as an input to their chemo-mechanical model. Major concerns: 1. The biochemical model is highly heuristic and several major assumptions are poorly justified. Thus, the authors explicitly introduce recruitment of Ect2 by myosin, something apparently based on the experimental observations by Longhini and Glotzer in 2022, which had not been biochemically confirmed since with a clear molecular mechanism. 2. The contribution of AurA is introduced highly schematically as a term based on enzyme inhibition biochemistry that increases the off rate of Ect2. The major assumption of the model is that AurA phosphorylates Ect2 strictly on the membrane (cortex) of the cell. Why? No molecular justification is given. If the authors cannot provide clear justification, this major assumption has to be clearly declared as such. The phosphorylation/dephosphorylation dynamics of Ect2 is not considered at all. 3. In the equation for myosin, the authors introduce disassembly/ inactivation term proportional to the fourth order of concentration of myosin. Why? This is a major assumption, which appears to be derived from the work by Michaux et al. 2018. There the authors (Michaux et al.) postulated that the rate of inactivation of RhoA GTPase was somehow proportional to the fourth power of RhoA concentration. It appears that Maxian et al. further assume that the myosin concentration is fast variable enslaved by Rho, so that M ~ [RhoA]. They then presumably assume that if the rate of degradation/ inactivation of Rho is proportional to the forth power of Rho concentration, so is true for myosin (M). This is a logical error and is not justified. An important question, why do the current authors need this unusual assumption with such a high power of M disassembly/inactivation? Perhaps, this is because without this rather dubious term the cortex flow produces a blow-up of myosin concentration? This would be expected in their mechanical model - the continuous flow of actomyosin not compensated by cortex disassembly generally causes blow-up of biochemical concentrations transported by the flow, this is a known problem of the "simple" active gel model used by the authors. Maxian et al. have to provide clear derivation of the term -kfb*M^4 and also demonstrate why they need this exotic assumption. 4. The equation for myosin M has a membrane-binding term, which is second order in concentration of Ect2 ~E^2, without which the model will not show the instability that the authors need. The only justification given is that "some nonlinearity is required". A proper derivation should be given here. 5. The diffusion coefficients for Ect2 and myosin are chosen to be the same. Why? Clearly these molecules so different in size - myosin being a gigantic cluster monster of ~300 nm believed to be bound to actin, should have a much smaller diffusion coefficient? 6. There are confusing statements regarding the role of actomyosin flows. In the beginning of the manuscript, the authors seem to state that since Ect2 has a high off rate, the effect of the flow on Ect2 localization is negligible in comparison with direct binding to myosin. Later, the authors state that flows are absolutely essential for the patterning. The authors need to clearly explain where and how the flows are important or not. Minor points: 1. page 9. Why is the rate of dephosphorylation of AurA is named Koff? 2. page 10. "Note that the model is calibrated to predict... which matches experimental observations" - this sentence needs changing. You want to say that you fit the model to experiments in the Longhini and Glotzer paper. There is no prediction here. 3. page 14. "A plot of Ect-2 accumulation as a function of distance from the nearest cortex..." - clearly the word "centrosome" is meant here instead of "cortex". 4. page 16. "Inactive, non-phosphorylatable version of Ect-2..." - non-phosphorylatable is clear, but why inactive?

Significance

This reviewer sees limited significance of this manuscript to the field in general. The modeling approach is hardly novel as it is based on a variety of published models, all cited by the authors, to be precise. The model, being very simplistic and heuristic, is not predictive. The main novelty of the current manuscript is the introduction of the effect of Aurora A on the activity of the actomyosin cortex. Since this is taken to be very schematic, simply via the effective increase in the off rate of Ect2, the model is showing that it is consistent with the earlier published experimental results by Longhini and Glotzer. This is to be expected. The main claim of the authors, that the model fitted to the polarization data also qualitatively describes the cytokinesis (there are no quantitative data to compare to) is probably valid, but the result is not surprising either. At best, the model can be labeled as fitted to the data and confirming the experimental results. Since it contains several postulated heuristic terms not properly justified on the mechanistic level, this is also not surprising.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this article, Maxian et al. propose a model combining 1-d simulations of ECT-2 and Myosin concentration at the cortex through binding/unbinding and advection at the cortex, with an input for AIR-1 cortical concentration based on the spatial localisation of the centrosomes in the cytoplasm. The objective of the authors is to recapitulate the role of (1) AIR-1, (2) its effector ECT-2 and (3) the downstream effector, driver of cortical flows, the molecular motors Myosin, in two key physiological processes, polarization and cell division. This is important as work over the last 10 years have emphasized the role of AIR-1 in embryo polarization. Previous biochemical-mechanical models have focused on RhoA/Myosin interactions (Nishikawa et al, 2017), the importance of a negative feedback and excitable RhoA dynamics (Michaux et al, 2018), or anterior PARs/posterior PARs/Myosin (Gross et al, 2019). The authors thus attempt to provide a new descriptive model in which RhoA is implicit, instead focusing on the role of centrosome localization on AIR-1 localization, and providing a framework to explore polarity establishment and cell division based on these 3 simple players. The first part of the model is very reminiscent of previously published models, while the second instead provides a link between the initial polarizing cue AIR-1 and polarization. Based on this description, the model is precisely tuned to achieve polarization while matching experimental observations of flow speed and ECT-2 A/P enrichment shape. The results are therefore certainly new and interesting.

Major comments:

1. The authors use the position of the centrosomes as a static entry, resulting in a static AIR-1 input. Is this true, or are the positions of the centrosomes dynamically modulated over the course of the different processes simulated here (for example as a consequence of cortical flows?), and if so, is the assumption of immobile position?
2. While in its principle the model is quite simple and elegant, the detailed form of the equations describing the interactions between the players is more complex. Are all these required? If they are crucially important for the behavior of the model, these should be described more thoroughly, and if possible rooted more directly in experimental results, in particular:
   • k(ME)MEc (Linear enhancement term): why would myosin impact E concentration? The authors state, p.7, "There is a modest increase in the recruitment rate of ECT-2 due to cortical myosin (directly or indirectly), in a myosin concentration-dependent manner (Longhini and Glotzer, 2022)." I could not find the data supporting this assumption - Longhini and Glotzer apparently rather point to a modulation of cortical flows. ("During anaphase, asymmetric ECT-2 accumulation is also myosin-dependent, presumably due to its role in generating cortical flows."). Embedding this effect in the recruitment rate instead of expecting it from the model thus appears awkward. Could the authors specify how they came to this conclusion, which the authors might have derived from observations made in their previous work, but maybe did not fully document there?
   • k(EM)E^2Mc (ECT-2 non-linear impact on Myosin): does the specific of the value to convey the enhancement (square form) have an impact on the results?
   • k(fb)*M^4 "The form of this term is a coarse-grained version of previously-published work (Michaux et al., 2018)." Myosin feedback on myosin localization proportionally to M^4 does not seem to directly derive from Michaux et al... Please detail this points more extensively and detail the derivation, in the supplements if not in the main text. P23. Parameter values: "This is 1.5 times longer than the estimate for single molecules (Nishikawa et al., 2017; Gross et al., 2019) to reflect the more long-lived nature of myosin foci during establishment phase (Munro et al., 2004)." Not sure what the authors mean by more long-lived duration of foci during establishment phase. Seems rather arbitrary.
3. It would be very helpful (and indeed more convincing) to include a direct comparison between modeling results and experimental counterpart whenever possible. This might not be possible for some data (e.g. Fig. 3d from Cowan et al), but should be possible for other, in particular Fig. 3c and Fig. 5b, for the flow speed and ECT-2 profiles. In Fig. 5b in particular, previously published experimental data could be produced to give the reader to compare model with experiments (possibly provided as an inset, at least for the wild type conditions).

Minor comments

Fig. 5b: ECT-2 C 6A(dhc-1) do not seem to be referenced or discussed in the main text. Also, why present the results for the flow for 2 conditions and the ECT-2 localisation for 4? Or does the variation of ECT-2 not impact the flow profile?

p.6: Eqn 1a: ^ missing on 3rd E?

p.6: Given that the non-normalized data is used in the main text, and the normalized only appears in the supplemental, maybe star the dimensionless and remove all hats from the main for greater legibility?

p.14: replace "embryo treatment" with "experimental conditions"?

p.21, S4a: add A=Â/Atot

p.22: "L = 134.6 μm" - please write 134µm to retain the precision of original measurements

p.22: Please provide formula for all dimensionless values as a table at the end of the supplemental for the eager but less-mathematically proficient reader.

The authors' attention to providing specific citations including figure number corresponding to the specific point they reference in the papers they cite is appreciated.

Significance

General assessment:

This modeling paper interestingly leverages existing experimental data to develop a new mathematical model of embryo polarization and cell division focusing on the role of AIR-1/Aurora Kinase. It combines classical 1-d advection/diffusion-reaction scheme with an upstream cue, AIR-1/Aurora Kinase, the profile of which is defined by the localization of the two centrosomes, and use the model as a framework to explore cortical flows and ECT-2 and Myosin cortical localization. Calibrated using information from polarization phase, the model recapitulates without any further tuning, in a variety of mutants, key localisation hallmarks of Ect-2 during cell division, simply based on the localization of the centrosomes. Finally, it provides strong, experimentally testable predictions of the validity of the proposed model.

Advance:

In particular, this study provide compelling evidence showing that their model, based on dynamics during polarization, is sufficient to explain the ultra-sensitivity of cortical ECT-2 accumulation to centrosome distance during cell division. Their model further predicts that short ECT-2 cortical residence time is required to prevent advection-mediated counter-flows of ECT-2 that would otherwise prevent polarization, a prediction testable experimentally by engineering modifications of ECT-2 cortical residence time.

Audience:

This is primarily a modeling paper. Although the bulk of the article is written to capture the interest of cellular biologists with a sound backgrounds in mathematics and an interest in minimal models of cell division and polarization, the overall conclusions and prediction are further-reaching and would be of interest to a larger audience with an interest in cell division, polarization, and the role of Aurora Kinase in these processes.

Expertise:

Developmental biology / Cell biology / Biological physics

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Reply to the reviewers

Referee #1 Major concerns:

1) One major concern that I have about the sexual dimorphism in tolerance to nutrient deprivation is that the papers cited by the authors, and paradigms that are used broadly in the field, all use adult flies. The authors must show that in larvae, a completely different life stage from their citations, there is a sexual dimorphism in tolerance to nutrient deprivation.

In our descriptions of previous literature that describes tolerance to nutrient deprivation, we have added language that specifies that the results from nutrient deprivation mentioned therein were performed in adults (lines 82, 91, 96, highlighted in the preliminary revision).

In response to the concern from this reviewer that our data do not assay for nutrient deprivation in larvae, we would like to clarify that our “stress tolerance assay” more specifically demonstrates that developmental nutrient deprivation compromises male survival through pupariation to adulthood. While the effects of acute nutrient deprivation on developmental delay can be assayed in foraging or earlier larval stages, we have not tested whether ATF4 signaling is present and dimorphic in these stages and believe it to be beyond the scope of this study. In the revision, we will edit the text to be more precise in our conclusions with these data.

Interestingly, Diaz et al 2023 (Genetics) show that male larvae have greater fat stores than female larvae. Considering fat is the main determinant of tolerance to nutrient deprivation it's not clear that females will actually survive nutrient deprivation longer as larvae. This is an essential test of whether female larvae do have increased tolerance to nutrient deprivation, which is the basic foundation of the authors' model.

We thank the reviewer for making this clarifying point about the relationship between fat stores and nutrient deprivation. ____In response to the concern our data do not assay for nutrient deprivation in larvae (major point #1), we would like to clarify that our “stress tolerance assay” more specifically demonstrates that developmental nutrient deprivation compromises male survival through pupariation to adulthood. While the effects of acute nutrient deprivation on developmental delay can be assayed in foraging or earlier larval stages, we have not tested whether ATF4 signaling is present and dimorphic in these stages and believe it to be beyond the scope of this study. In the revision, we will edit the text to be more precise in our conclusions with these data.

2) Another concern is the way that the authors "genetically induce nutrient deprivation using methioninase overexpression". As they acknowledge in the discussion (Line 381-390), methioninase overexpression will have many cellular effects. While there is no doubt that methionine levels would be lower in their model, it is less certain whether this is the main driver of the male-specific lethality.

There are two potential solutions to this problem. First, the authors could change the text throughout the paper to more accurately describe their paradigm as "methioninase-induced lethality" rather than "nutrient deprivation". This would limit the scope of their scientific question and the conclusions they draw, but would eliminate the need for more experiments.

The second solution would be to complete experiments to establish the following points: i) methioninase overexpression causes all the classical features of nutrient deprivation (e.g. changes to canonical signaling pathways such as TOR); ii) using other genetic means of nutrient deprivation such as slimfast-RNAi to see if those manipulations phenocopies the male-specific lethality they see with methioninase overexpression; iii) testing a role for ATF4 in mediating sex differences (if any) in other contexts such as slimfast-RNAi. This will take 2-3 months but is essential to draw any conclusions about whether their paradigm is truly a model for nutrient deprivation.

We agree that methionine depletion is not the only cellular change effected by methioninase over-expression. For example, a molecular byproduct of methioninase metabolism via methioninase is the production of ammonia, which has recently been shown to indue ISR signaling in the context of____ alcohol-associated liver disease (Song et al. 2024, PMID 37995805). We believe our experimental controls and genetic rescues account for this and other possible effects in the interpretation of our data. ____To further establish the utility of methioninase overexpression as a genetic means of methionine deprivation (first described in Parkhitko et al. 2021, PMID 34588310), we will perform ____slimfastRNAi_ in the fat (another genetic means of reducing intracellular amino acid levels) per the reviewer’s suggestion. In these animals we will evaluate 1) ATF4 activity in L3 adipocytes using 4E-BPintron-GFP (1.5 months) , and 2) male vs. female lethality (as determined by counting eclosed adults) (2 months. If male lethality is observed with _UAS-slimfastRNAi _as with _methioninase ____expression, we will test the requirement for dimorphic ATF4 signaling in the fat for such male susceptibility to lethality/female resistance to lethality. (3 months)

3) Another important point is that the authors state that sexually dimorphic ATF4 activity in the fat body is instructed by sexual identity in a cell-autonomous manner. Despite a clear decrease in ATF4 reporter levels in tra mutants, the fat body-specific tra-RNAi effect on the ATF4 reporter was less convincing. Together with the fact that changes to tra in the fat body affect insulin secretion from the insulin-producing cells, it is possible that the effect on ATF4 is not cell-autonomous. To conclusively test if sexual identity regulates ATF4 in a cell-autonomous manner the authors should use the flp-out system to make Tra-expressing or tra-RNAi-expressing clones in the fat body. This would take approximately 1.5 months to make the strain and test this.

We thank the reviewer for making the astute observation that the effect of fat body-specific ____tra_ knockdown on female ATF4 reporter activity was more modest than whole-animal _tra_ mutants. We ascribe this to RNAi knockdown efficiency rather than non-autonomous effects of sexual identity on ATF4 expression in the fat. This is underscored by our data showing fat body knockdown of _spenito_ (_nito_), a sex determinant upstream of _tra____ that is shown to instruct female sexual identity in the larval fat (Diaz et al. 2023, PMID 36824729), does indeed reduce ATF4 levels in female fat to that of control male fat (Fig. 2K).

4) As the authors show for the UAS-methioninase, other UAS lines used in the paper such as UAS-traF, UAS-tra-RNAi, UAS-dsx-RNAi may have leaky effects on gene/reporter expression. The authors must include a UAS only control to establish that the tra-RNAi, UAS-traF, UAS-dsx-RNAi do not affect gene/reporter expression.

We thank the reviewer for suggesting that we evaluate the “leakiness” of all UAS lines used in this study (major point #4). To do this, we will quantify ATF4 reporter activity in the fat (4E-BPintron-GFP) in the presence of UAS lines but in the absence of GAL4 for ____UAS-traRNAi_, _UAS-traF_, and _UAS-dsxRNAi____ (1.5 months)

5) I have concerns about the statistics used. In the methods and legends only t-tests are mentioned; however, when three groups are compared a one-way ANOVA with post-hoc tests must be used to correct for multiple comparisons. To compare differential responses to genetic/environmental manipulations between the sexes, a two-way ANOVA must be used. For example, to conclude that males and females have different responses in the two-way ANOVA, there must be a significant genotype:sex interaction. The p-values for comparisons between genotypes in either the one-way or two-way ANOVA must be derived from post-hoc tests within the ANOVA analysis.

__We thank the reviewer for carefully assessing our usage of statistical analyses to interpret the data in the study. To the best of our understanding, such ANOVA analyses are helpful in evaluating significance when comparing multiple sample groups simultaneously. However, in all our analyses we are only ever comparing two samples at a time, making a two-tailed Student’s t-test with Welch’s correction (assuming unequal variance) to be the best statistical method. __

Referee #1 Minor points

1) Please ensure to make the reader aware of which life stage was tested in the literature cited supporting sexually dimorphic tolerance to nutrient deprivation.

We thank the reviewer for pointing out this ambiguity in our description of previous and current work on nutrient deprivation tolerance. We address this minor point in tandem with major point #1 above ____by adding language that specifies that the results from nutrient deprivation mentioned therein were performed in adults (lines 82, 91, 96, highlighted in the preliminary revision).

2) Published data about sex-specific mechanisms of metabolic regulation mean that the introduction should be more fully cited than it is. Even in the introduction "the molecular basis of these differences and how they impact tolerance to nutrient deprivation is still under investigation" is inaccurate, as there are published studies identifying some mechanisms (work on gut hormones and sex-specific effects on starvation resistance and body fat, role of ecdysone on body fat and feeding, sex-specific roles for brummer and Akh in regulating body fat, intestinal transit and gut size and feeding). Please adjust the paper to acknowledge this growing body of knowledge.

We thank the reviewer for appropriately highlighting that there are other relevant studies in the context of sex-specific mechanisms of metabolic regulation in addition to those referenced in the original manuscript. Specifically, we will include additional citations and appropriate descriptions of previous work, such as those that report on sex-specific effects of starvation (i.e. Millington et al. 2022, PMID 35195254) and sex-specific roles for metabolic regulators such as Brummer/ATGL (Wat et al. 2020, PMID 31961851) and Adipokinetic hormone (Wat et al. 2021, PMID 34672260) in _Drosophila fat storage._ __

3) Please list the ingredients per L so that individuals can replicate the diet easily.

__We thank the reviewer for requesting additional details on the diet fed to animals in this study, which will improve the reproducibility of our findings. In the Methods section, we have now included additional details on the specific diet fed to animals used in this study (lines 465-468 in the preliminary revision).____ __

4) Please cite grant numbers for all the community resources (e.g. Bloomington, DSHB), and please acknowledge FlyBase and its grants as well. For example, here are the instructions for citing BDSC https://bdsc.indiana.edu/about/acknowledge.html and similar instructions are available for the other resources.

We thank the reviewer for underscoring the importance of citing grant numbers for all community resources used. We have added to the Acknowledgements section statements and grant numbers regarding use of community resources such as FlyBase, Bloomington Drosophila Stock Center, and DSHB (lines 533-538 in the preliminary revision).

Referee #2:

1. Figure 4 is an important part of this study, where the authors show a male-specific vulnerability to methioninase expression. They show that ATF4 RNAi confers vulnerability to methioninase expression even in females. An obvious question is whether ATF4 overexpression is sufficient to enhance resistance to methionine deprivation in males.

We thank the reviewer for pointing out that the ability of increased ATF4 in male fat to enhance resistance to methionine deprivation was not interrogated. To examine this, ____we will quantify survival rates of males and females following dual over-expression of methioninase and ATF4 (3 months). We would like to state here that experimental over-expression of ATF4 at the levels induced by GAL4 activity is sometimes lethal, so this experiment may be difficult to execute/interpret due to technical limitations.

Methioninase expression results (Figure 4) are interesting. Are the levels of methioninase expression similar between males and females?

We thank the reviewer for asking for clarification on whether methioninase induction is similar between males and females. Whether methioninase induction is sexually dimorphic is likely a function of whether there is sexual dimorphism in the strength of the GAL4 driver used. While the drivers employed in this study are widely used for fat body expression, to our knowledge relative expression of ____Dcg-GAL4_ in males versus females has never been reported. Thus, we will perform qPCR to compare GAL4 and methioninase transcript levels in _Dcg-GAL4; UAS-methioninase____ male and female fat bodies (1 month).

1. This manuscript focuses on ATF4, but there could be additional possible reasons for the sexually dimorphic ISR activity. For example, the degree of physiological stress that activates ISR could be different between males and females. I suggest comparing the levels of Phospho-eIF2alpha (or any other markers upstream of ATF4) in both sexes.

We thank the reviewer for suggesting additional checks for sexual dimorphism in ISR activity in the fat, such as degree of eIF2α phosphorylation, which is directly upstream of ATF4 induction. Per their suggestion, we will compare p-eIF2α staining in male and female larval adipocytes (1.5 months).

In Figures 1 to 3, the authors examine the intensity of ATF4 signaling after perturbing the sexual determination pathway. The methioninase experiments in Figure 4 are interesting, but there is nothing in this Figure linking male-specific vulnerability to sex determination genes. Examining the vulnerability to methioninase expression after perturbing the sexual determination genes would make Figure 4 integrate better with the rest of the manuscript.

We thank the reviewer for highlighting that the role of male sexual identity in vulnerability of males to methioninase expression was not interrogated. Similar to our genetic interaction study proposed in point #1 from this reviewer, we will test whether feminizing male fat bodies (using UAS-traF over-expression) will change survival rate of males in our methioninase-expression paradigm (3 months).

1. The authors write that they generated 4EBP intron-GFP because the 4EBP intron-DsRed signal was frequently observed in the cytoplasm (line 122). They seem to suggest that the DsRed reporter is less reliable than the GFP reporter. However, they continue to mix results using 4EBP intron-GFP (Fig. 4A) and 4EBP intron-DsRed (Fig. 4F). The two figures examine slightly different conditions (Fig 4A shows tra1 KO females, while Fig. 4F shows traF males). If the DsRed reporter is less reliable due to the signal from the cytoplasm, the authors should show results with the GFP reporter in traF males.

We thank the reviewer for raising the legitimate concern that the ____4EBPintron-DsRed_ reporter used for some of the included quantifications in Fig. 3 might be less reliable then _4EBPintron-GFP_ that was generated for this study. We have updated the manuscript text (in the Results section) to more accurately describe the justification for building the _4EBPintron-GFP____ line (lines 122-127 in the preliminary revision).

1. In Figure S1, the authors label 4EBP intron-GFP as Thor2p-GFP, which is confusing. There are other parts in the methods section referring to Thor2p. I suggest using consistent terminology throughout the manuscript.

We thank the reviewer for pointing out this typo. We have modified the text accordingly in Figure S1.

Referee #3 Major concerns:

1) Sexually dimorphic ATF4 activity (Figure 1 and associated supplemental figure) as evidenced by reporter expression is the basis of this study, yet a detailed description of the immunofluorescence quantification is lacking. The methods sections needs to include information on how a) images were acquired (Were the same acquisition settings used across all images?), b) the intensity measurements were taken (What software was used? Does each data point in the distribution represent a single nucleus (the assumption is yes)? Is nuclear size adjusted for? Panels A' and B' have obvious differences in nuclear size which would in turn affect total intensity measurements), c) the sample size (How many fat images taken per animal per sample/genotype? How many trials were performed?)

We thank the reviewer for requesting additional information describing the immunofluorescence quantification methods. ____We have now added an additional paragraph to the Methods section detailing image acquisition for quantifying reporter activity (lines 483-494 in the preliminary revision).

2) While the authors nicely address the lack in specificity for two of the Gal4 driver lines used in the study limitation section, the fact that the one driver that is fat body-specific, 3.1Lsp2-Gal4, shows a modest, not statistically significant decrease in Figure 4C still raises some concern. There is another Lsp2-Gal4 line described in Lazareva et al., 2007 (PLoS Genetics) that drives expression in larval fat, perhaps to combat the issue of 3.1Lsp2-Gal4 have low activity, as mentioned by the authors. Alternatively, this phenotype could be assessed using Gal4 lines that only drive expression in the other tissues (if available). Otherwise, the conclusion that ISR/ATF4 signaling specifically in the fat mediates the starvation response needs to be toned down.

We thank the reviewer for carefully analyzing our data showing survival during methioninase over-expression using different GAL4 drivers. ____The reviewer raises a valid concern that the GAL4 driver with highest specificity for the fat body (that is, with the least off-target tissue expression), ____3.1 Lsp2-GAL4_, induces the most modest methioninase-induced lethality (major point #2). We attribute this to the fact that _3.1 Lsp2-GAL4_ is reportedly (and in our hands) a weaker driver than _Dcg-GAL4_ in the larval fat body. We will demonstrate this experimentally by performing UAS-nucGFP expression using both _Dcg-GAL4_ and _3.1 Lsp2-GAL4____ side by side and quantifying nuclear GFP intensity in the larval fat (2 months).

The reviewer also mentions that the other drivers with more statistically significant effects on male lethality (____Dcg-GAL4_ and _r4-GAL4_, Fig. 4) are not restricted to the fat body. Importantly, both these drivers are also expressed in the blood lineage (hemocytes). To examine whether ISR activation in hemocytes contributes to the female stress tolerance (and/or male lethality) observed upon methioninase induction, we will quantify male and female survival rate following methioninase induction in the blood lineage using a blood-specific driver, _HHLT-GAL4____ (Mondal et al 2014, PMID 25201876). (2.5 months)

3) Several analyses rely on RNAi, and this is understandably important for tissue-specific knockdown of gene expression. At least one of the two following issues needs to be addressed: a) the efficiency of knockdown for each gene are not provided or reported on and b) only single RNAi lines were used for each gene targeted for knockdown.

We thank the reviewer for pointing out that the original manuscript does not report on knockdown efficiencies of the RNAi lines used in the study. The RNAi lines from the Harvard Transgenic RNAi Project (TRiP) collection (traRNAi, dsxRNAi, nitoRNAi) have been verified in Yan & Perrimon 2015 (PMID 26324914). The ATF4RNAi line was verified in Grmai et al. 2024 (PMID 38457339). We have included all citations for these validation studies in Table S1 in the preliminary revision.

Referee #3 Moderate concerns:

1) Lines 137-141: It would be nice to see a gel that confirms that these newly designed primers detect the expected isoforms (supplemental perhaps).

We thank the reviewer for requesting confirmation of isoform specificity of the primers used to detect ATF4 transcript in the fat body in Fig. 2B-C. Because these are qPCR primers, they were all designed to produce amplicons of nearly equal size. There is currently no reliable method to specifically deplete one ____ATF4_ isoform at a time, which would be the only way to experimentally demonstrate isoform specificity of each primer set. However, we have designed each primer pair to specifically detect isoform-specific regions of _ATF4_ mRNA and have verified specificity (and lack of off-target products in the _D. melanogaster_ genome) _in silico____ using Primer-BLAST (NCBI).

2) Lines 278-282 and Figure 4D: Shouldn't the second and fourth bars be compared? Based on the hypothesis and conclusion, second bar females can resist nutrient stress because they have ATF4, but fourth bar females can't because they don't have ATF4 - is this difference statistically significant?

We thank the reviewer for pointing out this missing statistical report that compares the second and fourth bars in Figure 4D ____(females expressing methioninase, with and without ATF4 knockdown). We have now performed this analysis and reported the p-value in text (lines 282-285 in the preliminary revision).

3) For all scatter plot graphs, figure legends should indicate what the horizontal line represents (is this the average?). Also, error bars and what they represent (SD or SEM) are not included or described.

We thank the reviewer for asking for additional details on our graph annotations. We have added language to explain that 1) horizontal lines on ATF4 reporter quantification graphs denote mean intensity (Fig. 1 legend, lines 567-568 in the preliminary revision) and 2) error bars on qPCR graphs represent SEM (Fig. 2 legend, line 583 in the preliminary revision).

Referee #3 Minor concerns:

1) Line 27: "counter parts" should be one word 2) Line 33: should the word "nutrient" be included before "stress" 3) Line 42: It would be nice to see a couple of examples of the "well documented across species" statement 4) Line 44-45: Add in the word "human" before population and use "women" instead of "females" 5) Line 53: There seems to be an issue with comma placement or word usage in the section of the sentence that reads "coincident with, or a comorbidity, for" 6) Lines 82-83: Mention of a couple examples would be nice 7) Line 104: Perhaps add the word "cellular" before "sexual" 8) Line 204: Delete the word "and" after "expression" 9) Line 234: Delete "a" before "significantly" 10) Line 276: Should "adult" be "adulthood" 11) For the discussion, a model schematic would nicely depict the findings as a whole 12) Line 330: May consider incorporating the following studies - Stobdan et al., 2019 and De Groef et al., 2021 13) Related to the point above: It would be great to see discussion/speculation of potential ATF4 targets that might be mediating this effect 14) Line 374: The placement of "yet unidentified" makes it seem like other ATF4 target genes aren't known, but really what is meant is that their sexually dimorphic expression is not known 15) Line 535: (beta-gal) "protein" instead of "gene"? 16) Figure S2: Please indicate what the two horizontal dotted lines are supposed to point out

We thank the reviewer for carefully pointing out these minor yet critical text concerns. ____We have addressed all minor concerns raised by the reviewer in text edits to the preliminary revision, which are each highlighted in yellow in lines 27, 33, 44, 53, 105, 204, 236, 279, 375,554, 624 in the preliminary revision. The exceptions are points 3, 6, 11, 13, which we will address in the subsequent revision as described in the previous section.

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Referee #3

Evidence, reproducibility and clarity

Summary: Using a combination of genetic and molecular tools, Grmai and colleagues present data showing the sexually dimorphic expression of ATF4, a transcription factor that mediates the integrated stress response, in larval fat tissue. Moreover, they find that higher basal ATF4 activity in female larvae supports the stronger resistance to nutrient deprivation that females exhibit compared to male larvae. The data are clearly described and nicely laid out in well-organized figures. Some major, moderate, and minor concerns, delineated below, regarding the approach and conclusions should be addressed prior to acceptance for publication.

Major concerns:

1. Sexually dimorphic ATF4 activity (Figure 1 and associated supplemental figure) as evidenced by reporter expression is the basis of this study, yet a detailed description of the immunofluorescence quantification is lacking. The methods sections needs to include information on how a) images were acquired (Were the same acquisition settings used across all images?), b) the intensity measurements were taken (What software was used? Does each data point in the distribution represent a single nucleus (the assumption is yes)? Is nuclear size adjusted for? Panels A' and B' have obvious differences in nuclear size which would in turn affect total intensity measurements), c) the sample size (How many fat images taken per animal per sample/genotype? How many trials were performed?)
2. While the authors nicely address the lack in specificity for two of the Gal4 driver lines used in the study limitation section, the fact that the one driver that is fat body-specific, 3.1Lsp2-Gal4, shows a modest, not statistically significant decrease in Figure 4C still raises some concern. There is another Lsp2-Gal4 line described in Lazareva et al., 2007 (PLoS Genetics) that drives expression in larval fat, perhaps to combat the issue of 3.1Lsp2-Gal4 have low activity, as mentioned by the authors. Alternatively, this phenotype could be assessed using Gal4 lines that only drive expression in the other tissues (if available). Otherwise, the conclusion that ISR/ATF4 signaling specifically in the fat mediates the starvation response needs to be toned down.
3. Several analyses rely on RNAi, and this is understandably important for tissue-specific knockdown of gene expression. At least one of the two following issues needs to be addressed: a) the efficiency of knockdown for each gene are not provided or reported on and b) only single RNAi lines were used for each gene targeted for knockdown.

Moderate concerns:

1. Lines 137-141: It would be nice to see a gel that confirms that these newly designed primers detect the expected isoforms (supplemental perhaps).
2. Lines 278-282 and Figure 4D: Shouldn't the second and fourth bars be compared? Based on the hypothesis and conclusion, second bar females can resist nutrient stress because they have ATF4, but fourth bar females can't because they don't have ATF4 - is this difference statistically significant?
3. For all scatter plot graphs, figure legends should indicate what the horizontal line represents (is this the average?). Also, error bars and what they represent (SD or SEM) are not included or described.

Minor concerns:

1. Line 27: "counter parts" should be one word
2. Line 33: should the word "nutrient" be included before "stress"
3. Line 42: It would be nice to see a couple of examples of the "well documented across species" statement
4. Line 44-45: Add in the word "human" before population and use "women" instead of "females"
5. Line 53: There seems to be an issue with comma placement or word usage in the section of the sentence that reads "coincident with, or a comorbidity, for"
6. Lines 82-83: Mention of a couple examples would be nice
7. Line 104: Perhaps add the word "cellular" before "sexual"
8. Line 204: Delete the word "and" after "expression"
9. Line 234: Delete "a" before "significantly"
10. Line 276: Should "adult" be "adulthood"
11. For the discussion, a model schematic would nicely depict the findings as a whole
12. Line 330: May consider incorporating the following studies - Stobdan et al., 2019 and De Groef et al., 2021
13. Related to the point above: It would be great to see discussion/speculation of potential ATF4 targets that might be mediating this effect
14. Line 374: The placement of "yet unidentified" makes it seem like other ATF4 target genes aren't known, but really what is meant is that their sexually dimorphic expression is not known
15. Line 535: (beta-gal) "protein" instead of "gene"?
16. Figure S2: Please indicate what the two horizontal dotted lines are supposed to point out

Significance

Study novelty: This work begins to shed light on the underlying molecular mechanisms that mediate differential responses to nutrient deprivation in male and female larvae. The knowledge gained from Drosophila studies will very likely have implications for human adipose physiology given the known sex differences in adipose associated physiology and pathophysiology in men and women.

General assessment: This study makes excellent use of the Drosophila melanogaster genetic toolkit to better understand the involvement of the ISR in mediating sexually dimorphic responses to nutrient deprivation. In addition, carefully thought-out figure layouts make the data easy to visualize. Limitations of the study include lack of specificity of fat body-specific driver lines and thus a potentially overstated conclusion.

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Referee #2

Evidence, reproducibility and clarity

It is now well-established that the Integrated Stress Response (ISR) mediated by ATF4 plays important roles in metabolism and proteostasis. This manuscript by Grmai and colleagues reports that the sex determination genes tra and dsx allow higher levels of ATF4 expression in Drosophila. They further show that female flies depend on ATF4 to survive under conditions of metabolic stress.

The presented data are technically sound, and the manuscript is generally very well written. It is a concise study with four Figures. The authors could have chosen to expand the scope: For example, they have shown the requirement, but not the sufficiency, of ATF4 in the sexually dimorphic nature of vulnerability to nutrient deprivation. They also demonstrate that ATF4 affects male-specific survival upon metabolic stress, which could be improved with additional experiments. These and other technical points are outlined below:

1. Figure 4 is an important part of this study, where the authors show a male-specific vulnerability to methioninase expression. They show that ATF4 RNAi confers vulnerability to methioninase expression even in females. An obvious question is whether ATF4 overexpression is sufficient to enhance resistance to methionine deprivation in males.
2. Methioninase expression results (Figure 4) are interesting. Are the levels of methioninase expression similar between males and females?
3. This manuscript focuses on ATF4, but there could be additional possible reasons for the sexually dimorphic ISR activity. For example, the degree of physiological stress that activates ISR could be different between males and females. I suggest comparing the levels of Phospho-eIF2alpha (or any other markers upstream of ATF4) in both sexes.
4. In Figures 1 to 3, the authors examine the intensity of ATF4 signaling after perturbing the sexual determination pathway. The methioninase experiments in Figure 4 are interesting, but there is nothing in this Figure linking male-specific vulnerability to sex determination genes. Examining the vulnerability to methioninase expression after perturbing the sexual determination genes would make Figure 4 integrate better with the rest of the manuscript.
5. The authors write that they generated 4EBP intron-GFP because the 4EBP intron-DsRed signal was frequently observed in the cytoplasm (line 122). They seem to suggest that the DsRed reporter is less reliable than the GFP reporter. However, they continue to mix results using 4EBP intron-GFP (Fig. 4A) and 4EBP intron-DsRed (Fig. 4F). The two figures examine slightly different conditions (Fig 4A shows tra1 KO females, while Fig. 4F shows traF males). If the DsRed reporter is less reliable due to the signal from the cytoplasm, the authors should show results with the GFP reporter in traF males.
6. In Figure S1, the authors label 4EBP intron-GFP as Thor2p-GFP, which is confusing. There are other parts in the methods section referring to Thor2p. I suggest using consistent terminology throughout the manuscript.

Significance

Overall, the authors report a novel and interesting observation because the sex determination pathway was not previously associated with ISR signaling. As many metabolic diseases show sex-specific outcomes, the main findings of this study will draw broad interest.

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Referee #1

Evidence, reproducibility and clarity

Summary

This study aims to explore sexual dimorphism in tolerance to nutrient deprivation using Drosophila larvae as a model. In particular the authors focus on sex differences in the larval fat body. They show that ATF4, an ISR transcription factor, has higher mRNA levels in female fat bodies. ATF4 transcriptional activity is also higher based on a reporter of ATF4 function, where this female bias in expression is influenced by sex determination factors. When the authors

Overall, this study is interesting, as it identifies previously unrecognized sex-specific regulation of ATF4, an important transcription factor that mediates cellular stress responses. The study also shows that sex determination genes regulate ATF4. However, I have concerns about the paradigms of nutrient deprivation used in the study, and about data interpretation and statistical analysis that should be addressed prior to publication to support the authors' conclusions.

Major concerns

1. One major concern that I have about the sexual dimorphism in tolerance to nutrient deprivation is that the papers cited by the authors, and paradigms that are used broadly in the field, all use adult flies. The authors must show that in larvae, a completely different life stage from their citations, there is a sexual dimorphism in tolerance to nutrient deprivation.

Interestingly, Diaz et al 2023 (Genetics) show that male larvae have greater fat stores than female larvae. Considering fat is the main determinant of tolerance to nutrient deprivation it's not clear that females will actually survive nutrient deprivation longer as larvae. This is an essential test of whether female larvae do have increased tolerance to nutrient deprivation, which is the basic foundation of the authors' model. 2. Another concern is the way that the authors "genetically induce nutrient deprivation using methioninase overexpression". As they acknowledge in the discussion (Line 381-390), methioninase overexpression will have many cellular effects. While there is no doubt that methionine levels would be lower in their model, it is less certain whether this is the main driver of the male-specific lethality.

There are two potential solutions to this problem. First, the authors could change the text throughout the paper to more accurately describe their paradigm as "methioninase-induced lethality" rather than "nutrient deprivation". This would limit the scope of their scientific question and the conclusions they draw, but would eliminate the need for more experiments.

The second solution would be to complete experiments to establish the following points: i) methioninase overexpression causes all the classical features of nutrient deprivation (e.g. changes to canonical signaling pathways such as TOR); ii) using other genetic means of nutrient deprivation such as slimfast-RNAi to see if those manipulations phenocopies the male-specific lethality they see with methioninase overexpression; iii) testing a role for ATF4 in mediating sex differences (if any) in other contexts such as slimfast-RNAi. This will take 2-3 months but is essential to draw any conclusions about whether their paradigm is truly a model for nutrient deprivation. 3. Another important point is that the authors state that sexually dimorphic ATF4 activity in the fat body is instructed by sexual identity in a cell-autonomous manner. Despite a clear decrease in ATF4 reporter levels in tra mutants, the fat body-specific tra-RNAi effect on the ATF4 reporter was less convincing. Together with the fact that changes to tra in the fat body affect insulin secretion from the insulin-producing cells, it is possible that the effect on ATF4 is not cell-autonomous. To conclusively test if sexual identity regulates ATF4 in a cell-autonomous manner the authors should use the flp-out system to make Tra-expressing or tra-RNAi-expressing clones in the fat body. This would take approximately 1.5 months to make the strain and test this. 4. As the authors show for the UAS-methioninase, other UAS lines used in the paper such as UAS-traF, UAS-tra-RNAi, UAS-dsx-RNAi may have leaky effects on gene/reporter expression. The authors must include a UAS only control to establish that the tra-RNAi, UAS-traF, UAS-dsx-RNAi do not affect gene/reporter expression. 5. I have concerns about the statistics used. In the methods and legends only t-tests are mentioned; however, when three groups are compared a one-way ANOVA with post-hoc tests must be used to correct for multiple comparisons. To compare differential responses to genetic/environmental manipulations between the sexes, a two-way ANOVA must be used. For example, to conclude that males and females have different responses in the two-way ANOVA, there must be a significant genotype:sex interaction. The p-values for comparisons between genotypes in either the one-way or two-way ANOVA must be derived from post-hoc tests within the ANOVA analysis.

Minor points

1. Please ensure to make the reader aware of which life stage was tested in the literature cited supporting sexually dimorphic tolerance to nutrient deprivation.
2. Published data about sex-specific mechanisms of metabolic regulation mean that the introduction should be more fully cited than it is. Even in the introduction "the molecular basis of these differences and how they impact tolerance to nutrient deprivation is still under investigation" is inaccurate, as there are published studies identifying some mechanisms (work on gut hormones and sex-specific effects on starvation resistance and body fat, role of ecdysone on body fat and feeding, sex-specific roles for brummer and Akh in regulating body fat, intestinal transit and gut size and feeding). Please adjust the paper to acknowledge this growing body of knowledge.
3. Please list the diet ingredients per L so that individuals can replicate the diet easily.
4. Please cite grant numbers for all the community resources (e.g. Bloomington, DSHB), and please acknowledge FlyBase and its grants as well. For example, here are the instructions for citing BDSC https://bdsc.indiana.edu/about/acknowledge.html and similar instructions are available for the other resources.

Significance

This study identifies for the first time the sex-specific regulation of ATF4, and reveals the sex determination genes that mediate this effect. A strength of the study is the characterization of sex-specific ATF4 regulation. Limitations of the study include the paradigm for nutrient deprivation, need for additional controls, and statistical analysis. If the concerns above are addressed, this study will be of interest to researchers studying organismal and cellular stress responses, stress signaling, and builds upon a growing body of knowledge of sex differences in stress responses (e.g. autophagy, infection responses).

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Reviewer 1:

Evidence, reproducibility and clarity

The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation. Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

Major Comments

Comment____:

The major issue is related to the overall model the authors seem to build based on their data - or at least the overall model the reader may get from the paper. This model suggests that the loss / decrease in WWOX levels in RGs leads to Myc overexpression, that in turn affects the cell cycle and prevents neuronal differentiation. This model is highly attractive, but is probably incomplete, in the sense that it does not fully recapitulate the complicated picture. Indeed, all three types of mutated WWOX COs (WWOX k/o, WOREE patient-derived organoids, and SCAR12 patient-derived organoids) demonstrate strong - but equal levels of Myc upregulation. Yet the under-differentiation in each of these three types is different, as described above, and the disease manifestations among WOREE vs. SCAR12 patients are also different. Thus, another player (in addition to Myc) must be at place, that is differentially affected by the partial null mutations in WOREE and missense mutations in SCAR12. This point - ideally to be addressed experimentally - should be at least faced directly by the authors in the Discussion. Perhaps they can already point to such additional players based on their transcriptomics analysis.

Response____:

We thank the reviewer for this important point. We agree with the reviewer that the model of WWOX loss / decrease levels in RGs leading to MYC overexpression is incomplete, and that it is a limitation of our model. It seems plausible that other players have a high impact on the genotype and are potentially differently affected, resulting in this complexed phenotype. Following the reviewer advice, we plan to address this in the discussion as a limitation of the model, and we will compare how the expression levels of MYC change based on the genotype in comparison to the WT, using the single cell RNA-sequencing data. We would also like to clarify that MYC upregulation we observed in the patient lines in SOX2+/MYC+ populations, does not quantify expression levels of MYC, but rather positive/negative nuclear staining, in contrast to the high-resolution of scRNA-seq data.

Minor Comments

Comment____: 1. It would be useful if a table (perhaps supplementary) describing the details of the WWOX__ mutations__ in all the COs models studied in this paper were presented.

Response____:

We thank the reviewer for this suggestion, and we plan to prepare a table summarizing all the mutations in the COs models presented in the paper.

Comment____: 2. For the new WOREE individual with complex genetics in WWOX: it is not clear why any WWOX protein is still present in this patient in Fig. S1D (please give an explanation or speculation); it is not clear which tissue was used for the Western blot in Fig. S1D; the data in Fig. S1D need to be quantified.

Response____:

We thank the reviewer for their observation and would like to clarify that the ‘upper’ band seen in WWOX bands in a nonspecific one that appears in the parent lines and the mutant offspring. We will quantify the WB levels and clearly state that they are the IPSCs in the figure legend.

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Comment____: 3. Western blot, quantified, should be performed on all COs under study, to compare the WWOX expression levels. Please also change the immunofluorescence shown in Fig. 1B (e.g. show WWOX in a different color), as the figure provided shows WWOX poorly in wild-type CO, and it is not clear how much it is removed in the mutant organoids. Why should there be no signal in the SCAR12 COs?

Response____:

We thank the reviewer for their observation, we will provide protein levels of WWOX in patient and KO cerebral organoids which will better clarify the decreased WWOX levels, specifically in SCAR12 (see WB figure below). We will also perform any necessary changes to the figure to enhance visualization of WWOX.

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Significance

The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation.

Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

Response____:

We sincerely thank the reviewer for their thoughtful and constructive comments, which have greatly helped us improve the clarity and rigor of our manuscript. We appreciate the recognition of our work’s significance and the careful evaluation of both our major findings and methodological details. We have addressed all the raised points to the best of our ability and believe the manuscript will be substantially strengthened as a result. We are grateful for the reviewer’s time and valuable insights.

Reviewer 2:

Evidence, reproducibility and clarity

Summary

In this study, Steinberg et al aim to elucidate the role of WWOX in human neurogenesis and model WOREE and SCAR12 syndromes which are rare neurodevelopmental disorders. They chose to investigate its function in human brain organoids after generating WWOX KO and patient-derived iPSC lines. Their major finding is that radial glial cells, the main neural progenitor population during corticogenesis, are affected. Via single-cell-RNA-sequencing, they try to decipher the perturbed molecular mechanisms identifying MYC, a proto-oncogene, as a major player. At the end of their study, they proceed to gene therapy restoration and suggest that this could become a potential therapeutic intervention for WOREE and SCAR12 syndromes. The study aims to elucidate major cellular and molecular mechanisms that modulate neurodevelopment and neurodevelopmental disorders. Although sc-RNA-seq could potentially be of great interest and unravel major mechanisms, the authors do not follow this part, but only discuss potential future avenues. Here are some suggestions that could be useful to the authros.

Major comments

Comment____:

A big part of the paper focuses on generating the iPSCs and characterizing the generated brain organoids and gene restoration of the phenotype via restoration of the WWOX gene expression (Fig.1, Fig.6, Fig.S1, Fig.S8, Fig.S10 and potentially Fig.S9 - this figure is not included) however, this has already been done by the same authors (first and last authors) in a previous publication. What are the differences in the line that have been generated in previous publication (Steinberg et al 2021, EMBO Mol. Med.)? If there are differences, the authors should make a thought comparison and explain why they generated different lines. If there is no difference, the authors should reduce to minimum this part and place it to supplementary.

Response____:

We thank the reviewer for pointing this out. We would like to clarify that some iPSC lines used in this publication were not introduced in the previous one (Steinberg et al 2021, EMBO Mol. Med.), including the wildtype JH-iPS11, and the new compound heterozygous WOREE line LM-iPS. In this paper, we aimed to widen our understanding of the effects of WWOX mutations through advanced techniques not applied before, and by adding these lines we were able to better generalize our findings as we did not depend on a single patient for both WOREE and SCAR12. Additionally, WWOX rescue in this current paper relies on AAV9-hSynI targeting that is more clinically relevant to gene therapy, as opposed to lenti-WWOX and AAVS1 WWOX in the previous publication. We will include the differences in a summary table.

Comment____:

• Fig.1E: in the pictures shown, the majority of the Satb2+ cells are colocalized with SOX2. Although a small portion of neurons have been shown from many studies that in brain organoids are co-localized to SOX2, in the pictures depicted this percentage is big. Also in ctrl condition the VZ-CP like areas are not easily recognized. The authors should check if this co-localization is a more general phenotype and if not choose more representative pictures.

Response____:

We thank the reviewer for their observation. We will check for the presence of a trend of colocalization between SATB2 and SOX2 and address the concern experimentally if needed. We will also choose pictures that better display VZ-CO areas in the control line.

Comment____: Information about the number of organoids per batch used in each figure is not included. This needs to be added for each experiment. Data (at least the majority of them) should be collected from brain organoids from at least two batches.

Response____:

We thank the reviewer for their point, and we plan to better clarify the technical parts of the experiments, and if needed will include data from more batches.

Comment____: The expression of WWOX in cortical development has been shown in the previous publication. Although sc-RNA data are validating the previous data and are adding more information, these data should be put as supplementary. Besides, in Fig.3G where authors aim to compare WWOX expression to MYC that fits nicely with their results depicting MYC as the most affected gene in KO and mutant line, when one looks at the WWOX expression only it seems that its expression is higher in CP that VZ. This is contrary to the conclusion that WWOX is mainly characterizes RGs. Why is that? Authors should at least discuss this.

Response____: We agree with the reviewer that Fig.3G can be misleading, and we acknowledge that it can lead to the opposite conclusion of WWOX being mainly characterized in RGs. In Fig.3G the x axis displays positive values on the left, and negative scores on the right. Following the reviewer’s suggestion, we modified the graph to show positive values on the right, demonstrating how WWOX expression is higher in the VZ compared to the CP.

Comment____: In this study, authors show that progenitors are reduced in WWOX-KO organoids, however in the previous publication SOX2 population is not majorly affected. Why are there such differences? Given that RGs are the main population affected as authors propose in this study, these differences must be at least discussed. Similar comments regarding neurons: in previous publication there is a minimal reduction of neurons in WWOX-KO brain organoids, while here authors describe major differences.

Response____:

We thank the reviewer for their remark and agree that it should be mentioned in our discussion. We believe that this is unfortunately due to the inherent issue of heterogeneity between organoids and could partly be attributed to the difference in the age of organoids at that timepoint (week 10 organoids in previous paper, week 7 organoids in this one), and the difference in control lines (WiBR3 hESCs vs JH-iPSCs). Additionally, while percentages of SOX2+ populations in WT organoids vary between the previous publication and this one, WWOX-KO organoids display similar levels of SOX2 upregulation in precious and current papers: 69% and 74%, respectively. We would also like to point out that calculations in scRNA-seq data convey the phenotype at a much higher resolution, as it identifies radial glia populations that are not necessarily SOX2+, further strengthening the validation of the SOX2+ RG quantifications that are present in this study.

Comment____: Data from sc-RNA-seq analysis highlighting MYC as major differentially regulated gene are very interesting and seem to be key to the molecular pathway affected as authors suggest. Authors also validate this with immunostainings in brain orgnaoids. However, in Fig.3J MYC expression in ctrl is not depicted, even though in the respective graph it seems that 20% of SOX2+ cells co-express MYC. Please choose a more representative picture.

Response____:

We agree with the reviewer’s comments that the current image size and resolution limits the ability to appreciate the MYC staining in the control, and plan to use a more representative figure of the phenotype.

Comment____: One of the main findings in this study is the cell cycle changes observed in WWOX-KO and mutant organoids. Given that the major novelty of the publication is the cellular and molecular mechanism implicated in WOREE and SCAR12 syndromes, authors should perform additional experiments towards this direction. One suggestion would be to perform stainings in brain organoids using markers of the different cell cycle phases (eg. KI67, cyclin a, BrdU/EdU, ph3). Also, treatment of organoids with different BrdU/EdU chase experiments would be important so as to measure exactly the length of each cell cycle phase.

Response____:

We appreciate the reviewer’s suggestions and plan to validate the findings through staining and quantifying percentages of proliferative RGs in WT vs mutant WWOX lines.

Comment____: Regarding the molecular cascade, is WWOX directly affecting MYC of Wnt genes? Do they have information on upstream and downstream factors in the affected molecular pathway?

Response____:

We thank the reviewer for highlighting this important point. To address this question, encouraged by our results, we will compile genes belonging to the regulon of MYC and study the upstream and downstream factors in our transcriptional data. Additionally, we will look at protein expression levels of WNT genes in our organoid samples.

Comment____: Restoration of phenotype via reinsertion of WWOX gene has already been done in the previous publications by the same authors. But what about MYC? Is MYC manipulation able to rescue the phenotype?

Response____:

We thank the reviewer for this insightful suggestion. We fully agree that understanding the role of MYC in the observed phenotype is of great interest. However, due to the essential and widespread role of MYC in both radial glia and neurons, we refrained from direct perturbation of MYC levels—either through knockdown or overexpression—as such manipulations may have broad, uncontrolled effects that could confound the interpretation of our findings. The potential deleterious consequences of MYC modulation in radial glia have been originally discussed in the Discussion section of the manuscript. In our revisions, we will further explore the role of MYC regulons in our scRNA-seq dataset to better understand their contribution to the WWOX-related phenotype.

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Comment____: Finally, MYC association to ribosome biogenesis as mentioned by the authors in discussion is very interesting. The authors should consider investigating this direction, as it will be a great addition to the mechanisms that regulate WOREE and SCAR12 syndromes which is the main focus of this study.

Response____:

We thank the reviewer for highlighting this point, and we agree that MYC's association with ribosome biogenesis is a fascinating topic to discuss. This could be connected to the alterations of the proliferative potential and to the anabolic state of the cell, and we plan to expand the discussion of this observation and its implication in the context of RGs and neurons*. *

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Minor comments

Comment: - Line 115: authors say that the data they discuss are found in Fig.S2A, maybe they mean Fig.S1A?

Response:

We thank the reviewer for their observation, we will correct Fig.S2A to Fig.S1A and B.

Comment: - Fig.S9 is missing, in the current version this Fig is the same with Fig.S10. Please change it.

Response____:

We thank the reviewer for pointing this out and apologize for this oversight. We acknowledge the error and will correct the duplication by replacing Fig. S9 with the intended figure in the revised version of the manuscript.

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Significance

This study is the continuation of a previous publication the authors have published. The topic is very interesting and novel especially in modelling neurodevelopmental disorders in a human context, however, given that the main phenotype has already been published, the authors should include more effort in the molecular cascade. Clinical interventions if the molecular cascade is described would be of great importance to the field.

Response____:

We sincerely thank the reviewer for their thoughtful, constructive, and detailed review. We appreciate the time and effort taken to carefully read our manuscript and provide insightful suggestions, taking into consideration also our previous published work. The suggestion raised, especially regarding MYC-WNT axis and its potential link to ribosome biogenesis, will help us clarify, strengthen, and expand the scope of our study. We have carefully addressed each of the points raised and have incorporated the necessary experimental validations, clarifications, and revisions accordingly. We believe these changes have substantially improved the manuscript.

Reviewer 3:

Summary:

The manuscript by Steinberg and colleagues describes cellular and molecular changes linked to mutations in WWOX, a gene implicated in rare neurodevelopmental disorders, WOREE and SCAR12 syndromes. By comparing immunofluorescene and single cell trascriptomics of unguided brain organoids from control and WWOX-knockout iPSCs, as well as 2D NSCs and in vivo fetal brain expression datasets, the authors identified radial glia as relevant cell types in which WWOX is expressed and affected by WWOX deficiency. Using immunofluorescence, single cell trascriptomics analysis and western blotting on week 16 organoids, the authors show that WWOX deficiency results in increased abundance of radial glia cells at the expenses of neuronal production. These changes are accompanied by accumulation of cells in G2/M and S phases, overexpression of c-MYC and Wnt activation. In addition to this, the authors characterize unguided brain organoids generated from iPSCs reprogrammed from patients affected by WOREE or SCARE12 syndromes. Using immunofluoresce and single cell trascriptomics, they find that, while RG abundance changes were very modest, patients's iPSC-derived neurons are enriched for signatures related to early development, suggesting delayed differentiation. Finally, the authors use patch clumping, calcium imaging and gene therapy in 16 weeks old organoids derived from control and patients-derived iPSCs, to demonstrate that WWOX restoration normalized hyperexcitability phenotypes in both WOREE and SCAR12 organoids. These results thus provide a proof-of-concept evidence that WWOX restoration in human cells is a valid strategy to correct for hyperexcitability pehnotypes in WWOX related syndromes.

Major ____C____omments

The study's main conclusions regarding neurodevelopmental phenotypes linked to WWOX deficiency and genotype-phenotype relationships are based on iPSC-derived brain organoid models analyzed using immunofluorescence, single-cell transcriptomics, and excitability recordings (cell-attached patch clamping, calcium imaging). While the analyses involve a diverse collection of iPSCs and two time points (7 and 16 weeks), the study falls short in providing sufficient experimental details and validation to fully support its conclusions. Additional quantification, replication, and functional validation would be necessary to solidify the study's conclusions. Some of these validations are achievable within a reasonable timeframe, while others would require a more substantial investment of time and resources as detailed below.

Comment____:

A key concern is the lack of experimental details and replicability. Number of individual organoids, number of images per organoid for IF, and whether multiple batches were used are only partially provided. While the authors report generating multiple WWOX knockout clones, the legends and methods do not specify whether multiple clones were used across different organoid experiments. The study states that four organoids were used for scRNA-seq, but it is unclear whether this means four organoids per genotype or one organoid per genotype was analyzed. These ambiguities make the claims appear rather preliminary.

Response____:

We thank the reviewer for pointing this out, and we acknowledge that the clarity of our description of the batch used in each experiment can be improved. Therefore, we will provide all these details, adding information on additional batches adopted for the different validations that were not included in the manuscript.

Comment____:

Another issue is the limited validation of scRNA-seq observations. Since scRNA-seq is often performed on a limited number of organoids, orthogonal validation is crucial to strengthen the findings. For example, changes in radial glia abundance and neuronal production observed in scRNA analyis (Figure 2-5) could be validated using immunofluorescence across genotypes and batches. Currently, IF stainings for Sox2 and TUBB3 are shown only at 7 weeks in Figure 1B, but no quantificative assessment is provided. Also, it is not clear if quantifications provided in Figure 1F refer to multiple organoids or batches.

Response:

We thank the reviewer for this important point. We would like to clarify that in Figure 1B, TUBB3 staining is primarily used for visualization purposes to provide anatomical context and delineate the overall architecture of the organoids, rather than for quantitative assessment of neuronal output. As such, the focus of our quantification in Figure 1F was on SOX2+ radial glial cells. That said, we agree that clearly stating the number of organoids and batches used in the quantification is important, and we will include this information in the figure legend for clarity.

Comment____:

Furthermore, the observations on cell cycle arrest, DNA damage, senescence, metabolic alterations, Wnt activation obtained via scRNA-seq could be further validated on organoid tissues using specific antibodies that the lab used before (e.g. yH2AX antibody in PMID: 34268881) or assays that have been developed elsewhere (some examples are reviewed in PMID: 38759644). As for feasibility, immunofluorescence validation of existing tissues is realistic, requiring validated antibodies and procedures, some additional imaging time and analysis (estimated 1-2 months, with some budget to purchase antibodies and cover imaging time costs). Feasibility of efforts related to validation across organoids and batches depends on the number of organoids used so far and available tissues. Generating new organoids would be indeed more time-consuming (≈ 6 months) and expensive (but extact costs would depend on number of clones, organoids and batches used), but feasible.

Response____:

We appreciate the reviewer’s thoughtful feedback and for drawing our attention to the review by Sandoval, Soraya O., Anderson, Stewart, et al. We also thank the reviewer for their suggestions and intend to explore the proposed modifications through immunostaining, particularly to address questions related to cell cycle changes, and Wnt pathway. However, regarding DNA damage, senescence, and cell cycle arrest, we do not believe additional validation is necessary, as our current manuscript does not present findings related to these aspects.

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Comment____:

Another limitation is the lack of functional relevance of MYC alterations. The study confirms increased MYC expression via both scRNA-seq and immunofluorescence in organoid tissues. However, these results remain correlative and demonstrating the functional requirement of MYC overexpression in mediating WWOX-deficiency-related changes would significantly strengthen the study's conclusions. This would require additional differentiation experiments, including MYC overexpression or knockout models, to assess its direct impact. These efforts would represent a major conceptual advance by linking RG effects to MYC function and highlighting MYC-related therapeutic directions. These additonal experiments would require a substantial investment to generate the necessary regents (e.g. WWOX-KO and WT iPSCs with altered MYC levels) and additional time and costs for organoid analysis, mostly by immunofluorescence (estimated 6-8 months).

Response____:

We thank the reviewer for this insightful comment and fully agree that elucidating the functional contribution of MYC alterations in the context of WWOX deficiency would represent a major conceptual advance. We acknowledge that our current findings are correlative, based on scRNA-seq and immunostaining, and that direct manipulation of MYC could help establish causality.

However, due to MYC’s essential and pleiotropic role in both progenitor and neuronal populations—including its regulation of cell cycle, metabolism, and apoptosis—we refrained from genetic overexpression or silencing approaches in this study. Such perturbations often lead to widespread, non-specific effects that can obscure the interpretation of lineage-specific phenotypes, particularly in a complex model like brain organoids.

That said, we agree that further insight into the functional role of MYC is crucial. To this end, we plan to leverage our scRNA-seq dataset to analyze the activation state of MYC regulons across genotypes and cell types, and to assess how these regulons intersect with cell cycle dysregulation observed in WWOX-deficient radial glia. We also aim to integrate available transcriptomic data from primary cortical tissue to support the relevance of MYC pathway alterations in human development. While these analyses cannot replace experimental perturbation, we believe they can provide strong, hypothesis-generating evidence for MYC’s mechanistic involvement and help prioritize targeted experiments in future studies.

Comment____:

Another issue is the lack of patterning analysis in unguided organoids, which are known to exhibit high variability in regional identity (PMID: 28283582). While the authors acknowledge this limitation to some extent-abstaining from fine-resolution analysis (Lines 173-174)-this variability, combined with the limited number of organoids used, could be a major confounding factor in the phenotypic analyses, even at a broad resolution. Indeed, some of the reported differences across genotypes may stem from variability in organoid patterning rather than true genotype-driven effects. For example, the reduced SATB2 expression in KO and patient-derived organoids from Figure 1E-F could result from impaired cortical patterning rather than a direct effect of WWOX deficiency. Additionally, in Figure 6D and 6E, the fact that WOREE iPSC-derived organoids - but not SCAR12 organodis- show lower levels of both CTIP2 and SATB2, might reflect a shift toward a non-cortical identity rather than a direct WWOX-dependent phenotype. To rule out patterning variability as a contributing factor, the authors should analyze organoid regional identity across genotypes using immunostaining for dorsal and ventral forebrain markers. This would allow a more solid inference of genotype-specific effects on neurodevelopmental phenotypes. Patterning validation can be performed on existing organoid tissues (week 7) using validated antibodies (PMID: 28283582). As such, this analysis is expected to be relatively straightforward and feasible in a few weeks time. If the generation of new organoids is needed, such patterning validation should still be relatively feasible, as week 7 organoids are ideal for assessing regional identity. Analysis of patterning effects should also extend to 2D NSC cultures. In the 2D NSC models derived from WWOX-KO lines (Figure 3L, Figure S4A), the differentiation protocol includes patterning factors that promote ventral fates (SAG and IWP2). Interestingly, the quantification of MYC expression from unguided organoids and 2D NSCs (Figure 3K-L) reveals a major difference in the fraction of MYC-positive cells in WT conditions across the two culture models. A possible changes in the dorsal and ventral patterning of 3D and 2D cultures might explain these differences and implementing immunostaining for patterning markers in 2D would help clarify patterning contributions.

Response____:

We thank the reviewer for this thoughtful and constructive comment. We fully agree that regional identity variability in unguided cerebral organoids is a well-recognized challenge, and that systematic assessment of dorso-ventral patterning is important to confidently interpret genotype-driven phenotypes.

We would like to clarify that the cerebral organoid protocol used here has consistently been shown to favor a dorsal forebrain identity (PMID: 23995685, 28562594, 32483384, 33328611), and in our previous work (PMID: 34268881), we demonstrated that WWOX mutations did not substantially alter dorsal identity in this model. Nevertheless, to directly address the reviewer’s concern, we plan to perform additional immunostaining for regional patterning markers on our existing week 7 organoid tissues and explore our scRNA-seq data to evaluate potential shifts in regional identity and rule out patterning-related confounders.

Regarding the 2D NSC cultures, while the differentiation protocol included the ventralizing factor SAG, it did not include IWP2. We acknowledge the importance of validating patterning outcomes in this model as well and will do so using immunostaining.

Comment____:

There are also some concerns regarding WOREE and SCAR12 phenotypes. First, the genotypes of the patient-derived iPSCs are not clearly defined, making it difficult to establish clear genotype-phenotype relationships. The study uses iPSCs from four different patients (2 WOREE, 2 SCAR12), some of which were validated in a previous study (PMID: 34268881). However, it remains unclear how they were validated, and detailed genomic alterations of the four patients are not explicitly reported. Additionally, it is uncertain whether all variants result in a full loss of WWOX function, as protein loss evidence is only provided for one WOREE patient (Figure S1D). Also, the authors state that SCAR12 should have a milder phenotype (line 168), but it is unclear whether this claim is based on clinical evidence or genomic data from these specific patients. To improve genotype-phenotype comparisons, the authors should consider including a clear schematic summarizing the genomic alterations in all patient-derived lines and their expected disease severity.

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Response____:

We thank the reviewer for this suggestion, and we agree that including a schematic summarizing the genomic alterations in all patient-derived lines and their severity will improve the genotype-phenotype comparison. We will include this clarification and provide additional information on how the mutations affect the protein level, and the genotype-phenotype correlations in WWOX mutants based on clinical and genetic evidence.

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Comment____:

Second, the experimental design lacks appropriate controls for patient-derived iPSCs. All patient-derived iPSC comparisons are performed against a single reference male iPSC line, which is neither isogenic to WOREE nor SCAR12 iPSCs. This complicates the interpretation of differences between healthy and patient-derived organoids, as well as comparisons between WOREE and SCAR12 phenotypes. Given this design, it is impossible to draw solid conclusions about genotype-phenotype relationships. A more robust approach would involve including multiple healthy controls to account for genetic background variability or using isogenic parental or genetically corrected lines, which would provide a cleaner genetic comparison. A recent study (PMID: 36385170) discusses different study designs that could strengthen this aspect and might be useful for the authors to consult.

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Response____:

We thank the reviewer for highlighting this and pointing us to the work of Brunner, Lammertse, van Berkel et al. While we agree that isogenic controls for each mutant line would be the ideal wild-type reference, generating these through genomic editing is particularly challenging, specifically for the compound heterozygous mutants. Instead and as suggested, we plan to include additional wild-type lines derived from healthy individuals, collected from different batches. We will use these to validate our key findings, including analyses of RG and SATB2+ cell populations, as well as MYC expression through immunofluorescence.

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Comment____:

Third, the study presents seemingly conflicting results regarding WOREE and SCAR12 phenotypes. The authors present immunofluorescence (IF) and scRNA-seq data indicating that changes in radial glia (RG) abudance are not observed in these patient-derived organoids. However, using same methodologies, they indicate that neuronal production is affected, leading to the accumulation of early neuronal signatures in both WOREE and SCAR12 neurons. The study does not explore whether RG signatures might be altered in a way that could contribute to neuronal phenotypes. Also, Figure 1F suggests that while Sox2+ cell counts are not increased in SCAR12 organoids, SATB2 levels are still altered, indicating that Sox2 and SATB2 trends are not tightly coupled across genotypes.

Furthermore, Figure 1 and 6 show that while both syndromes exhibit similar hyperexcitability, data in Figure 6 report that only WOREE organoids display reductions in SATB2 and CTIP2 counts and that this can be rescued by WWOX restoration. Some of these discrepancies could stem from patterning variability as discussed above. Also, neuronal firing rate across WOREE and SCAR12 iPSC-derived organoids (Figure 6B) was different at later stages, but was rather comparable at an earlier stages (Figure S1G). The reasons for these differences are not thoroughly discussed.

To strengthen the discussion, the authors should address how RG alterations (if any) might contribute to neuronal phenotypes, provide a more detailed comparison between WOREE and SCAR12 organoids and the WWOX-KO model and elaborate on the distinct phenotypes of the two syndromes, including possible explanations for observed functional and molecular discrepancies.

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Response____:

We thank the reviewer and agree that further investigation into the proposed link between WWOX deficiency and MYC-related alterations in radial glia would provide deeper insight into the downstream effects on neuronal populations. To this end, we will first illustrate how our model of radial glia alterations accounts for changes in neuronal production without affecting overall RG abundance. Second, we will expand our comparisons of RGs and MYC expression using patient-derived and control single-cell RNA-sequencing datasets. Third, we will address the discrepancies between neuronal hyperexcitability and SATB2/CTIP2 counts more comprehensively. Notably, while SATB2 is an early marker for several cortical neuron subtypes, it is not expressed in all neurons. In contrast, SOX2 is considered a pan-radial glia marker, which may help explain the differing expression trends observed.

Comment:

Lastly, the conclusions drawn about WWOX restoration via gene therapy are weakened by the lack of replication and validation (see points above).

First, the authors claim successful WWOX restoration in neurons, but provide limited evidence that the infected population consists of neurons. NeuN staining (Figure 6A and S10) suggests some neuronal expression, but quantification of WWOX+ NeuN+ / WWOX+ total cells is missing. Given that IF data are already available, this additional quantification could be completed within a few weeks and would significantly strengthen the claim.

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Response____:

We thank the reviewer for the suggestion, and based on this, we will quantify the WWOX+ NeuN+ / WWOX+ total cells, incorporating data from additional batches to strengthen the analysis.

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Comment____:

Second, the rationale for restoring WWOX in neurons is unclear, given that WT neurons do not normally express WWOX. Is WWOX being considered a functional neuronal maturation factor? If so, this should be explicitly discussed in the manuscript.

Third, the authors propose that WWOX deficiency might lead to a delay in neuronal maturation. However, to demonstrate delayed maturation, the study should show that, given additional time, affected organoids can eventually produce late-stage neuronal signatures. Since this additional experiment may be technically challenging and time-consuming, the claim should instead be rephrased as speculative and discussed accordingly in the text.

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Response____:

*We thank the reviewer for highlighting this point. We will discuss and re-phrase the rationale for restoring WWOX in the neurons and the WWOX deficiency-associated delayed maturation. *

Comment____:

Lastly, the study lacks key details necessary for reproducibility in multiple aspects. In addition to details about organoid numbers and batches discussed above, all IF images are shown as insets, making it difficult to assess broader reproducibility within the whole organoid tissue section. Also, whether distinct iPSC clones/sections/organoids were used across IF experiments - which is critical for ensuring reproducibility - is not specified.

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Response____:

We thank the reviewer for mentioning this problem. We will include the details needed for reproducibility, including the number of batches and organoids.

Comment____:

As for details about experimental and bioinformatics methods, the bioinformatics pipeline is not fully described, making it impossible to verify or reproduce the computational analysis. No information is provided regarding batch correction procedures for scRNA-seq data (Lines 695-697) and on how clusters were mapped (lines 695-697) for cell type identification. Legends in Figure 1F, 2K-L, 6B, S10 do not specify what the error bars represent (e.g., standard deviation or standard error). Many catalog numbers for critical cell culture reagents are not provided, which is essential for experimental replication. The Western blot methods lack crucial details.

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Response____:

*We thank the reviewer for highlighting this point. We acknowledge that our clarity in our methods could be improved, therefore, we will expand the bioinformatics pipeline description, the reagents used, and the details for the Western Blot. *

Minor comments:

Comment:

• One relevant study (PMID: 32581702) that examines WWOX function in rat models and human fetal brains from patients has not been referenced or discussed. Notably, this study characterizes molecular changes associated with WWOX knockdown in human ESC-derived NPCs. Given its direct relevance to the current study, these findings should be acknowledged and integrated into the discussion to provide a more comprehensive understanding of WWOX-related neurodevelopmental alterations. Response____:

We thank the reviewer for suggesting the work of Iacomino et al. which we are very aware of and shall cite appropriately in our revised version.

Comment____:

• For WKO-1C and 2C the exact mutations in exon1 identified by Sanger sequencing are not reported. Also, validation for WWOX protein loss in all the lines used is also missing. Information about cell line genome integrity check are also missing. Response____:

We thank the reviewer for bringing up these important points. We will provide the exact mutations identified in exon 1 of WKO-1C and WKO-2C as determined by Sanger sequencing and include this information in the revised manuscript. Additionally, we will present data additional data regarding WWOX protein loss in all the cell lines used in the study.

Comment:

• Line 116 and 394, reference Steinberg et al is not formatted. Response:

We apologize for this oversight and the formatting will be fixed.

Comment:

• Figure S1A: Localization of WWOX seems to be cytoplasmic and/or membrane-bound in organoids, while staining in IPSCs shows cytoplasmic and nuclear signals. Perphaps an orthogonal valiation with another anti-WWOX antibody would be appropriate to confirm subcellular localization. Response____:

We thank the reviewer for their comment. WWOX localization was previously confirmed using anti-WWOX HPA050992 (Sigma), as reported in our prior publication (PMID: 34268881). While the images were not included due to a lack of novelty, we acknowledge the importance of confirming the observed patterns. The difference in localization between organoids (cytoplasmic/membrane-bound) and iPSCs (cytoplasmic/nuclear) may be attributed to differences in cell morphology, with RGs in 3D organoid sections exhibiting distinct characteristics compared to iPSCs cultured in 2D (Supplementary Figure S1A). In fact, in 2D cultures of NSCs (Supplementary Figure S4A), WWOX also shows a nuclear localization, similar to iPSCs. We will clarify this point in the manuscript.

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Comment____:

In Figure 1, authors use week 7 organoids and claim that they are enriched for early born preplate neurons (line 141). However the authors decide to look at SATB2, which is not an eary-born preplate neuron. So while the rationale for using Satb2 is not clear, the reported staining in Figure 1E shows an unusal overlap beetween Sox2 and Satb2 nuclear signals in wt organoids. The authors needs to recheck that the correct antibodies were used in this analysis.

Response____:

We thank the reviewer highlighting this. We will better define the rationale for the usage of SATB2 as a marker expressed in many types of young neurons (not specifically preplate neurons), and add DCX as a marker for neurogenesis.

Comment____:

Figure 1 Panel F: legend states that n indiactes 3 neurons. Please specifify what n referes to.

Response____:

We thank the reviewer for the keen eyes and apologize for this mistake. We will correct the legend and specify that n is indeed referring to organoids and not single neurons.

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Comment____:

Figure 3J: MYC staining appears to be nuclear in WWOX-KO organoids but more cytoplasmic in SCAR12 organoids. Also in WOREE organoids, both Sox2 and MYC staining appears different from what seen in other panels/ genotypes from the same figure panel.

Response____:

• *We thank the reviewer for their comment. Upon repeated staining, we consistently observe this MYC localization across organoids and more. Similarly, the differences in Sox2 and MYC staining in WOREE organoids are reproducible. While these results may seem divergent, they accurately represent the findings. We will, however, review the staining protocols and ensure that representative images are carefully selected to best reflect the data.

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Comment____:

Figures 3 and related legend: Authors use the term w for weeks but they need to specify whether this refers to gestational weeks or post-conception weeks.

Response____:

*We thank the reviewer for pointing this out. We will add in the legend that “w” refers to post-conceptional weeks. *

Comment____:

Figure 4: The UMAP in B, E and G seems to be blurred in the bottom parts. Is this an intentional choice? If so, what would be intent? Also, title and legend for E mention metabolic alterations but data presented are not related to metabolic patwhays.

Response____:

We thank the reviewer for addressing this. The blurred parts of the UMAP are intentional, we will add a description of why and what it represents.

Comment____:

Figure 6. The same exact images from A and C are also reported in Figure S8 and S9 respectively.

Response____:

*We thank the reviewer for pointing this out, we will better clarify that figures S8 and S9 are an expansion of the panels shown in Figure 6, showing ROIs per cell line and rather than per genotype. *

Comment____:

Figure S1D: WWOX antibody seems to give an extra band at higehr molecular weight. This is also evident from S4B, where the upper band seems overrepresented in KO2. Also, are the healthy parents haploinsufficient for WWOX? what are the levels compared to wt (unrelated) controls?

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Response____:____

• *We thank the reviewer for raising this point. We will quantify the bands in the WOREE patient samples and compare them to wild-type controls. We would like to clarify that the "upper" band is a nonspecific band, and its overrepresentation in KO2 samples is not indicative of WWOX expression. Additionally, we will address the question of WWOX haploinsufficiency in healthy parents and provide a comparison of WWOX levels to unrelated wild-type controls.

Comment____:

Figure S2: In B, what is the difference between top and bottom UMAPs? In C-D, what is NP? Correlation map suggests that the NP clusters 7 and 8 are different from cluster 11. What is the rational for labelling them all NP cluster?

Response____:

We would like to thank the reviewer, and we will add a clarification for the differences between top and bottom UMAPs and the rationale behind NP labeling.

Comment____:

Figure S6: In the legend, full description of cluster labels are missing. Also legends specifes A-D while the figure contains only A-C.

Response____:

We thank the reviewer and will alter the figure and its legend to clarify this.

Comment:

Figure S4A: The staining for TUBB3 is very different between KO1 and KO2.

Response____:

We thank the reviewer and will examine the pictures and if need be will replace them with more representative pictures.

Comment____:

Figure S8: The legend indicates n as 4 organoids but images are not quantified so there is no evidence that these patterns have been replicated in 4 organoids.

Response____:

We thank the reviewer for pointing this out. We will add the quantifications of NEUN+/WWOX+.

Comment____:

Figure S9: The title is duplicated and not corresponding to the data in the figure. The whole figure is duplicated in Figure S10 (which is wrongly labelled as Figure 10 in the legend).

Response____:

We thank the reviewer; we will fix the figures and corresponding titles and legends.

Comment:

Line 330: Figure S6 F-H should be corrected in Figure 6 F-H.

Response____:

We thank and agree with the observation; we will correct it.

Comment____:

Line 353: reference needs to be added for "our earlier findings”.

Response____:

We thank the reviewer, and we will re-phrase to clarify.

Comment____:

Lines 383 and 392: The authors describe several possible MYC roles but which ones could relevant in this contex is not discussed.

Response____:

We thank the reviewer and agree with their observation and would like to clarify that as we are not aware of any relevant literature examining the relationship between WWOX and MYC in non-tumor settings, we refrained from drawing any conclusions in any one direction without further experimental exploration. Nonetheless, we will re-phrase the sentences to draw clearer conclusions.

Comment____:

Lines 402 and 403: The authors state that the study "highlights the critical role of Wnt signalling" but they fail to provide evidence that Wnt is functionally involved, as Wnt perturbation experiments are not applied.

Response____:

• *We thank the reviewer for their comment. We agree that further clarification is needed regarding the functional involvement of Wnt signaling. While we have previously shown that Wnt is inappropriately activated in WWOX-KO, WOREE, and SCAR12 organoids (PMID: 34268881), and demonstrated Wnt activation in RGs via our scRNA-seq data (Figure 4I), we recognize that direct perturbation experiments would strengthen this aspect. In light of this, we will examine the levels of Wnt target genes in our transcriptomic data to provide more direct evidence of Wnt signaling involvement and its functional relevance in the context of WWOX deficiency.

Comment:

Line 473: "at X concentration" needs to be correct to specify the concentration used.

Response:

We thank the reviewer for noticing this missing information and we will add the final puromycin concentration (1 mg/ml) .

Comment____:

Line 478: The authors state that "inform consent is under approval". Does this mean that the study was conducted before approval was obatined?

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Response____:

We thank the reviewer for raising this concern. To clarify, approval was obtained prior to the commencement of experimentation. The sentence should read: "Skin biopsies and blood samples were obtained with informed consent, under the approval of the Kaplan Medical Center Helsinki Committee," indicating that the study was conducted in full compliance with ethical requirements, with prior approval from the committee.

Comment____:

Line 525: which orbital shaker and which speed was used?

Response____:

We thank the reviewer and will add orbital shaker details and speed.

Comment____:

Line 537: what is GC in GC/ul?

Response____:

We thank the reviewer and clarify that this is the accepted units for viral load. GC is Genome Copies, and this is often used in qPCR assays to estimate the amount of viral genetic material in a sample. It is often used interchangeably with vg/µL (Viral Genome per microliter).

Comment____:

Line 629: samples were centrifgues at which speed and for how long?

Response____:

We thank the reviewer and will fix to include details about centrifugation.

Comment____:

Line 639: "All primer sequence" should be plural.

Response____:

We thank the reviewer and will correct the typing mistake.

Referees cross-commenting

All four reviews appear fair and complementary to each other. Reviewers have consistently highlighted concerns regarding unclear genomic alterations in patients' iPSCs and experimental reproducibility in organoid cultures, emphasizing the need for further validation of the reported findings and the underlying molecular cascade. Additionally, they have noted some inconsistencies, with Reviewer #2 specifically identifying a major discrepancy in the WWOX-KO phenotypes compared to those previously described by the same team.

General assessment:

The strengths of this study lie in its focus on disease phenotypes in a human context and the use of patient-derived iPSC lines, which provide valuable translational relevance. Additionally, the study employs a complementary set of analyses, including functional assays, immunofluorescence (IF), and single-cell RNA sequencing (scRNA-seq), which enhance its depth. However, the study has several critical weaknesses, primarily related to suboptimal experimental design and limited reproducibility. These are discussed in section A and also indicated below:

• Lack of isogenic controls or patient-derived lines and presence of conflicting data for patient-derived organoids, making genotype-based comparisons for patients' lines less robust; examples of studies using iPSC isogenic controls for dissecting neurodevelopmental disorders are found here (PMID: 35084981; PMID: 26186191).
• Limited reproducibility, due to a small number of organoids used and the lack of orthogonal validation for key findings.
• Absence of functional validation for MYC's contribution, making its proposed role unclear. Advance:

This study builds upon and expands previous efforts by the same team to characterize brain organoid models obtained from patient-derived iPSCs, as well as to explore gene therapy restoration approaches (PMID: 34268881, PMID: 34747138). Some of the bioinformatics analyses appear to have been developed elsewhere, and technically, the study offers only a limited methodological advance.

Instead, the key advancement of this work is more conceptual: it proposes potential underlying mechanisms of WWOX-related neurodevelopmental disorders. If the study's limitations were addressed, it could provide valuable insights into WWOX's role as a key regulator of radial glia proliferation and differentiation, as well as potential functions in neuronal maturation. These findings would be relatively novel in the context of WWOX-related neurodevelopmental disorders. WWOX has been extensively studied in rodent models, where WWOX -/- mice exhibit growth retardation and brain malformations (PMID: 32000863, PMID: 18487609, PMID: 15026124). Additionally, studies in rats and human fetal cortical tissue from patients (PMID: 32581702) have linked WWOX deficiency to migration defects and cortical cytoarchitectural alterations. Previous work in mice by the same team suggested that neurons are the key population affected, linking WWOX deficiency to hyperexcitability and intractable epilepsy (PMID: 33914858). However, the relevance of radial glia and cell-type specific molecular alterations linked to WWOX mutations have remained poorly defined. Through scRNA-seq, this study offers some insights into cell-type-specific molecular changes, especially in radial glia cells. These changes are linked to MYC fucntion, cell cycle arrest and altered differentiation trajectories. However, these insights remain preliminary due to the study's design limitations.

Another potential advancement of this study is its exploration of syndrome-specific alterations in WOREE and SCAR12 patients and their rescue through WWOX gene therapy-an aspect that has been difficult to study in animal models and remains largely unexplored. While the brain organoid model offers a promising approach, the true conceptual advance of this study remains uncertain, as its current limitations hinder the ability to draw definitive conclusions.

Audience: This study could be particularly relevant to a specialized audience, including basic research scientists working in developmental biology and the molecular basis of neurodevelopmental disorders, as well as those interested in translational approaches. Additionally, given WWOX's known roles beyond neurodevelopment and potential involvement of MYC, the findings may also be of interest to cancer biologists.

Expertise: My expertise lies in iPSCs and brain organoid modeling of neurodevelopmental disorders, with a strong focus on organoid phenotypic analysis, particularly immunofluorescence and transcriptomics. However, I do not have a strong background in bioinformatics and therefore lack sufficient expertise to evaluate the bioinformatic methodologies utilized in the study.

Response:

We thank the reviewer for their valuable feedback and for acknowledging the strengths of our study. We agree with the reviewer that additional validation and replication are needed to strengthen our conclusions. We acknowledge the limitations in experimental design, and we are committed to enhancing the reproducibility of our findings. We also appreciate the reviewer's comments on the study's conceptual advancements, which we believe offer new insights into WWOX's role in neurodevelopmental disorders.

We are confident that with the additional experiments outlined, our study will provide valuable contributions to understanding WWOX-related syndromes. Thank you again for your thoughtful suggestions.

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Reviewer 4:

Summary

The article deals with WWOX gene deficiency related neural diseases such as WOREE and SCAR12 syndromes. While there is no available drugs for treatment, the authors used organoid approach to study the development of the potential of disease development. The authors utilized neural organoids and single-cell transcriptomics and identified radial glial cells (RGs) as preferentially affected. The RG cells have disrupted cell cycle arrests in the leading G2/M and S phases, along with MYC overexpression and concomitant reduction in neuronal generation. The study also included detecting neural hyperexcitability and restoring defective WWOX gene for functional assessment. The study is important in understanding the function of WWOX and its mutated states, especially in identifying RG in the potential disease progression.

My concerns are:

Comment____:

1.Although organoids are good models for in vitro simulation of disease progression, I am not convinced that RG is the only cell type affected initially.

Response____:

We thank the reviewer for their thoughtful comment. We would like to clarify that we do not suggest that only radial glia cells are affected. As mentioned in both the current manuscript and our previous work (Steinberg et al., EMBO Mol. Med. 2021, and Repudi et al., Brain, 2021), other cell populations, including neurons and oligodendrocytes, are also impacted by WWOX deficiency. WWOX is widely expressed in the mature brain, and we are actively investigating whether these effects are cell-autonomous. In this study, we focus on WWOX in RGs due its high expression and possible importance in maintaining RG homeostasis. We will further clarify this point in the revised manuscript.

Comment____:

Functional characterization of RG needs further strengthening. I suggest utilizing a proteomic approach to compare the diseased-ongoing RG versus regular RG and identify which proteins are involved for functional characterization. Finally, the functional alterations in the mitochondria due to WWOX deficiency should be checked.

Response____:

We thank the reviewer for their suggestion and agree that performing proteomic analysis on RG populations will strengthen our understanding of the underlying mechanism, however, the experiment itself was attempted and proved to be technically challenging at this size, and for now is beyond the scope of this paper.

Comment____:

WWOX-deficient radial glia cells are expected not to guide neurons' migration normally during neural development. Please note that neuronal heterotopia occurs frequently in the WWOX deficiency. Neurons tend to exhibit groups of cells coming together in the neocortex. Purified RG cells are used to run versus typical neurons or RG cells. One can expect WWOX-deficient cells to run away from the normal cells, and they may kill each other, leading to compromise. The authors should run the real-time cell migration experiments using normal neurons versus WWOX-deficient radial glia cells and see the behavior of both cell types upon encountering each other. This will provide better insight regarding the deficiency of WWOX in radial glia cells.

Response____:

We thank the reviewer for their insightful suggestion regarding the validation of neuronal heterotopia in WWOX-deficient cells through real-time migration experiments. While we recognize the potential value of this approach for investigating the behavior of WWOX-deficient radial glia cells, we believe that such experiments would extend beyond the scope of the current study. However, we are considering them as part of our future research to further explore the impact of WWOX deficiency on cell migration and neuronal positioning. Thank you again for your valuable input.

Significance

The study is significant in our understanding the progression of syndromes associated with WWOX deficiency. My suggestions are shown in the above section.

Response____:

We thank the reviewer for their thoughtful and constructive feedback. We especially appreciate the suggestions regarding the broader involvement of additional cell types and the importance of exploring radial glia function through real-time migration assays. These insights will help us refine the focus and interpretation of our findings, and we will address the relevant clarifications and improvements in the revised manuscript.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #4

Evidence, reproducibility and clarity

The article deals with WWOX gene deficiency related neural diseases such as WOREE and SCAR12 syndromes. While there is no available drugs for treatment, the authors used organoid approach to study the development of the potential of disease development. The authors utilized neural organoids and single-cell transcriptomics and identified radial glial cells (RGs) as preferentially affected. The RG cells have disrupted cell cycle arrests in the leading G2/M and S phases, along with MYC overexpression and concomitant reduction in neuronal generation. The study also included detecting neural hyperexcitability and restoring defective WWOX gene for functional assessment. The study is important in understanding the function of WWOX and its mutated states, especially in identifying RG in the potential disease progression. My concerns are:

1. Although organoids are good models for in vitro simulation of disease progression, I am not convinced that RG is the only cell type affected initially.
2. Functional characterization of RG needs further strengthening. I suggest utilizing a proteomic approach to compare the diseased-ongoing RG versus regular RG and identify which proteins are involved for functional characterization. Finally, the functional alterations in the mitochondria due to WWOX deficiency should be checked.
3. WWOX-deficient radial glia cells are expected not to guide neurons' migration normally during neural development. Please note that neuronal heterotopia occurs frequently in the WWOX deficiency. Neurons tend to exhibit groups of cells coming together in the neocortex. Purified RG cells are used to run versus typical neurons or RG cells. One can expect WWOX-deficient cells to run away from the normal cells, and they may kill each other, leading to compromise. The authors should run the real-time cell migration experiments using normal neurons versus WWOX-deficient radial glia cells and see the behavior of both cell types upon encountering each other. This will provide better insight regarding the deficiency of WWOX in radial glia cells.

Significance

The study is significant in our understanding the progression of syndromes associated with WWOX deficiency. My suggestions are shown in the above section.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript by Steinberg and colleagues describes cellular and molecular changes linked to mutations in WWOX, a gene implicated in rare neurodevelopmental disorders, WOREE and SCAR12 syndromes. By comparing immunofluorescene and single cell trascriptomics of unguided brain organoids from control and WWOX-knockout iPSCs, as well as 2D NSCs and in vivo fetal brain expression datasets, the authors identified radial glia as relevant cell types in which WWOX is expressed and affected by WWOX deficiency. Using immunofluorescence, single cell trascriptomics analysis and western blotting on week 16 organoids, the authors show that WWOX deficiency results in increased abundance of radial glia cells at the expenses of neuronal production. These changes are accompanied by accumulation of cells in G2/M and S phases, overexpression of c-MYC and Wnt activation. In addition to this, the authors characterize unguided brain organoids generated from iPSCs reprogrammed from patients affected by WOREE or SCARE12 syndromes. Using immunofluoresce and single cell trascriptomics, they find that, while RG abundance changes were very modest, patients's iPSC-derived neurons are enriched for signatures related to early development, suggesting delayed differentiation. Finally, the authors use patch clumping, calcium imaging and gene therapy in 16 weeks old organoids derived from control and patients-derived iPSCs, to demonstrate that WWOX restoration normalized hyperexcitability phenotypes in both WOREE and SCAR12 organoids. These results thus provide a proof-of-concept evidence that WWOX restoration in human cells is a valid strategy to correct for hyperexcitability pehnotypes in WWOX related syndromes.

Major comments:

The study's main conclusions regarding neurodevelopmental phenotypes linked to WWOX deficiency and genotype-phenotype relationships are based on iPSC-derived brain organoid models analyzed using immunofluorescence, single-cell transcriptomics, and excitability recordings (cell-attached patch clamping, calcium imaging). While the analyses involve a diverse collection of iPSCs and two time points (7 and 16 weeks), the study falls short in providing sufficient experimental details and validation to fully support its conclusions. Additional quantification, replication, and functional validation would be necessary to solidify the study's conclusions. Some of these validations are achievable within a reasonable timeframe, while others would require a more substantial investment of time and resources as detailed below. A key concern is the lack of experimental details and replicability. Number of individual organoids, number of images per organoid for IF, and whether multiple batches were used are only partially provided. While the authors report generating multiple WWOX knockout clones, the legends and methods do not specify whether multiple clones were used across different organoid experiments. The study states that four organoids were used for scRNA-seq, but it is unclear whether this means four organoids per genotype or one organoid per genotype was analyzed. These ambiguities make the claims appear rather preliminary. Another issue is the limited validation of scRNA-seq observations. Since scRNA-seq is often performed on a limited number of organoids, orthogonal validation is crucial to strengthen the findings. For example, changes in radial glia abundance and neuronal production observed in scRNA analyis (Figure 2-5) could be validated using immunofluorescence across genotypes and batches. Currently, IF stainings for Sox2 and TUBB3 are shown only at 7 weeks in Figure 1B, but no quantificative assessment is provided. Also, it is not clear if quantifications provided in Figure 1F refer to multiple organoids or batches. Furthermore, the observations on cell cycle arrest, DNA damage, senescence, metabolic alterations, Wnt activation obtained via scRNA-seq could be further validated on organoid tissues using specific antibodies that the lab used before (e.g. yH2AX antibody in PMID: 34268881) or assays that have been developed elsewhere (some examples are reviewed in PMID: 38759644). As for feasibility, immunofluorescence validation of existing tissues is realistic, requiring validated antibodies and procedures, some additional imaging time and analysis (estimated 1-2 months, with some budget to purchase antibodies and cover imaging time costs). Feasibility of efforts related to validation across organoids and batches depends on the number of organoids used so far and available tissues. Generating new organoids would be indeed more time-consuming (≈ 6 months) and expensive (but extact costs would depend on number of clones, organoids and batches used), but feasible. Another limitation is the lack of functional relevance of MYC alterations. The study confirms increased MYC expression via both scRNA-seq and immunofluorescence in organoid tissues. However, these results remain correlative and demonstrating the functional requirement of MYC overexpression in mediating WWOX-deficiency-related changes would significantly strengthen the study's conclusions. This would require additional differentiation experiments, including MYC overexpression or knockout models, to assess its direct impact. These efforts would represent a major conceptual advance by linking RG effects to MYC function and highlighting MYC-related therapeutic directions. These additonal experiments would require a substantial investment to generate the necessary regents (e.g. WWOX-KO and WT iPSCs with altered MYC levels) and additional time and costs for organoid analysis, mostly by immunofluorescence (estimated 6-8 months). Another issue is the lack of patterning analysis in unguided organoids, which are known to exhibit high variability in regional identity (PMID: 28283582). While the authors acknowledge this limitation to some extent-abstaining from fine-resolution analysis (Lines 173-174)-this variability, combined with the limited number of organoids used, could be a major confounding factor in the phenotypic analyses, even at a broad resolution. Indeed, some of the reported differences across genotypes may stem from variability in organoid patterning rather than true genotype-driven effects. For example, the reduced SATB2 expression in KO and patient-derived organoids from Figure 1E-F could result from impaired cortical patterning rather than a direct effect of WWOX deficiency. Additionally, in Figure 6D and 6E, the fact that WOREE iPSC-derived organoids - but not SCAR12 organodis- show lower levels of both CTIP2 and SATB2, might reflect a shift toward a non-cortical identity rather than a direct WWOX-dependent phenotype. To rule out patterning variability as a contributing factor, the authors should analyze organoid regional identity across genotypes using immunostaining for dorsal and ventral forebrain markers. This would allow a more solid inference of genotype-specific effects on neurodevelopmental phenotypes. Patterning validation can be performed on existing organoid tissues (week 7) using validated antibodies (PMID: 28283582). As such, this analysis is expected to be relatively straightforward and feasible in a few weeks time. If the generation of new organoids is needed, such patterning validation should still be relatively feasible, as week 7 organoids are ideal for assessing regional identity. Analysis of patterning effects should also extend to 2D NSC cultures. In the 2D NSC models derived from WWOX-KO lines (Figure 3L, Figure S4A), the differentiation protocol includes patterning factors that promote ventral fates (SAG and IWP2). Interestingly, the quantification of MYC expression from unguided organoids and 2D NSCs (Figure 3K-L) reveals a major difference in the fraction of MYC-positive cells in WT conditions across the two culture models. A possible changes in the dorsal and ventral patterning of 3D and 2D cultures might explain these differences and implementing immunostaining for patterning markers in 2D would help clarify patterning contributions. There are also some concerns regarding WOREE and SCAR12 phenotypes. First, the genotypes of the patient-derived iPSCs are not clearly defined, making it difficult to establish clear genotype-phenotype relationships. The study uses iPSCs from four different patients (2 WOREE, 2 SCAR12), some of which were validated in a previous study (PMID: 34268881). However, it remains unclear how they were validated, and detailed genomic alterations of the four patients are not explicitly reported. Additionally, it is uncertain whether all variants result in a full loss of WWOX function, as protein loss evidence is only provided for one WOREE patient (Figure S1D). Also, the authors state that SCAR12 should have a milder phenotype (line 168), but it is unclear whether this claim is based on clinical evidence or genomic data from these specific patients. To improve genotype-phenotype comparisons, the authors should consider including a clear schematic summarizing the genomic alterations in all patient-derived lines and their expected disease severity. Second, the experimental design lacks appropriate controls for patient-derived iPSCs. All patient-derived iPSC comparisons are performed against a single reference male iPSC line, which is neither isogenic to WOREE nor SCAR12 iPSCs. This complicates the interpretation of differences between healthy and patient-derived organoids, as well as comparisons between WOREE and SCAR12 phenotypes. Given this design, it is impossible to draw solid conclusions about genotype-phenotype relationships. A more robust approach would involve including multiple healthy controls to account for genetic background variability or using isogenic parental or genetically corrected lines, which would provide a cleaner genetic comparison. A recent study (PMID: 36385170) discusses different study designs that could strengthen this aspect and might be useful for the authors to consult. Third, the study presents seemingly conflicting results regarding WOREE and SCAR12 phenotypes. The authors present immunofluorescence (IF) and scRNA-seq data indicating that changes in radial glia (RG) abudance are not observed in these patient-derived organoids. However, using same methodologies, they indicate that neuronal production is affected, leading to the accumulation of early neuronal signatures in both WOREE and SCAR12 neurons. The study does not explore whether RG signatures might be altered in a way that could contribute to neuronal phenotypes. Also, Figure 1F suggests that while Sox2+ cell counts are not increased in SCAR12 organoids, SATB2 levels are still altered, indicating that Sox2 and SATB2 trends are not tightly coupled across genotypes. Furthermore, Figure 1 and 6 show that while both syndromes exhibit similar hyperexcitability, data in Figure 6 report that only WOREE organoids display reductions in SATB2 and CTIP2 counts and that this can be rescued by WWOX restoration. Some of these discrepancies could stem from patterning variability as discussed above. Also, neuronal firing rate across WOREE and SCAR12 iPSC-derived organoids (Figure 6B) was different at later stages, but was rather comparable at an earlier stages (Figure S1G). The reasons for these differences are not thoroughly discussed. To strengthen the discussion, the authors should address how RG alterations (if any) might contribute to neuronal phenotypes, provide a more detailed comparison between WOREE and SCAR12 organoids and the WWOX-KO model and elaborate on the distinct phenotypes of the two syndromes, including possible explanations for observed functional and molecular discrepancies. Lastly, the conclusions drawn about WWOX restoration via gene therapy are weakened by the lack of replication and validation (see points above). First, the authors claim successful WWOX restoration in neurons, but provide limited evidence that the infected population consists of neurons. NeuN staining (Figure 6A and S10) suggests some neuronal expression, but quantification of WWOX+ NeuN+ / WWOX+ total cells is missing. Given that IF data are already available, this additional quantification could be completed within a few weeks and would significantly strengthen the claim. Second, the rationale for restoring WWOX in neurons is unclear, given that WT neurons do not normally express WWOX. Is WWOX being considered a functional neuronal maturation factor? If so, this should be explicitly discussed in the manuscript. Third, the authors propose that WWOX deficiency might lead to a delay in neuronal maturation. However, to demonstrate delayed maturation, the study should show that, given additional time, affected organoids can eventually produce late-stage neuronal signatures. Since this additional experiment may be technically challenging and time-consuming, the claim should instead be rephrased as speculative and discussed accordingly in the text. Lastly, the study lacks key details necessary for reproducibility in multiple aspects. In addition to details about organoid numbers and batches discussed above, all IF images are shown as insets, making it difficult to assess broader reproducibility within the whole organoid tissue section. Also, whether distinct iPSC clones/sections/organoids were used across IF experiments - which is critical for ensuring reproducibility - is not specified. As for details about experimental and bioinformatics methods, the bioinformatics pipeline is not fully described, making it impossible to verify or reproduce the computational analysis. No information is provided regarding batch correction procedures for scRNA-seq data (Lines 695-697) and on how clusters were mapped (lines 695-697) for cell type identification. Legends in Figure 1F, 2K-L, 6B, S10 do not specify what the error bars represent (e.g., standard deviation or standard error). Many catalog numbers for critical cell culture reagents are not provided, which is essential for experimental replication. The Western blot methods lack crucial details.

Minor comments:

• One relevant study (PMID: 32581702) that examines WWOX function in rat models and human fetal brains from patients has not been referenced or discussed. Notably, this study characterizes molecular changes associated with WWOX knockdown in human ESC-derived NPCs. Given its direct relevance to the current study, these findings should be acknowledged and integrated into the discussion to provide a more comprehensive understanding of WWOX-related neurodevelopmental alterations.
• For WKO-1C and 2C the exact mutations in exon1 identified by Sanger sequencing are not reported. Also, validation for WWOX protein loss in all the lines used is also missing. Information about cell line genome integrity check are also missing.
• Line 116 and 394, reference Steinberg et al is not formatted.
• Figure S1A: Localization of WWOX seems to be cytoplasmic and/or membrane-bound in organoids, while staining in IPSCs shows cytoplasmic and nuclear signals. Perphaps an orthogonal valiation with another anti-WWOX antibody would be appropriate to confirm subcellular localization.
• In Figure 1, authors use week 7 organoids and claim that they are enriched for early born preplate neurons (line 141). However the authors decide to look at SATB2, which is not an eary-born preplate neuron. So while the rationale for using Satb2 is not clear, the reported staining in Figure 1E shows an unusal overlap beetween Sox2 and Satb2 nuclear signals in wt organoids. The authors needs to recheck that the correct antibodies were used in this analysis.
• Figure 1 Panel F: legend states that n indiactes 3 neurons. Please specifify what n referes to.
• Figure 3J: MYC staining appears to be nuclear in WWOX-KO organoids but more cytoplasmic in SCAR12 organoids. Also in WOREE organoids, both Sox2 and MYC staining appears different from what seen in other panels/ genotypes from the same figure panel.
• Figures 3 and related legend: Authors use the term w for weeks but they need to specify whether this refers to gestational weeks or post-conception weeks.
• Figure 4: The UMAP in B, E and G seems to be blurred in the bottom parts. Is this an intentional choice? If so, what would be intent? Also, title and legend for E mention metabolic alterations but data presented are not related to metabolic patwhays.
• Figure 6. The same exact images from A and C are also reported in Figure S8 and S9 respectively.
• Figure S1D: WWOX antibody seems to give an extra band at higehr molecular weight. This is also evident from S4B, where the upper band seems overrepresented in KO2. Also, are the healthy parents haploinsufficient for WWOX? what are the levels compared to wt (unrelated) controls?
• Figure S2: In B, what is the difference between top and bottom UMAPs? In C-D, what is NP? Correlation map suggests that the NP clusters 7 and 8 are different from cluster 11. What is the rational for labelling them all NP cluster?
• Figure S6: In the legend, full description of cluster labels are missing. Also legends specifes A-D while the figure contains only A-C.
• Figure S4A: The staining for TUBB3 is very different between KO1 and KO2.
• Figure S8: The legend indicates n as 4 organoids but images are not quantified so there is no evidence that these patterns have been replicated in 4 organoids.
• Figure S9: The title is duplicated and not corresponding to the data in the figure. The whole figure is duplicated in Figure S10 (which is wrongly labelled as Figure 10 in the legend).
• Line 330: Figure S6 F-H should be corrected in Figure 6 F-H.
• Line 353: reference needs to be added for "our earlier findings"
• Lines 383 and 392: The authors describe several possible MYC roles but which ones could relevant in this contex is not discussed.
• Lines 402 and 403: The authors state that the study "highlights the critical role of Wnt signalling" but they fail to provide evidence that Wnt is functionally involved, as Wnt perturbation experiments are not applied.
• Line 473: "at X concentration" needs to be correct to specify the concentration used.
• Line 478: The authors state that "inform consent is under approval". Does this mean that the study was conducted before approval was obatined?
• Line 525: which orbital shaker and which speed was used?
• Line 537: what is GC in GC/ul?
• Line 629: samples were centrifgues at which speed and for how long?
• Line 639: "All primer sequence" should be plural

Referees cross-commenting

All four reviews appear fair and complementary to each other. Reviewers have consistently highlighted concerns regarding unclear genomic alterations in patients' iPSCs and experimental reproducibility in organoid cultures, emphasizing the need for further validation of the reported findings and the underlying molecular cascade. Additionally, they have noted some inconsistencies, with Reviewer #2 specifically identifying a major discrepancy in the WWOX-KO phenotypes compared to those previously described by the same team.

Significance

General assessment:

The strengths of this study lie in its focus on disease phenotypes in a human context and the use of patient-derived iPSC lines, which provide valuable translational relevance. Additionally, the study employs a complementary set of analyses, including functional assays, immunofluorescence (IF), and single-cell RNA sequencing (scRNA-seq), which enhance its depth. However, the study has several critical weaknesses, primarily related to suboptimal experimental design and limited reproducibility. These are discussed in section A and also indicated below:

• Lack of isogenic controls or patient-derived lines and presence of conflicting data for patient-derived organoids, making genotype-based comparisons for patients' lines less robust; examples of studies using iPSC isogenic controls for dissecting neurodevelopmental disorders are found here (PMID: 35084981; PMID: 26186191).
• Limited reproducibility, due to a small number of organoids used and the lack of orthogonal validation for key findings.
• Absence of functional validation for MYC's contribution, making its proposed role unclear.

Advance:

This study builds upon and expands previous efforts by the same team to characterize brain organoid models obtained from patient-derived iPSCs, as well as to explore gene therapy restoration approaches (PMID: 34268881, PMID: 34747138). Some of the bioinformatics analyses appear to have been developed elsewhere, and technically, the study offers only a limited methodological advance. Instead, the key advancement of this work is more conceptual: it proposes potential underlying mechanisms of WWOX-related neurodevelopmental disorders. If the study's limitations were addressed, it could provide valuable insights into WWOX's role as a key regulator of radial glia proliferation and differentiation, as well as potential functions in neuronal maturation. These findings would be relatively novel in the context of WWOX-related neurodevelopmental disorders. WWOX has been extensively studied in rodent models, where WWOX -/- mice exhibit growth retardation and brain malformations (PMID: 32000863, PMID: 18487609, PMID: 15026124). Additionally, studies in rats and human fetal cortical tissue from patients (PMID: 32581702) have linked WWOX deficiency to migration defects and cortical cytoarchitectural alterations. Previous work in mice by the same team suggested that neurons are the key population affected, linking WWOX deficiency to hyperexcitability and intractable epilepsy (PMID: 33914858). However, the relevance of radial glia and cell-type specific molecular alterations linked to WWOX mutations have remained poorly defined. Through scRNA-seq, this study offers some insights into cell-type-specific molecular changes, especially in radial glia cells. These changes are linked to MYC fucntion, cell cycle arrest and altered differentiation trajectories. However, these insights remain preliminary due to the study's design limitations. Another potential advancement of this study is its exploration of syndrome-specific alterations in WOREE and SCAR12 patients and their rescue through WWOX gene therapy-an aspect that has been difficult to study in animal models and remains largely unexplored. While the brain organoid model offers a promising approach, the true conceptual advance of this study remains uncertain, as its current limitations hinder the ability to draw definitive conclusions.

Audience: This study could be particularly relevant to a specialized audience, including basic research scientists working in developmental biology and the molecular basis of neurodevelopmental disorders, as well as those interested in translational approaches. Additionally, given WWOX's known roles beyond neurodevelopment and potential involvement of MYC, the findings may also be of interest to cancer biologists.

Expertise: My expertise lies in iPSCs and brain organoid modeling of neurodevelopmental disorders, with a strong focus on organoid phenotypic analysis, particularly immunofluorescence and transcriptomics. However, I do not have a strong background in bioinformatics and therefore lack sufficient expertise to evaluate the bioinformatic methodologies utilized in the study.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, Steinberg et al aim to elucidate the role of WWOX in human neurogenesis and model WOREE and SCAR12 syndromes which are rare neurodevelopmental disorders. They chose to investigate its function in human brain organoids after generating WWOX KO and patient-derived iPSC lines. Their major finding is that radial glial cells, the main neural progenitor population during corticogenesis, are affected. Via single-cell-RNA-sequencing, they try to decipher the perturbed molecular mechanisms identifying MYC, a proto-oncogene, as a major player. At the end of their study, they proceed to gene therapy restoration and suggest that this could become a potential therapeutic intervention for WOREE and SCAR12 syndromes. The study aims to elucidate major cellular and molecular mechanisms that modulate neurodevelopment and neurodevelopmental disorders. Although sc-RNA-seq could potentially be of great interest and unravel major mechanisms, the authors do not follow this part, but only discuss potential future avenues. Here are some suggestions that could be useful to the authros.

Major comments:

• A big part of the paper focuses on generating the iPSCs and characterizing the generated brain organoids and gene restoration of the phenotype via restoration of the WWOX gene expression (Fig.1, Fig.6, Fig.S1, Fig.S8, Fig.S10 and potentially Fig.S9 - this figure is not included) however, this has already been done by the same authors (first and last authors) in a previous publication. What are the differences in the line that have been generated in previous publication (Steinberg et al 2021, EMBO Mol. Med.)? If there are differences, the authors should make a thought comparison and explain why they generated different lines. If there is no difference, the authors should reduce to minimum this part and place it to supplementary.
• Fig.1E: in the pictures shown, the majority of the Satb2+ cells are colocalized with SOX2. Although a small portion of neurons have been shown from many studies that in brain organoids are co-localized to SOX2, in the pictures depicted this percentage is big. Also in ctrl condition the VZ-CP like areas are not easily recognized. The authors should check if this co-localization is a more general phenotype and if not choose more representative pictures.
• Information about the number of organoids per batch used in each figure is not included. This needs to be added for each experiment. Data (at least the majority of them) should be collected from brain organoids from at least two batches.
• The expression of WWOX in cortical development has been shown in the previous publication. Although sc-RNA data are validating the previous data and are adding more information, these data should be put as supplementary. Besides, in Fig.3G where authors aim to compare WWOX expression to MYC that fits nicely with their results depicting MYC as the most affected gene in KO and mutant line, when one looks at the WWOX expression only it seems that its expression is higher in CP that VZ. This is contrary to the conclusion that WWOX is mainly characterizes RGs. Why is that? Authors should at least discuss this.
• In this study, authors show that progenitors are reduced in WWOX-KO organoids, however in the previous publication SOX2 population is not majorly affected. Why are there such differences? Given that RGs are the main population affected as authors propose in this study, these differences must be at least discussed. Similar comments regarding neurons: in previous publication there is a minimal reduction of neurons in WWOX-KO brain organoids, while here authors describe major differences.
• Data from sc-RNA-seq analysis highlighting MYC as major differentially regulated gene are very interesting and seem to be key to the molecular pathway affected as authors suggest. Authors also validate this with immunostainings in brain orgnaoids. However, in Fig.3J MYC expression in ctrl is not depicted, even though in the respective graph it seems that 20% of SOX2+ cells co-express MYC. Please choose a more representative picture.
• One of the main findings in this study is the cell cycle changes observed in WWOX-KO and mutant organoids. Given that the major novelty of the publication is the cellular and molecular mechanism implicated in WOREE and SCAR12 syndromes, authors should perform additional experiments towards this direction. One suggestion would be to perform stainings in brain organoids using markers of the different cell cycle phases (eg. KI67, cyclin a, BrdU/EdU, ph3). Also treatment of organoids with different BrdU/EdU chase experiments would be important so as to measure exactly the length of each cell cycle phase.
• Regarding the molecular cascade, is WWOX directly affecting MYC of Wnt genes? Do they have information on upstream and downstream factors in the affected molecular pathway?
• Restoration of phenotype via reinsertion of WWOX gene has already been done in the previous publications by the same authors. But what about MYC? Is MYC manipulation able to rescue the phenotype?
• Finally, MYC association to ribosome biogenesis as mentioned by the authors in discussion is very interesting. The authors should consider investigating this direction, as it will be a great addition to the mechanisms that regulate WOREE and SCAR12 syndromes which is the main focus of this study.

Minor comments:

• Line 115: authors say that the data they discuss are found in Fig.S2A, maybe they mean Fig.S1A?
• Fig.S9 is missing, in the current version this Fig is the same with Fig.S10. Please change it.

Significance

This study is the continuation of a previous publication the authors have published. The topic is very interesting and novel especially in modelling neurodevelopmental disorders in a human context, however, given that the main phenotype has already been published, the authors should include more effort in the molecular cascade. Clinical interventions if the molecular cascade is described would be of great importance to the field

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Referee #1

Evidence, reproducibility and clarity

The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation. Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones. The major issue is related to the overall model the authors seem to build based on their data - or at least the overall model the reader may get from the paper. This model suggests that the loss / decrease in WWOX levels in RGs leads to Myc overexpression, that in turn affects the cell cycle and prevents neuronal differentiation. This model is highly attractive, but is probably incomplete, in the sense that it does not fully recapitulate the complicated picture. Indeed, all three types of mutated WWOX COs (WWOX k/o, WOREE patient-derived organoids, and SCAR12 patient-derived organoids) demonstrate strong - but equal levels of Myc upregulation. Yet the under-differentiation in each of these three types is different, as described above, and the disease manifestations among WOREE vs. SCAR12 patients are also different. Thus, another player (in addition to Myc) must be at place, that is differentially affected by the partial null mutations in WOREE and missense mutations in SCAR12. This point - ideally to be addressed experimentally - should be at least faced directly by the authors in the Discussion. Perhaps they can already point to such additional players based on their transcriptomics analysis.

The minor issues are as follows.

1. It would be useful if a table (perhaps supplementary) describing the details of the WWOX mutations in all the COs models studied in this paper were presented.
2. For the new WOREE individual with complex genetics in WWOX: it is not clear why any WWOX protein is still present in this patient in Fig. S1D (please give an explanation or speculation); it is not clear which tissue was used for the Western blot in Fig. S1D; the data in Fig. S1D need to be quantified.
3. Western blot, quantified, should be performed on all COs under study, to compare the WWOX expression levels. Please also change the immunofluorescence shown in Fig. 1B (e.g. show WWOX in a different color), as the figure provided shows WWOX poorly in wild-type CO, and it is not clear how much it is removed in the mutant organoids. Why should there be no signal in the SCAR12 COs?

Significance

The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation. Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

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Reply to the reviewers

Reviewer #1

__Evidence, reproducibility and clarity __

The manuscript explores mild physiological and metabolic disturbances in patient-derived fibroblasts lacking G6Pase expression, suggesting that these cells retain a "distinctive disease phenotype" of GSD1a. The manuscript is well written with well-designed experiments. However, it remains unclear whether these phenotypes genuinely reflect the pathology of GSD1a-relevant tissues. The authors did not validate these findings in a liver-specific G6pc knockout mouse model, raising concerns about the study's relevance to GSD1a. Additionally, the lack of sufficient in vivo evidence undermines the therapeutic potential of GHF201 for this disease. Overall, the study lacks a few key pieces of evidence to completely justify its conclusions at both fundamental and experimental levels.

__Reply:__We thank the reviewer for this general comment which gives us the opportunity to better explain the scope of our work. The purpose and focus of this work are not to test the pathological relevance of skin fibroblasts to GSD1a pathology. We do not claim that skin fibroblasts are involved in GSD1a pathogenesis. It is also not a developmental work claiming to uncover GSD1a pathogenic axis throughout embryonic development. As a matter of fact, since skin fibroblasts originate from the mesoderm embryonic germ layer and hepatocytes develop from the endoderm embryonic germ layer, it would even be unlikely that the pathological phenotype found in skin fibroblasts directly contributes to GSD1a pathology in model mice or in patients. Indeed, we are not aware of any dermatological contribution to GSD1a pathology in patients. However, our results suggest that in addition to the established and mutated organ (liver in the liver-specific G6pc knockout mouse model), other, relatively less studied, patho-mechanisms in distal tissues may also contribute to GSD1a pathology. Notably, this work is also not testing a therapeutic modality for GSD1a. Our work uses GSD1a disease models as a tool for demonstrating, or reviving, the concept of epigenomic landscape (Waddington, 1957): Different cell phenotypes, such as healthy and diseased, are established by innate metabolic differences between their respective cell environments, which impose epigenetic changes generating these different phenotypes. In this respect, our manuscript has a similar message to the one in the recently published paper Korenfeld et al (2024) Nucleic Acids Res 53:gkae1161. doi: 10.1093/nar/gkae1161: The Kornfeld et al paper shows that intermittent fasting generates an epigenetic footprint in PPARα-binding enhancers that is "remembered" by hepatocytes leading to stronger transcriptional response to imposed fasting by up-regulation of ketogenic pathways. In the same way, the diseased GSD1a status imposes metabolic changes, as detailed here, leading to permanent epigenetic changes, also described here, which are "remembered" by GSD1a fibroblasts and play a major role in the transcription of pathogenic genes in these patient's cells. This in turn is how the diseased state is preserved, even in cells not expressing the G6Pase mutant, which is the direct cause of the disease. We added this perspective to the Discussion to better highlight the key takeaway from our manuscript.Naturally, research such as ours with a claim on biological memory would involve ex vivo experiments where tissues are isolated from their in-situ environments and tested for preservation of the original in situ phenotype. The few in vivo experiments we performed (Fig. 5) are mainly aimed at demonstrating that not only the phenotype, but also therapy response is "remembered" ex vivo: In the same way that the G6PC-loss-of-function liver responded positively to GHF201 therapy in situ, ex vivo cells not expressing G6PC also responded positively to the same therapy. This observation only demonstrates further support for "memorization" of the disease phenotype by cell types not expressing the mutant: Both the diseased phenotype and response to therapy were preserved ex vivo.Lastly, while interesting, validation of our findings in vivo (as suggested by the reviewer) is not related to the scope of this manuscript. Such experiments, using the liver-targeted G6pc knockout mouse model, are the follow-up story, which is related to the origin of inductive signals that cause the curious and novel phenotype mechanism in GSD1a fibroblasts described in this manuscript. The scope and volume of such research constitute a novel manuscript.

Since dietary restriction is the only management strategy for GSD1a, the authors should clarify whether the patient fibroblast donors were on a dietary regimen and for how long. Given that fibroblasts do not express G6Pase, it is possible that the observed phenotype could be influenced by the patient's diet history.

__Reply:__We thank the reviewer for this important comment, we agree that it is important to note the dietary regimen assigned to the cohort of patients described in this study. We added an explanation to the manuscript on patient's diets as shown below.Briefly, all patients besides patient 6894 were treated with the recommended dietary regimen for GSD1a as explained in Genereviews (Bali et al (2021)). This dietary treatment (now added to the Methods section in the manuscript) allows to maintain normal blood glucose levels, prevent secondary metabolic derangements, and prevent long-term complications. Specifically, this dietary treatment includes- nocturnal nasogastric infusion of a high glucose formula in addition to usual frequent meals during. By constantly maintaining a nearly normal level of blood glucose, this treatment causes a remarkable decrease, although not normalization, of blood lactate, urate and triglyceride levels, as well as bleeding time values. A second layer in the treatment includes the use of uncooked starch in the dietary regimen to allow maintenance of a normal blood glucose levels for long periods of time. Patient 6894 did not tolerate well the uncooked cornstarch and therefore was treated with a tailored dietary treatment planned by metabolic disease specialists and dedicated certified dieticians highly experienced with the management of pediatric and adult patients with GSDs and other inborn errors of metabolism. The biopsies of patients were taken in the range of 3 month to several years from receiving the aforementioned dietary regimen.Importantly, the strict metabolic diet imposed on GSD1a patients might influence the observed phenotype described throughout the manuscript. This concept aligns with our claim that the GSD1a skin cells are affected by the dysregulated metabolism in patients in comparison to healthy individuals. Interestingly, while patient 0762 harbors a mutation in the SI gene in addition to the G6PC mutation and patient 6894 did not receive the same dietary regimen as other patients (as explained above), all patients do show similar disease related phenotypes, perhaps highlighting the role of an early programing process that affected these cells due to the severe metabolic aberrations presented in this disease from birth.One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.

One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.

__Reply:__We thank the reviewer for this important comment. We added glycogen levels of HC to Figure S2A and accordingly also edited the relevant text in the Results section.

Figure S2A - As mentioned above, the authors should present healthy control vs. patient fibroblast glycogen data. Without this, the rationale for using GHF201 is questionable.

__Reply:__We thank the reviewer for this important comment. We added glycogen levels of HC to Figure S2A as mentioned above.

Figure S2B-C - If the authors propose that GHF201 reduces glycogen and increases intracellular glucose in GSD1a fibroblasts, they need direct evidence. Either directly quantifying glycogen levels or even better would be a labeling experiment to confirm that the free intracellular glucose originates from glycogen. Additionally, the reduction in sample size from N=24 in glycogen analysis to N=3 in the glucose assay needs justification.

__Reply:__We thank the reviewer for this comment. To clarify, the results shown in Figure S2A left are based on PAS assay, directly quantifying glycogen in cells with and without GHF201 treatment. We have now added HC glycogen levels as requested above. Regarding N, this is explained in Methods: In imaging experiments N was determined based on wells from the experiments done in three independent plates following the rationale that each well is independent from the others and reflects a population of hundreds of cells as previously described in (Lazic SE, Clarke-Williams CJ, Munafò MR (2018) What exactly is 'N' in cell culture and animal experiments?. PLOS Biology 16(4):e2005282. https://doi.org/10.1371/journal.pbio.2005282, Gharaba S, Sprecher U, Baransi A, Muchtar N, Weil M. Characterization of fission and fusion mitochondrial dynamics in HD fibroblasts according to patient's severity status. Neurobiol Dis. 2024 Oct 15;201:106667. doi: 10.1016/j.nbd.2024.106667. Epub 2024 Sep 14. PMID: 39284371.). Figure S2A right shows the glucose quantification experiment that we think the reviewer is referring to. Glucose increase is normally concomitant with glycogen reduction and we therefore show these results in support of the glycogen reduction results. These glucose results are part of our metabolomics results done on the same cells (Figure 6), where glucose is one of the metabolites analyzed. This metabolomics analysis was repeated three times; therefore, N is 3. In summary, these results show that GHF201 directly contributes to glycogen reduction in GSD1a fibroblasts and concomitantly increases glucose levels.

Figure S2B-C- It is not shown how GHF201 increases intracellular glucose? If glycophagy is a possibility, the authors should do an experiment to confirm this.

__Reply:__Assuming the reviewer's comment is related to Figure S2A right, glucose levels are only shown to validate the glycogen reduction results (also see point 4): When glycogen levels are reduced, especially by inhibition of glycogen synthesis, glucose levels are supposed to concomitantly rise, being spared as an indirect substrate of glycogen synthesis. There is no proof, and as a matter of fact we also do not assume, that the GHF201-mediated reduction in glycogen levels is a result of increased glycophagy: Glycophagy has been described in cell types with high glycogen turnover, e.g., muscle and liver cells, not fibroblasts. Additionally, glycophagy is a glycogen-selective process implicating STBD1 whose expression in skin fibroblasts is negligible (https://www.proteinatlas.org/ENSG00000118804-STBD1/tissue).On the other hand, glycogen in GSD1a does not accumulate in lysosomes. It is built up in the cytoplasm (Hicks et al (2011) Ultrastr Pathol 35: 183-196; Hannah et al (2023) Nat Rev Dis Primers DOI: 10.1038/s41572-023-00456-z). Therefore, we do not believe that GHF201 reduced glycogen by enhancing glycophagy. As we show, GHF201 activated several key catabolic pathways. It is more likely that activation of one of these pathways, the AMPK pathway, inhibited glycogen synthesis via phosphorylation and ensuing inhibition of glycogen synthase. Alternatively, excessive cytoplasmic glycogen might enter lysosomes by bulk autophagy, or microautophagy (not by glycophagy) and GHF201 might induce lysosomal glycogenolysis by alpha glucosidase as an established lysosomal activator (Kakhlon et al (2021)). However, since, as explained, the mechanism of action of GHF201 is not the topic of this manuscript and therefore we did not dwell more into that.

Figure 2- How can GSD1a fibroblasts have significantly reduced OCR (Fig. 2B) but increased mitochondrial ATP production (Fig. 2H)?

__Reply:__We thank the reviewer for highlighting this important topic. OCR, measured in Fig. 2B, is an indirect measure of ATP production. Therefore, changes in OCR only measure the capacity of the mitochondria to produce ATP, and not the direct quantity of ATP. Other factors might influence ATP production, e.g., substrate availability and the activity of other metabolic pathways. On the other hand, the ATP Rate Assay (Figure 2h), provides a real-time direct measurement of ATP levels incorporating coupling efficiency and P/O ratio assumptions. Therefore, these two measurements do not necessarily match. We will add this information to the relevant segment in the text to clarify why OCR is reduced and mitochondrial ATP production increased in GSD1a cells.

Why do GSD1a fibroblasts show reduced glycolytic ATP (Figure 2h) despite increased glycolysis and glycolytic capacity (Fig 2J-K)?

__Reply:__We thank the reviewer for highlighting this important topic. ECAR measures medium acidification and thus reflects the production of lactic acid, which is a byproduct of glycolysis. However, medium acidification is also influenced by other factors that can acidify the extracellular environment, especially CO2 production which can originate from the intramitochondrial Krebs cycle which produces reductive substrates for mitochondrial respiration, or OCR. Moreover, the buffering capacity of the Seahorse mito stress assay medium might mask changes in lactic acid production, leading to an underestimation of glycolytic activity. On the other hand, glycolytic ATP production measured by the ATP rate assay directly quantifies the rate of ATP production from glycolysis. Notably, there is a major difference between ECAR and the ATP rate assay: The ATP rate assay is less sensitive to variations in buffering capacity than ECAR measurements. This is because the ATP rate assay relies on inhibitor-driven changes in OCR and ECAR, rather than absolute pH values.Teleologically, as indicated, the increased ECAR in GSD1a cells represents a known compensatory response to deficient ATP production which is stimulation of glycolysis (Figure 2i). To test the success of this known compensatory attempt, we applied the real-time ATP rate assay, but as explained they do not report the same entities. We will add this information to the relevant segment in the text to clarify how reduced glycolytic ATP can be co-observed with increased glycolytic capacity.

The authors should clarify how many healthy control and patient fibroblast lines were compared per experiment. Given the wide age range, the unexpectedly small error bars raise concerns about variability and statistical robustness.

Reply:__We thank the reviewer for raising this topic. Number of samples per experiment is reported in the Methods section. As for the age range, patients age was matched to healthy controls to account for age differences and experiments were performed under similar passages range. This procedure allowed us to control for technical differences between samples that might arise due to different passages and ages. Importantly, the cohort of samples used in this manuscript included GSD1a patients with different ages further implying the strength of the observed disease phenotype found in patients' cells which exists regardless of the different age and gender of patients. The HC samples were chosen to match age and gender and passages were used in the recommended range (L. Hayflick,The limited in vitro lifetime of human diploid cell strains,Experimental Cell Research,Volume 37, Issue 3,1965,Pages 614-636, änzelmann S, Beier F, Gusmao EG, Koch CM, Hummel S, Charapitsa I, Joussen S, Benes V, Brümmendorf TH, Reid G, Costa IG, Wagner W. Replicative senescence is associated with nuclear reorganization and with DNA methylation at specific transcription factor binding sites. Clin Epigenetics. 2015 Mar 4;7(1):19. doi: 10.1186/s13148-015-0057-5. PMID: 25763115; PMCID: PMC4356053., Magalhães, S.; Almeida, I.; Pereira, C.D.; Rebelo, S.; Goodfellow, B.J.; Nunes, A. The Long-Term Culture of Human Fibroblasts Reveals a Spectroscopic Signature of Senescence. Int. J. Mol. Sci. __2022, 23, 5830. https://doi.org/10.3390/ijms23105830). Finally, for the error bars, assuming the reviewer is addressing this for all experiments, this means that results are consistent across each compared group and reflects robustness of the results. Further, to ensure statistical robustness we used bootstrapping, 95% confidence intervals and other statistical methodologies that were designed to increase the validity of the conclusions drawn from different experiments.

Figure 5- The study should include Tamoxifen-untreated mice as a control to properly assess the efficacy of GHF201 in regulating glucose-6-P and glycogen levels.

__Reply:__GHF201 reduced liver glucose-6-phosphate (G6P) with p-/-* mice livers and their normalization by GHF201.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

General comments: the authors propose a very intriguing concept, that metabolic abnormalities trigger epigenetic changes in tissues distal from the disease site, even in cells in which the affected gene is not expressed. This is demonstrated in primary fibroblasts from patients with Glycogen Storage Disease type 1a (GSD1a). The authors provide a large amount of data to support the compelling concept of "Disease-Associated Programming", a term that they have coined to describe this effect. The level of novelty is very high and so is the impact of the study, since the above may apply to many different pathological conditions. Although, the study is well performed and employs multiple approaches and analyses to address the raised hypothesis, there are some limitations and concerns that need to be addressed by the authors.

__Reply:__We thank the reviewer for this comment and will address each comment raised.

The different phenotypic characteristics are only demonstrated in skin fibroblasts which is not sufficient to support the conclusions made in the Discussion about the general applicability of the proposed disease-induced, metabolite-driven epigenetic programming to all cells and tissues. The authors should discuss this as a limitation of the study and general conclusions should be formulated with more caution.

__Reply:__We concur with this comment and accept that this is a general limitation of the study. We added a reservation clause at the beginning of the Discussion section.

The authors describe a range of alterations in patients' fibroblasts as compared to healthy control fibroblasts. However, they draw parallels to the liver which is the organ primarily affected by GSD1a, stating that tissues other than the liver such as skin fibroblasts phenocopy the liver pathology (Discussion). Extrapolation of the findings to the liver is also made in the section "ATAC-seq, RNA-seq and EPIC methylation data integration". Here, the authors comment on the finding that identified genes are associated with tumour formation and draw parallels to hepatocellular carcinoma which is an important co-morbidity of GSD1a. These correlations, although interesting, should be presented as indications and not as "strong links". A major difference between fibroblasts and liver cells in the case of GSD1a is the massive accumulation of glycogen in the liver. This is a major metabolic feature which largely defines the disease's pathology. In addition to the similarities in the pathological features between the liver and other tissues such as fibroblasts, the authors should highlight this major difference and discuss their findings within this context.

__Reply:__We thank the reviewer for this important comment. We have toned down the language correlating the regulation of gene expression between fibroblasts and liver in GSD1a. We have also alluded to the key metabolic difference between fibroblasts and liver - glycogen levels and turnover - in the second paragraph of the Discussion. We are aware that if our deep analyses were conducted on a different tissue with different basal metabolism the results might have been different. However, the GSD1a-pathogenic findings in fibroblasts suggest that they might also contribute to pathology in situ, perhaps by modulating the expression of functionally redundant genes.

For basically all experiments performed in the study the authors follow the approach of culturing cells for 48 hours under serum and glucose starvation, followed be 24-hour cultivation in complete medium. This was practiced in a previous study by the authors (PMID: 34486811) to enhance the levels of glycogen in skin fibroblasts of patients with Adult Polyglucosan Body Disease. For the current study the selection of this treatment protocol is not sufficiently justified. Although, differences are described between patients' fibroblasts and controls under these conditions, it would have been interesting to address the reported parameters also at standard culturing conditions. This might be too much to ask for the purposes of this revision, but the authors may provide a better justification for the selection of the above treatment protocol and discuss whether the described phenotypic features are constitutive abnormalities present at all times or are induced by the metabolic stress imposed to the cells through this treatment.

__Reply:__We thank the reviewer for pointing this important topic. Previously, we used the 72 h condition (48 h starvation followed by 24 h glucose supplementation) to attain two goals: generation of glycogen burden by excessive glucose re-uptake after glucose starvation and induction of basal autophagy by serum starvation so as to sensitize detection of the action of the autophagic activator GHF201 on a background of already induced autophagy. As stated, this 72 h condition was used previously in other GSD cell models (Kakhlon et al (2021) - GSDIV, Mishra et al (2024) - GSDIII, GSDII - in preparation), so we decided to use it in this work as well to enable cross-GSD comparison of GHF201 efficacy in GSD cell models. Moreover, as shown in Figure 1, the largest differences between HC and GSD1a fibroblasts, especially in lysosomal and mitochondrial features, were observed at the 72 h time condition. We therefore used this condition in all other fibroblasts experiments presented in this manuscript. Our ultimate aim was to test whether the metabolic reprograming induced in situ by the patients' diseased state before culturing generates stable epigenetic modifications withstanding seclusion from the original in situ environment. Thus, using the non-physiological 72 h condition, after the fibroblasts were cultured in full media remote from the in situ environment, can only confirm the stability and environment-independence of these metabolically-driven epigenetic modulations. We now provide this justification at the beginning of the Results section.

In the Figures, the authors provide comparisons between controls and patient fibroblasts (+/- GHF201). Although the authors provide the respective p values in all figures, it is not clear which differences are considered significant and which are not. Since some of the indicated p values are > 0.0. The authors should indicate which of these changes are significant or non-significant and these should be presented and discussed accordingly in the text.

__Reply:__We thank the reviewer for highlighting this important topic. We will add this information to the methods segment. Throughout the manuscript, p https://doi.org/10.1080/00031305.2018.1529624, Cumming, G. (2013). The New Statistics: Why and How. Psychological Science, 25(1), 7 29. https://doi.org/10.1177/0956797613504966 (Original work published 2014)). Along with the p values we presented all data points in each comparison and added bootstrap mediated 95 % confidence intervals as well. Since our sample size was small, we chose to focus on effect sizes, to use a higher p value threshold and to implement various advanced methodologies that allowed us to find important biological patterns.

In Figure S2A, the authors show a reduction of glycogen levels in GSD1a fibroblasts following treatment with GHF201. Glycogen accumulation is central to this study, since a) is considered by the authors "a disease marker which is reversed by GHF201" - this is demonstrated in the liver of L.G6pc-/- mice and, according to the authors, replicated in the fibroblasts, b) as suggested by the authors it is the biochemical aberration that drives epigenetic modifications generating "disease memory". It is therefore important to appreciate whether GSD1a cells display pathologically increased levels of glycogen. This is also pertinent to the lack of G6PC expression in fibroblasts. The authors should include in Fig. S2A glycogen measurements of HC control fibroblasts cultured under the same conditions to compare with the levels present in GSD1a cells.

__Reply:__We thank the reviewer for highlighting this issue. We added glycogen levels of HC to Figure 2SA as requested. Expectedly, glycogen levels are similar between HC and GSD1a fibroblasts because neither wild type G6PC1 in HC, or mutated G6PC1 in GSD1a fibroblasts is expressed. We have now corrected the manuscript text suggesting that glycogen is accumulated in GSD1a fibroblasts and rephrased the text to express the more versatile state where epigenetic modulation could be mediated by different metabolic perturbations according to the expression profile: G6PC1 mutant expressers (notably liver and kidney cells) could inhibit p-AMPK by glycogen accumulation, while non-expressers could inhibit p-AMPK by lowering NAD+. Text changes related to this new concept are found in the Results section "Exploring epigenetics as a phenotypic driver in GSD1a fibroblasts by ATAC-seq analysis" and in the Discussion section "Metabolic-driven, disease-associated programming of cell memory."

Comparisons between protein levels (AMPK/pAMPK, Sirt1, TFEB, p62 ane PGC1a) are made on the basis of fluorescence intensity in immunostained cells. These results need to be supported by relevant western blot images to exclude that binding of the antibodies to unspecific sites contributes to the measured fluorescence.

__Reply:__We thank the reviewer for this comment allowing us to clarify the reasoning behind the selected methods for the main markers identification. Throughout the manuscript we employed both Western blot and immunofluorescence experiments. We believe that immunofluorescence present as a more robust and efficient method for the following reasons: i. It allows to focus on proteins in their native state; ii. Immunofluorescence allows to observe proteins in relation to their location in the cells (for example TFs in nuclei area); iii. Immunofluorescence allows to focus on each cell and exclude cells which are dead, stressed or with a low viability characteristic; iv. Immunofluorescence allows to generate much more data. For the following reasons, the main proteins explored in this work we used immunofluorescence, in each immunofluorescence experiment we added a control for the secondary antibody alone, verifying the signal is related to the antibodies only. This information can be added if requested. Importantly, some of the antibodies used were recommended for immunofluorescence and not for Western blot. As the reviewer requested, we now provide western blot results for proteins that produced a signal with the antibodies in Western blots, all markers mentioned except TFEB were added to Figure S3 d.

The authors demonstrate that treatment of GSD1a fibroblasts with histone deacetylase inhibitors reverses some of the phenotypic alterations. Given that GHF201 also improves these phenotypic differences it would be interesting to address whether GHF201 has any effect on histone acetylation.

Reply: We strongly agree with this comment and have therfore tested for the effect of GHF201 on H3K27 acetylation levels as shown in Fiugre 3f and on the deacetylase -SIRT-1 as shown in Figure 3e, Figure S3d and representative images in Figure S2b.

The authors report reduced levels of the transcription factors PGC1α and TFEB in GSD1a fibroblasts. Does this correlate with lower levels of expression of PGC1α and TFEB target genes in the RNA-seq experiments?

Reply:

We thank the reviewer for raising this topic, since there were thousands of differentially expressed genes and we cannot mention all we focused on the most important ones that comprise key pathways we wanted to highlight as described in the Results section. We have now linked in the Results section examples of PGC1α and TFEB target genes that were reduced due to lower levels of these transcription factors in GSD1a, as compared to HC cells. Importantly, a full list of the genes from the RNA-seq experiment can be found in Table S3. Genes regulated by TFEB contain the CLEAR (Coordinated Lysosomal Expression and Regulation) motif. Two notable genes regulated by CLEAR binding TFs such as TFEB, which are very important biologically, are cathepsin L and S (Figure 6A right) both of which were reduced in GSD1a and are now elaborated in the Results section referring to Figure 6a right. Additionally, Table S3 shows differentially expressed genes in GSD1a cells where there are many other lysosomal related genes that are downmodulated in GSD1a, we now added another important example, ATP6V0D2 to the Discussion as the reviewer suggested. As for PGC1alpha, a notable gene whose expression is up-modulated by PGC1alpha, which is down-modulated in GSD1a, is ALDH1A1 (Figure 6a right). In addition, we have now added PPARG and its coactivators alpha and beta to the discussion as requested by the reviewer, these genes are shown in Table S3 and are downmodulated in GSD1a. Finally, the transcriptional effect of PGC1alpha and TFEB is also mentioned in the Discussion within the cell phenotyping section, where we describe the deep impact of dysregulation of NAD+/NADH-Sirt-1-TFEB regulatory axis on the cell phenotype at all the levels described in the manuscript.

Please revise the following sentences as the statements made are not adequately supported by the provided data a. "This NAD+/NADH increase correlated with reduced cytotoxicity and increased cell confluence (Figure 3d) suggesting that NAD+ availability prevails over ATP availability as an effector of cell thriving in GSD1a cells."

__Reply:__If one ranks treatments according to NAD+/NADH (Figure 3c) and according to cytotoxicity (Figure 3d left) and cell confluence (Figure 3d right), then the mentioned correlation can be supported. ATP availability is compromised by gramicidin, yet gramicidin, which also increased NAD+/NADH, reduced cytotoxicity and enhanced cell confluence.

b. "....in further support that respiration-dependent NAD+ availability mediate GHF201's corrective effect in GSD1a cells."

__Reply:__Our data (Figure 3c) show that GHF201 increased NAD+/NADH both alone and with gramicidin.

Please indicate on the densitometry graph of Fig. 10b the treatment (HDACi), for better visibility.

__Reply:__We agree and have corrected the Figure as requested.

The reference list (n=160) is probably too long for a research article.

__Reply:__The number of references reflect the length and depth of the manuscript and we believe that each reference merits its place. We agree that the number of references is large but we are not sure which criteria to use to exclude some references and to reduce them to a more acceptable number that we assume would be determined by the publishing journal.

The study is of high novelty and impact, as it proposes a so far undescribed biological mechanism contributing to disease pathology that could apply for general pathological conditions. Although this is a compelling concept, it is only demonstrated in skin fibroblasts which limits its applicability at an organismal level.

__Reply:__We thank the reviewer for this comment and for raising the important comments that allowed us to improve our manuscript, please see our reply to point 1.

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Referee #2

Evidence, reproducibility and clarity

General comments: the authors propose a vey intriguing concept, that metabolic abnormalities trigger epigenetic changes in tissues distal from the disease site, even in cells in which the affected gene is not expressed. This is demonstrated in primary fibroblasts from patients with Glycogen Storage Disease type 1a (GSD1a). The authors provide a large amount of data to support the compelling concept of "Disease-Associated Programming", a term that they have coined to describe this effect. The level of novelty is very high and so is the impact of the study, since the above may apply to many different pathological conditions. Although, the study is well performed and employs multiple approaches and analyses to address the raised hypothesis, there are some limitations and concerns that need to be addressed by the authors.

1. The different phenotypic characteristics are only demonstrated in skin fibroblasts which is not sufficient to support the conclusions made in the Discussion about the general applicability of the proposed disease-induced, metabolite-driven epigenetic programming to all cells and tissues. The authors should discuss this as a limitation of the study and general conclusions should be formulated with more caution.
2. The authors describe a range of alterations in patients' fibroblasts as compared to healthy control fibroblasts. However, they draw parallels to the liver which is the organ primarily affected by GSD1a, stating that tissues other than the liver such as skin fibroblasts phenocopy the liver pathology (Discussion). Extrapolation of the findings to the liver is also made in the section "ATAC-seq, RNA-seq and EPIC methylation data integration". Here, the authors comment on the finding that identified genes are associated with tumour formation and draw parallels to hepatocellular carcinoma which is an important co-morbidity of GSD1a. These correlations, although interesting, should be presented as indications and not as "strong links". A major difference between fibroblasts and liver cells in the case of GSD1a is the massive accumulation of glycogen in the liver. This is a major metabolic feature which largely defines the disease's pathology. In addition to the similarities in the pathological features between the liver and other tissues such as fibroblasts, the authors should highlight this major difference and discuss their findings within this context.
3. For basically all experiments performed in the study the authors follow the approach of culturing cells for 48 hours under serum and glucose starvation, followed be 24-hour cultivation in complete medium. This was practiced in a previous study by the authors (PMID: 34486811) to enhance the levels of glycogen in skin fibroblasts of patients with Adult Polyglucosan Body Disease. For the current study the selection of this treatment protocol is not sufficiently justified. Although, differences are described between patients' fibroblasts and controls under these conditions, it would have been interesting to address the reported parameters also at standard culturing conditions. This might be too much to ask for the purposes of this revision, but the authors may provide a better justification for the selection of the above treatment protocol and discuss whether the described phenotypic features are constitutive abnormalities present at all times or are induced by the metabolic stress imposed to the cells through this treatment.
4. In the Figures, the authors provide comparisons between controls and patient fibroblasts (+/- GHF201). Although the authors provide the respective p values in all figures, it is not clear which differences are considered significant and which are not. Since some of the indicated p values are > 0.0. The authors should indicate which of these changes are significant or non-significant and these should be presented and discussed accordingly in the text.
5. In Figure S2A, the authors show a reduction of glycogen levels in GSD1a fibroblasts following treatment with GHF201. Glycogen accumulation is central to this study, since a) is considered by the authors "a disease marker which is reversed by GHF201" - this is demonstrated in the liver of L.G6pc-/- mice and, according to the authors, replicated in the fibroblasts, b) as suggested by the authors it is the biochemical aberration that drives epigenetic modifications generating "disease memory". It is therefore important to appreciate whether GSD1a cells display pathologically increased levels of glycogen. This is also pertinent to the lack of G6PC expression in fibroblasts. The authors should include in Fig. S2A glycogen measurements of HC control fibroblasts cultured under the same conditions to compare with the levels present in GSD1a cells.
6. Comparisons between protein levels (AMPK/pAMPK, Sirt1, TFEB, p62 ane PGC1a) are made on the basis of fluorescence intensity in immunostained cells. These results need to be supported by relevant western blot images to exclude that binding of the antibodies to unspecific sites contributes to the measured fluorescence.
7. The authors demonstrate that treatment of GSD1a fibroblasts with histone deacetylase inhibitors reverses some of the phenotypic alterations. Given that GHF201 also improves these phenotypic differences it would be interesting to address whether GHF201 has any effect on histone acetylation.
8. The authors report reduced levels of the transcription factors PGC1α and TFEB in GSD1a fibroblasts. Does this correlate with lower levels of expression of PGC1α and TFEB target genes in the RNA-seq experiments?

Minor points

1. Please revise the following sentences as the statements made are not adequately supported by the provided data

a. "This NAD+/NADH increase correlated with reduced cytotoxicity and increased cell confluence (Figure 3d) suggesting that NAD+ availability prevails over ATP availability as an effector of cell thriving in GSD1a cells."

b. "....in further support that respiration-dependent NAD+ availability mediate GHF201's corrective effect in GSD1a cells." 2. Please indicate on the densitometry graph of Fig. 10b the treatment (HDACi), for better visibility. 3. The reference list (n=160) is probably too long for a research article.

Significance

The study is of high novelty and impact, as it proposes a so far undescribed biological mechanism contributing to disease pathology that could apply for general pathological conditions.

Although this is a compelling concept, it is only demonstrated in skin fibroblasts which limits its applicability at an organismal level.

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Referee #1

Evidence, reproducibility and clarity

Major Comments:

1. Since dietary restriction is the only management strategy for GSD1a, the authors should clarify whether the patient fibroblast donors were on a dietary regimen and for how long. Given that fibroblasts do not express G6Pase, it is possible that the observed phenotype could be influenced by the patient's diet history.
2. One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.
3. Figure S2A - As mentioned above, the authors should present healthy control vs. patient fibroblast glycogen data. Without this, the rationale for using GHF201 is questionable.
4. Figure S2B-C - If the authors propose that GHF201 reduces glycogen and increases intracellular glucose in GSD1a fibroblasts, they need direct evidence. Either directly quantifying glycogen levels or even better would be a labeling experiment to confirm that the free intracellular glucose originates from glycogen. Additionally, the reduction in sample size from N=24 in glycogen analysis to N=3 in the glucose assay needs justification.
5. Figure S2B-C- It is not shown how GHF201 increases intracellular glucose? If glycophagy is a possibility, the authors should do an experiemnt to confirm this.
6. Figure 2- How can GSD1a fibroblasts have significantly reduced OCR (Fig. 2B) but increased mitochondrial ATP production (Fig. 2H)?
7. Why do GSD1a fibroblasts show reduced glycolytic ATP (Fig. 2H) despite increased glycolysis and glycolytic capacity (Fig. 2J-K)? The authors should clarify how many healthy control and patient fibroblast lines were compared per experiment. Given the wide age range, the unexpectedly small error bars raise concerns about variability and statistical robustness.
8. Figure 5- The study should include Tamoxifen-untreated mice as a control to properly assess the efficacy of GHF201 in regulating glucose-6-P and glycogen levels.
9. Fig. 5B-C - The authors should explain how GHF201 reduces glucose-6-P levels. Additionally, they should demonstrate whether GHF201 activates lysosomal pathways and induces autophagy in the liver of G6pc knockout mice, as claimed in the fibroblast experiments.

Significance

The manuscript explores mild physiological and metabolic disturbances in patient-derived fibroblasts lacking G6Pase expression, suggesting that these cells retain a "distinctive disease phenotype" of GSD1a. The manuscript is well written with well designed experiments. However, it remains unclear whether these phenotypes genuinely reflect the pathology of GSD1a-relevant tissues. The authors did not validate these findings in a liver-specific G6pc knockout mouse model, raising concerns about the study's relevance to GSD1a. Additionally, the lack of sufficient in vivo evidence undermines the therapeutic potential of GHF201 for this disease. Overall, the study lacks a few key pieces of evidence to completely justify its conclusions at both fundamental and experimental levels.

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Reply to the reviewers

1. General Statements [optional]

We* thank all three Reviewers for appreciating our work and for sharing constructive feedback to further enhance the quality of our work. It is really gratifying to read that the Reviewers believe that this work will be of interest to broad audience and will be suitable for a high profile journal. Further, the experiments suggested by the reviewers will add value to the work and will substantiate our findings. It is important to highlight that we have already performed most of the suggested experiments except a couple of experiments that we have plan to carry out during full revision. Please find below the details of experiments performed and planned to address the reviewers comments. *

2. Description of the planned revisions

Reviewer #1

Comment 6. In Figure 6A, B, does the Orai3 western blot show any of the heavier bands seen in the ubiquitination IP if you show the whole blot? It should.

Reviewer #2

Comment 5. Fig. 6A and 6B. Show the full Orai3 and Ubiquitin WBs. As presented the figure current just shows that there are ubiquitin proteins in Orai3 pull down, not that Orai3 is ubiquitinated.

Reviewer #3

Comment 3. In the scheme in Fig. 10, the authors highlight that Orai3 is ubiquitinated. Do they have any idea where the site of action of ubiquitination in Orai3 is located?

Response: We thank the Reviewer 1, 2 and 3 regarding their query on the co-immunoprecipitation assays performed for studying Orai3 ubiquitination. The reviewers are asking for ubiquitination status of Orai3 and the potential sites for Orai3 ubiquitination. To address these comments, we are planning to perform co-immunoprecipitation assays with mutated Orai3 with mutations of potential ubiquitination sites. We have already performed bioinformatic analysis and it revealed presence of three potential ubiquitination sites on Orai3: K2 (present on N-terminal region), K274 and K279 (present on C-terminal region). We would mutate these lysine residues on Orai3 protein via site-directed mutagenesis and check the Orai3 ubiquitination status. These experiments will answer the question raised by Reviewers and strengthen the Orai3 ubiquitination data.

Please refer to below diagrammatic illustration showing potential ubiquitination sites on Orai3:

Reviewer #2

Comment 7. Also, all the imaging and pull down do not prove conclusively direct interaction between MARCH8 and Orai3, they rather show that the proteins are in the same complex. Although it is unlikely best for the text to be moderated accordingly.

Response: We understand the concern raised by Reviewer 2 regarding direct or indirect interaction of MARCH8 and Orai3. Hence, we are planning to perform co-immunoprecipitation assays in which we delete the MARCH8 interacting domain in Orai3 protein and check the for direct interaction of these proteins. Bioinformatic analysis and literature survey have highlighted two possible MARCH8 interacting domains in Orai3. The first domain is present in 2nd loop region, present between the 2nd and 3rd transmembrane domains at the LMVXXXL (AA113-120) motif and the second domain is present at the GXXXG (AA235-239) motif, present in the 3rd loop region of Orai3. We will remove these domains from Orai3 protein individually and check its effect on MARCH8 interaction. These experiments will provide conclusive evidence of direct interaction between Orai3 and MARCH8.

Please refer to below diagrammatic illustration displaying potential MARCH8 binding sites on Orai3:

3. Description of the revisions that have already been incorporated in the transferred manuscript

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Reviewer #1

Comment 1. The observation that both transcriptional regulation and protein degradation of Orai3 is regulated downstream of one transcription factor is not, in and of itself, entirely surprising. All proteolytic components are transcriptionally regulated and this phenomenon is likely relatively common. However, what I do think is both impressive and important is that the authors have characterized both components of the pathway within a disease context. While I am not going to search the literature for how often transcription and proteolysis are co-regulated for other proteins, it is the case for many short-lived proteins and perhaps many others. As such, discussion throughout the abstract and introduction that co-regulation of these processes is unprecedented should be removed.

Response: We thank the Reviewer for thinking that our work is both impressive and important. Further, we understand the Reviewer’s point that transcription and proteolysis may be co-regulated for other proteins. However, our extensive literature search did not resulted in such scenarios. Therefore, to best of our knowledge, we are revealing for the first time that same transcription factor regulates both transcription and protein degradation of the same target in a context dependent manner in a single study. In case, Reviewer would still recommend to modify the text in abstract and introduction, we would do it.

Comment 2. In discussing figure 1, the authors switch from claiming to be studying NFATc binding to studying NFAT expression. This use of 2 different naming conventions is certain to confuse readers; the authors should use the approved current naming system in referring to NFAT isoforms. In which case NFAT2 is NFATc1.

Response: We would like to thank the Reviewer for highlighting this point. We have effectively addressed this comment by changing the nomenclature of NFAT2 to NFATc1 throughout the manuscript text and figures.

Comment 3. The ChIP analyses in figures 1H and 7D are important findings, however, there is missing information. Typically, ChIP is used to validate putative binding sites; as such, one would expect 3 separate qPCR reactions for Orai3, not one. It is also important to note that qPCR products should be uniform in size and under 100 bp; here, the product size is not stated. Finally, demonstrating that an antibody targeting ANY other NFAT isoform fails to pull down whatever product this is would increase confidence considerably.

Also, the gold standard for validating ChIP is to mutate the sites and eliminate binding. The "silver" standard would be to mutate them in your luciferase vector and demonstrate that NFATc1 no longer stimulates luciferase expression. Since neither of these was done, the ChIP data provided should not be considered formally validated.

Response: We thank the Reviewer for raising this highly relevant concern. In this revised manuscript, we have addressed this comment by performing several additional experiments. The new data provided in the revised manuscript corroborates our earlier results. Indeed, this data has notably strengthen our work.

In the revised manuscript, we performed ChIP assay where we increased the number of sonication cycles to 35 so as to make sheared chromatin of around 100 bp. Next, we designed primers to amplify individual NFATc1 binding sites on Orai3 promoter, but due to close proximity of the NFATc1 binding sites, we could design two primer sets. The primer first set to amplify the -1017 bp binding site and the second set to amplify the -990 and -920 bp. Further, as suggested by the Reviewer, we performed immunoprecipitation with the four isoforms of NFAT. Our results show that only NFATc1 pulldown shows significant enrichment of Orai3 promoter with both the primer sets as compared to the IP mock samples and other NFAT isoforms (Figure 1J). Hence, our data reveals that only NFATc1 binds to these predicted sites on the Orai3 promoter and it doesn’t show a preference among these binding sites.

Further, as suggested by the Reviewer, we mutated the Orai3 promoter in luciferase vector with deletions of the individual NFATc1 binding sites and also cloned a truncated Orai3 promoter with no NFATc1 binding sites into the luciferase vector. The luciferase assays with these mutant and truncated promoters show that upon co-expression of NFATc1, the luciferase activity of the mutant Orai3 promoter with deletion of individual NFATc1 binding site is significantly reduced in comparison to wild type Orai3 promoter. Furthermore, the maximum decrease in luciferase activity was seen with the truncated Orai3 promoter with no NFATc1 binding sites (Figure 1I). These results show that NFATc1 binds to the predicted binding sites on Orai3 promoter. Taken together, the additional ChIP assays with the four isoforms of NFAT and luciferase assays with mutated & truncated Orai3 promoters validates the transcriptional regulation of Orai3 by NFATc1.

Comment 4. In figures 2 and 3, only one cell line is used to represent each of 3 conditions of pancreatic cancer. That is insufficient to make generalized conclusions; some aspects of this figure (expression and stability, not function) should be extended to 2 to 3 cell lines/condition. TCGA data validating this point would also be helpful.

Response: We really appreciate the feedback given by Reviewer 1. To strengthen our manuscript, we have addressed this comment by performing experiments in 2 cell lines/condition of pancreatic cancer. This new data in the revised manuscript provides substantial evidence for the dichotomous regulation of Orai3 by NFATc1.

In the revised manuscript, we carried out NFATc1 overexpression and NFAT inhibition via VIVIT studies in three additional cell lines: BXPC-3 (non-metastatic), ASPC-1 (invasive) and SW1990 (metastatic). The results in these cell-lines support our earlier findings as both overexpression of NFATc1 and VIVIT mediated NFAT inhibition leads to transcriptional upregulation of Orai3 in BXPC-3 (non-metastatic) (Figure S3A, D), ASPC-1 (invasive) (Figure S3G, J) and SW1990 (metastatic) (Figure S3M, P). These results are similar to our earlier data from MiaPaCa-2 (non-metastatic), PANC-1 (invasive) and CFPAC-1 (metastatic) cells. Further, NFATc1 overexpression leads to an increase in Orai3 protein levels in BXPC-3 (non-metastatic) (Figure S3B, C) and a decrease in Orai3 protein levels in ASPC-1 (invasive) (Figure S3H, I) and SW1990 (metastatic) (Figure S3N, O). Moreover, VIVIT transfection leads to a decrease in Orai3 protein levels in BXPC-3 (non-metastatic) (Figure S3E, F) and an increase in Orai3 protein levels in ASPC-1 (invasive) (Figure S3K, L) and SW1990 (metastatic) (Figure S3Q, R). The findings in these cell lines recapitulates the data obtained earlier from MiaPaCa-2 (non-metastatic), PANC-1 (invasive) and CFPAC-1 (metastatic) cell lines. Therefore, this new data supports our conclusion regarding the dichotomous regulation of Orai3 by NFATc1 across the three conditions of pancreatic cancer.

Comment 5. Upon finding that NFAT inhibition stimulates Orai3 transcription (same as O/E), the authors essentially conclude that this confirms regulation of Orai3 by NFAT and that there must be compensation. This is not supported by any data; the use of siRNA validates that Orai3 has some dependence on NFATc1 for transcription, but the nature of this relationship is not adequately explained.

Response: We thank the Reviewer for asking this question. In our manuscript, we performed NFATc1 inhibition studies using VIVIT and siRNA-mediated NFATc1 knockdown. Both of these assays show increase in Orai3 mRNA levels in all non-metastatic, invasive and metastatic pancreatic cancer cell lines. To understand if the increase in Orai3 mRNA levels is via transcriptional regulation, we performed luciferase assay which showed that VIVIT mediated NFAT inhibition leads to increase in luciferase activity suggesting the binding of other transcription factors on the Orai3 promoter. To corroborate this hypothesis, in our revised manuscript, we performed luciferase assay in wild type Orai3 promoter and truncated Orai3 promoter with no NFATc1 binding sites. NFAT inhibition via VIVIT transfection led to an increase in luciferase activity in both wild type and truncated Orai3 promoter (Figure S2A). Hence, removal of NFATc1 binding sites had no significant effect on luciferase activity suggesting that apart from NFATc1, other endogenous transcription factors are involved in regulating Orai3 transcription. We have not identified all the transcription factors that can modulate Orai3 upon NFAT inhibition as it is beyond the scope of this study. We sincerely hope the Reviewer 1 would be satisfied with this additional data.

Reviewer #2

Comment 1. Figure 1 all overexpression no evidence of endogenous NFAT2 regulating Orai3. I realize there may be limitations on available NFAT isoform specific antibodies so it is not essential to directly show this but a comment to that effect in the paper would be useful.

Response: We apologize to the Reviewer for not highlighting the NFAT2 (NFATc1) loss of function data effectively. Actually, in the __Figure 3 __and __Supplementary Figure 2 __of the original manuscript, we showed VIVIT mediated NFAT inhibition and siRNA induced NFATc1 silencing data to provide the evidence that endogenous NFATc1 regulates Orai3.

Comment 2. Figure 1F. Show RNA levels of Orai3 following overexpression of the other NFAT isoforms.

Response: As suggested by the Reviewer, in the revised manuscript, we overexpressed the four NFAT isoforms: NFATc2, NFATc1, NFATc4 & NFATc3 and checked Orai3 mRNA levels. qRT-PCR analysis shows that overexpression of NFATc1 results in the highest and significant increase in Orai3 mRNA levels compared to the empty vector and other NFAT isoforms (Figure 1F). This data corroborates the western blot data of NFAT isoforms overexpression highlighting the transcriptional regulation of Orai3 by NFATc1.

Comment 3. Fig. S3D, E. For both MARCH3 and 8 higher expression levels correlate with better survival whereas in the text it is stated that this is the case only for MARCH8. Please correct.

Response: The survival analysis of pancreatic cancer patients with low MARCH3 and MARCH8 levels shows that around 30% of patients with low MARCH3 levels survived for 5.5 years, whereas in case of MARCH8 30% of patients with high MARCH8 levels survived for >7.5 years. Hence high MARCH8 expression in pancreatic cancer patients provided significant survival advantage compared to high MARCH3 levels. Therefore, in the text, we meant that compared to MARCH3, higher MARCH8 levels correlate with better survival. As suggested by the Reviewer, we have modified the text to make this point clearer.

Comment 4. For the 2APB stimulation experiments there is a large variation in the level of the response between experiments even for the same cell type. For example, compare the level of the 2APB-stimulated Orai3 influx between Fig. 4H and 5C on the MiaPaCa-2 cells. Also there doesn't seem to be a correlation between the levels of Orai3 protein from WB and the 2APB stimulated entry among the different cell lines. This needs to be addressed and differences explained.

Response: We understand the concern raised by Reviewer 2 regarding calcium imaging experiments in MiaPaCa-2 cell line. Therefore, in the revised manuscript, we repeated calcium imaging experiments in MiaPaCa-2 and updated the representative traces as well as quantitative analysis (Figure 2D, E, 3D, E, 4H, I, S2L, M). Further, we have discussed this point in the text of the manuscript.

Comment 6. Fig. 6C and 6D. Show the line in 6C from which the intensity profile in 6D was generated. Also give the details of the imaging setup in methods: size of the pinhole, imaging mode, etc. The colocalization is not very convincing.

Response: As recommended by the Reviewer, in the revised manuscript, we have indicated the region used for intensity profile generation by drawing a line in the representative image (Figure 6D). Further, we have updated the methodology of colocalization microscopy with details of the size of the pinhole and imaging mode.

Comment 8. May be worth showing that overexpression of MARCH8 in the metastatic cell lines decreases their migration and metastasis as the argument is that these cells need high Orai3 but not too high. So, it would be predicted that overexpression of MARCH8 should lower Orai3 levels enough to prevent their metastasis.

Response: We would like to thank the Reviewer for this highly relevant suggestion. In our revised manuscript, we carried out transwell migration assays with MARCH8 overexpression as well as MARCH8 knockdown in CFPAC-1 (metastatic) cells. Our data shows that stable lentiviral knockdown of MARCH8 increased the number of migrated CFPAC-1 cells compared to shNT CFPAC-1 cells while MARCH8 overexpression decreased the number of migrated CFPAC-1 cells compared to empty vector control cells (Figure 9F, G). Therefore, as pointed out by the Reviewer, MARCH8 overexpression lowers Orai3 levels in metastatic pancreatic cancer cells and hinders their metastatic potential.

Comment 9. Fig. 10. Show higher levels of Orai3 protein in the metastatic side.

Response: As suggested, we have updated the summary figure (Figure 10) showing higher Orai3 protein levels in the metastatic side.

Comment 10. Please show all full WBs in the supplementary data.

Response: As recommended by the Reviewer, we have provided all full western blots in a supplementary file (Supplementary File 1).

Reviewer #3

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Comment 1. The authors show that MARCH8 physically associates with Orai3 using Co-IP and Co-localization studies. For the co-localization studies the authors should still provide a quantitative analysis. Furthermore, can the authors detect FRET between March and Orai3? Can you please state the labels used in the co-localization experiments also in the figure legend.

Response: As suggested by Reviewer 3, in the revised manuscript, we have provided quantitative analysis of Orai3 and MARCH8 co-localization. Further, we have stated the labels used in the co-localization experiment in the figure legend of the revised manuscript. Unfortunately, we could not perform FRET assay between Orai3 and MARCH8 due to limited resources. Instead, as discussed in the planned revisions section, we are planning to perform co-immunoprecipitation assay with mutated Orai3 protein in which the MARCH8 interacting domains are deleted to investigate direct interaction of Orai3 and MARCH8. We believe that Reviewer 3 will be satisfied with this experiment.

Comment 2. In the abstract it is only getting clear at the end that pancreatic cancer cells are used. It would be great if the authors could introduce this fact already more at the beginning of the abstract.

Response: As recommended by the Reviewer, in the revised manuscript, we have introduced the use of pancreatic cancer cells at the beginning of the abstract.

Comment 4. In other cancer types recent reports suggest a co-expression of Orai1 and Orai3 and even the formation of heteromers. Does only Orai3 or also Orai1 play a role in pancreatic cancer cells? Could there we difference in degradation when Orai3 forms homomers or heteromers with Orai1.

Response: We thank the reviewer for asking this interesting question. There is only one report on Orai1’s role in pancreatic cancer. It was suggested that Orai1 can contribute to apoptotic resistance of pancreatic cancer cells (Kondratska et al. BBA-Molecular Cell Research, 2014). However, only one cell line i.e. PANC-1 was used in this study. While our earlier work and other studies have demonstrated that Orai3 drives pancreatic cancer metastasis (Arora et al. Cancers, 2021) and proliferation (Dubois et al. BBA-Molecular Cell Research, 2021) respectively. Therefore, emerging literature suggests that both Orai1 and Orai3 can contribute to different aspects of pancreatic cancer progression. But whether Orai1 and Orai3 form heteromers in pancreatic cancer cells remains unexplored. Further, we believe that the degradation machinery and the underlying molecular mechanisms would be analogous for both Orai3 homomers and heteromers. Nonetheless, the rate of degradation may differ for Orai3 homomers and heteromers as literature suggests that usually proteins are more stable in large heteromeric protein complexes.

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Referee #3

Evidence, reproducibility and clarity

The study by Raju et al. demonstrated that NFAT2 drives both Orai3 transcription and protein degradation. They find a clearly distinct mechanism between non-metastatic cancerous and metastatic cells. While in non-metastatic cells NFAT2 drives Orai3 transcritpion and increases Orai3 expression, in invasive and metastatic cells degradation of Orai3 is driven. They find a physical interaction of MARCH8 with Orai3 resulting in degradation. This degradation is not happening in non-metastatic cells as MARCH8 promotor is highly methylated. This study is highly interesting for a broad readerships and provides a solid basis for the development of novel therapeutic strategies for cancer treatment. Before publication the authors should address a few minor comments.

1. The authors show that MARCH8 physically associates with Orai3 using Co-IP and Co-localization studies. For the co-localization studies the authors should still provide a quantitative analysis. Furthermore, can the authors detect FRET between March and Orai3? Can you please state the labels used in the co-localization experiments also in the figure legend.
2. In the abstract it is only getting clear at the end that pancreatic cancer cells are used. It would be great if the authors could introduce this fact already more at the beginning of the abstract
3. In the scheme in Fig. 10, the authors highlight that Orai3 is ubiquitinated. Do they have any idea where the site of action of ubiquitination in Orai3 is located?
4. In other cancer types recent reports suggest a co-expression of Orai1 and Orai3 and even the formation of heteromers. Does only Orai3 or also Orai1 play a role in pancreatic cancer cells? Could there we difference in degradation when Orai3 forms homomers or heteromers with Orai1.

Significance

The authors highlight and decode a dual role of NFAT2 in controling Orai3 expression, which is highly interestingly to gain insight in different states of cancer cells (non-metastatic, metastatic). The findings form a great basis for a deeper understanding of potential therapeutic targets.

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Referee #2

Evidence, reproducibility and clarity

Raju et al. presents a nice comprehensive study of the differential regulation of Orai3 at the transcriptional and stability levels in metastatic versus non-metastatic pancreatic cancer (PC) cells. They convincingly show that NFAT2 regulates Orai3 transcription in all PC cells but interestingly, in the metastatic PC cells NFAT2 also upregulates the expression of MARCH8 an E3 ubiquitin ligase that targets Orai3 for lysosomal degradation. The MARCH8 locus is hypermethylated in the non-metastatic cell line, thus preventing MARCH8 upregulation in those cells. The data is convincing and complementary. I only a few suggestions below.

Specific Comments:

1. Figure 1 all overexpression no evidence of endogenous NFAT2 regulating Orai3. I realize there may be limitations on available NFAT isoform specific antibodies so it is not essential to directly show this but a comment to that effect in the paper would be useful.
2. Figure 1F. Show RNA levels of Orai3 following overexpression of the other NFAT isoforms.
3. Fig. S3D,E. For both MARCH3 and 8 higher expression levels correlate with better survival whereas in the text it is stated that this is the case only for MARCH8. Please correct.
4. For the 2APB stimulation experiments there is a large variation in the level of the response between experiments even for the same cell type. For example compare the level of the 2APB-stimulated Orai3 influx between Fig. 4H and 5C on the MiaPaCa-2 cells. Also there doesn't seem to be a correlation between the levels of Orai3 protein from WB and the 2APB stimulated entry among the different cells lines. This needs to be addressed and differences explained.
5. Fig. 6A and 6B. Show the full Orai3 and Ubiquitin WBs. As presented the figure current just shows that there are ubiquitin proteins in Orai3 pull down, not that Orai3 is ubiquitinated.
6. Fig. 6C and 6D. Show the line in 6C from which the intensity profile in 6D was generated. Also give the details of the imaging setup in methods: size of the pinhole, imaging mode, etc. The colocalization is not very convincing.
7. Also all the imaging and pull down down do not prove conclusively direct interaction between MARCH8 and Orai3, they rather show that the proteins are in the same complex. Although it is unlikely best for the text to be moderated accordingly.
8. May be worth showing that overexpression of MARCH8 in the metastatic cell lines decreases their migration and metastasis as the argument is that these cells need high Orai3 but not too high. So it would be predicted that overexpression of MARCH8 should lower Orai3 levels enough to prevent their metastasis.
9. Fig. 10. Show higher levels of Orai3 protein in the metastatic side.
10. Please show all full WBs in the supplementary data.

Significance

SIgnificant and relevant study that will be of great interest to the cancer and calcium signaling fields.

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Referee #1

Evidence, reproducibility and clarity

The manuscript entitled, "NFAT2 drives both Orai3 transcription and protein degradation by harnessing the differences in epigenetic landscape of MARCH8 E3 ligase" offers an extensive study of how Orai3 levels are controlled during pancreatic cancer progression. The central hypothesis is that NFAT2 stimulates both Orai3 and MARCH8 transcription, resulting in both Orai3 transcription and degradation. They further establish that MARCH8 expression/Orai3 degradation is epigenetically regulated in PDAC, with a progressive loss of methylation during cancer progression leading to increased Orai3 transcription, stability and Ca2+ entry.

Overall, I'm certain that there is new information to be learned here. However, as detailed below, the manuscript makes a number of general claims about what happens during PDAC progression, but this is based on only one cell line per disease state. While they should not be expected to do a complete analysis in more cell lines, a demonstration that Orai3 and MARCH8 expression are correlated with disease progression in a panel of cell lines and/or on the TCGA database would increase enthusiasm considerably. In addition, although I found the work with MARCH8 to be highly convincing, the fact that NFAT2 knockdown increased rather than reduced Orai3 transcription does not support the central hypothesis. The explanation that this results from compensation is not very meaningful; that NFAT2 drives Orai3 transcription is in the title of the paper. These observations clearly demonstrate that this relationship is more complicated than suggested. Finally, there are a number of missing controls and unclear aspects to the authors' ChIP data that could help explain some of these discrepancies.

Specific Comments:

1. The observation that both transcriptional regulation and protein degradation of Orai3 is regulated downstream of one transcription factor is not, in and of itself, entirely surprising. All proteolytic components are transcriptionally regulated and this phenomenon is likely relatively common. However, what I do think is both impressive and important is that the authors have characterized both components of the pathway within a disease context. While I am not going to search the literature for how often transcription and proteolysis are co-regulated for other proteins, it is the case for many short-lived proteins and perhaps many others. As such, discussion throughout the abstract and introduction that co-regulation of these processes is unprecedented should be removed.
2. In discussing figure 1, the authors switch from claiming to be studying NFATc binding to studying NFAT expression. This use of 2 different naming conventions is certain to confuse readers; the authors should use the approved current naming system in referring to NFAT isoforms. In which case NFAT2 is NFATc1.
3. The ChIP analyses in figures 1H and 7D are important findings, however, there is missing information. Typically, ChIP is used to validate putative binding sites; as such, one would expect 3 separate qPCR reactions for Orai3, not one. It is also important to note that qPCR products should be uniform in size and under 100 bp; here, the product size is not stated. Finally, demonstrating that an antibody targeting ANY other NFAT isoform fails to pull down whatever product this is would increase confidence considerably.

Also, the gold standard for validating ChIP is to mutate the sites and eliminate binding. The "silver" standard would be to mutate them in your luciferase vector and demonstrate that NFATc1 no longer stimulates luciferase expression. Since neither of these was done, the ChIP data provided should not be considered formally validated. 4. In figures 2 and 3, only one cell line is used to represent each of 3 conditions of pancreatic cancer. That is insufficient to make generalized conclusions; some aspects of this figure (expression and stability, not function) should be extended to 2 to 3 cell lines/condition. TCGA data validating this point would also be helpful. 5. Upon finding that NFAT inhibition stimulates Orai3 transcription (same as O/E), the authors essentially conclude that this confirms regulation of Orai3 by NFAT and that there must be compensation. This is not supported by any data; the use of siRNA validates that Orai3 has some dependence on NFATc1 for transcription, but the nature of this relationship is not adequately explained. 6. In Figure 6A,B, does the Orai3 western blot show any of the heavier bands seen in the ubiquitinization IP if you show the whole blot? It should.

Significance

My expertise is in calcium signaling, particularly within the context of disease states. I currently have a PDAC study in its late stages, but I have worked more with melanoma.

Issues about significance were raised in my comments above; generalization of these observations requires the appropriate use of a panel of cell lines and/or TCGA usage. In addition, some observations require additional investigation for confidence; necessary to achieve significance.

The extent of the advance is quite reasonable for a high profile paper in this field, should the issues I and the other reviewers raise be formally and thoroughly addressed.

Given that the study crosses lines between signaling, cancer, epigenetics, transcription and ubiquitination, I think that it is of potential interest to a general audience.

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Reply to the reviewers

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

__* SUMMARY

This study utilizes the developing chicken neural tube to assess the regulation of the balance between proliferative and neurogenic divisions in the vertebrate CNS. Using single-cell RNAseq and endogenous protein tagging, the authors identify Cdkn1c as a potential regulator of the transition towards neurogenic divisions. Cdkn1c knockdown and overexpression experiments suggest that low Cdkn1c expression enhances neurogenic divisions. Using a combination of clonal analysis and sequential knockdown, the authors find that Cdkn1c lengthens the G1 phase of the cell cycle via inhibition of cyclinD1. This study represents a significant advance in understanding how cells can transition between proliferative and asymmetric modes of division, the complex and varying roles of cycle regulators, and provides technical advance through innovative combination of existing tools.

MAJOR AND MINOR COMMENTS *__

Overall Sample numbers are missing or unclear throughout for all imaging experiments. The authors should add numbers of cells analysed and/or numbers of embryos for their results to be appropriately convincing.

This information is now provided in the figure legends (numbers of cells analyzed and/or numbers of embryos) except for data in Figure 5, which are presented in a new Supplementary Table

Values and error bars on graphs must be defined throughout. Are the values means and error bars SD or SEM?

We have used SD throughout the study. This information has now been added in figure legends.

Results 2

____A reference should be provided for cell type distribution in spinal neural tube, where the authors state that cell bodies of progenitors reside within the ventricular zone.

We now cite a recent review on spinal cord development (Saade and E. Marti, Nature Reviews Neuroscience, 2025) to illustrate this point

The authors state that Cdkn1c "was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside, and markedly increased in a domain immediately adjacent to this zone which is enriched in nascent neurons on their way to the mantle zone. In contrast, the transcript was completely excluded from the mantle zone, where HuC/D positive mature neurons accumulate." It is not clear if this is referring only to E4 or also to E3 embryos. Indeed, Cdkn1c expression appears to be much more salt and pepper at E3 and only resolves into a clear domain of high expression adjacent to the mantle zone at E4. It may be helpful if this expression pattern could be described in a bit more detail highlighting the changes that occur between E3 and E4.

We have now reformulated this paragraph as follows: "At E3, the transcript was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside (Saade and Marti, 2025)). One day later, at E4, this salt and pepper expression was still detected in the ventricular zone, while it markedly increased in the region of the mantle zone that is immediately adjacent to the ventricular zone. This region is enriched in nascent neurons on their way to differentiation that are still HuC/D negative. In contrast, the transcript was completely excluded from the more basal region of the mantle zone, where mature HuC/D positive neurons accumulate.

It would be useful to annotate the ISH images in Fig 2A to show the ventricular and mantle zones as defined by immunofluorescence.

Thank you for the suggestion. We have now added a dotted line that separates the ventricular zone from the mantle zone at E3 and E4 in Figure 2A

Reference should be included for pRb expression dynamics.

This section has been rewritten in response to comments from Reviewer #3, and now contains several references regarding pRb expression dynamics. See detailed response to Reviewer #3 for the new version

Could the Myc tag insertion approach disrupt protein function or turnover? ____Why was the insertion target site at the C terminus chosen?

The first reason was practical: at the time when we decided to generate a KI in Cdkn1c, we had already generated several successful KIs at C-termini of other genes, in particular using the P2A-Gal4 approach (see Petit-Vargas et al, 2024), and had not yet experimented with N-terminal Gal4-P2A. We therefore decided to use the same approach for Cdkn1c.

We also chose to target the C-terminus to avoid affecting the active CKI domain which is located at the N-terminus.

Nevertheless, the C-terminal targeting may have an impact on the turnover: it has been described that CDK2 phosphorylation of a Threonin close to the C-terminus of Cdkn1c leads to its targeting for degradation by the proteasome from late G1 (Kamura et al, PNAS, 2003; doi: 10.1073/pnas.1831009100). We can therefore not rule out that the addition of the Myc tags close to this phosphorylation site modulates the dynamics of Cdkn1c degradation. We note, however, that we observed little overlap between the Cdkn1c-Myc and pRb signals in cycling progenitors, suggesting that Cdkn1c is effectively degraded from late G1.

OPTIONAL Could a similar approach be used to tag Cdkn1c with a fluorescent protein to enable live imaging of dynamics?

Although it could be done, we have not attempted to do this for CDKN1c because our current experience of endogenous tagging of several genes with a similar expression level (based on our scRNAseq data) and nuclear localization (Hes5, Pax7) with a fluorescent reporter shows that the fluorescent signal is extremely low or undetectable in live conditions; Therefore we favored the multi-Myc tagging approach, and indeed we find that the Myc signal in progenitors is also very low even though it is amplified by the immunohistology method; this suggests that most likely, the only signal that would be detected -if any- with a fluorescent approach would be the peak of expression in newborn neurons.

In suppl Fig 1C nlsGFP-positive cells are shown in the control shRNA condition. How can this be explained and does it impact the interpretation of the findings?

The reviewer refers to the control gRNA condition in panel C, that shows that two small patches of GFP-positive cells are visible in the whole spinal cord of this particular embryo.

Technically, the origin of these "background" cells could be multiple. A spontaneous legitimate insertion at the CDKN1c locus by homologous recombination is possible, although we tend to think it is unlikely, given the extremely short length of the arms of homology; illegitimate insertions of the Myc-P2A-Gal4 cassette at off-target sites of the control gRNA is a possibility. Alternatively, a low-level leakage of Gal4 expression from the donor vector could lead to a detectable nls-GFP expression in a few cells via Gal4-UAS amplification.

In any case, these cells are observed at a very low frequency (1 or 2 patches of cells/embryo) relative to the signal obtained in presence of the CDKN1c gRNA#1 (probably several thousand positive cells per embryo). This suggests that if similar "background" cells are also present in presence of the CDKN1c gRNA, they would not significantly contribute to the signal, and would not impact the interpretation.

In Fig 2B, there are a number of Myc labelled cells in the mantle zone, whereas the in situ images show no appreciable transcript expression. Is this because the protein but not the transcript is present in these cells? Could the authors comment on this?

It is indeed possible that the CDKN1c protein is more stable than the transcript in newborn neurons and remains detectable in the mantle zone after the mRNA disappears. In Gui et al, 2006, where they use an anti-CDKN1c antibody to label the protein in mouse spinal cord transverse sections at E11.5 (Figure 1B), a few positive cells are also visible basally. They could correspond to neurons that have not yet degraded CDKN1c, although it is unclear in the picture whether these cells are really in the mantle zone or in the adjacent dorsal root ganglion; we note that a similar differential expression dynamics between mRNA and protein has been described for Tis21/Btg2 in the developing mouse cortex, where the protein, but not the mRNA, is detected in some differentiated bIII-tubulin-positive neurons (Iacopetti et al, 1999).

However, related to our response above to a previous comment from the same reviewer, we cannot rule out the possibility that the Myc tags modulate the turnover of CDKN1c protein and slow down the dynamics of its degradation in differentiating neurons.

We have added a sentence to indicate the presence of these cells: "In addition, a few Myc-positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript."

Results

It should be mentioned how mRNA expression levels were quantified in the shRNA validation experiment (supp Fig 2A).

We did not quantify the level of mRNA reduction, it was just evaluated by eye. The reason for choosing shRNA1 for the whole study was dictated by 1) the fact that we more consistently saw (by eye) a reduction in the signal on the electroporated side with this construct than with the other shRNAs, and 2) that the effect on neurogenesis was also more consistent.

We will perform additional experiments to provide some quantitation of the shRNA effect, as this is also requested by Reviewer #3.

As our Cdkn1c KI approach offers a direct read-out of the protein levels in the ventricular and mantle zones, and since our shRNA strategy of "partial knock-down" is based on the idea that the shRNA effect should be more complete in progenitors expressing Cdkn1c at low levels than in newborn progenitors that express the protein at a higher level, we propose to validate the shRNA in the Cdkn1c-Myc knock-in background, by comparing the Myc signal intensity between control and Cdkn1c shRNA conditions

Figure panels are not currently cited in order. Citation or figure order could be changed.

We have now added a common citation of the panels referring to analyses at 24 and 48 hours after electroporation (now Figure 3A-F), allowing us to display the experimental data on the figure according to the timing post electroporation, while the text details the phenotype at the later time point first.

The authors should provide representative images for the graphs shown in Fig 3A and 3B. These could go into supplementary if the authors prefer.

We have added images in a revised version of the Figure 3, as requested

A supplementary figure showing the Caspase3 experiment should be added.

We have added data showing Caspase3 experiments in Supplementary Figure 3D

OPTIONAL. Identification of sister cells in the clonal analysis experiments is based on static images and cannot be guaranteed. Could live imaging be used to watch divisions followed by fixation and immunostaining to confirm identity?

We agree with the reviewer that direct tracking is the most direct method for the identification of pairs of sister cells. However, it remains technically challenging, and the added value compared to the retrospective identification would be limited, while requiring a great workload, especially considering the many different experimental conditions that we have explored in this study.

Results 4

How did the authors quantify the intensity of endogenous Myc-tagged Cdkn1c to confirm the validity of the Pax7 locus knock in? Can they show that the expression level was consistently lower than the endogenous expression in neurons? Quantification and sample numbers should be shown.

We have not done these quantifications in the original version of the study. We will add a quantification of the signal intensity in the ventricular and mantle zones for the revised version of the manuscript, as also requested by reviewer #3.

In Fig 4B, the brightness of row 2 column 1 is lower than the same image in row 2 column 2, which is slightly misleading, since it makes the misexpressed expression level look lower than it is compared with endogenous in column 3. Is this because only a single z-section is being displayed in the zoomed in image? If so, this should be stated in the figure legend.

All images in the figure are single Z confocal images. Images in Column 2 (showing both electroporated sides of the same tube) were acquired with a 20x objective, whereas the insets shown in Columns 1 and 3 are 100x confocal images. 100x images on both sides were acquired with the same acquisition parameters, and the display parameters are the same for both images in the figure. The signal intensity can therefore be compared directly between columns 1 and 3.

We have modified the legend of the Figure to indicate these points: "The insets shown in Columns 1 and 3 are 100x confocal images acquired in the same section and are presented with the same display parameters".

In Fig 4D, the increase in neurogenic divisions is mainly because of the rise in terminal NN divisions according to the graph, but no clear increase in PN divisions. Could the authors comment on the significance of this?

Our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

We have modified the manuscript to elaborate on our interpretation of this result: "We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3H)."

Results 5 ____The proportion of pRb-positive progenitors having entered S phase was stated to be higher at all time points; however, it is not significantly higher until 6h30 and is actually trending lower at 2h30.

Thank you for pointing this out. We have modified the sentence in the main text.

"We found that the proportion of pRb positive progenitors having entered S phase (EdU positive cells) was significantly higher at all time points examined more than 4h30 after FT injection in the Cdkn1c knock-down condition compared to the control population (Figure 5D)"

OPTIONAL Could CyclinD1 activity be directly assessed?

This is an interesting suggestion. For example, using the fluorescent CDK4/6 sensor developed by Yang et al (eLife, 2020; https://doi.org/10.7554/eLife.44571) in a CDKN1c shRNA condition would represent an elegant experimental alternative to complement our rescue experiments with the double CDKN1c/CyclinD1 shRNA. However, we fear that setting up and calibrating such a tool for in vivo usage in the chick embryo represents too much of a challenge for incorporation in this study.

General ____Scale bars missing fig s1c s4d.

Thanks for pointing this out. Scale bars have been added in the figures and corresponding legends

OPTIONAL Some of the main findings be replicated in another species, for example, mouse or human to examine whether the mechanism is conserved.

OPTIONAL Could use approaches other than image analysis be used to reinforce findings, for example biochemical methods, RNAseq or FACS?

We agree that it will be interesting and important that our findings are replicated in other species, experimental systems, and even tissues, or by alternative experimental approaches. Nevertheless, it is probably beyond the scope of this study.

A model cartoon to summarise outcomes would be useful.

We thank the reviewer for the suggestion. We will propose a summary cartoon for the revised version of the manuscript.

Unclear how cells were determined to be positive or negative for a label. Was this decided by eye? If so, how did the authors ensure that this was unbiased?

Positivity or negativity was decided by eye. However, for each experiment, we ensured that all images of perturbed conditions and the relevant controls were analyzed with the same display parameters and by the same experimenter to guarantee that the criteria to determine positivity or negativity were constant.

Reviewer #1 (Significance (Required)):

SIGNIFICANCE

Strengths: This manuscript investigates the mechanisms regulating the switch from symmetric proliferative divisions to neurogenic division during vertebrate neuronal differentiation. This is a question of fundamental importance, the answer to which has eluded us so far. As such, the findings presented here are of significant value to the neurogenesis community and will be of broad interest to those interested in cell divisions and asymmetric cell fate acquisition. Specific strengths include:

• Variety of approaches used to manipulate and observe individual cell behaviour within a physiological context.
• A limitation of using the chicken embryo is the lack of available antibodies for immunostaining. The authors take advantage of recent advances in chicken embryo CRISPR strategy to endogenously tag the target protein with Myc, to facilitate immunostaining.
• Innovative combination of genetic and labelling tools to target cells, for example, use of FlashTag and EdU in combination to more accurately assess G1 length than the more commonly used method.
• Premature misexpression demonstrates that the previously observed dynamics indeed regulate cell fate.
• Mechanistic insight by examining downstream target CyclinD1.
• Clearly presented with useful illustrations throughout.
• Logic is clear and examination thorough.
• Conclusions are warranted on the basis of their findings. ____Limitations ____T____his study primarily used visual analysis of fixed tissue images to assess the main outcomes. To reinforce the conclusions, these could be supplemented with live imaging to appreciate dynamics, or biochemical techniques to look at protein expression levels.

Some aspects of quantification require explanation in order for the experiments to be replicated.

It is imperative that precise sample sizes are included for all experiments presented.

Advance: ____First functional demonstration role for Cdkn1c in regulating neurogenic transition in progenitors.

Conceptual advance suggesting Cdkn1c has dual roles in driving neurogenesis: promoting neurogenic divisions of progenitors and the established role of mediating cell cycle exit previously reported.

Technical advances in the form of G1 signposting and endogenous Myc tagging using CRISPR in chicken embryonic tissue.

Audience:

Of broad interest to developmental biologists. Could be relevant to cancer, since Cdkn1c is implicated.

Please define your field of expertise with a few keywords to help the authors contextualize your point

Developmental biology, vertebrate embryonic development, neuronal differentiation, imaging. Please note that we have not commented on RNAseq experiments as these are outside of our area of expertise.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The work by Mida and colleagues addresses important questions about neurogenesis in the embryo, using the chicken neural tube as their model system. The authors investigate the mechanisms involved in the transition from stem cell self-renewal to neurogenic progenitor divisions, using a combination of single cell, gene functional and tracing studies.

The authors generated a new single cell data set from the embryonic chicken spinal cord and identify a transitory cell population undergoing neuronal differentiation, which expresses Tis21, Neurog2 and Cdkn1c amongst other genes. They then study the role of Cdkn1c and investigate the hypothesis that it plays a dual role in spinal cord neurogenesis: low levels favour transition from proliferative to neurogenic divisions and high levels drive cell cycle exit and neuronal differentiation.

Major comments

I have only a general comment related to the main point of the paper. The authors claim that Cdkn1c onset in cycling progenitor drives transition towards neurogenic modes of division, which is different from its role in cell cycle exit and differentiation. Figures 3F and 4D are key figures where the authors analysed PP, PN and NN mode of divisions via flash tag followed by analysis of sister cell fate. If their assumption is correct, shouldn't they also see, for example in Fig. 4D, an increase in PN or is this too transient to be observed or is it bypassed?

As already stated in our response to a similar question from reviewer #1, our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

At the moment, the calculations of PN and NN frequencies are merged in the text, so perhaps describing PN and NN numbers separately will help better understand the dynamics of this gradual process (especially since there is little to no difference in PN).

Regarding the results of Pax7 overexpression presented in figure 4D (now Figure 4E in the revised version), we had made the choice to merge PN and NN values in the main text to focus on the neurogenic transition from PP to PN/NN collectively. We agree with this reviewer, as well as with reviewer #1, that it should be more detailed and better discussed. We therefore propose to modify the paragraph as follows (and as already indicated above in the response to reviewer #1):

"We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that Cdkn1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of Cdkn1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal Cdkn1c shRNA approach (see Figure 3F, now 3H)."

Could the increase in NN be compatible also with a role in cell cycle exit and differentiation, for example from cells that have been targeted and are still undergoing the last division (hence marked by flash tag) or there won't be any GFP cells marked by flash tag a day after expression of high levels of Cdkn1c?

It is likely that a proportion of cells that would normally have done a NN division are pushed to a direct differentiation that bypasses their last division in the Pax7-CDKN1c condition, and that they contribute to the general increase in neuron production observed in our quantification 48hae (Figure 3F -previously 3C). However, these cases would not contribute to the increase in the NN quantification in pairs of sister cells 6 hours after division at 24hae (Figure 4E - previously 4D), because by design they would not incorporate FlashTag. The rise in NN is therefore the result of a PN to NN conversion.

Basically, what would the effect of expressing higher levels of Cdkn1c be? I guess this will really help them distinguish between transition to neurogenic division rather than neuronal differentiation. If not experimentally, any further comments on this would be appreciated.

These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification

« We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 6A)."

* * Minor comments

Fig 3C my understanding is that HuC/D should be nuclear, but in fig 3C it seems more cytoplasmic (any comment?)

Some studies suggest that HuC/D can, under certain conditions, be observed in the nucleus of neurons. However, HuC/D is a RNA binding protein whose localization is mainly expected to be cytoplasmic. In our experience (Tozer et al, 2017), and in other publications using the antibody in the chick spinal cord (see, for example, le Dreau et al, 2014), it is observed in the cell body of differentiated neurons, as in the current manuscript.

Fig Suppl 3E (and related 4B), immuno for Cdkn1c-Myc: to help the reader understand the difference between the immuno signals when looking at the figure, I would suggest writing on the panel i) Pax7-Cdkn1c-Myc and ii) endogenous Cdkn1c-Myc, rather than 'misexpressed' and 'endogenous', which is slightly confusing (especially because what it is called endogenous expression is higher).

This has now been modified in the figures.

Literature citing: Introduction and discussion are very nicely written, although they could benefit from some more recent literature on the topic. For example, Cdkn1c role as a gatekeeper of stem cell reserve in the stomach, gut, (Lee et al, CellStemCell 2022 PMID: 35523142) or some other work on symmetric/asymmetric divisions and clonal analysis in zebrafish (Hevia et al, CellRep 2022 PMID: 35675784, Alexandre et al, NatNeur PMID: 20453852), mammals (Royal et al, Elife 2023 37882444, Appiah et al, EMBO rep 2023 PMID: 37382163). Also, similar work has been performed in the developing pancreatic epithelium, where mild expression of Cdkn1a under Sox9rtTa control was used to lengthen G1 without overt cell cycle exit and this resulted in Neurog3 stabilization and priming for endocrine differentiation (Krentz et al, DevCell 2017 PMID: 28441528), so similar mechanisms might be in in place to gradually shift progenitor towards stable decision to differentiate. Moreover, in the discussion, alongside Neurog2 control of Cdkn1c, it could be mentioned that the feedback loop between Cdk inhibitors and neurogenic factor is usually established via Cdk inhibitor-mediated inhibition of proneural bHLHs phosphorylation by CDKs (Krentz et al, DevCell 2017 PMID: 28441528, Ali et al, 24821983, Azzarelli et al 2017 - PMID: 28457793; 2024 - PMID:39575884). Further, in the discussion, could they mention anything about the following open questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis? Or is there another Cdk inhibitor expressed at later developmental stages, hence linking this with specific cell fate decisions?

We will modify the introduction and discussion in several instances, in order to address the above suggestions and we will:

• add references to its role in other contexts and/or species.

• expand the discussion on the cross talk between neurogenic factors and CDK inhibitors in other cellular contexts.

• add a dedicated paragraph in the discussion to answer reviewer#2's questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis or is there another Cdk inhibitor expressed at later developmental stages?

Reviewer #2 (Significance (Required)):

The work here presented has important implications on neural development and its disorders. The authors used the most advanced technologies to perform gene functional studies, such as CRISPR-HDR insertion of Myc-tag to follow endogenous expression, or expression under endogenous Pax7 promoter, often followed by flash tag experiments to trace sister cell fate, and all of this in an in vivo system. They then tested cell cycle parameters, clonal behaviour and modes of cell division in a very accurate way. Overall data are convincing and beautifully presented. The limitation is potentially in the resolution between the events of switching to neurogenic division versus neuronal differentiation, which might just warrant further discussion. This work advances our knowledge on vertebrate neurogenesis, by investigating a key player in proliferation and differentiation.

____I believe this work will be of general interest to developmental and cellular biologists in different fields. Because it addresses fundamental questions about the coordination between cell cycle and differentiation and fate decision making, some basic concepts can be translated to other tissues and other species, thus increasing the potential interested audience.

My work focuses on stem cell fate decisions in mammalian systems, and I am familiar with the molecular underpinnings of the work here presented. However, I am not an expert in the chicken spinal cord as a model and yet the manuscript was interesting. I am also not sufficiently expert in the bioinformatic analysis, so cannot comment on the technical aspects of Figure 1 and the way they decided to annotate their data.

__*

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

Summary: In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely.

By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

Major comments:

1.-The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions.

However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified.

Throughout their comments on the manuscript, this reviewer raises several points regarding the characterization of pRb expression in our model and of our use of this marker in our study. We take these comments into account and propose to expand on pRb characteristics in the first occurrence of pRb as a marker of cycling cells in the manuscript. The modifications rely on:

• the quotation of several studies showing that phosphorylation of Rb is regulated during the cell cycle, and that "it is not detectable during a period of variable length in early G1 in several cell types (Moser et al, 2018;Spencer et al, 2013; Gookin et al, 2017), including neural progenitors in the developing chick spinal cord (Molina et al, 2022). Apart from this absence in early G1, pRb is detected throughout the rest of the cell cycle until mitosis".

• a more detailed description of our own characterization of pRb dynamics in a synchronous cohort of cycling cells, which reveals a similar heterogeneity in the timing of the onset of Rb phosphorylation after mitosis. This description was initially shown in supplementary figure 3 and will be transferred to a new supplementary figure 2 to account for the fact that it will now be cited earlier in the manuscript.

Regarding the specific question the "suitability (of pRb) as a neurogenic division marker": we do not directly "use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors", but we use Rb phosphorylation to discriminate between progenitors (pRb+) and neurons (pRb-) identity in pairs of sister cells to retrospectively identify the mode of division of their mother.

Given that Rb is unphosphorylated during a period of variable length after mitosis (see references above), pRb is not a reliable marker of ALL cycling progenitors. We developed an assay to identify the timepoint (the maximal length of this "pRb-negative" phase) after which Rb is phosphorylated in all cycling progenitors (new Supplementary Figure 2). This assay relies on a time course of pRb detection in cohorts of FlashTag-positive pairs of sister cells born at E3. This time course experiment allowed us to identify a plateau after which the proportion of pRb-positive cells in the cohort remains constant. From this timepoint, this proportion corresponds to the proportion of cycling cells in the cohort. Rb phosphorylation therefore becomes a discriminating factor between cycling progenitors (pRb+) and non-cycling neurons (pRb-).

We are confident that this provides a solid foundation for the determination of the identity of pairs of sister cells in all our Flash-Tag based assays, which retrospectively identify the mode of division of a progenitor on the basis of the phosphorylation status of its daughter cells 6 hours after division.

We propose to modify the main text to describe the strategy and protocol more explicitly, by introducing the sentence highlighted in yellow in the following paragraph where the paired-cell analysis is first introduced (in the section on CDKN1c knock-down):

"This approach allows to retrospectively deduce the mode of division used by the mother progenitor cell. We injected the cell permeant dye "FlashTag" (FT) at E3 to specifically label a cohort of progenitors that undergoes mitosis synchronously (Baek et al., 2018; Telley et al., 2016 and see Methods), and let them develop for 6 hours before analyzing the fate of their progeny using pRb immunoreactivity (Figure 3D). Our characterization of pRb immunoreactivity in the tissue had established beforehand that 6 hours after mitosis, all progenitors can reliably be detected with this marker (Supplementary Figure 2, Methods). Therefore, at this timepoint after FT injection, two-cell clones selected on the basis of FT incorporation can be categorized as PP, PN, or NN based on pRb positivity (P) or not (N) (see Methods, new Figure 3G and new Supplementary Figures 2 and 4)."

We also modified accordingly the legend to Supplementary Figure 2 (previously Supplementary Figure 3, which describes the identification of the plateau of pRb.

Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c.

In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components.

We agree with the reviewer that Rb phosphorylation may be a direct or indirect target of Cdkn1c activity, and exploring the molecular aspects of the cellular and developmental phenomena that we describe in our manuscript would represent an interesting follow up study.

____A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

To complement our analyses of the modes of division, we propose to use a positive marker to assess neural identity in parallel to the absence of pRb within pairs of cells. This approach may be the most meaningful in the gain of function context (Pax7 driven expression of Cdkn1c) because in this context, the time-point to reach the plateau of Rb phosphorylation used in our FT-based assay may indeed be delayed. On the opposite, in the context of loss of functions, the plateau may be reached earlier, which would have no effect on this assay.

2.-Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division.

This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.

The reviewer probably mistyped and meant 6-hour post injection, which is the duration that we use for paired cell tracking. We would like to emphasize that in addition to the FlashTag label, we benefit from the electroporation reporter to assess clonality. Altogether, we combine 5 criteria to define a clonal relationship :

• 2 cells are positive for Flash Tag
• The Flash Tag intensity is similar between the 2 cells
• The 2 cells are positive for the electroporation reporter
• The electroporation reporter intensity is similar between the two cells
• the position of the two cells is consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995;__; __Loulier et al, 2014): they are found on a shared line along the apico-basal axis, and share the same Dorso-Ventral and Antero-Posterior position . This combination is already described in the Methods section. We propose to modify the paragraph to include the sentence highlighted in yellow in the text below;

"Cell identity of transfected GFP positive cells was determined as follows: cells positive for pRb and FT were classified as progenitors and cells positive for FT and negative for pRb as neurons. In addition, a similar intensity of both the GFP and FT signals within pairs of cells, and a relative position of the two cells consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995; Loulier et al, 2014) were used as criteria to further ascertain sisterhood. This combination restricts the density of events fulfilling all these independent criteria, and can confidently be used to ensure a robust identification of pairs of sister cells."

3.- The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.

- "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation.

We have entirely remodeled this section, which describes the expression of Myc-tagged Cdkn1c relative to pRb and now provide several references that describe the generally accepted view that pRb is specific of cycling cells, regulated during the cell cycle, and in particular absent in early G1. We also remove the mention of the "Restriction point" in the main text to avoid any confusion on the timing of phosphorylation, as the notion of restriction point is not useful in our study. The section now reads as follows:

"To ascertain that Cdkn1c is translated in neural progenitors, we used an anti-pRb antibody, recognizing a phosphorylated form of the Retinoblastoma (Rb) protein that is specifically detected in cycling cells (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) , including neural progenitors of the developing chick spinal cord (Molina et al., 2022). In the ventricular zone of transverse sections at E4 (48hae), we detected triple Cdkn1c-Myc/GFP/pRb positive cells (arrowheads in Figure 2B), providing direct evidence for the Cdkn1c protein in cycling progenitors. We also observed many double GFP/pRb positive cells that were Myc negative (arrowheads in Figure 2B). The observation of UAS-driven GFP in these pRb-positive cells is evidence for the translation of Gal4 and therefore provides a complementary demonstration that the Cdkn1c *transcript is translated in progenitors. The absence of Myc detection in these double GFP/pRb positive cells also suggests that Cdkn1c/Cdkn1c-Myc stability is regulated during the cell cycle. *

Finally, we observed double Myc/GFP-positive cells that were pRb-negative (Figure 2B; asterisks). One characteristic of Rb phosphorylation as a marker of cycling cells is a period in early G1 during which it is not detectable, as described in several cell types (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) including chick spinal cord neural progenitors (Molina et al., 2022). Using a method that specifically labels a synchronous cohort of dividing cells in the neural tube, we similarly observed a period in early G1 during which pRb is not detectable in some progenitors at E3 (See Supplementary Figure 2 and Methods). Hence, the double Myc/GFP positive and pRb negative cells may correspond to progenitors in early G1. Alternatively, they may be nascent neurons whose cell body has not yet translocated basally (see Figure 2C). Finally, we observed a pool of GFP positive/pRb negative nuclei with a strong Myc signal in the region of the mantle zone that is in direct contact with the ventricular zone (VZ), corresponding to the region where the transcript is most strongly detected (see Figure 2A). This pool of cells with a high Cdkn1c expression likely corresponds to immature neurons exiting the cell cycle and on their way to differentiation (Figure 2B; double asterisks). In addition, a few Myc positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript.

In summary, our dual Myc and Gal4 knock-in strategy which reveals the history of Cdkn1c transcription and translation confirms that Cdkn1c is expressed at low level in a subset of progenitors in the chick spinal neural tube, as previously suggested (Gui et al., 2007; Mairet-Coello et al., 2012). In addition, the restricted overlap of Cdkn1c-Myc detection with Rb phosphorylation suggests that in progenitors, Cdkn1c is degraded during or after G1 completion. "

This section will again be remodeled in a future revised version of the manuscript, in which we will add quantifications of Myc levels, as requested by Reviewer 1 above, and also by Reviewer #3 below.

Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).

In the original version of the manuscript, the section describing the dynamics of CDKN1c-Myc in the KI experiments presented in Figure 2 relied on the idea that the dynamics of pRb in chick spinal progenitors is similar to what I described in other tissues and cell types, without providing any references to substantiate this fact. Actually, Molina et al provide a characterization of pRb in combination with their cell cycle reporter and conclude that pRb negative progenitors are in G1 ("We also verified that phospho-Rb- and HuC/D-negative cells were in G1 by using our FUCCI G1 and PCNA reporters"). We will now cite this reference to support our claim. In addition, our characterization of Rb progressive phosphorylation in the synchronic Flash-Tag cohort of newborn sister cells provides a complementary demonstration that a fraction of the progenitors are pRb-negative when they exit mitosis (i.e. in early G1). This analysis was initially only introduced in the supplementary Figure 3, as support for the section that presents the Paired-cell assay used in Figure 3. We propose to introduce the data from Supplementary Figure 3 earlier in the manuscript (now Supplementary Figure 2), in order to better introduce the reader with the dynamics of pRb in cycling cells in our model. This will better support our description of the Cdkn1c-Myc dynamics in relation with pRb. We therefore propose to reformulate this whole section as follows.

- It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.

- It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).

These are two interesting suggestions. To complement our data with guide #1, we have performed Myc-immunostaining experiments on transverse sections in the context of guide #3, showing exactly the same pattern of Myc signal, with low expression in the VZ, and a peak of signal in the part of the mantle zone that is immediately touching the VZ. This confirms the specificity of the spatial distribution of the Cdkn1c-Myc signal. These data have been added in a revised version of Supplementary Figure 1.

We will perform the suggested quantifications using guides #1 and #3, which both show a good KI efficiency. We do not think it is useful to do these experiments with guide #2, whose efficiency is much lower, and which would lead to a very sparse signal.

- The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).

We will perform these experiments to validate guide cutting efficiency using the Tide method (Brinkman et al, 2014)

- In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.

We will perform genomic PCR experiments to confirm in-frame insertion of the Myc tags at the Cdkn1c locus

4.- In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c.

Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression.

All the predicted isoforms in the chick genome contain the sequence targeted by shRNA1, which is located in the CKI domain, the region of the protein that is most conserved between species. Besides, all the isoforms annotated in the mouse and human genomes also contain the region targeted by shRNA1. We are therefore confident that shRNA1 should target all chick isoforms.

A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2).

This approach (qRT-PCR on sorted cells) would enable us to focus solely on electroporated cells, but it would result in an averaged quantification of Cdkn1c depletion. In order to obtain additional information on the shRNA-dependent decrease in Cdkn1C in the different neural cell populations (progenitor versus differentiating neuron), we propose an alternative approach consisting in monitoring the level of Cdkn1c protein, assessed through Cdkn1c-Myc signal in knock-in cells, in the presence versus absence of Cdkn1c shRNA.

- As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons.

We have addressed the case of pRb dynamics in progenitors above and added a reference documented pRb expression during the cell cycle of chick neural progenitors (Molina et al, 2022).

Regarding Sox2 persistence: we consistently detect a small fraction of double positive Sox2+/HuC/D+ cells in chick spinal cord transverse sections. We have shown that this marker of differentiating neurons (HuC/D) only becomes detectable more than 8 hours after mitosis in newborn neurons at E3 (Baek et al, 2018), indicating that Sox2 protein can persist for up to at least 8 hours in newborn neurons.

We now cite a paper showing that a similar persistence of Sox2 protein is reported in differentiating neurons of the human neocortex, where double Sox2/NeuN positive cells are frequently observed in cerebral organoids (Coquand et al, Nature Cell Biology 2024__)__

- The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption?

We do not think that these numbers are equal for both conditions, and we did not formulate this assumption. We only indicate (in the methods section) that this undefined/undetermined population (based on negativity for both markers) is a mix of two possible cell types. However, we do not offer any interpretation of the CDKN1c phenotypes based on the changes in this population. Indeed, our interpretation of the knock-down phenotype is solely based on the increase in pRb-positive and decrease in HuC/D-positive cells, which both suggest a delay in neurogenesis. We understand from the reviewer's comment that depicting an "undefined" population on the graph may cause some confusion. We therefore propose to present the data on pRb and HuC/D in different graphs, rather than on a combined plot, and to remove the reference to undefined cells in Figure 3, as well as in Figures 4 and 5 depicting the gain of function and double knock-down experiments. We have implemented these changes in updated versions of the figures.

- In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe?

We have now performed experiments with an anti phospho Histone 3 antibody, which labels mitotic cells, at 24 and 48 hours post electroporation. We do not see any ectopic mitoses upon Cdkn1c knock-down with this marker, and we have produced a Supplementary Figure with these data. This is consistent with the fact that we also do not see ectopic pRb or Sox2 positive cells in the mantle zone in the knock-down experiments. These data (pH3 and Sox2) have been added in the new Supplementary Figure 3E and F.

We have now modified the main text to include these data:

"In the context of a full knock-out of Cdkn1c in the mouse spinal cord, a reduction in neurogenesis was also observed, which was attributed to a failure of prospective neurons to exit the cell cycle, resulting in the observation of ectopic mitoses in the mantle zone (Gui et al, 2007). In contrast with this phenotype, using an anti phospho-Histone3 antibody, we did not observe any ectopic mitoses 24 or 48 hours after electroporation in our knock-down condition (Supplementary Figure 3E-F). This is consistent with the fact that we also do not observe ectopic cycling cells with pRb (Figure 3A and D) and Sox2 (Supplementary Figure 3E-F) antibodies. We therefore postulated that the reduced neurogenesis that we observe upon a partial Cdkn1c knock-down may result from a delayed transition of progenitors from the proliferative to neurogenic modes of division."

- The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye.

- Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells?

The key criterion for cells that are not directly touching each other is that their relative position corresponds to the classical "radial" organization of clones in this tissue (Leber and Sanes, 1995__; __Loulier et al, Neuron, 2014). In other words, we make sure that they are located on a same apico-basal axis, as is the case for the NN clone presented on the figure. As stated above in our response to major comment #2, we have modified the Methods section accordingly.

Can they provide further image examples of different types of clones?

We now provide additional examples in a new Supplementary Figure 4

- Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity?

The plateau for Rb phosphorylation in progenitors is reached before 6 hours post mitosis at E3. At the same age, we have previously shown (Baek et al, PLoS Biology 2018) in a similar time course experiment in pairs of FT+ cells that the HuC/D signal is not detected in newborn neurons 8 hours after mitosis. HuC/D only starts to appear between 8 and 12 hours, and still increases between 8 and 16 hours. The plateau would therefore be very delayed for HuC/D compared to pRb. This long delay in the appearance of this « positive » marker of neural differentiation is the main reason why we chose to use Rb phosphorylation status for the analysis of synchronous cohorts of pairs of sister cells, because pRb becomes a discriminating factor much earlier than HuC/D after mitosis.

- In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?

We have carried out similar experiments at E2, showing a plateau of 95% of pRb-positive cells in the FT-positive population (see graph on the right). This provides a retrospective estimate of the mode of division of the mother cells at this stage (roughly 90% of PP and 10% of PN) which is consistent with the vast majority of PP divisions described by Saade et al (2013, see Figure S1) at this stage.

5.- In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is:

- Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024.

We have now performed Pax7 immunostainings on transverse sections at 24 and 48 hours post electroporation, both with the Pax7-CDKN1c-Gal4 and with the Pax7-Gal4 control constructs. We present these data in the new supplementary figure 7. In both conditions, we find that the Pax7 protein is still present in KI-positive cells. We observe a modest increase in Pax7 signal intensity in these cells, suggesting either that the insertion of exogenous sequences stabilizes the Pax7 transcript, or that the C-terminal modification of Pax7 protein with the P2A tag increases its stability. This does not affect the interpretation of the CDKN1c overexpression phenotype, because we used the Pax7-Gal4 construct that shows the same modification of Pax7 stability as a control for this experiment. We have introduced this comment in the legend of Supplementary Figure 7.

- Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level?

Cdkn1c transcription is regulated by multiple transcription factors and non-coding RNAs (see for example Creff and Besson, 2020, or Rossi et al, 2018 for a review). To our knowledge, these studies focus more on the regulation of Cdkn1c global expression than on the regulation of its levels during cell cycle progression. Although it is very likely that transcriptional regulation contributes, post-translational regulation, and in particular degradation by the proteasome, is also a key factor in the cell cycle regulation of Cdkn1c activity

If so, how does this differ from the promoter activity of Pax7?

The transcriptional regulation of Pax7 and Cdkn1c is probably controlled by different regulators, since their expression profiles are very different. Regardless of the mechanisms that control their expression, the rationale for choosing Pax7 as a driver for Cdkn1c expression was that Pax7 expression precedes that of Cdkn1c in the progenitor population, and that it disappears in newborn neurons, when that of Cdkn1c peaks. This provided us with a way to advance the timing of Cdkn1c expression onset in proliferative progenitors.

- It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B.

We will carry out experiments similar to those shown in Figure 2B in order to characterise the dynamics of Cdkn1c in a context of overexpression, in relation to pRb.

In addition, we will include a more precise quantification of the "misexpressed" compared to "endogenous" Cdkn1c -Myc levels, as already mentioned in the answer to a request by reviewer1.

6.-In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons.

- In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845).

The Nowakowski non linear regression method has been used often in the literature in the same tissue, and is generally used to calculate fixed values for Tc, Ts, etc... This method is based on several selective criteria, and in particular the assumption that "all of the cells have the same cycle times". Yet, many studies have documented that cell cycle parameters change during the transition from proliferative to neurogenic modes of division during which our analysis is performed; live imaging data in the chick spinal cord have illustrated very different cell cycle durations at a given time point (see Molina et al). We therefore think that the proposed formulas do not reflect the heterogenous reality of neural progenitors of the embryonic spinal cord. However, the cumulative approach described by Nowakowski is useful to show qualitative differences between populations (e.g. a global decrease of the cycle length, like in our comparison between control and shRNA conditions). For these reasons, we prefer to display only the raw measurements rather than the regression curves.

- Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why?

Le Dreau et al count the EdU+ proportion of cells in the total population of electroporated cells located in the VZ (which includes progenitors, but also future neurons that have been labelled during the previous cycles -at least for the time points after 2hours- and have not yet translocated to the mantle zone), whereas we only consider pRb+ progenitors in the analysis. In addition, the experiments are not performed at the same developmental stage. Altogether, this may account for the different curves obtained in our study.

- It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

We will perform cumulative EDU incorporation experiments similar to that shown in Figure 5D to measure G1 length for the cdkn1c-sh - ccnd1-sh knock down double conditions, as well as in the Ccnd1 knock down condition alone.

Minor comments

__*Introduction:

• The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. ____Nature Communications 2020).*__

We will modify the introduction in several instances, in order to address suggestions by Reviewers #2 (see above) and #3, in particular to expand the description of the role of Cdkn1c during cortical development

1) Transcriptional signature of the neurogenic transition (Figure 1).

- In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).

We have now listed the genes used to determine the progenitor and neuron score in the main text of the result section

- Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).

We have now added the detail of what 'filtering' means in the diagram

- In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.

We have reworded this sentence, adding a reference to the expression profile of Tis 21. The paragraph now reads as follows:

« However, Cdkn1c expression is maintained longer and transiently peaks at high levels after Tis21 expression is switched off. Given that Tis21 is no more expressed in neurons (Iacopetti et al, 1999), this suggests that Cdkn1c expression is transiently upregulated in nascent neurons before fading off in more mature cells. »

- "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.

We have now added references linking the function and/or expression profile of these genes to the neurogenic transition: Dll1 (Henrique et al., 1995), the bHLH transcription factors Hes6 (Fior and Henrique, 2005), NeuroG1 and NeuroG2 (Lacomme et al., 2012; Sommer et al., 1996) and the coactivator Gadd45g (Kawaue et al., 2014).

- There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

- Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper.

We have corrected the colour code errors in Figure 1c and Supp Figure 3B (now changed to Supplementary Figure 5 in the modified revision)

____It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors.

We have so far refrained from performing the suggested combined analysis based on cell cycle and cell type scores, as the "neurogenic progenitor population" (based on neurogenic progenitor score values) in which Cdkn1c expression is initiated represents a small number of cells in our scRNAseq, and felt that the significance of such an analysis is uncertain. We will perform this analysis in the revised version

2) Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2).

- Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2).

The scale bar is actually valid for the whole panel A. The E2 section in the original figure appeared as "large" as the E3 section along the DV axis probably because the cutting angle was not perfectly transverse at E2, artificially lengthening the section. In a new version of the figure, we have replaced the E2 images with another section from the same experiment. The scale bar remains valid for the whole panel.

- Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A.

We have now added a diagram for the knock-in strategy in Figure 2B, and modified the legend of the figure accordingly.

- Indicate hours post-electroporation. Indicate which guide is used in the main text.

We have now added the post-electroporation timing and guide used in the main text.

3) Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3).

- In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed.

- Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region?

__We have modified this sentence as follows: "__Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal two thirds of the neural tube, except for the Pax7-Cdkn1c misexpression analysis, which was performed in the more dorsal Pax7 domain."

This is valid both for the whole population and clonal analyses

- Figure 3. Would have a better flow if 3C preceded 3A and 3B.

We have modified the Figure accordingly.

- Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification.

We have modified the Figure accordingly

- In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B.

This explanation corresponds indeed to Figure 5A. We have corrected this mistake in the new version of the manuscript.

4) Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).

- "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain.

We have added references to the expression profile of Pax7 in the dorsal neural tube (Jostes et al, 1990). In addition, the new Supplementary Figure 7 shows anti-Pax7 staining that confirm this expression pattern at E3 and E4

- "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells.

As stated in the response to Major Point 5 above, we will perform a quantification based on Myc immunofluorescence to compare endogenous Cdkn1c expression versus Cdkn1c expression upon overexpression.

- "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe.

We have added in the main text that the quantification was performed 48hae.

- Legend of figure 4D should indicate that the quantification has been done 24hpe.

We have added the timing of quantification in the legend of Figure 4D.

- "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment.

These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification:

« We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)."

- "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections.

This whole section has been modified in response to a question from reviewer 1. The new version does not contain percentages in the main text, and reads as follows:

« Using the FlashTag cohort labeling approach described above, we traced the fate of daughter cells born 24 hae. We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3F). Overall, these data show that inducing a premature low-level expression of Cdkn1c in cycling progenitors is sufficient to accelerate the transition towards neurogenic modes of division. »

- Figure sup 4C includes references to 3 gRNAs even when only one is used in the study.

The three guides listed in the original Supplementary Figure 4C correspond to the guides that we tested in Petit-Vargas et al. 2024. In this study, we only used the most efficient of these three guides. We have modified Figure 4C by quoting only this guide.

5) The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5)

- "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing

We have included references related to the activity of the CyclinD1/CDK4-6 complex in the developing CNS, and the antagonistic activities of CyclinD1 and Cdkn1c in this model

- "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation in the developing CNS (Lobjois et al, 2004, 2008, Lange 2009, Gui et al 2007), and is inhibited by Cdkn1c (Gui et al, 2007)."

- It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G.

We have added the experimental set-up information in Figure 5.

- Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

The analyses were carried out on two thirds of the neural tube (dorsal 2/3), excluding the ventral zone, as specified above (and in the Methods section)

- It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G.

- For Figure 4C and D, it would be valuable to add images to illustrate the quantification.

We have added images:

• in Supplementary Figure 7C to illustrate what is quantified in Figures 4C (now 4C and 4D);
• In Figure 5E to illustrate what is quantified in Figure 5D
• In Supplementary Figure 8B to illustrate what is quantified in Figure 5G (now Figure 5H and 5I) Regarding the requested images for Figures 4D and 5F, they correspond to the same types of images already shown in Figure 3E. Since we have now added several additional examples of representative pairs of each type of mode of division in the new Supplementary Figure 4, we do not think that adding more of these images in figures 4 and 5 would strengthen the result of the quantifications.

Discussion:

- "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).

We have now included the references suggested by the reviewer.

- "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

We propose to reformulate this paragraph in the discussion as follows to take this remark into account

"This allows us to re-interpret the role of Cdkn1c during spinal neurogenesis: while previously mostly considered as a binary regulator of cell cycle exit in newborn neurons, we demonstrate that Cdkn1c is also an intrinsic regulator of the transition from the proliferative to neurogenic status in cycling progenitors. This occurs through a change in their mode of division, and our double knock-down experiments suggest that the onset of Cdkn1c expression may promote this change by counteracting a CyclinD1/CDK6 complex dependent mechanism."

Other comments:

- To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.

We have modified the figures to systematically show the electroporated side of the neural tube on the same side of the image for single electroporations.

____- Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.

This information is now provided in the figure legends (numbers of cells analysed and/or numbers of embryos), except for data in Figure 5, which are presented in a new Supplementary Table 1.

All experiments were performed on vibratome sections, except for in situ hybridization experiments, which were performed on cryostat sections. This last information was already indicated in the relevant figure legends

- Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.

- There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.

We have now homogenized the nomenclature in the figures.

- "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

In the original version of the manuscript, the anti-Sox2 antibody was not used; we have now added experiments using this antibody in the modified version of the manuscript; this sentence in the Methods thus remains unchanged.

Reviewer #3 (Significance (Required)):

__*Significance:

In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. *__

The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

The strengths of the study include:

The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

- The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.

- The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

The aspects to improve:

- The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.

The sequencing dataset has been deposited in NCBI's Gene Expression Omnibus database. It is currently under embargo, but will be made available upon acceptance and publication of the peer reviewed manuscript. Access is nonetheless available to the reviewers via a token that can be retrieved from the Review Commons website.

The following information will be added in the final manuscript.

Data availability

Single cell RNA sequencing data have been deposited in NCBI's Gene Expression Omnibus (GEO) repository under the accession number GSE273710, and are available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273710."

- The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed. - Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

- Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.

- The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.

- The presentation of the existing literature could be more up to date.

- The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

Field of expertise of the reviewer: neural development, cell biology, embryology.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary:

In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely. By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

Majors comments:

1. The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions. However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified. Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
2. Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division. This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.
3. The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.
   • "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation. Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).
   • The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).
   • In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.
   • It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.
4. It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).
5. In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c. Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression. A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2). - As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons. - The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption? - In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe? - The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye. - Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells? Can they provide further image examples of different types of clones? - Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity? - In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?. 5. In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is: - Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024. - Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level? If so, how does this differ from the promoter activity of Pax7? - It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B. 6. In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons. - In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845). - Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why? - It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

Minor comments

Introduction:

• The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. Nature Communications 2020).

• Transcriptional signature of the neurogenic transition (Figure 1).

  • In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).
  • Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).
  • In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.
  • "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.
  • There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors. 2. Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2). - Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2). - Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A. - Indicate hours post-electroporation. Indicate which guide is used in the main text. 3. Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3). - In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed. - Figure 3. Would have a better flow if 3C preceded 3A and 3B. - Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification. - Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region? - Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper. - In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B. 4. Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).<br /> - "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain. - "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells. - For Figure 4C and D, it would be valuable to add images to illustrate the quantification. - "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe. - Legend of figure 4D should indicate that the quantification has been done 24hpe. - "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment. - "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections. - Figure sup 4C includes references to 3 gRNAs even when only one is used in the study. 5. The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5) - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing - It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G. - It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G. - Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

Discussion:

• "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).
• "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

Other comments:

• There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.
• To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.
• Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.
• Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.
• "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

Significance

In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

The strengths of the study include:

The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

• The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.
• The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

The aspects to improve:

• The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.
• The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed.
• Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
• Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.
• The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.
• The presentation of the existing literature could be more up to date.
• The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

Field of expertise of the reviewer: neural development, cell biology, embryology.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

The work by Mida and colleagues addresses important questions about neurogenesis in the embryo, using the chicken neural tube as their model system. The authors investigate the mechanisms involved in the transition from stem cell self-renewal to neurogenic progenitor divisions, using a combination of single cell, gene functional and tracing studies.

The authors generated a new single cell data set from the embryonic chicken spinal cord and identify a transitory cell population undergoing neuronal differentiation, which expresses Tis21, Neurog2 and Cdkn1c amongst other genes. They then study the role of Cdkn1c and investigate the hypothesis that it plays a dual role in spinal cord neurogenesis: low levels favour transition from proliferative to neurogenic divisions and high levels drive cell cycle exit and neuronal differentiation.

Major comments

I have only a general comment related to the main point of the paper. The authors claim that Cdkn1c onset in cycling progenitor drives transition towards neurogenic modes of division, which is different from its role in cell cycle exit and differentiation. Figures 3F and 4D are key figures where the authors analysed PP, PN and NN mode of divisions via flash tag followed by analysis of sister cell fate. If their assumption is correct, shouldn't they also see, for example in Fig. 4D, an increase in PN or is this too transient to be observed or is it bypassed? At the moment, the calculations of PN and NN frequencies are merged in the text, so perhaps describing PN and NN numbers separately will help better understand the dynamics of this gradual process (especially since there is little to no difference in PN). Could the increase in NN be compatible also with a role in cell cycle exit and differentiation, for example from cells that have been targeted and are still undergoing the last division (hence marked by flash tag) or there won't be any GFP cells marked by flash tag a day after expression of high levels of Cdkn1c? Basically, what would the effect of expressing higher levels of Cdkn1c be? I guess this will really help them distinguish between transition to neurogenic division rather than neuronal differentiation. If not experimentally, any further comments on this would be appreciated.

Minor comments

Fig 3C my understanding is that HuC/D should be nuclear, but in fig 3C it seems more cytoplasmic (any comment?)

Fig Suppl 3E (and related 4B), immuno for Cdkn1c-Myc: to help the reader understand the difference between the immuno signals when looking at the figure, I would suggest writing on the panel i) Pax7-Cdkn1c-Myc and ii) endogenous Cdkn1c-Myc, rather than 'misexpressed' and 'endogenous', which is slightly confusing (especially because what it is called endogenous expression is higher).

Literature citing: Introduction and discussion are very nicely written, although they could benefit from some more recent literature on the topic. For example, Cdkn1c role as a gatekeeper of stem cell reserve in the stomach, gut, (Lee et al, CellStemCell 2022 PMID: 35523142) or some other work on symmetric/asymmetric divisions and clonal analysis in zebrafish (Hevia et al, CellRep 2022 PMID: 35675784, Alexandre et al, NatNeur PMID: 20453852), mammals (Royal et al, Elife 2023 37882444, Appiah et al, EMBO rep 2023 PMID: 37382163). Also, similar work has been performed in the developing pancreatic epithelium, where mild expression of Cdkn1a under Sox9rtTa control was used to lengthen G1 without overt cell cycle exit and this resulted in Neurog3 stabilization and priming for endocrine differentiation (Krentz et al, DevCell 2017 PMID: 28441528), so similar mechanisms might be in in place to gradually shift progenitor towards stable decision to differentiate. Moreover, in the discussion, alongside Neurog2 control of Cdkn1c, it could be mentioned that the feedback loop between Cdk inhibitors and neurogenic factor is usually established via Cdk inhibitor-mediated inhibition of proneural bHLHs phosphorylation by CDKs (Krentz et al, DevCell 2017 PMID: 28441528, Ali et al, 24821983, Azzarelli et al 2017 - PMID: 28457793; 2024 - PMID:39575884). Further, in the discussion, could they mention anything about the following open questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis or is there another Cdk inhibitor expressed at later developmental stages, hence linking this with specific cell fate decisions?

Significance

The work here presented has important implications on neural development and its disorders. The authors used the most advanced technologies to perform gene functional studies, such as CRISPR-HDR insertion of Myc-tag to follow endogenous expression, or expression under endogenous Pax7 promoter, often followed by flash tag experiments to trace sister cell fate, and all of this in an in vivo system. They then tested cell cycle parameters, clonal behaviour and modes of cell division in a very accurate way. Overall data are convincing and beautifully presented. The limitation is potentially in the resolution between the events of switching to neurogenic division versus neuronal differentiation, which might just warrant further discussion. This work advances our knowledge on vertebrate neurogenesis, by investigating a key player in proliferation and differentiation.

I believe this work will be of general interest to developmental and cellular biologists in different fields. Because it addresses fundamental questions about the coordination between cell cycle and differentiation and fate decision making, some basic concepts can be translated to other tissues and other species, thus increasing the potential interested audience.

My work focuses on stem cell fate decisions in mammalian systems, and I am familiar with the molecular underpinnings of the work here presented. However, I am not an expert in the chicken spinal cord as a model and yet the manuscript was interesting. I am also not sufficiently expert in the bioinformatic analysis, so cannot comment on the technical aspects of Figure 1 and the way they decided to annotate their data.

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Referee #1

Evidence, reproducibility and clarity

Summary

This study utilizes the developing chicken neural tube to assess the regulation of the balance between proliferative and neurogenic divisions in the vertebrate CNS. Using single-cell RNAseq and endogenous protein tagging, the authors identify Cdkn1c as a potential regulator of the transition towards neurogenic divisions. Cdkn1c knockdown and overexpression experiments suggest that low Cdkn1c expression enhances neurogenic divisions. Using a combination of clonal analysis and sequential knockdown, the authors find that Cdkn1c lengthens the G1 phase of the cell cycle via inhibition of cyclinD1. This study represents a significant advance in understanding how cells can transition between proliferative and asymmetric modes of division, the complex and varying roles of cycle regulators, and provides technical advance through innovative combination of existing tools.

Major and Minor Comments:

Overall

• Sample numbers are missing or unclear throughout for all imaging experiments. The authors should add numbers of cells analysed and/or numbers of embryos for their results to be appropriately convincing.
• Values and error bars on graphs must be defined throughout. Are the values means and error bars SD or SEM?

Results 2

• A reference should be provided for cell type distribution in spinal neural tube, where the authors state that cell bodies of progenitors reside within the ventricular zone.
• The authors state that Cdkn1c "was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside, and markedly increased in a domain immediately adjacent to this zone which is enriched in nascent neurons on their way to the mantle zone. In contrast, the transcript was completely excluded from the mantle zone, where HuC/D positive mature neurons accumulate." It is not clear if this is referring only to E4 or also to E3 embryos. Indeed, Cdkn1c expression appears to be much more salt and pepper at E3 and only resolves into a clear domain of high expression adjacent to the mantle zone at E4. It may be helpful if this expression pattern could be described in a bit more detail highlighting the changes that occur between E3 and E4.
• It would be useful to annotate the ISH images in Fig 2A to show the ventricular and mantle zones as defined by immunofluorescence.
• Reference should be included for pRb expression dynamics.
• Could the Myc tag insertion approach disrupt protein function or turnover?
• Why was the insertion target site at the C terminus chosen?
• OPTIONAL Could a similar approach be used to tag Cdkn1c with a fluorescent protein to enable live imaging of dynamics?
• In suppl Fig 1C nlsGFP-positive cells are shown in the control shRNA condition. How can this be explained and does it impact the interpretation of the findings?
• In Fig 2B, there are a number of Myc labelled cells in the mantle zone, whereas the in situ images show no appreciable transcript expression. Is this because the protein but not the transcript is present in these cells? Could the authors comment on this?

Results 3

• It should be mentioned how mRNA expression levels were quantified in the shRNA validation experiment (supp Fig 2A).
• Figure panels are not currently cited in order. Citation or figure order could be changed.
• The authors should provide representative images for the graphs shown in Fig 3A and 3B. These could go into supplementary if the authors prefer.
• A supplementary figure showing the Caspase3 experiment should be added.
• OPTIONAL. Identification of sister cells in the clonal analysis experiments is based on static images and cannot be guaranteed. Could live imaging be used to watch divisions followed by fixation and immunostaining to confirm identity?

Results 4

• How did the authors quantify the intensity of endogenous Myc-tagged Cdkn1c to confirm the validity of the Pax7 locus knock in? Can they show that the expression level was consistently lower than the endogenous expression in neurons? Quantification and sample numbers should be shown.
• In Fig 4B, the brightness of row 2 column 1 is lower than the same image in row 2 column 2, which is slightly misleading, since it makes the misexpressed expression level look lower than it is compared with endogenous in column 3. Is this because only a single z-section is being displayed in the zoomed in image? If so, this should be stated in the figure legend.
• In Fig 4D, the increase in neurogenic divisions is mainly because of the rise in terminal NN divisions according to the graph, but no clear increase in PN divisions. Could the authors comment on the significance of this?

Results 5

• The proportion of pRb-positive progenitors having entered S phase was stated to be higher at all time points; however, it is not significantly higher until 6h30 and is actually trending lower at 2h30.
• OPTIONAL Could CyclinD1 activity be directly assessed?

General

• Scale bars missing fig s1c s4d.
• OPTIONAL Some of the main findings be replicated in another species, for example, mouse or human to examine whether the mechanism is conserved.
• OPTIONAL Could use approaches other than image analysis be used to reinforce findings, for example biochemical methods, RNAseq or FACS?
• A model cartoon to summarise outcomes would be useful.
• Unclear how cells were determined to be positive or negative for a label. Was this decided by eye? If so, how did the authors ensure that this was unbiased?

Significance

Strengths:

This manuscript investigates the mechanisms regulating the switch from symmetric proliferative divisions to neurogenic division during vertebrate neuronal differentiation. This is a question of fundamental importance, the answer to which has eluded us so far. As such, the findings presented here are of significant value to the neurogenesis community and will be of broad interest to those interested in cell divisions and asymmetric cell fate acquisition. Specific strengths include:

• Variety of approaches used to manipulate and observe individual cell behaviour within a physiological context.
• A limitation of using the chicken embryo is the lack of available antibodies for immunostaining. The authors take advantage of recent advances in chicken embryo CRISPR strategy to endogenously tag the target protein with Myc, to facilitate immunostaining.
• Innovative combination of genetic and labelling tools to target cells, for example, use of FlashTag and EdU in combination to more accurately assess G1 length than the more commonly used method.
• Premature misexpression demonstrates that the previously observed dynamics indeed regulate cell fate.
• Mechanistic insight by examining downstream target CyclinD1.
• Clearly presented with useful illustrations throughout.
• Logic is clear and examination thorough.
• Conclusions are warranted on the basis of their findings.

Limitations

• This study primarily used visual analysis of fixed tissue images to assess the main outcomes. To reinforce the conclusions, these could be supplemented with live imaging to appreciate dynamics, or biochemical techniques to look at protein expression levels.
• Some aspects of quantification require explanation in order for the experiments to be replicated.
• It is imperative that precise sample sizes are included for all experiments presented.

Advance:

• First functional demonstration role for Cdkn1c in regulating neurogenic transition in progenitors.
• Conceptual advance suggesting Cdkn1c has dual roles in driving neurogenesis: promoting neurogenic divisions of progenitors and the established role of mediating cell cycle exit previously reported.
• Technical advances in the form of G1 signposting and endogenous Myc tagging using CRISPR in chicken embryonic tissue.

Audience:

Of broad interest to developmental biologists. Could be relevant to cancer, since Cdkn1c is implicated.

 Please define your field of expertise with a few keywords to help the authors contextualize your point Developmental biology, vertebrate embryonic development, neuronal differentiation, imaging. Please note that we have not commented on RNAseq experiments as these are outside of our area of expertise.

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Reply to the reviewers

Manuscript number: RC-2025-02860

Corresponding author(s): Duncan, Sproul

[The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

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1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We thank the reviewers for recognizing that our work contributes 'both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development' and their insightful suggestions to improve the manuscript. We note that the reviewers suggest that the data are 'comprehensive', 'well-controlled', 'rigorously done' and 'diligently analysed'.

Our planned revisions focus on further elucidating the broader implications of our findings for partially methylated domain formation in cancer, the effects of the methylation changes we observe on gene expression and the potential mechanisms underpinning the formation of the hypermethylated domains we observe.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

We have reproduced the reviewer's comments in their entirety and highlighted them in blue italics.

February 21, 2025
*RE: Review Commons Refereed Preprint #RC-2025-02860 *

*Kafetzopoulos *

DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A

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

The DNA methylation landscape is frequently altered in cancers, which may contribute to genome misregulation and cancer cell behavior. One phenomenon is the emergence of "partially methylated domains (PMDs)": intermediately methylated regions of the genome that are generally heterochromatic and late replicating. The prevailing explanation is that the DNA methyltransferase, DNMT1, is not able to maintain DNAme levels at late replicating sites in proliferating cancer cells. This could result in genome instability. In this study, Kafetzopoulos and colleagues interrogated this possibility using a common laboratory colorectal cancer cell line (HCT116). Additionally, they utilized a DNMT1 mutant line that they refer to as a knockout, even though, more accurately, it is a hypomorphic truncation. They performed several genomic assays, such as whole genome bisulfite sequencing, ChIP and repli-seq, in order to assess the effect of reduced DNMT1 activity. While expectedly, global DNAme levels are decreased, they discovered a subset of PMDs gain DNA methylation, which they term hyperPMDs. There seems to be no impact on DNA replication timing, but the authors did go on to show that the de novo DNA methyltransferase, DNMT3Α, is likely responsible for this counterintuitive increase in DNAme levels.

*Reviewer #1 (Significance (Required)): *

Overall, I found the data well-presented and diligently analyzed, as we have come to expect from the Sproul group. However, I am somewhat at a loss to understand both the rationale for the experimental set-up and the meaning of the results. The HCT116 cell line is already transformed but was treated as though it was a wild-type control. I was more curious to see how the PMD chromatin state and replication compare to a healthy cell.

We focused on the comparison between WT and DNMT1 KO cells as we wanted to understand the role of DNMT1 in maintaining the organisation of the cancer methylome. We agree that, strictly, this could differ from its role in normal cells. However, we are unaware of a suitable cell line to test the consequences of DNMT1 KO in normal colon cells and testing this in vivo would be beyond the time-scale of a manuscript revision.

To further understand the relevance of our findings in the context of carcinogenesis, we propose to analyse data derived from normal and cancerous colon tissue in the revised manuscript. Preliminary analysis shows that HCT116 PMDs are hypomethylated in a colorectal tumour but not in the normal colon (revision plan figure 1). This suggests that HCT116 cells are a model that can be used to understand PMD formation in tumours and we will extend this analysis in the revised manuscript. We will also add discussion of the caveat that DNMT1 may function differently in normal tissues and cancer cells.

Note, revision plan figure 1 was included with the full submission but cannot be uploaded in this format.

Revision plan figure 1. HCT116 PMDs are hypomethylated in colorectal tumours. Heatmaps and pileup plots of HCT116, normal colon and colorectal tumour DNA methylation levels for HCT116 PMDs (n=546 domains) and HMDs (n=558 domains). DNA methylation levels are mean % mCpG. PMDs and HMDs are aligned and scaled to the start and end points of each domain and ranked based on their mean methylation levels in HCT116 cells. Colon and tumour data re-analysed from a previous publication (Berman et al 2011, PMID: 22120008).

Moreover, the link between late replication and PMDs would indicate that a DNMT1 gain-of- function line would potentially be more interesting: could more increased DNMT activity rescue the PMDs, and how would this impact the chromatin and replication states? Perhaps this is not trivial to create; I do not know if simply overexpressing DNMT1 and/or UHRF1 could act as a gain-of-function.

We agree with the reviewer that a DNMT1 overexpression or a gain-of-function mutation cell line would be interesting to analyse and potentially informative as to the mechanism of PMD formation. However, as the reviewer notes, this is a complex experiment that could require the overexpression of partners such as UHRF1 or generation of an unknown gain-of-function mutation. In addition, the full dissection of the implications of this separate experimental strategy would entail the repetition of the majority of our experiments in DNMT1 KO cells. Instead, in the revised manuscript, we will focus on a related experiment suggested by reviewer 2 and ask whether re-expression of DNMT1 rescues DNA methylation patterns DNMT1 KO cells.

Nevertheless, the appearance of hyperPMDs was a curious finding worth publishing. However, it is unclear what the biological relevance is. There is no effect on replication timing, and no assessment on cell behavior (eg, proliferation assays).* In other words, is DNMT3A performing some kind of compensatory action, or is it just a curiosity? Below in the significance section, I have highlighted some additional specific points *

PMDs are important to study because cancer-associated hypomethylation is believed to drive carcinogenesis through genomic instability (Eden et al 2003, PMID: 12702868). However, the mechanisms underpinning their formation remain unclear. At present the predominant hypothesis is that PMDs emerge in heterochromatin because their late replication timing leaves insufficient time for re-methylation following DNA replication (Zhou et al 2018, PMID: 29610480 and Petryk et al 2021, PMID: 33300031). We believe that our observations of hypermethylated PMDs in DNMT1 KO cells provides important evidence contrary to this hypothesis because they disconnect domain-level methylation patterns from the replication timing program. Our work instead suggests that the localization of de novo DNMTs plays a key role in the formation of PMDs by protecting euchromatin from hypomethylation.

To further explore this hypothesis, we propose to analyze data derived from tumours in our revised manuscript to understand the degree to which our findings are reflected in vivo. As shown above, our preliminary analysis suggests that HCT116 cell PMDs are also hypomethylated in a colorectal tumour but not the normal colon (revision plan figure 1). We will also analyze how the changes in methylome affect gene expression using our RNA-seq data.

- Why were DNMT3A and 3B transgenes used for ChIP instead of endogenous proteins? I know the authors cited work justifying this strategy, but this still merits explanation. Also, the expression level of transgenes compared to the endogenes was not shown (neither protein nor RNA level).

DNMT3A and B transgenes were used because antibodies against the endogenous proteins are not suitable for ChIP. Furthermore, performing these experiments using endogenously tagged proteins, required generating 3 knock-in tagged lines (we have already generated HCT116 cells with tagged DNMT3B, Masalmeh et al 2021, PMID: 33514701).

We have previously shown that our constructs do indeed result in overexpression of DNMT3B compared to endogenous protein in this system (Masalmeh et al 2021, PMID: 33514701). However, our previous results also demonstrate that overexpressed DNMT3B recapitulates the localization of the endogenously tagged protein to the genome (Taglini et al 2024, PMID: 38291337). Others have similarly demonstrated that ectopically expressed DNMT3A and DNMT3B can be used to understand their localization on the genome (Baubec et al 2015, PMID: 25607372 and Weinberg et al 2019, PMID: 31485078).

To address this point, we propose to add further justification of our approach and discussion of this potential limitation to a revised version of the manuscript.

- The DNMT3A binding profile appears as though it is on the edges of the PMDs and fairly depleted within (Fig 4A,D). Could the authors comment on this?

This is an interesting point. We note that although mean DNMT3A signal is indeed higher at the edges of hypermethylated PMDs than inside these domains, its levels are both above background and the levels observed in HCT116 cells. As suggested by reviewer 3, this could be consistent with H3K36me2 and DNMT3A spreading in from the boundaries of hypermethylated PMDs in DNMT1 KO cells. We propose to add discussion of this possibility to the revised version of the manuscript.

- A more compelling experiment would be to assess the loss of DNMT3A genetically. How would this affect PMD DNA methylation? Maybe in this case there would be an effect on replication timing. Could a KO or KD (eg, siRNA) strategy be employed to assess this on top of either the HCT116 or DNMT1 KO.

As the reviewer suggests, functional experiments aimed at understanding the role of DNMT3A in our system are likely to be informative. We therefore propose to include such experiments in a revised version of the manuscript.

- What is the major H3K36me2 methylatransferase in these cells? Could an Nsd1 KO or KD strategy be used, for example, to show that indeed H3K36 methylation is required for HyperPMDs? This would complement the DNMT3A experiment above.

H3K36 methylation is thought to be deposited in the mammalian genome by at least 8 different methyltransferase enzymes, NSD1, NSD2, NSD3, ASH1L, SETD2, SETMAR, SMYD2 and SETD3 (Wagner and Carpenter 2023, PMID: 22266761). To understand which of these might be responsible for the deposition of H3K36me2 in hypermethylated PMDs, we have examined their expression in HCT116 and DNMT1 KO cells using our RNA-seq data. This suggests that 5 of these enzymes are highly expressed in HCT116 cells and their expression levels are similar in DNMT1 KO cellsrevision plan figure 2). The other 3 putative methyltransferases have lower expression levels and, although SMYD2 is significantly upregulated in DNMT1 KO cells, its expression remains low (revision plan figure 2). It is currently unclear whether SMYD2 is a bona fide H3K36 methyltransferase (Wagner and Carpenter 2023, PMID: 22266761). We also note that in a recent study, cells lacking NSD1, NSD2, NSD3, ASH1L and SETD2 had no detectable H3K36 methylation, although expression levels of SMYD2 were not reported (Shipman et al, 2024. PMID: 39390582). Based on this analysis, it is therefore unclear which enzyme(s) might be responsible for H3K36me2 deposition in hypermethylated PMDs and delineation of this enzyme would require multiple perturbation and sequencing experiments. We therefore suggest that assessing the consequences of knocking out H3K36me2 methyltransferase activity on hypermethylated PMDs is beyond the scope of a manuscript revision. We propose to include discussion of the expression of the different H3K36me2 depositing enzymes in the revised manuscript.

Note, revision plan figure 2 was included with the full submission but cannot be uploaded in this format.

Revision plan figure 2. HCT116 cells express multiple H3K36 methyltransferases. Barplot of mean expression levels for putative mammalian H3K36 methyltransferases in HCT116 and DNMT1 KO cells. Expression levels are counts per million (CPM) derived from RNA-seq. Mean expression levels are derived from 9 and 4 independent cultures of HCT116 and DNMT1 KO cells respectively.

- Based on Figure 2C, it seems that a general predictive pattern of hyperPMDs is H3K9me3-enriched and H3K27me3-depleted. Is this an accurate interpretation? Given the authors' expertise in the relationship between DNMT3A and polycomb, could they perhaps give an explanation for this phenomenon?

The reviewer is correct. In HCT116 cells, those PMDs that become hypermethylated in DNMT1 KO cells are marked by H3K9me3 and are H3K27me3-depleted (except at their boundaries). DNMT3A is recruited to polycomb-marked regions associated with H3K27me3 through interaction of its N-terminal region with H2AK119ub. However, this mark is depleted from hypermethylated-PMDs in DNMT1 KO cells (current manuscript Figure S5D) meaning that this pathway of recruitment is unlikely to explain DNMT3A's localisation to these regions in DNMT1 KO cells. This is discussed in the current manuscript:

We and others have reported that DNMT3A is also recruited to the polycomb-associated H2AK119ub mark through its N-terminal region (Chen et al, 2024; Gretarsson et al, 2024; Gu et al, 2022; Wapenaar et al, 2024; Weinberg et al, 2021). However, we do not observe the polycomb-associated H3K27me3 mark, which is generally tightly correlated with H2AK119ub (Ku et al, 2008), at hypermethylated PMDs suggesting that H2AK119ub does not play a role in the recruitment of DNMT3A to these regions.

Furthermore, DNMT3A's localisation is predominantly driven by its PWWP-dependent H3K36me2 recruitment pathway unless its PWWP domain is mutated (Heyn et al 2019, PMID: 30478443, Sendžikaitė et al 2019, PMID: 31015495, Kibe et al 2021, PMID: 34048432 and Weinberg et al, 2021, PMID: 33986537). Our observations of DNMT3A at hypermethylated PMDs marked by H3K36me2 is therefore consistent with previous findings. We propose to discuss this point in the revised manuscript.

- This is a minor point, but calling the DNMT1 mutant a "KO" seemed a bit misleading, as it is a truncation mutant. Perhaps there is a more accurate way to describe this line.

We propose to amend the manuscript to reflect this point as suggested by the reviewer. To ensure our responses are consistent with the reviewer comments we continue to refer to this line as DNMT1 KO cells in our revision plan.

*Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

*In this study, Kafetzopoulos et al. investigated the role of DNMT1-mediated methylation maintenance in cancer partially methylated domains (PMDs) using DNMT1 knockout HCT116 colorectal cancer cells. They used a range of sequencing-based approaches, including whole-genome bisulfite sequencing (WGBS), chromatin immunoprecipitation sequencing (ChIP-seq), and replication timing sequencing (Repli-seq), to define the dynamics of DNA methylation loss and gain in PMDs during DNA synthesis. Interestingly, they demonstrate that specific PMDs marked by H3K9me3 undergo a gain of DNA methylation in DNMT1-deficient HCT116 cells. This increase in methylation is associated with the loss of H3K9me3, an enrichment of H3K36me2, and the recruitment of DNMT3A. These findings suggest that de novo methyltransferase activity plays a critical role in determining which genomic regions become PMDs in cancer. *

*The authors use a comprehensive and well-controlled set of sequencing-based techniques. While the sequencing depth for DNA methylation is somewhat limited, the inclusion of multiple biological replicates strengthens the reliability of the data. The study effectively integrates multiple layers of epigenomic information, providing a nuanced view of PMD regulation in the context of DNMT1 loss. *

*Overall, this paper provides valuable insights into the epigenetic regulation of PMDs in cancer, and its conclusions are well supported by the data. It significantly advances our understanding of how DNMT1 loss reshapes the epigenome and highlights the interplay between de novo and maintenance methylation mechanisms in cancers. *

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*Reviewer #2 (Significance (Required)): *

General assessment

-The main strength of the study lies in the clear presentation of the data, which follows a cohesive and well-defined storyline.

*-The authors demonstrate that both hypomethylated and hypermethylated domains occur at the late replication stage. They further investigate the dynamics of histone modifications and DNA methylation, focusing on the acquisition and loss of these marks, particularly in relation to DNMT3A and DNMT3B. *

Limitation

-Although the study is compelling, its primary limitation is the correlative nature of most of the data. While the high-level representations (e.g., tracks, heat maps) are convincing, the study would have been more informative if it had explored the impact of these changes on a specific set of genes or regions critical to cancer initiation and progression. For example, in the DNMT1 knockout model, how does the loss of H3K9me3, the gain of H3K36me2, and the recruitment of DNMT3A in hypermethylated PMDs affect the expression of key genes involved in colorectal cancer?

To understand how the remodeling of DNA methylation and chromatin structure in DNMT1 KO cells affects gene expression, we propose to include an analysis of our RNA-seq data in the revised manuscript. We will also cross reference these results and our ChIP-seq with lists of colorectal cancer genes.

Additional experiments that could provide deeper insights

-Cross-validation in other cancer cell lines would have enable to define if these signatures are observed beyond HCT116.

As the reviewer suggests, we propose to undertake analyses of additional samples in the revised manuscript to understand how our findings relate to domain-level methylation patterns beyond HCT116 cells. As noted above in response to reviewer 1, our preliminary analysis suggests our findings are relevant for primary colorectal tumours (revision plan figure 1).

-Are the observed signatures permanent, or could they be reversed by reinstating the full activity of DNMT1? Since DNMT1 might be dysregulated but never completely deleted.

To address this suggestion, we propose to include the results of a DNMT1 rescue experiment in the revised manuscript.

-Use knockdown and overexpression experiments to track the dynamics and occurrence of these molecular events over time, providing insight into the progression and reversibility of epigenetic changes.

This is an interesting suggestion. As the reviewer suggests, we propose to analyse data derived from time-course experiments to understand the dynamics of changes in different genomic compartments following perturbation of DNMT1.

Advances

-The study provides new insights into the establishment of PMD types in colorectal cancer cell lines.

-These findings contribute both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development.

Audience:

-This study will appeal to a broad audience, from researchers primarily focused on epigenetics and cancer biology to those interested in the mechanistic underpinnings of DNA methylation and its role in cancer progression. It will also be relevant to those exploring therapeutic strategies targeting epigenetic regulators in cancer.

We thank the reviewer for their kind comments on our manuscript.

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

Summary:
*Cancer is linked to the acquisition of an atypical DNA methylation landscape, with broad domains of partial DNA methylation (termed PMDs). This study investigates PMDs in a colorectal cancer cell line and evaluates the contribution of DNMT1 in maintaining PMDs, using a DNMT1 KO line. The authors find that PMDs preferentially lose DNA methylation upon loss of DNMT1, but they find a number of domains that paradoxically gain DNA methylation (hyperPMDs). They attribute this gain of methylation to the action of DNMT3A through the accumulation of H3K36me2 and loss of heterochromatin mark H3K9me3. Together this work sheds light on the dynamic mechanisms regulating the atypical DNA methylation landscape in colorectal cancer cells. *

General comments:
The introduction is informative and well written. Additionally, the work is rigorously done and analyses are clear. However, the conclusions and summary figure largely focus on the relationship between PMDs with H3K9me3 and H3K36me2, but I think the role for H3K27me3 should be revisited based on the results presented. H3K9me3 is present at PMDs and hyperPMDs, but H3K27me3 level appears to be a much more defining feature of whether they lose or gain methylation upon loss of DNMT1 (Figure 2, Figure S2C- D). There is a reported interplay between PRC2 and DNMT3A activity at DNA methylation valleys in other cell contexts (e.g., mouse embryogenesis, hematopoietic cells), so couldn't H3K27me3 be performing a 'boundary' function at PMDs and when sufficiently low, permits spread of H3K36me2 in the absence of DNMT1? I think it is worth further exploring the H3K27me3 data.

The reviewer makes an interesting point about the potential for H3K27me3 to act as a boundary preventing H3K36me2 spread into PMDs. Multiple studies have shown that H3K36me2 restricts H3K27me3 deposition in the genome (Streubel et al 2018, PMID: 29606589, Shirane et al 2020, PMID: 32929285 and Farhangdoost et al 2021, PMID: 3362635). The structural nature of this inhibitory effect has also been resolved, demonstrating that the PRC2 catalytic subunit, EZH2 directly binds H3K36 and this is inhibited when the residue is methylated (Jani et al 2019, PMID: 30967505, Finogenova et al 2020, PMID: 33211010 and Cookis et al 2025, PMID: 39774834). The effect of H3K27me3 on H3K36me2 is less well characterised. However, previous work has suggested that inhibiting EZH2 leads to elevated H3K36me2 being established on newly replicated chromatin (Alabert et al 2020, PMID: 31995760). Expression of the EZH2-inhibiting oncohistone H3.3K27M has also been reported to lead to increased H3K36me2 dependent on NSD1/2 in diffuse intrinsic pontine gliomas (DIPG) (Stafford et al 2018, PMID: 30402543 and Yu et al 2021, PMID: 34261657). However, this increase was not reported by an independent study of H3.3K27M DIPG cells (Harutyunyan et al 2020, PMID: 33207202) and the molecular basis of the effect of H3K27me3 on H3K36me2 remains unclear.

As the reviewer suggests, we propose to explore the relationship between H3K27me3 and H3K36me2 further in a revised manuscript along with the including further discussion of previous findings in this area.

Additionally, a key point that is illustrated in the summary figure, is the localization of H3K36me2 at HMDs and its mutual exclusivity with H3K9me3 (a mark typically associated with high DNA methylation). However, because the H3K36me2 is introduced quite late in the analysis, I feel that a rigorous evaluation of its enrichment and anti-correlation with H3K9me3 at highly methylated domains (HMDs) is missing.

The relationship between H3K36me2 and H3K9me3 is far less explored than that of H3K27me3 and H3K36me2. Interestingly, we note that a recent study reported that depletion of H3K36me2 results in H3K9me3 re-distribution suggesting that H3K9me3 is restricted by H3K36me2 (Padilla et al 2024, DOI: 10.1101/2024.08.10.607446, also cited in the original manuscript).

To understand this relationship further, we therefore propose to explore the relationship between H3K9me3 and H3K36me2 in our datasets as part of revised manuscript along with including additional discussion of relevant experimental findings.

In general, I also found that I was jumping between figures a lot and needed to look at the supplements to gain the full picture. It may be beneficial to re-organize the figures.

In accordance with the reviewer's suggestion, we propose to re-organise the revised manuscript to make it easier to follow.

Specific Comments/Questions:

• An expanded explanation of the truncated DNMT1 in the DNMT1 KO cells would be helpful for context**
* As suggested by the reviewer, we will amend the manuscript to include an expanded discussion of the DNMT1 truncation present in the cell line.

• Does the DNMT expression in HCT116 cells reflect the levels seen in primary colorectal cancers? Hence, do you think these cultured cells reflect aspects of DNA methylation dynamics that would be seen in tumors?**
*

While differences between cancer cell line and tumour methylation patterns have previously been noted (for example Anne Rogers et al 2018, PMID: 30559935), we have previously demonstrated that HCT116 cells recapitulate CpG island methylation patterns observed in colorectal tumours (Masalmeh et al 2021, PMID: 33514701). As stated above in response to reviewer 1, we have now examined the methylation status of HCT116 PMDs in a colorectal tumour. This analysis shows that HCT116 PMDs have reduced methylation levels in a colorectal tumour but not in the normal colon (revision plan figure 1). We propose to extend this analysis of colorectal tumour samples and add them to the revised manuscript to address this point.

Regarding the expression of DNMTs in colorectal tumours, DNMT1 is ubiquitously expressed to our knowledge. DNMT3B is reported to be overexpressed in 15-20% of cases of colorectal cancer, often as a result of amplification (Nosho et al 2009, PMID: 19470733, Ibrahim et al 2011, PMID: PMID: 21068132, Zhang et al 2018, PMID: 30468428 and Mackenzie et al 2020, PMID: 32058953). DNMT3A expression in colorectal tumours is less studied but one report suggests upregulation in at least some tumours (Robertson et al 1999, PMID: 10325416 and Zhang et al 2018, PMID: 30468428). We propose to add additional discussion of DNMT expression in colorectal cancer to the revised manuscript to clarify the degree to which our results reflect methylation regulation in primary colorectal tumours.

• Although DNMT3A/B mRNA levels are similar between DNMT1 KO and HCT116 cells, is the protein abundance altered? I think there would be value in showing a Western blot analysis, as the loss of DNMT1 protein may alter the stability of the de novo DNMTs. Is a similar level of expression of the ectopic T7-DNMT3A and T7-DNMT3B achieved in HCT116 and DNMT1 KO cells? A western blot showing this would also be valuable.**
*

As part of our work towards revising the manuscript, we have undertaken blots of DNMT3A in our cell lines. This shows that DNMT3A levels in DNMT1 KO cells are similar to those in HCT116 cells which (revision plan figure 3). We propose to include this in the revised manuscript alongside a similar analysis of DNMT3B. We will also include an analysis of T7-DNMT3A and T7-DNMT3B levels to understand whether they are expressed to similar levels in HCT116 and DNMT1 KO cells.

Note, revision plan figure 3 was included with the full submission but cannot be uploaded in this format.

Revision plan figure 3. DNMT3A protein levels are similar in HCT116 and DNMT1 KO cells. Left, representative DNMT3A Western blot. Right, bar plot quantifying relative DNMT3A levels. The bar height indicates the mean levels observed in protein extracts from 3 independent cell cultures. Individual points indicate the level of each replicate.

• Do you think that the increase in DNMT3A over HyperPMD compared to H3K9me3-marked PMDs is related to an increase in protein bound at these domains or an altered residence time?*

The reviewer makes an interesting point with regard to a potential alteration of DNMT3A residence at hypermethylated PMDs. Given that ChIP-seq signal is affected by residence time (Schmiedeberg et al 2009, PMID: 19247482), it is possible that our findings could reflect this rather than increased DNMT3A localisation. We propose to add discussion of this point as a limitation of the current study to the manuscript.

It would also be valuable to move the plot showing levels of DNMT3A/3B at HMDs, from the S4C/D to the main Figure 4, for reference. It would also be interesting to see the enrichment of DNMT3A/B at all PMDs (not just H3K9me3-marked PMDs).*
*

As the reviewer suggests, we will include the data on HMDs to the main Figure 4 and include enrichments at all PMDs in the supplementary figures.

• It appears that the same genomic locus is used multiple times across figures Fig 1A, Fig 2B, Fig 3A, Fig 4A, Fig 5B to illustrate the trends reported from the global analyses. While this has value in showing the dynamics across datasets at this region, I think it is important to illustrate that these trends can be observed elsewhere. Please add or replace some plots with additional loci. Furthermore, please add the genomic region coordinates to the figure or figure legend.*

We had shown a single locus for consistency and to not overcomplicate figures which already contain multiple panels. As the reviewer suggests, we will add additional loci in the supplementary figures of our revised manuscript. We had also included the chromosome co-ordinates in the figures. In the revised version we will ensure that the precise co-ordinates are included in the legends.

• The ChIP-seq data is quantified as IP/input. This quantitation can be prone to introducing artefacts into analyses if the input coverage is substantially uneven over AT-rich regions or CpG islands, or if the sequencing depth is insufficient. I would encourage the authors to check that the trends observed are still present if quantified without correcting against the inputs. If using IP/input, in the supplementary figures, I think it would be valuable to show the uncorrected quantitation of inputs across PMDs, to demonstrate that there is even coverage and this isn't contributing to any of the changes observed.**
*

We thank the reviewer for this point and we propose to examine the quantification of the ChIP-seq without normalizing to input to ensure that uneven input signal does not substantially contribute to our results.

• Generally, the n numbers for different groups of probes can be confusing and increased clarity would be helpful.*

We will clarify the explanation of n numbers in the revised manuscript.

*Reviewer #3 (Significance (Required)): *

This study adds to the accumulating body of evidence that DNMT3A recruitment is mediated primarily through H3K36me2 across cell contexts, shedding light on the interplay between histone modifications and de novo DNA methylation. Understanding these mechanisms is important to appreciate the role for DNMT3A in establishing DNA methylation in development and disease contexts. It does remain unclear why, upon loss of DNMT1 in colorectal cancer cells, some PMDs accumulate H3K36me2 and consequently DNA methylation, while others do not. Further study into the chromatin dynamics will be valuable in understanding determinants of the DNA methylation landscape in cancer.

We thank the reviewer for their insightful comments and believe that our proposed revisions will further clarify the points they raise.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

We have not yet incorporated revisions into the manuscript.

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

As stated in our responses to the reviewer comments above, we plan to address all comments. However, we suggest that two experiments proposed by the reviewers are beyond the scope of a manuscript revision and we will instead address these comments in the following manner:

Analysis of a DNMT1 gain-of-function line (Reviewer 1). As suggested by the reviewer such a line is non-trivial to generate. It would also require extensive profiling of this new line to fully understand its implications for our findings. We therefore believe it is outwith the scope of a manuscript revision. Instead, we propose to address this comment by undertaking the related experiment suggested by Reviewer 2 and perform a DNMT1 rescue experiment in the DNMT1 KO line. Analysis of H3K36me2 methyltransferase knockout cells (Reviewer 1). Our preliminary analysis suggests that HCT116 cells express multiple H3K36 methyltransferases and that their expression does not vary greatly in DNMT1 KO cels (revision plan figure 2). This means that it is unclear which enzyme(s) might be responsible for depositing H3K36me2 in hypermethylated PMDs. Delineation of this would require the generation and analysis of multiple knockouts and we suggest it is therefore outwith the scope of a manuscript revision. To address this point we will instead include discussion of the spectrum of H3K36 methyltransferases expressed in our cells in the revised manuscript as detailed in the specific response above.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Cancer is linked to the acquisition of an atypical DNA methylation landscape, with broad domains of partial DNA methylation (termed PMDs). This study investigates PMDs in a colorectal cancer cell line and evaluates the contribution of DNMT1 in maintaining PMDs, using a DNMT1 KO line. The authors find that PMDs preferentially lose DNA methylation upon loss of DNMT1, but they find a number of domains that paradoxically gain DNA methylation (hyperPMDs). They attribute this gain of methylation to the action of DNMT3A through the accumulation of H3K36me2 and loss of heterochromatin mark H3K9me3. Together this work sheds light on the dynamic mechanisms regulating the atypical DNA methylation landscape in colorectal cancer cells.

General comments:

The introduction is informative and well written. Additionally, the work is rigorously done and analyses are clear. However, the conclusions and summary figure largely focus on the relationship between PMDs with H3K9me3 and H3K36me2, but I think the role for H3K27me3 should be revisited based on the results presented. H3K9me3 is present at PMDs and hyperPMDs, but H3K27me3 level appears to be a much more defining feature of whether they lose or gain methylation upon loss of DNMT1 (Figure 2, Figure S2C-D). There is a reported interplay between PRC2 and DNMT3A activity at DNA methylation valleys in other cell contexts (e.g., mouse embryogenesis, hematopoietic cells), so couldn't H3K27me3 be performing a 'boundary' function at PMDs and when sufficiently low, permits spread of H3K36me2 in the absence of DNMT1? I think it is worth further exploring the H3K27me3 data.

Additionally, a key point that is illustrated in the summary figure, is the localization of H3K36me2 at HMDs and its mutual exclusivity with H3K9me3 (a mark typically associated with high DNA methylation). However, because the H3K36me2 is introduced quite late in the analysis, I feel that a rigorous evaluation of its enrichment and anti-correlation with H3K9me3 at highly methylated domains (HMDs) is missing.

In general, I also found that I was jumping between figures a lot and needed to look at the supplements to gain the full picture. It may be beneficial to re-organize the figures.

Specific Comments/Questions:

1. An expanded explanation of the truncated DNMT1 in the DNMT1 KO cells would be helpful for context.
2. Does the DNMT expression in HCT116 cells reflect the levels seen in primary colorectal cancers? Hence, do you think these cultured cells reflect aspects of DNA methylation dynamics that would be seen in tumors?
3. Although DNMT3A/B mRNA levels are similar between DNMT1 KO and HCT116 cells, is the protein abundance altered? I think there would be value in showing a Western blot analysis, as the loss of DNMT1 protein may alter the stability of the de novo DNMTs. Is a similar level of expression of the ectopic T7-DNMT3A and T7-DNMT3B achieved in HCT116 and DNMT1 KO cells? A western blot showing this would also be valuable.
4. Do you think that the increase in DNMT3A over HyperPMD compared to H3K9me3-marked PMDs is related to an increase in protein bound at these domains or an altered residence time? It would also be valuable to move the plot showing levels of DNMT3A/3B at HMDs, from the S4C/D to the main Figure 4, for reference. It would also be interesting to see the enrichment of DNMT3A/B at all PMDs (not just H3K9me3-marked PMDs).
5. It appears that the same genomic locus is used multiple times across figures Fig 1A, Fig 2B, Fig 3A, Fig 4A, Fig 5B to illustrate the trends reported from the global analyses. While this has value in showing the dynamics across datasets at this region, I think it is important to illustrate that these trends can be observed elsewhere. Please add or replace some plots with additional loci. Furthermore, please add the genomic region coordinates to the figure or figure legend.
6. The ChIP-seq data is quantified as IP/input. This quantitation can be prone to introducing artefacts into analyses if the input coverage is substantially uneven over AT-rich regions or CpG islands, or if the sequencing depth is insufficient. I would encourage the authors to check that the trends observed are still present if quantified without correcting against the inputs. If using IP/input, in the supplementary figures, I think it would be valuable to show the uncorrected quantitation of inputs across PMDs, to demonstrate that there is even coverage and this isn't contributing to any of the changes observed.
7. Generally, the n numbers for different groups of probes can be confusing and increased clarity would be helpful.

Significance

This study adds to the accumulating body of evidence that DNMT3A recruitment is mediated primarily through H3K36me2 across cell contexts, shedding light on the interplay between histone modifications and de novo DNA methylation. Understanding these mechanisms is important to appreciate the role for DNMT3A in establishing DNA methylation in development and disease contexts. It does remain unclear why, upon loss of DNMT1 in colorectal cancer cells, some PMDs accumulate H3K36me2 and consequently DNA methylation, while others do not. Further study into the chromatin dynamics will be valuable in understanding determinants of the DNA methylation landscape in cancer.

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Referee #2

Evidence, reproducibility and clarity

In this study, Kafetzopoulos et al. investigated the role of DNMT1-mediated methylation maintenance in cancer partially methylated domains (PMDs) using DNMT1 knockout HCT116 colorectal cancer cells. They used a range of sequencing-based approaches, including whole-genome bisulfite sequencing (WGBS), chromatin immunoprecipitation sequencing (ChIP-seq), and replication timing sequencing (Repli-seq), to define the dynamics of DNA methylation loss and gain in PMDs during DNA synthesis. Interestingly, they demonstrate that specific PMDs marked by H3K9me3 undergo a gain of DNA methylation in DNMT1-deficient HCT116 cells. This increase in methylation is associated with the loss of H3K9me3, an enrichment of H3K36me2, and the recruitment of DNMT3A. These findings suggest that de novo methyltransferase activity plays a critical role in determining which genomic regions become PMDs in cancer.

The authors use a comprehensive and well-controlled set of sequencing-based techniques. While the sequencing depth for DNA methylation is somewhat limited, the inclusion of multiple biological replicates strengthens the reliability of the data. The study effectively integrates multiple layers of epigenomic information, providing a nuanced view of PMD regulation in the context of DNMT1 loss.

Overall, this paper provides valuable insights into the epigenetic regulation of PMDs in cancer, and its conclusions are well supported by the data. It significantly advances our understanding of how DNMT1 loss reshapes the epigenome and highlights the interplay between de novo and maintenance methylation mechanisms in cancers.

Significance

General assessment

• The main strength of the study lies in the clear presentation of the data, which follows a cohesive and well-defined storyline.
• The authors demonstrate that both hypomethylated and hypermethylated domains occur at the late replication stage. They further investigate the dynamics of histone modifications and DNA methylation, focusing on the acquisition and loss of these marks, particularly in relation to DNMT3A and DNMT3B.

Limitation

• Although the study is compelling, its primary limitation is the correlative nature of most of the data. While the high-level representations (e.g., tracks, heat maps) are convincing, the study would have been more informative if it had explored the impact of these changes on a specific set of genes or regions critical to cancer initiation and progression. For example, in the DNMT1 knockout model, how does the loss of H3K9me3, the gain of H3K36me2, and the recruitment of DNMT3A in hypermethylated PMDs affect the expression of key genes involved in colorectal cancer?

Additional experiments that could provide deeper insights

• Cross-validation in other cancer cell lines would have enable to define if these signatures are observed beyond HCT116.
• Are the observed signatures permanent, or could they be reversed by reinstating the full activity of DNMT1? Since DNMT1 might be dysregulated but never completely deleted.
• Use knockdown and overexpression experiments to track the dynamics and occurrence of these molecular events over time, providing insight into the progression and reversibility of epigenetic changes.

Advances

• The study provides new insights into the establishment of PMD types in colorectal cancer cell lines.
• These findings contribute both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development.

Audience:

• This study will appeal to a broad audience, from researchers primarily focused on epigenetics and cancer biology to those interested in the mechanistic underpinnings of DNA methylation and its role in cancer progression. It will also be relevant to those exploring therapeutic strategies targeting epigenetic regulators in cancer.

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Referee #1

Evidence, reproducibility and clarity

The DNA methylation landscape is frequently altered in cancers, which may contribute to genome misregulation and cancer cell behavior. One phenomenon is the emergence of "partially methylated domains (PMDs)": intermediately methylated regions of the genome that are generally heterochromatic and late replicating. The prevailing explanation is that the DNA methyltransferase, DNMT1, is not able to maintain DNAme levels at late replicating sites in proliferating cancer cells. This could result in genome instability. In this study, Kafetzopoulos and colleagues interrogated this possibility using a common laboratory colorectal cancer cell line (HCT116). Additionally, they utilized a DNMT1 mutant line that they refer to as a knockout, even though, more accurately, it is a hypomorphic truncation. They performed several genomic assays, such as whole genome bisulfite sequencing, ChIP and repli-seq, in order to assess the effect of reduced DNMT1 activity. While expectedly, global DNAme levels are decreased, they discovered a subset of PMDs gain DNA methylation, which they term hyperPMDs. There seems to be no impact on DNA replication timing, but the authors did go on to show that the de novo DNA methyltransferase, DNMT3Α, is likely responsible for this counterintuitive increase in DNAme levels.

Significance

Overall, I found the data well-presented and diligently analyzed, as we have come to expect from the Sproul group. However, I am somewhat at a loss to understand both the rationale for the experimental set-up and the meaning of the results. The HCT116 cell line is already transformed but was treated as though it was a wild-type control. I was more curious to see how the PMD chromatin state and replication compare to a healthy cell. Moreover, the link between late replication and PMDs would indicate that a DNMT1 gain-of-function line would potentially be more interesting: could more increased DNMT activity rescue the PMDs, and how would this impact the chromatin and replication states? Perhaps this is not trivial to create; I do not know if simply overexpressing DNMT1 and/or UHRF1 could act as a gain-of-function. Nevertheless, the appearance of hyperPMDs was a curious finding worth publishing. However, it is unclear what the biological relevance is. There is no effect on replication timing, and no assessment on cell behavior (eg, proliferation assays). In other words, is DNMT3A performing some kind of compensatory action, or is it just a curiosity? Below in the significance section, I have highlighted some additional specific points

• Why were DNMT3A and 3B transgenes used for ChIP instead of endogenous proteins? I know the authors cited work justifying this strategy, but this still merits explanation. Also, the expression level of transgenes compared to the endogenes was not shown (neither protein nor RNA level).
• The DNMT3A binding profile appears as though it is on the edges of the PMDs and fairly depleted within (Fig 4A,D). Could the authors comment on this?
• A more compelling experiment would be to assess the loss of DNMT3A genetically. How would this affect PMD DNA methylation? Maybe in this case there would be an effect on replication timing. Could a KO or KD (eg, siRNA) strategy be employed to assess this on top of either the HCT116 or DNMT1 KO.
• What is the major H3K36me2 methylatransferase in these cells? Could an Nsd1 KO or KD strategy be used, for example, to show that indeed H3K36 methylation is required for HyperPMDs? This would complement the DNMT3A experiment above.
• Based on Figure 2C, it seems that a general predictive pattern of hyperPMDs is H3K9me3-enriched and H3K27me3-depleted. Is this an accurate interpretation? Given the authors' expertise in the relationship between DNMT3A and polycomb, could they perhaps give an explanation for this phenomenon?
• This is a minor point, but calling the DNMT1 mutant a "KO" seemed a bit misleading, as it is a truncation mutant. Perhaps there is a more accurate way to describe this line.

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Reply to the reviewers

We thank the reviewers for their feedback on our paper. We have taken all their comments into account in revising the manuscript. We provide a point-by-point response to their comments, below.

Reviewer #1

Major comments:

The manuscript is clearly written with a level of detail that allows others to reproduce the imaging and cell-tracking pipeline. Of the 22 movies recorded one was used for cell tracking. One movie seems sufficient for the second part of the manuscript, as this manuscript presents a proof-of-principle pipeline for an imaging experiment followed by cell tracking and molecular characterisation of the cells by HCR. In addition, cell tracking in a 5-10 day time-lapse movie is an enormous time commitment.

My only major comment is regarding "Suppl_data_5_spineless_tracking". The image file does not load. It looks like the wrong file is linked to the mastodon dataset. The "Current BDV dataset path" is set to "Beryl_data_files/BLB mosaic cut movie-02.xml", but this file does not exist in the folder. Please link it to the correct file.

We have corrected the file path in the updated version of Suppl. Data 5.

Minor comments:

The authors state that their imaging settings aim to reduce photo damage. Do they see cell death in the regenerating legs? Is the cell death induced by the light exposure or can they tell if the same cells die between the movies? That is, do they observe cell death in the same phases of regeneration and/or in the same regions of the regenerating legs?

Yes, we observe cell death during Parhyale leg regeneration. We have added the following sentence to explain this in the revised manuscript: "During the course of regeneration some cells undergo apoptosis (reported in Alwes et al., 2016). Using the H2B-mRFPruby marker, apoptotic cells appear as bright pyknotic nuclei that break up and become engulfed by circulating phagocytes (see bright specks in Figure 2F)."

We now also document apoptosis in regenerated legs that have not been subjected to live imaging in a new supplementary figure (Suppl. Figure 3), and we refer to these observations as follows: "While some cell death might be caused by photodamage, apoptosis can also be observed in similar numbers in regenerating legs that have not been subjected to live imaging (Suppl. Figure 3)."

Based on 22 movies, the authors divide the regeneration process into three phases and they describe that the timing of leg regeneration varies between individuals. Are the phases proportionally the same length between regenerating legs or do the authors find differences between fast/slow regenerating legs? If there is a difference in the proportions, why might this be?

Both early and late phases contribute to variation in the speed of regeneration, but there is no clear relationship between the relative duration of each phase and the speed of regeneration. We now present graphs supporting these points in a new supplementary figure (Suppl. Figure 2).

To clarify this point, we have added the following sentence in the manuscript: "We find that the overall speed of leg regeneration is determined largely by variation in the speed of the early (wound closure) phase of regeneration, and to a lesser extent by variation in later phases when leg morphogenesis takes place (Suppl. Figure 2 A,B). There is no clear relationship between the relative duration of each phase and the speed of regeneration (Suppl. Figure 2 A',B')."

Based on their initial cell tracing experiment, could the authors elaborate more on what kind of biological information can be extracted from the cell lineages, apart from determining which is the progenitor of a cell? What does it tell us about the cell population in the tissue? Is there indication of multi- or pluripotent stem cells? What does it say about the type of regeneration that is taking place in terms of epimorphosis and morphallaxis, the old concepts of regeneration?

In the first paragraph of Future Directions we describe briefly the kind of biological information that could be gained by applying our live imaging approach with appropriate cell-type markers (see below). We do not comment further, as we do not currently have this information at hand. Regarding the concepts of epimorphosis and morphallaxis, as we explain in Alwes et al. 2016, these terms describe two extreme conditions that do not capture what we observe during Parhyale leg regeneration. Our current work does not bring new insights on this topic.

Page 5. The authors mention the possibility of identifying the cell ID based on transcriptomic profiling data. Can they suggest how many and which cell types they expect to find in the last stage based on their transcriptomic data?

We have added this sentence: "Using single-nucleus transcriptional profiling, we have identified approximately 15 transcriptionally-distinct cell types in adult Parhyale legs (Almazán et al., 2022), including epidermis, muscle, neurons, hemocytes, and a number of still unidentified cell types."

Page 6. Correction: "..molecular and other makers.." should be "..molecular and other markers.."

Corrected

Page 8. The HCR in situ protocol probably has another important advantage over the conventional in situ protocol, which is not mentioned in this study. The hybridisation step in HCR is performed at a lower temperature (37˚C) than in conventional in situ hybridisation (65˚C, Rehm et al., 2009). In other organisms, a high hybridisation temperature affects the overall tissue morphology and cell location (tissue shrinkage). A lower hybridisation temperature has less impact on the tissue and makes manual cell alignment between the live imaging movie and the fixed HCR in situ stained specimen easier and more reliable. If this is also the case in Parhyale, the authors must mention it.

This may be correct, but all our specimens were treated at 37˚C, so we cannot assess whether hybridisation temperature affects morphological preservation in our specimens.

Page 9. The authors should include more information on the spineless study. What been is spineless? What do the cell lineages tell about the spineless progenitors, apart from them being spread in the tissue at the time of amputation? Do spineless progenitors proliferate during regeneration? Do any spineless expressing cells share a common progenitor cell?

We now point out that spineless encodes a transcription factor. We provide a summary of the lineages generating spineless-expressing cells in Suppl. Figure 6, and we explain that "These epidermal progenitors undergo 0, 1 or 2 cell divisions, and generate mostly spineless-expressing cells (Suppl. Figure 5)."

Page 10. Regarding the imaging temperature, the Materials and Methods state "... a temperature control chamber set to 26 or 27˚C..."; however, in Suppl. Data 1, 26˚C and 29˚C are indicated as imaging temperatures. Which is correct?

We corrected the Methods by adding "with the exception of dataset li51, imaged at 29{degree sign}C"

Page 10. Regarding the imaging step size, the Materials and Methods state "...step size of 1-2.46 µm..."; however, Suppl. Data 1 indicate a step size between 1.24 - 2.48 µm. Which is correct?

We corrected the Methods.

Page 11. Correct "...as the highest resolution data..." to "...at the highest resolution data..."

The original text is correct ("standardised to the same dimensions as the highest resolution data").

Page 11. Indicate which supplementary data set is referred to: "Using Mastodon, we generated ground truth annotations on the original image dataset, consisting of 278 cell tracks, including 13,888 spots and 13,610 links across 55 time points (see Supplementary Data)."

Corrected

p. 15. Indicate which supplementary data set is referred to: "In this study we used HCR probes for the Parhyale orthologues of futsch (MSTRG.441), nompA (MSTRG.6903) and spineless (MSTRG.197), ordered from Molecular Instruments (20 oligonucleotides per probe set). The transcript sequences targeted by each probe set are given in the Supplementary Data."

Corrected

Figure 3. Suggestion to the overview schematics: The authors might consider adding "molting" as the end point of the red bar (representing differentiation).

The time of molting is not known in the majority of these datasets, because the specimens were fixed and stained prior to molting. We added the relevant information in the figure legend: "Datasets li-13 and li-16 were recorded until the molt; the other recordings were stopped before molting."

Figure 4B': Please indicate that the nuclei signal is DAPI.

Corrected

Supplementary figure 1A. Word is missing in the figure legend: ...the image also shows weak...

Corrected

Supplementary Figure 2: Please indicate the autofluorescence in the granular cells. Does it correspond to the yellow cells?

Corrected

Video legend for video 1 and 2. Please correct "H2B-mREFruby" to "H2B-mRFPruby".

Corrected

Reviewer #2

Major comments:

MC 1. Given that most of the technical advances necessary to achieve the work described in this manuscript have been published previously, it would be helpful for the authors to more clearly identify the primary novelty of this manuscript. The abstract and introduction to the manuscript focus heavily on the technical details of imaging and analysis optimization and some additional summary of the implications of these advances should be included here to aid the reader.

This paper describes a technical advance. While previous work (Alwes et al. 2016) established some key elements of our live imaging approach, we were not at that time able to record the entire time course of leg regeneration (the longest recordings were 3.5 days long). Here we present a method for imaging the entire course of leg regeneration (up to 10 days of imaging), optimised to reduce photodamage and to improve cell tracking. We also develop a method of in situ staining in cuticularised adult legs (an important technical breakthrough in this experimental system), which we combine with live imaging to determine the fate of tracked cells. We have revised the abstract and introduction of the paper to point out these novelties, in relation to our previous publications.

In the abstract we explain: "Building on previous work that allowed us to image different parts of the process of leg regeneration in the crustacean Parhyale hawaiensis, we present here a method for live imaging that captures the entire process of leg regeneration, spanning up to 10 days, at cellular resolution. Our method includes (1) mounting and long-term live imaging of regenerating legs under conditions that yield high spatial and temporal resolution but minimise photodamage, (2) fixing and in situ staining of the regenerated legs that were imaged, to identify cell fates, and (3) computer-assisted cell tracking to determine the cell lineages and progenitors of identified cells. The method is optimised to limit light exposure while maximising tracking efficiency."

The introduction includes the following text: "Our first systematic study using this approach presented continuous live imaging over periods of 2-3 days, capturing key events of leg regeneration such as wound closure, cell proliferation and morphogenesis of regenerating legs with single-cell resolution (Alwes et al., 2016). Here, we extend this work by developing a method for imaging the entire course of leg regeneration, optimised to reduce photodamage and to improve cell tracking. We also develop a method of in situ staining of gene expression in cuticularised adult legs, which we combine with live imaging to determine the fate of tracked cells."

MC 2. The description of the regeneration time course is nicely detailed but also very qualitative. A major advantage of continuous recording and automated cell tracking in the manner presented in this manuscript would be to enable deeper quantitative characterization of cellular and tissue dynamics during regeneration. Rather than providing movies and manually annotated timelines, some characterization of the dynamics of the regeneration process (the heterogeneity in this is very very interesting, but not analyzed at all) and correlating them against cellular behaviors would dramatically increase the impact of the work and leverage the advances presented here. For example, do migration rates differ between replicates? Division rates? Division synchrony? Migration orientation? This seems to be an incredibly rich dataset that would be fascinating to explore in greater detail, which seems to me to be the primary advance presented in this manuscript. I can appreciate that the authors may want to segregate some biological findings from the method, but I believe some nominal effort highlighting the quantitative nature of what this method enables would strengthen the impact of the paper and be useful for the reader. Selecting a small number of simple metrics (eg. Division frequency, average cell migration speed) and plotting them alongside the qualitative phases of the regeneration timeline that have already been generated would be a fairly modest investment of effort using tools that already exist in the Mastodon interface, I would roughly estimate on the order of an hour or two per dataset. I believe that this effort would be well worth it and better highlight a major strength of the approach.

The primary goal of this work was to establish a robust method for continuous long-term live imaging of regeneration, but we do appreciate that a more quantitative analysis would add value to the data we are presenting. We tried to address this request in three steps:

First, we examined whether clear temporal patterns in cell division, cell movements or other cellular features can be observed in an accurately tracked dataset (li13-t4, tracked in Sugawara et al. 2022). To test this we used the feature extraction functions now available on the Mastodon platform (see link). We could discern a meaningful temporal pattern for cell divisions (see below); the other features showed no interpretable pattern of variation.

Second, we asked whether we could use automated cell tracking to analyse the patterns of cell division in all our datasets. Using an Elephant deep learning model trained on the tracks of the li13-t4 dataset, we performed automated cell tracking in the same dataset, and compared the pattern of cell divisions from the automated cell track predictions with those coming from manually validated cell tracks. We observed that the automated tracks gave very imprecise results, with a high background of false positives obscuring the real temporal pattern (see images below, with validated data on the left, automated tracking on the right). These results show that the automated cell tracking is not accurate enough to provide a meaningful picture on the pattern of cell divisions.

Third, we tried to improve the accuracy of detection of dividing cells by additional training of Elephant models on each dataset (to lower the rate of false positives), followed by manual proofreading. Given how labour intensive this is, we could only apply this approach to 4 additional datasets. The results of this analysis are presented in Figure 4.

MC 3. The authors describe the challenges faced by their described approach: Using this mode of semi-automated and manual cell tracking, we find that most cells in the upper slices of our image stacks (top 30 microns) can be tracked with a high degree of confidence. A smaller proportion of cell lineages are trackable in the deeper layers.

Given that the authors quantify this in Table 1, it would aid the reader to provide metrics in the manuscript text at this point. Furthermore, the metrics provided in Table 1 appear to be for overall performance, but the text describes that performance appears to be heavily depth dependent. Segregating the performance metrics further, for example providing DET, TRA, precision and recall for superficial layers only and for the overall dataset, would help support these arguments and better highlight performance a potential adopter of the method might expect.

In the revised manuscript we have added data on the tracking performance of Elephant in relation to imaging depth in Suppl. Figure 3. These data confirm our original statement (which was based on manual tracking) that nuclei are more challenging to track in deeper layers.

We point to these new results in two parts of the paper, as follows: "A smaller proportion of cells are trackable in the deeper layers (see Suppl. Figure 3)", and "Our results, summarised in Table 1A, show that the detection of nuclei can be enhanced by doubling the z resolution at the expense of xy resolution and image quality. This improvement is particularly evident in the deeper layers of the imaging stacks, which are usually the most challenging to track (Suppl. Figure 3)."

MC 4. Performance characterization in Table 1 appears to derive from a single dataset that is then subsampled and processed in different ways to assess the impact of these changes on cell tracking and detection performance. While this is a suitable strategy for this type of optimization it leaves open the question of performance consistency across datasets. I fully recognize that this type of quantification can be onerous and time consuming, but some attempt to assess performance variability across datasets would be valuable. Manual curation over a short time window over a random sampling of the acquired data would be sufficient to assess this.

We think that similar trade-offs will apply to all our datasets because tracking performance is constrained by the same features, which are intrinsic to our system; e.g. by the crowding of nuclei in relation to axial resolution, or the speed of mitosis in relation to the temporal resolution of imaging. We therefore do not see a clear rationale for repeating this analysis. On a practical level, our existing image datasets could not be subsampled to generate the various conditions tested in Table 1, so proving this point experimentally would require generating new recordings, and tracking these to generate ground truth data. This would require months of additional work.

A second, related question is whether Elephant would perform equally well in detecting and tracking nuclei across different datasets. This point has been addressed in the Sugawara et al. 2022 paper, where the performance of Elephant was tested on diverse fluorescence datasets.

Reviewer #3

Major comments:

The authors should clearly specify what are the key technical improvements compared to their previous studies (Alwes et al. 2016, Elife; Konstantinides & Averof 2014, Science). There, the approaches for mounting, imaging, and cell tracking are already introduced, and the imaging is reported to run for up to 7 days in some cases.

In Konstantinides and Averof (2014) we did not present any live imaging at cellular resolution. In Alwes et al. (2016) we described key elements of our live imaging approach, but we were never able to record the entire time course of leg regeneration. The longest recordings in that work were 3.5 days long.

We have revised the abstract and introduction to clarify the novelty of this work, in relation to our previous publications. Please see our response to comment MC1 of reviewer 2.

While the authors mention testing the effect of imaging parameters (such as scanning speed and line averaging) on the imaging/tracking outcome, very little or no information is provided on how this was done beyond the parameters that they finally arrived to.

Scan speed and averaging parameters were determined by measuring contrast and signal-to-noise ratios in images captured over a range of settings. We have now added these data in Supplementary Figure 1.

The authors claim that, using the acquired live imaging data across entire regeneration time course, they are now able to confirm and extend their description of leg regeneration. However, many claims about the order and timing of various cellular events during regeneration are supported only by references to individual snapshots in figures or supplementary movies. Presenting a more quantitative description of cellular processes during regeneration from the acquired data would significantly enhance the manuscript and showcase the usefulness of the improved workflow.

The events we describe can be easily observed in the maximum projections, available in Suppl. Data 2. Regarding the quantitative analysis, please see our response to comment MC2 of reviewer 2.

Table 1 summarizes the performance of cell tracking using simulated datasets of different quality. However only averages and/or maxima are given for the different metrics, which makes it difficult to evaluate the associated conclusions. In some cases, only 1 or 2 test runs were performed.

The metrics extracted from each of the three replicates, per dataset, are now included in Suppl. Data 4.

We consistently used 3 replicates to measure tracking performance with each of the datasets. The "replicates" column label in Table 1 referred to the number of scans that were averaged to generate the image, not to the replicates used for estimating the tracking performance. To avoid confusion, we changed that label to "averaging".

OPTIONAL: An imaging approach that allows using the current mounting strategy but could help with some of the tradeoffs is using a spinning-disk confocal microscope instead of a laser scanning one. If the authors have such a system available, it could be interesting to compare it with their current scanning confocal setup.

Preliminary experiments that we carried out several years ago on a spinning disk confocal (with a 20x objective and the CSU-W1 spinning disk) were not very encouraging, and we therefore did not pursue this approach further. The main problem was bad image quality in deeper tissue layers.

Minor comments:

The presented imaging protocol was optimized for one laser wavelength only (561 nm) - this should be mentioned when discussing the technical limitations since animals tend to react differently to different wavelengths. Same settings might thus not be applicable for imaging a different fluorescent protein.

In the second paragraph of the Results section, we explain that we perform the imaging at long wavelengths in order to minimise photodamage. It should be clear to the readers that changing the excitation wavelength will have an impact for long-term live imaging.

For transferability, it would be useful if the intensity of laser illumination was measured and given in the Methods, instead of just a relative intensity setting from the imaging software. Similarly,more details of the imaging system should be provided where appropriate (e.g., detector specifications).

We have now measured the intensity of the laser illumination and added this information in the Methods: "Laser power was typically set to 0.3% to 0.8%, which yields 0.51 to 1.37 µW at 561 nm (measured with a ThorLabs Microscope Slide Power Sensor, #S170C)."

Regarding the imaging system and the detector, we provide all the information that is available to us on the microscope's technical sheets.

The versions of analysis scripts associated with the manuscript should be uploaded to an online repository that permanently preserves the respective version.

The scripts are now available on gitbub and online repositories. The relevant links are included in the revised manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary

Çevrim et al. provide a method for confocal live imaging of the entire leg regeneration process in the crustacean Parhyale. This approach allows imaging for up to 10 days with cellular resolution. Subsequently, the data can be used for cell tracking and inferring lineage relationships. The authors explore strategies for doing so efficiently, as well as the associated tradeoffs. They also provide modifications to the HCR RNA FISH protocol that allow applying it in their system to asses marker gene expression at the imaging endpoints (and cross-referencing it with the live imaging data).

Major comments

• The authors should clearly specify what are the key technical improvements compared to their previous studies (Alwes et al. 2016, Elife; Konstantinides & Averof 2014, Science). There, the approaches for mounting, imaging, and cell tracking are already introduced, and the imaging is reported to run for up to 7 days in some cases.
• While the authors mention testing the effect of imaging parameters (such as scanning speed and line averaging) on the imaging/tracking outcome, very little or no information is provided on how this was done beyond the parameters that they finally arrived to.
• The authors claim that, using the acquired live imaging data across entire regeneration time course, they are now able to confirm and extend their description of leg regeneration. However, many claims about the order and timing of various cellular events during regeneration are supported only by references to individual snapshots in figures or supplementary movies. Presenting a more quantitative description of cellular processes during regeneration from the acquired data would significantly enhance the manuscript and showcase the usefulness of the improved workflow.
• Table 1 summarizes the performance of cell tracking using simulated datasets of different quality. However only averages and/or maxima are given for the different metrics, which makes it difficult to evaluate the associated conclusions. In some cases, only 1 or 2 test runs were performed.
• OPTIONAL: An imaging approach that allows using the current mounting strategy but could help with some of the tradeoffs is using a spinning-disk confocal microscope instead of a laser scanning one. If the authors have such a system available, it could be interesting to compare it with their current scanning confocal setup.

Minor comments

• The presented imaging protocol was optimized for one laser wavelength only (561 nm) - this should be mentioned when discussing the technical limitations since animals tend to react differently to different wavelengths. Same settings might thus not be applicable for imaging a different fluorescent protein.
• For transferability, it would be useful if the intensity of laser illumination was measured and given in the Methods, instead of just a relative intensity setting from the imaging software. Similarly,more details of the imaging system should be provided where appropriate (e.g., detector specifications).
• The versions of analysis scripts associated with the manuscript should be uploaded to an online repository that permanently preserves the respective version.

Significance

As the authors point out, live imaging the entirety of a regeneration process can provide valuable insights but is not easy to perform. Many organisms cannot be easily mounted or immobilized for a sufficiently long time, and the imaging conditions might be too stressful. The manuscript provides methods for overcoming these issues in Parhyale, an interesting and tractable arthropod model system for limb regeneration. Additional tools to analyze the acquired movies and cross-reference them with stainings at the endpoint are also provided. As such, it will be a valuable resource for researchers investigating regeneration in this this organism, or in similar settings where parts of the workflow can be adapted. In the present form, however, it is unclear what are the major improvements to previous work and the key steps to achieve them. Similarly, presenting a more detailed analysis of the already acquired data would not only serve to showcase the improved protocols but also make the study of interest to the broader regeneration community.

My expertise: post-embryonic development, whole-body regeneration, cell specification

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors describe a workflow for preparing and imaging leg regeneration in the marine crustacean Paryhale hawaiensis. The method relies on a heatshock-inducible fluorescent histone reporter and captures the regenerating limbs of a mid-sized adult. The authors expand on the prior work that established this transgenic line (Wolff et al. 2018), determined optimal spatial and temporal sampling rates (Alwes et al. 2016), and established the use of an incremental deep learning framework to perform robust automated cell tracking (Sugawara et al. 2022), by jointly optimizing the heatshock induction, imaging parameters, tracking implementation, and integrating in situ end-point analysis to capture the entirety of the regeneration process over an incredibly long window (up to 10 days).

Major comments

MC 1. Given that most of the technical advances necessary to achieve the work described in this manuscript have been published previously, it would be helpful for the authors to more clearly identify the primary novelty of this manuscript. The abstract and introduction to the manuscript focus heavily on the technical details of imaging and analysis optimization and some additional summary of the implications of these advances should be included here to aid the reader.

MC 2. The description of the regeneration time course is nicely detailed but also very qualitative. A major advantage of continuous recording and automated cell tracking in the manner presented in this manuscript would be to enable deeper quantitative characterization of cellular and tissue dynamics during regeneration. Rather than providing movies and manually annotated timelines, some characterization of the dynamics of the regeneration process (the heterogeneity in this is very very interesting, but not analyzed at all) and correlating them against cellular behaviors would dramatically increase the impact of the work and leverage the advances presented here. For example, do migration rates differ between replicates? Division rates? Division synchrony? Migration orientation? This seems to be an incredibly rich dataset that would be fascinating to explore in greater detail, which seems to me to be the primary advance presented in this manuscript. I can appreciate that the authors may want to segregate some biological findings from the method, but I believe some nominal effort highlighting the quantitative nature of what this method enables would strengthen the impact of the paper and be useful for the reader. Selecting a small number of simple metrics (eg. Division frequency, average cell migration speed) and plotting them alongside the qualitative phases of the regeneration timeline that have already been generated would be a fairly modest investment of effort using tools that already exist in the Mastodon interface, I would roughly estimate on the order of an hour or two per dataset. I believe that this effort would be well worth it and better highlight a major strength of the approach.

MC 3. The authors describe the challenges faced by their described approach:

Using this mode of semi-automated and manual cell tracking, we find that most cells in the upper slices of our image stacks (top 30 microns) can be tracked with a high degree of confidence. A smaller proportion of cell lineages are trackable in the deeper layers.

Given that the authors quantify this in Table 1, it would aid the reader to provide metrics in the manuscript text at this point. Furthermore, the metrics provided in Table 1 appear to be for overall performance, but the text describes that performance appears to be heavily depth dependent. Segregating the performance metrics further, for example providing DET, TRA, precision and recall for superficial layers only and for the overall dataset, would help support these arguments and better highlight performance a potential adopter of the method might expect.

MC 4. Performance characterization in Table 1 appears to derive from a single dataset that is then subsampled and processed in different ways to assess the impact of these changes on cell tracking and detection performance. While this is a suitable strategy for this type of optimization it leaves open the question of performance consistency across datasets. I fully recognize that this type of quantification can be onerous and time consuming, but some attempt to assess performance variability across datasets would be valuable. Manual curation over a short time window over a random sampling of the acquired data would be sufficient to assess this.

Significance

As a microscopist and practitioner of large-scale timelapse image acquisition and analysis, my general assessment is that the integration of such complex and data intensive experiments is non-trivial. The study's primary strengths include: 1. Novel capabilities for continuous recording and analysis of limb regeneration in a crustacean model where previous approaches were limited to piecemeal analyses. 2. The assessment of variability in the regeneration timecourse enabled by this approach. And 3. The integration of in situ endpoint analysis enabling retrospective analysis of cell lineage and terminal fate. The study's primary limitation is the lack of quantitative analysis of the resulting datasets, what this reviewer feels is one of the most promising capabilities afforded by this approach. The primary advances described in this manuscript are twofold. First, incremental optimization of imaging and image analysis approaches enabling continuous long-term imaging and robust cell tracking. Second, the potential for the integrated assessment of cellular scale behaviors and tissue level events during regeneration alongside analysis of cell fate endpoints that can be aligned to the time lapse data with cellular precision. This work will be of interest to a somewhat specialized audience, especially given the methods-intensive focus of the manuscript, particularly to microscopists, researchers interested in the biology of regeneration who may be interested in using the method, and developmental or cell biologists working with Paryhale who might benefit from adopting the long-term imaging protocols for other questions. Aligning with my expertise, my review focuses principally on the data analysis and tracking performance characterization aspects of the manuscript.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The manuscript by Çevrim et al. presents a live-imaging method that covers the entire regeneration process of crustacean legs at a resolution that allows for cell tracking and identification of cells based on their gene expression profiles.

Previous work by this group imaged and described the early events of leg regeneration in the crustacean Parhyale hawaiensis (Alwes et al., 2016). Parhyale was also used in the current study because it meets important criteria for live imaging studies: the leg cuticle is optically transparent and does not change in size prior to molting. The authors first recorded 22 time-lapse movies using confocal microscopy, starting immediately after amputation and ending with the fully regenerated leg. They used the movies to describe the entire regeneration of the leg and divided the process into three phases: 1) wound closure, 2) cell proliferation and morphogenesis, and 3) differentiation. There is variability in the duration of these phases, but by highlighting cellular and morphological differences, the authors were able to distinguish the phases from each other. The authors then used one of the 22 time-lapse movies to sub-sample and test the influence of different imaging parameters (z-spacing, time interval, and image quality) on the reliability of cell tracking and the time required for proofreading of the cell lineages, while keeping the phototoxicity on the embryo as low as possible. They achieved this for the upper tissue layer (top 30 µm). For combined semi-automated and manual cell tracking, they used an improved version of the Mastodon add-on Elephant. This cell-tracking software, previously published by the group (Sugawara et al., 2022), now has a backtracking function. It runs in Fiji and is open source. Finally, the regenerated leg used for cell tracking was fixed to perform in situ hybridisation against a gene called spineless. The spineless expressing cells were then aligned with the corresponding cells in the time-lapse movie. This allowed them to associate these cells with cell lineages and identify the progenitor cells at the time of leg amputation. To achieve this, they had to establish the HCR in situ protocol for adult legs. A previous study by the group provided them with transcriptomic data (Sinigaglia et al., 2022), which can inform them about the potential cell types in the leg for future studies.

In summary, this study presents a method to image the complete leg regeneration process at a spatial and temporal resolution, which allows for cell tracking and the addition of molecular information to the cells in the leg.

Major comments:

The manuscript is clearly written with a level of detail that allows others to reproduce the imaging and cell-tracking pipeline. Of the 22 movies recorded one was used for cell tracking. One movie seems sufficient for the second part of the manuscript, as this manuscript presents a proof-of-principle pipeline for an imaging experiment followed by cell tracking and molecular characterisation of the cells by HCR. In addition, cell tracking in a 5-10 day time-lapse movie is an enormous time commitment.

My only major comment is regarding "Suppl_data_5_spineless_tracking". The image file does not load. It looks like the wrong file is linked to the mastodon dataset. The "Current BDV dataset path" is set to "Beryl_data_files/BLB mosaic cut movie-02.xml", but this file does not exist in the folder. Please link it to the correct file.

Minor comments:

The authors state that their imaging settings aim to reduce photo damage. Do they see cell death in the regenerating legs? Is the cell death induced by the light exposure or can they tell if the same cells die between the movies? That is, do they observe cell death in the same phases of regeneration and/or in the same regions of the regenerating legs?

Based on 22 movies, the authors divide the regeneration process into three phases and they describe that the timing of leg regeneration varies between individuals. Are the phases proportionally the same length between regenerating legs or do the authors find differences between fast/slow regenerating legs? If there is a difference in the proportions, why might this be?

Based on their initial cell tracing experiment, could the authors elaborate more on what kind of biological information can be extracted from the cell lineages, apart from determining which is the progenitor of a cell? What does it tell us about the cell population in the tissue? Is there indication of multi- or pluripotent stem cells? What does it say about the type of regeneration that is taking place in terms of epimorphosis and morphallaxis, the old concepts of regeneration?

Page 5. The authors mention the possibility of identifying the cell ID based on transcriptomic profiling data. Can they suggest how many and which cell types they expect to find in the last stage based on their transcriptomic data?

Page 6. Correction: "..molecular and other makers.." should be "..molecular and other markers.."

Page 8. The HCR in situ protocol probably has another important advantage over the conventional in situ protocol, which is not mentioned in this study. The hybridisation step in HCR is performed at a lower temperature (37˚C) than in conventional in situ hybridisation (65˚C, Rehm et al., 2009). In other organisms, a high hybridisation temperature affects the overall tissue morphology and cell location (tissue shrinkage). A lower hybridisation temperature has less impact on the tissue and makes manual cell alignment between the live imaging movie and the fixed HCR in situ stained specimen easier and more reliable. If this is also the case in Parhyale, the authors must mention it.

Page 9. The authors should include more information on the spineless study. What been is spineless? What do the cell lineages tell about the spineless progenitors, apart from them being spread in the tissue at the time of amputation? Do spineless progenitors proliferate during regeneration? Do any spineless expressing cells share a common progenitor cell?

Page 10. Regarding the imaging temperature, the Materials and Methods state "... a temperature control chamber set to 26 or 27˚C..."; however, in Suppl. Data 1, 26˚C and 29˚C are indicated as imaging temperatures. Which is correct?

Page 10. Regarding the imaging step size, the Materials and Methods state "...step size of 1-2.46 µm..."; however, Suppl. Data 1 indicate a step size between 1.24 - 2.48 µm. Which is correct?

Page 11. Correct "...as the highest resolution data..." to "...at the highest resolution data..."

Page 11. Indicate which supplementary data set is referred to: "Using Mastodon, we generated ground truth annotations on the original image dataset, consisting of 278 cell tracks, including 13,888 spots and 13,610 links across 55 time points (see Supplementary Data)."

p. 15. Indicate which supplementary data set is referred to: "In this study we used HCR probes for the Parhyale orthologues of futsch (MSTRG.441), nompA (MSTRG.6903) and spineless (MSTRG.197), ordered from Molecular Instruments (20 oligonucleotides per probe set). The transcript sequences targeted by each probe set are given in the Supplementary Data."

Figure 3. Suggestion to the overview schematics: The authors might consider adding "molting" as the end point of the red bar (representing differentiation).

Figure 4B': Please indicate that the nuclei signal is DAPI.

Supplementary figure 1A. Word is missing in the figure legend: ...the image also shows weak...

Supplementary Figure 2: Please indicate the autofluorescence in the granular cells. Does it correspond to the yellow cells?

Video legend for video 1 and 2. Please correct "H2B-mREFruby" to "H2B-mRFPruby".

Mette Handberg-Thorsager, Jan Huisken

Significance

The optimisation of the imaging and cell tracking parameters presented in this study allows the authors to follow the cells throughout leg regeneration, a milestone that will increase the attractiveness of Parhyale for regeneration studies. The authors have also shown that it is possible to add a cell ID to the cells using HCR in situ staining, which will allow them to decipher the gene-regulatory-networks during cell specification and differentiation in future studies.

Why and how some animals regenerate are fascinating questions in biology. The process of regeneration is therefore being studied in many organisms. A key point is the development of methods to do so, in particular the development of cell lineage tracing techniques combined with molecular profiling of cells to understand the origin of the newly formed cells. In the case of Parhyale, Wolff et al. (2018) traced the cells during leg development. Previous work by the Averof group (Alwes et al. 2016) described the initial phases of leg regeneration. Therefore, the present manuscript represents an important step towards a full understanding of the complete leg regeneration process and the cells that contribute to the reformation of this tissue.

The potential audience interested in this manuscript is broad because of the variety of tools presented in this manuscript (microscopy, software development, cell tracking, and HCR) and the biological question behind the manuscript (cellular and molecular contribution to leg regeneration). This includes scientists working in live imaging, regeneration biology, and software developers.

My field of expertise:

Animal development and regeneration.

Live imaging (including long term) using spinning disk confocal microscopy and light sheet microscopy.

Cell tracking using Mastodon and TGMM.

Limited expertise:

Software development.

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Reply to the reviewers

Manuscript number: RC- 2025-02880

Corresponding author(s): Monica, Gotta

1. General Statements [optional]

We thank the reviewers for their useful comments that will improve our manuscript and make it clearer. We agree with Reviewer 1 that SDS-22 has more general functions in cellular processes by maintaining GSP-1/-2 levels, rather than only regulating cell polarity. We have now modified our conclusion in the text (all changes are highlighted in yellow) and we hope that it is now more clear and better explained. Below we address the reviewer’s comments one by one and indicate how we have or will address the comments in the final version. We expect the revisions to take 2-3 months.

2. Description of the planned revisions

Major comments

Reviewer 1

(1) Overall, the evidence supporting the core finding that SDS-22 is required for normal GSP-1/2 levels is strong and well documented. The experiments were performed well and controls, statistics, replicates were appropriate. Our only slight reservation was whether the effect of sds-22(RNAi) on stability may be overstated due to the use of GFP fusions to GSP-1/2 for this analysis. The authors note these alleles are hypomorphic, potentially raising the possibility that GFP tags destabilise the proteins and make them more prone to degradation. Ideally this would be repeated with an untagged allele via Western (e.g. Peel et al 2017 for relevant antibodies).

We thank the reviewer for the general comments. To address this important point on the protein levels we have requested GSP-1 and GSP-2 antibodies reported in Peel et al and Tzur et al (Peel et al, 2017; Tzur et al, 2012). The published GSP-1 antibody has been used in western blot, and the GSP-2 antibody has been used in both immunostaining and Western blot analysis. Despite our efforts, we were not able to detect GSP-2 neither on western blots nor on immunostainings with the aliquot we have received. On the opposite, GSP-1 antibodies worked well on western blot. We have already measured the GSP-1 levels in SDS-22 depleted embryos (N=2, see below) and we observed reduced levels, confirming our initial result. However, as the reviewer rightly pointed out, the levels are reduced by 20% (rather than about 50% as in the GFP strain), suggesting that indeed the GFP fusion does contribute to the instability. We will measure GSP-1 levels in at least an additional sds-22(RNAi) experiment and in sds-22(E153A) embryos.

Left, Western Blot of embryonic extracts from N2 in ctrl(RNAi) and sds-22(RNAi) embryos. Tubulin is used as a loading control. Right, Fold change of GSP-1 normalized to Tubulin levels. N = 2.

Since we could not detect endogenous GSP-2 with the antibodies we have received, we will generate an OLLAS-tagged GSP-2 strain. OLLAS is a commonly used tag consisting of 14 amino acids (Park et al, 2008), with an additional 4 amino acids as a linker. The tag is much smaller than mNeonGreen, which consists of approximately 270 amino acids. We will then measure the GSP-2 levels using the ollas antibody in sds-22(RNAi) embryos. We will also cross this strain with sds-22(E153A) and measure OLLAS::GSP-2 levels in this mutant. If this strain is not embryonic lethal, as in the case of the mNG::gsp-2; sds-22(E153A) (Fig EV6A), it will also suggest that ollas::gsp-2 does not behave as hypomorph.

These data will complement the data shown in Fig 6.

(2) The role for SDS-22 in polarity is rather weak. Both the SDS-22 depletion phenotypes and the ability of SDS-22 depletion to suppress pkc-3(ts) polarity phenotypes are modest (and weaker in than GSP-2 depletion). For example, the images in Figure 1B appear striking, but from Movie S1 it is clear that this isn't a full rescue as PAR-2 is initially uniformly enriched on the cortex (rather than mostly cytoplasmic) and it is never fully cleared. In the movie, the clearance at the point of pronuclear meeting is very modest. Quantitation might be helpful here (i.e. as in Figure 3G). As the authors state, it seems that SDS-22 does not have a specific role in polarity beyond the general effect on GSP-1/2 levels. This does not undermine the core message of the paper, but we would recommend downplaying the conclusions with respect to contributing to polarity establishment. For example "...suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos" implies a regulatory role for SDS-22 in polarity, but we would interpret it as simply helping reduce aberrant degradation of GSP-1/2 and this impacts a variety of cellular processes including polarity.

We agree with the reviewer that the rescue of pkc-3ts polarity defects by SDS-22 depletion is not as strong as GSP-2 depletion, and as suggested, we have re-quantified the phenotype, as we did in Fig 3G, as shown below in Fig 1C.

This has replaced Fig.1 in the manuscript.

Accordingly, we have clarified this in the text in several locations. We have added “partial” rescue in many places and modified conclusions in the results and discussion. The changes are all highlighted and the major ones are also below:

From Result Line 119-121, page 5:

“In contrast, depletion of SDS-22 resulted in PAR-2 localization being restricted to the posterior cortex in 87.5% of the one-cell stage embryos (Fig 1B) and PAR-2 was localized to the P1 blastomere after the first cell-division (Movie EV1).”

To: Result Line 122-125, page 5

“In contrast, depletion of SDS-22 resulted in PAR-2 localization being enriched in the posterior cortex in 87.5% of the one-cell stage embryos (Fig 1B,C) and PAR-2 was localized to the P1 blastomere after the first cell-division (Movie EV1).”

• *

From Result Line 172-175, page 7:

“Our data show that depletion of SDS-22 results in a smaller PAR-2 domain, suppresses the polarity defects of a pkc-3 temperature sensitive strain and the aberrant PAR-2 localization observed in the PAR-2(L165V) mutant strain. As SDS-22 is a conserved PP1 regulator, our data suggest that SDS-22 positively regulates GSP-2 in polarity establishment.”

To: Result Line 178-181, page 7

“Our data show that depletion of SDS-22 results in a smaller PAR-2 domain, partially suppresses the polarity defects of a pkc-3 temperature sensitive strain and the aberrant PAR-2 localization observed in the PAR-2(L165V) mutant strain. As SDS-22 is a conserved PP1 regulator, our data suggest that SDS-22 positively regulates GSP-2.”

From Result Line 256-257, page 10:

“suggesting that the interaction of SDS-22 with the PP1 phosphatases is important for polarity establishment.”

To: Result Line 264-265, page 10

“suggesting that the interaction of SDS-22 with the PP1 phosphatases contributes to polarity establishment”

• *

From Result Line 311-313, page 12:

To conclude, while our genetic data on PAR-2 cortical localization suggest that SDS-22 is not required to fully activate GSP-1 and/or GSP-2, depletion or mutation of SDS-22 results in a reduced activity of the phosphatases.

To: Result Line 319-322, page 12

To conclude, while our genetic data on PAR-2 cortical localization suggest that SDS-22 is not required to fully activate GSP-1 and/or GSP-2, depletion or mutation of SDS-22 results in a reduced activity of the phosphatases, as shown by phospho-histone H3 (Ser10) levels. This suggests that SDS-22 plays a general role in regulating GSP-1 and GSP-2, which is not specific to cell polarity.

From Result Line 391-392, page 15:

In summary, our results show that SDS-22 maintains the levels of GSP-1 and GSP-2 by protecting them

392 from proteasome mediated degradation.

To: Result Line 402-403, page 15

In summary, these data show that SDS-22 is important to maintain the levels of GSP-1 and GSP-2 by protecting them from proteasome mediated degradation.

We have also rephrased our conclusion according to Reviewer 1’s suggestion.

From Introduction Line 95-101, Page 4:

Here we show that SDS-22 depletion rescues the polarity defects caused by reduced PAR-2 phosphorylation in the pkc-3(ne4246) mutant at the semi-restrictive temperature (24°C), similarly to the depletion of GSP-2. Depletion of SDS-22 results in lower GSP-1 and GSP-2 protein levels which can be rescued by depleting proteasomal subunits. These results establish SDS-22 as a regulator of PAR polarity establishment in the C. elegans one-cell embryo and are consistent with and complement the recent data in mammalian cells showing that SDS22 is important to control the stability of the PP1 phosphatase (Cao et al., 2024).

To: Introduction Line 96-101, Page 4

*Here we show that SDS-22 depletion partially rescues the polarity defects caused by reduced PAR-2 phosphorylation in the pkc-3(ne4246) mutant at the semi-restrictive temperature (24°C). Depletion of SDS-22 results in lower GSP-1 and GSP-2 protein levels which can be rescued by depleting proteasomal subunits. These results establish that SDS-22 contributes to cell polarity by regulating GSP-1/-2 levels and are consistent with and complement the recent data in mammalian cells showing that SDS22 is important to control the stability of the PP1 phosphatase (Cao et al., 2024). *

From Discussion Line 417-420, page 17:

Depletion of SDS-22, or mutation of its E153 residue (E153A) important for SDS-22-PP1 interaction resulted in reduced GSP-1/-2 protein levels, decreased dephosphorylation of a PP1 substrate, and a smaller PAR-2 domain, suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos.

To: Discussion Line 426-429, page 17

*Here we find that a conserved PP1 regulator, SDS-22, when depleted, results in a smaller PAR-2 domain and can partially rescue the polarity defects of a pkc-3(ne4246) mutant. We demonstrate that SDS-22 contributes to the activity of GSP-1/-2 by protecting them from proteasomal degradation and maintaining their protein levels. *

Add new discussion to Discussion Line 429-432, page 17:

Taken together, our data suggest that the role of SDS-22 in polarity is indirect via the regulation of GSP-1/-2 levels. In support of this, SDS-22 depletion results in broader GSP-1/-2 dependent phenotypes such as increased Phospho-H3 (Ser10) (Fig 5) and centriole duplication defects in later-stage embryos (Peel et al., 2017).

• *

(3) Specificity of SDS-22 effects on polarity. SDS-22 (or GSP-1/2) depletion is likely to have effects on many pathways. We wondered whether some of the polarity phenotypes may not be specifically due to changes in the PAR-2 phosphorylation cycle as implied.

One candidate is the actomyosin cortex. It was noticeable that control and sds-22 embryos were different: In Movies S1, S2, and S3 control embryos show either stronger or more persistent cortical ruffling or pseudocleavage furrows. This is also visible in Figure 3A. Is it possible that disruption of SDS-22 reduces cortical flows (time, intensity or duration) and could this explain the small reduction in anterior PAR-2 spreading and thus the slightly smaller domain size measured in Figures 1B and 3A.

We have noticed that SDS-22 depletion results in less ruffling and reduced pseudocleavage furrows. To properly address this question we should have a condition in which we can rescue the cortical flow reduction in the SDS-22 depletion and measure the PAR-2 domain. Since we do not know how SDS-22 reduces the flows, we could not come up with a clean experiment to address this issue and are happy to have suggestions.

We believe that the most rigorous way to address this issue, as reviewer 1 points out, is to clearly address this limitation in the text. We have now added this in the discussion:

Discussion Line 463-466, page 18:

Consistent with GSP-2 reduced levels, SDS-22 depleted or E153A mutant embryos also have a smaller PAR-2 domain. However, since these embryos also show reduced cortical ruffling (Movie EV1,2) and are smaller (Fig EV2C) we cannot exclude that these two phenotypes also contribute to the smaller size of the PAR-2 domain.

• *

A potentially related issue could be embryo size. sds-22 embryos generally seem to be smaller than wild-type (e.g. Figure 1B(left), 4A(left column), and particularly EV3). Is this consistently true? Could cell size effects change the ability of embryos to clear anterior PAR-2 domains as described in EV3? Klinkert et al (2018, biorXiv) note that reducing the size of air-1(RNAi) embryos reduces the frequency of bipolar PAR-2 domains.

Quantification of perimeter of embryos at pronuclear meeting in live zygotes. Sample size (n) is indicated in the graph, each dot represents a single embryo and mean is shown. N = 5. The P value was determined using two-tailed unpaired Student’s t test.

We quantified the perimeter of the embryos and as seen by quantification, there is a weak but significant decrease of size in the absence of SDS-22, and in SDS-22(E153A) mutant, as shown above. We have now added the data of the RNAi in the supplementary information and mentioned it in the results.

Results Line 129, page 5:

SDS-22 depleted embryos also displayed a smaller size (Fig EV2C).

Klinkert et al reported that reducing the size of air-1(RNAi) embryos by depletion of ANI-2, a homolog of the actomyosin scaffold protein anillin, reduces the frequency of bipolar PAR-2 domains (Klinkert et al, 2018). In the image shown in the paper on bioRxiv, the PAR-2 domain appears small but there are no quantifications and these data have been removed from the published paper.

From published data, a smaller embryo size does not appear to correlate with smaller PAR-2 domain. Chartier et al show that depletion of ANI-2 reduces embryo size without changing the relative anterior PAR-6 domain (Chartier et al, 2011), thereby suggesting that the posterior PAR-2 domain should not change either. In addition, Hubatsch et al reported that small embryos depleted of ima-3 tend to have larger PAR-2 domains, whereas larger embryos depleted of C27D9.1 exhibit smaller PAR-2 domains (Hubatsch et al, 2019), which is the opposite of what we see. We do not believe that the smaller PAR-2 domain is the important message of our paper. Our main question was whether PAR-2 was cortical or not and since GSP-2 had a smaller domain, we decided to quantify the PAR-2 domain length in the different RNAi conditions and mutants. Since RNAi of C27D9.1 which makes embryos bigger, results in a small PAR-2 domain, again we do not know how to experimentally address this question, unless the reviewer has a suggestion. As for the point above, we will clearly highlight this limitation in the discussion (see our reply to the previous point, now it is in Discussion Line 463-466, page 18).

We would stress that these comments relate to interpreting the polarity phenotypes and do not undermine the core finding that SDS-22 stabilises GSP-1/2.

We thank the reviewer and we hope that by performing the experiments mentioned above and by changing the text, their comments are properly addressed.

Reviewer 2

Major comment: Consistent with the model that PP1 activity is reduced in the absence of SDS-22, the authors show that a surrogate PP1 target (phospho-histone H3) becomes hyper-phosphorylated. To strengthen the study, the authors could consider performing an OPTIONAL experiment (see below) of assaying the phosphorylation status of PAR-2 itself, as this is proposed to be the target of both PKC-3 and PP1, and represent the mechanism of PAR-2 polarization.

We thank the reviewer for this comment and also for pointing out that there is technical difficulty in the proposed experiment.

We have already attempted to address this point without success in Calvi et al (Calvi et al, 2022), using western blot analysis (see below). For this we used the GFP::PAR-2 strain and used a GFP antibody (shown below in the left panel), as none of the anti-PAR-2 antibodies (neither the ones produced by us nor the ones produced by other laboratories) were working on western blot. We observed several bands of GFP::PAR-2 but were not able to determine if these represented phosphorylated forms or to compare the ratio of phosphorylated to unphosphorylated PAR-2. We did use λ-PPase in the embryonic extracts but we did not always observe a clear difference. We show three experiments below.

Left, __Western blots of gfp::par-2 embryonic extract in the presence or absence of λ-PPase (+/- PhosSTOP) and probed with anti-GFP and anti-Tubulin antibodies. Right,__ Representative images of fixed embryos with indicated genotypes at one-, two- and four-cell stages. DNA (DAPI) is gay. Scale bars, 5 μm. Anterior is to the left and posterior to the right.

One possible explanation is that the role of GSP-1/-2 in PAR-2 dephosphorylation is specific to the very early embryos. As shown in the right panel above, despite PAR-2(RAFA) remaining cytoplasmic in one- and two-cell embryos due to lack of binding to GSP-1/-2, it can localize to internal cortices in four-cell stage embryos, similarly to the control and suggesting that in later embryos other mechanisms are intervening. One limitation of our Western Blot is that it is not possible to isolate only early embryos, which are a minority in a mixed population of embryos. This may mask difference of phosphorylation status of PAR-2 in the early stages.

For the revision, we plan to blot PAR-2 using GFP antibody in gfp::par-2 embryo lysates, with both control and sds-22(RNAi) treatment. We will also compare the GFP::PAR-2 bands between gfp::par-2 and gfp::par-2; sds-22(E153A) mutant samples. We are not very hopeful and our failures with gsp-1/2 RNAi (unpublished) are why we did not try with SDS-22 but it is definitely worth giving it a go and we will.

As for Hao et al (Hao et al, 2006) the result was quite clear. In this paper however, the authors used a transgene strain of PAR-2. We have never tried to use a transgene (the proteins are usually overexpressed) but we can deplete SDS-22 in a PAR-2 transgene as well and see if a difference is observed.

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Reviewer 3

Major comments: major issues affecting the conclusions

Overall, the authors' conclusions are supported by their data. The data and methods are presented clearly, with appropriate replicates and statistics. Here I propose two experiments to strengthen the link between some of their data and their claims. These experiments could take a month or two to complete.

Experiment 1

It would be helpful if the authors could show that blocking the proteasome in the zygote restores GSP-1/-2 levels in the absence of SDS-22 or even better in the SDS-22(E153A) mutant. This would provide more direct evidence to support their claim that SDS-22 regulates polarity by protecting PP1 from proteasomal degradation. While they are currently conducting this experiment in the germline, they cannot assess polarity there. However, in the zygote, they would be able to examine the PAR-2 domain (polarity). To do this, the authors could permeabilise the embryos and apply a proteasome inhibitor.

This would be a straightforward experiment if we were using culture cells. One problem with the set up is that much of the protein of the one-cell embryo is inherited from the egg and the reduction in SDS-22 depletion or mutant happens already in the germline (Fig 6-7). Even if the proteasome is inhibited in embryos, the whole division process only takes 20 minutes and we wonder whether the timing will be sufficient to inhibit the proteasome, produce more protein and rescue the phenotype. We will try, as only this will tell us.

One alternative approach would be to apply the proteasome inhibitor to adult worms in liquid culture for several hours before dissection. This would aim to inhibit degradation in the germline, therefore allowing us to test whether GSP-1/-2 levels are restored in the embryos with SDS-22 disruption. However, proteasome inhibition in the germline impairs oogenesis (Shimada et al, 2006), suggesting that we might incur in the same problem (unless we succeed in timing the inhibition).

One additional experiment that we will try is to deplete other proteasomal subunits that result in a lower level or proteasomal activity reduction. As reported by Fernando et al (Fernando et al, 2022), depletion of RPN-9, -10, or -12 impairs proteasomal activity, but worms remain fertile.

Quantification of mNG::GSP-2 and GFP::GSP-1fluorescence intensity in rpn-12, rpn-9, and rpn-10(RNAi) normalized to ctrl(RNAi). Mean is shown and error bars indicate SD. Dots in graphs represent individual embryo measurements and sample size (n) is indicated inside the bars in the graph. N = 1.

So far, our data suggest that the GSP-1/-2 levels are weakly but significantly increased in the embryos (16.8% for GSP-2 and 12.5% for GSP-1) following RPN-12 depletion (see above). We will co-deplete RPN-12 and SDS-22 to assess if the protein levels of GSP-1/-2 are rescued. We will also deplete RPN-12 in gfp::gsp-1; sds-22(E153A) strains to test if GSP-1 levels are rescued. We cannot measure GSP-2 levels in mNG::GSP-2; sds-22(E153A) because they are embryonic lethal (see details below in the reply to minor comments of Reviewer 3).

Left, Representative midsection images of gfp::gsp-1 and gfp::gsp-1;sds-22(E153A) embryos in ctrl(RNAi) and rpn-12(RNAi).__ Right, __Quantification of GFP::GSP-1 intensity levels. N = 1.

Our preliminary data showed that similar to germlines (Fig 7G-I), RPN-12 depletion in gfp::gsp-1; sds-22(E153A) rescued the reduction of GSP-1 levels in embryos (shown above). We will perform two additional experiments to quantify GSP-1 levels.

We will also test if the smaller PAR-2 domain in sds-22(E153A) mutant is rescued by RPN-12 depletion. With these experiments, we aim to answer if proteasome inhibition rescues the reduced levels of GSP-1/-2 and thereby rescues the reduced PAR-2 domain when SDS-22 is depleted or mutated.

Experiment 2

The posterior localization of PAR-2 after co-RNAi of GSP-1 and SDS-22 contrasts with the absence of PAR-2 at the cortex when both GSP-1 and GSP-2 are depleted. This difference may be due to the partial reduction of GSP-2 levels when SDS-22 is depleted, compared to the more substantial reduction of GSP-2 upon GSP-2 RNAi. Have the authors considered combining full depletion of GSP-1 with partial depletion of GSP-2 to see if PAR-2 remains present and localized to the posterior? This experiment could help clarify the discrepancy between the phenotypes and further support the role of SDS-22 in regulating GSP-2 protein levels. Additionally, by titrating PP1, the authors may be able to determine the minimum amount of PP1 needed to establish the PAR-2 domain.

We will try this experiment but, assuming we find a condition in which we can fully deplete GSP-1 and only half of GSP-2, one problem is that it is impossible to control the levels of both GSP-1 and 2 and measure the PAR-2 domain in the same embryos (which would be the most rigorous way to perform the experiment so that we know the amount of depletion and correlate with the PAR-2 domain length). The only thing we can do is the same depletion time in the 3 different strains (the mNG::gsp-2, the gfp::gsp-1 and the gfp::par-2) and assume that the depletion will work the same in the three different strains.

• *

Minor comments

Reviewer 1

Minor Points

• The link between lethality and polarity of the zygote is not always obvious and whether they are connected (or not) could probably be made clearer. Indeed, the source of lethality is unclear, particularly given that loss of SDS-22 on its own strongly impacts lethality with minimal effects on polarity (at least in the zygote).

In many cases, we have reported embryonic lethality as information, not with a precise scope to correlate the lethality with the phenotype. We apologize for the lack of clarity. We know that embryonic lethality is normally associated with severe polarity defects. As example, in the par-2(RAFA) mutant and in the pkc-3ts mutant at temperatures around 24-25°C cortical polarity is lost, embryos divide symmetrically and synchronously and die (Calvi et al., 2022; Rodriguez et al, 2017) and many more references for the PAR mutants (Kemphues et al, 1988; Kirby et al, 1990; Morton et al, 1992). We and others have also shown that depletion of GSP-2 can rescue the lethality of pkc-3(ts) but only at a semipermissive temperature when there is still residual PKC-3 activity (Calvi et al., 2022; Fievet et al, 2013). As our aim was to identify the regulator of GSP-2, we tested the potential regulators by RNAi in the pkc-3(ts), with the assumptions that a regulator, similar to GSP-2, would rescue the pkc-3(ts) polarity defects and lethality. As it turns out, SDS-22 is not a canonical regulator of GSP-2. The partial rescue of the polarity defects is most likely the result of the fact that SDS-22 lowers the level of GSP-2. However, SDS-22 is probably involved in many other functions that involve GSP-1 and GSP-2 (as shown for example:(Beacham et al, 2022; Peel et al., 2017)) and it is embryonic lethal. We do not know, however, whether the embryonic lethality is the results of the sum of the various functions of SDS-22 or it is due to a specific function.

To clarify it better, we have now explained the connection between polarity and lethality in the text,

From Result Line 111-114, page 5:

We first asked whether depletion of any of these three regulators suppress the embryonic lethality of pkc-3(ne4246); gfp::par-2 embryos at the semi-permissive temperature of 24°C (in which PKC-3 is partially active, temperature used in all experiments with the pkc-3(ne4246) mutant, unless otherwise stated), similar to depletion of the catalytic subunit GSP-2.

To Results Line 111-117, page 5:

*When the temperature sensitive mutant pkc-3(ne4246) is grown at semi-permissive temperature, the residual PKC-3 activity is not sufficient to exclude PAR-2 from the anterior cortex. These embryos cannot establish polarity and die. Depletion of the catalytic subunit GSP-2 in this strain suppresses PAR-2 mislocalization and the resulting polarity defects, thereby rescuing embryonic lethality. We first asked whether depletion of any of these three identified regulators suppresses the embryonic lethality of pkc-3(ne4246); gfp::par-2 embryos at the semi-permissive temperature of 24°C (temperature used in all experiments with the pkc-3(ne4246) mutant, unless otherwise stated) , similar to depletion of GSP-2. *

From Result Line 241-242, page 10:

We next asked whether sds-22(E153A) was able to rescue the lethality and the polarity defects of pkc-3(ne4246) embryos.

To Results Line 223-224, page 9:

Because of this, we decided to test whether sds-22(E153A) was able to rescue the lethality and the polarity defects of pkc-3(ne4246) embryos.

• Formally, the conclusion that reduced GSP-1/2 in SDS-22 depletion conditions is due to increased proteasomal degradation is not shown directly as there is no data on rates just steady-state levels. We agree that the genetic data is strongly suggestive of this model and it is consistent with work of other labs. Thus this is the most likely scenario, but could in principle reflect reduced expression that is balanced by reduced degradation.

We agree with the reviewer. To address this point, we will perform RT-PCR analysis to measure the gene expression levels of gsp-1 and gsp-2 from control, SDS-22 depletion and sds-22(E153A) embryos.

• It is interesting that sds-22(E153A) caused a stronger decrease in oocyte GSP-1 levels than sds-22(RNAi) (Fig 7). The authors may want to comment on this result.

As we performed depletion of SDS-22 by RNAi feeding from L4 stage, we might not see strong reduction of GSP-1 in oocytes compared to that in sds-22(E153A) mutant, which carries an endogenous mutation of SDS-22 throughout the life cycle.

Left, Representative images of gfp::gsp-1 germlines in ctrl(RNAi) and sds-22(RNAi), comparing to gfp::gsp-1; sds-22(E153A); ctrl(RNAi). __Right, __Quantification of GFP::GSP-1 intensity levels in the cytoplasm and nucleus of -1 and -2 oocytes. N = 1.

To address this point we have performed an experiment where we have depleted SDS-22 starting from L1s. As shown above, RNAi feeding of SDS-22 from L1 stage showed a similar reduction of GSP-1 (16.1% in the cytoplasm; 24.6% in the nucleus) as in gfp::gsp-1; sds-22(E153A), which was stronger comparing to feeding from L4 (8.8% in the cytoplasm; 17.4% in the nucleus, Fig 7D-E). This supports our hypothesis that the difference shown in Fig 7D-I might result from a relative short RNAi depletion of SDS-22 from L4 stage comparing to endogenous SDS-22(E153A) mutation. This experiment was done only once and will be repeated. If confirmed, we will add a sentence in the text. As RNAi feeding of SDS-22 from L1 stage impairs the formation of germlines, we will keep the protocol using SDS-22 RNAi feeding in L4 worms for other experiments in this study.

• "At polarity establishment, the PP1 phosphatases GSP-1/-2 dephosphorylate PAR-2 allowing its cortical posterior accumulation." This statement, possibly inadvertently, implies temporal regulation, which has not been shown.

We have changed the sentence, as suggested by the reviewer:

To Introduction Line 59-60, page 3:

The PP1 phosphatases GSP-1/-2 dephosphorylate PAR 2 allowing its cortical posterior accumulation and embryo polarization.

• It would be ideal if the authors could explicitly state how they define pronuclear meeting. For example in Figure 1B, the embryos look like they are a few minutes past pronuclear meeting (e.g. compared to Figure 3), but maybe the pronuclei tend to meet more centrally in these conditions? Given that PAR-2 clearance is changing in time in some of these cases (based on looking at the movies), staging needs to be very accurate to get the best comparisons.

We apologize for the lack of clarity. Pronuclear meeting is defined when the two pronuclei first contact each other.

As noted by Reviewer 1, it is true that the pronuclei in pkc-3ts mutant tend to meet more centrally compared to control embryos. The same finding was also observed on PKC-3 inhibition (through depletion, mutation or inhibitor treatment) by Rodriguez et al (Rodriguez et al., 2017). In addition, Kirby et al reported that mutations in the anterior PAR complex lead to the mislocalization of the pronuclei, causing them to meet more in the center (Kirby et al., 1990). We now specify this in the Material and Methods.

Add in Material and Methods Line 633-635, page 22:

*The stage of pronuclear meeting is defined when the two pronuclei first contact each other. In pkc-3(ne4246) embryos, the two pronuclei exhibited a tendency to meet more centrally compared to controls (Fig 1B, Movie EV1), as shown in (Kirby et al, 1990; Rodriguez et al, 2017). *

As Reviewer 1 mentioned, accurate staging is crucial, as PAR-2 clearance can vary over time. The measurements were done in the first frame where pronuclei touch each other. However, in Fig. 1B we had shown one pkc-3ts; sds-22(RNAi) embryo one frame (10 seconds) later. We have now corrected this (see the updated Figure 1B).

• In the interests of data-availability, upon publication the authors would deposit the raw mass spec data underlying Figure EV1.

The reviewer is right, this was forgotten. We have now added as supplementary material the Dataset EV1 and EV2.

Reviewer 3

Minor comments: important issues that can confidently be addressed

In the introduction (line 83), it's unclear what reconciles the contradictory data. I also have difficulty understanding this point in the discussion (line 435).

We apologize for the lack of clarity and have now modified the text:

From Introduction Line 82-84, page 4:

This underscores the complex roles of SDS22 in regulating PP1 function and reconciling the contradictory data obtained in vivo and in vitro (Cao et al., 2024; Cao et al, 2022; Kueck et al., 2024; Lesage et al, 2007).

To Introduction Line 81-85, page 4:

These two recent findings suggest that while SDS-22 is required for the biogenesis of PP1 holoenzymes, its removal is essential to have an active PP1. This dual role of SDS-22 explains how SDS22 behaves as an inhibitor in biochemical assays in vitro but as an activator in vivo (Cao et al., 2024; Cao et al, 2022; Kueck et al., 2024; Lesage et al, 2007).

From Discussion Line 435-436, page 17:

These data reconcile the contradictory in vivo and in vitro observations.

To Discussion Line 447-451, page 17:

Given that SDS-22 both stabilizes PP1 levels and inhibits its activity, this dual role clarifies the apparent contradiction: while SDS-22 is essential for PP1 activity in vivo (because it is essential for the biogenesis/stability), it inhibits PP1 activity in vitro (as it needs to be removed to have an active PP1), while in vivo it is removed by p97/Valosin resulting in active PP1.

Additionally, in the results section (line 389), it's not clear why the gonads cannot be studied in the strain with dead embryos. Are the gonads also altered in a way that prevents their observation?

We explained this in the material and methods part (Line 583-584, 588-592), page 21.

To clarify it better in the main text, we have now modified

Results Line 377-378, page 15:

Since depletion of these subunits results in worms with very little to no progeny (Fernando et al., 2022)

Results Line 396-401, page 15:

*Since we use the embryonic lethality phenotype of the mNG::gsp-2; sds-22(E153A) strain to recognize the homozygote sds-22(E153A), this precluded the possibility to analyze the germlines of homozygote mNG::gsp-2; sds-22(E153A) worms depleted of RNP-6.1 or RPN-7, as these worms do not have progenies (Fernando et al., 2022) and we therefore cannot distinguish the sds-22(E153A) homozygote from the sds-22(E153A) heterozygote (see material and methods for details). *

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

• *

We have re-quantified the data in Fig 1B and displayed as in Fig 1C.

We have double checked our data and corrected Fig 3G.

We have modified the text to address many of the comments of the reviewer about clarity and rigor.

We have added supplementary information Fig EV2C and Dataset EV1 and EV2.

Other experiments performed are still preliminary and only shown in this revision letter.

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

• *

We believe with the reply, the text changes and the experiments that we have proposed and started, we will address all comments of the reiewers.

• *

References

Beacham GM, Wei DT, Beyrent E, Zhang Y, Zheng J, Camacho MMK, Florens L, Hollopeter G (2022) The Caenorhabditis elegans ASPP homolog APE-1 is a junctional protein phosphatase 1 modulator. Genetics 222

Calvi I, Schwager F, Gotta M (2022) PP1 phosphatases control PAR-2 localization and polarity establishment in C. elegans embryos. J Cell Biol 221

Chartier NT, Salazar Ospina DP, Benkemoun L, Mayer M, Grill SW, Maddox AS, Labbe JC (2011) PAR-4/LKB1 mobilizes nonmuscle myosin through anillin to regulate C. elegans embryonic polarization and cytokinesis. Curr Biol 21: 259-269

Fernando LM, Quesada-Candela C, Murray M, Ugoaru C, Yanowitz JL, Allen AK (2022) Proteasomal subunit depletions differentially affect germline integrity in C. elegans. Front Cell Dev Biol 10: 901320

Fievet BT, Rodriguez J, Naganathan S, Lee C, Zeiser E, Ishidate T, Shirayama M, Grill S, Ahringer J (2013) Systematic genetic interaction screens uncover cell polarity regulators and functional redundancy. Nat Cell Biol 15: 103-112

Hao Y, Boyd L, Seydoux G (2006) Stabilization of cell polarity by the C. elegans RING protein PAR-2. Dev Cell 10: 199-208

Hubatsch L, Peglion F, Reich JD, Rodrigues NT, Hirani N, Illukkumbura R, Goehring NW (2019) A cell size threshold limits cell polarity and asymmetric division potential. Nat Phys 15: 1075-1085

Kemphues KJ, Priess JR, Morton DG, Cheng NS (1988) Identification of genes required for cytoplasmic localization in early C. elegans embryos. Cell 52: 311-320

Kirby C, Kusch M, Kemphues K (1990) Mutations in the par genes of Caenorhabditis elegans affect cytoplasmic reorganization during the first cell cycle. Dev Biol 142: 203-215

Klinkert K, Levernier N, Gross P, Gentili C, von Tobel L, Pierron M, Busso C, Herrman S, Grill SW, Kruse K et al (2018) Aurora A depletion reveals centrosome-independent polarization mechanism in C.elegans. bioRxiv: 388918

Morton DG, Roos JM, Kemphues KJ (1992) par-4, a gene required for cytoplasmic localization and determination of specific cell types in Caenorhabditis elegans embryogenesis. Genetics 130: 771-790

Park SH, Cheong C, Idoyaga J, Kim JY, Choi JH, Do Y, Lee H, Jo JH, Oh YS, Im W et al (2008) Generation and application of new rat monoclonal antibodies against synthetic FLAG and OLLAS tags for improved immunodetection. J Immunol Methods 331: 27-38

Peel N, Iyer J, Naik A, Dougherty MP, Decker M, O'Connell KF (2017) Protein Phosphatase 1 Down Regulates ZYG-1 Levels to Limit Centriole Duplication. PLoS Genet 13: e1006543

Rodriguez J, Peglion F, Martin J, Hubatsch L, Reich J, Hirani N, Gubieda AG, Roffey J, Fernandes AR, St Johnston D et al (2017) aPKC Cycles between Functionally Distinct PAR Protein Assemblies to Drive Cell Polarity. Dev Cell 42: 400-415 e409

Shimada M, Kanematsu K, Tanaka K, Yokosawa H, Kawahara H (2006) Proteasomal ubiquitin receptor RPN-10 controls sex determination in Caenorhabditis elegans. Mol Biol Cell 17: 5356-5371

Tzur YB, Egydio de Carvalho C, Nadarajan S, Van Bostelen I, Gu Y, Chu DS, Cheeseman IM, Colaiacovo MP (2012) LAB-1 targets PP1 and restricts Aurora B kinase upon entrance into meiosis to promote sister chromatid cohesion. PLoS Biol 10: e1001378

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Referee #3

Evidence, reproducibility and clarity

Summary: your understanding of the study and its conclusions

This is a follow up on Gotta's lab paper, which shows that the PP1 catalytic subunits GSP-1 and GSP-2 are involved in the polarization of the C. elegans zygote (10.1083/jcb.202201048). Here, the authors report that SDS-22, an interactor of PP1, regulates PP1 function in the zygote. Depleting SDS-22, similar to depleting GSP-2, rescues the polarity defects caused by the inactivation of aPKC in the zygote. This suggests that SDS-22 plays a role in promoting GSP-2's function in polarity. The mechanism behind this may involve SDS-22 protecting GSP-1 and GSP-2 from degradation by the proteasome.

Major comments: major issues affecting the conclusions

Overall, the authors' conclusions are supported by their data. The data and methods are presented clearly, with appropriate replicates and statistics. Here I propose two experiments to strengthen the link between some of their data and their claims. These experiments could take a month or two to complete.

Experiment 1

It would be helpful if the authors could show that blocking the proteasome in the zygote restores GSP-1/-2 levels in the absence of SDS-22 or even better in the SDS-22(E153A) mutant. This would provide more direct evidence to support their claim that SDS-22 regulates polarity by protecting PP1 from proteasomal degradation. While they are currently conducting this experiment in the germline, they cannot assess polarity there. However, in the zygote, they would be able to examine the PAR-2 domain (polarity). To do this, the authors could permeabilise the embryos and apply a proteasome inhibitor.

Experiment 2

The posterior localization of PAR-2 after co-RNAi of GSP-1 and SDS-22 contrasts with the absence of PAR-2 at the cortex when both GSP-1 and GSP-2 are depleted. This difference may be due to the partial reduction of GSP-2 levels when SDS-22 is depleted, compared to the more substantial reduction of GSP-2 upon GSP-2 RNAi. Have the authors considered combining full depletion of GSP-1 with partial depletion of GSP-2 to see if PAR-2 remains present and localized to the posterior? This experiment could help clarify the discrepancy between the phenotypes and further support the role of SDS-22 in regulating GSP-2 protein levels. Additionally, by titrating PP1, the authors may be able to determine the minimum amount of PP1 needed to establish the PAR-2 domain.

Minor comments: important issues that can confidently be addressed

In the introduction (line 83), it's unclear what reconciles the contradictory data. I also have difficulty understanding this point in the discussion (line 435). Additionally, in the results section (line 389), it's not clear why the gonads cannot be studied in the strain with dead embryos. Are the gonads also altered in a way that prevents their observation?

Referees cross-commenting

Overall, I agree with the other reviewers' comments. The suggested experiments would help strengthen the connection between SDS-22 and cell polarity, as well as its role in relation to the proteasomal-mediated degradation of GSP-1/-2 and its impact on cell polarity. These experiments seem feasible and could provide stronger support for the authors' claims about these regulatory mechanisms. Alternatively, the authors may consider moderating some of their conclusions if these experiments are not conducted.

Significance

General assessment: strengths and limitations

This study enhances our understanding of how phosphatases regulate cell polarity, specifically in the C. elegans zygote, a key model system for studying cell polarity. The study could be further strengthened by the experiments mentioned above. Additionally, see the comment on how to increase the impact of the work (Audience section).

Advance: compare the study to existing published knowledge This study is the first to characterize the role of SDS-22 in the polarization of the C. elegans zygote. As the authors discuss, their results align with and complement existing knowledge of SDS-22 in other cell types. Together with the literature, this work highlights the complexity of PP1 regulation, suggesting that different PP1 outcomes may be achieved by combining SDS-22 with various PP1 co-regulators.

Audience that will be interested or influenced by this research

These results will be of interest to scientists studying cell signalling and cell polarity. There is currently strong focus on understanding the regulation of phosphatases. In cell polarity research, the spatial regulation of phosphatases is particularly important for understanding the asymmetric activation of signalling pathways. SDS-22 does not appear to control the spatial localization or activity of PP1, but rather its overall protein levels. As the authors note in the discussion, this suggests that other factors may be involved in the polarization of PP1 signalling. In supplementary figure S1, the authors provide a volcano plot showing candidate PP1 interactors. Providing the list of positive hits would increase the impact of the study and benefit the research community. It would also help explain why the authors chose to follow up on SDS-22 in this study. Furthermore, this could advance the identification of factors involved in the polarization of PP1 signalling.

My expertise

Cell polarity, cell signalling, embryo development.

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Referee #2

Evidence, reproducibility and clarity

Summary: The authors present a logical and clear set of data that support the model that SDS-22 is an important regulator of cell polarity via its ability to stabilize Protein Phosphatase 1 (PP1).

The authors use a clever combination of genetic manipulations and quantitative imaging to show that loss of SDS-22 phenocopies loss of PP1, in that PAR-2 polarization is restored in nascent C. elegans zygotes following inactivation of PKC-3. Rescue of PAR-2 polarization also occurs when the authors mutate a conserved residue in SDS-2 that is predicted to form electrostatic interactions with PP1, suggesting that SDS-22 acts via PP1. Inactivation of SDS-22 results in decreased levels of PP1, and the authors provide evidence that this is via proteasomal degradation by showing that PP1 levels are restored by knockdown of proteasomal subunits.

Overall the manuscript is well-written, the experiments rigorous, and the methods and data likely to be reproducible.

Major comment: Consistent with the model that PP1 activity is reduced in the absence of SDS-22, the authors show that a surrogate PP1 target (phospho-histone H3) becomes hyper-phosphorylated. To strengthen the study, the authors could consider performing an OPTIONAL experiment (see below) of assaying the phosphorylation status of PAR-2 itself, as this is proposed to be the target of both PKC-3 and PP1, and represent the mechanism of PAR-2 polarization.

Referees cross-commenting

In principle I agree with many of the thoughtful comments by the other reviewers. They point out many potential areas for both enhancing the strength of the findings and including a more nuanced interpretation of the results. However, I also feel that the experiments proposed to deal with their concerns might not be so straightforward to pursue for unforeseen technical reasons and may actually take substantially longer than anticipated. The same is true for my proposed experiment to assess phosphorylation status of PAR-2, which is why I have indicated it as optional. I ask the other reviewers to consider if any of their proposed experiments might also be considered optional. I also thank them for their critical assessments of the paper! They were helpful for me and I'm sure will also be for the authors.

Significance

This study brings clarity to the contentious role of SDS-22 by showing that it appears to promote PP1 activity by counteracting the phosphatase degradation process in vivo. This complements a previously hypothesized function of SDS-22, while contrasting with other proposed functions of SDS-22 as a regulator of PP1 localization or stimulator of PP1 degradation. Thus, the authors' discoveries in C. elegans represent a significant advance in our understanding of protein phosphatase regulation, a long-standing question in biology and a central process in all cellular systems. The study also points to potential mechanisms for modulating phosphatase activity in other contexts, across different organisms and disease states. Basic science researchers will be interested in the findings, with potential to attract additional interest from physiologists and even drug designers.

One limitation of the study is that the authors use PAR-2 polarization as a readout of PKC-3 and PP1 activity without showing directly that PAR-2 phosphorylation status is changing in response to their genetic manipulations, including SDS-22 inactivation. The PAR-2 membrane localization is thought to be inhibited by PKC-3-dependent phosphorylation and promoted by PP1-dependent dephosphorylation. Is there a possibility of examining whether PAR-2 phosphorylation is elevated in SDS-22 RNAi or mutant animals? Previously, Hao et. al., 2006; doi.org/10.1016/j.devcel.2005.12.015. showed in Figure 2 that PAR-2 runs as a doublet band on western blots with the phosphorylated form of PAR-2 appearing to correlate with the slightly higher molecular weight band. This was used to infer the ratio of phosphorylated to dephosphorylated PAR-2. I'm wondering if it might be possible for the authors to perform a similar analysis of their existing GFP::PAR-2? It appears from their previous paper on PP1 regulation of PAR-2 polarization (Calvi et. al., 2022; doi: 10.1083/jcb.202201048.) that they might also be detecting a similar doublet (Figure S5F and associated source file), so perhaps it is a doable experiment? Regardless, this is not an essential experiment as the study is already significant and rigorous!

I am a cell biologist that uses C. elegans to understand the function of conserved protein complexes that regulate the development and function of animal tissues.

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Referee #1

Evidence, reproducibility and clarity

Summary

This work from Li et al. identifies a role for SDS-22 in maintaining normal levels of the PP1 subunits GSP-1/2 in C. elegans. Prior work in the lab identified a role for the catalytic PP1 subunits GSP-1/2 in opposing the phosphorylation of of the polarity protein PAR-2 by the polarity kinase PKC-3(aPKC). To identify potential regulatory subunits in this process, they performed IP-MassSpec on GSP-2 and pulled out a number of potential regulatory subunits, including SDS-22. While polarity was the primary motivation for the study, their subsequent analysis did not point to a specific function in cell polarity, but rather pointed to a general effect on stabilising levels of GSP-1/2 against potential proteasome-mediated degradation. Consistent with this hypothesis, SDS-22 depletion or mutation of the SDS-22-GSP-1/2 interaction partially recapitulated the phenotypes of GSP-1/2 depletion including increased phosphorylation of histone H3. Consistent with reduced PP1 activity, there were modest effects on polarity as seen in GSP-2 depleted embryos, including slightly reducing PAR-2 domain size and partially restoring PAR-2 asymmetry in embryos carrying a temperature sensitive pkc-3 mutation.

Major Comments:

1. Overall, the evidence supporting the core finding that SDS-22 is required for normal GSP-1/2 levels is strong and well documented. The experiments were performed well and controls, statistics, replicates were appropriate. Our only slight reservation was whether the effect of sds-22(RNAi) on stability may be overstated due to the use of GFP fusions to GSP-1/2 for this analysis. The authors note these alleles are hypomorphic, potentially raising the possibility that GFP tags destabilise the proteins and make them more prone to degradation. Ideally this would be repeated with an untagged allele via Western (e.g. Peel et al 2017 for relevant antibodies).
2. The role for SDS-22 in polarity is rather weak. Both the SDS-22 depletion phenotypes and the ability of SDS-22 depletion to suppress pkc-3(ts) polarity phenotypes are modest (and weaker in than GSP-2 depletion). For example, the images in Figure 1B appear striking, but from Movie S1 it is clear that this isn't a full rescue as PAR-2 is initially uniformly enriched on the cortex (rather than mostly cytoplasmic) and it is never fully cleared. In the movie, the clearance at the point of pronuclear meeting is very modest. Quantitation might be helpful here (i.e. as in Figure 3G). As the authors state, it seems that SDS-22 does not have a specific role in polarity beyond the general effect on GSP-1/2 levels. This does not undermine the core message of the paper, but we would recommend downplaying the conclusions with respect to contributing to polarity establishment. For example "...suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos" implies a regulatory role for SDS-22 in polarity, but we would interpret it as simply helping reduce aberrant degradation of GSP-1/2 and this impacts a variety of cellular processes including polarity.
3. Specificity of SDS-22 effects on polarity. SDS-22 (or GSP-1/2) depletion is likely to have effects on many pathways. We wondered whether some of the polarity phenotypes may not be specifically due to changes in the PAR-2 phosphorylation cycle as implied.

One candidate is the actomyosin cortex. It was noticeable that control and sds-22 embryos were different: In Movies S1, S2, and S3 control embryos show either stronger or more persistent cortical ruffling or pseudocleavage furrows. This is also visible in Figure 3A. Is it possible that disruption of SDS-22 reduces cortical flows (time, intensity or duration) and could this explain the small reduction in anterior PAR-2 spreading and thus the slightly smaller domain size measured in Figures 1B and 3A.

A potentially related issue could be embryo size. sds-22 embryos generally seem to be smaller than wild-type (e.g. Figure 1B(left), 4A(left column), and particularly EV3). Is this consistently true? Could cell size effects change the ability of embryos to clear anterior PAR-2 domains as described in EV3? Klinkert et al (2018, biorXiv) note that reducing the size of air-1(RNAi) embryos reduces the frequency of bipolar PAR-2 domains.

We would stress that these comments relate to interpreting the polarity phenotypes and do not undermine the core finding that SDS-22 stabilises GSP-1/2.

Minor Points

• The link between lethality and polarity of the zygote is not always obvious and whether they are connected (or not) could probably be made clearer. Indeed, the source of lethality is unclear, particularly given that loss of SDS-22 on its own strongly impacts lethality with minimal effects on polarity (at least in the zygote).
• Formally, the conclusion that reduced GSP-1/2 in SDS-22 depletion conditions is due to increased proteasomal degradation is not shown directly as there is no data on rates just steady-state levels. We agree that the genetic data is strongly suggestive of this model and it is consistent with work of other labs. Thus this is the most likely scenario, but could in principle reflect reduced expression that is balanced by reduced degradation.
• It is interesting that sds-22(E153A) caused a stronger decrease in oocyte GSP-1 levels than sds-22(RNAi) (Fig 7). The authors may want to comment on this result.
• "At polarity establishment, the PP1 phosphatases GSP-1/-2 dephosphorylate PAR-2 allowing its cortical posterior accumulation." This statement, possibly inadvertently, implies temporal regulation, which has not been shown.
• It would be ideal if the authors could explicitly state how they define pronuclear meeting. For example in Figure 1B, the embryos look like they are a few minutes past pronuclear meeting (e.g. compared to Figure 3), but maybe the pronuclei tend to meet more centrally in these conditions? Given that PAR-2 clearance is changing in time in some of these cases (based on looking at the movies), staging needs to be very accurate to get the best comparisons.
• In the interests of data-availability, upon publication the authors would deposit the raw mass spec data underlying Figure EV1.

Referees cross-commenting

We also generally agree with the comments of the other reviewers.

Our only concern with the main conclusion regarding GSP stabilization of GSP-1/2 is the impact of the gfp tags. Given that antibodies exist and have been used in Peel et al 2017 for exactly this purpose in C. elegans embryos, this does not seem excessively burdensome in our view and would strengthen the paper.

The remainder of our concerns can likely be addressed by modifications to the text and/or adding a caveats/limitations section to their discussion. As we noted, these mostly relate to the magnitude and specificity of the impact of SDS-22 on polarity and PAR-2 phosphorylation, which in our view is rather peripheral to the core conclusion (i.e. that SDS-22 stabilizes GSP-1/2).

Significance

Overall, this is a careful and well-executed study identifying a conserved role for SDS-22 in stabilising PP1 catalytic subunits in C. elegans embryos and shows that this can broadly impact PP1 activity in this system. A mechanistic role for SDS-22 in PP1 function was recently demonstrated in (Cao et al, 2024), where it was shown to stabilise nascent catalytic subunits, but also in subunit recycling (Kuetsch et al 2024). The data here suggest this role in stabilisation PP1 subunits is broadly relevant.

These data are also consistent with prior work from the lab demonstrating the role of PP1 in C. elegans zygote polarity. It adds to previous reports that compromised PP1 activity can impact cell polarity and further highlights the importance of considering regulation of protein phosphatases in cell polarity pathways. That said, the impact on polarity is rather modest, likely reflecting a general requirement for SDS-22 in supporting optimal PP1 activity rather than any specific role in polarity.

Field of expertise: cell polarity, cell and developmental biology.

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Reply to the reviewers

The authors do not wish to provide a response at this time

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Referee #3

Evidence, reproducibility and clarity

The study by Yasmin & colleagues tackles an important question, what is the molecular nature of specificity that arises from otherwise highly similar proteins. In this case, they focus on two proteins with epigenetic activity, DNMT3A and DNMT3B, using a functional readout of their ability to methylate DNA in a model that specifically requires DNMT3B function at a subset of the genome, i.e. DNMT3B-dependent regions. This includes characterizing the role of DNMT3B in these regions in stem cell-to-embryoid body differentiation experiments, using genomic assays to probe DNA methylation dynamics. By removing DNMT3B and ectopically expressing a variety of sophisticated mutants, the authors attempt to show the protein domains required for specificity. However, several questions remain about the strength of the data to support the claims, particularly with respect to the ectopically expressed mutant DNMT3B proteins.

Major comments:

1. The strength of this study is in the very nice addback strategy for probing DNMT3B specificity, where the designed mutants seem highly useful to ask critical questions. However, the stability of the mutant proteins (i.e. cellular expression levels) and question of protien levels in the nucleus are insufficient evidence for the conclusions stated in the paper. With the exception of the Dnmt3b1-KI clones (top panel fig 3B), it seems like most mutants are not expressed at wildtype levels. How much of the results are driven by differences in expression, relative to the wildtype protein? While this a technically challenging problem, there are various methods to establish roughly matched expression such as integration into a stronger locus for expression or tuning the promoter sequence for expression of a construct. Given the mutants are key for the main conclusions of the study, this seems critical to address, though would substantially increase effort required for the paper.
2. Characterisation of the datasets supporting effects seems lacking in several instances. For example, the text states that DMNT3B null cells behave similarly to wildtype cells but supporting data (FigS2A-C) or that Dnmt3b1-KI and Dnmt3b3-KI behave normally with respect to differentiation (FigS4C), seem insufficient evidence for this, with largely summary plots supporting the statements. Similarly, several of the MBDseq datasets seem discordant, such as FigS2G or FigS4D(right panel) where the x-y axis for scatterplots are clearly not equivalent suggesting global effects on the data. The authors should also clearly demonstrate the levels of DNMT3A throughout their EB timecourse for mutant lines, where this seems especially important given their readout is DNA methylation dynamics.
3. An optional analysis that could support the claims of the paper would be to contrast the effect sizes in their cellular model with existing datasets that profile DNA methylation dynamics in vivo, where these have been captured at early developmental levels. This would nicely show that their functional readout in relation to normal processes.

Minor comments:

1. Several figures require addressing, listed here:
   • Fig1B the points are not so legible when overplotted, consider reducing the size of the datapoint circles or turning into "*" representations.
   • Fig4I seems not to have a figure legend.
   • FigS2G should be represented as a square and not as a rectangle, as this visually condenses on axis relative to the other.
   • FigS3A is unclear, could more be added to the legend to describe what exactly is the schematic representing?
   • FigS3D the axis seems not aligned with the barplot positioning?
2. The Dnmt3b-PAS-KI clone 1 does not seem to well-cluster with the 2nd and 3rd clone, could this be a clonal effect at the global level?
3. The text states (page 7, third paragraph) that in the two differentiation models the identity of the CGIs that exhibit different dynamics largely match, though no direct comparison (i.e. delta-delta effect) is show, rather a summary plot of either is presented side-by-side. This seems insufficient evidence of the statement, and a direct comparison of the fold changes would help.
4. The clonal effect sizes would benefit from more quantitative comparisons throughout the manuscript, broken down to raw data. For example, the statement in page 8 paragraph two that the effects on independent clones were fully consistent is show from largely a PCA plot, which seems incomplete evidence that replicates behave consistently. More transparent analysis of clonality from the raw data would be helpful for the reader.
5. The statement in the discussion that the authors experimental system affords 'homogeneous and highly synchronised onset and progression of XCI", but it seems unclear from the data provided in the manuscript that cells exhibit differentiation in a synchronized manner. Softening this statement seems apt here.

Significance

The question of specificity is highly important, not just to the field of epigenetics and DNA methylation where this study is particularly relevant, but also to a broader audience. Many of our cells proteins are highly homologous but have nevertheless highly divergent activities. Molecular explanations of specificity are therefore critical to understand phenotype and how traits can be acquired through gene paralogue evolution. Here, by focusing on a particularly apt example, the similar DNMT3A/B proteins, this study offers a nice breakdown with the potential to tie back the results to locus specific activity in the genome. The strongest aspect is the comparison of sophisticated mutants in a matched experimental setting, however, the experiments do not seem sufficient to support the broad conclusions of the study. From a genomics standpoint, the experimental setup is impressive, but requires additional work to show that matched expression levels of wildtype/mutant proteins still maintains the phenotypes reported.

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Referee #2

Evidence, reproducibility and clarity

DNA methylation controls gene expression and genome stability during development and in healthy adult cells. It is frequently abnormal in diseases including cancer. Therefore, there is a clear impetus for the community to better understand the mechanisms that underlie DNA methylation patterns in mammalian genomes, including how the mark is deposited on specific regions. Of particular interest are CpG islands, which correspond to the majority of promoters, and are typically devoid of methylation. However, in specific cases, including during differentiation and X chromosome inactivation, CpG islands acquire extensive DNA methylation in a de novo methylation process. There are 2 de novo DNA methyltransferases expressed in the embryo: DNMT3A and DNMT3B. They are globally similar, but only DNMT3B contributes to de novo DNA methylation during X inactivation (Gendrel 2012, from the authors' lab). The question of this paper is: why is that?

The model system used by the authors is female mouse ES cells, which during differentiation in vitro inactivate one of their X chromosomes. They use a hybrid line to distinguish parental alleles, and a genetic trick to ensure that the same chromosome is inactivated in all cells. Figure 1 validates the system, showing that CGIs on the inactivated X acquire DNA methylation during the differentiation process into EBs, along with some autosomal CGIs (this is done by MBD-seq). Some are fast to gain methylation, some slower. A similar analysis is carried out during differentiation into NPCs.

The authors then move on to functional experiments by knocking out DNMT3B in their system. The KO clones are extensively characterized (Fig 2 and S2). They lack DNMT3B but retain DNMT3A levels similar to WT. However, they fail to methylate the majority of CGIs during X inactivation, confirming that DNMT3B (and not DNMT3A) is the principal actor in this process.

The next question is: which domain(s) of DNMT3B is/are involved in this function. For this, the authors rescue their KO clones with cDNAs encoding different isoforms of DNMT3B, namely 3B1 (active) and 3B3 (inactive). They found that 3B1 fully rescued proper DNA methylation and gene expression during differentiation, whereas 3B3 had no effect (Fig 3 and S4).

Having found that 3B1 expression fully rescues the DNMT3B KO, they move on to a more precise delineation of the important domains (Fig 4). Domain-swapping experiments show that the catalytic domain itself does not contribute to the specificity of DNMT3B (see my note on this experiment below). A similar strategy is then employed to test the contribution of the PWWP and ADD domains to 3B function. I did not find this part very clear. My understanding is that the swapped rescue construct has some activity on gene bodies even before differentiation. I gather from the lower part of Panel 4F that the PAS construct mostly fails to rescue DNA methylation during differentiation, but I am a little confused by the phrasing. It would be very easy to solve this with a summary graphic.

Lastly, they move on to an examination of the Nter part of DNMT3B, using their domain swapping/rescue approach (Figures 5 and S6). A first experiment suggests that the Nter of 3A cannot substitute for the Nter of 3B (see note below). A second set of experiments shows that the presence of residues 1-218 is necessary for DNMT3B to function, and that smaller deletions within this region also inactivate the protein (see notes below).

The paper is very well written. The figures are clear. The experiments are well controlled and correctly interpreted, except for the points below.

Fig 4A: it is looking like the DNMT3B1-3A-Cat rescue construct is vastly overexpressed relative to the endogenous protein. This is surprising as the DNMT3B1 rescue construct is not (Figure 3B). What is the reason for this? Is a different promoter or rescue method being used? I feel that the overexpression level weakens the conclusion that the catalytic domain of 3A can perfectly replace that of 3B.

Fig 4G: similarly, the two different DNMT3B-PAS-KI clones show widely different levels of DNMT3B expression. Are they both used to generate the data of Fig 4H? A third rescue clone is shown in Fig S5B. What experiment(s) was it used for?

The clarity of the section concerning DNMT3B-PAS-KI clones can be improved easily.

Fig 5B: the DNMT3a2(N)3b-KI clones show 3B expression levels that are ~10% of endogenous. I find it hard to conclude from this that the Nter of 3A cannot replace the Nter of 3B. Unless the authors can show that a 10% level of 3B expression is enough to fully rescue the KO of 3B.

Fig S6D: same comment for the DeltaA and DeltaB construct. I am not seeing the data for DeltaC. In the absence of expression data, the methylation data for this mutant are not interpretable.

Fig 5B. Is it clear that the Delta 1-218 protein is nuclear? What about the Delta A-E mutants?

I suggest that the authors add the following paper to their reference list: Wapenaar EMBO Rep 2024 PMID: 39528729

Significance

I feel that the target audience is somewhat specialized. In addition, the novelty of the paper is diminished by the existence of published papers, in particular: DNMT3B PWWP mutations cause hypermethylation of heterochromatin. Taglini F, ..., Sproul D. EMBO Rep. 2024 Mar;25(3):1130-1155. doi: 10.1038/s44319-024-00061-5. Epub 2024 Jan 30. PMID: 38291337

This paper uses a different system (human cancer cells), but arrives at the same conclusion, ie the Nter of DNMT3B is necessary for de novo DNA methylation. In addition, it shows that the Nter interacts with HP1.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

In this study the authors used a previously established mESCs iXist-ChrX cell line to investigate the mechanisms underpinning developmentally regulated CGI methylation through differentiation into embryoid bodies and MBD-seq profiling. They show that their system recapitulates developmentally regulated DNA methylation at CGIs on the X chromosome and autosomes before using a knockout and rescue system to determine that this is dependent on the DNMT3B1 isoform. Through domain swap experiments, they then go on to suggest that this requires the PWWP-ADD domain and that the N-terminal region of DNMT3B1.

Major comments:

• Are the key conclusions convincing?
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
• Are the data and the methods presented in such a way that they can be reproduced?
• Are the experiments adequately replicated and statistical analysis adequate?

Overall, this is an interesting study that provides insights into the role of different domains of DNMT3B in establishing DNA methylation patterns during development. However, it suffers from poor annotation and description throughout. More specific comments are:

1. The manuscript provides some details as to the replication of experiments but fails to show the replicate data in the vast majority of cases. Instead, representative data is presented alongside global correlations for some experiments. However, global correlations can mask differences between replicates. The data from all replicates should be shown in the manuscript and clear details provided regarding the replication strategy in the methods and figure legends. For example, were the different knock-in clones generated from independent DNMT3B knockout clones? For individual experiments this would be a minor point however, collectively this is a major point. It is particularly important given the variation highlighted in point 2 below.
2. Different DNMT3B knock-in clones show high variability in expression levels. Have the authors investigated whether the discrepancy in protein levels contributes to the differences in methylation patterns seen? A non-comprehensive list of examples is given in the minor comments section.
3. There is variation in the level of expression of different DNMT3B constructs detected by Western blot. Could this be caused by differences in protein stability? It would be helpful to see an assessment of protein stability to determine whether this contributes to the variable expression. For example, the DNMT3B3-KI has lower levels than the DNMT3B1-KI (Figure 3B) and this could potentially contribute to the differences in DNA methylation observed.
4. The study lacks statistical tests to support the conclusions drawn from the analysis of the sequencing data. For example, are the differences in CGI methylation between DNMT3B1-KI and DNMT3B3-KI statistically significant? For individual analyses this would be a minor comment but given the lack throughout the study, this classes as a major comment.
5. Chromatin marks play major roles in DNMT3A and DNMT3B recruitment (Tibben & Rothbart 2024) and the N-terminal region, PWWP and ADD domains have direct or indirect chromatin reading activity. However, the manuscript does not detail the chromatin environment of the CGIs studied. This could potentially be addressed through experiments, analysis of existing data or discussion.
6. As the authors state in their discussion, MBD-seq may only detect very dense methylation. This could potentially obscure lower levels of DNA in some conditions. Analysis of a few loci by alternative methods, such as targeted bsPCR or EM-PCR would help support key results and rule out the possibility that some of the rescue constructs are able to partially rescue DNA methylation patterns.
7. While some expression data is shown, there is currently no investigation as to whether the different DNMT3B domain swap constructs have impact on transcriptional silencing on Xi/autosomal sites.
8. The text relating to the section on the PWWP-ADD domain is very brief and currently unclear. Expanding this section and specifying which data are derived from ES cells vs differentiated cells would help to clarify. We also suggest that it would be clearer to move this data to the main figure and to move the results of the catalytic domain experiments, which are negative, to the supplementary.
9. The authors suggest that PWW-ADD domain region of DNMT3B is required for developmental methylation of Xi and autosomal CGIs. However, there is no further dissection as to whether this requirement is due to the PWWP, ADD or the intervening region.
10. Throughout the text autosomal and Xi CGIs are both analysed. The introduction highlights SMCHD1 as important in methylating CGIs on Xi but that PCGF6 complex for the autosomal targets. This suggests separate mechanisms target DNMT3B to these loci. However, based on the results presented here, these two different types of targets have similar DNMT3B1 domain requirements. It would be interesting to see discussion with regard to this point in the manuscript.

Minor comments:

• Specific experimental issues that are easily addressable.
• Are prior studies referenced appropriately?
• Are the text and figures clear and accurate?
• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
• Introduction: Methylation of tumour suppressor gene CGI promoters is mentioned alongside examples of developmental CGI methylation. It would be useful to place rare event in context. Methylation of CGIs is common in cancer but very few of these correspond to tumour suppressor genes. It would also be useful to discuss how DNMT3B might be involved in these events.
• Introduction: The authors mention the DNMT3A1's N-terminal region recruits it to H2AK119ub1. It would also be useful to discuss recent work on the N-terminal of DNMT3B which can bind HP1-alpha to mediate H3K9me3 recruitment (Taglini et al 2024). This paper is currently only cited in the discussion.
• Throughout the manuscript DNMT3B1 is referred to as the catalytic isoform, however it is not the only catalytic isoform of DNMT3B.
• Page 5: 'the presence of non-catalytic isoforms, notably DNMT3L and DNMT3B3'. This statement incorrectly suggests DNMT3L is a non-catalytic isoform of DNMT3B.
• The authors refer to the protein complex as heterodimers when referring to a DNMT3B -DNMT3L or DNMT3B3-DNMT3A complex. However, the consensus of structural studies is that they form tetramers.
• Marker sizes are not included on blots with the exception of Figure 3B.
• Western blots are cropped closely. It would be useful if full blots were shown in the supplementary particularly given the presence of extra bands in some blots and different DNMT3B isoforms.
• Details about the Western blot methods (eg visualisation and antibodies) are missing.
• It would be useful if the size of different groups was annotated in plots. This is given in Figure 1D for example but not in Figure 2E.
• The authors show data confirming DNMT3B knockout by western blot. However, they do not provide details of the strategy for generating the knockout (ie vector used, sgRNAs, screening process). Could the authors also provide additional details as to whether there is any sequencing to confirm the results on the knockout?
• Page 9: The authors state "Since Dnmt3b-/- cells have normal levels of DNMT3A", but show no data to support this statement. This is particularly relevant as they have generated new DNMT3B knockout clones for this study so they cannot be assumed to behave similarly to previous studies.
• Figure 1B: There is poor PCA clustering between replicates at some timepoints, particularly Day 8.
• Figure 1G: Different colours are used for the different timepoints in this figure. We are unclear if this is deliberate.
• Page 7: The authors state "... the two categories largely matched between the two differentiation systems (Figure S1C-G)". It is difficult to draw this interpretation from the data presented as no explicit overlap is shown.
• Figure 2E: MBD-seq peaks for DNMT3B-independent loci in WT sample have a dip in the middle of the peak (also seen in Figure 5F). Could the authors explain why this might be and why it only appears in some experiments?
• Clarification as to whether the DNMT3B -dependent and -independent loci are located on chromosome X or autosomes (e.g.: Figure 2D, E).
• Figure S2C: the chromatin RNA-seq is not explained in the text or figure.
• Figure S2E: Suggests WT is one of 5 replicates. The authors should show all replicates.
• Figure S2H: What genes are included in the metaplot?
• DNMT3B rescue knock-in clones. As shown in figure 2A, there are two different Dnmt3b-/- cell line clones. Could the authors clarify whether all the CRISPR KI clones are produced from the same parental Dnmt3b-/- cell line clone?
• Figure 3B: The two clones shown for Dnmt3b3-KI have variable expression. Do the individual replicates for the Dnmt3b3-KI clones show similar methylation patterns?
• Figure 3E: in the MBD-seq metaplots, there is a peak present at -/+ 4kb. What are these peaks and why do they appear at 4kb distance? Similar peaks are seen in other metaplots.
• In figure 3F, the signal for both Dnmt3b1-KI and Dnmt3b3-KI at DNMT3B-independent CGIs is higher than in the KO. This suggests that these may not be DNMT3B independent but this point is unaddressed in the text.
• Figure S3A: it is currently unclear what has been modelled in this figure, adding labels of what has been plotted along the x- and y- axis may aid in understanding.
• Figure 4A: Dnmt3b1-3a-Cat-KI appears very highly expressed. Is the WT shown the endogenous protein? Could this higher expression be because the chimeric protein is more stable than DNMT3B1? There are also multiple bands on this blot.
• Plots panels are inconsistently ordered, e.g.: Figure 3F is dependent then independent. 4F is independent then dependent.
• Figure 4G: the expression level of the Dnmt3b-PAS-KI varies significantly between the clones shown. There are also two bands on the blot, both for the wt and KI. Clarify if WT is endogenous.
• Figure 4H: The figure lacks a legend to indicate the scale of the colour density used.
• Figure 4F,H: Could the authors clarifying what data (clones and number of replicates) are presented in the representative plots. Does the different protein levels between the clones result in any differences in DNA methylation?
• Page 12: The authors cite Boyko et al when discussing potential differences between the ADD domains of DNMT3A and B. However, they do not cite the study of Lu et al., 2023 (https://doi.org/10.1093/nar/gkad972) which reaches a different conclusion.
• Figure S4A: The position of the 750 residue is inconsistent across the isoforms in this schematic.
• Figure 5A: Schematic suggests the chimeric protein is DNMT3A2(N)-3B. However, DNMT3A2 lacks the N terminal region so presumably this should be DNMT3A1(N)-3B. This applies other figure panels using this construct.
• Figure 5B: Many lanes on this blot are unlabelled and it would be useful to clarify what these extra lanes show.
• Figure 5C: For Dnmt3a2(N)3b-KI the levels of methylation appear to be lower than Dnmt3b-/- and it would be useful to understand why this might be the case.
• Figure 5D: would be helpful to indicate which CGIs are DNMT3B dependent and independent.
• Figure 5F: Dnmt3a2(N)3b-KI data not included for the autosomal peaks
• Figure 5G, H: It is difficult to see if there are any differences between the deletions in this heatmap. For example, it appears that levels of methylation on autosomal DNMT3B-dependent loci are very similar between the KO and rescue constructs. ∆D also appears to have a lesser effect than the other deletions on the Xi CGIs. A more quantitative representation of the data would help with interpretation.
• S5E has different colour scale to other heatmaps. Red is low and in other heatmaps red is high.
• Figure S6C: sequence conservation is shown for primates. However as mouse Dnmt3b is used throughout the paper, including the mouse NT would be a useful comparison. This is particularly relevant given that the NT is the region that varies the most between mouse and human proteins (Molaro et al 2020 https://doi.org/10.1093/molbev/msaa044 ).
• Figure S6D: There is variable expression levels between the clones of the different deletions. The deletion ∆C is also not shown in this figure meaning that no data is shown to support the statement that it is unstable.
• It would be useful to clarify in the text that "deletion of residues 98-146" corresponds to ∆C. It is also unclear why MBD-seq data for this deletion was included if it is unstable.
• Discussion, page 15: The authors propose that DNMT3B could directly bind to H3K9me3. However, a study they cite, Taglini et al., 2024 (Figure S8C, D), suggests this is not the case.
• Discussion: When discussing regulatory element methylation on Xi. An uncited statement is included: 'This observation may help to explain a prior observation that loss of DNMT3B1 alone does not result in significant de-repression of Xi during embryogenesis'. However this model appears contradictory to the observation that DNMT1 is not required for Xi silencing, given that DNMT1 KO embryos would be expected to have very low DNA methylation (eg Sado et al 2000, https://doi.org/10.1006/dbio.2000.9823 and Sado et al 2004, https://doi.org/10.1242/dev.00995).

Referee cross-commenting

Having reviewed the comments of the other reviewers, we agree that they are very similar and we have no issues with them. We note that Reviewer 3 notes that considering nuclear protein levels is important in the context of this study and we agree that this is an important additional consideration that we did not consider in our review.

Significance

The manuscript is an interesting study on the role of DNMT3B in X inactivation and development. It will be of interest to scientists who work on these fundamental processes. In addition, given the roles of DNA methylation in gene regulation, cancer, aging and disease more generally the findings are likely to be of interest to many others.

Our expertise is in epigenetics and its regulation in disease, with a specific focus on DNA methylation and DNMTs.

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Reply to the reviewers

Revision Plan (Response to Reviewers)

1. General Statements [optional]

Response: We are pleased the reviewers appreciate the power of this novel proteomics methodology that allowed us to uncover new depths on the complexity of the ribosome ubiquitination code in response to stress. We also appreciate that the reviewers think that this is a "very timely" study and "interesting to a broad audience" that can change the models of translation control currently adopted in the field. Characterizing complex cellular processes is critical to advance scientific knowledge and our work is the first of its kind using targeted proteomics methods to unveil the integrated complexity of ribosome ubiquitin signals in eukaryotic systems. We also appreciate the fairness of the comments received and below we offer a comprehensive revision plan substantially addressing the main points raised by the reviewers. According to the reviewers' suggestions, we will also expand our studies to two additional E3 ligases (Mag2 and Not4) known to ubiquitinate ribosomes, which will create an even more complete perspective of ubiquitin roles in translation regulation.

2. Description of the planned revisions

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

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

        __Response: __We appreciate the evaluation of our work and that the power of our proteomics method established a good foundation for the study. We also understand the reviewer's concerns and we will detail below a plan to enhance quantification and increase systematic comparisons. The experiments presented here were conducted with biological replicates, but in several instances, we focused on presence and absence of bands, or their pattern (mono vs poly-ub) because of the semi-quantitative nature of immunoblots. We will revise the figures and present their quantification and statistical analyses. In additional, we did not intend to use these stressors interchangeably, but instead, to use select conditions to highlight the complexity the stress response. In particular, we followed up with H2O2 *versus* 4-NQO because both chemicals are considered sources of oxidative stress. Even though it is unfeasible to compare every single stress condition in every strain background, in the revised version, we will include additional controls to increase the cohesion of the narrative, and expand the comparison between MMS, H2O2, and 4-NQO, as suggested. Details below.

To strengthen the work, the following revisions are essential:

R1.1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.

        __Response: __As requested, we will display quantification and statistical analysis of the suggested and new immunoblots that will be conducted during the revision period.

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R1.3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.

        __Response: __We will follow the reviewers' suggestion and redesign the analysis to increase consistency and prioritize data under identical conditions. To increase confidence in the mRNA data analysis, we intend to perform follow up experiments and analyze protein abundance of *ARG proteins* and *CTT1 *under different conditions. The remaining data using non-parallel comparisons will be moved to supplemental material and de-emphasized in the final version of the manuscript.

R1.4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

        __Response: __To ensure a better comparison across strains and conditions, we will re-run several experiments and focus on our main stress conditions. Specifically:

• 3D: We plan to re-run this experiment and include MMS

• 3E: We plan to perform the same panel of experiments in rad6D ,and display WT data as main figure.

• 4A-B: We plan to perform translation output (HPG incorporation) experiments with MMS as suggested

• 4C: We plan to re-run blots for p-eIF2a under MMS for improved comparison.

Reviewer #1 (Significance (Required)):

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6. It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted.

Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

        __Response: __We value the positive evaluation of our work. We also appreciate the notion that it meaningfully expands the knowledge on the complexity of the ribosome ubiquitination code, challenges the current models of translation control, and conducted well-performed, and nicely controlled experiments. To address the main concern of the reviewer, we will expand our work by studying two additional enzymes involved in ribosome ubiquitination (Mag2 and Not4) and provide a more comprehensive picture of this integrated system. Specifically, we will generate yeast strains deleted for *MAG2* and *NOT4*, and evaluate their impact in ribosome ubiquitination under our main conditions of stress. We will investigate the role of these additional E3s in translation output (HPG incorporation), and in inducing the integrated stress response via phosphorylated eIF2α and Gcn4 expression. Additional follow up experiments will be performed according to our initial results.

Reviewer #2 (Significance (Required)):

In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience. However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

        __Response: __We appreciate the comments and the balanced view that studies like ours will still be impactful and contribute to a number of fields in multiple and meaningful ways. With the new experiments proposed here, and used of additional mutants and strains, we intend to propose and provide a more unified model that explain this complex and dynamic relationship.

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

Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

Major Comments:

I consider the additional experiments essential to support the claims of the paper.

R3.1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.

        __Response: __We understand the importance of the suggested experiment. We have already requested and kindly received strains expressing these mutations, which will reduce the time required to successfully address this point. We will perform our translation and ISR assays such as the one referred by the reviewer in Figs. 4A-C and 5E, and results will determine the role of individual ribosome ubiquitination sites in translation control.

R3.2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

        __Response: __As was also requested by reviewer 1 and discussed above (point R1.1), we will conduct quantification and display statistical analyses for our immunoblots. In addition, we will re-run the aforementioned experiments to improve quantification following the reviewers' request (same gel & diluted control samples).

Reviewer #3 (Significance (Required)):

• General assessment:

Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

• Advance:

In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

• Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

• The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

__ Response:__ We appreciate that our work will be valuable to a number of fields in protein dynamics and that our method advances the field by measuring ribosome ubiquitination relatively and stoichiometrically in response to stress.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Response: All requested changes require experiments and data analyses, and a complete revision plan is delineated above in section #2.

• *

4. Description of analyses that authors prefer not to carry out

• *

R1.2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.

        __Response: __Although we understand the interest on the proposed result for consistency, this is the only requested experiment that we do not intend to conduct. Because of the lack of overall ubiquitination of ribosomal proteins in *rad6**D* in response to H2O2 (e.g., Silva et al., 2015, Simoes et al., 2022), we believe that this PRM experiment in unlikely to produce meaningful insight on the ubiquitination code. In this context, we expected that sites regulated by Hel2 will be the ones largely modified in rad6*D *and we followed up on them via immunoblot. Moreover, this experiment would not be time or cost-effective, and resources and efforts could be used to strengthen other important areas of the manuscript, such as including the E3's Mag2 and Not4 into our work.

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Referee #3

Evidence, reproducibility and clarity

Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

Major Comments:

I consider the additional experiments essential to support the claims of the paper.

1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.
2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

Significance

General assessment:

Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

Advance:

In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6.

It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted. Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

Significance

In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience.

However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

To strengthen the work, the following revisions are essential:

1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.
2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.
3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.
4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

Significance

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

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Reply to the reviewers

The response appears in a PDF document, which will be easier to read than plain text

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Referee #3

Evidence, reproducibility and clarity

This article investigates the phenomenon of intracellular protein agglomeration. The authors distinguish between agglomeration and aggregation, both physically characterising them and developing a simple but elegant assay to differentiate the two. Using microscopy and structural analysis, this research demonstrates that unlike aggregates, agglomerates retain their folded structures (and are not misfolded), and do not colocalise with chaperones or interact with the proteostasis machinery which targets and breaks down misfolded proteins. The inert nature of agglomerates was further confirmed in fitness assays, though they were observed to disrupt the yeast proteome. Overall, agglomerated proteins were described and characterised, and shown to be largely neutral in vivo.

The claims and conclusions were well supported by the data. Microscopy and CD spectra (previously published) were used to confirm the nature of agglomerates and to rule out colocalistion with proteostasis machinery. This was confirmed by testing ubiquitination.

The fitness of yeast cells carrying enzymatically-inactive agglomerates was assayed by generating growth curves over 24 hours. The growth rate and doubling time were taken from these growth curves as a proxy for relative fitness. The authors mention not wanting to mask differences in lag, log or stationary phases between mutants. This could be achieved by using the area under each growth curve, rather than growth rate or doubling time alone. No further experimentation would be needed, and area under the curve may provide a more holistic metric to measure fitness by.

The results indicate that agglomerates confer a slight fitness advantage. The authors do not speculate on a reason for this. I would be interested to know why they thought this might be.

Referees cross-commenting

I have read the reports from the other reviewers and agree with their comments.

Significance

Protein filamentation is observed across the tree of life, and contributes greatly to cell structure and organisation (Wagstaff, J., Löwe, J. Prokaryotic cytoskeletons: protein filaments organizing small cells. Nat Rev Microbiol 16, 187-201 (2018).). Recent work in this field has shown that self-assembly is also important for enzyme function (S. Lim, G. A. Jung, D. J. Glover, D. S. Clark, Enhanced Enzyme Activity through Scaffolding on Customizable Self-Assembling Protein Filaments. Small 2019, 15, 1805558.). Previous work from several of these authors demonstrated that the ability of a protein to filament is subject to selection (Garcia-Seisdedos H, Empereur-Mot C, Elad N, Levy ED. Proteins evolve on the edge of supramolecular self-assembly. Nature. 2017 Aug 10;548(7666):244-247. doi: 10.1038/nature23320. Epub 2017 Aug 2. PMID: 28783726.). It has become increasingly clear that protein assemblies are ubiquitous, evolvable and perhaps overlooked in research.

This research explores a specific type of filamentation, named agglomeration, unique in that the protein which assemble are not misfolded (Romero-Romero ML, Garcia-Seisdedos H. Agglomeration: when folded proteins clump together. Biophys Rev. 2023;15: 1987-2003.). This is particularly of biomedical interest due to its role in disease, such as sickle cell anaemia (J. Hofrichter, P.D. Ross, & W.A. Eaton, Kinetics and Mechanism of Deoxyhemoglobin S Gelation: A New Approach to Understanding Sickle Cell Disease*, Proc. Natl. Acad. Sci. U.S.A. 71 (12) 4864-4868, https://doi.org/10.1073/pnas.71.12.4864 (1974).) The current research adds to the field by specifically exploring agglomerates in the most detailed methodology to date.

The novelty of this research lies especially in two areas; (1) establishing a method for distinguishing between aggregation and agglomeration, and (2) the finding that agglomerates are largely innocuous in vivo. The method established for defining agglomerates is simple, elegant and well-described in this paper's methods. The authors then probe cellular responses to agglomeration via both proteostasis machinery and cellular fitness. They noted no disruption to fitness and observed little targeting of agglomerates by chaperones. The experiments were thorough, conclusive, and resulted in interesting findings.

The inertia of this type of protein filament is unexpected; agglomerates are large and have been associated with disease. The results of this study, however, indicate that agglomerates are non-toxic and well-tolerated in vivo. The authors speculate that agglomerates may have evolved in a non-adaptive process, which is evolutionary very interesting. They also posit that these results could lead to synthetic biology applications such as a tracking expression or as a molecular sensor. This work is of great interest and impact both in cell biology, biomedicine and in-vivo biology.

Personal note: I come from a background of enzyme evolution and have viewed the work in this light.

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Referee #2

Evidence, reproducibility and clarity

This is a very interesting paper investigating the fitness and cellular effects of mutations that drive dihedral protein complex into forming filaments. The Levy group have previously shown that this can happen relatively easily in such complexes and this paper now investigates the cellular consequences of this phenomenon. The study is very rigorous biophysically and very surprisingly comes up empty in terms of an effect: apparently this kind of self-assembly can easily be tolerated in yeast, which was certainly not my expectation. This is a very interesting result, because it implies that such assemblies may evolve neutrally because they fulfill the two key requirements for such a trajectory: They are genetically easily accessible (in as little as a single mutation), and they have perhaps no detrimental effect on fitness. This immediately poses two very interesting questions: Are some natural proteins that are known to form filaments in the cell perhaps examples of such neutral trajectories? And if this trait is truly neutral (as long as it doesn't affect the base biochemical function of the protein in question), why don't we observe more proteins form these kinds of ordered assemblies.

I have no major comments about the experiments as I find that in general very carefully carried out. I have two more general comments:

1. The fitness effect of these assemblies, if one exists, seems very small. I think it's worth remembering that even very small fitness effects beyond even what competition experiments can reveal could in principle be enough to keep assembly-inducing alleles at very low frequencies in natural populations. Perhaps this could be acknowledged in the paper somewhere.
2. The proteins used in this study I think were chosen such that they do not have an important function in yeast that could be disrupted by assembly This allows the effect of the large scale assemblies to be measured in isolation. If I deduced this correctly, this should probably be pointed out agin in this paper (I apologise if I missed this).
3. The model system in which these effects were tested for is yeast. This organism has a rigid cell wall and I was wondering if this makes it more tolerant to large scale assemblages than wall-less eukaryotes. Could the authors comment on this?

Minor points:

In Figure 2D, what are the fits? And is there any analysis that rules out expression effects on the mutant caused by higher levels of the wild-type? The error bars in Figure 2E are not defined.

Significance

This is a remarkably rigours paper that investigates whether self-assembly into large structures has any fitness effect on a single celled organism. This is very relevant, because a landmark paper from the Levy group showed that many proteins are very close in genetic terms to forming such assemblies. The general expectation I think would have been that this phenomenon is pretty harmful. This would have explained why such filaments are relatively rare as far as we know. This paper now does a large number of highly rigours experiments to first prove beyond doubt that a range of model proteins really can be coaxed into forming such filaments in yeast cells through a very small number of mutations. Its perhaps most surprising result is that this does not negatively affect yeast cells.

From an evolutionary perspective, this is a very interesting and highly surprising result. It forces us to rethink why such filaments are not more common in Nature. Two possible answers come to mind: First, it's possible that filamentation is not directly harmful to the cell, but that assembling proteins into filaments can interfere with their basic biochemical function (which was not tested for here).

Second, perhaps assembly does cause a fitness defect, but one so small that it is hard to measure experimentally. Natural selection is very powerful, and even fitness coefficients we struggle to measure in the laboratory can have significant effects in the wild. If this is true, we might expect such filaments to be more common in organisms with small effective population sizes, in which selection is less effective.

A third possibility is of course that the prevalence of such self-assembly is under-appreciated. Perhaps more proteins than we currently know assemble into these structures under some conditions without any benefit or detriment to the organism.

These are all fascinating implications of this work that straddle the fields of evolutionary genetics and biochemistry and are therefore relevant to a very wide audience. My own expertise is in these two fields. I also think that this work will be exciting for synthetic biologists, because it proves that these kinds of assemblies are well tolerated inside cells.

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Referee #1

Evidence, reproducibility and clarity

In this work, the authors used yeast cell as a model system to study the abovementioned question. They established a model protein system using fluorescently labeled proteins that can form both agglomerates and aggregates. Using imaging experiments, they arguably showed that agglomerates do not colocalize with the proteostasis machinery, echoing what was observed by proteomics results. The proteomics results after pull down assay to study the interactome revealed that agglomerate-size-dependent changes were dependent on the cell-wall and plasma-membrane proteins. On the other hand, as expected, the misfolded proteins (aggregates) showed heavy involvement of proteostasis network components.

Although the experiments still lack some controls and failed to support some of the conclusions, I found this work is a nice complement of the field to emphasize the point that "aggregates" and "agglomerates" are two different states, which is often mistaken by lots of researchers in recent years, in particular with the membraneless organelles (LLPS). I support its publication after the authors may consider the following suggestions and make necessary improvement.

Major concerns:

My major concern was raised by the lack of evidence to support the model system's folding state in the cell. 1. In Figure 1 and 2, I found the evidence to distinguish the folded state of proteins in the cells was limited. The concept of using hybrid imaging technique to prove the folding state is not a common experiment. The description of Figure 2 was very limited. I am sure the general audience can be convinced that the model proteins were actually folded and form agglomeration. 2. In addition, for mutants formed aggregates, the authors may consider to perform fractionation or crosslinking or native page experiment to show the evidence of protein misfolding and aggregation. 3. Have the authors considered to use FRAP assay to distinguish "aggregates" and "agglomerates" states in the cell? Does each of the state display different dynamics in the cell?

Minor concerns:

1. In Figure 3, it is very interesting to see such patten. I wonder why some of the chaperones were not responsive to misfolded proteins but some were very addicted to proteostasis. Could you elaborate more on this point? Are they chaperone sensitive, namely selective to 60/10, 70/40 or 90 system?
2. In Figure 6, I suggest to add GO analysis and KEGG analysis to distinguish pathways and functional mismatch between "aggregates" and "agglomerates" interactomics.
3. The quantity control for the proteomics studies is needed, namely the reproducibility of 3 repeats?
4. This may beyond the scope of this work. I am interested whether the authors could point out whether similar works can be done in mammalian cells. What is the model system for mammalian cell that can form "agglomerates".

Referees cross-commenting

I read through the other two reviewers' comments, which I found reasonable. It seems like all reviewers agreed that this work is of enough significance for the field only with several technical concerns.

Significance

The submitted manuscript emphasized on a very important but often misleading concept: "aggregates" and "agglomerates" are two different states of protein structures in the cell with distinct physiological roles. However, these two states are of very similar phenotype: punctate structure in the cell. While the proteostasis network has been well-established for its central role of protein quality control and coping with misfolded and aggregated proteome, the authors attempted to profile the mechanism and physiological impact of mutation-induced folded-state protein filamentation, namely a model of "agglomerates". Such overarching goal of this work clearly pointed out the novelty of this work. Clearly, this is a new angle and aspect remained to be clarified for the field.

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Reply to the reviewers

Manuscript number: RC-2024-02465

Corresponding author(s): Saravanan, Palani

1. General Statements

We would like to thank the Review Commons Team for handling our manuscript and the Reviewers for their constructive feedback and suggestions. In our revised manuscript, we have addressed and incorporated all the major suggestions of the reviewers, and we have also added new significant data on the role of Tropomyosin in regulation of endocytosis through its control over actin monomer pool maintenance and actin network homeostasis. We believe that with all these additions, our study has significantly gained in quality, strength of conclusions made, and scope for future work.

2. Point-by-point description of the revisions

Reviewer #1

Evidence, reproducibility and clarity

There are 2 Major issues -

Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.

Response: We would like to clarify that all mNG-Tpm constructs used in our study contain a 40 amino-acid (aa) flexible linker between the N-terminal mNG fluorescent protein and the Tpm protein as per our earlier published study (Hatano et al., 2022). During initial optimization, we have also experimented with linker length and the 40aa-linker length works optimally for clear visualization of Tpm onto actin cable structures in budding yeast, fission yeast (both S. pombe and S. japonicus), and mammalian cells (Hatano et al., 2022). These constructs have also been used since in other studies (Wirshing et al., 2023; Wirshing and Goode, 2024) and currently represents the best possible strategy to visualize Tpm isoforms in live cells. In our study, we characterized these proteins for functionality and found that both mNG-Tpm1 and mNG-Tpm2 were functional and can rescue the synthetic lethality observed in Dtpm1Dtpm2 cells. During our study, we observed that mNG-Tpm1 expression from a single-copy integration vector did not restore full length actin cables in Dtpm1 cells (Fig. 1B, 1C). We hypothesized that this could be a result of reduced binding affinity of the tagged tropomyosin due to lack of normal N-terminal acetylation which stabilizes the N-terminus. The 40aa linker is unstructured and may not be able to neutralize the charge on the N-terminal Methionine, thus, we tried to insert -Ala-Ser- dipeptide which has been routinely used in vitro biochemical studies to stabilize the N-terminal helix and impart a similar effect as the N-terminal acetylation (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) by restoring normal binding affinity of Tpm to F-actin (Monteiro et al., 1994; Greenfield et al., 1994). We observed that addition of the -Ala-Ser- dipeptide to mNG-Tpm fusion, indeed, restored full length actin cables when expressed in Dtpm1 cells, performing significantly better in our in vivo experiments (Fig. 1B, 1C). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may stabilize the N-terminus of Tpm and allow normal head-to-tail dimer formation (Greenfield et al., 1994; Monteiro et al., 1994; Frye et al., 2010). We have discussed this in our new Discussion section (Lines 350-372). Since, the addition of -AS- dipeptide was referred to as "acetyl-mimic (am)" in a previous study (Alioto et al., 2016), we continued to use the same nomenclature in our study. Now as per your suggestions and to be more accurate, we have renamed "mNG-amTpm" constructs as "mNG-ASTpm" throughout the study to not confuse or claim that -AS- addition mimics acetylation. In any case, we have not seen any other ill effect of -AS- dipeptide introduction in addition to our 40 amino acid linker suggesting that it can also be considered part of the linker. Although, we agree with the reviewer that biochemical characterization of the effect of linker would be important to determine, we strongly believe that it is currently outside the scope of this study and should be taken up for future work with these proteins. Our study has majorly aimed to understand the functionality and utility of these mNG-Tpm fusion proteins for cell biological experiments in vivo, which was not done earlier in any other model system.

My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.

Response: We agree with the reviewer that N-terminal tagging of tropomyosin may have effects on its function, but these constructs represent the only fluorescently tagged functional tropomyosin constructs available currently while C-terminal fusions are either non-functional (we were unable to construct strains with endogenous Tpm1 gene fused C-terminally to GFP) or do not localize clearly to actin structures (See Figure R1 showing endogenous C-terminally tagged Tpm2-yeGFP that shows almost no localization to actin cables). To our knowledge, our study represents a first effort to understand the question of spatial sorting of Tpm isoforms, Tpm1 and Tpm2, in S. cerevisiae and any future developments with better visualization strategies for Tpm isoforms without compromising native N-terminal modifications and function will help improve our understanding of these proteins in vivo. We have also discussed these possibilities in our new Discussion section (Lines 391-396).

Significance

This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly. The significance of this study, given the above, and the concerns raised is not clear to this reviewer.

Response: __Our study explores multiple facets of Tropomyosin (Tpm) biology. The lack of functional tagged Tpm has been a major bottleneck in understanding Tpm isoform diversity and function across eukaryotes. In our study, we characterize the first functional tagged Tpm proteins (Fig. 1, Fig. S1) and use them to answer long-standing questions about localization and spatial sorting of Tpm isoforms in the model organism S. cerevisiae (Fig. 2, Fig. 3, Fig. S2, Fig. S3). We also discover that the dual Tpm isoforms, Tpm1 and Tpm2, are functionally redundant for actin cable organization and function, while having gained divergent functions in Retrograde Actin Cable Flow (RACF) (Fig. 4, Fig. 5A-D, Fig. S4, Fig. S5, Fig. S6). We have now added new data on role of global Tpm levels controlling endocytosis via maintenance of normal linear-to-branched actin network homeostasis in S. cerevisiae (Fig. 5E-G)__. We respectfully differ with the reviewer on their assessment of our study and request the reviewer to read our revised manuscript which discusses the significance, limitations, and future perspectives of our study in detail.

Reviewer #2

Evidence, reproducibility and clarity

This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.

Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.

1. Functionality of the acetyl-mimic tagged tropomyosin constructs: The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.

__Response: __We would like to thank the reviewer for his feedback and suggestions. Based on the suggestions, we have now more accurately described the growth rescue observed by expression of mNG-ASTpm1 in Dtpm1 cells in the revised text. We have also removed the use of "completely functional" to describe mNG-Tpm functionality and corrected any errors in Figure citations in the revised manuscript.

As per reviewers' suggestion, we have now tested rescue of synthetic lethality of Dtpm1Dtpm2 cells by expression of all mNG-Tpm variants and we find that all of them are capable of restoring the viability of Dtpm1Dtpm2 cells when expressed under their native promoters via a high-copy plasmid (pRS425) (Fig. S1E) but only mNG-Tpm1 and mNG-ASTpm1 restored viability of Dtpm1Dtpm2 cells when expressed under their native promoters via an integration plasmid (pRS305) (Fig. S1F). These results clearly suggest that while both mNG-Tpm1 and mNG-Tpm2 constructs are functional, Tpm1 tolerates the presence of the N-terminal fluorescent tag better than Tpm2. These observations now enhance our understanding of the functionality of these mNG-Tpm fusion proteins and will be a useful resource for their usage and experimental design in future studies in vivo.

It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.

Response: __We agree with the reviewer's observation and for the sake of clarity and accuracy, we have now renamed "mNG-amTpm" with "mNG-ASTpm". The use of -AS- dipeptide is very routine in studies with Tpm (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) and its addition restores normal binding affinities to Tpm proteins purified from E. coli (Monteiro et al., 1994). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may help neutralize the impact of a freely protonated Met on the alpha-helical structure and stabilize the N-terminus helix of Tpm and allow normal head-to-tail dimer formation (Monteiro et al., 1994; Frye et al., 2010; Greenfield et al., 1994). Consistent with this, we also observe a highly significant improvement in actin cable length when expressing mNG-ASTpm as compared to mNG-Tpm in Dtpm1 cells, suggesting an improvement in function probably due to increased binding affinity (Fig. 1B, 1C). We have also discussed this in our answer to Question 1 of Reviewer 1 and the revised manuscript (Lines 350-372)__.

__ Localization of Tpm1 and Tpm2:__Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.

Response: __We thank the reviewer for this observation and their suggestions. We agree that relative concentrations of functional Tpm1 and Tpm2 in cells may influence the extent of their localizations. As per the reviewer's suggestion, we have now conducted our quantitative analysis in cells lacking endogenous Tpm1 and only expressing mNG-ASTpm1 from an integrated plasmid copy at the leu2 locus and the data is presented in new __Figure S3. We compared Tpm-bound cable length (Fig. S3A, S3B) __and Tpm-bound cable number (Fig. S3A, S3C) along with actin cable length (Fig. S3D, S3E) and actin cable number (Fig. S3D, S3F) in wildtype, Dbnr1, and Dbni1 cells. Our analysis revealed that mNG-ASTpm1 localized to actin cable structures in wildtype, Dbnr1, and Dbni1 cells and the decrease observed in Tpm-bound cable length and number upon loss of either Bnr1 or Bni1, was accompanied by a corresponding decrease in actin cable length and number upon loss of either Bnr1 or Bni1. Thus, this analysis reached the same conclusion as our earlier analysis (Fig. 2) that mNG-ASTpm1 does not show preference between Bnr1 and Bni1-made actin cables. mNG-ASTpm2 did not restore functionality, when expressed as single integrated copy, in Dtpm1Dtpm2 cells (new results in __Fig. S1E, S1F, S5A) thus, we could not conduct a similar analysis for mNG-ASTpm2. This suggests that use of mNG-ASTpm2 would be more meaningful in the presence of endogenous Tpm2 as previously done in Fig. 2D-F.

We have now also performed additional yeast mating experiments with cells lacking bnr1 gene and expressing either mNG-ASTpm1 or mNG-ASTpm2 and the data is shown in new Figure 3. From these observations, we observe that both mNG-ASTpm1 and mNG-ASTpm2 localize to the mating fusion focus in a Bnr1-independent manner (Fig. 3B, 3D) and suggests that they bind to Bni1-made actin cables that are involved in polarized growth of the mating projection. These results also add strength to our conclusion that Tpm1 and Tpm2 localize to actin cables irrespective of which formin nucleates them. Overall, these new results highlight and reiterate our model of formin-isoform independent binding of Tpm1 and Tpm2 in S. cerevisiae.

In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.

Response: __We thank the reviewer for pointing this out. Our data and analysis do not suggest that Tpm1 and Tpm2 show any preference for decoration of cables in either mother or bud compartment. As per the reviewer's suggestion, we have now quantified the ratio of mean mNG fluorescence in the bud to the mother (Bud/Mother) and the data is shown in __Figure. S2G. The bud-to-mother ratio was similar for mNG-ASTpm1 and mNG-ASTpm2 in wildtype cells, and the ratio increased in Dbnr1 cells and decreased in Dbni1 cells for both mNG-ASTpm1 and mNG-ASTpm2 (Fig. S2G). __This is consistent with the decreased actin cable signal in the mother compartment in Dbnr1 cells and decreased actin cable signal in the bud compartment in Dbni1 cells (Fig. S2A-D). Thus, our new analysis shows that both mNG-ASTpm1 and mNG-ASTpm2 have similar changes in their concentration (mean fluorescence) upon loss of either formins Bnr1 and Bni1 and show similar ratios in wildtype cells as well, suggesting no preference for binding to actin cables in either bud or mother compartment. The preference inferred by the reviewer seems to be a bias of the current representative images and thus, we have replaced the images in __Fig. 2A, 2D to more accurately represent the population.

The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?

__Response: __We did not use LifeAct for our analysis as LifeAct is known to cause expression-dependent artefacts in cells (Courtemanche et al., 2016; Flores et al., 2019; Xu and Du, 2021) and it also competes with proteins that regulate normal cable organization like cofilin. Use of LifeAct would necessitate standardization of expression to avoid such artefacts in vivo. Also, phalloidin staining provides the best staining of actin cables and allows for better quantitative results in our experiments. The use of LifeAct along with mNG-Tpm would also require optimization with a red fluorescent protein which usually tend to have lower brightness and photostability. However, during the revision of our study, a new study from Prof. Goode's lab has developed and optimized expression of new LifeAct-3xmNeonGreen constructs for use in S. cerevisiae (Wirshing and Goode, 2024). Thus, a similar strategy of using tandem copies of bright and photostable red fluorescent proteins can be explored for use in combination with mNG-Tpm in the future studies.

__ Complementation of tpm1∆ by Tpm2:__

I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue.

The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.

Response: __We thank the reviewer for pointing this out. We would like to clarify that in our RT-qPCR experiments, the primers were chosen within the Tpm1 and Tpm2 gene and do not distinguish between transcripts from endogenous or plasmid copy. We have now mentioned this in the Materials and Methods section of the revised manuscript. So, they represent a relative estimate of the total mRNA of these genes present in cells. We were consistently able to detect ~19 fold increase in Tpm2 total mRNA levels as compared to wildtype and ∆tpm1 cells (Fig. S4D) when tpm2 was expressed from a high-copy plasmid (pRS425). This increase in Tpm2 mRNA levels was accompanied by a rescue in growth (Fig. S4A) and actin cable organization (Fig. S4B) of ∆tpm1 cells containing pRS425-ptpm2TPM2. When tpm2 was expressed from a low-copy number centromeric plasmid (pRS316), we detected a ~2 fold increase in Tpm2 transcript levels when using the tpm1 promoter and no significant change was detected when using tpm2 promoter (Fig. S4E)__. We have made sure that these results are accurately described in the revised manuscript.

As per the reviewer's suggestion, we have now conducted a more extensive analysis to ascertain the expression levels of Tpm2 in our experiments and the data is now presented in new Figure S5. We used mNG-ASTpm1 and mNG-ASTpm2 to rescue growth of ∆tpm1 (Fig. S5A) and correlated growth rescue with protein levels using quantified fluorescence intensity (Fig. S5B, S5C) and western blotting (anti-mNG) (Fig. S5D, S5E). We find that ∆tpm1 cells containing pRS425-ptpm1mNG-ASTpm1 had the highest protein level followed by pRS425-ptpm2 mNG-ASTpm2, pRS305-ptpm1mNG-ASTpm1, and the least protein levels were found in pRS305-ptpm2 mNG-ASTpm2 containing ∆tpm1 cells in both fluorescence intensity and western blotting quantifications (Fig. S5C, S5E). Surprisingly, we were not able to detect any protein levels in ∆tpm1 cells containing pRS305-ptpm2 mNG-ASTpm2 with western blotting (Fig. S5D) which was also accompanied by a lack of growth rescue (Fig. S5A). This most likely due to weak expression from the native Tpm2 promoter which is consistent with previous literature (Drees et al., 1995). Taken together, this data clearly shows that the rescue observed in ∆tpm1 cells is caused due to increased expression of mNG-ASTpm2 in cells and supports our conclusion that increase in Tpm2 expression leads to restoration of normal growth and actin cables in ∆tpm1 cells.

__ Specific function of Tpm2:__

The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.

Response: __We agree with the reviewer and as per the reviewer's suggestion, we have performed another experiment which include wildtype, ∆tpm2 cells containing empty pRS316 vector or pRS316-ptpm2TPM1 or pRS316-ptpm1TPM1. We find that RACF rate increased in ∆tpm2 cells as compared to wildtype and was restored to wildtype levels by exogenous expression of Tpm2 but not Tpm1 (Fig. S6E, S6F). Since, actin cables were not detectable in ∆tpm1 cells, we measured RACF rates in ∆tpm1 cells expressing Tpm1 or Tpm2 from a plasmid copy, which restored actin cables as shown previously in __Fig. 5A-C. We observed that RACF rates were similar to wildtype in ∆tpm1 cells expressing either Tpm1 or Tpm2 (Fig. S6E, S6F), suggesting that Tpm1 is not involved in RACF regulation. Taken together, these results suggest a specific role for Tpm2, but not Tpm1, in RACF regulation in S. cerevisiae, consistent with previous literature (Huckaba et al., 2006).

Minor comments: __1.__The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).

Response: We thank the reviewer for pointing this out. We have now repeated the drop test multiple times (Fig. R2), but we see similar growth rates as the drop test already presented in Fig. S4A. __At this point, it would be difficult to ascertain the basis of this difference observed at 23{degree sign}C and 30{degree sign}C, but a recent study that links leucine levels to actin cable stability (Sing et al., 2022) might explain the faster growth of these ∆tpm1 cells containing a leu2 gene carrying high-copy plasmid. However, there is no effect on growth rate at 37{degree sign}C which is consistent with other spot assays shown in __Fig. S1D, S4F, S5A.

Significance

I am a cell biologist with expertise in both yeast and actin cytoskeleton.

The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.

We thank the reviewer for their positive assessment of our work and the constructive feedback that has greatly improved the quality of our study. After addressing the points raised by the reviewer, we believe that our study has significantly gained in consolidating the major conclusions of our work.

**Referees cross-commenting**

Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.

Response: __We acknowledge the reviewer's point about the effect of Ala-Ser dipeptide and would request the reviewer to refer to our response to Reviewer 1 (Question 1) for a more detailed discussion on this. We have also extensively addressed the question of Tpm2 expression levels as suggested by the reviewer (new data in __Figure S5) which has further strengthened the conclusions of our study.

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

Summary:__ The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.

Major and Minor Comments: 1. The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.

Response: __We agree with the reviewer's suggestions and have now updated our Materials and Methods section to describe the image analysis pipelines used in more detail. We have also added examples of quantification procedure for actin cable length/number and mitochondrial morphology as an additional Supplementary __Figure S7. Briefly, the following pipelines were used:

• Actin cable length and number analysis: This was done exactly as mentioned in McInally et al., 2021, McInally et al., 2022. Actin cables were manually traced in Fiji as shown in __ S7A__, and then the traces files for each cell were run through a Python script (adapted from McInally et al., 2022) that outputs mean actin cable length and number per cell.
• Mitochondria morphology: Mitochondria Analyzer plug-in in Fiji was used to segment out the mitochondrial fragments. The parameters used for 2D segmentation of mitochondria were first optimized using "2D Threshold Optimize" to find the most accurate segmentation and then the same parameters were run on all images. After segmentation of the mitochondrial network, measurements of fragment number were done using "Analyze Particles" function in Fiji. An example of the overall process is shown in __ S7B.__ As per the reviewer's suggestion, we have now included the description of the statistical test used in the Figure Legends of each Figure in the revised manuscript. We have used One-Way Anova with Tukey's Multiple Comparison test, Kruskal-Wallis test with Dunn's Multiple Comparisons, and Unpaired Two-tailed t-test using the in-built functions in GraphPad Prism (v.6.04).

**Referees cross-commenting**

I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.

Response: __We acknowledge the reviewer's concern about the Ala-Ser dipeptide and would request them to refer our earlier discussion on this in response to Reviewer 1 (Question 1) and Reviewer 2 (Question 2). We would also request the reviewer to refer to our answer to Reviewer 2 (Question 6) where we have extensively addressed the question of Tpm2 expression levels and their effect on rescue of Dtpm1 cells. This data is now presented as new __Figure S5 in our revised manuscript.

Reviewer#3 (Significance (Required)):

The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.

The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.

As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.

We thank the reviewer for their positive outlook and assessment of our study. We have incorporated all their suggestions, and we are confident that the revised manuscript has significantly improved in quality due to these additions.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.

Major and Minor Comments:

The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.

Referees cross-commenting

I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.

Significance

The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.

The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.

As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.

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Referee #2

Evidence, reproducibility and clarity

This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.

Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.

Functionality of the acetyl-mimic tagged tropomyosin constructs:

The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.

It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.

Localization of Tpm1 and Tpm2:

Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.

In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.

The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?

Complementation of tpm1∆ by Tpm2:

I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue. The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.

Specific function of Tpm2:

The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.

Minor comments:

The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).

Referees cross-commenting

Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.

Significance

I am a cell biologist with expertise in both yeast and actin cytoskeleton.

The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.

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Referee #1

Evidence, reproducibility and clarity

There are 2 Major issues:

1. Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.
2. My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.

Significance

This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly.

The significance of this study, given the above, and the concerns raised is not clear to this reviewer.

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Referee #3

Evidence, reproducibility and clarity

Lupu et al have identified a role for the RNA binding protein SRSF3 in epicardial development. In a well written manuscript containing many experiments the authors show that this protein is required at different time points in epicardial development to control a range of processes, in particular cell proliferation. This advances understanding of the complex roles of this RNA binding protein in the heart - and raises an important message about how incomplete Cre recombination needs to be considered in interpreting conditional mutant phenotypes. The following points should be addressed.

1. SRSF3 is known to play essential developmental roles in the myocardium where it regulates capping of transcripts involved in contraction. This point should be mentioned in addition to roles in proliferation. To facilitate understanding, the authors should say more about the subset of cardiomyocytes labelled by Gata5-Cre. For example, is this the result of stochastic activation of the transgene or is a specific subset of cells labelled? How much of the myocardium is targeted?
2. The authors show failure of ventricular compaction at E13.5 using Wt1-CreERT2 and go on to assess proliferation in epicardial cells. As epicardial-derived signals are known to promote compact myocardial growth, they should also show whether there are indirect defects in proliferation in compact layer myocardium that might explain the non-compaction phenotype. The authors should also indicate if any of the large number of genes bound by SRSF3 encode known or potential pro-proliferative signals from the epicardium or EPDCs to the myocardium and potentially validate their altered expression in mutant hearts.
3. The rescue by expansion of non-recombined cells is a most interesting aspect of this study. Can the authors see any such outcompeting in the explant experiments (for example in Figure 2)? Do the authors consider this to be an exclusively in vivo competition phenomenon? Given the known roles of Myc in cell competition can the authors use their single cell transcriptomic data to score Myc expression levels in cells from Srsf3 iKO hearts or determine if Myc transcripts are bound by SRSF3?
4. The authors suggest that this rescue occur by upregulation of Srsf3 in non-recombined cells. It would be helpful to provide additional lines of evidence supporting the hypothesis that SRSF3 expression is upregulated due to hypoxia. Do the CLIP experiments reveal whether SRSF3 binds to it's own transcript?
5. The authors imply that SRSF3 may regulate Ccnd1 mRNA stability. Can the authors directly evaluate this point? Please clarify if this gene is also affected in the knock-down experiments in MEC1 cells.
6. Please brighten the immunofluorescence panels in Figure 1 to more clearly show nuclear labelling and tissue structure.
7. Given the broad roles of SRSF3 is the adjective key necessary in the title?

Significance

This ms advances understanding of the complex roles of this RNA binding protein in the heart - and raises an important message about how incomplete Cre recombination needs to be considered in interpreting conditional mutant phenotypes. This study would be of interest to reseachers in the fields of heart development and RNA-protein interactions. Although there are a number of major points to be addressed, these could be potentially dealt with rapidly.

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Referee #2

Evidence, reproducibility and clarity

In this study, Lupu et al. analyzed the role of the RNA-binding protein SRSF3 for epicardial development. The authors found that Srsf3 is highly expressed in the proepicardial organ and during early stages of epicardial layer formation. Conditional inactivation of SRSF3 in the proepicardial organ stage using a Gata5-Cre driver line resulted in defective formation of the epicardium, accompanied by a proliferation arrest of the proepicardium, resulting in embryonic lethality at E12.5. In contrast, epicardial-specific Srsf3 deletion at later stages using the inducible Wt1CreERT2 line caused a less severe phenotype indicated by impaired coronary vasculature formation, reduced cardiac compaction, and myocardial hypoxia. Mosaic recombination yielded a small population of epicardial cells that upregulate Srsf3, hyperproliferate and compensate for the depleted Srsf3 negative lineage. Single-cell RNA sequencing of control and epicardial Srsf3 knock out hearts, combined with infrared CLIP to map SRSF3 binding sites in the transcriptome identified a number of putative SRSF3 targets involved in mitotic cell cycle control. Among others, SRSF3 binds directly to transcripts encoding key regulators of proliferation, such as Cyclin D1, and senescence, including MAP4K4. The authors conclude that SRSF3 exerts different functions in processing of RNAs, including splicing.

Overall, this is a well-written and well-organized manuscript, describing interesting findings in the field of epicardial development. However, the mechanistic part is not overly strong. The authors detected some moderate changes in the distribution of different Map4k4 splicing isoforms after knockdown of Srsf3 in an immortalized epicardial cell line but did not go any deeper. The cause for the reduced presence of transcripts for the SRSF3-target Ccnd1 after knockdown of Srsf3 remains enigmatic.

Significance

The authors raise a number of speculations why remaining Srsf3-expressing cell start to hyperproliferate after inactivation of Srsf3 but it does not become clear which mechanism is critical. How do non-targeted epicardial cells in the mosaic recombination model sense the loss of SRSF3 knock out cells, resulting in hyperproliferation and enhanced Srsf3 expression? Is a loss of lateral inhibition, e.g. by activated YAP/TAZ, causative for enhanced proliferation of the remaining epicardial cells and an elevated expression level of WT1 and SRSF3? Immunofluorescence staining and/or qRT-PCR of YAP/TAZ and TEADs might provide an answer.

Is the elevated expression of Srsf3 in non-targeted epicardial cells due to enhanced transcription and/or by altered post-transcriptional processes? How does this observation fit to previous reports indicating that Srsf3 overexpression promotes inclusion of an autoregulatory cassette exon (exon 4) containing a premature (in-frame) stop codon in Srsf3, thereby confining this SRSF3 isoform to nonsense mediated decay (NMD) (doi: 10.1093/emboj/16.16.5077, doi: 10.1186/gb-2012-13-3-r17, doi: 10.1038/srep14548, doi: 10.1161/CIRCRESAHA.118.31451)? In contrast, Srsf1 as well as PTBP1/2 have been previously reported to regulate Srsf3 expression by promoting exon 4 skipping. The authors should perform RNA seq and/or qRT-PCRs validation to check the inclusion of Srsf4 Exon4 as well as Srsf1 and PTBP1/2 expression levels in control and knock out epicardial cells.

It remains unclear by which mechanisms (alternative splicing, alternative polyadenylation, miRNA processing, or others) SRFS3 mainly exerts its function in the embryonic epicardial lineage. The selection and validation of Map4k4 as a splicing target is not based on an unbiased splicing analysis. In my opinion it is mandatory to provide a full assessment of splicing changes in Srsf3-deficient cells, either by long-range sequencing or by analysis of exon-junction reads.

Likewise, it is completely enigmatic what SRFS3 does to Ccnd1 transcripts. Does SRFS3 increase the half-life of Ccnd1, does it impact trafficking? At least, the authors have to determine changes in the half-life of Ccnd1 after depletion of SRFS3.

An unbiased bioinformatics analysis addressing alternative splicing, alternative polyadenylation, and mRNA processing is necessary. Ideally, primary epicardial cells should be used and not an immortalized epicardial cell lines. It is well known, that splicing in cell lines differs substantially from splicing in primary cells.

I am not convinced that the moderate changes of different Map4k4 splicing isoforms after knockdown of Srsf3 are really responsible for the rather drastic phenotype. Additional experiments are needed to prove a decisive function of a shift in Map4k4 splicing isoforms for hyperproliferation of epicardial cells.

The authors claim that inactivation of Srsf3 inhibits cell proliferation and causes a senescence-like phenotype. The claim for acquisition of senescence is solely based on transcriptional changes. No attempts were made to visualize an increase of senescent cells in Srsf3-mutant embryos. The authors need to perform SA-bGAL assays or use other techniques to analyse the appearance of senescent cells in the mutants.

Fig. 2E indicates that WT1 positive / tdTom negative epicardial cell population is enriched in a specific region of the pre-epicardial organ from Srsf3KOs. However it is not clear whether these cells proliferate. The authors should quantify Ki67positive cells in both the WT1-positive / tdTom-positive and the WT1positive / tdTom-negative epicardial population.

In the headline on page 6, the authors stated that "SRSF3 depletion in the PEO results in impaired ... migration of epicardial progenitor cells", which they deduced from the reduced outgrowth of ventricular epicardial explants. However, the reduced outgrowth from the PEO could be caused by both, reduced proliferation and/or reduced migration. Therefore, the authors should provide additional data clearly indicating reduced migration, e.g. by blocking transcription. Scratch assays of SRFS3 knockout/knockdown vs. control epicardial cells would strengthen the analysis, Is there a change in the GO term "regulation of migration"?

To prove the reduced proliferation ratio in Figure 4B, quantification of Cyclin D1 positive cells in both SRSF3 positive and negative cells is required.

Minor issues

Abstract line 12: a full stop is missing at the end of the sentence.

Figure 1A: E11.5, figure label 'DAPI WT1' is missing.

Page 8: Bracket in front of Fig. 4B is missing

Page 8: G2M phase change uniformly to G2/M phase

Page 9: 'Srsf3-depleted hearts also demonstrated an increased abundance of epicardial cells with upregulated expression of genes associated with quiescence, such as Clu48, and senescence, for example Map4k4, Tmem30a and Pofut249 (Fig. 4F)'. The sentence is misleading, implying Srfs3 inactivation in all cardiac cell types ('Srsf3-depleted hearts').

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Referee #1

Evidence, reproducibility and clarity

Summary:

The study by Irina Lupu and colleagues highlights SRSF3 as a key regulator of epicardial development by regulating epicardial cell proliferation. This was demonstrated via two murine knockout models; the first to assimilate the role SRSF3 plays in epicardial formation as a whole, and the second to address its importance post a pivotal maturation point. Through scRNA sequencing and irCLIP, several SRSF3 targets were ascertained and identified as cell cycle regulators. Those epicardial cells that did not lose SRSF3 compensated the loss of some of their mates by increasing SRSF3 expression and over-proliferating. Overall, the paper is interesting and the conclusions are largely supported by the provided data.

Major comments:

1. Authors claim that a "reduction in SRSF3 expression levels coincided with the downregulation of WT1 in the epicardium". This was evidenced by immunofluorescence imaging (figure 1A). I suggest conducting a qRT-PCR to quantify Wt1 expression over time, similar to the experiment they performed in figure 1B.
2. A western blot depicting SRSF3 protein production in controls compared to the knockout model may provide stronger evidence of its depletion (figure 1E).
3. Authors state that they were unable to directly identify the absence of exons 2 and 3 in individual cells. Please provide evidence that exons 2 and 3 have been knocked out, at least by performing a qRT-PCR.
4. To prove the functional implication in the observed phenotype of the identified SRSF3 targets, please interfere with Map4k4 activity or expression and check whether the defective epicardial cell proliferation is reverted. This should be done at least in vitro, ideally in vivo.

Minor comments:

1. Several minor typos and spacing issues were observed. Please correct.
2. It would be good for the reader if the authors would simplify their rationale for the use of the two mouse models. It is slightly convoluted and not easy to follow.
3. In figure 4, it is recommended to add a stacked bar plot to represent the percentage of each cell cluster/population after 4A. This would help the reader
4. Figure 4B. It is confusing for the reader to understand the fact that the majority of tdTomato+ sorted cells in Srsf3 iKO keep expressing Srsf3. Including the quantification of the image could help.

Significance

The paper will be of interest to readers in the field of cardiology, embryology and molecular biology. It will advance the field especially in the study of the development of the epicardium. The models are sophisticated and the experiments carefully performed.

My field is molecular cardiology, with interest in RNA-binding proteins.

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Reply to the reviewers

Manuscript number: RC-2024-02835

Corresponding author: Eglantine, Heude

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RESPONSE TO REVIEWERS

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Minor changes include:

- concerns of Reviewer #1 with additional work to complete a new Supp. Fig. 3.

- some text modifications to avoid ambiguity and misunderstanding, and to address the concerns of Reviewers #1 and #2.

- addition of three references to develop some aspects of the discussion following the suggestions from Reviewer #3.

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Reviewer #1

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

This manuscript investigates the role of Dlx5/6 in development of the ear and vocal tract components. They generate a new conditional mouse knockout of Dlx5/6 using a Sox10 driver, which deletes functional forms of these factors in the otic placode (the primordium of the entire inner ear) and in neural crest cells, which contribute to the middle and outer ear, the otic capsule, as well as the craniofacial skeleton and cartilage including that of the larynx, and tendons.

The authors describe the ear and craniofacial phenotypes in these mutants using marker analysis on sections, some whole mounts and microCT imaging. They confirm previously described ear and craniofacial phenotypes, and add additional information on tongue, thyroid and hyoid cartilages, and associated mesoderm-derived muscles. Finally, they assess innervation finding subtle changes innervations patterns.

They conclude that Dlx5/6 are required for normal development of auditory and vocal structures and suggest that these factors coordinate both systems and that this is important for their co-evolution.

The experiments presented are straightforward phenotypic analysis, which is well presented with high quality imaging. However, it is not clear how well the conclusions are supported by the data because

i) The number of animals analysed and showing the reported phenotypes is not clearly stated; some figure legends list some n numbers, but it is sometimes ambiguous whether they refer to controls or experimental specimen. In other cases, n=2 is too low to draw firm conclusions.

We thank the reviewer for highlighting this point and for giving us the opportunity to clarify it.

The number of specimens mentioned correspond to the number of mutants analyzed versus the equivalent number of controls. For most figures, at least 3 mutants and 3 corresponding controls have thus been studied independently, unless for the microCT analysis (n=2 each condition) that however complete histological data on sections (n=6 each condition) presented at equivalent stage. Our data showing highly penetrant and reproducible results, the n numbers appear significant in support of our conclusions. To avoid ambiguity, we have systematically completed the n numbers with the mention 'each condition' in the figure legends.

ii) The nature of the controls is not stated, so we cannot know what is compared. Are controls WT, heterozygous for Dlx5 and -6, single KOs, single hets?

The lack of clarity in methods and controls does not allow others to reproduce the findings easily.

To address this concern, we have now added the genotype of controls in the Materials and Methods section. For all experiments, the controls of mutant specimens were from the same littermate. We included as control genotypes the specimens heterozygous for the Cre and/or flox or homozygous for the flox only (genotypes equivalent to breeders), which we had previously validated as showing a normal phenotype compared to non-genetically modified 'wild-type' specimens. This observation is consistent with what had been described previously in constitutive inactivated Dlx5/6 mouse models, for which the development of heterozygous animals was unaffected by the mutation (e.g. Heude et al. 2010 PNAS).

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Based on the fairly limited data presented here, the authors make some far-reaching suggestions that are not supported by experimental findings. For example, they propose that their study points to "co-adaptation of the effector and receptor organs during acoustic communication diversification in land vertebrates" and that Dlx5/6 play a role in this process. This idea appears to be the main motivation for the current study, however, there is little evidence to support such conclusions.

Likewise, they suggest that a "common Sox10-Dlx5/6-BMP signaling axis coordinates the morphogenesis of both CNCC and otic placode derivatives for the proper formation of the vocal and auditory complex". There is no evidence provided that Sox10 controls any of the other genes and functional experiment related to Sox10 are not carried out, while the Dlx5/6-BMP link has been established previously.

Overall, the authors need to improve numbers, experimental information and controls, and given the descriptive nature of the manuscript they should refrain from wide-ranging conclusions.

We regret that Reviewer #1 took our discussion points as conclusions, which was not our purpose. Rather, Reviewer #3 stated that 'The authors are careful in the way they present the evidence, and only go beyond their data to add a few 'speculative' paragraphs at the end of the Discussion' (see below). We believe that the Discussion section is the appropriate space to formulate hypotheses based on experimental results, mainly while speculating on evolutionary aspects, that cannot be technically tested but are still conceptually of interest.

Concerning the Sox10-Dlx5/6-BMP axis, we agree with Reviewer #1 that we do not provide evidences that this common genetic program regulates the formation of vocal and auditory systems. We have added additional nuance to the text to avoid ambiguity, and we expect that our propositions will inspire future research on the issues raised.

The analysis could also be strengthened by focusing on the novel aspects, providing a developmental time series as to when phenotypes are first observed combined with marker analysis e.g. looking at different compartments of the otic vesicle, cell types etc., and providing higher magnification images to describe the phenotypes (e.g. those in figure 1) will strengthen the paper and might provide some novelty.

Reviewer #1 (Significance (Required)):

The authors present a new conditional knock out line for Dlx5/6. The major limitation of the study is that the data presented largely confirm what has already been published including the regulation of BMP pathway components and non-cell autonomous effects on muscle. As far as I am aware, the only new additions to the literature are the analysis of the cartilages and tongue.

The phenotypic analysis is minimal, there is no developmental time series and no other functional experiments to address some of the suggestions made by the authors.

My expertise is in ear formation, and I am aware of the craniofacial literature.

The novelty of our study is based on the generation of a new conditional mouse model to address the pleiotropic role of Dlx5/6 in coordinating the development of both the vocal tract and auditory components, an aspect that has never been considered before.

To this end, we have combined a variety of high-resolution imaging techniques to achieve an unprecedented analysis of critical markers of placode, neural crest and mesoderm derivatives (including a neural crest lineage analysis). We initially selected key early embryonic (E10.5-E11.5), fetal (E14.5) and perinatal (E16.5-E17.5) stages, but we agree with Reviewer #1 that an additional stage is relevant in our context to complete this developmental time series. We have now added in a new Supp. Fig. 3 including the analysis of a late embryonic stage (E12.5). In particular, our results show that primary chondrogenic condensations are affected at the level of the vocal tract and auditory compartments. Furthermore, our data reveal that the differentiation defects of the masticatory and tongue musculature occur at the transition between early (E11.5) and late (E12.5) embryonic stages. We are grateful to Reviewer #1 for his comments, which allowed us to present these new and interesting results.

Reviewer #2

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Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Sanchez-Garrido and colleagues analyze the consequences of Dlx5/6 deletion on cranial neural crest- and otic placode-derived structures development. The authors show that Dlx5/6 inactivation causes broad defects of the outer, middle and inner ear as well as malformations of the jaw, pharynx and larynx musculoskeletal systems. The authors conclude that Dlx5/6 play an important role in the regulation of vocal and auditory systems development in mammals.

Reviewer #2 (Significance (Required)):

The work is well-presented and illustrated, with appropriate description of methods. The role of this gene family in regulating craniofacial structures is not completely novel. The analysis is largely descriptive providing little mechanistic insights. The authors propose a possible link between Dlx5/6 activity and BMP signaling as the culprit for the defects observed in the mutants, however this is not supported experimentally. The work is viewed as too preliminary at this stage.

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We are appreciative of the feedback from Reviewer #2. Our aim was to gain insight into the genetic basis of the coordinated development of the vocal tract and hearing complex. As mentioned above for Reviewer #1, we have added modifications to the discussion to nuance and develop our propositions. We hope that our study will help to further develop some of the aspects we have explored.

Reviewer #3

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

This is a very good paper presenting results examining the role of DLX5/6 in the formation of ear and vocal tract structures by means of mutant mice. I believe the evidence offered in this paper in favor of such a role is strong and well presented.

Reviewer #3 (Significance (Required)):

I think this is a very good study, contributing to our knowledge of the action of DLX5/6, giving us insights into the genes' role in ear and vocal tract structures, critical for vocalization and the pairing of auditory and oral capacities. The authors are careful in the way they present the evidence, and only go beyond their data to add a few 'speculative' paragraphs at the end of the Discussion.

I would encourage the authors to consider making mention of Gokhman et al's (2021) work, where genes similarly impacting craniofacial and oral capacities are discussed: https://www.nature.com/articles/s41467-020-15020-6

I would also have liked the authors to cite work on GLI3 and larynx formation (given that both GLI3 and HAND2 control DLX5 and 6) and play an important role in Neural Crest-related processes: https://elifesciences.org/articles/77055 / https://elifesciences.org/articles/56450

In this context, the authors mention work on the role of Dlx5/6 in GABAergic neurons. Do the authors believe there to be a connection (mediated by the SHH pathway) between the ganglionic eminence and neural crest development, or are these two findings only convergent at the level of the phenotype?

We are grateful to Reviewer #3 for his positive assessment of our study and for his very relevant suggestions of works to consider in the context of our research. This has allowed us to complete and develop exciting points in our discussion, which we hope will stimulate further lines of research regarding the diversification of acoustic communication abilities during mammalian evolution, including the emergence of speech in humans.

To answer the reviewer's question, we do not believe that there is a link between the ganglionic eminences and neural crest derivatives, which develop independently in two embryonic compartments under the regulation of distinct genetic programs. However, the observation that Dlx5/6 expression in the brain regulates socialization and vocalization behaviors in adult mice suggest that the genes have a broader pleiotropic role in acoustic communication, both during development and in the adult at the cognitive level.

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Referee #3

Evidence, reproducibility and clarity

This is a very good paper presenting results examining the role of DLX5/6 in the formation of ear and vocal tract structures by means of mutant mice. I believe the evidence offered in this paper in favor of such a role is strong and well presented.

Significance

I think this is a very good study, contributing to our knowledge of the action of DLX5/6, giving us insights into the genes' role in ear and vocal tract structures, critical for vocalization and the pairing of auditory and oral capacities. The authors are careful in the way they present the evidence, and only go beyond their data to add a few 'speculative' paragraphs at the end of the Discussion.

I would encourage the authors to consider making mention of Gokhman et al's (2021) work, where genes similarly impacting craniofacial and oral capacities are discussed. https://www.nature.com/articles/s41467-020-15020-6

I would also have liked the authors to cite work on GLI3 and larynx formation (given that both GLI3 and HAND2 control DLX5 and 6) and play an important role in Neural Crest-related processes: https://elifesciences.org/articles/77055 https://elifesciences.org/articles/56450 In this context, the authors mention work on the role of Dlx5/6 in GABAergic neurons. Do the authors believe there to be a connection (mediated by the SHH pathway) between the ganglionic eminence and neural crest development, or are these two findings only convergent at the level of the phenotype?

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Sanchez-Garrido and colleagues analyze the consequences of Dlx5/6 deletion on cranial neural crest- and otic placode-derived structures development. The authors show that Dlx5/6 inactivation causes broad defects of the outer, middle and inner ear as well as malformations of the jaw, pharynx and larynx musculoskeletal systems. The authors conclude that Dlx5/6 play an important role in the regulation of vocal and auditory systems development in mammals.

Significance

The work is well-presented and illustrated, with appropriate description of methods. The role of this gene family in regulating craniofacial structures is not completely novel. The analysis is largely descriptive providing little mechanistic insights. The authors propose a possible link between Dlx5/6 activity and BMP signaling as the culprit for the defects observed in the mutants, however this is not supported experimentally. The work is viewed as too preliminary at this stage.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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Referee #1

Evidence, reproducibility and clarity

This manuscript investigates the role of Dlx5/6 in development of the ear and vocal tract components. They generate a new conditional mouse knockout of Dlx5/6 using a Sox10 driver, which deletes functional forms of these factors in the otic placode (the primordium of the entire inner ear) and in neural crest cells, which contribute to the middle and outer ear, the otic capsule, as well as the craniofacial skeleton and cartilage including that of the larynx, and tendons.

The authors describe the ear and craniofacial phenotypes in these mutants using marker analysis on sections, some whole mounts and CT imaging. They confirm previously described ear and craniofacial phenotypes, and add additional information on tongue, thyroid and hyoid cartilages, and associated mesoderm-derived muscles. Finally, they assess innervation finding subtle changes innervation patterns.

They conclude that Dlx5/6 are required for normal development of auditory and vocal structures and suggest that these factors coordinate both systems and that this is important for their co-evolution. The experiments presented are straightforward phenotypic analysis, which is well presented with high quality imaging. However, it is not clear how well the conclusions are supported by the data because

i) The number of animals analysed and showing the reported phenotypes is not clearly stated; some figure legends list some n numbers, but it is sometimes ambiguous whether they refer to controls or experimental specimen. In other cases, n=2 is too low to draw firm conclusions.

ii) The nature of the controls is not stated, so we cannot know what is compared. Are controls WT, heterozygous for Dlx5 and -6, single KOs, single hets?

The lack of clarity in methods and controls does not allow others to reproduce the findings easily. Based on the fairly limited data presented here, the authors make some far-reaching suggestions that are not supported by experimental findings. For example, they propose that their study points to "co-adaptation of the effector and receptor organs during acoustic communication diversification in land vertebrates" and that Dlx5/6 play a role in this process. This idea appears to be the main motivation for the current study, however, there is little evidence to support such conclusions. Likewise, they suggest that a "common Sox10-Dlx5/6-BMP signaling axis coordinates the morphogenesis of both CNCC and otic placode derivatives for the proper formation of the vocal and auditory complex". There is no evidence provided that Sox10 controls any of the other genes and functional experiment related to Sox10 are not carried out, while the Dlx5/6-BMP link has been established previously. Overall, the authors need to improve numbers, experimental information and controls, and given the descriptive nature of the manuscript they should refrain from wide-ranging conclusions. The analysis could also be strengthened by focusing on the novel aspects, providing a developmental time series as to when phenotypes are first observed combined with marker analysis e.g. looking at different compartments of the otic vesicle, cell types etc., and providing higher magnification images to describe the phenotypes (e.g. those in figure 1) will strengthen the paper and might provide some novelty.

Significance

The authors present a new conditional knock out line for Dlx5/6. The major limitation of the study is that the data presented largely confirm what has already been published including the regulation of BMP pathway components and non-cell autonomous effects on muscle. As far as I am aware, the only new additions to the literature are the analysis of the cartilages and tongue. The phenotypic analysis is minimal, there is no developmental time series and no other functional experiments to address some of the suggestions made by the authors.

My expertise is in ear formation, and I am aware of the craniofacial literature.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

The authors first use a Bio-ID approach to search for interactors of the basket proteins TPR and NUP153, identifying proteins involved in various nuclear process, including many splicing components, and confirm some of these interactions using IP and PLA assays. PLA experiments further suggest that these interactions occur primary at or close to the nuclear periphery. Moreover, inhibiting splicing, but not transcription, reduced these interactions. The authors then investigated the role of NUP153 in loading of the splicing machinery and found a lower association of the NUP98/SF3A1 but not AQR interaction (measured through PLA). Furthermore, DamID experiments identified NUP153 bound genes proximal to LAD domains that are actively transcribed, contain overall longer introns with low GC-content, and fall within a group of genes located at the outermost shell of the nucleus (when compared to previously published LaminI ID /PGseq data). Lastly, they interrogate whether depletion of NUP153 results in a splicing defect for NUP153 bound genes.

The authors identify many proteins in their BioID interaction screen, however, only a single nucleoporin (Nup35, an inner ring protein). Previous BioID studies have identified NUP153 in BioID experiments including proteins of the Y-complex (PMID: 24927568 and others).

The BioID list summarises interactions of proteins present across five datasets and among two cell lines, HEK293T and Jurkat T cells. As the reviewer pointed out, these stringent criteria excluded proteins originally present in the BioID datasets. Indeed, the original datasets across the cell lines include a wide range of nucleoporins Nup37, Nup43, Nup53, Nup85, Nup107, Nup188, and Nup205. Apart from this, several other proteins were consistently found on the BioID of the basket nucleopore that had been previously found in the literature, namely transcription-related and export-related proteins, as the reviewer can depict on Fig 1 C.

To ensure that the BioID experiments indeed probe for interactions of NUP152 and TPR at the NPC, the authors should include control experiments that show that their NUP153 and TPR-BirA fusions primarily localize to the NPC. If a significant fraction is not NPC bound, this has to be taken into account when interpreting/discussing their data.

Before conducting the DamID experiment, we validated the peripheral localization of the constructs used here. We provide here the images showing the distribution of the two nucleoporins

Figure R1: Immunofluorescent images obtained for Nup153 and TPR in Jurkat cells prior to BioID experiment.

The authors describe DDX39b/UAP56 as an early splicing factor; DDX39b/UAP56 main role however seems to be in mRNA export and mRNP compaction. The authors might want to include this in the interpretation of their data.

The reviewer is right; the role of DDX39b/UAP56 goes beyond pre-mRNA splicing. This is indeed involved in posttranscriptional maturation and export from the nucleus to the cytoplasm of cellular RNAs. Originally identified as a helicase involved in pre-mRNA splicing, UAP56 has been shown to facilitate the formation of the A complex during spliceosome assembly. This is the reason why we included it in our study. Additionally, DDX39b/UAP56 has been found to be critical for interactions between components of the exon junction and transcription and export complexes to promote the loading of export receptors, while more recently has also been identified as a DNA:RNA helicase involved in the resolution of R-loops (PMID:32439635). At present, we indeed cannot distinguish between multiple functions of this protein at the NPC, and this is now acknowledged in our manuscript.

• Concerning PLA experiment controls, the authors perform TPR-PML as a negative control, however, no negative control for NUP153 is shown (Figure 2). Such a control should be added to allow evaluating the specificity of NUP153 PLA interactions, and/or discussed why this was not done. We would like to clarify better how we selected the antibodies for the control PLA experiments. In PLA, antibodies from different species have to be used. Nup153 is an anti-mouse antibody, and the control we used, PML, is also an anti-mouse; therefore, the two antibodies cannot be used in the PLA experiment. The controls used (mouse) were conjugated with rabbit antibodies, either TPR (PML) or AQR (B23). Both TPR:PML and AQR:B23 showed insignificant PLA signals. Therefore, we can confidently conclude that the PLA spots seen for the NPC/splicing proteins are of measurable quantities. Moreover, the conclusion that there’s a pool of splicing machinery associated with the NPC is sustained on several pieces of evidence accumulated through other experiments, not only PLA. We have supporting evidence from super-resolution microscopy, coIPs as well as the referred PLA.

Figure R2: Control PLA assay with anti-AQR (rabbit) and Nucleophosmin (B23)(mouse) antibodies.

• Quantification of the distance of PLA-NUP153/TPR interactions show interactions mainly close to the nuclear periphery. The imaging data shown in Figure 2b indeed shows that TPR/NUP153 interactions are exclusively at the nuclear periphery, whereas NUP153/splicing factor interactions are sometimes at the edge of the DAPI signal, but mostly somehow internalized (Figure 2B, S2b). Quantification (Figure 2d) shows these distributions to be very similar, likely due to the way the quantification was performed / the bin size of plotting the relative distance of a spot to the nuclear periphery was chosen. Looking at the scale bar/nuclear size and the position of the PLA spots for the NUP153/splicing factors, it appears that spots are often hundreds of nanometers away from the periphery. As the nuclear basket is thought to reach only about 100nm onto the nuclear interior, the conclusion by the authors that these interactions occur at the NPC would not be consistent with the data. The authors should better incorporate this in their interpretation of the data. Nup153/TPR are peripherally located at the most outer shells (0, 40% of signal and 1, 60% of signal). Consistently, based on figure 2d, Nup153:DDX39b is similarly distributed (0, 40% of signal and 1, 60% of signal) and the vast majority of the Nup153:AQR and Nup153:SF31A1 signal is also present in these two shells (20% and 40%). Although our super-resolution microscopy excludes the presence of internal Nup153 staining we cannot exclude that PLA potentially increases the signal of a possible internal Nup153/splicing interaction due to the rolling circle amplification reaction. However, as referred above this is not where primarily our interaction is occurring.

• The conclusion ' Nup153 aids the loading of splicing machinery' is not sufficiently supported by the data. The authors observed a reduction in PLA signal for the NUP98-AQR interaction, but not the NUP98-SF3A1 (Figure 3g). Their conclusion has to reflect this discrepancy in their data. Moreover, the studies focus is to determine the role of NUP153/TPR in recruiting the splicing machinery to the NPC. As in the experiments the authors interrogate the interaction of only NUP98, who has to a large extend splicing factor interactions within the nuclear interior and not at the periphery, the relevance of the experiments in Figure 3 towards the main focus of the paper is unclear. The reviewer is right to point out that the study focuses on determining the role of Nup153/TPR in recruiting the splicing machinery to the NPC. As TPR is docked to the NPC through Nup153 (PMID: 12802065; 39127037) we investigated Nup98 and performed internal controls to show that 1) Nup98 wasn’t disrupted by shNup153 (Fig below) and technically 2) Nup98 was used in the shNup153 studies because of the availability of a reliable mouse antibody that could be coupled with the various rabbit antibodies used previously for SF3A1, DDX39, XAB2, and AQR in PLA experiments' for which mouse counterparts do not exist and would therefore hinder the continuation of the study as explained above. For TPR only a reliable rabbit antibody exists that works in our hands and therefore we wouldn’t have been able to perform the PLA experiments shown here

Figure R3: Nup98 staining in wild type and shNup153 depleted Jurkat cells. In (a) co-immunofluorescence between AQR and Nup98 showing predominant positioning of Nup98 in the nuclear periphery (at the NPC). (b) Nup98 staining at the periphery persists also in shNup153 depleted cells, indicating that this Nup can be used as an NPC marker in Nup153 depleted conditions.

When investigating the effect of NUP153 depletion on splicing, the authors observe a splicing phenotype for multiple NUP153 genes (Figure 5). The authors however show only a single negative control gene (CBX5). It would significantly strengthen their argument if the authors would investigate splicing defects of periphery located noneNUP153 bound genes as well as for genes located in the nuclear interior to better understand whether this splicing phenotype is indeed specific for NUP153 genes (at the nuclear periphery/NPC).

We agree with the reviewer that expanding our observations to other peripherally located genes would be interesting; however, most of the other known peripheral genes are LAD-associated and mostly not expressed. While it was not our intention to claim that splicing at the periphery is specific only for Nup153-bound genes, we had obviously focused on Nup153-bound genes to understand the dynamics between Nup153/splicing machinery interactions. As stated above, other peripherally located genes within LADs are repressed.

It Is out of the scope of this study to understand other relevant splicing hubs, as the reviewer knows splicing can occur throughout the nucleus at different sites (outside speckles). However, we do understand the reviewer's point of view, and to include more controls as requested by this and other reviewers, we have designed primers for additional non-Nup153 bound genes, and these additional experiments will be included in the manuscript.

Figure R4: Preliminary data showing splicing of other Non-Nup153 Bound genes upon shNup153

The authors state in the text describing the SABER-FISH experiments in Figure 5f that 'were able to visualize the presence of a site of transcription where accumulation of these probes was close to the periphery for all except for GSTK1, which showed a wider nuclear distribution, similar to CBX5 control region not bound by Nup153'. However, their statement is not supported by the images shown in Fig 5f, which show TS in control cells in the nuclear interior. Also, a single cell but no quantification is shown. Moreover, what distance from the periphery is considered as close to the periphery is not defined (see also earlier comment on the question what should be considered a periphery and/or NPC association).

Measurement of the distance of FISH signals to the nuclear periphery for each probe (ie transript) performed in n>100 cells were represented graphically in Fig. 5f. We then compared total number of signals for each probe, obtained in shCtrl and shNup153 cells, and represented them graphically in Fig 5g; representative images shown in Fig 5g are those that were measured in Fig5g and represent the accumulation of the signal in cells upon shNup153 (not necessarily all at the nuclear periphery).

We hope that this clarifies better what is represented.

Limitation of the study does not discuss the limitations of the study but rather reads like the extension of the discussion. This section should be rewritten.

We will take into consideration this comment and will expand the section in the revised manuscript

Minor comments:

Western in Figure 3c does not represent well the quantification in 3e.

Figure S3 is mislabelled (pannel h is panel g).

__Reviewer #1 (Significance (Required)): __

Reviewer #1 (Significance (Required)):

The manuscript interrogates an important question related to the role of the NPC in gene regulation, in particular how interaction of genes/pre-mRNAs with the NPC might stimulate expression of specific genes/mRNAs. Stimulating splicing would be one way that could contribute to efficient gene expression, and this is the question the authors address in this manuscript. This study is therefore important and relevant to a wide audience. However, as outlined in the section above, the conclusions drawn by the authors do not always reflect the experimental data, and it is therefore unclear whether the overall conclusion as stated in the title of the manuscript is valid. Moreover, conceptually, if intron containing genes are transcribed at or near nuclear pores, and splicing often occurs co-transcriptionally, it is to be expected to find splicing components close to nuclear pores. While it is relevant to show that this actually happens, and this is, at least in part, done by the authors. However, the experiments presented do not show that the splicing machinery actually actively docks to the NPC and is not just passively recruited close to NPCs because nascent pre-mRNAs are spliced where they are transcribed (the authors state in their title that the NUP153 docks the splicing machinery at the NPC). Showing this require identifying direct interactions between spliceosome components with NUP153/nuclear basket components to stimulate splicing at the NPC. If this would indeed be the case, these findings would describe a novel mechanistic step to stimulate efficient splicing and subsequently export of a selected set of NPC-associated genes. This would open other questions such as how to achieve specificity for only some pre-mRNAs/introns. While addressing this question is likely beyond the scope of this manuscript, the question whether the process described here is an active or passive process should be incorporated in the interpretation of the data.

We are grateful to the reviewer for highlighting the importance and relevance of our work for a broad audience. We now provide additional experimental evidence that will hopefully aid in substantiating our overall conclusions, as suggested by the reviewer.

__ Reviewer 2:__

Summary:

In this manuscript, using a combination of proximity labelling, immunoprecipitations and imaging, the authors report a physical interaction between splicing factors (SFs) and the nuclear basket of nuclear pore complexes (i.e. NUP153). Using DamID, they further identify a set of NUP153-bound genes characterized by long, GC-poor introns. Finally, based on molecular analyses for a set of candidate loci, they report that inactivation of NUP153 triggers a (modest) reduction of intron splicing, which may specifically affect NUP153-bound genes.

Major comments:

• The BioID experiments (Fig. 1) lack proper controls. Proteins biotinylated by NUP-BirA fusions need to be compared with those modified upon expression of a control BirA protein, as has been done previously, especially when other NUPs were used as baits in BioID experiments (PMID: 24927568, to be cited). This control fusion should ideally be targeted to the same compartment (i.e. the nucleus or the nuclear side of the nuclear envelope). In our experimental setting, we have opted to use an unrelated protein tagged with BirA (Lck-BirA) rather than BirA-only control. The peripheral membrane proteins of the Src family kinase (SFK) Lck and its GPF tagged version (LckN18.GFP) localize predominantly at the plasma membrane (PMID: 29588370), whereas the GFP only (non-biothinylated) shows a broader nuclear distribution. All MS-detected proteins from the Lckn18-BirA and GFP negative control experiments were excluded. Moreover, we have analysed carefully the published data of nuclear transporter receptors binding to the NPC and the respective controls (BirA alone or the shuttling NLS_NES_Dendra with C or N terminal tags) (PMID: 29254951), and we did not find that any of these protein controls interact with the proteins of the splicing machinery.

• Here, the chosen controls are inappropriate as the authors are probing interactions between NPC proteins (NUP153/TPR) and proteins restricted to a different nuclear compartment (e.g., nucleophosmin in the nucleolus). We have used two different controls in our PLA experiments - initially, we used B23 for nucleolus stain and then PML protein, major component of PML NBs, as PML can be found scattered throughout the nucleus and sometimes even resides at the nuclear periphery. Both of these controls showed negligible amounts of PLA spots in all our experiments. We take the opportunity to clarify that in PLA, antibodies from different species have to be used. Nup153 is an anti-mouse antibody, and the control we used, PML is also an anti- mouse; therefore the two antibodies cannot be used in the PLA experiment. The controls used (mouse) were conjugated with rabbit antibodies, either TPR (PML) or AQR (B23). Both TPR:PML and AQR:B23 showed insignificant PLA signals. Therefore we can confidently conclude that the PLA spots seen for the NPC/splicing proteins are of measurable quantities.

• Would a control soluble, diffusible nucleoplasmic protein be detected in the vicinity of the NPC and sometimes colocalized with Nups? Precisely for this purpose we have used PML protein, that can be found both disperse in nucleoplasm as well as in PML NBs.

• In order to assess these possibilities, the authors should perform their immunoprecipitation on extracts treated with benzonase, thus abrogating DNA- and RNA-dependent interactions. We have performed this experiment assessing the binding of Nup153 with the components of the IBC and observed that Nup153 interaction with these splicing factors is DNA or RNA independent, with some factors being more affected than others (probably passively recruited by protein-protein interactions with their splicing counterparts).

__Figure R5: __RNA or DNA do not affect the interaction between Nup153 and splicing proteins. Lysates from HEK293T cells transfected with eGFP-Nup153 or eGFP were treated with RNase or DNase or left untreated prior to Co-IP for GFP-Nup153. The membrane was probed for GFP (confirmed successful transfection), AQR, SF3A1, XAB2, and DDX39B. The graph shows quantified bands of the bound fractions, normalized to the input and untreated control from one experiment.

NUP153 inactivation appears to have a modest effect on splicing (Fig. 5; S6), which is poorly characterized here. It is also unclear whether this effect is direct or caused by side consequences of the depletion of this nucleoporin (e.g., changes in nucleocytoplasmic exchanges or gene expression).

We have indeed asked if the presence of splicing components at the periphery could be a consequence of protein trafficking. To address whether nucleocytoplasmic exchange has a role in these associations, we have pharmacologically inhibited nuclear import by ivermectin (IVM). ​​IVM has been shown to block the importin-α/β-mediated nuclear import by directly interacting with karyopherin importin-α.(PMID: 30826604). HEK293T cells transfected with eGFPNup153 or eGFP alone were treated with IVM for 2hr (24hrs post transfection). As biochemical fractionation demonstrated (data not shown) there were slightly decreased protein levels of AQR, DDX39B and SF3A1 in the nuclear insoluble fraction. However, we barely observed any decrease in interactions between Nup153 and the splicing components we tested, indicating that the interaction with the spliceosomal components is not a consequence of nucleocytoplasmic exchange.

Figure R6: Association of splicing components with Nup153 is not only due to nuclear import. HEK293T cells transfected with eGFP-Nup153 or eGFP and treated with import inhibitor IVM or DMSO were analyzed with Co-IP for GFP-Nup153. The membrane was probed for GFP (confirmed successful transfection), AQR, SF3A1, XAB2 and DDX39 B. Quantified bands of the bound fractions were normalized to the input fraction and DMSO control and the results of two experiments were shown in the graph.

Related to the gene expression levels, we have not observed any significant changes in total expression levels of tested genes, probed with designed exonic primers (as indicated with blue arrows in Figure 5a). Additional control genes will be added as suggested by this and other reviewers.

• To confirm the specificity of the effects of NUP153 depletion on the splicing of NUP153-bound genes, the authors need to provide additional splicing measurements for several genes that are bound by NUP153 "in the nucleoplasm" (e.g. excluded from their analysis by the cutoff of proximity to LAD borders, line 192) and for other "non-NUP153" genes (beyond the unique control shown in Fig. S6a).

We acknowledge the comment of the reviewer that our work will benefit from additional controls. We are currently designing primers and probes to amplify additional regions, Nup153 bound and non-LAD proximal or non-Nup153 bound; Please also see the comment below.

- From the few examples provided, it is difficult to evaluate the type of splicing events affected by NUP153 inactivation. Are they uniquely intron retention events? The authors should analyze available RNA-seq data obtained from NUP153-depleted cells (PMID:32917881) to characterize the types of alternative splicing events that are impacted by NUP153.

In Aksenova et al, only 28 differentially expressed genes were detected during rapid degron Nup153 depletion (2h). With this small number of genes, it is highly unlikely we would be able to perform a statistically significant and detailed analysis. Importantly, the depletion was performed in colorectal adenocarcinoma cell line (DLD-1), whereas here we are reporting on T lymphocytes. Based on the analysis which we performed and explained below, there seem to be significant cell type specific differences in Nup153 associations, as already reported by others (PMID: 27807035; 32451376; 28919367).

Splicing dynamics and speckle localization propensity have been proposed to depend on the overall GC content and the overall average intron size by several studies (PMID: 39413186; 38720076; 35182478; 22832277; 35182477). Prompted by our observation that Nup153 genes have longer than average introns and lower than average GC content (Figure 4f and 4g), we analyzed the data from HeLa cells, where genes were classified into groups A, B and C based on their speckle localization and dynamics (PMID 39413186). We intersected our Nup153 genes with the list of ABC genes from HeLa cells and found that 43 out of our 461 protein-coding genes were represented among non-speckle enriched group C genes, with the lowest GC content and longest average intron length.

Figure R7: Nup153 genes comparison to the A_B_C genes from Wu J et al study. GC content and number of introns, used to classify the identified genes from HeLa cells are plotted on X and Y axis. A genes in Red, B in green or C in turquoise from HeLa cells were compared with Nup153 genes from Jurkat cells in the graph on the left. Nup153 genes are represented as triangles. A subgroup of Nup153 genes, classified as C group genes (long introns with high GC content and spliced away from speckles) are shown as turquoise triangles. Graph on the right shows total pool of Nup153 genes in violet (not identified in HeLa cells) and a subgroup of C Nup153 genes as turquoise. The list of Nup153 C genes is shown below.

query

entrezgene

name

symbol

ENSG00000168615

8754

ADAM metallopeptidase domain 9

ADAM9

ENSG00000139154

121536

AE binding protein 2

AEBP2

ENSG00000112249

10973

activating signal cointegrator 1 complex subunit 3

ASCC3

ENSG00000176788

10409

brain abundant membrane attached signal protein 1

BASP1

ENSG00000153956

781

calcium voltage-gated channel auxiliary subunit alpha2delta 1

CACNA2D1

ENSG00000153113

831

calpastatin

CAST

ENSG00000134371

79577

cell division cycle 73

CDC73

ENSG00000188517

84570

collagen type XXV alpha 1 chain

COL25A1

ENSG00000182158

64764

cAMP responsive element binding protein 3 like 2

CREB3L2

ENSG00000109861

1075

cathepsin C

CTSC

ENSG00000153904

23576

dimethylarginine dimethylaminohydrolase 1

DDAH1

ENSG00000139734

81624

diaphanous related formin 3

DIAPH3

ENSG00000102580

5611

DnaJ heat shock protein family (Hsp40) member C3

DNAJC3

ENSG00000173852

23333

dpy-19 like C-mannosyltransferase 1

DPY19L1

ENSG00000151914

667

dystonin

DST

ENSG00000165891

144455

E2F transcription factor 7

E2F7

ENSG00000138829

2201

fibrillin 2

FBN2

ENSG00000115414

2335

fibronectin 1

FN1

ENSG00000075420

64778

fibronectin type III domain containing 3B

FNDC3B

ENSG00000114861

27086

forkhead box P1

FOXP1

ENSG00000090615

2802

golgin A3

GOLGA3

ENSG00000196591

3066

histone deacetylase 2

HDAC2

ENSG00000071794

6596

helicase like transcription factor

HLTF

ENSG00000145012

4026

LIM domain containing preferred translocation partner in lipoma

LPP

ENSG00000065833

4199

malic enzyme 1

ME1

ENSG00000087053

8898

myotubularin related protein 2

MTMR2

ENSG00000145555

4651

myosin X

MYO10

ENSG00000061676

10787

NCK associated protein 1

NCKAP1

ENSG00000185630

5087

PBX homeobox 1

PBX1

ENSG00000113448

5144

phosphodiesterase 4D

PDE4D

ENSG00000163110

10611

PDZ and LIM domain 5

PDLIM5

ENSG00000070087

5217

profilin 2

PFN2

ENSG00000152952

5352

procollagen-lysine,2-oxoglutarate 5-dioxygenase 2

PLOD2

ENSG00000106772

158471

prune homolog 2 with BCH domain

PRUNE2

ENSG00000173482

5797

protein tyrosine phosphatase receptor type M

PTPRM

ENSG00000164292

22836

Rho related BTB domain containing 3

RHOBTB3

ENSG00000067900

6093

Rho associated coiled-coil containing protein kinase 1

ROCK1

ENSG00000112701

26054

SUMO specific peptidase 6

SENP6

ENSG00000154447

57630

SH3 domain containing ring finger 1

SH3RF1

ENSG00000187164

57698

shootin 1

SHTN1

ENSG00000198887

23137

structural maintenance of chromosomes 5

SMC5

ENSG00000116754

9295

serine and arginine rich splicing factor 11

SRSF11

ENSG00000152818

7402

utrophin

UTRN

Table R1: List of Nup153 genes that are characterized as C group of genes.

Only two Nup153 gene were found among A and B genes (Serine and arginine rich splicing factor 11 SRSF11 among A, and Phosphodiesterase 4D PDE4D among B genes). Despite the cell type specific expression and splicing patterns it is worth noting that we find Nup153 genes enriched among C group genes that are spliced out of speckles. We are currently probing the splicing of some of these genes, and these data will be added to the list of control genes.

Considering all these new observations related to the Nup153 splicing events and the general interest and relevance of our initial observations, a new dedicated study will have to be designed to tackle all these important questions that go beyond these current findings

---

Minor comments:

• Several studies have shown that the nuclear basket contributes to a splicing quality control process preventing the nuclear export of improperly spliced transcripts, both in yeast and mammalian cells (PMID:14718167, 19127978, 24452287, 25845599, 22253824, 22661231). These studies have to be mentioned and discussed here.

• Line 31: "movement of active genes towards the NPC would be favorable for their transcription and export ". Please rephrase: "...transcription and mRNA export".

• Line 163: "NUP153 plays a role in harboring splicing machinery". Please rephrase.

• Line 200-202: Fig. 4d and 4e (instead of S4d and S4e)

• Line 186 and beyond: All conclusions about NUP153-bound genes (e.g., "Majority of NUP153 bound genes are proximal to LADs and expressed") are not accurately phrased since the authors selected NUP153-bound genes with a cutoff of proximity to LAD borders. The conclusions are thus only valid for a subpopulation of NUP153-bound regions located in the vicinity of LADs.

• Line 292: "transport Nups less likely interact with splicing machinery". The term "transport Nup" is not correct. Does this mean "nuclear transport receptors"? Or "FG-Nups" (which interact with NTRs)?

All the comments will be addressed in the revised version of the manuscript

Reviewer #2 (Significance (Required):

It is increasingly recognized that NPCs are involved in a number of cellular processes beyond nucleo-cytoplasmic transport and, in particular, contribute to several genomic functions. In this context, the identification of a physical and functional interaction between NPCs and the splicing machinery could be of conceptual interest in the NPC field, and more generally, in cell and genome biology, although it needs to be (i) carefully controlled and validated in view of the strong limitations mentioned above, and (ii) discussed in line with the known links between the nuclear basket and splicing quality control (see minor comments). This coupling would be particularly relevant for genes that have been shown to be positioned at NPCs during transcriptional activation, in line with the "gene gating" model mentioned by the authors.

We greatly appreciate these insightful comments and suggestions, and the time and effort that this reviewer invested in critical reading of our manuscript. We will certainly take the points into account as we revise the manuscript. Specifically, we will carefully address the concerns related to the NPC-splicing interaction, ensuring that the experimental validation is robust and well-controlled, and we will further discuss the connection between the nuclear basket and splicing quality control in the context of our findings.

Once again, thank you for your thoughtful and constructive feedback.

Reviewer #3

The authors discovered that the splicing machinery and nuclear baskets are sometimes in close proximity using Nup153 as a representative for the nuclear basket. They characterize this interaction using several different methods and propose that NUP153 is required to assemble the splicing machinery on genes that are transcribed in the nuclear periphery, which would supporting the gene gating model.

The manuscript is well written and structured and the experiments are carefully conducted and analyzed.

We thank the reviewer for the appreciation of our work. We have addressed all the major points here and will amend the manuscript text according to the suggestions.

Reviewer #3 (Significance (Required)):

The impression that I get from this manuscript is that we are looking at rather rare events with a small effect size. A definitive proof that the splicing machinery really assembles in the vicinity of NPCs docked via NUP153 is lacking. To assist in the revision process I will raise some questions to discuss but also propose some additional experiments to substantiate the claims.

1. It is not clear what NUP153 really binds to and which domain is important. The experiments shown suggest proximity and indirect interactions (Co-IPs), but it is not clear whether NUP153 binds to DNA, RNA or a specific splicing factor. The bioID experiment might label splicing factors because they are cargos that pass through the pores during import, or it labels splicing factors that remain bound to spliced mRNPs during mRNP export. For example DDX39b, also called UAP56 is an important subunit of the TREX complex and involved the final packaging of mRNPs at the NPC. In my opinion, this protein is not a good choice. Also, the negative control that was used in the experiments is a potassium channel in the plasmamembrane, which can exclude that signal occurs by chance. But it would have been better to use a nuclear protein as control to exclude these possibilities.

In line with a rare event, the Co-IP signals are very weak and barely higher than the GFP control. They should be repeated in the presence of RNase to confirm that this interaction occurs on nascent RNA during splicing and not e.g. to recycle or reroute splicing factors or during import.

We acknowledge all the points, some of them already brought to our attention by other reviewers, that we tried to address throughout our response here, and we will also incorporate our answers in the revised manuscript. In particular, we have already provided some evidence related to the role of DNA, RNA, as shown above in Figure R5 (Response to R2). We have also addressed the effect of nuclear-cytosolic transport by using IVM and described these results in Figure R6, showing that splicing factors interact with Nup153 even when cytosolic transport is blocked with IVM. We have also commented on the use of controls and on the additional control analysis that we performed (also mentioned in more detail in response to R2)

Moreover, we are also further trying to understand the binding of Nup153 to the splicing components. Intron Binding Complex, recently shown to be crucial for the activation of the spliceosome due to the activity of its helicase AQR (PMID: 37165190) is one of the protein complexes that we found bound to the NPC basket. We are interested in different functions of this helicase and have created the previously described mutant that has been shown to be defective in splicing. We have probed the interaction of Nup153 to this mutant, which we also characterized for its splicing inefficiency, and observed that Nup153 interacts with the splicing competent AQR, whereas the interaction with the splicing mutant seems to be less efficientThis set of additional data strengthens the bulk of data present here, details of which remain still to be further elucidated in an additional follow-up study.

__Figure R8 __Lysates from HEK 293T cells, transiently transfected with eGFP-NUP153 and AQR-His-FLAG or AQRK829A-His-FLAG were probed in co-IP experiments. Western blot of the co-IP performed with Dynabeads for His-tagged proteins; input (IN), unbound (U) and bound (B) fractions are shown. Densitometric analysis of the co-IP where eGFP-NUP153 intensity was quantified, bound fractions were normalized to input samples, and the results are expressed relative to the AQR-His-FLAG control (n = 2). pENTER is a control empty plasmid.

I do not understand why a NUP would be required to recruit or tether splicing factors to peripheral genes. Usually, splicing factors hitchhike on transcribing polymerase II or they are delivered by nuclear speckles which could happen also at the periphery. The authors should co-stain with a nuclear speckle marker to exclude this possibility.

__Figure R9 __STED microscopy resolves the position of sc-35, marker of splicing speckles with respect to AQR. Jurkat T cells were stained with anti AQR AB (rabbit) and anti - sc35 antibody (mouse) to probe the positioning of splicing speckles with respect to the splicing helicase AQR.

This is a very interesting remark, which we have addressed through co-staining experiments. Since the Nup153 antibody we used is anti-mouse, like SC-35, we instead co-stained SC-35 with AQR, a representative splicing factor. Our results show that a substantial number of AQR spots are detected away from nuclear speckles and near the nuclear rim, suggesting that a subset of splicing factors localize independently of speckles. While we have not directly stained the nuclear periphery, this pattern is consistent with the idea that splicing factors can be recruited outside of speckle-mediated delivery.

To further confirm that NPC-associated splicing does not rely on nuclear speckles, we are open to performing additional co-staining between Nup153 and SON, another speckle marker in a followup study. Furthermore, emerging evidence supports off-speckle splicing, particularly for genes with long introns and low GC content (PMID: 39413186, 38720076, 35182478, 22832277, 35182477). Our additional analysis (Figure R7) demonstrates that genes associated with Nup153 share characteristics with known off-speckle spliced genes, suggesting that these genes might be processed outside of speckles due to their transcription and splicing kinetics

What would be the advantage to splice in the vicinity of the pore? Given that genes with long introns take a long time to be transcribed, splicing would block the pores for hours and would prevent other activities. It would also be possible that splicing does occur at the periphery but NUP153 picks the mRNPs up at a later stage.

We thank the reviewer for these insightful comments. We would like to add here that there are numerous pores, according to our own estimations around 800 pores in Jurkat cells, which implies that there is also huge heterogeneity of the NPC which is at present largely unexplored. In yeast, some pores are basketless and the assembly of the basket is transcription dependent (PMID: 36220102) - suggesting that there’s more than one pore population. Similarly, looking into the statuses of genes that have been associated with pore, both polycomb-repressed and transcriptionally active genes have been found at pores- again pointing to an heterogeneity. This is a very interesting (and large) question that we pose ourselves and to the NPC field but not something we can address straight away.

One major drawback of the story is that the authors use very long-term depletion of NUP153 via shRNAs that will definitely screw up the import of many nuclear proteins; and a splicing inhibitor that has broad effects on nuclear architecture. Degron lines of Nup153 exist and should be used to substantiate at least some of the conclusions. Alternatively, a NUP153 mutant without zinc finger or IDR could be used to prevent DNA binding or basket association.

The reviewer is right, we have for over 2 years tried tirelessly to use the degron Nup153 system established in DLD-1 cells by Dasso’s Lab. This has been unsuccessful. Jurkats are difficult to transfect and more sensitive than other cell lines and they died with our trials. We have therefore used the shNup153 which has been used in X and Y and shown to not interfere with nucleocytoplasmic trafficking.

However, we understand the reviewers point and since then have tried to use a Nup153 mutant construct, containaing Nup153 N-terminus with and without a zinc finger (McKay et al 2009.), kindly sent by the Ulman Lab. Unfortunately, in our hands, the construct has low levels of GFP:Nup153 expression, not comparable to the ones we used in our coIP experiments and that would make conclusions hard.

We are now planning the cloning of our GFP:Nup153 construct to produce such plasmids,

N-terminus with/without the Zinc finger and use it in coIP experiments to understand the importance of the Zn finger domain in the splicing interactions.

Specific comments: Abstract: suggesting that a fraction of splicing occurs at the NPC. speckle-distant splicing events, it should be nuclear speckles, term not explained Line 20: Super enhancers not introduced

Line 39: Splicing and the different spliceosomal subcomplexes needs more explanation and introduction to understand the selection of proteins that were used in the study. Line 65: Choice of controls. LckN18 The genes should be written once in full and their choice should be explained better.

Line 123: The authors state: 'However, we detected a lower number of interacting spots between Nup98/DDX39b than with its Nup153 counterpart; a similar trend is followed between Nup98 and SF3A1 (Fig. S2g), suggesting that the interaction between splicing proteins and Nup98 might be further apart within the NPC structure.

A more likely explanation is that both proteins are shed from the mRNP at the basket as they are not shuttling with the mRNA and should not enter the pore.

Line 131: Mention right at the beginning of the sentence which splicing proteins were imaged here.

Line 141: Nuclear speckles are still not properly introduced. Why is SF3A1 not expected to be in nuclear speckles? It Should be co-stained for nuclear speckle markers. Lane 149: PlaB drastically changes the nuclear architecture. The reduced interactions can have also different reasons. The authors should image the distribution of the investigated factors in the presence of PlaB. Again the Co-IPs should be performed in the presence of RNase to confirm that the observed interactions depend on RNA. Line 181: The requirement of Nup153 to tether the splicing machinery to the NPC is not convincing from the presented data. The knockdown is way too long and the changes are tiny. Wouldn't it be better to use a Nup153 mutant without Zinc knuckle or IDR to show that now the splicing factors interactions are lost? Alternatively degron lines should be used.

Line 186: I do not understand the logic why NUP153 needs to bind to chromatin to fullfil its function in splicing. It could also bind to RNA with its zinc knuckle or IDRs. The authors should perform iCLIP or RIP to exclude this possibility? I also do not understand the logic to look to look for proximity to repressive LADs as a criterion, while investigating a function of NUP153 in splicing which requests actively transcribing genes. This has to be better motivated. Excluding the nucleoplasmic pool of NUP153 removes important data points that might be functionally relevant. Line 187: The entire paragraph on Dam-ID and all subsequent genome-wide analyses is way too densely written and hard to understand for non-experts. Analysis tools or thresholds are rarely given and it is unclear how the different data sets have been made and by who, what they mean and how they have been integrated. Chromatin patterns and expression profiles are very unspecific terms. Many of the used terms have not been introduced properly. The metaplots show very small differences. In the end I am not sure what we have learned from all the data integration. Is NUP153 bound to DNA, to nucleosomes, to nascent RNA or to splicing factors?

Lane 232: Unclear what NUP153 introns are. Is the entire gene where NUP153 binds to considered or only the intron with a NUP153 peak.

Lane 242: Again, the shRNA knockdown performed in this manuscript is way to long to observe a direct effect on splicing at the pore, which occurs at the level of minutes. Degrons should be used here to confirm this observation.

Line 249: More negative controls are needed for genes not bound by NUP153 for the splicing analysis. RNA-Seq analyzed for intron retention could be helpful.

All the text and specific comments will be addressed as suggested by the reviewer.

The experimental part to be included in the revised manuscript is explained in more detail above.

Reviewer expertise (keywords): nuclear pore complexes, nuclear organization, gene expression, mRNA biology

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

The authors discovered that the splicing machinery and nuclear baskets are sometimes in close proximity using Nup153 as a representative for the nuclear basket. They characterize this interaction using several different methods and propose that NUP153 is required to assemble the splicing machinery on genes that are transcribed in the nuclear periphery, which would supporting the gene gating model.

The manuscript is well written and structured and the experiments are carefully conducted and analyzed.

Significance

The impression that I get from this manuscript is that we are looking at rather rare events with a small effect size. A definitive proof that the splicing machinery really assembles in the vicinity of NPCs docked via NUP153 is lacking. To assist in the revision process I will raise some questions to discuss but also propose some additional experiments to substantiate the claims.

1. It is not clear what NUP153 really binds to and which domain is important. The experiments shown suggest proximity and indirect interactions (Co-IPs), but it is not clear whether NUP153 binds to DNA, RNA or a specific splicing factor. The bioID experiment might label splicing factors because they are cargos that pass through the pores during import, or it labels splicing factors that remain bound to spliced mRNPs during mRNP export. For example DDX39b, also called UAP56 is an important subunit of the TREX complex and involved the final packaging of mRNPs at the NPC. In my opinion, this protein is not a good choice. Also, the negative control that was used in the experiments is a potassium channel in the plasmamembrane, which can exclude that signal occurs by chance. But it would have been better to use a nuclear protein as control to exclude these possibilities. In line with a rare event, the Co-IP signals are very weak and barely higher than the GFP control. They should be repeated in the presence of RNase to confirm that this interaction occurs on nascent RNA during splicing and not e.g. to recycle or reroute splicing factors or during import.
2. I do not understand why a NUP would be required to recruit or tether splicing factors to peripheral genes. Usually, splicing factors hitchhike on transcribing polymerase II or they are delivered by nuclear speckles which could happen also at the periphery. The authors should co-stain with a nuclear speckle marker to exlcude this possibility.
3. What would be the advantage to splice in the vicinity of the pore? Given that genes with long introns take a long time to be transcribed, splicing would block the pores for hours and would prevent other activities. It would also be possible that splicing does occur at the periphery but NUP153 picks the mRNPs up at a later stage.
4. One major drawback of the story is that the authors use very long-term depletion of NUP153 via shRNAs that will definitely screw up the import of many nuclear proteins; and a splicing inhibitor that has broad effects on nuclear architecture. Degron lines of Nup153 exist and should be used to substantiate at least some of the conclusions. Alternatively, a NUP153 mutant without zinc finger or IDR could be used to prevent DNA binding or basket association.

Specific comments:

Abstract: suggesting that a fraction of splicing occurs at the NPC. speckle-distant splicing events, it should be nuclear speckles, term not explained

Line 20: Super enhancers not introduced

Line 39: Splicing and the different spliceosomal subcomplexes needs more explanation and introduction to understand the selection of proteins that were used in the study.

Line 65: Choice of controls. LckN18 The genes should be written once in full and their choice should be explained better.

Line 123: The authors state: 'However, we detected a lower number of interacting spots between Nup98/DDX39b than with its Nup153 counterpart; a similar trend is followed between Nup98 and SF3A1 (Fig. S2g), suggesting that the interaction between splicing proteins and Nup98 might be further apart within the NPC structure. A more likely explanation is that both proteins are shed from the mRNP at the basket as they are not shuttling with the mRNA and should not enter the pore.

Line 131: Mention right at the beginning of the sentence which splicing proteins were imaged here.

Line 141: Nuclear speckles are still not properly introduced. Why is SF3A1 not expected to be in nuclear speckles? It Should be co-stained for nuclear speckle markers.

Lane 149: PlaB drastically changes the nuclear architecture. The reduced interactions can have also different reasons. The authors should image the distribution of the investigated factors in the presence of PlaB. Again the Co-IPs should be performed in the presence of RNase to confirm that the observed interactions depend on RNA.

Line 181: The requirement of Nup153 to tether the splicing machinery to the NPC is not convincing from the presented data. The knockdown is way too long and the changes are tiny. Wouldn't it be better to use a Nup153 mutant without Zinc knuckle or IDR to show that now the splicing factors interactions are lost? Alternatively degron lines should be used.

Line 186: I do not understand the logic why NUP153 needs to bind to chromatin to fullfil its function in splicing. It could also bind to RNA with its zinc knuckle or IDRs. The authors should perform iCLIP or RIP to exclude this possibility? I also do not understand the logic to look to look for proximity to repressive LADs as a criterion, while investigating a function of NUP153 in splicing which requests actively transcribing genes. This has to be better motivated. Excluding the nucleoplasmic pool of NUP153 removes important data points that might be functionally relevant.

Line 187: The entire paragraph on Dam-ID and all subsequent genome-wide analyses is way too densely written and hard to understand for non-experts. Analysis tools or thresholds are rarely given and it is unclear how the different data sets have been made and by who, what they mean and how they have been integrated. Chromatin patterns and expression profiles are very unspecific terms. Many of the used terms have not been introduced properly. The metaplots show very small differences. In the end I am not sure what we have learned from all the data integration. Is NUP153 bound to DNA, to nucleosomes, to nascent RNA or to splicing factors?

Lane 232: Unclear what NUP153 introns are. Is the entire gene where NUP153 binds to considered or only the intron with a NUP153 peak.

Lane 242: Again, the shRNA knockdown performed in this manuscript is way to long to observe a direct effect on splicing at the pore, which occurs at the level of minutes. Degrons should be used here to confirm this observation.

Line 249: More negative controls are needed for genes not bound by NUP153 for the splicing analysis. RNA-Seq analyzed for intron retention could be helpful.

There is quite some typos and missing words in the text.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, using a combination of proximity labelling, immunoprecipitations and imaging, the authors report a physical interaction between splicing factors (SFs) and the nuclear basket of nuclear pore complexes (i.e. NUP153). Using DamID, they further identify a set of NUP153-bound genes characterized by long, GC-poor introns. Finally, based on molecular analyses for a set of candidate loci, they report that inactivation of NUP153 triggers a (modest) reduction of intron splicing, which may specifically affect NUP153-bound genes.

Major comments:

1. The data presented do not convincingly demonstrate a specific interaction between the NPC basket and the splicing machinery, mainly due to the lack of appropriate controls.
   • The BioID experiments (Fig. 1) lack proper controls. Proteins biotinylated by NUP-BirA fusions need to be compared with those modified upon expression of a control BirA protein, as has been done previously, especially when other NUPs were used as baits in BioID experiments (PMID: 24927568, to be cited). This control fusion should ideally be targeted to the same compartment (i.e. the nucleus or the nuclear side of the nuclear envelope).
   • NUP153 immunoprecipitates only very low levels of splicing factors, which are almost indistinguishable from those detected in control pull-downs (see for example AQR or XAB2 signals in blot images and error bars in the quantifications, Fig. 2a). In addition, in these analyses, the high/saturated signals do not allow comparison of the abundance of the proteins of interest in the input samples under different conditions. These limitations also apply to the interpretation of the changes in NUP153-SF association scored upon splicing inhibition (Fig. 3a), which also seems to affect SF abundance in inputs (e.g. AQR, SF3A1).
   • PLA experiments (e.g. Fig. 2b-d) also lack proper control. PLA has been shown to reveal artefactual signals for abundant proteins present in the same compartment (doi.org/10.1101/411355). Here, the chosen controls are inappropriate as the authors are probing interactions between NPC proteins (NUP153/TPR) and proteins restricted to a different nuclear compartment (e.g., nucleophosmin in the nucleolus). An abundant nucleoplasmic protein/epitope should be used as a control in these experiments. In addition, the authors need to show direct immunofluorescence images for each antibody used in PLA assays, in order to verify that the expression levels or localization of their targets are unchanged between conditions (e.g. upon PladB treatment, Fig. S3g, or NUP153 depletion, Fig. 3g).
   • The proximal localization of NUP153 and splicing factors in super resolution microscopy (Fig. 2e-f) is also not properly controlled. Would a control soluble, diffusible nucleoplasmic protein be detected in the vicinity of the NPC and sometimes colocalized with Nups?
   • Since NUP153 is located in the vicinity of peripheral genes (as also shown here through DamID), some of which contain introns, its association with the spliceosome could be indirect, i.e., mediated by DNA. Of note, the association of Mlp1 (the yeast ortholog of TPR) with the splicing factor SF1 has been shown to be mediated by RNAs (PMID:14718167). In order to assess these possibilities, the authors should perform their immunoprecipitation on extracts treated with benzonase, thus abrogating DNA- and RNA-dependent interactions.
2. NUP153 inactivation appears to have a modest effect on splicing (Fig. 5; S6), which is poorly characterized here. It is also unclear whether this effect is direct or caused by side consequences of the depletion of this nucleoporin (e.g., changes in nucleocytoplasmic exchanges or gene expression).
   • To confirm the specificity of the effects of NUP153 depletion on the splicing of NUP153-bound genes, the authors need to provide additional splicing measurements for several genes that are bound by NUP153 "in the nucleoplasm" (e.g. excluded from their analysis by the cutoff of proximity to LAD borders, line 192) and for other "non-NUP153" genes (beyond the unique control shown in Fig. S6a).
   • From the few examples provided, it is difficult to evaluate the type of splicing events affected by NUP153 inactivation. Are they uniquely intron retention events? The authors should analyze available RNA-seq data obtained from NUP153-depleted cells (PMID:32917881) to characterize the types of alternative splicing events that are impacted by NUP153.

Minor comments:

• Several studies have shown that the nuclear basket contributes to a splicing quality control process preventing the nuclear export of improperly spliced transcripts, both in yeast and mammalian cells (PMID:14718167, 19127978, 24452287, 25845599, 22253824, 22661231). These studies have to be mentioned and discussed here.
• Line 31: "movement of active genes towards the NPC would be favorable for their transcription and export ". Please rephrase: "...transcription and mRNA export".
• Line 163: "NUP153 plays a role in harboring splicing machinery". Please rephrase.
• Line 200-202: Fig. 4d and 4e (instead of S4d and S4e)
• Line 186 and beyond: All conclusions about NUP153-bound genes (e.g., "Majority of NUP153 bound genes are proximal to LADs and expressed") are not accurately phrased since the authors selected NUP153-bound genes with a cutoff of proximity to LAD borders. The conclusions are thus only valid for a subpopulation of NUP153-bound regions located in the vicinity of LADs.
• Line 292: "transport Nups less likely interact with splicing machinery". The term "transport Nup" is not correct. Does this mean "nuclear transport receptors"? Or "FG-Nups" (which interact with NTRs)?

Significance

It is increasingly recognized that NPCs are involved in a number of cellular processes beyond nucleo-cytoplasmic transport and, in particular, contribute to several genomic functions. In this context, the identification of a physical and functional interaction between NPCs and the splicing machinery could be of conceptual interest in the NPC field, and more generally, in cell and genome biology, although it needs to be (i) carefully controlled and validated in view of the strong limitations mentioned above, and (ii) discussed in line with the known links between the nuclear basket and splicing quality control (see minor comments). This coupling would be particularly relevant for genes that have been shown to be positioned at NPCs during transcriptional activation, in line with the "gene gating" model mentioned by the authors.

Reviewer expertise (keywords): nuclear pore complexes, nuclear organization, gene expression, mRNA biology

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Referee #1

Evidence, reproducibility and clarity

The authors first use a Bio-ID approach to search for interactors of the basket proteins TPR and NUP153, identifying proteins involved in various nuclear process, including many splicing components, and confirm some of these interactions using IP and PLA assays. PLA experiments further suggest that these interactions occur primary at or close to the nuclear periphery. Moreover, inhibiting splicing, but not transcription, reduced these interactions. The authors then investigated the role of NUP153 in loading of the splicing machinery and found a lower association of the NUP98/SF3A1 but not AQR interaction (measured through PLA). Furthermore, DamID experiments identified NUP153 bound genes proximal to LAD domains that are actively transcribed, contain overall longer introns with low GC-content, and fall within a group of genes located at the outermost shell of the nucleus (when compared to previously published LaminI ID /PGseq data). Lastly, they interrogate whether depletion of NUP153 results in a splicing defect for NUP153 bound genes.

The authors identify many proteins in their BioID interaction screen, however, only a single nucleoporin (Nup35, an inner ring protein). Previous BioID studies have identified NUP153 in BioID experiments including proteins of the Y-complex (PMID: 24927568 and others). To ensure that the BioID experiments indeed probe for interactions of NUP152 and TPR at the NPC, the authors should include control experiments that show that their NUP153 and TPR-BirA fusions primarily localize to the NPC. If a significant fraction is not NPC bound, this has to be taken into account interpreting/discussing their data.<br /> The authors should be more precise when describing the role of the different splicing factor identified in the BioID screen and their function in specific steps of splicing, as this is important when claiming that they identify factors acting at all steps of splicing. For example, the authors describe DDX39b/UAP56 as an early splicing factor; DDX39b/UAP56 main role however seems to be in mRNA export and mRNP compaction. The authors might want to include this in the interpretation of their data.

Concerning PLA experiment controls, the authors perform TPR-PML as a negative control, however, no negative control for NUP153 is shown (Figure 2). Such a control should be added to allow evaluating the specificity of NUP153 PLA interactions, and/or discussed why this was not done.

Quantification of the distance of PLA-NUP153/TPR interactions show interactions mainly close to the nuclear periphery. The imaging data shown in Figure 2b indeed shows that TPR/NUP153 interactions are exclusively at the nuclear periphery, whereas NUP153/splicing factor interactions are sometimes at the edge of the DAPI signal, but mostly somehow internalized (Figure 2B, S2b). Quantification (Figure 2d) shows these distributions to be very similar, likely due to the way the quantification was performed / the bin size of plotting the relative distance of a spot to the nuclear periphery was chosen. Looking at the scale bar/nuclear size and the position of the PLA spots for the NUP153/splicing factors, it appears that spots are often hundreds of nanometers away from the periphery. As the nuclear basket is thought to reach only about 100nm onto the nuclear interior, the conclusion by the authors that these interactions occur at the NPC would not be consistent with the data. The authors should better incorporate this in their interpretation of the data. The conclusion ' Nup153 aids the loading of splicing machinery' is not sufficiently supported by the data. The authors observed a reduction in PLA signal for the NUP98-AQR interaction, but not the NUP98-SF3A1 (Figure 3g). Their conclusion has to reflect this discrepancy in their data. Moreover, the studies focus is to determine the role of NUP153/TPR in recruiting the splicing machinery to the NPC. As in the experiments the authors interrogate the interaction of only NUP98, who has to a large extend splicing factor interactions within the nuclear interior and not at the periphery, the relevance of the experiments in Figure 3 towards the main focus of the paper is unclear. When investigating the effect of NUP153 depletion on splicing, the authors observe a splicing phenotype for multiple NUP153 genes (Figure 5). The authors however show only a single negative control gene (CBX5). It would significantly strengthen their argument if the authors would investigate splicing defects of periphery located noneNUP153 bound genes as well as for genes located in the nuclear interior to better understand whether this splicing phenotype is indeed specific for NUP153 genes (at the nuclear periphery/NPC). The authors state in the text describing the SABER-FISH experiments in Figure 5f that 'were able to visualize the presence of a site of transcription where accumulation of these probes was close to the periphery for all except for GSTK1, which showed a wider nuclear distribution, similar to CBX5 control region not bound by Nup153'. However, their statement is not supported by the images shown in Fig 5f, which show TS in control cells in the nuclear interior. Also, a single cell but no quantification is shown. Moreover, what distance from the periphery is considered as close to the periphery is not defined (see also earlier comment on the question what should be considered a periphery and/or NPC association).

Limitation of the study does not discuss the limitations of the study but rather reads like the extension of the discussion. This section should be rewritten.

Minor comments:

Western in Figure 3c does not represent well the quantification in 3e.

Figure S3 is mislabelled (pannel h is panel g).

Significance

The manuscript interrogates an important question related to the role of the NPC in gene regulation, in particular how interaction of genes/pre-mRNAs with the NPC might stimulate expression of specific genes/mRNAs. Stimulating splicing would be one way that could contribute to efficient gene expression, and this is the question the authors address in this manuscript. This study is therefore important and relevant to a wide audience. However, as outlined in the section above, the conclusions drawn by the authors do not always reflect the experimental data, and it is therefore unclear whether the overall conclusion as stated in the title of the manuscript is valid. Moreover, conceptually, if intron containing genes are transcribed at or near nuclear pores, and splicing often occurs co-transcriptionally, it is to be expected to find splicing components close to nuclear pores. While it is relevant to show that this actually happens, and this is, at least in part, done by the authors. However, the experiments presented do not show that the splicing machinery actually actively docks to the NPC and is not just passively recruited close to NPCs because nascent pre-mRNAs are spliced where they are transcribed (the authors state in their title that the NUP153 docks the splicing machinery at the NPC). Showing this require identifying direct interactions between spliceosome components with NUP153/nuclear basket components to stimulate splicing at the NPC. If this would indeed be the case, these findings would describe a novel mechanistic step to stimulate efficient splicing and subsequently export of a selected set of NPC-associated genes. This would open other questions such as how to achieve specificity for only some pre-mRNAs/introns. While addressing this question is likely beyond the scope of this manuscript, the question whether the process described here is an active or passive process should be incorporated in the interpretation of the data.

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Reply to the reviewers

Reply to the Reviewers

I would like to thank the reviewers for their comments and interest in the manuscript and the study.

Reviewer #1

1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of CTCF-binding motif sequence and RAD21-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-253, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

__2. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. __

Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 435 and 829: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

3. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). We found that the DNA-binding sites of the insulator-associated DBPs were statistically overrepresented in the 5 kb boundary sites more than other DBPs (Fig. 4d). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2. We also observed the directional biases of CTCF, RAD21, and SMC3 by using Micro-C chromatin interaction data as we estimated, but the directionality was more apparent to distinguish the differences between the four directions of FR, RF, FF, and RR using CTCF-mediated ChIA-pet chromatin interaction data (lines 287 and 288).

 I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. *Cell* 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay, and include less long-range interactions due to distance bias. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study.

 I discussed other causes in lines 614-622: Another reason for the difference may be that boundary sites are more closely associated with topologically associated domains (TADs) of chromosome than are insulator sites. Boundary sites are regions identified based on the separation of numerous chromatin interactions. On the other hand, we found that the multiple DNA-binding sites of insulator-associated DNA-binding proteins were located close to each other at insulator sites and were associated with distinct nested and focal chromatin interactions, as reported by Micro-C assay. These interactions may be transient and relatively weak, such as tissue/cell type, conditional or lineage-specific interactions.

 Furthermore, I have added the statistical summary of the analysis in lines 372-395 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

4. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 405 - 412: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

6. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

Reviewer #2

1. Introduction, line 95: CTCF appears two times, it seems redundant.

On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

 I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 501: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

 In Aljahani A et al. *Nature Communications* 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. *Nature Genetics* 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

I added the following sentence on lines 569-577: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

 FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. *Molecular Cell* 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. *Nucleic acids research* 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 556: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

 The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. *Nature Genetics* 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. *Proc Natl Acad Sci USA* 2021 ; Ortabozkoyun H et al. *Nature genetics* 2022 ; Wang R et al. *Nature communications* 2022). I have added the following sentences on lines 571-575: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 582-584: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

 As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. *Nature* 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. *Nature Reviews Molecular Cell Biology* 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. *EMBO Journal* 2024). I have added the following sentences on lines 543-547: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 577-582: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

 Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. *Elife* 2024) (Ke W et al. *Elife* 2024) (Fujioka M et al. *PLoS Genetics* 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 559-567: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

8. Do the authors think that the identified DBPs could work in that way as well?

The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

 Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. *Nucleic Acids Research* 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. *Cell Reports* 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 554: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 584-590: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 539 - 543 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

 To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the *Drosophila even skipped *locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. *PLoS Genetics* 2016).

Reviewer #3

Major Comments:

1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 257 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 929 - 931 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 974 - 976 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 356 - 360: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 593-597: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

Minor Comments:

1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., ____https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2____or ____https://pubmed.ncbi.nlm.nih.gov/37486787____/). The authors should discuss how that would impact their results.

The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, although the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

 As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

 Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 623-628: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

 Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Osato and Hamada propose a systematic approach to identify DNA binding proteins that display directional binding. They used a modified Deep Learning method (DEcode) to investigate binding profiles of 1356 DBP from GTRD database at promoters (30 of 100bp bins around TSS) and enhancers (200 bins of 10Kb around eSNPs) and use this to predict expression of 25,071 genes in Fibroblasts, Monocytes, HMEC and NPC. This method achieves a good prediction power (Spearman correlation between predicted and actual expression of 0.74). They then use PIQ, and overlap predicted binding sites with actual ChIP-seq data to investigate the motifs of TFs that are controlling gene expression. They find 99 insulator proteins showing either a specific directional bias or minor non-directional bias, corresponding to 23 DBP previously reported to have insulator function. Of the 23 proteins they identify as regulating enhancer promoter interactions, 13 are associated with CTCF. They also show that there are significantly more insulator proteins binding sites at borders of polycomb domains, transcriptionally active or boundary regions based on chromatin interactions than other proteins.

Major Comments:

1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.
2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.
3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.
4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

Minor comments:

1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.
2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

Referee Cross-Commenting

I would like to mention that I agree with the comments of reviewers 1 and 2.

Significance

General assessment:

This is the first study to my knowledge that attempts to use Deep Learning to identify insulators and directional biases in binding. One of the limitations is that no additional methods were used to show that these DBP have directional binding bias. It is not necessarily to employ additional methods, but it would definitely strengthen the paper.

Advancements:

This is a useful catalogue of potential DNA binding proteins of interest, beyond just CTCF. Some known TFs are there, but also new ones are found.

Audience:

Basic research mainly, with particular focus on chromatin conformation and TF binding fields.

My expertise:

ML/AI methods in genomics, TF binding models, epigenetics and 3D chromatin interactions.

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Referee #2

Evidence, reproducibility and clarity

In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.

In general, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see my points below). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the following list.

Also, I encourage the authors to integrate the current presentation of the data with other (published) data about chromatin architecture, to make more robust the claims and go deeper into the biological implications of the current work. Se my list below.

It follows a specific list of relevant points to be addressed:

Specific points:

1. Introduction, line 95: CTCF appears two times, it seems redundant;
2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?
3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS;
4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details;
5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.
6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?
7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?
8. Do the authors think that the identified DBPs could work in that way as well?
9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?
10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

Significance

In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.

In general, chromatin organization is an important topic in the context of a constantly expanding research field. Therefore, the work is timely and could be useful for the community. The paper appears overall well written and the figures look clear and of good quality. Nevertheless, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see list of specific points). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the above reported points.

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Referee #1

Evidence, reproducibility and clarity

The study by Osato and Hamada aims at computationally identifying a set of novel putative insulator-associated DNA binding proteins (DBPs) via estimation of their contribution to the expression of genes in the same chromosome region of their binding sites (+- 1Mbp from TSS). To achieve this, the authors leverage a deep learning architecture already published via which ChIP-seq peaks of DBPs in the TSS of a given gene are used to predict its expression level in four human cell lines.

Building on this, the authors used another tool called DeepLIFT to evaluate the weight of each DBP binding site on the final gene expression value. Hence they made the assumption that if a given DBP had an insulator function they could restrict the prediction of the gene's expression to the region included between pairs of that DBP binding sites, and evaluate the pair's motif directionality bias in the distribution of weights. They exemplify their approach's validity by the fact that they can predict the known directionality bias of CTCF/cohesin-bound sites as the highest of the lot, with the F-R orientation of the pairs the most enriched, recapitulating what already known in literature: i.e., that F-R chromatin interaction peaks are the most enriched. In addition, they find several new DBPs showing significant directionality bias; hence they could be candidates for insulation activity. They then provide correlation between these putative insulator binding sites and sites of transition between euchromatin and heterochromatin by independently using histone mark and gene expression datasets. This, of course, is not surprising because (a) there is insulation between regions with heterotypic chromatin identities, and (b) it was already known from the first papers describing insulated chromatin domains that their boundaries were well-enriched for active transcription and transcriptional regulators (e.g., Dixon et al, Nature 2012).

Finally, they use chromatin interaction (looping) sites to check the overlap between CTCF and all other DBPs and define a subset of putative insulator DBPs not overlapping CTCF peaks, suggesting potentially new insulatory mechanisms. These factors were all known transcriptional activators, but this part of the findings carry most of the novelty in the work and have the potential of opening up new directions for research in chromatin organization.

Overall, the methodology applied here is adequate, clear, and reproducible. The major issue, in our view, is that the entire manuscript's findings relies on the usage of deepLIFT, a tool which was not benchmarked previously or by the current study. In fact, deepLIFT is public as regards its code, and also appears as a preprint from 2017 on biorXiv and published in the Proceedings of Machine Learning Research conference. Also, this key tool was developed by the Kundaje lab (who produce high quality alogrithms), and not by the authors. Therefore, the manuscript is predominantly based on the execution of existing workflows to publicly-available data. This does not take anything away from the interesting question posed here, but at the same time does not provide the community with any new algorithm/workflow.

Finally, although I appreciate that the authors are purely computational and have likely no capacity for experimental validation of their claims of new DBPs having insulator roles, I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning. Using this kind of data, effects on gene expression can at least be tested in regard to the authors' predictions. Moreover, in terms of validation, Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

As secondary issues, we would point out that:

• The suggested alternative transcripts function, also highlighted in the manuscript;s abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.
• Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.
• Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

Significance

The scientific novelty of the work lies primarily in the identification of a set of DBPs that are proposed to confer insulator activity genome-wide. This has been long sought after in human data (whilst it is well understood and defined in Drosophila). The authors produce a quantitative ranking of the putative insulation effect of these DBPs and, most importantly, go on to identify a smaller subset that are apparently non-overlapping with anchors of CTCF-cohesin loop anchors; the presence of strong motif orientation biases in many DBPs can also be of broad interest, especially those that cannot be trivially ascribable to the loop extrusion process.

However, although these findings open the way for speculation on multiple insulation mechanisms via proteins with multiple regulatory functions, the manuscript provide no experimental or computational means to test the proposed roles of these DBPs - and, as such, this limits the potential impact of the work and mostly targets researchers in the field of genome organization that can test these findings. Having said this, if validated, this work can significantly broaden our understanding of how chromatin is organized in 3D nuclear space.

I typically identify myself to the authors: A. Papantonis, expertise in 3D genome architecture, chromatin biology, and genomics/bioinformatics.

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Reply to the reviewers

We thank the reviewers for their thoughtful comments

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Reviewer #1 (Evidence, reproducibility and clarity):

SUMMARY: The manuscript is well written, with excellent explanation and documentation of experimental approaches. All conclusions are well supported by the data. The discussion is balanced and appropriate. The data, including images and movies, are of high quality and beautifully presented. The experimental design and analysis, including quantification of parameters in the images, is rigorous. Additional rigor is provided by comparing different cell types. The rapalog and iLID dimerization strategies have been described previously, as has their use to recruit kinesin motors to membranous organelles. However, this is the first application of these strategies to recruit motors to intermediate filaments. The evidence that vimentin filaments can be redistributed locally is clear and convincing and offers appealing potential for future experimentation. The redistribution was not fully reversible in all cells, but this is not surprising given the entanglement that must result from the action of motors along the length of these long flexible polymers.

In terms of the biology of intermediate filaments, the authors show that vimentin redistribution had negligible effect on microtubule or F-actin organization, cell area, or the number of focal adhesions. Depletion of vimentin filaments locally reduced cell stiffness. Both ER and mitochondria segregated with vimentin filaments, but not lysosomes. These findings are consistent with published reports (e.g. comparing vimentin null and wildtype cell lines), but the acute and reversible nature of the motor recruitment strategy is a more elegant experimental approach, and the selectivity of the observed effects is evidence of its specificity. It is interesting that the ER network segregated with vimentin even in the absence of RNF26. While this is not explored further, it points to the potential power of this motor recruitment strategy for future studies on intermediate filament interactions.

• *

The following are some major and minor issues, which should all be easy for the authors to address.

MAJOR COMMENTS:

• • Fig. S1 shows that the Vim-mCherry-FKBP construct coassembles with endogenous vimentin, but similar data for the iLID constructs appears to be lacking. I would like to see data demonstrating the incorporation of the Vim-mCherry-SspB constructs into the vimentin filaments. This should include high magnification images of single filaments in the cytoplasm of the cells.*
• *

Response:

We have included a new Figure 2D, which illustrates the incorporation of the vimentin-mCherry-SspB construct into the vimentin network stained for endogenous vimentin.

• • The authors do not discuss the density of motor recruitment along the filaments. To address this, I'd like to see images showing the extent of recruitment of motors to the filaments using the rapalog and LID strategies. This should include high magnification images of single filaments in the cytoplasm of the cells.*
• *

Response:

We have included new Figure S1B,C and Figure S2A, which illustrate the recruitment of kinesin motors to vimentin filaments upon induction with rapalog or light, respectively, by using super-resolution imaging with an Airyscan microscope. The motors were stained with antibodies against GFP. These data are discussed in the text, lines 126-132 and 165-168.

• • For the experiments on vimentin and keratin organization, the authors do not explain that these proteins form distinct networks and do not coassemble. The authors should show this in the cell types examined. This should also be explained explicitly in the body of the manuscript, though the data could be placed in the supplementary data. This is important because many intermediate filaments can coassemble freely, and coassembled proteins would be expected to segregate together.*
• *

Response:

To address this important comment, we have now included images of vimentin and keratin in the three studied cell types using super-resolution imaging, both for cells expressing vimentin constructs (updated Figure 5) and endogenous filament staining in untransfected cells (updated Figure S4). These images illustrate that vimentin and keratin mostly form distinct filaments in HeLa cells. However, we do observe some degree of co-assembly of vimentin and keratin in COS-7 and U2OS cells. We were really surprised by this observation as, to our knowledge, it has not been clearly documented in the literature. These data help to explain why vimentin pulling causes keratin co-clustering in COS-7 and U2OS cells. We note that in a study where kinesin-1 mediated transport of vimentin and keratin has been previously investigated by the Gelfand lab in RPE1 cells, the two networks also appear to overlap quite strongly (Robert et al, 2019, FASEB J). Since no super-resolution microscopy was performed in that study, potential co-assembly of keratin and vimentin filaments was not discussed. Colocalization and coprecipitation of vimentin and keratin have been also described by Velez-delValle et al. in epithelial cells (Sci Rep 2016). Cell type-specific co-assembly of keratin and vimentin would require more investigation, and we make no strong conclusions about it, but we think that our data illustrate the usefulness of our methodology to address the co-dependence of different types of intermediate filaments.

MINOR COMMENTS:

• • The authors refer to selecting cells within an "optimized expression range" for their transiently expressed recombinant proteins. They should state the proportion of the cells that met this criterion in their transient transfection experiments as this is important information for other researchers that might wish to use this approach in their own studies*. Response:

These numbers are now included in lines 137 -142 and 173-176 of the revised paper. For the FRB-FKP system, ~50% of transfected cells could be used for analysis, for the light-induced system, ~40% were in the optimal range.

• • In Fig. 1F there should be a statistical comparison between cells transfected with the Kin14 construct and control (untransfected) cells in the absence of rapalog*
• *

Response:

This comparison has been added.

• • In Fig. 1G there should be a statistical comparison between cells expressing Kin14 and KIF5A in the absence of rapalog.*
• *

Response:

This comparison has been added.

• • The depletion of the ER network in the cell periphery is not evident in Fig. 7B, though the perinuclear accumulation is evident. Perhaps the authors could select another example or explain to the reader what exactly to look for in these images.*
• *

Response:

We note that Figure 7B is a line scan of the image shown in Figure 7A. We assume that the reviewer meant Figure 7C, which is discussed in detail below.

• • In Fig. 7C, the intensity of the mCherry declines markedly over time. This is presumably due to photobleaching but should be explained in the legend.*
• *

Response:

We have now improved Figure 7 by adding additional quantifications of ER and vimentin intensity and distribution in Figures 7D and E. We also extended the corresponding text (lines 288-297), which now reads; “Using the optogenetic tool, we observed that ER sheets and matrices, but not tubules, were pulled along with vimentin, confirming their previously described direct connections (Cremer et al., 2023) (black arrows, Figure 7C; Video S5). Most of the vimentin and ER repositioning occurred within approximately 10 minutes (Figure 7C, D, Video S5). While initially this resulted in a sparser tubular ER network at the cell periphery, over time, the network became denser, with smaller polygonal structures. This effect could also be observed in the ratio of perinuclear to peripheral intensity, where a subset of ER initially follows vimentin to the perinuclear region but then redistributes again towards the cell periphery (Figure 7D). It should be noted that while photobleaching of the ER channel was negligible, there was a 40% reduction in total Vim-mCh-SspB intensity over the course of the experiment due to photobleaching (Figure 7E).”

• *

Reviewer #1 (Significance):

SUMMARY: The authors show that chemical-induced and light-induced dimerization strategies can be used to recruit microtubule motors to vimentin filaments, allowing rapid and reversible experimental manipulation of vimentin filament organization either locally or globally in cells. These strategies provide an experimental approach for investigating the physical interaction of intermediate filaments with organelles and other cytoskeletal component, as well as a method for probing the role of intermediate filaments in cell mechanics, cytoskeletal dynamics, etc. This is a technical improvement over previous experimental strategies, which have relied largely on chronic manipulation such as global disassembly or genetic deletion of intermediate filaments, e.g. comparison of vimentin null and wild type cells.

The principal weakness of this study is that it offers limited insight into intermediate filament biology. As such, it might be most appropriate for a tools or techniques section of a journal. The dimerization strategies have been reported previously, so that is not new, but the application to intermediate filaments is novel.

• *

Response:

We agree that our paper is primarily of technical nature and thus would be most appropriate for the tools and techniques section of a journal. We also agree that we used motor recruitment strategies that we and others have employed previously. However, we would like to emphasize that the demonstration that the tools work very well for intermediate filaments is entirely novel, as are the observations that these tools can be used to very rapidly alter cell stiffness or probe the links between intermediate filaments and organelles. Most importantly, the intermediate filament field currently lacks rapid specific manipulation strategies, and our tools will allow revisiting many important pending questions in the field. For example, they will allow to distinguish short-term and direct effects of intermediate filaments on cell polarity, adhesion and migration from their function in signaling and gene expression. We also report some new biology, such as evidence of some degree of co-assembly of vimentin and keratin.

AUDIENCE: This paper will be of interest to cell biologists who study cytoskeletal interactions, particularly the interaction of intermediate filaments with other cellular organelles or cytoskeletal polymers, or the role of intermediate filaments in cellular mechanics.

REVIEWER EXPERTISE: This reviewer has expertise on the cytoskeleton, cytoskeletal dynamics, and intracellular transport including intermediate filament biology.

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Reviewer #2 (Evidence, reproducibility and clarity):

Summary: The manuscript presents a novel methodology for acute manipulation of vimentin intermediate filaments (IFs) using chemical genetic and optogenetic tools. By recruiting microtubule-based motors to vimentin via inducible dimerization systems, the authors achieve precise temporal and spatial control over vimentin distribution. Apart from the significant advancement in terms of methods development, key findings include:

* Vimentin's role in organelle positioning: Mitochondria and ER are repositioned with vimentin, while lysosomes are less dependent on its organization.

* Cytoskeletal interactions: Vimentin clustering minimally impacts actin and microtubule networks in the short term.

* Cell stiffness: Vimentin repositioning reduces cell stiffness, indicating its significant role in cellular mechanics.

* Cell-type-specific keratin interactions: The study highlights diverse interactions between vimentin and keratin-8 across cell lines.

The study demonstrates methodological advancements enabling rapid vimentin manipulation and provides insights into vimentin's interactions with cellular structures.

A major shortcoming is the unclear narrative, what do the authors want to present? This aspect requires significant attention.

Response:

By “unclear narrative” the reviewer meant that we should have provided a more balanced discussion of the insights that could be obtained using our new method compared to previously published literature, and we have modified our narrative accordingly.

General Comments and Overall Assessment

The manuscript represents an interesting contribution to the cytoskeletal field, addressing limitations of long-term perturbation methods. The tools developed are innovative, allowing controlled and reversible vimentin reorganization with minimal off-target effects. The findings are robust and provide important insights into the role of vimentin in cellular mechanics and organelle positioning.

Strengths:

Methodological novelty with broad applicability - this is the most exciting aspect.

Comprehensive validation of the tools in multiple cell lines.

Clear differentiation between vimentin's short- and long-term roles.

Addressing gaps in understanding vimentin-organelle interactions.

Limitations:

* The manuscript is a little bit all over the place. While the method development is clear, the manuscript makes claims way beyond the method development. The message and narrative needs to be improved, and in the respect the whole structure needs an overhaul.

Response:

We have carefully modified the manuscript to avoid the impression that we make any claims that go beyond the immediate and quantifiable effects of vimentin repositioning on different cellular structures.

* Unclear how much the differences in expression levels impact results and reproducibility.

Response:

Quantifications of expression levels and their discussion are included in Figures 1G-I, 2G-H, S2B and lines 137-142 and 173-176.

* Would be good to discuss some findings that are specific to a given experimental cell line. How generalizable are these results?

Response:

Cell line-specific findings concerned mostly the co-displacement of keratin together with vimentin, which occurred in COS-7 and U2OS cells but in in HeLa cells. This interesting finding is discussed in the text, lines 246-269 and 375-383 (see also our answers on page 3 above and page 7 below).

Major Comments

Evidence and Claims:

* While the methodological aspect is very strong the balance between presenting a novel method and presenting specific cell biological findings needs to be improved. Now it is quite unclear what the manuscript wants to present.

* The abstract needs a complete overhaul. From reading the abstract, it is not clear what the manuscript wants to present.

Response:

We have modified the abstract to make it more clear that we do not make any general claims on the impact of vimentin on the interactions and functions of different organelles, but rather describe what can be directly observed after the acute displacement of vimentin and which conclusions can be made from these observations.

Regarding the research findings there are a number of things for the authors to consider. Since the methods aspect is, in the eyes of this reviewer, in focus, I have not stringently assessed the experimental findings. Hence, the comments below are things to be considered in order to make the findings related to IF research stronger:

• *

* Cell-specific keratin interactions: The manuscript could benefit from some further validation of the physical interactions between vimentin and keratin-8 across different cell types.

Response:

We have improved the images of keratin and vimentin by using super-resolution (Airyscan) microscopy to show that they indeed form distinct filaments in HeLa cells, whereas in COS-7 and U2OS cells, where their co-displacement occurs, they can also incorporate into the same filaments. This observation was very surprising but agrees with the data published by the Gelfand lab on similarity in the distribution pattern and co-transport of vimentin and keratin in RPE1 cells (Robert et al, 2019, FASEB J). Colocalization and coprecipitation of vimentin and keratin has been also described by Velez-delValle at al. in epithelial cells (Sci Rep 2016).

* Impact on microtubules: The disorganization of stable microtubules in cells expressing KIF5A was attributed to overexpression effects. It would be helpful to include additional controls, such as expressing KIF5A without vimentin constructs, to confirm this claim.

Response:

This control has been included in the new Figure S3. We note that this observation fully aligns with data published by another lab (Andreu-Carbó et al, 2024, Nat Comm).

* ER-vimentin linkages: The observation that ER-vimentin interactions persist in RNF26 knockout cells is intriguing. The manuscript would benefit from a discussion on possible candidates for alternative linkers.

Response:

We have added a short discussion (lines 394-398) about the potential involvement of nesprins, such as nesprin-3, because they can connect the nuclear envelope to intermediate filaments, and might also partly participate in ER sheet-IF connections because ER and nuclear membranes are continuous and show some overlap in proteome.

* Construct variability: Do the authors have some data on how much Expression level differences significantly affect the outcomes (e.g., incomplete recovery)?

Response:

We have added a figure (Figure S2B), which shows that incomplete recovery of vimentin clustering does not correlate with protein expression levels and likely depends on other factors, which could possibly be the cell cycle phase or degree of vimentin entanglement after repositioning. This point is discussed in revised text, lines 194-197.

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Reviewer #2 (Significance):

Significance

General Assessment: The study represents a significant technical advance in the study of cytoskeletal dynamics. The tools developed address critical limitations of traditional vimentin perturbation methods, allowing for spatiotemporally precise manipulation without long-term effects on gene expression or signaling pathways.

Novelty:

This is, to my knowledge, the first demonstration of reversible and acute vimentin repositioning using optogenetics. The study extends understanding of vimentin's short-term mechanical and organizational roles, distinguishing them from compensatory effects observed in knockdown models.

Audience and Impact: The manuscript will appeal to researchers in cytoskeletal dynamics, cell mechanics, and organelle biology. The tools have broader applicability in studying other cytoskeletal systems and could inspire translational applications, such as investigating the role of vimentin in cancer or fibrosis.

The reference list provide a relatively representative selection of articles relevant for the article. However, the authors may consider whether there could be relevant information in the relatively recent special edition of Current Opinion in Cell Biology, which focused on IFs, specially featuring vimentin https://www.sciencedirect.com/special-issue/10TFHK2QCKW

Response:

We thank the reviewer for this excellent suggestion, and we have included some additional references from this issue.

Field of Expertise

I specialize in cell biology, intermediate filaments, post-translational modifications, cytoskeletal dynamics, and advanced microscopy techniques.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary:

This is an excellent paper describing the use of chemical and light-induced heterodimerization of microtubule-based motors to rapidly disrupt the distribution of the vimentin cytoskeletal network. Rapid clustering of vimentin did not significantly affect the microtubule or actin networks, cell spreading or focal adhesions. Other organelles were repositioned together with vimentin. Interestingly, in some cell lines, keratin networks were displaced along with vimentin while in other cells they were not.

Major comments:

The conclusions are well supported by the data presented and appropriate controls are included.

Optional comments:

1. • The authors should expand on why they think the plus end directed KIF5A gives such a strong localization of vimentin to the perinuclear area.* Response:

We think that two factors can contribute to this counterintuitive effect. First, vimentin is strongly concentrated and entangled in the perinuclear region, and displacement of some vimentin filaments to the cell periphery can cause the collapse of the rest to the cell center, with kinesins being unable to pull the perinuclear network apart. Second, kinesin-1 KIF5A is a motor that strongly prefers stable, post-translationally modified microtubules, and our previous study has shown that a significant proportion of such microtubules are located with their minus ends facing towards the cell periphery (Chen et al., Elife 2016). This could contribute to the accumulation of vimentin in the cell center upon KIF5A recruitment. These considerations were added to the revised text, lines 344-347.

• Consideration should be given to the idea that the pulling of ER and mitochondria along with the vimentin could be due to trapping of these organelles within the vimentin matrix and not necessarily due to direct interactions. Such reasoning could explain the transient localization of lysosomes with the center aggregate since lysosomes are generally not thought to significantly bind to vimentin networks.*

Response:

This is an excellent point, and we have included it in the revised article, lines 333-335 and 405.

Reviewer #3 (Significance):

This study describes some valuable tools that should be useful to cell biologists interested in determining the role of the cytoskeleton and possibly other organelles in a variety of cellular contexts. It overcomes some of the existing shortcomings of the pharmacological reagents currently available for studying intermediate filament biology and will provide a useful adjunct to other more long-term manipulations of the cytoskeleton. While much of the data presented confirm results obtained by other methods, this is a significant technical advance as it provides a short time scale, and in one instance, reversible manipulation of the cytoskeleton.