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

      Summary Maintenance of the histone H3 variant CENP-A at centromeres is necessary for proper kinetochore assembly and correct chromosome segregation. The Mis18 complex recruits the CENP-A chaperone HJURP to centromeres to facilitate CENP-A replenishment. Here the authors characterise the Mis18 complex using hybrid structural biology, and determine complex interface separation-of-function mutants.

      Major Comments The SAXS and EM data on the full-length Mis18 components must be included in the main Figures, either as an additional figure or by merging/rearranging the existing figures. The authors discuss these results in three whole paragraphs, which are a very important part of the paper.

      We thank the reviewer for this constructive suggestion. We have now included an additional figure (new Fig. 2, attached below), that highlights the fit of the integrative model against the SAXS and EM data.

      Could the authors also compare the theoretical SAXS scattering curves generated by their final model(s) with the experimental SAXS curves? This would provide some additional evidence for the overall shape of their complex model beyond the consistency with the Dmax/Rg.

      We acknowledge the importance of this suggestion. We have now compared the theoretical SAXS scattering curve of the Mis18a/b core complex (named Mis18a/b DN), which lacks the flexible elements (disordered regions and the helical region flexibility connected to the Yippee domains). The theoretically calculated SAXS scattering curve of the model matches nicely with the experimental data with c2 value of 1.36. This data is now included in new Fig. 2 (Fig. 2f) and is referenced on page 9 line 21.

      Minor Comments

      While the introduction is clearly written, an additional cartoon schematic, representing the system/question would be helpful to a non-specialist reader to interpret the context of the study.

      We have now included a cartoon in the revised Fig. 1 to support the introduction on centromere maintenance and the central role of the Mis18a/b/BP1 complex in this process. Please find the new Fig. 1 below.

      No doubt the authors had a reason for choosing their figure allocation, but I wonder if more material couldn't be brought from the supplementary into the main figures?

      As addressed in our response to one of the major comments, we have now moved key CLMS, SAXS and EM data from the supplemental figure into the main figure, new Fig. 2.

      Page 6 "Mis18-alpha possesses an additional alpha-helical domain" - please make it clear in addition to what (I assume it's in addition to Mis18-beta).

      Apologies for the lack of clarity. We have now rephrased this sentence to highlight that this difference is in comparison with Mis18b on page 6 line 15.

      Page 7 - Report the RMSD of the Pombe vs. Human Mis18-alpha yipee structures?

      The S. pombe Mis18 Yippee structure superposes on to the Human Mis18a Yippee domain with an RMSD of 0.92 angstroms with is now mentioned on page 7 line 9.

      Page 7 - "We generated high-confidence structural models...." is there a metric for the confidence as reported by RaptorX? Perhaps includinging the PAE plots in the supplementary for the AlphaFold generated models would be useful?

      We thank the reviewer for the valid suggestion. We have now included the PAE plot corresponding to the AlphaFold model in the supplementary Fig. S1d and reference on page 7 line 18. RaptorX ranks models based on estimated error. We have now included this information in the new figure legend for Supplementary Fig. S1.

      Figure 1 - Perhaps label figure 1b as being experimentally determined, with the R values (as for Figure 1d), and 1c being a predicted model.

      We have included Rfree and Rwork values for the Mis18a Yippee homo dimer structure and labelled Mis18a/b Yippee hetero-dimer as the predicted model in Fig. 1c and 1d.

      Page 8 "This observation is consistent with the theoretically calculated pI of the Mis18alpha helix" This is a circular argument, of course this region has a low pI due to the amino acid composition. Please remove this statement.

      We have now removed this statement as suggested.

      Page 8 "...reveals tight hydrophobic interactions" these are presumably shown in Figure 1d rather than in the referenced 1e.

      We apologise for the oversight. We have now referred to the correct figure (Fig. 1f in the revised Fig. 1).

      Page 8 - The authors should briefly somewhere discuss why there is a difference between their results and those in Pan et al 2009. As I understand it, the Pan et al paper was based in part on modelling with CLMS data as restraints.

      We thank the reviewer for this suggestion. According to Pan et al., 2009, the model shown by them was generated using CCBuilder, and their CLMS data could not differentiate the two models with the 2nd Mis18a C-terminal helix in either parallel or anti-parallel orientation. We now briefly discuss this on page 8 and line 22 as follows: "Although the Pan et al., 2019 model presented the 2nd Mis18a in a parallel orientation, they did not rule out the possibility of this assembling in an anti-parallel orientation within the Mis18a/b C-terminal helical assembly (Pan et al., 2019)."

      Figure 1 - The labelling of the residues for Mis18-alpha in Figure 1d is problematic, they are black on dark purple (might be my printer/screen/eyes) suggest amending.

      We have now rearranged the label positions to overcome this issue. For clarity, the labels that could not be moved appropriately are shown in white.

      Figure S3a - Do the authors have some data to show the mass of the cross-linked complex that was loaded onto grids is consistent with what is expected?

      Unfortunately, the amount of material that we recover after performing GraFix is not sufficient enough to determine the molecular weight of the crosslinked sample by techniques such as SEC-MALS. However, GraFix fractions were analysed by SDS PAGE, and fractions that ran around the expected molecular weight were selected for EM analysis. We have now included the corresponding SDS-PAGE showing the migration of the crosslinked sample analysed by EM (Supplementary Fig. S3a).

      Figure S3b - scale bar

      Revised Fig. 2d now includes the scale bar shown.

      Figure S3c - Could the authors show or explain the differences between these different 3D reconstructions?

      The models mainly differ in the relative orientations of the bulkier structural features that are referred to as 'ear' and 'mouth' pieces of a telephone handset. This has been mentioned in the text, but we note that the figure is not referenced right next to this statement. We have now amended this (Page 9 line 19), and to make it clear, we have also highlighted the difference using an arrowhead in Fig. 2e and S3b. The different orientations are also stated in the corresponding figure legends.

      Page 9 - The use of "AFM" for AlphaFoldMultimer" is a little confusing since AFM is the established acronym for Atomic Force Microscopy. Perhaps AF2M?

      We have now replaced AFM with AF2M on page 9 to avoid confusion.

      Figure S4a - Control missing for Mis18-alpha wild-type

      Apology for the confusion, this control is present in Fig. 4a. We have now stated this in the figure legend of S4a for clarity.

      Figure S4 d and e - The contrast between the bands and the background is very bad (at least in my copy).

      We have now adjusted the contrast of the blots in Fig. S4d and S4e response to this comment.

      Page 13 "Our structural analysis suggests that two Mis18BP1 fragments.....". How did you arrive at this conclusion? Is this based on the AlphaFold/RaptorX model? What additional evidence do you have that the positioning of the Mis18BP1 is correct? Does the CLMS data support this?

      We confirm that this statement is based on AlphaFold model. We have now explicitly highlighted this on page 14, line 5. As noted in the same paragraph (page 14, line 19), this model agrees with the contacts suggested by the cross-linking mass spectrometry data presented here.

      Figure 4a - Would the authors like to consider using a different colour for Mis18BP1? The contrast is not great, especially in the electrostatic surface inset.

      In response to this suggestion, the Mis18BP1 helix is now shown in grey in the inset of Fig. 5a.

      Reviewer #1 (Significance (Required)):

      General Assessment The paper is extremely clearly written. Likewise the figures are beautifully presented and the data extremely clean and fully supportive of the authors conclusions. Indeed it is seldom that one sees the depth of the structural approaches (X-ray, CLMS, EM, SAXS) in one paper which is a huge strength of the manuscript. In addition the translation of this data into very clean cell biological experiments, makes the paper truly outstanding.

      Advance The authors provide the first model of the Mis18 complex, with extensive evidence to back up this model. The authors provide additional evidence as to how the deposition/renewal of CENP-A might be mediated by the Mis18 complex. The advance comes from both the level of clarity, detail, and scope achieved in this paper.

      Audience This will likely be of great interest to anyone with an interest in chromosome biology, plus be of interest to structural biologists as an outstanding example of hybrid structural biology.

      Expertise I am a biochemist with a background in structural biology with some familiarity with centromere biology

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

      Summary: The manuscript "structural basis for Mis18 complex assembly: implications for centromere maintenance" by Thamkachy and colleagues describes a study that uses structural analysis to test essential candidate residues in Mis18 complex components in CENP-A loading. For chromosomes to faithfully segregate during cell division, CENP-A levels must be maintained at the centromere. How CENP-A levels are maintained is therefore important to understand at the mechanistic level. The Mis18 complex has been found to be important, but how exactly the various Mis18 complex components interact and how they regulate new CENP-A loading remains not fully understood. This study set out to characterize the critical residues using X-ray crystallography, negative staining EM, SEC analysis, molecular modeling (Raptorx, AlphaFold2, and AlphaFold-multimer) to identify the residues of Mis18a and Mis18b that are critical for the formation of the Mis18a/b hetero-hexamer and which residues are important for Mis18a and Mis18BP1 interactions. A complex beta-sheet interface dictates the Mis18a and Mis18b interactions. Mutating the Mis18a residues that are important for the Mis18a/b interactions resulted in impaired pull-down of Mis18b and reduced centromeric levels of mutated Mis18a. The functional consequences of mutating residues that impair Mis18a/b interactions is that with reduced centomeric levels of Mis18a, also impaired new CENP-A loading. Interestingly, mutated Mis18b did not impact centromeric Mis18a levels and only modestly impaired new CENP-A loading. These data were interpreted that Mis18a is critical for new CENP-A loading, whereas Mis18b might be involved in finetuning how much new CENP-A is loaded. Overall, it is a very well described and well written study with exciting data.

      Major comments:

      • Overall, the structural data and the IF data support the importance of Mis18a residues 103-105 are critical for centromeric localization and new CENP-A loading, whereas Mis18b residues L199 and I203 are critical for centromeric localization, but only very modestly impair centromeric Mis18a localization and new CENP-A loading. In the discussion the authors argue that the N-terminal helical region of Mis18a mediate HJURP binding. This latter is postulated based on published work, but not tested in this work. This should be clarified as such.

      We thank the reviewer for this comment. Our very recent study aimed at understanding the licencing role of Plk1, independent of the work reported here, serendipitously has now validated this suggestion and demonstrates that a Plk1-mediated phosphorylation cascade activates the Mis18a/b complex via a conformational switch of the N-terminal helical region of Mis18a, which facilitates a robust HJURP-Mis18a/b interaction (Parashara et al. bioRxiv 2024). An independent study from the Musacchio lab (Conti et al. bioRxiv, 2024) also reports similar findings, mutually strengthening our independent conclusions. Overall, these studies highlight the importance of the critical structural insights into the Mis18 complex this study reports. We now explicitly discuss the validation of our original hypothesis by citing our recent work along with that of the Musacchio lab. The corresponding section of the last paragraph now reads as follows (page 17 line 10): "Previously published work identified amino acid sequence similarity between the N-terminal region of Mis18a and R1 and R2 repeats of the HJURP that mediates Mis18a/b interaction (Pan et al., 2019). Deletion of the Mis18a N-terminal region enhanced HJURP interaction with the Mis18 complex (Pan et al., 2019). Here, we show that the N-terminal helical region of Mis18a makes extensive contact with the C-terminal helices of Mis18a and Mis18b, which had previously been shown to mediate HJURP binding by Pan et al., 2019. Collectively these observations suggest that the N-terminal region of Mis18a might directly interfere with HJURP - Mis18 complex interaction. Two independent recent studies (Parashara et al., 2024, Conti et al., 2024) reveal that this is indeed the case and a Plk1-mediated phosphorylation cascade involving several phosphorylation and binding events of the Mis18 complex subunits relieve the intramolecular interactions between the Mis18a N-terminal helical region and the HJURP binding surface of the Mis18a/b C-terminal helical bundle. This facilitates robust HJURP-Mis18a/b interaction in vitroand efficient HJURP centromere recruitment and CENP-A loading in cells. Overall, these studies also highlight the importance of the critical structural insights into the Mis18 complex we report here."

      • Overall, the authors clearly describe their data and methodology and use adequate statistical analyses. The structural data of the Mis18a/b complex being a hetero-hexamer is convincing, but the validation in vivo is missing. As structural experiment are not performed under physiological conditions, it is important to establish the stoichiometry in vivo to further support the totality of the findings of the structural experiments and modeling. The data for the hierarchical assembly of Mis18a and Mis18b at the centromere and its importance in new CENP-A loading is convincing. An additional open question is whether "old" centromeric CENP-A or HJURP:new CENP-A complex is needed to recruit Mis18a to the centromere and whether the identified residues have a role in Mis18a centromeric localization. These data would provide a solid link between the Mis18 complex and how it is directly linked to new CENP-A loading.

      We agree that establishing the stoichiometry of Mis18 subunits of the Mis18 complex in vivo would be insightful. However, considering that the Mis18 complex assembles in a specific window of the cell cycle (late Mitosis and early G1), we think characterising the stoichiometry in cells is extremely difficult and technically challenging. However, consistent with our structural model, several lines of independent evidence (Pan et al., 2017 and Spiller et al., 2017) using different biophysical methods (Analytical Ultra Centrifugation (Pan et al., 2017), SEC-MALS (Spiller et al., 2017)) showed that recombinantly purified Mis18 complex (irrespective of the expression host, from both E. Coli or insect cells) is a hetero-octamer made of a hetero-hexameric Mis18a/b (4 Mis18a and 2 Mis18 b) complex bound to two copies of Mis18BP1. These observations suggested that hetero-hexamerisation of the Mis18a/b complex may be needed to bind and dimerise Mis18BP1 in cells. Previously published cellular studies support the in vivo requirement of the hetero-octameric Mis18 assembly as: (i) Perturbing the hetero-hexamerisation of the Mis18a/b complex (by introducing mutations at the Mis18a/b Yippee dimerisation interface, which while did not disrupt Mis18a/b complex formation, perturbed its hetero-hexamerisation and resulted in a hetero-trimeric Mis18a/b complex made of 2 Mis18aand 1 Mis18b) abolished Mis18BP1 binding in vitro and in cells, consequently abolished CENP-A deposition (Spiller et al., 2017) and (ii) artificial dimerisation of Mis18BP1, by expressing Mis18BP1 as a GST-tagged protein, enhanced the centromere localisation of Mis18BP1 highlighting the requirement of Mis18a/b hexameric assembly mediated dimerization of Mis18BP1 in cells (Pan et al., 2017). While these studies highlighted the importance of maintaining the right stoichiometry (hetero-octamer of 4 Mis18a, 2 Mis18b and 2 Mis18BP1), lack of structural information on how this essential biological assembly is established remained a major knowledge gap. Our work presented here fills this critical knowledge gap by showing that a segment of Mis18BP1 (aa 20-51) also binds at the Yippee dimerisation interface. To highlight this, we have included the following statements in the introduction on page 5 and 20 "Perturbing the Yippee domain-mediated hexameric assembly of Mis18a/b (that resulted in a Mis18a/b hetero-trimer, 2 Mis18a and 1 Mis18b) abolished its ability to bind Mis18BP1 in vitro and in cells (Spiller et al., 2017), emphasising the requirement of maintaining correct stoichiometry of Mis18a/b subunits. Consistent with this, artificial dimerisation of Mis18BP1, by expressing Mis18BP1 as a GST-tagged protein, enhanced the centromere localisation of Mis18BP1 (Pan et al., 2017)." and in the Results section on page 14 line 12: "Mis18BP120-51 contains two short b strands that interact at Mis18a/b Yippee interface extending the six-stranded-b sheets of both Mis18a and Mis18b Yippee domains. This provides the structural rationale for why Yippee domains-mediated Mis18a/b hetero-hexamerisation is crucial for Mis18BP1 binding (Spiller et al., 2017)."

      Regarding the question "whether 'old' centromeric CENP-A or HJURP:new CENP-A complex is needed to recruit Mis18a centromere localisation and whether identified residues have a role in Mis18a centromere localisation": According to the published literature, the Mis18 complex associates with centromeres through interaction with CCAN components CENP-C and CENP-I (Shono et al., 2015, Dambacher et al., 2012, Moree et al., 2011, Hoffmann et al., 2020). Considering CCAN assembles on CENP-A nucleosomes, and HJURP:new CENP-A centromere recruitment depends on the Mis18 complex, it will be reasonable to argue that the 'old' centromeric CENP-A contributes to the centromere localisation of the Mis18 complex. Amongst the components of the Mis18 complex, Mis18BP1 and Mis18bhave previously been suggested to interact with CENP-C. Within the Mis18 complex, we (Spiller et al., 2017) and others (Pan et al., 2017) have shown that Mis18a can directly interact with Mis18BP1, but it does so more efficiently when Mis18a hetero-oligomerises with Mis18b via their Yippee domains. Here, our structural analysis mapped the interaction interfaces and showed that Mis18a residues E103, D104 and T105 contribute to Mis18BP1 binding, as mutating these residues abolishes centromere localisation of Mis18a (Fig. 5c and 5d). To accentuate our findings, we have now included the following paragraph in the discussion section (page 17 line 26): "One of the key outstanding questions in the field is how does the Mis18 complex associate with the centromere. Previous studies identified CCAN subunits CENP-C and CENP-I as major players mediating the centromere localisation of the Mis18 complex mainly via Mis18BP1 (Shono et al., 2015, Dambacher et al., 2012, Moree et al., 2011), although Mis18b subunit has also been suggested to interact with CENP-C (Stellfox et al., 2016). Within the Mis18 complex, we and others have shown that the Mis18a/b Yippee hetero-dimers can directly interact with Mis18BP1. Here our structural analysis allowed us to map the interaction interface mediating Mis18a/b-Mis18BP1 binding. Perturbing this interface on Mis18a completely abolished Mis18a centromere localisation and reduced Mis18BP1 centromere levels. These observations show that Mis18a associates with the centromere mainly via Mis18BP1, and assembly of the Mis18 complex itself is crucial for its efficient centromere association, as previously suggested. Future work aimed at characterising the intermolecular contact points between the subunits of the Mis18 complex, centromeric chromatin and CCAN components and understanding if the Mis18 complex undergoes any conformational and/or compositional variations upon centromere association and/or during CENP-A deposition process, will be crucial to delineate the mechanisms underpinning the centromere maintenance."

      Minor comments:

      • The bar graphs shown ideally also show the individual data points for the authros to appreciate the spread of the data. These figures can be replicated in the Supplemental to avoid making the main figures look too busy.

      We thank the reviewers for this suggestion. Reviewer #3 made a similar comment and suggested we use Superplot, which allows visualisation of individual data points of independent experiments. We have now revised all bar graphs using Superplot to address both reviewers' suggestions.

      Reviewer #2 (Significance (Required)):

      • This study uses a broad range of structural techniques, including molecular modeling which were subsequently validated by in vitro pull-down assays, co-IP, and IF. This combination of these techniques is important because many structural techniques cannot be performed under physiological conditions. Validating the main findings of the structural results by IF and co-IP is therefore critical.
      • This work greatly advances our structural understanding how Mis18a, Mis18b, and Mis18BP1 form the Mis18 complex and how the critical residues in especially Mis18a help the Mis18 complex localize to the centromere and influence new CENP-A loading. This study also provides the first strong evidence in hierarchical assembly of the Mis18 complex.
      • How centromere identity is maintained is a critical question in chromosome biology and genome integrity. The Mis18 complex has been identified as an important complex in the process. Several structural and mutational studies (all adequately cited in this manuscript) have tried to address which residues guide the assembly and functional regions of the Mis18 complex. This work builds and expands our understanding how especially Mis18a holds a pivotal role in both Mis18 complex formation and its impact on maintaining centromeric CENP-A levels.
      • This work will be of interest to the chromosome field in general and anyone studying the mechanism of cell division.
      • Chromatin, centromere, CENP-A, cell division. This reviewer has limited expertise in structural biology.

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

      Centromere identity is defined by CENP-A loading to specific sites on genomic DNA. CENP-A loading is known to rely on the Mis18 complex, and several regulators are known; yet how the Mis18 complex achieves this complex process has remained puzzle. By elucidating the structural basis of Mis18 complex assembly using integrative structural approaches the authors show that multiple homo and heterodimeric interfaces of Mis18alpha, beta and Mis18BP1 are involved in centromere maintenance. The authors show that Mis18alpha can associate with centromeres and deposit CENP-A independent of Mis18 β. Mis18α functions in CENP-A deposition at centromeres independent of Mis18β. Mis18β is required for maintaining a specific level of CENP-A occupancy at centromeres. Thus, using structure-guided and separation-of-function mutants the study reveals how Mis18 complex ensures centromere maintenance. Major comments: This is an excellent study on centromere inheritance, combining structural and cell biology techniques. The comments here primarily refer to Cell biology aspect of the work.

      Figures show that new CENP-A deposits in Mis18βL199D/I203D mutants, but the level was reduced moderately. Based on this observation, the authors make a strong conclusion that Mis18β licenses the optimal levels of CENP-A at centromeres. Mis18α may be essential for both CENP-A incorporation and depositing a specific amount of CENP-A, as Mis18α and CENP-A levels are both reduced in Mis18βL199D/I203D mutants which failed to form the triple helical assembly with Mis18α as shown in Figure 3B and 3C. The authors may want to qualify some of these claims as preliminary or speculative.

      We thank the reviewer for this suggestion. We agree that although the reduction in CENP-A levels upon replacing WT Mis18b with Mis18b L199D/I203D is more prominent than the reduction in centromere localised Mis18a, one cannot completely rule out the contribution of reduced Mis18a on CENP-A loading. This also raises an interesting possibility where Mis18b ensures the correct amount of CENP-A deposition by facilitating the optimal level of Mis18a at centromeres. We now explicitly discuss this in the discussion as follows (page 16 line 26): "Whilst proteins involved in CENP-A loading have been well established, the mechanism by which the correct levels of CENP-A are controlled is yet to be thoroughly explored and characterised. The data presented here suggest that Mis18b mainly contributes to the quantitative control of centromere maintenance - by ensuring the right amounts of CENP-A deposition at centromeres - and maybe one of several proteins that control CENP-A levels. We also note that the Mis18b mutant, which cannot interact with Mis18a, moderately reduced Mis18a levels at centromeres, and hence, it is possible that Mis18b ensures the correct level of CENP-A deposition by facilitating optimal Mis18a centromere recruitment. Future studies will focus on dissecting the mechanisms underlying the Mis18b-mediated control of CENP-A loading amounts along with any other mechanisms involved."

      This work and others show that phosphorylation of Mis18BP1 by CDK1 can interfere with complex function (Spiller et al., 2017, Pan et al., 2017). Does the structure provide any insight into PLK1-mediated phosphorylation surfaces for activation of the complex? If yes, a brief discussion would help to link CDK1 and PLK1 mediated opposing actions will strengthen the work.

      As described in our response to the first major comment of Reviewer 2, our very recent study aimed at understanding the licencing role of Plk1, independent of the work reported here, identified and evaluated the functional contribution of Plk1 phosphorylation on the subunits of the Mis18 complex (Parashara et al., bioRxiv 2024). Serendipitously, this recent work has now validated our hypothesis proposed based on the structural characterisation reported here and demonstrates that a Plk1-mediated phosphorylation cascade activates the Mis18a/b complex via a conformational switch of the N-terminal helical region of Mis18a which facilitates a robust HJURP-Mis18a/b interaction (Parashara et al. bioRxiv 2024). An independent study from the Musacchio lab (Conti et al., bioRxiv 2024) also reports similar findings, mutually strengthening our independent conclusions. Overall, these studies highlight the importance of the critical structural insights into the Mis18 complex this study reports. We now explicitly discuss the validation of our original hypothesis by citing our recent work along with that of the Musacchio lab. The corresponding section of the last paragraph now reads as follows (page 17 line 10): "Previously published work identified amino acid sequence similarity between the N-terminal region of Mis18a and R1 and R2 repeats of the HJURP that mediates Mis18a/binteraction (Pan et al., 2019). Deletion of the Mis18a N-terminal region enhanced HJURP interaction with the Mis18 complex (Pan et al., 2019). Here, we show that the N-terminal helical region of Mis18a makes extensive contact with the C-terminal helices of Mis18a and Mis18b, which had previously been shown to mediate HJURP binding by Pan et al., 2019. Collectively these observations suggest that the N-terminal region of Mis18a might directly interfere with HJURP - Mis18 complex interaction. Two independent recent studies (Parashara et al., 2024, Conti et al., 2024) reveal that this is indeed the case and a Plk1-mediated phosphorylation cascade involving several phosphorylation and binding events of the Mis18 complex subunits relieve the intramolecular interactions between the Mis18a N-terminal helical region and the HJURP binding surface of the Mis18a/b C-terminal helical bundle. This facilitates robust HJURP-Mis18a/b interaction in vitro and efficient HJURP centromere recruitment and CENP-A loading in cells. Overall, these studies also highlight the importance of the critical structural insights into the Mis18 complex we report here."

      I am happy with the way cell biology data and the methods are presented so that they can be reproduced. The experiments are adequately replicated and the statistical analysis adequate. It will help to include sample size of cells or centromeres used for building the graphs.

      We have now included this information in figure legends of Fig. 3a, 3c, 4b, 4c, 5b, 5c and 5d.

      This is a strong interdisciplinary study using a variety of in vitro and in vivo techniques. Can the authors discuss if they expect chromatin associated Mis18 complex to host a similar structure as the soluble one? In other words, are they able to comment on any key differences between chromatin and non-chromatin associated Mis18 complexes.

      We thank the reviewer for the suggestion. We agree that one cannot rule out the possibility of the Mis18 complex undergoing compositional and/or conformational variations during the processes of CENP-A loading at centromeres. We now explicitly discuss this possibility in the last paragraph of the discussion section (page 18 line 10): "Future work aimed at characterising the intermolecular contact points between the subunits of the Mis18 complex, centromeric chromatin and CCAN components and understanding if the Mis18 complex undergoes any conformational and/or compositional variations upon centromere association and/or during CENP-A deposition process, will be crucial to delineate the mechanisms underpinning the centromere maintenance."

      Minor comments: -

      In cell biology experiments, fluorescence intensities could be presented as a superplot for added value across cells and repeats (instead of bar graphs). More on superplot:https://doi.org/10.1083/jcb.202001064.

      We thank the reviewers for this kind suggestion. We have now included graphs made using 'superplot' as suggested.

      In general, ACA levels do not appear to change significantly between WT and mutant expressing cells although new CENP-A loading is significantly absent in the presence of a few mutants - please comment if ACA used here can recognise CENP-A. Would this mean that old CENP-A remains normally?

      We thank the reviewer for this comment. While new CENP-A incorporated at centromeres is selectively labelled using the SNAP-tag, the ACA antibody used in these experiments can recognise CENP-A, CENP-B and CENP-C, with CENP-B being the primary target (Kallenberg, Clinical Rheumatology,1990). We would also like to note that ACA has commonly been used to locate the centromere in CENP-A loading assays where new CENP-A levels are assessed via selective labelling (e.g. McKinley 2014).

      It is unclear whether any of the mutant acted in a dominant negative fashion in the presence of endogenous Mis18 proteins. It would have been useful to test this particularly in the context of mis18alpha mutants that seem to fully abolish new CENP-A recruitment.

      As Mis18 subunits oligomerise (homo and hetero), we thought expressing these mutants in the presence of endogenous proteins might interfere with endogenous protein in a heterogenous manner and might make the interpretation difficult. Hence, we did not test this. Instead, as described in the manuscript we have tested these mutants in siRNA rescue experiments (Fig. 3, 4 and 5).

      In figure 3a, GFP panel (input lane, 1) is shown to mark a band corresponding to GFP. Is this expected? Please comment.

      Yes, as a control, an empty vector was transfected to express just GFP along with Mis18a-mCherry. These were used to show that there was no unspecific interaction between the beads used for IP or Mis18a-mCherry and GFP tag, and that any interaction seen was due to Mis18b. A similar control was used in S4b, where mCherry was expressed along with Mis18b-GFP. We have now clarified this in the corresponding legends of Fig. 4a and S4b.

      Would be useful to have the scale for the cropped images presented as insets. Figure 4B should read YFP and not YPF.

      We apologise for this typographical error. We have now corrected this.

      The authors may want to explain whether the tag differences matter for their study (Case in point: His-SUMO-Mis18a191-233 WT and mutant His-MBP-Mis18b188-229 proteins).

      The MBP tag was chosen to perform amylose pull-down assays, whereas the SUMO tag was chosen to increase the protein size. This is crucial as the C-terminal fragments of Mis18a and Mis18b are less than 50 amino acids long and are not easy to visualise by the band intensity in the Coomassie-stained SDS PAGE gels.

      Reviewer #3 (Significance (Required)):

      This work elucidates the structural basis of Mis18 complex assembly and the intermolecular interfaces essential for Mis18 functions. This is a significant advance in the field as it helps researchers in the field better understand CENP-A deposition and mechanism underpinning the maintenance of centromere identity. This is a broad area of research benefitting those studying cell division, genome stability, centromere identity and epigenetics might all be interested in and influenced by these findings. Novelty and strength lies in combining structural and cell biology work. Strengths of the work are structural details of the Mis18 complex. Minor weakness is the link between Mis18 structure and Centromere inheritance is limited to one immunostaining assay (I have mentioned this as a minor comment because addressing this may not be within the scope of this manuscript and is likely to require a repeat of a vast majority of the work with additional reagents which may not directly add value to the current manuscript).

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      Referee #3

      Evidence, reproducibility and clarity

      Centromere identity is defined by CENP-A loading to specific sites on genomic DNA. CENP-A loading is known to rely on the Mis18 complex, and several regulators are known; yet how the Mis18 complex achieves this complex process has remained puzzle. By elucidating the structural basis of Mis18 complex assembly using integrative structural approaches the authors show that multiple homo and heterodimeric interfaces of Mis18alpha, beta and Mis18BP1 are involved in centromere maintenance. The authors show that Mis18alpha can associate with centromeres and deposit CENP-A independent of Mis18 β. Mis18α functions in CENP-A deposition at centromeres independent of Mis18β. Mis18β is required for maintaining a specific level of CENP-A occupancy at centromeres. Thus, using structure-guided and separation-of-function mutants the study reveals how Mis18 complex ensures centromere maintenance.

      Major comments:

      This is an excellent study on centromere inheritance, combining structural and cell biology techniques. The comments here primarily refer to Cell biology aspect of the work.

      1. Figures show that new CENP-A deposits in Mis18βL199D/I203D mutants, but the level was reduced moderately. Based on this observation, the authors make a strong conclusion that Mis18β licenses the optimal levels of CENP-A at centromeres. Mis18α may be essential for both CENP-A incorporation and depositing a specific amount of CENP-A, as Mis18α and CENP-A levels are both reduced in Mis18βL199D/I203D mutants which failed to form the triple helical assembly with Mis18α as shown in Figure 3B and 3C. The authors may want to qualify some of these claims as preliminary or speculative.
      2. This work and others show that phosphorylation of Mis18BP1 by CDK1 can interfere with complex function (Spiller et al., 2017, Pan et al., 2017). Does the structure provide any insight into PLK1-mediated phosphorylation surfaces for activation of the complex? If yes, a brief discussion would help to link CDK1 and PLK1 mediated opposing actions will strengthen the work.
      3. I am happy with the way cell biology data and the methods are presented so that they can be reproduced. The experiments are adequately replicated and the statistical analysis adequate. It will help to include sample size of cells or centromeres used for building the graphs.
      4. This is a strong interdisciplinary study using a variety of in vitro and in vivo techniques. Can the authors discuss if they expect chromatin associated Mis18 complex to host a similar structure as the soluble one? In other words, are they able to comment on any key differences between chromatin and non-chromatin associated Mis18 complexes.

      Minor comments:

      In cell biology experiments, fluorescence intensities could be presented as a superplot for added value across cells and repeats (instead of bar graphs). More on superplot: https://doi.org/10.1083/jcb.202001064. In general, ACA levels do not appear to change significantly between WT and mutant expressing cells although new CENP-A loading is significantly absent in the presence of a few mutants - please comment if ACA used here can recognise CENP-A. Would this mean that old CENP-A remains normally?

      It is unclear whether any of the mutant acted in a dominant negative fashion in the presence of endogenous Mis18 proteins. It would have been useful to test this particularly in the context of mis18alpha mutants that seem to fully abolish new CENP-A recruitment.

      In figure 3a, GFP panel (input lane, 1) is shown to mark a band corresponding to GFP. Is this expected? Please comment. Would be useful to have the scale for the cropped images presented as insets.

      Figure 4B should read YFP and not YPF.

      The authors may want to explain whether the tag differences matter for their study (Case in point: His-SUMO-Mis18a191-233 WT and mutant His-MBP-Mis18b188-229 proteins).

      Significance

      This work elucidates the structural basis of Mis18 complex assembly and the intermolecular interfaces essential for Mis18 functions. This is a significant advance in the field as it helps researchers in the field better understand CENP-A deposition and mechanism underpinning the maintenance of centromere identity. This is a broad area of research benefitting those studying cell division, genome stability, centromere identity and epigenetics might all be interested in and influenced by these findings. Novelty and strength lies in combining structural and cell biology work.

      Strengths of the work are structural details of the Mis18 complex. Minor weakness is the link between Mis18 structure and Centromere inheritance is limited to one immunostaining assay (I have mentioned this as a minor comment because addressing this may not be within the scope of this manuscript and is likely to require a repeat of a vast majority of the work with additional reagents which may not directly add value to the current manuscript).

  2. Mar 2024
    1. 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

      We would like to thank all reviewers for taking the time to evaluate our manuscript. Many helpful suggestions and discussion points were raised. These comments were instrumental to provide more data that strengthen our conclusion about the relevance of centrin condensation in vivo, expand our findings to other organisms, and improve the manuscript in general. Details are given in the following individual replies.

      All line numbers given below refer to the document with the tracked changes.

      1. Point-by-point description of the revisions

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

      Voss and colleagues show calcium-dependent assembly of Plasmodium falciparum centrins in vitro and in parasites. This assembly is dependent on the EF-hands of centrin and an N-terminal disordered region.

      Major concerns:

      1. The very definitive title is not wholly supported by the data. This should be qualified by specifying the conditions under which the centrins can accumulate in this way.

      We understand this comment by the reviewer. There are multiple dimensions to the potential of centrins to condensate, such as the specific centrin family member, in vivo vs in vitro situation, and media conditions. Naturally it is difficult to represent these various conditions in a concise and compelling title but in line with the suggestion by Reviewer 2 we are changing the title to “Malaria parasite centrins__ can__ assemble by Ca2+-inducible condensation” to reflect the conditionality of this process.

      1. A major concern is whether this behaviour of centrins represents a biologically relevant mechanism in centriolar plaque formation. Is this limited to high overexpression conditions or in vitro high concentrations? Or is it a result of the tagging of the P. falciparum centrins?...

      Centrin accumulation at the centriolar plaque and assembly of the centriolar plaque itself must be differentiated. Although compelling we are already very careful in the text about extrapolating our findings about centrin accumulation in cells to centriolar plaque or centrosomal assembly in general. We, however, thank the reviewer for this important comment and now have carried out hexanediol treatment of wild type parasites to test the effect on centrin in a native context. After IFA staining we failed to detect any centrin foci at the centriolar plaques, suggesting that they can be resolved by inhibiting weak hydrophobic interactions that are typical for phase separation (now Fig. 6, lines 283ff).

      Concerning the effect of tagging we have generated new data of cells overexpressing an untagged version of PfCen1 in parasites, which still shows formation of ECCAs as revealed by IFA (now Fig. 4H-K, lines 243ff). This significantly alleviates the concern that the observed phenomenon is only a consequence of GFP-tagging. Our in vitro data already showed that native and tagged PfCentrin1 & 3 can undergo condensation.

      Concerning the critical concentration of our in vitro assay we find it to be around 10-15 µM without the addition of crowding agents such as PEG (now Fig. S3C, lines 120ff). To our understanding it is challenging to select an in vitro concentration that is adequate to define a threshold for “biological relevance” due to so many additional factors playing a role in vivo. Those factors can also favor a phase separation locally when total saturation concentration is not reached as we now discuss in more detail (lines 440ff). For reference the critical concentration of FUS, which is one of the most studied phase separating proteins in model system, is around 2 µM, but concentrations below 15 µM are well within the range of what is observed for in vitro LLPS. Additionally, it is important to consider that we find Cen1/3 and HsCen2 LLPS is inducible and reversible and that very homologous proteins i.e. Cen2 and 4 serve as an adequate internal control.

      … A convincing approach to addressing this issue would be to knock-in a fluorescent tag to the centrin loci. Roques et al. (ref. 12 in this submission) report the GFP tagging of centrin-4 in P. berghei, although they note that centrins-1 to -3 were refractory to tagging in this organism. It is unclear whether Voss et al. attempted this tagging in P. falciparum. This should be clarified and relevant data presented.

      We indeed attempted several unsuccessful iterations of tagging Cen1/3 with HA and GFP tag and now explain this in the text more clearly (lines 81ff). We did not attempt tagging Cen2 and 4 as they do not display phase separation in vitro or carry IDRs.

      If the tagged molecules used in the biochemical parts of this study are functional, it is challenging to understand why the centrins cannot be tagged in P. falciparum. If the tags render the P. falciparum centrins dysfunctional, the study becomes significantly less useful.

      Our data shows that in vitro Cen1-GFP can undergo Ca2+-inducible and reversible LLPS and that GFP-tagged centrins can still localize to the centriolar plaque. Centrin function, however, certainly goes beyond its ability to condensate and localize. It is easily conceivable that interaction with critical binding partners at the centriolar plaque is inhibited by tagging a protein as small as centrin, which prohibits tagging the endogenous version, while its ability to phase separate remains unaltered. To dynamically study a protein in cells tagging is, however, unavoidable. Even though tagging affects any proteins function to highly variable degree we are still convinced that studying those proteins still provides useful information. Our mutant versions of PfCen1 in vivo shows that non-condensating version display different localization. Importantly, as mentioned above, we now provide images of cells overexpressing an untagged Cen1 version, which still causes ECCA formation (Fig. 5H-K). Ultimately, even though tagged versions might not be fully functional, our observations are compatible with the ability of centrins to condensate in vivo.

      If a knock-in cannot be achieved, it must be shown that the transgenic expression of tagged Plasmodium centrins does not confound the analysis of centrin behaviour. It is known that these proteins can behave anomalously when overexpressed (Yang et al. 2010, PMID: 20980622; Prosser et al. 2009, PMID: 19139275), at least in other species.

      Thank you for this comment. Transgenic expression of proteins can in principle influence their behavior. In the context of this study the overexpression is, however, used intentionally since protein concentration correlates with the phase separation. Here, transgenic overexpression is used as a tool, rather than being a confounding factor, and ECCA formation can be used as quantifiable phenotype. The observation that ECCAs appear significantly earlier the higher they are expressed is in our opinion one of the stronger points of evidence that this result from phase separation in vivo. Yet centrins maintain their centriolar plaque localization and no significant impact on growth is observed. To definitely answer whether phase separation of endogenous centrin is occurring during centriolar plaque accumulation is challenging. These challenges and limitations are now addressed in the significantly extended discussion. As explained above untagged Cen1 also forms ECCAs.

      A previous description of centriolar plaque from the authors' lab (Simon et al. 2021, PMID: 34535568) shows an organized structure of an established size. It should be demonstrated whether the structures formed with the GFP tagged centrins show the same dimensions and dynamics as those in wild-type parasites. The extent of the overexpression of the GFP-tagged centrins should also be demonstrated.

      We thank the reviewer for this suggestion. We have now added spatial measurements of the centrin signal dimensions at the centriolar plaque of mitotic spindle containing nuclei in PfCen1-GFP overexpressing vs non-induced cell lines. We found that the width of the centrin-signal at the centriolar plaque was unaltered while the height only increased by 11% (Fig. S9). Further, we found no significant growth phenotype in overexpressing parasites, which indicates that the centriolar plaque is functional.

      Due to several confounding factors, we were, unfortunately, unable to clearly quantify the extent of overexpression. Most notably the induction of overexpression only works in about 50% of the cells (Fig. S6). The mean intensity after induction further displays quite some variability. Furthermore, the expression kinetics along the IDC of endogenous centrin and our overexpression system that we use as a tool differ. Lastly, our centrin antibodies display crossreactivity (see also Fig. S12) making it impossible to identify how much of the endogenous pool we are labeling in comparison to the GFP- tagged Cen1 protein.

      It would also be useful to remove the His tag from the recombinantly expressed and purified centrins for the in vitro analyses, particularly if concern remains about the impact of tags on Plasmodium centrin behaviour.

      Based on the published in vitro studies on other centrins, we did not anticipate the His-tag to change LLPS properties. Also, Cen1 and 3 and Cen2 and 4 would need to be differentially affected by the tag. We further have experimented with N-terminally tagged 6His-Cen3 protein and found no significant differences in our turbidity assays. Nevertheless, we expressed new versions of the recombinant PfCen1-4 proteins with a TEV cleavage site inserted after the His-tag to purify untagged proteins and found no fundamental differences in our LLPS assay aside some slight variation in the kinetics (Fig. S3E).

      The discussion is very short and does not consider the findings presented here in the context of the literature, with respect to centrins, Plasmodium MTOC assembly mechanisms, or to general considerations around biological condensates. Andrea Musacchio's recent commentary (ref. 44 in the current submission) advocates caution in ascribing phase separation as an assembly mechanism for organelles in vivo, particularly on the basis of in vitro experiments with high concentrations of homogeneous protein. It is not clear that the concentration dependence of extracentrosomal centrin accumulations (ECCAs) at the onset of schizogony provides sufficient justification of a phase separation model in vivo. The authors' recent description of the involvement of an SFI1-like protein, SIp (Wenz et al. 2023 PMID: 37130129), in the centriolar plaque makes a case for non-homotypic interactions also driving assembly and alternative models for ECCA are not convincingly excluded. The absence of a robust discussion of such considerations is unhelpful to the reader.

      We very much thank the reviewer for this suggestion, which helped to significantly improve the manuscript. We have purposefully included the commentary by Andrea Musacchio to highlight a different (possibly the most antipodal) point of view on the role of biomolecular condensation in membraneless organelle formation for the unfamiliar readers that might be just getting to know the field of phase separation. In the absence of word limitations, the reviewer is right to point out the lack of more extensive discussion. We now have significantly extended this section and address the suggested points including the potential role of the novel centriolar plaque protein Slp, which was not published upon submission of our previous version (lines 450ff.)

      It is also unclear whether the analysis of human centrin is suggested to indicate a phase separation mechanism for centrins in human cells. As this is readily testable, this notion could be considered further. Although its experimental examination may lie outside the theme of this study, one would expect some discussion of the significance of the data presented in the study.

      Since it is the first description of phase separation of centrin, it would indeed be interesting to explore the functional relevance in other organisms such as humans. We are considering approaching this in the future. We have, as requested above, significantly extended the discussion and now also include this aspect. Earlier reports have e.g. shown centriole overduplication in human cells upon centrin overexpression.

      Minor points

      There are only three centrins in humans. Centrin 4 is a pseudogene (Gene ID: 729338 on NCBI).

      Thank you for detecting this error, which we now corrected (line 60). Centrin 4 seems only to be an expressed gene in mice.

      Line 175 should say 'temporally', rather than 'temporarily. The Abstract should say 'evolutionarily conserved', rather than 'evolutionary conserved'. 'To condensate' is not ideal as a phrase- 'to form a condensate' would be clearer.

      Thank you for those suggestions. The text has been modified accordingly.

      **Referees cross-commenting**

      I think the other 2 reviewers have made fair, cogent and constructive points. There is good convergence between the reviewers on the significant issues around the study. These concern in vivo and in vitro effects of tagging and of high concentrations.

      Reviewer #1 (Significance (Required)):

      The biology of the Plasmodium centriolar plaque is of great interest as an alternative MTOC structure, with obvious additional interest deriving from the role of this organism in malaria. Much remains to be learned about this structure, so the topic of this paper is likely to attract a broad readership. Furthermore, the centrins are a widely-expressed and evolutionarily conserved family of eukaryotic proteins, with multiple roles; a new model for their behaviour, such as is suggested here, would be of interest to many cell biologists.

      With that in mind, significant additional data should be provided to substantiate the model proposed by the authors.

      We appreciate that the reviewer considers our manuscript of interest for a broad audience. We feel that our modifications of the text including a more thorough contextualization and addition of some new experimental data now sufficiently supports our claims.


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

      The authors analyzed the properties of the four Centrin proteins of the malaria parasite using a combination of in vitro and in vivo approaches. Their findings indicate that two of the four Plasmodium Centrin proteins, PfCen1 and PfCen3, as well as the human Centrin protein HsCen2, exhibit features of biomolecular condensates. Moreover, analysis of cells overexpressing PfCen1 indicates that such biomolecular condensates become more numerous as cells approach mitosis and are dissolved thereafter.

      Major comments

      A) A critical point that requires clarification is how the protein concentrations used in the in vitro and in vivo assays (20-200 microM in vitro, and not estimated in vivo) compare to that of the endogenous components. This is important because it may well be that 6His-tagged PfCen1, PfCen3 and HsCen2 can form biomolecular condensates when present in vast excess, but not when present in physiological concentrations. The authors should report the estimated cellular concentration of PfCen1-4, as well as that achieved upon PfCen1-GFP overexpression (on top of endogenous PfCen1), for instance using semi-quantitative immunoblotting analysis. Given this limitation, the authors may also want to temper their title by introducing the word "can" after "centrins".

      In the context of phase separation, protein concentration is of course a critical metric. However, in vitro and in vivo concentrations cannot be directly compared as the composition of the surrounding solute has a significant impact on the effective saturation concentration. In vitro we find a saturation concentration for Cen1 of 10-15 µM (Fig. S3C), which is within a range that is frequently found other in vitro studies as listed in the in vitro LLPS data base (PMID: 35025997). We now more explicitly discuss this in the text (lines 422ff). At this point, unfortunately, we have no means of investigating the absolute concentrations of centrin in vivo and to our knowledge no such data is available for apicomplexan. Additionally, one has to keep in mind the presence of other centrin family members in the cell which can interact and co-condensate as well as other centriolar plaque proteins, like PfSlp, but are difficult to separate through analysis. Further we now discuss several contexts that modify the saturation concentration in vivo (lines 440ff).

      As explained above in a response to Reviewer 1, we were not able to produce a satisfactory quantification of the overexpression levels. We are repasting the previous response here:

      “Due to several confounding factors we were, unfortunately, unable to clearly quantify the extent of overexpression. Most notably the induction of overexpression only works in about 50% of the cells (Fig. S6). The mean intensity after induction further displays quite some variability. Lastly the expression kinetics along the IDC of endogenous centrin and our overexpression system that we use as a tool differ. Lastly, our centrin antibodies display crossreactivity (see also Fig. S12) making it impossible to identify how much of the endogenous pool we are labeling in comparison to the GFP- tagged Cen1 protein. “

      Concerning the title, as explained above, we followed the suggestion and added the word “can”.

      B) Movies S1 and S2 (and the related Fig. 1D and 1E) are not the most convincing to support the notion that the observed assemblies are biomolecular condensates, as not much activity is going on during the recordings. Likewise, Movies S3, and even more so Movie S4, as out of focus for a large fraction of the time, making it difficult to assess what happens at the beginning of the process. Moreover, it appears that fusion events, while occurring, are rather rare. The movies should be exchanged for ones that are in focus, and ideally a rough quantification of fusion events as a function of biomolecular condensate size provided.

      We thank the reviewer for requesting clarification. Movies S1 and S2 are by no means direct evidence for biomolecular condensation and we do not claim them to be but rather say that they are “…reminiscent of biomolecular condensates…”. We think that this is an appropriate entry into the subsequent analyses. For Movie S1 it is noteworthy that the shape of the accumulation, which can only be resolved by super-resolution microscopy in live cells, is round as would be expected for a liquid condensate in the absence of forces and on these short time scales. Nevertheless, the centriolar plaque must be duplicated which might be the process partly depicted in Movie S2. The observation that centrin can be still change its shape at least suggests that it is not a solid aggregate. In the context of centriolar plaque biology and the technological advance of applying live cell STED in P. falciparum, we think these data are still worth reporting.

      Concerning Movies S3 and S4 we have carefully selected the focal plane to highlight all the hallmarks of LLPS. Since the protein droplets freely move in 3D throughout the entire imaged liquid volume there is no z-plane that is in focus. Our positioning of the focal plane presents the best compromise between showing round droplet shape, droplet fusion events, and surface wetting. All those observations demonstrate the liquid nature of the condensates. Fusion events are indeed relatively rare, and we do not go beyond this qualitative statement that it can be seen.

      C) An important control is missing from Fig. 2, namely assaying PfCen1-4 without the 6His tag, to ensure that the tag does not contribute to the observed behavior (although it can of course not be sufficient as evidenced by the lack of biomolecular condensates for PfCen2 and PfCen4).

      Thank you for this suggestion. Since reviewer 1 made a similar comment, I’m reiterating our previous reply here: Generally speaking, and based on the published in vitro studies on other centrins, we didn’t anticipate the very small His-tag to change LLPS properties. Also, Cen1 and 3 and Cen2 and 4 would need to be differentially affected by the tag. We further have experimented with N-terminally tagged 6xHis-Cen3 protein and found no significant differences in our turbidity assays. However, we expressed new versions of the recombinant PfCen1-4 proteins with a TEV cleavage site inserted after the His-tag to purify untagged proteins and found no significant differences in our LLPS assay (Fig. S3E).

      D) The authors should test whether the assemblies formed by PfCen1 and PfCen3 are sensitive to 1,6-hexanediol treatment, as expected for biomolecular condensates.

      This is an interesting and helpful suggestion. We now tested 1,6-hexanediol addition to recombinant PfCen1 and wildtype parasites (now Fig. 6). Interestingly the dissolving effect of hexanediol on PfCen1 in vitro was moderate, which we attribute to the polar component in centrin assembly, which has been documented earlier (Tourbez et al. 2004). In vivo, however, only 5 min of treatment caused a striking dissolution of most centrin foci in wild type parasites, which is compatible with the interpretation that centrin or centriolar plaque assembly could be driven by biomolecular condensation.

      E) The fact that HsCen2 also forms biomolecular condensates is very intriguing, but further investigation would be needed to assess the generality of these findings. For instance, the authors could test in vitro also S. cerevisiae Cdc31, the founding member of the Centrin family of proteins to further enhance the impact of their study.

      We thank the reviewer for this suggestion. It would of course be exciting to investigate in more detail how widely this biochemical property of some centrins is conserved. To take a first step in that direction, we have recombinantly expressed centrins containing some N-terminal IDRs from C. reinhardtii, T. brucei and S. cerevisiae to represent organism of significant evolutionary distance. Using our in vitro phase separation assays, we found a very similar behavior to PfCen1 for two centrins while yeast Cdc31, although forming droplets, had a much higher saturation concentration, which could be explained by the significantly lower intrinsic disorder in its sequence (now new Fig. 3).

      Minor comments

      1) For the experiments reported in Fig. 3D, the same concentrations as those used in Fig. 3A-C (namely 10 microM, and not 30 microM as in Fig. 3D) should be used. Moreover, it would be informative to test whether PfCen2 and PfCen4 as PfCen3 when added to PfCen1.

      Unfortunately, this experiment is not feasible since Cen3 does not produce droplets at 10 µM. Hence, in Fig. 3D we aimed to test if Cen1 is incorporated into preformed droplets i.e. whether there is still some interaction between them. We have, however, tested the addition of Cen2 to Cen1 and Cen3 and as expected from the inability PfCen2 to condensate we did not find the same synergistic effect as for Cen1 and 3 together (now Fig. S6). The combination of Cen1/2/3 still enabled co-condensation while addition of Cen4 did not further improve droplet formation. Taken together this strongly suggests that only Cen1 and 3 contribute to the phase separation in vitro (lines 184ff).

      2) The authors mention that the effect of Calcium in inducing biomolecular condensates is specific, as Magnesium was not effective (lines 94-95). However, an examination of Fig. S3B indicates that the Magnesium also exhibits some activity, albeit less potent than Calcium. The authors should discuss this point and rectify the wording in the main text.

      Thank you for pointing this out. While PfCen1 is not reactive to Magnesium, PfCen3 and HsCen2 do display a small reaction, which we now more clearly mention in the text (lines 118ff). Of note Mg2+ and other divalent cation are known to generally promote phase separation.

      3) Do the authors think that PfCen2 and PfCent4 localize to the centriolar plaque in vivo using another mechanism that deployed by PfCen1 and PfCent3? It would be good to discuss this point.

      This is indeed a point worth discussing. Centrins can of course still interact in the absence of biomolecular condensation and their localization to the centriolar plaque is not dependent on their ability to phase-separate as seen for PfCen2 and 4. We have recently described a novel centriolar plaque protein PfSlp that interacts with centrins and might assist recruitment (Wenz et al. 2023). Cellular condensates are, however, often separated into scaffold proteins, which actually phase separate and client protein which get recruited into those condensates. It is easily conceivable that Cen1 and 3 participate in formation of the biomolecular condensate into which Cen2 and 4 as well as other centriolar plaque proteins might be recruited. Unfortunately, we were not yet able to establish a recruitment hierarchy by e.g. dual-labeling of centrins to test whether PfCen1 and 3 might appear prior to PfCen2 and 4. We now include those aspects in the extended discussion.

      4) Given that the EFh-dead mutant exhibits no activity in vitro and fails to localize in vivo, one potential concern is that the protein is misfolded. The authors should conduct a CD spectrum to investigate this.

      Thank you for suggesting this relevant control experiment. We have carried out CD spectroscopy of wild type and EFh-dead PfCen1 and find no difference in secondary structure distribution. We now added these data to the supplemental information (now Fig. S14).

      5) It is not entirely clear from the main text in lines 103-104, as well as from the legend, what Fig. S3B shows. When was EDTA added in this case?

      Thank you for requesting clarification. We will assume the reviewer is referring to Fig S4B. We wanted to show that contrary to PfCen3 that PfCen1 droplets can still be resolved after an elongated period of incubation with calcium but forgot to mark the timepoint of EDTA addition at 180 min in the graph. We have now corrected this and further reworded the sentence for more clarity (lines 132ff).

      6) Fig. S7: the correlation between PfCen1-GFP expression levels and ECCA appearance is modest at best. What statistical test was applied? This should be spelled out. Moreover, the authors should combine the two data sets, as this will provide further statistical power to assess whether a correlation is truly present.

      Indeed, the correlation is modest but statistically significant, which is why we decided to place this data in the supplemental information. The used statistical test was an F-test provided by Prism, which compares two competing regression models, which we now mention in the legend. Combining the two data sets is unfortunately not possible since they arise from two independent sets of measurements where different imaging settings had to be used to adjust for the very different fluorescent protein levels in both lines after induction.

      7) The authors may want to discuss how their findings can be reconciled with the notion that Centrin assemble into a helical polymer on the inside of the centriole (doi: 10.1126/sciadv.aaz4137).

      This is an interesting point. Although centrin does localize to the inside of the centriole (https://doi.org/10.15252/embj.2022112107), more precisely one pool at the distal part and one pool at the core, there is no evidence that it is itself part of the helical inner scaffold described by the authors even though it might localize in close proximity to it. Further, there are several examples where polymers such as microtubules act as seeding point for biomolecular condensates or the other way around, and our work suggest this could be a potential working model for centrins. We have discussed our results extensively with the two corresponding authors of the aforementioned study (i.e. Virginie Hamel and Paul Guichard) and agreed that our data are not conflicting. Nevertheless, we include the inner centriole localization and potential association with polymer structures of centrin in our extended discussion.

      9) Likewise, the authors may want to speculate regarding what their findings signify for the role of Centrin proteins in detection of nucleotide excision repair (doi: 10.1083/jcb.201012093).

      We appreciate the comment by the reviewer. Centrins seem to have many different potential roles that remain to be clarified. While we are excited about this, we think it is too early to speculate about the impact of centrin condensation on less well studied aspects of centrins such as nucleotide excision repair. We, however, now cite this study in the discussion to highlight the functional diversity of centrins.

      Small things

      • Fig. 1A: change color for microtubules as red on red is difficult to discern.

      Throughout our publications we use this shade of magenta to label microtubules in schematics and have therefore opted to use a slightly brighter shade of red for the RBCs instead to improve visibility.

      • Fig. 1C: the indicated boxes in the top row do not seem to correspond exactly to the insets shown in the bottom row.

      We have verified the position of the boxes and found them to be accurate. Possibly the different imaging modality used for both panels (confocal vs STED) creates this impression.

      • line 266: typo, promotor > promoter.

      Has been corrected.

      • line 360: a reference should be provided for the GFP-booster, including the concentration at which it was used.

      Has been added.

      • line 363: "an" missing before "HC".

      Has been corrected.

      • line 428: it would be best to deposit the macros on Github or an analogous repository.

      Macros have been deposited on https://github.com/SeverinaKlaus/ImageJ-Macros (line 737)

      • line 461: "to the" is duplicated.

      Has been corrected.

      • Fig. S5A: maybe draw the lines in red (as red in Fig. S5B correspond to the proteins that do not have IDRs).

      Since we cannot easily change the line colors of the IDR graphs, we have inverted the font color for Fig. S5B instead.

      • Movie S7, legend: left frames shows PfCen1-GFP, not microtubules as currently stated.

      Has been corrected.

      Reviewer #2 (Significance (Required)):

      This is a provocative study that extends initial observations regarding self-assembly properties of Centrin proteins, and posits that some members of this evolutionarily conserved family can form biomolecular condensates. After the above outstanding issues have been properly addressed, these data could have important implications for understanding Centrin function in centriole biology and DNA repair. Therefore, these findings will be of interest to a cell biology audience.

      Field of expertise: cell biology.


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

      Summary:

      The authors have provided a comprehensive characterisation of centrin proteins in Plasmodium falciparum. Through expression of episomal GFP-tagged centrin for in vitro, they were able to observe co-localisation of centrin with centriolar plaques during the replicative stage of the parasite. They also utilised live cell STED microscopy to track dynamic changes in centrin morphology. They have also demonstrated calcium-dependent phase separation dynamics in bacterially-expressed P. falciparum centrin and human centrin 2. The formation of liquid-liquid phase separation in PfCen1, 3 and HsCen2 tied well with IUPred3 predictions of intrinsically disordered regions in these proteins. Using an inducible DiCre overexpression system with two promoters of varying strengths, the authors have shown accumulation of centrin1 outside of centrosomes and premature appearance of centriolar plaques. Finally, changes on the centrin1 protein, i.e., N-terminal deletion, and mutations in calcium binding sites in the EFh domains, have shown a reduction in the formation of ECCAs during overexpression and inability to form LLPS in vitro, respectively.

      Major comments:

      1. Given that parasites cannot tolerate endogenous C-terminal tagging of some centrins (but not all, as PbCen4 was successfully tagged), has N-terminal tagging been attempted either by the authors or in previous publications? Note that this is not a request for further experimentation; rather, maybe this can be noted in the manuscript; and line 62 can be rephrased for transparency.

      We have not attempted N-terminal tagging ourselves but through personal communication with Rita Tewari we were informed that neither N- nor C-terminal tagging for PbCen1-3 was successful in the context of the study published by Roques et al 2018. We have only unsuccessfully attempted C-terminal tagging in several iterations. Due to importance of N-terminus for interaction and function in other organisms it is plausible that N-terminal tagging is even more unlikely to work. Since we have not exhaustively attempted every tagging strategy on every centrin we, as suggested, rephrased the text accordingly (lines 81ff).

      Is there a possibility that by adding a C-terminal tag, centrin may lose a specific function or cause change in the physicochemical properties of the protein (thus making C-terminal tagging lethal)? Was His tag removal attempted so the native protein can be used in the LLPS experiments? IUPred3 analysis showed potential IDR at the C-terminal end of PfCen4. Could the C-terminal tag have caused the protein to not form droplets in the presence of Ca2+?

      As we could show for PfCen1-GFP, the tag did not impair its ability to undergo LLPS which is at least partly mediated by the N-terminus, and that it could still properly localizes to the centriolar plaque. The fact that some endogenous centrins cannot be tagged suggest that there is a functional relevance to the C-terminus that could e.g. be an interaction with other essential centriolar plaque components. As suggested in a reply to Reviewer 1, we consider a substantial and centrin-specific effect of the small His-tag on phase separation unlikely. To be sure, we have repeated our turbidity assays with tag-free versions of PfCen1-4 and found no change in phase separation properties (now Fig. S3E).

      It has been shown by the authors that different tagged centrins co-condense which may support the localisation data (Figure 1C). However, is there a way to show that the episomally- and endogenously-expressed centrin co-localise with each other (e.g., confocal microscopy with anti-centrin vs anti-gfp in PfCen-GFP lines, that is if the authors have access to anti-centrin antibodies)? Has endogenous centrin been demonstrated to form ECCAs (in previous publications or by the authors)?

      These are important questions by the reviewer. Due to the high sequence homology centrin antibodies, even if raised against a specific centrin (such as PfCen3 in this study), will likely cross-react with other centrins. So far, we have not been able to produce a staining were the anti-GFP-positive foci are devoid of anti-centrin3 staining, which limits the interpretation of these data. The outer centriolar plaque compartment containing centrin is, however, well defined by now and the localization pattern of endogenous centrin and Centrin1 and 4-GFP seems identical. In a more recent study from our lab Cen1-GFP IP has identified other endogenous centrins as interaction partners (Wenz et al 2023), like the Roques et al. 2018 study did for PbCen4-GFP indicating that the tag does not abolish interaction between centrins. So far, we have never detected any ECCAs, nor have we identified any similar structure in the literature. This suggest that this is indeed a consequence of excessive centrin concentration. Importantly we now have added data from a new parasite line overexpressing untagged PfCen1 using the T2A skip peptide (pFIO+_GFP-T2A-Cen1) which displays ECCAs upon induction, showing that this effect is not a mere consequence of tagging (now Fig. 5H-K).

      Minor comments:

      1. How were the times (post addition of Ca2+) presented in Figure 2A determined?

      We noted down the time of calcium addition and cross-referenced it with the timestamps available in the metadata of the movie files (e.g. file creation timepoint marks the start of the movie). We now mention this in the legend.

      Line 126: Figure 1B should be Figure 1C

      Line 145: Figure 1C-D should be Figure 1D-E

      Line 151: Figure 3A should be Figure 4A

      Thank you for spotting these mistakes, which now have been corrected.

      Line 152: Suggest rephrasing "placing the gene of interest in front of the promoter" to "placing the gene of interest immediately downstream of the promoter" or something similar

      Thank you for this good suggestion.

      Any growth phenotype changes observed in the overexpressors?

      The parasite lines seem to silence the Cen1-4-GFP expression plasmids readily, which suggest that there might be a growth disadvantage. However, repeated attempts to quantify a growth phenotype were unsuccessful due to high variability in the data, which might be partly connected to the fact that the fraction of GFP positive cells after induction can vary between lines and replicas.

      How often are ECCAs observed in pARL strains, or are they not observed at all? This might be good to mention.

      ECCAs in the pArl strains have been observed on very limited instances but are too rare to be quantified. We now mention this in the text (lines 217ff).

      Line 192 and Figure S8: n {less than or equal to} 33 (either a typographical error and should have been {greater than or equal to}, otherwise, it may be expressed as a range)

      It was indeed a typographical error that was now corrected.

      Line 258: Methods on the generation of FIO/FIO+ was a bit difficult to understand. Maybe a simple plasmid schematic with the restriction sites (at least for the original plasmid) in the supplementary may help clarify this.

      Cloning strategy has been expanded with additional information for clarity.

      Line 295: include abbreviation of cRPMI here rather than in Line 303

      Has been corrected.

      Line 322: typographical error on WR99210 working concentration?

      Has been corrected.

      Line 372: Last sentence on area and raw integrated density measurement is unclear.

      We have reformulated the sentence for more clarity.

      Line 461: typographical error in last sentence

      Has been corrected.

      Line 532: Figure 4E should be Figure 4F

      Has been corrected.

      Reviewer #3 (Significance (Required)):

      DNA replication is vital to the survival of malaria parasites. A deeper understanding on their unusual form of replication may be exploited to find drug targets uniquely directed to the parasite. Biological insights from this work can also provide a jump-off point for unravelling unusual replication in other organisms. Data on the physicochemical analysis of centrin is not just of great interest for those in the field of parasitology, but also for those in the much wider fields of biology, physics and chemistry. Techniques presented in this work (e.g., DiCre overexpression with different promoters) can definitely be utilised for the elucidation of protein function within and outside the field of parasitology.

      My field of expertise is in Plasmodium spp., particularly in parasite replication, molecular and cellular biology, and epigenetics.

      We thank the reviewer for the appreciation of our work in terms of insight and technology development.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      In this manuscript, Hoskins et al describe analyses of the effects of sequence variation on RNA levels, protein levels, and ribosome loading for the COMT gene. They use multiple experimental approaches to assay these levels and report on how sequence differences affect expression. Overall, the paper is interesting in that it presents a very deep dive into the effects of sequence variation on gene expression, including in coding sequences. However, there are some issues with the polysome loading assay technique and there are substantial issues with the figure presentation, which is often confusing.

      __Response: __Thanks for the positive assessment of our manuscript and the constructive feedback regarding the issues with the figure presentation. We have addressed all of these below and they have significantly improved the clarity.

      • Major comments:*

      • 1) Figures:*

      • --Fig 1C needs a cartoon description to show where the UTRs are. Y-axis should say "Ribo-seq CPM"*

      __Response: __Fig 1C now includes a schematic and the y-axis is updated. Locations of the uORFs are also now included in Fig 1A.

      • --Sup Fig 1A confusing, what is "start" what is the point of this panel?*

      __Response: __We apologize for the confusing labeling of the panels in Sup Fig 1. “Start” refers to the MB-COMT start codon. We removed this annotation as it is irrelevant to the figure. We included Supplementary Figure 1A to show RNA probing data for the entire transcript. Figure 1A and B only show the regions that encompass the variants assayed in our study.

      • --Sup Fig 1B what is PCBP del?*

      Response: “PCBP del” refers to deletion of PCBP1/PCBP2 RNA binding protein motifs. The legend now specifies this.

      • --Sup Fig 1C what is "uORF B restore"? The description in the figure legend is not interpretable. Draw diagrams of the mutations that tell the reader what was assayed and why it was assayed. Why are there multiplication factors listed (e.g. 1.33X)? The data are depicted on a log scale, which makes it difficult to appreciate the fold-effects of the mutations (e.g. does uORFA mutation increase expression 1.5-fold?). Please calculate median expression values and report them on a bar graph or something like that so readers can interpret the results.*

      Response: “uORF B restore” refers to restoration of the endogenous uORF B frame with a silent variant in the Flag tag of the transgene. The multiplication factors listed were the fold change in median fluorescence between each mutant and the template (wild-type) transgene. We retained the figures as they show the raw distribution of fluorescence in each cell line, but in response to the reviewer’s suggestion we included a new figure displaying the effects as a bar graph (Supplementary Figure 1E).

      • --Fig 2A. It's hard to understand the cartoon diagram of the expression reporter construct. Why is +Dox shown here? Does that induce transcription?*

      __Response: __The reviewer is correct. “+Dox” indicated addition of Doxycycline to induce transcription before the data collection step. We agree that there may have been too much detail in this diagram and have now removed this for simplicity and indicated this in the Methods section.

      • --Fig 2B. What's on the x-axis? is it Log2(RNA/gDNA) from sequencing? is it Log2 or Log10 or Ln?*

      __Response: __Variant effects in each figure were derived from ALDEx2 analysis, which reports effect size as the median standardized difference between groups. The effect size is not directly interpretable as a log fold change; it takes into account the difference between groups as well as the dispersion. This analysis strategy has been previously demonstrated for analysis of SELEX experiments (Fernandes et al. 2014), which are used to select small populations of cells with specific phenotypes.

      ALDEx2 is a robust and principled choice for the analysis of count-compositional datasets, particularly after selection (e.g. sorted cell populations or low-input RNA fractions arising from polysome profiling). While we understand that this choice leads to less easily interpretable effect sizes, the mathematical advantages make ALDEx2 a more appropriate choice for this type of data. In the past, we had used other methods to analyze log frequencies (limma, a frequency based normalization-dependent analysis, as previously employed in Hoskins et al. 2023. Genome Biology) that directly reported fold changes. In our experience, the ALDEx2-derived effect sizes are well-correlated with those estimates (Pearson correlation 0.93 for variants significant at a FDR

      • --Fig 2C. What's on the y-axis (same question). I think it's LogX(mutant/wt)RNA level?*

      __Response: __For consistency with other figures, we replaced Figure 2C to report the effect size statistic as described above.

      • --Fig 2D. What's on the y-axis now? Fold-difference (not log transformed)?*

      __Response: __Please see our response above.

      • --Fig 2E. The scale bar is flipped vs. normal convention. This is also log transformed, but it's not labeled. Please label as log(whatever) and put the negative values on the left side of the bar (red on the left, blue on the right).*

      __Response: __Thanks for the suggestion, we have now updated the scale bar.

      --Fig 2F y-axis should say Ribo-seq CPM.

      __Response: __Done

      • --Fig 3A - please separate the graphs more. Did you sort cells from ROI2 into populations, or just cells from ROI1?*

      __Response: __Thanks for the suggestion, we now separate the graphs further. Cells were sorted for both ROI 1 and ROI 2 libraries.

      • --Fig3C-F What's the "effect size" mean on these graphs?*

      __Response: __Please see the response above regarding the effect size estimate from ALDEx2.

      • --Fig3D It looks like the colors have switched for positive / negative "effects" on the heat map*

      • compared to Figure 2E. Please define what "median effect" means and be consistent with*

      • comparison to figure 2E.*

      __Response: __We intentionally inverted colors for Figure 3. The rationale is that a variant causing low protein abundance corresponds to enrichment in P3 compared to gDNA, as opposed to depletion in P3. On the other hand, for effects on RNA abundance and ribosome load, a variant leading to low abundance for these measures is depleted.

      • --Figure 4 what does effect size mean, what's the log-transformed scale (log2, 10, etc) same issues from earlier figures.*

      __Response: __Please see response above.

      • --Figure 5 "effect size"*

      __Response: __The same definition of effect size was used with the exception that effect sizes are multiplied by -1 so that color schemes are consistent for deleterious effects.

      • 2) "Codon stability" should always be "Codon Stability Coefficient", maybe use "CSC". Otherwise it's confusing.*

      __Response: __Thanks for the suggestion. This has been updated throughout the manuscript.

      3) Flow cytometry section talks about "RNA fluorescence", which is confusing. You need to explain that it's IRES-driven mCherry as a proxy for the level of RNA first. It would also help to state explicitly that you sorted the cells into four populations, and define them all first before describing the results.

      __Response: __We apologize for the use of imprecise language with respect to this reporter. We revised the text to emphasize that mCherry is a proxy for RNA abundance and described the populations first as suggested.

      4) What are DeMask scores? How are they related to conservation or amino acid properties? If you define these, you can help the reader interpret the result.

      __Response: __Thanks for the suggestion. We now include a conceptual interpretation of the DeMask score in the relevant section. We also include a comparison to a recent large language model for variant effect prediction (ESM1b, Brandes et al. 2023) which is now reported in Supplementary Figure 5C.

      5) There are several issues with the Polysome gradient fractionation. The gradients did not separate 40S, 60S, and monosomal fractions, so it's hard to tell how many ribosomes correspond to each peak on the gradient graph in Figure S5. This is probably because the authors used a 20-50% gradient instead of a lower percentage on top. More significantly, variations in the coding region of COMT are likely affecting the polysome association in ways the authors didn't consider. Nonsense codons will simply make the orf a lot shorter, hence fewer ribosomes. This may have nothing to do with NMD. Silent and missense variants may have unpredictable effects because they may make translation faster (fewer ribosomes) or slower (more ribosomes) on the reporter. This could lead to more ribosomes with less protein or fewer ribosomes with more protein. The reporter RNA also has an IRES loading mCherry on it, which probably helps blunt or dampen the effects of the COMT sequence variants on polysome location distribution. Overall, the design of the polysome assay is probably very limited in power to detect changes in ribosome loading (four fractions, limited separation by 20-50 gradient, IRES loading, etc). This is partially addressed in the limitations section, but these issues could be discussed in more detail.

      __Response: __Given high polysomal association of endogenous COMT and our COMT transgene (Supplementary Figure 2B, Supplementary Figure 5B-C), we chose a 20-50% sucrose gradient to better resolve changes in ribosome load among heavy polysomes.

      We thank the reviewer for offering another valid explanation regarding the depletion of nonsensense variants. We have now included a sentence in the discussion to indicate lower ribosome load for nonsense variants may be due to a shorter ORF as opposed to NMD. We further include the potential limitation of the assay due to the presence of the IRES-mCherry.

      We agree that variants may have unpredictable effects due to effects on the dynamics of translation elongation. To address this potential limitation, we attempted to devise a selective ribosome profiling strategy by immunoprecipitating N-terminal Flag tagged peptides to enrich ribosomes translating COMT. However, we were unable to achieve significant enrichment, limiting our ability to measure variant effects on elongation in a high-throughput manner.

      Significance

      The study is novel in that it assays both 5' UTR and a wide range of protein coding sequence variants for effects on RNA and protein levels from a clinically important gene, COMT. The manuscript reports that most protein coding variants have modest effects on RNA levels, and that the minority of variants that do affect RNA levels are not predictable due to their affect on codon usage. The work also determines the distribution of effects of variants on protein levels, finding a variety of effects on expression. Interestingly, the authors found SNPs that affect ribosome loading generally affect RNA structure of the COMT coding region, rather than affecting codon usage.

      This should appeal to many different communities of biologists - gene expression experts, geneticists, and clinical neurobiologists who focus on COMT. So there is a potential for fairly broad interest. The main limitations to the work are in a lack of clarity in the figures and perhaps in the underdeveloped nature of the discussion section. The discussion section reports new results (SNP associations that affect expression). These would make more sense in the results section, such that the discussion could do a better job relating the impact of sequence variants on expression levels to prior work to highlight the novelty.

      __Response: __We thank reviewer #1 for their positive assessment of the broad significance of our study. We also thank them for constructive suggestions that led to increased clarity in the presentation. We have moved the analysis of gnomAD variants to the Results section and expanded the discussion.

      Reviewer #2

      Evidence, reproducibility and clarity

      Summary:

      Hoskins and colleagues expressed a reporter containing all silent, missense, and nonsense codons at 58 amino acid positions in the human COMT gene in HEK293T cells and measured levels of DNA, bulk RNA, and pooled polysomal mRNA. They included a C-terminal translational GFP fusion and a downstream transcriptional mCherry fusion in the reporter in order to also bin variants by their relative protein and mRNA levels by flow cytometry. They hypothesized that RNA structure, in-part by mediating uORF translation, influences COMT gene expression. The authors conclude by identifying previously-uncharacterized COMT variants that, in this reporter system, affect RNA abundance and ribosome load. We generally found the results of this paper convincing and clear. We do not have major comments, but have many minor comments that we hope the authors can address. These comments mostly deal with clarification on analysis metrics and giving recommendations on data presentation.

      __Response: __Thanks for highlighting the strengths of our study and the constructive suggestions to improve the presentation.

      Minor comments:

      In Figure 2C, the vertical axis reads "Median between-group difference". How was this metric calculated and normalized? We also agree that nonsense mutations having consistently-detrimental effects on RNA abundance is reassuring, but recommend more explanation as to why the difference in the effects of silence and missense mutations between regions may be biologically relevant.

      __Response: __Variant effects in each figure derive from ALDEx2 analysis, which reports effect size as the median standardized difference between groups. In particular, to avoid any distributional assumptions for standardization, ALDEx2 uses a permutation based non-parametric estimate of dispersion. The effect size is not directly interpretable as a log fold change; it takes into account the difference between groups as well as the max dispersion of the groups. We have now provided explicit references to the specific R functions that were used to calculate the effect size.

      ALDEx2 is robust for analysis of count-compositional datasets, particularly after selection and bottlenecking (e.g. sorted cell populations or low-input RNA fractions arising from polysome profiling). While we have used other methods to analyze log frequencies (limma, a frequency based normalization-dependent analysis, as previously employed in Hoskins et al. 2023. Genome Biology), we opted for the less-interpretable but more robust ALDEx2 analysis to report variant effects between varying nucleic acid inputs.

      We currently lack a mechanistic interpretation for the difference in RNA abundance effects between ROI 1 and 2. However, we observed consistent results using a different analysis framework, which makes use of variant frequencies (as in Hoskins et al. 2023 Genome Biology) instead of the centered log ratios used in ALDEx2 analysis, further supporting a biological difference between the two.

      In Figure 3, we believe that the authors are claiming that lower RNA abundance causes lower protein abundance in some variants. However, this data only reports on protein abundance relative to transcript abundance, not absolute protein abundance. We think the claim should be revised to (1) clarify that the authors are measuring protein per mRNA, and (2) express that lower mRNA amounts are more likely to co-occur with lower protein amounts, but that this data does not support any causative model.

      __Response: __Thanks for the suggestion. We have now included an explicit description of the experimental design in the results section and noted that we are unable to assign protein abundance effects to underlying RNA abundance effects. In the current setup, we did not sort cells based on the ratio of moxGFP/mCherry fluorescence (protein per mRNA), but rather we defined gates based on the 2D plot of moxGFP versus mCherry. This is explicitly marked in Figure 3A.

      On page 9, the authors claim that their data supports a model that rs4633 increases RNA

      abundance, leading to higher COMT expression. Can the authors rule out a model whereby rs4633 facilitates translation initiation, as suggested by Tsao et al. 2011, leading to both an increase in mRNA and protein abundance?

      __Response: __Thanks for this question and opportunity to clarify. We have now added a sentence to the Discussion and included the following paragraph in the Supplementary Note:

      “Importantly, our study does not rule out a model where rs4633 facilitates translation initiation. Nevertheless, our data suggest a potential concurrent mechanism where rs4633 leads to higher protein abundance in human cell lines and in an in vitro translation assay (Tsao et al. 2011) by increasing RNA abundance. We note that Tsao et al did not directly measure RNA abundance in their study. In Supplementary Figure 3A of Nackley et al 2006, the APS haplotype containing rs4633 C>T showed slightly higher total RNA abundance compared to the LPS haplotype (in our study, the wild-type template). However, this was not statistically significant and was only observed for the S-COMT isoform. It is possible that our observations are compatible with the conclusions in Tsao et al. 2011. For example, increased translation of rs4633 C>T may lead to stabilization of the RNA.”

      The paper references "effect size" at multiple points (e.g. "polysome effect size") but we could not find this term explicitly defined (for example: for the polysome effect size, were RNA counts for each polysome fraction divided by the relative abundance of that RNA in total RNA?)

      __Response: __We apologize for this confusion. Please see our response above. We have also stated the definition of effect size explicitly in the revised manuscript.

      Could you elaborate on how you define "protein abundance and "effect size: in Figure 5G? How is enrichment in P3 or P1 calculated?

      __Response: __Effect size is defined as described above. Enrichment in P3 or P1 is calculated with respect to the abundance in gDNA (unsorted cells).

      Were 3396 variants considered for all readouts in this paper? How many of these variants were present in each ROI? It may be worth clarifying sample sizes.

      __Response: __Thanks for the suggestion. The reviewer is correct: 3396 variants were present in all biological replicates and all readouts (after excluding polysome metafractions 1 and 2 and flow cytometry population 4). The Methods were updated to include all readouts that were dropped. The number of variants in each ROI are now included in this section of the main text.

      How did Twist generate these mutagenized sequences? We assumed that they used error-prone PCR due to the mention of multiple nucleotide polymorphisms, but couldn't find an explicit answer.

      __Response: __Twist generates these mutagenized inserts using degenerate primers. This allows all alternate codons to be assayed (all silent, missense changes). This is now noted in the Methods.

      https://www.twistbioscience.com/resources/technical-note/solid-phase-dna-synthesis-allows-tight-control-combinatorial-library

      In the methods, it may be worth elaborating on the composition of the HsCD00617865 plasmid. For example: this COMT reporter is under the control of a constitutively-expressed T7 promoter, correct?

      __Response: __The HsCD00617865 plasmid was only used as a template for PCR amplification and generation of the transgene. The transgene is cloned into a vector containing attB sites for recombination into the landing pad cell line (Matreyek et al 2020). Transcription is induced by Doxycycline from the landing pad locus. Plasmid maps used for transfection into the landing pad line are now included in the GitHub repository.

      In Supplementary Figures 4 and 5, it would be helpful to explicitly say that you are reporting Pearson correlations between biological replicates.

      __Response: __Thanks for the suggestion. The legends have been updated accordingly.

      "After summarizing biological replicates (N=4) for each readout...": how did the authors summarize biological replicates? Were counts averaged?

      __Response: __Biological replicates were summarized using the median. This is now clarified in the Methods.

      The authors used pairwise correlations between flow cytometry fractions, polysome fractions, and total RNA/gDNA as indications of data quality. Do the authors expect for these counts to be strongly correlated? We would not necessarily expect to see a strong correlation between ribosome load and RNA/gDNA.

      __Response: __We used replicate correlation as an indicator of data quality. Our readouts of ribosome load reflect the abundance of a variant in a particular polysome fraction. Given that variants that are highly abundant in the RNA pool will on average be more highly represented in polysome fractions, we would expect a correlation between the abundance of a variant in total RNA and in polysome fractions.

      The authors may need to check that their standard deviations on fold changes are properly reported.

      __Response: __iIn the Figures and the main text, we specified the confidence intervals as calculated by ALDEx2 method instead of reporting standard deviations on fold changes,. Specifically, the confidence intervals were determined by Monte Carlo methods that produce a posterior probability distribution of the observed data given repeated sampling. Variants in which the confidence intervals do not cross 0 are considered true discoveries (section 5.4.1 of the ALDEx2 vignette on Bioconductor).

      https://www.bioconductor.org/packages/devel/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#541_The_effect_confidence_interval

      We would expect standard deviation bounds to be symmetric for log fold changes, but not on unlogged fold changes - for example see page 8, for the sentence "our point estimate for nonsense variant effects on COMT RNA abundance was approximately a two-fold decrease relative to the gDNA frequency (fold change of 0.43 +/- 0.13; mean +/- standard deviation; Methods)."

      __Response: __Thanks for the suggestion. To avoid any confusion about the symmetry, we replaced the +/- notation, and explicitly noted the mean and standard deviation. To help the reader gain an intuition of the magnitude of variant effects, we conducted a frequency based normalization-dependent analysis using limma (as previously employed in Hoskins et al. 2023. Genome Biology). We now report a fold change (unlogged) for RNA abundance compared to gDNA abundance. The point estimate is the mean and s.d. across all nonsense variants.

      On page 10, the authors say that their data suggests that hydrophobicity in the early coding region of COMT may be important for COMT folding. If this is the case, would we expect to see this effect in flow cytometry data (which is affected by protein degradation) and not polysome profiling (which is unaffected by post-translational protein degradation)?

      __Response: __We apologize as we are uncertain about the reviewer’s intended question. The section that refers to the importance of hydrophobicity indeed refers to the flow cytometry data. While there are specific instances in which the amino acid properties encoded by the mRNA influences translation dynamics, these are not universally true. Consequently, we did not expect these impacts to be observed at the level of polysome profiling.

      We believe that we would have some trouble replicating the analysis from this paper from the raw data, given that the bulk of the analysis on GitHub is presented as a single R Markdown file, with references to local files to which we do not have access. We recommend that the authors add additional documentation to their repository to facilitate re-analysis.

      __Response: __Thanks for the opportunity to address this issue of critical importance. To facilitate replication, we have now deposited all analysis files to Zenodo and refactored the code to enable replication by simply running a markdown file.

      In Figure 1B, indicating that more signal indicates less structure (in the legend or the figure itself) may assist readers who are unfamiliar with DMS-seq.

      __Response: __Thanks for the suggestion. This is now updated.

      Figure 1C does a great job presenting evidence for the translation of uORFs, but does not seem to flow with the overall argument of the paper, so may fit better in the supplement.

      __Response: __We considered this suggestion, and opted for keeping its placement as it gives evidence that our transgene is translated primarily as the MB-COMT isoform. This ensures that, for variants upstream of the S-COMT isoform, we can assay effects on ribosome load that are tied to mechanisms of translation elongation and codon stability.

      We believe there is a typo in the Figure 1 legend that should read "K562" instead of "H562".

      __Response: __Thank you, this was indeed a typo.

      You also gated to separate into P1-P4, correct? Can you also show the bounds of that gating

      strategy in Figure 3A?

      __Response: __This has been updated. We also added the gating strategy in response to comments from reviewer #1.

      We find Figure 3F very compelling. Do you have any theories as to why mutating I59-H66 to

      nonpolar, uncharged residues leads to increased COMT expression?

      __Response: __We do not have any theories for why this may be. However, we noted that with the exception of V63, residues I59-H66 are not evolutionarily constrained (based on DeMask entropy values). This suggests mutational tolerance for nonpolar, uncharged residues in this region (with the exception of V63 and H66; see Figure 3D).

      There appears to be a non-negligible proportion of di- and tri- nucleotide polymorphisms in Supplementary Figure 4. Were these excluded in downstream analyses?

      __Response: __These variants are expected from the Twist mutagenesis strategy and included in analysis. We believe they are at lower frequency compared to SNPs due to less favorable annealing of the degenerate primers.

      A minor typo in the discussion reads "fluoresce".

      __Response: __Done

      Significance

      Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      This work investigated the regulatory effects of thousands of coding variants in the COMT gene, focusing on two regions with clinical significance, by using high-throughput reporter assays. The results from this will be useful for clinical scientists interested in understanding the impacts of COMT mutations and be a useful framework for other systems/computational biologists to understand the impacts of coding mutations across different levels of regulatory function. Mutations in protein regions, if having a function, are classically known to interfere with protein function. There are fewer large-scale efforts to understand the impacts of coding mutations affecting expression through potentially changing of RNA structure or codon optimization - this work has contributed towards that frontier.

      Place the work in the context of the existing literature (provide references, where appropriate). This is (as far as I am aware) the first paper that has integrated high-throughput screens massively parallel reporter assays from RNA degradation, ribosomal load, and flow cytometry. Previous papers have tended to measure on expression regulation on only one dimension (i.e. Greisemer et al. 2023 on RNA degradation, Sample et al. 2019 on ribosomal load, and de Boer at al. 2020 on protein expression).

      __Response: __Thanks for highlighting the novelty of our approach compared to existing strategies in the literature.

      State what audience might be interested in and influenced by the reported findings.

      Clinicians/researchers interested in COMT, computational biologists, geneticists and potentially structural biologists interested in understanding the consequences of amino acid mutations on RNA/protein expression

      __Response: __Thanks for noting the broad significance of our study.

      Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Genomics, Massively parallel reporter assays, High-throughput regulatory screens.

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

      *This manuscript reports on transcript sequence variants that affect expression of the gene COMT. Targeted analysis of SNPs identifies 5' UTR variants that affect COMT, leading to the identification of translated uORFs. Common coding sequence SNPs do not affect COMT expression, however. Massively parallel analyses of mRNA abundance, protein abundance, and translation are combined to look more broadly at coding sequence variants. These analyses focus on regions of predicted structure in the COMT transcript. Both silent and missense mutations that increase mRNA abundance are identified. Protein abundance is then measured and many missense mutations are found to change protein levels. To address translation directly, analysis of polysome loading is performed and significant differences are identified, although technical challenges limit data quality in these experiments. These different experiments are then analyzed jointly to classify mutation effects and identify a class of silent mutations with expression effects, leading to a proposal that these act through structure. *

      *The joint, integrative analysis of COMT variants through a range of methods allows clearer insights into interconnected post-transcriptional effects. The massively parallel experiments generate high-quality data, although targeted validation of key results would strengthen the work. The findings advance our understanding of silent variant effects, which remains an open question, and technical innovations could find broader applications. *

      __Response: __Thanks for the positive assessment of the quality of the data generated and the potential for the broader application of the technical innovations.

      *I do have concerns with the present version of this work. *

        • There is no validation presented for high-throughput experimental data. I would say that validating the effects of M152T and V63V variants from Figure 2B would substantially strengthen the work and support key conclusions. * __Response: __Our experiments collectively enabled nearly 10,000 measurements of variant effect (summed over three layers of gene expression). The goal of our study was to identify broad mechanisms of variant effect. While we are excited about the specific variants uncovered, targeted experimental methods for validating changes to RNA abundance, such as RT-qPCR, are unlikely to be sufficiently sensitive. For example, RNA abundance effects in our study had a median effect size of 1.47 for variants up in RNA, and 0.4 for variants down in RNA. This likely corresponds to less than one Ct difference between the variant and the reference allele. Indeed, previous studies such as Findlay et al., 2018 Nature that reported similar effect sizes (FGF7 and FOS, respectively (Figure 4B).

      Thus, for time and cost concerns, we respectfully suggest that targeted experiments involving V63V and M152T are beyond the scope of our study. Nevertheless, to further strengthen our conclusions, we have computationally confirmed our findings using a different analysis framework. We found 75/76 of the variants significant by ALDEx2 analysis were also significant by limma analysis (a frequency based normalization-dependent analysis, as previously employed in Hoskins et al. 2023. Genome Biology) using the same FDR (0.1).

      • In the fluorescent reporter scheme, it seems that variants reducing mRNA abundance should be enriched in the "P2" gate region relative to "P1", as they would have lower mRNA abundance and correspondingly lower protein abundance. However, this analysis is not performed, and instead P1 and P3 are compared (Figure 3G), which would seem to focus on protein-level effects. *

      __Response: __Our initial hesitation in comparing P2 to P1 is that the P2 population may be enriched for cells that underwent inefficient induction of transcription with Doxycycline. Hence technical factors as opposed to the effect of the variants may dominate this comparison. In response to the reviewer’s comments, we carried out the suggested analysis (new Supplementary Figure 5B). We found that variants that are down in RNA are enriched in P2 relative to P1 as expected. This is now noted in the Results section.

      • In general the work classifies variants in several different ways and it would help to be a little clearer in naming these classes. For instance, in describing the FACS-based analysis of variant expression it is written, "protein fluorescence conditioned on RNA fluorescence" which is confusing at best-it's a fluorescence-based measurement that is used indirectly to measure COMT reporter abundance. *

      __Response: __Thanks for the suggestion. We agree that our initial word-choice was imprecise. We rewrote this section to indicate mCherry fluorescence is an indirect proxy for RNA abundance.

      • Likewise, the populations with shifted GFP/mCherry ratio in this assay are described as "uncorrelated" populations, which is opaque and somewhat inaccurate-there seems to be a correlation in this group but at a different ratio. *

      __Response: __We have revised the language in the manuscript. We opted for “low or high RNA/protein abundance” to indicate the relationship between GFP and mCherry fluorescence in populations P3 and P4.

      • In the same way, "deleterious variants" is used to describe protein abundance changes, but this term implies a fitness effect and is not very specific. *

      __Response: __We apologize for the confusing word choice. We did away with this term in favor of “variants with low protein abundance”.

      • In discussing the effects of missense COMT variants on protein levels, there is an implicit assumption that degradation of mis-folded protein (or perhaps properly-folded protein with excess hydrophobic exposure?) explains these effects. This is plausible, but it would help to lay out this reasoning more clearly. *

      __Response: __Thanks for the suggestion. We have added a sentence at the end of the section that specifies this assumption and cites a recent study reporting that rare missense variants in COMT may be misfolded and degraded by the proteasome (Larsen et al. 2023).

      • It is written that,"In line with codon stability as a predictor of translational efficiency (Presnyak et al., 2015), variants with low codon optimality were depleted from polysomes compared to variants with optimal codons". However, this mis-states the conclusions of the cited study, which notes, "Importantly, under normal conditions the ribosome occupancy of the HIS3 opt and non-opt constructs was determined to be similar (Fig. 6B)". *

      __Response: __We apologize for mis-stating the conclusions of Presnyak et al. 2015. We have now revisited the relevant literature to more accurately place our conclusions in the context of literature. While Presnyak et al. and several other studies (Bazzini et al., 2016; Mauger et al., 2019) have clearly linked the association between codon choice and mRNA stability. We now reference Mauger et al. 2019 who used elegant experiments to demonstrate that mRNA secondary structure is a driver of increased protein production and synergizes with codon optimality (Figure 5B). Their results further support the role of codon optimality on RNA stability while providing evidence of additive impact on translation efficiency.

      • It is written that, "One intriguing possibility is to develop multiplexed assays of variant effect on RNA folding, using mutational profiling RNA probing methods (Weng et al., 2020; Zubradt et al., 2017)." How would this differ from the "Mutate and Map" approach in doi://10.1038/nchem.1176 and subsequent work from the same group? *

      __Response: __Thanks for pointing out the more recent work following the initial papers in 2010-2011. We have missed the work from the Das lab that extended the Mutate and Map approach to utilize mutational profiling (Cheng and Kladwang et al., 2017). We updated our Discussion to indicate that the proposed assay has been pioneered and is a viable approach for high-throughput determination of variant effects on RNA folding.

      Because mutational profiling methods leverage reverse transcriptase readthrough and mismatch incorporation, they enable deeper and more uniform coverage of sequencing reads, particularly for longer transcripts. A key design principle of the proposed assay is to mutagenize only certain types of variants in the library such that they do not overlap RT mismatch signatures arising from the RNA probing reagent/RT enzyme. For example, readthrough of DMS base adducts largely generates A>N or C>N mismatches, so a variant library would be designed to only contain variants at G or T bases. This ensures variants in the library can be differentiated from signals of the RNA probing method.

      ***Referees cross-commenting** *

      *I generally agree with the other reviewers and found that many small points on the figures were confusing, and in some cases the values being computed and displayed were under-specified. *

      *I agree with Reviewer 1 that the polysome fractionation probably has limited power due to experimental design, and that the interpretation of changed ribosome loading is subtle. *

      __Response: __In response to these helpful comments, we have clarified the points highlighted by the reviewers and expanded the limitations section related to the ribosome loading assay. Thanks for these constructive suggestions to strengthen our study.

      *Reviewer #3 (Significance (Required)): *

      *The joint, integrative analysis of COMT variants through a range of methods allows clearer insights into interconnected post-transcriptional effects. The massively parallel experiments generate high-quality data, although targeted validation of key results would strengthen the work. The findings advance our understanding of silent variant effects, which remains an open question, and technical innovations could find broader applications. *

      __Response: __Thanks for pointing out the high-quality of the generated data and the broad significance of our study. The goal of our study was to identify broad mechanisms of variant effect instead of focusing on differential expression for any specific variants.

    1. 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

      1. General Statements

      We thank the reviewers for their time and both thoughtful and constructive comments. Their specific points are addressed below but a general point that we would like to comment on is that in the original version it appears we did not make our model clear enough. The dogma in the field is that Rab7 is recruited to endosomes from a cytosolic pool via exchange with Rab5 (mediated by Mon1/Ccz1). Our work instead indicates that the majority of Rab7 is delivered to Dictyostelium phagosomes by fusion with other endocytic compartments. It was not our intention to imply there was no canonical recruitment of Rab7 from a cytosolic pool, and indeed we provide data to show this happens at a low level and discuss this in the manuscript. Nonetheless, we clearly over-stated the exclusivity of Rab7 recruitment to phagosomes via fusion at several points and our original model cartoon, and have tried to better explain or more nuanced model with multiple routes for Rab7 acquisition in this revision, including a completely redrawn model figure (Fig. 7).

      2. Description of the planned revisions

      Reviewer 1:

      1. The observation that macropinosomes undergo retrograde fusion with newly formed phagosomes to facilitate phagosome maturation is an interesting notion that challenges the traditional model. However, not all phagocytes exhibit a high level of macropinocytosis, and axenic Dictyostelium cells used in the study may be an exception. Thus, it remains unclear whether fusion with macropinosomes is universally required for phagosome maturation. WT Dictyostelium cells or axenic cells cultured under SorMC/Ka condition (Paschke et al., PLoS One, 2018) exhibit significantly reduced macropinocytosis. The authors could examine whether the accumulation of Rab7 and V-ATPase on large-sized phagosomes is delayed in these cells. These experiments may help broaden the applicability of the authors’ finding.

      As our previous work (Buckley et al. PloS pathogens 2019) demonstrated that bacterially-grown PIKfyve mutants are also defective in bacterial killing and growth it is highly likely that cells also are defective in V-ATPase and Rab7 acquisition. However, we agree that formally testing this will further support our conclusions and improve the paper and should be quite straightforward.

      We will therefore co-express GFP-V-ATPase and RFP-Rab7 in both Ax2 and non-axenic cells grown on bacteria and repeat our analysis of recruitment to phagosomes – with the caveat that non-axenic cells do not phagocytose large particles such as yeast (Bloomfield et al. eLife 2015), so the imaging and quantification will be more challenging in this case.

      PIKfyve seems to play a specific role in the maturation of phagosomes but not macropinosomes. The differences may be driven by signaling from phagocytic receptors, as the author suggested. Alternatively, the large size of the yeast-containing phagosomes may require additional steps for efficient lysosomal delivery. The authors should consider examining whether PIKfyve is needed for the delivery of Rab7 and V-ATPase to phagosomes of comparable size to regular macropinosomes, such as those containing K. aerogenes or small beads. In addition, whether the process also involves fusion between phagosomes and macropinosomes should be verified.

      Whilst it is possible that large size of yeast-containing phagosomes requires additional mechanisms to process them, our previous data demonstrate that PIKfyve is also required to kill much smaller bacteria such as Klebsiella and Legionella (Buckley et al. PloS pathogens 2019). Furthermore, in this paper we also showed that loss of PIKfyve disrupts phagosomal proteolysis using 3um beads, and showed that V-ATPase recruitment was reduced on purified phagosomes containing 1um beads. We therefore find consistent defects on phagosomes of different size, with different cargos. Nonetheless, the experiments above, observing V-ATPase and Rab7 in cells grown on bacteria should directly address this point.

      As suggested, we will also perform a dextran pulse-chase prior to addition of bacteria to test if we can observe macropinocytic delivery to bacteria-containing phagosomes - perhaps using E. coli as their elongated shape may help phagosome visualisation.

      In the previous study from the authors' group (Buckley et al., PLoS Pathog, 2019), it was shown that the accumulation of V-ATPase on phagosomes begins immediately after internalization in both PIKfyve mutant and WT, although V-ATPase accumulation reaches only half of the levels seen in WT. This partial accumulation of V-ATPase differs from the almost complete absence of Rab7 recruitment found in this study, which raises the question of whether there exists yet another population of fusogenic vesicles that are positive for V-ATPase but negative for Rab7. This could be checked by simultaneously examining the dynamics of V-ATPase and Rab7 during yeast phagocytosis in the PIKfyve mutant.

      We agree with the referee that there are multiple pools of V-ATPase, and we show that there is both a very early PIKfyve-independent recruitment of both V-ATPase and Rab7 as well as a later and more substantial pool delivered in a PIKfyve-dependent manner. It is clear that V-ATPase and Rab7 do not always co-localise however - the clearest example being on the contractile vacuole, which has lots of V-ATPase but no Rab7 (the large bright magenta structure in Fig 2G.).

      We suspect that the dramatically reduced, but not completely absent levels of both V-ATPase and Rab7 recruitment in the absence of PIKfyve are similar, but the challenges with imaging these very small low levels means we cannot formally exclude that there is a pool of V-ATPase vesicles that lack Rab7 which fuse to very early phagosomes. Nonetheless, as we will already be looking at V-ATPase and Rab7 in PIKfyve KO's in the experiments above will also attempt to unequivocally differentiate a pool of V-ATPase positive/Rab7 negative vesicles fusing with phagosomes.

      Reviewer 2:

      (1) The authors show that deletion of PIKfyve results "in an almost complete block in Rab7 delivery to phagosomes" (page 17) indicating that the delivery of Rab7 depends on fusion with Rab7-positive structures. This would suggest that the Rab7-GEF Mon1-Ccz1 is not localized to the membrane of the phagosomes. Could the authors test for the presence of Mon1-Ccz1 in either fluorescence microscopy experiments or on purified phagosomes to exclude the possibility of a "canonical" Rab7 recruitment by its GEF? If the GEF is found on phagosomal membranes it would indicate that a Rab-transition from Rab5 to Rab7 occurs on the phagosome during maturation, but on a low level. The later fusion event might be a homotypic fusion of two Rab7-positive compartments. The observed fusion events could still deliver the bulk of Rab7 and other endolysosomal proteins to the phagosome. If the Rab7-GEF is not found on phagosomes how do the authors envision that the organelle keeps its identity? Is it solely dependent on PI(3,5)P2? What is the fate of the Rab7-negative phagosome in ∆PIKfyve cells if Rab7 is not delivered to the membrane, is there degradation happening over longer periods of time?

      This is an excellent suggestion, for which we thank the reviewer. Mon1 and Ccz1 are highly conserved, with clear Dictyostelium orthologues that have never been studied. Our model is that there is a small proportion of Rab7 driven by this canonical pathway so would expect Ccz1/Mon1 to coincide with loss of Rab5 and be unaffected by loss of PIKfyve - although subsequent Rab7 delivery would be lost. This is easy to test by cloning and expressing GFP-fusions of both Ccz1 and Mon1 and would be highly informative. Note we do not exclude canonical Rab7 recruitment in our model (see discussion), our data just indicate this has a minor contribution.

      Reviewer 3:

      The focus is on their manuscript is loading of Rab7 on phagosomes, but there's no indication about Rab7 activation (GTP-loading). Would the RILP-C33 probe work in Dictyostelium? If not possible, the activation state of Rab7 should still be discussed. Despite Rab7 on other organelles in PIKfyve-inhibited cells, is this active or not?

      The GTP-loading status of Rab7 is a good question, although the general dogma is that membrane-localised Rabs are active. We will try the RILP-C33 probe in Dictystelium as suggested, but as these cells lack an endogenous RILP orthologue there is a high chance it will not work. Sadly, reliable tools to asses active Rab status are a general limitation for the field, so if the RILP-C33 probe does not work we will add this caveat to the discussion.

      The authors need to better address the confusing kinetics of early Rab7 recruitment, followed by SnxA (Fig. 4G, same for VatM - Fig. 4I ) - which is counterintuitive if PIKfyve activity is required to recruit Rab7. How do the authors explain this? Are phagosomes prevented from acquiring Rab7 in PIKfyve deficient cells because of a defect on phagosomes or the endo-lysosomes loaded with Rab7 (but not active).

      We believe this again relates to the over-simplification of our model. Our data indicate both PIKfyve dependent and independent Rab7 recruitment. In contrast to the abrupt recruitment of SnxA at ~120 seconds (Vines et al. JCB 2023), both Rab7 and VatM accumulate gradually over time starting from almost immediately following engulfment (Buckley et al. 2019, and Figure 2F). Our data indicate that the first stage of this is PIKfyve independent, and is responsible for ~10% of the total Rab7/V-ATPase accumulation by both the imaging in this paper, and Western blot for V-ATPase on purified phagosomes in Buckley et al. PLoS pathogens 2019. The arrival of some Rab7/V-ATPase prior to PI(3,5)P2 therefore supports our model where there are multiple sources of Rab7.

      As the reviewer quite rightly points out, interpretation of the defects observed in the absence of PIKfyve becomes complex and we cannot completely differentiate between a defect on the phagosome, or the Rab7 compartments that fuse with them (or indeed both). In fact, we already note that small Rab7 compartments that we observe in wild-type cells are much more sparse in PIKfyve mutants. Therefore whilst the requirement for PI(3,5)P2 in the clustering and fusion of macropinosomes with phagosomes is clear, additional effects on the PI(3,5)P2-independent Rab7 compartments cannot be excluded.

      The experiments above using the RILP-C33 active Rab7 biosensor as well as observation of the Mon1/Ccz complex should further clarify this, but we will also add further discussion of these points.

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

      Reviewer 1:

      Minor comments.

      1. It is unclear how the experiment in Figure 3G was conducted. If microscopic analysis was involved, the corresponding images should be included.

      We apologise that we overlooked this and have now added a full description in the materials and methods (P8 L16-21). Fluorescence measurements were performed using a plate reader, so there are no images.

      Page 11-Line 2, the sentence "there was no obvious clustering around the nascent phagosome (Figure 2D)." It is Figure 2E, not Figure 2D.

      Corrected.

      There is an inconsistency regarding the description of fluorescent fusion proteins. For example, both GFP (RFP)-2xFyve and 2xFyve-GFP (RFP), as well as GFP-Rab5 and Rab5-GFP, were used. Typically, placing GFP (or RFP) before a gene suggests N-terminal tagging, while placing it after the gene implies C-terminal tagging. The authors should clarify the position of the fluorescent tag and ensure consistency in their descriptions.

      We apologise for this oversight, and have been through and corrected all fusion protein references accordingly.

      One of the videos was not referred in the manuscript or described in the Video legends. This video seems to correspond to Figure 5A, albeit with a different pseudo-color scheme.

      This has been corrected. Video 7 does correspond to Fig 5A, and we have corrected the colour scheme to match and added references to the video in the text and figure legend.

      Reviewer 2:

      (2) In their abstract, the authors state that they "...delineate multiple subpopulations of Rab7-positive endosomes that fuse sequentially with phagosomes" (page 2, line 14,15). However, the data provides only evidence for V-ATPase or PI(3,5) P2-containing structures and the authors conclude to my understanding that macropinosomes are the main source for vesicular structures fusing with phagosomes. I would ask the authors to please be clear on the identity of the "Rab7-donor"-structures throughout the manuscript. Saying that they delineate multiple subpopulations of endosomes seems to be overstated.

      We identify that macropinosomes are one source (subpopulation) of Rab7/PI(3,5)P2 vesicles but our data clearly show that they are the only source of Rab7 - there is clearly an additional early Rab positive / PI(3,5)P2-negative subpopulation of vesicles that cluster and fuse too at earlier stages. For example, in Figure 4F we co-express Rab7a/SnxA and show that whilst all the SnxA vesicles also contain Rab7 (and dextran), there is a clear separate population of small and early-fusing population of Rab7-containing vesicles that do not possess PI(3,5)P2. This is further validated in Figure 5B and C. To our mind this clearly demonstrates and defines different Rab7 endosomal populations, although we do not yet know the origins of the initial Rab7-positive/PI(3,5)P2 negative population - as discussed in our response to their point (3) below.

      Minor points:

      (1) The sentence "...which both deactivates and dissociates Rab5, and recruits and activates Rab7 on endosomes" is at least problematic as it suggests that Mon1-Ccz1 directly drives GTP-hydrolysis of Rab5 and dissociates it from the membrane. Indeed, Mon1-Ccz1 is shown to interfere with the positive feedback loop of the Rab5-GEF by interacting with Rabex (Poteryaev et al., 2010), so a rather indirect effect of Mon1-Ccz1. A GAP and the GDI are needed for Rab5 deactivation and dissociation from the membrane. How both are involved in the endosomal Rab-conversion is not clarified.

      We have changed the text to better represent this complexity (P4 L4-6)

      (2) Signals of RFP-labeled proteins are difficult to interpret throughout the experiments. What are the structures that show a strong accumulation of red signal in Fig. 1A,B, Fig 2G and Fig4A (20sec.) If these are fluorescently labeled proteins it would suggest that most of the proteins cluster/accumulate in the cell. Can the authors provide better images?

      We appreciate that some of these reporters with multiple localisations can be difficult to interpret. This is major challenge for these sort of studies and main reason we use the large and easily-identified yeast containing phagosomes for quantification. In Fig. 1 the large structure is the large peri-nuclear cluster of Rab5 previously reported (Tu et al. JCB 2022). In Fig. 2G the bright structure is the recruitment of V-ATPase on the CV. Both these large structures easily distinguished from the phagosomal pool we are interested in. Whilst we would love to provide better images, this is simply not possible - both these other structures are unavoidable and we are already using some of the best microscopy methods available. We have however clarified the additional localisations seen in these images in the revised figure legends.

      (3) On page 11 the authors state "...macropinosomes in ∆PIKfyve cells still appeared much larger. Quantification of their size and fluorescence intensity demonstrated that although macropinosomes started off the same size,...". This statement is not reflected in the data depicted in Fig. 3A,B. The size of the single labeled macropinosome appears to be larger in wildtype than in ∆PIKfyve cells from the beginning on. However, the quantification in Fig 3F is clear. So, are these bad examples in 3A,B, are they swapped or is this due to the additional expression of GFP-Rab7A? Could you please comment on the effect that the (over-)expression of GFP-tagged Rab-GTPases might have on the observations described in this paper in the discussion part?

      As you can see from the error bars in Figure 3F, macropinosomes are extremely variable in size - ranging from ~0.2-5 microns in size in axenic Dicytostelium. The image in Figure 3B is therefore indicative of this heterogeneity, rather than being a "bad example". This is why we designed the experiment to quantify several hundred vesicles in order to make any conclusions - as well as doing it in the absence of any GFP-fusion expression.

      Although we have not noticed any issues (enlarged vesicles are also clear in GFP-Rab7 expressing cells in Figure 1B), we do of course accept that GFP-Rab7 expression itself may have some detrimental effects on maturation and this is why we quantified macropinosome size in untransformed cells. We have clarified this in the results section (P12 L28).

      (4) In Fig. 6E it is hard to distinguish if the dextran is accumulating inside the phagosome. I would suggest conducting a 3D reconstruction of these images to allow judging if macropinosomes fused with the phagosomes or if they cluster around the neck of the phagosome.

      This would be nice, but not possible as these images are from single confocal sections, rather than a complete high-resolution Z-stack. We have however added an enlargement of both Figure 6D and E which we feel now more clearly shows the presence of dextran within the bounding PI(3)P membrane of the phagosome.

      (5) In the discussion, the authors state that the small pool of "PIKfyve-independent Rab7" is "insufficient to for subsequent fusion with other Rab7A-positive compartments, further Rab7 enrichment, and lysosomal fusion." What is the rationale for this conclusion? Is it shown how many Rabs are necessary to induce a tethering and fusion event? It would be good to revise this part of the discussion also in respect of the first major point of my comments above.

      Our data show that in the absence of PIKfyve, phagosomes still remove Rab5 and gain a small pool of Rab7 but progress no further. This is consistent with some block in the HOPS-mediated homotypic fusion of Rab7 compartments. However, we accept that this is not necessarily due to simply not having enough Rab's so have rephrased the discussion accordingly.

      (6) The intention of the paragraph about phagosomal ion channels is for this reviewer somehow out of context. It is not clear to me how the authors relate this to their findings. It would be could to bring this into a broader context.

      __ __We mention ion channels in the background as they represent the main class of PI(3,5)P2 effectors known so far. We feel this is important background context, even if our studies do not directly relate to this.

      Reviewer 3:

      Their disclosure and use of statistics is incomplete and/or inconsistent, and potentially wrong in some cases. For example, the authors disclose the number biological repeats in a few experiments (Fig. 3C, F) but not in the majority. Instead, they state the number of phagosomes without indicating biological repeats (eg. Fig. 2 and others). So, it is not possible to know if their data are reproducible. Despite not indicating independent experiments in some cases, they speak of SEM, which applies to mean of means from biological repeats. In other cases, none of this is disclosed (eg Fig. 3G). Often there is no indication of what statistical test was done OR if a statistical test was done (eg. Fig. 3G, Fig. 4, etc). I would recommend the authors review the excellent resource paper published in JCB on SuperPlots to better follow statistical expectations. This is essential to improve reproducibility and confidence in their observations.

      We apologise if this was unclear for the referee, but we have tried to be clear in each case. The confusion likely lies in the definition of a biological repeat, which depends on the type of experiment. For quantification of phagocytic events over time, we feel it reasonable to take each individual event (each from an individual organism) as a biological repeat. This is because events are relatively rare and taken from multiple different movies, and it is not technically possible to film both mutants and controls simultaneously. In all these sort of experiments (e.g. Figure 2) we have shown standard deviation, which indicates the reproducibility between phagocytic events. We have clarified that these events are from movies obtained on at least 3 independent days in the methods.

      In other cases, such as Figure 3C and F and Figures 5-6, we are able to take measurements across multiple cells simultaneously at each timepoint. It is therefore appropriate to average over multiple independent experimental repeats rather than individual cells. We have therefore used SEM in our analysis, and both the number of individual cells and independent repeats are stated on the graphs and legend. This was incomplete in a few cases but has now been clarified in all cases.

      Regarding statistical tests, which ones were used now been clarified in each figure legend. Note that in Fig 3G, we do not apply any test as both lines essentially overlap and it is clear there would not be any convincing differences. In Figure 4, the graphs all compare co-expression of different reporters rather than different mutants or conditions and are from single events. We therefore feel statistical tests are unnecessary and inappropriate. Comparison of the same reporters between strains averaged across multiple events, with statistical analysis is shown in Fig 2 instead. All these points have now been added to the statistics section of the methods (P9 L1-6)

      Minor Comments

      It is interesting that 2FYVE-GFP stays on phagosomes for 50 min or more - this is distinct from macrophages. Please comment. Have the authors tried other PI(3)P probes to see if the same (PX-GFP).

      We have not used other probes but we have no reason to believe 2xFYVE does not behave as predicted as it is the same probe used for most macrophage studies (FYVE domain from human Hrs), and gets removed from macropinosomes exactly as expected. We did not originally comment in this manuscript but PI3P dynamics are even more interesting as our previous data indicate that latex-bead containing phagosomes lose PI3P after 10 minutes (Buckley et al 2019, Figure 4F-G) This indicates phagosome maturation can be regulated by the cargo (under further investigation). Importantly however, both bead and yeast-containing phagosomes have comparable defects in the absence of PIKfyve. This is more fully discussed in our previous paper (Vines et al. JCB 2023) where we characterise PI(3)P and PI(3,5)P2 dynamics in more detail.

      Fig. 7 model: the macropinosome in the diagram seems like a dead end as depicted - is there any arrow or change that could be added to show that it doesn't just sit there in the middle? Also, the light green on yellow hurts the eyes!

      We apologise, there was actually supposed to be an arrow there but it was lost somewhere in the drafting process. The whole figure has now been updated to more clearly describe our full and more complex model.

      Fig. 3F, could be converted to volume assuming macropinosomes are spheres.

      This is true, however as these images are taken from single planes we cannot know where in the sphere the slices are and therefore what the maximum diameter would be. We therefore prefer to keep it as area so as not to confuse and over-interpret the data.

      Pg. 10, line 10 - Vps34 is Class III PI3K, not Class II.

      Corrected.

      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.

      • *

      Reviewer 2:

      (3) ("OPTIONAL") Optionally, the authors could also try to clarify these structures' identity by including further colocalization studies with additional early and late endosomal marker proteins. Are they for example positive for early or late endosomal markers like EEA1, ESCRT or Retromer? How about organelle-specific SNAREs? This would give further insights into the character of the "Rab7-donor" structures and would allow to clarify if multiple subpopulations are contributing to phagosome maturation in a sequential order as stated in the abstract. As I am not an expert on Dictyostellium I can`t estimate the effort that would go into such an experimental setup. However, since the time scale of the events in the cell is nicely worked out in this study, these colocalization studies would not need to be conducted as live-cell microscopy experiments.

      This is a sensible suggestion that would in theory help define these populations. However many of these markers are poorly defined with respect to phagosomes and/or Dictyostelium. Dictyostelium does not posses an EEA orthologue, but our data also indicate that these vesicles do not possess PI3P so cannot be canonical early endosomes. We have previously characterised WASH/retromer and whilst it is recruited to phagosomes at around the time of Rab5/7 transition Retromer appears to be recruited from the cytosol and drive recycling rather than being delivered on endosomes that fuse (see King et al. PNAS 2016). We have also previously looked at ESCRT (Lopez-Jimenez et al. PLoS Pathogens 2018) which also does not appear to have any recruitment to early phagosomes that would be consistent with a Rab7-sub-population. The SNAREs are yet to be studied in any detail, as they are often too divergent to assign a direct mammalian orthologue.

      Therefore, whilst this is a sensible suggestion, and something we would like to follow up in the future, this is not straight-forward and we feel outside the scope of the current study. We have however included additional discussion of this in the revised manuscript (P20 L21-26).

      Reviewer 3:

      Major Comments:

      1. Based on the current data, I am not entirely convinced that Rab7 is delivered mostly by fusion with other compartments. At least the data as provided cannot exclude other models. For example, Rab7-containing organelles that cluster with phagosomes may form contact sites that provide a local environment to load cytosolic Rab7. There's also a possibility that some of their Rab7 clusters are membrane sub-domains and not vesicles. Or perhaps, there is a first wave of cytosolic Rab7 recruitment, which then initiates fusion with Rab7 compartments, i.e., there is a two-phase Rab7 recruitment. While this last possibility is consistent with recruitment of Rab7 by fusion (the second phase), the authors present a model that is too simplistic and conclusive based on the data. The authors may be right, but they need to strengthen their evidence towards their claim. Maybe EM could help determine some of these issues. Perhaps better would be the use of FRAP, photo-activation, or optigenetics of Rab7. For example, if Rab7 is acquired on phagosomes after photobleaching clusters of Rab7, this would suggest a cytosolic Rab7 contribution, and if not, this would support their model. I recognize that these experiments are not necessarily trivial, but either the authors augment their data (as suggested or with other approaches) or significantly pare down their conclusions.

      We agree with the Referee that we cannot completely exclude other models, and as we talk about in the discussion, we do not wish to do so. We apologise if the role of fusion was over-stated but the model we propose is as the referee suggests: there is likely an early first wave of canonical Rab7 recruitment from the cytosol that is independent of PIKfyve before the majority of Rab7 is subsequently delivered by fusion in a PIKfyve-dependent manner. Our data indicate that the second wave is both quantitively and functionally more significant (see functional data in Buckley et al. 2019).

      We do however agree with the referee that we cannot formally exclude things such as contact-site mediated recruitment from the cytosol or sub-domains but not fusion however there is no data to support these either. In contrast, the hypothetical clustered Rab7 contacts/subdomains often (but not always) contain the transmembrane V-ATPase complex (Figure 2G) which must be delivered by fusion.

      However we do not wish to over-simplify our conclusions and as we state in the discussion, we do think there is probably a small amount of Rab7 recruited from the cytosol by the canonical pathway. We accept that our cartoon in Figure 7 is over-focussed on fusion so we have substantially revised this, as well as the discussion to give a more balanced and complex view.

      Regarding the proposed experiments, unfortunately, the imaging required to acquire these movies is already at the very limit of what is possible so we do not believe it would be technically feasible to employ methods such as FRAP and optogenetics on these relatively fast-moving phagosomes with the temporal resolution required. Furthermore, to differentiate recruitment from a cytosolic pool, every GFP-Rab7 cluster would need to be photobleached, which could not be reliably achieved.

      However, this point will be largely addressed by the suggestion of Reviewer 2 to look at the Mon1/Ccz complex. The presence or absence of this will give strong evidence for canonical Rab5/7 transition and Rab7 recruitment from the cytosol which would significantly clarify our model and define the two different mechanisms of Rab7 recruitment to phagosomes.

      Early macropinosomes fuse with early phagosomes more readily than 10-min old macropinosomes. Do 10-min old macropinosomes not fuse with older phagosomes? Is this not an issue of mismatched age?

      This is an interesting point that we have clarified in the text. We agree with reviewer that it appears the ages of the macropinosomes and phagosomes must match but our data indicate this only occurs when both parties possess PI(3,5)P2 as macropinosome fusions appears to happen in a single burst at about 240 seconds (Figure 6F) rather than as a continuous process. We also do not start to see any fusion of these older macropinosomes when the phagosomes get past the initial first 10 minutes of maturation (Figure 6G).

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      Reply to the reviewers

      We would like to thank our reviewers for their constructive criticism and for their appreciation and enthusiasm for our study. Some reviewers expressed opposing views, particularly when it came to the function and identity of the Cdt1-related protein in Toxoplasma gondii. To avoid redundancy in our response, we would like to make a brief statement. Toxoplasma gondii and other apicomplexan parasites utilize unique and highly unusual modes of cell division; numerous studies suggest that multiple phases can run concurrently in apicomplexan cell cycles. The best-known examples include the asynchronous S/M cycles in schizogony and concurrent mitosis and budding in Toxoplasma endodyogeny. These overlapping phases are not a feature exclusive to apicomplexans, since in budding yeast, cytokinesis initiates in G1 phase by marking the location of budding on the surface of the mother. Based on years of previous research and from our experience, we adjusted our approach by focusing on the processes that are associated with each cell cycle phase rather than on their temporal order. While the model of a conventional cell cycle guides our studies, we “follow the breadcrumbs” that we discover and the published studies to create a more accurate model of apicomplexan cell cycle instead of relying on the traditional cell cycle map employed by distantly related eukaryotes. Below are point-to-point responses to reviewers’ comments.

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

      Summary: Hawkins et al. employ a reverse genetic approach to analyze the molecular function of the Toxoplasma gondii kinase Crk4 and the Toxoplasma gondii cyclin 4. The authors combine inducible depletion with imaging, (phospho-)proteomics, molecular modeling, and protein-protein interaction studies.

      Major comments: - The major conclusion of the manuscript is that TgCrk4/TgCyc4 regulate entry into mitosis and that the primary role of TgCrk4 is to suppress DNA re-replication and chromosome re-duplication (lines 105-106). The authors also provide evidence that TgCrk4 interacts with TgCdt1, a DNA licensing factor ("TgCdt1" is missing in line 107). (had been corrected) By sequence homology, the authors found homologues of TgCrk4 only in apicomplexan parasites with binary division and concluded that the dominant division mode, presumably schizogony, is repressed in these organisms in favor of binary division. Indeed, internal budding and daughter cell formation is defective in the inducible depletion mutants of TgCrk4 and most experiments focus on this developmental stage. However, the analysis of preceding events, such as DNA replication is rather brief. If G2 is indeed regulated by TgCrk4/TgCyc4, one would assume that the parasites are post-S phase and the nucleus contains two copies of the genome, as indicated in Fig. 2C. The data shown in Fig. 3H and 7A, however, show that the TgCrk4 and TgCTD1 depletion induces a developmental arrest pre-S phase. This contradicts the main conclusions of the manuscript.

      *We agree that the G2 location is odd for a conventional cell cycle model. Given the high possibility that cell cycle phases can overlap in apicomplexans, we determined the relative position of G2 phase in Toxoplasma endodyogeny by instead focusing solely on the processes that are attributed to a specific cell cycle phase (such as DNA replication for S phase, DNA re-replication for G2 phase, DNA segregation for mitosis). Our approach shows that Toxoplasma G2/M checkpoint operates upstream of SAC, which led to enrichment of parasites with replicated DNA (Fig. 3H and Fig. 7A), which places G2 at the end of S-phase. Our focus in the present study is on the G2 functions, the control of centrosome and chromosome reduplication, but we appreciate the suggestion to examine DNA replication in Toxoplasma, which could be investigated in future studies. *

      Indeed, many data of this manuscript could support an alternative conclusion, i.e., that TgCrk4 regulates entry into S-phase (similar to Plasmodium falciparum Crk4: PMID: 28211852). This alternative conclusion is supported by the data showing that TgCyc4 is in the nucleus during S-phase (Fig. 1H) and that TgCrk4 interacts with TgCdt1, which has a well-known role in origin of replication licensing and loading of the MCM complex. MCM subunits were less phosphorylated in absence of TgCrk4, which could also suggest a role for TgCrk4 in S phase. Together, it seems more parsimonious to interpret the data as a DNA replication phenotype rather than a phenotype in G2.

      *We understand some confusion from prior data, but PfCrk4 is not orthologous to TgCrk4 (Alvarez & Suvorova, 2017); The true TgCrk4 ortholog had not been found in Plasmodium genomes. Our understanding is that nuclear accumulation of TgCyc4 in S-phase activates TgCrk4, which leads to repression of the DNA reduplication. One of the possible mechanisms involves interfering with loading of the MCM complex on chromatin mediated by hyper-phosphorylated TgiRD1 (former TgCdt1), which has been reported in other eukaryotes. We also believe that increased MCM phosphorylation indicates entry into or active S-phase, while the reduced phosphorylation that was detected in Crk4-depleted cells supports a block at the end of S-phase (G2). *

      • *

      The currently provided data on the DNA content are, however, clearly insufficient to draw firm conclusions. The gating strategy (dotted lines in Figs. 3H, 7A) is unclear. Why are populations, e.g., not separated at the lowest part of the depression in the histogram, but shifted towards lower DNA content? This seems to overestimate the percentage of cells that have a higher DNA content and the statement in lines 269-271, i.e., that TgCrk4 deficient parasites break the "once and only once" rule, is not supported by data.

      *We corrected the gating of the FACScan plots to separate G1, S, G2+M, and parasites with over-duplicated DNA. Please note that, in general, the cell cycle gating of FACScan data is relative and somewhat subjective when it comes to the gaussian curve. Independent of the chosen gates, our data show that removal of either TgCrk4 or TgiRD1 led to substantial decrease of the G1 population (reduction of 1N peak) accompanied by increase of parasites in the process of replication, completed replication (increase of 1.8 N peak), as well as undergoing DNA re-replication, which supports our claim in lines 269-271. In the case of TgiRD1, the number of parasites with re-duplicated DNA nearly doubled upon 8h of factor deficiency. *

      • *

      It is also unclear how may biological replicates are represented by these data (Figs. 3H, 7A), a critical wild type control at t = 4 h is missing, as well as a statistical analysis. Alternatively, the authors could use microscopy to quantify the DNA content of individual nuclei, which would yield a direct read out on whether a nucleus is in pre-S phase, S-phase or post-S phase. Defining the onset of S-phase indirectly by the number of centrosomes per cell seems imprecise, given the small size of the structure and the resolution of the microscope. Without solving these issues, the major conclusions and several minor statements throughout the manuscript are in question.

      *Thank you for your point, we performed a minimum of three independent experiments to evaluate the DNA content of TgCrk4- or TgiRD1- (former TgCdt1) depleted tachyzoites and have now indicated this in the figure legends. The 0h time point is a “wild type” control, since the parasites that expressed factors were incubated without auxin (mock treated) for 4h. The DNA content of Toxoplasma has been thoroughly studied and we are thus confident our 0h data is a good representation of asynchronous healthy populations. Although the parental strain had been examined, due to the data density mentioned in the reviews, we included only relative results (control and two experimental points) for clarity. Our concern with using microscopy to analyze DNA content is that it can be highly subjective, hinging on the quality of staining and imaging, while flow cytometry produces more unbiased datasets. We have considered the concern that the start of centrosome duplication can be difficult to identify, but the centrin-positive centrosomes move apart by the middle of S-phase. The independent structures are then distinct and easy to resolve, providing a popular means of marking G1/S transition in Toxoplasma. *

      • Lines 187-189: The mentioned checkpoint is unclear and so is the "specific cell cycle population". Fig. 2B analyses budding, but as the final step in the cell cycle, the knock down parasites may have arrested at various other stages of the cell cycle. In addition, it is unclear on which primary data Fig. 2B is based. It appears these may be at least partially shown in Fig. 3. If so, please reorganize as this is highly misleading.

      *“A checkpoint” in the indicated lines refers to G2/M and SAC, which are regulated by TgCrk4 and TgCrk6, respectively. We refer to “specific cell cycle population” since each transgenic parasite that is subject to G2/M or SAC arrest can allow us to isolate very different cell cycle stages. TgCrk6-dependent arrest had been confirmed by the presence of unresolved centrocone (not shown but was previously reported in Hawkins et al., 2022), while we thoroughly examined the novel TgCrk4-dependent block by focusing on many parameters, such as joint centrosomes, single-bud assembly, or unresolved apicoplast. Fig. 2 and Fig. S2 summarize our rigorous quantifications of these phenotypes. For convenience, we used budding efficiency as a readout to compare arrest and release of G2/M and SAC, which was incorporated in Fig. 2B. Table S4 contains the primary data used in all figures in the manuscript, including Fig. 2B. *

      • Line 246-254: It is unclear how many biological replicates were performed and how many cells were analyzed to conclude that TgCrk4 deficient parasites cannot form a bipolar spindle (Fig. 2H, S3B). This, together with the possibility that the developmental arrest occurs pre-S phase (Fig. 3H), does not support the statement, that the G2/M transition is regulated by the novel TgCrk4-TgCyc4 complex.

      We have indicated our replicates in the M&M. As addressed for Fig. 3H above, these IFA experiments were performed in at least three independent experiments.

      * * Minor comments: - Throughout the manuscript, please reorganize and present the figures in order of appearance in the text. Also, Fig. 1G summarizes data that are only presented in Fig. 1H. Please reorder. Similarly, Fig. 2C appears to summarize data that are only presented later.

      *Thank you for the suggestion, however we must abide by the standards of the publishers. The order of the figures must be maintained, but there is a substantial degree of freedom in organizing panels within figures. Fig. 1G summarizes data shown in Fig. 1F, H, while Fig. 2C summarizes many panels including preceding Fig. 2B and Fig. S2. Most of our schematics are placed at the top of figures to provide guidance for the relevant experiments. *

      • Why was only the "G1" timepoint quantified in Fig. 1H? Do the other images shown in F and H represent the majority of cells analyzed?

      *You are correct, we indicated the percentage of factor-positive parasites only when the factor emerges during a specific cell cycle phase. For example, the TgCyc4-positive parasites with 1 centrin dot were quantified to show that TgCyc4 emerges in the middle of G1 phase. The lack of a number indicates that the image represents all the parasites progressing through this phase; we have added this explanation to the figure legends. *

      • Several micrographs lack scale bars (Fig. 1B, D; 2E, F, H, I; 6D; 7F, H and S2G, S3A, B; S5A, B, D).

      *Thank you, we have added the scale bars to indicated images.

      *

      • Lines 83-85 and 93-95: Recently several publications investigated the cell cycle of the apicomplexan parasite Plasmodium and data are accumulating, showing that there may be a gap between the last S phase and segmentation (e.g., PMID: 35731838; PMID: 35353560), which may be interpreted as a G2 phase. Thus, these statements could be revised to reflect the current literature.

      *The studies mentioned provide very valuable insights into S-phase dynamics; the gap that was detected between S-phase and segmentation includes mitotic events such as prophase, metaphase, and anaphase prior to telophase (karyokinesis to segmentation). However, studies using means like stage-specific markers could help resolve the composition and order of events in the apicomplexan cell cycle. We used processes specific to G2 (repression of DNA and centrosome reduplication) and identified TgCrk4/TgCyc4 as the first G2 markers in apicomplexans. *

      • Fig. 4 shows the effect on protein abundance and phosphorylation upon TgCrk4 depletion. Fig. 4B seems somewhat redundant as a more detailed analysis with two timepoints is shown in the rest of the figure.

      *Fig. 4B is provided in contrast to the plot in Fig. 4A. It demonstrates that TgCrk4 depletion results in a far more pronounced effect on global phosphorylation rather than on proteolysis. While Fig. 4B highlights the checkpoint arrest, panels C and D are dedicated to the search for TgCrk4 substrates: the phospho-sites that immediately lost intensity of phosphorylation and remained low during the 4h block. *

      *

      *

      • Lines 146-148: This statement is confusing in light of the expression data in Fig.1 F and H. If they stabilize each other, how is TgCrk4 stabilized in G1, when TgCyc4 is absent?

      We believe that multiple mechanisms contribute to the stability and function of TgCrk4. We tested one and found that depleting the cyclin partner led to reduced expression of TgCrk4, and were able to conclude that the complex is stable when both subunits are expressed. Please note that we probed the mixed cell cycle populations by WB, and our proteomics data show that TgCrk4 interacts with many partners (Fig. 1E). Thus, it is likely that G1 stability may have been mediated by other partners, or by a higher transcription/translation rate, which could be evaluated in further experiments that focus on the regulation of TgCrk4/TgCyc4 complex.

      • *

      • Fig. 2D, and G: Please provide representative images of what has been quantified, as E/F and H/I are apparently UxEM images.

      The corresponding images are included in Fig. S2.

      • Line 236-243: This statement seems to be based on a single IFA shown in Fig. 2K. If so, the manuscript would benefit from clearly stating that this is a singular observation.

      *Thank you, we have provided clarification as described in previous points. *

      *

      *

      • Lines 301-304: In the cited publication, the TgOTUD3A knockout could not be complemented, which raises the possibility that other factors are involved. Thus, this statement would benefit from revision.

      *The lack of TgOTUD3A KO complementation is an example of the unappreciated complexity of apicomplexan cell cycle regulation by controlled proteolysis. We highlighted the similarity of TgCrk4 and TgOTUD3A deficiencies, which indirectly confirms their partnerships in the G2 network. Fig. 8A shows that, in addition to TgOTUD3A, the G2 network contains numerous factors. *

      *

      *

      • Lines 421-422: PfCdt1 was annotated in PlasmoDB some time ago and this statement needs to be revised.

      *Please see our response to comments made by Reviewer 2. Briefly, we agree with Reviewer 2 comment that TgCdt1 does not function as conventional DNA replication licensing factor CDT1. Therefore, we named TGME49_247040 TgiRD1 – inhibitor of DNA and centrosome ReDuplication 1. *

      • *

      • Lines 448-450 and Fig. 6F: Are these data from a single biological replicate and how many cells were analyzed for the different time points? Given the insufficient data on the DNA content, the paper would benefit form more conservative conclusions on the role of TgCdt1. The numbers of biological replicates were added throughout the text, also please refer to our response to Reviewer 2 and the comment above.

      Reviewer #1 (Significance (Required)):

      • This manuscript investigates the role of TgCrk3, TgCyc4 and TgCdt1s and provides a large amount of data.
      • These data will contribute to our understanding of the unusual division modes of Apicomplexa, a field of research that recently gained momentum.
      • These data will be interesting to the community of cell and molecular biologist, which work on the fundamental biology of eukaryotic microorganisms.
      • My field of expertise is the cell biology of Apicomplexa.

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

      Summary: In this Manuscript, Hawkins et. al. describe advances in the apicomplexan parasite cell cycle, which is reminiscent but distinct from mammalian cell cycle regulation. These differences include a presumed lack of G2 phase and the ability to replicate in either a multinuclear (schizogony) or binary (endodyogeny) manner. Using Toxoplasma gondii (TG) as a model, the authors seek to expand the current understanding of how these highly variable parasitic cell cycles are regulated by describing a previously unreported G2 phase. Building on the authors earlier work, this manuscript defines the function of TgCrk4 and identifies a novel binding partner, TgCyc4. Crk4 and Cyc4 control a G2/M checkpoint by regulating centrosome duplication and separation.

      The authors also identify 247040, a protein with previously no known function, as a binding partner and substrate of TgCrk4/TgCyc4 and several replication fork proteins such as MCM and PCNA. Results indicate that the protein negatively regulates replication and centrosome duplication. The authors propose to rename this protein TgCDT1 despite "low sequence similarity" and having a completely opposite function to eukaryotic CDT1. Using Swiss-Prot modeling the authors claim 247040 bears a "partial resemblance" to mammalian CDT1. Indeed, both of these proteins show high intrinsic disorder and have 2 folded domains. While 247040, like hCDT1, does contain cyclin interacting motifs (Cy), a collection degrons (not all shared with other CDT1 orthologs), and an NLS, the list of nuclear cell cycle proteins that also contain Cy and degron motifs would be very long. Further, 247040 is regulated in an opposite manner to all other CDT1 orthologs because it is absent in TG G1 and present in TG S phase; eukaryotic CDT1 is either degraded or relocalized to the cytoplasm in S phase, and evidence for degradation via APC/C is minimal. Crucially, loss of 247040 resulted in inappropriate replication ("re-replication"), whereas all other eukaryotic CDT1 orthologs are essential for replication. Re-replication in eukaryotic cells can be caused by excess or hyper-active CDT1, not by loss of CDT1 activity as shown here for 247040. Clearly 247040 is a negative regulator of DNA replication, and as such, is not a candidate for the TgCDT1 ortholog. If anything, it is functionally analogous to metazoan geminin, the negative regulator of metazoan CDT1; of note, geminin also has centrosome-related phenotypes. We cannot support naming 247040 TgCDT1 because it will cause confusion in the field.

      Aside from this major issue, the study is well-executed, rigorous, quantitative, and thorough; it has many strengths from the unbiased interaction screens. The authors' sequence analysis also suggests broader possibilities for cyclin structures than had previously been appreciated. We appreciate the legend in Figure 2 to the organism-specific terminology.

      Major comments: The spatiotemporal dynamics of 247040, its role in repressing TG DNA replication, lack of PIP motif and winged helix domain indicate that some other nomenclature, other than TgCdt1 will be a better name for this protein of previous unknown function.

      We would like to thank Reviewer 2 for this highly insightful comment. We agree that TGME49_247040 functions as a CDT1 inhibitor rather than as CDT1 itself, so conserving the name would produce confusion in the cell cycle field. Based on TGME49_247040 protein function we decided to name this factor TgiRD1 – inhibitor of DNA and centrosome ReDuplication 1. We revisited our data, looked deeper into the protein structure, and adjusted our conclusions. Our new Figure S5 shows differences in the predicted folding of HsCDT1 and TgiRD1. We could not ignore the fact that TgiRD1 is phylogenetically related to CDT1 in ancestral branches and metazoans (Fig. 6B), but we identified substantial differences that may indicate a selective loss (or inheritance) of protein features. For example, TgiRD1 does not interact with ORCs that are critical for the licensing step, but TgiRD1 retained an MCM binding domain (winged helix-turn-helix) that plays a role in licensing and firing. Rather than CRL4Cdt2 degrons, TgiRD1 contains APC/C degrons that would be activated late in mitosis (similar to regulation of Geminin). Together with the lack of DNA licensing control in G1 and its opposing expression profile, we concluded that TgiRD1 represents a Cdt1-related protein that controls DNA and centrosome reduplication in S and G2 phases.

      Minor comments:

      1. For clarity, please include the number of replicates in the figure legends where appropriate. We added the requested information.

      For microscopy/imaging, how were representative cells/images chosen? The representative images constituted the most common phenotype of the feature we aimed to highlight, and most are accompanied by quantifications.

      In addition to the ELM analysis, the authors could also employ fold recognition software (such as Promal) to analyze 247040 structural models to show similarity to known protein structures.

      We use a variety of folding prediction software, including AlphaFold2, PyMol, and template-based SWISS-PRO module to examine protein structures in our study, indicated in the text and figure legends. Our new TgiRD1 (former TgCdt1) analysis is based on an AlphaFold2 prediction (Fig. S5). All the software we used is listed in the M&M section.

      Line 107: missing words "TgCdt1"

      *We corrected the sentence.

      *

      Line 141: the interpretation that the C terminus is "unstable" is misleading if it is simply that the protein cannot tolerate a fusion to the C-terminus.

      *We successfully incorporated a tag at the C-terminus (confirmed by sequencing across the recombinant gene) but could not detect protein expression. If our protein could not tolerate a recombinant tag, the transgenic parasites would not survive because TgCyc4 is essential protein. Therefore, since the parasites survived, we concluded that the lack of TgCyc4-AID-HA expression was due to native truncation at the C-tail (instability). *

      Line 221: word choice "reminisced" We have changed the wording.

      Line 348 refers to Orc4 expression in Figure 4A, but the data point is not labelled. Fig. 4A references GO group (DNA replication/licensing factors), and the raw data is included in Table S6, which is now indicated in the text.

      Lines 407-8 and 510-11: Reference Fig 1E We added the reference.

      Line 408: please define what is meant by "dominant interactor" We meant that TgiRD1 is the most prominent interactor of TgCrk4 and TgCyc4. To clarify the confusion, we changed the wording to “primary interactor”.

      Reviewer #2 (Significance (Required)):

      This manuscript makes great strides in defining apicomplexan cell cycle control and genome replication. These strides include defining a previously unrecognized G2/M checkpoint controlled by TgCrk4 and the novel TgCyc4. Further, the authors identify a binding partner and substrate of the novel Crk4/Cyc4 kinase complex, 247040 that acts as a repressor of replication.

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

      Summary The present study Hawkins et al have described the important role of Cyclin-CDK complex in an apicomplexan parasite Toxoplasma(Tg) which exhibit binary mode of cell division like many other eukaryotes. In the apicomplexan field it is generally shown that G2 phase of cel cycle is either absent or has very little role. The authors here demonstrate that the combination of Tg CRK4 and Tg Cyclin4 works during the G2 phase of cell cycle such as chromosome rereplication and centrosome reduplication. In order to show the function of Cyclin-CRK function they used Auxin degradation system to down regulate or deplete the protein and study parasite growth during cell cycle as well as they used tagged parasite to identify the protein complex with these two molecules. In the study they showed that these two molecules Cyc4 and cRK4 formed the complex in protein pulldown method and show identical function in the cell cycle. In addition to thiese two proteins they also found another interacting partner Cdt1 that was further analysed to be involved in controlling Chromosome rereplication and centrosome. So overall the study is nicely performed and three molecules of Cyclin4-CRk4-Cdt1 and their role is illustrated in the binary mode of cell division in Toxoplasma.

      Comments 1.Though no new experiments need to be performed but it will be good if some details are given as to which stage of tachyzoite cycle the protein complex were performed and if there is difference in the various phases of cell cycle especially the s phase and the M phase. Are these period changed. Since G2 is suppose to be absent in many apicomplexan do the authors suggest that G2 phase is only coupled to binary mode of cell division. Please discuss how it is then linked to the other part of cell cycle.

      *You are correct, we propose that the presence of G2 phase is linked to binary division in apicomplexans and our hypothesis is supported by the overall evolution of the cell cycle (see Discussion section). We also entertained the hypothesis that G2 operates in multinuclear division since all apicomplexans encode TgiRD1 orthologs (please, see the Discussion section). For the first time, we identified the major functions of G2 functions (repression of the DNA and centrosome reduplication) in the apicomplexan cell cycle. However, given the unresolved organization of the Toxoplasma (or any apicomplexan) cell cycle, it is currently impossible to define the boundaries of G2. According to our study, TgCrk4 and TgCyc4 control G2/M transition or the end of G2 phase, and we still lack markers of G2 entry. In our comparative synchronization study (Fig 2), we uncovered the temporal link between G2/M and SAC regulatory points, which is discussed in the results section. *

      Ganter et al have studied CRK4 in Plasmodium previously and they do find in their phosphoproteome study the similar association with the DNA replication machinery with CRK4 but no cyclin was identified in their study. In the cyclin study by Roques et al it has been shown that no cell cycle cyclins are found in Apicomplexan so can the author discuss more how these complex can be different in two apicomplexan species. They describe that Crk4 is novel cell cycle kinase though this has been studied earlier. Authors have almost not discussed these previous finding with respect to their in this study.

      *We would like to clarify this confusion. We have not discussed Ganter et al. studies because PfCRK4 is not orthologous to TgCrk4, but rather it is related to TgCrk6. Unfortunately, the Plasmodium and Toxoplasma Crk nomenclature was published almost concurrently. Our previous (Alvarez & Suvorova, 2017) and current study show that Plasmodium and other apicomplexans that divide by multinuclear division do not encode TgCrk4 orthologs (and/or TgCyc4). Additionally, the mentioned studies by Roques and Ganter were released prior to newer genome annotations that include additional cyclin-domain proteins, including 10 Toxoplasma cyclins (5 new) that we categorized in our recent publication (Hawkins et al., 2022). Although the newly annotated cyclins are not related to conventional cell cycle cyclins, we had proven empirically that TgCyc1 together with TgCrk6 controls SAC, and now, the specific interaction of TgCyc4 with TgCrk4 controls G2 processes. Lastly, we call TgCrk4 “a novel” kinase only in the meaning that it is a novel cyclin-dependent kinase that is not related to known CDKs in other eukaryotes. The identification of TgCrk4 in our previous study (Alvarez & Suvorova, 2017) is described in the Introduction section and at the opening of the Results. *

      The manuscript is too dense, in terms of both figures and text. At times loses the focus and hence can be organised with most important finding in the figure and text. Especially Fig2, Fig4 and Fig7. Fig5 does not give too much in terms of the real finding an in fact take away from the focus. Some parts of these figures can be simplified or moved to supplementary. Some of the figures in Fig2 and 7 are missing the scale bars.

      We respectfully disagree with some conclusions made by the Reviewer. Our study contains ample material that is intended to guide the reader through the complexity of the Toxoplasma cell cycle and the intricate structures contained in the parasite. We have also introduced a few novel approaches that require additional schematics and dedicated discussions.

      • Fig 2*. The G2/M block, as well as the G2 phase, had never been detected in apicomplexans. We created a new approach to determine the timing of the G2/M checkpoint, which involves comparison to a known cell cycle block. Panels A, B, and C provide visuals and summarize our findings. The main events are highlighted with arrows (Panel C), while graphs (panel B) show differences in responses. The rest of the figure is devoted to quantification of the primary events caused by TgCrk4 deficiency, since the G2 block had never been examined. While the U-ExM images of the entire vacuole (2-4 parasites) may seem overwhelming, they represent that the deficiency is consistent. *
      • Fig 7* is devoted to the major Crk4/Cyc4 interactor TgiRD1 (former TgCdt1). This is one of the first mechanistic studies of central cell cycle regulators in Toxoplasma. This Cdt1-related protein was examined at the molecular level to support the main claims of its control of G2 Nevertheless, we moved two panels from Fig. 7 into the supplement. *
      • 4* is organized as follows. Top row: panels A, B visualize the G2/M checkpoint block at the protein level. Middle row: panels C, D, and E represent the workflow to find TgCrk4 substrates. Bottom row: panels F, G highlight TgCrk4 substrates of interest that are discussed in the paper. *
      • 5* is an in-depth analysis of the central cell cycle regulators across Apicomplexa phylum, a key figure of the study. Its comparative nature supports our main message: binary division is regulated by TgCrk4/TgCyc4, which are only expressed in a subgroup of apicomplexans that divide in a binary mode. *

      May be bit more discussion of ORC in relation to their Cyclin-CRK complex as they did find upregulation of the ORC in their genome profiling. So may be instead of CDT1 these are more important in the licencing of DNA replication.

      *Our choice to focus on Cdt1-related protein was driven by the fact this protein is a major component of the TgCrk4/TgCyc4 complex, while the ORCs act downstream (as TgCrk4 substrates). Shifting focus to ORCs opens an entire new project, which will be explored in the future. *

      5 The model in Fig8B does not take Cyc4 into consideration and I feel is bit oversimplified as there are many factors that may be responsible for centrosome non separation. The S and G2 are no separated in the Cell cycle as given in this Fig.

      Referring to comment 3, we focused on empirically supported, central findings and created the first model of centrosome cycle regulation in T. gondii. We intentionally drew focus to TgCrk4, which was extensively studied, while TgCyc4 received less attention due to difficulties in modulating its expression. We have used transcriptional downregulation to evaluate TgCyc4 (tet-OFF model), which is unfavorable for cell cycle studies because it exceeds the duration of the cell cycle. The unclear cell cycle borders are addressed in the introduction to this response. Briefly, the organization of apicomplexan cell cycle is currently unclear, thus most of the schematics are approximate.

      It is not clear from the data with CDt1 if this linking the inner and outer centrocone or its down regulation breaks the bipartite centrosome. May be some reflection it will be useful.

      *Our model suggests that both TgCrk4 and TgiRD1 (former TgCdt1) affect only the inner core of the centrosome, which we propose is comprised of two types of linkers. The arrows in Fig. 8 point specifically to the linkers whose stability depends on the expression of TgCrk4 or TgiRD1. *

      Minor comments

      I what is SAINT analysis as it is not described in methods.

      *We added the description of our SAINT analysis to M&M.

      *

      How was budding quantified

      *We supplemented the figure legend with the required information. *

      Western blot can have predicted size

      *Due to density of the figures, we did not supply the predicted MW of the proteins when they display the proper PAGE motility. *

      what does red star mean in Blot 1C

      *We added the description to the figure legend.

      *

      What does the number in Fig1H means please explain in the legends and same for Fig6F. In fig 1, removing the inhibition for 5 hours led to very less budding, but in fig 3, removing inhibition showed increased budding (50% in 2 hours). Please explain

      *Please see our response to the reviewer 1 minor comment regarding Fig. 1H and 6F. *

      *We presume that there is some confusion regarding figure numbers. Perhaps the Reviewer refers to Fig. 2B. Indeed, the 4h block at G2/M led to reduced budding (Fig. 2B), while release from the block for 2 hours (Fig. 3C, post-recovery) allows parasites to continue cell cycle progression and reach the next stage –budding. The numbers over the Fig. 3A, B, and C panels are from the plots in Fig. 2B to help give a comprehensive representation of the analyzed timepoint. *

      Fig2 has no scale bars -please add- this figure is too dense. May be fig2A, B,C can be in supplementary, legend in the figure can be in the figure legend.

      Please see our response to comment 3. We have included scale bars.

      Also this figure2 H and I in not quoted in line 231. Also this figure2 has no panel J but goes directly from I to K

      *The alphabetical order was corrected, and the reference added. *

      Fig3 the FigG can be more relevant in the Figure 8 while describing about the Crk4 and Cyc4 and CDt1 in binary mode of cell division. Also please define what stars mean either in legend or methods section in terms of significance.

      *Thank you for the suggestion. The Fig. 3G schematics summarize the overall findings of the Figure and acts as an intermediate conclusion in this study. We added the meaning of the stars in the M&M section. *

      Line 107 the sentence is incomplete

      We have corrected the sentence.

      Line 217 may be the figure could be referred as then it is not cleat about the description.

      Due to the density of the figures and well-established dynamics of the centrocone and basal rings, we included the reference to a publication rather than as a figure panel.

      **Referees cross-commenting**

      The study is quite rigrous and with analyses of CRK4-CYC4 and CDT. However it will be better if authors please revisit their conclusions on G2 phase of cell cycle in Toxoplasma based on their findings. The study will have important bearing on the community studying apicomplexan parasites and DNA replication as well as who work on eukaryotic cell cycle.

      Reviewer #3 (Significance (Required)):

      Significance In the manuscript by Hawkins etal have illustrated that in the apicomplexan parasite that have binary mode of cell division present a Cyclin-Crk complex with detailed analysis of Tg Crk4-Cyc4 that are novel in these group pf parasite infect humans and animal alike like malaria parasite and ones affecting cattle and chicken. So these finding are novel as very little is known about this interaction. The significant finding is to show how the G2 phase of cell cycle may be regulated in these parasites and how DNA licencing factor Cdt1 is highly divergent but part of this CRK-Cyclin complex.

      So though it discusses more on the Toxoplasma but it may be of interest to the scientist working on eukaryotes with divergent mode of cell cycle.

      General Assessment - The findings are novel but the manuscript is too dense and at time loses the focus. May be both text and Figures could be made less dense so that important finding are revealed in better way.

      Advance - It does give important insight into the cell cycle in apicomplexan parasite and how even though there are no cell cycle cyclin in Apicomplexa. The findings here suggest how different complexes can substitute for the function. It does extend the knowledge in the field of Cell division in divergent parasites both in terms of mechanistic, functional and technical way.

    1. Reviewer #1 (Public Review):

      Summary:

      Hippo pathway activity is required for pancreas morphogenesis, but its role in endocrine pancreas function remains elusive. The author aims to study the function of the TEAD1 gene in b-cells.

      Strengths:

      The authors generated TEAD1 conditional knockout animals by crossing the TEAD1f/f mice with three Cre strains (RIP-Cre, Ins1-Cre, and MIP-CreERT). In all of them, the KO animals showed progressive loss of insulin secretion with normal beta cell mass. Further characterization of the animals indicated glucose-induced insulin secretion defect and increased beta cell proliferation rate. RNA-Seq and ChIP-Seq experiments identified Pdx1, MafA, and Glut2, etc. as direct targets of TEAD1, which might be responsible for the insulin secretion defect in the animals. Of interest, the authors also uncovered the cell cycle-related gene p16 as a direct target of TEAD1. Reduction of p16 is likely to drive the beta cell proliferation in the TEAD1 knockout model. Thus, they proposed that TEAD1 is a regulator of the proliferative quiescence process in beta cells. Overall, the evidence provided by the authors is highly relevant and supports their conclusion.

      Weaknesses:

      (1) The authors don't explicitly mention that some results appeared in a previous publication (https://doi.org/10.1093/nar/gkac1063) from them.

      (2) The authors begin their story by introducing TEAD1 as part of the Hippo pathway. They showed Taz expression data in Figure 1. Did they do any experiments to detect Taz in their TEAD1 model? Did the authors detect any expression changes in CTGF following TEAD1 knockout? I could not see this changed. The phenotype characterization data presented here contrasts with what has been shown in TAZ b-cell knockout mice (https://doi.org/10.1101/2022.05.31.494216). Based on the data presented here, Hippo is not involved, which should at least be discussed in length.

      (3) Figure 1B - TAZ staining looks different in the three-month age group.

      (4) TEAD ChIP-seq data doesn't look very convincing to me. It's hard to tell whether those highlighted regions in Figures 3A and 5J were signals or background noise. Although the authors also performed ChIP-qPCR in MIN6, it's unclear whether these binding events occur in vivo. The analysis of ChIP-seq dataset is limited as well. How many peaks called? What proportion of differentially expressed genes are bound by TEAD1? Was TEAD1 also detectable at NGN3 and NEUROD1 gene regions? If acquiring enough cells is not possible, the authors could try CUT&RUN or CUT&Tag to improve the data quality.

      (5) The authors should perform RNA-seq or gene expression studies in MIP-CreERT to confirm, which could help narrow down the actual targets of TEAD1 as well.

      (6) Figure 6 - the experiment lacks a control: Ezh2 beta cell KO. In addition to p16, Ezh2, and PRC2 have other targets in beta-cells, the authors could not rule out the contribution of those to the phenotype, so the implication of this experiment is vague.

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      Reply to the reviewers

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

      Summary Maintenance of the histone H3 variant CENP-A at centromeres is necessary for proper kinetochore assembly and correct chromosome segregation. The Mis18 complex recruits the CENP-A chaperone HJURP to centromeres to facilitate CENP-A replenishment. Here the authors characterise the Mis18 complex using hybrid structural biology, and determine complex interface separation-of-function mutants.

      Major Comments The SAXS and EM data on the full-length Mis18 components must be included in the main Figures, either as an additional figure or by merging/rearranging the existing figures. The authors discuss these results in three whole paragraphs, which are a very important part of the paper.

      We thank the reviewer for this constructive suggestion. We have now included an additional figure (new Fig. 2, attached below), that highlights the fit of the integrative model against the SAXS and EM data.

      Could the authors also compare the theoretical SAXS scattering curves generated by their final model(s) with the experimental SAXS curves? This would provide some additional evidence for the overall shape of their complex model beyond the consistency with the Dmax/Rg.

      We acknowledge the importance of this suggestion. We have now compared the theoretical SAXS scattering curve of the Mis18a/b core complex (named Mis18a/b DN), which lacks the flexible elements (disordered regions and the helical region flexibility connected to the Yippee domains). The theoretically calculated SAXS scattering curve of the model matches nicely with the experimental data with c2 value of 1.36. This data is now included in new Fig. 2 (Fig. 2f) and is referenced on page 9 line 21.

      Minor Comments

      While the introduction is clearly written, an additional cartoon schematic, representing the system/question would be helpful to a non-specialist reader to interpret the context of the study.

      We have now included a cartoon in the revised Fig. 1 to support the introduction on centromere maintenance and the central role of the Mis18a/b/BP1 complex in this process. Please find the new Fig. 1 below.

      No doubt the authors had a reason for choosing their figure allocation, but I wonder if more material couldn't be brought from the supplementary into the main figures?

      As addressed in our response to one of the major comments, we have now moved key CLMS, SAXS and EM data from the supplemental figure into the main figure, new Fig. 2.

      Page 6 "Mis18-alpha possesses an additional alpha-helical domain" - please make it clear in addition to what (I assume it's in addition to Mis18-beta).

      Apologies for the lack of clarity. We have now rephrased this sentence to highlight that this difference is in comparison with Mis18b on page 6 line 15.

      Page 7 - Report the RMSD of the Pombe vs. Human Mis18-alpha yipee structures?

      The S. pombe Mis18 Yippee structure superposes on to the Human Mis18a Yippee domain with an RMSD of 0.92 angstroms with is now mentioned on page 7 line 9.

      Page 7 - "We generated high-confidence structural models...." is there a metric for the confidence as reported by RaptorX? Perhaps includinging the PAE plots in the supplementary for the AlphaFold generated models would be useful?

      We thank the reviewer for the valid suggestion. We have now included the PAE plot corresponding to the AlphaFold model in the supplementary Fig. S1d and reference on page 7 line 18. RaptorX ranks models based on estimated error. We have now included this information in the new figure legend for Supplementary Fig. S1.

      Figure 1 - Perhaps label figure 1b as being experimentally determined, with the R values (as for Figure 1d), and 1c being a predicted model.

      We have included Rfree and Rwork values for the Mis18a Yippee homo dimer structure and labelled Mis18a/b Yippee hetero-dimer as the predicted model in Fig. 1c and 1d.

      Page 8 "This observation is consistent with the theoretically calculated pI of the Mis18alpha helix" This is a circular argument, of course this region has a low pI due to the amino acid composition. Please remove this statement.

      We have now removed this statement as suggested.

      Page 8 "...reveals tight hydrophobic interactions" these are presumably shown in Figure 1d rather than in the referenced 1e.

      We apologise for the oversight. We have now referred to the correct figure (Fig. 1f in the revised Fig. 1).

      Page 8 - The authors should briefly somewhere discuss why there is a difference between their results and those in Pan et al 2009. As I understand it, the Pan et al paper was based in part on modelling with CLMS data as restraints.

      We thank the reviewer for this suggestion. According to Pan et al., 2009, the model shown by them was generated using CCBuilder, and their CLMS data could not differentiate the two models with the 2nd Mis18a C-terminal helix in either parallel or anti-parallel orientation. We now briefly discuss this on page 8 and line 22 as follows: "Although the Pan et al., 2019 model presented the 2nd Mis18a in a parallel orientation, they did not rule out the possibility of this assembling in an anti-parallel orientation within the Mis18a/b C-terminal helical assembly (Pan et al., 2019)."

      Figure 1 - The labelling of the residues for Mis18-alpha in Figure 1d is problematic, they are black on dark purple (might be my printer/screen/eyes) suggest amending.

      We have now rearranged the label positions to overcome this issue. For clarity, the labels that could not be moved appropriately are shown in white.

      Figure S3a - Do the authors have some data to show the mass of the cross-linked complex that was loaded onto grids is consistent with what is expected?

      Unfortunately, the amount of material that we recover after performing GraFix is not sufficient enough to determine the molecular weight of the crosslinked sample by techniques such as SEC-MALS. However, GraFix fractions were analysed by SDS PAGE, and fractions that ran around the expected molecular weight were selected for EM analysis. We have now included the corresponding SDS-PAGE showing the migration of the crosslinked sample analysed by EM (Supplementary Fig. S3a).

      Figure S3b - scale bar

      Revised Fig. 2d now includes the scale bar shown.

      Figure S3c - Could the authors show or explain the differences between these different 3D reconstructions?

      The models mainly differ in the relative orientations of the bulkier structural features that are referred to as 'ear' and 'mouth' pieces of a telephone handset. This has been mentioned in the text, but we note that the figure is not referenced right next to this statement. We have now amended this (Page 9 line 19), and to make it clear, we have also highlighted the difference using an arrowhead in Fig. 2e and S3b. The different orientations are also stated in the corresponding figure legends.

      Page 9 - The use of "AFM" for AlphaFoldMultimer" is a little confusing since AFM is the established acronym for Atomic Force Microscopy. Perhaps AF2M?

      We have now replaced AFM with AF2M on page 9 to avoid confusion.

      Figure S4a - Control missing for Mis18-alpha wild-type

      Apology for the confusion, this control is present in Fig. 4a. We have now stated this in the figure legend of S4a for clarity.

      Figure S4 d and e - The contrast between the bands and the background is very bad (at least in my copy).

      We have now adjusted the contrast of the blots in Fig. S4d and S4e response to this comment.

      Page 13 "Our structural analysis suggests that two Mis18BP1 fragments.....". How did you arrive at this conclusion? Is this based on the AlphaFold/RaptorX model? What additional evidence do you have that the positioning of the Mis18BP1 is correct? Does the CLMS data support this?

      We confirm that this statement is based on AlphaFold model. We have now explicitly highlighted this on page 14, line 5. As noted in the same paragraph (page 14, line 19), this model agrees with the contacts suggested by the cross-linking mass spectrometry data presented here.

      Figure 4a - Would the authors like to consider using a different colour for Mis18BP1? The contrast is not great, especially in the electrostatic surface inset.

      In response to this suggestion, the Mis18BP1 helix is now shown in grey in the inset of Fig. 5a.

      Reviewer #1 (Significance (Required)):

      General Assessment The paper is extremely clearly written. Likewise the figures are beautifully presented and the data extremely clean and fully supportive of the authors conclusions. Indeed it is seldom that one sees the depth of the structural approaches (X-ray, CLMS, EM, SAXS) in one paper which is a huge strength of the manuscript. In addition the translation of this data into very clean cell biological experiments, makes the paper truly outstanding.

      Advance The authors provide the first model of the Mis18 complex, with extensive evidence to back up this model. The authors provide additional evidence as to how the deposition/renewal of CENP-A might be mediated by the Mis18 complex. The advance comes from both the level of clarity, detail, and scope achieved in this paper.

      Audience This will likely be of great interest to anyone with an interest in chromosome biology, plus be of interest to structural biologists as an outstanding example of hybrid structural biology.

      Expertise I am a biochemist with a background in structural biology with some familiarity with centromere biology

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

      Summary: The manuscript "structural basis for Mis18 complex assembly: implications for centromere maintenance" by Thamkachy and colleagues describes a study that uses structural analysis to test essential candidate residues in Mis18 complex components in CENP-A loading. For chromosomes to faithfully segregate during cell division, CENP-A levels must be maintained at the centromere. How CENP-A levels are maintained is therefore important to understand at the mechanistic level. The Mis18 complex has been found to be important, but how exactly the various Mis18 complex components interact and how they regulate new CENP-A loading remains not fully understood. This study set out to characterize the critical residues using X-ray crystallography, negative staining EM, SEC analysis, molecular modeling (Raptorx, AlphaFold2, and AlphaFold-multimer) to identify the residues of Mis18a and Mis18b that are critical for the formation of the Mis18a/b hetero-hexamer and which residues are important for Mis18a and Mis18BP1 interactions. A complex beta-sheet interface dictates the Mis18a and Mis18b interactions. Mutating the Mis18a residues that are important for the Mis18a/b interactions resulted in impaired pull-down of Mis18b and reduced centromeric levels of mutated Mis18a. The functional consequences of mutating residues that impair Mis18a/b interactions is that with reduced centomeric levels of Mis18a, also impaired new CENP-A loading. Interestingly, mutated Mis18b did not impact centromeric Mis18a levels and only modestly impaired new CENP-A loading. These data were interpreted that Mis18a is critical for new CENP-A loading, whereas Mis18b might be involved in finetuning how much new CENP-A is loaded. Overall, it is a very well described and well written study with exciting data.

      Major comments:

      • Overall, the structural data and the IF data support the importance of Mis18a residues 103-105 are critical for centromeric localization and new CENP-A loading, whereas Mis18b residues L199 and I203 are critical for centromeric localization, but only very modestly impair centromeric Mis18a localization and new CENP-A loading. In the discussion the authors argue that the N-terminal helical region of Mis18a mediate HJURP binding. This latter is postulated based on published work, but not tested in this work. This should be clarified as such.

      We thank the reviewer for this comment. Our very recent study aimed at understanding the licencing role of Plk1, independent of the work reported here, serendipitously has now validated this suggestion and demonstrates that a Plk1-mediated phosphorylation cascade activates the Mis18a/b complex via a conformational switch of the N-terminal helical region of Mis18a, which facilitates a robust HJURP-Mis18a/b interaction (Parashara et al. bioRxiv 2024). An independent study from the Musacchio lab (Conti et al. bioRxiv, 2024) also reports similar findings, mutually strengthening our independent conclusions. Overall, these studies highlight the importance of the critical structural insights into the Mis18 complex this study reports. We now explicitly discuss the validation of our original hypothesis by citing our recent work along with that of the Musacchio lab. The corresponding section of the last paragraph now reads as follows (page 17 line 10): "Previously published work identified amino acid sequence similarity between the N-terminal region of Mis18a and R1 and R2 repeats of the HJURP that mediates Mis18a/b interaction (Pan et al., 2019). Deletion of the Mis18a N-terminal region enhanced HJURP interaction with the Mis18 complex (Pan et al., 2019). Here, we show that the N-terminal helical region of Mis18a makes extensive contact with the C-terminal helices of Mis18a and Mis18b, which had previously been shown to mediate HJURP binding by Pan et al., 2019. Collectively these observations suggest that the N-terminal region of Mis18a might directly interfere with HJURP - Mis18 complex interaction. Two independent recent studies (Parashara et al., 2024, Conti et al., 2024) reveal that this is indeed the case and a Plk1-mediated phosphorylation cascade involving several phosphorylation and binding events of the Mis18 complex subunits relieve the intramolecular interactions between the Mis18a N-terminal helical region and the HJURP binding surface of the Mis18a/b C-terminal helical bundle. This facilitates robust HJURP-Mis18a/b interaction in vitroand efficient HJURP centromere recruitment and CENP-A loading in cells. Overall, these studies also highlight the importance of the critical structural insights into the Mis18 complex we report here."

      • Overall, the authors clearly describe their data and methodology and use adequate statistical analyses. The structural data of the Mis18a/b complex being a hetero-hexamer is convincing, but the validation in vivo is missing. As structural experiment are not performed under physiological conditions, it is important to establish the stoichiometry in vivo to further support the totality of the findings of the structural experiments and modeling. The data for the hierarchical assembly of Mis18a and Mis18b at the centromere and its importance in new CENP-A loading is convincing. An additional open question is whether "old" centromeric CENP-A or HJURP:new CENP-A complex is needed to recruit Mis18a to the centromere and whether the identified residues have a role in Mis18a centromeric localization. These data would provide a solid link between the Mis18 complex and how it is directly linked to new CENP-A loading.

      We agree that establishing the stoichiometry of Mis18 subunits of the Mis18 complex in vivo would be insightful. However, considering that the Mis18 complex assembles in a specific window of the cell cycle (late Mitosis and early G1), we think characterising the stoichiometry in cells is extremely difficult and technically challenging. However, consistent with our structural model, several lines of independent evidence (Pan et al., 2017 and Spiller et al., 2017) using different biophysical methods (Analytical Ultra Centrifugation (Pan et al., 2017), SEC-MALS (Spiller et al., 2017)) showed that recombinantly purified Mis18 complex (irrespective of the expression host, from both E. Coli or insect cells) is a hetero-octamer made of a hetero-hexameric Mis18a/b (4 Mis18a and 2 Mis18 b) complex bound to two copies of Mis18BP1. These observations suggested that hetero-hexamerisation of the Mis18a/b complex may be needed to bind and dimerise Mis18BP1 in cells. Previously published cellular studies support the in vivo requirement of the hetero-octameric Mis18 assembly as: (i) Perturbing the hetero-hexamerisation of the Mis18a/b complex (by introducing mutations at the Mis18a/b Yippee dimerisation interface, which while did not disrupt Mis18a/b complex formation, perturbed its hetero-hexamerisation and resulted in a hetero-trimeric Mis18a/b complex made of 2 Mis18aand 1 Mis18b) abolished Mis18BP1 binding in vitro and in cells, consequently abolished CENP-A deposition (Spiller et al., 2017) and (ii) artificial dimerisation of Mis18BP1, by expressing Mis18BP1 as a GST-tagged protein, enhanced the centromere localisation of Mis18BP1 highlighting the requirement of Mis18a/b hexameric assembly mediated dimerization of Mis18BP1 in cells (Pan et al., 2017). While these studies highlighted the importance of maintaining the right stoichiometry (hetero-octamer of 4 Mis18a, 2 Mis18b and 2 Mis18BP1), lack of structural information on how this essential biological assembly is established remained a major knowledge gap. Our work presented here fills this critical knowledge gap by showing that a segment of Mis18BP1 (aa 20-51) also binds at the Yippee dimerisation interface. To highlight this, we have included the following statements in the introduction on page 5 and 20 "Perturbing the Yippee domain-mediated hexameric assembly of Mis18a/b (that resulted in a Mis18a/b hetero-trimer, 2 Mis18a and 1 Mis18b) abolished its ability to bind Mis18BP1 in vitro and in cells (Spiller et al., 2017), emphasising the requirement of maintaining correct stoichiometry of Mis18a/b subunits. Consistent with this, artificial dimerisation of Mis18BP1, by expressing Mis18BP1 as a GST-tagged protein, enhanced the centromere localisation of Mis18BP1 (Pan et al., 2017)." and in the Results section on page 14 line 12: "Mis18BP120-51 contains two short b strands that interact at Mis18a/b Yippee interface extending the six-stranded-b sheets of both Mis18a and Mis18b Yippee domains. This provides the structural rationale for why Yippee domains-mediated Mis18a/b hetero-hexamerisation is crucial for Mis18BP1 binding (Spiller et al., 2017)."

      Regarding the question "whether 'old' centromeric CENP-A or HJURP:new CENP-A complex is needed to recruit Mis18a centromere localisation and whether identified residues have a role in Mis18a centromere localisation": According to the published literature, the Mis18 complex associates with centromeres through interaction with CCAN components CENP-C and CENP-I (Shono et al., 2015, Dambacher et al., 2012, Moree et al., 2011, Hoffmann et al., 2020). Considering CCAN assembles on CENP-A nucleosomes, and HJURP:new CENP-A centromere recruitment depends on the Mis18 complex, it will be reasonable to argue that the 'old' centromeric CENP-A contributes to the centromere localisation of the Mis18 complex. Amongst the components of the Mis18 complex, Mis18BP1 and Mis18bhave previously been suggested to interact with CENP-C. Within the Mis18 complex, we (Spiller et al., 2017) and others (Pan et al., 2017) have shown that Mis18a can directly interact with Mis18BP1, but it does so more efficiently when Mis18a hetero-oligomerises with Mis18b via their Yippee domains. Here, our structural analysis mapped the interaction interfaces and showed that Mis18a residues E103, D104 and T105 contribute to Mis18BP1 binding, as mutating these residues abolishes centromere localisation of Mis18a (Fig. 5c and 5d). To accentuate our findings, we have now included the following paragraph in the discussion section (page 17 line 26): "One of the key outstanding questions in the field is how does the Mis18 complex associate with the centromere. Previous studies identified CCAN subunits CENP-C and CENP-I as major players mediating the centromere localisation of the Mis18 complex mainly via Mis18BP1 (Shono et al., 2015, Dambacher et al., 2012, Moree et al., 2011), although Mis18b subunit has also been suggested to interact with CENP-C (Stellfox et al., 2016). Within the Mis18 complex, we and others have shown that the Mis18a/b Yippee hetero-dimers can directly interact with Mis18BP1. Here our structural analysis allowed us to map the interaction interface mediating Mis18a/b-Mis18BP1 binding. Perturbing this interface on Mis18a completely abolished Mis18a centromere localisation and reduced Mis18BP1 centromere levels. These observations show that Mis18a associates with the centromere mainly via Mis18BP1, and assembly of the Mis18 complex itself is crucial for its efficient centromere association, as previously suggested. Future work aimed at characterising the intermolecular contact points between the subunits of the Mis18 complex, centromeric chromatin and CCAN components and understanding if the Mis18 complex undergoes any conformational and/or compositional variations upon centromere association and/or during CENP-A deposition process, will be crucial to delineate the mechanisms underpinning the centromere maintenance."

      Minor comments:

      • The bar graphs shown ideally also show the individual data points for the authros to appreciate the spread of the data. These figures can be replicated in the Supplemental to avoid making the main figures look too busy.

      We thank the reviewers for this suggestion. Reviewer #3 made a similar comment and suggested we use Superplot, which allows visualisation of individual data points of independent experiments. We have now revised all bar graphs using Superplot to address both reviewers' suggestions.

      Reviewer #2 (Significance (Required)):

      • This study uses a broad range of structural techniques, including molecular modeling which were subsequently validated by in vitro pull-down assays, co-IP, and IF. This combination of these techniques is important because many structural techniques cannot be performed under physiological conditions. Validating the main findings of the structural results by IF and co-IP is therefore critical.
      • This work greatly advances our structural understanding how Mis18a, Mis18b, and Mis18BP1 form the Mis18 complex and how the critical residues in especially Mis18a help the Mis18 complex localize to the centromere and influence new CENP-A loading. This study also provides the first strong evidence in hierarchical assembly of the Mis18 complex.
      • How centromere identity is maintained is a critical question in chromosome biology and genome integrity. The Mis18 complex has been identified as an important complex in the process. Several structural and mutational studies (all adequately cited in this manuscript) have tried to address which residues guide the assembly and functional regions of the Mis18 complex. This work builds and expands our understanding how especially Mis18a holds a pivotal role in both Mis18 complex formation and its impact on maintaining centromeric CENP-A levels.
      • This work will be of interest to the chromosome field in general and anyone studying the mechanism of cell division.
      • Chromatin, centromere, CENP-A, cell division. This reviewer has limited expertise in structural biology.

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

      Centromere identity is defined by CENP-A loading to specific sites on genomic DNA. CENP-A loading is known to rely on the Mis18 complex, and several regulators are known; yet how the Mis18 complex achieves this complex process has remained puzzle. By elucidating the structural basis of Mis18 complex assembly using integrative structural approaches the authors show that multiple homo and heterodimeric interfaces of Mis18alpha, beta and Mis18BP1 are involved in centromere maintenance. The authors show that Mis18alpha can associate with centromeres and deposit CENP-A independent of Mis18 β. Mis18α functions in CENP-A deposition at centromeres independent of Mis18β. Mis18β is required for maintaining a specific level of CENP-A occupancy at centromeres. Thus, using structure-guided and separation-of-function mutants the study reveals how Mis18 complex ensures centromere maintenance. Major comments: This is an excellent study on centromere inheritance, combining structural and cell biology techniques. The comments here primarily refer to Cell biology aspect of the work.

      Figures show that new CENP-A deposits in Mis18βL199D/I203D mutants, but the level was reduced moderately. Based on this observation, the authors make a strong conclusion that Mis18β licenses the optimal levels of CENP-A at centromeres. Mis18α may be essential for both CENP-A incorporation and depositing a specific amount of CENP-A, as Mis18α and CENP-A levels are both reduced in Mis18βL199D/I203D mutants which failed to form the triple helical assembly with Mis18α as shown in Figure 3B and 3C. The authors may want to qualify some of these claims as preliminary or speculative.

      We thank the reviewer for this suggestion. We agree that although the reduction in CENP-A levels upon replacing WT Mis18b with Mis18b L199D/I203D is more prominent than the reduction in centromere localised Mis18a, one cannot completely rule out the contribution of reduced Mis18a on CENP-A loading. This also raises an interesting possibility where Mis18b ensures the correct amount of CENP-A deposition by facilitating the optimal level of Mis18a at centromeres. We now explicitly discuss this in the discussion as follows (page 16 line 26): "Whilst proteins involved in CENP-A loading have been well established, the mechanism by which the correct levels of CENP-A are controlled is yet to be thoroughly explored and characterised. The data presented here suggest that Mis18b mainly contributes to the quantitative control of centromere maintenance - by ensuring the right amounts of CENP-A deposition at centromeres - and maybe one of several proteins that control CENP-A levels. We also note that the Mis18b mutant, which cannot interact with Mis18a, moderately reduced Mis18a levels at centromeres, and hence, it is possible that Mis18b ensures the correct level of CENP-A deposition by facilitating optimal Mis18a centromere recruitment. Future studies will focus on dissecting the mechanisms underlying the Mis18b-mediated control of CENP-A loading amounts along with any other mechanisms involved."

      This work and others show that phosphorylation of Mis18BP1 by CDK1 can interfere with complex function (Spiller et al., 2017, Pan et al., 2017). Does the structure provide any insight into PLK1-mediated phosphorylation surfaces for activation of the complex? If yes, a brief discussion would help to link CDK1 and PLK1 mediated opposing actions will strengthen the work.

      As described in our response to the first major comment of Reviewer 2, our very recent study aimed at understanding the licencing role of Plk1, independent of the work reported here, identified and evaluated the functional contribution of Plk1 phosphorylation on the subunits of the Mis18 complex (Parashara et al., bioRxiv 2024). Serendipitously, this recent work has now validated our hypothesis proposed based on the structural characterisation reported here and demonstrates that a Plk1-mediated phosphorylation cascade activates the Mis18a/b complex via a conformational switch of the N-terminal helical region of Mis18a which facilitates a robust HJURP-Mis18a/b interaction (Parashara et al. bioRxiv 2024). An independent study from the Musacchio lab (Conti et al., bioRxiv 2024) also reports similar findings, mutually strengthening our independent conclusions. Overall, these studies highlight the importance of the critical structural insights into the Mis18 complex this study reports. We now explicitly discuss the validation of our original hypothesis by citing our recent work along with that of the Musacchio lab. The corresponding section of the last paragraph now reads as follows (page 17 line 10): "Previously published work identified amino acid sequence similarity between the N-terminal region of Mis18a and R1 and R2 repeats of the HJURP that mediates Mis18a/binteraction (Pan et al., 2019). Deletion of the Mis18a N-terminal region enhanced HJURP interaction with the Mis18 complex (Pan et al., 2019). Here, we show that the N-terminal helical region of Mis18a makes extensive contact with the C-terminal helices of Mis18a and Mis18b, which had previously been shown to mediate HJURP binding by Pan et al., 2019. Collectively these observations suggest that the N-terminal region of Mis18a might directly interfere with HJURP - Mis18 complex interaction. Two independent recent studies (Parashara et al., 2024, Conti et al., 2024) reveal that this is indeed the case and a Plk1-mediated phosphorylation cascade involving several phosphorylation and binding events of the Mis18 complex subunits relieve the intramolecular interactions between the Mis18a N-terminal helical region and the HJURP binding surface of the Mis18a/b C-terminal helical bundle. This facilitates robust HJURP-Mis18a/b interaction in vitro and efficient HJURP centromere recruitment and CENP-A loading in cells. Overall, these studies also highlight the importance of the critical structural insights into the Mis18 complex we report here."

      I am happy with the way cell biology data and the methods are presented so that they can be reproduced. The experiments are adequately replicated and the statistical analysis adequate. It will help to include sample size of cells or centromeres used for building the graphs.

      We have now included this information in figure legends of Fig. 3a, 3c, 4b, 4c, 5b, 5c and 5d.

      This is a strong interdisciplinary study using a variety of in vitro and in vivo techniques. Can the authors discuss if they expect chromatin associated Mis18 complex to host a similar structure as the soluble one? In other words, are they able to comment on any key differences between chromatin and non-chromatin associated Mis18 complexes.

      We thank the reviewer for the suggestion. We agree that one cannot rule out the possibility of the Mis18 complex undergoing compositional and/or conformational variations during the processes of CENP-A loading at centromeres. We now explicitly discuss this possibility in the last paragraph of the discussion section (page 18 line 10): "Future work aimed at characterising the intermolecular contact points between the subunits of the Mis18 complex, centromeric chromatin and CCAN components and understanding if the Mis18 complex undergoes any conformational and/or compositional variations upon centromere association and/or during CENP-A deposition process, will be crucial to delineate the mechanisms underpinning the centromere maintenance."

      Minor comments: -

      In cell biology experiments, fluorescence intensities could be presented as a superplot for added value across cells and repeats (instead of bar graphs). More on superplot:https://doi.org/10.1083/jcb.202001064.

      We thank the reviewers for this kind suggestion. We have now included graphs made using 'superplot' as suggested.

      In general, ACA levels do not appear to change significantly between WT and mutant expressing cells although new CENP-A loading is significantly absent in the presence of a few mutants - please comment if ACA used here can recognise CENP-A. Would this mean that old CENP-A remains normally?

      We thank the reviewer for this comment. While new CENP-A incorporated at centromeres is selectively labelled using the SNAP-tag, the ACA antibody used in these experiments can recognise CENP-A, CENP-B and CENP-C, with CENP-B being the primary target (Kallenberg, Clinical Rheumatology,1990). We would also like to note that ACA has commonly been used to locate the centromere in CENP-A loading assays where new CENP-A levels are assessed via selective labelling (e.g. McKinley 2014).

      It is unclear whether any of the mutant acted in a dominant negative fashion in the presence of endogenous Mis18 proteins. It would have been useful to test this particularly in the context of mis18alpha mutants that seem to fully abolish new CENP-A recruitment.

      As Mis18 subunits oligomerise (homo and hetero), we thought expressing these mutants in the presence of endogenous proteins might interfere with endogenous protein in a heterogenous manner and might make the interpretation difficult. Hence, we did not test this. Instead, as described in the manuscript we have tested these mutants in siRNA rescue experiments (Fig. 3, 4 and 5).

      In figure 3a, GFP panel (input lane, 1) is shown to mark a band corresponding to GFP. Is this expected? Please comment.

      Yes, as a control, an empty vector was transfected to express just GFP along with Mis18a-mCherry. These were used to show that there was no unspecific interaction between the beads used for IP or Mis18a-mCherry and GFP tag, and that any interaction seen was due to Mis18b. A similar control was used in S4b, where mCherry was expressed along with Mis18b-GFP. We have now clarified this in the corresponding legends of Fig. 4a and S4b.

      Would be useful to have the scale for the cropped images presented as insets. Figure 4B should read YFP and not YPF.

      We apologise for this typographical error. We have now corrected this.

      The authors may want to explain whether the tag differences matter for their study (Case in point: His-SUMO-Mis18a191-233 WT and mutant His-MBP-Mis18b188-229 proteins).

      The MBP tag was chosen to perform amylose pull-down assays, whereas the SUMO tag was chosen to increase the protein size. This is crucial as the C-terminal fragments of Mis18a and Mis18b are less than 50 amino acids long and are not easy to visualise by the band intensity in the Coomassie-stained SDS PAGE gels.

      Reviewer #3 (Significance (Required)):

      This work elucidates the structural basis of Mis18 complex assembly and the intermolecular interfaces essential for Mis18 functions. This is a significant advance in the field as it helps researchers in the field better understand CENP-A deposition and mechanism underpinning the maintenance of centromere identity. This is a broad area of research benefitting those studying cell division, genome stability, centromere identity and epigenetics might all be interested in and influenced by these findings. Novelty and strength lies in combining structural and cell biology work. Strengths of the work are structural details of the Mis18 complex. Minor weakness is the link between Mis18 structure and Centromere inheritance is limited to one immunostaining assay (I have mentioned this as a minor comment because addressing this may not be within the scope of this manuscript and is likely to require a repeat of a vast majority of the work with additional reagents which may not directly add value to the current manuscript).

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      Referee #3

      Evidence, reproducibility and clarity

      Centromere identity is defined by CENP-A loading to specific sites on genomic DNA. CENP-A loading is known to rely on the Mis18 complex, and several regulators are known; yet how the Mis18 complex achieves this complex process has remained puzzle. By elucidating the structural basis of Mis18 complex assembly using integrative structural approaches the authors show that multiple homo and heterodimeric interfaces of Mis18alpha, beta and Mis18BP1 are involved in centromere maintenance. The authors show that Mis18alpha can associate with centromeres and deposit CENP-A independent of Mis18 β. Mis18α functions in CENP-A deposition at centromeres independent of Mis18β. Mis18β is required for maintaining a specific level of CENP-A occupancy at centromeres. Thus, using structure-guided and separation-of-function mutants the study reveals how Mis18 complex ensures centromere maintenance.

      Major comments:

      This is an excellent study on centromere inheritance, combining structural and cell biology techniques. The comments here primarily refer to Cell biology aspect of the work.

      1. Figures show that new CENP-A deposits in Mis18βL199D/I203D mutants, but the level was reduced moderately. Based on this observation, the authors make a strong conclusion that Mis18β licenses the optimal levels of CENP-A at centromeres. Mis18α may be essential for both CENP-A incorporation and depositing a specific amount of CENP-A, as Mis18α and CENP-A levels are both reduced in Mis18βL199D/I203D mutants which failed to form the triple helical assembly with Mis18α as shown in Figure 3B and 3C. The authors may want to qualify some of these claims as preliminary or speculative.
      2. This work and others show that phosphorylation of Mis18BP1 by CDK1 can interfere with complex function (Spiller et al., 2017, Pan et al., 2017). Does the structure provide any insight into PLK1-mediated phosphorylation surfaces for activation of the complex? If yes, a brief discussion would help to link CDK1 and PLK1 mediated opposing actions will strengthen the work.
      3. I am happy with the way cell biology data and the methods are presented so that they can be reproduced. The experiments are adequately replicated and the statistical analysis adequate. It will help to include sample size of cells or centromeres used for building the graphs.
      4. This is a strong interdisciplinary study using a variety of in vitro and in vivo techniques. Can the authors discuss if they expect chromatin associated Mis18 complex to host a similar structure as the soluble one? In other words, are they able to comment on any key differences between chromatin and non-chromatin associated Mis18 complexes.

      Minor comments:

      In cell biology experiments, fluorescence intensities could be presented as a superplot for added value across cells and repeats (instead of bar graphs). More on superplot: https://doi.org/10.1083/jcb.202001064. In general, ACA levels do not appear to change significantly between WT and mutant expressing cells although new CENP-A loading is significantly absent in the presence of a few mutants - please comment if ACA used here can recognise CENP-A. Would this mean that old CENP-A remains normally?

      It is unclear whether any of the mutant acted in a dominant negative fashion in the presence of endogenous Mis18 proteins. It would have been useful to test this particularly in the context of mis18alpha mutants that seem to fully abolish new CENP-A recruitment.

      In figure 3a, GFP panel (input lane, 1) is shown to mark a band corresponding to GFP. Is this expected? Please comment. Would be useful to have the scale for the cropped images presented as insets.

      Figure 4B should read YFP and not YPF.

      The authors may want to explain whether the tag differences matter for their study (Case in point: His-SUMO-Mis18a191-233 WT and mutant His-MBP-Mis18b188-229 proteins).

      Significance

      This work elucidates the structural basis of Mis18 complex assembly and the intermolecular interfaces essential for Mis18 functions. This is a significant advance in the field as it helps researchers in the field better understand CENP-A deposition and mechanism underpinning the maintenance of centromere identity. This is a broad area of research benefitting those studying cell division, genome stability, centromere identity and epigenetics might all be interested in and influenced by these findings. Novelty and strength lies in combining structural and cell biology work.

      Strengths of the work are structural details of the Mis18 complex. Minor weakness is the link between Mis18 structure and Centromere inheritance is limited to one immunostaining assay (I have mentioned this as a minor comment because addressing this may not be within the scope of this manuscript and is likely to require a repeat of a vast majority of the work with additional reagents which may not directly add value to the current manuscript).

  3. Feb 2024
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      Reply to the reviewers

      Manuscript number: RC-2023-02270

      Corresponding author(s): Usha Vijayraghavan

      General Statements

      We thank all three Reviewers for their thorough assessment of our manuscript and their constructive feedback and comments.

      Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      Reviewer #1

      We are encouraged by the very positive comments made on the significance of our study that it provides convincing insights on alternative modes of nuclear positioning and division which is an important question in cell biology. We also took all possible suggestions to improve the interpretation of our results, have also added some newer data to address the constructive points raised by the reviewer.

      Major comments:

      1. A) I am concerned about the lethal phenotype caused by slu7 deprivation. Slu7 deficiency causes defective nuclear positioning at the bud in late G2. This phenotype per se should not cause defective mitosis, so slu7 deficiency may also be interfering with other aspects of mitosis which might indeed impinge on cell viability.

      Response: Our data indeed show Slu7 knockdown has severe growth defect when grown on non-permissive media (YPD) where a two-fold difference in O.D. was seen by 12 hours (Supplementary figure 2.B).

      We agree with the reviewer that defective mitosis, arises from several aspects of cell cycle including those in mitosis. The data we present show G2 arrest, small-budded cells with unsegregated nuclei and large-budded cells with segregated nuclei, all which do not progress through cell cycle phases and contribute to the severe growth defect. Further, GO enrichment analysis of deregulated pathways on knockdown of Slu7 support the above findings as various cell cycle related pathways are abnormal in their expression levels. In this study, we have focused on an in depth analysis of the role of Slu7 in a particular window and uncover how it controls nuclear position for progress G2-M phase cell cycle progression. The likely targets and mechanisms by which Slu7 regulates other phases of the cell cycle which needs similar other deeper investigations in future. Our detailed analysis of nuclear movement in Slu7 knockdown cells grown in YPD for 12 hours showed no nuclear movement (Supplementary figure 3B) which is the terminal phenotype. To examine events that lead to nuclear mispositioning phenotype we investigated the dividing slu7kd cells grown in non-permissive media for only 6 hours; under these conditions Slu7 protein is still detected at lower amount (Supplementary figure 1D). From the studies of nuclear position, mitotic spindle position and dynein distribution in mother and daughter cell, we propose that in the dividing cells, the nucleus does not experience enough force to move inside the daughter bud during mitosis. Further, we delineate the role of Slu7 in the splicing of transcripts for PAC1 encoding a protein whose homolog in S. cerevisiae has a proven role in nuclear migration. In live imaging of slu7kd cells that show nuclear segregation at the start of live imaging, new bud was not formed till the end of 60 minutes, implying that are arrested after transition to mitosis. We could speculate a role for Slu7 through regulation of genes involved in mitotic exit or cytokinesis.

      1. B) Supp. Fig4 shows defective mitosis in TBZ, so TBZ may be exacerbating defective mitosis of slu7-deficient cells.

      __Response: __Studies with yeast and mammalian model systems have revealed that the mobility and repair of damaged DNA are compromised upon disruption of microtubules (Wu et al, 2008; Chung et al, 2015; Lottersberger et al, 2015; Lawrimore et al, 2017; Oshidari et al, 2018; Laflamme et al, 2019). These data point to reasons why the mutants in DNA damage checkpoint genes are sensitive to TBZ. In this context, we observed that CnSlu7 knockdown is also sensitive to MMS stress (shown below). In addition, recent work on human Slu7 in Hela cell lines has elucidated the its role in the maintenance of genome integrity by preventing the formation of R-loops (Jiménez et al, 2019). We suggest that TBZ may exacerbate the defective mitosis of Slu7 depleted cells, however, whether it is particular only to mitosis or to the other cellular processes where the microtubules are involved needs further investigation.

      Throughout the figures it can be observed uneven chromosome/nuclear segregation in cells deprived of slu7, however, these mitotic defects have not been mentioned or explored in depth. From Supp Figure 3C it can be inferred that CENP-A segregation is uneven. Is this correct? Is CENP-A-GFP segregation normal?

      __Response: __ It should be noted that in Cryptococcus, the kinetochore remains unclustered during the early phase of cell cycle, cluster to a single punctum at the end of G2 phase and then de-cluster at the end of mitosis. Since this is a highly dynamic process, its technically challenging to measure the intensity CENP-A in mother and daughter cell. In the fixed cell imaging or live imaging data, there are no appreciable differences in intensity of the GFP signal of the tagged proteins (H4 and CENPA). The uneven chromosome/nuclear segregation observed in certain panels images presented are due to technical issues in that particular stack while generating the montage. This has been re-examined and we infer that there are no major differences in the signals from GFP-H4 and GFP - CENPA through mitosis.

      Additionally, taking the cue from the reviewer’s comment, we examined the likelihood of improper chromosome segregation by evaluating if there are any appreciable cell populations that are aneuploid. We revisited our flow cytometry data, we found no significant difference in the population of aneuploid cells between the knockdown strain and wildtype strain grown in non-permissive condition for 12 hours. This data was assessed again in new experiments where we also analyzed by flow cytometry the ipl1 mutant where aneuploidy is reported (Varshney et al, 2019). It has been reported in Cryptococcus neoformans that aneuploid cells are resistance to anti-fungal drug fluconazole. Preliminary experiments showed that slu7kd cells were sensitive to fluconazole and in this assay were similar to wildtype cells. Hence, we speculate that chromosome segregation is normal in Slu7 depleted cells.

      If chromosome segregation is altered upon slu7 deprivation, this might also explain the drop in cell viability and slow growth rates of this condition.

      __Response: __ From live microscopy imaging and flow cytometry data, we believe that the chromosome segregation is normal in Slu7 depleted cells. Dilution spotting in permissive media after growth in non-permissive media revealed that slu7kd cells resumed growth without losing viability, indicating the arrest phenotype associated with the depletion of Slu7 is largely reversible and does not cause chromosome mis-segregation (figure is now added to manuscript as supplementary figure 2D). Prolonged arrest at various cell cycle phase might lead to cell death and hence drop in cell viability.

      The manuscript will improve if authors analyse chromosome segregation for example, by showing time-lapse images of chromosome dynamics during mitosis.

      __Response: __Chromosome dynamics during the mitotic phase is given below. We observe that the chromosome segregation is equal in both mother and daughter bud. The uneven chromosome/nuclear segregation observed in certain panels images presented in original manuscript were due to technical issues while generating the montage.

      The authors perform an RNA seq comparing wild-type cells with slu7 deficiency and detect changes in gene expression, however, they do not explore from this data the percentage of un-spliced introns genome-wide which might be very informative, even more than changes in gene expression, which many of them, might be an indirect consequence of Slu7 deficiency. Authors should re-analyze the RNA seq data looking for unprocessed mRNAs and provide information about the overall impact of slu7 in intron processing.

      __Response: __ A very detailed bioinformatic analysis of the impact on slu7 on global transcriptome and splice pattern, is an ongoing study in the laboratory. The findings are indeed giving good leads which are being validated by further experiments using mini-gene exon-intron constructs. These studies are extensive and form a future manuscript identifying and characterizing intronic features which predispose an intron towards Slu7 dependency. Therefore, it falls outside the scope for this study on the cell biological role of Slu7 on mitosis, specifically nuclear position to ensure faithful mitotic segregation.

      Minor comments:

      __ __1. "Previous studies of slu7 mutants in S. cerevisiae and the conditional knockdown of its S. pombe homolog". Consider replacing homolog with Ortholog.

      Response: The suggestion is well taken, and the word “homolog” has been replaced with word “ortholog”.

      1. A) Taking these results together, we conclude that the inability of the conditional mutant to grow in the non-permissive media is due to impaired progression through the G2-M phase of the cell cycle. Is the G2/M delay the cause of the slow growth phenotype of the Slu7 deficiency?

      Response: From the live microscopy, we note that even when the budding index for mitosis has been reached the nucleus in slu7kd cells is still in the mother cell and spends more time here rather than reaching the bud or bud neck. We present G2/M delay as ONE of the reasons for the slow growth of Slu7 depleted cells. Although we have showed that Slu7 depletion does not activate MAD2 dependent Spindle Assembly Checkpoint, we have not investigated the activation of other cell cycle checkpoints such as G2 DNA damage checkpoint. These are potential new leads as we infer from our RNA seq datasets that CHK1, TEL1, BDR1 and RAD51 show increased expression in Slu7 knockdown condition when compared to wildtype. It is therefore reasonable to conclude that Slu7 might play a role at various cell cycle phases through direct or indirect effect on genes involved in these phases. Delayed positioning of the nucleus during G2/M is one of the major effects that is investigated in depth in this study.

      1. B) If so, growth defects of slu7 deficiency could be suppressed by ectopic expression of G2/M activators.

      Response: We have not tested this possibility, but we predict that expression of G2/M activators would at best offer only partial rescue the growth defect of Slu7 depleted cells since multiple pathways are adversely affected in cells depleted of Slu7.

      In this line of investigation, we have tested the consequences of PAC1 overexpression, as PAC1 expression levels and splicing are affected by loss of Slu7. We report a partial rescue of nuclear position defect during mitosis, yet these cells were arrested at cytokinesis. Further, the unavailability of an array of suitable auxotrophic (or other) markers in this model system makes it technically challenging to do rescue experiments by overexpression of multiple candidate downstream genes.

      Supp Figure 3C, remove the drawing on the right. Adjust times relative to panels.

      Response: The drawing has been removed and the time points have been adjusted.

      1. Tracking the nucleus in wild-type cells with a small bud showed that the nucleus moved into the daughter bud, divided into two, and one-half migrated to the mother bud (Supplementary Figure 3B, top row).

      Please replace the sentence: "one-half" with "one of the daughter nuclei". Additionally, as this nuclear positioning occurring during late mitosis is due to spindle elongation, I would not use the term migrated but "positioned" or "moved". Nuclear movement into the bud, which is referred to as "moved", can indeed be named "migrated".

      Response: The word “migrated” in the above sentence has been replaced with the word “moved”.

      1. Indicates in Figure 2B the marker used (GFP-H4), as in Fig Supp 3B.

      Response: The marker has been indicated in the figure.

      1. Nuclear division initiates in the bud, and one of the divided nuclei with segregated chromosomes migrates back to the mother cell (Figure 2B, top panel, wildtype, quantified in Figure 2C grey bar).

      As mentioned before, I would not name this, nuclear migration as it is the result of spindle elongation, and it can be confusing or misleading for non-expert readers.

      Response: The word “migrate” in the above sentence has been replaced with the word “move”.

      1. These two conclusions should be revised and described in temporal/sequential order.
      2. Thus, we identify that the depletion of CnSlu7 severely affects the temporal and spatial sequence of events during mitosis, particularly nuclear migration and division.
      3. Together, these results confirmed that without affecting the kinetochore clustering, depletion of Slu7 affects nuclear migration during the G2 to mitotic transition in Cryptococcus neoformans.

      Response: We thank the reviewer for bringing out the clarity in the concluding statements. These has now been revised to read as follows:

      “Together, these results confirm that without affecting the kinetochore clustering, depletion of Slu7 affects nuclear movement during the G2 to mitotic transition in Cryptococcus neoformans. Thus, we identify that the depletion of CnSlu7 severely affects the temporal and spatial sequence of events during mitosis, particularly nuclear migration, and division.”

      1. In slu7d cells, in cells with small buds, numerous cMTs were nucleated from the MTOCs, and as the cell cycle progressed, they organized to form the unipolar mitotic spindle (Figure 3A, slu7kd GFP-TUB1 panel, time point 55 mins).

      Please, revise whether the term unipolar mitotic spindle is correct here.

      Response: The word unipolar has been removed.

      1. I suggest including page and line numbers in the manuscript to facilitate revision.

      Response: We regret missing out this formatting guideline. The Page and line numbers have provided.

      Reviewer #2

      We are thankful by the very positive comments on the significance of our work, its novelty and findings being of broad interest to microbiology; splicing; cell cycle and cell division communities. We respond to all comments raised below.

      1. The authors test the Mad2-dependent spindle assembly checkpoint and show that it is not relevant for slu7-depletion. This is as expected if the defect is in nuclear positioning. They could test other checkpoint pathways that would monitor nuclear positioning in budding yeasts. Perhaps they have considered this: Bub2, Bfa1, Tem1, Lte1 mutants? I don't think this experiment is essential for publication, but it could strongly support their model.

      Response: We appreciate the comment on other checkpoints operating during mitosis. However, we have not done these experiments to examine role of components that arrest mitosis (Bub2, Tem1 etc.) in response to spindle or kinetochore damage. We hope the reviewer appreciates that this line of work would require the generation of bub2Δ strain and extensive characterization for their role in checkpoint in Cryptococcus before it can be brought into strains compromised for Slu7.

      __ Minor comments:__ 1. in Figure 3, Dyn1-GFP is imaged and in many of the cells in which Slu7 is depleted, nothing (or very little) can be seen. It is later argued that this is an indirect effect, due to defects in Pac1 and associated functions. Have the authors attempted a Dynein western blot (the 3xGFP tag should be quite sensitive)? It would be good to demonstrate that the Dynein motor complex hasn't simply fallen apart and Dynein been degraded in the slu7-depletion.

      Response: A study in S. cerevisiae has reported the dynein expression does not change in pac1Δ cells (Lee et al., 2003). Since the molecular weight of CnnDYN1 along with the tag is 630kDa, we did attempt the very challenging experiment of western blot to check for the expression levels this very large protein in wildtype and slu7kd cells. Based on the reviewer’s suggestion, we have attempted dot blot of protein lysates from wild type and from slu7kd cells probed with anti GFP antibody for estimating DYN-GFP levels. Untagged WT H99 strain was used as negative control. The same blot was stripped and re-probed for PSTAIRE which served as a loading control. This experiment revealed that dynein levels are same in both wildtype and slu7kd cells.

      in Figure 7: have any intronless genes been tested for rescue of the post-mitotic delay/arrest? This is not necessary for publication, but if any have been tested already, they could be listed here.

      Response: We have not tested intronless genes for their role in the rescue of post mitotic delay/arrest. From the RNA seq data, we observed that most of the genes involved in mitotic exit network (MEN) and cytokinesis were highly expressed in slu7kd cells as compared to the wildtype indicating and indirect role for Slu7 in their expression level. So, we had validated three candidates MOB2, CDC12 and DBF2 by qRT PCR (Supplementary 7.D) and found they were upregulated in slu7kd cells and hence speculate that deregulation of these transcript could contribute to the post mitotic arrest in slu7kd.

      In SFig2C legend make it clear that these cells are HU arrested at time zero. Are the cells in glucose or galactose during HU treatment.?

      Response: We regret the lack of clarity in the legend and the required details have been added. The cells were initially grown in non-permissive media for 2 hours to deplete Slu7 and then HU was added to the non-permissive media and the cell were allowed to grow for 4 hours.

      in SFig4, the TBZ sensitivity isn't very convincing as the slu7kd strain is struggling to grow at all on YPD.

      Response: We agree with the reviewer comment on the growth of slu7kd cells on media YPD containing TBZ. TBZ may exacerbate the defective mitosis of Slu7 depleted cells, however whether it pertains only to mitosis or any cellular processes where microtubules are involved requires further investigation.

      In SFig5 legend the volcano plot needs to be better explained. What are the dashed lines etc. ?

      Response: We regret missing these details on the volcano plot which has now been added to the legend.

      __Reviewer #3 __

      We appreciate the views that our work provides strong evidence to support out conclusions that Cryptococcus neoformans Slu7 controls mitotic progression by efficient splicing of cell cycle regulators and cytoskeletal elements. We have taken all comments of the reviewer into account to revise our manuscript with additional data, and by improving the presentation. The key additional data are summarized below.

      Major comments:

      1) The authors claimed that CnSlu7 is the most divergent among the fungal homologs and closer to its human counterpart (Fig. 1A, Supplementary Fig 1A). -Just based on the phylogenetic tree including limited members, as in Supplementary Fig. 1, it cannot be concluded that CnSlu7 is closer to its human counterpart since the basidiomycete yeast such as C. neoformans itself is more closely positions to humans compared to the ascomycete yeasts S. cerevisiae and Sch. pombe in phylogenetic tree analysis. It is strongly recommended to include other fungal species from the Basidiomycota, such as Ustilago maydis, in phylogenetic analysis in Supplementary Fig. 1. - Conservation analysis among diverse eukaryotes is more meaningful data that the conservation withing the fungi group, so that it is recommended that the data of Fig. 1 A would be replaced with the revised Supplementary Fig 1. -The analysis data on amino acid identities among Slu7 homologues should be presented to support the claim.

      Response: We agree with the reviewer that our data would be better served by an improved analysis of the phylogenetic relationship between various Slu7 homologs. We have therefore reconstructed the phylogenetic tree by including other fungal groups. This is presented here and also in the revised manuscript Supplementary Figure 1A. These data too, show that Cryptococcus (deneoformans and neoformans) Slu7 is the most diverged among its homologs from various fungal species with its closest homologs being other pathogens Puccinia graminis and Ustilago maydis.

      2) Despite that CnSlu7 is the main key subject, the comparative analysis of CnSlu7 to the previously reported Slu7 homologues, in the aspect of functional domain organization, is not provided in the present manuscript. - It was reported that Slu7 contains the four motifs that control its cellular localization and canonical function as a splicing factor, such as a nuclear location signal, a zinc knuckle motif, four stretches of leucine repeats and a lysine-rich domain. Notably, human Slu7 protein is 204 amino acids longer than S. cerevisiae homolog with only 24% identity in the zinc knuckle motif (Molecular Biology of the Cell Vol. 15, 3782-3795). Thus, it is strongly recommended to provide additional information on the conserved and diverged features of CnSlu7 compared to other Slu7 homologs as a part of revised Figure.

      Response: The multiple sequence alignment of Cryptococcus neoformans Slu7 with its fungal and higher eukaryote homologs such as human Slu7 and plant Slu7 proteins revealed that only the CCHC zinc finger motif is highly conserved. We do not detect conservation in the nuclear localization signal, stretch of leucine repeats and lysine rich domain except for leucine 3 stretch near the C terminal. This additional information is presented in revised Figure 1A.

      3) The manuscript clearly demonstrated that one of key targets of Slu7-mediated splicing is PAC1 in C. neoformans. Considering, Pac1 is also conserved from S. cerevisiae to human, it could be speculated that the defect of Slu7 can affect nuclear migration in other fungal species and human cells by inefficient splicing of PAC1, despite striking differences in their nuclear position during cell division. Please discuss this possibility or provide the qRT-PCR analysis data of PAC1 homologs in the available fungal Slu7 mutant strains.

      Response: Cell cycle arrest phenotypes of splicing factor mutants (studied largely in budding and fission yeast) results from inefficient pre-mRNA splicing of cell cycle-related genes. Slu7 is a well characterized second step splicing factor in S. cerevisiae where in vitro splicing assays with ACT1 minigene transcripts with a modified single intron showed ScSlu7 is dispensable for splicing when the branchpoint to 3'SS distance is less than seven nucleotides in the mini transcript (Brys and Schwer, 1996). In fission yeast we reported the effects of metabolic depletion of Slu7, which is an essential gene (Banerjee et al., 2013) and showed unexpectedly that in addition to BrP to 3'SS distance new intronic features contributors of dependency of fission yeast intron containing transcripts on Slu7 functions. The work also showed in multi-intronic transcripts its role is intron-specific and thus the candidate gene/ transcript is likely to be to dependent on Slu7 by virtue of the intronic features and not its biological function. In this study a splicing dependent role of CnSlu7 in cell cycle progression is investigated where based on a strong nuclear mis-positioning phenotype we narrowed on PAC1 transcripts as one of targets. We show PAC1, encoding a cytoskeletal factor, has introns dependent on CnSlu7 for efficient splicing and show partial rescue of nuclear position in strain complemented with expression of an intronless PAC1 gene. In this scenario, while it is likely that in other species where PAC1 exon-introns nucleotide sequences are similar to that in Cryptococcus a role for Slu7 may be predicted, for validation by other experimentalists.

      Interestingly, PAC1 in S. cerevisiae is an intronless gene and its homolog is not annotated in S. pombe. In human cell lines, knockdown of Slu7 by siRNA resulted in metaphase arrest by inefficient splicing of soronin – which is crucial in sister chromatid cohesion and correct spindle assembly, according to recent research in human cell lines (Jiménez et al., 2019).

      Hence the roles of splicing factor in cell cycle is through splicing of targets involved in cell cycle wherein the targets regulated by splicing factor may or may not be conserved in other species.

      Minor comments:

      General points 1) Provide information on the marker sizes in the data of qRT-PCR analysis presented in Figures 5 and 6, and Supplementary Fig 2A.

      Response: We regret the omission of this technical data and have corrected the same by providing the marker sizes in all the figures.

      2) Please unify the format of gene names. Some genes were written with superscript of "+", such as CLN1+ and PAC1+ in Fig. 4. What does "+" mean in the gene names?

      Response: We have taken the suggestion to carefully review the nomenclature of genes and their expressed transcripts as is typical for Cryptococcus neoformans. To depict the wildtype form of transcript we had used +. Thus CLN1+ was used to denote Cyclin 1 cellular transcript from expressed from its own locus without any modification of promoter or the intronic features.

      3) Supplementary Figure 1 C: Please correct "Slu7KD" 6 hrs YPD to "slu7kd" 6 hrs YPD.

      Response: This error has been corrected.

      4) Supplementary Figure 2A: What do "mRNA" and "No RT29X/", respectively, indicate?

      Response: The mRNA indicates the spliced form across any intron after intron is spliced out, so denotes exon-exon sequences in the mRNA. The reactions marked as “No RT 29 X” denote semi- quantitative PCR performed on DNase treated RNA sample, without reverse transcription to generate the cDNA. These reactions were done to confirm that there is no genomic DNA present in the RNA sample used for reverse transcription reaction of the cellular transcripts. Some of these details are now included in the Supp Fig 2A legend.

      5) Supplementary Figure 4C: Please provide brief explanation in the text on why the authors employed mad2Δ slu7kd cells.

      Response: In Page 8, line 6, we had provided the rationale for generating and studying mad2Δ slu7kd strain. This is recapitulated below:

      “To investigate whether Slu7 knockdown triggers the activation of spindle assembly checkpoint (SAC), we generated a strain with conditional slu7kd in cells with mad2Δ allele and the GFP-H4 nuclear marker.”

      6) Supplementary Figure 6D legend: Please correct the description of "slu7kd SH:Slu7 FL" from "expressing intronless PAC1" to "expressing full length of SLU7".

      Response: The error in the legend is regretted and this has been corrected.

      7) Supplementary Figure 7D: The authors confirmed that MOB2, CDC12, and DFB1 were expressed at higher levels in slu7kd when compared to wildtype. Please briefly explain in the text why the expression level of these genes in slu7kd was mentioned.

      Response: slu7kd cells expressing intronless Pac1 arrest post nuclear division. Revisiting our transcriptomic data, we found that genes involved in mitosis exit network and cytokinesis, such as DFB1, MOB2, CDC12, BUD4, and CHS2, were deregulated in slu7kd when compared to wildtype. We confirmed the same by performing qRT PCRs for three candidates, MOB2, DBF1 and CDC12 and that these transcript were expressed at high levels in knockdown when compared to wildtype.

      8) The species name should be written as abbreviation after the first mention. For example, please correct Cryptococcus neoformans to C. neoformans throughout manuscript.

      Response: The suggestion is well taken, and the required edits have been made throughout the text.

      9) Please unify the format of paper titles listed in References.

      Response: This formatting error is regretted and corrected to have all references in a single format.

      10) No page information for Hoffmann et al (2010) in References.

      Response: This omission is corrected.

      11) Update the information on the published journal of Chatterjee et al. (2021) in References.

      Response: This omission is regretted and is now corrected.

      12) Information on the authors, title, published journal and pages should be provided for the papers (Yadav and Sanyal, 2018; Sridhar et al., 2021) in Supplementary Table 1, which were not included in the main Reference list.

      Response: The references are now added to the main list.

      References used for addressing the reviewer’s comments:

      1. Chung DKC, Chan JNY, Strecker J, Zhang W, Ebrahimi-Ardebili S, Lu T, Abraham KJ, Durocher D, Mekhail K (2015) Perinuclear tethers license telomeric DSBs for a broad kinesin- and NPC-dependent DNA repair process. Nat Commun doi:10.1038/NCOMMS8742.
      2. Jiménez M, Urtasun R, Elizalde M, Azkona M, Latasa MU, Uriarte I, Arechederra M, Alignani D, Bárcena-Varela M, Alvarez-Sola G et al (2019) Splicing events in the control of genome integrity: Role of SLU7 and truncated SRSF3 proteins. Nucleic Acids Res 47: 3450–3466. doi:10.1093/nar/gkz014.
      3. Laflamme G, Sim S, Leary A, Pascariu M, Vogel J, D’Amours D (2019) Interphase Microtubules Safeguard Mitotic Progression by Suppressing an Aurora B-Dependent Arrest Induced by DNA Replication Stress. Cell Rep 26: 2875-2889.e3. doi:10.1016/J.CELREP.2019.02.051.
      4. Lawrimore J, Barry TM, Barry RM, York AC, Friedman B, Cook DM, Akialis K, Tyler J, Vasquez P, Yeh E et al (2017) Microtubule dynamics drive enhanced chromatin motion and mobilize telomeres in response to DNA damage. Mol Biol Cell 28: 1701–1711. doi:10.1091/MBC.E16-12-0846.
      5. Lee WL, Oberle JR, Cooper JA (2003) The role of the lissencephaly protein Pac1 during nuclear migration in budding yeast. J Cell Biol. doi:10.1083/jcb.200209022.
      6. Lottersberger F, Karssemeijer RA, Dimitrova N, De Lange T (2015) 53BP1 and the LINC Complex Promote Microtubule-Dependent DSB Mobility and DNA Repair. Cell 163: 880–893. doi:10.1016/J.CELL.2015.09.057.
      7. Oshidari R, Strecker J, Chung DKC, Abraham KJ, Chan JNY, Damaren CJ, Mekhail K (2018) Nuclear microtubule filaments mediate non-linear directional motion of chromatin and promote DNA repair. Nat Commun doi:10.1038/S41467-018-05009-7.
      8. Varshney N, Som S, Chatterjee S, Sridhar S, Bhattacharyya D, Paul R, Sanyal K (2019) Spatio-temporal regulation of nuclear division by Aurora B kinase Ipl1 in Cryptococcus neoformans. PLoS Genet doi:10.1371/journal.pgen.1007959.
      9. Wu G, Zhou L, Khidr L, Guo XE, Kim W, Lee YM, Krasieva T, Chen PL (2008) A novel role of the chromokinesin Kif4A in DNA damage response. Cell Cycle 7: 2013–2020. doi:10.4161/CC.7.13.6130.
    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This important study combines a range of advanced ultrastructural imaging approaches to define the unusual endosomal system of African trypanosomes. Compelling images show that instead of a distinct set of compartments, the endosome of these protists comprises a continuous system of membranes with functionally distinct subdomains as defined by canonical markers of early, late and recycling endosomes. The findings suggest that the endocytic system of bloodstream stages has evolved to facilitate the extraordinarily high rates of membrane turnover needed to remove immune complexes and survive in the blood, which is of interest to anyone studying infectious diseases.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Bloodstream stages of the parasitic protist, Trypanosoma brucei, exhibit very high rates of constitutive endocytosis, which is needed to recycle the surface coat of Variant Surface Glycoproteins (VSGs) and remove surface immune complexes. While many studies have shown that the endo-lysosomal systems of T. brucei BF stages contain canonical domains, as defined by classical Rab markers, it has remained unclear whether these protists have evolved additional adaptations/mechanisms for sustaining these very high rates of membrane transport and protein sorting. The authors have addressed this question by reconstructing the 3D ultrastructure and functional domains of the T. brucei BF endosome membrane system using advanced electron tomography and super-resolution microscopy approaches. Their studies reveal that, unusually, the BF endosome network comprises a continuous system of cisternae and tubules that contain overlapping functional subdomains. It is proposed that a continuous membrane system allows higher rates of protein cargo segregation, sorting and recycling than can otherwise occur when transport between compartments is mediated by membrane vesicles or other fusion events.

      Strengths:

      The study is a technical tour-de-force using a combination of electron tomography, super-resolution/expansion microscopy, immune-EM of cryo-sections to define the 3D structures and connectivity of different endocytic compartments. The images are very clear and generally support the central conclusion that functionally distinct endocytic domains occur within a dynamic and continuous endosome network in BF stages.

      Weaknesses:

      The authors suggest that this dynamic endocytic network may also fulfil many of the functions of the Golgi TGN and that the latter may be absent in these stages. Although plausible, this comment needs further experimental support. For example, have the authors attempted to localize canonical makers of the TGN (e.g. GRIP proteins) in T. brucei BF and/or shown that exocytic carriers bud directly from the endosomes?

      We agree with the criticism and have shortened the discussion accordingly and clearly marked it as speculation. However, we do not want to completely abandon our hypothesis.

      The paragraph now reads:

      Lines 740 – 751:

      “Interestingly, we did not find any structural evidence of vesicular retrograde transport to the Golgi. Instead, the endosomal ‘highways’ extended throughout the posterior volume of the trypanosomes approaching the trans-Golgi interface. It is highly plausible that this region represents the convergence point where endocytic and biosynthetic membrane trafficking pathways merge. A comparable merging of endocytic and biosynthetic functions has been described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019). As we could not find structural evidence for the existence of a TGN we tentatively propose that trypanosomes may have shifted the central orchestrating function of the TGN as a sorting hub at the crossroads of biosynthetic and recycling pathways to the endosome. Although this is a speculative scenario, it is experimentally testable.”

      Furthermore, we removed the lines 51 - 52, which included the suggestion of the TGN as a master regulator, from the abstract.

      Reviewer #2 (Public Review):

      The authors suggest that the African trypanosome endomembrane system has unusual organisation, in that the entire system is a single reticulated structure. It is not clear if this is thought to extend to the lysosome or MVB. There is also a suggestion that this unusual morphology serves as a trans-(post)Golgi network rather than the more canonical arrangement.

      The work is based around very high-quality light and electron microscopy, as well as utilising several marker proteins, Rab5A, 11 and 7. These are deemed as markers for early endosomes, recycling endosomes and late or pre-lysosomes. The images are mostly of high quality but some inconsistencies in the interpretation, appearance of structures and some rather sweeping assumptions make this less easy to accept. Two perhaps major issues are claims to label the entire endosomal apparatus with a single marker protein, which is hard to accept as certainly this reviewer does not really even know where the limits to the endosomal network reside and where these interface with other structures. There are several additional compartments that have been defined by Rob proteins as well, and which are not even mentioned. Overall I am unconvinced that the authors have demonstrated the main things they claim.<br /> The endomembrane system in bloodstream form T. brucei is clearly delimited. Compared to mammalian cells it is tidy and confined to the posterior part of the spindleshaped cell. The endoplasmic reticulum is linked to one side of the longitudinal cell axis, marked by the attached flagellum, while the mitochondrion locates to the opposite side. Glycosomes are easily identifiable as spheres, as are acidocalcisomes, which are smaller than glycosomes and – in electron micrographs – are characterized by high electron density. All these organelles extend beyond the nucleus, which is not the case for the endosomal compartment, the lysosome and the Golgi. The vesicles found in the posterior half of the trypanosome cell are quantitatively identifiable as COP1, CCVI or CCVII vesicles, or exocytic carriers. The lysosome has a higher degree of morphological plasticity, but this is not topic of the present work. Thus, the endomembrane system in T. brucei is comparatively well structured and delimited, which is why we have chosen trypanosomes as cell biological model.

      We have published EP1::GFP as marker for the endosome system and flagellar pocket back in 2004. We have defined the fluid phase volume of the trypanosome endosome in papers published between 2002 and 2007. This work was not intended to represent the entirety of RAB proteins. We were only interested in 3 canonical markers for endosome subtypes. We do not claim anything that is not experimentally tested, we have clearly labelled our hypotheses as such, and we do not make sweeping assumptions.

      The approaches taken are state-of-the-art but not novel, and because of the difficulty in fully addressing the central tenet, I am not sure how much of an impact this will have beyond the trypanosome field. For certain this is limited to workers in the direct area and is not a generalisable finding.

      To the best of our knowledge, there is no published research that has employed 3D Tokuyasu or expansion microscopy (ExM) to label endosomes. The key takeaway from our study, which is the concept that "endosomes are continuous in trypanosomes" certainly is novel. We are not aware of any other report that has demonstrated this aspect.

      The doubts formulated by the reviewer regarding the impact of our work beyond the field of trypanosomes are not timely. Indeed, our results, and those of others, show that the conclusions drawn from work with just a few model organisms is not generalisable. We are finally on the verge of a new cell biology that considers the plethora of evolutionary solutions beyond ophistokonts. We believe that this message should be widely acknowledged and considered. And we are certainly not the only ones who are convinced that the term "general relevance" is unscientific and should no longer be used in biology.

      Reviewer #3 (Public Review):

      Summary:

      As clearly highlighted by the authors, a key plank in the ability of trypanosomes to evade the mammalian host’s immune system is its high rate of endocytosis. This rapid turnover of its surface enables the trypanosome to ‘clean’ its surface removing antibodies and other immune effectors that are subsequently degraded. The high rate of endocytosis is likely reflected in the organisati’n and layout of the endosomal system in these parasites. Here, Link et al., sought to address this question using a range of light and three-dimensional electron microscopy approaches to define the endosomal organisation in this parasite.

      Before this study, the vast majority of our information about the make-up of the trypanosome endosomal system was from thin-section electron microscopy and immunofluorescence studies, which did not provide the necessary resolution and 3D information to address this issue. Therefore, it was not known how the different structures observed by EM were related. Link et al., have taken advantage of the advances in technology and used an impressive combination of approaches at the LM and EM level to study the endosomal system in these parasites. This innovative combination has now shown the interconnected-ness of this network and demonstrated that there are no ‘classical’ compartments within the endosomal system, with instead different regions of the network enriched in different protein markers (Rab5a, Rab7, Rab11).

      Strengths:

      This is a generally well-written and clear manuscript, with the data well-presented supporting the majority of the conclusions of the authors. The authors use an impressive range of approaches to address the organisation of the endosomal system and the development of these methods for use in trypanosomes will be of use to the wider parasitology community.

      I appreciate their inclusion of how they used a range of different light microscopy approaches even though for instance the dSTORM approach did not turn out to be as effective as hoped. The authors have clearly demonstrated that trypanosomes have a large interconnected endosomal network, without defined compartments and instead show enrichment for specific Rabs within this network.

      Weaknesses:

      My concerns are:

      i) There is no evidence for functional compartmentalisation. The classical markers of different endosomal compartments do not fully overlap but there is no evidence to show a region enriched in one or other of these proteins has that specific function. The authors should temper their conclusions about this point.

      The reviewer is right in stating that Rab-presence does not necessarily mean Rabfunction. However, this assumption is as old as the Rab literature. That is why we have focused on the 3 most prominent endosomal marker proteins. We report that for endosome function you do not necessarily need separate membrane compartments. This is backed by our experiments.

      ii) The quality of the electron microscopy work is very high but there is a general lack of numbers. For example, how many tomograms were examined? How often were fenestrated sheets seen? Can the authors provide more information about how frequent these observations were?

      The fenestrated sheets can be seen in the majority of the 37 tomograms recorded of the posterior volume of the parasites. Furthermore, we have randomly generated several hundred tiled (= very large) electron micrographs of bloodstream form trypanosomes for unbiased analyses of endomembranes. In these 2D-datasets the “footprint” of the fenestrated flat and circular cisternae is frequently detectable in the posterior cell area.

      We now have included the corresponding numbers in all EM figure legends.

      iii) The EM work always focussed on cells which had been processed before fixing. Now, I understand this was important to enable tracers to be used. However, given the dynamic nature of the system these processing steps and feeding experiments may have affected the endosomal organisation. Given their knowledge of the system now, the authors should fix some cells directly in culture to observe whether the organisation of the endosome aligns with their conclusions here.

      This is a valid criticism; however, it is the cell culture that provides an artificial environment. As for a possible effect of cell harvesting by centrifugation on the integrity and functionality of the endosome system, we consider this very unlikely for one simple reason. The mechanical forces acting in and on the parasites as they circulate in the extremely crowded and confined environment of the mammalian bloodstream are obviously much higher than the centrifugal forces involved in cell preparation. This becomes particularly clear when one considers that the mass of the particle to be centrifuged determines the actual force exerted by the g-forces. Nevertheless, the proposed experiment is a good control, although much more complex than proposed, since tomography is a challenging technique. We have performed the suggested experiment and acquired tomograms of unprocessed cells. The corresponding data is now included as supplementary movie 2, 3 and 4. We refer to it in lines 202 – 206: To investigate potential impacts of processing steps (cargo uptake, centrifugation, washing) on endosomal organization, we directly fixed cells in the cell culture flask, embedded them in Epon, and conducted tomography. The resulting tomograms revealed endosomal organization consistent with that observed in cells fixed after processing (see Supplementary movie 2, 3, and 4).

      We furthermore thank the reviewer for the experiment suggestion in the acknowledgments.

      iv) The discussion needs to be revamped. At the moment it is just another run through of the results and does not take an overview of the results presenting an integrated view. Moreover, it contains reference to data that was not presented in the results.

      We have improved the discussion accordingly.

      Recommendations for the authors:

      The reviewers concurred about the high calibre of the work and the importance of the findings.

      They raised some issues and made some suggestions to improve the paper without additional experiments - key issues include

      (1) Better referencing of the trypanosome endocytosis/ lysosomal trafficking literature.

      The literature, especially the experimental and quantitative work, is very limited. We now provide a more complete set of references. However, we would like to mention that we had cited a recent review that critically references the trypanosome literature with emphasis on the extensive work done with mammalian cells and yeast.

      (2) Moving the dSTORM data that detracts from otherwise strong data in a supplementary figure.

      We have done this.

      (3) Removal of the conclusion that the continuous endosome fulfils the functions of TGN, without further evidence.

      As stated above, this was not a conclusion in our paper, but rather a speculation, which we have now more clearly marked as such. Lines 740 to 751 now read:

      “Interestingly, we did not find any structural evidence of vesicular retrograde transport to the Golgi. Instead, the endosomal ‘highways’ extended throughout the posterior volume of the trypanosomes approaching the trans-Golgi interface. It is highly plausible that this region represents the convergence point where endocytic and biosynthetic membrane trafficking pathways merge. A comparable merging of endocytic and biosynthetic functions was already described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019). As we could not find structural evidence for the existence of a TGN we tentatively propose that trypanosomes may have shifted the central orchestrating function of the TGN as a sorting hub at the crossroads of biosynthetic and recycling pathways to the endosome. Although this is a speculative scenario, it is experimentally testable.”

      (4) Broader discussion linking their findings to other examples of organelle maturation in eukaryotes (e.g cisternal maturation of the Golgi)

      We have improved the discussion accordingly.

      Reviewer #1 (Recommendations For The Authors):

      What are the multi-vesicular vesicles that surround the marked endosomal compartments in Fig 1. Do they become labelled with fluid phase markers with longer incubations (e.g late endosome/ lysosomal)?

      The function of MVBs in trypanosomes is still far from being clear. They are filled with fluid phase cargo, especially ferritin, but are devoid of VSG. Hence it is likely that MVBs are part of the lysosomal compartment. In fact, this part of the endomembrane system is highly dynamic. MVBs can be physically connected to the lysosome or can form elongated structures. The surprising dynamics of the trypanosome lysosome will be published elsewhere.

      Figure 2. The compartments labelled with EP1::Halo are very poorly defined due to the low levels of expression of the reporter protein and/or sensitivity of detection of the Halo tag. Based on these images, it would be hard to conclude whether the endosome network is continuous or not. In this respect, it is unclear why the authors didn't use EP1-GFP for these analyses? Given the other data that provides more compelling evidence for a single continuous compartment, I would suggest removing Fig 2A.

      We have used EP1::GFP to label the entire endosome system (Engstler and Boshart, 2004). Unfortunately, GFP is not suited for dSTORM imaging. By creating the EP1::Halo cell line, we were able to utilize the most prominent dSTORM fluorescent dye, Alexa 647. This was not primarily done to generate super resolution images, but rather to measure the dynamics of the GPI-anchored, luminal protein EP with single molecule precision. The results from this study will be published separately. But we agree with the reviewer and have relocated the dSTORM data to the supplementary material.

      The observation that Rab5a/7 can be detected in the lumen of lysosome is interesting. Mechanistically, this presumably occurs by invagination of the limiting membrane of the lysosome. Is there any evidence that similar invagination of cytoplasmic markers occurs throughout or in subdomains of the endocytic network (possibly indicative of a 'late endosome' domain)?

      So far, we have not observed this. The structure of the lysosome and the membrane influx from the endosome are currently being investigated.

      The authors note that continuity of functionally distinct membrane compartments in the secretory/endocytic pathways has been reported in other protists (e.g T. cruzi). A particular example that could be noted is the endo-lysosomal system of Dictyostelium discoideum which mediates the continuous degradation and eventual expulsion of undigested material.

      We tried to include this in the discussion but ultimately decided against it because the Dictyostelium system cannot be easily compared to the trypanosome endosome.

      Reviewer #2 (Recommendations For The Authors):

      Abstract

      Not sure that 'common' is the correct term here. Frequent, near-universal..... it would be true that endocytosis is common across most eukaryotes.

      We have changed the sentence to “common process observed in most eukaryotes” (line 33).

      Immune evasion - the parasite does not escape the immune system, but does successfully avoid its impact, at least at the population level.

      We have replaced the word “escape” with “evasion” (line 35).

      The third sentence needs to follow on correctly from the second. Also, more than Igs are internalised and potentially part of immune evasion, such as C3, Factor H, ApoL1 etcetera.

      We believe that there may be a misunderstanding here. The process of endocytic uptake and lysosomal degradation has so far only been demonstrated in the context of VSGbound antibodies, which is why we only refer to this. Of course, the immune system comprises a wide range of proteins and effector molecules, all of which could be involved in immune evasion.

      I do not follow the logic that the high flux through the endocytic system in trypanosomes precludes distinct compartmentalisation - one could imagine a system where a lot of steps become optimised for example. This idea needs expanding on if it is correct.

      Membrane transport by vesicle transfer between several separate membrane compartments would be slower than the measured rate of membrane flux.

      Again I am not sure 'efficient' on line 40. It is fast, but how do you measure efficiency? Speed and efficiency are not the same thing.

      We have replaced the word “efficient” with “fast” (line 42).

      The basis for suggesting endosomes as a TGN is unclear. Given that there are AP complexes, retromer, exocyst and other factors that are part of the TGN or at least post-G differentiation of pathways in canonical systems, this seems a step too far. There really is no evidence in the rest of the MS that seems to support this.

      Yes, we agree and have clarified the discussion accordingly. We have not completely removed the discussion on the TGN but have labelled it more clearly as speculation.

      I am aware I am being pedantic here, but overall the abstract seems to provide an impression of greater novelty than may be the case and makes several very bold claims that I cannot see as fully valid.

      We are not aware of any claim in the summary that we have not substantiated with experiments, or any hypothesis that we have not explained.

      Moreover, the concept of fused or multifunctional endosomes (or even other endomembrane compartments) is old, and has been demonstrated in metazoan cells and yeast. The concept of rigid (in terms of composition) compartments really has been rejected by most folks with maturation, recycling and domain structures already well-established models and concepts.

      We agree that the (transient) presence of multiple Rab proteins decorating endosomes has been demonstrated in various cell types. This finding formed the basis for the endosomal maturation model in mammals and yeast, which has replaced the previous rigid compartment model.

      However, we do not appreciate attempts to question the originality of our study by claiming that similar observations have been made in metazoans or yeast. This is simply wrong. There are no reports of a functionally structured, continuous, single and large endosome in any other system. The only membrane system that might be similar was described in the American parasite Trypanosoma cruzi, however, without the use of endosome markers or any functional analysis. We refer to this study in the discussion.

      In summary, the maturation model falls short in explaining the intricacies of the membrane system we have uncovered in trypanosomes. Therefore, one plausible interpretation of our data is that the overall architecture of the trypanosome endosomes represents an adaptation that enables the remarkable speed of plasma membrane recycling observed in these parasites. In our view, both our findings and their interpretation are novel and worth reporting. Again, modern cell biology should recognize that evolution has developed many solutions for similar processes in cells, about whose diversity we have learned almost nothing because of our reductionist view. A remarkable example of this are the Picozoa, tiny bipartite eukaryotes that pack the entire nutritional apparatus into one pouch and the main organelles with the locomotor system into the other. Another one is the “extreme” cell biology of many protozoan parasites such as Giardia, Toxpoplasma or Trypanosoma.

      Higher plants have been well characterised, especially at the level of Rab/Arf proteins and adaptins.

      We now mention plant endosomes in our brief discussion of the trypanosome TGN. Lines 744 – 747:

      “A comparable merging of endocytic and biosynthetic functions was already described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019).”

      The level of self-citing in the introduction is irritating and unscholarly. I have no qualms with crediting the authors with their own excellent contributions, but work from Dacks, Bangs, Field and others seems to be selectively ignored, with an awkward use of the authors' own publications. Diversity between organisms for example has been a mainstay of the Dacks lab output, Rab proteins and others from Field and work on exocytosis and late endosomal systems from Bangs. These efforts and contributions surely deserve some recognition?

      This is an original article and not a review. For a comprehensive overview the reviewer might read our recent overview article on exo- and endocytic pathways in trypanosomes, in which we have extensively cited the work of Mark Field, Jay Bangs and Joel Dacks. In the present manuscript, we have cited all papers that touch on our results or are otherwise important for a thorough understanding of our hypotheses. We do not believe that this approach is unscientific, but rather improves the readability of the manuscript. Nevertheless, we have now cited additional work.

      For the uninitiated, the posterior/anterior axis of the trypanosome cell as well as any other specific features should be defined.

      In lines 102 - 110 we wrote:

      “This process of antibody clearance is driven by hydrodynamic drag forces resulting from the continuous directional movement of trypanosomes (Engstler et al., 2007). The VSG-antibody complexes on the cell surface are dragged against the swimming direction of the parasite and accumulate at the posterior pole of the cell. This region harbours an invagination in the plasma membrane known as the flagellar pocket (FP) (Gull, 2003; Overath et al., 1997). The FP, which marks the origin of the single attached flagellum, is the exclusive site for endo- and exocytosis in trypanosomes (Gull, 2003; Overath et al., 1997). Consequently, the accumulation of VSG-antibody complexes occurs precisely in the area of bulk membrane uptake.”

      We think this sufficiently introduces the cell body axes.

      I don't understand the comment concerning microtubule association. In mammalian cells, such association is well established, but compartments still do not display precise positioning. This likely then has nothing to do with the microtubule association differences.

      We have clarified this in the text (lines 192 – 199). There is no report of cytoplasmic microtubules in trypanosomes. All microtubules appear to be either subpellicular or within the flagellum. To maintain the structure and position of the endosomal apparatus, they should be associated either with subpellicular microtubules, as is the case with the endoplasmic reticulum, or with the more enigmatic actomyosin system of the parasites. We have been working on the latter possibility and intend to publish a follow-up paper to the present manuscript.

      The inability to move past the nucleus is a poor explanation. These compartments are dynamic. Even the nucleus does interesting things in trypanosomes and squeezes past structures during development in the tsetse fly.

      The distance between the nucleus and the microtubule cytoskeleton remains relatively constant even in parasites that squeeze through microfluidic channels. This is not unexpected as the nucleus can be highly deformed. A structure the size of the endosome will not be able to physically pass behind the nucleus without losing its integrity. In fact, the recycling apparatus is never found in the anterior part of the trypanosome, most probably because the flagellar pocket is located at the posterior cell pole.

      L253 What is the evidence that EP1 labels the entire FP and endosomes? This may be extensive, but this claim requires rather more evidence. This is again suggested at l263. Again, please forgive me for being pedantic, but this is an overstatement unless supported by evidence that would be incredibly difficult to obtain. This is even sort of acknowledged on l271 in the context of non-uniform labelling. This comes again in l336.

      The evidence that EP1 labels the entire FP and endosomes is presented here: Engstler and Boshart, 2004; 10.1101/gad.323404).

      Perhaps I should refrain from comments on the dangers of expansion microscopy, or asking what has actually been gained here. Oddly, the conclusion on l290 is a fair statement that I am happy with.

      An in-depth discussion regarding the advantages and disadvantages of expansion microscopy is beyond the manuscript's intended scope. Our approach involved utilizing various imaging techniques to confirm the validity of our findings. We appreciate that our concluding sentence is pleasing.

      F2 - The data in panel A seem quite poor to me. I also do not really understand why the DAPI stain in the first and second columns fails to coincide or why the kinetoplast is so diffuse in the second row. The labelling for EP1 presents as very small puncta, and hence is not evidence for a continuum. What is the arrow in A IV top? The data in panel B are certainly more in line with prior art, albeit that there is considerable heterogeneity in the labelling and of the FP for example. Again, I cannot really see this as evidence for continuity. There are gaps.... Albeit I accept that labelling of such structures is unlikely to ever be homogenous.

      We agree that the dSTORM data represents the least robust aspect of the findings we have presented, and we concur with relocating it to the supplementary material.

      F3 - Rather apparent, and specifically for Rab7, that there is differential representation - for example, Cell 4 presents a single Rab7 structure while the remaining examples demonstrate more extensive labelling. Again, I am content that these are highly dynamic strictures but this needs to be addressed at some level and commented upon. If the claim is for continuity, the dynamics observed here suggest the usual; some level of obvious overlap of organellar markers, but the representation in F3 is clever but not sure what I am looking at. Moreover, the title of the figure is nothing new. What is also a bit odd is that the extent of the Rab7 signal, and to some extent the other two Rabs used, is rather variable, which makes this unclear to me as to what is being detected. Given that the Rab proteins may be defining microdomains or regions, I would also expect a region of unique straining as well as the common areas. This needs to at least be discussed.

      The differences in the representation result from the dynamics of the labelled structures. Therefore, we have selected different cells to provide examples of what the labelling can look like. We now mention this in the results section.

      The overlap of the different Rab signals was perhaps to be expected, but we now have demonstrated it experimentally. Importantly, we performed a rigorous quantification by calculating the volume overlaps and the Pearson correlation coefficients.

      In previous studies the data were presented as maximal intensity projections, which inherently lack the complete 3D information.

      We found that Rab proteins define microdomains and that there are regions of unique staining as well as common areas, as shown in Figure 3. The volumes do not completely overlap. This is now more clearly stated in lines 315 – 319:

      “These objects showed areas of unique staining as well as partially overlapping regions. The pairwise colocalization of different endosomal markers is shown in Figure 3 A, XI - XIII and 3 B. The different cells in Figure 3 B were selected to represent the dynamic nature of the labelled structures. Consequently, the selected cells provide a variety of examples of how the labelling can appear.”

      This had already been stated in lines 331 – 336:

      “In summary, the quantitative colocalization analyses revealed that on the one hand, the endosomal system features a high degree of connectivity, with considerable overlap of endosomal marker regions, and on the other hand, TbRab5A, TbRab7, and TbRab11 also demarcate separated regions in that system. These results can be interpreted as evidence of a continuous endosomal membrane system harbouring functional subdomains, with a limited amount of potentially separated early, late or recycling endosomes.”

      F4-6 - Fabulous images. But a couple of issues here; first, as the authors point out, there is distance between the gold and the antigen. So, this of course also works in the z-plane as well as the x/y-planes and some of the gold may well be associated with membraneous figures that are out of the plane, which would indicate an absence of colinearity on one specific membrane. Secondly, in several instances, we have Rab7 essentially mixed with Rab11 or Rab5 positive membrane. While data are data and should be accepted, this is difficult to reconcile when, at least to some level, Rab7 is a marker for a late-endosomal structure and where the presence of degradative activity could reside. As division of function is, I assume, the major reason for intracellular compartmentalisation, such a level of admixture is hard to rationalise. A continuum is one thing but the data here seem to be suggesting something else, i.e. almost complete admixture.

      We are grateful for the positive feedback regarding the image quality. It is true that the "linkage error," representing the distance between the gold and the antigen, also functions to some extent in the z-axis. However, it's important to note that the zdimension of the section in these Figures is 55 nm. Nevertheless, it's interesting to observe that membranes, which may not be visible within the section itself but likely the corresponding Rab antigen, is discernible in Figure 4C (indicated by arrows).

      We have clarified this in lines 397 – 400:

      “Consequently, gold particles located further away may represent cytoplasmic TbRab proteins or, as the “linkage error” can also occur in the z-plane, correspond to membranes that are not visible within the 55 nm thickness of the cryosection (Figure 4, panel C, arrows). “

      The coexistence of different Rabs is most likely concentrated in regions where transitions between different functions are likely. Our focus was primarily on imaging membranes labelled with two markers. We wanted to show that the prevailing model of separate compartments in the trypanosome literature is not correct.

      F7 - Not sure what this adds beyond what was published by Grunfelder.

      First, this figure is an important control that links our results to published work (Grünfelder et al. (2003)). Second, we include double staining of cargo with Rab5, Rab7, and Rab11, whereas Grünfelder focused only on Rab11. Therefore, our data is original and of such high quality that it warrants a main figure.

      F8 - and l583. This is odd as the claim is 'proof' which in science is a hard thing to claim (and this is definitely not at a six sigma level of certainty, as used by the physics community). However, I am seeing structures in the tomograms which are not contiguous - there are gaps here between the individual features (Green in the figure).

      We have replaced the term "proof". It is important to note that the structures in individual tomograms cannot all be completely continuous because the sections are limited to a thickness of 250 nm. Therefore, it is likely that they have more connectivity above and below the imaged section. Nevertheless, we believe that the quality of the tomograms is satisfactory, considering that 3D Tokuyasu is a very demanding technique and the production of serial Tokuyasu tomograms is not feasible in practice.

      Discussion - Too long and the self-citing of four papers from the corresponding author to the exclusion of much prior work is again noted, with concerns about this as described above. Moreover, at least four additional Rab proteins are known associated with the trypanosome endosomal system, 4, 5B, 21 and 28. These have been completely ignored.

      We have outlined our position on referencing in original articles above. We also explained why we focused on the key marker proteins associated with early (Rab5), late (Rab7) and recycling endosomes (Rab11). We did not ignore the other Rabs, we just did not include them in the present study.

      Overall this is disappointing. I had expected a more robust analysis, with a clearer discussion and placement in context. I am not fully convinced that what we have here is as extreme as claimed, or that we have a substantial advance. There is nothing here that is mechanistic or the identification of a new set of gene products, process or function.

      We do not think that this is constructive feedback.

      This MS suggests that the endosomal system of African trypanosomes is a continuum of membrane structures rather than representing a set of distinct compartments. A combination of light and electron microscopy methods are used in support. The basic contention is very challenging to prove, and I'm not convinced that this has been. Furthermore, I am also unclear as to the significance of such an organisation; this seems not really addressed.

      We acknowledge and respect varying viewpoints, but we hold a differing perspective in this matter. We are convinced that the data decisively supports our interpretation. May future work support or refute our hypothesis.

      Reviewer #3 (Recommendations For The Authors):

      Line 81 - delete 's

      Done.
      

      Generally, the introduction was very well written and clearly summarised our current understanding but the paragraph beginning line 134 felt out of place and repeated some of the work mentioned earlier.

      We have removed this paragraph.

      For the EM analysis throughout quantification would be useful as highlighted in the public review. How many tomograms were examined, and how often were types of structures seen? I understand the sample size is often small but this would help the reader appreciate the diversity of structures seen.

      We have included the numbers.

      Following on from this how were the cells chosen for tomogram analysis? For example, the dividing cell in 1D has palisades associating with the new pocket - is this commonly seen? Does this reflect something happening in dividing cells. This point about endosomal division was picked up in the discussion but there was little about in the main results.

      This issue is undoubtedly inherent to the method itself, and we have made efforts to mitigate it by generating a series of tomograms recorded randomly. We have refrained from delving deeper into the intricacies of the cell cycle in this manuscript, as we believe that it warrants a separate paper.

      As the authors prosecute, the co-localisation analysis highlights the variable nature of the endosome and the overlap of different markers. When looking at the LM analysis, I was struck by the variability in the size and number of labelled structures in the different cells. For example, in 3A Rab7 is 2 blobs but in 3B Cell 1 it is 4/5 blobs. Is this just a reflection of the increase in the endosome during the cell cycle?

      The variability in representation is a direct consequence of the dynamic nature of the labelled structures. For this reason, we deliberately selected different cells to represent examples of how the labelling can look like. We have decided not to mention the dynamics of the endosome during the cell cycle. This will be the subject of a further report.

      Moreover, Rab 11 looks to be the marker covering the greatest volume of the endosomal system - is this true? I think there's more analysis of this data that could be done to try and get more information about the relative volumes etc of the different markers that haven't been drawn out. The focus here is on the co-localisation.

      Precisely because we recognize the importance of this point, we intend to turn our attention to the cell cycle in a separate publication.

      I appreciate that it is an awful lot of work to perform the immuno-EM and the data is of good quality but in the text, there could be a greater effort to tie this to the LM data. For example, from the Rab11 staining in LM you would expect this marker to be the most extensive across the networks - is this reflected in the EM?

      For the immuno-EM there were no numbers, the authors had measured the position of the gold but what was the proportion of gold that was in/near membranes for each marker? This would help the reader understand both the number of particles seen and the enrichment of the different regions.

      Our original intent was to perform a thorough quantification (using stereology) of the immuno-EM data. However, we later realized that the necessary random imaging approach is not suitable for Tokuyasu sections of trypanosomes. In short, the cells are too far apart, and the cell sections are only occasionally cut so that the endosomal membranes are sufficiently visible. Nevertheless, we continue to strive to generate more quantitative data using conventional immuno-EM.

      The innovative combination of Tokuyasu tomograms with immuno-EM was great. I noted though that there was a lack of fenestration in these models. Does this reflect the angle of the model or the processing of these samples?

      We are grateful to the referee, as we have asked ourselves the same question. However, we do not attribute the apparent lack of fenestration to the viewing angle, since we did not find fenestration in any of the Tokuyasu tomograms. Our suspicion is more directed towards a methodological problem. In the Tokuyasu workflow, all structures are mainly fixed with aldehydes. As a result, lipids are only effectively fixed through their association with membrane proteins. We suggest that the fenestration may not be visible because the corresponding lipids may have been lost due to incomplete fixation.

      We now clearly state this in the lines 563 – 568.

      “Interestingly, these tomograms did not exhibit the fenestration pattern identified in conventional electron tomography. We suspect that this is due to methodological reasons. The Tokuyasu procedure uses only aldehydes to fix all structures. Consequently, effective fixation of lipids occurs only through their association with membrane proteins. Thus, the lack of visible fenestration is likely due to possible loss of lipids during incomplete fixation.”

      The discussion needs to be reworked. Throughout it contains references to results not in the main results section such as supplementary movie 2 (line 735). The explicit references to the data and figures felt odd and more suited to the results rather than the discussion. Currently, each result is discussed individually in turn and more effort needs to be made to integrate the results from this analysis here but also with previous work and the data from other organisms, which at the moment sits in a standalone section at the end of the discussion.

      We have improved the discussion and removed the previous supplementary movies 2 and 3. Supplementary movie 1 is now mentioned in the results section.

      Line 693 - There was an interesting point about dividing cells describing the maintenance of endosomes next to the old pocket. Does that mean there was no endosome by the new pocket and if so where is this data in the manuscript? This point relates back to my question about how cells were chosen for analysis - how many dividing cells were examined by tomography?

      The fate of endosomes during the cell cycle is not the subject of this paper. In this manuscript we only show only one dividing cell using tomography. An in-depth analysis focusing on what happens during the cell cycle will be published separately.

      Line 729 - I'm unclear how this represents a polarization of function in the flagellar pocket. The pocket I presume is included within the endosomal system for this analysis but there was no specific mention of it in the results and no marker of each position to help define any specialisation. From the results, I thought the focus was on endosomal co-localisation of the different markers. If the authors are thinking about specialisation of the pocket this paper from Mark Field shows there is evidence for the exocyst to be distributed over the entire surface of the pocket, which is relevant to the discussion here. Boehm, C.M. et al. (2017) The trypanosome exocyst: a conserved structure revealing a new role in endocytosis. PLoS Pathog. 13, e1006063

      We have formulated our statement more cautiously. However, we are convinced that membrane exchange cannot physically work without functional polarization of the pocket. We know that Rab11, for example, is not evenly distributed on the pocket. By the way, in Boehm et al. (2017) the exocyst is not shown to cover the entire pocket (as shown in Supplementary Video 1).

      We now refer to Boehm et al. (Lines 700 – 703):

      “Boehm et al (2017) report that in the flagellar pocket endocytic and exocytic sites are in close proximity but do not overlap. We further suggest that the fusion of EXCs with the flagellar pocket membrane and clathrin-mediated endocytosis take place on different sites of the pocket. This disparity explains the lower colocalization between TbRab11 and TbRab5A.”

      Line 735 - link to data not previously mentioned I think. When I looked at this data I couldn't find a key to explain what all the different colours related to.

      We have removed the previous supplementary movies 2 and 3. We now reference supplementary movie 1 in the results section.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This important study elucidates the molecular divergence of caspase 3 and 7 in the vertebrate lineage. Convincing biochemical and mutational data provide evidence that in humans, caspase 7 has lost the ability to cleave gasdermin E due to changes in a key residue, S234. However, the physiological relevance of the findings is incomplete and requires further experimental work.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary

      In this study, Xu et al. provide insights into the substrate divergence of CASP3 and CASP7 for GSDME cleavage and activation during vertebrate evolution vertebrates. Using biochemical assays, domain swapping, site-directed mutagenesis, and bioinformatics tools, the authors demonstrate that the human GSDME C-terminal region and the S234 residue of human CASP7 are the key determinants that impede the cleavage of human GSDME by human CASP7.

      Strengths

      The authors made an important contribution to the field by demonstrating how human CASP7 has functionally diverged to lose the ability to cleave GSDME and showing that reverse-mutations in CASP7 can restore GSDME cleavage. The use of multiple methods to support their conclusions strengthens the authors' findings. The unbiased mutagenesis screen performed to identify S234 in huCASP7 as the determinant of its GSDME cleavability is also a strength.

      Weaknesses

      While the authors utilized an in-depth experimental setup to understand the CASP7-mediated GSDME cleavage across evolution, the physiological relevance of their findings are not assessed in detail. Additional methodology information should also be provided.

      Specific recommendations for the authors

      (1) The authors should expand their evaluation of the physiological relevance by assessing GSDME cleavage by the human CASP7 S234N mutant in response to triggers such as etoposide or VSV, which are known to induce CASP3 to cleave GSDME (PMID: 28045099). The authors could also test whether the human CASP7 S234N mutation affects substrate preference beyond human GSDME by testing cleavage of mouse GSDME and other CASP3 and CASP7 substrates in this mutant.

      (1) The physiological relevance was discussed in the revised manuscript (lines 328-340). Our study revealed the molecular mechanism underlying the divergence of CASP3- and CASP7-mediated GSDME activation in vertebrate. One of the physiological consequences is that in humans, CASP7 no longer directly participates in GSDME-mediated cell death, which enables CASP7 to be engaged in other cellular processes. Another physiological consequence is that GSDME activation is limited to CASP3 cleavage, thus restricting GSDME activity to situations more specific, such as that inducing CASP3 activation. The divergence and specialization of the physiological functions of different CASPs are consistent with and possibly conducive to the development of refined regulations of the sophisticated human GSDM pathways, which are executed by multiple GSDM members (A , B, C, D, and E), rather than by GSDME solely in teleost, such as Takifugu. More physiological consequences of CASP3/7 divergence in GSDME activation need to be explored in future studies.

      With respect to the reviewer’s suggestion of assessing GSDME cleavage by the human CASP7 S234N mutant in response to triggers such as etoposide or VSV: (i) CASP7 S234N is a creation of our study, not a natural human product, hence its response to CASP7 triggers cannot happen under normal physiological conditions except in the case of application, such as medical application, which is not the aim of our study. (ii) CASP3/7 activators (such as raptinal) induced robust activation of the endogenous CASP3 (Heimer et al., Cell Death Dis. 2019;10:556) and CASP7 (Author response image 1, below) in human cells. Since CASP3 is the natural activator of GSDME, the presence of the triggers inevitably activates GSDME via CASP3. Hence, under this condition, it will be difficult to examine the effect of CASP7 S234N.

      Author response image 1.

      HsCASP7 activation by raptinal. HEK293T cells were transfected with the empty vector (-), or the vector expressing HsCASP7 or HsCASP7-S234N for 24 h. The cells were then treated with or without (control) 5 μM raptinal for 4 h. The cells were lysed, and the lysates were blotted with anti-CASP7 antibody.

      (2) As suggested by the reviewer, the cleavage of other CASP7 substrates, i.e., poly (ADP-ribose) polymerase 1 (PARP1) and gelsolin, by HsCASP7 and S234N mutant was determined. The results showed that HsCASP7 and HsCASP7-S234N exhibited similar cleavage capacities. Figure 5-figure supplement 1 and lines 212-214.

      (2) It would also be interesting to examine the GSDME structure in different species to gain insight into the nature of mouse GSDME, which cannot be cleaved by either mouse or human CASP7.

      Because the three-dimensional structure of GSDME is not solved, we are unable to explore the structural mechanism underlying the GSDME cleavage by caspase. Since our results showed that the C-terminal domain was essential for caspase-mediated cleavage of GSDME, it is likely that the C-terminal domain of mouse GSDME may possess some specific features that render it to resist mouse and human CASP7.

      (3) The evolutionary analysis does not explain why mammalian CASP7 evolved independently to acquire an amino acid change (N234 to S234) in the substrate-binding motif. Since it is difficult to experimentally identify why a functional divergence occurs, it would be beneficial for the authors to speculate on how CASP7 may have acquired functional divergence in mammals; potentially this occurred because of functional redundancies in cell death pathways, for example.

      According to the reviewer’s suggestion, a speculation was added. Lines 328-340.

      (4) For the recombinant proteins produced for these analyses, it would be helpful to know whether size-exclusion chromatography was used to purify these proteins and whether these purified proteins are soluble. Additionally, the SDS-PAGE in Figure S1B and C show multiple bands for recombinant mutants of TrCASP7 and HsCASP7. Performing protein ID to confirm that the detected bands belong to the respective proteins would be beneficial.

      The recombinant proteins in this study are soluble and purified by Ni-NTA affinity chromatography. Size-exclusion chromatography was not used in protein purification.

      For the SDS-PAGE in Figure 4-figure supplement 1B and C (Figure S1B and C in the previous submission), the multiple bands are most likely due to the activation cleavage of the TrCASP7 and HsCASP7 variants, which can result in multiple bands, including p10 and p20. According to the reviewer’s suggestion, the cleaved p10 was verified by immunoblotting. Figure 4-figure supplement 1B and C.

      (5) For Figures 3C and 4A, it would be helpful to mention what parameters or PDB files were used to attribute these secondary structural features to the proteins. In particular, in Figure 3C, residues 261-266 are displayed as a β-strand; however, the well-known α-model represents this region as a loop. Providing the parameters used for these callouts could explain this difference.

      For Figure 3C, in the revised manuscript, we used the structure of mouse GSDMA3 (PDB: 5b5r) for the structural analysis of HsGSDME. As indicated by the reviewer, the region of 261-266 is a loop. The description was revised in lines 172 and 174, Figure 3C and Figure 3C legend.

      For Figure 4A, the alignment of CASP7 was constructed by using Esprit (https://espript.ibcp.fr/ESPript/cgi-bin/ESPript.cgi) with human CASP7 (PDB:1k86) as the template. The description was revised in the Figure legend.

      (6) Were divergent sequences selected for the sequence alignment analyses (particularly in Figure 6A)? The selection of sequences can directly influence the outcome of the amino acid residues in each position, and using diverse sequences can reduce the impact of the number of sequences on the LOGO in each phylogenetic group.

      In Figure 6A, the sequences were selected without bias. For Mammalia, 45 CASP3 and 43 CASP7 were selected; for Aves, 41 CASP3 and 52 CASP7 were selected; for Reptilia, 31CASP3 and 39 CASP7 were selected; for Amphibia, 11 CASP3 and 12 CASP7 were selected; for Osteichthyes, 40 CASP3 and 43 CASP7 were selected. The sequence information was shown in Table 1 and Table 2.

      (7) For clarity, it would help if the authors provided additional rationale for the selection of residues for mutagenesis, such as selecting Q276, D278, and H283 as exosite residues, when the CASP7 PDB structures (4jr2, 3ibf, and 1k86) suggest that these residues are enriched with loop elements rather than the β sheets expected to facilitate substrate recognition in exosites for caspases (PMID: 32109412). It is possible that the inability to form β-sheets around these positions might indicate the absence of an exosite in CASP7, which further supports the functional effect of the exosite mutations performed.

      According to the suggestion, the rationale for the selection of residues for mutagenesis was added (lines 216-222). Unlike the exosite in HsCASP1/4, which is located in a β sheet, the Q276, D278, and H283 of HsCASP7 are located in a loop region (Figure 5-figure supplement 2), which may explain the mutation results and the absence of an exosite in HsCASP7 as suggested by the reviewer.

      Reviewer #2 (Public Review):

      The authors wanted to address the differential processing of GSDME by caspase 3 and 7, finding that while in humans GSDME is only processed by CASP3, Takifugu GSDME, and other mammalian can be processed by CASP3 and 7. This is due to a change in a residue in the human CAPS7 active site that abrogates GSDME cleavage. This phenomenon is present in humans and other primates, but not in other mammals such as cats or rodents. This study sheds light on the evolutionary changes inside CASP7, using sequences from different species. Although the study is somehow interesting and elegantly provides strong evidence of this observation, it lacks the physiological relevance of this finding, i.e. on human side, mouse side, and fish what are the consequences of CASP3/7 vs CASP3 cleavage of GSDME.

      Our study revealed the molecular mechanism underlying the divergence of CASP3- and CASP7-mediated GSDME activation in vertebrate. One of the physiological consequences is that in humans, CASP7 no longer directly participates in GSDME-mediated cell death, which enables CASP7 to be engaged in other cellular processes. Another physiological consequence is that GSDME activation is limited to CASP3 cleavage, thus restricting GSDME activity to situations more specific, such as that inducing CASP3 activation. The divergence and specialization of the physiological functions of different CASPs are consistent with and possibly conducive to the development of refined regulations of the sophisticated human GSDM pathways, which are executed by multiple GSDM members (A , B, C, D, and E), rather than by GSDME solely in teleost, such as Takifugu. More physiological consequences of CASP3/7 divergence in GSDME activation need to be explored in future studies. Lines 328-340.

      Fish also present a duplication of GSDME gene and Takifugu present GSDMEa and GSDMEb. It is not clear in the whole study if when referring to TrGSDME is the a or b. This should be stated in the text and discussed in the differential function of both GSDME in fish physiology (i.e. PMIDs: 34252476, 32111733 or 36685536).

      The TrGSDME used in this study belongs to the GSDMEa lineage of teleost GSDME. The relevant information was added. Figure 1-figure supplement 1 and lines 119, 271, 274-276, 287 and 288.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) For the chimeric and truncated constructs, such as HsNT-TrCT, TrNT-HsCT, Hsp20-Trp10, Trp20-Hsp10, etc., the authors should provide a table denoting which amino acids were taken from each protein to create the fusion or truncation.

      According to the reviewer’s suggestion, the information of the truncate/chimeric proteins was provided in Table 4.

      (2) Both reviewers agree that functional physiological experiments are needed to increase the significance of the work. Specifically, the physiological relevance of these findings can be assessed by using western blotting to monitor GSDME cleavage by the human CASP7 S234N mutant compared with wild type CASP7 in response to triggers such as etoposide or VSV, which are known to induce CASP3 to cleave GSDME (PMID: 28045099).

      Additionally, the authors can assess cell death in HEK293 cells, HEK293 cells transfected with TrGSDME, HEK293 cells expressing TrCASP3/7 plus TrGSDME, and TrCASP3/7 plus the D255R/D258A mutant. These cells can be stimulated, and pyroptosis can be assessed by using ELISA to measure the release of the cytoplasmic enzyme LDH as well as IL-1β and IL-18, and the percentage of cell death (PI+ positive cells) may also be assessed.

      (1) With respect to the physiological relevance, please see the above reply to Reviewer 1’s comment of “Specific recommendations for the authors, 1”.

      (2) As shown in our results (Fig. 2), co-expression of TrCASP3/7 and TrGSDME in HEK293T cells induced robust cell death without the need of any stimulation, as evidenced by LDH release and TrGSDME cleavage. In the revised manuscript, similar experiments were performed as suggested, and cell death was assessed by Sytox Green staining (Figure 2-figure supplement 3A and B) and immunoblot to detect the cleavage of both wild type and mutant TrGSDME (Figure 2-figure supplement 3C). The results confirmed the results of Figure 2.

      Reviewer #2 (Recommendations For The Authors):

      Abstract:

      Although the authors try to summarize the principal results of this study, please rewrite the abstract section to make it easier to follow and to empathise the implications of their results.

      We have modified the Abstract as suggested by the reviewer.

      Introduction:

      The authors do not mention anything about the implication of the inflammasome activation to get pyroptosis by GSDM cleave by inflammatory caspases. Please consider including this in the introduction section as they do in the discussion section.

      The introduction was modified according to the reviewer’s suggestion. Lines 58-61.

      From the results section the authors name the human GSDM as HsGSDM and the human CASP as HsCASP, maybe the author could use the same nomenclature in the introduction section. The same for the fish GSDM (Tr) and CASP.

      According to the reviewer’s suggestion, the same nomenclature was used in the introduction.

      Line 39. Remove the word necrotic.

      “necrotic” was removed .

      Line 42. Change channels by pores. In the manuscript, change channels by pores overall.

      “channels” was replaced by “pores”.

      Line 42: Include that: by these pores can be released the proinflammatory cytokines and if these pores are not solved then pyroptosis occurs. Please rephrase this statement.

      According to the reviewer's suggestion, the sentence was rephrased. Lines 46-48.

      Line 45. GSDMF is not an approved gene name, its official nomenclature is PJVK (Uniprot Q0ZLH3). Please use PJVK instead GSDMF.

      GSDMF was changed to PJVK.

      Line 103: Can the authors explain better the molecular determinant?

      The sentence was revised, line 109.

      Results:

      Line 110: Reference for this statement. The reference for this statement was added in line 116.

      Figure 1A, B: Concentration or units used of HsCASP?

      The unit (1 U) of HsCASPs was added to the figure legend (line 661).

      Line 113: Add Hs or Tr after CASP would be helpful to follow the story.

      “CASP” was changed to “HsCASP”.

      Fig 1D: Why the authors do not use the DMPD tetrapeptide (HsGSDME CASP3 cut site) in this assay? Comparing with the data obtained in Fig 3B the TrCASP3 activity is going to be very closer to that obtained for VEID o VDQQD in the CASP3 panel.

      The purpose of Figure 1D was to determine the cleavage preference of TrCASPs. For this purpose, a series of commercially available CASP substrates were used, including DEVD, which is commonly used as a testing substrate for CASP3. Figure 3B was to compare the cleavage of HsCASP3/7 and TrCASP3/7 specifically against the motifs from TrGSDME (DAVD) and HsGSDME (DMPD).

      Figure 1D and Figure 3B are different experiments and were performed under different conditions. In Figure 1D, CASP3 was incubated with the commercial substrates at 37 ℃ for 2 h, while in Figure 3B, CASP3/7 were incubated with non-commercial DAVD (motif from TrGSDME) and DMPD (motif from HsGSDME) at 37 ℃ for 30 min. More experimental details were added to Materials and Methods, lines 443 and 447.

      Fig 1H: What is the concentration used of the inhibitors?

      The concentration (20 μM) was added to the figure legend (line 669).

      Does the Hs CASP3/7 fail to cleave the TrGSDME mutants (D255R and D258A)? the authors do not show this result so they cannot assume that HsCASP3/7 cleave that sequence (although this is to be expected).

      The result of HsCASP3/7 cleavage of the TrGSDME mutants was added as Figure 1-figure supplement 2 and described in Results, line 133.

      Line 132-133: Can the author specify where is placed the mCherry tag? In the N terminal or C terminal portion of the different engineered proteins?

      The mCherry tag is attached to the C-terminus. Figure 2 legend (line 676).

      Fig 2A: Although is quite clear, a column histogram showing the quantification is going to be helpful.

      The expression of TrGSDME-FL, -NT and -CT was determined by Western blot, and the result was added as Figure 2-figure supplement 1.

      Fig 2A, B, C: After how many hours of expression are the pictures taken? Can the authors show a Western blot showing that the expression of the different constructions is similar?

      The time was added to Figure 2 legend and Materials and Methods (line 466). The expression of TrGSDME-FL, -NT and -CT was determined by Western blot, and the result was added as Figure 2-figure supplement 1.

      Fig 2C: Another helpful assay can be to measure the YO-PRO or another small dye internalization, to complete the LDH data.

      According the reviewer’s suggestion, in addition to LDH release, Sytox Green was also used to detect cell death. The result was added as Figure 2-figure supplement 2 and described in Results, line 146.

      Fig 2C: In the figure y axe change LHD by LDH.

      The word was corrected.

      Fig 2D: Change HKE293T by HEK293T in the caption.

      The word was corrected.

      Fig 2G: Please add the concentration used with the two plasmids co-transfection. A Western blot showing CASP3/7 expression vs TrGSDME is missing. Is that assay after 24h? please specify better the methodology.

      The concentration of plasmid used in co-transfection and the time post transfection were added to the Materials and Methods (lines 422 and 424). In addition, the expression of CASP3/7 was added to Figure 2I.

      Fig 2 J, K: Change HKE293T by HEK293T in the figure caption. The concentration of the caspase inhibitors is missing. Depending on the concentration used, these inhibitors used could provoke toxicity on the cells by themselves.

      The word was corrected in the figure caption. The inhibitor concentration (10 μM) was added to the figure legend (line 690).

      Line 151: TrCASP3/7 instead of CASP3/7

      CASP3/7 was changed to TrCASP3/7.

      Fig 3A, 3B: Please add the units used of the HsCASP

      The unit was added to the figure legends (lines 697).

      Fig 3A: Can the authors add the SDS-PAGE to see the Nt terminal portion as has been done in Fig 1A? Maybe in a supplementary figure.

      The SDS-PAGE was added as Figure 3-figure supplement 1.

      Fig 3B: If the authors could add some data about the caspase activity using any other CASP such as CASP2, CASP1 to compare the activity data with CASP3 and CASP7 would be helpful.

      The proteolytic activity of TrCASP1 was provided as Figure 3-figure supplement 2.

      Fig 3C: To state this (Line 160), the authors should use another prediction software to reach a consensus with the sequences of the first analysis. In fact, what happens when GSDME is modelled 3-dimensionally by comparing it to crystalized structures such as mouse GSDMA? If the authors add an arrow indicating where the Nt terminal portion ends and where Ct portion begins would make the figure clearer.

      According to the suggestions of both reviewers, in the revised manuscript, we used mouse GSDMA3 (PDB: 5b5r) for the structural analysis of HsGSDME, which showed that the 261-266 region of HsGSDME was a loop. As a result, Figure 3C was revised. Relevant change in Results: lines 172 and 174.

      As suggested by the reviewer, we modelled the three-dimensional structure of HsGSDME by using SWISS-MODEL with mouse GSDMA3 as the template (Author response image 2, below).

      Author response image 2.

      The three-dimensional structure model of HsGSDME. (A) The structure of HsGSDME was modeled by using mouse GSDMA3 (MmGSDMA3) as the template. The N-terminal domain (1-246 aa) and the C-terminal domain (279-468 aa) of HsGSDME are shown in red and blue, respectively. (B) The superposed structure of HsGSDME (cyan) and MmGSDMA3 (purple).

      Fig 3F: if this is an immunoblotting why NT can be seen? In other Western blots only the CT is detected, why? The use of the TrGSDME mouse polyclonal needs more details (is a purify Ab, was produced for this study, what are the dilution used...)

      Since the anti-TrGSDME antibody was generated using the full-length TrGSDME, it reacted with both the N-terminal and the C-terminal fragments of TrGSDME in Figure 3F. In Figure 3G, the GSDME chimera contained only TrGSDME-CT, so only the CT fragment was detected by anti-TrGSDME antibody. More information on antibody preparation and immunoblot was added to “Materials and Methods” (lines 390 and 391).

      Fig 4B: Can the authors show in which amino acid the p20 finish for each CASP? (Similarly, as they have done in panel 3E)

      Fig 4B was revised as suggested.

      Fig 5F: With 4 units of WT CASP7 the authors show a HsGSDME Ct in the same proportion than when the S234N mutant is used (at lower concentrations). How do the authors explain this?

      The result showed that the cleavage by 4U of HsCASP7 was comparable to the cleavage by 0.25U of HsCASP7-S234N, indicating that S234 mutation increased the cleavage ability of HsCASP7 by 16 folds.

      Line 203: Can the authors show an alignment between this region of casp1/4 and 7? Maybe in supplementary figures.

      As reported by Wang et. al (PMID: 32109412), the βIII/βIII’ sheet of CASP1/4 forms the exosite critical for GSDMD recognition. The structural comparison among HsCASP1/4/7 and the sequence alignment of HsCASP1/4 βIII/βIII’ region with its corresponding region in HsCASP7 were added as Figure 5-figure supplement 2.

      Line 205: A mutation including S234N with the exosite mutations (S234+Q276W+D278E+H283S) is required to support this statement.

      The sentence of “suggesting that, unlike human GSDMD, HsGSDME cleavage by CASPs probably did not involve exosite interaction” was deleted in the revised manuscript.

      Fig 5I, 5J: which is the amount of HsGSDME and TrGSDME? I would place these figures in supplementary material.

      The protein expression of TrGSDME/HsGSDME was shown in the figure. Fig 5I and 5J were moved to Figure 5-figure supplement 3.

      Line 218: I would specify that this importance is in HUMAN CASP7 to cleavage Human GSDME.

      “CASP7” and “GSDME” were changed to “HsCASP7” and “HsGSDME”, respectively.

      Fig 6C: 4 units is the amount of S234N mutant needed to see an optimal HsGSDME cleavage in Fig 5F.

      In Figure 6C, the cleavage efficacy of HsCASP3-N208S was apparently decreased compared to that of HsCASP3, and 4U of HsCASP3-N208S was roughly equivalent to 1U of HsCASP3 in cleavage efficacy. In Figure 5F, cleavage by 4U of HsCASP7 was comparable to the cleavage by 0.25U of HsCASP7-S234N. Together, these results confirmed the critical role of S234/N208 in HsCASP3/7 cleavage of HsGSDM.

      Fig 6I: Could be the fact that the mouse GSDME has a longer Ct than human GSDME affect the interaction with CASP7? Less accessible to the cut site? Needs a positive control of mouse GSDME with mouse Caspase 3.

      Although mouse GSDME (MmGSDME) (512 aa) is larger than HsGSDME (496 aa), the length of the C-terminal domain of MmGSDME (186 aa) is comparable to that of HsGSDME (190 aa).

      Author response image 3.

      Conserved domain analysis of mouse (upper) and human (lower) GSDME.

      As suggested by the reviewer, the cleavage of MmGSDME by mouse caspase-3 (MmCASP3) was added as Figure 6-figure supplement 2 and described in Results, lines 258.

      Material and Methods:

      -Overall, concentrations or amounts used in this study regarding the active enzyme or plasmids used are missing and need to be added.

      The missing concentrations of the enzymes and plasmids were added in Material and Methods (lines 421, 453, 457, and 470) or figure legends (Figure 1 and 3).

      -It would be helpful if the authors label in the immunoblotting panels what is the GSDME that they are using. (Hs GSDME FL...).

      As suggested, the labels were added to Figures 1A ,1B, and 3.

      -Add the units of enzyme used.

      The units of enzyme were added to figure legends (Figure 1A, 3A, 3D, and 3F) or Material and Methods (lines 453 and 457).

      The GSDME sequence obtained for Takifugu after amplification of the RNA extracted should be shown and specified (GSDMEa or GSDMEb). From which tissue was the RNA extracted?

      The details were added to Materials and Methods (lines 398 and 402).

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1:

      Summary:

      In this study, Yan et al. investigate the molecular bases underlying mating type recognition in Tetrahymena thermophila. This model protist possesses a total of 7 mating types/sexes and mating occurs only between individuals expressing different mating types. The authors aimed to characterize the function of mating type proteins (MTA and MTB) in the process of self- and non-self recognition, using a combination of elegant phenotypic assays, protein studies, and imaging. They showed that the presence of MTA and MTB in the same cell is required for the expression of concavalin-A receptors and for tip transformation - two processes that are characteristic of the costimulation phase that precedes cell fusion. Using protein studies, the authors identify a set of additional proteins of varied functions that interact with MTA and MTB and are likely responsible for the downstream signaling processes required for mating. This is a description of a fascinating self- and non-self-recognition system and, as the authors point out, it is a rare example of a system with numerous mating types/sexes. This work opens the door for the further understanding of the molecular bases and evolution of these complex recognition systems within and outside protists.

      The results shown in this study point to the unequivocal requirement of MTA and MTB proteins for mating. Nevertheless, some of the conclusions regarding the mode of functioning of these proteins are not fully supported and require additional investigation.

      Strengths:

      (1) The authors have established a set of very useful knock-out and reporter lines for MT proteins and extensively used them in sophisticated and well-designed phenotypic assays that allowed them to test the role of these proteins in vivo.

      (2) Despite their apparent low abundance, the authors took advantage of a varied set of protein isolation and characterization techniques to pinpoint the localization of MT proteins to the cell membrane, and their interaction with multiple other proteins that could be downstream effectors. This opens the door for the future characterization of these proteins and further elucidation of the mating type recognition cascade.

      Weaknesses:

      The manuscript is structured and written in a very clear and easy-to-follow manner. However, several conclusions and discussion points fall short of highlighting possible models and mechanisms through which MT proteins control mating type recognition:

      (1) The authors dismiss the possibility of a "simple receptor-ligand system", even though the data does not exclude this possibility. The model presented in Figure 2 S1, and on which the authors based their hypothesis, assumes the independence of MTA and MTB proteins in the generation of the intracellular cascade. However, the results presented in Figure 2 show that both proteins are required to be active in the same cell. Coupled with the fact that MTA and MTB proteins interact, this is compatible with a model where MTA would be a ligand and MTB a receptor (or vice-versa), and could thus form a receptor-ligand complex that could potentially be activated by a non-cognate MTA-MTB receptor-ligand complex, leading to an intracellular cascade mediated by the identified MRC proteins. As it stands, it is not clear what is the proposed working model, and it would be very beneficial for the reader for this to be clarified by having the point of view of the authors on this or other types of models.

      We are very grateful that Reviewer #1 proposed the possibility that MTA and MTB form a receptor-ligand complex in which one acting as the ligand and the other as the receptor. We considered this hypothesis when asking how dose MTRC function, too. However, our current results do not support this idea. For instance, if MTA were a ligand and MTB a receptor, we would expect a mating signal upon treatment with MTAxc protein, but not with MTBxc. Contrary to this expectation, our experiments revealed that both MTAxc and MTBxc exhibit very similar effects (Figure 5, green and blue), and their combined treatment produces a stronger effect (Figure 5, teal). This suggests a mixed function for both proteins. (We incorporated this discussion into the revised version [line 120-121, 240-244].) It is pity that our current knowledge does not provide a detailed molecular mechanism for this intricate system. We are actively investigating the protein structures of MTA, MTB, and the entire MTRC, hoping to gain deeper insights into the molecular functions of MTA and MTB.

      Additionally, we also realized that the expression we used in the previous version, “simple receptor-ligand model”, is not clearly defined. As Reviewer #1 pointed out, in this section, we examined whether the individual proteins of MTA and MTB act as a couple of receptor and ligand. We think this is the simplest possibility as a null hypothesis for Tetrahymena mating-type recognition. We have clarified it in the revised version (line 90-91, 104-106). According to this section, we proposed that MTA and MTB may form a complex that serves as a recognizer (functioning as both ligand and receptor) (line 117-118).

      (2) The presence of MTA/MTB proteins is required for costimulation (Figure 2), and supplementation with non-cognate extracellular fragments of these proteins (MTAxc, or MTBxc) is a positive stimulator of pairing. However, alone, these fragments do not have the ability to induce costimulation (Figure 5). Based on the results in Figures 5 and 6 the authors suggest that MT proteins mediate both self and non-self recognition. Why do MTAxc and MTBxc not induce costimulation alone? Are any other components required? How to reconcile this with the results of Figure 2? A more in-depth interpretation of these results would be very helpful, since these questions remain unanswered, making it difficult for the reader to extract a clear hypothesis on how MT proteins mediate self- and non-self-recognition.

      Several factors could contribute to the inability of MTA/Bxc to induce costimulation. It is highly likely that additional components are necessary, given that MTA/B form a protein complex with other proteins. Moreover, the expression of MTA/Bxc in insect cells, compared with Tetrahymena, might result in differences in post-translational modifications. Additionally, there are variations in protein conditions; on the Tetrahymena membrane, these proteins are arranged regularly and concentrated in a small area, while MTA/Bxc is randomly dispersed in the medium. The former condition could be more efficient. If there is a threshold required to stimulate a costimulation marker, MTA/Bxc may fail to meet this requirement. Much more studies are needed to fully answer this question. We acknowledged this limitation in the revised version (line 244-248).

      Reviewer #2:

      This manuscript reports the discovery and analysis of a large protein complex that controls mating type and sexual reproduction of the model ciliate Tetrahymena thermophila. In contrast to many organisms that have two mating types or two sexes, Tetrahymena is multi-sexual with 7 distinct mating types. Previous studies identified the mating type locus, which encodes two transmembrane proteins called MTA and MTB that determine the specificity of mating type interactions. In this study, mutants are generated in the MTA and MTB genes and mutant isolates are studied for mating properties. Cells missing either MTA or MTB failed to co-stimulate wild-type cells of different mating types. Moreover, a mixture of mutants lacking MTA or MTB also failed to stimulate. These observations support the conclusion that MTA and MTB may form a complex that directs mating-type identity. To address this, the proteins were epitope-tagged and subjected to IP-MS analysis. This revealed that MTA and MTB are in a physical complex, and also revealed a series of 6 other proteins (MRC1-6) that together with MTA/B form the mating type recognition complex (MTRC). All 8 proteins feature predicted transmembrane domains, three feature GFR domains, and two are predicted to function as calcium transporters. The authors went on to demonstrate that components of the MTRC are localized on the cell surface but not in the cilia. They also presented findings that support the conclusion that the mating type-specific region of the MTA and MTB genes can influence both self- and non-self-recognition in mating.

      Taken together, the findings presented are interesting and extend our understanding of how organisms with more than two mating types/sexes may be specified. The identification of the six-protein MRC complex is quite intriguing. It would seem important that the function of at least one of these subunits be analyzed by gene deletion and phenotyping, similar to the findings presented here for the MTA and MTB mutants. A straightforward prediction might be that a deletion of any subunit of the MRC complex would result in a sterile phenotype. The manuscript was very well written and a pleasure to read.

      Thanks for the valuable comments and suggestions. We are currently in the process of constructing deletion strains for these genes. As of now, we have successfully obtained ΔMRC1-3 and MRC4-6 knockdown strains. Our preliminary observations indicate that ΔMRC1-3 strains are unable to undergo mating. However, we prefer not to include these results in the current manuscript, as we believe that more comprehensive studies are still needed.

      Reviewer #3:

      The authors describe the role, location, and function of the MTA and MTB mating type genes in the multi-mating-type species T. thermophila. The ciliate is an important group of organisms to study the evolution of mating types, as it is one of the few groups in which more than two mating types evolved independently. In the study, the authors use deletion strains of the species to show that both mating types genes located in each allele are required in both mating individuals for successful matings to occur. They show that the proteins are localized in the cell membrane, not the cilia, and that they interact in a complex (MTRC) with a set of 6 associated (non-mating type-allelic) genes. This complex is furthermore likely to interact with a cyclin-dependent kinase complex. It is intriguing that T. thermophila has two genes that are allelic and that are both required for successful mating. This coevolved double recognition has to my knowledge not been described for any other mating-type recognition system. I am not familiar with experimental research on ciliates, but as far as I can judge, the experiments appear well performed and mostly support the interpretation of the authors with appropriate controls and statistical analyses.

      The results show clearly that the mating type genes regulate non-self-recognition, however, I am not convinced that self-recognition occurs leading to the suppression of mating. An alternative explanation could be that the MTA and MTB proteins form a complex and that the two extracellular regions together interact with the MTA+MTB proteins from different mating types. This alternative hypothesis fits with the coevolution of MTA and MTB genes observed in the phylogenetic subgroups as described by Yan et al. (2021 iScience). Adding MTAxc and/or MTBxc to the cells can lead to the occupation of the external parts of the full proteins thereby inhibiting the formation of the complex, which in turn reduces non-self interactions. Self-recognition as explained in Figure 2S1 suggests an active response, which should be measurable in expression data for example. This is in my opinion not essential, but a claim of self-recognition through the MTA and MTB should not be made.

      We express our gratitude to Reviewer #3 for proposing the occupation model and have incorporated this possibility into the manuscript. We believe it is possible that occupation may serve as the molecular mechanism through which self-recognition negatively regulates mating. If there is a physical interaction between mating-type proteins of the same type, but this interaction blocks the recognition machinery rather than initiating mating, it can be considered a form of self-recognition. This aligns with the observation that strains expressing MTA/B6 and MTB2 mate normally with WT cells of all mating types except for VI and II (line 203-204). A concise discussion on this topic is included in the manuscript (line 288-293, 659-661). We are actively investigating the downstream aspects of mating-type recognition, and we hope to provide further insights into this question soon.

      The authors discuss that T. thermophila has special mating-type proteins that are large, while those of other groups are generally small (lines 157-160 and discussion). The complex formed is very large and in the discussion, they argue that this might be due to the "highly complex process, given that there are seven mating types in all". There is no argument given why large is more complex, if this is complex, and whether more mating types require more complexity. In basidiomycete fungi, many more mating types than 7 exist, and the homeodomain genes involved in mating types are relatively small but highly diverse (Luo et al. 1994 PMID: 7914671). The mating types associated with GPCR receptors in fungi are arguably larger, but again their function is not that complex, and mating-type specific variations appear to evolve easily (Fowler et al 2004 PMID: 14643262; Seike et al. 2015 PMID: 25831518). The large protein complex formed is reminiscent of the fusion patches that develop in budding or fission yeasts. In these species, the mating type receptors are activated by ligand pheromones from the opposite mating type that induce polarity patch formation (see Sieber et al. 2023 PMID: 35148940 for a recent review). At these patches, growth (shmooing) and fusion occur, which is reminiscent (in a different order) of the tip transformation in T. thermophilia. The fusion of two cells is in all taxa a dangerous and complex event that requires the evolution of very strict regulation and the existence of a system like the MTRC and cyclin-dependent complex to regulate this process is therefore not unexpected. The existence of multiple mating types should not greatly complicate the process, as most of the machinery (except for the MTA and MTB) is identical among all mating types.

      We are very grateful that Reviewer #3 provide this insightful view and relevant papers. In response to the feedback, we removed the sentences regarding “multiple mating types greatly complicate the process” in the revised version. Instead, we have introduced a discussion section comparing the mating systems of yeasts and Tetrahymena (line 279-286).

      The Tetrahymena/ciliate genetics and lifecycle could be better explained. For a general audience, the system is not easy to follow. For example, the ploidy of the somatic nucleus with regards to the mating type is not clear to me. The MAC is generally considered "polyploid", but how does this work for the mating type? I assume only a single copy of the mating type locus is available in the MAC to avoid self-recognition in the cells. Is it known how the diploid origin reduces to a single mating type? This does not become apparent from Cervantes et al. 2013.

      In T. thermophila, the MIC (diploid) contains several mating-type gene pairs (mtGP, i.e., MTA and MTB) organized in a tandem array at the mat locus on each chromosome. In sexual reproduction, the new MAC of the progeny develops from the fertilized MIC through a series of genome editing events, and its ploidy increases to ~90 by endoreduplication. During this process, mtGP loss occurs, resulting in only one mtGP remaining on the MAC chromosome. The mating-type specificity of mtGPs on each chromosome within one nucleus becomes relatively pure through intranuclear coordination. After multiple assortments (possibly caused by MAC amitosis during cell fission), only mtGPs of one mating-type specificity exist in each cell, determining the cell’s mating type.

      It is pity that the exact mechanisms involved in this complicated process remain a black box. The loss of mating-type gene pairs is hypothesized to involve a series of homologous recombination events, but this has not been completely proven. Furthermore, there is no clear understanding of how intranuclear coordination and assortment are achieved. While we have made observations confirming these events, a breakthrough in understanding the molecular mechanism is yet to be achieved.

      We included more information in the revised version (line 672-683). Given the complexity of these unusual processes, we recommend an excellent review by Prof. Eduardo Orias (PMID: 28715961), which offers detailed explanations of the process and related concepts (line 685-686).

      Also, the explanation of co-stimulation is not completely clear (lines 49-60). Initially, direct cell-cell contact is mentioned, but later it is mentioned that "all cells become fully stimulated", even when unequal ratios are used. Is physical contact necessary? Or is this due to the "secrete mating-essential factors" (line 601)? These details are essential, for interpretation of the results and need to be explained better.

      Sorry that we didn’t realize the term “contact” is not precise enough. In Tetrahymena, physical contact is indeed necessary, but it can refer to temporary interactions. Unlike yeast, Tetrahymena cells exhibit rapid movement, swimming randomly in the medium. Occasionally, two cells may come into contact, but they quickly separate instead of sticking together. Even newly formed loose pairs often become separated. As a result, one cell can come into contact with numerous others and stimulate them. We have clarified this aspect in the revised version (line 50-51, 57).

      Abstract and introduction: Sexes are not mating types. In general, mating types refer to systems in which there is no obvious asymmetry between the gametes, beyond the compatibility system. When there is a physiological difference such as size or motility, sexes are used. This distinction is of importance because in many species mating types and sexes can occur together, with each sex being able to have either (when two) or multiple mating types. An example are SI in angiosperms as used as an example by the authors or mating types in filamentous fungi. See Billiard et al. 2011 [PMID: 21489122] for a good explanation and argumentation for the importance of making this distinction.

      We have clarified the expression in the revised version (line 20, 38, 40, 45).

      Recommendations for the authors:

      Reviewer #1:

      I really enjoyed reading this manuscript and I think a few tweaks in the writing/data presentation could greatly improve the experience for the reader:

      (1) The information about your previous work in identifying downstream proteins CDK19, CYC9, and CIP1 (lines 170-173) could be directly presented in the introduction.

      We have moved this information in the introduction in the revised version (line 74-77).

      (2) For a reader who is not familiar with Tetrahymena, a few more details on how reporter and knock-out lines are generated would be beneficial.

      We introduced the knock-out method in Figure 2 – figure supplement 1B, HA-tag method in Figure 3A, and MTB2-eGFP construction method in Figure 4E. In addition, we introduced how co-stimulation markers observed in Materials and Methods (line 404-410)

      (3) Figures 5 and 6: clarify the types of pairing and treatments that were done directly in the figure (eg. adding additional labels). As of now, it is necessary to go through the text and legend to try and understand in detail what was done.

      Cell types and treatments were directly introduced in the revised figure (Figure 5 and 6).

      (4) The logical transition in lines 136-142 is hard to follow.

      We rewrote this paragraph in the revised version (lines 143-156). Additionally, we added a figure to illustrate the theoretical mating-type recognition model between WT cells and ΔCDK19, ΔCYC9 cells, MTAxc, MTBxc proteins, and ΔMTA, ΔMTB cells (Figure 2 – figure supplement 1D-G).

      (5) Lines 191-196: the fact that cells expressing multiple mating types can self goes against an active self-rejection system - if this is the case there should be self-rejection among all expressed mating types. Unless non-self recognition is an active process and self-recognition is simply the absence of non-self recognition. The authors briefly mention this in lines 263-265, but it would be interesting to expand and clarify this.

      We appreciate that Reviewer #1 notice the interesting selfing phenotype of the MTB2-eGFP (MTVI background) strain. We further discussed it in the revised manuscript (line 298-306).

      (6) The authors briefly mention the possibility of different mating types using different recognition mechanisms (lines 255-260), based on the big differences in the size of the mating-specific region of MT proteins. Following this and the weakness nr. 2, I think it would be pertinent to gather and present more information on the properties and structures of the mating-type specific regions of MT proteins. Simple in silico analysis of motifs, structure, etc. could help clarify the role of these regions. It seems more parsimonious that MT proteins would have variable mating type specific regions that account for the recognition of the different mating types, and conserved cytoplasmic functions that could trigger a single downstream signaling cascade. It would be interesting to know the authors' opinion on this.

      We are very grateful for this suggestion. Actually, we are currently working on determining the 3D structure of MTRC. The Alphafold2 prediction indicates that the MT-specific region is comprised of seven global β-sheets, resembling the structure of immunoglobulins (Ig). Our most recent cryo-EM results have revealed a ~15Å structure, aligning well with the prediction. However, the main challenge lies in the low expression levels, both in Tetrahymena and insect/mammal cells. We anticipate obtaining more detailed results soon. Therefore, we prefer to present the MT recognition model with robust experimental evidence in the future, and didn’t discuss too much on this aspect in the current manuscript.

      (7) Adding a figure including a proposed model, as well as expanding the discussion on the points presented as "weaknesses" would help clarify the ideas/hypothesis on how the mating recognition works. I think this would really elevate the paper and help highlight the results.

      We added a figure to introduce the model and the weaknesses in the revised version (Figure 7, line 656-665).

      (8) Line 202-203: It is far-fetched to infer subcellular localization based on the data presented here, couterstaining with other dyes and antibodies specific to certain cell components, as well as negative control images, are required.

      Thanks for the suggestion. We attempted to stain cell components using various dyes and antibodies. Unfortunately, we found that cell surface and cilia (especially oral cilia) is very easy to give a false positive signal. We think this issue seriously affects the credibility of the results. It may seem like splitting hairs, but we are trying to be precise.

      Meanwhile, we still believe the mating-type proteins localizes to cell surface because MTA-HA is identified in the isolated cell surface proteins.

      Regarding negative control, as shown in Fig. 4G, where a MTB2-eGFP cell is pairing with a WT cell, no GFP signal is observed in the WT cell.

      (9) Lines 131: clarify the sentence - expression of Con-A receptors requires both MTA and MTB (MTA to receive the signal).

      We modified the sentence in the revised version (line 139-140).

      Reviewer #2:

      Minor points.

      (1) Line 194-196. Why are these cells able to self?

      These cells able to self may because the MTRC contain heterotypic mating-type proteins (MTA6 and MTB2), which activate mating when they interact with another heterotypic MTRC (line 207-208).

      (2) Line 232. What do the authors mean by the term synergistic effect here? Definition and statistics?

      Sorry about the confusion. The synergistic effect refers to the effect of MTAxc and MTBxc become stronger when using together. We clarified it in the revised version (line 232).

      (3) For Figure 4 panel D, are there antibodies that are available as a control for cilia? If so, then blotting this membrane would show that cilia-associated proteins are in the cilia preparation, which is a standard control for sub-cellular fractionation.

      Thanks for the suggestion. Unfortunately, we didn’t find a suitable cilia-specific antibody yet. Instead, we employed MS analysis to confirm the presence of cilia proteins in this sample (line 195-196, Figure 4–Source data 1). We also observed the sample under the microscope, which directly revealed the presence of cilia (Figure 4C).

      (4) At least one reference cited in the text was not present in the reference list. The authors should go through the references cited to ensure that all have made it into the reference list.

      We have checked all the references.

      Some minor edits:

      (1) MTA and MTB are presented in both roman and italics (e.g. line 209) in the manuscript. Maybe all should be in italics? Or is this a distinction between the gene and the protein?

      The italics word (MTA) refers to gene, and non-italics word (MTA) refers to protein.

      (2) Line 251. Change "achieving" to "achieve".

      We have corrected this word (line 266).

      Reviewer #3:

      Line 101. It would help to explain this expectation earlier in this paragraph.

      We explained the expectation in the revised version (line 92-97, 104-106).

      Line 109. How is a co-receptor different from the MTRC complex?

      We have rewritten the relevant sentences to enhance clarity (line 116-119). The molecular function of the MTRC complex could involve acting as a co-receptor or recognizer (functioning as both ligand and receptor). Based on the results presented in this section, we propose that MTA and MTB may function as a complex, but the confirmation of this hypothesis (MTRC) is provided in a later section. Therefore, we did not use the term “MTRC” here. These sentences briefly discuss the molecular function of this complex and explain why MTRC does not appear to function as a co-receptor.

      Line 251: which "dual approach" is referred to?

      Dual approach is referred to both self and non-self recognition. We explained it in the revised version (line 265-266).

      Line 258: what "different mechanisms" do the authors have in mind? Why would a different mechanism be expected? The different sizes could have evolved for (coevolutionary?) selection on the same mechanism.

      Sorry about the confusion. We clarified it in the revised version (line 269-278).

      What we intended to express is that we are uncertain whether the mating-type recognition model we discovered in T. thermophila is applicable to all Tetrahymena species due to significant differences in the length of the mating-type-specific region. We believe it is important to highlight this distinction to avoid potential misinterpretations in future studies involving other Tetrahymena species. At the same time, we look forward to future research that may provide insights into this question.

      Fig 2 C&D. Is it correct that these figures show the strains only after 'preincubation'? This is not apparent from the caption of the text. Additionally, the order of the images is very confusing. Write in the figures (so not just in the caption) what the sub-script means.

      These panels are re-organized in the revised version (Fig. 2C&D). There are three kinds of pictures: “not incubated”, “WT pre-incubated by mutant” and “mutant pre-incubated by WT”.

      The methods used to generate Figure 5 are not clearly described. I understand that the obtained xc proteins were added to the cells, and then washed, after which a test was performed mixing WT-VI and WT-VII cells. Were both cells treated? Or only one of the strains? The explanation for the reused washing medium is not clear and the method is not indicated.

      Both cells are treated. More details are provided in the revised manuscript (line 230-231, 633-634, 637-639, Fig. 5). To prepare the starvation medium containing mating-essential factors, cells were starved in fresh starvation medium for ~16 hours. Subsequently, cells were removed by three rounds of centrifugation (1000 g, 3 min) (line 330-332).

      In general, the figures are difficult to understand without repeated inquiries in the captions. Give more information in the figures themselves.

      More information is introduced in the figure (Fig. 2C, Fig. 3B, Fig. 4A, B, D, Fig. 5 and Fig. 6).

  4. Jan 2024
    1. Author Response

      The following is the authors’ response to the original reviews.

      First of all, we'd like to thank the three reviewers for their meticulous work that enable us to present now an improved manuscript and substantial changes were made to the article following reviewers' and editors' recommendations. We read all their comments and suggestions very carefully. Apart from a few misunderstandings, all comments were very pertinent. We responded positively to almost all the comments and suggestions, and as a result, we have made extensive changes to the document and the figures. This manuscript now contains 16 principal figures and 15 figure supplements.

      The number of principal figures is now 16 (1 new figure), and additional panels have been added to certain figures. On the other hand, we have added 7 additional figures (supplement figures) to answer the reviewers' questions and/or comments.

      Main figures

      ▪ Figures 1, 4, 5, 10, 11, 12, 13, 14: unchanged ▪ Figure 7 and 8 were switched.

      ▪ Figure 2: we added panel F in response to reviewer 3's and request for sperm defect statistics

      ▪ Figure 3: the contrast in panel B has been taken over to homogenize colors

      ▪ Figure 6: This figure was recomposed. The WB on testicular extract was suppressed and we present a new WB allowing to compare the presence of CCDC146 in the flagella fraction. Using an anti-HA Ab, we demonstrate that the protein is localized in the flagella in epididymal sperm. Request of the 3 reviewers.

      ▪ Figure 7 (old 8): to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. Moreover, the WB was removed and is now presented in figure 6 (improved as requested).

      ▪ Figure 8. Was old figure 7

      ▪ Figure 9: figure 9 was recomposed and improved for increased clarity as suggested by reviewer 2 and 3.

      ▪ Figure 16 was before appendix 11

      Figure supplements and supplementary files

      ▪ Figure 1-Figure supplement 1 New. Sperm parameters of the 2 patients. requested by editor (remark #1) by the reviewer 1 (Note #3)

      ▪ Figure 2-Figure supplement 1 new. Sperm parameters of the line 2 (KO animals) requested by the reviewer 1 (Note #5)

      ▪ Figure 4-Figure supplement 1 New. Experiment to evaluate the specificity of the human CCDC146 antibody. Minimal revision request and reviewer 1 note #8

      ▪ Figure 6-Figure supplement 1 New. Figure recomposed; Asked by reviewer 2 note #4 and reviewer 3

      ▪ Figure 8-Figure supplement 1 New. We now provide new images to show the non-specific staining of the midpiece of human sperm by secondary Abs in ExM experiments; Asked by reviewer 2

      ▪ Figure 10-Figure supplement 1 New. We added new images to show the non-specific staining of the midpiece of mouse sperm by secondary Abs in IF (panel B). Rewiever 1 note #9 and reviewer 2 note #5

      ▪ Figure 12-Figure supplement 1 New. Control requested by reviewer 3 Note #23

      ▪ Figure 13-Figure supplement 1 New. We provide a graph and a statistical analysis demonstrating the increase of the length of the manchette in the Ccdc146 KO. Requested by editor and reviewer 3 Note 24

      ▪ Figure 15-Figure supplement 1 New. Control requested by reviewer 2. Minor comments

      ▪ Figure supplementary 1 New. Answer to question requested by reviewer 2 note #1

      All the reviewers' and editors’ comments have been answered (see our point to point response) and we resubmit what we believe to be a significantly improved manuscript. We strongly hope that we meet all your expectations and that our manuscript will be suitable for publication in "eLife". We look forward to your feedback,

      Point by point answer

      Please note that there has been active discussion of the manuscript and the summarize points below is the minimal revision request that the reviewers think the authors should address even under this new review model system. It was the reviewers' consensus that the manuscript is prepared with a lot of oversights - please see all the minor points to improve your manuscript.

      All minimal revision requests have been addressed

      Minimal revision request

      1) Clinical report/evaluation of the two patients should be given as it was not described even in their previous study as well as full description of CCDC146.

      We provide now a new Figure 1-figure supplement 1 describing the patients sperm parameters

      2) Antibody specificity should be provided, especially given two of the reviewers were not convinced that the mid piece signal is non-specific as the authors claim. As both KO and KI model in their hands, this should be straightforward.

      To validate the specificity of the Antibody, we transfected HEK cells with a human DDK-tagged CCDC146 plasmid and performed a double immunostaining with a DDK antibody and the CCDC146 antibody. We show that both staining are superimposable, strongly suggesting that the CCDC146 Ab specifically target CCDC146. This experiment is now presented in Figure 4-Figure supplement 1. Next, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

      3) The authors should improve statistical analysis to support their experimental results for the reader can make fair assessment. Combined with clear demonstration of ab specificity, this lack of statistical analysis with very few sample number is a major driver of dampening enthusiasm towards the current study.

      Several statistical analyses were carried out and are now included:

      1) distribution of the HA signal in mouse sperm cells (see point 2 Figure 7 panel B)

      2) quantification and statistical analyses of the defect observed in Ccdc146 KO sperm (figure 2 panel E)

      3) Quantification and statistical analyses of the length of the manchette in spermatids 13-15 steps (Figure 13-Figure supplement 1 new)

      4) The authors need to clarify (peri-centriolar vs. centriole)

      In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein in somatic cells. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein, and this is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described, and the best example is maybe gamma-tubulin (PMID: 8749391).

      or tone down (CCDC146 to be a MIP) of their claim/description.

      Concerning its localization in sperm, we agree with the reviewer that our demonstration that CCDC146 is MIP would deserve more results. Because of that, we have toned down the MIP hypothesis throughout the manuscript. See lines 491495

      Testis-specific expression of CCDC146 as it is not consistent with their data.

      We have also modified our claim concerning the testis-expression of CCDC146. Line 176

      Reviewer #1 (Recommendations For The Authors):

      Major comments

      1) As described in general comments, this study limits how the CCDC146 deficiency impairs abnormal centriole and manchette formation. The authors should explain their relationship in developing germ cells.

      In fact, there are limited information about the relationship between the manchette and the centriole. However, few articles have highlighted that both organelles share molecular components. For instance, WDR62 is required for centriole duplication in spermatogenesis and manchette removal in spermiogenesis (Commun Biol. 2021; 4: 645. doi: 10.1038/s42003-021-02171-5). Another study demonstrates that CCDC42 localizes to the manchette, the connecting piece and the tail (Front. Cell Dev. Biol. 2019 https://doi.org/10.3389/fcell.2019.00151). These articles underline that centrosomal proteins are involved in manchette formation and removal during spermiogenesis and support our results showing the impact of CCDC146 lack on centriole and manchette biogenesis. This information is now discussed. See lines 596-603

      2) The authors generated knock-in mouse model. If then, are the transgene can rescue the MMAF phenotype in CCDC146-null mice? This reviewer strongly suggest to test this part to clearly support the pathogenicity by CCDC146.

      We indeed wrote that we created a “transgenic mice”, which was misleading. We actually created a CCDC16 knock-in expressing a tagged-protein. The strain was actually made by CRISPR-Cas9 and a sequence coding for the HA-tag was inserted just before the first amino acid in exon 2, leading to the translation of an endogenous HA-tagged CCDC146 protein. We have removed the word transgenic from the text and made changes accordingly (see lines 250-253). We can therefore not use this strain to rescue the MMAF phenotype as suggested by the reviewer.

      3) Although the authors cite the previous study (Coutton et al., 2019), the study does not describe any information for CCDC146 and clinical information for the patients. The authors must show the results for clinical analysis to clarify the attended patients are MMAF patients without other phenotypic defects.

      We have now inserted a table, indicating all sperm parameters for the patients harboring a mutation in the CCDC146 gene (Figure 1-Figure supplement 1) and is now indicated lines 159-160

      4) The authors describe CCDC146 expression is dominant in testes, However, the level in testis is only moderate in human (Supp Figure 1). Thus, this description is not suitable.

      In Figure 1-figure supplement 2 (old FigS1), the median of expression in testis is around 12 in human, a value considered as high expression by the analysis software from Genevestigator. However, for mouse, it is true that the level of expression is medium. We assumed that reviewer’s comment concerned testis expression in mouse. To take into account this remark, we changed the text accordingly. See line 176.

      5) Although the authors mentioned that two mice lines are generated, only one line information is provided. Authors must include information for another line and provide basic characterization results to support the shared phenotype within the lines.

      We now provide a revised Figure 2-figure supplement 1CD, presenting the second line and the corresponding text in the main text is found lines 178-183.

      6) In somatic cells, the CCDC146 localizes at both peri-centriole and microtubule but its intracellular localization in sperm is distinguished. The authors should explain this discrepancy.

      The multi-localization of a centriolar protein is already discussed in detail in discussion lines 520-526. We have written:

      “Despite its broad cellular distribution, the association of CCDC146 with tubulin-dependent structures is remarkable. However, centrosomal and axonemal localizations in somatic and germ cells, respectively, have also been reported for CFAP58 [37, 55], thus the re-use of centrosomal proteins in the sperm flagellar axoneme is not unheard of. In addition, 80% of all proteins identified as centrosomal are found in multiple localizations (https://www.proteinatlas.org/humanproteome/subcellular/centrosome). The ability of a protein to home to several locations depending on its cellular environment has been widely described, in particular for MAP. The different localizations are linked to the presence of distinct binding sites on the protein…. “

      7) Authors mention CCDC146 is a centriolar protein in the title and results subtitle. However, the description in results part depicts CCDC146 is a peri-centriolar protein, which makes confusion. Do the authors claim CCDC146 is centrosomal protein?

      In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein in somatic cells, and is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described and the best example is maybe gamma-tubulin (PMID: 8749391).

      8) Verification of the antibody against CCDC146 must be performed and shown to support the observed signal are correct. 2nd antibody only signal is not proper negative control.

      It is a very important remark. The commercial antibody raised against human CCDC146 was validated in HEK293-cells expressing a DDK-tagged CCDC146 protein. Cells were co-marked with anti-DDK and anti-CCDC146 antibodies. We have a perfect colocalization of the staining. This experiment is now presented in Figure 4-figure supplement 1 and presented in the text (lines 206-208).

      9) In human sperm, conventional immunostaining reveals CCDC146 is detected from acrosome head and midpiece. However, in ExM, the signal at acrosome is not detected. How is this discrepancy explained? The major concern for the ExM could be physical (dimension) and biochemical (properties) distortion of the sample. Without clear positive and negative control, current conclusion is not clearly understood. Furthermore, it is unclear why the authors conclude the midpiece signal is non-specific. The authors must provide experimental evidence.

      Staining on acrosome should always be taken with caution in sperm. Indeed, numerous glycosylated proteins are present at the surface of the plasma membrane regarding the outer acrosomal membrane for sperm attachment and are responsible for numerous nonspecific staining. Moreover, this acrosomal staining was not observed in mouse sperm, strongly suggesting that it is not specific.

      Concerning the staining in the midpiece observed in both conventional and Expansion microscopy, it also seems to be nonspecific and associated with secondary Abs.

      For IF, we now provide new images showing clearly the nonspecific staining of the midpiece when secondary Ab were used alone (see Figure 10-figure supplement 1B).

      For ExM, we provide new images in Figure 8-figure supplement 1B (POC5 staining) showing a staining of the midpiece (likely mitochondria), although POC5 was never described to be present in the midpiece. Both experiments (CCDC146 and POC5 staining by ExM) shared the same secondary Ab and the midpiece signal was likely due to it.

      Moreover, we now provide new images (figure 7C) in ExM on mouse sperm showing no staining in the midpiece and demonstrating that the punctuated signal is present all along the flagellum. Finally, we would like to underline that we now provide new IF results, using an anti-HA conjugated with alexafluor 488 and confirming the ExM results.

      These points are now discussed lines 498-502 for acrosome and lines 503-511 for midpiece staining.

      10) For intracellular localization of the CCDC146 in mouse sperm, the authors should provide clear negative control using WT sperm which do not carry the transgene.

      This experiment was performed.

      To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

      11) Current imaging data do not clearly support the intracellular localization of the CCDC146. Although western blot imaging reveal that CCDC146 is detected from sperm flagella, this is crude approach. Thus, this reviewer highly recommends the authors provide more clear experimental evidence, such as immuno EM.

      We provide now a WB comparing the presence of the protein in the flagellum and in the head fractions; see new figure 6. We show that CCDC146 is only present in the flagellum fraction; The detection of the band appeared very quickly at visualization and became very strong after few minutes, demonstrating that the protein is abundant in the flagella. It is important to note that epididymal sperm do not have centrioles and therefore this signal is not a centriolar signal. We also now provide new statistical analyses showing that the immuno-staining observed in the principal piece is very specific (Figure 7B). Altogether, these results demonstrate unequivocally the intracellular localization of CCDC146 in the flagellum. This point is now discussed lines 480-489

      12) Although sarkosyl is known to dissociate tubulin, it is not well understood and accepted that the enhanced detection of CCDC146 by the detergent indicates its microtubule inner space. Sperm axoneme to carry microtubule is also wrapped peri-axonemal components with structural proteins, which are even not well solubilized by high concentration of the ionic detergent like SDS.

      We agree with the reviewer that the solubilization of the protein by sarkozyl is not a proof of the presence of the protein inside microtubule. Taking into account this point, the MIP hypothesis was toned down and we now discuss alternative hypothesis concerning these results; See discussion lines 490-497

      13) SEM image is not suitable to explain internal structure (line 317-323).

      We agree with the reviewers and changes were made accordingly. See lines 354-357

      Minor comments

      1) In main text, supplementary figures are cited "Supp Figure". And the corresponding legends are written in "Appendix - Figure". Please unify them.

      Done Labelled now “Figure X-figure supplement Y”

      2) Line 159, "exon 9/19" is not clear.

      We have written now exons 9 and indicated earlier that the gene contains 19 exons

      3) Line 188, "positive cells" are vague.

      Positive was changed by “fluorescent”

      4) Representative TUNEL assay image for knockout testes were not shown in Supp Figure 3B.

      It was a mistake now Figure 2-figure supplement 2C

      5) Please provide full description for "IF" and "AB" when described first.

      Done

      6) Line 262, It is unclear what is "main piece".

      Changed to principal piece

      7) Line 340, Although the "stage" information might be applicable, this is information for "seminiferous tubule" rather than "spermatid". This reviewer suggests to provide step information rather than stage information.

      We agree with the reviewer that there was a confusion between “stage” and “step”. We change to step spermatids

      8) Line 342, Step 1 is not correct in here.

      OK corrected. now steps 13-15 spermatids

      9) Line 803, "C." is duplicated.

      Removed

      10) Figure 3A, it will be good to mark the defective nuclei which are described in figure legends.

      These cells are now indicated by white arrow heads

      11) Figure 5, Please provide what MT stands for.

      Now explained in the legend of figure 5

      12) Figure 6. Author requires clear blot images for C. In addition, Panel B information is not correct. If the blot was performed using HA antibody, then how "WT" lane shows bands rather than "HA" bands?

      The reviewer is correct. It was a mistake; The figure was recomposed and improved.

      Reviewer #2 (Recommendations For The Authors):

      Overall, editing oversights are present throughout the manuscript, which has made the review process quite difficult. Some repetitive figures can be removed to streamline to grasp the overall story easier. Some claims are not fully supported by evidence that need to tone down. Some figures not referenced in the main text need to be mentioned at least once.

      All figures are now referenced in the text

      Major comments:

      1) 163-164 - Please clarify the claim that there is going to be an absence of the protein or nonfunctional protein, especially for the patient with a deletion that could generate a truncated protein at two third size of the full-length protein. Similarly, 35% of the protein level is present for the patient with a nonsense mutation. Some in silico structural analysis or analysis of conserved domains would be beneficial to support these claims.

      Both mutations are predicted to produce a premature stop codons: p.Arg362Ter and p.Arg704serfsTer7, leading either to the complete absence of the protein in case of non-sense mediated mRNA decay or to the production of a truncated protein missing almost two third or one fourth of the protein respectively. CCDC146 is very well conserved throughout evolution (Figure supplementary 1), including the 3’ end of the protein which contains a large coil-coil domain (Figure 1B). In view of the very high degree of conservation, it is most likely that the 3’ end of the protein, absent in both subjects, is critical for the CCDC146 function and hence that both mutations are deleterious. This explanation is now added to the discussion. see lines 439-448

      2) 173, 423 - Please clearly state a rationale of your mouse model design (i.e., why a mouse model that recapitulate human mutation is not generated) as the truncations identified in human patients are located further towards the C-terminus, and it is not clear whether truncated proteins are present, and if so, they could still be functional. Basically, the current mouse model supports the causality of the human mutations.

      This is an important question, which goes beyond the scope of this article, and raises the question of how to confirm the pathogenicity of mutations identified by high-throughput sequencing. The production of KO or KI animals is an important tool to help confirm one’ suspicions but the first element to take into consideration is the nature of the genetic data.

      Here we had two patients with homozygous truncating variants. In human, it is well established that the presence of premature stop codons usually induces non-sense mediated mRNA decay (NMD), inducing the complete absence of the protein or a strong reduction in protein production. In the unlikely absence of NMD in our two patients, the identified variants would induce the production of proteins missing 60% and 30% of their C terminal part. Often (and it is particularly true for structural proteins) the production of abnormal proteins is more deleterious than the complete absence of the protein (and it is most likely the purpose of NMD, to limit the production of abnormal “toxic” proteins). For these reasons, to try to recapitulate the most likely consequences of the human variants, without risking obtaining an even more severe effect, we decided to introduce a stop codon in the first exon in order to remove the totality of the protein in the KO mice.

      The second element is to interpret the phenotype of the KO animals. Here, the human sperm phenotype is perfectly recapitulated in the KO mice.

      Overall, we have strong genetic arguments in human and the reproduction of the phenotype in KO mice confirming the pathogenicity of the variants identified in men.

      This point is now discussed see lines 433-438

      3) Figure 6A - the labelling is misleading as it seems to suggest that the specific cells were isolated from the testes for RT-PCR.

      We have modified the labelling to avoid any confusion.

      Figure 6B -Signal of HA-tag is shown in WT, not in transgenic. Please check the order of the labels. Figure 6C - This blot is NOT a publication-quality figure. The bands are very difficult to observe, especially in lane D18. Because it is one of the important data of this study, replacing this figure is a must.

      The figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed.

      4) Supplementary fig 6 is also not a publication-level figure, and the top part seems largely unnecessary (already in the figure legend).

      The figure has been completely remade as well (now Figure 6-Figure Supplement 1).

      5) 261/267- The conclusion that mitochondrial staining in the flagellum (in both mice and humans) is non-specific is not convincing. Supplementary fig 8 shows that the signal from secondary only IF possibly extends beyond the midpiece - but it is hard to determine as no mitochondrial-specific staining is present. Either need to tone down the conclusion or provide supporting experimental evidence.

      First, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. These experiments are now described lines 271-279

      Second, we provide new images of the signal obtained with secondary Abs only that shows more clearly that the secondary Ab gave a non-specific staining (Figure 10-Figure supplement 1B). This point is discussed lines 503-511

      6) Figure 9 A - Please relate the white line to Fig. 9B label in X-axis. The information from Fig 9A+D and 9E+F are redundant. The main text nor the figure legends indicate why these specific two sperm were chosen for quantification and demonstrating the outcomes. One of them could be moved to supplementary information or removed, or the two could be combined.

      As suggested by the reviewer, we have combined the two sperm to demonstrate that CCDC146 staining is mostly located on microtubule doublets. Moreover, the figure was recomposed to make it clearer.

      Minor comments:

      All of the supplementary figures are referred to as Supp Fig X in the text, however, they are actually titled Appendix - Figure X. This needs to be consistent.

      The figures are now referred as figure supplement x in both text and figures

      Line 125 - edit spacing.

      We think this issue (long internet link) will be curated later and more efficiently by the journal, during the step of formatting necessary for publication.

      144 - With which to study  with which we studied?

      We made the change as suggested.

      151 - Supp Fig 1 - the text says that the gene is highly transcribed in human and mouse testes, but the information in the figure states that the level in mouse tissues is "medium"

      We have corrected this mistake in the text; See line 176

      165 - The two mutations are most likely deleterious. Please specifically mention what analyses done to predict the deleterious nature to support these claims.

      Both variants, c.1084C>T and c.2112del, are extremely rare in the general population with a reported allele frequency of 6.5x10-5 and 6.5x10-06 respectively in gnomAD v3. Moreover, these variants are annotated with a high impact on the protein structure (MoBiDiC prioritization algorithm (MPA) score = 10, DOI: 10.1016/j.jmoldx.2018.03.009) and predicted to induce each a premature termination codon, p.(Arg362Ter) and p.(Arg704SerfsTer7) respectively, leading to the production of a truncated protein. This information is now given line 164-169

      196-200/Figure 4 - As serum starved cells/basal body (B) are not mentioned in the main text, as is, Fig 4A would be sufficient/is relevant to the text. Please make the text reflect the contents of the whole figure, or re/move to supplement.

      We agree with the reviewer that the full description of the figure should be in the text. We added two sentences to describe figure 4B see lines 217-218.

      224 - spermatozoa (plural) fits better here, not spermatozoon

      OK changed accordingly

      236 - According to the figure legend, 6B is only showing data from the epididymal sperm, not postnatal time points; should be referencing 6C. Alignment of Marker label

      As indicated above, the figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed. The corresponding text was changed accordingly see lines 249-266

      255-256 - Referenced figure 7B3, however, 7B3 only shows tubulin staining, so no CCDC146 can be observed. Did authors mean to reference fig 7B as a whole?

      Sorry for this mistake. We agree and the text is now figure 8B6 (figure 7 and 8 were switched)

      305 - "of tubules" - I presume it is meant to be microtubules?

      Yes; The text was changed as suggested

      317-321 - a diagram of HTCA would be useful here

      We have added a reference where HTCA diagram is available see line 363. Moreover, a TEM view of HTCA is presented figure 12A

      322/Fig 11A - an arrow denoting the damage might be useful, as A1 and A3 look similar. The size of the marker bar is missing. Please update the information on figure legend.

      Concerning, the comparison between A1 and A3, the take home message is that there is a great variability in the morphological damages. This point is now underlined in the corresponding text. We updated the size of the marker bar as suggested (200 nm). See line 365-367

      323 - Please mark where capitulum is in the figure

      Capitulum was changed for nucleus

      Since Fig 11B2 is not referenced in the main text, it does not seem to add anything to the data, and could be removed/moved to supplement.

      We added a sentence to describe figure 11B2 line 370

      342-343 - manchette in step I is not seen clearly - the figure needs to be annotated better. However, DPY19L2 is absent in step I in the KO, but the main text does not reflect that - why is that?

      We do not understand the remark of the reviewer “manchette in step I is not seen clearly”. The figure shows clearly the manchette (red signal) in both WT and KO (Figure 13 D1/D2).

      For steps 13-15 WT spermatids, the size of the manchette decreases and become undetectable. In KO spermatids, the shrinkage of the manchette is hampered and in contrast continue to expand (Figure 13D2). We also provide a new Figure 13-figure supplement 1 for other illustrations of very long manchettes and a statistical analysis. In the meantime, the acrosome is strongly remodeled, as shown in figure 16-new, with detached acrosome (panel H). This morphological defect may induce a loss of the DPY19L2 staining (Figure 13 D2 stage I-III). This explanation is now inserted in the text line 396399

      Figure 15B and 15C only show KO, corresponding images from the WT should be present for comparison.

      WT images are now provided in Figure 1-figure supplement 1 new

      Figure 12 - Figure 12 - JM?.

      JM was removed. It does not mean anything

      Figure 12C and Supplementary Fig 10 - structures need to be labelled, as it is unclear what is where

      Done

      338 - text mentions step III, but only sperm from step VII are shown in Figure 13

      As suggested by reviewer 3, we changed stage by step. The text was modified to take into account this remark see lines 388-396

      360 - This is likely supposed to say Supp Figure 11E-G, not 13??

      Yes, it is a mistake. Corrected

      388 Typo "in a in a".

      Yes, it is a mistake. Corrected

      820 - Fig 3 legend - in KO spermatid nuclei were elongated - could this be labelled by arrows? I am not convinced this phenotype is that different from the WT.

      In fact, the nuclei of elongating KO spermatids are elongated and also very thin, a shape not observed in the WT; We have added arrow heads and modified the text to indicate this point line 200.

      836 - Figure 5 legend says that in yellow is centrin, but that is not true for 5A, where the figure shows labelling for y-tubulin (presumably, according to the figure itself).

      We have modified the text of the legend to take into account the remark

      837- 5A supposedly corresponds to synchronized HEK293T cells, but the reasoning behind using synchronized cells is not mentioned at all in the main text; furthermore, how this synchronization is achieved is not explained in materials and methods (serum starvation? Thymidine block?).

      Yes, figure 5A was obtained with synchronized cells. We have added one paragraph in the MM section. For cell synchronization experiments, cells underwent S-phase blockade with thymidine (5 mM, SigmaAldrich) for 17 h followed by incubation in a control culture medium for 5 h, then a second blockade at the G2-M transition with nocodazole (200 nM, Sigma-Aldrich) for 12 h. Cells were then fixed with cold methanol at different times for IF labelling. See line 224 for changes made in the result section and lines 700-704 for changes made in the MM section.

      845- figure legend says that the RT-PCR was done on CCDC146-HA tagged mice, but the main text does not reflect that.

      We made changes and the description of the KI is now presented before (line 240) the RT-PCR experiment (line 257).

      949 - it is likely supposed to say A2, not B1 (B1 does not exist in Fig 15)

      Yes, it is a mistake. Corrected

      971 - Appendix Fig 3 legend - I believe that the description for B and C are swapped.

      Yes, it is a mistake. Corrected

      Furthermore, some questions to address in A would be: Which cross sections were from which animal/points? How many per animal? Were they always in the same location?

      Yes, we have a protocol for arranging and orienting all testes in the same way during the paraffin embedding phase. The cross-sections are therefore not taken at random, and we can compare sections from the same part of the testis. The number of animals was already indicated in the figure legend (see line 1128)

      Reviewer #3 (Recommendations For The Authors):

      1) There are a number of grammatical and orthographical errors in the text. Careful proofreading should be performed.

      We have sent the manuscript to a professional proofreader

      2) The author should also check for redundancies between the introduction and the discussion.

      The discussion has modified to take into account reviewers’ remarks. Nevertheless, we did our best to avoid redundancies between introduction and discussion.

      3) Can the authors provide a rationale why they have chosen to tag their gene with an HA tag for localisation? One would rather think of fluorescent proteins or a Halo tag.

      Because the functional domains of the protein are unknown, adding a fluorescent protein of 24 KDa may interfere with both the localization and the function of CCDC146. For this reason, we choose a small tag of only 1.1 KDa, to limit as such as possible the risk of interfering with the structure of the protein. This rational is now indicated in the manuscript lines 251-254. It is worth to note, that the tagged-strain shows no sperm defect, demonstrating that the HA-tag does not interfere with CCDC146 function.

      4) In the abstract, line 53, "provide evidence" is not the right term for something that is just suggestive. The term "suggests" would be more appropriate.

      The text was modified to take into account this remark

      5) Line 74: "genetic deficiency" sounds strange here, do the authors mean simply "mutation"?

      Infertility may be due to several genetic deficiency such as chromosomal defects (XXY (Klinefelter syndrome)), microdeletion of the Y chromosome or mutations in a single gene. Therefore, mutation is too restrictive. Nevertheless, we modified the sentence which is now “…or a genetic disorder including chromosomal or single gene deficiencies”

      6) Lines 163-164: the authors describe the mutations (premature stop mutations) and say that they could either lead to complete absence of the gene product, or the expression of a truncated protein. Did they test this, for example, with some immuno blot analyses?

      As stated above, unfortunately, we were unable to verify the presence of RNA-decay in these patients for lack of biological material.

      7) Line 184 and Fig 2E: the sperm head morphologies should be quantitatively assessed.

      We provide now a full statistical analysis of the observed defects: see new panel in Figure 2 F

      8) Fig 3: The annotation should be more precise - KO certainly means CDCC146-KO. The colours of the IH panels is different, which attracts attention but is clearly a colour-adjustment artefact. Colours should be adjusted for the panels to look comparable. It would be also helpful to add arrowheads into the figure to point at the phenotypes that are highlighted in the text.

      We have added Ccdc146 KO in all figures. We have added arrow heads to point out the spermatids showing a thin and elongated nucleus. Concerning adjustment of colors, we attempted to make images of panel B comparable. See new figure 3.

      9) Fig 6A: the authors use RT PCR to determine expression dynamics of their gene of interested, and use actin (apparently) as control. However, actin and CDCC146 expression levels follow the same trend. How is the interpreted?

      The reviewer did not understand the figure. The orange bars do not correspond to actin expression and the grey bars to Ccdc146 expression but both bars represent the mRNA expression levels of Ccdc146 relative to Actb (orange) and Hprt (grey) expression in CCDC146-HA mouse pups’ testes. We tested two housekeeping genes as reference to be sure that our results were not distorted by an unstable expression of a housekeeping gene. We did not see significant difference between both house keeping genes. Actin was not used.

      10) In line 235, the authors suggest posttranslational modifications of their protein as potential cause for a slightly different migration in SDS PAGE as predicted from the theoretical molecular weight. This is not necessarily the case, some proteins do migrate just differently as predicted.

      We have changed the text accordingly and now provide alternative explanation for the slightly different migration. See lines 258-259

      11) The annotation of Fig 6 panels is problematic. First, why do the authors write "Laemmli" as description of the gel? It would be more helpful to write what is loaded on the gel, such as "sperm". Second, in panels B and C it would be helpful to add the antibodies used. It is not clear why there is a signal in the WT lane of panel B, but not in the HA lane (supposing an anti-HA antibody is used: why has WT a specific HA band?). In panel C, it is not clear why the blot that has so beautifully shown a single band in panel B suddenly gives such a bad labelling. Can the authors explain this? Also, they cut off the blot, likely because to too much background, but this is bad practice as full blots should be shown. In the current state, the panel C does not allow any clear conclusion. To make it conclusive, it must be repeated.

      Several mistakes were present in this figure. This figure was recomposed. The WB on testicular extract was suppressed and we now present a new WB allowing to compare the presence of CCDC146 in the flagella and head fractions from WT and HA-CCDC146 sperm. Using an anti-HA Ab, we demonstrate that in epididymal sperm the protein is localized in the flagella only. See new figure 6. The corresponding text was changed accordingly.

      12) The authors have raised an HA-knockin mouse for CDCC146, which they explained by the unavailability of specific antibodies. However, in Fig 7, they use a CDCC146 antibody. Can they clarify?

      The commercial Ab work for HUMAN CCDC146 but not for MOUSE CCDC146. We have added few words to make the situation clearer, we have added the following information “the commercial Ab works for human CCDC146 only”. See line 240

      13) In Fig 7A (line 258), the authors hypothesise that they stain mitochondria - why not test this directly by co-staining with mitochondria markers?

      We chose another solution to resolve this question:

      To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the whole flagellum.

      14) It seems that in both, Fig 7 and 8, the authors use expansion microscopy to localise CDCC146 in sperm tails. However, the staining differs substantially between the two figures. How is this explained?

      In figure 8 we used the commercial Ab in human sperm, whereas in figure 7 we used the anti-HA Abs in mouse sperm. Because the antibodies do not target the same part of the CCDC146 protein (the tag is placed at the N-terminus of the protein, and the HPA020082 Ab targets the last 130 amino acids of the Cter), their accessibility to the antigenic site could be different. However, it is important to note that both antibodies target the flagellum. This explanation is now inserted see lines 304-312

      15) Fig 8D and line 274: the authors do a fractionation, but only show the flagella fraction. Why?

      Showing all fractions of their experiment would have underpinned the specific enrichment of CDCC146 in the flagella fraction, which is what they aim to show. Actually, given the absence of control proteins, the fact that the band in the flagellar fraction appears to be weaker than in total sperm, one could even conclude that there is more CDCC146 in another (not analysed) fraction of this experiment. Thus, the experiment as it stands is incomplete and does not, as the authors claim, confirm the flagellar localisation of the protein.

      We agree with the reviewer’s remark. We provide now new results showing both flagella and nuclei fractions in new figure 6A. This experiment is presented lines 253-256

      16) Line 283, Fig 9D,F: The description of the microtubules in this experiment is not easy to understand. Do the authors mean to say that the labelling shows that the protein is associated with doublet microtubules, but not with the two central microtubules? They should try to find a clearer way to explain their result.

      As suggested by reviewer 2, we have changed the figure to make it clearer. The text was changed accordingly. See new figure 9 and new corresponding legend lines 1006.

      17) Fig 9G - how often could the authors observe this? Why is the axoneme frayed? Does this happen randomly, or did the authors apply a specific treatment?

      Yes, it happens randomly during the fixation process.

      18) Line 300 and Fig 10A - the authors talk about the 90-kDa band, but do say anything about what they think this band is representing.

      We have now added the following sentence lines 340-342: “This band may correspond to proteolytic fragment of CCDC146, the solubilization of microtubules by sarkosyl may have made CCDC146 more accessible to endogenous proteases.”

      19) Fig 11A, lines 321-322: the authors write that the connecting piece is severely damaged. This is not obvious for somebody who does not work in sperm. Perhaps the authors could add some arrow heads to point out the defects, and briefly describe them in the text.

      We realized from your remark that our message was not clear. In fact, there is a great variability in the morphological damages of the HTCA. For instance, the HTCA of Ccdc146 KO sperm presented in figure 10A2 is quite normal, whereas that in figure 10A4 is completely distorted. This point is now underlined in the corresponding text. See lines 367-369

      We also added the size of the marker bar (200 nm), which were missing in the figure’s legend.

      20) Line 323: it will be important to name which tubulin antibody has been used to identify centrioles, as they are heavily posttranslationally modified.

      The different types of anti-tubulin Abs are described in the corresponding figure’s legend

      21) Fig 11B - phenotypes must be quantified to make these observations meaningful.

      We agree that a quantification would improve the message. However, testicular sperm are obtained by enzymatic separation of spermatogenic cells and the number of testicular sperm are very low. Moreover, not all sperm are stained. Taking these two points into account, it seems to us that quantification could be difficult to analyze. For this reason, the quantification was not done; however, it is important to note that these defects were not observed in WT sperm, demonstrating that these defects are cased by the lack of CCDC146. We have added a sentence to underline this point; See lines 374-375

      22) Line 329: Figure 12AB - is this a typo - should it read Figure 12B?

      We have split the panel A in A1 and A2 and changed the text accordingly. See line 378

      23) Why are there not wildtype controls in Fig 12B, C?

      We provide now as Figure 12-figure supplement 1, a control image for fig 12B. For figure 12C, the emergence of the flagellum from the distal centriole in WT is already shown in Fig 12A1

      24) Fig 13: the authors write that the manchette is "clearly longer and wider than in WT cells" (lines 342-343). How can they claim this without quantitative data?

      We now provide a statistical analysis of the length of the manchette. See figure 13-figure supplement 1A. We also provide a new a new image illustrating the length of the manchette in Ccdc146 KO spermatids; See Figure 13-figure supplement 1B.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Koumoundourou et al., identify a pathway downstream of Bcl11b that controls synapse morphology and plasticity of hippocampal mossy fiber synapses. Using an elegant combination of in vivo, ex vivo, and in vitro approaches, the authors build on their previous work that indicated C1ql2 as a functional target of Bcl11b (De Bruyckere et al., 2018). Here, they examine the functional implications of C1ql2 at MF synapses in Bcl11b cKO mice and following C1ql2 shRNA. The authors find that Bcl11b KO and shRNA against C1ql2 significantly reduces the recruitment of synaptic vesicles and impairs LTP at MF synapses. Importantly, the authors test a role for the previously identified C1ql2 binding partner, exon 25b-containing Nrxn3 (Matsuda et al., 2016), as relevant at MF synapses to maintain synaptic vesicle recruitment. To test this, the authors developed a K262E C1ql2 mutant that disrupts binding to Nrxn3. Curiously, while Bcl11b KO and C1ql2 KD largely phenocopy (reduced vesicle recruitment and impaired LTP), only vesicle recruitment is dependent on C1ql2-Nrxn3 interactions. These findings provide new insight into the functional role of C1ql2 at MF synapses. While the authors convincingly demonstrate a role for C1ql2-Nrxn3(25b+) interaction for vesicle recruitment and a Nrxn3(25b+)independent role for C1ql2 in LTP, the underlying mechanisms remain inconclusive. Additionally, a discussion of how these findings relate to previous work on C1ql2 at mossy fiber synapses and how the findings contribute to the biology of Nrxn3 would increase the interpretability of this work.

      As suggested by reviewer #1, we extended our discussion of previous work on C1ql2 and additionally discussed the biology of Nrxn3 and how our work relates to it. Moreover, we extended our mechanistic analysis of how Bcl11b/C1ql2/Nrxn3 pathway controls synaptic vesicle recruitment as well as LTP (please see also response to reviewer #2 points 5 and 8 and reviewer #3 point 4 of public reviews below for detailed discussion).

      Reviewer #2 (Public Review):

      This manuscript describes experiments that further investigate the actions of the transcription factor Bcl11b in regulating mossy fiber (MF) synapses in the hippocampus. Prior work from the same group had demonstrated that loss of Bcl11b results in loss of MF synapses as well as a decrease in LTP. Here the authors focus on a target of Bcl11b a secreted synaptic organizer C1ql2 which is almost completely lost in Bcl11b KO. Viral reintroduction of C1ql2 rescues the synaptic phenotypes, whereas direct KD of C1ql2 recapitulates the Bcl1 phenotype. C1ql2 itself interacts directly with Nrxn3 and replacement with a binding deficient mutant C1q was not able to rescue the Bcl11b KO phenotype. Overall there are some interesting observations in the study, however there are also some concerns about the measures and interpretation of data.

      The authors state that they used a differential transcriptomic analysis to screen for candidate targets of Bcl11b, yet they do not present any details of this screen. This should be included and at the very least a table of all DE genes included. It is likely that many other genes are also regulated by Bcl11b so it would be important to the reader to see the rationale for focusing attention on C1ql2 in this study.

      The transcriptome analysis mentioned in our manuscript was published in detail in our previous study (De Bruyckere et al., 2018), including chromatin-immunoprecipitation that revealed C1ql2 as a direct transcriptional target of Bcl11b. Upon revision of the manuscript, we made sure that this was clearly stated within the main text module to avoid future confusion. In the same publication (De Bruyckere et al., 2018), we discuss in detail several identified candidate genes such as Sema5b, Ptgs2, Pdyn and Penk as putative effectors of Bcl11b in the structural and functional integrity of MFS. C1ql2 has been previously demonstrated to be almost exclusively expressed in DG neurons and localized to the MFS.

      There it bridges the pre- and post-synaptic sides through interaction with Nrxn3 and KAR subunits, respectively, and regulates synaptic function (Matsuda et al., 2016). Taken together, C1ql2 was a very good candidate to study as a potential effector downstream of Bcl11b in the maintenance of MFS structure and function. However, as our data reveal, not all Bcl11b mutant phenotypes were rescued by C1ql2 (see supplementary figures 2d-f of revised manuscript). We expect additional candidate genes, identified in our transcriptomic screen, to act downstream of Bcl11b in the control of MFS.

      All viral-mediated expression uses AAVs which are known to ablate neurogenesis in the DG (Johnston DOI: 10.7554/eLife.59291) through the ITR regions and leads to hyperexcitability of the dentate. While it is not clear how this would impact the measurements the authors make in MF-CA3 synapses, this should be acknowledged as a potential caveat in this study.

      We agree with reviewer #2 and are aware that it has been demonstrated that AAV-mediated gene expression ablates neurogenesis in the DG. To avoid potential interference of the AAVs with the interpretability of our phenotypes, we made sure during the design of the study that all of our control groups were treated in the same way as our groups of interest, and were, thus, injected with control AAVs. Moreover, the observed phenotypes were first described in Bcl11b mutants that were not injected with AVVs (De Bruyckere et al., 2018). Finally, we thoroughly examined the individual components of the proposed mechanism (rescue of C1ql2 expression, over-expression of C1ql3 and introduction of mutant C1ql2 in Bcl11b cKOs, KD of C1ql2 in WT mice, and Nrxn123 cKO) and reached similar conclusions. Together, this strongly supports that the observed phenotypes occur as a result of the physiological function of the proteins involved in the described mechanism and not due to interference of the AAVs with these biological processes. We have now addressed this point in the main text module of the revised ms.

      The authors claim that the viral re-introduction "restored C1ql2 protein expression to control levels. This is misleading given that the mean of the data is 2.5x the control (Figure 1d and also see Figure 6c). The low n and large variance are a problem for these data. Moreover, they are marked ns but the authors should report p values for these. At the least, this likely large overexpression and variability should be acknowledged. In addition, the use of clipped bands on Western blots should be avoided. Please show the complete protein gel in primary figures of supplemental information.

      We agree with reviewer #2 that C1ql2 expression after its re-introduction in Bcl11b cKO mice was higher compared to controls and that this should be taken into consideration for proper interpretation of the data. To address this, based also on the suggestion of reviewer #3 point 1 below, we overexpressed C1ql2 in DG neurons of control animals. We found no changes in synaptic vesicle organization upon C1ql2 over-expression compared to controls. This further supports that the observed effect upon rescue of C1ql2 expression in Bcl11b cKOs is due to the physiological function of C1ql2 and not as result of the overexpression. These data are included in supplementary figure 2g-j and are described in detail in the results part of the revised manuscript.

      Additionally, we looked at the effects of C1ql2 overexpression in Bcl11b cKO DGN on basal synaptic transmission. We plotted fEPSP slopes versus fiber volley amplitudes, measured in slices from rescue animals, as we had previously done for the control and Bcl11b cKO (Author response image 1a). Although regression analysis revealed a trend towards steeper slopes in the rescue mice (Author response image 1a and b), the observation did not prove to be statistically significant, indicating that C1ql2 overexpression in Bcl11b cKO animals does not strongly alter basal synaptic transmission at MFS. Overall, our previous and new findings support that the observed effects of the C1ql2 rescue are not caused by the artificially elevated levels of C1ql2, as compared to controls, but are rather a result of the physiological function of C1ql2.

      Following the suggestion of reviewer #2 all western blot clipped bands were exchanged for images of the full blot. This includes figures 1c, 4c, 6b and supplementary figure 2g of the revised manuscript. P-value for Figure 1d has now been included.

      Author response image 1.

      C1ql2 reintroduction in Bcl11b cKO DGN does not significantly alter basal synaptic transmission at mossy fiber-CA3 synapses. a Input-output curves generated by plotting fEPSP slope against fiber volley amplitude at increasing stimulation intensities. b Quantification of regression line slopes for input-output curves for all three conditions. Control+EGFP, 35 slices from 16 mice; Bcl11b cKO+EGFP, 32 slices from 14 mice; Bcl11b cKO+EGFP-2A-C1ql2, 22 slices from 11 mice. The data are presented as means, error bars represent SEM. Kruskal-Wallis test (non-parametric ANOVA) followed by Dunn’s post hoc pairwise comparisons. p=0.106; ns, not significant.

      Measurement of EM micrographs: As prior work suggested that MF synapse structure is disrupted the authors should report active zone length as this may itself affect "synapse score" defined by the number of vesicles docked. More concerning is that the example KO micrographs seem to have lost all the densely clustered synaptic vesicles that are away from the AZ in normal MF synapses e.g. compare control and KO terminals in Fig 2a or 6f or 7f. These terminals look aberrant and suggest that the important measure is not what is docked but what is present in the terminal cytoplasm that normally makes up the reserve pool. This needs to be addressed with further analysis and modifications to the manuscript.

      As requested by reviewer #2 we analyzed and reported in the revised manuscript the active zone length. We found that the active zone length remained unchanged in all conditions (control/Bcl11b cKO/C1ql2 rescue, WT/C1ql2 KD, control/K262E and control/Nrxn123 cKO), strengthening our results that the described Bcl11b/C1ql2/Nrxn3 mechanism is involved in the recruitment of synaptic vesicles. These data have been included in supplementary figures 2c, 4h, 5f and 6g and are described in the results part of the revised manuscript.

      We want to clarify that the synapse score is not defined by the number of docked vesicles to the plasma membrane. The synapse score, which is described in great detail in our materials and methods part and has been previously published (De Bruyckere et al., 2018), rates MFS based on the number of synaptic vesicles and their distance from the active zone and was designed according to previously described properties of the vesicle pools at the MFS. The EM micrographs refer to the general misdistribution of SV in the proximity of MFS. Upon revision of the manuscript, we made sure that this was clearly stated in the main text module to avoid further confusion.

      The study also presents correlated changes in MF LTP in Bcl11b KO which are rescued by C1ql2 expression. It is not clear whether the structural and functional deficits are causally linked and this should be made clearer in the manuscript. It is also not apparent why this functional measure was chosen as it is unlikely that C1ql2 plays a direct role in presynaptic plasticity mechanisms that are through a cAMP/ PKA pathway and likely disrupted LTP is due to dysfunctional synapses rather than a specific LTP effect.

      The inclusion of functional experiments in this and our previous study (de Bruyckere et al., 2018) was first and foremost intended to determine whether the structural alterations observed at MFB disrupt MFS signaling. From the signaling properties we tested, basal synaptic transmission (this study) and short-term potentiation (de Bruyckere et al., 2018) were unaltered by Bcl11b KO, whereas MF LTP was found to be abolished (de Bruyckere et al., 2018). Indeed, because MF LTP largely depends on presynaptic mechanisms, including the redistribution of the readily releasable pool and recruitment of new active zones (Orlando et al., 2021; Vandael et al., 2020), it appears to be particularly sensitive to the specific structural changes we observed. We therefore believe that it is valuable information that MF LTP is affected in Bcl11b cKO animals - it conveys a direct proof for the functional importance of the observed morphological alterations, while basic transmission remains largely normal. Furthermore, it subsequently provided a functional marker for testing whether the reintroduction of C1ql2 in Bcl11b cKO animals or the KD of C1ql2 in WT animals can functionally recapitulate the control or the Bcl11b KO phenotype, respectively.

      We fully agree with the reviewer that C1ql2 is unlikely to directly participate in the cAMP/PKA pathway and that the ablation of C1ql2 likely disrupts MF LTP through an alternative mode of action. Our original wording in the paragraph describing the results of the forskolin-induced LTP experiment might have overstressed the importance of the cAMP pathway. We have now rephrased that paragraph to better describe the main idea behind the forskolin experiment, namely to circumvent the initial Ca2+ influx in order to test whether deficient presynaptic Ca2+ channel/KAR signaling might be responsible for the loss of LTP in Bcl11b cKO. The results are strongly indicative of a downstream mechanism and further investigation is needed to determine the specific mechanisms by which C1ql2 regulates MFLTP, especially in light of the result that C1ql2.K262E rescued LTP, while it was unable to rescue the SV recruitment at the MF presynapse. This raises the possibility that C1ql2 can influence MF-LTP through additional, yet uncharacterized mechanisms, independent of SV recruitment. As such, a causal link between the structural and functional deficits remains tentative and we have now emphasized that point by adding a respective sentence to the discussion of our revised manuscript. Nevertheless, we again want to stress that the main rationale behind the LTP experiments was to assess the functional significance of structural changes at MFS and not to elucidate the mechanisms by which MF LTP is established.

      The authors should consider measures that might support the role of Bcl11b targets in SV recruitment during the depletion of synapses or measurements of the readily releasable pool size that would complement their findings in structural studies.

      We fully agree that functional measurements of the readily releasable pool (RRP) size would be a valuable addition to the reported redistribution of SV in structural studies. We have, in fact, attempted to use high-frequency stimulus trains in both field and single-cell recordings (details on single-cell experiments are described in the response to point 8) to evaluate potential differences in RRP size between the control and Bcl11b KO (Figure for reviewers 2a and b). Under both recording conditions we see a trend towards lower values of the intersection between a regression line of late responses and the y-axis. This could be taken as an indication of slightly smaller RRP size in Bcl11b mutant animals compared to controls. However, due to several technical reasons we are extremely cautious about drawing such far-reaching conclusions based on these data. At most, they suffice to conclude that the availability of release-ready vesicles in the KO is likely not dramatically smaller than in the control.

      The primary issue with using high-frequency stimulus trains for RRP measurements at MFS is the particularly low initial release probability (Pr) at these synapses. This means that a large number of stimulations is required to deplete the RRP. As the RRP is constantly replenished, it remains unclear when steady state responses are reached (reviewed by Kaeser and Regehr, 2017). This is clearly visible in our single-cell recordings (Author response image 2b), which were additionally complicated by prominent asynchronous release at later stages of the stimulus train and by a large variability in the shapes of cumulative amplitude curves between cells. In contrast, while the cumulative amplitude curves for field potential recordings do reach a steady state (Author response image 2a), field potential recordings in this context are not a reliable substitute for single cell or, in the case of MFB, singlebouton recordings. Postsynaptic cells in field potential recordings are not clamped, meaning that the massive release of glutamate due to continuous stimulation depolarizes the postsynaptic cells and reduces the driving force for Na+, irrespective of depletion of the RRP. This is supported by the fact that we consistently observed a recovery of fEPSP amplitudes later in the trains where RRP had presumably been maximally depleted. In summary, high-frequency stimulus trains at the field potential level are not a valid and established technique for estimating RRP size at MFS.

      Specialized laboratories have used highly advanced techniques, such as paired recordings between individual MFB and postsynaptic CA3 pyramidal cells, to estimate the RRP size of MFB (Vandael et al., 2020). These approaches are outside the scope of our present study which, while elucidating functional changes following Bcl11b depletion and C1ql2 rescue, does not aim to provide a high-end biophysical analysis of the presynaptic mechanisms involved.

      Author response image 2.

      Estimation of RRP size using high-frequency stimulus trains at mossy fiber-CA3 synapses. a Results from field potential recordings. Cumulative fEPSP amplitude in response to a train of 40 stimuli at 100 Hz. All subsequent peak amplitudes were normalized to the amplitude of the first peak. Data points corresponding to putative steady state responses were fit with linear regression (RRP size is indirectly reflected by the intersection of the regression line with the yaxis). Control+EGFP, 6 slices from 5 mice; Bcl11b cKO+EGFP, 6 slices from 3 mice. b Results from single-cell recordings. Cumulative EPSC amplitude in response to a train of 15 stimuli at 50 Hz. The last four stimuli were fit with linear regression. Control, 5 cells from 4 mice; Bcl11b cKO, 3 cells from 3 mice. Note the shallow onset of response amplitudes and the subsequent frequency potentiation. Due to the resulting increase in slope at higher stimulus numbers, intersection with the y-axis occurs at negative values. The differences shown were not found to be statistically significant; unpaired t-test or Mann-Whitney U-test.

      Bcl11b KO reduces the number of synapses, yet the I-O curve reported in Supp Fig 2 is not changed. How is that possible? This should be explained.

      We agree with reviewer #2– this apparent discrepancy has indeed struck us as a counterintuitive result. It might be that synapses that are preferentially eliminated in Bcl11b cKO are predominantly silent or have weak coupling strength, such that their loss has only a minimal effect on basal synaptic transmission. Although perplexing, the result is fully supported by our single-cell data which shows no significant differences in MF EPSC amplitudes recorded from CA3 pyramidal cells between controls and Bcl11b mutants (Author response image 3; please see the response below for details and also our response to Reviewer #1 question 2).

      Matsuda et al DOI: 10.1016/j.neuron.2016.04.001 previously reported that C1ql2 organizes MF synapses by aligning postsynaptic kainate receptors with presynaptic elements. As this may have consequences for the functional properties of MF synapses including their plasticity, the authors should report whether they see deficient postsynaptic glutamate receptor signaling in the Bcl11b KO and rescue in the C1ql2 re-expression.

      We agree that the study by Matsuda et al. is of key importance for our present work. Although MF LTP is governed by presynaptic mechanisms and we previously did not see differences in short-term plasticity between the control and Bcl11b cKO (De Bruyckere et al., 2018), the clustering of postsynaptic kainate receptors by C1ql2 is indeed an important detail that could potentially alter synaptic signaling at MFS in Bcl11b KO. We, therefore, re-analyzed previously recorded single-cell data by performing a kinetic analysis on MF EPSCs recorded from CA3 pyramidal cells in control and Bcl11b cKO mice (Figure for reviewers 3a) to evaluate postsynaptic AMPA and kainate receptor responses in both conditions. We took advantage of the fact that AMPA receptors deactivate roughly 10 times faster than kainate receptors, allowing the contributions of the two receptors to mossy fiber EPSCs to be separated (Castillo et al., 1997 and reviewed by Lerma, 2003). We fit the decay phase of the second (larger) EPSC evoked by paired-pulse stimulation with a double exponential function, yielding a fast and a slow component, which roughly correspond to the fractional currents evoked by AMPA and kainate receptors, respectively. Analysis of both fast and slow time constants and the corresponding fractional amplitudes revealed no significant differences between controls and Bcl11b mutants (Figure for reviewers 3e-h), indicating that both AMPA and kainate receptor signaling is unaffected by the ablation of C1ql2 following Bcl11b KO.

      Importantly, MF EPSC amplitudes evoked by the first and the second pulse (Author response image 3b), paired-pulse facilitation (Author response image 3c) and failure rates (Author response image 3d) were all comparable between controls and Bcl11b mutants. These results further corroborate our observations from field recordings that basal synaptic transmission at MFS is unaltered by Bcl11b KO.

      We note that the results from single cell recordings regarding basal synaptic transmission merely confirm the observations from field potential recordings, and that the attempted measurement of RRP size at the single cell level was not successful. Thus, our single-cell data do not add new information about the mechanisms underlying the effects of Bcl11b-deficiency and we therefore decided not to report these data in the manuscript.

      Author response image 3.

      Basal synaptic transmission at mossy fiber-CA3 synapses is unaltered in Bcl11b cKO mice. a Representative average trace (20 sweeps) recorded from CA3 pyramidal cells in control and Bcl11b cKO mice at minimal stimulation conditions, showing EPSCs in response to paired-pulse stimulation (PPS) at an interstimulus interval of 40 ms. The signal is almost entirely blocked by the application of 2 μM DCG-IV (red). b Quantification of MF EPSC amplitudes in response to PPS for both the first and the second pulse. c Ratio between the amplitude of the second over the first EPSC. d Percentage of stimulation events resulting in no detectable EPSCs for the first pulse. Events <5 pA were considered as noise. e Fast decay time constant obtained by fitting the average second EPSC with the following double exponential function: I(t)=Afaste−t/τfast+Aslowe−t/τslow+C, where I is the recorded current amplitude after time t, Afast and Aslow represent fractional current amplitudes decaying with the fast (τfast) and slow (τslow) time constant, respectively, and C is the offset. Starting from the peak of the EPSC, the first 200 ms of the decaying trace were used for fitting. f Fractional current amplitude decaying with the fast time constant. g-h Slow decay time constant and fractional current amplitude decaying with the slow time constant. For all figures: Control, 8 cells from 4 mice; Bcl11b cKO, 8 cells from 6 mice. All data are presented as means, error bars indicate SEM. None of the differences shown were found to be statistically significant; Mann-Whitney U-test for nonnormally and unpaired t-test for normally distributed data.

      Reviewer #3 (Public Review):

      Overall, this is a strong manuscript that uses multiple current techniques to provide specific mechanistic insight into prior discoveries of the contributions of the Bcl11b transcription factor to mossy fiber synapses of dentate gyrus granule cells. The authors employ an adult deletion of Bcl11b via Tamoxifen-inducible Cre and use immunohistochemical, electron microscopy, and electrophysiological studies of synaptic plasticity, together with viral rescue of C1ql2, a direct transcriptional target of Bcl11b or Nrxn3, to construct a molecular cascade downstream of Bcl11b for DG mossy fiber synapse development. They find that C1ql2 re-expression in Bcl11b cKOs can rescue the synaptic vesicle docking phenotype and the impairments in MF-LTP of these mutants. They also show that C1ql2 knockdown in DG neurons can phenocopy the vesicle docking and plasticity phenotypes of the Bcl11b cKO. They also use artificial synapse formation assays to suggest that C1ql2 functions together with a specific Nrxn3 splice isoform in mediating MF axon development, extending these data with a C1ql2-K262E mutant that purports to specifically disrupt interactions with Nrxn3. All of the molecules involved in this cascade are disease-associated and this study provides an excellent blueprint for uncovering downstream mediators of transcription factor disruption. Together this makes this work of great interest to the field. Strengths are the sophisticated use of viral replacement and multi-level phenotypic analysis while weaknesses include the linkage of C1ql2 with a specific Nrxn3 splice variant in mediating these effects.

      Here is an appraisal of the main claims and conclusions:

      1) C1ql2 is a downstream target of Bcl11b which mediates the synaptic vesicle recruitment and synaptic plasticity phenotypes seen in these cKOs. This is supported by the clear rescue phenotypes of synapse anatomy (Fig.2) and MF synaptic plasticity (Fig.3). One weakness here is the absence of a control assessing over-expression phenotypes of C1ql2. It's clear from Fig.1D that viral rescue is often greater than WT expression (totally expected). In the case where you are trying to suppress a LoF phenotype, it is important to make sure that enhanced expression of C1ql2 in a WT background does not cause your rescue phenotype. A strong overexpression phenotype in WT would weaken the claim that C1ql2 is the main mediator of the Bcl11b phenotype for MF synapse phenotypes.

      As suggested by reviewer #3, we carried out C1ql2 over-expression experiments in control animals. We show that the over-expression of C1ql2 in the DG of control animals had no effect on the synaptic vesicle organization in the proximity of MFS. This further supports that the observed effect upon rescue of C1ql2 expression in Bcl11b cKOs is due to the physiological function of C1ql2 and not a result of the artificial overexpression. These data are now included in supplementary figure 2g-j and are described in detail in the results part of the revised manuscript. Please also see response to point 3 of reviewer #2.

      2) Knockdown of C1ql2 via 4 shRNAs is sufficient to produce the synaptic vesicle recruitment and MFLTP phenotypes. This is supported by clear effects in the shRNA-C1ql2 groups as compared to nonsense-EGFP controls. One concern (particularly given the use of 4 distinct shRNAs) is the potential for off-target effects, which is best controlled for by a rescue experiment with RNA insensitive C1ql2 cDNA as opposed to nonsense sequences, which may not elicit the same off-target effects.

      We agree with reviewer #3 that the usage of shRNAs could potentially create unexpected off-target effects and that the introduction of a shRNA-insensitive C1ql2 in parallel to the expression on the shRNA cassette would be a very effective control experiment. However, the suggested experiment would require an additional 6 months (2 months for AAV production, 2-3 months from animal injection to sacrifice and 1-2 months for EM imaging/analysis and LTP measurements) and a high number of additional animals (minimum 8 for EM and 8 for LTP measurements). We note here, that before the production of the shRNA-C1ql2 and the shRNA-NS, the individual sequences were systematically checked for off-target bindings on the murine exome with up to two mismatches and presented with no other target except the proposed (C1ql2 for shRNA-C1ql2 and no target for shRNA-NS). Taking into consideration our in-silico analysis, we feel that the interpretation of our findings is valid without this (very reasonable) additional control experiment.

      3) C1ql2 interacts with Nrxn3(25b+) to facilitate MF terminal SV clustering. This claim is theoretically supported by the HEK cell artificial synapse formation assay (Fig.5), the inability of the K262-C1ql2 mutation to rescue the Bcl11b phenotype (Fig.6), and the altered localization of C1ql2 in the Nrxn1-3 deletion mice (Fig.7). Each of these lines of experimental evidence has caveats that should be acknowledged and addressed. Given the hypothesis that C1ql2 and Nrxn3b(25b) are expressed in DG neurons and work together, the heterologous co-culture experiment seems strange. Up till now, the authors are looking at pre-synaptic function of C1ql2 since they are re-expressing it in DGNs. The phenotypes they are seeing are also pre-synaptic and/or consistent with pre-synaptic dysfunction. In Fig.5, they are testing whether C1ql2 can induce pre-synaptic differentiation in trans, i.e. theoretically being released from the 293 cells "post-synaptically". But the post-synaptic ligands (Nlgn1 and and GluKs) are not present in the 293 cells, so a heterologous synapse assay doesn't really make sense here. The effect that the authors are seeing likely reflects the fact that C1ql2 and Nrxn3 do bind to each other, so C1ql2 is acting as an artificial post-synaptic ligand, in that it can cluster Nrxn3 which in turn clusters synaptic vesicles. But this does not test the model that the authors propose (i.e. C1ql2 and Nrxn3 are both expressed in MF terminals). Perhaps a heterologous assay where GluK2 is put into HEK cells and the C1ql2 and Nrxn3 are simultaneously or individually manipulated in DG neurons?

      C1ql2 is expressed by DG neurons and is then secreted in the MFS synaptic cleft, while Nrxn3, that is also expressed by DG neurons, is anchored at the presynaptic side. In our work we used the well established co-culture system assay and cultured HEK293 cells secreting C1ql2 (an IgK secretion sequence was inserted at the N-terminus of C1ql2) together with hippocampal neurons expressing Nrxn3(25b+). We used the HEK293 cells as a delivery system of secreted C1ql2 to the neurons to create regions of high concentration of C1ql2. By interfering with the C1ql2-Nrxn3 interaction in this system either by expression of the non-binding mutant C1ql2 variant in the HEK cells or by manipulating Nrxn expression in the neurons, we could show that C1ql2 binding to Nrxn3(25b+) is necessary for the accumulation of vGlut1. However, we did not examine and do not claim within our manuscript that the interaction between C1ql2 and Nrxn3(25b+) induces presynaptic differentiation. Our experiment only aimed to analyze the ability of C1ql2 to cluster SV through interaction with Nrxn3. Moreover, by not expressing potential postsynaptic interaction partners of C1ql2 in our system, we could show that C1ql2 controls SV recruitment through a purely presynaptic mechanism. Co-culturing GluK2-expressing HEK cells with simultaneous manipulation of C1ql2 and/or Nrxn3 in neurons would not allow us to appropriately answer our scientific question, but rather focus on the potential synaptogenic function of the Nrxn3/C1ql2/GluK2 complex and the role of the postsynaptic ligand in it. Thus, we feel that the proposed experiment, while very interesting in characterization of additional putative functions of C1ql2, may not provide additional information for the point we were addressing. In the revised manuscript we tried to make the aim and methodological approach of this set of experiments more clear.

      4) K262-C1ql2 mutation blocks the normal rescue through a Nrxn3(25b) mechanism (Fig.6). The strength of this experiment rests upon the specificity of this mutation for disrupting Nrxn3b binding (presynaptic) as opposed to any of the known postsynaptic C1ql2 ligands such as GluK2. While this is not relevant for interpreting the heterologous assay (Fig.5), it is relevant for the in vivo phenotypes in Fig.6. Similar approaches as employed in this paper can test whether binding to other known postsynaptic targets is altered by this point mutation.

      It has been previously shown that C1ql2 together with C1ql3 recruit postsynaptic GluK2 at the MFS. However, loss of just C1ql2 did not affect the recruitment of GluK2, which was disrupted only upon loss of both C1ql2 and C1ql3 (Matsuda et al., 2018). In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 can recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (KARs and BAI3; Fig.5; please also see response above). Furthermore, we have now performed a kinetic analysis on single-cell data which we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b KO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling is altered upon the loss of C1ql2 following Bcl11b cKO (Author response image 3e-h; please also see our response to reviewer #2 point 8). Thus, we have no experimental evidence supporting the idea that a loss of interaction between C1ql2.K262E and GluK2 would interfere with the examined phenotype. However, to exclude that the K262E mutation disrupts interaction between C1ql2 and GluK2, we performed co-immunoprecipitation from protein lysate of HEK293 cells expressing GluK2myc-flag and GFP-C1ql2 or GluK2-myc-flag and GFP-K262E and could show that both C1ql2 and K262E had GluK2 bound when precipitated. These data are included in supplementary figure 5k of the revised manuscript.

      5) Altered localization of C1ql2 in Nrxn1-3 cKOs. These data are presented to suggest that Nrx3(25b) is important for localizing C1ql2 to the SL of CA3. Weaknesses of this data include both the lack of Nrxn specificity in the triple a/b KOs as well as the profound effects of Nrxn LoF on the total levels of C1ql2 protein. Some measure that isn't biased by this large difference in C1ql2 levels should be attempted (something like in Fig.1F).

      We acknowledge that the lack of specificity in the Nrxn123 model makes it difficult to interpret our data. We have now examined the mRNA levels of Nrxn1 and Nrxn2 upon stereotaxic injection of Cre in the DG of Nrxn123flox/flox animals and found that Nrxn1 was only mildly reduced. At the same time Nrxn2 showed a tendency for reduction that was not significant (data included in supplementary figure 6a of revised manuscript). Only Nrxn3 expression was strongly suppressed. Of course, this does not exclude that the mild reduction of Nrxn1 and Nrxn2 interferes with the C1ql2 localization at the MFS. We further examined the mRNA levels of C1ql2 in control and Nrxn123 mutants to ensure that the observed changes in C1ql2 protein levels at the MFS are not due to reduced mRNA expression and found no changes (data are included in supplementary figure 6b of the revised manuscript), suggesting that overall protein C1ql2 expression is normal.

      The reduced C1ql2 fluorescence intensity at the MFS was first observed when non-binding C1ql2 variant K262E was introduced to Bcl11b cKO mice that lack endogenous C1ql2 (Fig.6). In these experiments, we found that despite the overall high protein levels of C1ql2.K262E in the hippocampus (Fig. 6c), its fluorescence intensity at the SL was significantly reduced compared to WT C1ql2 (Fig. 6d-e). The remaining signal of the C1ql2.K262E at the SL was equally distributed and in a punctate form, similar to WT C1ql2. Together, this suggests that loss of C1ql2-Nrxn3 interaction interferes with the localization of C1ql2 at the MFS, but not with the expression of C1ql2. Of course, this does not exclude that other mechanisms are involved in the synaptic localization of C1ql2, beyond the interaction with Nrxn3, as both the mutant C1ql2 in Bcl11b cKO and the endogenous C1ql2 in Nrxn123 cKOs show residual immunofluorescence at the SL. Further studies are required to determine how C1ql2-Nrxn3 interaction regulates C1ql2 localization at the MFS.

      Reviewer #1 (Recommendations For The Authors):

      In addition to addressing the comments below, this study would benefit significantly from providing insight and discussion into the relevant potential postsynaptic signaling components controlled exclusively by C1ql2 (postsynaptic kainate receptors and the BAI family of proteins).

      We have now performed a kinetic analysis on single-cell data that we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b cKO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling differ between controls and upon the loss of C1ql2 following Bcl11b cKO (Author response image 3e-h; please also see our response to Reviewer #2 point 8). This agrees with previous findings that C1ql2 regulates postsynaptic GluK2 recruitment together with C1ql3 and only loss of both C1ql2 and C1ql3 results in a disruption of KAR signaling (Matsuda et al., 2018). In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 can recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (KARs and BAI3; Fig.5; please also see our response to reviewer #3 point 4 above). We believe that further studies are needed to fully understand both the pre- and the postsynaptic functions of C1ql2. Because the focus of this manuscript was on the role of the C1ql2-Nrxn3 interaction and our investigation on postsynaptic functions of C1ql2 was incomplete, we did not include our findings on postsynaptic current kinetics in our revised manuscript. However, we increased the discussion on the known postsynaptic partners of C1ql2 in the revised manuscript to increase the interpretability of our results.

      Major Comments:

      The authors demonstrate that the ultrastructural properties of presynaptic boutons are altered after Bcl11b KO and C1ql2 KD. However, whether C1ql2 functions as part of a tripartite complex and the identity of the postsynaptic receptor (BAI, KAR) should be examined.

      Matsuda and colleagues have nicely demonstrated in their 2016 (Neuron) study that C1ql2 is part of a tripartite complex with presynaptic Nrxn3 and postsynaptic KARs. Moreover, they demonstrated that C1ql2, together with C1ql3, recruit postsynaptic KARs at the MFS, while the KO of just C1ql2 did not affect the KAR localization. In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 is able to recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (Fig. 5; please also see our response to reviewer #3 point 4 above). Moreover, we were able to show that the SV recruitment depends on C1ql2 interaction with Nrxn3 through the expression of a non-binding C1ql2 (Fig. 6) that retains the ability to interact with GluK2 (supplementary figure 5k of revised manuscript) or by KO of Nrxns (Fig. 7). Furthermore, we have now performed a kinetic analysis on single-cell data which we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b cKO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling differ between controls and Bcl11b mutants (Author response image 3e-h; please also see our response to Reviewer #2 question 8). Together, we have no experimental evidence so far that would support that the postsynaptic partners of C1ql2 are involved in the observed phenotype. While it would be very interesting to characterize the postsynaptic partners of C1ql2 in depth, we feel this would be beyond the scope of the present study.

      Figure 1f: For a more comprehensive understanding of the Bcl11b KO phenotype and the potential role for C1ql2 on MF synapse number, a complete quantification of vGlut1 and Homer1 for all conditions (Supplement Figure 2e) should be included in the main text.

      In our study we focused on the role of C1ql2 in the structural and functional integrity of the MFS downstream of Bcl11b. Bcl11b ablation leads to several phenotypes in the MFS that have been thoroughly described in our previous study (De Bruyckere et al., 2018). As expected, re-expression of C1ql2 only partially rescued these phenotypes, with full recovery of the SV recruitment (Fig. 2) and of the LTP (Fig. 3), but had no effect on the reduced numbers of MFS nor the structural complexity of the MFB created by the Bcl11b KO (supplementary figure 2d-f of revised manuscript). We understand that including the quantification of vGlut1 and Homer1 co-localization in the main figures would help with a better understanding of the Bcl11b mutant phenotype. However, in our manuscript we investigate C1ql2 as an effector of Bcl11b and thus we focus on its functions in SV recruitment and LTP. As we did not find a link between C1ql2 and the number of MFS/MFB upon re-expression of C1ql2 in Bcl11b cKO or now also in C1ql2 KD (see response to comment #4 below), we believe it is more suitable to present these data in the supplement.

      Figure 3/4: Given the striking reduction in the numbers of synapses (Supplement Figure 2e) and docked vesicles (Figure 2d) in the Bcl11b KO and C1ql2 KD (Figure 4e-f), it is extremely surprising that basal synaptic transmission is unaffected (Supplement Figure 2g). The authors should determine the EPSP input-output relationship following C1ql2 KD and measure EPSPs following trains of stimuli at various high frequencies.

      We fully acknowledge that this is an unexpected result. It is, however, well feasible that the modest displacement of SV fails to noticeably influence basal synaptic transmission. This would be the case, for example, if only a low number of vesicles are released by single stimuli, in line with the very low initial Pr at MFS. In contrast, the reduction in synapse numbers in the Bcl11b mutant might indeed be expected to reflect in the input-output relationship. It is possible, however, that synapses that are preferentially eliminated in Bcl11b cKO are predominantly silent or have weak coupling strength, such that their loss has only a minimal effect on basal synaptic transmission. Finally, we cannot exclude compensatory mechanisms (homeostatic plasticity) at the remaining synapses. A detailed analysis of these potential mechanisms would be a whole project in its own right.

      As additional information, we can say that the largely unchanged input-output-relation in Bcl11b cKO is also present in the single-cell level data (Author response image 3; details on single-cell experiments are described in the response to Reviewer #2 point 8).

      As suggested by the reviewer, we have now additionally analyzed the input-output relationship following C1ql2 KD and again did not observe any significant difference between control and KD animals. We have incorporated the respective input-output curves into the revised manuscript under Supplementary figure 3c-d.

      Figure 4: Does C1ql2 shRNA also reduce the number of MFBs? This should be tested to further identify C1ql2-dependent and independent functions.

      As requested by reviewer #1 we quantified the number of MFBs upon C1ql2 KD. We show that C1ql2 KD in WT animals does not alter the number of MFBs. The data are presented in supplementary figure 4d of the revised manuscript. Re-expression of C1ql2 in Bcl11b cKO did not rescue the loss of MFS created by the Bcl11b mutation. Moreover, C1ql2 re-expression did not rescue the complexity of the MFB ultrastructure perturbed by the Bcl11b ablation. Together, this suggests that Bcl11b regulates MFs maintenance through additional C1ql2-independent pathways. In our previously published work (De Bruyckere et al., 2018) we identified and discussed in detail several candidate genes such as Sema5b, Ptgs2, Pdyn and Penk as putative effectors of Bcl11b in the structural and functional integrity of MFS (please also see response to reviewer #2- point 1 of public reviews).

      Figure 5: Clarification is required regarding the experimental design of the HEK/Neuron co-culture: 1. C1ql2 is a secreted soluble protein - how is the protein anchored to the HEK cell membrane to recruit Nrxn3(25b+) binding and, subsequently, vGlut1?

      C1ql2 was secreted by the HEK293 cells through an IgK signaling peptide at the N-terminus of C1ql2. The high concentration of C1ql2 close to the secretion site together with the sparse coculturing of the HEK293 cells on the neurons allows for the quantification of accumulation of neuronal proteins. We have now described the experimental conditions in greater detail in the main text module of the revised manuscript

      2) Why are the neurons transfected and not infected? Transfection efficiency of neurons with lipofectamine is usually poor (1-5%; Karra et al., 2010), while infection of neurons with lentiviruses or AAVs encoding cDNAs routinely are >90% efficient. Thus, interpretation of the recruitment assays may be influenced by the density of neurons transfected near a HEK cell.

      We agree with reviewer #1 that viral infection of the neurons would have been a more effective way of expressing our constructs. However, due to safety allowances in the used facility and time limitation at the time of conception of this set of experiments, a lipofectamine transfection was chosen.

      However, as all of our examined groups were handled in the same way and multiple cells from three independent experiments were examined for each experimental set, we believe that possible biases introduced by the transfection efficiency have been eliminated and thus have trust in our interpretation of these results.

      3) Surface labeling of HEK cells for wild-type C1ql2 and K262 C1ql2 would be helpful to assess the trafficking of the mutant.

      We recognize that potential changes to the trafficking of C1ql2 caused by the K262E mutation would be important to characterize, in light of the reduced localization of the mutant protein at the SL in the in vivo experiments (Fig. 6e). In our culture system, C1ql2 and K262E were secreted by the HEK cells through insertion of an IgK signaling peptide at the N-terminus of the myc-tagged C1ql2/K262E. Thus, trafficking analysis on this system would not be informative, as the system is highly artificial compared to the in vivo model. Further studies are needed to characterize C1ql2 trafficking in neurons to understand how C1ql2-Nrxn3 interaction regulates the localization of C1ql2. However, labeling of the myc-tag in C1ql2 or K262E expressing HEK cells of the co-culture model reveals a similar signal for the two proteins (Fig. 5a,c). Nrxn-null mutation in neurons co-cultured with C1ql2-expressing HEK cells disrupted C1ql2 mediated vGlut1 accumulation in the neurons. Selective expression of Nrxn3(25b) in the Nrxn-null neurons restored vGlut1 clustering was (Fig. 5e-f). Together, these data suggest that it is the interaction between C1ql2 and Nrxn3 that drives the accumulation of vGlut1.

      Figure 6: Bcl11b KO should also be included in 6f-h.

      As suggested by reviewer #1, we included the Bcl11b cKO in figures 6f-h and in corresponding supplementary figures 5c-j.

      Figure 7b: What is the abundance of mRNA for Nrxn1 and Nrxn2 as well as the abundance of Nrxns after EGFP-Cre injection into DG?

      We addressed this point raised by reviewer #1 by quantifying the relative mRNA levels of Nrxn1 and Nrxn2 via qPCR upon Nrxn123 mutation induction with EGFP-Cre injection. We have now examined the mRNA levels of Nrxn1 and Nrxn2 upon stereotaxic injection of Cre in the DG of Nrxn123flox/flox animals and found that Nrxn1 was only mildly reduced. At the same time Nrxn2 showed a tendency for reduction that was not significant. The data are presented in supplementary figure 6a of the revised maunscript.

      Minor Comments for readability:

      Synapse score is referred to frequently in the text and should be defined within the text for clarification.

      'n' numbers should be better defined in the figure legends. For example, for protein expression analysis in 1c, n=3. Is this a biological or technical triplicate? For electrophysiology (e.g. 3c), does "n=7" reflect the number of animals or the number of slices? n/N (slices/animals) should be presented.

      Figure 7a: Should the diagrams of the cre viruses be EGFP-Inactive or active Cre and not CRE-EGFP as shown in the diagram?

      Figure 7b: the region used for the inset should be identified in the larger image.

      All minor points have been fixed in the revised manuscript according to the suggestions.

      Reviewer #3 (Recommendations For The Authors):

      -Please describe the 'synapse score' somewhere in the text - it is too prominently featured to not have a clear description of what it is.

      The description of the synapse score has been included in the main text module of the revised manuscript.

      -The claim that Bcl11b controls SV recruitment "specifically" through C1ql2 is a bit stronger than is warranted by the data. Particularly given that C1ql2 is expressed at 2.5X control levels in their rescue experiments. See pt.2

      Please see response to reviewer #3 point 1 of public reviews. To address this, we over-expressed C1ql2 in control animals and found no changes in the synaptic vesicle distribution (supplementary figure 2g-j of revised manuscript). This supports that the observed rescue of synaptic vesicle recruitment by re-expression of C1ql2 is due to its physiological function and not due to the artificially elevated protein levels. Of course, we cannot exclude the possibility that other, C1ql2-independent, mechanisms also contribute to the SV recruitment downstream of Bcl11b. Our data from the C1ql2 rescue, C1ql2 KD, the in vitro experiments and the interruption of C1ql2-Nrxn3 in vivo, strongly suggest C1ql2 to be an important regulator of SV recruitment.

      -Does Bcl11b regulate Nrxn3 expression? Considering the apparent loss of C1ql2 expression in the Nrxn KO mice, this is an important detail.

      We agree with reviewer #3 that this is an important point. We have previously done differential transcriptomics from DG neurons of Bcl11b cKOs compared to controls and did not find Nrxn3 among the differentially expressed genes. To further validate this, we now quantified the Nrxn3 mRNA levels via qPCR in Bcl11b cKOs compared to controls and found no differences. These data are included in supplementary figure 5a of the revised manuscript.

      -It appears that C1ql2 expression is much lower in the Nrxn123 KO mice. Since the authors are trying to test whether Nrxn3 is required for the correct targeting of C1ql2, this is a confounding factor. We can't really tell if what we are seeing is a "mistargeting" of C1ql2, loss of expression, or both. If the authors did a similar analysis to what they did in Figure 1 where they looked at the synaptic localization of C1ql2 (and quantified it) that could provide more evidence to support or refute the "mistargeting" claim.

      Please also see response to reviewer #3 point 5 of public reviews. To exclude that reduction of fluorescence intensity of C1ql2 at the SL in Nrxn123 KO mice is due to loss of C1ql2 expression, we examined the mRNA levels of C1ql2 in control and Nrxn123 mutants and found no changes (data are included in supplementary figure 6b of the revised manuscript), suggesting that C1ql2 gene expression is normal. The reduced C1ql2 fluorescence intensity at the MFS was first observed when non-binding C1ql2 variant K262E was introduced to Bcl11b cKO mice that lack endogenous C1ql2 (Fig.6). In these experiments, we found that despite the overall high protein levels of C1ql2.K262E in the hippocampus (Fig. 6c), its fluorescence intensity at the SL was significantly reduced compared to WT C1ql2 (Fig. 6d-e). The remaining C1ql2.K262E signal in the SL was equally distributed and in a punctate form, similar to WT C1ql2. Together, this indicates that the loss of C1ql2-Nrxn3 interaction interferes with the localization of C1ql2 along the MFS, but not with expression of C1ql2. Of course, this does not exclude that additional mechanisms regulate C1ql2 localization at the synapse, as both the mutant C1ql2 in Bcl11b cKO and the endogenous C1ql2 in Nrxn123 cKO show residual immunofluorescence at the SL.

      We note here that we have not previously quantified the co-localization of C1ql2 with individual synapses. C1ql2 is a secreted molecule that localizes at the MFS synaptic cleft. However, not much is known about the number of MFS that are positive for C1ql2 nor about the mechanisms regulating C1ql2 targeting, transport, and secretion to the MFS. Whether C1ql2 interaction with Nrxn3 is necessary for the protection of C1ql2 from degradation, its surface presentation and transport or stabilization to the synapse is currently unclear. Upon revision of our manuscript, we realized that we might have overstated this particular finding and have now rephrased the specific parts within the results to appropriately describe the observation and have also included a sentence in the discussion referring to the lack of understanding of the mechanism behind this observation.

      -Title of Figure S5 is "Nrxn KO perturbs C1ql2 localization and SV recruitment at the MFS", but there is no data on C1ql2 localization.

      This issue has been fixed in the revised manusript.

      -S5 should be labeled more clearly than just Cre+/-

      This issue has been fixed in the revised manuscript.

      References

      Castillo, P.E., Malenka, R.C., Nicoll, R.A., 1997. Kainate receptors mediate a slow postsynaptic current in hippocampal CA3 neurons. Nature 388, 182–186. https://doi.org/10.1038/40645

      De Bruyckere, E., Simon, R., Nestel, S., Heimrich, B., Kätzel, D., Egorov, A.V., Liu, P., Jenkins, N.A., Copeland, N.G., Schwegler, H., Draguhn, A., Britsch, S., 2018. Stability and Function of Hippocampal Mossy Fiber Synapses Depend on Bcl11b/Ctip2. Front. Mol. Neurosci. 11. https://doi.org/10.3389/fnmol.2018.00103

      Kaeser, P.S., Regehr, W.G., 2017. The readily releasable pool of synaptic vesicles. Curr. Opin. Neurobiol. 43, 63–70. https://doi.org/10.1016/j.conb.2016.12.012

      Lerma, J., 2003. Roles and rules of kainate receptors in synaptic transmission. Nat. Rev. Neurosci. 4, 481–495. https://doi.org/10.1038/nrn1118

      Orlando, M., Dvorzhak, A., Bruentgens, F., Maglione, M., Rost, B.R., Sigrist, S.J., Breustedt, J., Schmitz, D., 2021. Recruitment of release sites underlies chemical presynaptic potentiation at hippocampal mossy fiber boutons. PLoS Biol. 19, e3001149. https://doi.org/10.1371/journal.pbio.3001149

      Vandael, D., Borges-Merjane, C., Zhang, X., Jonas, P., 2020. Short-Term Plasticity at Hippocampal Mossy Fiber Synapses Is Induced by Natural Activity Patterns and Associated with Vesicle Pool Engram Formation. Neuron 107, 509-521.e7. https://doi.org/10.1016/j.neuron.2020.05.013

    1. Author Response

      The following is the authors’ response to the original reviews.

      We are very grateful to both reviewers for taking the time to review our manuscript and data in great detail. We thank you for the fair assessment of our work, the helpful feedback, and for recognizing the value of our work. We have done our best to address your concerns below:

      eLife assessment This work reports a valuable finding on glucocorticoid signaling in male and female germ cells in mice, pointing out sexual dimorphism in transcriptomic responsiveness. While the evidence supporting the claims is generally solid, additional assessments would be required to fully confirm an inert GR signaling despite the presence of GR in the female germline and GR-mediated alternative splicing in response to dexamethasone treatment in the male germline. The work may interest basic researchers and physician-scientists working on reproduction and

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Cincotta et al set out to investigate the presence of glucocorticoid receptors in the male and female embryonic germline. They further investigate the impact of tissue-specific genetically induced receptor absence and/or systemic receptor activation on fertility and RNA regulation. They are motivated by several lines of research that report inter and transgenerational effects of stress and or glucocorticoid receptor activation and suggest that their findings provide an explanatory mechanism to mechanistically back parental stress hormone exposure-induced phenotypes in the offspring.

      Strengths:

      A chronological immunofluorescent assessment of GR in fetal and early life oocyte and sperm development.

      RNA seq data that reveal novel cell type specific isoforms validated by q-RT PCR E15.5 in the oocyte.

      2 alternative approaches to knock out GR to study transcriptional outcomes. Oocytes: systemic GR KO (E17.5) with low input 3-tag seq and germline-specific GR KO (E15.5) on fetal oocyte expression via 10X single cell seq and 3-cap sequencing on sorted KO versus WT oocytes both indicating little impact on polyadenylated RNAs

      2 alternative approaches to assess the effect of GR activation in vivo (systemic) and ex vivo (ovary culture): here the RNA seq did show again some changes in germ cells and many in the soma.

      They exclude oocyte-specific GR signaling inhibition via beta isoforms.

      Perinatal male germline shows differential splicing regulation in response to systemic Dex administration, results were backed up with q-PCR analysis of splicing factors. Weaknesses:

      COMMENT #1: The presence of a protein cannot be entirely excluded based on IF data

      We agree that very low levels of GR could escape the detection by IF and confocal imaging. We feel that our IF data do match transcript data in our validation studies of the GR KO using (1) qRT-PCR on fetal ovary in Fig 2E and (2) scRNA-seq in germ cells and ovarian soma in Fig S2B.

      COMMENT #2: (staining of spermatids is referred to but not shown).

      You are correct that this statement was based on a morphological identification of spermatids using DAPI morphology. We have performed a co-stain for GR with the spermatocyte marker SYCP3, and the spermatid/spermatozoa marker PNA (Peanut Agglutinin; from Arachis hypogaea) in adult testis tissue. We have updated Figure 4D to reflect this change, as well as the corresponding text in the Results section.

      COMMENT #3: The authors do not consider post-transcriptional level a) modifications also triggered by GR activation b) non-coding RNAs (not assessed by seq).

      We thank the reviewer for raising this very important point about potential post-transcriptional (non-genomic) effects of GR in the fetal oocyte. We agree that while our RNA-seq results show only a minimal transcriptional response, we cannot rule out a non-canonical signaling function of GR, such as the regulation of cellular kinases (as reviewed elsewhere1), or the regulation of non coding RNAs at the post-transcriptional level, and we have amended the discussion to include a sentence on this point. However, while we fully acknowledge the possibility of GR regulating non-genomic level cellular signaling, we chose not to explore this option further based on the lack of any overall functional effect on meiotic progression when GR signaling was perturbed- either by KO (Figure 2D) or dex-mediated activation (Figure S3C).

      COMMENT #4: Sequencing techniques used are not total RNA but either are focused on all polyA transcripts (10x) or only assess the 3' prime end and hence are not ideal to study splicing

      We thank the reviewer for raising this concern, however this statement is not correct and we have clarified this point in the Results section to explain how the sequencing libraries of the male germ cell RNA-seq were prepared. We agree that certain sequencing techniques (such as 3’ Tag-Seq) that generate sequencing libraries from a limited portion of an entire transcript molecule are not appropriate for analysis of differential splicing. This was not the case, however, for the RNA-seq libraries prepared on our male germ cells treated with dexamethasone. These libraries were constructed using full length transcripts that were reverse transcribed using random hexamer priming, thus accounting for sequencing coverage across the full transcript length. As a result, this type of library prep technique should be sufficient for capturing differential splicing events along the length of the transcript. We do, however, point out that these libraries were constructed on polyA-enriched transcripts. Thus while we obtained full length transcript coverage for these polyA transcripts, any differential splicing taking place in non poly-adenylated RNA moieties were not captured. While we are excited about the possibility of exploring GR-mediated splicing regulation of other RNA species in the future, we chose to focus the scope of our current study on polyA mRNA molecules specifically.

      COMMENT #5: The number of replicates in the low input seq is very low and hence this might be underpowered

      While the number of replicates (n=3-4 per condition) is sufficient for performing statistical analysis of a standard RNA-seq experiment, we do acknowledge and agree with the reviewer that low numbers of FACS-sorted germ cells from individual embryos combined with the low input 3’ Tag-Seq technique could have led to higher sample variability than desired. Given that we validated our bulk RNA-seq analysis of GR knockout ovaries using an orthogonal single-cell RNA-seq approach, we feel that our conclusions regarding a lack of transcriptional changes upon GR deletion remain valid.

      COMMENT #6: Since Dex treatment showed some (modest) changes in oocyte RNA - effects of GR depletion might only become apparent upon Dex treatment as an interaction.

      We may be missing the nuance of this point, but our interpretation of an effect that is seen only when the KO is treated with Dex would be that the mechanism would not be autonomous in germ cells but indirect or off-target.

      COMMENT #7: Effects in oocytes following systemic Dex might be indirect due to GR activation in the soma.

      As both the oocytes and ovarian soma express GR during the window of dex administration, we agree that it is possible that the few modest changes seen in the oocyte transcriptome are the result of indirect effects following robust GR signaling in the somatic compartment. However, given that these modest oocyte transcript changes in response to dex treatment did not significantly alter the ability of oocytes to progress through meiosis, we chose not to explore this mechanism further.

      COMMENT #8: Even though ex vivo culture of ovaries shows GR translocation to the nucleus it is not sure whether the in vivo systemic administration does the same.

      AND

      The conclusion that fetal oocytes are resistant to GR manipulation is very strong, given that "only" poly A sequencing and few replicates of 3-prime sequencing have been analyzed and information is lacking on whether GR is activated in germ cells in the systemically dex-injected animals.

      If we understand correctly, the first part refers to a technical limitation and the second part takes issue with our interpretation of the data. For the former, we appreciate this astute insight on the conundrum of detecting a response to systemic dex in fetal oocytes, which is generally monitored by nuclear translocation of GR. As shown in Figure 1A and 1B, GR localization is overwhelmingly nuclear in fetal oocytes of WT animals at E13.5 without addition of any dex. We could not, therefore, use GR translocation as a proxy for activation in response to dex treatment. We instead used ex vivo organ culture to monitor localization changes, as we were able to maintain fetal ovaries ex vivo in hormone-depleted and ligand negative conditions. As shown in Fig. 3, these defined culture conditions elicited a shift of GR to the cytoplasm of fetal oocytes. This led us to conclude that GR is capable of translocating between nucleus and cytoplasm in fetal oocytes, and we were able to counteract this loss in nuclear localization by providing dex ligand in the media.

      We feel that our conclusion that oocytes are resistant to manipulation of glucocorticoid signaling despite their possession of the receptor and capacity for nuclear translocation is substantiated by multiple results: meiotic phenotyping, bulk RNA-seq and scRNA-seq analysis of both GR KO and dex dosed mice. Our basis for testing the timing and fidelity of meiotic prophase I was the coincident onset of GR expression in female germ cells at E13, and the disappearance of GR in neonatal oocytes as they enter meiotic arrest. The lack of transcriptional changes observed in oocytes in response to dex has made it even more challenging to demonstrate a bona fide “activation” of GR. Observation of a dose-dependent induction of the canonical GR response gene Fkbp5 in the somatic cells of the fetal ovary (Figure S3A and 3A) affirmed that dex traverses the placenta. We agree with the reviewer that it remains possible that dex or GR KO could lead to changes in epigenetic marks or small RNAs in oocytes, and have mentioned these possibilities in the discussion, but we note that even epigenetic perturbations during oocyte development such as the loss of Tet1 or Dnmt1 result in measurable changes in the transcriptome and the timing of meiotic prophase 2–4.

      COMMENT #9: This work is a good reference point for researchers interested in glucocorticoid hormone signaling fertility and RNA splicing. It might spark further studies on germline-specific GR functions and the impact of GR activation on alternative splicing. While the study provides a characterization of GR and some aspects of GR perturbation, and the negative findings in this study do help to rule out a range of specific roles of GR in the germline, there is still a range of other potential unexplored options. The introduction of the study eludes to implications for intergenerational effects via epigenetic modifications in the germline, however, it does not mention that the indirect effects of reproductive tissue GR signaling on the germline have indeed already been described in the context of intergenerational effects of stress.

      The reviewer raises an excellent point that we have not made sufficient distinction in our manuscript between prior studies of gestational stress and preconception stress and the light that our work may shed on those findings. We have revised the introduction to clarify this difference, and added reference to an outstanding study that identifies glucocorticoid-induced changes to microRNA cargo of extracellular vesicles shed by epididymal epithelial cells that when transferred to mature sperm can induce changes in the HPA axis and brain of offspring 5. Interestingly, this GR-mediated effect in the epididymal epithelial cells concurs with our observation in the adult testis that GR can be detected only cKit+ spermatogonia but not in subsequent stages of spermatids.

      COMMENT #10: Also, the study does not assess epigenetic modifications.

      We agree with the reviewer that exploring the role of GR in regulating epigenetic modifications within the germline is an area of extreme interest given the potential links between stress and transgenerational epigenetic inheritance. As this is a broader topic that requires a more thorough and comprehensive set of experiments, we have intentionally chosen to keep this work separate from the current study, and hope to expand upon this topic in the future.

      COMMENT #11: The conclusion that the persistence of a phenotype for up to three generations suggests that stress can induce lasting epigenetic changes in the germline is misleading. For the reader who is unfamiliar with the field, it is important to define much more precisely what is referred to as "a phenotype". Furthermore, this statement evokes the impression that the very same epigenetic changes in the germline have been observed across multiple generations.

      We see how this may be misleading, and we have amended the text of the introduction and discussion accordingly to avoid the use of the term “phenotype”.

      COMMENT #12: The evidence of the presence of GR in the germline is also somewhat limited - since other studies using sequencing have detected GR in the mature oocyte and sperm.

      As described above in response to Comment #2, we have included immunostaining of adult testis in a revised Figure 4D and shown that we detect GR in PLZF+ and cKIT+ spermatogonia. We also show low/minimal expression in some (SYCP3+) early meiotic spermatocytes, but not in (Lectin+) spermatids. We are not aware of any studies that have shown expression of GR protein in the mature oocyte.

      COMMENT #13: The discussion ends again on the implications of sex-specific differences of GR signaling in the context of stress-induced epigenetic inheritance. It states that the observed differences might relate to the fact that there is more evidence for paternal lineage findings, without considering that maternal lineage studies in epigenetic inheritance are generally less prevalent due to some practical factors - such as more laborious study design making use of cross-fostering or embryo transfer.

      We thank the reviewer for this valid point, and we have amended the discussion section.

      Reviewer #2 (Public Review):

      Summary:

      There is increasing evidence in the literature that rodent models of stress can produce phenotypes that persist through multiple generations. Nevertheless, the mechanism(s) by which stress exposure produces phenotypes are unknown in the directly affected individual as well as in subsequent offspring that did not directly experience stress. Moreover, it has also been shown that glucocorticoid stress hormones can recapitulate the effects of programmed stress. In this manuscript, the authors test the compelling hypothesis that glucocorticoid receptor (GR)-signaling is responsible for the transmission of phenotypes across generations. As a first step, the investigators test for a role of GR in the male and female germline. Using knockouts and GR agonists, they show that although germ cells in male and female mice have GR that appears to localize to the nucleus when stimulated, oocytes are resistant to changes in GR levels. In contrast, the male germline exhibits changes in splicing but no overt changes in fertility.

      Strengths:

      Although many of the results in this manuscript are negative, this is a careful and timely study that informs additional work to address mechanisms of transmission of stress phenotypes across generations and suggests a sexually dimorphic response to glucocorticoids in the germline. The work presented here is well-done and rigorous and the discussion of the data is thoughtful. Overall, this is an important contribution to the literature.

      Reviewer #1 (Recommendations For The Authors):

      RECOMMENDATION #1: To assess whether in females the systemic Dex administration directly activates GR in oocytes it would be great to assess GR activation following Dex administration, and ideally to see the effects abolished when Dex is administered to germline-specific KO animals.

      In regard to the recommendation to assess GR activation in response to systemic dex administration, we refer the reviewer back to our response in Comment #8 highlighting the difficulties defining and measuring GR activation in the germline.

      This therefore has made it difficult to assess whether any of the modest effects seen in response to dex are abolished in our germline-specific KO animals. While repeating our RNA-seq experiment in dex-dosed germline KO animals would address whether the ~60 genes induced in oocytes are the result of oocyte-intrinsic GR activity, we have decided not to explore this mechanism further due to the overall lack of a functional effect on meiotic progression in response to dex (Figure S3C).

      RECOMMENDATION #2: To further strengthen the link between GR and alternative splicing it would be great to see the dex administration experiment repeated in germline specific GR KO's.

      While we understand the reviewer’s suggestion to explore whether deletion of GR in the spermatogonia is sufficient to abrogate the dex-mediated decreases in splice factor expression, we chose not to explore the details of this mechanism given that deletion of GR in the male germline does not impair fertility (Figure 6).

      RECOMMENDATION #3: I am wondering how much a given reduction in one of the splicing factors indeed affects splicing events. Can the authors relate this to literature, or maybe an in vitro experiment can be done to see whether the level of differential splicing events detected is in a range that can be expected in the case of the magnitude of splicing factor reduction?

      It has been shown in many instances in the literature that a full genetic deletion of a single splice factor leads to impairments in spermatogenesis, and ultimately infertility 6–16. We suspect that dex treatment leads to fewer differential splicing events than a full splice factor deletion, given that dex treatment causes a broader decrease in splice factor expression without entirely abolishing any single splice factor. We have amended the discussion section to include this point. While we share the reviewer’s curiosity to compare the effects of dex vs genetic deletion of splicing machinery on the overall magnitude of differential splicing events, we unfortunately do not have access to mice with a floxed splice factor at this time. While we have considered knocking out one or more splice factors in an ex vivo cultured testis to compare alongside dex treatment, our efforts to date have proven unsuccessful due to high cell death upon culture of the postnatal testis for more than 24 hours.

      RECOMMENDATION #4: It is unclear from the methods whether in germline-specific KO's also the controls received tamoxifen.

      We thank the reviewer for catching this missing piece of information. All control embryos that were assessed received an equivalent dose of tamoxifen to the germline-specific KO embryos. The only difference between cKOs and controls was the presence of the Cre transgene. We have updated the Materials and Methods 3’ Tag-Seq sample preparation section to include the sentence: “Both GRcKO/cKO and control GRflox/flox embryos were collected from tamoxifen-injected dams, and thus were equally exposed to tamoxifen in utero”.

      Reviewer #2 (Recommendations For The Authors):

      I just have only a few comments/questions.

      RECOMMENDATION #5: It is somewhat surprising that GR is expressed in female germ cells, yet there doesn't seem to be a requirement. Is there any indication of what it does? Is the long-term stability of the germline compromised?

      We thank the reviewer for these questions, and we agree that it was quite surprising to find a lack of GR function in the female germline despite its robust expression. The question of whether loss of GR affects the long-term stability of the female germline is interesting, given that similar work in GR KO zebrafish has shown impairments to female reproductive capacity, yet only upon aging 17–19.

      While we have shared interest in this question, technical limitations thus far have prevented us from properly assessing the effect of GR loss in aged females. Homozygous deletion of GR results in embryonic lethality at approximately E17.5. Conditional deletion of GR using Oct4-CreERT2 with a single dose of tamoxifen (2.5 mg / 20g mouse) at E9.5 results in complete deletion of GR by E10.5, although dams consistently suffer from dystocia and are no longer able to deliver viable pups. While using the more active tamoxifen metabolite (4OHT) at 0.1 mg / 20g has allowed for successful delivery, the resulting deletion rate is very poor (see qPCR results in panel below, left). While using half the dose of standard tamoxifen (1.25 mg / 20g mouse) at E9.5 has on rare occasions led to a successful delivery, the resulting recombination efficiency is insufficient (Author response image 1 right panel).

      Author response image 1.

      While a Blimp1-Cre conditional KO model was used to assess male fertility on GR deletion, we believe this model may not be ideal for studying fertility in the context of aging. While Blimp1-Cre is highly specific to the germ cells within the gonad, there are many cell types outside of the gonad that express Blimp1, including the skin and certain cells of the immune system. It is unclear, particularly over the course of aging, whether any effects on fertility seen would be due to an oocyte-intrinsic effect, or the result of GR loss elsewhere in the body. While we hope to explore the role of GR in the aging oocyte further using alternative Cre models in the future, this is currently outside the scope of this work.

      RECOMMENDATION #6: Figure 5b: what is the left part of that panel? Is it the same volcano plot for germ cells as shown in part a but with splicing factors?

      We apologize if this panel was unclear. Yes, the left panel of Figure 5B is in fact the same volcano plot in 5A, labeled with splicing factors instead of top genes. We have edited Figure 5B and corresponding figure legend to clarify this.

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      3. Eymery, A., Liu, Z., Ozonov, E.A., Stadler, M.B., and Peters, A.H.F.M. (2016). The methyltransferase Setdb1 is essential for meiosis and mitosis in mouse oocytes and early embryos. Development 143, 2767–2779. 10.1242/dev.132746.

      4. Chan, J.C., Morgan, C.P., Leu, N.A., Shetty, A., Cisse, Y.M., Nugent, B.M., Morrison, K.E., Jašarević, E., Huang, W., Kanyuch, N., et al. (2020). Reproductive tract extracellular vesicles are sufficient to transmit intergenerational stress and program neurodevelopment. Nat Commun 11, 1499. 10.1038/s41467-020-15305-w.

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      7. Li, H., Watford, W., Li, C., Parmelee, A., Bryant, M.A., Deng, C., O’Shea, J., and Lee, S.B. (2007). Ewing sarcoma gene EWS is essential for meiosis and B lymphocyte development. J Clin Invest 117, 1314–1323. 10.1172/jci31222.

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    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment:

      This important study represents a comprehensive computational analysis of Plasmodium falciparum gene expression, with a focus on var gene expression, in parasites isolated from patients; it assesses changes that occur as the parasites adapt to short-term in vitro culture conditions. The work provides technical advances to update a previously developed computational pipeline. Although the findings of the shifts in the expression of particular var genes have theoretical or practical implications beyond a single subfield, the results are incomplete and the main claims are only partially supported.

      The authors would like to thank the reviewers and editors for their insightful and constructive assessment. We particularly appreciate the statement that our work provides a technical advance of our computational pipeline given that this was one of our main aims. To address the editorial criticisms, we have rephrased and restructured the manuscript to ensure clarity of results and to support our main claims. For the same reason, we removed the var transcript differential expression analysis, as this led to confusion.

      Public Reviews:

      Reviewer #1:

      The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

      Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

      One concern is that the abstract highlights "Unpredictable var gene switching..." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites...". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

      Thank you for this comment. We were happy to modify the wording in the abstract to have consistency with the results presented by highlighting that modest but unpredictable var gene switching was observed while substantial changes were found in the core transcriptome. Moreover, any differences observed in core transcriptome between ex vivo samples from naïve and pre-exposed patients are diminished after one cycle of cultivation making inferences about parasite biology in vivo impossible.

      Therefore, – to our opinion – the statement in the last sentence is well supported by the data presented.

      Line 43–47: “Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.” Nevertheless, we would like to note that this study was in a unique position to assess changes at the individual patient level as we had successive parasite generations. This comparison is not done in most cross-sectional studies and therefore these small, unpredictable changes in the var transcriptome are missed.

      Reviewer #2:

      In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

      Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

      The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

      We are grateful for this suggestion and agree that a table comparing the pros and cons of all existing methods would be helpful for the general reader and also highlight the key advantages of our new approach. A table comparing previous methods for var gene and transcript characterisation has been added to the manuscript and is referenced in the introduction (line 107).

      Author response table 1.

      Comparison of previous var assembly approaches based on DNA- and RNA-sequencing.

      Reviewer #3:

      This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

      The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately, there is no attempt to describe the rationale for the different approaches and how they might inform one another.

      It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

      We agree with the reviewer that the different methods and results of the var transcriptome analysis can be difficult to reconcile. To address this, we have included a summary table with a brief description of the rationale and results of each approach in our analysis pipeline.

      Author response table 2.

      Summary of the different levels of analysis performed to assess the effect of short-term parasite culturing on var and core gene expression, their rational, method, results, and interpretation.

      Additionally, the var transcript differential expression analysis was removed from the manuscript, because this study was in a unique position to perform a more focused analysis of var transcriptional changes across paired samples, meaning the per-patient approach was more suitable. This allowed for changes in the var transcriptome to be identified that would have gone unnoticed in the traditional differential expression analysis.

      We thank the reviewer for his highly important comment about adjusting for life cycle stage. Var gene expression is highly stage-dependent, so any quantitative comparison between samples does need adjustment for developmental stage. All life cycle stage adjustments were done using the mixture model proportions to be consistent with the original paper, described in the results and methods sections:

      • Line 219–221: “Due to the potential confounding effect of differences in stage distribution on gene expression, we adjusted for developmental stage determined by the mixture model in all subsequent analyses.”

      • Line 722–725: “Var gene expression is highly stage dependent, so any quantitative comparison between samples needs adjustment for developmental stage. The life cycle stage proportions determined from the mixture model approach were used for adjustment.“

      The rank-expression analysis did not have adjustment for life cycle stage as the values were determined as a percentage contribution to the total var transcriptome. The var group level and the global var gene expression analyses were adjusted for life cycle stages, by including them as an independent variable, as described in the results and methods sections.

      Var group expression:

      • Line 321–326: “Due to these results, the expression of group A var genes vs. group B and C var genes was investigated using a paired analysis on all the DBLα (DBLα1 vs DBLα0 and DBLα2) and NTS (NTSA vs NTSB) sequences assembled from ex vivo samples and across multiple generations in culture. A linear model was created with group A expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. The same was performed using group B and C expression levels.“

      • Line 784–787: “DESeq2 normalisation was performed, with patient identity and life cycle stage proportions included as covariates and differences in the amounts of var transcripts of group A compared with groups B and C assessed (Love et al., 2014). A similar approach was repeated for NTS domains.”

      Gobal var gene expression:

      • Line 342–347: “A linear model was created (using only paired samples from ex vivo and generation 1) (Supplementary file 1) with proportion of total gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. This model showed no significant differences between generations, suggesting that differences observed in the raw data may be a consequence of small changes in developmental stage distribution in culture.”

      • Line 804–806: “Significant differences in total var gene expression were tested by constructing a linear model with the proportion of gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as an independent variables and the patient identity included as a random effect.“

      The analysis of the conserved var gene expression was adjusted for life cycle stage:

      • Line 766–768: “For each conserved gene, Salmon normalised read counts (adjusted for life cycle stage) were summed and expression compared across the generations using a pairwise Wilcoxon rank test.”

      And life cycle stage estimates were included as covariates in the design matrix for the domain differential expression analysis:

      • Line 771–773: “DESeq2 was used to test for differential domain expression, with five expected read counts in at least three patient isolates required, with life cycle stage and patient identity used as covariates.”

      Reviewer #1:

      1. In the legend to Figure 1, the authors cite "Deitsch and Hviid, 2004" for the classification of different var gene types. This is not the best reference for this work. Better citations would be Kraemer and Smith, Mol Micro, 2003 and Lavstsen et al, Malaria J, 2003.

      We agree and have updated the legend in Figure 1 with these references, consistent with the references cited in the introduction.

      1. In Figures 2 and 3, each of the boxes in the flow charts are largely filled with empty space while the text is nearly too small to read. Adjusting the size of the text would improve legibility.

      We have increased the size of the text in these figures.

      1. My understanding of the computational method for assessing global var gene expression indicates an initial step of identifying reads containing the amino acid sequence LARSFADIG. It is worth noting that VAR2CSA does not contain this motif. Will the pipeline therefore miss expression of this gene, and if so, how does this affect the assessment of global var gene assessment? This seems relevant given that the authors detect increased expression of var2csa during adaptation to culture.

      To address this question, we have added an explanation in the methods section to better explain our analysis. Var2csa was not captured in the global var gene expression analysis, but was analyzed separately because of its unique properties (conservation, proposed role in regulating var gene switching, slightly divergent timing of expression, translational repression).

      • Line 802/3: “Var2csa does not contain the LARSFADIG motif, hence this quantitative analysis of global var gene expression excluded var2csa (which was analysed separately).”
      1. In Figures 4 and 7, panels a and b display virtually identical PCA plots, with the exception that panel A displays more generations. Why are both panels included? There doesn't appear to be any additional information provided by panel B.

      We agree and have removed Figure 7b for the core transcriptome PCA as it did not provide any new information. The var transcript differential analysis (displayed in Figure 4) has been removed from the manuscript.

      1. On line 560-567, the authors state "However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at higher levels." What are the high levels being referred to here?

      We have replaced this sentence to make it clearer what the different levels are (global var gene expression, var domain and var type).

      • Line 526/7: “However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at the var domain, var type and global var gene expression level.”

      Reviewer #2:

      The authors make no mention or assessment of previously published var gene assembly methods from clinical samples that focus on genomic or transcriptomic approaches. These include:

      https://pubmed.ncbi.nlm.nih.gov/28351419/

      https://pubmed.ncbi.nlm.nih.gov/34846163/

      These methods should be compared to the method for var gene assembly outlined by the co-authors, especially as the authors say that their method "overcomes previous limitations and outperforms current methods" (128-129). The second reference above appears to be a method to measure var expression in clinical samples and so should be particularly compared to the approach outlined by the authors.

      Thank you for pointing this out. We have included the second reference in the introduction of our revised manuscript, where we refer to var assembly and quantification from RNA-sequencing data. We abstained from including the first paper in this paragraph (Dara et al., 2017) as it describes a var gene assembly pipeline and not a var transcript assembly pipeline.

      • Line 101–105: “While approaches for var assembly and quantification based on RNA-sequencing have recently been proposed (Wichers et al., 2021; Stucke et al., 2021; Andrade et al., 2020; TonkinHill et al., 2018, Duffy et al., 2016), these still produce inadequate assembly of the biologically important N-terminal domain region, have a relatively high number of misassemblies and do not provide an adequate solution for handling the conserved var variants (Table S1).”

      Additionally, we have updated the manuscript with a table (Table S1) comparing these two methods plus other previously used var transcript/gene assembly approaches (see comment to the public reviews).

      But to address this particular comment in more detail, the first paper (Dara et al., 2017) is a var gene assembly pipeline and not a var transcript assembly pipeline. It is based on assembling var exon 1 from unfished whole genome assemblies of clinical samples and requires a prior step for filtering out human DNA. The authors used two different assemblers, Celera for short reads (which is no longer maintained) and Sprai for long reads (>2000bp), but found that Celera performed worse than Sprai, and subsequently used Sprai assemblies. Therefore, this method does not appear to be suitable for assembling short reads from RNA-seq.

      The second paper (Stucke et al. 2021) focusses more on enriching for parasite RNA, which precedes assembly. The capture method they describe would complement downstream analysis of var transcript assembly with our pipeline. Their assembly pipeline is similar to our pipeline as they also performed de novo assembly on all P. falciparum mapping and non-human mapping reads and used the same assembler (but with different parameters). They clustered sequences using the same approach but at 90% sequence identity as opposed to 99% sequence identity using our approach. Then, Stucke et al. use 500nt as a cut-off as opposed to the more stringent filtering approach used in our approach. They annotated their de novo assembled transcripts with the known amino acid sequences used in their design of the capture array; our approach does not assume prior information on the var transcripts. Finally, their approach was validated only for its ability to recover the most highly expressed var transcript in 6 uncomplicated malaria samples, and they did not assess mis-assemblies in their approach.

      For the methods (619–621), were erythrocytes isolated by Ficoll gradient centrifugation at the time of collection or later?

      We have updated the methods section to clarify this.

      • Line 586–588: “Blood was drawn and either immediately processed (#1, #2, #3, #4, #11, #12, #14, #17, #21, #23, #28, #29, #30, #31, #32) or stored overnight at 4oC until processing (#5, #6, #7, #9, #10, #13, #15, #16, #18, #19, #20, #22, #24, #25, #26, #27, #33).”

      Was the current pipeline and assembly method assessed for var chimeras? This should be described.

      Yes, this was quantified in the Pf 3D7 dataset and also assessed in the German traveler dataset. For the 3D7 dataset it is described in the result section and Figure S1.

      • Line 168–174: “However, we found high accuracies (> 0.95) across all approaches, meaning the sequences we assembled were correct (Figure 2 – Figure supplement 1b). The whole transcript approach also performed the best when assembling the lower expressed var genes (Figure 2 – Figure supplement 1e) and produced the fewest var chimeras compared to the original approach on P. falciparum 3D7. Fourteen misassemblies were observed with the whole transcript approach compared to 19 with the original approach (Table S2). This reduction in misassemblies was particularly apparent in the ring-stage samples.” - Figure S1:

      Author response image 1.

      Performance of novel computational pipelines for var assembly on Plasmodium falciparum 3D7: The three approaches (whole transcript: blue, domain approach: orange, original approach: green) were applied to a public RNA-seq dataset (ENA: PRJEB31535) of the intra-erythrocytic life cycle stages of 3 biological replicates of cultured P. falciparum 3D7, sampled at 8-hour intervals up until 40hrs post infection (bpi) and then at 4-hour intervals up until 48 (Wichers al., 2019). Boxplots show the data from the 3 biological replicates for each time point in the intra-erythrocytic life cycle: a) alignment scores for the dominantly expressed var gene (PF3D7_07126m), b) accuracy scores for the dominantly var gene (PF3D7_0712600), c) number of contigs to assemble the dominant var gene (PF3D7_0712600), d) alignment scores for a middle ranking expressed vargene (PF3D7_0937800), e) alignment scores for the lowest expressed var gene (PF3D7_0200100). The first best blast hit (significance threshold = le-10) was chosen for each contig. The alignment score was used to evaluate the each method. The alignment score represents √accuracy* recovery. The accuracy is the proportion of bases that are correct in the assembled transcript and the recovery reflects what proportion of the true transcript was assembled. Assembly completeness of the dominant vargene (PF3D7 071200, length = 6648nt) for the three approaches was assessed for each biological f) biological replicate 1, g) biological replicate 2, h) biological replicate 3. Dotted lines represent the start and end of the contigs required to assemble the vargene. Red bars represent assembled sequences relative to the dominantly whole vargene sequence, where we know the true sequence (termed “reference transcript”).

      For the ex vivo samples, this has been discussed in the result section and now we also added this information to Table 1.

      • Line 182/3: “Remarkably, with the new whole transcript method, we observed a significant decrease (2 vs 336) in clearly misassembled transcripts with, for example, an N-terminal domain at an internal position.”

      • Table 1:

      Author response table 3.

      Statistics for the different approaches used to assemble the var transcripts. Var assembly approaches were applied to malaria patient ex vivo samples (n=32) from (Wichers et al., 2021) and statistics determined. Given are the total number of assembled var transcripts longer than 500 nt containing at least one significantly annotated var domain, the maximum length of the longest assembled var transcript in nucleotides and the N50 value, respectively. The N50 is defined as the sequence length of the shortest var contig, with all var contigs greater than or equal to this length together accounting for 50% of the total length of concatenated var transcript assemblies. Misassemblies represents the number of misassemblies for each approach. **Number of misassemblies were not determined for the domain approach due to its poor performance in other metrics.

      Line 432: "the core gene transcriptome underwent a greater change relative to the var transcriptome upon transition to culture." Can this be shown statistically? It's unclear whether the difference in the sizes of the respective pools of the core genome and the var genes may account for this observation.

      We found 19% of the core transcriptome to be differentially expressed. The per patient var transcript analysis revealed individually highly variable but generally rather subtle changes in the var transcriptome. The different methods for assessing this make it difficult to statistically compare these two different results.

      The feasibility of this approach for field samples should be discussed in the Discussion.

      In the original manuscript we reflected on this already several times in the discussion (e.g., line 465/6; line 471–475; line 555–568). We now have added another two sentences at the end of the paragraph starting in line 449 to address this point. It reads now:

      • Line 442–451: “Our new approach used the most geographically diverse reference of var gene sequences to date, which improved the identification of reads derived from var transcripts. This is crucial when analysing patient samples with low parasitaemia where var transcripts are hard to assemble due to their low abundancy (Guillochon et al., 2022). Our approach has wide utility due to stable performance on both laboratory-adapted and clinical samples. Concordance in the different var expression profiling approaches (RNA-sequencing and DBLα-tag) on ex vivo samples increased using the new approach by 13%, when compared to the original approach (96% in the whole transcript approach compared to 83% in Wichers et al., 2021. This suggests the new approach provides a more accurate method for characterising var genes, especially in samples collected directly from patients. Ultimately, this will allow a deeper understanding of relationships between var gene expression and clinical manifestations of malaria.”

      MINOR

      The plural form of PfEMP1 (PfEMP1s) is inconsistently used throughout the text.

      Corrected.

      404-405: statistical test for significance?

      Thank you for this suggestion. We have done two comparisons between the original analysis from Wichers et al., 2021 and our new whole transcript approach to test concordance of the RNAseq approaches with the DBLα-tag approach using paired Wilcoxon tests. These comparisons suggest that our new approach has significantly increased concordance with DBLα-tag data and might be better at capturing all expressed DBLα domains than the original analysis (and the DBLα-approach), although not statistically significant. We describe this now in the result section.

      • Line 352–361: “Overall, we found a high agreement between the detected DBLα-tag sequences and the de novo assembled var transcripts. A median of 96% (IQR: 93–100%) of all unique DBLα-tag sequences detected with >10 reads were found in the RNA-sequencing approach. This is a significant improvement on the original approach (p= 0.0077, paired Wilcoxon test), in which a median of 83% (IQR: 79–96%) was found (Wichers et al., 2021). To allow for a fair comparison of the >10 reads threshold used in the DBLα-tag approach, the upper 75th percentile of the RNA-sequencingassembled DBLα domains were analysed. A median of 77.4% (IQR: 61–88%) of the upper 75th percentile of the assembled DBLα domains were found in the DBLα-tag approach. This is a lower median percentage than the median of 81.3% (IQR: 73–98%) found in the original analysis (p= 0.28, paired Wilcoxon test) and suggests the new assembly approach is better at capturing all expressed DBLα domains.”

      Figure 4: The letters for the figure panels need to be added.

      The figure has been removed from the manuscript.

      Reviewer #3:

      It is difficult from Table S2 to determine how many unique var transcripts would have enough coverage to be potentially assembled from each sample. It seems unlikely that 455 distinct vars (~14 per sample) would be expressed at a detectable level for assembly. Why not DNA-sequence these samples to get the full repertoire for comparison to RNA? Why would so many distinct transcripts be yielded from fairly synchronous samples?

      We know from controlled human malaria infections of malaria-naive volunteers, that most var genes present in the genomic repertoire of the parasite strain are expressed at the onset of the human blood phase (heterogenous var gene expression) (Wang et al., 2009; Bachmann et al, 2016; Wichers-Misterek et al., 2023). This pattern shifts to a more restricted, homogeneous var expression pattern in semi-immune individuals (expression of few variants) depending on the degree of immunity (Bachmann et al., 2019).

      Author response image 2.

      In this cohort, 15 first-time infections are included, which should also possess a more heterogenous var gene expression in comparison to the pre-exposed individuals, and indeed such a trend is already seen in the number of different DBLa-tag clusters found in both patient groups (see figure panel from Wichers et al. 2021: blue-first-time infections; grey–pre-exposed). Moreover, Warimwe et al. 2013 have shown that asymptomatic infections have a more homogeneous var expression in comparison to symptomatic infections. Therefore, we expect that parasites from symptomatic infections have a heterogenous var expression pattern with multiple var gene variants expressed, which we could assemble due to our high read depth and our improved var assembly pipeline for even low expressed variants.

      Moreover, the distinct transcripts found in the RNA-seq approach were confirmed with the DBLα tag data. To our opinion, previous approaches may have underestimated the complexity of the var transcriptome in less immune individuals.

      Mapping reads to these 455 putative transcripts and using this count matrix for differential expression analysis seems very unlikely to produce reliable results. As acknowledged on line 327, many reads will be mis-mapped, and perhaps most challenging is that most vars will not be represented in most samples. In other words, even if mapping were somehow perfect, one would expect a sparse matrix that would not be suitable for statistical comparisons between groups. This is likely why the per-patient transcript analysis doesn't appear to be consistent. I would recommend the authors remove the DE sections utilizing this approach, or add convincing evidence that the count matrix is useable.

      We agree that this is a general issue of var differential expression analysis. Therefore, we have removed the var differential expression analysis from this manuscript as the per patient approach was more appropriate for the paired samples. We validated different mapping strategies (new Figure S6) and included a paragraph discussing the problem in the result section:

      • Line 237–255: “In the original approach of Wichers et al., 2021, the non-core reads of each sample used for var assembly were mapped against a pooled reference of assembled var transcripts from all samples, as a preliminary step towards differential var transcript expression analysis. This approach returned a small number of var transcripts which were expressed across multiple patient samples (Figure 3 – Figure supplement 2a). As genome sequencing was not available, it was not possible to know whether there was truly overlap in var genomic repertoires of the different patient samples, but substantial overlap was not expected. Stricter mapping approaches (for example, excluding transcripts shorter than 1500nt) changed the resulting var expression profiles and produced more realistic scenarios where similar var expression profiles were generated across paired samples, whilst there was decreasing overlap across different patient samples (Figure 3 – Figure supplement 2b,c). Given this limitation, we used the paired samples to analyse var gene expression at an individual subject level, where we confirmed the MSP1 genotypes and alleles were still present after short-term in vitro cultivation. The per patient approach showed consistent expression of var transcripts within samples from each patient but no overlap of var expression profiles across different patients (Figure 3 – Figure supplement 2d). Taken together, the per patient approach was better suited for assessing var transcriptional changes in longitudinal samples. It has been hypothesised that more conserved var genes in field isolates increase parasite fitness during chronic infections, necessitating the need to correctly identify them (Dimonte et al., 2020, Otto et al., 2019). Accordingly, further work is needed to optimise the pooled sample approach to identify truly conserved var transcripts across different parasite isolates in cross-sectional studies.” - Figure S6:

      Author response image 3.

      Var expression profiles across different mapping. Different mapping approaches Were used to quantify the Var expression profiles of each sample (ex Vivo (n=13), generation I (n=13), generation 2 (n=10) and generation 3 (n=l). The pooled sample approach in Which all significantly assembled van transcripts (1500nt and containing3 significantly annotated var domains) across samples were combined into a reference and redundancy was removed using cd-hit (at sequence identity = 99%) (a—c). The non-core reads of each sample were mapped to this pooled reference using a) Salmon, b) bowtie2 filtering for uniquely mapping paired reads with MAPQ and c) bowtie2 filtering for uniquely mapping paired reads with a MAPQ > 20. d) The per patient approach was applied. For each patient, the paired ex vivo and in vitro samples were analysed. The assembled var transcripts (at least 1500nt and containing3 significantly annotated var domains) across all the generations for a patient were combined into a reference, redundancy was removed using cd-hit (at sequence identity: 99%), and expression was quantified using Salmon. Pie charts show the var expression profile With the relative size of each slice representing the relative percentage of total var gene expression of each var transcript. Different colours represent different assembled var transcripts with the same colour code used across a-d.

      For future cross-sectional studies a per patient analysis that attempts to group per patient assemblies on some unifying structure (e.g., domain, homology blocks, domain cassettes etc) should be performed.

      Line 304. I don't understand the rationale for comparing naïve vs. prior-exposed individuals at ex-vivo and gen 1 timepoints to provide insights into how reliable cultured parasites are as a surrogate for var expression in vivo. Further, the next section (per patient) appears to confirm the significant limitation of the 'all sample analysis' approach. The conclusion on line 319 is not supported by the results reported in figures S9a and S9b, nor is the bold conclusion in the abstract about "casting doubt" on experiments utilizing culture adapted

      We have removed this comparison from the manuscript due to the inconsistencies with the var per patient approach. However, the conclusion in the abstract has been rephrased to reflect the fact we observed 19% of the core transcript differentially expressed within one cycle of cultivation.

      Line 372/391 (and for the other LMM descriptions). I believe you mean to say response variable, rather than explanatory variable. Explanatory variables are on the right hand side of the equation.

      Thank you for spotting this inaccuracy, we changed it to “response variable” (line 324, line 343, line 805).

      Line 467. Similar to line 304, why would comparisons of naïve vs. prior-exposed be informative about surrogates for in vivo studies? Without a gold-standard for what should be differentially expressed between naïve and prior-exposed in vivo, it doesn't seem prudent to interpret a drop in the number of DE genes for this comparison in generation 1 as evidence that biological signal for this comparison is lost. What if the generation 1 result is actually more reflective of the true difference in vivo, but the ex vivo samples are just noisy? How do we know? Why not just compare ex vivo vs generation 1/2 directly (as done in the first DE analysis), and then you can comment on the large number of changes as samples are less and less proximal to in vivo?

      In the original paper (Wichers et al., 2021), there were differences between the core transcriptome of naïve vs previously exposed patients. However, these differences appeared to diminish in vitro, suggesting the in vivo core transcriptome is not fully maintained in vitro.

      We have added a sentence explaining the reasoning behind this analysis in the results section:

      • Lines 414–423: “In the original analysis of ex vivo samples, hundreds of core genes were identified as significantly differentially expressed between pre-exposed and naïve malaria patients. We investigated whether these differences persisted after in vitro cultivation. We performed differential expression analysis comparing parasite isolates from naïve (n=6) vs pre-exposed (n=7) patients, first between their ex vivo samples, and then between the corresponding generation 1 samples. Interestingly, when using the ex vivo samples, we observed 206 core genes significantly upregulated in naïve patients compared to pre-exposed patients (Figure 7 – Figure supplement 3a). Conversely, we observed no differentially expressed genes in the naïve vs pre-exposed analysis of the paired generation 1 samples (Figure 7 – Figure supplement 3b). Taken together with the preceding findings, this suggests one cycle of cultivation shifts the core transcriptomes of parasites to be more alike each other, diminishing inferences about parasite biology in vivo.”

      Overall, I found the many DE approaches very frustrating to interpret coherently. If not dropped in revision, the reader would benefit from a substantial effort to clarify the rationale for each approach, and how each result fits together with the other approaches and builds to a concise conclusion.

      We agree that the manuscript contains many different complex layers of analysis and that it is therefore important to explain the rationale for each approach. Therefore, we now included the summary Table 3 (see comment to public review). Additionally, we have removed the var transcript differential expression due to its limitations, which we hope has already streamlined our manuscript.

  5. Nov 2023
    1. Lovely. I guess what I'm trying to define is some methodology for practicing. Many times I simply resort to my exhaustive method, which has worked for me in the past simply due to brute force.Thank you for taking the time to respond and for what look like some very interesting references.

      reply to u/ethanzanemiller at https://www.reddit.com/r/Zettelkasten/comments/185xmuh/comment/kb778dy/?utm_source=reddit&utm_medium=web2x&context=3

      Some of your methodology will certainly depend on what questions you're asking, how well you know your area already, and where you'd like to go. If you're taking notes as part of learning a new area, they'll be different and you'll treat them differently than notes you're collecting on ideas you're actively building on or intriguing facts you're slowly accumulating. Often you'll have specific questions in mind and you'll do a literature review to see what's happing around that area and then read and take notes as a means of moving yourself closer to answering your particular questions.

      Take for example, the frequently asked questions (both here in this forum and by note takers across history): how big is an idea? what is an atomic note? or even something related to the question of how small can a fact be? If this is a topic you're interested in addressing, you'll make note of it as you encounter it in various settings and see that various authors use different words to describe these ideas. Over time, you'll be able to tag them with various phrases and terminologies like "atomic notes", "one idea per card", "note size", or "note lengths". I didn't originally set out to answer these questions specifically, but my interest in the related topics across intellectual history allowed such a question to emerge from my work and my notes.

      Once you've got a reasonable collection, you can then begin analyzing what various authors say about the topic. Bring them all to "terms" to ensure that they're talking about the same things and then consider what arguments they're making about the topic and write up your own ideas about what is happening to answer those questions you had. Perhaps a new thesis emerges about the idea? Some have called this process having a conversation with the texts and their authors or as Robert Hutchins called it participating in "The Great Conversation".

      Almost anyone in the forum here could expound on what an "atomic note" is for a few minutes, but they're likely to barely scratch the surface beyond their own definition. Based on the notes linked above, I've probably got enough of a collection on the idea of the length of a note that I can explore it better than any other ten people here could. My notes would allow me a lot of leverage and power to create some significant subtlety and nuance on this topic. (And it helps that they're all shared publicly so you can see what I mean a bit more clearly; most peoples' notes are private/hidden, so seeing examples are scant and difficult at best.)

      Some of the overall process of having and maintaining a zettelkasten for creating material is hard to physically "see". This is some of the benefit of Victor Margolin's video example of how he wrote his book on the history of design. He includes just enough that one can picture what's happening despite his not showing the deep specifics. I wrote a short piece about how I used my notes about delving into S.D. Goitein's work to write a short article a while back and looking at the article, the footnotes, and links to my original notes may be illustrative for some: https://boffosocko.com/2023/01/14/a-note-about-my-article-on-goitein-with-respect-to-zettelkasten-output-processes/. The exercise is a tedious one (though not as tedious as it was to create and hyperlink everything), but spend some time to click on each link to see the original notes and compare them with the final text. Some of the additional benefit of reading it all is that Goitein also had a zettelkasten which he used in his research and in leaving copies of it behind other researchers still actively use his translations and notes to continue on the conversation he started about the contents of the Cairo Geniza. Seeing some of his example, comparing his own notes/cards and his writings may be additionally illustrative as well, though take care as many of his notes are in multiple languages.

      Another potentially useful example is this video interview with Kathleen Coleman from the Thesaurus Linguae Latinae. It's in the realm of historical linguistics and lexicography, but she describes researchers collecting masses of data (from texts, inscriptions, coins, graffiti, etc.) on cards which they can then study and arrange to write their own articles about Latin words and their use across time/history. It's an incredibly simple looking example because they're creating a "dictionary", but the work involved was painstaking historical work to be sure.

      Again, when you're done, remember to go back and practice for yourself. Read. Ask questions of the texts and sources you're working with. Write them down. Allow your zettelkasten to become a ratchet for your ideas. New ideas and questions will emerge. Write them down! Follow up on them. Hunt down the answers. Make notes on others' attempts to answer similar questions. Then analyze, compare, and contrast them all to see what you might have to say on the topics. Rinse and repeat.

      As a further and final (meta) example, some of my answer to your questions has been based on my own experience, but the majority of it is easy to pull up, because I can pose your questions not to my experience, but to my own zettelkasten and then quickly search and pull up a variety of examples I've collected over time. Of course I have far more experience with my own zettelkasten, so it's easier and quicker for me to query it than for you, but you'll build this facility with your own over time.

      Good luck. 🗃️

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This important paper exploits new cryo-EM tomography tools to examine the state of chromatin in situ. The experimental work is meticulously performed and convincing, with a vast amount of data collected. The main findings are interpreted by the authors to suggest that the majority of yeast nucleosomes lack a stable octameric conformation. Despite the possibly controversial nature of this report, it is our hope that such work will spark thought-provoking debate, and further the development of exciting new tools that can interrogate native chromatin shape and associated function in vivo.

      We thank the Editors and Reviewers for their thoughtful and helpful comments. We also appreciate the extraordinary amount of effort needed to assess both the lengthy manuscript and the previous reviews. Below, we provide our point-by-point response in bold blue font. Nearly all comments have been addressed in the revised manuscript. For a subset of comments that would require us to speculate, we have taken a conservative approach because we either lack key information or technical expertise: Instead of adding the speculative replies to the main text, we think it is better to leave them in the rebuttal for posterity. Readers will thereby have access to our speculation and know that we did not feel confident enough to include these thoughts in the Version of Record.

      Reviewer #1 (Public Review):

      This manuscript by Tan et al is using cryo-electron tomography to investigate the structure of yeast nucleosomes both ex vivo (nuclear lysates) and in situ (lamellae and cryosections). The sheer number of experiments and results are astounding and comparable with an entire PhD thesis. However, as is always the case, it is hard to prove that something is not there. In this case, canonical nucleosomes. In their path to find the nucleosomes, the authors also stumble over new insights into nucleosome arrangement that indicates that the positions of the histones is more flexible than previously believed.

      Please note that canonical nucleosomes are there in wild-type cells in situ, albeit rarer than what’s expected based on our HeLa cell analysis and especially the total number of yeast nucleosomes (canonical plus non-canonical). The negative result (absence of any canonical nucleosome classes in situ) was found in the histone-GFP mutants.

      Major strengths and weaknesses:

      Personally, I am not ready to agree with their conclusion that heterogenous non-canonical nucleosomes predominate in yeast cells, but this reviewer is not an expert in the field of nucleosomes and can't judge how well these results fit into previous results in the field. As a technological expert though, I think the authors have done everything possible to test that hypothesis with today's available methods. One can debate whether it is necessary to have 35 supplementary figures, but after working through them all, I see that the nature of the argument needs all that support, precisely because it is so hard to show what is not there. The massive amount of work that has gone into this manuscript and the state-of-the art nature of the technology should be warmly commended. I also think the authors have done a really great job with including all their results to the benefit of the scientific community. Yet, I am left with some questions and comments:

      Could the nucleosomes change into other shapes that were predetermined in situ? Could the authors expand on if there was a structure or two that was more common than the others of the classes they found? Or would this not have been found because of the template matching and later reference particle used?

      Our best guess (speculation) is that one of the class averages that is smaller than the canonical nucleosome contains one or more non-canonical nucleosome classes. However, we do not feel confident enough to single out any of these classes precisely because we do not yet know if they arise from one non-canonical nucleosome structure or from multiple – and therefore mis-classified – non-canonical nucleosome structures (potentially with other non-nucleosome complexes mixed in). We feel it is better to leave this discussion out of the manuscript, or risk sending the community on wild goose chases.

      Our template-matching workflow uses a low-enough cross-correlation threshold that any nucleosome-sized particle (plus minus a few nanometers) would be picked, which is why the number of hits is so large. So unless the noncanonical nucleosomes quadrupled in size or lost most of their histones, they should be grouped with one or more of the other 99 class averages (WT cells) or any of the 100 class averages (cells with GFP-tagged histones). As to whether the later reference particle could have prevented us from detecting one of the non-canonical nucleosome structures, we are unable to tell because we’d really have to know what an in situ non-canonical nucleosome looks like first.

      Could it simply be that the yeast nucleoplasm is differently structured than that of HeLa cells and it was harder to find nucleosomes by template matching in these cells? The authors argue against crowding in the discussion, but maybe it is just a nucleoplasm texture that side-tracks the programs?

      Presumably, the nucleoplasmic “side-tracking” texture would come from some molecules in the yeast nucleus. These molecules would be too small to visualize as discrete particles in the tomographic slices, but they would contribute textures that can be “seen” by the programs – in particular RELION, which does the discrimination between structural states. We are not sure what types of density textures would side-track RELION’s classification routines.

      The title of the paper is not well reflected in the main figures. The title of Figure 2 says "Canonical nucleosomes are rare in wild-type cells", but that is not shown/quantified in that figure. Rare is comparison to what? I suggest adding a comparative view from the HeLa cells, like the text does in lines 195-199. A measure of nucleosomes detected per volume nucleoplasm would also facilitate a comparison.

      Figure 2’s title is indeed unclear and does not align with the paper’s title and key conclusion. The rarity here is relative to the expected number of nucleosomes (canonical plus non-canonical). We have changed the title to:

      “Canonical nucleosomes are a minority of the expected total in wild-type cells”.

      We would prefer to leave the reference to HeLa cells to the main text instead of as a figure panel because the comparison is not straightforward for a graphical presentation. Instead, we now report the total number of nucleosomes estimated for this particular yeast tomogram (~7,600) versus the number of canonical nucleosomes classified (297; 594 if we assume we missed half of them). This information is in the revised figure legend:

      “In this tomogram, we estimate there are ~7,600 nucleosomes (see Methods on how the calculation is done), of which 297 are canonical structures. Accounting for the missing disc views, we estimate there are ~594 canonical nucleosomes in this cryolamella (< 8% the expected number of nucleosomes).”

      If the cell contains mostly non-canonical nucleosomes, are they really non-canonical? Maybe a change of language is required once this is somewhat sure (say, after line 303).

      This is an interesting semantic and philosophical point. From the yeast cell’s “perspective”, the canonical nucleosome structure would be the form that is in the majority. That being said, we do not know if there is one structure that is the majority. From the chromatin field’s point of view, the canonical nucleosome is the form that is most commonly seen in all the historical – and most contemporary – literature, namely something that resembles the crystal structure of Luger et al, 1997. Given these two lines of thinking, we added the following clarification as lines 312 – 316:

      “At present, we do not know what the non-canonical nucleosome structures are, meaning that we cannot even determine if one non-canonical structure is the majority. Until we know the non-canonical nucleosomes’ structures, we will use the term non-canonical to describe all the nucleosomes that do not have the canonical (crystal) structure.”

      The authors could explain more why they sometimes use conventional the 2D followed by 3D classification approach and sometimes "direct 3-D classification". Why, for example, do they do 2D followed by 3D in Figure S5A? This Figure could be considered a regular figure since it shows the main message of the paper.

      Since the classification of subtomograms in situ is still a work in progress, we felt it would be better to show one instance of 2-D classification for lysates and one for lamellae. While it is true that we could have presented direct 3-D classification for the entire paper, we anticipate that readers will be interested to see what the in situ 2-D class averages look like.

      The main message is that there are canonical nucleosomes in situ (at least in wild-type cells), but they are a minority. Therefore, the conventional classification for Figure S5A should not be a main figure because it does not show any canonical nucleosome class averages in situ.

      Figure 1: Why is there a gap in the middle of the nucleosome in panel B? The authors write that this is a higher resolution structure (18Å), but in the even higher resolution crystallography structure (3Å resolution), there is no gap in the middle.

      There is a lower concentration of amino acids at the middle in the disc view; unfortunately, the space-filling model in Figure 1A hides this feature. The gap exists in experimental cryo-EM density maps. See Author response image 1 for an example (pubmed.ncbi.nlm.nih.gov/29626188). The size of the gap depends on the contour level and probably the contrast mechanism, as the gap is less visible in the VPP subtomogram averages. To clarify this confusing phenomenon, we added the following lines to the figure legend:

      “The gap in the disc view of the nuclear-lysate-based average is due to the lower concentration of amino acids there, which is not visible in panel A due to space-filling rendering. This gap’s visibility may also depend on the contrast mechanism because it is not visible in the VPP averages.”

      Author response image 1.

      Reviewer #2 (Public Review):

      Nucleosome structures inside cells remain unclear. Tan et al. tackled this problem using cryo-ET and 3-D classification analysis of yeast cells. The authors found that the fraction of canonical nucleosomes in the cell could be less than 10% of total nucleosomes. The finding is consistent with the unstable property of yeast nucleosomes and the high proportion of the actively transcribed yeast genome. The authors made an important point in understanding chromatin structure in situ. Overall, the paper is well-written and informative to the chromatin/chromosome field.

      We thank Reviewer 2 for their positive assessment.

      Reviewer #3 (Public Review):

      Several labs in the 1970s published fundamental work revealing that almost all eukaryotes organize their DNA into repeating units called nucleosomes, which form the chromatin fiber. Decades of elegant biochemical and structural work indicated a primarily octameric organization of the nucleosome with 2 copies of each histone H2A, H2B, H3 and H4, wrapping 147bp of DNA in a left handed toroid, to which linker histone would bind.

      This was true for most species studied (except, yeast lack linker histone) and was recapitulated in stunning detail by in vitro reconstitutions by salt dialysis or chaperone-mediated assembly of nucleosomes. Thus, these landmark studies set the stage for an exploding number of papers on the topic of chromatin in the past 45 years.

      An emerging counterpoint to the prevailing idea of static particles is that nucleosomes are much more dynamic and can undergo spontaneous transformation. Such dynamics could arise from intrinsic instability due to DNA structural deformation, specific histone variants or their mutations, post-translational histone modifications which weaken the main contacts, protein partners, and predominantly, from active processes like ATP-dependent chromatin remodeling, transcription, repair and replication.

      This paper is important because it tests this idea whole-scale, applying novel cryo-EM tomography tools to examine the state of chromatin in yeast lysates or cryo-sections. The experimental work is meticulously performed, with vast amount of data collected. The main findings are interpreted by the authors to suggest that majority of yeast nucleosomes lack a stable octameric conformation. The findings are not surprising in that alternative conformations of nucleosomes might exist in vivo, but rather in the sheer scale of such particles reported, relative to the traditional form expected from decades of biochemical, biophysical and structural data. Thus, it is likely that this work will be perceived as controversial. Nonetheless, we believe these kinds of tools represent an important advance for in situ analysis of chromatin. We also think the field should have the opportunity to carefully evaluate the data and assess whether the claims are supported, or consider what additional experiments could be done to further test the conceptual claims made. It is our hope that such work will spark thought-provoking debate in a collegial fashion, and lead to the development of exciting new tools which can interrogate native chromatin shape in vivo. Most importantly, it will be critical to assess biological implications associated with more dynamic - or static forms- of nucleosomes, the associated chromatin fiber, and its three-dimensional organization, for nuclear or mitotic function.

      Thank you for putting our work in the context of the field’s trajectory. We hope our EMPIAR entry, which includes all the raw data used in this paper, will be useful for the community. As more labs (hopefully) upload their raw data and as image-processing continues to advance, the field will be able to revisit the question of non-canonical nucleosomes in budding yeast and other organisms. 

      Reviewer #1 (Recommendations For The Authors):

      The manuscript sometimes reads like a part of a series rather than a stand-alone paper. Be sure to spell out what needs to be known from previous work to read this article. The introduction is very EM-technique focused but could do with more nucleosome information.

      We have added a new paragraph that discusses the sources of structural variability to better prepare readers, as lines 50 – 59:

      “In the context of chromatin, nucleosomes are not discrete particles because sequential nucleosomes are connected by short stretches of linker DNA. Variation in linker DNA structure is a source of chromatin conformational heterogeneity (Collepardo-Guevara and Schlick, 2014). Recent cryo-EM studies show that nucleosomes can deviate from the canonical form in vitro, primarily in the structure of DNA near the entry/exit site (Bilokapic et al., 2018; Fukushima et al., 2022; Sato et al., 2021; Zhou et al., 2021). In addition to DNA structural variability, nucleosomes in vitro have small changes in histone conformations (Bilokapic et al., 2018). Larger-scale variations of DNA and histone structure are not compatible with high-resolution analysis and may have been missed in single-particle cryo-EM studies.”

      Line 165-6 "did not reveal a nucleosome class average in..". Add "canonical", since it otherwise suggests there were no nucleosomes.

      Thank you for catching this error. Corrected.

      Lines 177-182: Why are the disc views missed by the classification analysis? They should be there in the sample, as you say.

      We suspect that RELION 3 is misclassifying the disc-view canonical nucleosomes into the other classes. The RELION developers suspect that view-dependent misclassification arises from RELION 3’s 3-D CTF model. RELION 4 is reported to be less biased by the particles’ views. We have started testing RELION 4 but do not have anything concrete to report yet.

      Line 222: a GFP tag.

      Fixed.

      Line 382: "Note that the percentage .." I can't follow this sentence. Why would you need to know how many chromosome's worth of nucleosomes you are looking at to say the percentage of non-canonical nucleosomes?

      Thank you for noticing this confusing wording. The sentence has been both simplified and clarified as follows in lines 396 – 398:

      “Note that the percentage of canonical nucleosomes in lysates cannot be accurately estimated because we cannot determine how many nucleosomes in total are in each field of view.”

      Line 397: "We're not implying that..." Please add a sentence clearly stating what you DO mean with mobility for H2A/H2B.

      We have added the following clarifying sentence in lines 412 – 413:

      “We mean that H2A-H2B is attached to the rest of the nucleosome and can have small differences in orientation.”

      Line 428: repeated message from line 424. "in this figure, the blurring implies.."

      Redundant phrase removed.

      Line 439: "on a HeLa cell" - a single cell in the whole study?

      Yes, that study was done on a single cell.

      A general comment is that the authors could help the reader more by developing the figures and making them more pedagogical, a list of suggestions can be found below.

      Thank you for the suggestions. We have applied all of them to the specific figure callouts and to the other figures that could use similar clarification.

      Figure 2: Help the reader by avoiding abbreviations in the figure legend. VPP tomographic slice - spell out "Volta Phase Plate". Same with the term "remapped" (panel B) what does that mean?

      We spelled out Volta phase plate in full and explained “remapped” the additional figure legend text:

      “the class averages were oriented and positioned in the locations of their contributing subtomograms”.

      Supplementary figures:

      Figure S3: It is unclear what you mean with "two types of BY4741 nucleosomes". You then say that the canonical nucleosomes are shaded blue. So what color is then the non-canonical? All the greys? Some of them look just like random stuff, not nucleosomes.

      “Two types” is a typo and has been removed and “nucleosomes” has been replaced with “candidate nucleosome template-matching hits” to accurately reflect the particles used in classification.

      Figure S6: Top left says "3 tomograms (defocus)". I wonder if you meant to add the defocus range here. I have understood it like this is the same data as shown in Figure S5, which makes me wonder if this top cartoon should not be on top of that figure too (or exclusively there).

      To make Figures S6 (and S5) clearer, we have copied the top cartoon from Figure S6 to S5.

      Note that we corrected a typo for these figures (and the Table S7): the number of template-matched candidate nucleosomes should be 93,204, not 62,428.

      The description in the parentheses (defocus) is shorthand for defocus phase contrast and was not intended to also display a defocus range. All of the revised figure legends now report the meaning of both this shorthand and of the Volta phase plate (VPP).

      To help readers see the relationship between these two figures, we added the following clarifying text to the Figure S5 and S6 legends, respectively:

      “This workflow uses the same template-matched candidate nucleosomes as in Figure S6; see below.”

      “This workflow uses the same template-matched candidate nucleosomes as in Figure S5.”

      Figure S7: In the first panel, it is unclear why the featureless cylinder is shown as it is not used as a reference here. Rather, it could be put throughout where it was used and then put the simulated EM-map alone here. If left in, it should be stated in the legend that it was not used here.

      It would indeed be much clearer to show the featureless cylinder in all the other figures and leave the simulated nucleosome in this control figure. All figures are now updated. The figure legend was also updated as follows:

      “(A) A simulated EM map from a crystal structure of the nucleosome was used as the template-matching and 3-D classification reference.”

      Figure S18: Why are there classes where the GFP density is missing? Mention something about this in the figure legend.

      We have appended the following speculations to explain the “missing” GFP densities:

      “Some of the class averages are “missing” one or both expected GFP densities. The possible explanations include mobility of a subpopulation of GFPs or H2A-GFPs, incorrectly folded GFPs, or substitution of H2A for the variant histone H2A.Z.”

      Reviewer #2 (Recommendations For The Authors):

      My specific (rather minor) comments are the following:

      1) Abstract:

      yeast -> budding yeast.

      All three instances in the abstract have been replaced with “budding yeast”.

      It would be better to clarify what ex vivo means here.

      We have appended “(in nuclear lysates)” to explain the meaning of ex vivo.

      2) Some subtitles are unclear.

      e.g., "in wild-type lysates" -> "wild-type yeast lysates"

      Thank you for this suggestion. All unclear instances of subtitles and sample descriptions throughout the text have been corrected.

      3) Page 6, Line 113. "...which detects more canonical nucleosomes." A similar thing was already mentioned in the same paragraph and seems redundant.

      Thank you for noticing this redundant statement, which is now deleted.

      4) Page 25, Line 525. "However, crowding is an unlikely explanation..." Please note that many macromolecules (proteins, RNAs, polysaccharides, etc.) were lost during the nuclei isolation process.

      This is a good point. We have rewritten this paragraph to separate the discussion on technical versus biological effects of crowding, in lines 538 – 546:

      “Another hypothesis for the low numbers of detected canonical nucleosomes is that the nucleoplasm is too crowded, making the image processing infeasible. However, crowding is an unlikely technical limitation because we were able to detect canonical nucleosome class averages in our most-crowded nuclear lysates, which are so crowded that most nucleosomes are butted against others (Figures S15 and S16). Crowding may instead have biological contributions to the different subtomogram-analysis outcomes in cell nuclei and nuclear lysates. For example, the crowding from other nuclear constituents (proteins, RNAs, polysaccharides, etc.) may contribute to in situ nucleosome structure, but is lost during nucleus isolation.”

      5) Page 7, Line 126. "The subtomogram average..." Is there any explanation for this?

      Presumably, the longer linker DNA length corresponds to the ordered portion of the ~22 bp linker between consecutive nucleosomes, given the ~168 bp nucleosome repeat length. We have appended the following explanation as the concluding sentence, lines 137 – 140:

      “Because the nucleosome-repeat length of budding yeast chromatin is ~168 bp (Brogaard et al., 2012), this extra length of DNA may come from an ordered portion of the ~22 bp linker between adjacent nucleosomes.”

      6) "Histone GFP-tagging strategy" subsection:

      Since this subsection is a bit off the mainstream of the paper, it can be shortened and merged into the next one.

      We have merged the “Histone GFP-tagging strategy” and “GFP is detectable on nucleosome subtomogram averages ex vivo” subsections and shortened the text as much as possible. The new subsection is entitled “Histone GFP-tagging and visualization ex vivo”

      7) Page 16, Line 329. "Because all attempts to make H3- or H4-GFP "sole source" strains failed..." Is there a possible explanation here? Cytotoxic effect because of steric hindrance of nucleosomes?

      Yes, it is possible that the GFP tag is interfering with the nucleosomes interactions with its numerous partners. It is also possible that the histone-GFP fusions do not import and/or assemble efficiently enough to support a bare-minimum number of functional nucleosomes. Given that the phenotypic consequences of fusion tags is an underexplored topic and that we don’t have any data on the (dead) transformants, we would prefer to leave out the speculation about the cause of death in the attempted creation of “sole source” strains.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      "MAGIC" was introduced by the Rong Li lab in a Nature letters article in 2017. This manuscript is an extension of this original work and uses a genome wide screen the Baker's yeast to decipher which cellular pathways influence MAGIC. Overall, this manuscript is a logical extension of the 2017 study, however the manuscript is challenging to follow, complicated by the data often being discussed out of sequence. Although the manuscripts make claims of a mechanism being pinpointed, there are many gaps and the true mechanisms of how the factors identified in the screen influence MAGIC is not clear. A key issue is that there are many assumptions drawn on previous literature, but central aspects of the mechanisms being proposed are not adequately shown.

      Key comments:

      1. Reasoning and pipelines presented in the first two sections of the results are disordered and do not follow figure order. In some instances, the background to experimental analyses such as detailing the generation of spGFP constructs in the YKO mutant library, or validation of Snf1 activation are mentioned after respective results are discussed. This needs to be fixed.

      We thank the reviewer for pointing out potential confusion to readers. We have revised the first two sections according to reviewer’s suggestion. (Page 4-6)

      1. In general there is a lack of data to support microscopy data and supporting quantification analysis. The validity of this data could be significantly strengthened with accompanying western blots showing accumulation of a given constructs in mitochondrial sub compartments (as was the case in the lab’s original paper in 2017).

      We appreciate the reviewer’s suggestion on biochemical validations. However, the validity of this imaging-based assay for detecting import of cytosolic misfolded proteins into mitochondria, including the use of FlucSM as a model misfolding-prone protein, was carefully established in our previous study by using appropriate controls, super resolution imaging, APEX-based proximity labeling, and classical biochemical fractionation and protease protection assay (Ruan et al., 2017 Nature, ref. 10). We have reminded readers of these validation experiments in the previous study on Page 4, line 14-17.

      In recent years, advancements in imaging-based tools have allowed many protein interactions and dynamic processes, which were previously examined by using biochemical assays in lysates of populations of cells, to be observed with various level of quantitation in live cells with intact cellular compartments. Many of these assays, e.g., the RUSH assay for ER to Golgi transport, FRAP-based analysis for nuclear/cytoplasmic shuttling of proteins, or FRET-based assays for protein-protein interactions, have been well accepted and even embraced by the respective fields of study once validated with genetic and biochemical approaches. The advantages for live-cell imaging-based assays are often their unique ability to report dynamic processes or unstable molecular species with spatiotemporal sensitivity. Respectfully, it is our view, based on our own experience, that the traditional protease protection assay is not adequate or sufficiently quantitative for examining the presence of unstable misfolded proteins in mitochondrial sub-compartments, given the obligatorily lengthy in vitro cell lysis and mitochondrial isolation process, during which the unstable proteins are continuously being degraded. This likely explains our previous biochemical fractionation result that only weak protein signals were detected in the matrix fraction (Ruan et al., 2017 Nature, ref. 10). In addition, unlike stably folded, native mitochondrial matrix proteins, misfolded/unfolded proteins such as Lsg1 or FlucSM are highly susceptible to protease treatment. This sensitivity makes the assay unreliable for detecting such proteins if trace amount of the protease penetrates mitochondrial membranes during cell lysis even without detergent treatment.

      While we agree that protease protection assay is highly valuable for qualitative detection of the presence of a protein in certain mitochondrial compartments or determining its topology on membranes, this assay (regrettably in our hands) does not allow quantitative comparisons that were necessary for this study, because of inherent sample to sample variation, yet the laborious and low throughput nature of this assay makes it difficult for adequate statistical analysis. Furthermore, the level of protein detection in various fractions is highly sensitive to how the sample is treated with protease and detergent. Our imaging-based quantification, on the other hand, allows us to compare increased or decreased presence of GFP11-tagged proteins in mitochondria under different metabolic conditions or in different mutant or wild-type strains. Data from hundreds of cells and at least three independent biological replicates allowed us to apply adequate statistical analysis to aid our conclusion.

      1. Much of the mechanisms proposed relies on the Snf1 activation. This is however not shown but assumed to be taking place. Given that this activation is central to the mechanism proposed, this should be explicitly shown here - for example survey the phosphorylation status of the protein.

      Both REG1 deletion and low glucose conditions have been demonstrated extensively for Snf1 phosphorylation and activation in yeast (e.g., many seminal papers from Marian Carlson’s and other lab, such as ref. 24-28). In our study, we have indeed corroborated this by showing that Mig1 was exported from the nucleus in Δreg1 mutant and in low glucose conditions (Figure 1—figure supplement 2H and I. The mechanism of Snf1-mediated nuclear export of Mig1 has been characterized in detail as well (e.g., ref. 29-31).

      Recommendations for the authors: please note that you control which, if any, revisions, to undertake

      Reviewer #1 (Recommendations For The Authors):

      SPECIFIC COMMENTS

      Genetic Screen o Line 20 - the narrative moves to SNF1, but the reasoning for the selection of this Class I substrate is not defined. What was the basis for this selection - what happened to the other Class I substrates. It is stated in the text that the other Class I proteins show the same increase in spGFP signal. The data showing this should be included in the Supp Figure 1 for transparency.

      We have moved the narratives of Snf1 function to the second section and clarified that we were interested in this gene due to its central role in metabolism and mitochondrial functions that may influence MAGIC (Page 5: line 16-20). Other genes in class 1 were shown in Table S1. Detailed discussion of other genes in this category is beyond the scope of this study.

      Snf1/AMPK prevents MP accumulation in mitochondria:

      The FlucDM data in human RPE-1 mitochondria seems to be added to only increase the significance of the work. The mechanisms suggested here with Hap4 would not be possible in human cells as there is no homologue of this protein in human cells. Making generalisations that these pathways are conserved based on this one experiment is not appropriate.

      We appreciate this feedback. Although the focus of this study is the regulation of MAGIC by the yeast AMPK Snf1, we would like to share our initial observation that suggests a similar role of AMPK in human RPE-1 cells. We acknowledge that the underlying mechanisms regarding the downstream transcription factors and pathway for misfolded protein import could be different in mammalian cells, but the overall effect of AMPK in mitochondrial biogenesis is well known to resemble that of Snf1. To avoid making over-generalization, we changed our statement of conclusion to: ‘These results suggest that AMPK in human cells regulates MP accumulation in mitochondria following a similar trend as in yeast, although the underlying mechanisms might differ between these organisms.’ (Page 7: line 2-4)

      Mechanisms of MAGIC regulation by Snf1:

      While the lysosome is ruled out here the authors have not considered the proteasomes. Is there a reason for this? Given accumulation of aggregates outside of mitochondria, and previous connections of the proteasome to mitochondrial quality control this would be an obvious thing to check. We examined the role of lysosomal degradation here because it is known to be activated under Snf1active condition (ref. 37). We appreciate this feedback and have included a new analysis on MG132treated FlucSM spGFP strains in which PDR5 gene was deleted to avoid drug efflux.

      This result suggests that the proteosome inhibitor did not ablate the difference in FlucSM accumulation between these conditions. That MG132 promoted mitochondrial accumulation of FlucSM in both high glucose and low glucose conditions was not surprising, as FlucSM is also degraded by proteasome in the cytosol (Ruan et al., 2017 Nature, ref. 10), and preventing this pathway could divert more of such protein molecules toward MAGIC. (Page 7: line 26-29).

      Line 13 "we hypothesized that elevated expression of mitochondrial preproteins induced by the activation of Snf1-Hap4 axis (REF) may outcompete MPs for import channels". This statement has some assumptions. The authors have not shown that Snf1 is activated in thier models and more importantly that they have an accumulation of mitochondrial preproteins. The data that follows using the cytosolic domains of the receptors is hard to rationalise without seeing evidence that there is in fact pre-protein accumulation or impacts on the mitochondrial proteome in this system.

      As stated in our response to main point [3], Snf1 activation in reg1 mutant or in low glucose is evidenced by our data showing Mig1 export from nucleus to cytoplasm and had also been shown in many previous publications. A recent study (Tsuboi et al., 2020 eLife) also showed a dramatic increase in mitochondrial volume fraction in Δreg1 cells and wild-type cells in respiratory conditions, further supporting the role of Snf1 in mitochondrial biogenesis. We have provided relevant references in the manuscript (ref. 24-28).

      The ability of Tom70 cytosolic domain (Tom70cd), which can bind mitochondrial preproteins but not localize to mitochondria due to lack of N-terminal targeting sequence, to compete with endogenous Tom70 for mitochondrial preproteins has been well documented (ref. 47-49). However, we agree with the reviewer that a future quantitative proteomics study to measure changes in mitochondrial proteome under Tom70cd over-expression could allow more accurate interpretation of our experimental result.

      AMPK protects cellular fitness during proteotoxic stress:

      The inhibition of preprotein import by overexpressing the cytosolic domains of receptors is not supported with some proof of principle data. If this was working as the authors assume, it is not clear why only an effect with Tom70 is observed. The majority of the mitochondrial proteome is imported via Tom20/Tom22 so this does not align with what the authors are suggesting. Is the Tom70CD and any associated Hsp proteins facilitating the observed changes to the MPs?

      We thank the reviewer for raising this point. We expressed different TOM receptor cytosolic domains but found that Tom70cd had the strongest rescue on MAGIC under AMPK activation conditions. It is possible that certain Tom70 substrates or Tom70-assoicated heat shock proteins inhibit the import of MAGIC substrates. We admit that a clear explanation of this unexpected observation necessitates a better understanding of how native and MAGIC substrates are selected and imported by the outer-membrane channel. We can only offer our best interpretation based on the current state of the understanding, and we feel that we have been careful to acknowledge such in the manuscript.

      While the effect of AMPK inactivation reducing FUS accumulation was striking, this was all in the context of overexpression and may not be physiologically relevant - or may occur very transiently under basal conditions. Is GST an appropriate control here, why not use WT FUS? Likewise, one representative image is shown in Figure 5 - can the authors show western blotting that mitochondrial accumulation of FUS can be reduced with AMPK activation?

      We thank the reviewer for this suggestion, however, overexpressed FUS WT is also aggregation prone (Zhihui Sun et al., 2011, PloS Biology; Shulin Ju, 2011, PloS Biology; Jacqueline C. Mitchell et., 2013, Acta Neuro). We believe that GST, as a well-folded protein, is an appropriate control (Ruan et al., 2017 Nature, ref. 10). As we discussed in response to main point [1], the in vitro assay involving protease protection and western blots do not allow reliable quantitative comparison in our hands.

      In text changes.

      The analysis pipeline of the YKO mutant library should be introduced at the very start of the first paragraph, not the end.

      Addressed on Page 4, second paragraph

      "Fluc" should be introduced as "Firefly luciferase" within the first paragraph of the first section, also need to define SM and DM in FlucSM/FlucDM - these appear to be missing.

      Addressed in both Introduction (Page 2: line 29; Page 3: line 8-9) and re-clarified in Result (Page 5: line 27-29)

      The role of Reg1 should be explicitly stated in the text, not just in the figure.

      Addressed on Page 6: line 3-6

      Figure 1H legend states Reg1 (WT) is Snf1-inactive and Reg1 KO is Snf1-active. This wording is confusing and is not supported by data, but by assumption. If the authors want to use this wording then evidence needs to be provided - as suggested above.

      We have changed this and other legends to only show genotypes and medium conditions.

      "Tom70cd overexpression also exacerbated growth rate reduction due to FlucSM expression in HG medium (Figure 4A; Figure 4 - figure supplement 1A)" should be figure supplement 1B.

      Fixed on Page 10: line 10

      "These results suggest that glucose limitation protects mitochondria and cellular fitness during FlucSM induced proteotoxic stress through Snf1-dependent inhibition of MP import into mitochondria". The phrase "Snf1-dependent inhibition of MP import into mitochondria" may be misleading, as Snf1 isn't modulating import directly but is acting on transcriptional regulators to modulate mitochondrial import under stress.

      We restated the conclusion as follows: ‘These results suggest that Snf1 activation under glucose limitation protects mitochondrial and cellular fitness under FlucSM-associated proteotoxic stress.’ (Page 10: line 20- 21)

      "... Significantly increased the fraction of spGFP-positive and MMP-low cells in both HG and LG medium (Figure 4G-K)" should be (Figure 4J-K).

      Fixed on Page 11: line 3

      Reviewer #2 (Public Review):

      Work of Rong Li´s lab, published in Nature 2017 (Ruan et al, 2017), led the authors to suggest that the mitochondrial protein import machinery removes misfolded/aggregated proteins from the cytosol and transports them to the mitochondrial matrix, where they are degraded by Pim1, the yeast Lon protease. The process was named mitochondria as guardian in cytosol (MAGIC).

      The mechanism by which MAGIC selects proteins lacking mitochondrial targeting information, and the mechanism which allows misfolded proteins to cross the mitochondrial membranes remained, however, enigmatic. Up to my knowledge, additional support of MAGIC has not been published. Due to that, MAGIC is briefly mentioned in relevant reviews (it is a very interesting possibility!), however, the process is mentioned as a "proposal" (Andreasson et al, 2019) or is referred to require "further investigation to define its relevance for cellular protein homeostasis (proteostasis)" (Pfanner et al, 2019).

      Rong Li´s lab now presents a follow-up story. As in the original Nature paper, the major findings are based on in vivo localization studies in yeast. The authors employ an aggregation prone, artificial luciferase construct (FlucSM), in a classical split-GFP assay: GFP1-10 is targeted to the matrix of mitochondria by fusion with the mitochondrial protein Grx5, while GFP11 is fused to FlucSM, lacking mitochondrial targeting information. In addition the authors perform a genetic screen, based on a similar assay, however, using the cytosolic misfolding-prone protein Lsg1 as a read-out.

      My major concern about the manuscript is that it does not provide additional information which helps to understand how specifically aggregated cytosolic proteins, lacking a mitochondrial targeting signal could be imported into mitochondria. As it stands, I am not convinced that the observed FlucSM-/Lsg1-GFP signals presented in this study originate from FlucSM-/Lsg1-GFP localized inside of the mitochondrial matrix. The conclusions drawn by the authors in the current manuscript, however, rely on this single approach.

      In the 2017 paper the authors state: "... we speculate that protein aggregates engaged with mitochondria via interaction with import receptors such as Tom70, leading to import of aggregate proteins followed by degradation by mitochondrial proteases such as Pim1." Based on the new data shown in this manuscript the authors now conclude "that MP (misfolded protein) import does not use Tom70/Tom71 as obligatory receptors." The new data presented do not provide a conclusive alternative. More experiments are required to draw a conclusion.

      In my view: to confirm that MAGIC does indeed result in import of aggregated cytosolic proteins into the mitochondrial matrix, a second, independent approach is needed. My suggestion is to isolate mitochondria from a strain expressing FlucSM-GFP and perform protease protection assays, which are well established to demonstrate matrix localization of mitochondrial proteins. In case the authors are not equipped to do these experiments I feel that a collaboration with one of the excellent mitochondrial labs in the US might help the MAGIC pathway to become established.

      We thank Reviewer 2 for these suggestions, but we would like to respectfully offer our difference in opinion:

      a. Regarding the suggestion “to isolate mitochondria from a strain expressing FlucSM-GFP and perform protease protection assays”, in our previous study (Ruan et al., 2017 Nature, ref. 10), we have indeed applied two independent biochemical approaches: APEX-mitochondrial matrix proximity labeling and classic protease protection assay using non-spGFP strains, both consistently confirmed the entry of misfolded proteins into mitochondria under proteotoxic stress. Our super-resolution imaging further confirmed the import of the split GFP-labeled proteins to be inside mitochondria. Moreover, as we discussed in response to Reviewer 1’s main point [2], while the suggested biochemical assay is useful for validating topology within mitochondria, it is not quantitative and may not reliably report the in vivo accumulation of misfolded proteins in mitochondria due to the isolation process that takes hours, during which the unstable proteins could be continuously degraded within mitochondria.

      While we agree with the reviewer that we do not yet understand how misfolded proteins are imported into mitochondria, it would be unfair to state “as it stands, I am not convinced..” simply because the underlying mechanism remains to be elucidated. We would like to point out that targeting sequences for many well-established mitochondrial proteins are still not well defined. It is well known that mitochondrial targeting sequences are not as uniformly predictable as, for example, nuclear targeting sequences. Our finding that deletion of TOM6 enhances the import of misfolded proteins suggest that their import may involve the TOM channel in a more promiscuous conformation, which may reduce the requirement for a specific sequence-based targeting signal associated with the substrate.

      b. Regarding the role of Tom70, in our 2017 study, using proteomics and subsequently immunoprecipitation we validated the binding, albeit not necessarily direct, between misfolded protein FlucSM and Tom70. Therefore, “we speculate that protein aggregates engaged with mitochondria via interaction with import receptors such as Tom70”. Recent studies from different labs confirmed the interactions between Tom70 and aggregation prone proteins (Backes et al., 2021, Cell Reports; Liu et al., 2023, PNAS). In the current study, surprisingly, knockout of TOM70 did not block MAGIC, suggesting redundant components of mitochondria import system may facilitate the recruitment of misfolded proteins in the absence of Tom70, and this does not contradict the notion that Tom70 helps tether protein aggregates to mitochondria.

      c. Regarding other studies also showing the import of misfolding or aggregation-prone cytosolic proteins into mitochondria, there have been at least several recent studies in the literature for mammalian cells involving either model substrates or disease proteins (e.g., ref. 12-15; 56-58; Vicario, M. et al. 2019 Cell Death Dis.). The studies are briefly mentioned in Introduction (Page 3, paragraph 2). The present manuscript documents a major effort from our group using whole genome screen in yeast to understand the mechanism and regulation of MAGIC. Many of the screen hits have yet to be studied in detail. We full agree that much remains to be understood about whether and how this pathway affects proteostasis and what might be the evolutionary origin for such a mechanism.

      Additional comments:

      The genetic screen:

      The genetic screen identified five class 1 deletion strains, which lead to enhanced accumulation of Lsg1GFP and a larger set of class 2 mutants, which lead to reduced accumulation. Please note, in my opinion it is not clear that accumulation of the reporters occurs inside the mitochondria. In any case, the authors selected one single protein for further analysis: Snf1, the catalytic subunit of the yeast SNF complex, which is required for respiratory growth of yeast.

      The results of the screen are not discussed in any detail. The authors mention that ribosome biogenesis factors are abundant among class 2 mutants. Noteworthy, Lsg1 is involved in 60S ribosomal subunit biogenesis. As Lsg1-GFP11 is overexpressed in the screen this should be discussed. Class 2 mutants also .include several 40S ribosomal subunit proteins (only one of the 60S subunit). What does this imply for the MAGIC model? Also, it should be discussed that the screen did not identify reg1 and hap4, which I had expected as hits based on the data shown in later parts of the manuscript.

      We apologize for the confusion, but the GFP11 tag was in fact knocked into the C-terminus of Lsg1 in the endogenous LSG1 locus, and so Lsg1 was not overexpressed in the screen. We have made sure that this information is clearly conveyed in the revised manuscript (Page 4: line 20-22). How the ribosome small subunit affects MAGIC is beyond the focus of the current study and will be pursued in the future.

      Regarding why certain mutants did not come out of our initial screen, this is not unexpected as the YKO collection, although extremely valuable to the community, is known to be potentially affected by false knockouts, suppressor accumulation and cross contamination (for references, e.g., Puddu et al., 2019 Nature). Additionally, high-through screens can also miss real hits. In our experience using this collection in several studies, we often found additional hits from analysis of genes implicated by known genetic or biochemical interactions.

      Mutant yeast strains and growth assays:

      The Δreg1 strain grows poorly in all growth conditions and frequently accumulates extragenic suppressor mutations (Barrett et al, 2012). It would be good to make sure that this is not the case in the strains employed in this study. My suggestion is to do (and show) standard yeast plating assays with the relevant mutant strains including Δreg1, snf1, hap4, Δreg1Δhap4 without the split GFP constructs and also with them (i.e. the strains that were used in the assays).

      We thank the reviewer for the suggestion. We were indeed aware of potential accumulation of suppressor mutations from the YKO library. Therefore, deletion mutants like Δreg1 and loss of TFs downstream of Snf1 that we used in the study after the initial screen were all freshly made and validated. At least 3 independent colonies were analyzed for each mutant (mentioned in Methods & Materials; Page 33, line 57). Moreover, the plating assay suggested here may not reveal additional information other than growth, which was taken into consideration during our experiments.

      Activation of Snf1 in the relevant strains should be tested with the commercially available antibody recognizing active Snf1, which is phosphorylated at Snf1-T210.

      Snf1 activation was validated by the Mig1 exporting from the nucleus. We also noted above that many studies have clearly demonstrated Snf1 activation in reg1 mutant and under low glucose growth (e.g., ref. 24-28).

      Effects of Snf1, Reg1, Hap4 and respiratory growth conditions:

      The authors show that split GFP reporters show enhanced accumulation during fermentative growth, in Δsnf1, and Δreg1Δhap4 and fail to accumulate during respiratory growth, in Δreg1 and upon overexpression of HAP4. Analysis of Δhap4 should be included in Fig. 2. The suggestion that upon activation of Snf1 enhanced Hap4-dependent expression "outcompetes" misfolded protein import seems unlikely as only a fraction of mitochondrial genes is under control of Hap4. Without further experimental evidence I do not find that a valid assumption. More likely, the membrane potential plays a role: it is low during fermentative growth, in Δsnf1 and Δreg1Δhap4, and high during respiratory growth and in Δreg1 (Hübscher et al, 2016). Such an effect of the membrane potential seems to contradict the findings in the 2017 paper and the issue should be clarified and discussed. In any case, these data do not reveal that GFP reporters accumulate inside of the mitochondria. Based on the currently available evidence they may accumulate in close proximity/attached to the mitochondria. This has to be tested directly (see above).

      We have included our analysis of Δhap4 in Page 8: line 14-15 and Figure 2—figure supplement 1H. Consistent with our result for Δreg1Δhap4 in glucose-rich medium, HAP4 deletion also resulted in a significant increase in mitochondrial accumulation of FlucSM in low glucose medium compared to WT. It did not have effect in high glucose condition in which Snf1 is largely inactive.

      It is our view that the importance of Hap4 should not be judged by the number of nuclear encoded mitochondrial proteins they regulate. Still, this sub-group comprises a considerable number of proteins (at least 55 genes upregulated by Hap4 overexpression, ref. 43), and certain substrates may be more competitive with misfolded cytosolic proteins for import. Our genetic data strongly suggest that the inhibitory effect of active Snf1 on MAGIC is through Hap4, although we agree with the reviewer that detailed mechanism on how Hap4 substrates may compete with misfolded proteins need to be addressed in future studies.

      Membrane potential is important for mitochondrial import. During respiratory growth and in Δreg1, membrane potential is well known to be elevated comparing to fermentative condition (e.g., Figure 4C). Our observation that the import of misfolded proteins into mitochondria is reduced under these conditions simply suggests that this reduction is not due to a lack of membrane potential. This is not in any way contradictory to our 2017 finding that misfolded protein import requires membrane potential (ref. 10).

      Again, the accumulation of misfolded proteins in mitochondria, especially the model protein FlucSM, has been validated by using super resolution imaging (Figure 1—figure supplement 1A) in addition to the protease protection assay in our 2017 study.

      Introduction and Discussion:

      Both are really short, too short in my view. Please provide some background of the general principals of mitochondrial protein import and information of how exactly translocation of cytosolic, aggregated proteins (lacking targeting information) is supposed to work. I do not understand exactly how the authors actually envisage the process.

      We thank the reviewer for the suggestion. In the revised manuscript, we have extended both Introduction (Page 2-3) and Discussion section (Page 11-13)

      The results from the 2022 eLife paper (Liu et al, 2022), which suggests that Tom70 may "regulate both the transcription/biogenesis and import of mitochondrial proteins so the nascent mitochondrial proteins do not compromise cytosolic proteostasis or cause cytosolic protein aggregation" should be discussed with regard to the data obtained with overexpression of the Tom70 soluble domain.

      We thank the reviewer for pointing out that study and we have included a brief comment in Discussion section (Page 12: line 13-16). As the function of Tom70 appears to be complex, we cannot exclude the possibility that overexpression of the cytosolic domain has additional or indirect effects in addition to that due to preprotein binding.

      Andreasson, C., Ott, M., and Buttner, S. (2019). Mitochondria orchestrate proteostatic and metabolic stress responses. EMBO Rep 20, e47865.

      Barrett, L., Orlova, M., Maziarz, M., and Kuchin, S. (2012). Protein kinase A contributes to the negative control of Snf1 protein kinase in Saccharomyces cerevisiae. Eukaryot Cell 11, 119-128.

      Hubscher, V., Mudholkar, K., Chiabudini, M., Fitzke, E., Wolfle, T., Pfeifer, D., Drepper, F., Warscheid, B., and Rospert, S. (2016). The Hsp70 homolog Ssb and the 14-3-3 protein Bmh1 jointly regulate transcription of glucose repressed genes in Saccharomyces cerevisiae. Nucleic Acids Res. 44, 5629-5645.

      Liu, Q., Chang, C.E., Wooldredge, A.C., Fong, B., Kennedy, B.K., and Zhou, C. (2022). Tom70-based transcriptional regulation of mitochondrial biogenesis and aging. Elife 11

      Pfanner, N., Warscheid, B., and Wiedemann, N. (2019). Mitochondrial proteins: from biogenesis to functional networks. Nat Rev Mol Cell Biol 20, 267-284.

      Ruan, L., Zhou, C., Jin, E., Kucharavy, A., Zhang, Y., Wen, Z., Florens, L., and Li, R. (2017). Cytosolic proteostasis through importing of misfolded proteins into mitochondria. Nature 543, 443-446.

      I prefer to have "all in one", also due to time limitation.

      It would be great to be able to upload the review file as otherwise formatting and symbols get lost.

      Reviewer #3 (Public Review):

      In this study, Wang et al extend on their previous finding of a novel quality control pathway, the MAGIC pathway. This pathway allows misfolded cytosolic proteins to become imported into mitochondria and there they are degraded by the LON protease. Using a screen, they identify Snf1 as a player that regulates MAGIC. Snf1 inhibits mitochondrial protein import via the transcription factor Hap4 via an unknown pathway. This allows cells to adapt to metabolic changes, upon high glucose levels, misfolded proteins an become imported and degraded, while during low glucose growth conditions, import of these proteins is prevented, and instead import of mitochondrial proteins is preferred.

      This is a nice and well-structured manuscript reporting on important findings about a regulatory mechanism of a quality control pathway. The findings are obtained by a combination of mostly fluorescent protein-based assays. Findings from these assays support the claims well.

      While this study convincingly describes the mechanisms of a mitochondria-associated import pathway using mainly model substrates, my major concern is that the physiological relevance of this pathway remains unclear: what are endogenous substrates of the pathway, to which extend are they imported and degraded, i.e. how much does MAGIC contribute to overall misfolded protein removal (none of the experiments reports quantitative "flux" information). Lastly, it remains unclear by which mechanism Snf1 impacts on MAGIC or whether it is "only" about being outcompeted by mitochondrial precursors.

      We thank Reviewer 3 for the positive and encouraging comments on our manuscript. We agree with the reviewer that identifying MAGIC endogenous substrates and understanding what percentage of them are degraded in mitochondria are very important issues to be addressed. We are indeed carrying out projects to address these questions. We also agree with Reviewer 3 that the effect of Snf1 on MAGIC may have additional mechanisms in addition to precursors competition, such as Tom6 mediated conformational changes of TOM pores. In the revised manuscript, we had added a discussion to address these comments (Page 12: line 21-28).

      Reviewer #3 (Recommendations For The Authors):

      1. In their screen, the authors utilize differences in GFP intensity as a measure for import efficiency. However, reconstitution of the GFP from GFP1-10 and GFP11 in the matrix might also be affected (folding factors, differential degradation).

      Upon Snf1 activation, the protein abundance of mitochondrial chaperones such as Hsp10, Hsp60, and Mdj1, and mitochondrial proteases such as Pim1 are not significantly changed (ref. 35). Therefore, it is unlikely that the folding and degradation capacity of mitochondrial matrix is drastically affected by Snf1 activation.

      To examine the effect of Snf1 activation on spGFP reconstitution, Grx5 spGFP strain was constructed in which the endogenous mitochondrial matrix protein Grx5 was C-terminally tagged with GFP11 at its genomic locus, and GFP1-10 was targeted to mitochondria through cleavable Su9 MTS (MTS-mCherryGFP1-10) (ref. 10). Only modest reduction in Grx5 spGFP intensity was observed in LG compared to HG, and no significant difference after adjusting the GFP1-10 abundance (spGFP/mCherry ratio) (Figure 1— figure supplement 3A-D). These data suggest that any effect on spGFP reconstitution is insufficient to explain the drastic reduction of MP accumulation in mitochondria under Snf1 activation. Overall, our results demonstrate that Snf1 activation primarily prevents mitochondrial accumulation of MPs, but not that of normal mitochondrial proteins. (Page 6: line 17-25).

      We admit, however, that to fully rule out these factors, specific intra-mitochondrial folding or degradation reporter assays would be needed.

      1. Scoring of protein import always takes place using fluorescence-based assays. These always require folding of the "sensors" in the matrix. An additional convincing approach that would not rely on matrix folding could be pulse chase approaches coupled to fractionation assays and immunoprecipitation.

      We thank reviewer 3 for this suggestion. In our previous study, we applied two different biochemical assays: APEX proximity labeling, and mitochondrial fractionation followed by protease protection. Both confirmed the entry of misfolded proteins into mitochondria as observed by using split GFP. As we discussed in response to Reviewer 1’s main point [3], the fractionation assays are not quantitative enough for the comparisons made in our study. In particular, during the over 2-hour assay, misfolded proteins continue to be degraded within mitochondria. By using proper controls, our spGFP system provides quantitative comparisons for mitochondrial accumulation of misfolded proteins in non-disturbed physiological conditions.

      1. Could the pathway be reconstituted in vitro with isolated mitochondria to test for the "competition hypothesis"

      This is an excellent suggestion, but setting up such a reconstituted system is a project on its own. The study documented in this manuscript already encompasses a large amount of work that we feel should be published timely.

      1. Fluorescence figures are not colour blind friendly (red-green). This should be improved by changing the color scheme.

      We thank reviewer 3 for pointing this out and sincerely apologize for any inconvenience. However, we are unfortunately unable to change all images within a limited time. We will adopt another color scheme in future work.

      1. spGFP in human cells appears to form "spot-like" structures. What are these granules?

      We indeed observed granule-like structures by spGFP labeled FUS in mitochondria, which is interesting, but we did not investigate this further because it is a not a focus of this study.

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      Reply to the reviewers

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

      *In their study, Yamano et al. dissect the mechanism of TBK1 activation and downstream effects, especially in its relation to mitophagy adaptor OPTN. The authors find that OPTN's interaction with ubiquitin and the autophagy machinery, forming contact sites between mitochondria and autophagic membranes, results in TBK1 accumulation and subsequent autophosphorylation. Based on these findings, the authors propose a self-propagating feedback loop wherein OPTN phosphorylation by TBK1 promotes recruitment and accumulation of OPTN to damaged mitochondria and specifically the autophagosome formation site. This formation site is then involved in TBK1 autophosphorylation, and the activated TBK1 can then further phosphorylate other pairs of OPTN and TBK1. A OPTN monobody investigation strengthens their findings. *

      *Critique: *

      • It would be helpful if the authors could more clearly highlight the previous findings in OPTN-TBK1 relationship and which gaps in the understanding their study addresses.* We thank the reviewer for this comment. As suggested, we have highlighted previous findings and detailed in the Discussion how the study advances our understanding of TBK1 activation.

      • It is not always clear whether experiments have been replicated sufficiently; this should be indicated in the figure descriptions.* In the original manuscript, most of the data shown was derived from duplicated experiments. For the revised version, we repeated experiments as needed to generate the replication necessary (i.e, N = 3) for determining statistical significance. Error bars and statistical significance have been added to the graphs and figure legends accordingly.

      • During the discussion, references to the figures that indicate conclusions should be added where appropriate.* We thank the reviewer for the suggestion. References to figures have been added were appropriate to the Discussion.

      *Figure 1 / Result "OPTN is required for TBK1 phosphorylation and subsequent autophagic Degradation": *

      *o In a) the TBK1 and TOMM20 blots feature an image artefact that makes it appear like the blots are stitched together or there was a problem with the digital imager. The quantification in b) seems to be missing replications. *

      We found that the artifact came from an automatic pixel interpolation process in Adobe Photoshop when the image was rotated by a small angle. We have provided the original immunoblotting data below as evidence that the data were not stitched from separate images. More accurate representations of the images without the artifact are now shown in Fig1 A of the revised manuscript.

      For Fig 1b, the experiment was independently replicated three times with error bars added to each plot on the graph.

      *o g) should feature the wt cell line on the same blot for better comparability as well as quantification and replication like done in f) *

      As suggested, we have included the WT cell line in the immunoblot (See Fig 1g). In addition, Reviewer 2 asked that we provide data for Penta KO cells without exogenous expression of the autophagy adaptors and expressed concern regarding the lower expression of NDP52 relative to OPTN. To address these issues, we repeated the mitophagy experiments and detected phosphorylated TBK1 in six different cell lines: WT, Penta KO, Penta KO stably expressing OPTN at both low and high expression levels, and Penta KO stably expressing NDP52 at low and high expression levels. Immunoblots of phos-TBK1(pS172), TBK1, OPTN, NDP52, TOMM20, and actin were generated under four different conditions (DMSO, valinomycin for 1 hr, valinomycin for 3 hrs, and valinomycin in the presence of bafilomycin for 3 hrs). In addition, phos-TBK1 abundance in the six cell lines was determined in response to val and baf for 3 hrs and the expression levels of NDP52 and OPTN were similarly determined in response to DMSO. Error bars based on three independent experiments have been incorporated into the data, which are shown in Figure 1g and 1h of the revised manuscript.

      *o h) is missing the blots for controls actin and TOMM20 *

      Immunoblots for actin and TOMM20 have been added, please see Fig 1i in the revised manuscript.

      *o In the text to e/f), the authors write that NDP52 KO effect on pS172 are comparable to controls, though the quantitation in f) indicates that pS172 signal is indeed significantly reduced compared to wt *

      The reviewer is correct, the phos-TBK1 (pS172) signal in NDP52 KO cells is reduced compared to that in WT cells, but is only moderately lower in NDP52 KO cells relative to OPTN KO. We regret the error, which has been corrected in the revised manuscript.

      *o In the text to h/i), the authors write "there was a significant increase in the TBK1 pS172 signal in cells overexpressing OPTN", though the quantification in i) does not indicate significance levels *

      We performed statistical analyses on the phos-TBK1 (pS172) levels between cells with or without OPTN overexpression and have added the degree of significance to Fig 1j. As indicated in the original manuscript, there was a significant increase in phos-TBK1 (pS172) levels when OPTN was overexpressed.

      *Figure 2 / Result "OPTN association with the autophagy machinery is required for TBK1 activation": ** o In b), pTBK1 at val 1 hr only features one dot/experiment per cell line *

      Three independent replicates of the experiment (val 1 hr) were performed. The levels of phos-TBK1 (pS172), total TBK1, and actin were quantified, and the graph was remade with error bars and statistical significance incorporated. Please see Fig 2b in the revised manuscript.

      *o In the text to c), the authors claim that the mutants reduce/abolish the recruitment of OPTN to the autophagosome site. A costain for LC3, as done for SupFig 1b, would be necessary to support that specific claim. *

      To address the reviewer’s concern regarding the recruitment of OPTN mutants to the autophagosomal formation site, we performed two different experiments. First, when OPTN WT is recruited to the contact site between the autophagosomal formation site and damaged mitochondria, it should be heterogeneously distributed across mitochondria. In contrast, OPTN mutants that are unable to associate with the autophagosome formation sites should be largely localized to damaged mitochondria since the mutants are still capable of binding ubiquitin. When we examined the mitochondrial distribution of OPTN WT following valinomycin treatment for 1 hr, more than 80% of the Penta KO cells exhibited a heterogeneous distribution, whereas only 10% of the cells showed a similar distribution for OPTN 4LA or OPTN 4LA/F178A (please see Fig 2g in the revised manuscript). Although the OPTN F178A mutant exhibited 50% heterogeneous distribution (Fig 2g), this may be because OPTN F178A retains the ability to interact with ATG9A vesicles. In fact, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB), which are strongly correlated with OPTN mitochondrial distribution, showed that the OPTN F178A mutant moderately (~ 60%) induced mitochondrial degradation. This degradation effect was slightly higher (80%) with OPTN WT but significantly lower (9%) with the 4LA/F178A mutant. In the second experiment, Penta KO cells expressing either OPTN WT or the OPTN mutants were immunostained for exogenous FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (a mitochondrial marker) after valinomycin treatment for 1 hr (see Fig 2e and 2f in the revised manuscript). Because LC3B is assembled on the autophagosomal formation site as well as completed autophagosomes, we detected endogenous WIPI2 because WIPI2 is only recruited to autophagosomal formation sites (Dooley et al. 2014 Mol Cell). Confocal microscopy images and their associated quantification data indicate that WIPI2 foci formation during mitophagy was reduced in Penta KO cells expressing the OPTN mutants (4LA, F178A and 4LA/F178A) as compared to Penta KO cells expressing OPTN WT.

      *o d) and g) as simple confirmations of KO/KD efficiency might be better suited for the supplemental part, or blots for FIP/ATG be included with the blots in e) and h) *

      Based on the reviewer comments, we performed additional experiments related to Figure 2 and have incorporated the new data into the revised figure. The original Figure 2d, e, f, g, h, and I have been moved to supplemental Figure 5.

      *o In the text to e), the authors claim that the levels of pS172 in the KO cell lines did not increase during mitophagy, though the blot and quantification in f) seem to indicate an increase. The results therefore don't seem to align completely with the claims that pS172 generation in response to mitophagy requires the autophagy machinery, or that FIP200 and ATG9A rather than ATG5 are critical for TBK1 phosphorylation. *

      Although newly generated pS172 TBK1 was reduced in FIP200 KO and ATG9A KO cells relative to WT cells, the signals gradually increased. In the autophagy KO cell lines (FIP200 KO and ATG9A KO), phos-TBK1 accumulates prior to mitophagy stimulation. Although suggesting it is mitophagy-independent, phos-TBK1 accumulation prior to mitophagy stimulation in autophagy KO cell lines complicated interpretation of the results. To avoid this issue, we used siRNA to transiently knock down FIP200 and ATG9A. As shown in the original manuscript (Fig 2g, h, I in the original manuscript, supplementary Fig 5d, e, f in the revised manuscript), knockdown of FIP200 and ATG9A prior to mitophagy induction allowed us to observe mitophagy-dependent phosphorylation of TBK1. This result strongly suggests that the autophagy machinery does induce TBK1 phosphorylation in response to Parkin-mediated mitophagy. However, TBK1 phosphorylation still increases, albeit very slightly, in the FIP200 and ATG9A knock down cells. Thus, it may be reasonable to assume that OPTN-dependent phosphorylation of TBK1 can occur to a certain degree even in the absence of autophagy components. We have noted this in the Discussion.

      While conducting experiments for the revised manuscript, we determined that TAX1BP1 is responsible for the accumulation of phos-TBK1 in the autophagy KO cell lines under basal conditions. When TAX1BP1 is knocked down in FIP200 KO or ATG9A KO cells, the basal accumulation of phos-TBK1 was eliminated and then we could observe mitophagy-specific TBK1 phosphorylation (please see Fig 2h, i, j, k in the revised manuscript). These results showed that mitophagy-dependent phos-TBK1 is largely attenuated in FIP200KO and was almost completely eliminated in ATG9A KO cells (Fig 2k in the revised manuscript).

      *o f) is missing significance indications. Its description has a typo: "bad" instead of "baf" *

      Newly synthesized pTBK1 (pS172) during mitophagy was quantified and statistical significance incorporated into the figure (please see supplementary Fig 5c). The identified typo has been corrected.

      *Figure 3 / Result "TBK1 activation does not require OPTN under basal autophagy conditions": *

      *o In the text to SupFig2, the authors claim that pS172 levels are significantly elevated, but no significance levels are indicated *

      Statistical significance was determined for all proteins shown in original supplementary Fig 2 and the results have been incorporated into the relevant figure. The original supplementary Fig 2 is now supplementary Fig 6.

      *o In the text to a), NBR1 is claimed to colocalize with Ub, but no costaining with Ub is shown. The claimed lacking colocalization of OPTN with Ub is not obvious from the images; a quantification might be appropriate. *

      Since the anti-NBR1 antibody used in the original manuscript is derived from mouse, we were unable to use it in conjunction with the mouse ubiquitin antibody. Because ubiquitin-positive foci and NBR1-positive foci contain p62 (original Fig 3a) and NBR1 and p62 are known to tightly interact each other (Kirkin et al. 2009 Mol Cell and Sanchez-Martin et al. 2020 EMBO Rep), we stated that "NBR1 colocalizes with Ub". However, the reviewer is correct. To remedy this confusion, we obtained a rabbit anti-NBR1 antibody (a gift from the Masaaki Komatsu group) and used it to co-immunostain with anti-Ub antibodies (please see supplementary Fig 7a of the revised manuscript). NBR1 foci colocalize with both ubiquitin and p62 in FIP200 KO and ATG9A KO cells. Further, based on comments from Reviewer 2, we purchased several anti-TBK1 antibodies and identified one that was able to detect endogenous TBK1 by immunostaining (see Figure 1 for reviewers in our response to Reviewer 2 below). Using this anti-TBK1 antibody, we showed that a part of TBK1 also associates with ubiquitin and p62-positive aggregates.

      *o In the text to b), the authors make reference to significant changes, but replication/ quantification/ significance testing is missing. *

      We independently performed the same experiments three times. The levels of TBK1, phos-TBK1 (pS172), all five autophagy adaptors, and TOMM20 in both the supernatants and pellets have been quantified with error bars and statistical significance indicated. These results have been incorporated into Figure 3c in the revised manuscript.

      *Figure 4b) is missing the pTBK1 data that is referenced in the text. In the text to figure 5 c/d), the authors claim that certain mutants have no significant effect on mitophagy, though d) is missing significance testing *

      *Figure 6 c/d/i) appear to be missing replication. *

      For Figure 4b, phos-TBK1 was immunoblotted (See Fig 4b of the revised manuscript). For Figure 5b and d, statistical significance was determined for the effect of TBK1 mutations on autophosphorylation and OPTN phosphorylation and the effect of the TBK1 mutants on Parkin-mediated mitophagy. For Figure 6 c/d/I, the experiment was repeated; error bars and statistical significance have been added to the associated graphs.

      *Reviewer #1 (Significance (Required)): Removal of damaged mitochondria by the mitophagy pathway provides an important safeguarding mechanism for cells. The Pink1/Parkin mechanism linked to numerous modulators and adaptor proteins ensures an efficient targeting of damaged mitochondria to the phagophore. The Ser/Thr kinase TBK1, in addition of multiple roles in innate immunity, is a major mitophagy regulator as has been revealed by the Dikic and Youle groups in 2016 (Richter et al., PNAS). The mechanistic insights provided by this manuscript add to a growing body of studies of how the autophagy machinery interconnects with cellular signalling networks. Although parts of the results need to be further validated, the data shown is of high quality, revealing an important conceptual advance. The paper is interesting and of general relevance beyond the signalling and autophagy community. *

      We would like to thank Reviewer 1 for the comments and suggestions, many of which improved our manuscript. We hope that the reviewer’s comments have been adequately addressed in the revised manuscript.

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary In this manuscript, Yamano and colleagues show that as for Sting-mediated TBK1 activation, Optn provides a platform for TBK1 activation by autophosphorylation and that TBK1 is activated after the interaction of Optn with the autophagy machinery and ubiquitin and not before. They show that TBK1 phosphorylation is blocked by bafilomycine A1, an inhibitor of vacuolar ATPases that blocks the late phase of autophagy. Furthermore, they demonstrate that Optn is require for TBK1 phosphorylation since variation of Optn expression regulates TBK1 phosphorylation in response to PINK/Parkin-mediated autophagy. Interestingly, using immunofluorescence microscopy, they show that Optn forms sphere like structures at the surface of damage mitochondria which are more dispersed in the absence of TBK1. In addition, TBK1 is also recruited at the surface of damage mitochondria and as Optn and NDP52 (but not p62) colocalize with LC3B in response to PINK/Parkin-mediated mitophagy. Next, it is demonstrated that the Leucin zipper and LIR domains of Optn (which modulate Optn interaction with autophagosome) play an important role for TBK1 activation. Additionally, the autophagy core is shown to be required for TBK1 activation. Under basal conditions, depletion of the autophagosome machinery leads to an increase in autophagy receptors (except Optn) and TBK1 phosphorylation which colocalize with ubiquitin in insoluble moieties. In contrast, Optn remains cytosolic and is dispensable for TBK1 activation in these conditions. Then, using the fluoppi technic, the authors demonstrate that the generation of Optn-Ubiquitin condensates recruits and activates TBK1. They express in HCT116 TBK1-deficient cells engineered or pathological ALS mutations of TBK1 that affect ubiquitin interaction, structure, dimerization and kinase activity of TBK1. The expression level of TBK1 was only affected by the dimerization-deficient mutations. None of the mutations impaired Optn and TBK1 ubiquitination. Interestingly, some ALS-associated mutations affect TBK1 activity and it is said in the text that the dimerization-deficient mutations of TBK1 affect its activity proportionally to their level of expression, which is not really correct (the expression level of the mutants is very heterogenous and not always correlate to their activity). Regarding their effect on mitophagy, the authors claim that the phosphorylation of TBK1 correlate with mitophagy which is not really the case. By using TBK1 inhibitor or TBK1-depleted cells, the authors conclude that TBK1 is the only kinase phosphorylating Optn. However, BX-795 is not completely specific to TBK1. Finally, the authors use monobodies against Optn effective in inhibiting mitophagy in NDP52 KO cells. Some of the monobodies have been shown to form a ternary complex with Optn and TBK1, while others compete for the interaction between Optn and TBK1 which involves the amino-terminal region of Optn and the C-terminal region of TBK1. Monobodies that compete for the interaction of Optn with TBK1 could alter the cellular distribution of Optn and inactivate TBK1, but they do not alter the ubiquitination of Optn. Finally, these monobodies inhibit 50% of mitophagy. *

      *Major and minor points: Introduction The first paragraph of the Introduction section is confused and difficult to read. First and second paragraphs (page 3 and top of page 4) are dedicated to macroautophagy processes but ended with one sentence on Parkin-mediated autophagy without further introduction, while all processes regarding mitophagy are detailed in the next paragraph. Links between ideas developed are also somewhat missing. For example, in page 6, the three last sequences detailed the phosphorylation of autophagosome component, the fact that Optn and TBK1 genes are involved in neurodegenerative diseases and autophosphorylation of TBK1 as a pre-requirement for TBK1 activation without evident links between them, except "interestingly". *

      In response to the reviewer’s suggestion, we have rewritten the Introduction. The first paragraph focused on introducing the molecular mechanism underlying macroautophagy and the second paragraph focused on Parkin-mediated mitophagy. As the reviewer indicated, the ALS mutations and TBK1 phosphorylation during Parkin-mediated mitophagy are not well related, so we moved the background material on the relationship between OPTN and TBK1 in neurodegenerative diseases to the beginning of the section describing Figure 5. We believe these changes have made the Introduction easier to read and understand.

      *Results *

      *Major points: *

      *1- Results are often over-interpreted regarding data obtained leading to inadequate conclusions (see below for details); *

      We regret the reviewer’s concerns regarding over-interpretation. To address this issue, we have carefully considered the data, performed additional experiments where necessary, and rewritten the results accordingly. Please see our point-by-point responses below.

      *2- Quantification of protein levels detected by western blot are provided as "relative intensities" without referring to specific loading control or to total protein when -phosphorylated forms are quantified (Fig. 1b, 1d, 1f, 1i, 2b, 2f, 2i, 5b, 7b, supplemental figures 2b). *

      For the immunoblots, we loaded the same amount of total cell lysate and the phosphorylated forms were quantified relative to the total protein input. This has been mentioned in the Materials and Methods.

      *3- In western blotting experiments, authors described slower migrating bands as "ubiquitinated" forms of detected proteins, but never provided experimental evidences that it could be the case. Use of non-specific deubiquitinase incubation of extracts prior to western blot could help to correctly identified ubiquitination versus other post-translational modifications such as phosphorylation, glycosylation, acetylation etc... *

      We appreciate the reviewer’s suggestion. The cell lysates after mitophagy induction were incubated in vitro with a recombinant USP2 core domain (non-specific DUB), and then immunoblotted. As shown in supplemental Fig 1 of the revised manuscript, the slower migrating OPTN bands disappeared in a USP2-dependent manner. The slower migrating NDP52 and TOMM20 bands likewise disappeared. These results confirm that the slower migrating OPTN, NDP52, and TOMM20 bands are ubiquitinated.

      *4- Conclusions from data obtained by immunofluorescent imaging are often drawn from only one image presented without further statistical analysis. *

      Statistical significance was determined for the immunofluorescent data (original figures 1j, 2c and 3a). Please see Fig 1l, 2f, 2g, and 3a in the revised manuscript.

      *Page 7: - authors referred to TBK1 phosphorylation induced by mitophagy induction as "TBK1 phosphorylation induced by Parkin-mediated ubiquitination" while mitophagy can be induced independently of Parkin (ex: via mitochondrial receptors) and without any evidence (according to referee's knowledge) of a link between ubiquitination by Parkin and TBK1 phosphorylation. *

      As the reviewer indicated, Parkin-independent and ubiquitination-independent mitophagy pathways are also known (i.e. receptor-mediated mitophagy driven by NIX, BNIP3, BCL2L13, FKBP8, FUNDC1, or Atg32). Therefore, references to "mitophagy" in our manuscript were reworded as "Parkin-mediated mitophagy". Since TBK1 phosphorylation is observed before mitochondria are degraded and is dependent on Parkin-mediated ubiquitin (for example, see Fig 1c), we use the phrase "TBK1 phosphorylation triggered by Parkin-mediated OMM ubiquitination".

      *Fig 1g: Western blots performed in Penta KO cells without exogene expression of any autophagy receptors should be provided as control. Furthermore, lower expression of NDP52 relative to that of Optn (using flag antibodies) should be discussed as it can explained the differential levels in TBK1 phosphorylation observed. *

      As suggested, we repeated the experiment using Penta KO cells in the absence of exogeneous autophagy adaptor expression. Furthermore, we expressed different amounts of NDP52 and OPTN (indicated as low and high in the figure) in Penta KO cells to rule out the possibility that higher TBK1 phosphorylation is induced by simple overexpression of autophagy adaptor (please see Fig 1g and h in the revised manuscript). At high NDP52 expression (2.5-3.0-fold higher than endogenous NDP52), phosphorylated TBK1 was reduced to ~30% the level of that observed in WT cells after 3 hrs with val and baf. In contrast, Penta KO cells with higher OPTN expression (3.0-fold higher than endogenous OPTN) had phosphorylated TBK1 signals that were 2-fold higher than those in WT cells. Based on these results, we concluded that OPTN is an important adaptor for TBK1 activation during Parkin-mediated mitophagy.

      *Page 8: Supplemental Fig 1a: - The inability of authors to observe TBK1 endogenous signal in HeLa cells using commercially available antibodies is surprising as many publications reported successful staining (see Figure 1 of Suzuki et al. 2013 Cell type-specific subcellular localization of phospho-TBK1 in response to cytoplasmic viral DNA. PLoS One. 8:e83639 among others) as well as commercial promotion (see Anti-NAK/TBK1 antibody from Abcam reference: ab235253). *

      For the original manuscript, anti-TBK1 antibodies purchased from abcam (ab235253), CST (#3013S), Proteintech (28397-1-AP), and GeneTex (GTX12116) for immunostaining were unable to yield TBK1-positive signals (please see Fig 1 for reviewers below). WT and TBK1-/- HCT116 cells stably expressing Parkin were treated with valinomycin for 1 hr and immunostained with the indicated antibodies. Anti-phos-TBK1 antibody (CST, #5483) was used as a positive control. Based on these results, we stated in the original manuscript that the "endogenous TBK1 signal could not be observed using commercially available antibodies". At the reviewer’s suggestion, we purchased anti-TBK1 antibodies from abcam (ab40676) and CST (#38066). As shown in the figure below, the immunofluorescent signals generated by these antibodies were detected in WT, but not in TBK1-/- cells. The CST (#38066) antibody yielded a stronger signal, most of which was on damaged mitochondria. Thanks to this suggestion, we repeated the experiment using the new anti-TBK1 antibody. Furthermore, based on a suggestion from Reviewer 3, we detected mitochondrial recruitment of TBK1 during mitophagy stimulation (valinomycin for 30 min or 2 hrs in the presence and absence of bafilomycin; supplemental Fig 2 in the revised manuscript). We also detected association of endogenous TBK1 with ubiquitin-positive condensates in WT, FIP200KO, and ATG9A KO cells (Fig 3a and supplementary Fig 7a in the revised manuscript).

      *- Conclusions of the localization of signal on mitochondria (dispersed, in the periphery or at contact sites) are clearly over-interpreted in the absence of other membrane or autophagosome specific labeling and statistical colocalization analyses of multiple images. It is particularly difficult to assess any difference between Tax1BP1, p62 and NBR1 localization on mitochondria subdomains. *

      We previously expressed each FLAG-tagged autophagy adaptor in Penta KO cells and observed their localization during Parkin-mediated mitophagy and found that exogenous FLAG-tagged OPTN and NDP52, but not p62, colocalized with LC3B (Yamano et al 2020 JCB). No one has assessed and compared the localization of all five endogenous autophagy adaptors. Although we still believe that the results (supplemental Fig1 in the original manuscript) are informative for researchers in the autophagy field, we decided to remove that data from the revised manuscript since they are not the main focus of the study. We will consider publishing those data elsewhere in the future after co-staining with autophagosome markers and assessing the statistical significance of colocalization as the reviewer suggested.

      *Page 9: *

      *- First part of results ended without any conclusions. *

      As detailed in the previous response, we have removed results for mitophagic recruitment of autophagy adaptors (supplementary Figure 1 in the original manuscript).

      *- The observation that "TBK1 phosphorylation was not apparent in the Optn mutant cell lines, even after 3 hrs of valinomycin, ..." is inconsistent with detection of bands with anti-pS172-TBK1 antibodies in Fig 2a detected at 1hr (with F178A) and 3 hrs (4LA, F178A, and 4LA/F178A mutants) of treatment. *

      We apologize for the confusion. This statement was clearly our mistake. We had intended to state when "all autophagy adaptors are deleted" no phosphorylated TBK1 was observed. We have rewritten this part as "TBK1 phosphorylation was not apparent in the Penta KO cells even after 3 hrs with valinomycin".

      *- Similarly, decreased levels of phosphorylated TBK1 stated for F178A mutant was only observed at 1 but not 3hrs or at 3hrs in the presence of bafilomycin. *

      Based on the mitophagy assay previously reported (Yamano et al 2020 JCB), the F178A mutant only moderately inhibited mitophagy (60% mitophagy with the F178A mutant vs 80% mitophagy with OPTN WT). Conversely, the 4LA mutant and 4LA/F178A double mutant had stronger inhibitory effects on mitophagy (35% for 4LA and 9% mitophagy for 4LA/F178A). Therefore, the levels of phos-TBK1 after 1 hr with valinomycin or 3 hrs with valinomycin in the presence of bafilomycin are consistent with mitophagy progression. When mitophagy proceeds efficiently, the amount of phos-TBK1 in the 1 hr val samples is reduced relative to the 3 hr val samples due to autophagic degradation.

      To more clearly observe and compare the levels of mitophagy-dependent phos-TBK1 among Penta KO cells expressing OPTN WT and the mutants, we treated cells with valinomycin in the presence of bafilomycin for 0, 0.5, 1, and 2 hrs and quantified phos-TBK1. The results are shown in Fig 2c and d in the revised manuscript. The phos-TBK1 signal increased over time with val and baf treatment in all OPTN expressing cells. Cells with OPTN WT generated the most phos-TBK1, whereas the signal generated by the F178A mutant was 75% that of the OPTN WT-expressing cells and the 4LA and 4LA/F178A mutants were about 40%. The experiments were independently replicated three times and error bars and statistical significance were incorporated into the associated graph. These results indicate that OPTN association with the autophagy machinery, in particular ATG9A vesicles, is important for TBK1 activation.

      *Page 10: *

      *The results and their repartition between figure 2 d, e, f, g, h, I and figure 3 is a bit confusing. In these experiments, it is shown Figure 2 that the absence or depletion of the autophagy machinery increase the phosphorylation of TBK1 and in Figure 3 it is shown that not only the phosphorylation of TBK1 accumulate but also the expression of NDP52, Tax1BP1 and p62. Is it because their degradation by autophagy is blocked (like for phosphoTBK1)? *

      The reviewer is correct that autophagy adaptors other than OPTN (especially TAX1BP1, p62 and NBR1) are constantly degraded by macro/micro autophagy (Mejlvang et al. 2018 J Cell Biol and Yamano et al. 2021 BBA Gen Subj). Therefore, these adaptors accumulate in autophagy deficient cell lines (original Fig 3). In this study, we found that in the absence of mitophagy stimulation phos-TBK1 accumulates in autophagy deficient cell lines. This suggests that the accumulated autophagy adaptors induce TBK1 phosphorylation under basal conditions. In the original manuscript, we claimed that TBK1 phosphorylation under basal conditions does not require OPTN since in FIP200 KO and ATG9A KO cells it did not accumulate and did not primarily colocalize with ubiquitin- and TBK1-positive foci (original Fig 3). To gain more direct evidence for the revised manuscript, we performed additional experiments and discovered that TAX1BP1 is the adaptor responsible for TBK1 autophosphorylation under basal autophagy. We treated FIP200KO and ATG9A KO cells with siRNAs against OPTN, NDP52, TAX1BP, p62, and NBR1, and immunoblotted total cell lysates with an anti-phos-TBK antibody. As shown in Fig 3f in the revised manuscript, TAX1BP1 siRNA treatment decreased phos-TBK1 levels without affecting total TBK1. This result indicates that the accumulation of TAX1BP1 in the FIP200 KO and ATG9A KO cells induced TBK1 autophosphorylation under basal conditions. Considering this result, we treated WT, FIP200 KO, and ATG9A KO cells with TAX1BP1 siRNA, and then induced Parkin-mediated mitophagy with valinomycin in the presence of bafilomycin. This strategy eliminated the basal accumulation of phos-TBK1 and allowed us to focus on mitophagy-dependent TBK1 phosphorylation. Please see revised Fig 2h, I, j, and k. The results showed that mitophagy-dependent phos-TBK1 is predominantly attenuated in FIP200 KO and ATG9A KO cells. In Figs 2 and 3, we would like to emphasize that OPTN is required for TBK1 phosphorylation in response to Parkin-mediated mitophagy, whereas TAX1BP1 is required for TBK1 phosphorylation in basal autophagy. Since Reviewer 3 commented that interpretation of the data in original Figs 2d, e, and f was challenging, we elected to move those results to the supplemental figures. We have incorporated the newly acquired data (mitophagy using FIP200 KO or ATG9A KO with TAX1BP1 siRNA cells) into the main figure. We believe that this makes the text easier for readers to understand.

      *- Fig 2c: conclusions on *

      *the reduction of recruitment of Optn mutants on autophagosome formation seem over-interpreted as: *

      *1- no labeling with LC3 has been used to identified autophagsome, *

      *2- immunofluorescent signals observed with mutants are dispersed throughout the entire mitochondria network (see the merged images) rendering impossible to distinguish between autophagosome-associated mitochondria and others. *

      *The following conclusive sentence stating that association of Optn to damaged mitochondria is not sufficient for TBK1 activation based solely on IF of figure 2c seems therefore unrelated to the obtained data. *

      To address concerns about the recruitment of OPTN mutants to the autophagosome formation site, we performed additional experiments. Penta KO cells and those expressing OPTN WT and mutants were treated with valinomycin for 1 hr, and FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (mitochondrial marker) were detected by immunostaining. We detected endogenous WIPI2 because WIPI2 is recruited only to autophagosome formation sites (Dooley et al. 2014 Mol Cell), whereas LC3B assembles on autophagosome formation sites and is also associated with completed autophagosomes. Confocal microscopy images showed that cup-shaped OPTN WT that had been recruited to damaged mitochondria colocalized with WIPI2. Quantification further showed that during mitophagy the number of WIPI2 foci seen in cells expressing OPTN WT decreased in Penta KO cells and cells expressing OPTN mutants (4LA, F178A and 4LA/F178A). These data are shown in Fig 2e and f in the revised manuscript. In addition, we quantified the number of cells that either exhibited heterogeneous or homogeneous recruitment of OPTN to damaged mitochondria after treatment with valinomycin for 1 hr. More than 80% of Penta KO cells with OPTN WT had heterogeneous OPTN recruitment, whereas this distribution was only present in 10% of cells expressing either OPTN 4LA or OPTN 4LA/F178A. Although cells expressing the OPTN F178A mutant exhibited 50% heterogeneous recruitment, this may be because the mutant can interact with ATG9A. As mentioned above, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB) showed that the OPTN F178A mutant induced ~60% mitochondrial degradation (which is correlated strongly with OPTN distribution), whereas it was 80% with OPTN WT and 9% with 4LA/F178A.

      *- Fig 2d: authors should explain why ATG KO cells displayed lipidated LC3B in the absence of efficient autophagy processes. *

      We thank the reviewer for the suggestion. We added the following sentence to explain the function of ATG5 in LC3B lipidation. "Since LC3B lipidation is catalyzed by ATG5, but not FIP200 and ATG9A, the lipidated form disappears only in ATG5 KO cells (Hanada et al 2007 J Biol Chem). "

      *- Fig 2e: despite authors statement that TBK1 phosphorylation did not increase during mitophagy in ATG KO cells, increased pS172-TBK1 is visible in FIP200 and ATG5 KO cells especially between 1 and 3 hrs of stimulation, leading to inaccurate conclusions that TBK1 phosphorylation requires the autophagy machinery. Therefore, overall assumption that both ubiquitination and autophagy subunits are required for TBK1 autophosphorylation appears erroneous. *

      As the reviewer indicated, phos-TBK1 levels gradually increased in ATG KO cells. The main text was rewritten to more accurately reflect this increase. Based on experiments using the monobodies and those conducted during the revision process, it is apparent that although the autophagy machinery may not be completely essential for TBK1 phosphorylation, it clearly facilitates TBK1 phosphorylation in response to Parkin-mediated mitophagy.

      *Page 12: *

      *- Fig 3a: conclusion that Optn signal is more cytosolic and did not localize with Ub condensates seems speculative as based on: *

      *1- only one immunofluorescence image without statistical analysis *

      *2- Optn and Ub signals are lower in images with Optn is analyzed compared to other images in which NDP52, TAX1BP1 and NBR1 are detected. *

      To address these concerns, we compared and quantified the signal intensities of all endogenous autophagy adaptors in FIP200 KO and ATG9A KO cells. The quantification data are shown in Fig 3a and the immunofluorescence images are shown in supplementary Fig 6a of the revised manuscript.

      *- Fig 3b: interpretation of western blot data is uncertain due to lack of appropriate loading control, especially with pellets (P) extracts. In addition, it is not clear how to conclude from the experiments in Fig 3b that autophagy adaptors other than Optn mediate TBK1 phosphorylation. *

      When autophagy is inhibited, p62 accumulates in the cytosol as aggregates (Komatsu et al. 2007 Cell). Therefore, p62 should be a positive control. Indeed, Fig 3b in the original manuscript (Fig 3b and c in the revised manuscript) showed that the amount of p62 in the pellet fraction was elevated in FIP200 KO and ATG9A KO cells. Furthermore, these aggregates were also observed in the imaging data (Fig 3a and supplementary Fig 7 in the revised manuscript). As the reviewer indicated, the original manuscript did not clarify whether autophagy adaptors other than OPTN mediated TBK1 phosphorylation; however, our revised results clearly demonstrate that TAX1BP1 is the adaptor responsible inducing TBK1 autophosphorylation when basal autophagy is impaired (please see Fig 3f in the revised manuscript).

      *Minor point: reference is missing in the last sentence of the paragraph stating that K48-linked chains dominate when autophagy pathways are impaired. *

      While several autophagy adaptors preferentially interact with K48-linked ubiquitin chains (Donaldson et al. 2003 PNAS etc), TRAF6 is recruited to ubiquitin-condensates via p62-mediated K63-linked ubiquitination (Linares et al. 2013 Mol Cell). Furthermore, K33-linked ubiquitin chains are also present in p62-positive condensates (Nibe et al. 2018 Autophagy). Because it’s not clear which ubiquitin-linkage is dominant in the condensates, we decided to delete the sentence. We regret the confusion.

      *Page 13: *

      *Conversely to Optn, they find that the other autophagic receptors localize in insoluble fractions (what does it mean?) independently of TBK1 expression (experiments with DKO cells) and also independently of Optn (where is this shown?). Altogether, these experiments are far from the message of the manuscript. The title of the paragraph "TBK1 activation does not require Optn under basal autophagy conditions" is not correct because even if the level of expression of autophagic receptors and TBK1 phosphorylation are increase in response to the depletion of the autophagy machinery, it does not increase autophagy. *

      According to the suggestion, we changed the title of the paragraph to "TAX1BP1, but not OPTN, mediates TBK1 phosphorylation when basal autophagy is impaired." In addition, we rewrote this section.

      *- Fig 3d: authors should mention the nature of the upper band observed in Optn western blot and show the same experiment in since solely TBK1 depleted cells since they stated that "electrophoretic migration of Optn was not affected by TBK1 deletion". In addition, suggesting from these sole experiments that "NP52, TAX1BP1, p62, NBR1 and AZI2 form Ub-positive condensates where TBK1 is activated" seems over-interpretated. *

      Reviewer 3 suggested we characterize the upper band in the OPTN blot (Fig 3d in the original manuscript). To determine if the band is genuine OPTN, we used phostag-PAGE to analyze cell lysates from cells treated with control siRNA or OPTN siRNA and found that both the lower and upper bands were OPTN species (please see "Figure 2 for reviewers" in our response to Reviewer 3). The same pattern was reported by the Wade Harper group (Heo et al. 2015 Mol Cell). They showed that the OPTN double band pattern on phos-tag PAGE was not affected by TBK1 deletion. We have cited this literature where appropriate in the revised manuscript. In WT cells, it is difficult to detect phosphorylation of autophagy adaptors by TBK1 because basal autophagy constantly degrades them. That’s why we used autophagy KO cell lines.

      *Page 14: *

      *- Fig 4: TBK1 phosphorylation was analyzed in Fig4d and not in Fig4b as stated. In addition, it is rather difficult to conclude from artificial multimerization experiments, as the authors have done, that interaction between Optn and autophagy components contributes to Optn multimerization in genuine conditions. *

      Detection of phos-TBK1 has been corrected to Fig 4b. Although artificial, the fluoppi assay provides insights into how OPTN activates TBK1 and how the autophagy machinery contributes to TBK1 activation via OPTN. To determine if artificial OPTN multimerization could bypass the autophagy machinery requirement, we used the fluoppi assay. This assay was important for us to conclude that the autophagy machinery and Parkin-mediated ubiquitination allow OPTN to be assembled in close proximity to where TBK1 is activated. The main text was rewritten to better convey the benefits of the fluoppi assay.

      *Page 15: *

      *This work could have therapeutic consequences but the pathological mutants of TBK1 used affect ALS (Figure 5) while in the discussion it is proposed that monobodies could have a therapeutic interest in familial forms of glaucoma due to the E50K mutation of Optn. It should be better to target only one pathology. *

      Both TBK1 and OPTN are causative genes for ALS and many pathogenic mutations are known to impact their function. In this study, we focused on ALS mutations in TBK1 that affect self-dimerization and investigated their impact in response to Parkin-mediated mitophagy. We created the monobodies as a tool to physically inhibit OPTN assembly at the contact site. Although our monobodies inhibit Parkin-mediated mitophagy, they would not be a useful therapeutic strategy for ALS due to the loss of function with the TBK1 mutations. However, because TBK1 E50K is a glaucomatous mutation that causes OPTN-TBK1 to bind more tightly, our monobodies might be applicable to glaucomatous pathology since they could disrupt this interaction. We thus feel that it is appropriate to mention the potential of the monobodies and their future utility in the Discussion.

      *- Fig 5c, d: Authors stated that degree of TBK1 autophosphorylation correlated with OPTN phosphorylation at S177 whereas phosphorylated TBK1 is unaffected by L693Q and V700Q mutants that display decreased phosphorylated Optn In addition, authors interpretation of Figure 5 data is clearly problematic as they stated that: *

      *1- neither 693Q and V700Q mutants had "significant effect on mitophagy", while decreasing efficiency from 78% to 37-51% *

      *2- but conclude that 49.7% mitophagy levels of R357Q mutant is significant mitochondrial degradation. *

      *Overall conclusion that mitophagy efficiency is correlated with phosphorylated TBK1 levels is therefore inaccurate. *

      We regret that this section did not sufficiently describe the data. Reviewer 3 also noted that the text referencing Fig 5 was difficult to interpret. One of the reasons for the complicated data interpretation is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit Parkin-mediated mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutations only moderately affected Parkin-mediated mitophagy. Furthermore, both the L693Q and V700Q mutations were engineered based on the X-ray structure, rather than being authentic pathogenic ALS mutations. To avoid any potential confusion, we decided to remove the L693Q and V700A data. We have re-evaluated the other data and have rewritten this section accordingly. Please see the revised main text.

      *Discussion *

      *Minor points: *

      *page 20: - reference is missing in the sentence "Optn cannot oligomerize on its own on ubiquitin-decorated mitochondria". *

      We have provided the appropriate reference.

      *Major points: *

      *Authors stated that they showed that Optn recruitment to damaged mitochondria, itself, is insufficient for TBK1 autophosphorylation, but did not show experiment of Optn recruitment to mitochondria and its consequences on TBK1 phosphorylation in the absence of mitophagy induction signal. Authors could for example target HA-Ash-6Ub to mitochondria in order to artificially recruit hAG-Optn to "ubiquitinated" mitochondria in the absence of mitophagy signal. *

      We showed that the efficiency of TBK1 autophosphorylation was reduced in cells expressing the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (Fig 2c and d in the revised manuscript). Cells with FIP200 or ATG9A knockdown also have reduced phos-TBK1 (pS172) as shown in supplementary Fig 5e and f. The rate of phos-TBK1 (pS172) generation in ATG9AKO cells during Parkin-mediated mitophagy is reduced relative to that in WT cells (Fig 2j and k). Since a small amount of phos-TBK1 was generated in both ATG9A knockdown and KO cells (supplementary Fig 5e, f, Fig 2j and k), we concur that it would be premature to conclude that phosphorylation of TBK1 does not occur at all when autophagy core components are absent. A small amount of phos-TBK1 may be generated by OPTN that is freely distributed on the outer mitochondrial membrane. In the revised manuscript, we mention the possibility that TBK1 might be phosphorylated by OPTN independent of the autophagy machinery and were careful to avoid over-interpretation.

      As shown in Fig 4, fusing OPTN with an Azami-Green tag can induce artificial multimerization and trigger the generation of phos-TBK1 (pS172). Therefore, we expect that mitochondria-targeted HA-Ash-6Ub would induce TBK1 phosphorylation in a hAG-OPTN-dependent manner as was observed with cytosolic HA-Ash-6Ub (Fig 4). The accumulation of OPTN at the contact site in Parkin-mediated mitophagy is important for TBK1 phosphorylation. Even if OPTN is forced to anchor to the mitochondria, this would induce isolation membrane formation and subsequent autophosphorylation of TBK1. Therefore, the utility of forcing OPTN to anchor to mitochondria is questionable.

      *Similarly, experimental approaches used by authors lack dynamics parameters to conclude on formation and elongation of isolation membranes and contacts sites that could be probably obtained through video microscopy. *

      Based on the reviewer’s comment, we performed time-lapse microscopy to observe OPTN recruitment to the contact site and followed its movement along with the elongation of isolation membranes during Parkin-mediated mitophagy. HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin (please see Fig 2l in the revised manuscript). Initial recruitment of GFP-OPTN near mitochondria was evident as small dot-like structures that then elongated over time to become cup-shaped structures and culminated in the formation of spherical structures. Considering the colocalization of OPTN with WIPI1/WIPI2 (markers of autophagosome formation site) in Fig 2e and supplementary Fig 2a, the time-lapse images strongly suggest that OPTN assembles at contact sites followed by elongation in tandem with isolation membranes during Parkin-mediated mitophagy.

      *Finally, the model proposed by the authors does not take into account data showing that Optn basally interacts with ubiquitinated mitochondria and LC3 family members (see Wild et al., Phosphorylation of the autophagy receptor optineurin restricts Salmonella growth. Science. 2011 333:228-33), although at lower levels compared to induced conditions, relativizing the impact of the proposed model. *

      According to the Reviewer 2 comment, we again read the Science paper (Wild et al. 2011) but could not find data showing that OPTN basally interacts with ubiquitinated mitochondria. At least, we think that under steady state conditions without mitophagy induction, mitochondrial ubiquitination and mitochondrial localization of OPTN are undetectable as shown in supplementary Figure 2 in our revised manuscript.

      *In conclusion, this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. *

      ***Referees cross-commenting** *

      *In my opinion, what emerges from these 3 reviews is that the results lack controls or have not been repeated enough to support the message that the interaction of Optn with ubiquitin and the ubiquitination machinery is sufficient to activate TBK1. In particular, as reviewer 1 says, the phosphorylation kinetics shown in Figure 1a are not consistent with TBK1 phosphorylation following the interaction of Optn with the ubiquitination machinery and ubiquitin. In Figure 1e, there is a decrease in TBK1 phosphorylation in contrast to WTcells as mentioned by Reviewer 1. In agreement with Reviewer 1, we believe that the WT cells are missing in Figure 1g. *

      *With regard to Figure 2c, we agree with reviewer 1 that an LC3 label is missing in order to be able to interpret the data. In Figure 2e and f, we agree with reviewer 1 that it is difficult to understand why TBK1 phosphorylation increases in the absence of the autophagy machinery (FIP200 KO and ATG5KO). In Figure 3, loading controls are missing for 3b and c. The TBK1 KO cells alone are missing in Fig 2d. In Figure 2b, pTBK1 is missing. In agreement with reviewer 3, we believe that the data with fluoppi contradict the message of the manuscript since they show that TBK1 can be phosphorylated by ubiquitin in the absence of the ubiquitination machinery. In agreement with reviewer 3, we believe that the experiments in Figure 5 are very difficult to interpret. The first reviewer is right to ask the question of the replicates for figures 6c and d. *

      We appreciate the summary of the reviewers’ comments. To address their concerns, we have included the appropriate controls and included the results of three independent experiments in the graphs, which now include appropriate error bars and statistical significance. Thus, we believe we have answered the most critical comments concerning the lack of controls.

      In Fig 1a, phos-TBK1 was maximal following 30 min of valinomycin treatment. We confirmed using microscopy-based observations that recruitment of endogenous TBK1 and OPTN and the generation of phos-TBK1 and phos-OPTN at contact sites (marked by WIPI1) near damaged mitochondria was also maximal after 30 min of valinomycin treatment (supplementary Fig 2 and 3). Therefore, the kinetics of phos-TBK1 and phos-OPTN generation are consistent with the recruitment of OPTN-TBK1 to the contact site.

      The data presented in Fig 2 clearly indicate that the autophagy components are involved in phos-TBK1 generation during Parkin-mediated mitophagy. Therefore, the claim that ubiquitination machinery is sufficient for TBK1 activation is incorrect. Although we agree that the autophagy gene deletions cannot completely inhibit TBK1 autophosphorylation, mitophagy-dependent generation of phos-TBK1 is largely impaired by ATG9A KO (Fig 2j and k). Thus, there is no doubt that isolation membrane formation is important for TBK1 activation following Parkin-mediated mitophagy.

      Fig 1e - The reviewer is correct that phos-TBK1 is reduced in the NDP52 knockout. We have rewritten the main text. It is also true that NDP52 has a smaller effect on TBK1 autophosphorylation as compared to OPTN.

      Fig 1g - Immunoblots using total cell lysates prepared from six different cell lines (WT, Penta KO alone, Penta KO stably expressing low or high OPTN or NDP52) under four different conditions (DMSO, valinomycin 1 hr, valinomycin 3 hrs, valinomycin + bafilomycin 3 hrs) showed that OPTN is a rate-limiting factor for TBK1 phosphorylation. Please see Fig 1g and h in the revised manuscript

      Fig 2c - The recruitment of OPTN WT and associated mutants to the contact site was re-examined by immunostaining with WIPI2 labeling. We found that OPTN WT was both efficiently recruited to and formed the contact site. In contrast, the OPTN 4LA/F178A mutant was unable to interact with FIP200/LC3/ATG9A and was uniformly (i.e. homogenously) distributed on damaged mitochondria with the rate of autophagosome site formation reduced. Please see Fig 2e, f, g in the revised manuscript.

      Fig 2e and f - KO of the autophagy core components FIP200 and ATG9A increased phos-TBK1 under basal, non-mitophagy-associated conditions (see Fig 3). The levels of autophagy adaptors other than OPTN also increased in FIP200 KO and ATG9A KO cells. Furthermore, as shown in Fig 3a and supplementary Fig 7, both phos-TBK1 and the autophagy adaptors accumulated in Ub-positive condensates. Based on previous reports (Mejlvang 2018 J Cell Biol), TAX1BP1, p62, and NBR1 have short half-lives and are quicky degraded by macro/micro autophagy. The accumulation of phos-TBK1 in the absence of autophagy occurs because autophagy-dependent degradation of TAX1BP1 (and other adaptors) is inhibited. This allows for the formation of Ub-positive condensates, which brings TBK1 into sufficient proximity for activation. This has been noted in the revised manuscript.

      Fig 3b and 3c - We wonder if the "loading controls are missing for Fig 3b and 3c" statement might be a misinterpretation by the reviewer as TOMM20 was used as the loading control in the original Fig 3b. It was recovered in the supernatant fractions of WT, FIP200 KO, and ATG9A KO cells, indicating that the accumulation of autophagy adaptors in the pellet fractions depends on autophagy gene deletion. Similarly, actin and TOMM20 were used as loading controls in the original manuscript Fig 3c.

      Fig 2d (perhaps meant to be Fig 3d) – A previous study reported that phos-tag PAGE blot of OPTN in TBK1 KO cells alone revealed no differences between WT and TBK1 KO cells (Heo et al 2015 Mol Cell). We cited this reference in the revised manuscript.

      Fig 2b (perhaps meant to be Fig 4b) - Immunoblots of phos-TBK1 have been incorporated into the results of Fig 4b in the revised manuscript.

      Fig 4 - We show in Fig 2 that induction of Parkin-mediated mitophagy promotes OPTN accumulation at contact sites formed by isolation membranes and ubiquitinated mitochondria, and that autophagy core subunits are required for efficient generation of phos-TBK1. Fig 3 shows that phos-TBK1 accumulates in Ub-positive condensates with TAX1BP1, rather than OPTN, and that it is responsible for phos-TBK1 accumulation. Together, these results suggest a model in which TBK1 is activated when OPTN and TBK1 are positioned near each other. We hypothesized that if we could force OPTNs into close proximity the autophagy machinery requirement for TBK1 activation might be bypassed. To assess this model, we designed the fluoppi assay shown in Fig 4. This assay was critical in that it provided an important clue for the molecular mechanism that OPTN and the autophagy machinery use to cooperatively induce TBK1 trans-autophosphorylation. Because the original manuscript may not have sufficiently conveyed our reasoning for the fluoppi analysis, we have rewritten this section. The main point of the fluoppi assay is that engineered OPTN multimerization was able to bypass the autophagy requirement for TBK1 activation.

      Fig 5 - For easier interpretation, the L693Q and V700Q data, which are not related to ALS pathology, have been removed.

      Fig 5d – Statistical significance has been determined for the mitophagy results and the main text has been rewritten for better clarity.

      Fig 6c, d, and I – The experiments were independently replicated more than three times with statistical support and error bars incorporated into the associated graphs.

      *Reviewer #2 (Significance (Required)): *

      *this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. The manuscript is for a broad audience. *

      For the revised manuscript, additional experiments were carefully performed with appropriate controls and the manuscript was rewritten to address concerns regarding over-interpretation. We hope that we have adequately addressed the reviewer’s comments.

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

      *The authors investigated the mechanisms by which TBK1 is phosphorylated and thus activated in PINK1/Parkin-mediated mitophagy. They show data indicating that OPTN, by interacting both with ubiquitin-coated mitochondria and with the autophagy machinery, provides a platform where OPTN-bound TBK1 can be hetero-autophosphorylated by adjacent TBK1. *

      *According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *

      *This is an important topic. Overall, the experimental data are of high scientific quality. For the most part, the manuscript is clearly written. The figures have been made with great care. The novel insights are relevant. However, a number of issues need to be addressed or clarified. *

      *Major comments: *

      • Fig. 1a-b shows that pTBK1 (pS172) formation already peaks after 30 min of valinomycin. Even when bafilomycin is added, pTBK1 level already reaches a near maximum after 30 min of valinomycin. If the model proposed by the authors is correct and pTBK1 (pS172) formation requires extensive interaction of OPTN with both ubiquitin and autophagic isolation membranes, they should be able to show (by immunostaining) that OPTN already extensively forms peri-mitochondrial cup/sphere-shaped structures that colocalize with isolation membrane markers after only 30 min of valinomycin. In the present manuscript, they only show formation of such structures after 1-3 h of valinomycin.* We thank the reviewer for the critical comments. Based on the suggestion, we performed immunostaining to observe the recruitment of TBK1 and OPTN to damaged mitochondria as well as the generation of phos-TBK1 (pS172) and phos-OPTN (pS177). HeLa cells stably expressing Parkin and 3HA-WIPI1 were treated with valinomycin for 30 min, and then TBK1, OPTN, phos-TBK1, and phos-OPTN were immunostained along with 3HA-WIPI1 (a marker of the autophagosome formation site) and TOMM20 (a mitochondria marker). Please see supplementary Fig 2a and 3a in the revised manuscript. The TBK1, OPTN, phos-TBK1, and phos-OPTN signals formed dot-like, cup-shaped, and/or spherical structures, most of which were peri-mitochondrial and colocalized with 3HA-WIPI1. In separate experiments, HeLa cells stably expressing Parkin were treated with valinomycin in the presence or absence of bafilomycin for 30 min or 2 hrs and then immunostained. Please see supplementary Fig 2b in the revised manuscript. After 30 min valinomycin in the absence of bafilomycin, many TBK1 and OPTN signals were observed on damaged mitochondria. These signals were quantified from more than 160 cells for each of the four conditions. Each microscopic image generated contained 18-36 cells and corresponds to one dot in supplementary Fig 2c. Based on these results, the abundance of TBK1 and OPTN on mitochondria after 30 min of valinomycin was much higher than that after 2 hrs with valinomycin (supplementary Fig 2c). Similar results were obtained for phos-TBK1 and phos-OPTN (supplementary Fig 3b and c). These results are consistent with the immunoblot data (Fig1a and b).

      Furthermore, we show that Parkin expression levels affect the amount of phos-TBK1 generated during mitophagy. Please see supplementary Fig 4 in the revised manuscript. When PARKIN was integrated into HeLa cells under a CMV promoter via an AAVS1 (Adeno-associated virus integration site 1)-locus, the resultant cell line (referred to as high-Parkin) had higher Parkin levels than HeLa cells in which PARKIN was introduced by retrovirus infection (referred to as low-Parkin). In high-Parkin HeLa cells, phos-TBK1 levels reached a maximum after 30 min with valinomycin, while in low-Parkin HeLa cells, phos-TBK1 levels were comparable after 30 min and 1 hr. High-Parkin HeLa was used for Fig 1a, b, c, and d as well as supplementary Fig 1, 2, 3 and 4. For all other Figs, PARKIN genes were introduced by retrovirus infection. This is one of the reasons why val was used for 30 min in Fig1, but 1-3 hrs for the other Figs. Because 3 hrs valinomycin treatment may be unsuitable for evaluating OPTN recruitment to mitochondria/isolation membrane contact sites, we deleted the original Fig 2c and replaced it with the val 1 hr data (Please see Fig 2e in the revised manuscript).

      • The authors propose that OPTN needs to interact both with ubiquitin on mitochondria and with isolation membrane proteins such as ATG9A to allow TBK1 phosphorylation. However, their fluoppi experiments in Fig. 4 seem to contradict this. In the fluoppi experiments, the authors generate multimeric OPTN-Ub foci and this is apparently sufficient to induced TBK1 phosphorylation at S172 (shown in 4d,f). In this experiment, there is no induction of autophagy or formation of isolation membranes, and TBK1 nevertheless gets activated.*

      Figure 2 demonstrates that both ubiquitin on mitochondria and formation of the isolation membranes are needed to provide a platform for OPTN to assemble in close proximity to each other and subsequently induce TBK1 autophosphorylation. To determine if OPTN proximity is sufficient for TBK1 autophosphorylation (i.e., if engineered OPTN multimerization can bypass the autophagy machinery requirement for TBK1 autophosphorylation), we used the fluoppi assay. The results clearly showed that engineered OPTN multimerization induced TBK1 autophosphorylation without the need for the autophagy machinery. Although this is not a mitophagy experiment, the fluoppi assay provided crucial insights into the molecular mechanism underlying OPTN-mediated TBK1 autophosphorylation. The main text was rewritten to provide more clarity regarding the purpose of the fluoppi experiments.

      • Can the authors be more concrete/specific in the discussion about the molecular mechanisms that explain why this 'platform' that is created by OPTN-autophagy machinery interactions is so crucial for TBK1 activation? If I understand the model in Fig. 7D correctly, the OPTN-autophagy machinery interactions are mainly important because they reduce the distance between OPTN-bound TBK1 molecules so that they can trans-phosphorylate each other. But if TBK1 autophosphorylation was just a matter of proximity between OPTN-bound TBK1 molecules, interaction of OPTN with densely ubiquitinated mitochondria should already be sufficient for TBK1 phosphorylation. When multiple OPTN molecule bind to one ubiquitin chain or to closely adjacent ubiquitin chains (similar to the fluoppi experiments), TBK1 molecules binding to OPTN would be expected to be already closely enough to one another for trans-autophosphorylation.*

      The amount of phos-TBK1 during Parkin-mediated mitophagy was reduced in cells with the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (e.g. FIP200, ATG9A, and LC3) but can be targeted to mitochondria (see Fig 2c, d). ATG9AKO cells also had reduced amounts of phos-TBK1 relative to WT cells (See Fig 2j, k). Therefore, rather than OPTN-ubiquitin freely diffusing laterally on the outer membrane, we suggest that the contact site OPTN forms with ubiquitin and the autophagy machinery provides a more suitable platform for TBK1 autophosphorylation because it maintains TBK1 in a proximal position for a longer period of time.

      The OPTN UBAN domain binds a ubiquitin-chain composed of two ubiquitin molecules (Oikawa et al. 2016 Nat Comm), and during Parkin-mediated mitophagy only shorter length poly-ubiquitin chains are generated on the mitochondrial surface (Swatek et al. 2019 Nature). Based on those findings, it is unlikely that multiple OPTN bind to one ubiquitin chain. Of course, we cannot rule out the possibility that TBK1 autophosphorylation does not occur on mitochondria in the absence of autophagy components. While full activation of TBK1 requires OPTN to associate with the isolation membrane, initial TBK autophosphorylation at the onset of mitophagy may occur based only on the OPTN-ubiquitin interaction. These explanations have been added to the Discussion in the revised manuscript.

      Furthermore, based on comments from Reviewer 2, we performed time-lapse microscopy to observe OPTN dynamics during Parkin-mediated mitophagy (please see Fig 2l). HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin. GFP-OPTN was initially a peri- mitochondrial dot-like structure that elongated over time to a cup-shaped structure and which eventually ended up forming a spherical structure. The time-laps imaging showed that, at least in WT cells, OPTN is directly recruited to the contact sites and elongates along with the isolation membranes. We thus concluded that TBK1 is activated (autophosphorylated) at the contact site rather than on the outer membrane where OPTN-TBK can move freely.

      • Fig. 5c,d and P. 16: the mitophagy experiments in TBK1-/- cells expressing the different mutant forms of TBK1 are hard to interpret because it is not clear which mitophagy differences are statistically significant. The main text about this part (p. 16) is also confusing.*

      We regret the confusion. Reviewer 2 also noted that the main text for Fig 5 was difficult to interpret. One of the reasons that complicated interpretation of the data is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutants only moderately affected Parkin-mediated mitophagy. Furthermore, both L693Q and V700Q were engineered based on the X-ray structure and are not ALS pathogenic mutations. To simplify the data and to make data interpretation easier, we decided to delete the L693Q and V700A data. We also determined statistical significance and rewrote this section.

      • Many graphs lack statistics: Fig. 2b (pTBK1), Fig. 2f, Fig. 5b, Fig. 5d, Fig. 6c.*

      We apologize for the lack of statistical analyses. We repeated experiments (if the experiments had not been independently performed more than three times) with statistical significance and error bars incorporated into the relevant figures.

      *Other comments: *

      • Fig. 1a: how do they know that the upper OPTN band is ubiquitinated OPTN? Reviewer 2 raised the same question. To demonstrate that the upper OPTN band is ubiquitinated, cell lysates after mitophagy induction were incubated in vitro* with a recombinant USP2 core domain, and the samples immunoblotted. As shown in supplementary Fig 1 in the revised manuscript, the upper OPTN band disappeared in a USP2-dependent manner. The upper NDP52 and TOMM20 bands similarly disappeared. Therefore, the upper OPTN, NDP52 and TOMM20 bands observed after mitophagy induction are ubiquitinated.

      • Fig. 1a,b: the bafilomycin stabilization of pTBK1, OPTN and pOPTN indicates that these proteins are substantially degraded by autophagy within 30-60 minutes. This seems extremely fast for mitophagy completion. Please discuss.*

      According to Kulak et al. (2014 Nat Methods), autophagy adaptor abundance (OPTN: 2.32E+4 and NDP52: 3.34E+4 in HeLa cell line) is low compared to that of mitochondria (TOMM20: 1.45E+6 in HeLa cell line). This is one of the reasons why autophagic degradation of adaptors is easier to see. Degradation of phos-TBK1 was likewise easy to detect, whereas total TBK1 was not. This discrepancy is likely based on differences in the abundance of phos-TBK1 and total TBK1. In addition, because autophagy adaptors are localized outside of the mitochondrial membrane they may be easier targets for lysosomal degradation than matrix proteins, which are localized inside the outer and inner membranes.

      • Fig. 1a and rest of the manuscript: is there a reason why the authors only looked at S177 phosphorylation of OPTN and not also at OPTN S473, which is also phosphorylated by TBK1?*

      Both mass spectrometry and mutational analyses indicated that OPTN S473 is phosphorylated during Parkin-mediated mitophagy and that OPTN phosphorylated at S473 strongly binds ubiquitin chains (Richter et al. 2016 PNAS and Heo et al. 2015 Mol Cell). However, because a phos-S473 OPTN antibody is, to the best of our knowledge, currently not commercially available, we did not focus on S473 phosphorylation.

      • Fig. 1e-f: the main text states that "NDP52 KO effects on the pS172 signal were comparable to controls", but the blot in 1e and the graph in 1f indicate a difference between NDP52KO and WT (significant difference shown in 1f). This is confusing.*

      We regret the over-interpretation. As the reviewer indicated, the amount of phos-TBK generated in response to mitophagy was reduced in NDP52 KO cells relative to that in WT cells. This has been corrected. We would like to emphasize that, unlike OPTNdeletion, NDP52 deletion has relatively minor effects on TBK1 phosphorylation.

      • P. 9: "TBK1 phosphorylation however was not apparent in the OPTN mutant lines, even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation (Fig. 2a)". However, the pTBK1 blot in Fig. 1a does show pTBK1 formation in the OPTN mutant (4LA etc.) lines. This is confusing.*

      We apologize for this error. We intended to state “TBK1 phosphorylation was not apparent in the Penta KO cells without OPTN expression even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation”. This sentence has been corrected in the revised manuscript.

      • P. 10: "we subtracted the basal phosphorylation signal from that generated post-valinomycin (1 hr) and bafilomycin (3 hr)". Do they mean "from that generated post-valinomycin (3 hr) and bafilomycin (3 hr)?*

      The reviewer is correct, we have corrected the error.

      • P. 10, same paragraph: "the phosphorylation signal was ~90 but was less than 30 in ATG9A KO cells." Unclear what they mean by 90 and 30. 90% and 30%? 90-fold and 30-fold?*

      The newly generated pTBK1 levels following Parkin-mediated mitophagy were calculated as pTBK1 [val & baf 3 hrs] minus pTBK1 [DMSO]. Since pTBK1 [val & baf 3 hrs] in WT cells is set to 100%, the newly generated pTBK1 in WT cells was 100% - 5% = 95%. The calculated values for pTBK1 [DMSO] and pTBK1 [val & baf 3 hrs] in ATG9A KO cells were ~55% and ~85%, respectively. Consequently, newly generated pTBK1 in the ATG9A KO cells is ~85% - ~55% = 30%. For clarity, we modified the figure to make the meaning of the numbers more apparent.

      • Fig. 3a: Do they have an idea what kind of ubiquitinated substrates are contained in the ubiquitin-positive condensates that accumulate in FIP200 KO and ATG9A KO cells (i.e. without valinomycin treatment)?*

      According to Kishi-Itakura et al. (2014 J Cell Sci), ferritin accumulates in the p62 condensates in FIP200 KO and ATG9A KO cells. However, it is unknown if the ferritin in the condensates is ubiquitinated. In the original manuscript, we confirmed by immunostaining that the p62-NBR1 condensates contain ferritin (Fig 3a in the original manuscript and supplementary Fig 7b in the revised manuscript).

      • P. 12 and Fig. 3a: please explain why they look at ferritin, to improve readability.*

      We thank the reviewer for the suggestion. As mentioned, ferritin is a known substrate that accumulates in p62 condensates, we thus sought to confirm its presence. We have included this explanation in the revised manuscript.

      • Fig. 3a: please also include Ub stain for NBR1.*

      We thank the reviewer for the suggestion. We obtained a rabbit anti-NBR1 antibody that allowed us to co-immunostain with the mouse anti-ubiquitin antibody (please see supplementary Fig 7b in the revised manuscript).

      • Fig. 3d: the OPTN blot shows 2 OPTN bands. What does the upper OPTN band represent here?*

      To determine if the two bands are genuine OPTN, total cell lysates prepared from HeLa cells treated with control siRNA or OPTN siRNA were subjected to phos-tag PAGE followed by immunoblotting with an anti-OPTN antibody. As shown below (Figure 2 for reviewers), the two bands (indicated as blue arrowheads) were absent in the OPTN knock down cells, indicating that both are derived from OPTN. Since phosphorylated species migrate slower in phos-tag PAGE, the upper band might be a phosphorylated form. The specific Ser/Thr phosphorylated in OPTN, however, remains to be determined. Heo et al. (2015 Mol Cell) also reported the two OPTN bands on phos-tag PAGE and that both were unchanged in TBK1 KO cells, suggesting that at least the upper band is not affected by TBK1.

      • P. 14 and Fig. 4b: "Here, we found that phosphorylation of ... TBK1 (S172) was induced by the OPTN-ub fluoppi (Fig. 4b)." However, Fig 4b does not show a pTBK1 blot.*

      We immunoblotted phos-TBK1. Please see Fig 4b in the revised manuscript.

      *Reviewer #3 (Significance (Required)): *

      *The novel insights are relevant. *

      *According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *

      Based on our time-lapse microscopy observations (Fig 2l), OPTN recruited to the vicinity of mitochondria was visible as a small dot-like structures that likely correspond to contact sites between mitochondria and the isolation membrane since OPTN colocalizes with WIPI1 (please see supplementary Fig 2). These results support our proposed model that OPTN interacts with both isolation membranes and ubiquitin at the onset of mitophagy. Without TBK1 activation, OPTN can interact with ATG9A vesicles, a seed for isolation membrane formation (Yamano et al 2020 JCB), and TBK1 can interact with the PI3K complex (Nguyen et al 2023 Mol Cell). Therefore, OPTN-TBK1 can be recruited to the contact site from the very beginning of mitophagy induction prior to TBK1 being fully activated. Furthermore, the proposed model also includes an OPTN-TBK1 positive feedback loop; however, the earliest reactions in the positive feedback loop are too difficult to observe. For example, it’s widely known that PINK1 and Parkin form a positive feedback loop to generate ubiquitin-chains on damaged mitochondria, but the initial reaction has yet to be observed. It remains unclear if PINK1 is the first to phosphorylate mitochondrial ubiquitin (if this is the case, it remains unknown how ubiquitin comes to mitochondria) or if cytosolic Parkin first adds ubiquitin to the outer membrane albeit with very weak activity. Similarly, in our proposed model, we cannot determine the earliest OPTN-TBK1 reaction. As described in the Discussion in the revised manuscript, it remains possible that in the absence of autophagy machinery OPTN distributed freely on the outer membrane can induce trans-autophosphorylation, albeit weakly, as the earliest reaction.

      We would like to thank Reviewer 3 for the critical comments and suggestions. We have performed several of the suggested experiments, added new data, and rewritten the text. We hope that these changes have sufficiently addressed the reviewer’s concerns.

    1. When does annotating books become a distraction? .t3_17pitv9._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #8c8c8c; --postTitleLink-VisitedLinkColor: #8c8c8c; --postBodyLink-VisitedLinkColor: #989898; }

      reply to u/Low-Appointment-2906 at https://www.reddit.com/r/books/comments/17pitv9/when_does_annotating_books_become_a_distraction/

      Through the middle ages, bookmakers would not only leave significant margins for readers to annotate, but they also illuminated books and included drolleries which readers in the know would use in conjunction with the arts of memory (from rhetoric) to memorize portions of texts more easily. I strongly suspect this isn't what booktokkers are doing; their practice is likely more like the sorts of decorative #ProductivityPorn one sees in the Bullet journal and journaling spaces. It's performative content creation.

      Those interested in refining their practices of "reading with a pen in hand", continuing the "great conversation" or having "conversations with their texts" might profitably start with Mortimer J. Adler's essay: “How to Mark a Book” (Saturday Review of Literature, July 6, 1941). In his 1975 KCET series How to Read a Book, which was based on their book of the same name, Adler mentioned to Charles Van Doren that he would buy new copies of books so he could re-annotate them without being distracted by his older annotations.

      Some have solved the problem of distracting annotations by interleaving their books so they've got lots of blank space to write their notes. It's a rarer practice now, but some publishers still print Bibles with blank pages every other page for this practice. Others put their annotations and notes into commonplace books or on index cards for their card index/zettelkasten.

      As some have mentioned, friends and lovers through time have shared books with annotations as a way of sharing their thoughts. George Custer and his wife Elizabeth did this with Tennyson.

      If you're interested in annotating digitally online, perhaps check out Hypothes.is where I've seen teachers and students using social annotation to read and make sense of books [example]. I've also seen groups of people use this tool for hosting online book groups/clubs.

      If you're in it for fun, you might appreciate:

      And those wishing to delve more deeply into the history and power of annotation might look at: Kalir, Remi H., and Antero Garcia. Annotation. The MIT Press Essential Knowledge Series. MIT Press, 2019. https://mitpressonpubpub.mitpress.mit.edu/annotation.

      Good luck annotating! 📝

    1. Analog zettelkasten for natural sciences .t3_17kui2u._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; --postBodyLink-VisitedLinkColor: #989898; }

      Reply to u/Wooden-School-4091 at https://www.reddit.com/r/Zettelkasten/comments/17kui2u/analog_zettelkasten_for_natural_sciences/

      Given that Carl Linnaeus "invented" the standardized 3x5 inch index card and used it heavily in his scientific work (read Isabelle Charmantier and Staffan Müller-Wille's works for more on his practice), and a variety of others including me, use it for mathematics, physics, chemistry, biology, etc., Zettelkasten can certainly be used for STEM, STEAM, and any of the natural sciences.

      See also, notes and links at: https://hypothes.is/users/chrisaldrich?q=tag%3A%22zettelkasten+for+studying%22

      If I were using it for classes/university/general studying via lectures, I'd base my practice primarily on Cornell Notes in combination with creating questions/cards for spaced repetition and/or a variation on Leitner's System.

      Some of the best material on spaced repetition these days can be found via:

      and other material on their sites.

      Beyond this, I'd focus my direct zettelkasten practice less on the learning portion and more on the developing or generating ideas portion of the work. Some of my practice with respect to mathematics can be found here: https://www.reddit.com/r/Zettelkasten/comments/17bqztm/applying_zettelkasten_for_math_heavy_subjects/

      For those interested, it may bear mentioning that Bjornstad, an engineer at Remnote, has a TiddlyWiki-based zettelkasten at https://zettelkasten.sorenbjornstad.com/#PublicHomepage:PublicHomepage which he demonstrates with a walk through at https://www.youtube.com/watch?v=GjpjE5pMZMI

  6. Oct 2023
    1. Author Response

      Reviewer #1 (Public Review):

      The work by Yijun Zhang and Zhimin He at al. analyzes the role of HDAC3 within DC subsets. Using an inducible ERT2-cre mouse model they observe the dependency of pDCs but not cDCs on HDAC3. The requirement of this histone modifier appears to be early during development around the CLP stage. Tamoxifen treated mice lack almost all pDCs besides lymphoid progenitors. Through bulk RNA seq experiment the authors identify multiple DC specific target gens within the remaining pDCs and further using Cut and Tag technology they validate some of the identified targets of HDAC3. Collectively the study is well executed and shows the requirement of HDAC3 on pDCs but not cDCs, in line with the recent findings of a lymphoid origin of pDC.

      1) While the authors provide extensive data on the requirement of HDAC3 within progenitors, the high expression of HDAC3 in mature pDCs may underly a functional requirement. Have you tested INF production in CD11c cre pDCs? Are there transcriptional differences between pDCs from HDAC CD11c cre and WT mice?

      We greatly appreciate the reviewer’s point. We have confirmed that Hdac3 can be efficiently deleted in pDCs of Hdac3fl/fl-CD11c Cre mice (Figure 5-figure supplement 1 in revised manuscript). Furthermore, in those Hdac3fl/fl-CD11c Cre mice, we have observed significantly decreased expression of key cytokines (Ifna, Ifnb, and Ifnl) by pDCs upon activation by CpG ODN (shown in Author response image 1). Therefore, HDAC3 is also required for proper pDC function. However, we have yet to conduct RNA-seq analysis comparing pDCs from HDAC CD11c cre and WT mice.

      Author response image 1.

      Cytokine expression in Hdac3 deficient pDCs upon activation

      2) A more detailed characterization of the progenitor compartment that is compromised following depletion would be important, as also suggested in the specific points.

      We thank the reviewer for this constructive suggestion. We have performed thorough analysis of the phenotype of hematopoietic stem cells and progenitor cells at various developmental stages in the bone marrow of Hdac3 deficient mice, based on the gating strategy from the recommended reference. Briefly, we analyzed the subpopulations of progenitors based on the description in the published report by "Pietras et al. 2015", namely MPP2, MPP3 and MPP4, using the same gating strategy for hematopoietic stem/progenitor cells. As shown in Author response image 2 and Author response image 3, we found that the number of LSK cells was increased in Hdac3 deficient mice, especially the subpopulations of MPP2 and MPP3, whereas no significant changes in MPP4. In contrast, the numbers of LT-HSC, ST-HSC and CLP were all dramatically decreased. This result has been optimized and added as Figure 3A in revised manuscript. The relevant description has been added and underlined in the revised manuscript Page 6 Line 164-168.

      Author response image 2.

      Gating strategy for hematopoietic stem/progenitor cells in bone marrow.

      Author response image 3.

      Hematopoietic stem/progenitor cells in Hdac3 deficient mice

      Reviewer #2 (Public Review):

      In this article Zhang et al. report that the Histone Deacetylase-3 (HDAC3) is highly expressed in mouse pDC and that pDC development is severely affected both in vivo and in vitro when using mice harbouring conditional deletion of HDAC3. However, pDC numbers are not affected in Hdac3fl/fl Itgax-Cre mice, indicating that HDCA3 is dispensable in CD11c+ late stages of pDC differentiation. Indeed, the authors provide wide experimental evidence for a role of HDAC3 in early precursors of pDC development, by combining adoptive transfer, gene expression profiling and in vitro differentiation experiments. Mechanistically, the authors have demonstrated that HDAC3 activity represses the expression of several transcription factors promoting cDC1 development, thus allowing the expression of genes involved in pDC development. In conclusion, these findings reveals HDAC3 as a key epigenetic regulator of the expression of the transcription factors required for pDC vs cDC1 developmental fate.

      These results are novel and very promising. However, supplementary information and eventual further investigations are required to improve the clarity and the robustness of this article.

      Major points

      1) The gating strategy adopted to identify pDC in the BM and in the spleen should be entirely described and shown, at least as a Supplementary Figure. For the BM the authors indicate in the M & M section that they negatively selected cells for CD8a and B220, but both markers are actually expressed by differentiated pDC. However, in the Figures 1 and 2 pDC has been shown to be gated on CD19- CD11b- CD11c+. What is the precise protocol followed for pDC gating in the different organs and experiments?

      We apologize for not clearly describing the protocols used in this study. Please see the detailed gating strategy for pDC in bone marrow, and for pDC and cDC in spleen (Figure 4 and Figure 5). These information are now added to Figure1−figure supplement 3, The relevant description has been underlined in Page 5 Line 113-116, in revised manuscript.

      We would like to clarify that in our study, we used two different panels of antibody cocktails, one for bone marrow Lin- cells, including mAbs to CD2/CD3/TER-119/Ly6G/B220/CD11b/CD8/CD19; the other for DC enrichment, including mAbs to CD3/CD90/TER-119/Ly6G/CD19. We included B220 in the Lineage cocktails to deplete B cells and pDCs, in order to enrich for the progenitor cells from bone marrow. However, when enriching for the pDC and cDC, B220 or CD8a were not included in the cocktail to avoid depletion of pDC and cDC1 subsets . For the flow cytometry analysis of pDCs, we gated pDCs as the CD19−CD11b−CD11c+B220+SiglecH+ population in both bone marrow and spleen. The relevant description has been underlined in the revised manuscript Page 16 Line 431-434.

      2) pDC identified in the BM as SiglecH+ B220+ can actually contain DC precursors, that can express these markers, too. This could explain why the impact of HDAC3 deletion appears stronger in the spleen than in the BM (Figures 1A and 2A). Along the same line, I think that it would important to show the phenotype of pDC in control vs HDAC3-deleted mice for the different pDC markers used (SiglecH, B220, Bst2) and I would suggest to include also Ly6D, taking also in account the results obtained in Figures 4 and 7. Finally, as HDCA3 deletion induces downregulation of CD8a in cDC1 and pDC express CD8a, it would important to analyse the expression of this marker on control vs HDAC3-deleted pDC.

      We agree with the reviewer’s points. In the revised manuscript, we incorporated major surface markers, including Siglec H, B220, Ly6D, and PDCA-1, all of which consistently demonstrated a substantial decrease in the pDC population in Hdac3 deficient mice. Moreover, we did notice that Ly6D+ pDCs showed higher degree of decrease in Hdac3 deficient mice. Additionally, percentage and number of both CD8+ pDC and CD8- pDC were decreased in Hdac3 deficient mice (Author response image 4). These results are shown in Figure1−figure supplement 4 of the revised manuscript. The relevant description has been added and underlined in the revised manuscript Page 5 Line 121-125.

      Author response image 4.

      Bone marrow pDCs in Hdac3 deficient mice revealed by multiple surface markers

      3) How do the authors explain that in the absence of HDAC3 cDC2 development increased in vivo in chimeric mice, but reduced in vitro (Figures 2B and 2E)?

      As shown in the response to the Minor point 5 of Reviewer#1. Briefly, we suggested that the variabilities maybe explained by the timing of anaysis after HDAC3 deletion. In Figure 2C, we analyzed cells from the recipients one week after the final tamoxifen treatment and observed no significant change in the percentage of cDC2 when further pooled all the experiment data. In Figure 2E, where tamoxifen was administered at Day 0 in Flt3L-mediated DC differentiation in vitro, the DC subsets generated were then analyzed at different time points. We observed no significant changes in cDCs and cDC2 at Day 5, but decreases in the percentage of cDC2 were observed at Day 7 and Day 9. This suggested that the cDC subsets at Day 5 might have originated from progenitors at a later stage, while those at Day 7 and Day 9 might originate form the earlier progenitors. Therefore, based on these in vitro and in vivo experiments, we believe that the variation in the cDC2 phenotype might be attributed to the progenitors at different stages that generated these cDCs.

      4) More generally, as reported also by authors (line 207), the reconstitution with HDAC3-deleted cells is poorly efficient. Although cDC seem not to be impacted, are other lymphoid or myeloid cells affected? This should be expected as HDAC3 regulates T and B development, as well as macrophage function. This should be important to know, although this does not call into question the results shown, as obtained in a competitive context.

      In this study, we found no significant influence on T cells, mature B cells or NK cells, but immature B cells were significantly decreased, in Hdac3-ERT2-Cre mice after tamoxifen treatment (Figure 6). However, in the bone marrow chimera experiments, the numbers of major lymphoid cells were decreased due to the impaired reconstitution capacity of Hdac3 deficient progenitors. Consistent with our finding, it has been reported that HDAC3 was required for T cell and B cell generation, in HDAC3-VavCre mice (Summers et al., 2013), and was necessary for T cell maturation (Hsu et al., 2015). Moreover, HDAC3 is also required for the expression of inflammatory genes in macrophages upon activation (Chen et al., 2012; Nguyen et al., 2020).

      5) What are the precise gating strategies used to identify the different hematopoietic precursors in the Figure 4 ? In particular, is there any lineage exclusion performed?

      We apologize for not describing the experimental procedures clearly. In this study we enriched the lineage negative (Lin−) cells from the bone marrow using a Lineage-depleting antibody cocktail including mAbs to CD2/CD3/TER-119/Ly6G/B220/CD11b/CD8/CD19. We also provide the gating strategy implemented for sorting LSK and CDP populations from the Lin− cells in the bone marrow (Author response image 5), shown in the Figure 3A and Figure4−figure supplement 1 of revised manuscript.

      Author response image 5.

      Gating strategy for LSK, CD115+ CDP and CD115− CDP in bone marrow

      6) Moreover, what is the SiglecH+ CD11c- population appearing in the spleen of mice reconstituted with HDAC3-deleted CDP, in Fig 4D?

      We also noticed the appearance of a SiglecH+CD11c− cell population in the spleen of recipient mice reconstituted with HDAC3-deficient CD115−CDPs, while the presence of this population was not as significant in the HDAC3-Ctrl group, as shown in Figure 4D. We speculate that this SiglecH+CD11c− cell population might represent some cells at a differentiation stage earlier than pre-DCs. Alternatively, the relatively increased percentage of this population derived from HDAC3-deficient CD115−CDP might be due to the substantially decreased total numbers of DCs. This could be clarified by further analysis using additional cell surface markers.

      7) Finally, in Fig 4H, how do the authors explain that Hdac3fl/fl express Il7r, while they are supposed to be sorted CD127- cells?

      This is indeed an interesting question. In this study, we confirmed that CD115−CDPs were isolated from the surface CD127− cell population for RNA-seq analysis, and the purity of the sorted cells were checked (Author response image 6), as shown in Figure4−figure supplement 1 in revised manuscript.

      The possible explanation for the expression of Il7r mRNA in some HDAC3fl/fl CD115−CDPs, as revealed in Figure 4H by RNA-seq analysis, could be due to a very low level of cell surface expression of CD127, these cells therefore could not be efficiently excluded by sorting for surface CD127- cells.

      Author response image 6.

      CD115−CDPs sorting from Hdac3-Ctrl and Hdac3-KO mice

      8) What is known about the expression of HDAC3 in the different hematopoietic precursors analysed in this study? This information is available only for a few of them in Supplementary Figure 1. If not yet studied, they should be addressed.

      We conducted additional analysis to address the expression of Hdac3 in various hematopoietic progenitor cells at different stages, based on the RNA-seq analyis. The data revealed a relatively consistent level of Hdac3 expression in progenitor populations, including HSC, MMP4, CLP, CDP and BM pDCs (Author response image 7). That suggests that HDAC3 may play an important role in the regulation of hematopoiesis at multiple stages. This information is now added in Figure1−figure supplement 1B of revised manuscript.

      Author response image 7.

      Hdac3 expression in hematopoietic progenitor cells

      9) It would be highly informative to extend CUT and Tag studies to Irf8 and Tcf4, if this is technically feasible.

      We totally agree with the reviewer. We have indeed attempted using CUT and Tag study to compare the binding sites of IRF8 and TCF4 in wild-type and Hdac3-deficient pDCs. However, it proved that this is technically unfeasible to get reliable results due to the limited number of cells we could obtain from the HDAC3 deficient mice. We are committed to explore alternative approaches or technologies in future studies to address this issue.

    1. Any recommendations on Analog way of doing it? Not the Antinet shit

      reply to u/IamOkei at https://www.reddit.com/r/Zettelkasten/comments/17beucn/comment/k5s6aek/?utm_source=reddit&utm_medium=web2x&context=3

      u/IamOkei, I know you've got a significant enough practice that not much of what I might suggest may be helpful beyond your own extension of what you've got and how it is or isn't working for you. Perhaps chatting with a zettelkasten therapist may be helpful? Does anyone have "Zettelkasten Whisperer" on a business card yet?! More seriously, I occasionally dump some of my problems and issues into a notebook, unpublished on my blog, or even into a section of my own zettelkasten, which I never index or reconsult, as a helpful practice. Others like Henry David Thoreau have done something like this and there's a common related practice of writing "Morning Pages" that you can explore. My own version is somewhat similar to the idea of rubber duck debugging but focuses on my own work. You might try doing something like this in one of Bob Doto's cohorts or by way of private consulting sessions. Another free version of this could be found by participating in Will's regular weekly posts/threads "Share with us what is happening in your ZK this week" at https://forum.zettelkasten.de/. It's always a welcoming and constructive space. There are also some public and private (I won't out them) Discords where some of the practiced hands chat and commiserate with each other. Even the Obsidian PKM/Zettelkasten Discord channels aren't very Obsidian/digital-focused that you couldn't participate as an analog practitioner. I've even found that participating in book clubs related to some of my interests can be quite helpful in talking out ideas before writing them down. There are certainly options for working out and extending your own practice.

      Beyond this, and without knowing more of your specific issues, I can only offer some broad thoughts which expand on some of the earlier discussion above.

      I recommend stripping away Scheper's religious fervor, some of which he seems to have thrown over lately along with the idea of a permanent note or "main card" (something I think is a grave mistake), and trying something closer to Luhmann's idea of ZKII.

      An alternate method, especially if you like a nice notebook or a particular fountain pen, might be to take all of your basic literature/fleeting notes along with the bibliographic data in a notebook and then just use your analog index cards/slips to make your permanent notes and your index.

      Ultimately it's all a lot of the same process, though it may come down to what you want to call it and your broad philosophy. If you're anti-antinet, definitely quit using the verbiage for the framing there and lean toward the words used by Ahrens, Dan Allosso, Gerald Weinberg, Mark Bernstein, Umberto Eco, Beatrice Webb, Jacques Barzun & Henry Graff, or any of the dozens of others or even make up your own. Goodness knows we need a lot more names and categories for types of notes—just like we all need another one page blog post about how the Zettelkasten method works by someone who's been at it for a week. Maybe someone will bring all these authors to terms one day?

      Generally once you know what sorts of ideas you're most interested in, you take fewer big notes on administrivia and focus more of your note taking towards your own personal goals and desires. (Taking notes to learn a subject are certainly game, but often they serve little purpose after-the-fact.) You can also focus less on note taking within your entertainment reading (usually a waste) and focusing more heavily on richer material (books and journal articles) that is "above you" in Adler's framing. You might make hundreds of highlights and annotations in a particular book, but only get two or three serious ideas and notes out of it ultimately. Focus on this and leave the rest. If you're aware of the Pareto principle or the 80/20 rule, then spend the majority of your time on the grander permanent notes (10-20%), and a lot less time worrying about the all the rest (the 80-90%).

      In the example above relating to Marx, you can breeze through some low level introductory material for context, but nothing is going to beat reading Marx himself a few times. The notes you make on his text will have tremendously more value than the ones you took on the low level context. A corollary to this is that you're highly unlikely to earn a Ph.D. or discover massive insight by reading and taking note posts on Twitter, Medium, or Substack (except possibly unless your work is on the cultural anthropology of those platforms).

      A lot of the zettelkasten spaces focus heavily on the note taking part of the process and not enough on the quality of what you're reading and how you're reading it. This portion is possibly more valuable than the note taking piece, but the two should be hand-in-glove and work toward something.

      I suspect that most people who have 1000 notes know which five or ten are the most important to where they're going and how they're growing. Focus on those and your "conversations with texts" relating to those. The rest is either low level context for where you're headed or either pure noise/digital exhaust.

      If you think of ideas as incunables, which notes will be worth of putting on your tombstone? In other words: What are your "tombstone notes"? (See what I did there? I came up with another name for a type of note, a sin for which I'm certainly going to spend a lot of time in zettelkasten purgatory.)

  7. Sep 2023
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We thank the reviewers for their careful reading of the document and feedback which will help us to improve our manuscript. We will go through their comments one by one.

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

      This study would be much convincing if additional line of eukaryotic cells can be used to demonstrate the GEF-GAP synergy tis important for cell physiology. In addition, it would be best to demonstrate the spatiotemporal interaction of GEF-GAP using high-resolution live cell imaging.

      Response from the authors:

      The reviewer requests additional in vivo data to support our in vitro findings:

      (1) The reviewer requests in vivo data showing that GEF-GAP synergy is important for cell physiology. We believe that in order to show GEF-GAP synergy in vivo, Cdc42 cycling rates would need to be measured in vivo. For that single-molecule resolution is required – to track a single Cdc42 molecule and measure its GTPase cycling. We agree that such data would indeed be interesting, but are unaware of established techniques that would facilitate measurements of Cdc42 cycling rates in vivo.

      (2) The reviewer requests in vivo data showing the spatiotemporal interaction of GEF-GAP. Cdc24 and Rga2 are shown to interact (direct or mediated by another protein) (McCusker et al. 2007, Breitkreutz et al. 2010, Chollet et al. 2020). Cdc24 and Rga2 share 11 binding partners (https://thebiogrid.org/31724/table/saccharomyces-cerevisiae-s288c/cdc24.html, https://thebiogrid.org/32438/table/saccharomyces-cerevisiae-s288c/rga2.html) and have been found at the polarity spot (Gao et al. 2011). Live cell imaging of fluorescently tagged Cdc24 and Rga2 will show that they exhibit some interaction, but not specify the role of the interaction nor if the interaction is direct or mediated by one of the shared binding partners. In order to show a direct interaction between Cdc24 and Rga2, one could consider (A) super-resolution imaging or (B) FRET experiments: For both fluorescently tagged Cdc24 and Rga2 cell lines would need to be constructed.

      (A) Super-resolution imaging could show direct interaction between Cdc24 and Rga2, but even with the techniques available this would be on the limit. Further, it is usually done in fixed cells, and not in live cells (as requested from the reviewer).

      (B) To show a direct interaction of Cdc24 and Rga2 using FRET, suitable protein constructs would need to be engineered. We believe that the main obstacle in showing direct binding of Cdc24 and Rga2 using FRET is to design the fluorophore linker. The linker would need to be designed in such a way that it is flexible enough to give a FRET signal even if the two large proteins bind on the opposite sites of the fluorophore, but also is stiff/short enough to not show binding if both proteins are in close proximity through binding to a common binding partner.

      __We believe that an investigation of GEF GAP binding in vivo is beyond the scope of this study. Instead, we will further explore one possible mechanism underlying GEF GAP synergy - Cdc24 Rga2 binding - through conducting Size-Exclusion Chromatography Multi-Angle Light Scattering experiments with purified Cdc24 and Rga2 (alone and in combination). __

      Reviewer #1 (Significance (Required)):

      The revised study would provide first line evidence that GEF-GAP synergy to be general regulatory property in eukaryotic kingdom.

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

      The study entitled, "The GEF Cdc24 and GAP Rga2 synergistically regulate Cdc42 GTPase cycling" by Tschirpke et al., uses an in vitro GTPase assay to examine the GTPase cycle of Cdc42 in combination with its GEF and GAP effectors. The authors find that the Cdc24 GEF activity scales non-linearly with its concentration and the GAP Rga2 has substantially weaker effect on stimulating Cdc42 GTPase activity. Not surprisingly, the combined addition of Cdc24 and Rga2 lead to a substantial increase in Cdc42 GTPase activity.

      **Referees cross-commenting**

      In Zheng, Y., Cerione, R., and Bender, A. (1994) J. Biol. Chem. 269: 2369-2372 (Fig. 3C), the authors show that Cdc24 combined with the GAP Bem3 lead to a large synergy in boosting Cdc42 GTPase activity.

      Reviewer #2 (Significance (Required)):

      There is very little new information in this manuscript. Previous studies (Rapali et al. 2017) have shown that the scaffold protein Bem1 enhances the GEF activity of Cdc24. It is expected that the reconstitution of a GEF and GAP protein promote the GTPase cycle and indeed Zheng et al. (1994) showed that that Cdc24 combined with the GAP Bem3 lead to a large synergy in boosting Cdc42 GTPase activity. Hence the only potentially interesting finding in this work is that, in solution Cdc24 activity scales non-linearly with its concentration. However as this GEF and Cdc42 are associated with the membrane, the relevance of solution studies are less clear and furthermore the mechanistic basis for the non-linearity is not explored in detail. Given the limited new information from this work, the findings are, in their current form, too preliminary.

      Response from the authors:

      __We appreciate the reviewer recognizing our work on the non-linear concentration-dependence of Cdc24’s activity. We disagree that this is the only new finding in our study: __

      We explore the effect of Cdc24 and Rga2 on Cdc42’s entire GTPase cycle and show that Cdc24 and Rga2 synergistically upregulate Cdc42 cycling. So-far Cdc42 effectors were only characterized in isolation (with the exception of Cdc24-Bem1 (Rapali et al. 2017)) and through how they affect a specific GTPase cycle step. The regulation of single GTPase cycle steps through an effector yields mechanistic insight into this specific GTPase cycle step. However, it does not show how the effector affects overall GTPase cycling of Cdc42 – a process Cdc42 constantly undergoes in vivo. Our approach allows us to study synergistic effects between proteins affecting different GTPase cycle steps. Synergies are another regulatory layer of the polarity system, adding further complexity: Which polarity proteins exhibit synergy, to which extend? The assay employed here, which studies the entire GTPase cycle, enables studying the effect of any GTPase cycle regulator, alone and in combination with another regulator.

      The reviewer states that the GEF GAP synergy is to be expected, as it was already shown in Zheng et al. 1994. In Fig. 3C Zheng et al. shows the time course of the GTPase activity of Cdc42 in presence of Cdc24, Bem3, and Cdc24 plus Bem3. Fig. 3C is the only data in which the combined effect of a GEF (Cdc24) and a GAP (Bem3) is investigated. The data indicates synergy, but is neither discussed as such in the text of the publication, nor analyzed quantitatively. Further, only one concentration of each effector (GEF/GAP) is used and the study uses a Bem3 peptide containing codons 751-1128 (30%) of the full-length BEM3 gene. Zheng et al. 1994 gives an early indication of GEF GAP synergy, but does not claim, discuss, or further investigate the synergy as such. In contrast, we use full-length Rga2 (not Bem3) as GAP, conduct several concentration-dependent assays, and analyze them quantitatively. We thank the reviewer for pointing out the pioneering character of Zheng et al.‘s study and will mention it more prominently in our report. However, we disagree that Zheng et al. sufficiently studied the GEF GAP interaction. To our awareness no theoretical studies include a GEF GAP synergy term, which we would expect if GEF GAP synergy is well-established in the field.

      The reviewer criticizes the relevance of bulk in vitro studies (lacking membranes) of proteins that bind to membranes in vivo. We agree that the presence of a membrane can affect the protein’s property, and we can not exclude that membrane-binding could alter the magnitude of a GEF GAP synergy. However, we believe that membrane-binding does not impede the GEF GAP synergy altogether. If membrane binding would influence GTPase properties that strongly, other studies on Cdc42’s GTPase activity and GEF and GAP activity, that do not include a membrane, would be inconclusive as well (e.g. Zheng et al. 1993, Zheng et al. 1994, Zheng et al. 1995, Zhang et al. 1997, Zhang et al. 1998, Zhang et al. 1999, Zhang et al. 2000, Zhang et al. 2001, Smith et al. 2002, Rapali et al. 2017). Both studies mentioned by the reviewer (Zheng et al. 1994, Rapali et al. 2017) were also conducted without membranes present.

      We believe that an inclusion of membrane-binding into reconstituted Cdc42 systems will enhance our understanding of Cdc42 and recognize it as a next step, which could be enabled by the assay used in our study.

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

      This work reports a biochemical analysis of the effects of a recombinant yeast GEF (Cdc24) and GAP (Rga2) on Cdc42 GTPase cycling in vitro. The central conclusion is that the GEF and GAP act "synergistically", which occurs "due to proteins enhancing each other's effects". By this they appear to mean that the GEF enhances the GAP's activity and vice versa. I was not persuaded that this is correct, and was confused by many aspects of the approach and interpretation, as outlined below.

      1. GEF and GAP are expected to accelerate GTPase cycle synergistically even with no effect on each other's activity:

      The Cdc42 GTPase cycle is understood to occur via distinct steps (GDP release, GTP binding, and GTP hydrolysis): GDP release and GTP hydrolysis are intrinsically slow steps that are accelerated by GEFs (GDP release) and GAPs (GTP hydrolysis). This fundamental biochemistry was established in the 1990s using biochemical assays that measure each step independently. Here instead the authors use an assay that measures [GTP] decline in a mix with 5 uM starting GTP, 1 uM Cdc42, plus or minus some amount of GEF or GAP. They assume exponential decline of [GTP] with time, yielding a cycling "rate". If that is so, then one would expect that added GEF would accelerate only the first step, leaving a slow GTP hydrolysis step that limits the overall cycling rate, while added GAP would accelerate only the last step, leaving a slow GDP release step that limits the overall cycling rate. Adding both together would speed up both steps, and should therefore "synergistically" accelerate cycling. This would be expected based on previous work and does not imply that GEF or GAP are affecting each other's action (except trivially by providing substrate for the next reaction). If the authors wish to demonstrate that something more complex is indeed happening, they need to use assays that directly measure the sub-reaction of interest, as done by prior investigators.

      Response from the authors:

      The reviewer raises the point that we do not consider a simpler, rate-limiting model and that this rate-limiting model could explain our synergy between GAP and GEF in accelerating the GTPase cycle.

      We very much welcome this consideration of the reviewer! We will add a clarification to our manuscript to explain why a rate-limiting model/interpretation does not match our data.

      Intuitively, the rate-limiting model is appealing, as it permits interpretation of cycle rate increases in terms of individual biochemical steps. So, a consideration of this model is indeed relevant. However, as also noted by the reviewer in the next points, data from e.g., figure 3e are not compatible with a simple rate-limiting model with two steps (hydrolysis and nucleotide exchange). We will explain how the acceleration of the total rate by both GAP and GEF individually does not match the rate-limiting model, even if we assume maximal effects of adding GAPs and GEF to the cycle. For this purpose, we consider the rate-limiting model scenario where the sensitivity of the GTPase cycle to adding GAP/GEF is maximized, so the best case-scenario for the rate limiting step-model.

      In the rate-limiting step model, we assume that we have a GTPase cycle in which at least one of the three GTPase cycle steps is rate-limiting: (A) GTP binding, (B) GTP hydrolysis, and (C) GDP release.

      We assume that the addition of a GEF and GAP only accelerates GDP release and GTP hydrolysis respectively. Biochemically, all three steps in the GTPase cycle are expected to be relevant. However, here we will consider only the final two steps, as sensitivity to rate limitation by GAP/GEF is maximized when time spent in the GAP/GEF-independent step in the cycle (step A: GTP) is negligible (i.e. never rate-limiting). The two-step model thus consists of (1) a nucleotide exchange step (step C+A) which is dominated by GDP release (step C) and assumed to be accelerated exclusively by the GEF, and (2) a GTP hydrolysis step (step B) exclusively enhanced by the GAP.

      In the rate limiting step model GEF-GAP synergy can appear if one of the conditions applies:

      1. the addition of a GAP speeds up the GTP hydrolysis step so much that the hydrolysis step stops (or almost stops) being the rate-limiting step, or
      2. the addition of a GEF speeds up the GDP release step so much that the release step stops (or almost stops) being the rate-limiting step. In these conditions, the acceleration of the GTPase cycle, accomplished by adding only a GAP or adding only a GEF, is interdependent. Therefore, we consider the possible acceleration of the GTPase cycle by GAP and GEF individually, and compare these to our observations to determine whether the rate-limiting step model can explain our data.

      The GTPase cycle time Tc is thus composed of hydrolysis Th and nucleotide exchange time Te, and the rates r are connected through:

      1/rc=1/rh + 1/re

      If we compare the ratio of the rates with protein (GAP/GEF) added in the assay (index 1) with the basal rate without protein added (index 0), we obtain the cycle acceleration factor alpha:

      alpha=rc1/rc0=(1/rh0 + 1/re0)/(1/rh1 + 1/re1)=(re0 + rh0)/(re0*rh0/rh1 + rh0*re0/re1)

      Here, rc1 and rc0 are the total GTPase cycle rate with and without effector respectively, rh1 and rh0 are the GTP hydrolysis rate with and without effector respectively, and re1 and re0 are the nucleotide exchange rate with and without effectors respectively.

      There is indeed an interdependence created between how much the GAP and GEF can both accelerate the total cycle, if the GAP and GEF are assumed to only accelerate GTP hydrolysis and nucleotide exchange respectively. E.g., how much the total GTPase cycle rate rc is accelerated by an increase in GTP hydrolysis rate rh depends on and can be limited by the current nucleotide exchange rate re. However, this interdependence is too strict to match the data in Figure 3e, as we will explain in the next paragraphs:

      When we only add a GAP and the GAP accelerates only the GTP hydrolysis rate (re1=re0), then the maximal total GTPase cycle rate acceleration alphaGAP that the GAP can accomplish is when rh1>>rh0,re0:

      alphaGAP=rc1/rc0=(1/rh0 +1/re0)/(1/rh1+1/re0)=(re0+rh0)/(re0*rh0/rh1+rh0)

      ~(re0+rh0)/rh0=1+ re0/rh0

      We thus assume the GAP accelerates the cycle so much that the hydrolysis step is much faster than the exchange step, at which point the effect of adding more GAP would saturate. We note that we do not consider the GAP concentration regime where we see saturation, thus in reality the acceleration by the GAP is more restricted than predicted here.

      Analogously, if the GEF accelerates only the nucleotide exchange rate (rh1=rh0), then the maximum GTPase cycle rate ratio will be when re1>>re0,rh0 , yielding acceleration factor alphaGEF :

      alphaGEF= rc1/rc0=1+ rh0/re0

      Again, note we assume the GEF accelerates the cycle so much that the exchange step is much faster than the hydrolysis step, at which point the effect of adding more GEF would saturate. We note that we do not observe the GEF concentration regime where we see saturation, thus in reality the acceleration by the GEF is more restricted than predicted here.

      We see that the maximum gain in rates for GAP-only and GEF-only assays is limited by the same basal GTP hydrolysis and nucleotide exchange rates (rh0 and re0), leading to the following interdependence:

      alphaGAP=1+ 1/(alphaGEF -1)=alphaGEF/(AlphaGEF -1)

      In our GAP-only and GEF-only assays (Fig. 3e, Tab. 2), we see both a 2-fold and 100-fold increase in the total rate respectively. A 100-fold acceleration factor of the GEF would maximize the GAP acceleration factor to 1.01 (or alternatively, the 2-fold GAP acceleration would maximize the GEF acceleration to 2), which are both significantly lower than what we observe. So even though we made favorable assumptions for the rate-limiting model to maximize rate sensitivity to GAP/GEF, namely neglecting nucleotide binding and assuming GAP/GEF concentrations that saturate in their effects, we still cannot reproduce the acceleration factors in our GAP-only and GEF-only assays.

      Moreover, a rate-limiting step model would also imply saturation effects as stated in the next point of the reviewer. While we observe saturation in total rate acceleration for certain GAP concentrations, we use GEF and GAP concentrations in the combined protein assays for which no saturation effects were observed. Absence of saturation in both cycle steps simultaneously is also not reconcilable with the rate-limiting step model, as will be further discussed in the next point of the reviewer.

      In summary, this means that the rate-limiting model is not sufficient to explain our results: the GAP/GEF synergy we observe is not simply resulting from GEF and GAP independently lifting two different rate-limiting steps.

      Model-based interpretation of the GTPase assay is poorly supported:

      The assay employed measures overall GTP concentration with time. It is assumed (but not well documented-see below) that [GTP] declines exponentially, and that the rate constant for a particular condition can be fit by the sum of a series of terms that are linear or quadratic in the concentrations of Cdc42, GEF, and GAP. There is no theoretical derivation of this model from the elementary reactions, and the assumptions involved are not well articulated.

      As discussed in point 1 above, one would expect that a GEF or GAP alone could only accelerate the cycle to a certain point, where the other (slow) reaction becomes rate limiting. But that does not appear to be true for their phenomenological model, where slow steps (small terms in the sum) will always be overwhelmed by fast steps. This is not the traditional understanding of how GTPases operate.

      Response from the authors:

      The reviewer expresses the concern that because we do not derive our coarse-grained model from elementary reactions, we miss important effects that can occur when adding GAP and GEFs, particularly saturation.

      We understand the concern of the reviewer that if a rate-limiting step model is considered, saturation effects of GAP/GEF will limit the amount with which these effectors can speed up the total cycle. Our coarse-grained model indeed does not account for this saturation. However, as discussed in the previous point of the reviewer, we do not opt for the rate-limiting model interpretation, as the GAP and GEF effects are not compatible with the rate-limiting step model.

      Secondly, we agree that for high enough concentrations of GEF and GAPs, we would experience a saturation in the effect of adding the effectors. We are aware of this possibility, and we verify that we are not in saturation regimes with our added proteins by checking the plots of the individual protein titrations (see Figure 3a-d). If we enter the saturation regime, we expect a negative second derivative in the rate as function of protein concentration (the curve shallows off). We do not see this for any protein except for Rga2 at some point, as discussed in our main text of the manuscript. However, for this protein we only use the data in the linear regime for further analysis. In short, we understand the concern of the author but we empirically check that we are not in the saturation regime.

      Data that do not conform to expectation are not explained: Strangely, the data (as interpreted by the model assumptions) also appear inconsistent with the expectation of rate-limiting steps. GEF addition (alone) is said to accelerate cycling 100-fold, while GAP addition (alone) accelerates it 2-fold. But that would seem to imply that GDP release takes up >99% of the basal cycle (so accelerating that step alone reduces cycling time 100-fold), while GTP hydrolysis takes up >50% of the basal cycle (so accelerating that step alone reduces cycling time 2-fold). In the conventional understanding of GTPase cycles, these cannot both be be true (as the steps would then add to >100% of the basal cycle). There is no attempt to reconcile these findings with previous work.

      Response from the authors:

      The reviewer raises the point that our findings do not match the expectations of the rate-limiting model perspective.

      We fully agree with the reviewer that our data is not compatible with the rate-limiting step model. The 100-fold and 2-fold gain of the total cycle rates for GEF-only and GAP-only assays are one of our arguments against the rate-limiting model view, as described in the first point of the reviewer. Also, our lack of saturation as described in the previous point of the reviewer provides another argument against using expectations based on rate-limiting steps to interpret our findings.

      Lack of detailed timecourse data:

      The decline in [GTP] with time is stated to be exponential, allowing extraction of an overall cycling "rate". But this claim is supported only weakly (S3 Fig. 1 uses only 3 timepoints, is not plotted on semi-log axis, and does not report fit to exponential vs other models) and only for the Cdc42-alone scenario: no data at all are presented to support exponential decline in reactions with GEF or GAP. Most assays seem to measure only a single timepoint, so extraction of a "rate" is very heavily influenced by the unsupported assumption of exponential decline. And if the decline is not exponential, it becomes extremely difficult to interpret what a single timepoint means.

      Response from the authors:

      The reviewer requests additional timeseries data with GEF and GAP to support the assumption of an exponential decline of GTP in the assay and requests to plot it on a semi-log axis.

      We will add data for Cdc42 + Cdc24 and for Cdc42 + Rga2 with two to three time points, and plot it as requested on a semi-log axis.

      Other issues with interpretation of the data:

      (i) It is unclear why the authors chose to employ an assay that is much harder to interpret than the biochemical assays used by others. In biochemical studies, assays that report an output of multiple reactions are always harder to interpret than assays targeting a single reaction. As well-established assays are available for each individual step in GTPase cycles, any conclusions must be supported using such assays.

      Response from the authors:

      The reviewer wonders why an assay that investigates several GTPase steps at once was chosen over assays that investigate sub-steps of the GTPase cycle, given that these give more mechanistic insights.

      We agree that assays investigating GTPase cycle substeps can give more mechanistic insights into these specific steps. However, they do not allow to study how proteins affecting different steps act together. We were interested in investigating the overall GTPase cycle of Cdc42 and a possible interplay of GEFs and GAPs. Cdc42 GTPase cycling was found to be a requirement for polarity establishment (Wedlich-Soldner et al. 2004) and Cdc42 GTPase cycling is physiologically relevant. Ultimately, we hope that in vitro results provide stepping stones towards understanding the complex and less controlled in vivo environment. The in vivo environment often entails the output of many reactions combined, so there is every incentive to study aggregated effects of a full cycle which are not necessarily the sum of individual outputs.

      __We believe that both assay types – assays that investigate sub-steps and yield mechanistic details, and assays that investigate the entire cycle – are important and disagree that one assay type is superior to the other. Instead, we believe they complement each other. __

      (ii) The reported basal (and GEF/GAP-accelerated) rates are very slow, perhaps due to poor folding of recombinant proteins. This raises the possibility that much of the Cdc42 is inactive. If so, then accelerated GTP hydrolysis could come from increasing the active fraction of Cdc42, rather than catalyzing a specific step.

      Response from the authors:

      The reviewer wonders whether the reported rates are slow due to poor folding of recombinant Cdc42. We used S. cerevisae Cdc42, for which it has been shown that it has a significantly lower basal GTPase activity than Cdc42 of other organisms (see Zhang et al. 1999). Many other studies on Cdc42 were conducted with human Cdc42, which has a significantly higher basal GTPase activity (Zhang et al. 1999). We assessed the activity of several recombinantly expressed Cdc42 constructs previously (Tschirpke et al. 2023). We there observed that most constructs had a similar GTPase activity, only some purification batches and constructs had a significantly reduced GTPase activity (which might be linked to poor folding). The Cdc42 construct used here shows a similar activity as the active Cdc42 constructs in Tschirpke et al. 2023, and we therefore believe that it exhibits proper folding. If recombinant Cdc42 folds poorly, we would expect greater variations between Cdc42 constructs and purification batches (caused by different levels of folding/ a different fraction of active Cdc42) than what we observed previously (see Tschirpke et al. 2023).

      Tschirpke et al. 2023:

      Tschirpke et al. A guide to the in vitro reconstitution of Cdc42 activity and its regulation (2023) BioRxiv. (https://doi.org/10.1101/2023.04.24.538075) (in submission at Current Protocols)

      (iii) The GEF and GAP preparations include multiple partial degradation products and it is unclear whether the measured activities come from full-length proteins or more active fragments.

      Response from the authors:

      We agree with the reviewer that the Cdc24 and Rga2 preparations contain degradation products.

      It would be more ideal if the protein purifications were entirely pure, but this is experimentally very difficult to achieve for the used proteins (which are large and partially unstructured, making them prone to partial degradation). Further, it is not uncommon to use protein preparations where some degradation products were present (e.g. Zheng et al. 1993, Zheng et al. 1994). Other studies did not show their purified preparations.

      The vast majority of the Cdc24 preparation is the full-length protein. We therefore expect that the degradation fragments only contribute in a small extend to the overall protein behavior.

      The Rga2 preparation contains a higher amount of degradation product, but only larger size protein fragments (> 60kDa), suggesting that the fragments contain at least and more than 1/3 of the full-length protein (the protein fragments are thus the size or larger than of the GAP peptides used previously). The fragments could in principle have a higher or lower activity. We account for fragments of no/lower activity by comparing our cycling rates to those of BSA/Casein, which has no specific effect on Cdc42. The cycling rate Rga2 is almost an order of magnitude greater than that of BSA/Casein, suggesting that the effect of the full-length protein dominates. We could only imagine that a Rga2 fragment has a higher GAP activity if the fragment consists mainly of the GAP domain and if in Rga2 the activity of the GAP domain is downregulated. Nevertheless, we will do an additional experiment using a purified GAP domain peptide to assess that if a GAP domain by itself has a higher GAP activity than our Rga2 preparation. Using that data, we will discuss possible implication of the GAP fragments in our manuscript.

      (iv) Cdc42 cycling is also accelerated by BSA and casein, suggesting that there are poorly understood aspects of the assay and that GEF and GAP actions may (like BSA and casein) involve non-canonical effects on Cdc42. As GEF and GAP are expected to interact better with Cdc42 than BSA or casein, these effects could dominate the observed changes in GTP levels.

      Response from the authors:

      The reviewer raises the concern that the effects of the added effector proteins on the rates could be caused by non-canonical effects. We do not believe non-canonical effects play a relevant role in our assays. While BSA and casein accelerate the GTPase cycle in our assays, the GAP effect and GEF effect are orders of magnitude stronger.

      (v) Cdc42-alone cycling assays are said to be reproducible. However, assays with added GEF/GAP/BSA/Casein yield rates that vary almost an order of magnitude between replicates. This poor reproducibility further reduces confidence in the findings.

      Response from the authors:

      The reviewer is concerned about the variations in Cdc42 effector rates.

      __We disagree that the variations are concerning and believe to have accounted for them in our analysis: __The Cdc42 (Cdc42 alone) data is very reproducible (see Tschirpke et al. 2023). The GTPase assay is generally sensitive to small concentration changes and errors introduced through pipetting small volumes (as required for the assay). We believe that the small variation observed for Cdc42 alone is because Cdc42 has such a low basal rate and therefore the small concentration changes due to pipetting have a smaller effect. Once other effectors are added, especially highly GTPase stimulating ones as Cdc24, small concentration changes due to pipetting can lead to larger variations between assays (small variations in Cdc24 concentration lead to larger changes in remaining GTP due to Cdc24’s strong and non-linear effect on Cdc42). We conduct the assays multiple times to account for these variations. In our analysis we do not compare single rate numbers but the orders of magnitude of the rate, and report the variations present. Even given the present variations, the differences in effect sizes are still significant. We map and discuss assay variation in (Tschirpke et al. 2023), to which we refer to several times throughout the manuscript.

      Tschirpke et al. 2023:

      Tschirpke et al. A guide to the in vitro reconstitution of Cdc42 activity and its regulation (2023) BioRxiv. (https://doi.org/10.1101/2023.04.24.538075) (in submission at Current Protocols)

      (vi) It is unclear what timepoint was used for the different assays. 1.5 h at 30 degrees seems to be the standard here for the Cdc42-alone assays, but I assume that cannot be what was measured to assess GTP decline for GEF-containing assays as there would be very little GTP left at 1.5 h.

      Response from the authors:

      We used 60-100 min as incubation times for all assays. The assay data will be published on a data server, where all these numbers can be checked. We further added a clarification to the materials and methods section. In order to still have remaining GTP for the Cdc42 GEF mixtures after 60-100 min, we lowered the used protein concentrations.

      (vii) The graph reporting GEF activity is plotted only for [GEF]Response from the authors:

      The graphs show the full range of protein concentrations used.

      In order to calculate K1, K2, K3,Cdc24, K3,Rga2, K3,Cdc24,Rga2 from k1, k2, k3,Cdc24, k3,Rga2, k3,Cdc24,Rga2, …, a protein concentration has to be included in the term (as K1 = k1 [Cdc42], ….). In order to make K comparable, we chose to use 1uM for all protein concentrations. This was done to compare the cycling rate values of different proteins. 1uM was a choice, in the same fashion 0.2uM could have been chosen.

      __We will further discuss in the manuscript how the choices in protein concentration affect the effector strength on Cdc42. __

      (viii) S8 Data with casein seems very noisy and it is no longer at all clear that the quadratic fit for [Cdc24] is justified. Also, the symbol colors are very similar so it is hard to tell what data corresponds to what condition. The synergy between Cdc24 and Rga2 is also very noisy and the fits seem arbitrary.

      Response from the authors:

      The reviewer is concerned with (1) the noise in the S8 data, and (2) the Cdc42-Cdc24-Rga2 fits.

      (1) We acknowledge in the manuscript that the S8 data is noisy and should be viewed with caution. We do not put much emphasis on these data sets and their interpretation and show them only in the supplement.

      (2) We disagree that the Cdc42-Cdc24-Rga2 fits are arbitrary. The fits contain several data points per protein, and reproduce the rate values from Cdc42-Cdc24 and Cdc42-Rga2 assays well.

      The reviewer is concerned with the color scheme choice in the fits.

      __We will adapt the color scheme of the fits to make the colors more distinguishable. __

      (ix) It is disturbing that different Cdc42 constructs behave quite differently (S4). This suggests that protein behavior is influenced by the various added epitope tags and protease cleavage sites (they also leave the C-terminal CAAX box rather than removing the AAX as would happen in vivo). These features raise the concern that these findings may not be directly relevant to the situation with endogenous yeast Cdc42. Of course, it is also the case that relevant Cdc42 biochemistry occurs with prenylated Cdc42 on membranes.

      Response from the authors:

      The reviewer is concerned that the behavior of the Cdc42 constructs is influenced by their tags. In a previous manuscript (Tschirpke et al. 2023) we explored the effect of various N- and C-terminal tags on Cdc42, by comparing it to Cdc42 that is not tagged in that position. We found that most tags, including the tags present in the Cdc42 construct used here, do not affect Cdc42’s properties.

      Instead, we found a general, tag independent, heterogeneity in Cdc42 behavior (which can occur between purification batches and between constructs (but not between different assays)): in some batches GTPase activity depended quadratically on its concentration, others showed a linear relationship. Most batches exhibited a mixed behavior. The differences between the batches are generally small, and only visible in the activity to concentration plots and because of the assay’s high accuracy. We use a two-parameter fit (k1 [Cdc42] + k2 [Cdc42]2) to phenomenologically account for this heterogeneity, and to estimate the basal Cdc42 GTPase activity. We do not interpret this heterogeneity, as more research is needed. We believe that Cdc42 still has unexplored properties, of which this heterogeneous behavior can be one. We speculate in Tschirpke et al. 2023 that it is linked to Cdc42 dimerization mediated by its polybasic region, a relationship that is far from being fully understood yet. __We believe that it is of scientific interest to point out heterogeneous behaviors to encourage more research. __

      Tschirpke et al. 2023:

      Tschirpke et al. A guide to the in vitro reconstitution of Cdc42 activity and its regulation (2023) BioRxiv. (https://doi.org/10.1101/2023.04.24.538075) (in submission at Current Protocols)

      The reviewer is concerned that our findings are biologically not relevant, as our experiments (1) included Cdc42 that was not prenylated and (2) did not include membranes.

      (1) We here used recombinantly purified proteins, which do not contain posttranslational modifications, such as prenylations. So-far Cdc42’s prenyl group, which is responsible for binding it to membranes, has not been linked to its GTPase properties. We therefore believe that unprenylated Cdc42 is an equal choice to prenylated Cdc42 when studying Cdc42’s GTPase cycle. Further, the use of recombinantly purified proteins can be of advantage: when proteins are purified from their native host, the post-translationally modified protein is purified. However, many proteins contain a multitude of post-translational modifications (PTMs). Thus, the purified protein is a mixture of protein with different PTMs. For example, S. cerevisae Cdc42 undergoes ubiquitinylation (Swaney et al. 2013, Back, Gorman, Vogel, & Silva 2019), phosphorylation (Lanz et al. 2021), farnesylation and geranyl-geranylation (Caplin, Hettich, & Marshall 1994). We here used protein preparations that do not contain PTMs, and show how they behave. Natively purified proteins would be mixtures of various PTMs, and the observed protein behavior would be that of the mixture. If Cdc42’s PTMs affect it’s GTPase behavior, the observed behavior of natively purified Cdc42 would represent the average behavior of the mixture. It then would require additional work to disentangle which PTMs affect the GTPase cycling in which way. The use of recombinantly expressed Cdc42 does not require this work, and can set the baseline for how Cdc42 without PTMs behaves. If in the future a link between Cdc42’s GTPase behavior and PTMs are found, the work here could be used as a baseline for Cdc42’s behavior when it is without PTMs.

      (2) The concern about missing membranes was also raised by reviewer 2 (significance), and we like to refer to our response there.

      Reviewer #3 (Significance (Required)):

      The basic biochemistry of Cdc42 cycles was figured out about 30 years ago. However, those studies did not examine how combinations of Cdc42 regulators (as opposed to individual regulators) might interact to produce effects not expected from combining their individual actions. Recently, this combination approach did lead to interesting findings by Rapali et al. This approach is worthwhile and addresses a major question of interest to the broader field of GTPase biochemistry.

      One main limitation of this study is technical: the main assay is less informative (though perhaps easier) than traditional assays, and it is unclear whether the recombinant proteins employed retain their normal activities. Another limitation is the model-based interpretation of the assay that does not include the potential for rate-limiting steps.

      Response from the authors:

      We thank the reviewer for the detailed comments.

      One important point of confusion originated from our lack of discussion concerning a rate-limiting step model, which is an obvious starting point for modelling the GTPase cycle. We thank the reviewer for pointing this out, and we will include an explanation in our manuscript why we reject this model and instead opt for a coarse-grained model.

      Firstly, a rate-limiting model would generate saturation effects that we would observe when adding GEF and/or GAPs. In assays exploring GEF GAP synergy we use GEF and GAP concentrations for which no saturation effects were observed.

      Secondly, in our data we observed a two-fold increase of the total GTPase cycling rate when adding a GAP and a 100-fold rate increase when a GEF is added. These increases are not compatible with a model where either hydrolysis or nucleotide exchange limits the GTPase cycle. While a synergy could arise from the rate-limiting model perspective, the incompatibility of the rate-limiting model with the GAP-only and GEF-only assay data excludes this synergy explanation. Finally, through coarse-graining our model we avoid using single step parameters from literature which are incompatible in terms of proteins/buffers used. (For example; the mayor studies that kinetically characterized the individual GTPase steps of Cdc42 used human Cdc42 (Zhang et al. 1997, Zhang et al. 2000). Because human Cdc42 exhibits a higher basal GTPase activity (Zhang et al. 1999) we are skeptical how useful it is to transfer these parameters to S. cerevisae Cdc42.)

      At the same time, coarse-graining our model permits absorbing unidentified molecular details which is essential when we wish to incorporate BSA and casein rate contributions.

      The reviewer finds our assay, which investigates the GTPase cycle as a whole, less informative. Assays investigating single GTPase cycle sub-steps give more mechanistic insights into these steps. We opted for an assay that studies GTPase cycling as a whole instead, as we were interested in studying how proteins effecting different steps act together. We believe that both assay types are important as they complement each other.

      The reviewer is concerned about our use of recombinant proteins, and whether they retain their normal activities. We assessed Cdc42’s GTPase activity and the influence of added purification tags extensively (Tschirpke et al. 2023), and found that added tags do not affect Cdc42’s GTPase properties. We checked Cdc24’s GEF activity using the GTPase assay and found that it bound strongly to Bem1, as expected (Tschirpke et al. 2023). The Cdc24 concentrations needed to affect Cdc42’s GTPase activity were similar to those used previously (Rapali et al. 2017), suggesting that it is fully active. A similar comparison for Rga2 was not possible, as so-far only domains of Rga2 were used (Smith et al. 2002). We here used recombinantly purified proteins, which do not contain posttranslational modifications (PTMs). To our knowledge the PTMs of the herein used proteins are not linked to their GTPase/GEF/GAP properties. Thus, a lack of PTMs does not diminish our findings. Further, when proteins are purified from their native host, the post-translationally modified protein is purified. However, many proteins contain a multitude of post-translational modifications in vivo. Natively purified proteins would be mixtures of various PTMs, and the observed protein behavior would be that of the mixture. We here used protein preparations that do not contain PTMs, and show how they behave, setting the baseline for proteins without PTMs behaves. If in the future a link between GTPase behavior and PTMs are found, the work here could be used as a baseline for the proteins behavior when it is without PTMs.

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

      Summary

      The GTPase cdc42 is a key determinant of yeast polarization. Its activity is amplified at the site of polarization through a poorly defined positive feedback mechanism, and depends on numerous GAPs regulating GTP hydrolysis and the GEF cdc24 that regulates GDP release. These components have previously been evaluated for their quantitative effects on the individual steps in the GTPase cycle that they modulate, but potential interactions between the cdc24 GEF and any GAP could not be examined based on these assays. The authors validate and employ a bulk assay of the total GTPase cycle based on GTP consumption to study the activities of and potential interactions between cdc24 and the GAP Rga2. Fitting their data to a mathematical model, they come to three central conclusions: (1) the activating activity of cdc24 to activate cdc42 GTPase activity is nonlinear, showing a quadratic relationship, (2) Rga2 shows a much lower activating activity that is linear at low levels before saturating, and (3) there is a strongly synergistic interaction between the activating activities of cdc24 and Rga2. Some hypotheses for the mechanistic bases of these findings are hypothesized, but not further investigated. Their conclusions are well supported by the data which appears to be of sufficient rigor.

      Major comments

      The three main conclusions of the manuscript are well supported by the data and associated modeling.

      One unresolved issue is the discrepancy between the authors' conclusion that the non-linear activation by cdc24 is likely a result of oligomerization, whereas Mionnet et al 2008 reach the opposite conclusion. It seems that the authors wish to discount the Mionnet results because they used truncated constructs to test deficient oligomerization and an engineered construct to test induced oligomerization. If the authors are correct, then a relatively easy test would be to introduce the oligomerization deficient mutants defined by Mionnet into their fuill length construct and compare to wild type protein. While the authors' measured results don't depend on the offered mechanism and this experiment is therefore optional, their explanation is quite unsatisfying, especially since an experiment to resolve the difference is entirely feasible and not very strenuous.

      Response from the authors:

      __The reviewer suggests to conduct experiments with oligomerization deficient Cdc24 mutants to test our hypothesis that the non-linear concentration dependence of Cdc24’s activity is due to Cdc24 oligomerization. __

      We agree that this is an insightful experiment, and will conduct it. In order to observe the effect in our GTPase assays, we require a mutant that is oligomerizes substantially less than wild-type protein. Mionnet et al. constructed several Cdc24 mutants, but none were entirely oligomerization deficient. However, the DH5 (L339A/E340A) mutant showed a 10-fold reduction in oligomerization and the DH3 (F322A) mutant exhibited 2.5-fold reduction in oligomerization. We will therefore use the DH5 and DH3 mutant for two additional experiments.

      Minor comments

      The results in Fig S4 serve as assay validation, and this should be pointed out early in the Results section. I was initially concerned when the assay was described as based on consumption of GTP that a significantly diminished pool would alter the rate and thereby distort results, and being made aware of the S4 result would have alleviated that concern as I read further.

      Response from the authors:

      We believe that the reviewer refers to S3 (not S4). We appreciate this suggestion and now mention it earlier.

      On page 4 and Fig S4 the authors mention several cdc42 constructs, some of which show linear activity curves and others slightly non-linear curves. I was unable to find where these constructs or their differences are discussed. The authors should also tell us if the construct used for the remaining experiments was one of the two shown in S4, or a different one.

      Response from the authors:

      We added the requested information and explanations to the manuscript.

      It seems that in Fig 4 and Fig S8, some points are missing from the graphs. Were all concentrations for each condition not always assayed, or is some data omitted for some reason? For example, for the 0.125 microM Rga2 condition, only two points are shown vs 4 for some other conditions, and the two missing ones are expected to not be excluded by the >5% GTP remaining criterion.

      Response from the authors:

      The reviewer wonders whether Fig.4 and Fig. S8 miss data points. This is not the case, and __we added clarifying information to the manuscript. __

      In detail: Not all assays contain the same amount of data points/ concentrations for each protein. We first assessed Cdc42 alone using several Cdc42 concentration. We then examined the individual Cdc42 – effector mixtures, using a larger number of effector concentrations. We included a reduced number of effector concentrations in the assays containing two effectors and Cdc42. It would be ideal to include more concentrations, but this is not always feasible: The assay involves a multitude of pipetting steps and is sensitive to any pipetting errors. Further, assays can vary slights from each other, therefore all samples that ought to be compared need to be included in each assay.

      Each three-protein assay contains samples shown (Cdc42, Cdc42 + effector 1, Cdc42 + effector 2, Cdc42 + effector 1 + effector 2) and additional ‘buffer’ wells used for normalization. Each data point shown corresponds to the average of 3-4 replica samples per assay. We therefore did not include all concentrations in all conditions. As pointed out, Fig. 4a only shows two data points for the 0.125uM Rga2 axis (Rga2 + Cdc42 and Rga2 + Cdc24 + Cdc42). The rational was the following: We included three Cdc24 concentrations (for proper fitting for K3,Cdc24), three Rga2 concentrations (for proper fitting for K3,Rga2), and 5 mixtures of the used Cdc24 and Rga2 concentrations (for proper fitting for K3,Cdc24,Rga2).

      The Cdc42-Rga2-BSA and Cdc42-Rga2-Casein data is rather sparse and would benefit from additional data points. However, we only use those as control experiments and are cautious in their interpretation.

      In these graphs, a diamond symbol of slightly varying color is used for the different conditions. The different colors are hard to distinguish. Please use different shape symbols for the different conditions, and choose colors that are more distinct.

      Response from the authors:

      We will adapt the color scheme of the fits to make the colors more distinguishable.

      There are a few sentences that are of unclear meaning, for example on page 10, "It was suggested that each GAP plays a distinct role in Cdc42 regulation, of which the level of GAP activity could be a part of [Smith et al., 2002]." There are also typos and grammatical errors that should be fixed.

      Response from the authors:

      __We will further check the document for potentially unclear sentences and will try to clarify them, as well as further check for grammatical and spelling errors. __

      Reviewer #4 (Significance (Required)):

      Significance

      The most novel and important finding is the strong synergy observed between cdc24 and Rga2 in activating cdc42 GTPase activity. This is undoubtedly an important mechanism underlying positive feedback in polarization. The measured non-linear activity of cdc24 alone is also quite important given that availability of cdc24 is thought to be a critical in vivo stimulus for polarization. However, the unexplained discrepancy between this result and that of Mionnet leaves one to wonder which result is more reliable. Only Mionnet attempts to directly test whether oligomerization is important in cdc24 activity.

      The conclusions are of importance to a broad audience of cell biologists, though the lack of any mechanism for the synergy or the non-linearity of cdc24 activity somewhat diminishes significance.

      Note that my expertise and that of my co-reviewer is in the biology, and while we are able to follow the contributions of the modeling, we do not have the expertise to critically evaluate for potential errors or weaknesses in the modeling itself.

      The reviewer wonders whether our data or the data of Mionnet et al. on the link between Cdc24 oligomerization and its GEF activity is more reliable and suggests to conduct experiments with oligomerization deficient Cdc24 mutants.

      We thank the reviewer for this recommendation and we will do the suggested experiments to resolve the seemingly contradicting observations by us and Mionnet et al..

      The reviewer would find mechanistic insights into (2) the non-linear concentration dependence of Cdc24’s activity and (2) the Cdc24-Rga2 synergy useful.

      (1) We will conduct experiments with partially oligomerization deficient Cdc24 mutants, as suggested by the reviewer.

      (2) We speculate that Cdc24-Rga2 binding could lead to the synergy. ____We will add data on Cdc24 – Rga2 binding (in vitro: Size-Exclusion Chromatography Multi-Angle Light Scattering) to this study.

    1. Reset Background color CSS Ask Question Asked 7 years, 11 months ago Modified 7 years, 11 months ago Viewed 5k times Report this ad This question shows research effort; it is useful and clear 2 This question does not show any research effort; it is unclear or not useful Save this question. Show activity on this post. I am developing a project where I am supposed to make a particular part of div flash, (or blink only once) The HTML : <p style="color:#f47321; font-size:16px; font-weight:bold;" id="divtoBlink" >Current Price</p> and the CSS <style> #divtoBlink{ background: #008800; animation-duration: 1000ms; animation-name: blink; animation-iteration-count: 1; animation-direction: alternate; } @keyframes blink { from { opacity: 1; } to { opacity: 0; } } </style> It blinks, and changes colour to green. But the color stays green. I want to reset the background: #008800; to white or transparent again. Is there a property or tweak that I can use? Any help is appreciated. htmlcsscss-animations ShareShare a link to this question Copy linkCC BY-SA 3.0 Follow Follow this question to receive notifications edited Oct 5, 2015 at 12:08 Harry 87.6k2525 gold badges203203 silver badges215215 bronze badges asked Oct 5, 2015 at 11:58 ShahsaysShahsays 42111 gold badge77 silver badges2525 bronze badges 3 why not use jquery ? – Farrukh Faizy Oct 5, 2015 at 12:00 6 @MuhammadFarrukhFaizy: Because these sort of things can be handled without using jQuery. – Harry Oct 5, 2015 at 12:01 @MuhammadFarrukhFaizy Why not use Assembler? Yes right, because it is much to complicated to get such a task done using Asembler. Or a scripting language incl. a complete application framework (like jQuery)… – feeela Oct 5, 2015 at 12:19 Add a comment  |  2 Answers 2 Sorted by: Reset to default Highest score (default) Trending (recent votes count more) Date modified (newest first) Date created (oldest first) This answer is useful 5 This answer is not useful Save this answer. Show activity on this post. I think what you need is only for the background to become transparent after blink and for the text to remain visible. If that is the case, use the below snippet. When opacity is animated from 1 to 0, the whole element along with its content would become invisible. Instead, animating just the background should be enough. #divtoBlink { background: #008800; animation-duration: 1000ms; animation-name: blink; animation-iteration-count: 1; animation-direction: alternate; animation-fill-mode: forwards; } @keyframes blink { from { background: #008800; } to { background: transparent; } } <script src="https://cdnjs.cloudflare.com/ajax/libs/prefixfree/1.0.7/prefixfree.min.js"></script> <p style="color:#f47321; font-size:16px; font-weight:bold;" id="divtoBlink">Current Price</p> Run code snippetHide resultsExpand snippet Original Answer: All that is needed is to add animation-fill-mode: forwards so that the element holds the state as at its final keyframe (which is opacity: 0 or transparent). Currently the animated element reverts back to its original state (background: #008800) once the animation is complete. #divtoBlink { background: #008800; animation-duration: 1000ms; animation-name: blink; animation-iteration-count: 1; animation-direction: alternate; animation-fill-mode: forwards; } @keyframes blink { from { opacity: 1; } to { opacity: 0; } } <script src="https://cdnjs.cloudflare.com/ajax/libs/prefixfree/1.0.7/prefixfree.min.js"></script> <p style="color:#f47321; font-size:16px; font-weight:bold;" id="divtoBlink">Current Price</p> Run code snippetHide resultsExpand snippet ShareShare a link to this answer Copy linkCC BY-SA 3.0 Follow Follow this answer to receive notifications edited Oct 5, 2015 at 12:15 answered Oct 5, 2015 at 12:04 HarryHarry 87.6k2525 gold badges203203 silver badges215215 bronze badges 4 Well,after blinking it faded out everything inside the div tag. – Shahsays Oct 5, 2015 at 12:10 1 @FaizanShah: Yes, isn't that what you wanted? If not, can you please clarify more. (Edit: I think you are maybe looking for only the background to become transparent but content to be visible. If yes, please refer the first snippet in my answer now.) – Harry Oct 5, 2015 at 12:11 You see this is a label which is supposed to say Current Price. applying css, the color changes to green, but stays green. applying your method, it removes the green AND the current price. – Shahsays Oct 5, 2015 at 12:14 @FaizanShah: Glad to be of help. Please don't forget to accept the answer (click on the hollow tick mark below the voting icon). – Harry Oct 5, 2015 at 12:19 Add a comment  |  Report this ad This answer is useful 1 This answer is not useful Save this answer. Show activity on this post. I think in your situation it is easier to change the pattern. the initial color is white, then let it blink to green and reset again to your wished color (white or transparent). easy solution via custom defined keyframes. (look at the fiddle) #divtoBlink{ background: #fff; animation-duration: 1000ms; animation-name: blink; animation-iteration-count: 1; animation-direction: alternate; } @keyframes blink { 0% { background: #008800;} 50% { background: #fff;} // optional sugar any color between.. 100% { background: #fff; } } http://jsfiddle.net/a2pg246h/ ShareShare a link to this answer Copy linkCC BY-SA 3.0 Follow Follow this answer to receive notifications answered Oct 5, 2015 at 12:14 MarcMarc 2,66933 gold badges3434 silver badges4141 bronze badges 0 Add a comment  |  Your Answer StackExchange.ifUsing("editor", function () { StackExchange.using("externalEditor", function () { StackExchange.using("snippets", function () { StackExchange.snippets.init(); }); }); }, "code-snippets"); StackExchange.ready(function() { var channelOptions = { tags: "".split(" "), id: "1" }; initTagRenderer("".split(" "), "".split(" "), channelOptions); StackExchange.using("externalEditor", function() { // Have to fire editor after snippets, if snippets enabled if (StackExchange.settings.snippets.snippetsEnabled) { StackExchange.using("snippets", function() { createEditor(); 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      how are you doing this

    1. 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 #1

      Evidence, reproducibility and clarity

      Summary

      The VAP proteins are well established as tail anchored proteins of the ER membrane. VAPs mediates co-operation between the ER and other organelles by creating a transient molecular tether with binding partners on opposing organelles to form a membrane contact site over which lipids and metabolites are exchanged. Proteins which bind VAPs generally contain a short FFAT motif, of varying sequence which binds the MSP domain of VAP. More recently the FFAT motif has been more extensively analysed in multiple different proteins and differential phosphorylation of the FFAT motif has been shown to either enhance or block VAP binding depending on the position of the phosphosite.

      Recent work conducted by the authors demonstrated that a small population of VAPB is not exclusively localised to the ER and can also reach the inner nuclear membrane. They also identified ELYS as a potential interaction partner of VAPB in a screening approach. ELYS is a nucleoporin that can be found at the nuclear side of the nuclear envelope where it forms part of nuclear pore complexes. During mitosis, ELYS serves as an assembly platform that bridges an interaction between decondensing chromosomes and recruited nucleoporin subcomplexes to generate new nuclear pore complexes for post-mitotic daughter cells. In this manuscript, James et al seek to explore this enigmatic potential interaction between ELYS and VAPB to address why VAPB may be found at the inner nuclear membrane.

      Peptide binding assays and some co-immunoprecipitation experiments are used to demonstrate that interactions occur via the MSP-domain of VAPB and FFAT-like motifs within ELYS. In addition, it is demonstrated that, for the ELYS FFAT peptides, the interaction is dependent on the phosphorylation status of serine residues of a particular FFAT-motif that can either promote or reduce its affinity to VAPB. Of most relevance is a serine in the acidic tract (1314) which, when phosphorylated increases VAPB binding. This is completely in line with what is already known about the FFAT motif and so is not surprising, in particular when using a peptide in an in vitro assay.

      The authors then utilise cell synchronisation techniques to provide evidence that both phosphorylation of ELYS and its binding to VAPB are heightened during mitosis. Immunofluorescence and proximity ligation assays are used to demonstrate that the proteins co-localise specifically during anaphase and at the non-core regions of segregating chromosomes.

      The manuscript is concluded by investigating the effect of VAPB depletion on mitosis with some evidence to suggest that transition from meta-anaphase is delayed and defects such as lagging chromosomes are observed.

      Major comments

      Overall, this manuscript is well written and the data presented in Figures 1-3 convincingly show the nature of the interaction between ELYS and VAPB. Clearly the proteins interact via FFAT motifs and this interaction appears to be enhanced during mitosis. However, the work as is, relies heavily on peptide binding assays and would benefit from additional experiments to further support the results. The authors need to more clearly show that this specific phosphorylation happens during mitosis, they may have this data but it is not clearly explained. In addition, the data that VAPB-ELYS interaction contributes to temporal progression of mitosis (as per the title) is not sufficiently clear. VAPB silencing appears to have some impact on mitosis but this is not the same thing. So this section needs to be strengthened before this statement can be made.

      The authors claim that the study "suggests an active role of VAPB in recruiting membrane fragments to chromatin and in the biogenesis of a novel nuclear envelope during mitosis". Given the data presented in Figures 4 and 5, this appears to be rather speculative with little evidence to support it, so data should be provided or this statement toned down. Currently, without additional supporting data the authors may wish to revise the overarching conclusions of the study and change the title.

      Specific points.

      Peptide pull down assays clearly show which FFAT-like motifs are important in facilitating binding. The co-immunoprecipitation systems used in Figure 2 also provide useful information on the interaction in a cell context. The authors should combine these findings by introducing full length ELYS mutants with altered FFAT-like motifs into their stably expressing GFP-VAPB HeLa cell line and then performing Co-IPs to help identify which FFAT motif/s drive the mitotic interaction. Other mutants of ELYS harbouring either phosphomimetic or phospho-resistant residues may also be introduced to further investigate mechanisms of the molecular switch in a cellular environment to support the work currently done with peptides alone. This is an obvious gap in the work which, based on the other data the authors have shown, should presumably be straightforward and would also lead directly into the next major point.

      • Whilst silencing VAPB does appear to delay mitosis, no reference is made to ELYS throughout Figure 5 nor as part of its associated discussion. Given that VAPB has more than 250 proposed binding partners, the observed aberration of mitotic progression could result from a huge number of indirect processes. Further work is needed to link the experiment specifically to the VAPB-ELYS interaction and not just loss of VAPB. We would suggest generating a complementation system where ELYS is either knocked out or silenced and then wild-type ELYS and an ELYS FFAT mutant (which cannot interact with VAPB),and/or a phospho mutant (whose interaction cannot be regulated during mitosis) are introduced. Then the observed effects can be better attributed to the VAPB-ELYS interaction and not just loss of VAPB.
      • The immunofluorescence and PLA results in Figure 4 could be strengthened by including other ER markers. This would show that co-localisation of ELYS at the non-core region is specific to VAPB protein, not any ER protein or rather than an artefact of the ER being pushed out of the organelle exclusion zone during mitosis and therefore 'bunching' at the periphery of the nuclear envelope. It would be worthwhile repeating these experiments with candidates such as VAPA, other ER membrane proteins or at least GFP-KDEL, to make this phenomenon more convincing. As part of this the authors should ideally generate a complemented ELYS KO (see point above) to avoid the residual activity attributed to endogenous background in the PLA Figure 4E.
      • Authors should clarify if the phosphorylation events (in particular S1314) only occur or are increased during mitosis. This may be data they have from the MS experiment in Figure 3 or it could also be shown using a phospho-antibody (although this can be challenging if a suitable antibody cannot be made).
      • The authors should clarify why they need to do these semi in-vitro assays with purified GST-VAPB-MSP on beads and then lysates added and not just a standard co-IP. If this is simply signal intensity due to a very small proportion of VAPB binding to ELYS then this is fine but this should be stated and it should be made clear that ELYS is not a major binding partner - most of VAPB is on the ER. Otherwise, this is misleading.

      I estimate that the suggested alterations above would incur approximately 3-6 months of additional experimental work, depending on if KO cell lines were required.

      Minor comments

      • To show that the observed interactions and potential role of VAPB-ELYS interaction is universal it would be useful to have at least a subset of experiments also shown in another cell line or system - this is now also a requirement for some journals.
      • Consider re-wording the title of the manuscript to better reflect the data presented within the study. Alternatively, provide further evidence that VAPB-ELYS interactions directly affect temporal progression of mitosis to validate this claim, as discussed above.
      • Quantification of blots in Figure 2A could allow measurement of relative binding affinities between VAPB-ELYS throughout the cell cycle. The same could be applied to the effect of phosphorylation on binding affinity in Figure 2D.
      • The cells used are never clearly mentioned in the text - I assume this is always in HeLa but this should be added in all cases for clarity
      • Page 8: "As shown in Fig. 2A,a large proportion of GFP-VAPB was precipitated under our experimental conditions." - I don't understand how this is shown in this figure as the non-bound fraction is not shown?
      • Please provide some controls to demonstrate the extent to which the samples used are asyn, G1/M or M.
      • Page 9 - why are Phos-tag gels not shown as this would make this result more convincing?
      • Figure 3A - I find the SDS-PAGE gel confusing. Why not show the whole gel and why is the band size apparently reduced in the mitotic fraction when previously it was increased (by phosphorylation)? It would also be useful to see if there were any other band shifts.
      • "FFAT-2 of ELYS is regulated by phosphorylation" The way you have setup the experiment leads the reader to think you are going to show which sites are differentially phosphorylated in mitosis, but then this is not the case - so there seems no purpose to doing the experiment this way. If you used TMT MS approach you would be able to potentially quantify the change in phosphorylation at the FFAT motif sites in mitosis. Otherwise what is the purpose of using these 2 samples, mitotic and AS?
      • For all of the antibodies used, in particular for the PLA, please provide evidence of validation of the antibodies.
      • Just a minor point to consider - In the methods for your lysis buffer you use 400mM NaCl - might this slightly reduce the VAPB-FFAT interaction? Worth considering reducing this?
      • "The rather small difference observed between the wild-type and the mutant protein observed in this experiment probably results from the presence of endogenous VAPB in the stable cell lines, which could form dimers with the exogeneous HA-tagged versions." If this is the case then please demonstrate that this is happening, or use the KO approach in the major points above.
      • "we now show that the proteins can indeed interact with each other, without the need for additional bridging factors (Figs. 1 and 3)." You show that the peptides can bind - but this is not the same thing as the peptide in the full context of the protein - so this should be toned down or removed.
      • "Remarkably, this region is highly conserved between species, suggesting that it is important for protein functions (data not shown)". Please show the alignments so the reader can judge for themselves. It is conserved in ALL species and the phosphosites are also conserved??
      • "In our experiments, knockdown of VAPA alone did not lead to a delay in mitosis (data not shown). " Why not show this data - as this is a very interesting and potentially important observation? Also add the validation of knockdown of VAPA.
      • I find the end to the discussion to the paper rather abrupt. It would be interesting to discuss further how VAPB, but not apparently VAPA reaches the INM and if so why this function is required of an ER adaptor and not another more obvious adaptor protein. In short - why would VAPB be performing this role?

      Referees cross-commenting

      I agree with the comments of the other reviewers, and they are very much in line with my own review. We all seem convinced that VAPB binds ELYS via a pFFAT, and that this interaction is enhanced during mitosois. However the role of this interaction in mitotic progression remains unclear and based on this data should not be claimed in the title or discussion of the paper.

      Significance

      Overall, if the manuscript could be improved with the suggested changes, then this could be a considerable conceptual advance in how we understand the VAP proteins, showing functions beyond those as an ER adaptor. This would be significant for the field.

      In the context of the existing literature the work does not advance our knowledge of FFAT-VAP interactions, this has already been shown, but it would give a nice example of how this can be regulated during mitosis and how VAP can contribute beyond just as an ER adaptor at membrane contact sites.

      There would be a wide audience in the cell biology field and more widely as mutations in VAPB cause a form of ALS, and many people are working in this area.

      My field of expertise is in organelle cell biology and membrane contact sites.

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      Reply to the reviewers

      We would like to thank all reviewers for taking the time to evaluate our manuscript. Many helpful suggestions and discussion points were raised. These comments were instrumental to provide more data that strengthen our conclusion about the relevance of centrin condensation in vivo, expand our findings to other organisms, and improve the manuscript in general. Details are given in the following individual replies.

      Reviewer #1 (Evidence, reproducibility and clarity):

      Voss and colleagues show calcium-dependent assembly of Plasmodium falciparum centrins in vitro and in parasites. This assembly is dependent on the EF-hands of centrin and an N-terminal disordered region.

      Major concerns:

      1. The very definitive title is not wholly supported by the data. This should be qualified by specifying the conditions under which the centrins can accumulate in this way.

      We understand this comment by the reviewer. There are multiple dimensions to the potential of centrins to condensate, such as the specific centrin family member, in vivo vs in vitro situation, and media conditions. Naturally it is difficult to represent these various conditions in a concise and compelling title but in line with the suggestion by Reviewer 2 we are changing the title to “Malaria parasite centrins can assemble by Ca2+-inducible condensation” to reflect the conditionality of this process.

      1. A major concern is whether this behaviour of centrins represents a biologically relevant mechanism in centriolar plaque formation. Is this limited to high overexpression conditions or in vitro high concentrations? Or is it a result of the tagging of the P. falciparum centrins?...

      Centrin accumulation at the centriolar plaque and assembly of the centriolar plaque itself must be differentiated. Although compelling we are already very careful in the text about extrapolating our findings about centrin accumulation in cells to centriolar plaque or centrosomal assembly in general. We, however, thank the reviewer for this important comment and now have carried out hexanediol treatment of wild type parasites to test the effect on centrin in a native context. After IFA staining we failed to detect any centrin foci at the centriolar plaques, suggesting that they can be resolved by inhibiting weak hydrophobic interactions that are typical for phase separation (now Fig. 6, lines 283ff).

      Concerning the effect of tagging we have generated new data of cells overexpressing an untagged version of PfCen1 in parasites, which still shows formation of ECCAs as revealed by IFA (now Fig. 4H-K, lines 243ff). This significantly alleviates the concern that the observed phenomenon is only a consequence of GFP-tagging. Our in vitro data already showed that native and tagged PfCentrin1 & 3 can undergo condensation.

      Concerning the critical concentration of our in vitro assay we find it to be around 10-15 µM without the addition of crowding agents such as PEG (now Fig. S3C, lines 120ff). To our understanding it is challenging to select an in vitro concentration that is adequate to define a threshold for “biological relevance” due to so many additional factors playing a role in vivo. Those factors can also favor a phase separation locally when total saturation concentration is not reached as we now discuss in more detail (lines 440ff). For reference the critical concentration of FUS, which is one of the most studied phase separating proteins in model system, is around 2 µM, but concentrations below 15 µM are well within the range of what is observed for in vitro LLPS. Additionally, it is important to consider that we find Cen1/3 and HsCen2 LLPS is inducible and reversible and that very homologous proteins i.e. Cen2 and 4 serve as an adequate internal control.

      … A convincing approach to addressing this issue would be to knock-in a fluorescent tag to the centrin loci. Roques et al. (ref. 12 in this submission) report the GFP tagging of centrin-4 in P. berghei, although they note that centrins-1 to -3 were refractory to tagging in this organism. It is unclear whether Voss et al. attempted this tagging in P. falciparum. This should be clarified and relevant data presented.

      We indeed attempted several unsuccessful iterations of tagging Cen1/3 with HA and GFP tag and now explain this in the text more clearly (lines 81ff). We did not attempt tagging Cen2 and 4 as they do not display phase separation in vitro or carry IDRs.

      If the tagged molecules used in the biochemical parts of this study are functional, it is challenging to understand why the centrins cannot be tagged in P. falciparum. If the tags render the P. falciparum centrins dysfunctional, the study becomes significantly less useful.

      Our data shows that in vitro Cen1-GFP can undergo Ca2+-inducible and reversible LLPS and that GFP-tagged centrins can still localize to the centriolar plaque. Centrin function, however, certainly goes beyond its ability to condensate and localize. It is easily conceivable that interaction with critical binding partners at the centriolar plaque is inhibited by tagging a protein as small as centrin, which prohibits tagging the endogenous version, while its ability to phase separate remains unaltered. To dynamically study a protein in cells tagging is, however, unavoidable. Even though tagging affects any proteins function to highly variable degree we are still convinced that studying those proteins still provides useful information. Our mutant versions of PfCen1 in vivo shows that non-condensating version display different localization. Importantly, as mentioned above, we now provide images of cells overexpressing an untagged Cen1 version, which still causes ECCA formation (Fig. 5H-K). Ultimately, even though tagged versions might not be fully functional, our observations are compatible with the ability of centrins to condensate in vivo.

      1. If a knock-in cannot be achieved, it must be shown that the transgenic expression of tagged Plasmodium centrins does not confound the analysis of centrin behaviour. It is known that these proteins can behave anomalously when overexpressed (Yang et al. 2010, PMID: 20980622; Prosser et al. 2009, PMID: 19139275), at least in other species.

      Thank you for this comment. Transgenic expression of proteins can in principle influence their behavior. In the context of this study the overexpression is, however, used intentionally since protein concentration correlates with the phase separation. Here, transgenic overexpression is used as a tool, rather than being a confounding factor, and ECCA formation can be used as quantifiable phenotype. The observation that ECCAs appear significantly earlier the higher they are expressed is in our opinion one of the stronger points of evidence that this result from phase separation in vivo. Yet centrins maintain their centriolar plaque localization and no significant impact on growth is observed. To definitely answer whether phase separation of endogenous centrin is occurring during centriolar plaque accumulation is challenging. These challenges and limitations are now addressed in the significantly extended discussion. As explained above untagged Cen1 also forms ECCAs.

      A previous description of centriolar plaque from the authors' lab (Simon et al. 2021, PMID: 34535568) shows an organized structure of an established size. It should be demonstrated whether the structures formed with the GFP tagged centrins show the same dimensions and dynamics as those in wild-type parasites. The extent of the overexpression of the GFP-tagged centrins should also be demonstrated.

      We thank the reviewer for this suggestion. We have now added spatial measurements of the centrin signal dimensions at the centriolar plaque of mitotic spindle containing nuclei in PfCen1-GFP overexpressing vs non-induced cell lines. We found that the width of the centrin-signal at the centriolar plaque was unaltered while the height only increased by 11% (Fig. S9). Further, we found no significant growth phenotype in overexpressing parasites, which indicates that the centriolar plaque is functional.

      Due to several confounding factors, we were, unfortunately, unable to clearly quantify the extent of overexpression. Most notably the induction of overexpression only works in about 50% of the cells (Fig. S6). The mean intensity after induction further displays quite some variability. Furthermore, the expression kinetics along the IDC of endogenous centrin and our overexpression system that we use as a tool differ. Lastly, our centrin antibodies display crossreactivity (see also Fig. S12) making it impossible to identify how much of the endogenous pool we are labeling in comparison to the GFP- tagged Cen1 protein.

      1. It would also be useful to remove the His tag from the recombinantly expressed and purified centrins for the in vitro analyses, particularly if concern remains about the impact of tags on Plasmodium centrin behaviour.

      Based on the published in vitro studies on other centrins, we did not anticipate the His-tag to change LLPS properties. Also, Cen1 and 3 and Cen2 and 4 would need to be differentially affected by the tag. We further have experimented with N-terminally tagged 6His-Cen3 protein and found no significant differences in our turbidity assays. Nevertheless, we expressed new versions of the recombinant PfCen1-4 proteins with a TEV cleavage site inserted after the His-tag to purify untagged proteins and found no fundamental differences in our LLPS assay aside some slight variation in the kinetics (Fig. S3E).

      1. The discussion is very short and does not consider the findings presented here in the context of the literature, with respect to centrins, Plasmodium MTOC assembly mechanisms, or to general considerations around biological condensates. Andrea Musacchio's recent commentary (ref. 44 in the current submission) advocates caution in ascribing phase separation as an assembly mechanism for organelles in vivo, particularly on the basis of in vitro experiments with high concentrations of homogeneous protein. It is not clear that the concentration dependence of extracentrosomal centrin accumulations (ECCAs) at the onset of schizogony provides sufficient justification of a phase separation model in vivo. The authors' recent description of the involvement of an SFI1-like protein, SIp (Wenz et al. 2023 PMID: 37130129), in the centriolar plaque makes a case for non-homotypic interactions also driving assembly and alternative models for ECCA are not convincingly excluded. The absence of a robust discussion of such considerations is unhelpful to the reader.

      We very much thank the reviewer for this suggestion, which helped to significantly improve the manuscript. We have purposefully included the commentary by Andrea Musacchio to highlight a different (possibly the most antipodal) point of view on the role of biomolecular condensation in membraneless organelle formation for the unfamiliar readers that might be just getting to know the field of phase separation. In the absence of word limitations, the reviewer is right to point out the lack of more extensive discussion. We now have significantly extended this section and address the suggested points including the potential role of the novel centriolar plaque protein Slp, which was not published upon submission of our previous version (lines 450ff.)

      1. It is also unclear whether the analysis of human centrin is suggested to indicate a phase separation mechanism for centrins in human cells. As this is readily testable, this notion could be considered further. Although its experimental examination may lie outside the theme of this study, one would expect some discussion of the significance of the data presented in the study.

      Since it is the first description of phase separation of centrin, it would indeed be interesting to explore the functional relevance in other organisms such as humans. We are considering approaching this in the future. We have, as requested above, significantly extended the discussion and now also include this aspect. Earlier reports have e.g. shown centriole overduplication in human cells upon centrin overexpression.

      Minor points

      1. There are only three centrins in humans. Centrin 4 is a pseudogene (Gene ID: 729338 on NCBI).

      Thank you for detecting this error, which we now corrected (line 60). Centrin 4 seems only to be an expressed gene in mice.

      1. Line 175 should say 'temporally', rather than 'temporarily. The Abstract should say 'evolutionarily conserved', rather than 'evolutionary conserved'. 'To condensate' is not ideal as a phrase- 'to form a condensate' would be clearer.

      Thank you for those suggestions. The text has been modified accordingly.

      Referees cross-commenting

      I think the other 2 reviewers have made fair, cogent and constructive points. There is good convergence between the reviewers on the significant issues around the study. These concern in vivo and in vitro effects of tagging and of high concentrations.

      Reviewer #1 (Significance):

      The biology of the Plasmodium centriolar plaque is of great interest as an alternative MTOC structure, with obvious additional interest deriving from the role of this organism in malaria. Much remains to be learned about this structure, so the topic of this paper is likely to attract a broad readership. Furthermore, the centrins are a widely-expressed and evolutionarily conserved family of eukaryotic proteins, with multiple roles; a new model for their behaviour, such as is suggested here, would be of interest to many cell biologists.

      With that in mind, significant additional data should be provided to substantiate the model proposed by the authors.

      We appreciate that the reviewer considers our manuscript of interest for a broad audience. We feel that our modifications of the text including a more thorough contextualization and addition of some new experimental data now sufficiently supports our claims.

      Reviewer #2 (Evidence, reproducibility and clarity):

      The authors analyzed the properties of the four Centrin proteins of the malaria parasite using a combination of in vitro and in vivo approaches. Their findings indicate that two of the four Plasmodium Centrin proteins, PfCen1 and PfCen3, as well as the human Centrin protein HsCen2, exhibit features of biomolecular condensates. Moreover, analysis of cells overexpressing PfCen1 indicates that such biomolecular condensates become more numerous as cells approach mitosis and are dissolved thereafter.

      Major comments

      A) A critical point that requires clarification is how the protein concentrations used in the in vitro and in vivo assays (20-200 microM in vitro, and not estimated in vivo) compare to that of the endogenous components. This is important because it may well be that 6His-tagged PfCen1, PfCen3 and HsCen2 can form biomolecular condensates when present in vast excess, but not when present in physiological concentrations. The authors should report the estimated cellular concentration of PfCen1-4, as well as that achieved upon PfCen1-GFP overexpression (on top of endogenous PfCen1), for instance using semi-quantitative immunoblotting analysis. Given this limitation, the authors may also want to temper their title by introducing the word "can" after "centrins".

      In the context of phase separation, protein concentration is of course a critical metric. However, in vitro and in vivo concentrations cannot be directly compared as the composition of the surrounding solute has a significant impact on the effective saturation concentration. In vitro we find a saturation concentration for Cen1 of 10-15 µM (Fig. S3C), which is within a range that is frequently found other in vitro studies as listed in the in vitro LLPS data base (PMID: 35025997). We now more explicitly discuss this in the text (lines 422ff). At this point, unfortunately, we have no means of investigating the absolute concentrations of centrin in vivo and to our knowledge no such data is available for apicomplexan. Additionally, one has to keep in mind the presence of other centrin family members in the cell which can interact and co-condensate as well as other centriolar plaque proteins, like PfSlp, but are difficult to separate through analysis. Further we now discuss several contexts that modify the saturation concentration in vivo (lines 440ff).

      As explained above in a response to Reviewer 1, we were not able to produce a satisfactory quantification of the overexpression levels. We are repasting the previous response here:

      “Due to several confounding factors we were, unfortunately, unable to clearly quantify the extent of overexpression. Most notably the induction of overexpression only works in about 50% of the cells (Fig. S6). The mean intensity after induction further displays quite some variability. Lastly the expression kinetics along the IDC of endogenous centrin and our overexpression system that we use as a tool differ. Lastly, our centrin antibodies display crossreactivity (see also Fig. S12) making it impossible to identify how much of the endogenous pool we are labeling in comparison to the GFP- tagged Cen1 protein. “

      Concerning the title, as explained above, we followed the suggestion and added the word “can”.

      B) Movies S1 and S2 (and the related Fig. 1D and 1E) are not the most convincing to support the notion that the observed assemblies are biomolecular condensates, as not much activity is going on during the recordings. Likewise, Movies S3, and even more so Movie S4, as out of focus for a large fraction of the time, making it difficult to assess what happens at the beginning of the process. Moreover, it appears that fusion events, while occurring, are rather rare. The movies should be exchanged for ones that are in focus, and ideally a rough quantification of fusion events as a function of biomolecular condensate size provided.

      We thank the reviewer for requesting clarification. Movies S1 and S2 are by no means direct evidence for biomolecular condensation and we do not claim them to be but rather say that they are “…reminiscent of biomolecular condensates…”. We think that this is an appropriate entry into the subsequent analyses. For Movie S1 it is noteworthy that the shape of the accumulation, which can only be resolved by super-resolution microscopy in live cells, is round as would be expected for a liquid condensate in the absence of forces and on these short time scales. Nevertheless, the centriolar plaque must be duplicated which might be the process partly depicted in Movie S2. The observation that centrin can be still change its shape at least suggests that it is not a solid aggregate. In the context of centriolar plaque biology and the technological advance of applying live cell STED in P. falciparum, we think these data are still worth reporting.

      Concerning Movies S3 and S4 we have carefully selected the focal plane to highlight all the hallmarks of LLPS. Since the protein droplets freely move in 3D throughout the entire imaged liquid volume there is no z-plane that is in focus. Our positioning of the focal plane presents the best compromise between showing round droplet shape, droplet fusion events, and surface wetting. All those observations demonstrate the liquid nature of the condensates. Fusion events are indeed relatively rare, and we do not go beyond this qualitative statement that it can be seen.

      C) An important control is missing from Fig. 2, namely assaying PfCen1-4 without the 6His tag, to ensure that the tag does not contribute to the observed behavior (although it can of course not be sufficient as evidenced by the lack of biomolecular condensates for PfCen2 and PfCen4).

      Thank you for this suggestion. Since reviewer 1 made a similar comment, I’m reiterating our previous reply here: Generally speaking, and based on the published in vitro studies on other centrins, we didn’t anticipate the very small His-tag to change LLPS properties. Also, Cen1 and 3 and Cen2 and 4 would need to be differentially affected by the tag. We further have experimented with N-terminally tagged 6xHis-Cen3 protein and found no significant differences in our turbidity assays. However, we expressed new versions of the recombinant PfCen1-4 proteins with a TEV cleavage site inserted after the His-tag to purify untagged proteins and found no significant differences in our LLPS assay (Fig. S3E).

      D) The authors should test whether the assemblies formed by PfCen1 and PfCen3 are sensitive to 1,6-hexanediol treatment, as expected for biomolecular condensates.

      This is an interesting and helpful suggestion. We now tested 1,6-hexanediol addition to recombinant PfCen1 and wildtype parasites (now Fig. 6). Interestingly the dissolving effect of hexanediol on PfCen1 in vitro was moderate, which we attribute to the polar component in centrin assembly, which has been documented earlier (Tourbez et al. 2004). In vivo, however, only 5 min of treatment caused a striking dissolution of most centrin foci in wild type parasites, which is compatible with the interpretation that centrin or centriolar plaque assembly could be driven by biomolecular condensation.

      E) The fact that HsCen2 also forms biomolecular condensates is very intriguing, but further investigation would be needed to assess the generality of these findings. For instance, the authors could test in vitro also S. cerevisiae Cdc31, the founding member of the Centrin family of proteins to further enhance the impact of their study.

      We thank the reviewer for this suggestion. It would of course be exciting to investigate in more detail how widely this biochemical property of some centrins is conserved. To take a first step in that direction, we have recombinantly expressed centrins containing some N-terminal IDRs from C. reinhardtii, T. brucei and S. cerevisiae to represent organism of significant evolutionary distance. Using our in vitro phase separation assays, we found a very similar behavior to PfCen1 for two centrins while yeast Cdc31, although forming droplets, had a much higher saturation concentration, which could be explained by the significantly lower intrinsic disorder in its sequence (now new Fig. 3).

      Minor comments

      1) For the experiments reported in Fig. 3D, the same concentrations as those used in Fig. 3A-C (namely 10 microM, and not 30 microM as in Fig. 3D) should be used. Moreover, it would be informative to test whether PfCen2 and PfCen4 as PfCen3 when added to PfCen1.

      Unfortunately, this experiment is not feasible since Cen3 does not produce droplets at 10 µM. Hence, in Fig. 3D we aimed to test if Cen1 is incorporated into preformed droplets i.e. whether there is still some interaction between them. We have, however, tested the addition of Cen2 to Cen1 and Cen3 and as expected from the inability PfCen2 to condensate we did not find the same synergistic effect as for Cen1 and 3 together (now Fig. S6). The combination of Cen1/2/3 still enabled co-condensation while addition of Cen4 did not further improve droplet formation. Taken together this strongly suggests that only Cen1 and 3 contribute to the phase separation in vitro (lines 184ff).

      2) The authors mention that the effect of Calcium in inducing biomolecular condensates is specific, as Magnesium was not effective (lines 94-95). However, an examination of Fig. S3B indicates that the Magnesium also exhibits some activity, albeit less potent than Calcium. The authors should discuss this point and rectify the wording in the main text.

      Thank you for pointing this out. While PfCen1 is not reactive to Magnesium, PfCen3 and HsCen2 do display a small reaction, which we now more clearly mention in the text (lines 118ff). Of note Mg2+ and other divalent cation are known to generally promote phase separation.

      3) Do the authors think that PfCen2 and PfCent4 localize to the centriolar plaque in vivo using another mechanism that deployed by PfCen1 and PfCent3? It would be good to discuss this point.

      This is indeed a point worth discussing. Centrins can of course still interact in the absence of biomolecular condensation and their localization to the centriolar plaque is not dependent on their ability to phase-separate as seen for PfCen2 and 4. We have recently described a novel centriolar plaque protein PfSlp that interacts with centrins and might assist recruitment (Wenz et al. 2023). Cellular condensates are, however, often separated into scaffold proteins, which actually phase separate and client protein which get recruited into those condensates. It is easily conceivable that Cen1 and 3 participate in formation of the biomolecular condensate into which Cen2 and 4 as well as other centriolar plaque proteins might be recruited. Unfortunately, we were not yet able to establish a recruitment hierarchy by e.g. dual-labeling of centrins to test whether PfCen1 and 3 might appear prior to PfCen2 and 4. We now include those aspects in the extended discussion.

      4) Given that the EFh-dead mutant exhibits no activity in vitro and fails to localize in vivo, one potential concern is that the protein is misfolded. The authors should conduct a CD spectrum to investigate this.

      Thank you for suggesting this relevant control experiment. We have carried out CD spectroscopy of wild type and EFh-dead PfCen1 and find no difference in secondary structure distribution. We now added these data to the supplemental information (now Fig. S14).

      5) It is not entirely clear from the main text in lines 103-104, as well as from the legend, what Fig. S3B shows. When was EDTA added in this case?

      Thank you for requesting clarification. We will assume the reviewer is referring to Fig S4B. We wanted to show that contrary to PfCen3 that PfCen1 droplets can still be resolved after an elongated period of incubation with calcium but forgot to mark the timepoint of EDTA addition at 180 min in the graph. We have now corrected this and further reworded the sentence for more clarity (lines 132ff).

      6) Fig. S7: the correlation between PfCen1-GFP expression levels and ECCA appearance is modest at best. What statistical test was applied? This should be spelled out. Moreover, the authors should combine the two data sets, as this will provide further statistical power to assess whether a correlation is truly present.

      Indeed, the correlation is modest but statistically significant, which is why we decided to place this data in the supplemental information. The used statistical test was an F-test provided by Prism, which compares two competing regression models, which we now mention in the legend. Combining the two data sets is unfortunately not possible since they arise from two independent sets of measurements where different imaging settings had to be used to adjust for the very different fluorescent protein levels in both lines after induction.

      7) The authors may want to discuss how their findings can be reconciled with the notion that Centrin assemble into a helical polymer on the inside of the centriole (doi: 10.1126/sciadv.aaz4137).

      This is an interesting point. Although centrin does localize to the inside of the centriole (https://doi.org/10.15252/embj.2022112107), more precisely one pool at the distal part and one pool at the core, there is no evidence that it is itself part of the helical inner scaffold described by the authors even though it might localize in close proximity to it. Further, there are several examples where polymers such as microtubules act as seeding point for biomolecular condensates or the other way around, and our work suggest this could be a potential working model for centrins. We have discussed our results extensively with the two corresponding authors of the aforementioned study (i.e. Virginie Hamel and Paul Guichard) and agreed that our data are not conflicting. Nevertheless, we include the inner centriole localization and potential association with polymer structures of centrin in our extended discussion.

      9) Likewise, the authors may want to speculate regarding what their findings signify for the role of Centrin proteins in detection of nucleotide excision repair (doi: 10.1083/jcb.201012093).

      We appreciate the comment by the reviewer. Centrins seem to have many different potential roles that remain to be clarified. While we are excited about this, we think it is too early to speculate about the impact of centrin condensation on less well studied aspects of centrins such as nucleotide excision repair. We, however, now cite this study in the discussion to highlight the functional diversity of centrins.

      Small things

      • Fig. 1A: change color for microtubules as red on red is difficult to discern.

      Throughout our publications we use this shade of magenta to label microtubules in schematics and have therefore opted to use a slightly brighter shade of red for the RBCs instead to improve visibility.

      • Fig. 1C: the indicated boxes in the top row do not seem to correspond exactly to the insets shown in the bottom row.

      We have verified the position of the boxes and found them to be accurate. Possibly the different imaging modality used for both panels (confocal vs STED) creates this impression.

      • line 266: typo, promotor > promoter.

      Has been corrected.

      • line 360: a reference should be provided for the GFP-booster, including the concentration at which it was used.

      Has been added.

      • line 363: "an" missing before "HC".

      Has been corrected.

      • line 428: it would be best to deposit the macros on Github or an analogous repository.

      Macros have been deposited on https://github.com/SeverinaKlaus/ImageJ-Macros (line 737)

      • line 461: "to the" is duplicated.

      Has been corrected.

      • Fig. S5A: maybe draw the lines in red (as red in Fig. S5B correspond to the proteins that do not have IDRs).

      Since we cannot easily change the line colors of the IDR graphs, we have inverted the font color for Fig. S5B instead.

      • Movie S7, legend: left frames shows PfCen1-GFP, not microtubules as currently stated.

      Has been corrected.

      Reviewer #2 (Significance):

      This is a provocative study that extends initial observations regarding self-assembly properties of Centrin proteins, and posits that some members of this evolutionarily conserved family can form biomolecular condensates. After the above outstanding issues have been properly addressed, these data could have important implications for understanding Centrin function in centriole biology and DNA repair. Therefore, these findings will be of interest to a cell biology audience.

      Field of expertise: cell biology.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Summary:

      The authors have provided a comprehensive characterisation of centrin proteins in Plasmodium falciparum. Through expression of episomal GFP-tagged centrin for in vitro, they were able to observe co-localisation of centrin with centriolar plaques during the replicative stage of the parasite. They also utilised live cell STED microscopy to track dynamic changes in centrin morphology. They have also demonstrated calcium-dependent phase separation dynamics in bacterially-expressed P. falciparum centrin and human centrin 2. The formation of liquid-liquid phase separation in PfCen1, 3 and HsCen2 tied well with IUPred3 predictions of intrinsically disordered regions in these proteins. Using an inducible DiCre overexpression system with two promoters of varying strengths, the authors have shown accumulation of centrin1 outside of centrosomes and premature appearance of centriolar plaques. Finally, changes on the centrin1 protein, i.e., N-terminal deletion, and mutations in calcium binding sites in the EFh domains, have shown a reduction in the formation of ECCAs during overexpression and inability to form LLPS in vitro, respectively.

      Major comments:

      1. Given that parasites cannot tolerate endogenous C-terminal tagging of some centrins (but not all, as PbCen4 was successfully tagged), has N-terminal tagging been attempted either by the authors or in previous publications? Note that this is not a request for further experimentation; rather, maybe this can be noted in the manuscript; and line 62 can be rephrased for transparency.

      We have not attempted N-terminal tagging ourselves but through personal communication with Rita Tewari we were informed that neither N- nor C-terminal tagging for PbCen1-3 was successful in the context of the study published by Roques et al 2018. We have only unsuccessfully attempted C-terminal tagging in several iterations. Due to importance of N-terminus for interaction and function in other organisms it is plausible that N-terminal tagging is even more unlikely to work. Since we have not exhaustively attempted every tagging strategy on every centrin we, as suggested, rephrased the text accordingly (lines 81ff).

      1. Is there a possibility that by adding a C-terminal tag, centrin may lose a specific function or cause change in the physicochemical properties of the protein (thus making C-terminal tagging lethal)? Was His tag removal attempted so the native protein can be used in the LLPS experiments? IUPred3 analysis showed potential IDR at the C-terminal end of PfCen4. Could the C-terminal tag have caused the protein to not form droplets in the presence of Ca2+?

      As we could show for PfCen1-GFP, the tag did not impair its ability to undergo LLPS which is at least partly mediated by the N-terminus, and that it could still properly localizes to the centriolar plaque. The fact that some endogenous centrins cannot be tagged suggest that there is a functional relevance to the C-terminus that could e.g. be an interaction with other essential centriolar plaque components. As suggested in a reply to Reviewer 1, we consider a substantial and centrin-specific effect of the small His-tag on phase separation unlikely. To be sure, we have repeated our turbidity assays with tag-free versions of PfCen1-4 and found no change in phase separation properties (now Fig. S3E).

      1. It has been shown by the authors that different tagged centrins co-condense which may support the localisation data (Figure 1C). However, is there a way to show that the episomally- and endogenously-expressed centrin co-localise with each other (e.g., confocal microscopy with anti-centrin vs anti-gfp in PfCen-GFP lines, that is if the authors have access to anti-centrin antibodies)? Has endogenous centrin been demonstrated to form ECCAs (in previous publications or by the authors)?

      These are important questions by the reviewer. Due to the high sequence homology centrin antibodies, even if raised against a specific centrin (such as PfCen3 in this study), will likely cross-react with other centrins. So far, we have not been able to produce a staining were the anti-GFP-positive foci are devoid of anti-centrin3 staining, which limits the interpretation of these data. The outer centriolar plaque compartment containing centrin is, however, well defined by now and the localization pattern of endogenous centrin and Centrin1 and 4-GFP seems identical. In a more recent study from our lab Cen1-GFP IP has identified other endogenous centrins as interaction partners (Wenz et al 2023), like the Roques et al. 2018 study did for PbCen4-GFP indicating that the tag does not abolish interaction between centrins. So far, we have never detected any ECCAs, nor have we identified any similar structure in the literature. This suggest that this is indeed a consequence of excessive centrin concentration. Importantly we now have added data from a new parasite line overexpressing untagged PfCen1 using the T2A skip peptide (pFIO+_GFP-T2A-Cen1) which displays ECCAs upon induction, showing that this effect is not a mere consequence of tagging (now Fig. 5H-K).

      Minor comments:

      1. How were the times (post addition of Ca2+) presented in Figure 2A determined?

      We noted down the time of calcium addition and cross-referenced it with the timestamps available in the metadata of the movie files (e.g. file creation timepoint marks the start of the movie). We now mention this in the legend.

      1. Line 126: Figure 1B should be Figure 1C

      2. Line 145: Figure 1C-D should be Figure 1D-E

      3. Line 151: Figure 3A should be Figure 4A

      Thank you for spotting these mistakes, which now have been corrected.

      1. Line 152: Suggest rephrasing "placing the gene of interest in front of the promoter" to "placing the gene of interest immediately downstream of the promoter" or something similar

      Thank you for this good suggestion.

      1. Any growth phenotype changes observed in the overexpressors?

      The parasite lines seem to silence the Cen1-4-GFP expression plasmids readily, which suggest that there might be a growth disadvantage. However, repeated attempts to quantify a growth phenotype were unsuccessful due to high variability in the data, which might be partly connected to the fact that the fraction of GFP positive cells after induction can vary between lines and replicas.

      1. How often are ECCAs observed in pARL strains, or are they not observed at all? This might be good to mention.

      ECCAs in the pArl strains have been observed on very limited instances but are too rare to be quantified. We now mention this in the text (lines 217ff).

      1. Line 192 and Figure S8: n {less than or equal to} 33 (either a typographical error and should have been {greater than or equal to}, otherwise, it may be expressed as a range)

      It was indeed a typographical error that was now corrected.

      1. Line 258: Methods on the generation of FIO/FIO+ was a bit difficult to understand. Maybe a simple plasmid schematic with the restriction sites (at least for the original plasmid) in the supplementary may help clarify this.

      Cloning strategy has been expanded with additional information for clarity.

      1. Line 295: include abbreviation of cRPMI here rather than in Line 303

      Has been corrected.

      1. Line 322: typographical error on WR99210 working concentration?

      Has been corrected.

      1. Line 372: Last sentence on area and raw integrated density measurement is unclear.

      We have reformulated the sentence for more clarity.

      1. Line 461: typographical error in last sentence

      Has been corrected.

      1. Line 532: Figure 4E should be Figure 4F

      Has been corrected.

      Reviewer #3 (Significance):

      DNA replication is vital to the survival of malaria parasites. A deeper understanding on their unusual form of replication may be exploited to find drug targets uniquely directed to the parasite. Biological insights from this work can also provide a jump-off point for unravelling unusual replication in other organisms. Data on the physicochemical analysis of centrin is not just of great interest for those in the field of parasitology, but also for those in the much wider fields of biology, physics and chemistry. Techniques presented in this work (e.g., DiCre overexpression with different promoters) can definitely be utilised for the elucidation of protein function within and outside the field of parasitology.

      My field of expertise is in Plasmodium spp., particularly in parasite replication, molecular and cellular biology, and epigenetics.

      We thank the reviewer for the appreciation of our work in terms of insight and technology development.

  8. Aug 2023
  9. www.dreamsongs.com www.dreamsongs.com
    1. Because this PDF does not include outline metadata, I have inserted jump points by highlighting the names of the chapter on the page where that chapter begins for each chapter in the book. These can be filtered by the "chapter heading" tag.

    1. Why is the index card half full?

      reply to u/ManuelRodriguez331 at https://www.reddit.com/r/Zettelkasten/comments/15ehcy5/why_is_the_index_card_half_full/

      There has been debate about the length of notes on slips since the invention of slips and it shows no signs of coming to broad consensus other than everyone will have their personal opinion.

      If you feel that A6 is is too big then go down a step in size to A7. One of the benefits of the DIN A standard is that you can take the next larger card size and fold it exactly in half to have the next size smaller. This makes it easier to scale up the size of your cards if you prefer most of them to be smaller to save space, just take care not to allow larger folded cards to "taco" smaller cards in a way they're likely to get lost. If you really needed more space, you could easily use an A1 or A2 and fold it down to fit inside of your collection! (Sadly 4x6 and 3x5 cards don't have this affordance.)

      Fortunately there are a variety of available sizes, so you can choose what works best for yourself. Historically some chose large 5x8", 6x9", or even larger "slips". Some have also used different sizes for different functions. For example some use 3x5 for bibliographic cards and 4x6 for day-to-day ideas. I've seen stacked wooden card catalog furniture that had space for 3x5, 4x6, and 8.5x11 in separate drawers within the same cabinet. Some manufacturers even made their furniture modular to make this sort of mixed use even easier.

      One of the broadly used pieces of advice that does go back centuries is to use "cards of the same size" (within a particular use case). This consensus is arrived at to help users from losing smaller cards between larger/taller cards. Cards of varying sizes, even small ones, are also much more difficult to sort through. Slight of hand magicians will be aware of the fact that shaving small fractions of length off of playing cards is an easy way of not only marking them, but of executing a variety of clever shuffling illusions as well as finding some of them very quickly by feel behind the back. Analog zettelkasten users will only discover that smaller, shorter cards are nearly guaranteed to become lost among the taller cards. It's for this reason that I would never recommend one to mix 4x6, A6, or even the very closely cut Exacompta Bristol cards, which are neither 4x6 nor A6!

      I once took digital notes and printed them on paper and then cut them up to fit the size of the individual notes to save on space and paper. I can report that doing this was a painfully miserable experience and positively would NOT recommend doing this for smaller projects much less lifelong ones. Perhaps this could be the sort of chaos someone out there might actually manage to thrive within, but I suspect it would be a very rare individual.

      As for digital spacing, you may win out a bit here for "saving" paper space, but you're also still spending on storage costs in electronic formatting which historically doesn't have the longevity of physical formats. Digital also doesn't offer the ease of use of laying cards out on a desktop and very quickly reordering them for subsequent uses.

      There are always tradeoffs, one just need be aware of them to guide choices for either how they want to work or how they might work best.

      Personally, I use 4x6" cards because I often write longer paragraphs on them. Through experimentation I found that I would end up using two or more 3x5 cards more often than I would have had mostly blank 4x6 cards and used that to help drive my choice. I also find myself revisiting old cards and adding to them (short follow ups, links to other cards, or other metadata) and 3x5 wouldn't allow that as easily.

      As ever, YMMV...

      See also: [[note lengths]] and/or [[note size]].

  10. Jul 2023
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Please find our point-to-point response to the reviewer’s comments below, where we marked all changes implemented in the manuscript in italics.

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

      With the emergence and spread of resistance to Artemisinin (ART), a key component of current frontline malaria combination therapies, there is a growing effort to understand the mechanisms that lead to ART resistance. Previous work has shown that ART resistant parasites harbour mutations in the Kelch13 protein, which in turn leads to reduced endocytosis of host haemoglobin. The digestion of haemoglobin is thought to be critical for the activation of the artemisinin endoperoxide bridge, leading to the production of free radicals and parasite death. However, the mechanisms by which the parasites endocytose host cell haemoglobin remain poorly understood.

      Previous work by the authors identified several proteins in the proximity of K13 using proximity-based labelling (BioID) (Birnbaum et al. 2020). The authors then went on to characterise several of these proteins, showing that when proteins including EPS15, AP2mu, UBP1 and KIC7 are disrupted, this leads to ART resistance and defects in endocytosis leading to the hypothesis that these two processes are inextricably linked.

      In this manuscript, Schmidt et al. set themselves the task of characterising more K13 component candidates identified in their previous work (Birnbaum et al. 2020) that were not previously validated or characterised. They chose 10 candidates and investigated their localisations, and colocalisation with K13, and their involvement in endocytosis and in vitro ART resistance, 2 processes mediated by K13 and some members of the K13 compartments

      The authors show that of their 10 candidates, only 4 can be co-localised with K13. Then, using a combination of targeted gene disruption (TGD) as well as knock sideways (KS), they characterised these 4 proteins found in the K13 compartment. They show that MyoF and KIC12 are involved in endocytosis and are important for parasite growth, however their disruption does not lead to a change in ART sensitivity. The authors also confirm the findings of their previous publication (Birnbaum et al. 2020), using a slightly different TGD

      (note from the authors: we apologise if this has not properly transpired from the manuscript but the difference between the TGDs is substantial and relevant: one has less than 3% of the protein left and hence can be considered to fully inactivate MCA2 and has a growth defect whereas the other contains about two thirds of the protein (1344 amino acids/~66% are left), has no growth defect, although it lacks the MCA2 domain (hence that domain can not be critical for the growth defect)),

      that MCA2 is involved in ART resistance, however they did not check whether its disruption impacts haemoglobin uptake. They also show that KIC11 is not involved in mediating haemoglobin uptake or ART resistance. To finish, the authors used AlphaFold to identify new domains in the proteins of the K13 compartment. This led them to the conclusion that vesicle trafficking domains are enriched in proteins of the K13 compartment involved in endocytosis and in vitro ART resistance.

      The majority of the experiments conducted by the authors are performed to a good standard in biological and technical replicates, with the correct controls. Their findings provide confirmation that their 4 candidate genes seem to be important for parasite growth, and show that some of their candidates are involved in endocytosis. While the KD and KS approaches employed by the authors to study their candidate genes each have their own advantages and can be excellent tools for studying a large sets or genes, this manuscript highlights the many limitations of these approaches. For example, the large tag used for the KS approach can mislocalise proteins or disrupt their function (as is the case for MyoF), resulting in spurious results, or indeed the inability to generate the tagged line (as is the case for MCA2). The KS approach also makes the results of a protein with a dual localisation, like KIC12, extremely difficult to interpret.

      We thank the reviewer for this thorough and insightful review.

      The limitations mentioned above were addressed in the response to the main points and a general detailed response in regards to the systems used for this research are added at the end of this rebuttal. Briefly summarised here: while we agree that there are limitations of the system used, we are convinced that

      • the advantages of using a large tag in most cases outweighs the drawbacks as it permits to track the inactivation of the target, if need be on the individual cell level

      • while not optimal for MyoF, the partial inactivation actually helps in its functional study as detailed in major point 23&28 or reviewer#3 major point 11: it shows a consistent correlation of the phenotype with different causes and degrees of inactivation (this is now better illustrated in Figure 1L1M). Further, regarding the concern of the large tag: the effect of the tag based on localisation was overestimated in the review by what seems to have been a mix up comparing numbers from MyoF with a number from MCA2 (there is a difference, but it is only small) (see reviewer#1 major point #23).

      • KS is the optimal method for most of the assays in this work (e.g. bloated food vacuole assays and RSAs); these assays would be impossible or difficult to use with other inactivation systems currently used in P. falciparum research (see details in the response to the specific points and after the rebuttal)

      In regards to the difficulty to interpret KIC12 data: this is only true for measuring absolute essentiality, everything else we believe we actually have the optimal method. If not KS, which method targets a specific pool of a protein with a dual localisastion? Again, our assays targeting the K13 pool and revealing the specific function would have been difficult or impossible with any other system.

      Ultimately the question is whether any other system would have resulted in a different conclusion on the function of the proteins studied. At present we are confident this would not be the case and other systems probably would not have delivered the specific functional data shown in this work. Clearly, more in depth work will provide more nuanced and detailed insights into the proteins analysed in this work and this likely will also include the use of other systems for specific aspects they are most suitable for. However, this (e.g. different complementations in a diCre cKO) is complex and therefore beyond what fits into this work which had the goal to assess which proteins are true positives for the K13 compartment and to place them into functional groups in regards to endocytosis.

      Moreover, the manuscript is disjointed at times, with the authors choosing to conduct certain experiments for only a subset of genes, but not for others. For example, considering that the aim of this paper was to identify more proteins involved in ART resistance and endocytosis, it is confusing why the authors do not perform the endocytosis assays for all their selected proteins, and why they do not do this for the proteins they identify in their domain search. There is significant room for improvement for this manuscript, and a generally interesting question.

      The reviewer remarks that not every experiment was done for every target. Based on the rebuttal we tried to amend this but also note that there was some sentiment by the reviewers to better stick to the point and not make the manuscript more disjointed. We attempted to balance that as much as possible and hope we were able to honour both aspects (amendments were done as detailed in the point by point response below).

      In regards to endocytosis and choice of targets: We did do endocytosis assays for all proteins that showed a growth phenotype upon inactivation in this work. We therefore assume the reviewer here refers to major point #40 asking for endocytosis assays with KIC4 and KIC5 (which were not studied in this manuscript) as well as MCA2 (point 17). We fully agree with the reviewer that this would fill a gap in the work on K13 compartment proteins but such assays are difficult with TGDs (there are issues with non-comparable samples and compensatory effects) and proteins that are not essential (and hence likely have a smaller impact on endocytosis when truncated). We nevertheless now carried them out, but due to the limitations to do this with these lines would be hesitant to draw definite conclusions (see major point 17 and 40 for details and outcomes).

      But in it's current format, other than confirming that MCA2 is involved in ART resistance (which was already known from the Birnbaum paper), the authors do not further expand our understanding of the link between ART resistance and endocytosis in this manuscript.

      We would like to point out that the importance of the K13 compartment and endocytosis goes beyond ART resistance (see e.g. also newly published papers on the K13 compartment in Toxoplasma, (Wan et al., 2023; Koreny et al., 2023)). Endocytosis is an essential and prominent process in blood stages. However, in contrast to processes such as invasion, our understanding about endocytosis is only rudimentary. Hence, this manuscript provides important insights on an emerging topic that in our opinion deserves more attention:

      • it identifies novel proteins at the K13 compartment and provides 2 new proteins in endocytosis (MyoF and KIC12); getting an as complete as possible list of proteins involved in the process will be critical to study and understand it

      • it leads to the realisation that not all growth-relevant proteins detected at the K13 compartment are needed for endocytosis

      • it provides domains and stage specificity of function for several K13 compartment proteins, overall bolstering the model of endocytosis in ART resistance and providing a framework critical to direct future studies on endocytosis and their detailed mechanistic function at the cytostome

      • the identified vesicle trafficking domains (for instance now also found in UBP1) are expected to strengthen the support for the role of endocytosis of the K13 compartment; this and also the above points are important as (based on the current literature) there still seems to be prominent sentiment in the field that (in part due to the involvement of UBP1 and K13) the cause of ART resistance is due to various unclearly defined stress response pathways

      • with MyoF it also shows the first protein in connection with the K13 compartment that acts downstream of the generation of hemoglobin-filled containers in the parasite and provides the first protein that explains the suspected involvement of actin in endocytosis (so far this was only based on CytD studies)

      Overall we therefore believe this manuscript contains critical information and a framework for future studies on endocytosis and the K13 compartment. We hope the relevance of endocytosis as one of the most prominent and essential processes in the parasites and the connection to various aspects linked with many commercial drugs (in addition to the role of endocytosis in ART resistance), is adequately explained in the introduction. We also would like to mention that the main focus of the work is reflected in the title of the manuscript which does not mention ART susceptibility.

      Major Comments

      1) line 31: please change defined to characterised - defined suggests that novel proteins were identified in this study, which is not the case.

      We apologise, but we do not fully understand this comment. We did identify novel proteins not before known to be at the K13 compartment (MCA2 (admittedly this one was likely but had not previously been verified), MyoF, KIC11 and KIC12). In our view "further defining the composition of the K13 compartment" therefore is an accurate statement. Additionally, the identification of previously not-discovered domains, the stage-specificity and function of these proteins helped to further define the K13 compartment.

      If the reviewer is referring to the fact that the proteins analysed in this study were taken from a previously generated list of hits, we would like to stress that the presence in such a list (obtained from a BioID, but also if from an IP etc) can not be equalled for them to be true positives, they are merely candidates that still need to be experimentally validated. This is what we did in this work to find out which further proteins from the list can be classified as K13 compartment proteins (for hits with lower FDRs this is even more relevant as illustrated by the fact that 6 of the here analysed hits were not at the K13 compartment). In an attempt to address this comment in the manuscript, we changed the wording of this sentence to (line 31): "Here we further defined the composition of the K13 compartment by analysing more hits from a previous BioID, showing that MyoF and MCA2 as well as Kelch13 interaction candidate (KIC) 11 and 12 are found at this site."

      2) line 37: please change 'second' to "another". As explained further below, the authors identified 3 classes of proteins (confer ART resistance + involved in HCCU, involved in HCCU only, or involved in neither).

      We realized that the groups description wasn’t clear in the abstract. Please see response to major comment #41 for a detailed answer to this (endocytosis is an overarching criterion, ART resistance is a subgroup and applies only to those proteins with a function in endocytosis in ring stages). To clarify this (see also major point #8) we added an explanation on the influence of stage-specificity of endocytosis on ART susceptibility to the introduction (line 76): In contrast to K13 which is only needed for endocytosis in ring stages (the stage relevant for in vitro ART resistance), some of these proteins (AP2µ and UBP1) are also needed for endocytosis in later stage parasites (Birnbaum et al., 2020). At least in the case of UBP1, this is associated with a higher fitness cost but lower resistance compared to K13 mutations (Behrens et al., 2021; Behrens et al., 2023). Hence, the stage-specificity of endocytosis functions is relevant for in vitro ART resistance: proteins influencing endocytosis in trophozoites are expected to have a high fitness cost whereas proteins not needed for endocytosis in rings would not be expected to influence resistance.” The abstract was changed in response to this and other comments and hope it is now clearer in regards to the groups.

      3) Line 40: You define KIC11 as essential but according to your data some parasites are still alive and replicating 2 cycles after induction of the knock sideways. Please consider changing "essential" to "important for asexual parasite growth".

      We fully agree with the reviewer, we reworded the sentence as suggested.

      4) Line 40: please change 'second group' to 'this group'

      We reworded this part of the abstract and it know reads: (line 38): “While this strengthened the link of the K13 compartment to endocytosis, many proteins of this group showed unusual domain combinations and large parasite-specific regions, indicating a high level of taxon-specific adaptation of this process.”

      5) line 41: state here that despite it being essential, it is unknown what it is involved in.

      With the newly added data we show that this protein either has a function in invasion or very early ring development although we did not see any evidence for the latter. We therefore changed the sentence to (line 43): “We here identified the first protein of this group that is important for asexual blood stage development and showed that it likely is involved in invasion*..” *

      6) Line 50: the authors should state here that there is actually a reversal in this trend over the last few years.

      Done as suggested.

      7) Line 54: please separate out the references for each of the two statements made in this line (a: that ART resistance is widespread in SEA, and b: that ART resistance is now in Africa) Reference 14 also seems to reference ART resistance in Amazonia - which is not covered by the statement made by the authors (in which case the authors should state ART is now present in Africa and South America). The authors should also reference PMID: 34279219 for their statement that ART resistance is now found in Africa (albeit a different mutation to the one found in SEA).

      Done as suggested.

      8) Line 65: it is also worth mentioning here that there are other mutations in proteins other than K13, such as AP2mu and UBP1 (PMID: 24994911;24270944) that can lead to ART resistance.

      As suggested by the reviewer, we included a sentence about non-K13 mutations linked with reduced ART susceptibility in the introduction (line 74): Beside K13 mutations in other genes, such as Coronin (Demas et al., 2018) UBP1 (Borrmann et al., 2013; Henrici et al., 2020b; Birnbaum et al., 2020; Simwela et al., 2020) or AP2µ (Henriques et al., 2014; Henrici et al., 2020b)* have also been linked with reduced ART susceptibility." *

      We here also added data on fitness cost that is related to this and is also relevant for the issue of proteins with a stage-specific function in endocytosis, making a transition for this statement which might help clarifying the grouping of K13 compartment proteins (see also major point #2).

      9) Line 80, 86: ref 43 is misused. Reference 43 refers to Maurer's clefts trafficking which takes place in the erythrocyte cytosol and is not involved in haemoglobin uptake as far as I know. Please replace ref 43 with one showing the role of actin in haemoglobin uptake.

      We thank the reviewer for pointing this out, Ref 43 was removed from the manuscript.

      10) Line 98: the authors state here that they 'identified' further candidates from the K13 proxiome. This suggests that they identified new proteins in this paper, when in fact the list was already generated in ref 26. All they did was characterise proteins from that list that were not previously characterised. The authors should therefore remove identified from this statement.

      We agree with the reviewer that we did not identify further candidates, we identified new K13 compartment proteins from the list of potential K13 compartment proteins. We therefore changed “identified further candidates” into “identified further K13 compartment proteins” (line 116). Please see also response to major comment #1.

      11) Line 107-108: it is not clear from this sentence why these proteins were left out of the initial analysis in Ref 26. A sentence here explaining this would be valuable for the reader.

      This is a good point. One reason why we did not analyse more in our previous publication was that we had to stop somewhere and adding more would have been very difficult to fit into what was already a packed paper. However, as shown in this work, the list does contain further interesting candidates (e.g. K13 compartment proteins that are involved in endocytosis).

      We altered the relevant part of the introduction to highlight that we previously analysed the top hits, clarifying that the 'remaining' hits analysed in this work were further down in the list. This now reads: (line 113)“We reasoned that due to the high number of proteins that turned out to belong to the K13 compartment when validating the top hits of the K13 BioID (Birnbaum et al., 2020), the remaining hits of these experiments might contain further proteins belonging to the K13 compartment.” We hope this clarifies that we simply moved further down in the candidate list.

      12) Line 117-123: The authors say that PF3D7_0204300, PF3D7_1117900 and PF3D7_1016200 were not studied because they were not in the top 10 hits. However, the current organisation of Supplementary Table 1 shows all 3 proteins among the top 10 hits (MyoF, KIC12, UIS14 and 0907200 being after them). I think the authors should reorganise their table. It is also unclear according to what the proteins in the table are ranked. Could the authors indicate the metric used for the ranking?

      We thank the reviewer for alerting us to this. The issue here is that the 3 non-analysed proteins belong to a 'lower stringency' group comprising hits significant with FDRThe information about ranking is now also included as “Table legend” in the revised manuscript and the Table heading has been changed to: List of putative K13 compartment proteins, proteins selected for further characterization in this manuscript are highlighted.”

      13) Line 129-141: Can the authors be clearer with their explanations of the identification of mutation Y1344Stop? One dataset (ref 61) shows that 52% of African parasites have a mutation in MCA2 in position 1344 leading to a STOP codon. But another dataset (ref 62) shows that the next base is also mutated, reverting the stop codon. That should have been seen in the first dataset as well. Could the authors please clarify.

      This mutation was first spotted in the MalariaGEN database (https://www.malariagen.net) (MalariaGEN et al., 2021), which allows online accessing of the data by using the “variant catalogue” tool, which is in a table format of frequency rather than in a sequence context. Hence, only after further research later on it became evident to us, that this mutation does not occur alone when looking at individual MCA2 sequences from patient samples in (Wichers et al., 2021b). We hope this is accurately reflected in our results section.

      14) Line 147: the authors say that MCA2 is expressed throughout the intraerythrocytic cycle as shown by live cell imaging. In Birnbaum et al 2020 fig 4I, the authors show that MCA2 is mainly expressed between 4 and 16hpi. But in Figure 1B of this manuscript there is a clear multiplication of MCA2 signal between trophozoite and schizont. How do the authors explain this discrepancy? Could expression of the truncated MCA2 be different than the full length? This cannot be assessed as expression and localisation of the full-length HA tag MCA2 is not shown in Schizonts.

      The key difference lies in transcription vs protein expression (usually protein levels peak after mRNA levels peak and - depending on turnover - protein levels can stay high even after mRNA levels have declined). Figure 4 of the Birnbaum et al paper presents transcriptomic data, but with a peak in trophozoites (The axis label in Fig. 4l of that publication is a bit confusing, as hour 0 is at the top, 48 h at the bottom; it is clearer in Fig. S13 of that paper) which would fit very well with the multiplication of the signal between trophozoites and schizonts mentioned by the reviewer. So, overall, the temporal peaks of transcripts and protein of that protein fit well.

      For the signal in rings: Likely the protein has a turnover rate that is sufficiently low for some protein to be taken into the new cycle after re-invasion. Also different transcriptomic datasets e.g. (Otto et al., 2010; Wichers et al., 2019; Subudhi et al., 2020) available on plasmoDB show some mRNA present across the complete asexual development cycle, with each dataset showing maximum peak at a slightly different stage.

      Even when located in foci and hence aiding detection of small amounts of protein (as is the case for MCA2-Y1344-GFP), the MCA2 signal in rings is not strong. For MCA2-TGD, the GFP signal is dispersed and therefore likely below our detection limit, while the same amount of protein concentrated at the K13 compartment is visible as foci in the MCA2-Y1344 cell line. Please note that MCA2-TGD has only 2.8% of the protein left whereas MCA2-Y1344 has 66.5% left and based on our manuscript is almost fully functional, hence fitting the different locations between the two versions.

      Overall we believe this shows that there are actually no significant discrepancies of the expression of the different MCA2 versions.

      15) Line 158: would it not have been more useful for the authors to have episomally expressed MCA2-3xHA in their MCA2Y1344STOP-GFPENDO line to make sure that the truncated protein is indeed going to the correct compartment? The experiments done by the authors suggests that the MCA2Y1344STOP goes to the right location but does not really confirm it.

      We appreciate the reviewers caution here. However, considering that MCA2Y1344STOP-GFPendo co-locates with mCherryK13 and endogenously HA-tagged full length MCA2 does the same to a similar extent, there is in our opinion little doubt that MCA2 is found at the K13 compartment and that this is similar with both constructs. If there are minor differences, these might as well occur if MCA2 is episomally (as suggested in the comment) instead of endogenously expressed. Given the limited insight, we therefore decided against the episomal overexpression (which due to its size of > 6000bp may also be somewhat less straight forward than it may sound).

      16) Line 191: it is stated that MCA2 confers resistance independently of the MCA domain, however in both the MCA2-TGD and MCA2Y1344STOP-GFPENDO parasites, the MCA domain is deleted, and for both parasites, there is resistance (albeit to a lower level in the MCA2Y1344STOP-GFPENDO line). Therefore, how can the authors state that the ART resistance is independent of the MCA domain? This statement should be that resistance is dependent on the loss of the MCA domain.

      We agree that this can’t be categorically excluded. However, a ~5 fold difference in ART sensitivity was observed between the parasites with MCA2 truncated at amino acid 57 compared to those with MCA at amino acid 1344 even though both do not contain the MCA2 domain. Hence, at least this difference is not dependent on the MCA2 domain. The larger construct missing the MCA domain shows only a very moderate reduction in RSA survival, again suggesting the MCA domain is not the main factor. We amended our statement in an attempt to more accurately reflect the data (line 487): This considerable reduction in ART susceptibility in the parasites with the truncation at MCA2 position 57 compared to the parasites still expressing 1344 amino acids of MCA2, despite both versions of the protein lacking the MCA domain, indicates that the influence on ART resistance is not, or only partially due to the MCA domain.” We would be hesitant to state the reviewer's conclusion that “resistance is dependent on the loss of the MCA domain”, as the larger construct missing the MCA2 domain has a milder RSA effect compared to MCA2-TGD, which suggests the reduction in ART susceptibility is independent of the MCA domain. These considerations also agree with the fact that the parasites with the longer MCA2 version (in contrast to the MCA2-TGD) do not have any detectable growth defect which indicates that the protein can fulfil its function without the MCA2 domain.

      17) Line 192: Why did the authors not check if MCA2 is involved in endocytosis? They state later on in the manuscript that they did not do endocytosis assays with TGD lines, however if the authors include the correct controls, this could be easily done. It would also be really interesting to see whether endocytosis gets progressively worse going from WT to MCA2Y1344STOP to MAC2TGD. This experiment (as well as doing endocytosis assays for KIC4 and KIC5 TGD lines) would drastically increase the impact of this study. These experiments would not take more than 3 weeks to perform, and would not require the generation of new lines.

      So far were very hesitant to do bloated FV assays with TGDs (even though TGDs were available for the genes encoding MCA2 and KIC4 and KIC5). The reason for this was:

      1. the fact that these proteins could be disrupted indicated either redundancy or only a partial effect on endocytosis which might lead to only small effects that likely are difficult to pick up in an assay scoring for the rather absolute phenotype of bloated vs non-bloated. Using the refined assay measuring FV size could partly amend this but we note that also FV without hemoglobin have a certain size, reducing the relative effect if there are smaller differences.
      2. a TGD line does not permit tightly controlled inactivation of the target which makes comparing the outcome of bloated food vacuole assays difficult if there are smaller growth and stage differences to the 3D7 control.
      3. in contrast to conditional inactivation parasites, the TGD lines had ample times to adapt to loss of the target protein (compensatory mechanisms are well known for endocytosis, for instance in clathrin mediated endocytosis loss of individual components can be compensated (Chen and Schmid, 2020)). We nevertheless see the reviewer's point that this should at least be attempted and now conducted these assays (see also major point 40). For MCA2 (as requested in this point), the data is shown in Figure S5C-E. This assay showed that in MCA2-TGD, MCA2Y1344STOP-GFPendo (similar to the 3D7 control) >95% of parasites developed bloated food vacuoles. Additionally, we also measured the parasite and food vacuole size of individual cells in an attempt to solve some of the problems with TGDs with such assays. In order to specifically solve problem 2 mentioned above, we analysed the food vacuoles of similarly sized parasites, however, they were non-distinguishable between the three lines. Of note, in agreement with the reduced parasite proliferation rate (Birnbaum et al., 2020) a general effect on parasite and food vacuole size was observed for MCA2-TGD parasites, indicating reduced development speed in these parasites. Hence, it is possible that a potential endocytosis reduction was accompanied by a slowed growth, and the comparison of similarly sized parasites may have obscured the effect. It is therefore not sure if there indeed is no endocytosis phenotype, although we can exclude a strong effect in trophozoites.

      Based on the RSA results at least rings can be expected to have a reduced endocytosis in the MCA2-TGD. Apart from options 1-3 mentioned above, it is therefore possible there is an effect restricted to rings, although in that case the reduced growth in trophozoites would be due to other functions of MCA2. Overall, we can conclude that the MCA2-TGD parasites do not have a strongly reduced endocytosis, but given the fact that the parasites are viable, this is not surprising. Whether the MCA2-TGD has no effect at all on endocytosis we would be very hesitant to postulate based on these results.

      18) The authors should consider re-organising the MCA2 section, first showing that the 3xHA tagged line colocalises with K13, then performing the new truncation.

      We attempted to re-organise as suggested but because we now included additional fluorescence microscopy images of schizont and merozoites (in response to reviewer 2 major comment 3) the main figure would become even larger. To prevent this, we kept the 3xHA data in the supplement.

      19) Line 197: Once again ref 43 is not correct to illustrate that actin/myosin is involved in endocytosis

      We thank the reviewer for pointing this out – we removed Ref 43.

      20) Line 202: the authors state that MyoF localises near the food vacuole from ring stage/trophs onwards. However, how can this statement be made in schizonts based on these images (Fig. 2A), where it doesn't look like MyoF is anywhere near the FV? This statement can only be made for schizonts if co-localised with a FV marker (which is done in Fig. 2B), however, based on the number of MyoF foci, it appears that this was not done for schizonts. Please either remove the statement that MyoF is near the food vacuole from trophs onwards (because it is only seen near the FV up until trophs) or show the data in Fig. 2B of schizonts to substantiate these claims.

      This is a valid point. We originally did not focus on schizonts because most markers end up in some focal area in the forming merozoite but other proteins (such as e.g. K13) also have one or more additional foci at the FV, making interpretation unclear, particularly if the schizont is still organizing to become fully segmented. This is why we generally focused the K13 co-localisations on the trophozoite stage to obtain the clearest information on endocytosis. However, given the fact that this manuscript gives the first localization of MyoF in P. falciparum parasites, we now provide a comprehensive time course (Figure 1C, S1A) including schizonts, which show quite a complex pattern: while the MyoF-GFP localization in trophozoites appeared as multiple foci close to K13 and also the FV, the MyoF-GFP pattern changes in late schizonts (fully segmented) and merozoites, appearing as elongated foci no longer close to K13 or the FV. Of note, this pattern has been previously reported for MyoE in P. berghei (Wall et al., 2019).

      We therefore revised the statement about MyoF localization in schizont to better reflect the observed localization: (line 175): In late schizonts and merozoite the MyoF-GFP signal was not associated with K13, but showed elongated GFP foci (Figure 1C, S2A) reminiscent of the MyoE signal previously reported in P. berghei schizonts (Wall et al., 2019).”

      21) Line 204-206: what does this statement bring to the paper? Is it to show that it is the real localisation of MyoF because 2 tag cell line show the same localisation? I don't think this is needed, especially as later in the manuscript an HA-tag MyoF line is used and show similar localisation.

      We see the reviewers point, but prefer to keep this data included in the supplement, particularly because potential differences in the location of tagged MyoF were a major concern.

      Related to the tag issue: in order to get a better understanding of the effect of C-terminally tagging with different sized tags we now performed a more detailed analysis of the MyoF-3xHA cell line (Figure S2F-G), showing that this cell line shows a growth rate similar to the 3D7 wild type parasites, and has less vesicles than the 2x-FKBP-GFP-2xFKBP cell line, but still slightly, but significantly more than 3D7 parasites. Overall, this indicates that the smaller 3xHA tag has less effect on the parasite, than the larger 2x-FKBP-GFP-2xFKBP tag (see also new Figure 1L, showing a correlation of level of inactivation and the endocytosis phenotype for MyoF).

      22) Line 212: The overlap of K13 with MyoF in Figure 2C 3rd panel (1st trophozoite panel) is not obvious, especially as the MyoF signal seems inexistant. I would advise the authors to replace with a better image. Also, why are there no images of schizonts shown in Figure 2C?

      As suggested we exchanged the trophozoite image of panel Figure 2 C (now Figure 1C) and expanded this panel with images covering the complete asexual development cycle including schizonts in response to this and the previous points. As indicated above (point 20), schizont stages are complex to interpret. While late schizonts likely are not very relevant for endocytosis this is the first description of the location of the protein in this parasite and we therefore now provide a more thorough representation of the MyoF location across asexual stages in Figure1C and S2A.

      23) Line 217: the spatial association of MyoF with K13 is very different when it is tagged with GFP and when it is tagged with 3xHA. The way the authors word it here, it seems that there is agreement with the two datasets, when this is not in fact the case (59% overlap for MyoF-GFP and only 16% overlap with MyoF-3xHA). These data suggest that the GFP and the multiple FKBP tags are doing something to the protein and therefore maybe the ensuing results using this line should not be trusted or be taken with a pinch of salt.

      We agree with the reviewer that the location of this MyoF-GFP in the cell might differ due to the partial inactivation but in contrast to this comment, the data does not indicate any large differences. It seems the reviewer mixed something up (the 59% mentioned might come from the MCA2 figure?). The data with the two lines with differently tagged MyoF co-localised with K13 are actually quite comparable: GFP-tagged vs HA-tagged MyoF overlapping with K13 was 8% vs 16% full overlap, 12% vs 19% partially overlapping foci, 36% vs 63% foci that were touching but not overlapping (compare what now is Figure 1D and Figure S2C). Only in the 'no overlap' there is a much smaller proportion in the HA-tagged line. However, given that these are IFAs which on the one hand are more sensitive to see small protein pools but on the other hand also have pitfalls due to fixing of the cells (e.g. tiny increase in focus size due to fixing could increase the number of touching foci that in live cells might be close but did not touch), some variation can be expected to the live cells. We agree though that the partly reduced functionality of MyoF might be the reason for the consistent tendency of a lower overlap even though the difference is much less than indicated in the comment. We added "with a tendency for higher overlap with K13 which might be due to the partial inactivation of the GFP-tagged MyoF" to the sentence "IFA confirmed the focal localisation of MyoF and its spatial association with mCherry-K13 foci"

      While we expect the fact that the difference between these parasites is only small somewhat reduces the "pinch of salt" with the MyoF line, we do agree that the partial functional inactivation of the GFP-tagged MyoF line may have some impact. However, we do not think that this means the results with the MyoF-GFP line are untrustworthy. On the contrary, it provides insights into its function that in some ways is equivalent to a knock down or TGD. Overall all the MyoF lines show: few vesicles occur in the MyoF-HA-line, more in the MyoF-GFP line and even more after knock sideways of MyoF-GFP. Importantly the severity of this phenotype correlates with the growth rates in these lines. Hence, together with the bloated food vacuole assays, this provides consistent data indicating that MyoF has a role in the transport of HCC to the FV and its level of activity correlates with the number of vesicles and growth. To better highlight this, it is now summarised in Figure 1M.

      24) Line 219: the authors state here that they could not detect MyoF-GFP in rings, when in Figure 2C they show MyoF-GFP in rings, and also show that they could detect MyoF in Sup Fig. 3B with the 3xHA tagged line. Is this a labelling mistake in Figure 2C? If the authors could indeed not see MoyF-GFP in rings, this statement should have been made when Figure 2A was presented, and not so late in the manuscript, which causes confusion.

      We thank the reviewer for pointing this out. We now provide a detailed time course (see also previous points) which shows that there is no detectable MyoF-GFP signal during ring stage development until the stage where the parasites starts the transition to trophozoites (i.e. MyoF-GFP signal could only be observed in parasites already containing hemozoin). In addition to the extended time course in Figure 1C (previously 2C) we included a panel of example ring stage images below to further highlight this. We also changed the labelling of the parasite with MyoF-GFP signal the reviewer mentions in Figure 1C to “late ring stage” (it already contains hemozoin) to clarify this.

      The description of Figure 1A is now changed to: (line 153) *“The tagged MyoF was detectable as foci close to the food vacuole from the stage parasites turned from late rings to young trophozoite stage onwards, while in schizonts multiple MyoF foci were visible (Figure 1A, S2A).” *

      Please see our answer to major comment #45 where we provide an explanation for the difference between MyoF-3xHA and MyoF-GFP signal in ring stage parasites.

      [Figure MyoF]

      25) Line 237: Showing a DNA marker (DAPI, Hoecht) for Figure 2E, and subsequent figures using mislocalisation to the nucleus, would help the reader assess efficiency of the mislocalisation.

      Please see response to major comment #64 for a detailed answer on why we did not include DNA staining in the imaging used to assess mislocalization upon knock-sideways.

      26) Line 254-256: authors should show the results of the bloating assay for parental 3D7 parasites (+ and - rapalog) to see whether the MyoF line - rapalog has increased baseline bloating. This applies to all subsequent FV bloating assays.

      We did do several controls for bloated assays (including +/- rapalog of an irrelevant knock sideways line as well as using a chemical insult for which the control was 3D7 without treatment) in previous work (Birnbaum et al., 2020), which indicated that there is no effect of rapalog to reduce bloating. Although these controls are more stringent, we nevertheless did a 3D7 +/- rapalog control and added this to the manuscript (Figure S2I). As it is not possible to do this side by side with the assays that are already in the manuscript and the +/- rapalog 3D7 cells consistently showed no or very low numbers of cells without bloating (and stringent controls in the past equally did not show an effect), we believe adding this control once suffices.

      27) Line 254-257: The authors say that because fewer parasites show a bloated food vacuole upon inactivation of MyoF it means that less hemoglobin reached the food vacuole. I understand the authors statement, however, shouldn't they look at the size of the food vacuole, instead of the number of parasites with bloated FV, to make such a statement? This has been done for KIC12 so why not doing it for MyoF?

      This was now done and is provided as Figure 1J-K, S2J. The results confirm the assessment scoring bloated vs non-boated food vacuoles.

      28) Line 259-261: these results would be difficult to interpret namely because the authors have dying parasites, which is exacerbated with the protein being knocked sideways. The authors should mention the pitfalls their knock sideways and tagging design here. Line 260-261: RSA is an assay relying on measuring parasite growth 1 cycle after a challenge with ART for 6 hours.

      Fortunately, this concern is unfounded, as the survival (measured by parasitemia after one cycle) of the same sample + and - DHA is assessed, isolating the DHA effect independent of potential growth defects which are cancelled out. Hence, if there were parasites dying in the MyoF line (please note that they might not actually die, but simply grow more slowly), this factor applies for both the + and - ART condition. As we are testing for a decreased susceptibility to ART which would manifest as an increased survival in RSA surfacing above 1%, antagonistic effects of reduced MyoF function and ART treatment would not result in detectable differences as without effect, the RSA survival is always close to zero.

      The same applies for the knock sideways where we assess the survival of +rapalog between +ART and -ART. If the reduced MyoF activity of the knock sideways leads to a decreased survival, this applies to both +ART and -ART. Please also note that rapalog was lifted after the DHA pulse (see e.g. Figure S2K).

      That effects on growth are cancelled out is nicely illustrated for proteins where there is a stronger and more rapid effect on growth upon their conditional inactivation. For instance when KIC7 is knocked aside, there is a considerable increased of RSA survival, even though continued inactivation of KIC7 would have a severe growth defect (Birnbaum et al., 2020). Vice versa, a growth defect alone does not result in reduced RSA susceptibility as evident from knock sideways of an unrelated protein or using a chemical insult (Figure 4H in (Birnbaum et al., 2020) or simply slowing the ring stage by e.g. reducing EXP1 levels (Mesén-Ramírez et al., 2019). Hence, a growth reduction is not expected to alter the RSA outcome. And even if it did, it would only lead to an underestimation of the readout if growth is too severely affected (which would be obvious in the + rapalog without DHA sample, which was not the case).

      In that respect it is valuable to have the rapid kinetics of knock sideways which permit inactivation of a protein before severe growth defects occur (although the only partial responsiveness of MyoF clearly is not the most optimal). In contrast, the absolute loss of a gene (as is the case if diCre is used) prevents (or at least makes it extremely difficult as the timing would need to exactly hit sufficient protein reduction without killing the parasite until the end of the RSA) using this system in these experiments (again see (Mesén-Ramírez et al., 2021) where in a EXP1 diCre based knock out RSA was only possible because we complemented with a lowly, episomally expressed EXP1 copy to have parasites with only a partial phenotype to do this assay).

      29) Line 261-263: the authors sate that MyoF has a function in endocytosis but at a different step compared to K13 compartment proteins. I am not sure what they mean here. Can this be clarified?

      The different steps in endocytosis are explained in the introduction and we now tried to further clarify this (line 98). So far VPS45 (Jonscher et al., 2019), Rbsn5 (Sabitzki et al., 2023), Rab5b (Sabitzki et al., 2023), the phosphoinositide-binding protein PX1 (Mukherjee et al., 2022), the host enzyme peroxiredoxin 6 (Wagner et al., 2022) and K13 and some of its compartment proteins (Eps15, AP2µ, KIC7, UBP1) (Birnbaum et al., 2020) have been reported to act at different steps in the endocytic uptake pathway of hemoglobin. While inactivation of VPS45, Rbsn5, Rab5b, PX1 or actin resulted in an accumulation of hemoglobin filled vesicles (Lazarus et al., 2008; Jonscher et al., 2019; Mukherjee et al., 2022; Sabitzki et al., 2023), indicative of a block during endosomal transport (late steps in endocytosis), no such vesicles were observed upon inactivation of K13 and its compartment proteins (Birnbaum et al., 2020), suggesting a role of these proteins during initiation of endocytosis (early steps in endocytosis).

      VPS45 has not apparent spatial connection to the K13 compartment but the fact that MyoF does - and its inactivation also results in vesicle accumulation - indicates that it is downstream of vesicle initiation, providing the first connection from the initiation phase to the transport phase. More evidence for these different steps of endocytosis has been published in a recent preprint from our lab, where we simultaneously inactivated a protein of both “endocytosis steps” (Sabitzki et al., 2023).

      To clarify this in the results as requested, we changed the statement to: (line 256) Overall, our results indicate a close association of MyoF foci with the K13 compartment and a role of MyoF in endocytosis albeit not in rings and at a step in the endocytosis pathway when hemoglobin-filled vesicles had already formed and hence is subsequent to the function of the other so far known K13 compartment proteins.”

      30) Do the authors mean that it is involved in endocytosis but not in ART resistance? If so, this is a very difficult statement to make since the parasites are dying. Is there any evidence of point mutations in MyoF in the field?

      We split this point to address all issues raised here. Please see response to point 29 which clarifies that this was meant in a different way and our response to point 28 which explains why the dying parasite issue is not expected to affect the RSA (please also note that we do not have evidence of actually dying parasites in the MyoF-2xFKBP-GFP-2xFKBP line, most likely the growth is slowed).

      The mutation issue is interesting. In fact evidence exists that MyoF mutations may be associated with resistance (Cerqueira et al., 2017) (please note that there it is still called MyoC) but in a recent preprint from our lab we did not find any evidence for a significantly changed RSA survival in 12 tested mutations in the corresponding gene (Behrens et al., 2023).

      To clarify this we added the following statement to the discussion (line 709): "Of note, mutations in myoF have previously been found to be associated with reduced ART susceptibility (Cerqueira et al., 2017), but 12 mutations tested in the laboratory strain 3D7 did not result in increased RSA survival (Behrens et al., 2023)*. *

      31) Line 298: the authors state that there is no growth defect in the first cycle when rapalog is added to the KIC11 line, however based on Figure 3D, there is evidently a 25% reduction in growth compared to - rapalog at day 1 post treatment, and a 60% reduction by day 2, which is still within the 1st growth cycle. The authors should either revise their statement or provide an explanation for these findings. The authors should also explain why their Giemsa data in Fig. 3E is not in accordance with their FACS data.

      We think there is a misunderstanding here, as our figure legend was not detailed enough and we apologise if this had been misleading. The growth effect is restricted to invasion or possibly the first hours of ring stage development (see point 4&5, reviewer 2), which in asynchronous cultures more rapidly takes effect as the culture also contains schizonts that immediately generate cells that re-invade but can't due to inactivation of KIC11 (due to the rapid action of the knock sideways, KIC11 is already inactivated). In contrast, in highly synchronous cultures, this effect can only be evident once the parasites reached the schizont stage (starting with rings this takes close to 2 days). We now clarify that Figure 2E (previously Figure 3D) shows growth data obtained with an asynchronous parasite culture, while in Figure 2F the growth assay is performed with tightly synchronized (4h window) parasites as stated in the Figure legend.

      We now explicitly state in each Figure legend and for each growth experiment throughout the manuscript whether we used asynchronous or synchronized parasites for growth assays.

      Related to this, the incorrect y-axis label of what is now Figure 2E mentioned in major comment #58 is now corrected.

      32) Line 301: KIC11 could also be important very early for establishment of the ring stage for example for establishment of the PV. Also, was mislocalisation assessed in rapalog-treated parasites at 72 hours or in cycle 3?

      This is a valid point and this has now been addressed. We performed an invasion/egress assay revealing similar schizont rupture rates, but significantly reduced numbers of newly formed ring stage parasites (Figure 2H, S3G), indicating an effect of KIC11 inactivation either on invasion or possibly the first hours of ring stage development. A very similar point was raised by Reviewer 2, please see reviewer 2; major comment #4. This is now also reflected in line 302, which now reads: ”… indicating an invasion defect or an effect on parasite viability in merozoites or early rings but no effect on other parasite stages (Figure 2F-H, Figure S3F-G).”

      We further included an assessment of mislocalization 80 hours after the induction of knock-sideways by addition of rapalog in Figure S3E which showed mislocalization of KIC11 to the nucleus.

      33) Line 311: the authors should change the sentence from 'not related to endocytosis' to 'not related to endocytosis or ART resistance'.

      Done as suggested.

      34) Line 323-325: Authors say that a nuclear GFP signal can be observed in early schizonts for KIC12. According to the pictures provided in Figure 4A and Figure S5A it is not very obvious. Also faint cytoplasmic GFP signal could only be background as we can see that exposure is higher for schizont pictures

      We changed the sentence (line 339) to: “…nuclear signal and a faint uniform cytoplasmic GFP signal was detected in late trophozoites and early schizonts and these signals were absent in later schizonts and merozoites (Figure 3A, Figure S4A,B).” in order to emphasize that the nuclear signal disappears early during schizont development.

      35) Line 326-328: The authors say that kic12 transcriptional profile indicate mRNA levels peak (no s at peak) in merozoites. Should they show live cell imaging of merozoites then? Because from the Figure 4A schizont pictures where schizonts are almost fully segmented no signal can be observed.

      The observation that mRNA levels of early ring stage expressed proteins tend to increase already in mature schizonts and merozoites is well established (e.g. (Bozdech et al., 2003)). A very good example for this are exported proteins of which most show a transcription peak in schizonts but the proteins are only detected in rings see e.g. (Marti et al., 2004). Hence, our observation for KIC12 is quite typical.

      We originally did not include merozoites, as in the last row of Figure 3B fully developed merozoites within a schizont with already ruptured PVM are shown and no GFP signal can be detected in these parasites. We now provide images of free merozoites in Figure S4A-B showing again no detectable GFP signal.

      We thank the reviewer for pointing out the typo, "peak" has been corrected.

      36) Line 347: The authors state that using the Lyn mislocaliser the nuclear pool of KIC12 is inactivated by mislocalisation to the PPM. This tends to suggest that only the nuclear pool of KIC12 is mislocalised. How is it possible that only the nuclear pool is mislocalised?

      The Lyn mislocaliser is at the PPM which is continuous with the cytostomal neck where the K13 compartment likely is found. The effect of the Lyn mislocalizer on the KIC12 protein pool localizing at the K13 compartment is therefore somewhat unclear. For this reason we already had the following statement in the original submission (line 400): “Foci were still detected in the parasite periphery and it is unclear whether these remained with the K13 compartment or were also in some way affected by the Lyn-mislocaliser.” We would like to stress here that the same does not apply to the nuclear mislocaliser, which is only a trafficking signal delivering KIC12 to the nucleus and hence likely does not affect the nuclear pool of KIC12, only the K13 compartment pool (the main interest of this manuscript).

      We realised that the statement towards the end of this paragraph was unnecessarily ambiguous in regards to the K13 compartment pool of KIC12 which might have caused some confusion about the function of this pool of KIC12 and therefore modified it to (line 374): "Due to the possible influence on the K13 compartment located foci of KIC12 with the Lyn mislocaliser, a clear interpretation in regard to the functional importance of the nuclear pool of KIC12 other than that it confirms the importance of this protein for asexual blood stages is not possible. In contrast, the results with the nuclear mislocaliser indicate that the K13 located pool of KIC12 is important for efficient parasite growth.". It is also important to note that this limitation does not apply to the NLS knock sideways in regard to the K13 compartment and that the endocytosis function of this pool of KIC12 seems solid which with this statement is enforced.

      37) Line 368-369: Effect was also only partial for MyoF. Why didn't you measure the same metrics for MyoF?

      This was now done and is provided as Figure 1J-K, S2J, confirming our previous interpretation, see also point #27 which raises the same point.

      38) Line 379: you don't know if all proteins acting later in endocytosis will have an increased number of vesicles as a phenotype

      This is based on our current definition as stated in the introduction. It assumes a directional vesicular transport of hemoglobin to the food vacuole where inhibition of early stages will prevent transport before HCC-filled autonomous vesicular containers have formed and entered the cell. In contrast later inhibition stops such containers from further transport, leading to their accumulation. Such an accumulation is visible after VPS45-inactivation and other proteins (Jonscher et al., 2019; Mukherjee et al., 2022; Sabitzki et al., 2023) or treatment with cytochalasin D (Lazarus et al., 2008). While it is possible that there may be smaller intermediates formed at the K13 compartment that later on unite or fuse with the compartment evident after VPS45 inactivation and these might be missed due to small size (i.e. inhibition of a step between K13 compartment and an early endosome or equivalent), this would still be upstream of the VPS45 induced containers and hence would be earlier. We therefore believe that based on the framework given in the introduction (see also (Spielmann et al., 2020)) to assume that a phenotype manifesting as reduced food vacuole bloating without formation of detectable vesicles likely signifies inhibition of the process early whereas reduced bloating but with vesicles signifies inhibition later in the process.

      39) Line 413-414: The authors state that no growth defect was observed upon KS of 1365800. Is growth alone enough to say that there is no impact on endocytosis?

      This is an interesting point. The endocytosis proteins we studied so far indicate that efficient impairment of endocytosis manifests as a severe growth defect. Hence, lack of a growth defect can be assumed to be an indicator for absence of an important role for endocytosis (or any other growth relevant process). Clearly there is a gradual response, such as seen in the different MyoF versions resulting in proportional growth and vesicle appearance phenotypes. Hence, a protein with a minor role might have slipped our attention but then it probably is also not a very important protein in endocytosis.

      To further strengthen our assessment of PF3D7_1365800 importance for asexual blood stage development, we now also generated a cell line expressing the PPM Mislocalizer, enabling knock sideways to the PPM. This was done because this protein consistently has a focus at the nucleus that may be within the nucleus. Again this revealed no growth defect upon inactivation (Figure S7D).

      40) Line 432: in this section, the authors state that KIC4 and KIC5 seem to have domains that may suggest these proteins are involved in endocytosis, based on the alpha fold data that is publicly available. Considering the authors have TGD-SLI versions of these lines (Birnbaum et al. 2020) and have already confirmed in this previous publication that they confer resistance to ART; it would make sense to look at endocytosis for these genes. This would be a relatively simple and straightforward experiment, taking no longer than two to three weeks, and would require no additional reagents or line generation. Doing these experiments would add a lot more weight to this final section. The authors later state that KIC4 and 5 are TGD lines, so not the best for endocytosis assays. It is unclear why this would be difficult to do if an adequate control is contained in the experiment (such as parental 3D7). It explains why they did not perform the MCA2 endocytosis assays further up, but in my opinion, an attempt at doing these assays is important and would significantly increase the impact of this paper. Identical as major comment #17.

      As stated in the manuscript and above, we were originally hesitant to do these assays due to the fact that we can't induce inactivation which is less ideal than comparing the identical parasite population split into plus and minus and is further complicated by the likely smaller effect as the TGDs still permitted growth. However, we see the point of the reviewer and now performed these assays using 3D7 as controls and taking extra care to account for stage differences between the TGD lines and 3D7. However, there was no significant difference in the bloated food vacuole assays with these cell lines. Due to the reasons mentioned in major point 17, we are not sure this indeed means these proteins have no role in endocytosis. One possible reason why we were able to obtain these TGDs may have been because the effect on endocytosis is less than in the essential proteins (or is ring stage specific) and in a TGD an endocytosis defect may therefore not be detectable with our assays (see details and further possible explanations in response to point 17).

      In an attempt to address the TGD issue, we generated knock sideways cell lines for KIC4 and KIC5. Unfortunately, the mislocalization of KIC5 to the nucleus was inefficient (see figure below). As this did not result in a growth defect (in contrast to the clear KIC5-TGD growth defect (Birnbaum et al., 2020)), this line is not suitable to study a potential role of this protein in endocytosis. Therefore, we performed the bloated food vacuole assay only with KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser parasites. However, this revealed no effect on HHC uptake, which is in line with the normal growth of KIC4-TGD parasites (Birnbaum et al., 2020) and suggests that this protein could only have a minor or redundant role in endocytosis (it is the line that shows the smallest effect in RSA). As the KIC4 and KIC5 knock sideway lines did not permit any conclusions, we did not include them into the revised manuscript but they can be found here:

      [Figure KIC4 knock sideways & KIC5 knocksideways]

      Figure legend: (A) Live-cell microscopy of knock sideways (+ rapalog) and control (without rapalog) KIC4-2xFKBP-GFP-2xFKBPendo+ 1xNLS mislocaliser parasites 4 and 20 hours after the induction of knock-sideways by addition of rapalog. Scale bar, 5 µm. Relative growth of asynchronous KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser plus rapalog compared with control parasites over five days. Three independent experiments were performed. Growth of knock sideways (+ rapalog) compared to control (without rapalog) KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser (blue) or KIC5-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser (red) parasites over five days. Mean relative parasitemia ± SD is shown. (B) Live-cell microscopy of knock sideways (+ rapalog) and control (without rapalog) KIC5-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser parasites 4 and 20 hours after the induction of knock-sideways by addition of rapalog. Scale bar, 5 µm. Growth of asynchronous KIC5-2xFKBP-GFP-2xFKBPendo+ 1xNLSmislocaliser plus rapalog compared with control parasites over five days. Four independent experiments were performed. __(C) __Bloated food vacuole assay with KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser parasites 8 hours after inactivation of KIC4 (+rapalog). Cells were categorized as with ‘bloated FV’ or ‘non-bloated FV’ and percentage of cells with bloated FV is displayed; n = 3 independent experiments with each n=19-30 (mean 21.4) parasites analysed per condition. Representative DIC are displayed. Area of the FV, area of the parasite and area of FV divided by area of the corresponding parasites were determined. Mean of each independent experiment indicated by coloured symbols, individual datapoints by grey dots. Data presented according to SuperPlot guidelines (Lord et al., 2020); Error bars represent mean ± SD. P-value determined by paired t-test. Area of FV of individual cells plotted versus the area of the corresponding parasite. Line represents linear regression with error indicated by dashed line.

      41) Line 490-493: the authors state that the K13 compartment proteins fall in two groups, some that are involved in ART resistance AND endocytosis, and some that have different functions. However, in this manuscript the authors have demonstrated 3 flavours that K13 compartment proteins can come in: • Some that confer ART resistance and are involved in HCCU (MCA2) • Some that are involved in HCCU but not ART resistance (MyoF & KIC12) • Some that are involved in neither (KIC11) The authors should therefore revise this statement.

      We agree that this was not well phrased. To account for the fact that not all endocytosis proteins confer increased RSA survival to the parasites when inactivated we changed this statement (line 604): "This analysis suggests that proteins detected at the K13 compartment can be classified into at least two groups of which one comprises proteins involved in endocytosis or in vitro ART resistance whereas the other group might have different functions yet to be discovered.

      Generally, we believe that endocytosis is the overarching criterion and we therefore would like to keep the definitions of the main groups (endocytosis or not). As indicated by the title, the focus of the manuscript is on the K13 compartment for which so far endocytosis is the only experimentally associated function. That this group contains proteins that do not confer reduced ART susceptibility when conditionally inactivated (KIC12 and MyoF) is explained by their stage-specificity, making this a subgroup of the overarching endocytosis group.

      We realise that with the endocytosis data on the KIC4, KIC5 and MCA2 TGD there is now also a subgroup we were unable to demonstrate an endocytosis effect in trophozoites although they show changes in RSA survival. However, as indicated above, we would be hesitant to fully exclude some role of these proteins in endocytosis in rings. Particularly as a comparably small reduction in endocytosis protein activity or abundance is sufficient to increase RSA survival (Behrens et al., 2023). A principal classification of "endocytosis or ART resistance" or "neither endocytosis nor ART resistance" still accounts for this and therefore seems to us to be the most useful, particularly also in light of our domain identification that then can be linked with one or the other group.

      42) Line 508: the authors state that they expanded the repertoire of K13 compartments, when in fact they functionally analysed them - they did not do another BioID to identify more candidates.

      We respectfully disagree with the reviewer in this point, we did expand the repertoire of known K13 compartment proteins. Only independently experimentally validated proteins from proximity biotinylation experiments can be considered part of the K13 compartment (or any other cellular site or complex). Without validation of the location, the identified proteins can only be considered candidates. This is highlighted in this manuscript by the finding that several proteins of the list did not localize at the K13 compartment.

      43) Line 570-572: has anyone ever tested whether CytoD or JAS treatment in rings, is sufficient to mediate ART resistance? Something similar to what was done in PMID 21709259 with protease inhibitors. If not this would be a pretty interesting experiment for the authors to do that could shed more light on the MyoF data. It would take maybe 2 weeks to do and not require the generation of any new lines. This would clarify whether other Myosins other than MyoF are involved in endocytosis, as is suggested by previous publications (PMID: 17944961).

      We now included this experiment. In agreement with a lacking need of MyoF in rings and no effect on RSA survival, there was no increased survival of the parasites in RSA (neither on 3D7 nor on K13 C580Y parasites) after cytD treatment (new part in Figure 1M). We thank the reviewer for pointing out that this experiment might also inform on whether other myosins influence endocytosis in ring stages. We added (line 250): Similarly, also incubation with the actin destabilising agent Cytochalasin D (Casella et al., 1981), had no effect on RSA survival in 3D7 or K13C580Y (Birnbaum et al., 2020) parasites, indicating an actin/myosin independent endocytosis pathway in ring stage parasites (Figure 1M) and speaking against other myosins taking over the MyoF endocytosis function in rings.”

      44) Line 608: inhibitors targeting the metacaspase domain of MCA2 may inadvertently inactivate other essential parts of the protein. They authors should acknowledge this possibility in the text.

      The inhibitors used in the cited studies (Kumari et al., 2018) are validated metacaspase inhibitors, such as Z-FA-FMK (Lopez-Hernandez et al., 2003). Activity against the other parts of PfMCA2 - which apart from the MCA domain shows no homology to other proteins - is therefore unlikely.

      45) Line 624-625: the authors state that MyoF is 'lowly expressed in rings' - indeed this is the case in their MyoF-2xFKBP-GFP-2xFKBP line which the authors established has defects due to the tag, but it appears from their MyoF-3xHA tagged line that it is expressed in rings. The authors should therefore revise their statement, and be careful of making claims based on their defective line and using fluorescence imaging as their only metric. If they do want to make the statement that it is not there in rings, they should also do a western blot, which is much more sensitive since it amplifies the signal compared to an image of one parasite.

      This comment is related to major point #24. We also would like to stress that while the MyoF-GFP line already shows a phenotype, the impression of defectiveness based on its location is due to a mix up (see major point #23).

      We now provide a comprehensive time course of the MyoF-GFP signal (Figure 1C, S2A) showing that there is no detectable MyoF-GFP signal until the transition from ring to trophozoite stage. As this is all under the endogenous promoter, we do not think the partial functional inactivation of the tagging is the reason for the absence of the signal. If anything, we would have expected adding a stably folded structure such as GFP to increase the stability of the protein. The main reason for the discrepancy of MyoF signal in rings between the GFP-tagged line (of note there is also no detectable MyoF-GFP signal in MyoF-2xFKBP-GFP ring stage parasites (Figure S2B)) and the HA-tagged line likely is that IFA is much more sensitive than live GFP detection (similar to the high sensitivity the reviewer mentions in regards to WB). This discrepancy therefore is likely due to the fact that the lowly expressed MyoF only become apparent with the HA-tagged line due to the IFA. We therefore believe that MyoF is 'lowly expressed in rings' is an appropriate description of our results obtained with three different cell lines (MyoF-2xFKBP-GFP-2xFKBP, MyoF-2xFKBP-GFP and MyoF-3xHA). We hope this is sufficiently well reflected in the manuscript where we write ‘a low level of expression of MyoF in ring stage parasites.’ not that it is ‘not there in rings’ (line 174).

      46) Line 635: arguably this is the 3rd variety and not the 2nd (the authors already mentioned 2 types - ones that are involved in HCCU AND ART and those involved in HCCU only). See comment for line 490-493 above.

      See response for major comment #41, we now consistently used "or" instead of "and". See line 490-493 how this was resolved for what previously was line 635.

      47) Line 785: Bloated food vacuole assay/E64 hemoglobin uptake assay method specify that a concentration of 33mM E64protease inhibitor was used. However, in reference 44, cited in the manuscript, a concentration of 33µM E64 was used. Please confirmed if this is just a typo or if 1000x E64 concentration was used which renders the experiment invalid.

      We thank the reviewer for pointing this out, we corrected this typo and will look out for symbol font conversion errors for the resubmission.

      48) Line 788: it is unclear from this section what is considered a bloated food vacuole - is there an area above which the FV is considered bloated? Do the authors do these measurements manually or use an addon in FIJI/ImageJ? What is the cutoff for if a FV is bloated? Please clarify. Additionally, for the representative images + rapalog for Figures 2H and 4H, it would be useful to see where the authors delineate the FV (add a white circle showing what is actually measured).

      The bloated FV assay is well established (Jonscher et al., 2019; Birnbaum et al., 2020; Sabitzki et al., 2023). Although the bloating of the FV is a human judgment call, it is actually quite obvious: bloating appears as an easily spotted bulging of the FV in DIC. As also minor bloating is scored as 'bloated', it is a very conservative assay. Using an-add on to measure this is not straight forward. It is unclear how this bulging effect of the FV in DIC could be spotted by a software and due to the obviousness to human operators, potentially lengthy and complicated efforts to design appropriate machine learning options were not undertaken. The situation faced by the scorer of the assay is evident from Figure S4F-G which contains close to 50 "on rapalog" cells and close to 50 control cells, giving representative cells from all replicas of bloated FV assays with KIC12. Please note that these images shows the most complicated situation as far as bloated assays go, because the phenotype is not 100% (see Figure 3F) compared to e.g. KIC7 inactivation which leads to lack of bloating in almost all cells (see (Birnbaum et al., 2020) Figure 3E) but nevertheless the difference is still obvious. We are aware that in such situations (less than absolute inhibition) this assay scoring of "yes" or "no" is a surrogate for the actual level of inhibition and may be more subjective. This is why in this case we also did the FV size measurements (which are less dependent on human judgment) to further support this and give a better quantifiable measure. Of note, the bloated food vacuole judgments are done "blinded", i.e. the examiner does not know which sample they are looking at.

      In response to this reviewer's point we now also added the FV size refinement of the assay for MyoF inactivation which is one of the cases where inhibition of bloating is not in 100% of the cells (see major comment #27). Please also note here the advantage of the rapidly acting knock sideways technique for these assays which shows the sum of effect 8 h after initiating inactivation and for which we carefully control size of the cells which shows that there is no significant growth reduction over the assay time, excluding secondary effects due to a generally reduced viability. Compared to slower acting systems suggested to have been used instead (see introductory part and significance of this review), the rapid speed of knock sideways reduces the risk of potential pleiotropic or compensatory effects due to the time needed for proteins to be depleted if the gene or mRNA is targeted instead.

      The suggestion to include a ‘white circle’ (raised also as minor comment#27) is useful as an aid to see the food vacuole. However, in contrast to the Figures in (Birnbaum et al., 2020) (where we did add such a circle), we here included the DHE staining images in the figure, labelling the parasite cytosol which readily shows the FV (the FV corresponds to the region where there is no DHE staining). As this shows the position of the FV we would prefer to not obscure the DIC images with additional features to permit the reader to see the difference between bloated or non-bloated food vacuoles and keeping the image as natural as possible.

      49) Line 863-864: this sentence seems to be out of place.

      We thank the reviewer for pointing this out, the details of nucleus staining were moved to the correct part.

      50) Line 875: the authors state that there is a light blue wedge, when the circle consists of grey and black wedges. Please revise this.

      This has been corrected.

      51) Line 1059-1061: it is unclear whether the individual growth curves are different clones or whether they are just the same experiment repeated? If it is the latter, then why are they not combined, as is traditionally done?

      These are the individual replicates of the growth curves shown in Figure 1G of the same cell lines done on a different occasion. We always try to show as much of the primary data as possible and believe that showing individual data points from the different experiments is better than only the combined values which obscure the actual course of each experiment.

      52) Line 919-924: the authors mention a blue and red line, but there is only a black line in figure 3D. Moreover, the experiment of using the LYN mislocaliser was only done for KIC12 according to the manuscript. Additionally, the y axis of the figure states relative growth day 4[%] compared to rapalog, but then on the x axis there are several days. In the text it says there is no growth defect until the second cycle, but from this graph it appears the growth defect is evident as early as 1 day post rapalog treatment. Can the authors please clarify and correct the issues pointed out.

      We thank the reviewer for pointing this out, this was due to a copy & paste error in the figure legend that was now amended. We also fixed the incorrect axis label. For the last part (growth defect) please see detailed answer to Major comment#31 raising the same concern for KIC11 (in synchronous parasites the defect only takes effect once the cells reached the relevant stage whereas in asynchronous cultures there are always cells in the relevant stage that due to the rapid effect of the knock sideways already have a growth phenotype).

      53) Figure 1 panel B & C: the label of the figure where the signal from MCA2Y1344STOP-GFP is shown with the DAPI signal overlayed is deceptive since it suggests that this is the signal of full length MCA2. Please change the label of this panel from MAC2/DAPI to MCA2Y1344STOP/DAPI. The same is true for Panel C for the image labeled MCA2/K13 - please change this to MCA2Y1344STOP/K13.

      Done as requested.

      54) Figure 2B: what stages are these parasites? Please state this in the figure. Based on the MyoF pattern, it looks like rings in the upper panel and trophs in the bottom pannel. Why were schizonts not shown?

      Both are trophozoites (early trophozoite in top panel and late trophozoite in bottom panel). This is now labelled in what now is figure 1B. As stated above, schizont stages are less relevant for the topic of this manuscript and in order to prevent the manuscript from getting more disjointed and keeping it more focussed on the main topic, we decided to not include a schizont in the manuscript. Nevertheless, we included an example image below.

      [Figure MyoF_p40px schizont]

      55) Figure 2D&F: it is not very meaningful when growth assays are shown as a final bar after 4 days of growth. It is much more useful and informative to see a growth curve instead (as is shown in the supplementary), since it shows if the defect is apparent in the first growth cycle or later. With the way the data is currently shown, this is not apparent. I would advise the authors to switch the graph in 2F out of a combined graph of all the biological replicates growth curves for S3D - showing error bars.

      While we in principle fully agree with the reviewer in showing the course of the full experiment (which is available in Figure S2E), the key here is to show the overall difference. Hence, we would like to keep this comparison of the overall effect on growth in what now is Figure 1E and G. It is part of the argument to the doubts this reviewer raises to the function of MyoF (mainly in the overall assessment and the significance statement) to show that the phenotype is actually very consistent (partial inactivation through tagging or further inactivation using knock sideways increases endocytosis phenotypes, correlating with parasite viability).

      Please also note, that the growth curves upon knock sideways shown in Figure 1G, S2E are performed with asynchronous parasite cultures, which doesn’t allow us to draw direct conclusions about growth cycle effects.

      Nevertheless, we now also included the suggested combined data representation in Figure S2E.

      56) Figure 3: why were the calculation of FV area, parasite area and FV/parasite area only done for KIC12 and not done for MyoF? It would be interesting to see if any of these values are different for MyoF - whether the parasites are smaller in area and therefore FV smaller. Please present them Figure 2. Images should be already available and would not require further experiments to be done, only the analysis.

      This now has been done (confirming our results) and is included as Figure 1J-K, S2J. This point was also raised as major comment #37, please also see detailed answer there.

      57) Figure 3B: why is there no spatial association assessment for KIC11 and K13 as was done for the MCA2 and MyoF? The authors should show a pie chart showing the degree of association here as was done for the other proteins.

      This is now included in Figure 2C.

      58) Figure 3D: The y axis of the figure states relative growth day 4[%] compared to rapalog, but then on the x axis the experiment takes place over several days. Is this a typo in the y axis? Additionally, the authors state in line 287-290 that the growth defect upon addition of rapalog is only seen in the second cycle, but from this graph it appears the growth defect is already evident 1 day post rapalog addition. The figure legend also does not make sense for this figure since it mentions a blue and a red line, when there is only a black line present. The legend also mentions the LYN mislocaliser which was used for KIC12 not KIC 11 (see above).

      We apologise for the inadequate legend and colour issues, this was amended. This point was also raised in major comment #31 and #52, please find detailed answer there.

      59) Figure 3E: the colour for Control and Rapalog 4 hpi are very similar and very hard to discern. Please choose an alternative colour or add a pattern to one of the samples. The y axis is also missing a label. Is this supposed to be parasitemia (%)?

      We thank the reviewer for pointing this out, the missing label is now included and the colour has been adapted to make them better distinguishable.

      60) Figure 4A: the ring shown in this figure does not appear to be a ring (it is far too large and appears to have multiple nuclei?). Do the authors have any other representative images to show instead?

      This is in fact a ring, but we realize that we accidentally included an incorrect size bar in the ring image of Figure 4A (now Figure 3A) (size bar for 63x objective instead of the correct one for the 100x objective), we apologise for this oversight. We don’t think this parasite has multiple nuclei, instead the Hoechst signal shows the often elongated nucleus seen in rings that can appear as two foci in Giemsa stained smears which leads to the typical diagnostic feature of P. falciparum rings in diagnostics. In order to exclude any doubts about the nuclear localization of KIC12 in rings, we here attached a panel with more examples of KIC12-2xFKBP-GFP-2xFKBP ring stage parasites.

      [Figure KIC12]

      61) Figure 4B: why is there no spatial association assessment for KIC12 and K13 as was done for the MCA2 and MyoF? The authors should show a pie chart showing the degree of association here as was done for the other proteins. This should be done for the different life cycle stages considering the changing localisation of KIC12.

      This is now provided in Figure S4A. As suggested by the reviewer, we independently quantified the association for ring stage, early trophozoite and late trophozoites stage. As there is no KI12 signal in schizonts, we did not include a quantification for this stage.

      62) Figures 4C&E: it is extremely important to show the DNA stain in both these samples considering that a portion of KIC12 is in the nucleus! Please add the DAPI signal for these figures (as for all other figures!).

      Please see major comment #64 for a detailed answer why we did not include DNA staining in the imaging used to assess mislocalization upon knock-sideways.

      63) Figure 4E: this figure should be presented before 4D (considering the line being presented in 4E is used in an experiment in 4D). The authors should switch the order of these two.

      We see the point the reviewer is raising here, Figure 4D (now Figure 3D) also contains the data with the Lyn mislocaliser while we first talk about the NLS mislocaliser. This permits a better comparison between the two mislocaliser lines. However, first explaining the Lyn-mislocaliser and then going back to the NLS would make it rather complicated for the reader to follow the storyline and therefore we would like to keep the order as it is. We realise that this means the reader has to go back one figure part for seeing the Lyn growth data, but believe this is worth the benefit that the data is there compared to the NLS result.

      64) It is unclear why in many of the fluorescence images the authors do not show the DAPI signal - particularly when colocalising with K13 and when doing the knock sideways experiments. Please add these images to the figures - I would assume they have already been taken, so would simply involved adding the images to the panel.

      We did not include DNA staining (DAPI or Hoechst) for any of the images used to assess the efficacy of mislocalization, as we would prefer to keep the parasites as representative of a viable parasites in culture as possible. Hence they were imaged without DNA stain (these stains are toxic). We would like to point out that a DNA stain is not necessary, as the mislocaliser already marks the nucleus (in the case of the NLS mislocaliser), actually even somewhat more accurately, as it fills the entire nuclear space rather than only the DNA which is marked by DAPI or Hoechst.

      For LYN this admittedly is not the case, there the mislocaliser marks the plasma membrane. However, we think the proper control for efficient mislocalisation is the comparison between the GFP-tagged protein of interest and the mCherry mislocaliser to show mislocalisation, as previously done in our lab (e.g. (Birnbaum et al., 2017; Jonscher et al., 2019; Birnbaum et al., 2020)).

      Due to their toxicity, we also avoided nuclear staining in some other parts of the manuscript when we were of the opinion that a nucleus signal was not necessary.

      65) Throughout the manuscript, there is no western blot confirming the correct size of their modified proteins. This should be provided.

      We did perform Western blot analysis for both MCA2 cell lines. MCA2 is the only gene-product for which we generated a disruption for this work, and together with the severe truncation from previous work, we provided a Western blot-based confirmation of the correct size.

      The MCA2 disruptions are at least partially dispensable for in vitro parasite growth, hence if degradation occurred, this might not have been noticed. In that case we considered it relevant to show that the truncations were of the expected size. The other proteins in the main figures are essential for growth. Hence, if the tagging approach would lead to unexpected changes in protein integrity (which we assume is what was intended by this concern to be assessed with a Western blot), the parasites expressing the tagged MyoF, KIC11 and KIC12 would - due to their importance for asexual blood stage development - not have been obtained. Hence, we can assume the integrity of the tagged protein is very unlikely to have been affected in a functionally relevant way.

      66) None of the figures are appropriate for individuals with colour blindness, limiting their accessibility to the paper. Please change the colour schemes for all fluorescent images using magenta/green or an alternative colour combination appropriate for colourblind individuals.

      We thank the reviewer for this comment. This has now been amended, individual channels of fluorescence microscopy images are now shown in greyscale, while the overlay was changed to green/magenta.

      Minor Comments

      1) line 29: remove 'are'.

      Done.

      2) Line 29: the text says "HCCU is critical for parasite survival but is poorly understood, with the K13 compartment proteins are among the few proteins so far functionally linked to this process." The sentence should be: 'HCCU is critical for parasite survival but is poorly understood, with the K13 compartment proteins among the few proteins so far functionally linked to this process."

      Done.

      3) line 44: remove 'the'

      Done.

      4) Line 48: consider mentioning here that malaria is caused by the parasite Plasmodium - otherwise the first mention of parasite in line 52 is confusing for the non-specialist reader.

      Done.

      5) Line 49: estimated malaria-related death and case numbers are from the 2021 WHO World malaria report. You cite the 2020 WHO World malaria report.

      We now cite the newest WHO report.

      6) Line 53: please insert the word 'have' between now and also.

      Done.

      7) Line 54: please change 'was linked' to is linked

      Done

      8) Line 72: I would specify that free heme is toxic to the parasite. Especially as you mention that hemozoin is nontoxic.

      Sentence would be "where digestion results in the generation of free heme, toxic to the parasite, which is further converted into nontoxic hemozoin"

      Done.

      9) Line 90: authors should either say "in previous works" or "in a previous work"

      The text has been altered to say: “ in a previous work”.

      10) Line 91: "We designated these proteins as K13 interaction candidates (KICs)"

      Done.

      11) Line 95: please change 'rate' to number

      Done.

      12) Line 109: Please include a coma before (ii).

      Done.

      13) Line 112: as shown by Rudlaff et al in the paper you are citing, PPP8 is actually associated with the basal complex. You can say that "(ii) were either linked or had been shown to localise to the inner membrane complex (IMC) or the basal complex (PF3D7...).

      Done.

      14) Line 114: Protein PF3D7_1141300 is called APR1 in the manuscript but ARP1 in Supplementary Table 1. Please correct.

      Done.

      15) Line 131: please define SNP - this is the first use of the acronym.

      Done.

      16) Line 133-134: South-East Asia instead of "South Asia"

      Done.

      17) Line 135: please explain what TGD is - it is referred to over and over again in the manuscript without ever being explained.

      We apologise for this oversight. We now explain what is meant with TGD at the suggested point of the manuscript.

      18) Line 145: change 'Western blot' to western blot - only Southern blot is capitalised since it is named after an individual, while the other techniques are not.

      To the best of our knowledge this issue has not been resolved, some Journals capitalize the “W” (e.g. Science), while others don’t (e.g. Nature). We would prefer to continue to capitalize the “W”, as this is consistent with the original publication from (Burnette, 1981), but if there are strong objections, we would be happy to change this____.

      19) Line 152: add "the" between 'and spatial'

      Done.

      20) Line 158: please define SLI as selected linked integration, since it is the first use of the acronym.

      Done.

      21) Line 178: introduce a coma after protein. Sentence should be "Proliferation assays with the MCAY1344STOP-GFPendo parasites which express a larger portion of this protein, yet still lacking the MCA domain (Figure 1), indicated no growth ...

      Done.

      22) Line 195: the authors could mention that MyoF was previously called MyoC in the Birnbaum 2020 paper. I wanted to check back in the Birnbaum 2020 paper and could not find MyoF

      Good point, this was done.

      23) Line 200: "Expression and localisation of the fusion protein was analysed by fluorescent microscopy". Why expression was not analysed also by western Blot same as for MCA2?

      Please see major comment #64 for a detailed answer.

      24) Line 204: I could not find any mention of MyoF (Pf3D7_1329100) in reference 65. Please remove reference 65 if not correct. Also reference 66 looks at Plasmodium chabaudii transcriptomes so I would specify that "This expression pattern is in agreement with the transcriptional profile of its Plasmodium chabaudii orthologue"

      Reference 65 (Wichers et al., 2019) provides an RNAseq transcriptome dataset for asexual blood stage development of 3D7 (originating from the same source as the 3D7 used in this study). While Ref 66 (Subudhi et al., 2020) indeed contain transcriptomic data from P. chabaudi, the authors also provide a nice 2h window RNAseq transcriptome dataset for asexual blood stage development of Plasmodium falciparum. Both datasets are therefore suitable as reference for the statement about myoF transcription pattern. Both datasets are also easily accessible and show the pattern in a graph in PlasmoDB.

      25) Line 208: Please indicate a reference for P40 being a marker of the food vacuole

      Done.

      26) Line 220-224: The authors should consider changing to " Taken together these results show that MyoF is in foci that are mainly close to K13 and, at times, overlapping, indicating that MyoF is found in a regular close spatial association with the K13 compartment."

      The suggested wording introduces "mainly" for "frequently" and likely was in part motivated by the discrepancy in location between cell lines that we hope we now could clarify to be only minor (see major point #23). We therefore think the original wording appropriately summarises the findings (line 178): “*Taken together these results show that MyoF is in foci that are frequently close or overlapping with K13, indicating that MyoF is found in a regular close spatial association with the K13 compartment and at times overlaps with that compartment.” *

      27) Line 255: In Figure 2H, and subsequent figures showing bloated FV assay, I would delineate the food vacuole with dashed line as in Birnbaum et al. 2020 to help the reader understanding where the food vacuole is.

      In contrast to the Figures in Birnbaum et al. 2020, we here included the DHE staining (parasite cytosol) in images of bloated FV assays which visualizes the FV. We therefore decided to avoid any further marking, to keep the image as unprocessed as possible (see also major point 48).

      28) Line 265-266: Here the title says that KIC11 is a K13 compartment associated protein, but the title of Figure 3 says KIC11 is a K13 compartment protein. I noticed that you make the difference between K13 compartment protein et K13 compartment associated protein for MyoF for example which is not clearly associated with the K13 compartment. Which one is it for KIC11?

      The interpretation of the reviewer is correct, we indeed graded this subconsciously based on level of overlap. Based on the newly added quantification shown in Figure 2C, we describe KIC11 now as K13 compartment protein.

      29) Line 309-310: indicate a reference for your statement "which is in contrast to previously characterised essential K13 compartment proteins".

      Done, we now included Birnbaum et al. 2020 as reference for this.

      30) Line 377: Figure 4I, please correct 1st panel Y axis legend

      Done.

      31) Line 404: replace "dispensability" with dispensable

      Done.

      32) Line 416: can the authors provide any speculation as to why they observed these proteins as hits in the BioID experiments?

      As some of these proteins were less well or less consistently enriched, they could be background of the experiment. Alternatively, some could be proteins that only transiently interact with the K13 compartment.

      33) Line 451: Where the "97% of proteins containing these domains also contain an Adaptin_N domain and function in vesicle adaptor complexes as subunit a" come from. Do you have a reference?

      The statement now includes references and reads (with small changes to original submission): "More than 97% of proteins containing these domains also contain an Adaptin_N (IPR002553) domain (Blum et al., 2021) and in this combination typically function in vesicle adaptor complexes as subunit α (Hirst and Robinson, 1998; Traub et al., 1999) (Figure 5D) but no such domain was detectable in KIC5."

      34) Line 465-467: the same could be said for KIC4 as it also has a VHS domain.

      The critical issue is the combination of domains and their position within the protein. While KIC4 also contains a VHS domain, the VHS domain in KIC4 is N-terminal, not in a central position and it is also not the first structural domain to be identified in KIC4. The similarity to adaptin domains was already described ((Birnbaum et al., 2020) and annotated in PlasmoDB) and these domains are also involved in vesicle formation and trafficking. These aspects of the statement can therefore not be extended to KIC4. With regards to VHS domains being involved in vesicle trafficking, this is already stated in line 538: «KIC4 contained an N-terminal VHS domain (IPR002014), followed by a GAT domain (IPR004152) and an Ig-like clathrin adaptor α/β/γ adaptin appendage domain (IPR008152) (Figure 5A-C, Figure S8). This is an arrangement typical for GGAs (Golgi-localised gamma ear-containing Arf-binding proteins) which are vesicle adaptors first found to function at the trans-Golgi (Dell’Angelica et al., 2000; Hirst et al., 2000)

      35) Line 477-479: Can be rephrased to "However, we found this protein as being likely dispensable for intra-erythrocytic parasite development and no colocalisation with K13 could be demonstrated, suggesting a limited role for PF3D7_1365800 in endocytosis. Or something like that. Makes it clearer.

      We rephrased this sentence and it now reads (line 592): However, we found this protein as being likely dispensable for intra-erythrocytic parasite development and no colocalisation with K13 was observed, suggesting PF3D7_1365800 is not needed for endocytosis“.

      36) Line 535: Have AP-2a or AP-2b been shown to be at the K13 compartment?

      AP2m is at the K13 compartment (Birnbaum et al., 2020). Adaptor complexes are heterotetramers and their subunits do not typically function on their own and this is conserved across evolutionarily distant organisms. In agreement that this is also the case in P. falciparum, Henrici et al. (Henrici et al., 2020a) showed that both, AP-2a and AP-2b, were present in an AP2µ Co-IP, indicating that the AP2 complex consist of the ‘classical’ subunits in P. falciparum. Therefore, the presence of all subunits at the K13 compartment is very likely, although this has only been experimentally confirmed for AP2µ. Of note, for Toxoplasma gondii the presence of AP-2a and AP-2b at the micropore has been experimentally confirmed (Wan et al., 2023; Koreny et al., 2023) and interaction suggested by presence in the same IP as DRPC (Heredero-Bermejo et al., 2019).

      37) Line 569: reference 43 is wrong

      We thanks the reviewer for pointing this out – we removed Ref 43.

      38) Line 746: typo "ot" instead of or.

      Changed.

      39) Line 801: method for Domain Identification using AlphaFold specify that RMSDs of under 5Å over more than 60 amino acids are listed in the results. However, there is a typo in Figure 5B for KIC5 where it says "RMSD 4.0 Å over 8 aa". Please correct.

      Done. In addition, we have now applied a more stringent cut-off of 4Å over more than 60 amino acids to ensure a higher reliability of our hits. This decision was based on results from our preprint (Behrens and Spielmann, 2023). Because of this the phosphatase domain in KIC12 is no longer included in this manuscript and accordingly the following sentence has been deleted. In KIC12 we identified a potential purple acid phosphatase (PAP) domain. However, with the high RMSD of 4.9 Å, the domain might also be a divergent similar fold, such as a C2 domain, which targets proteins to membranes.”

      40) Line 856: In Figure 1E, please use the same Y axis legend as in Figure 2D "relative growth at day 4 [%] compared with 3D7"

      Done.

      41) Figure S1: Some PCR gels check for integration are presented as 5', 3' and ori whereas other gels are presented as ori, 5' and 3'. This is confusing.

      We agree that ideally the order of sample loading should be consistent and we apologise for this. The explanation for this is that these gels were run by different people at different times before we were able to better standardize the loading scheme. However, in the interest of not unnecessarily using resources for something that has a similar meaning, we would prefer not to repeat these PCRs and re-run them only for consistency reasons (as the conclusion is not affected by the different loading schemes).

      42) Figure S1: Why was the expression of only MCA2 was verified by Western blot? What about the other proteins?

      See response to major comment 56.

      43) Line 493: Considering KIC11 was not involved in HCCU or ART resistance it might be worth mentioning in this section that it is of note that there are no domains detected that would be involved in endocytosis.

      We agree that this is the case, however it is also the case for all other proteins that either are not involved in endocytosis and/or lowered susceptibility to ART. We therefore now added a summary statement addressing this in line 602: In contrast, the K13 compartment proteins where no role in ART resistance (based on RSA) or endocytosis was detected, KIC1, KIC2, KIC6, KIC8, KIC9 and KIC11, do not contain such domains (Figure 5E).” We did not add this at the suggested part of the manuscript as at that point the domain search results are not yet introduced and doing this each time for all the individual proteins would disconnect the flow of the manuscript.

      44) Line 503-506: is it wise to generate more drugs that target a pathway that is already highly susceptible to mutations? The authors should add a statement explaining how this might be avoided.

      The only protein for which mutations do not have a large fitness cost is K13 (see also our preprint on fitness cost of ubp1 mutation (Behrens et al., 2023) and even with K13 the level of resistance seems to be limited by amino acid deprivation when endocytosis is reduced (Mesén-Ramírez et al., 2021). We therefore do not think that this pathway is particularly prone for mutations. Further, the number of commercial drugs targeting the "endproduct" of endocytosis (hemoglobin digestion and detoxification of heme) highlight it as the most prominent vulnerability for drug-based intervention if we go by number of commercially available drugs acting on things associated with a single process.

      45) Throughout, scale bars are stated in the figure legends at the end of the legend. This is a slightly confusing format. The authors should consider stating the scale bar for each sub-legend where a fluorescence image is taken.

      Done.

      ** Referees cross-commenting**

      After reading reviewer 2 and 3's comments, I think there are significant overlaps in the key points raised in terms of questions about fusion proteins and their potential partial mis-localisation, better descripton of results and target selection. Overall I think we agree that the work has potential, but in its current form does not represent a major advance. It would be immensely helpful if the manuscript would be carefully edited for a better flow and linear description of results.

      We now rearranged the manuscript for better flow but would like to highlight that the many requests for smaller experimental issues (and "better description of results") worked somewhat in the opposite way of a more linear description. We hope the rearranged version acceptably balances these two issues. The issues raised in regards to target selection and potential partial mis-localisation are addressed in our responses mainly to this reviewer. Please also see comments on systems used at the end of the rebuttal.

      Reviewer #1 (Significance (Required)):

      The authors set out to test whether other proteins that are in the vicinity of K13 are involved in mediating ART resistance and endocytosis. This is an interesting question. However, other than MCA2 which was already known to be involved in mediating ART resistance (and was not tested for its involvement in endocytosis), none of their candidate proteins seem to be involved in mediating both these functions. The authors show that the other proteins tested appear important for parasite growth, with KIC12 and MyoF involved in mediating endocytosis. While these findings are novel, the KS approach used by the authors casts some doubt over the findings, and would mean that these findings would have to be re-tested with a more reliable approach, such as the GlmS system or generating a conditional knockout using the DiCre system. Despite not advancing our understanding of ART resistance, or identifying further players involved in this process, this manuscripts provides two candidates that are involved in mediating endocytosis and a further candidate that appears to be important for parasite growth. Further work on these proteins will be required to understand their exact roles. As stated above, there is currently limited interest for these results (limited to researchers working on endocytosis in apicomplexan parasites and possibly the wider endocytosis field from an evolutionary perspective), however with further work, this could increase the impact and interest of this work substantially.

      The authors do not describe any novel methods/approaches within this work.

      In the significance statement the reviewer indicates that other systems would have been more reliable for the work here. This is addressed in our response above and in a detailed considerations on the properties of conditional inactivation systems at the end of the rebuttal. The systems used in this work were not only chosen because they permit rapid targeting of many different proteins, but because they have merits that are beneficial for our assays. In fact many of the functional assays in this manuscript are difficult or impossible to carry with the suggested conditional inactivation systems (please note that we have extensive experience with the systems considered preferable:

      • DiCre (Birnbaum et al., 2017; Mesén-Ramírez et al., 2019; Mesén-Ramírez et al., 2021; Wichers et al., 2022; Kimmel et al., 2023)

      • glmS (Wichers et al., 2021c; Wichers et al., 2021a; Wichers et al., 2022; Wichers-Misterek et al., 2023)).

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

      In a previous publication the Spielmann lab identified the molecular mechanism of ART resistance in P. falciparum by connecting reduced levels of the protein K13 to decreased endocytosis (uptake of hemoglobin from the RBC cytosol), which results in reduced ART susceptibility. Using quantitative BioID the authors further identified proteins belonging to a K13 compartment, highlighting an unusual endocytosis mechanism.

      In the present manuscript the authors follow up on this work and closely examine ten more proteins of the K13/Eps15-related "proxiome". They successfully link MCA2 to ART resistance in vitro, while the proteins MyoF and KIC12 are involved in endocytosis but do not confer in vitro ART resistance when impaired. They further characterize one candidate (KIC11) that partially colocalizes with K13 in trophozoites but to a lesser degree in schizonts. Growth assays suggest an important function for KIC11 in late stages of the intraerythrocytic developmental cycle. Five analyzed proteins however do not colocalize with the K13 compartment, while a sixth was refractory to endogenous tagging.

      Using AlphaFold predictions of the KIC protein structures the author identify domains in most constituents of the K13 compartment, highlighting vesicle trafficking-related features that were not identified on primary sequence level before.

      The combination of functional data together with structure predictions leads them to propose a refinement of the K13 compartment as being divided into proteins participating in endocytosis and proteins that have an unknown function.

      We thank the reviewer for the assessment of the manuscript and the constructive comments.

      Major comments:

      1) -Table 1 is missing

      We apologise for this mistake; Table 1 is now included.

      2) -Lines 117-123: Given the total list of uncharacterized candidates encompasses 13 proteins, can the author gives the reason why only the top 10 and not all 13 were characterized in this study?

      A similar point has been raised by Reviewer 1 in major comment #12, please see our response there for an explanation why we chose which targets.

      3) -Line 174: 20% of observed MCA2 foci show no overlap with K13 and 21% only partially overlap, can the author confirm that the observed MCA2 foci in schizonts are the ones that co-localize with K13. (Addition of a schizont stage image in Fig 1C would be sufficient).

      We now extended Figure 4C with images of MCA2-Y1344STOP-GFP+mCherryK13 parasites covering the schizont and merozoite stage, showing that the majority of the MCA2 foci in schizonts are also mCherry-K13 positive.

      4) -The localization and observed phenotype of KIC11 is interesting but unfortunately the authors do not explore it further. Does KIC11 localize with markers of e.g. the secretory organelles (micronemes or rhoptries) in schizonts and could therefore be involved in RBC invasion?

      While we intended to focus mainly on the endocytosis aspect of these proteins, we see the reviewer's point and now generated new cell lines enabling assessment of spatial association of KIC11 with markers for rhoptry (ARO), micronemes (AMA1), and inner membrane complex (IMC1c). This revealed that the KIC11-GFP signal in schizonts does not overlap with apical organelle markers and the signal does not resemble a typical apical localization. In addition, we assessed all three organelle markers after inactivating KIC11 by knock sideways which showed that KIC11 inactivation has no apparent effect on the appearance of these markers, suggesting no major alterations in schizont morphology in respect to apical markers. These results are now presented as Figure S3A and in line 304 of the results.

      5) Can the author distinguish if KIC11 is involved in RBC invasion or in establishment of the ring-stage parasite?

      In order to look into this, we performed egress/invasion assays, quantifying schizont and ring stage parasites in tightly synchronized parasites at two different time points (pre-egress: 38-42 hpi & post-egress: 46-50 hpi). This revealed a significant decrease in newly formed ring stage parasite per ruptured schizont in parasites with inactivated KIC11, while the egress efficacy remained unaffected. This indicated an invasion or very early ring stage development defect (new Figure 2H, Figure S3G). To further determine at which point exactly the phenotype occurs (ie during invasion or early after invasion) would require extensive experimentation that goes beyond the scope of this study (e.g. invasion assays using video microscopy with a representative number of parasites or sophisticated flow based quantification assays). We hope by excluding egress and gross changes of apical organelles as well as no indication for similar number of early rings (indicating it is invasion or a very early ring-establishment phenotype) will sufficiently narrow down the phenotype for labs interested in invasion to more definitely answer this question.

      Minor comments:

      1) Table S1: Please add the criterion for the order of proteins (abundance in "proxiome"?) in the table as a separate column. I would also suggest adding a new column that highlights the 10 proteins investigated in this study as I found the color-coding slightly confusing.

      Done as suggested: we now include the “average log2 Ratio normalized Kelch13” values from the four DiQ-BioID experiments performed with K13 in (Birnbaum et al., 2020), as well as the suggested column to highlight the investigated proteins. Please also see reviewer 1 major point # 12 for additional information on the selection criteria and how this was added to the manuscript.

      2) -154-155: There is a discrepancy between the text and Fig1C regarding the % of partial overlapping and non-overlapping foci.

      We thank the reviewer for pointing this out, this was corrected.

      3) -The y-axis label is missing in Fig 3E

      Done.

      4) -Fig 4I left graph, the superscript 2 is missing in μm2

      We thank the reviewer for pointing this out, this is now changed.

      5) -Did the author colocalize KIC11 in schizonts with other proteins found in the K13 compartment group of proteins not involved in endocytosis/ART resistance? This may help to further subgroup these proteins.

      This is an interesting point but would actually be technically challenging to do. For this we would need to generate a KIC11endo parasite line for each of these KICs and then do co-localisation in schizonts. However, the outcome of this likely would not be very clear. The reason for this is as follows. There are foci of KIC11 that do overlap with K13 in schizonts. One can expect that these foci show KIC11 at the K13 compartment and that the other KICs would overlap with KIC11 in these K13 foci in schizonts. Hence, we would also need to see K13 to find the non-K13 compartment KIC11 foci and see if these contained the KIC of interest. This is technically challenging because it would mean we would need a third fluorescent protein which is not that trivial to do. Due to the difficulty to do this and the large amount of work involved and the already considerable amount of data in this manuscript, we believe this will be better suited for a different study.

      6) -As a general comment: to make the beautiful IFAs more accessible to a broader readership, I would encourage the authors to switch the color-coding to green/magenta/blue or an equivalent color system or add grayscale images.

      This was done as suggested, all fluorescence images are now provided as greyscale images and the overlays are shown in magenta/green.

      Reviewer #2 (Significance (Required)):

      Characterizing the molecular components involved in Plasmodium endocytosis will not only reveal interesting biology in these highly adapted parasites, but will more importantly lead to a better understanding and potentially open new avenues for intervention of ART resistance. The here presented manuscript is a carefully executed follow-up on previous work done in Dr. Spielmann's lab focusing on the K13 compartment. The authors use established assays to characterize novel components and reveal three new players in endocytosis with one mediating ART resistance in vitro. The proposition that parts of the K13 compartment have a function other than endocytosis is interesting, but will have to await more data from future studies. Taken together this manuscript adds significantly to our understanding of endocytosis in P. falciparum.

      This work is of interest for cell and molecular biologists working on Apicomplexa, but especially for the Plasmodium community.

      We thank the reviewer for this positive assessment.

      I am a cell and molecular biologist working on Toxoplasma gondii

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

      Summary: The authors characterized 4 proteins from P. falciparum via cellular (co-)localization, endocytosis, parasite growth, and artemisinin resistance assays. These proteins have been identified as candidates for Kelch13 compartment and a possible role in endocytosis in their previously work with quantitative BioID for potential proximity to K13 and Eps15 (Birnbaum et al. 2020). In the current work, additional 6 proteins were not confirmed as being associated to the K13 compartment. This experimental work was complemented by an in-silico analysis of protein domains based on AlphaFold algorithm. For this protein structure evaluation all proteins were chosen, which were experimentally confirmed to be linked to the K13 compartment in the current publication and previous work. With the work 3 novel proteins linked to artemisinin resistance or endocytosis could be functionally described (KIC12, MCA2, and MyoF) and a number of hypotheses were generated.

      We thank the reviewer for the assessment of the manuscript and the constructive comments.

      Major comments:

      The quality of the presented work is solid, the experimental design is adequate, and methods are presented clearly. The publication contains a lot of results both presented in text and in the figures and it is not always straight forward for the reader to follow the descriptions due to many details presented and a lack of context for some of these experiments.

      We thank the reviewer for this overall positive assessment.

      We now reordered the results section in an attempt to increase the flow of the manuscript. We also made changes to improve the context for the results. Given the further (very valid) requests for data on schizonts and invasion, there was an increased danger for a less linear manuscript that we hope to have acceptably managed with the re-arrange.

      Specific suggestions for consideration by the authors to improve the manuscript. Abstract: 1) R 31: Mention how the 4 proteins were identified as candidates, you need to refer to previous work to clarify this

      To clarify this the sentence was changed to (line 31): "Here we further defined the composition of the K13 compartment by analysing more hits from a previous BioID, showing that MyoF and MCA2 as well as Kelch13 interaction candidate (KIC) 11 and 12 are found at this site."

      2) R38: "Second group of proteins" is confusing - different from the 4 mentioned above? Significance to endocytosis unclear. Please unify terminology in the manuscript, see also comment below on proxiome.

      We changed the wording to clarify the group issue in the abstract as follows line 34: "Functional analyses, tests for ART susceptibility as well as comparisons of structural similarities using AlphaFold2 predictions of these and previously identified proteins showed that canonical vesicle trafficking and endocytosis domains were frequent in proteins involved in resistance or endocytosis (or both), comprising one group of K13 compartment proteins, While this strengthened the link of the K13 compartment to endocytosis, many proteins of this group showed unusual domain combinations and large parasite-specific regions, indicating a high level of taxon-specific adaptation of this process. Another group of K13 compartment proteins did not influence endocytosis or ART susceptibility and lacked detectable vesicle trafficking domains. We here identified the first protein of this group that is important for asexual blood stage development and showed that it likely is involved in invasion.”

      3) Abstract can only be understood after reading the full publication

      We attempted to amend this by expanding the abstract, particularly the changes highlighted in the previous two points.

      Results: 4) Table 1 is missing from the submitted materials

      We apologise for this mistake. Table 1 is now included.

      5) Consider to shorten and stratify the result section to focus on the significant data

      We rearranged the results in an attempt to streamline this section and are now starting with MyoF in the revised manuscript. However, as highlighted by the requests from reviewer 1, many details need to be available to support our conclusions. For instance the fact that GFP-tagging partially inactivated MyoF asked for further data to support our conclusion (HA-tagged version, showing that the location of the GFP-tagged version was consistent with the HA-tagged version, showing to what extent the different constructs affected growth and correlated with number of vesicles and bloating, see new figure 1M) or that KIC12 has two locations. Overall, we are therefore hesitant to remove data or description from the result part.

      6) Unclear how the localization and functionalization assays might be impaired by the fusion proteins Significance of ART resistance assay is not clear, in presence of strong growth effects due to inactivation or truncation of genes/proteins

      As indicated also in the example given in the previous point (this reviewer #5), the use of different cell lines (GFP-tagged live cells and small epitope tag in IFA) for targets with an indication for an effect of the tagging confirm that the location we assigned is reasonable. In the case of MyoF, the HA-tagged line, the partial inactivation due to GFP and the further inactivation in the GFP-tagged line by knock sideways show plausible increase of phenotypes (vesicle accumulation and bloated FV assays). Thereby the GFP-tagged line can be seen as a partial inactivation line that further supports our conclusions and overall this paints a consistent picture of the function of this protein in endocytosis (see new Figure 1M better illustrating this). Please note that the difference in location shown by this line compared to the HA-tagged proteins is only small (see also reviewer 1 major point 23ff). See also general discussion on tags at the end of this rebuttal.

      Significance of ART resistance assay: The ‘ART resistance assay’ is done comparing +/- ART (DHA) in identical parasites (originating from the same culture and the same condition). Hence, any growth effects are cancelled out and effects in reducing ART susceptibility would - if at all - be underestimated (see more detailed response to point 28, reviewer 1 and controls in Birnbaum et al., 2020 where we tested an unrelated essential protein, unrelated chemical insult and rapalog on 3D7 and did not detect any effect on RSA survival).

      MCA 7) Stratify results, order by significance of findings, it appears to be described in chronological order, improve readability/flow, eg ART resistance if mentioned in r138, but only reported in r183ff

      We attempted to stratify, but then the reason for generating the partial MCA2 disruption parasite line becomes very arbitrary and would leave the reader wondering why we at all truncated the protein at two thirds of the protein. Hence, we do not see a way around this chronological reporting. However, this part is now not at the start of the experimental results section anymore, possibly making it overall a bit more palatable.

      MyoF 8) R195 to 197 - consider moving to discussion as it is distracting here

      This was shortened and additional information (asked for by reviewer 1, major point 22) to clarify that MyoF was previously called MyoC, was added (line 147): “The presence of MyosinF (MyoF; PF3D7_1329100 previously also MyoC), in the K13 proxiome could indicate an involvement of actin/myosin in endocytosis in malaria parasites. "

      9) Term proxiome is introduced above, but not used in result section - suggest to unify language, eg r195 uses "K13 compartment DiQ-BioIDs" instead, which is not very convenient for the reader

      We carefully reviewed this and made this more consistent.

      10) What is the enrichment factor? Please provide for this and the following proteins, eg in Table 1

      The enrichment factor is log2 enrichment over control and this is now provided in table S1 (see also detailed answer for Reviewer 1 major point 12).

      11) R225 to 243 - overall significance of the growth experiments with mislocaliser is not clear, consider removing from manuscript or explain relevance more clearly

      See also point 28, reviewer 1: This experiment is actually quite important. It shows that if we conditionally inactivate the GFP-tagged MyoF, the growth is further reduced, as stated in line 208. It might have been confusing that the mislocalisation is only partial, but this is equivalent to a partial knock down and hence is useful. This becomes even more relevant with the specific assays following in the next paragraph: while the tagging of MyoF already resulted in vesicles, conditional inactivation with KS generated even more vesicles, showing that the same phenotype was rapidly increased when MyoF was further inactivated by a different means and this also correlated with growth. Hence, this is actually a very consistent phenotype that despite some shortcomings of the tools available to analyse this protein (due to the partial inactivation by the GFP tag) in our eyes looks very convincing. We now added a graph showing the correlation of growth and phenotypes to illustrate this (Figure 1L).

      We also tried to make this clearer by changing line 200 to: Hence, conditional inactivation of MyoF further reduced growth despite the fact that the tag on MyoF already led to a substantial growth defect, indicating an important role for MyoF during asexual blood stage development.” And line 208 to:“ This was even more pronounced upon conditional inactivation of MyoF by KS (Figure 1H), suggesting this is due to a reduced function of MyoF.”

      12) KIC11/KIC12 Enrichment factor?

      The enrichment (’average log2 Ratio normalized Kelch13 from Birnbaum et al. 2020’) is 1.65 for KIC11 and 1.32 for KIC12, which is now also explicitly shown in column D of Table S1.

      ** Referees cross-commenting**

      I would like to applaud reviewer #1 for a great, very thorough review and lots of detailed suggestions. I agree with the conclusions mentioned in the significance evaluation from reviewer #1 and #2: the work presented does not contain novel methods and the scope is rather narrow with the current results. (I am working on clinical studies with novel antimalarial agents)

      Reviewer #3 (Significance (Required)):

      On the one hand side, the authors have wrapped up some of the remaining protein candidates of the K13 compartment and could verify 4 of 10 proteins. The work is of interest for the scientific community working on endocytosis and malaria drug resistance mechanisms. Overall, the conclusions and findings from the previous work, Birnbaum et al. 2020, could be confirmed and extended mainly using the methods previously described. On the other hand, the authors made use of progress in protein structure predictions and identified domains linking the K13 compartment proteins to putative functions. The overlaid protein folds of the newly identified domains in figure 5 look convincing, but I can't comment on the technical details or cut-off used for this in-silico analysis.

      Extended general remarks on the systems used for this work:

      Mainly reviewer 1 suggest (in the general comments and the significance statement) that other systems would have been better suited to use for this work, namely glmS and diCre and also has concerns about the large tag which is seconded by a comment of reviewer 3. In light of this we here provide some extended considerations on the properties for conditional systems and tagging in regards to the goals of this work.

      We would like to point out that we do have experience with the systems considered better-suited by the reviewer (one of the first authors has extensively used glmS (Wichers et al., 2021c; Wichers et al., 2021a; Wichers et al., 2022; Wichers-Misterek et al., 2023) and our lab was one of the first to adopt the diCre system in P. falciparum parasites and we regularly us it (Birnbaum et al., 2017; Mesén-Ramírez et al., 2019; Kimmel et al., 2023)). Clearly, these methods have a lot of strengths but there are a number of issues to be considered for the assays we use in this work (see the next section on conditional inactivation systems). In a nutshell, we believe diCre would give a more reliable readout of the absolute level of "essentiality" (i.e. importance for growth) but is unsuitable or at least difficult to use for the assays that reveal the function of our interest in this work. GlmS basically combines the drawbacks of diCre and knock sideways and hence for most targets is not expected to give a better readout of level of "essentiality" but is similarly difficult to use for our specific assays. The fact that both of these systems are possible to use without adding a tag to the target may be an advantage but without tag one loses some very important features that can be critical to understand the outcome with a given system (see considerations on the tag further below).

      Conditional inactivation systems:

      1. __ speed of inactivation:__ glms acts on mRNA and diCre on the gene level, which makes them slower than techniques acting directly on the protein such as DD or KS. With diCre, mRNA and protein is still left, even if the gene is very rapidly excised. For instance for Kelch13 it takes 3-4 days after excising the gene until protein levels have waned enough that this manifests in a reduced growth (Birnbaum et al., 2017). While in some instances diCre permits same cycle analyses if the protein has a very rapid turn-over (e.g. Rab5a, (Birnbaum et al., 2017)), control in a few hours is still difficult. For vesicle accumulation and bloated food vacuole assays, which are done over comparably short time frames and with specific stages, it is rather challenging to hit the correct time of induction to have all the cells at the correct stage with suitably (and uniformly, ie all cells) sufficiently reduced target protein levels during the assay time. Slow acting systems are also more prone to secondary effects. The more immediate the inactivation, the closer it is to the core of the affected function. With vesicle trafficking processes this is particularly relevant as all vesicle trafficking in a cell is interconnected and there are always recycling pathways that maintain the membrane and protein homeostasis of individual compartments. Particularly for endocytosis there seem to be compensatory capacities at least in other organisms (see e.g. (Chen and Schmid, 2020)). One reason why knock sideways was developed is that it permitted to avoid compensatory changes when vesicle adaptors are inactivated (Robinson et al., 2010).

      The comparably short time frame for malaria parasites to go through different stages during blood stage development also is an issue relevant for inactivation speed. The advantage of speed and the danger of obscured phenotypes is highlighted by our work on VPS45 which showed that in trophozoites this protein is involved in the transport of hemoglobin to the FV whereas in late stages it also has a role in secretory processes. Both of these functions we were able to specifically assess in the same growth cycle using KS to rapidly inactivate the protein (Bisio et al., 2020) but with a slower system would have been more complicated to dissect.

      Speed of effect with glmS: unless the KS does not work well, glmS is slower acting than KS (it does not target the already synthesised protein which can remain in the cell) and also often suffers from only partial inactivation, hence the benefit of using it here is unclear. The option to have an untagged protein is a plus, however it also is a minus, as assessing efficiency (particularly in live cells e.g. for bloated assays etc a fluorescent tag is the only direct option to assess inactivation of target) is critical to ensure the phenotype manifests at the stage of interest.

      lethality/absolute phenotypic effects are detrimental to some assays to study the functions we are interested in for this work: no RSA can be conducted, if the gene is lost and the parasites die. Again, with diCre, one could attempt to hit the point when the parasites have lost sufficient amounts of the target protein when they are placed under ART but then the parasites need to continue growing for ~3 days, which is not possible if the cKO is lethal except for very slowly turning over proteins. However, in that latter case, the parasites likely still had full functionality of the target protein at the beginning of the RSA, when the drug pulse happens and there would be no effect. Knock sideways solves these problems by permitting knock sideways inactivation only under ART (or with a few hours pre-incubation depending on the inactivation speed) to not yet affect growth in a severe manner but inhibiting the process the protein is involved in. It may be possible to use glmS for RSAs, but the slow speed would complicate it (it would not permit control of target protein levels in a matter of a few hours to inactivate the target protein and then re-install it).

      None-absolute inactivation is also a strength for some functional assays. While we really like using diCre, in the case of EXP1 it made it necessary to complement the exp1 cKO parasites with low levels of EXP1 to be able to do functional assays without killing the parasites (Mesén-Ramírez et al., 2019; Mesén-Ramírez et al., 2021). While the lethality issue does not apply to glmS (like knock sideways, it also can be tuned), it is unclear what would be gained over knock sideways. Knockdown levels with glmS vary from gene to gene and cannot be predicted, it is in most cases considerably slower than KS, it requires glucosamine which becomes toxic at higher concentrations and might introduce off target effects and tracking protein levels during the assay would equally need GFP tagging.

      Integration of properties of conditional systems

      Given the above discussed properties, several factors have to be considered to be able to use a system for a given assay. Stage-specific transcription is one example. For diCre a protein not expressed in e.g. rings permits to remove the gene and the protein is never made in that parasite development cycle. We exploited this for instance for two proteins only expressed from the trophozoite stage onwards (Kimmel et al., 2023). However, if lethal (absolute effect problem), this also means one can also only see the phenotype on onset of expression of the target (e.g. if in mitosis, the first nuclear division in case the protein is absolutely essential for the process). This is just one example of such issues. Expression timing, turnover of the protein and homogeneity of stage-specific loss of protein will all influence how clearly the phenotype can be determined. All this will decide the exact time of loss/inactivation of the target protein to levels generating a phenotype and ideally therefore can be monitored during an assay (see considerations on tagging).

      For these reasons vesicle accumulation or bloated food vacuole assays are difficult with slow systems as ideally the target should rapidly be inactivated at the trophozoite stage and the result monitored before the cells have moved to the schizont stage. For this a well responding knock sideways is ideal as the protein can be rapidly taken away (sometimes within seconds) to visualise the immediate, direct effect in the cell.

      As shown for KIC11, there is also no disadvantage of using KS for proteins with other assays or proteins that result in different phenotypes. It permits stage-specific same cycle inactivation without having to worry about the turnover of mRNA and protein (Fig. 2F,G). Thus, besides the advantages of knock sideways for endocytosis related assays and RSAs, we also see no disadvantage of using knock sideways for the functional study of KIC11 which has a role other than endocytosis. KS also permits to specifically target the K13 pool of KIC12, something impossible or very difficult to do with other systems. Hence, we are of the opinion that the system for inactivation was adequate for most of the proteins analysed in this manuscript.

      Large tag: we agree that GFP-tagging can be a disadvantage but in our opinion its benefits often outweigh the drawbacks because it permits easy and immediate (on individual cell level, if need be) monitoring of the presence/location of the target protein (e.g. after KS, but given the discrepancy of the timing between gene excision and protein loss, it might be even more important for techniques such as diCre). No fixing/permeabilisation (prone to artifacts, prevents immediate view of cells) to detect a target with specific antibodies or via a small tag is needed with GFP. Similarly, the use of Western blots to do this is time consuming and impractical if monitoring of left-over protein in the course of an assay such as a bloated food vacuole assay is needed.

      In many cases, adding GFP has no negative effect. In addition, if the bulky folded structure of GFP is tolerated, it usually also tolerates the 2 to 4 12kDa FKBP domains in our standard tag. We also typically add a linker. This approach has worked for a large number of different proteins, including many essential ones for which we would not otherwise have obtained the integration cell lines (Birnbaum et al., 2017; Jonscher et al., 2019; Hoeijmakers et al., 2019; Birnbaum et al., 2020; Kimmel et al., 2023; Sabitzki et al., 2023). Hence, whenever a cell line is obtained with it, this tag in most cases is not a disadvantage. Admittedly an exception in this is MyoF and to some extent maybe MCA2 (we would like to stress that in the case of MCA2 the reason for not being able to obtain the full length tagged cell line is unclear: the protein can be severely truncated to less than 3% of its amino acid sequence and a GFP-tag is tolerated on the version with 2/3s of the protein left, which gives no good reason why the full length was not obtained; a potential reason could be a dominant negative effect). However, we obtained the full length with a small tag detected by IFA for both, MyoF and MCA2 and the location of these agreed well with the GFP tagged versions, indicating that the GFP-tagged versions are useful to show the location of these proteins in live cells.

      There are also tricks to attempt monitoring the effect of e.g. diCre without tagging the target. For instance, if a fluorescent protein is connected to excision without actually being fused to the target (ie excision of the gene leads to its expression of e.g. GFP), which would avoid adding a tag to the target itself. However, the problem with this is that expression of GFP does only show excision, but mRNA producing the target protein and left over target protein may still be there in the cell. All in all, the GFP-tag on the target, while with some drawbacks, is still our preferred method to control to monitor the target protein in the cell (in principle permitting quantification of ablation efficiency on the individual cell level).

      Conclusion on these considerations for this manuscript

      Based on these considerations we do not see the immediate benefit of changing the system for the conclusions drawn from this study and are unsure if they are indeed better suited for this work as suggested. While a more exact readout of "essentiality" might be possible with the diCre system we are of the opinion this is less important than learning the function of a protein which - as outlined above - we believe to be considerably more difficult with diCre and even more so with glmS considering our target functions. The same applies to target specific cellular pools of a protein as done here for KIC12. Clearly MyoF is one example where the employed systems shows limitations, but with the new Figure part showing consistency in phenotype with degree of inactivation (importantly with two different forms of inactivation) and the clarification that the location of the GFP-tagged and HA-tagged versions are actually quite similar in location, we do not think employing an extra system is warranted for the conclusions of this work. Admittedly, the apparent lack of need in ring stags might give an opening to attack MyoF using diCre (by excision before its major expression peak), but depending on lethality this might preclude extended analyses (possibly vesicle assays, for sure not RSAs).

      In the end the question is, if our approach provides the function of target analysed in this work and based on the data in our manuscript and the arguments in the rebuttal, we are reasonably confident that this is the case. It is not very likely the other mentioned techniques would result in a different conclusion on the function of the here studied proteins. In fact, we expect other commonly used techniques to be less suitable for the key assays in this work.

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    1. Author Response

      Reviewer #2 (Public Review):

      The manuscript by Ma et al, "Two RNA-binding proteins mediate the sorting of miR223 from mitochondria into exosomes" examines the contribution of two RNA-binding proteins on the exosomal loading of miR223. The authors conclude that YBX1 and YBAP1 work in tandem to traffic and load miR223 into the exosome. The manuscript is interesting and potentially impactful. It proposes the following scenario regarding the exosomal loading of miR223: (1) YBAP1 sequesters miR223 in the mitochondria, (2) YBAP1 then transfers miR223 to YBX1, and (3) YBX1 then delivers miR223 into the early endosome for eventual secretion within an exosome. While the authors propose plausible explanations for this phenomenon, they do not specifically test them and no mechanism by which miR223 is shuttled between YBAP1 and YBX1, and the exosome is shown. Thus, the paper is missing critical mechanistic experiments that could have readily tested the speculative conclusions that it makes.

      Comments:

      1) The major limitation of this paper is that it fails to explore the mechanism of any of the major changes it describes. For example, the authors propose that miR223 shuttles from mitochondrially localized YBAP1 to P-body-associated YBX1 to the exosome. This needs to be tested directly and could be easily addressed by showing a transfer of miR223 from YBAP1 to YBX1 to the exosome.

      Testing this idea using fluorescently labeled miR223 would indeed be an ideal experiment. However, miRNA imaging presents challenges. As reviewer 1 pointed out, and we have now confirmed, the atto-647 dye itself localizes to mitochondria. We will continue our efforts to identify a suitable fluorescent label for miR223in order to be in a position to evaluate the temporal relationship between mitochondrial and endosomal miR223.

      2) If YBAP1 retains miR223 in mitochondria, what is the trigger for YBAP1 to release it and pass it off to YBX1? The authors speculate in their discussion that sequestration of mito-miR223 plays a "role in some structural or regulatory process, perhaps essential for mitochondrial homeostasis, controlled by the selective extraction of unwanted miRNA into RNA granules and further by secretion in exosomes...". This is readily testable by altering mitochondria dynamics and/or integrity.

      A previous study has reported that YBAP1 can be released from mitochondria to the cytosol during HSV-1 infection (Song et al., 2021). However, due to restrictions, we are unable to conduct experiments using HSV to verify this condition. We attempted to induce mitochondrial stress by using different concentrations of CCCP, but we did not observe the release of YBAP1 from mitochondria after CCCP treatment. We speculate that not all mitochondrial stress conditions can trigger YBAP1 release. Investigating the mechanism of mito-miR223 release from mitochondria is one of our interests that we aim to explore in future studies.

      3) Much of the miRNA RT-PCR analysis is presented as a ratio of exosomal/cellular. This particular analysis assumes that cellular miRNA is unaffected by treatments. For example, Figure 1a shows that the presence of exosomal miR223 is significantly reduced when YBX1 is knocked out. This analysis does not consider the possibility that YBX1-KO alters (up or down-regulates) intracellular miR223 levels. Should that be the case, the ratiometric analysis is greatly skewed by intracellular miRNA changes. It would be better to not only show the intracellular levels of the miRs but also normalize the miRNA levels to the total amount of RNA isolated or an irrelevant/unchanged miRNA.

      Our previous publications demonstrated that miR223 levels are increased in YBX1-KO cells and decreased in exosomes derived from YBX1 KO cells. However, no significant changes were observed in miR190 levels (Liu et al., 2021; Shurtleff et al., 2016). The repeated data has been included in Figure 1a.

      For the analysis of other miRNAs by RT-PCR, we assessed changes in intracellular and exosomal miRNA levels in the corresponding figures. In the qPCR analysis, miRNA levels were normalized to the total amount of RNA.

      4) In figure 1, the authors show that in YBX1-KO cells, miR223 levels are decreased in the exosome. They further suggest this is because YBX1 binds with high affinity to miR223. This binding is compared to miR190 which the authors state is not enriched in the exosome. However, no data showing that miR190 is not present in the exosome is shown. A figure showing the amount of cellular and exosomal miR223 and 190 should be shown together on the same graph.

      In previous publications we demonstrated that miR190 is not localized in exosomes and not significantly changed in YBX1 knockout (KO) cells and exosomes derived from YBX1 KO cells (Liu et al., 2021; Shurtleff et al., 2016). The repeated data has been included in Figure 1a.

      5) Figure 2 Supplement 1 - As to determine the nucleotides responsible for interacting with YBX1, the authors made several mutations within the miR223 sequence. However, no explanation is given regarding the mutant sequences used or what the ratios mean. Mutant sequences need to be included. How do the authors conclude that UCAGU is important when the locations of the mutations are unclear? Also, the interpretation of this data would benefit from a binding affinity curve as shown in Fig 2C.

      The ratio is of labeled miR223/unlabeled miR223 (wt and mutant). All mutant sequences of miR223 have been included in Figure 2 supplement 1.

      6) While the binding of miR223mut to YBX1 is reduced, there is still significant binding. Does this mean that the 5nt binding motif is not exact? Do the authors know if there are multiple nucleotide possibilities at these positions that could facilitate binding? Perhaps confirming binding "in vivo" via RIP assay would further solidify the UCAGU motif as critical for binding to YBX1.

      The binding affinity of miR223mut with YBX1 is reduced approximately 27-fold compared to miR223. We speculate that the secondary structure of miR223 may contribute to the interaction with YBX1.

      Our EMSA data, in vitro packaging data, and exosome analysis reinforce the conclusion that UCAGU is critical for YBX1 binding. These findings suggest that the presence of the UCAGU motif in miR223 is crucial for its interaction with YBX1 and subsequent sorting into exosomes.

      7) Figures 2g, h - It would be nice to show that miR190mut also packages in the cell-free system. This would confirm that the sequence is responsible. Also, to confirm that the sorting of miR223 is YBX1-dependent, a cell-free reaction using cytosol and membranes from YBX1 KO cells is needed.

      Although we have not performed the suggested experiment, we purified exosomes from cells overexpressing miR190sort and observed an increase in the enrichment of miR190sort in exosomes compared to miR190. This finding confirmed that the UCAGU motif facilitates miRNA sorting into exosomes.

      Regarding the in vitro packaging assay, our previously published paper demonstrated that cytosol from YBX1 knockout (KO) cells significantly reduces the protection of miR223 from RNase digestion. We concluded that the sorting of miR223 into exosomes is dependent on YBX1 (Shurtleff et al., 2016).

      8) In Figure 3a, the authors show that miR223 is mitochondrially localized. Does the sequence of miR223 (WT or Mut) matter for localization? Does it matter for shuttling between YBAP1 and YBX1?

      The localization of miR223mut has not been tested in our current study. We plan to conduct these experiments in the future.

      9) Supplement 3c - Is it strange that miR190 is not localized to any particular compartment? Is miR190 present ubiquitously and equally among all intracellular compartments?

      Most mature miRNAs are predominantly localized in the cytoplasm. Although there is no specific subcellular localization reported for miR190 in the literature, our experimental findings indicate a relatively high expression of miR190 in 293T cells. It is likely that most of miR190 is localized in the cytosol. However, it is also possible that a small fraction of miR190 may associate with a membrane, which could explain its distribution in various subcellular structures. Importantly, we did not observe enrichment of miR190 in the mitochondria or exosomes.

      10) Figure 3h - Why would the miR223 levels increase if you remove mitochondria? Does CCCP also cause miR223 upregulation? I would have thought miR223 would just be mis-localized to the cytosol.

      We report that the levels of cytoplasmic miR223 increase following the removal of mitochondria using CCCP treatment. While we cannot rule out the possibility that upregulation of miR223 is directly caused by CCCP treatment, we suggest that miR223 becomes mis-localized to the cytosol upon mitochondrial removal. Our data suggests that mitochondria contribute to the secretion of miR223 into exosomes. When mitochondria are removed by mitophagy, cytosolic miR223 is not efficiently secreted, which provides an alternative explanation for the observed increase in miR223 level after mitochondrial removal.

      11) Figure 3i - What is the meaning of "Urd" in the figure label? This isn't mentioned anywhere.

      “Urd” represents Uridine. Uridine is now spelled out in figure 3i. The absence of mitochondria can impact the function of the mitochondrial enzyme dihydroorotate dehydrogenase, which plays a role in pyrimidine synthesis. To address this issue, one approach is to supplement the cell culture medium with Urd. A previous study demonstrated that primary fibroblasts showed positive responses when Urd was added to the cell culture medium, resulting in improved cell viability for extended periods of time (Correia-Melo et al., 2017).

      12) Figure 3j - The data is presented as a ratio of EV/cell. Again, this inaccurately represents the amount of miR223 in the EV. This issue is apparent when looking at Figures 3h and 3j. In 3h, CCCP causes an upregulation of intracellular miR223. As such, the presumed decrease in EV miR233 after CCCP (3j) could be an artifact due to increased levels of intracellular miR223. Both intracellular and EV levels of miRs need to be shown.

      Both the intracellular and exosomal levels of miR223 have been included in Figure 3j.

      13) In Figure 4, the authors show that when overexpressed, YBX1 will pulldown YBAP1. Can the authors comment as to why none of the earlier purifications show this finding (Figure 1 for example)? Even more curious is that when YBAP1 is purified, YBX1 does not co-purify (Figure 4 supplement 1a, b).

      In Figure 4a-b, human YBX1 fused with a Strep II tag was purified from 293T cells using Strep-Tactin® Sepharose® resin in a one-step purification process. Our data has shown that YBAP1 is expressed in 293T cells.

      In Figure 1 and Figure 4 Supplement 1a, human YBX1 or YBAP1 fused with His and MBP tags were purified from insect cells using a three-step purification process involving Ni-NTA His-Pur resin, amylose resin, and Superdex-200 gel filtration chromatography.

      One possibility is that human YBX1 or YBAP1 may not interact well with insect YBAP1 or YBX1, which could result in separate tagged forms of YBX1 or YBAP1 isolated from insect cells.

      Another possibility is that the expression levels of insect YBAP1 and YBX1 may be too low. Consequently, tagged forms YBX1 or YBAP1 expressed in insect cells may copurify with partners not readily detected by Coomassie blue stain. However, in Figure 4 Supplement 1b, human YBX1 fused with His and MBP tags was co-expressed with non-tagged human YBAP1, and both bands of YBX1 and YBAP1 were visible on the Coomassie blue gel after purification using Ni-NTA His-Pur resin, amylose resin, and Superdex-200 gel filtration chromatography.

      14) Figure 4f, g - The text associated with these figures is very confusing, as is the labeling for the input. Also, what is "miR223 Fold change" in this regard? Seeing as your IgG should not have IP'd anything, normalizing to IgG can amplify noise. As such, RIP assays are typically presented as % input or fold enrichment.

      The RIP assay results have been calculated and presented as a % input in Figure 4g.

      15) Figure 4h - The authors show binding between miR223 and YBAP1 however it is not clear how significant this binding is. There is more than a 30-fold difference in binding affinity between miR223 and YBX1 than between miR223 and YBAP1. Even more, when comparing the EMSAs and fraction bound from figures 1 and 2 to those of Figure 4h, the binding between miR223 and YBAP1 more closely resembles that of miR190 and YBX1, which the authors state is a non-binder of YBX1. The authors will need to reconcile these discrepancies.

      We agree that the binding of YBAP and YBX1 differ quite significantly in the affinity of their interaction with miR223. It is difficult to draw conclusions from a comparison of the affinities of YBX1 for miR190 and YBAP1 for miR223. Nonetheless, a quantitative difference in the interaction of YBAP1 with miR223 and miR190 is apparent (Fig. 4 h, I, j) and we observed no enrichment miR190 in isolated mitochondria (Fig. 3 supplement 1a) whereas YBAP1 selectively IP’d miR223 from isolated mitochondria (Fig. 4 f and g).

      16) Can the authors present the Kd values for EMSA data?

      The Kd values for the EMSA data have been added to the respective figures.

      17) Figure 5 - Does YBAP1-KO affect mitochondrial protein integrity or numbers?

      We generated stable cell lines expressing 3xHA-GFP-OMP25 in both 293T WT and YBAP1-KO cells, but we did not observe any alterations in mitochondrial morphology (Author response image 1).

      Author response image 1.

      Additionally, we performed a comparison of different mitochondrial markers using immunoblot in 293T WT cells and YBAP1-KO cells and did not observe any changes in these markers (data has been included in Figure 5b.).

      18) Figure 6a - Are the authors using YBAP1 as their mitochondrial marker? Please include TOM20 and/or 22.

      In Figure 4c and 4e, our data clearly demonstrate that the majority of YBAP1 is localized in the mitochondria.

      To further validate this localization, we performed immunofluorescence staining using antibodies against endogenous Tom20 and YBX1. The immunofluorescence images document YBX1 associated with mitochondria (Author response image 2 and new Fig 6a.).

      Author response image 2.

      19) Figure 6b - Rab5 is an early endosome marker and may not fully represent the organelles that become MVBs. Co-localization at this point does not suggest that associating proteins will be present in the exosome, and it is possible that the authors are looking at the precursor of a recycling endosome. Even more, exosome loading does not occur at the early endosome, but instead at the MVB. Perhaps looking at markers of the late endosome such as Rab7 or ideally markers of the MVB such as M6P or CD63 would help draw an association between YBX1, YBAP1, and the exosome. Also, If the authors want to make the claim that interactions at the early endosome leads to secretion as an exosome, the authors should show that isolated EVs from Rab5Q79L-expressing cells contain miR223.

      We have previously used overexpressed Rab5(Q79L) to monitor the localization of exosomal content, specifically CD63 and YBX1, in enlarged endosomes (Liu et al. 2021, Fig. 4A, B). These endosomes exhibit a mixture of early and late endocytic markers, including CD63. (Wegner et al., 2010). Hence, the presence of Rab5(Q79L)-positive enlarged endosomes does not solely indicate early endosomes.

      20) The mentioning of P-bodies is interesting but at no time is an association addressed. This is therefore an overly speculative conclusion. Either show an association or leave this out of the manuscript.

      In a previous paper we demonstrated that YBX1 puncta colocalize with P-body markers EDC4, Dcp1 and DDX6 (Liu et al., 2021).

      21) In lines 55-58, the authors make the comment "However, many of these studies used sedimentation at ~100,000 g to collect EVs, which may also collect RNP particles not enclosed within membranes which complicates the interpretation of these data." Do RNPs not dissolve when secreted? Can the authors give a reference for this statement?

      In a previous paper, we demonstrated that the RNP Ago2 does not dissolve in the conditioned medium and is not in vesicles but sediments to the bottom of a density gradient (Temoche-Diaz et al., 2019).

  11. Jun 2023
    1. Author Response:

      Reviewer #1 (Public Review):

      The study investigates the nature of "trailblazer" cells in distinct tumor models, including luminal B (MMTV/PyMT) and triple negative (TNBC) tumors (C3-TAg). The authors note that the trail-blazer phenotypes in the TNBC model are more complex relative to the Luminal B model and represent distinct EMT programs associated with the expression of distinct EMT-TFs (Zeb1, Zeb2 and Fra-1). They demonstrated that of numerous EMT-TFs, Zeb1 and Fra-1 were required for increased cancer cell migration and invasion. They reveal that TGF-beta and EGF-mediated signaling are required for the diverse EMT states that are required for trailblazer cell activity and increased cell migration/invasion. TGF-beta signaling engaged Zeb 1 and Zeb2 while EGF sig-naling activated Fra-1. Indeed, inhibitors of either TGF-beta or EGF signaling could impair cell migration/invasion. While both pathways contributed to trailblazer phenotypes, EGF signaling was shown to interfere with certain TGF-beta induced transcriptional response, including the ex-pression of genes encoding extracellular matrix proteins.

      One concern was the heavy reliance of the C3-TAg as the sole TNBC model in which the dis-tinct trailblazer phenotypes were described. The data in Fig. 3 of the submission reveals that the phenotypes observed in the C3-TAg model could be recapitulated in a TNBC patient-derived xenograft model (PDX). Using this PDX, the authors were able to show vimentin expression in lung metastatic TNBC cells that were intravascular, those that had extravasated and clusters of cancer cells fully within the lung parenchyma. This was an important addition to the manuscript. The additional experiments to investigate the role of Zeb1 and Zeb1 more fully, beyond the focus on Fra-1 in the initial submission was an additional strength of the new submission. Additional clarifications to the discussion also clarified the concepts articulated in the study. The study em-ploys multiple breast cancer models, utilizes numerous in vitro and in vivo assessments of the trailblazer phenotypes, and the experimental design is rigorous and the interpretation of the data is sound. The manuscript will be of general interest to the research community.

      Thank you for the supportive comments. We are glad that the revisions addressed your prior concerns.

      Reviewer #2 (Public Review):

      This represents an important study that demonstrates a high degree of heterogeneity within trailblazer cells in clusters that participate in collective migration. Solid methods highlight this het-erogeneity and show that in TNBC cancers, trailblazer cells are defined by vimentin (and not Keratin 14) and are dependent on both TGFbeta and EGFR signaling. Additional, single cell stud-ies would further support this work.

      Thank you for the suggestion. Our current data establishes that trailblazer cells are heterogene-ous using FACS, immunostaining and functional studies of fresh tumor organoids and estab-lished tumor organoid lines. In addition, our RNA-seq experiments provided deep insight into the nature of gene expression changes that corresponded with the evolution of new trailblazer states. This discovery of trailblazer cell heterogeneity was one of multiple key new discoveries in this manuscript, along with revealing a Krt14-independent invasion mechanism, the regulation of trailblazer cells by Tgfβ and Egfr signaling and a new compromise mode of signal integration. We agree that our results support further investigation of the nature and function of basal-like breast cancer heterogeneity during the progression to metastasis. However, a comprehensive implementation of scRNA-seq is mostly likely required to further unravel new aspects of hetero-geneity that substantially advance upon the conclusions supported by our current data. Such an undertaking is beyond the scope of this investigation.

      We agree that scRNA-seq would be confirmatory of trailblazer cell heterogeneity that has been demonstrated with multiple approaches rather than a new discovery of heterogeneity.

      Strengths:

      The paper highlights that collective migration, and the nature of trailblazer cells can be highly heterogeneous. This is important as it suggests that the ability to move between states may su-persede a singular phenotype.

      The paper uses animal models and organoids and in several areas attempts to correlate find-ings to human tissues.

      The experiments are logically described.

      Reviewer #3 (Public Review):

      Cancer is a disease of many faces and in particular, the ability of cancers cells to change their phenotypes and cell behaviors - cancer cell plasticity - is a major contributor to cancer lethality and therapeutic challenge of treating this disease. In this study, Nasir, Pearson et al., investigate tumor cell plasticity through the lens of invasive heterogeneity, and in particular in models of tri-ple-negative breast cancer (TNBC), a subtype of breast cancer with particularly poor clinical prognosis and more limited treatment modalities. Using organoid models in a variety of matrix systems, microscopy, and signaling pathway inhibitors, they find that invading TNBC breast tu-mors, primarily in the C31-Tag genetically engineered mouse model of TNBC, are composed of heterogeneous invasive/"trailblazer" type tumor cells that in many cases express vimentin, a classical intermediate filament marker of epithelial-mesenchymal transition, and reduced keratin-14, another filament marker of basal epithelial cells associated with collective invasion in differ-ent breast cancer models. Supportive genetic and pharmacologic evidence is provided that gen-eration of these cells is TGF-beta signaling pathway driven, likely in vivo from the surrounding tumor microenvironment, in accord with published studies in this space. Another important as-pect of this study is the good transcriptional evidence for multiple migratory states showing dif-fering degrees of partial overlap with canonical EMT programs, dependent on TGF-beta, and suggestive but at present incomplete understanding of a parallel program involving Egfr/Fra-1 mediated effects on invasion. When taken in context with other recent studies (Grasset et al. Science Translational Medicine 2022), these data are broadly supportive of concept of targeting vimentin-dependent invasion programs in TNBC tumors.

      The core conclusions of this paper are generally supported by the data, but there are some conceptual and technical considerations that should be taken into account when interpreting this study. Specific comments:

      1) The contribution of the different vimentin-positive trailblazer cells to distant metastasis was not directly confirmed in vivo in this study. Given the limited proliferative potential of many fully EMT'd cells and in light of recent studies indicating that invasion can be uncoupled from meta-static potential, it seems important to directly test whether the different C31-tag isolates, varying in invasive potential in this study, produce metastases and if so do metastases abundance corre-late with the invasive potential in 3D culture. The collection of lungs at 34 days post injection de-scribed in methods is too short to evaluate metastatic frequency.

      We agree that it is important to determine the contribution of trailblazer cells towards metastatic dissemination. In this manuscript, we show that Vimentin expressing cells in a triple negative breast cancer (TNBC) PDX model disseminate to the lungs (Figure 3F). We have also shown that Vimentin expressing SUM159 breast cancer (BC) trailblazer cells spontaneously metasta-size to the lungs in previous publications (Fig. 2–figure supplement 1C) and (Westcott et al, J Clin Invest, 2015, 10.1172/JCI77767 and Maine et al, Oncotarget, 2016, 10.18632/oncotarget.7408). Notably, the depletion of genes specifically expressed in trailblazer cells reduced spontaneous metastasis without significantly impinging on primary tumor growth (Westcott et al, J Clin Invest, 2015, 10.1172/JCI77767 and Maine et al, Oncotarget, 2016, 10.18632/oncotarget.7408). Our new results in Figure 5D show that Tgfβ activates genes that define the trailblazer state in the metastatic SUM159 trailblazer cell model. Thus, features of the Tgfβ regulated trailblazer program in the C3-TAg cells is active in the SUM159 trailblazer model of spontaneous metastasis. In addition, commonly employed BC cell line metastasis models, such as MDAMB231 derivatives are highly mesenchymal (Fig. 2–figure supplement 1C) and (Kang et al, Cell, 2003, 10.1016/S1535-6108(03)00132-6 and Minn et al, Nature, 2005, 10.1038/nature03799, as examples).

      It is not technically feasible to establish a correlation between the relative invasion of The C3-TAg GEMM primary tumors and spontaneous metastasis. C3-TAg GEMM primary tumors de-velop rapidly and the mice must be euthanized prior to the detection of metastasis. This limitation of the model is mentioned in the Results section “Trailblazer cells are specified by Vimentin ex-pression in basal-like breast cancer patient tumors”. The aggressive primary tumor growth and limited spontaneous metastasis of the the C3-TAg model has also been previously reported by others (Green et al, Oncogene, 2000, 10.1038/sj.onc.1203280). Surgical resection of the original primary tumor is not feasible option to allow metastases to form since additional tumors develop in multiple mammary glands.

      In response to reviewer requests, we initiated the growth of orthotopic primary tumors from con-trol or Tgfβ treated 1339-org cells to address the relationship between induction of the trailblazer state and primary tumor cell dissemination. We had to euthanize the mice at day 34 (d34) be-cause tumors within both cohorts had reached the maximum permitted diameter of 2 cm. This will be indicated in the Methods section with revised text. We detected CTCs from the mice bearing control and Tgfβ treated 1339-org cell tumors. However, no micrometastases were de-tected, which is indicated in the text describing Figure 4–figure supplement 3A-B. Thus, per-forming surgical resection in new experiments would not be expected to allow the later detection of metastasis, as there did not appear to be DTCs in the lungs that could initiate colonization. In addition, we would have to resect the tumors prior to d34 to successfully and humanely remove the primary tumors, further reducing the odds of metastases developing. We will continue our work to identify an experimental balance that permits sufficient primary tumor growth to initiate spontaneous metastasis. However, the time scale of resolving this technical challenge is uncer-tain and we believe that our published analysis of trailblazer cell metastasis and new findings here showing the dissemination of Vimentin expressing cells in a PDX model addresses the question of whether Vimentin expressing trailblazer cells metastasize.

      We agree that certain cell states induced by EMT programs can limit the proliferative potential of tumor cells. As described in the Introduction, we previously found that the induction of a trailblaz-er state in a subset of breast cancer cell line models triggers a collateral cost in fitness that limits the ability of trailblazer cells to initiate tumor growth (Westcott et al, Cancer Res, 2020, 10.1158/0008-5472.CAN-20-0014). The traits that distinguish trailblazer cells which are capable of tumor initiation and metastasis versus trailblazer cells with reduced fitness have begun to be delineated. Our prior report suggested that cells that were dependent on p63 for growth lost their proliferative capacity when converting to a trailblazer state (Westcott et al, Cancer Res, 2020, 10.1158/0008-5472.CAN-20-0014). C3-TAg cells are not dependent on p63 for growth, which is indicated by the vast majority of the tumor cells lacking p63 expression in primary tumors and primary tumor organoids (Westcott et al, Cancer Res, 2020, 10.1158/0008-5472.CAN-20-0014), similar to the metastatic SUM159 breast cancer cell line model. We were also able to derive clonal trailblazer cell lines that lacked detectable p63 expression from a C3-TAg tumor (Figure 2—figure supplement 1B) and grow organoids even when the limited extent of p63 expression was further reduced by Tgfβ (Figure 5C). Additionally, the persistent Tgfβ treated 1339-org cells, which were enriched for trailblazer cells and had reduced p63 expression, were capable of initiating primary tumor growth (Figure 4F). Together, these results indicate that C3-TAg trail-blazer cells are capable of initiating metastatic colonization. However, given the heterogeneity in trailblazer states that we discovered, it is possible that a subset of trailblazer cell states have re-duced proliferative capacity. Our analysis approach in this manuscript would not necessarily de-tect these low fitness trailblazer cells if they were a relatively small fraction of the total trailblazer population. We will clarify this point in the Discussion section in the revised manuscript. Our re-sults have begun to reveal mechanisms for the transcriptional regulation of trailblazer cell heter-ogeneity. We plan to continue delineating the regulatory programs conferring specific transcrip-tion state, defining approaches for the prospective isolation of distinct trailblazer subpopulations and determining trailblazer subpopulation specific biomarkers to understand the specific contri-bution of distinct trailblazer subpopulations towards metastasis. Given the scope of this analysis, it is not feasible to incorporate these future studies into this manuscript.

      2) The invasion of cancer cells is dependent on 3D matrix composition. In other studies, collec-tive cancer invasion is performed in exclusively collagen type 1 gels or in other instances entirely in 3D reconstituted basement membrane gel, e.g. lung cancer invasion studies. In this study, the authors use a mixture composed of both matrices. Given the invasion suppressive effects of matrigel, particularly for epithelial type cells, further studies would be important to determine whether the invasion phenotypes seen in this study are generalizable across matrix environ-ments.

      The invasion of C3-TAg and PyMT organoids embedded in a 100% pure reconstituted base-ment is shown in Fig. 1–figure supplement 1G. We will emphasize that trailblazer invasion was evaluated in multiple ECM compositions with revised text and figure graphic. We also provide images for the reviewer showing that C3-TAg organoids collectively invade in a pure Collagen I ECM. Importantly, these findings are consistent with our results showing that Vimentin express-ing cells are associated with basal-like mammary tumor cell invasion in the complex ECM of C3-TAg GEMM primary tumors (Figure 2G) and patient primary tumors (Figure 3D). Moreover, Vimentin expressing cells disseminated to the lungs in the TNBC PDX that we evaluated (Figure 3F).

      The ECM composition selected for experiments is dictated by the experimental question(s) being addressed. It is unlikely that mammary tumor cells would only ever collectively invade through an ECM that is either pure Collagen I or pure reconstituted basement membrane (BM). Indeed, it has been proposed that mixtures of Collagen I and BM proteins best reconstitute the complexity of primary tumor ECM (Hooper et al, Methods Enzymol, 2006, 10.1016/S0076-6879(06)06049-6). In line this observation, mixtures of Collagen I and BM proteins have been routinely used for the past 20 years to define mechanisms of 3D invasion; Xiang and Muthuswamy, Methods En-zymol, 2006, 10.1016/S0076-6879(06)06054-X; Calvo et al, Nat Cell Biol, 2013 10.1038/ncb2756; and Kato et al, eLife, 2023, 10.7554/eLife.76520, as examples).

      Consistent with the known complexity of the ECM in the tumor microenvironment (TME), we detect Collagen I and Collagen IV (a key component of experimental BM) in the TME of primary breast cancer tumor models (Westcott et al, J Clin Invest, 2015, 10.1172/JCI77767). Important-ly, we have found that a mixture of collagen I and experimentally derived BM proteins reliably reveals breast cancer trailblazer cell invasion mechanisms that promote the malignant progres-sion and metastasis of primary tumors and whose expression correlates with poor patient out-come (Westcott et al, J Clin Invest, 2015, 10.1172/JCI77767 and Westcott et al, Cancer Res, 2020, 10.1158/0008-5472.CAN-20-0014, as examples). Notably, the relative differences in trail-blazer and opportunist cell invasive phenotypes are not dictated by the ECM composition used in our 3D assays. We have previously tested the invasion of trailblazer and opportunist subpopula-tions in different ECM compositions using both spheroid vertical invasion assays (Westcott et al, J Clin Invest, 2015, 10.1172/JCI77767). Increasing collagen I concentration enhanced the rela-tive rate of trailblazer cell invasion, with trailblazer cells always showing a significantly enhanced invasion relative to opportunist cells.

      The relationship between trailblazer and opportunist cells that we have detected in primary tu-mors is recapitulated when using mixtures of Collagen I and BM proteins in our past publications and in this manuscript. The clonal opportunist cell lines derived from a C3-TAg tumor expressed high levels of the transcription factor p63 (Figure 2–figure supplement 1A-B). We previously showed that p63 restricts induction of a trailblazer state in human breast cancer trailblazer cell lines (Westcott et al, Cancer Res, 2020, 10.1158/0008-5472.CAN-20-0014). Notably, we showed that p63 expressing C3-TAg cells were not able to initiate collective invasion in the same ECM composition used in our current manuscript. Moreover, p63 cells in primary C3-TAg tumors were noninvasive opportunist cells that were limited to trailing p63-low trailblazer cells when collective-ly invading in primary tumors and in organoids (Westcott et al, Cancer Res, 2020). We now show that p63 expressing opportunist cell lines are limited to invading behind primary C3-TAg trailblazer cells and trailblazer cell lines in our 3D invasion assays (Figure 1B and Figure 1–figure supplement 1D-E). Together, these results indicate that the ECM employed in our 3D assays reveals the mechanistic underpinnings of both trailblazer and opportunist cell invasion in primary tumors.

      With respect to lung cancer invasion, leader cells that we would classify as trailblazer cells have been isolated from 2 non-small cell lung cancer cell line spheroid models grown in pure reconsti-tuted BM extract (Konen et al, Nat Comm, 2017, 10.1038/ncomms15078). However, it unclear whether these cell line derived NSCLC trailblazer cells are more intrinsically invasive than non-trailblazer siblings in primary NCSCLC tumors or if the traits associated cell line NSCLC trail-blazer cells are required for metastasis. These tests have never been reported to the best of our knowledge. Similarly, it is not clear whether these NSCLC cell line derived trailblazer cells reflect features of primary NSLC primary tumor cells, as we are unaware of any such comparisons be-ing reported. Thus, there is no reason to consider pure reconstituted BM to be an equivalent or preferred experimental option to define trailblazer cell features. Nevertheless, as we mentioned before, our discovery approach identifies trailblazer cells that are intrinsically more invasive than opportunist siblings across multiple ECM conditions, including pure reconstituted BM and, im-portantly, in primary tumors.

      3) TGF-beta is well known to induce EMT. Although this study identifies potential transcriptional mediators of the invasion/trailblazer program, is this program reversible?

      We have previously shown the breast cancer trailblazer cells can convert to an opportunist state, demonstrating that trailblazer states are reversible (Westcott et al, J Clin Invest, 2015, 10.1172/JCI77767). In this manuscript. we show that C3-TAg organoid lines derived in the Tgfbr1 inhibitor A83-01 have few if any cells with a trailblazer phenotype relative to C3-TAg pri-mary tumors, suggesting a reversion of the trailblazer state (Fig. 4C and Figure 4–figure sup-plement 2A-C). However, our results do not entirely rule out the possibility that only non-trailblazer cells grew to establish the organoid lines. Indeed, the problem of tracing phenotypic conversions when evaluating heterogeneous populations is a systemic challenge that extends beyond our analysis of trailblazer cells. Clearly defining the conversion rates for trailblazer cells will require multiple genetic markers to distinguish the different trailblazer states we have now identified, in addition to phenotypic and molecular analysis over multiple days, or possibly weeks. Thus, further definition of the rate of reversion of different trailblazer cells is worthy line of future investigation rather than a feasible objective of this study.

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      Referee #4

      Evidence, reproducibility and clarity

      Summary

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

      Major comments

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

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

      The authors note that recovery of DNMT1 after wash out is to a lesser extent in the NADNMT1/DNMT3B-/- background. The authors do not speculate why would this be. Past reports of degron tagged proteins show that after treatment endogenous protein levels can recover. Does this hint towards a viability issue of the line due to excessive hypomethylation? While difficult to prove it would be useful to speculate why this effect occurs. - The authors employ DNAme arrays to assess the DNA methylation loss after degradation of DNMT1 and study where in the genome this occurs. Specifically, the authors look on differentially methylated probes between treated/non treated samples and demonstrate their abundance over different genomic regions (figure 2E and S2 H, I, J, K). However, this way of visualising the data is a bit difficult to interpret as differences can be small. Furthermore, number of probes across the genome is not uniformly distributed, so it would be useful to include these numbers. It would be helpful if authors can provide genome browser snapshots with methylation levels and accompanying histone marks (from available data, Rokavec et al., 2017?) like done in figure 4F, S4B and S5C to show representative regions that showcase their observations. Coverage of the EPIC array will mean that these tracks will not have high coverage and thus gaps, and ideally one would need whole genome bisulfite data, however hopefully some snapshots can demonstrate locus specific changes better.

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

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

      Minor comments:

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

      Significance

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

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

    1. Author Response:

      The following is the authors' response to the original reviews.

      Reply to Public Reviews:

      Reply to Reviewer #1:

      This is a carefully performed and well-documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.

      Response: We would like to thank the reviewer for her/his time for evaluating our manuscript. We are very happy to see the reviewer for highly appreciating our manuscript.

      1. Overall this is a nicely done study with nice quantitation. It remains somewhat unclear from the data and discussions in exactly what way the authors mean that FUS is an RNA chaperone: is FUS changing the structure of the repeat or does FUS binding prevent it from folding into alternative in vivo structure?

      Response: We appreciate the reviewer’s constructive comments. Indeed, we showed that FUS changes the higher-order structures of GGGGCC [G4C2] repeat RNA in vitro, and that FUS suppresses G4C2 RNA foci formation in vivo. According to the established definition of RNA chaperone, RNA chaperones are proteins changing the structures of misfolded RNAs without ATP use, resulting in the maintenance of proper RNAs folding (Rajkowitsich et al., 2007). Thus, we consider that FUS is classified into RNA chaperone. To clarify these interpretations, we revised the manuscript as follows.

      (1) On page 10, line 215-219, the sentence “These results were in good agreement with our previous study on SCA31 showing the suppressive effects of FUS and other RBPs on RNA foci formation of UGGAA repeat RNA as RNA chaperones …” was changed to “These results were in good agreement with … RNA foci formation of UGGAA repeat RNA through altering RNA structures and preventing aggregation of misfolded repeat RNA as RNA chaperones …”.

      (2) On page 17, line 363-366, the sentence “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure, as evident by CD and NMR analyses (Figure 5), suggesting its functional role as an RNA chaperone.” was changed to “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure as evident by CD and NMR analyses (Figure 5, Figure 5—figure supplement 2), and suppresses RNA foci formation in vivo (Figures 3A and 3B), suggesting its functional role as an RNA chaperone.”

      Reply to Reviewer #2:

      Fuijino et al. provide interesting data describing the RNA-binding protein, FUS, for its ability to bind the RNA produced from the hexanucleotide repeat expansion of GGGGCC (G4C2). This binding correlates with reductions in the production of toxic dipeptides and reductions in toxic phenotypes seen in (G4C2)30+ expressing Drosophila. Both FUS and G4C2 repeats of >25 are associated with ALS/FTD spectrum disorders. Thus, these data are important for increasing our understanding of potential interactions between multiple disease genes. However, further validation of some aspects of the provided data is needed, especially the expression data.

      Response: We would like to thank the reviewer for her/his time for evaluating our manuscript and also for her/his important comments that helped to strengthen our manuscript.

      Some points to consider when reading the work:

      1. The broadly expressed GMR-GAL4 driver leads to variable tissue loss in different genotypes, potentially confounding downstream analyses dependent on viable tissue/mRNA levels.

      Response: We thank the reviewer for this constructive comment. In the RT-qPCR experiments (Figures 1E, 3C, 4G, 6D and Figure 1—figure supplement 1C), the amounts of G4C2 repeat transcripts were normalized to those of gal4 transcripts expressed in the same tissue, to avoid potential confounding derived from the difference in tissue viability between genotypes, as the reviewer pointed out. To clarify this process, we have made the following change to the revised manuscript.

      (1) On page 30, line 548-550, the sentence “The amounts of G4C2 repeat transcripts were normalized to those of gal4 transcripts in the same sample” was changed to “The amounts of G4C2 repeat transcripts were normalized to those of gal4 transcripts expressed in the same tissue to avoid potential confounding derived from the difference in tissue viability between genotypes”.

      2. The relationship between FUS and foci formation is unclear and should be interpreted carefully.

      Response: We appreciate the reviewer’s important comment. We apologize for the lack of clarity. We showed the relationship between FUS and RNA foci formation in our C9-ALS/FTD fly, that is, FUS suppresses RNA foci formation (Figures 3A and 3B), and knockdown of endogenous caz, a Drosophila homologue of FUS, enhanced it conversely (Figures 4E and 4F). We consider that FUS suppresses RNA foci formation through altering RNA structures and preventing aggregation of misfolded G4C2 repeat RNA as an RNA chaperone. To clarify these interpretations, we revised the manuscript as follows.

      (1) On page 10, line 215-219, the sentence “These results were in good agreement with our previous study on SCA31 showing the suppressive effects of FUS and other RBPs on RNA foci formation of UGGAA repeat RNA as RNA chaperones …” was changed to “These results were in good agreement with … RNA foci formation of UGGAA repeat RNA through altering RNA structures and preventing aggregation of misfolded repeat RNA as RNA chaperones …”.

      (2) On page 17, line 363-366, the sentence “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure, as evident by CD and NMR analyses (Figure 5), suggesting its functional role as an RNA chaperone.” was changed to “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure as evident by CD and NMR analyses (Figure 5, Figure 5—figure supplement 2), and suppresses RNA foci formation in vivo (Figures 3A and 3B), suggesting its functional role as an RNA chaperone.”

      Reply to Reviewer #3:

      In this manuscript Fujino and colleagues used C9-ALS/FTD fly models to demonstrate that FUS modulates the structure of (G4C2) repeat RNA as an RNA chaperone, and regulates RAN translation, resulting in the suppression of neurodegeneration in C9-ALS/FTD. They also confirmed that FUS preferentially binds to and modulates the G-quadruplex structure of (G4C2) repeat RNA, followed by the suppression of RAN translation. The potential significance of these findings is high since C9ORF72 repeat expansion is the most common genetic cause of ALS/FTD, especially in Caucasian populations and the DPR proteins have been considered the major cause of the neurodegenerations.

      Response: We would like to thank the reviewer for her/his time for evaluating our manuscript. We are grateful to the reviewer for the insightful comments, which were very helpful for us to improve the manuscript.

      1. While the effect of RBP as an RNA chaperone on (G4C2) repeat expansion is supposed to be dose-dependent according to (G4C2)n RNA expression, the first experiment of the screening for RBPs in C9-ALS/FTD flies lacks this concept. It is uncertain if the RBPs of the groups "suppression (weak)" and "no effect" were less or no ability of RNA chaperone or if the expression of the RBP was not sufficient, and if the RBPs of the group "enhancement" exacerbated the toxicity derived from (G4C2)89 RNA or the expression of the RBP was excessive. The optimal dose of any RBPs that bind to (G4C2) repeats may be able to neutralize the toxicity without the reduction of (G4C2)n RNA.

      Response: We appreciate the reviewer’s constructive comments. We employed the site-directed transgenesis for the establishment of RBP fly lines, to ensure the equivalent expression levels of the inserted transgenes. We also evaluated the toxic effects of overexpressed RBPs themselves by crossbreeding with control EGFP flies, showing in Figure 1A. To clarify them, we have made the following changes to the revised manuscript.

      (1) On page 8, line 166-168, the sentence “The variation in the effects of these G4C2 repeat-binding RBPs on G4C2 repeat-induced toxicity may be due to their different binding affinities to G4C2 repeat RNA, and their different roles in RNA metabolism.” was changed to “The variation in the effects of these G4C2 repeat-binding RBPs on G4C2 repeat-induced toxicity may be due to their different binding affinities to G4C2 repeat RNA, and the different toxicity of overexpressed RBPs themselves.”.

      (2) On page 29, line 519-522, the sentence “By employing site-specific transgenesis using the pUASTattB vector, each transgene was inserted into the same locus of the genome, and was expected to be expressed at the equivalent levels.” was added.

      2. In relation to issue 1, the rescue effect of FUS on the fly expressing (G4C2)89 (FUS-4) in Figure 4-figure supplement 1 seems weaker than the other flies expressing both FUS and (G4C2)89 in Figure 1 and Figure 1-figure supplement 2. The expression level of both FUS protein and (G4C2)89 RNA in each line is important from the viewpoint of therapeutic strategy for C9-ALS/FTD.

      Response: We appreciate the reviewer’s important comment. The FUS-4 transgene is expected to be expressed at the equivalent level to the FUS-3 transgene, since they are inserted into the same locus of the genome by the site-directed transgenesis. Thus, we suppose that the weaker suppressive effect of FUS-4 coexpression on G4C2 repeat-induced eye degeneration can be attributed to the C-terminal FLAG tag that is fused to FUS protein expressed in FUS-4 fly line. Since the caz fly expresses caz protein also fused to FLAG tag at the C-terminus, we used this FUS-4 fly line to directly compare the effect of caz on G4C2 repeat-induced toxicity to that of FUS.

      3. While hallmarks of C9ORF72 are the presence of DPRs and the repeat-containing RNA foci, the loss of function of C9ORF72 is also considered to somehow contribute to neurodegeneration. It is unclear if FUS reduces not only the DPRs but also the protein expression of C9ORF72 itself.

      Response: We thank the reviewer for this comment. We agree that not only DPRs, but also toxic repeat RNA and the loss-of-function of C9ORF72 jointly contribute to the pathomechanisms of C9-ALS/FTD. Since Drosophila has no homolog corresponding to the human C9orf72 gene, the effect of FUS on C9orf72 expression cannot be assessed. Our fly models are useful for evaluating gain-of-toxic pathomechanisms such as RNA foci formation and RAN translation, and the association between FUS and loss-of function of C9ORF72 is beyond the scope of this study.

      4. In Figure 5E-F, it cannot be distinguished whether FUS binds to GGGGCC repeats or the 5' flanking region. The same experiment should be done by using FUS-RRMmut to elucidate whether FUS binding is the major mechanism for this translational control. Authors should show that FUS binding to long GGGGCC repeats is important for RAN translation.

      Response: We would like to thank the reviewer for these insightful comments. Following the reviewer’s suggestion, we perform in vitro translation assay again using FUS-RRMmut, which loses the binding ability to G4C2 repeat RNA as evident by the filter binding assay (Figure 5A), instead of BSA. The results are shown in the figures of Western blot analysis below. The addition of FUS to the translation system suppressed the expression levels of GA-Myc efficiently, whereas that of FUS-RRMmut did not. FUS decreased the expression level of GA-Myc at as low as 10nM, and nearly eliminated RAN translation activity at 100nM. At 400nM, FUS-RRMmut weakly suppressed the GA-Myc expression levels probably because of the residual RNA-binding activity. These results suggest that FUS suppresses RAN translation in vitro through direct interactions with G4C2 repeat RNA.

      Unfortunately, RAN translation from short G4C2 repeat RNA was not investigated in our translation system, although the previous study reported the low efficacy of RAN translation from short G4C2 repeat RNA (Green et al., 2017).

      Author response image 1.

      (A) Western blot analysis of the GA-Myc protein in the samples from in vitro translation.

      (B) Quantification of the GA-Myc protein levels.

      We have made the following changes to the revised manuscript.

      (1) Figure 5F was replaced to new Figures 5F and 5G.

      (2) On page 14-15, line 326-330, the sentence “Notably, the addition of FUS to this system decreased the expression level of GA-Myc in a dose-dependent manner, whereas the addition of the control bovine serum albumin (BSA) did not (Figure 5F).” was changed to “Notably, upon the addition to this translation system, FUS suppressed RAN translation efficiently, whereas FUS-RRMmut did not. FUS decreased the expression levels of GA-Myc at as low as 10nM, and nearly eliminated RAN translation activity at 100nM. At 400nM, FUS-RRMmut weakly suppressed the GA-Myc expression levels probably because of the residual RNA-binding activity (Figure 5F and 5G).”.

      (3) On page 15, line 330-332, the sentence “Taken together, these results indicate that FUS suppresses RAN translation from G4C2 repeat RNA in vitro as an RNA chaperone.” was changed to “Taken together, these results indicate that FUS suppresses RAN translation in vitro through direct interactions with G4C2 repeat RNA as an RNA chaperone.”.

      (4) On page 37, line 720-723, the sentence “For preparation of the FUS protein, the human FUS (WT) gene flanked at the 5¢ end with an Nde_I recognition site and at the 3¢ end with a _Xho_I recognition site was amplified by PCR from pUAST-_FUS.” was changed to “For preparation of the FUS proteins, the human FUS (WT) and FUS-RRMmut genes flanked at the 5¢ end with an Nde_I recognition site and at the 3¢ end with a _Xho_I recognition site was amplified by PCR from pUAST-_FUS and pUAST- FUS-RRMmut, respectively.”.

      (5) On page 41, line 816-819, the sentence “FUS or BSA at each concentration (10, 100, and 1,000 nM) was added for translation in the lysate.” was changed to “FUS or FUS-RRMmut at each concentration (10, 100, 200, 400, and 1,000 nM) was preincubated with mRNA for 10 min to facilitate the interaction between FUS protein and G4C2 repeat RNA, and added for translation in the lysate.”.

      5. It is not possible to conclude, as the authors have, that G-quadruplex-targeting RBPs are generally important for RAN translation (Figure 6), without showing whether RBPs that do not affect (G4C2)89 RNA levels lead to decreased DPR protein level or RNA foci.

      Response: We appreciate the reviewer’s critical comment. Following the suggestion by the reviewer, we evaluate the effect of these G-quadruplex-targeting RBPs on RAN translation. We additionally performed immunohistochemistry of the eye imaginal discs of fly larvae expressing (G4C2)89 and these G-quadruplex-targeting RBPs. As shown in the figures of immunohistochemistry below, we found that coexpression of EWSR1, DDX3X, DDX5, and DDX17 significantly decreased the number of poly(GA) aggregates. The results suggest that these G-quadruplex-targeting RBPs regulate RAN translation as well as FUS.

      Author response image 2.

      (A) Immunohistochemistry of poly(GA) in the eye imaginal discs of fly larvae expressing (G4C2)89 and the indicated G-quadruplex-targeting RBPs.

      (B) Quantification of the number of poly(GA) aggregates.

      We have made the following changes to the revised manuscript.

      (1) Figures 6E and 6F were added.

      (2) On page 6-7, line 135-137, the sentence “In addition, other G-quadruplex-targeting RBPs also suppressed G4C2 repeat-induced toxicity in our C9-ALS/FTD flies.” was changed to “In addition, other G-quadruplex-targeting RBPs also suppressed RAN translation and G4C2 repeat-induced toxicity in our C9-ALS/FTD flies.”.

      (3) On page 15, line 344-346, the sentence “As expected, these RBPs also decreased the number of poly(GA) aggregates in the eye imaginal discs (Figures 6E and 6F).” was added.

      (4) On page 15, line 346-347, the sentence “Their effects on G4C2 repeat-induced toxicity and repeat RNA expression were consistent with those of FUS.” was changed to “Their effects on G4C2 repeat-induced toxicity, repeat RNA expression, and RAN translation were consistent with those of FUS.”

      (5) On page 16, line 355-357, the sentence “Thus, some G-quadruplex-targeting RBPs regulate G4C2 repeat-induced toxicity by binding to and possibly by modulating the G-quadruplex structure of G4C2 repeat RNA.” was changed to “Thus, some G-quadruplex-targeting RBPs regulate RAN translation and G4C2 repeat-induced toxicity by binding to and possibly by modulating the G-quadruplex structure of G4C2 repeat RNA.”

      (6) On page 19, line 417-421, the sentence “We further found that G-quadruplex-targeting RNA helicases, including DDX3X, DDX5, and DDX17, which are known to bind to G4C2 repeat RNA (Cooper-Knock et al., 2014; Haeusler et al., 2014; Mori et al., 2013a; Xu et al., 2013), also alleviate G4C2 repeat-induced toxicity without altering the expression levels of G4C2 repeat RNA in our Drosophila models.” was changed to “We further found that G-quadruplex-targeting RNA helicases, … ,also suppress RAN translation and G4C2 repeat-induced toxicity without altering the expression levels of G4C2 repeat RNA in our Drosophila models.”.

      Reply to Recommendations For The Authors:

      1) It is not clear from the start that the flies they generated with the repeat have an artificial vs human intronic sequence ahead of the repeat. It would be nice if they presented somewhere the entire sequence of the insert. The reason being that it seems they also tested flies with the human intronic sequence, and the effect may not be as strong (line 234). In any case, in the future, with a new understanding of RAN translation, it would be nice to compare different transgenes, and so as much transparency as possible would be helpful regarding sequences. Can they include these data?

      Response: We thank the editors and reviewers for this comment. We apologize for the lack of clarity. We used artificially synthesized G4C2 repeat sequences when generating constructs for (G4C2)n transgenic flies, so these constructs do not contain human intronic sequence ahead of the G4C2 repeat in the C9orf72 gene, as explained in the Materials and Methods section. To clarify the difference between our C9-ALS/FTD fly models and LDS-(G4C2)44GR-GFP fly model (Goodman et al., 2019), we have made the following change to the revised manuscript.

      (1) Schema of the LDS-(G4C2)44GR-GFP construct was presented in Figure 3—figure supplement 1.

      Furthermore, to maintain transparency of the study, we have provided the entire sequence of the insert as the following source file.

      (2) The artificial sequences inserted in the pUAST vector for generation of the (G4C2)n flies were presented in Figure 1—figure supplement 1—source data 1.

      2) It is really nice how they quantitated everything and showed individual data points.

      Response: We thank the editors and reviewers for appreciating our data analysis method. All individual data points and statistical analyses are summarized in source data files.

      3) So when they call FUS an RNA chaperone, are they simply meaning it is changing the structure of the repeat, or could it just be interacting with the repeat to coat the repeat and prevent it from folding into whatever in vivo structures? Can they speculate on why some RNA chaperones lead to presumed decay of the repeat and others do not? Can they discuss these points in the discussion? Detailed mechanistic understanding of RNA chaperones that ultimately promote decay of the repeat might be of highly significant therapeutic benefit.

      Response: We appreciate these critical comments. Indeed, we showed that FUS changes the higher-order structures of G4C2 repeat RNA in vitro, and that FUS suppresses G4C2 RNA foci formation. According to the established definition of RNA chaperone, RNA chaperones are proteins changing the structures of misfolded RNAs without ATP use, resulting in the maintenance of proper RNAs folding (Rajkowitsich et al., 2007). Thus, we consider that FUS is classified into RNA chaperone. To clarify these interpretations, we revised the manuscript as follows.

      (1) On page 10, line 215-219, the sentence “These results were in good agreement with our previous study on SCA31 showing the suppressive effects of FUS and other RBPs on RNA foci formation of UGGAA repeat RNA as RNA chaperones …” was changed to “These results were in good agreement with … RNA foci formation of UGGAA repeat RNA through altering RNA structures and preventing aggregation of misfolded repeat RNA as RNA chaperones …”.

      (2) On page 17, line 363-366, the sentence “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure, as evident by CD and NMR analyses (Figure 5), suggesting its functional role as an RNA chaperone.” was changed to “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure as evident by CD and NMR analyses (Figure 5, Figure 5—figure supplement 2), and suppresses RNA foci formation in vivo (Figures 3A and 3B), suggesting its functional role as an RNA chaperone.”

      Besides these RNA chaperones, we observed the expression of IGF2BP1, hnRNPA2B1, DHX9, and DHX36 decreased G4C2 repeat RNA expression levels. In addition, we recently reported that hnRNPA3 reduces G4C2 repeat RNA expression levels, leading to the suppression of neurodegeneration in C9-ALS/FTD fly models (Taminato et al., 2023). We speculate these RBPs could be involved in RNA decay pathways as components of the P-body or interactors with the RNA deadenylation machinery (Tran et al., 2004; Katahira et al., 2008; Geissler et al., 2016; Hubstenberger et al., 2017), possibly contributing to the reduced expression levels of G4C2 repeat RNA. To clarify these interpretations, we revised the manuscript as follows.

      (3) On page 18, line 392-398, the sentences “Similarly, we recently reported that hnRNPA3 reduces G4C2 repeat RNA expression levels, leading to the suppression of neurodegeneration in C9-ALS/FTD fly models (Taminato et al., 2023). Interestingly, these RBPs have been reported to be involved in RNA decay pathways as components of the P-body or interactors with the RNA deadenylation machinery (Tran et al., 2004; Katahira et al., 2008; Geissler et al., 2016; Hubstenberger et al., 2017), possibly contributing to the reduced expression levels of G4C2 repeat RNA.” was added.

      4) What is the level of the G4C2 repeat when they knock down caz? Is it possible that knockdown impacts the expression level of the repeat? Can they show this (or did they and I miss it)?

      Response: We thank the editors and reviewers for this comment. The expression levels of G4C2 repeat RNA in (G4C2)89 flies were not altered by the knockdown of caz, as shown in Figure 4G.

      5) A puzzling point is that FUS is supposed to be nuclear, so where is FUS in the brain in their lines? They suggest it modulates RAN translation, and presumably, that is in the cytoplasm. Is FUS when overexpressed now in part in the cytoplasm? Is the repeat dragging it into the cytoplasm? Can they address this in the discussion? If FUS is never found in vivo in the cytoplasm, then it raises the point that the impact they find of FUS on RAN translation might not reflect an in vivo situation with normal levels of FUS.

      Response: We appreciate these important comments. We agree with the editors and reviewers that FUS is mainly localized in the nucleus. However, FUS is known as a nucleocytoplasmic shuttling RBP that can transport RNA into the cytoplasm. Indeed, FUS is reported to facilitate transport of actin-stabilizing protein mRNAs to function in the cytoplasm (Fujii et al., 2005). Thus, we consider that FUS binds to G4C2 repeat RNA in the cytoplasm and suppresses RAN translation in this study.

      6) When they are using 2 copies of the driver and repeat, are they also using 2 copies of FUS? These are quite high levels of transgenes.

      Response: We thank the editors and reviewers for this comment. We used only 1 copy of FUS when using 2 copies of GMR-Gal4 driver. Full genotypes of the fly lines used in all experiments are described in Supplementary file 1.

      7) In Figure5-S1, FUS colocalizing with (G4C2)RNA is not clear. High-magnification images are recommended.

      Response: We appreciate this constructive comment on the figure. Following the suggestion, high-magnification images are added in Figure 5—figure supplement 1.

      8) I also suggest that the last sentence of the Discussion be revised as follows: Thus, our findings contribute not only to the elucidation of C9-ALS/FTD, but also to the elucidation of the repeat-associated pathogenic mechanisms underlying a broader range of neurodegenerative and neuropsychiatric disorders than previously thought, and it will advance the development of potential therapies for these diseases.

      Response: We appreciate this recommendation. We have made the following change based on the suggested sentence.

      (1) On page 20-21, line 455-459, “Thus, our findings contribute not only towards the elucidation of repeat-associated pathogenic mechanisms underlying a wider range of neuropsychiatric diseases than previously thought, but also towards the development of potential therapies for these diseases.” was changed to “Thus, our findings contribute to the elucidation of the repeat-associated pathogenic mechanisms underlying not only C9-ALS/FTD, but also a broader range of neuromuscular and neuropsychiatric diseases than previously thought, and will advance the development of potential therapies for these diseases.”.

      Authors’ comment on previous eLife assessment:

      We thank the editors and reviewers for appreciating our study. We mainly evaluated the function of human FUS protein on RAN translation and G4C2 repeat-induced toxicity using Drosophila expressing human FUS in vivo, and the recombinant human FUS protein in vitro. To validate that FUS functions as an endogenous regulator of RAN translation, we additionally evaluated the function of Drosophila caz protein as well. We are afraid that the first sentence of the eLife assessment, that is, “This important study demonstrates that the Drosophila FUS protein, the human homolog of which is implicated in amyotrophic lateral sclerosis (ALS) and related conditions, …” is somewhat misleading. We would be happy if you modify this sentence like “This important study demonstrates that the human FUS protein, which is implicated in amyotrophic lateral sclerosis (ALS) and related conditions, …”.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Reply to the reviewers

      Manuscript number: RC-2023-01932

      Corresponding author(s): Dennis KAPPEI

      We would like to thank all reviewers for their recognition of our approach and the quality of our work as well as their constructive criticism.

      Reviewer #1

      Reviewer #1: The manuscript by Yong et. al. describes a comparison of various chromatin immunoprecipitation-mass spectrometric (ChIP-MS) methods targeting human telomeres in a variety of systems. By comparing antibody-based methods, crosslinkers, dCas9 and sgRNA targeted methods, KO cells and various controls, they provide a useful perspective for readers interested in similar experiments to explore protein-DNA interactions in a locus-specific manner.

      Response: We would like to thank the reviewer for the feedback and the appreciation of our work.

      Reviewer #1: While interesting, I found it somewhat difficult to extract a clear comparison of the methods from the text. It was also difficult to compare as data and findings from each method was discussed in its own context. Perhaps it is not in their interest to single out a specific method and it is indeed true that there are caveats with each of the methods.

      Response: Across our manuscript we have established one single workflow, for which we present some technical comparisons (e.g. using single or double cross-linking in Fig. 2a/b), technical recommendations such as the use of loss-of-function controls (e.g. Fig. 1c v. Fig. 2a and Extended Data Fig. 3g vs. 3i) and an application to unique loci using dCas9 (Fig. 3f). Based on the suggestions below, we believe that we will improve the clarity of communicating our approach.

      Reviewer #1: I think the manuscript would be of interest but I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

      Response: We thank the reviewer for highlighting this point. We do not think that the ChIP-MS comparison between U2OS WT and ZBTB48 KO clones (Fig. 2a) has experiment-specific caveats. Instead the KO controls as well as the dTAGV-1 degron system for MYB ChIP-MS (Extended Data Fig. 3) reveal antibody-specific off-targets, which are indeed false-positives. Please see below for further details.

      Reviewer #1: Ln 57: What is "standard double cross-linking ChIP reactions" in this context? Is it the two different crosslinkers? The two proteins? The reciprocal IPs of one protein, and blotting for another? It's not clear here or from Extended Fig 1A. Upon further reading, it seems to pertain to the two crosslinkers - if so, the authors should briefly describe their workflow to help readers.

      Response: As the reviewer correctly concludes, we indeed intended to highlight the use of two separate crosslinkers (formaldehyde/FA and DSP). This combination is important as illustrated in the side-by-side comparison of Fig. 2a and Fig. 2d. Here, we performed ZBTB48 ChIP-MS in five U2OS WT and five U2OS ZBTB48 KO clones. While in both experiments the bait protein ZBTB48 was abundantly enriched in the samples that were fixed with formaldehyde we lose about half of the telomeric proteins that are known to directly bind to telomeric DNA independent of ZBTB48 and all of their interaction partners. For instance, while the FA+DSP reaction in Fig. 2a enriched all six shelterin complex members, the FA only reaction in Fig. 2d only enriches TERF2. These data suggest that the use of a second cross-linker helps to stabilise protein complexes on chromatin fragments. This is a critical message of our manuscript as ChIP-MS only truly lives up its name if we can enrich proteins that genuinely sit on the same chromatin fragment without protein interactions to the bait protein. We will expand on this in both the text and our schematics in Fig. 1a and 3a to make this clearer for the readers.

      Reviewer #1: Ln 95: It is surprising and quite unclear to me why it is that the WT ZBTB48 U2OS pulldown in Fig 1B shows 83 hits for the WT vs Ig control experiment but 27 hits for the WT vs KO condition in Fig 2A. The two WT experiments have the same design and reagents, shouldn't they be as close as technical replicates and provide very similar hits?

      The authors seem to make the claim that most of the 'extra' proteins in WT vs Ig are abundant and false positives, but if this is so, shouldn't they bind non-specifically to the beads and be enriched equally in Ig control and ZBTB48 WT IPs?

      Response: We again thank the reviewer for raising this point and the need to explain in more detail why we interpret the difference between 83 hits (anti-ZBTB48 antibody vs. IgG; Fig. 1c) and 27 hits (anti-ZBTB48 antibody used in both U2OS WT and ZBTB48 KO cells; Fig. 2a) primarily as false-positives. The KO controls in Fig. 2a allow to keep the ZBTB48 antibody as a constant variable while instead comparing the presence (WT) or absence (KO) of the bait protein. Hence, proteins that were enriched in the IgG comparison in Fig. 1c but that are lost in the WT vs. KO comparison in Fig. 2a are likely directly (or indirectly) recognised by the ZBTB48 antibody, akin to off-targets to this particular reagent. In a Western blot this would be equivalent to seeing multiple bands at different molecular weights with only the band belonging to the protein-of-interest disappearing in KO cells. To illustrate this we would like to refer to Extended Data Fig. 2, in which we have replotted the exact same data from Fig. 2a. However, in addition we have here highlighted proteins that were enriched in the IgG comparison in Fig. 1c. 46 proteins (in pink) are indeed quantified in the WT vs. KO comparison, but these proteins are found below the cut-offs (and most of them with very poor fold changes and p-values). In contrast to the other several hundred proteins common between both experiments that can be considered common background non-specifically bound to the protein G beads, these 46 proteins represent antibody-specific false-positives.

      The above consideration is not unique to ChIP-MS as illustrated by the Western blot example. We also do not claim novelty on the experimental logic, e.g. pre-CRISPR in 2006 Selbach and Mann demonstrated the usefulness of RNAi controls in immunoprecipitations (IPs) (PMID: 17072306). However, our data suggests that ChIP-MS is particularly vulnerable to this type of false-positives given that the approach requires (double-)cross-linking to sufficiently stabilise true-positives on the same chromatin fragment.

      To supplement the WT vs. ZBTB48 KO comparison, we had included a second experiment in the manuscript that illustrates the same point in even more dramatic fashion. First, KO controls are very clean in principle, but they themselves might come with caveats if e.g. the expression levels between WT and KO samples differ greatly. This might create a situation that the reviewer hinted to, i.e. differential expression of abundant proteins that would proportionally to their expression levels stick to the beads, resulting in “fold enrichments”. The resulting false positives could e.g. be controlled by matched expression proteomes. For ZBTB48 we have previously measured this (PMID: 28500257) and demonstrated that only a small number of genes are differentially expressed (~10) and hence we can interpret the WT vs. ZBTB48 KO comparison quite cleanly. However, for other classes of proteins such as transcription factors that regulate a large number of genes, E3 ligases etc. this might present a more serious concern. Therefore, we extended our loss-of-function comparison to such a transcription factor, MYB, by using the dTAGV-1 degron system. Importantly, the MYB antibody has been used in previous work for ChIP-seq applications (e.g. PMID: 25394790). Here, instead of 186 hits in the MYB vs. IgG comparison using the same MYB antibody in control-treated and dTAGV-1-treated cells (upon 30 min of treatment only) we only detect 9 hits. Again, similar to the WT vs. ZBTB48 KO comparison, 180 proteins are quantified in the DMSO vs. dTAGV-1 comparison, but these proteins fall below the cut-offs (Extended Data Fig. 3g vs. 3i). Again, we believe that this quite drastically illustrates how vulnerable ChIP-MS data is to large numbers of false-positives. This is not only a technical consideration as such datasets are frequently used in downstream pathway/gene set enrichment analyses etc. Such large false discovery rates would obviously lead to error-carry-forward and additional (unintended) misinterpretations. We will carefully expand our textual description across the manuscript to make these points much clearer. In addition, we will move the previous Extended Data Fig. 3 into the main manuscript to more clearly highlight this important point.

      Reviewer #1: Volcano plots in Figs 1, 2, and Suppl. Tables etc: Are the plotted points the mean of 5 replicates? Was each run normalized between the replicates in each group, for e.g. by median normalization of the log2 MS intensities? This does not appear to be the case upon inspection of the Suppl Tables. Given the variability in pulldown efficiency, gel digest and peptide recovery, this would certainly be necessary.

      Response: All volcano plots are indeed based on 4-5 biological replicates (most stringently in the WT vs. KO comparisons in Fig. 2 based on each 5 independent WT and ZBTB48 KO single cell clones). The x-axis of each volcano plot represents the ratio of mean MS1-based intensities between both experimental conditions in log2 scale. However, precisely to account for the variation that the reviewer highlighted we did not base our analysis on raw MS1 intensities but we used the MaxLFQ algorithm (PMID: 24942700) as part of the MaxQuant analysis software (PMID: 19029910) for genuine label-free quantitation across experimental conditions and replicates. In this context, we would also like to refer to a related comment by reviewer #2 based on which we will now addd concordance information for each replicate (heatmaps for Pearson correlations and PCA plots). We will improve this both in the text and methods section accordingly.

      Reviewer #1: Ln 125: The authors make the claim that the ChIP-MS experiments are inherently noisy, with examples from WT cells, dTAG system and IgG controls. This is likely the case, yet their experiments with WT vs KO cells do not identify as many proteins overall. I find this inconsistency somewhat unclear and does not seem to match the claim of ChIP-MS experiments and crosslinking adding to non-specificity. Can the authors add the total number of identified proteins in each volcano plot, for easier reference?

      Response: The number of identified proteins does not vary majorly between matched IgG and loss-of-function comparisons and for instance the single cross-linking (FA only) experiment in Fig. 2c has the largest number of quantified proteins among all ZBTB48 IPs. But we will of course add the requested information to all plots.

      Reviewer #1: I think the manuscript is interest as it provides important benchmarks for ChIP-proteomics experiments. I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

      Response: We would like to thank the reviewer for recognising our work as a source for important benchmarks for ChIP-MS experiments. We hope that with a more detailed description and discussion the highlighted aspects will be more clearly communicated. We originally conceived our manuscript as a short report and now realised that some of the information became too condensed and might therefore benefit from more extensive explanations.

      Reviewer #2

      Reviewer #2: Summary: In this manuscript, Yong and colleagues have introduced a optimized technique for studying actors on chromatin in specific regions with a localized approach thanks to revisited ChIP-mass spectrometry (MS) with label-free quantitative (LFQ). The authors exhibited the utility of their approach by demonstrating its effectiveness at telomeres from cell culture (human U2OS cells) to tissue samples (liver, mouse embryonic stem cells). As a proof of concept, this technique was tested by the authors with proteins from complex shelterin specific to telomeres (TERF2 and ZBTB48), transcription factors (MYB), and through dCas9-driven locus-specific enrichment. Notably, the authors created a U2OS dCas9-GFP clone and then introduced sgRNAs to target either telomeric DNA (sgTELO) or an unrelated control (sgGAL4). The cells expressing sgTELO exhibited a significant localization of telomeres and an enriched amount of telomeric DNA in ChIP with dCas9. They also found the proteins previously identified as known to be enriched at telomeres (for example, the 6 shelterin members).

      Moreover, the authors illustrated the importance of double crosslinking (formaldehyde (FA) and dithiobis(succinimidyl propionate) (DSP) in ChIP-MS. Their data demonstrated also that ChIP-MS is inclined towards false-positives, possibly owing to its inherent cross-linking. However, by utilizing loss-of-function conditions specific to the bait, it can be tightly managed.

      • Can you show the concordance between biological replicates for each ChIP with LFQ? (heatmap of Pearson correlation and PCA plot). This will confirm the robustness of the use of LFQ.

      Response: We will add the requested concordance data for all volcano plots both in the form of heatmaps of Pearson correlation and PCA plots. Across our datasets, the replicates from the same experimental condition clearly cluster with each other and replicates have high concordance values of >0.9. As expected replicates for the target/bait samples have slightly higher concordance values compared to the negative controls (IgG or loss-of-function samples). We thank the reviewer for this suggestion as the new Extended Data panel will strengthen the illustration of our robust LFQ data.

      Reviewer #2: You say that your technique is " a simple, robust ChIP-MS workflow based on comparably low input quantities » (line 139). What would be really interesting for a technical paper would be: a schematic and a table illustrating the differences between your method and the previously published methods (amount of material, timeline,...) to really highlight the novelty in your optimized techniques.

      Response: We will add a comparison table with previous publications using ChIP-MS and for reference include some complementary approaches as requested by reviewer #3. On this note, we would like to stress that we are not “only” intending to use less material and to have an easy-to-adopt protocol. A cornerstone of our manuscript is to apply rigorous expectations to ChIP-MS experiments, in particular the ability to enrich proteins that independently bind to the same chromatin fragments as the bait protein (regardless of whether this is an endogenous protein or a exogenous, targeted bait such as dCas9). Otherwise, such experiments risk to be regular protein IPs under cross-linking conditions, which as illustrated by our loss-of-function comparisons are prone to yield particularly large fractions of false-positives.

      Reviewer #2: It would be interesting to perform the dCas9 ChIP experiment in telomeric regions with and without LFQ. Since the novelty lies in this parameter, at no time does the paper show that LFQ really allows to have as many or more proteins identified but in a simpler way and with less material. A table allowing to compare with and without LFQ would be interesting.

      Response: We do not fully understand what the suggestion “without LFQ” refers to exactly. We assume that this reviewer might suggest to use a different quantitative mass spectrometry approach other than LFQ, e.g. SILAC labelling, TMT labelling etc. Please note that we do not claim that LFQ quantification is per se superior to the various quantification methods that had been developed and widely used across the proteomics community especially before instrument setups and analysis pipelines were stable enough for label-free quantification (a name that is strongly owed to this historic order of development). However, a central goal of our workflow is to make robust and rigorous ChIP-MS accessible to the myriad of laboratories using ChIP-qPCR/-seq and that may not be extensively specialised in mass spectrometry. Both metabolic and isobaric labelling come not only at a higher cost but also present an experimental hurdle to non-specialists compared to performing biological replicates without any labelling, essentially the same way as for any ChIP-qPCR etc. experiment. We will further elaborate on these points in the manuscript to more clearly convey these notions.

      In general, with the right effort different quantitative methods should and will likely yield qualitatively similar results. However, comparisons between LFQ approaches (MaxLFQ, iBAQ,…) and labelling approaches (SILAC, TMT, iTRAQ) have already been better explored and verbalised elsewhere (e.g. PMID: 31814417 & 29535314). Therefore, we believe that this will add relatively little value to our manuscript.

      Reviewer #2: Put a sentence to explain "label free quantification". For a reader who is not at all familiar with this technique, it would be interesting to explain it and to quote the advantages compared to PLEX.

      Response: Thanks for highlighting this. In line with the point above as well as a similar comment by reviewer #1 we will improve this both in the main text and manuscript to clearly explain the terminology, the MaxLFQ algorithm (PMID: 24942700) used and to highlight the advantages compared to labelling approaches.

      Reviewer #2: what does the ranking on the right of each volcano plot represent (figure 1 b-e, figure 2a,d,e for example)? top of the most enriched proteins in the mentioned categories? Not very clear when we look on the volcano plot. it must be specified in the legend.

      Response: The numbering these panels is meant to link protein names to the data points on the volcano plots. The order of hits is ranked based on strongest fold enrichment, i.e. from right to center. We will clarify this in the figure legends.

      Reviewer #2: General assessment/Advance: The authors explain in their article that the ChIP exploiting the sequence specificity of nuclease-dead Cas9 (dCas9) to target specific chromatin loci by directly enriching for dCas9 was already published. Here, the novelty of this study lies in the use of LFQ mass spectrometry to optimize the technique and make it easier to handle. Some comparisons with previous papers or data generated by the lab will be interesting to really show the improvement and the advantage to use LFQ and therefore, to highlight better the novelty of the study.

      Response: We thank the reviewer for this assessment and as mentioned above we will include such a comparison table. dCas9 has been used previously in a ChIP-MS approach termed CAPTURE (PMID: 28841410). While this is clearly a landmark paper that illustrated the dCas9 enrichment concept across multiple omics applications (i.e. not limited to proteomics) in their application to telomeres, the authors enriched only 3 out of the 6 shelterin proteins with quite moderate fold enrichments (POT1: 0.99, TERF2: 2.13, TERF2IP: 1.06; in log2 scale). Based on this alone, POT1 and TERF2IP would not have qualified for our cut-off criteria. In addition, while the authors had performed three replicates, detection is only reported in 1-2 out of 3 replicates. While it is difficult to reconstruct statistical values based on the publicly accessible data, it is therefore unlikely that even these 3 proteins would have robustly be considered hits in our datasets. Similarly, using recombinant dCas9 with a sgRNA targeting telomeres that was in vitro reconstituted with sonicated chromatin extracts from 500 million HeLa cells (CLASP; PMID: 29507191) the authors identified only up to 3 shelterin subunits (TERF2, TERF2IP and TPP1/ACD) based on 1 unique peptide each only. For comparison, in our dCas9 ChIP-MS dataset all 6 shelterin subunits are identified with 9-19 unique peptides, contributing to our robust quantification. Even when considering cell line-specific differences (HeLa cells have shorter telomeres and hence provide less biochemical material for enrichment per cell), these comparisons illustrate that prior attempts struggled to robustly replicate even the most abundant telomeric complex members.

      Based on these findings, others had suggested that dCas9 “might exclude some relevant proteins from telomeres in vivo” (PMID: 32152500), implying that dCas9 ChIP-MS might inherently not be feasible including at repetitive regions such as telomeres. Therefore, we believe that our dCas9 ChIP-MS data is a proof-of-concept that the method has the genuine ability to robustly enrich key proteins at individual loci. In concordance with the comment above we will include a comparison table with previous papers and expand on these points in the discussion.

      Reviewer #2: By presenting this technical paper, the authors allow laboratories across different fields to use this technique to gain insights into protein enrichment in specific chromatin regions such as the promoter of a gene of interest or a particular open region in ATACseq in a easier way and with less materials. This paper holds value in enabling researchers to answer many pertinent questions in various fields.

      Response: We again thank the reviewer for this encouraging assessment and we do indeed hope that this manuscript makes a contribution to a much wider use of ChIP-MS approaches as a promising complement to existing genome-wide epigenetics analyses.

      Reviewer #3

      Reviewer #3: Strengths of the study:

      The study is well-structured and provides a robust workflow for the application of ChIP-MS to investigate chromatin composition in various contexts.

      The use of telomeres as a model locus for testing the developed ChIP-MS approach is appropriate due to its well-characterized protein composition.

      The comparison of WT vs KO lines for ZBTB48 is a rigorous way to control for false-positives, providing more confidence in the results.

      The direct comparison of double vs only FA-crosslinking provides valuable insights into the benefit of additional protein-protein crosslinking in ChIP-MS workflows.

      Response: We thank the reviewer for this assessment and we agree that the above are several of the key features of our manuscript.

      Reviewer #3: Areas for improvement: The novelty of the method is more than questionable as both ChIP-MS coupled to LFQ and dCas9 usage for locus-specific proteomics have been previously reported. The fact that the authors directly pulldown dCas9 instead of using a dCas9-fused biotin ligase and subsequent streptavidin pulldown is only a very minor change to previous methods (not even improvement). It would be more accurate for the authors to present their study as an optimization and rigorous validation of existing techniques rather than a novel approach.

      Response: While we appreciate where the reviewer is coming from, it occurs to us that most of the reviewer’s comments equate ChIP approaches with other complementary methods, in particular proximity labelling. The latter is indeed a powerful experimental strategy and in fact we are ourselves avid users. As highlighted to reviewer #1 as well, our manuscript was originally conceived as a shorter report and based on the feedback we will now expand our discussion to more broadly incorporate related approaches.

      However, we would like to stress that dCas9 ChIP-MS and dCas9-biotin ligase fusions are not the same thing and this is not a minor tweak to an existing protocol. While both approaches have converging aims – to identify proteins that associate with individual genomic loci – the experimental workflows differ fundamentally. Biotin ligases use a “tag and run” approach by promiscuously leaving a biotin tag on encountered proteins. Subsequently, cellular proteins are extracted and in fact proteins can even be denatured prior to enrichment with streptavidin beads. While this is an in vivo workflow that (depending on the biotin ligase used) may provide sensitivity advantages, it does not retain complex information. The latter is inherently part of ChIP workflows due to the use of cross-linkers. One obvious future application would be to maintain (= not to reverse as we have done here) the crosslink during the mass spectrometry sample preparation in order to read out cross-linked peptides to gain insights into interactions and structural features. We will now more clearly incorporate such notions into our discussion.

      In addition, we would like to stress that while this reviewer focuses primarily on the dCas9 aspect of our manuscript, we believe that our general ChIP-MS workflow including the combination with label-free quantitation is useful and important already by itself as e.g. recognised by both reviewers #1 and #2.

      Reviewer #3: The authors should more thoroughly discuss previous works using ChIP-MS and dCas9 for locus-specific proteomics. This would give readers a better understanding of how the current work builds on and improves these earlier methods. For a paper that aims on presenting an optimized ChIP-MS workflow it is crucial to showcase in which use cases it outperforms previously published methods.

      E.g., compare locus-specific dCas9 ChIP-MS to CasID (doi.org/10.1080/19491034.2016.1239000) and C-Berst (doi.org/10.1038/s41592- 018-0006-2); how does your method perform in comparison to these?

      Response: Again, while we will now incorporate more extensively comparisons with previous ChIP-MS publications (and the few prior manuscripts that included dCas9) as well as related techniques, we would like to stress that dCas9 ChIP-MS is not the same approach as CasID and C-BERST, which rely on dCas9 fusions to BirA* and APEX2, respectively. dCas9-APEX2 strategies were also published by two additional groups as CASPEX (back-to-back with the C-BERST manuscript; PMID: 29735997) and CAPLOCUS (PMID: 30805613). All of these methods target specific loci with dCas9 and promiscuously biotinylate proteins that are in proximity to the dCas9-biotin ligase fusion protein. As described above, while the application of the BioID principle (PMID: 22412018) to chromatin regions has converging aims with the dCas9 ChIP-MS part of our manuscript, they do not test the same. ChIP carries chromatin complexes through the entire workflow while the CasID approaches are independent of that. This is the same scenario if we were to compare IP-MS reactions (such as the ChIP-MS reactions presented here for endogenous proteins) and BioID-type experiments for proximity partners of the same bait proteins.

      Reviewer #3: Compare likewise the described protein interactomes to previously published interactomes.

      Response: We will add comparisons in form of Venn diagrams with previously published interactomes. However, we would like to stress that a key aspect of our manuscript is the smaller yet rigorous hit lists based on e.g. loss-of-function controls, higher stringencies and specificity. Simply comparing final interactomes remains reductionist relative to the importance of other variables such as experimental design, number of replicates, data analysis etc.

      Reviewer #3: The authors use sgGAL4 as a control for the telomeric targeting of dCas9. The IF results (Fig3b) show that sgGAL4 barely localizes to the nucleus with very faint signals. It would be helpful to use a control with homogenous nuclear localization of dCas9 to further strengthen the author's conclusions.

      Response: dCas9-EGFP in the presence of sgGAL4 localises diffusely to the nucleus as expected. We have here used a very widely used non-targeting sgRNA control that has been originally used for imaging purposes (PMID: 24360272) and has since been used in a variety of studies (e.g. PMID: 26082495, 32540968, 28427715) including a previous dCas9 ChIP-MS attempt (PMID: 28841410). In addition, to the diffuse nuclear, non-telomeric localisation we provide complementary validation of clean enrichment of telomeric DNA specifically in the sgTELO samples. Therefore, we do not see how other non-targeting sgRNAs would provide for better controls or improve our data.

      Reviewer #3: The extrapolation of results from the use of telomeres as a proof-of-concept to other loci is not a given considering the highly repetitive structure of telomeric DNA. The authors should either be more cautious about generalizing the results to other loci or demonstrate that their method can also capture locus-specific interactomes at non-repetitive regions.

      Response: We agree that the adoption of any locus-specific approach to single genomic loci is a steep additional hurdle and warrants rigorous data on well characterised loci with very clear positive controls. We will expand on these challenges in our discussion. However, we would like to stress that we did not make any such statement in our original manuscript apart from simply referring to our telomeric experiment as proof-of-concept evidence that locus-specific approaches are feasible by ChIP.

      Reviewer #3: What are concrete biological insights from this optimized ChIP-MS workflow that previous methods failed to show?

      Response: We explicitly used telomeres as an extensively studied locus with clear positive controls that at the same time allows us to evaluate likely false positives. As such the intention of the manuscript was not to yield concrete biological insights but to develop a new methodological workflow.

      As also highlighted in a response to reviewer #2, based on other prior attempts to enrich telomers in ChIP-like approaches with dCas9 (PMID: 28841410 & 29507191), it had been suggested that dCas9 “might exclude some relevant proteins from telomeres in vivo” (PMID: 32152500), implying that dCas9 ChIP-MS might inherently not be feasible including at repetitive regions such as telomeres. Therefore, recapitulating the set of well-described telomeric proteins was no trivial feat and our ChIP-MS workflow (both targeted and applied to individual proteins) represents a well-validated method to in the future systematically interrogate changes in chromatin composition. As one example at telomeres, this may include chromatin changes upon the induction of telomeric fusions or general DNA damage.

      Reviewer #3: For instance, the authors could compare their mouse and human TERF2 interactomes and discuss similarities and differences between both species.

      Response: We thank the reviewer for this suggestion, but the comparison between mouse and human TERF2 interactomes is not suitable across the datasets that we generated. U2OS is a human osteosarcoma cell line that relies on the Alternative Lengthening of Telomeres (ALT) pathway while our mouse data is based on embryonic stem cells (mESCs) and mouse liver tissue. Even the latter, in contrast to adult human tissue, expresses telomerase. We can certainly still pinpoint (as already done in our original manuscript) individual differences among known factors, e.g. the fact that proteins such as NR2C2 are more abundantly found at ALT telomeres (PMID: 19135898, 23229897, 25723166) vs. the detection of the CST complex as telomerase terminator (PMID: 22763445) in the mouse samples. However, the TERF2 datasets contain hundreds of proteins as “hits” above our cut-offs and a key message of our manuscript is that the majority of them are likely false positives. Here, differences are likely extending to expression differences between U2OS cells, mESCs and liver samples. So while appealing in theory, this cross data set comparison would remain rather superficial and error prone at this point. As a biology focused follow-up study, this would need to be rigorously conceived based on an appropriate choice of human and murine cell line models. In addition, this would likely require the generation of FKBP12-TERF2 knock-in fusion clones to allow for rapid depletion of TERF2 for a clean loss-of-function control since sustained loss of TERF2 leads to chromosomal fusions and eventually cell death in most cell types.

      Reviewer #3: The authors should also describe which interaction partners are novel and try to validate some of these using orthogonal methods.

      Response: We will now highlight more explicitly two proteins, POGZ and UBTF, that are most robustly and reproducibly enriched on telomeric chromatin across datasets, including the U2OS WT vs. ZBTB48 KO comparison (Fig. 2a). However, we would like to abstain from a molecular characterization at this point. As mentioned above, the discovery of novel telomeric proteins is not the focus of this manuscript, which is primarily dedicated to method development. In addition, these type of validations in methods papers are often limited to a few assays (e.g. can 1 or 2 proteins be enriched by ChIP? Do you see some localisation by IF? etc.). However, our research group has a history of publishing in-depth mechanistic papers on the characterisation of novel telomeric proteins (e.g. PMID: 23685356, 28500257, 20639181, doi.org/10.1101/2022.11.30.518500). Therefore, a genuine validation of such factors would require functional insights and clearly warrants independent follow-up work.

      Reviewer #3: Human Terf2 ChIP-MS (Fig1A) seems to be much more specific than the mouse counterpart (Fig1D) (32 TERF2 interactors out of 176 hits in human vs 12 TERF2 interactors out of 500 hits in mouse). Could the authors explain this notable difference?

      Response: As eluded to above, Fig. 1A and 1D cannot be directly compared, starting with the difference in complexity in the input material – cell line vs. tissue. For comparison, the Terf2 ChIP-MS data from mouse embryonic stem cells tallies up to 19 out of 169 hits, which is much closer to the U2OS results. Again, we deem the majority of hits from the TERF2 ChIP-MS data to be false-positives and the more complex input material from mouse livers likely accounts for the difference in these numbers.

      Reviewer #3: The authors used much higher cell numbers than previously published ChIP-MS experiments; while this is understandable for dCas9-based pulldowns, the cell number is expected to be down-scalable for the other IPs (TERF2, ZBTB48, MYB). Since this work primarily describes an optimized Chip-MS workflow, the authors should show that they can reasonably downscale to at least 15 Mio cells per replicate; one way of achieving this could be through digesting on the beads and not in-gel.

      Response: As we will illustrate in the comparison table that was also requested by reviewer 2, our approach does not use higher cell numbers than previous ChIP-MS approaches – quite the contrary. In addition, we would like to highlight that while we state 50 million cells in Fig. 1a, we only inject 50% of our samples for MS analysis to retain a back-up sample in case of technical issues with the instruments. In other words, our workflow is already effectively based on 25 million cells and thereby pretty close to the requested 15 million cells while simultaneously requiring substantially less reagents.

      Importantly, our examples are based on rather lowly expressed bait proteins such as ZBTB48 (not detected within DDA-based proteomes of ~10,000 proteins in U2OS cells). While the workflow can be applied across proteins, exact input numbers might vary depending on the bait protein, e.g. histones and its modifications would likely require less for the same absolute sample enrichment. For instance, PMID 25990348 and 25755260 performed ChIP-MS on common histone modifications but still used 300-800 million cells per replicate. Considering that we worked on substantially less abundant proteins, we here present a workflow with comparably low input samples.

      Reviewer #3: It is not clear from the text or figure what the authors are trying to show in Fig2c. They should either explain this further or take the figure out.

      Response: We are trying to illustrate the following: As in any IP reaction the bait protein is the most enriched protein with very high relative intensities, e.g. TERF2 in the TERF2 ChIP-MS data. Direct protein interaction partners – here the other shelterin members – follow at about 1 order of magnitude lower signal intensities. In contrast, proteins that are enriched via an interaction with the same DNA molecule (i.e. that do not physically interact with the bait protein) such as NR2C2, HMBOX1 and ZBTB48 further trail by at least 1 more order of magnitude. These are information that are not easily visualised within the volcano plots and mainly “buried” within the Supplementary Tables. However, these relative intensities displayed in Fig. 2c clearly illustrate the dynamic range challenge that ChIP-MS poses for proteins that independently bind to the same chromatin fragment. We have now modified our text to make this point more clear.

      Reviewer #3: Was there any benefit in using a Q Exactive HF vs timsTOF flex?

      Response: Yes, measuring the same samples (e.g. the 50% backup mentioned above) on both instruments enriches more telomeric proteins/shelterin proteins in e.g. the dCas9 ChIP-MS data set on the timsTOF fleX. However, given the difference in age of these instruments/technologies between a Q Exactive HF and a timsTOF fleX (in the context of these experiments the equivalent of a timsTOF Pro 2), this is not a fair comparison beyond concluding that a more recent instrument like the timsTOF fleX achieves better coverage and is more sensitive with otherwise comparable measurement parameters. As we did not have the opportunity to run matched samples on e.g. an Exploris 480, we would not want to make claims across vendors. As stated in the discussion we are expecting that even newer generation of mass spectrometers, such as the very recently released Orbitrap Astral or timsTOF Ultra would further improve the sensitivity and/or allow to reduce the amount of input material. Therefore, the main conclusion is that improvements in the mass spec generations improve proteomics data quality and our samples are no exception, i.e. this is not specifically pertinent to our approach.

      Reviewer #3: How did the authors analyze the PTM data? This is not described in the methods section. In addition, it would be important to validate the novel PTMs described for NR2C2.

      Response: We apologise for the oversight and we will add the description of PTMs as variable modifications during our MaxQuant search in the methods section. The originally deposited datasets already include this and we had simply missed this in our methods text.

      While we are not 100% sure to understand the request for validation correctly, we would like to point out that the PTMs on NR2C2 have been previously reported in several high-throughput datasets and for S19 in functional work on NR2C2 (PMID: 16887930). However, the relevance in our data set is as follows: While the PTMs on TERF2 as the bait protein could occur both on telomere-bound TERF2 as well as on nucleoplasmic TERF2, NR2C2 is only enriched in the TERF2 ChIP-MS reactions due to its direct interaction with telomeric DNA. The co-detection of its modifications therefore implies that at least some of the telomere-bound NR2C2 carries these modifications. We showcase this example as an additional angle of how such ChIP-MS datasets can be analysed.

      While the robust, MS2-based detection of these modified peptides in our data set and several other publicly available datasets provides strong evidence that these modifications are genuine, further functional validation would involve rather labour-intensive experiments and resource generation (e.g. phospho-site specific antibodies). We hope that the reviewer agrees with us that this would require an independent follow-up study and that this goes beyond the scope of our current manuscript.

      Reviewer #3: For this kind of methods paper one would expect to see the shearing results of the ChIP-MS experiments since variations in DNA shearing can impact the detection of false-positives in the ChIP-MS experiments

      Response: We will include agarose gel pictures of our sonicates, which we indeed routinely quality controlled prior to ChIP experiments as stated in our methods description.

      Reviewer #3: Overall, the current state of the manuscript neither provides direct evidence that the "optimized" ChIP-MS workflow is better in certain aspects/use cases than previously published methods nor does it provide novel biological insights. At the current state it even cannot be considered as a validation of previously published methods since it does not discuss them.

      Response: We politely disagree with this conclusion. Again, as mentioned above we are under the impression that this reviewer somehow equates our entire manuscript to a comparison with dCas9-biotin ligase fusions.

      Instead, we here provide a workflow for ChIP-MS that incorporates label-free quantification as the experimentally easiest, most intuitive quantification method for non-mass spectrometry experts. This offers a particularly low barrier to entry aimed at making ChIP-MS more widely accessible as a complement to commonly used ChIP-seq applications. Furthermore, we showcase that as a gold standard ChIP-MS – to truly live up to its name – should have the ability to enrich proteins independently binding to the same chromatin fragment. We demonstrated that double cross-linking is critical for these assays and in return illustrate how rigorous loss-of-function controls (both KOs and degron systems) can mitigate prevalent false-positives that are exacerbated due to the cross-linking. Finally, we applied this workflow to different types of endogenous proteins (transcription factors, telomeric proteins) in cell lines and tissue and extend our work to dCas9 ChIP-MS as a targeted method.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      1. General Statements [optional]

      Thank you for your letter dated on May 5, 2023 concerning our manuscript (MS# RC-2023-01906) entitled “Activation of Nedd4L Ubiquitin Ligase by FCHO2-generated Membrane Curvature.”

      We thank the reviewers for their constructive comments and suggestions. We have considered all reviewers’ comments and plan to revise our manuscript accordingly.

      We believe that our revision plan will greatly improve the quality of our manuscript.

      1. Description of the planned revisions

      __Reviewer #1 __

      I enjoyed reading the paper by Sakamoto and colleagues, where they show that Nedd4L ubiquitin ligase activity is stimulated by membranes and in particular positive membrane curvature. This paper is a conceptual advance that hopefully will be extended by many other groups where membranes topology participates in the activation of associated enzymes, giving rise to added complexity but also specificity and further compartmentalization. It is an important paper for all cell biologists to understand.

      1. My comments are all relatively minor and I hope can improve the readability of the paper, but will not alter the overall conclusion as this is well backed up. In general I would like to see more/better statistics/quantitation and better figure legends. I found that often one had to read the paper to understand a figure where reading the figure legend should suffice.

      __Reply: __According to the reviewer’s comment, we will quantify the experiments (Fig. 1C, Fig. 2, Fig. 9B, and Fig. 10B) and add descriptions of statistics (Fig. 5, Fig. 6, B and D, and Fig. 7C). We will also write better figure legends to enable the readers to easily understand experiments.

      1. This paper reminds me of a paper from Gilbert Di Paolo's lab on the activation of synaptojanin PIP2 hydrolysis by high membrane curvature. One would expect that there may be many such proteins whose activities will be dependent on their membrane environment. I find it conceptually rather likely that a protein which interacts with membranes via a C2 domain (which has membrane insertions and will thus likely be curvature sensitive) will likely show some positive curvature sensitivity. Can I suggest this paper is referenced and discussed in the light of the discussion statement "Thus, our findings provide a new concept of signal transduction in which a specific degree of membrane curvature serves as a signal for activation of an enzyme that regulates a number of substrates."

      Reply: __According to the reviewer’s comment, we will cite the paper entitled “synaptojanin-1-mediated PI(4,5)P2 hydrolysis is modulated by membrane curvature and facilitates membrane fission” by Chang-Ileto et al. (Dev. Cell __20, 206–18 , 2011). We will also discuss this paper in the light of the discussion statement.

      1. Where the paper could be improved (or I have not understood fully). In figure 1 there is a robust endocytosis of ENaC that is FCHo2 and Nedd4L sensitive. There is a rescue for FCHo2 in a fluorescence image (unquantified), so it would be good to have the more quantitative approach of rescue with both FCHo2 and Nedd4L in the biochemical assay.

      __Reply: __Although the reviewer suggests a rescue experiment in the biochemical assay, the experiment is difficult because the transfection efficiency is low (about 50%). On the other hand, we agree with the reviewer that a quantitative approach is required in the rescue experiment (Fig. 1C). Therefore, we plan to quantify the rescue experiment for FCHO2 in the immunofluorescence assay. The reviewer also suggests a rescue experiment for Nedd4L as well as FCHO2. However, since the involvement of Nedd4L in ENaC endocytosis is well established, we do not think that the rescue experiment for Nedd4L is further required.

      1. In figure 2 there is nice co-localisation between clathrin/FCHo2 and ENaC but not with Nedd4L. It would be good to have some quantitation of the co-localisation. But also one should use a Nedd4L mutant or a mutant of ENaC and so be able to visualise co-localisation between receptor and ub-ligase. I find it strange that there is no (or much less) Nedd4L-GFP visible in the cells overexpressing ENaC... Is there an explanation? Does overexpression of ENaC lead to more auto-ubiquitination of Nedd4L. Also the Nedd4L-GFP signal in other cells is punctate, while in the next figure Myc-Nedd4L is not.

      __Reply: __According to the reviewer’s comment, we will perform quantitative colocalization analysis in Fig. 2.

      We have found that a catalytically inactive Nedd4L mutant, C922A, co-localizes with cell-surface αENaC and FCHO2 in αβγENaC-HeLa cells. According to the reviewer’s comment, these data will be added in the revised manuscript.

      In Fig. 2C, Nedd4L was transiently transfected in cells stably expressing ENaC. In Nedd4L-transfected cells, overexpression of Nedd4L stimulated ENaC internalization, resulting in the disappearance of ENaC at the cell surface. On the other hand, in non-transfected cells, cell-surface ENaC was detected. Thus, Nedd4L-negative cells are non-transfected cells (cell-surface ENaC positive cells). This explanation will be added in the revised manuscript.

      The staining pattern of Nedd4L depends on what section of the cell a confocal microscope was focused on. Nedd4L-GFP signals were punctate at the bottom section of the cell in Fig. 2, whereas Myc-Nedd4L was diffusely distributed at the upper section (cytoplasm) of the cell (Fig. 3). Thus, Nedd4L shows distribution throughout the cytoplasm and punctate staining at the bottom (cell surface). The staining pattern of Nedd4L is also affected by the expression amount of Nedd4L in cells. When Nedd4L was highly expressed in COS7 and HEK293 cells in Fig. 3, the punctate staining was hardly detected. This localization pattern of Nedd4L will be clearly described in the revised manuscript.

      1. In figure 3 it appears to me that there is co-localization between ENaC and amphiphysin. Is this not a positive piece of information? I am not sure that FBP17 is a good F-BAR domain to use given its oligomerization may well prevent membrane association of Nedd4L. Minor comment: I don't see tubules for amphiphysin in panel B.

      __Reply: __The reviewer states that there is co-localization between Nedd4L and amphiphysin1 (Fig. 3A). However, Nedd4L was not recruited to membrane tubules generated by amphiphysin1. We will clearly show that there is no colocalization between Nedd4L and amphiphysin1.

      The reviewer states that FBP17 may not be a good F-BAR domain to use because its oligomerization may well prevent membrane association of Nedd4L. However, we have shown that FCHO2 as well as FBP17 forms oligomer (Uezu et al. Genes Cells, 16, 868-878, 2011). Furthermore, we have found that FCHO2 inhibits the membrane binding and catalytic activity of Nedd4L when the PS percentage in liposomes is elevated (unpublished data and Fig. 9C). Thus, since FBP17 and FCHO2 probably have similar properties, we presume that FBP17 is a good F-BAR domain to use.

      As the reviewer pointed out, membrane tubules generated by amphiphysin1 were hardly detected in HEK293 cells (Fig. 3B). It showed punctate staining, but did not co-localized with Nedd4L. This description will be added in the revised manuscript.

      1. Figure 5: The affinity of Nedd4 C2 domain for calcium is quite high given we normally assume a cytosolic concentration of 100nM (approximate). The authors have rightly buffered the calcium with EGTA. Normally we would check that the buffering is sufficient by varying the protein concentration and making sure the affinity is still the same, so can I suggest the authors use 3 or 4 times the amount of C2 domain and make sure the curve does not change (provided liposomes are not limiting). Minor comment: How many experiments and what are error bars (SD?).

      __Reply: __According to the reviewer’s comment, we will check that the buffering is sufficient by varying the protein concentration (Fig. 5). We will also add a description of statistics to the legend to Fig. 5.

      1. Figure 6: Controls have been performed to ensure that liposomes are pelleted, according to methods. In Figure 6B can the authors show that there is the same amount of liposomes in each sample by showing more of the coomassie gel so that the reader can see the Neutravidin band is the same in each sample. Also I believe a student t-test should not be used in this experiment (but perhaps an Anova test), and in panel D there does not appear to be a description of statistics.

      __Reply: __To ensure that the same amounts of liposomes were pelleted, the reviewer suggests that we show more of the Coomassie gel to present the neutravidin bands in Fig. 6B. However, as the molecular weight of neutravidin is about 15 kDa, neutravidin run out of the gel (7% SDS-PAGE gel) where Nedd4L (As the reviewer pointed out, we will use an Anova test in Fig. 6B. We will also add a description of statistics in Fig. 6D.

      1. Figure 11: In panel B I note that the FCHo2 BAR domain on small liposomes appears to inhibit Ubiquitination. Is this consistent with the BAR domain not preventing Nedd4L binding?

      __Reply: __The FCHO2 BAR domain enhances the liposome binding and catalytic activity of Nedd4L when the strength of interaction of Nedd4L with liposomes (20% PS) is weak. In contrast, we have also found that the FCHO2 BAR domain inhibits the membrane binding and catalytic activity of Nedd4L when the interaction of Nedd4L with liposomes is increased by elevating the PS percentage in liposomes (unpublished data and Fig. 9C). The reason for the different effects of FCHO2 on Nedd4L is considered as follows: When liposomes (20% PS) are used (the interaction of Nedd4L with PS in liposomes is weak), Nedd4L binds to liposomes mainly through ENaC (Fig. 8F). The liposome binding is hardly mediated by PS. Addition of the FCHO2 BAR domain increases the strength of interaction Nedd4L with PS by generating membrane curvature. Consequently, the FCHO2 BAR domain newly induces the PS-mediated liposome binding of Nedd4L, resulting in the enhancement of liposome binding and catalytic activity of Nedd4L. On the other hand, when the interaction of Nedd4L with PS in liposomes is increased by elevating the PS percentage in liposomes (50% PS), the liposome binding of Nedd4L is mainly mediated by PS. Addition of the FCHO2 BAR domain inhibits the PS-mediated liposome binding of Nedd4L. Since both FCHO2 and Nedd4L are PS-binding proteins, they compete with each other to bind to PS in liposomes. Therefore, the results in Fig. 11B are consistent, because the interaction of Nedd4L with PS is increased by 0.05 µm pore-size liposomes. This explanation will be added in the revised manuscript.

      __Reviewer #2 __

      The authors have reported the involvement of the BAR domain-containing protein FCHO2 in the Nedd4L-mediated endocytosis of ENaC. They propose a model in which the membrane curvature induced by the BAR domain-FCHO2 relieves the auto-inhibition of E3 ligase causing its activation and recruitment. The paper describes a series of in vitro reconstituted experiments that are interesting but not fully connected with the mechanism of ENaC endocytosis. Additional experiments are needed to fully support the authors' conclusions.

      Major comments:

      1. Although the data reported by the authors regarding FCHO2 and Nedd4L involvement in ENaC endocytosis are convincing, it is suggested that the authors perform the same ENaC endocytosis assay presented in Fig.1B under conditions of FBP17 and amphiphysin1 siRNA to formally prove the selective involvement of FCHO2 in the process among other BAR-containing proteins.

      __Reply: __The reviewer suggests the same ENaC endocytosis assay presented in Fig. 1B under conditions of FBP17 and amphiphysin1 siRNA to prove the selective involvement of FCHO2 in ENaC endocytosis. There seems to be a misunderstanding. Similar to FCHO2, FBP17 and amphiphysin are well known to be involved in clathrin-mediated endocytosis. As ENaC is internalized through clathrin-mediated endocytosis, FBP17 and amphiphysin siRNA presumably inhibit ENaC endocytosis. We cannot understand the significance of FBP17 and amphiphysin1 siRNA in the ENaC endocytosis assay.

      1. According to the previous point, it will be interesting to see not only a snapshot image of the internalisation assay performed by immunofluorescence (Fig.1C) but a more quantitative analysis of the different time points (as in Fig.1B) in condition of FCHO2 siRNA and eventually FBP17 and amphiphysin1 siRNA.

      __Reply: __According to the reviewer’s comment, we will perform a quantitative analysis in Fig. 1C. The reviewer also suggests the immunofluorescence assay at the different time point in Fig. 1C. However, we show the time course of ENaC internalization in Fig. 1B. We do not think that the time course in the immunofluorescence assay is further required. As for FBP17 and amphiphysin siRNA, our response is the same as that to the comment 1 of this reviewer.

      1. In Fig.2B, overexpression of the catalytically inactive version of Nedd4L (Nedd4L C922A) would help to see Nedd4L-ENaC co-localization.

      __Reply: __This comment is the same as the comment 4 of the reviewer#1.

      1. In Fig.4D, the authors need to analyse ENaC ubiquitination in the same experimental setting as Fig. 4A instead of transfecting cells with increasing amounts of Nedd4L in the presence or absence of FCHO2 BAR. It is also recommended to include Nedd4L C922A as an additional control.

      __Reply: __The reviewer requests us to analyse ENaC ubiquitination in the same setting as Fig. 4A. However, an in vivo autoubiquitination assay is widely used to determine the catalytic activity of E3 Ub ligase, because the E3 activity is typically reflected in their autoubiquitination. Therefore, the autoubiquitination assay is sufficient to show that Nedd4L is specifically activated by membrane tubules generated by FCHO2 in cells. Furthermore, we have found it very difficult to compare ENaC ubiquitination among many GFP-BAR proteins (GFP alone, GFP-FCHO2, GFP-FBP17, amphiphysin1-GFP, GFP-FCHO2 mutant) in the same experimental setting as Fig. 4A. In Fig. 4A, three types of cDNAs (HA-Ub, Myc-Nedd4L, and GFP-BAR protein) were transfected in cells. The expression amounts of Myc-Nedd4L were similar among the GFP-BAR proteins. On the other hand, in Fig. 4D, four types of cDNA (HA-Ub, Myc-Nedd4L, GFP-BAR protein, and FLAG-αENaC) were transfected in cells. Under these conditions, it is very difficult to adjust the expression amounts of Nedd4L and αENaC among many GFP-BAR proteins. Even when comparing two GFP-BAR proteins (GFP alone and GFP-FCHO2), it was necessary to assess the expression amounts of Nedd4L by transfection with various cDNA amounts of Nedd4L (Fig. 4D). Moreover, as shown in Fig. 4D, enhancement of ENaC ubiquitination by FCHO2 is decreased at higher expression of Nedd4L (1.0 and 1.5 μg DNA), although the reason is unknown. Therefore, we are not sure that we will able to accurately analyse ENaC ubiquitination in the same setting as Fig. 4A instead of transfecting cells with increasing amounts of Nedd4L.

      According to the reviewer’s comment, we will examine the effect of Nedd4L C922A on ENaC ubiquitination.

      1. While discussing the role of hydrophobic residues in Nedd4L C2 domain,the authors never mentioned the publication by Escobedo et al., Structure 2014 (DOI:10.1016/j.str.2014.08.016), which highlighted how I37 and L38 are directly involved in Ca2+ binding. This aspect should be discussed since the authors show the importance of Ca2+ for PS binding in the sedimentation assay.

      __Reply: __According to the reviewer’s comment, we will cite the reference (Escobedo et al.) and discuss the aspect (I37 and L38 are directly involved in Ca2+ binding).

      1. As stated by the authors those two residues I37 and L38 are also involved in E3 enzyme activation by relieving C2-HECT interaction. It is important to further demonstrate the effect of these mutations on ENaC substrate.

      __Reply: __To prove that the I37 and F38 residues are involved in E3 enzyme activation by relieving C2-HECT interaction, the reviewer requests us to further demonstrate the effect of Nedd4L I37A+F38A on ENaC ubiquitination. However, these two residues are critical noy only for Nedd4L activation but also for membrane binding and curvature sensing of Nedd4L. We also show that membrane binding of Nedd4L is critical for ENaC ubiquitination. Actually, we have found that Nedd4L I37A+F38A mutant, which loses membrane binding, shows little ENaC ubiquitination (unpublished data), whereas it enhances autoubiquitination (Fig. 4C). Thus, the effect of the I37A+F38A mutant on ENaC ubiquitination is not appropriate to prove that the two residues are involved in E3 enzyme activation.

      1. There are some concerns regarding the in vitro ubiquitination assay performed in Fig.8 and following figures. The Nedd4L proteins used during the assay has been produced as His tagged at the C-terminus, it was reported (Maspero et al, Nat Struct Mol Biol 2013 DOI: 10.1038/nsmb.2566), at least for the isolated HECT domain, that modification of the C-terminal residue of the protein affects its activity. It would be important to judge the activity of the purified proteins used in the assay. Moreover, as additional control it is suggested the introduction of a mSA-ENaC PY mutant protein. The authors claimed the importance of membrane localized PY motif for recruitment and activation of Nedd4L, it would be informative to perform the experiment in presence of PY mutated ENaC.

      __Reply: __The reviewer states that there are some concerns regarding His-tagged Nedd4L proteins. We have prepared Nedd4L that has no tag at its N- or C-terminus. N-terminal GST-tagged, C-terminal untagged Nedd4L was expressed in E. coli and purified by Glutathione-Sepharose column chromatography. The GST tag was cleaved off and Nedd4L was further purified by Mono Q anion-exchange column chromatography. Using this purified sample, we have examined the catalytic activity of untagged Nedd4L. We have found that concerning Ca2+-dependency, PS-dependency, and curvature-sensing, the properties of untagged Nedd4L are similar to those of C-terminal His-tagged Nedd4L (unpublished data).

      According to the reviewer’s comment, we will perform the experiment in the presence of PY-mutated ENaC.

      1. It is not clear why increasing the concentration of PS (from 20% to 50%) the presence of BAR domain doesn't allow ENaC ubiquitination (Fig.9C), is Nedd4L not recruited to the pellet? It would be interesting to see the sedimentation experiment of Fig.9A done in presence of 50% PS.

      __Reply: __This comment is essentially the same as the comment 8 of the reviewer#1. We have found that FCHO2 BAR domain inhibits the membrane binding of Nedd4L when the PS percentage in liposomes is elevated (~50%) (unpublished data). According to the reviewer’s comment, these data will be added in the revised manuscript.

      1. This reviewer is not an expert of lipids biology, thus the explanations related to the effect of FCHO2 BAR in presence of PI(4,5)P2 (Fig. 10) or 0.05 pore-size liposomes (Fig.11) were not clear. Does FCHO2 BAR have a different effect in inducing membrane tubulation in these two conditions? Is this parameter measurable by tubulation assay?

      __Reply: __According to the reviewer’s comment, we will write more clearly the explanation related to the effect of FCHO2 BAR domain in the presence of PI(4,5)P2 or 0.05 μm pore-size liposomes.

      Minor Comments

      1. It would be appreciated if a nuclei staining panel is included in all immunofluorescence images, as it would help to identify the number of cells in the field of view (e.g., Fig. 1C, Fig. 2B).

      __Reply: __According to the reviewer’s comment, we will show immunofluorescence images to identify the number of cells in Fig. 1C and Fig. 2B.

      1. It would be recommended to include colocalization analysis, such as Pearson's correlation coefficient or Manders coefficient in immunofluorescence images.

      __Reply: __According to the reviewer comment, we plan to perform quantitative colocalization analysis in Fig. 2.

      1. It is not clear how the quantitation of mSA-ENaC ubiquitination in Fig.8D, 8C, and 9B was performed. Did the authors normalise the detected Ub signal over the amount of unmodified mSA-ENaC?

      __Reply: __We did not normalize the detected Ub signals over the amount of unmodified mSA-ENaC, because the same amount of mSA-ENaC was added in each assay. The chemiluminescence intensity of Ub signals was quantified by scanning using ImageJ. According to the reviewer’ comment, we will clearly describe how the quantification of mSA-ENaC ubiquitination was performed.

      __Reviewer #3 __

      --- Summary ---

      The manuscript by Sakamoto et al. describes how the ubiquitin ligase Nedd4L is activated by membrane curvature generated by the endocytic protein FCHO2. For their experiments, the authors use the epithelial sodium channel (ENaC) as a model Nedd4L target and CME cargo. The authors start their manuscript by showing in cells the importance of FCHo2 and Nedd4L in ENaC internalization. Using a combination of experiments in cells and biochemistry, the authors show that Nedd4L binds preferentially to membranes with the same curvature generated by FCHO2. Next, the authors show that a combination of membrane composition (PS), calcium concentration, PY domain presence and membrane curvature all act in concert to recruit Nedd4L to membranes and fully release its ubiquitination activity. Crucially, the authors show that role of FCHO2 in Nedd4L recruitment is not direct, with FCHO2 simply generating an optimal membrane curvature for Nedd4L binding. Taken together, the authors suggest a mechanism by which the curvature of early clathrin coated pits, generated by FCHO1/2 define an optimal environment for the recruitment and activation of the ubiquitin ligase Nedd4L.

      The manuscript convincingly shows the membrane curvature-dependent mechanism of Nedd4L activation. The biochemistry experiments in the manuscript are well designed and the results are of clear. The quality of these experiments is very high. The experiments in cells are, however, not of the same level of quality.

      --- Major comments ---

      1) The results do not show convincingly that Nedd4L is recruited to CCPs. There is plenty of indirect evidence, but to support the model shown in the last figure, authors need to show more than the staining in figure 2C. Live-cell imaging showing the post-FCHo2 recruitment of Nedd4L would be required. I understand that the recruitment would possibly occur in a fraction of events and may be difficult to catch. The cmeAnalysis script from the danuser lab(https://doi.org/10.1016/j.devcel.2013.06.019 can facilitate the identification of these events.

      __Reply: __According to the reviewer comment, we plan to examine by live-cell TIRF microscopy that Nedd4L is recruited to CCPs.

      2) What happens to ENaC in Nedd4L and FCHO2 knockdown cells? One would expect accumulation of the receptor on the surface.

      __Reply: __We have found that upon Nedd4L or FCHO2 knockdown, αENaC accumulates at the cell surface in αβγENaC-HeLa cells. According to the reviewer’s comment, we will show these data in the revised manuscript.

      *3) In the experiments in figure 1, it would be important to use a standard CME cargo as an internal control (transferrin). This will serve as a functional confirmation of FCHO2 knockdown and help the reader to put the Need4L knockdown experiments into the context of CME. *

      __Reply: __According to the reviewer’s comment, we will use a standard CME cargo as an internal control (transferrin).

      *4) Quantification for the rescue experiment is required (figure 1C). if not possible, at least a picture where the reader can see transfected and non-transfected cells side-by-side is necessary. *

      Reply: This comment is the same as those of the reviewer#1 (comment 3) and reviewer#2 (comment 2). According to the reviewer’s comment, we plan to quantify the rescue experiment (Fig. 1C).

      *--- Minor comments --- *

      *1) The experiments in figure 3 must be presented in order as they are in the text. For example, figure 3E is cited in the text into the context of figure 7. It is very confusing. *

      __Reply: __According to the reviewer’ s comment, we will present the experiments in Fig. 3 in order they are in the text.

      *2) A better explanation of the assay in 1C would facilitate its understanding for the non-specialist reader. The reader needs to read the methods section to understand how it was done. *

      __Reply: __According to the reviewer’ comment, we will write a better explanation of the assay in the Fig. 1C legend to enable the readers to understand how it was done.

  12. May 2023
    1. @Will Thanks for always keeping up with your regular threads and considerations.

      I've been keeping examples of people talking about the "magic of note taking" for a bit. I appreciate your perspectives on it. Personally I consider large portions of it to be bound up with the ideas of what Luhmann termed as "second memory", the use of ZK to supplement our memories, and the serendipity of combinatorial creativity. I've traced portions of it back to the practices of Raymond Llull in which he bound up old mnemonic techniques with combinatorial creativity which goes back to at least Seneca.

      A web search for "combinatorial creativity" may be useful, but there's a good attempt at what it entails here: https://fs.blog/seneca-on-combinatorial-creativity/

    1. @chrisaldrich I think the is an underated idea more broadly. I would love to see this done with other authors books that use an index card system, like Robert Greene. I think it would be a useful illustration to help people better understand the research and writing process. I've been wanting to and created a few experimental vaults where I do a similar thing except for a podcast (all of Sean Carroll's Mindscape transcripts are free) or a textbook (Introduction to Psychology). But I never followed through on the projects just because of how much work it takes to due it right. This also makes me wish for a social media type zettelkasten, where a community can keep a shared vault, creating a social cognition of sorts. I know this was kind of happening with the shared vaults Dan Alloso was experimenting with but his seemed more focused than random/chaotic. I'm also not sure if he continued it for later books.

      Reply to Nick at https://forum.zettelkasten.de/discussion/comment/17926/#Comment_17926

      Some pieces of social media come close to the sort of sense making and cognition you're talking about, but none does it in a pointed or necessarily collaborative way. The Hypothes.is social annotation tool comes about as close to it as I've seen or experienced beyond Wikipedia and variations which are usually a much slower boil process. As an example of Hypothes.is, here's a link to some public notes I've been taking on the "zettekasten output problem" which I made a call for examples for a while back. The comments on the call for examples post have some rich fodder some may appreciate. Some of the best examples there include videos by Victor Margolin, Ryan Holiday (Robert Greene's protoge), and Dustin Lance Black along with a few other useful examples that are primarily text-based and require some work to "see".

      For those interested, I've collected a handful of fascinating examples of published note collections, published zettelkasten, and some digitized examples (that go beyond just Luhmann) which one can view and read to look into others' practices, but it takes some serious and painstaking work. Note taking archaeology could be an intriguing field.

      Dan Allosso's Obsidian book club has kept up with additional books (they're just finishing Rayworth's Doughnut Economics and about to start Simon Winchester's new book Knowing What We Know, which just came out this month.) Their group Obsidian vault isn't as dense as it was when they started out, but it's still an intriguing shared space. For those interested in ZK and knowledge development, this upcoming Winchester book looks pretty promising. I'd invite everyone to join if they'd like to.

    1. Wittgenstein, Luhmann, Conrad Gessner, Leibniz, Linnaeus and Walter Benjamin are some I can think of off the top of my head.

      reply to u/muhlfriedl by way of reply to u/chounosumuheya at https://www.reddit.com/r/Zettelkasten/comments/13s6dsg/comment/jlpt8ai/?utm_source=reddit&utm_medium=web2x&context=3

      Examples of zettelkasten users

      S.D. Goitein, Beatrice Webb, Ludwig Wittgenstein, Harold Innis, Victor Margolin, Eminem, Aby Warburg, Antonin Sertillanges, Jacques Barzun, C. Wright Mills, Gotthard Deutsch, Roland Barthes, Umberto Eco, Vladimir Nabokov, Gerald Weinberg, Michael Ende, Twyla Tharp, Hans Blumenberg, Keith Thomas, Arno Schmidt, Mario Bunge, Sönke Ahrens, Dan Allosso for a few more. If you go with those who used commonplace books and waste books, which are notebook-based instead of index card-based, there are thousands upon thousands more.

      Historically the easier question might be: what creators didn't use one of these systems and was successful?!? The broad outlines of these methods go back much, much farther than Niklas Luhmann. These patterns are not new...

      Personally, I've used my own slip box to write large portions of the articles on my website. I also queried it to compile this reply.

    1. Tinderbox Meetup - Sunday, May 7, 2023 Video: Connect with Sönke Ahrens live, the author of How to Take Smart Notes

      reply for Fidel at https://forum.eastgate.com/t/tinderbox-meetup-sunday-may-7-2023-video-connect-with-sonke-ahrens-live-the-author-of-how-to-take-smart-notes/6659

      @fidel (I'm presuming you're the same one from the meetup on Sunday, if not perhaps someone might tag the appropriate person?), I was thinking a bit more on your question of using physical index cards for writing fiction. You might find the examples of both Vladimir Nabokov and Dustin Lance Black, a screenwriter, useful as they both use index card-based workflows.

      Vladimir Nabokov died in 1977 leaving an unfinished manuscript in note card form for the novel The Original of Laura . Penguin later published the incomplete novel with in 2012 with the subtitle A Novel in Fragments . Unlike most manuscripts written or typewritten on larger paper, this one came in the form of 138 index cards. Penguin's published version recreated these cards in full-color reproductions including the smudges, scribbles, scrawlings, strikeouts, and annotations in English, French, and Russian. Perforated, one could tear the cards out of the book and reorganize in any way they saw fit or even potentially add their own cards to finish the novel that Nabokov couldn't. Taking a look at this might give you some ideas of how Nabokov worked and how you might adapt the style for yourself. Another interesting resource is this article with some photos/links about his method with respect to writing Lolita: https://www.openculture.com/2014/02/the-notecards-on-which-vladimir-nabokov-wrote-lolita.html

      You might also find some useful tidbits on his writing process (Bristol cards/Exacompta anyone?) in: Gold, Herbert. “Vladimir Nabokov, The Art of Fiction No. 40.” The Paris Review, 1967. https://www.theparisreview.org/interviews/4310/the-art-of-fiction-no-40-vladimir-nabokov.

      Carl Mydans photographed Nabokov while writing in September 1958 and some of those may be interesting to you as well.

      Dustin Lance Black outlines his index card process in this video on YouTube: https://www.youtube.com/watch?v=vrvawtrRxsw

      If you dig around you'll also find Michael Ende and a variety of other German fiction writers who used index cards on the Zettelkasten page on Wikipedia, but I suspect most of the material on their processes are written in German.

      Index cards for fiction writing may allow some writers some useful affordances/benefits. By using small atomic pieces on note cards, one can be far more focused on the idea and words immediately at hand. It's also far easier in a creative and editorial process to move pieces around experimentally.

      Similarly, when facing Hemmingway's "White Bull", the size and space of an index card is fall smaller. This may have the effect that Twitter's short status updates have for writers who aren't faced with the seemingly insurmountable burden of writing a long blog post or essay in other software. They can write 280 characters and stop. Of if they feel motivated, they can continue on by adding to the prior parts of a growing thread.

      However, if you can, try to use a card catalog drawer with a rod so that you don't spill all of your well-ordered cards the way the character in Robert M. Pirsig's novel Lila (1991) did.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We thank the reviewers for their comments and constructive suggestions to improve the manuscript. We are encouraged to see that both reviewers acknowledge how the results from our manuscript uses state-of-art technologies to advance molecular underpinnings of centriole length, integrity and function regulation. Both reviewers also highlighted that the manuscript is well laid out and presents clear, rigorous, and convincing data. Reviewer#1 described our manuscript of highest experimental quality and broad interest to the field of centrosome and cell biology form a basic research and genetics/clinical point of view. Here, we explain the revisions, additional experimentations and analyses planned to address the points raised by the referees. We will perform most of the experimentations and corrections requested by the reviewers. We have already made several revisions and are currently working on additional experiments.

      Our responses to each reviewer comment in bold are listed below. References mentioned here are listed in the references section included at the of this document.

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

      Summary: __In this manuscript, Arslanhan and colleagues use proximity proteomics to identify CCDC15 as a new centriolar protein that co-localizes and interacts with known inner scaffold proteins in cell culture-based systems. Functional characterization using state-of-the-art expansion microscopy techniques reveals defects in centriole length and integrity. The authors further reveal intriguing aberrations in the recruitment of other centriole inner scaffold proteins, such as POC1B and the SFI1/centrin complex, in CCDC15-deficient cells, and observe defects in primary cilia. __

      We thank the reviewer for the accurate summary of the major conclusions of our manuscript.

      Major points:

      1) The authors present a high-quality manuscript that identifies a novel centriolar protein by elegantly revealing and comparing the proximity proteomes of two known centriolar proteins, which represents an important component for the maintenance of centrioles.

      We thank the reviewer for highlighting that our manuscript is of high quality and presents important advances for the field.

      __2) Data are often presented from two independent experiments (n = 2), which is nice, but also the minimum for experiments in biology. It is strongly recommended to perform at least three independent experiments. __

      We agree with the reviewer that analysis of data form three experimental replicates is ideal for statistical analysis. We performed three replicates for the majority of experiments in the manuscript. However, as the reviewer pointed out, we included analysis from two experiments for the following figures:

      • Fig. 4H: quantification of CCDC15 total cellular levels throughout the cell cycle by western blotting
      • Fig. 5A: CCDC15-positive centrioles in control and CCDC15 siRNA-transfected cells
      • Fig. 6B: % centriolar coverage of POC5, FAM161A, POC1B and Centrin-2 in control and CCDC15 siRNA-transfected cells
      • Fig. 6C, 6E: Centrin-2 or SFI1-positive centrioles in control and CCDC15 siRNA-transfected cells
      • Fig. 6J, K: normalized tubulin length and percentage of defective centrioles in cells depleted for CCDC15 or co-depleted for CCDC15 and POC1B
      • Fig. 7F, H: SMO-positive cilia and basal body IFT88 levels in control and CCDC15 siRNA-transfected cells
      • Fig. S3H: centriole amplification in HU-treated control and CCDC15 siRNA-transfected cells (no)
      • Fig. S3A: centrosomal levels upon CCDC15 depletion There are two reasons for why we performed two experimental replicates for these experiments: 1) results from the two experimental replicates were similar, 2) quantification of data by U-ExM is laborious. To address the reviewer’s comments, we will perform the third experimental replicate for the sets of data that led to major conclusions of our manuscript, which are Figures 4H, 6C, 6E, 6J, 6K, 7F, 7H and S3A.

      3) The protein interaction studies presented in Fig. 3 could be of higher quality. While it is great that the authors compared interactions to the centriolar protein SAS6, which is not expected to interact with CCDC15, the presented data raise many questions.

      __a) In most cases, co-expression of tagged CCDC15 stabilizes the tested interaction partners, such that the overall abundance seems to be higher. The increase in protein abundance is substantial for Flag-FAM161A (Fig. 3D) and GFP-Centrin-2 (Fig. 3E) and is even higher for the non-interactor SAS6 (Fig. 3G), while it cannot be assessed for GFP-POC1B (Fig. 3F). Hence, the higher expression levels under these conditions make it more likely that these proteins are "pulled down" and therefore do not represent appropriate controls. __

      We agree with the reviewer that the differences in protein abundance of the prey proteins upon expression of CCDC15 relative to control might impact the interpretation of the interaction data. To address this concern, we will perform the following experiments:

      • To account of the potential stabilizing effects of CCDC15 expression, we will change the relative ratio of plasmids expressing proteins of interest and assess the expression of bait and prey protein levels. We will then repeat the co-immunoprecipitation experiments in conditions where prey expression levels are similar.
      • To avoid the potential stabilizing effects of CCDC15 overexpression, we will perform immunoprecipitation experiments in cells expressing GFP or V5-tagged inner scaffold proteins and assess their potential physical or proximity interaction by blotting for endogenous CCDC15. __b) All Co-IP experiments are lacking negative controls in the form of proteins that are not pulled down under the presented conditions. __

      For the co-IP experiments, we only included a specificity control for the interaction of the bait protein with the tag of the prey protein (i.e. GBP pulldown of GFP or GFP-CCDC15-expressing cells). As the reviewer suggested, we will also include a specificity control for the interaction of bait with the tag of the prey protein for co-immunoprecipitation experiments (i.e. GFP pulldown of cells expressing GFP-CCDC15 with V5-BirA* or V5-BirA*-FAM161A).

      __c) The amounts of co-precipitation of the tested proteins appears very different. Could this reflect strong or weak interactors, or does it reflect the abundance of the respective proteins in centrioles? __

      We agree with the reviewer that the quantity of the co-precipitated prey proteins might be a proxy for the interaction strength if the abundance of the bait proteins is similar. However, the expression levels of bait and prey proteins in co-transfected cells are different and thus, cannot be used to derive a conclusion on the interaction strength. For the revised manuscript, we will repeat the IP experiments and comment on this in the discussion section.

      __4) The observation that IFT88 is supposedly decreased at the base of cilia in CCDC15-depleted cells requires additional experiments/evidence. Fig. 7G shows the results of n = 2 and more importantly, a similar reduction of gamma-tubulin in siCCDC15. Could the observed reduction in IFT88 be explained by a decrease in accessibility to immunofluorescence microscopy? Would the reduction in IFT88 at the base also be apparent when the signals were normalized to gamma-tubulin signals? __

      To address the reviewer’s concern, we quantified the basal body gamma-tubulin and IFT88 levels in control and CCDC15-depleted cells and plotted the basal body IFT88 levels normalized to gamma-tubulin levels in Fig. 7H. Similar to the reduction in IFT88 levels, gamma-tubulin-normalized IFT88 levels was significantly less relative to control cells. Moreover, the gamma-tubulin basal body levels were similar between control and CCDC15 cells. We revised the gamma-tubulin micrographs in Fig. 7G to represent this. These results indicate that the reduction in basal body IFT88 levels upon CCDC15 depletion in specific.

      __5) The observed Hedgehog signaling defects are described as follows: "CCDC15 depletion significantly decreased the percentage of SMO-positive cells". It is similarly described in the figure legend. If this was true, the simplest explanation would be that it reflects the reduction in ciliation rate (which is in a similar range). If SMO-positive cilia (instead of "cells") were determined, the text needs to be changed accordingly. __

      As the reviewer pointed out, we quantified SMO-positive cilia, but not cells. We are sorry for this typo. We corrected SMO-positive cells as SMO-positive cilia in the manuscript text, Fig. 7 and figure legends.

      __6) OPTIONAL: While expansion microscopy is slowly becoming one of the standard super-resolution microscopy methods, which is particularly well validated for studying centrioles, the authors should consider confirming part of their findings (as a proof of principle, surely not in all instances) by more established techniques. This could serve to convince critical reviewers that may argue that the expansion process may induce architectural defects of destabilized centrioles, as observed after disruptions of components, such as in Fig. 6. Alternatively, the authors could cite additional work that make strong cases about the suitability of expansion microscopy for their studies, ideally with comparisons to other methods. __

      • SIM imaging was previously successfully applied for nanoscale mapping of other centriole proteins including CEP44, MDM1 and PPP1R35 (Atorino et al., 2020; Sydor et al., 2018; Van de Mark et al., 2015). To complement the U-ExM analysis, we have started imaging cells stained for CCDC15 and different centriole markers (i.e. distal appendage, proximal linker, centriole wall) using a recently purchased 3D-SIM superresolution microscope. We already included the SIM imaging data for CCDC15 localization in centrosome fractions purified from HEK293T cells in Fig. S5B. In the revised manuscript, we will replace confocal imaging data in Fig. 3A and 3B with SIM imaging data.
      • As the reviewer noted, expansion microscopy has been successfully used for the analysis of a wide range of cellular structures and scientific questions including nanoscale mapping of cellular structures across different organisms. In particular, U-ExM of previously characterized centrosome proteins various centriole proteins have significantly advanced our understanding of centriole ultrastructure. In our manuscript, we used the U-ExM protocol that was validated for centrioles by comparative analysis of U-ExM and cryo-ET imaging by our co-authors (Gambarotto et al., 2019; Hamel et al., 2017). To clarify these points, we included the following sentence along with the relevant references in the introduction: “Application of the U-ExM method to investigate known centrosome proteins has started to define the composition of the inner scaffold as well as other centriolar sub-compartments (Chen et al., 2015; Gambarotto et al., 2021; Gambarotto et al., 2019; Kong and Loncarek, 2021; Laporte et al., 2022; Mahen, 2022; Mercey et al., 2022; Odabasi et al., 2023; Sahabandu et al., 2019; Schweizer et al., 2021; Steib et al., 2022; Tiryaki et al., 2022; Tsekitsidou et al., 2023).”

      Minor points:

      1) Text, figures, and referencing are clear and accurate, apart from minor exceptions.

      We clarified and corrected the points regarding text, figures and references as suggested by the two reviewers.

      __ 2) The title suggests a regulator role for CCDC15 in centriole integrity and ciliogenesis, which has formally not been shown. __

      We revised the title as “CCDC15 localizes to the centriole inner scaffold and functions in centriole length control and integrity”.

      __3) As the authors observe changes in centriole lengths in the absence of CCDC15, it would be very insightful to compare these phenotypes to other components that affect centriolar length, such as C2CD3, human Augmin complex components (as HAUS6 is identified in Fig. 1) or others. These could be interesting aspects for discussion, additional experiments are OPTIONAL. __

      We agree with the reviewer that comparative analysis of centriole length phenotypes for CCDC15 and other components that regulate centriole length will provide insight into how these components work together at the centriole inner core. To this end, we phenotypically compared CCDC15 loss-of-function phenotypes to that of other components of the inner scaffold (POC5, POC1B, FAM161A) that interact with CCDC15. In agreement with their previously reported functions in U2OS or RPE1 cells, we found that POC5 depletion resulted in a 4% slight but significant increase in centriole length and POC1B depletion resulted in a 15% significant decrease. In contrast, FAM161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, our analysis of their centriolar localization dependency and regulatory roles during centriole length suggest that CCDC15 and POC1B might form a functional complex as positive regulators of centriole length. In contrast, POC5 functions as a negative regulator and might be part of a different pathway for centriole length regulation. We integrated the following sub-paragraph in the results section and also included discussion of this data in the discussion section:

      “Moreover, we quantified centriole length in control cells and cells depleted for POC5 or POC1B. While POC5 depletion resulted in longer centrioles, POC1B resulted in shorter centrioles (POC5: siControl: 414.1 nm±38.3, siPOC5: 432.7±44.8 nm, POC1B: siControl: 400.6±36.1 nm, siPOC1B: 341.5±44.39 nm,). FAMA161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, these results suggest that CCDC15 might cooperate with POC1B and compete with POC5 to establish and maintain proper centriole length.”

      __ 4) While the reduced ciliation rate in the absence of CCDC15 is convincing, the authors did not investigate "ciliogenesis", i.e. the formation of cilia, and hence should re-phrase. The sentence in the discussion that "CCDC15 functions during assembly" should be removed. __

      To clarify that we only investigated the role of CCDC15 in the ability of cells to form cilia, we replaced sentences that indicates “CCDC15 functions in cilium assembly” with “CCDC15 is required for the efficiency of cilia formation”.

      __5) The existence of stably associated CCDC15 pools with centrosomes (Fig. 2) requires further evidence. The recovery of fluorescence after photobleaching in FRAP experiments is strongly dependent on experimental setups and is only semi-quantitative. A full recovery is unrealistic, hence, it is ideally compared to a known static or known mobile component. I personally think this experiment -as it is presented now- is of little value to the overall fantastic study. The authors may consider omitting this piece of data. __

      We agree with the reviewer that FRAP data by itself does not prove the existence of stably associated CCDC15 pool. As controls in these experiments, we use FRAP analysis of GFP-CCDC66, which has a 100% immobile pool at the cilia and 50% immobile pool at the centrosomes as assessed by FRAP (Conkar et al., 2019). To address these points, we toned down the conclusions derived from this experiment by revising the sentence as follows:

      Additionally, we note that the following data provides support for the stable association of CCDC15 at the centrioles:

      • About 49.6% (± 3.96) of the centrioles still had CCDC15 fluorescence signal at one of the centrioles upon CCDC15 siRNA treatment (Fig. 5A, 5B). The inefficient depletion of the mature centriole pool of CCDC15 is analogous to what was observed upon depletion of other centriole lumen and inner scaffold proteins including WDR90 and HAUS6 (Schweizer et al., 2021; Steib et al., 2020). __6) The data that CCDC15 is a cell cycle-regulated protein is not very convincing (see Fig. 3H), as the signals area weak and the experiment has been performed only once (n= 1). This piece of data does not appear to be very critical for the main conclusions of the manuscript and may be omitted. Otherwise, this experiment should be repeated to allow for proper statistical analysis. __

      We will perform these experiments two more times, quantify cellular abundance of CCDC15 in synchronized populations from three experimental replicates and plot it with proper statistical analysis.

      __7) Experimental details on how "defective centrioles" are determined are missing. __

      We included the following experimental details to the methods section:

      “Centrioles were considered as defective when the roundness of the centriole was lost or the microtubule walls were broken or incomplete. In the longitudinal views of centrioles, defective centrioles were visualized as heterogenous acetylated signal along the centriole wall or irregularities in the cylindrical organization of the centriole wall (Fig. 5F). We clarified these points in the methods section.

      __ 8) For figures, in which the focus should be on growing centrioles (see Fig. 4), it could be helpful to guide the reader and indicate the respective areas of the micrographs by arrows. __

      We added arrows to point to the respective areas of the micrographs in Fig. 4F.

      __ 9) Page18: "centriole length shortening" could be changed to "centriole shortening". __

      We corrected this description as suggested.

      __10) It is unclear how the authors determine distal from proximal ends of centrioles in presented micrographs (see Fig. 5D). __

      We determined the proximal and distal ends of the centrioles by taking the centriole pairs as a proxy. Even though we only represent a micrograph containing a single centriole in some of the U-ExM figures including Fig. 5D, the uncropped micrographs contain two centrioles, which are oriented orthogonally and tethered to each other at their proximal ends in interphase cells. We added the following sentence to the methods section to clarify this point:

      *“Since centrioles are oriented orthogonally and tethered to each other at their proximal ends in interphase cells, we also used the orientation of the centriole pairs as a proxy to determine the proximal and distal ends of the centrioles.” *

      __11) Fig. 7A is missing scale bars and Fig.7 overall is lacking rectangle indicators of the areas that are shown at higher magnification in the insets. __

      We added scale bar to Fig. 7A and rectangle indicators for zoomed in regions in Fig. A, E, G.

      12) Fig. 7C displays cilia that appear very short, especially when comparing to the micrographs and bar graphs presented. The authors may want to explain this discrepancy.

      We thank the reviewer for the comment. The zoomed in representative cilia is 4.1 µM in control cells and 1.4 µM in CCDC15-depleted cells. Therefore, the representative cilia is in agreement with the quantification of cilia in Fig. 7C.

      Reviewer #1 (Significance (Required)):From a technical point of view the authors use two state-of-the-art technologies, namely proximity labeling combined with proteomics and ultrastructure expansion microscopy, that are both challenging and very well suited to address the main questions of this study. ____ • General assessment: The presented study is of highest experimental quality. Despite being very challenging, the expansion microscopy and proximity proteomics experiments have been designed and performed very well to allow solid interpretation. The results of the central data are consistent and allow strong first conclusions about the putative function of the newly identified centriolar protein CCDC15. The study presents a solid foundation for future hypothesis-driven, mechanistic analysis of CCDC15 and inner scaffold proteins in centriole length control and maintaining centriole integrity. The only limitation of the study is that the technically simpler experiments should be repeated to allow proper statistical assessment, which can be addressed easily. • Advance: This is the first study that identifies CCDC15 as a centriolar protein and localizes it to the inner scaffold. It further describes a function for CCDC15 in centriole length control and shows its importance in maintaining centriole integrity with consequences for stable cilia formation in tissue culture. The study provides further functional insights into the interdependence of inner scaffold proteins and the role of CCDC15 in the recruitment of the SFI1/centrin distal complex. • Audience: The manuscript will be of broad interest to the fields of centrosome and cell biology, both from a basic research and genetics/clinical point of view due to the association with human disorders. The state-of-the-art technologies applied will be of interest to a broader cell and molecular biology readership that studies subcellular compartments and microtubules. • Reviewer's field of expertise: Genetics, imaging, and protein-protein interaction studies with a focus on centrosomes and cilia.

      We thank the reviewer for recognizing the importance of our work and for supportive and insightful comments that will further strengthen the conclusions of our manuscript. Our planned revisions will address the only major technical limitation raised by the reviewer that requires adding one more experimental replicate for analysis of the data detailed in major point#1. Notably, we also thank the reviewer to specifying the experiments that are not essential or will be out of the scope of our manuscript as “optional”.

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

      Summary:

      __In this study, Arslanhan et al. propose CCDC15 as a novel component of the centriole inner scaffold structure with potential roles in centriole length control, stability and the primary cilium formation in cultured epithelial cells. Using proximity labelling they explore the common interactors of Poc5 and Centrin-2, two resident molecules of the centriole inner scaffold, to hunt for novel regulators of this structure. The authors leverage expansion microscopy-based localization and siRNA-dependent loss-of-function experiments to follow up on one such protein they identify, CCDC15, with the aforementioned roles in centriole and cilia biology.

      This study is designed and laid out nicely; however, to be able to support some of the important claims regarding their proximity labelling results and exploration on the roles of CCDC15, there are several major technical and reproducibility concerns that deem major revision. Similarly, the introduction (perhaps inadvertently) omits much of the recent studies on centriole size control that have highlighted the complexity of this biological problem. As such, addressing the following major points will be essential in further considering this work for publication. __

      __We thank the reviewer for recognizing the importance of our work and appreciate the positive reflections on our manuscript and the feedback comments that were well thought-out and articulated and will further strengthen the conclusions of our manuscript. Our planned revisions focus on addressing the reviewer’s comments especially in further supporting our conclusions for proximity-labeling, phenotypic characterization and immunoprecipitation experiments, examining CCDC15 centriole localization in an additional cell line and investigating how CCDC15 works together during centriole length control with known components of the inner scaffold. __

      Major points:

      __1a) The authors use Poc5 and Centrin-2 molecules as joint baits to reveal the interactome of the centriole inner scaffold, however the work lacks appropriate experimental and analytical controls to argue that this is a proximity mapping "at the centriole inner scaffold". In its current state, it is simply an interactome of total Poc5 and Centrin-2, and it might be misleading to call it an interactome at the centriole inner scaffold (the statistical identification of shared interactors cannot do full justice to their biology at the centrosome). Appropriate expression data needed to delineate how large the centrosomal vs. cytoplasmic (or nucleoplasmic) fraction is for either of these molecules, both without and upon the addition of biotin (to see whether the bulk of interaction data stem from the cytoplasm/nucleoplasm or the centrioles themselves). The authors can test this by selectively blotting a lysate fraction containing the centrosomes after centrifugation, and compare them with the simultaneous blot of the supernatant (which were readily used for the blots presented in Fig. 1B). This experiment also becomes very relevant for the case of Centrin-2, as it also heavily localizes to the nucleoplasm as the authors found out (see Fig. 1A and Fig. S1A). __

      __ Additionally, an orthogonal approach should be taken to perform bio-image analysis on their biotin/streptavidin imaging data to demonstrate the exact ratios between the centrosomal vs. cytoplasmic/nucleoplasmic biotin activation with appropriate signal normalization between the biotin/streptavidin images. This is particularly important, as although the authors claim that these cells stably express the V5BirA*, it seems that there is partial clonality to the expression. Some cells in both the Poc5 and Centrin-2 fusion constructs appear to lack the V5/Streptavidin signals upon Biotin addition (such as the two cells in the centre right in Poc5, and again a cell in the centre right for Centrin-2 images). In its current form, Fig. 1A lacks signal quantification and does not report any information about the replicates and distributions of the data. I worry that this may raise concerns on the reproducibility if published in its current form. __a) We agree with the reviewer that the proximity maps of POC5 and

      a) Centrin-2 are not specific to the centriole inner scaffold and thus, do not represent the inner scaffold interactome. The proximity maps identified interactions across different pools of POC5 and Centrin-2 in nucleus, cytoplasm and centrosomes (Fig. 1, S1). To highlight these important points, we already included extensive analysis of the different cellular compartments and biological processes identified by the POC5 and Centrin-2 proximity maps in the results section (pg. 9-10).

      We think that there are two reasons that caused the misinterpretation of the use of these proximity maps as the “inner scaffold interactome”: 1) the way we introduced the motivation for proximity mapping studies, 2) proposing the use of the resulting interactomes as resources for identification of the full repertoire of the inner scaffold proteins. To clarify these points, we revised the manuscript in all relevant parts that might have led to misinterpretation. Following are the specific revisions:

      • To clarify that the proximity maps are not specific to the inner scaffold pools of POC5 and Centrin-2, we revised the title of the results section for Fig. 1 and 2 as follows: “Proximity mapping of POC5 and Centrin-2 identifies new centriolar proteins”.

      • To indicate that POC5 and Centrin-2 localizes to the cytoplasm and/or nucleus in addition to the centrosome, we added the following sentence to the result section: In addition to centrosomes, both fusion proteins also localized to and induced biotinylation diffusely in the cytoplasm and/or nucleus (Fig. 1A).”

      • In the introduction, we revised the following sentence “Here, we used the known inner scaffold proteins as probes to identify the molecular makeup of the inner scaffold in an unbiased way.” as follows: *“Here, we used the known inner scaffold proteins as probes to identify new components of the inner scaffold”. *

      • To highlight the different cellular pools of POC5 and Centrin-2 and identification of their interactors in these pools, we included the following sentence in the results section: “As shown in Fig. S1, Centrin-2 and POC5 proximity interactomes were enriched for GO categories that are relevant for their published functions during centrosomal, cytoplasmic and/or nuclear biological processes and related cellular compartments (Azimzadeh et al., 2009; Dantas et al., 2013; Heydeck et al., 2020; Khouj et al., 2019; Resendes et al., 2008; Salisbury et al., 2002; Steib et al., 2020; Yang et al., 2010; Ying et al., 2019).”

      • We replaced the “interactome” statement with “proximity interaction maps” or “proximity interactors” throughout the manuscript to prevent the conclusion that the proximity maps represent the inner scaffold interactome. b) As the reviewer noted, most centrosome proteins have multiple different cellular pools including the centrosome. For most proteins like gamma-tubulin and centrin, their cytoplasmic/nucleoplasmic pools are more abundant than their centrosomal pools (Moudjou et al., 1996; Paoletti et al., 1996). For the Firat-Karalar et al. Current Biology 2015 paper, I compared the biotinylation levels of centrosomal fractions versus cytoplasmic fractions and confirmed that this is also true in cells expressing myc-BirA* fusions of CDK5RAP2, CEP192, CEP152 and CEP63 (unpublished) (Firat-Karalar et al., 2014). For the revised manuscript, we will compare the biotinylation level of centrosomal, nuclear and cytoplasmic pools of V5Bir*-POC5 and V5BirA*-Centrin-2 using the stable lines. To this end, we will use published centrosome purification protocols. We will include this data in Fig. S1 to highlight that the proximity interactomes represent the different pools of the bait proteins and to show the relative levels of the baits across their different pools.

      c) BioID approach has been successfully used to probe centrosome interactions by my lab and other labs in the field. In fact, proximity interaction maps of over 50 centrosome proteins were published as resource papers by Pelletier&Gingras labs (Gheiratmand et al., 2019; Gupta et al., 2015). Analogous to our strategy in this manuscript, these studies generated proximity maps of centrosome proteins by creating cell lines that stably express BioID-fusions of centrosome proteins followed by streptavidin pulldowns from whole cell extracts and mass spectrometry analysis. Since majority of centrosome proteins also have pools in multiple cellular locations, the published BioID proximity maps for centrosome proteins are not specific to centrosomes. However, the proximity maps included all known centrosome proteins and identified new proteins, which shows that centrosome interactions are represented in pulldowns form whole cell lysates. Moreover, maps form whole cell lysates are also advantageous as they are are unbiased and can be used in future studies as resources for studying the functions and interactions of the bait proteins in different contexts.

      In the Firat-Karalar et al. Current Biology 2015 paper, I combined centrosome purifications with BioID pulldowns to enrich for the centrosomal interactions in the proximity maps of centriole duplication proteins(Firat-Karalar et al., 2014). However, I started the purification with cells transiently transfected with the BioID-fusion constructs, which resulted in high ectopic expression of the fusions in the cytoplasm and/or nucleus. Therefore, centrosome enrichments were useful as an additional step before mass spectrometry. Comparative analysis of the data for proximity maps of 4 centrosome proteins generated from stable lines or centrosome fractions of transiently transfected cells substantially overlap as compared in the Gupta et al. Cell 2015 study and were more comprehensive (Table S2) (Gupta et al., 2015). Therefore, we are confident that the proximity interactomes we generated for POC5 and Centrin-2 include their centrosomal interactions.

      __1b) Similarly, it is not clear whether the expression of Poc5 and Centrin-2 fusion molecules somehow interfere with their endogenous interactions or function. At least some loss-of-function (e.g., RNAi) experiments should be performed where the depletion of endogenous proteins should be attempted to rescue by the fusion constructs. This will help evaluate whether the fusion proteins can rescue the depletion of their endogenous counterparts and behave as expected from a wild-type scenario. __

      The reviewer raises an important concern regarding the physiological relevance of the POC5 and Centrin-2 proximity maps. In the manuscript, we showed and discussed the validation of their proximity interactomes by two lines of evidence, which are: 1) the interactomes identified the previously described cellular compartments, biological processes or interactors of POC5 and Centrin-2, 2) the interactomes led to the identification of CCDC15 as a new inner scaffold protein.

      As the reviewer indicated, stable expression of POC5 and Centrin-2 in the presence of their endogenous pools might affect cellular physiology and thereby the landscape of the interactomes. We plan to address this using the following experiments:

      a) We will perform a set of functional assays to assess whether stable V5BirA*-Centrin-2 and V5BirA*-POC5 cells behaves like control cells in terms of their centrosome number, cell cycle profiles and mitotic progression. We will specifically quantify:

      • centrosome number (immunofluorescence analysis for gamma-tubulin and centrin)
      • their mitotic index (immunofluorescence analysis by DAPI)
      • spindle polarity and percentage of multinucleation (immunofluoerescence analysis for microtubules, gamma-tubulin and DAPI)
      • cell cycle profiles (flow cytometry and immunofluorescence)
      • apoptosis (immunoblotting for caspase 3) Together, results from these experiments indicate that the V5BirA*-POC5 or Centrin-2-expressing stable lines do not exhibit defects associated with their stable expression.

      b) We will perform expansion microscopy in V5BirA*-Centrin-2 and V5BirA*-POC5 cells to assess whether the fusion protein specifically localizes to the centriole inner scaffold, which will provide support for the presence of inner scaffold proteins in their proximity maps. Specifically, we plan to stain the fusion proteins by V5 or BirA antibodies and include the data for the antibody that works for expansion microscopy. This experiment will address whether their stable expression results in specific localization of these proteins at the centriole inner scaffold.

      1c) Overall, as the entire claim around the proximity mapping revolve around its assumption about the centriole inner scaffold, these controls seem imperative to substantiate the ground truth of the biology presented in the manuscript.

      In the revised manuscript, we toned down and made it clear that Centrin-2 and POC5 proximity maps are not specific to the inner scaffold and do not represent the inner scaffold interactome. Since the maps were generated from the whole cell extract, they will provide a resource for future studies aimed at studying functions and mechanisms of POC5 and Centrin-2 across their different cellular pools including the centrosome.

      We would like to also highlight that the proximity maps of POC5 and Centrin-2 are not the major advances of our manuscript. The major advance of our manuscript is the identification of CCDC15 as a new inner scaffold protein that is required for regulation of centriole size and architectural integrity and thereby, for maintaining the ability of centrioles to template the assembly of functional cilia. Importantly, our results identified CCDC15 as the first dual regulator of centriolar recruitment of inner scaffold protein POC1B and the distal end SFI1/Centrin complex and provided important insight into how inner scaffold proteins work together during centriole integrity and size regulation. The new set of experiments we will perform for the revisions of the paper will strengthen these conclusions.

      __2) I am curious about the choices of the cell lines in this work. The proximity mapping to reveal CCDC15 as a candidate protein for centriole inner scaffold was performed in HEK293T cells (human embryonic kidney), however its immunostaining was performed using RPE1 and U2OS cells (human retinal and osteosarcoma epithelial cells respectively). This raises questions regarding the generality of CCDC15 as a centriole inner scaffold protein. Could CCDC15 be simply unique to the centriole inner scaffold of epithelial cells such as RPE1 and U2OS cells? Or could the authors demonstrate any information/data on whether it's similarly localized to the inner scaffold in embryonic kidney cells or other cell types? If not, the claims should be moderated to reflect this fine detail. __

      To test whether CCDC15 localizes to the inner scaffold in other cell types, we performed U-ExM analysis of CCDC15 localization relative to the centriolar microtubules in differentiating multiciliated epithelial cultures (MTEC). As shown in Fig. S3A, CCDC15 localized to the inner scaffold in the centrioles in MTEC ALI+4 cells. Given that the inner scaffold proteins including CCDC15 and previously characterized ones have not been studied in multiciliated epithelia, this result is important and provides support for potential role of the inner scaffold in ensuring centriole integrity during ciliary beating. Additionally, we examined CCDC15 localization by 3D-SIM in centrosomes purified from HEK293T cells, which showed that CCDC15 localizes between the distal centriole markers CEP164 and Centrin-3 and proximal centriole markers gamma-tubulin and rootletin (Fig. S3B).

      3) Discussions and data on the localization of CCDC15 to centriolar satellites appear anecdotal and not fully convincing (Fig. S2D). Given that the authors test the relevance of PCM1 for CCDC15's centriolar localization, it is key to have quantitative data supporting their claim that centriolar satellites can help recruit CCDC15 to the centriole. Could the authors quantify what proportion of CCDC15 localize to the centriolar satellites? One way to do this could be to quantify the colocalization coefficience of CCDC15 and PCM1 signals.

      We only observed co-localization of CCDC15 with the centriolar satellite marker PCM1 in cells transiently transfected with mNG-CCDC15. In Fig. S2E, we included the quantification of the percentage of U2OS and RPE1 cells that exhibit co-localization of PCM1 (100% of U2OS cells, about 80% of RPE1 cells). Like CCDC15, ectopic expression of WDR90 revealed its centriolar satellite localization, suggesting a potential link between centriolar satellites and inner scaffold proteins that can be investigated in future studies (Steib et al., 2020). We now included these results in the discussion section as follows:

      As assessed by co-localization with the centriolar satellite marker PCM1, mNG-CCDC15 localized to centriolar satellites in all U2OS cells and in about 80% of RPE1 cells (Fig. S2C-E). Association of CCDC15 with centriolar satellites is further supported by its identification in the centriolar satellite proteomes(Gheiratmand et al., 2019; Quarantotti et al., 2019).”

      Even though endogenous staining for CCDC15 did not reveal its localization to centriolar satellites, following lines of data support the presence of a dynamic and low abundance pool of CCDC15 at the centriolar satellites: 1) CCDC15 was identified in the centriolar satellite proteome and interactome (Gheiratmand et al., 2019; Quarantotti et al., 2019). 2) CCDC15 centrosomal targeting is in part regulated by PCM1 (Fig. S2F, S2G). For majority of the proteins identified in the centriolar satellite proteome, their satellite pool can only be observed upon ectopic expression. This might be because their centriolar satellite pool is of low abundance and transient as satellite interactions are extensively identified only in proximity mapping studies, but not in traditional pulldowns

      __4) Similar to above (#3), there is no quantitative information on the co-localization or partial co-localization of the signal foci in Fig. 3A and 3B. The authors readily study CCDC15's localization in wonderful detail in their expansion microscopy data, so they could actually consider taking out Fig. 3A and 3B, as the data seem redundant without any quantification. __

      To address the reviewer’s concern, we included plot intensity profile analysis of CCDC15 and different centriole markers along a line drawn at the centrioles in Fig. 3A and 3B, which shows the extent of their overlap. As part of our revision plan, we will replace the confocal imaging data in Fig. 3A and 3B with 3D-SIM imaging data of CCDC15 relative to different centriole markers together with plot profile analysis. We already included 3D-SIM imaging of centrosomes purified form HEK293T cells in Fig. S3B. 3D-SIM imaging data will complement the localization data revealed by U-ExM.

      __5) Do the authors also feel that CCDC15 localize to the core lumen in a somehow helical manner (Fig. 1A, Fig. 1F top and bottom panels, Fig. 5A etc.)? Le Guennec et al. 2020's helical lattice proposal for the inner scaffold further reaffirms that CCDC15 is indeed a likely major component of the inner scaffold. In my view, authors should state this physical similarity explicitly to further support their findings on CCDC15. __

      As the reviewer indicated, cryo–electron tomography and subtomogram averaging of centrioles from four evolutionarily distant species showed that centriolar microtubules are bound together by a helical inner scaffold covering ~70% of the centriole length (Le Guennec et al., 2020). Although U-ExM data do not have enough resolution to show that CCDC15 localizes in a helical manner, we agree with the reviewer that the discussion of this possibility is important and thus we included the following sentence in the results:

      “Longitudinal views suggest potential helical organization of CCDC15 at the inner scaffold, which is consistent with its reported periodic, helical structure (Le Guennec et al., 2020).”

      __6a) The data on the link between the CCDC15 recruitment and the centriole growth (Fig. 4F) or the G2 phase of the cell cycle (Fig. 4H) are not fully convincing without quantitative data. For Fig. 4F, the authors should consider plotting the daughter centriole length vs the daughter CCDC15 intensities against each another, to see whether more elongated daughters truly tend to have more CCDC15. __

      To address the reviewer’s concern, we will plot the daughter centriole length versus CCDC15 intensity at different stages of centriole duplication. In asynchronous cultures that we analyzed with U-ExM, we were not able to find enough cells to perform such quantification. To overcome this limitation, we will perform U-ExM analysis of cells fixed at different points after mitotic shake-off and stained for CCDC15 and tubulin. We will include minimum 10 different representative U-ExM data for different stages of centriole duplication in the revised manuscript along with quantification of length versus signal.

      As detailed in the results section, the goal of these experiments was to determine when CCDC15 is recruited to the procentrioles during centriole duplication, but not to suggest a role for CCDC15 in centriole growth. We clarified this by including the following sentence:

      “To investigate the timing of CCDC15 centriolar recruitment during centriole biogenesis, we examined CCDC15 localization relative to the length of procentrioles that represent cells at different stages of centriole duplication (Fig. 4F).”

      __6b) For Fig. 4H, the argument regarding the cell cycle regulation requires quantification of the bands from several WB repeats, normalized to the expression of GAPDH within each blot (this is particularly relevant, as the bands of CCDC15 do not look dramatically different enough to draw conclusions by eye). __

      We will perform these experiments two more times, quantify cellular abundance of CCDC15 in synchronized populations from three experimental replicates and plot it with proper statistical analysis.

      __7a) The authors find herein that CCDC15 depletion lead to centrioles that are ~10% shorter than the controls. With the depletion of Poc5 and Wdr90 (other proposed components of the inner scaffold), the centrioles end up larger however (Steib et al., 2020). If the role of inner scaffold in promoting centriole elongation is structural, why are these two results the opposite of each other? I realize there is a brief discussion about this at the end of the paper, however, this requires a detailed discussion and speculation on the relevance of these findings. It would be key to clarify whether the inner scaffold as a structure inhibits or promotes centriole growth - or somehow both? If so, how? __

      We agree with the reviewer that comparative analysis of centriole length phenotypes for CCDC15 and other components that regulate centriole length will provide insight into how these components work together at the centriole inner core. To this end, we phenotypically compared CCDC15 loss-of-function phenotypes to that of other components of the inner scaffold (POC5, POC1B, FAM161A) that interact with CCDC15. In agreement with their previously reported functions in U2OS or RPE1 cells, we found that POC5 depletion resulted in a 4% slight but significant increase in centriole length and POC1B depletion resulted in a 15% significant decrease. In contrast, FAM161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, our analysis of their centriolar localization dependency and regulatory roles during centriole length suggest that CCDC15 and POC1B might form a functional complex as positive regulators of centriole length. In contrast, POC5 functions as a negative regulator and might be part of a different pathway for centriole length regulation. We integrated the following sub-paragraph in the results section in pg. 19 and also included discussion of this data in the discussion section in pg. 23:

      “Moreover, we quantified centriole length in control cells and cells depleted for POC5 or POC1B. While POC5 depletion resulted in longer centrioles, POC1B resulted in shorter centrioles (POC5: siControl: 414.1 nm±38.3, siPOC5: 432.7±44.8 nm, POC1B: siControl: 400.6±36.1 nm, siPOC1B: 341.5±44.39 nm,). FAMA161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, these results suggest that CCDC15 might cooperate with POC1B and compete with POC5 to establish and maintain proper centriole length.”

      __7b) There might be some intriguing opposing regulatory action of Poc5 and CCDC15 as demonstrated here, where CCDC15 depletion leads to slightly over-recruitment of Poc5, and vice versa. Does this suggest that a tug-of-war going on between different molecules that localize to the inner scaffold? Does this provide some dynamicity to this structure, which might in turn regulate centriole length both positively and negatively? This may be analogous to how opposing forces of dyneins and kinesins provide robust length control for mitotic spindles. I am speculating here, but hopefully these may provide some useful grounds for further discussion in the paper. If the authors deem it interesting experimentally, they can test whether the two molecules indeed regulate centriole length by opposing each other's action, by a double siRNA of CCDC15 and Poc5 to see if this retains the centriole length at its control siRNA size (like how they do a similar test for Poc1's potential co-operativity with CCDC15 in Fig. 6J). __

      We thank the reviewer for proposing excellent ideas on how inner scaffold proteins work together to regulate centriole length. As proposed by the reviewer, different proteins oppose each other analogous to how dynein and kinesin regulate mitotic spindle length. Loss-of-function and localization dependency data support that CCDC15 cooperates with POC1B, which was supported by phenotypic characterization of co-depleted cells (Fig. 6I-K).

      The increase in POC5 levels and coverage at the centrioles upon CCDC15 depletion and vice versa (Fig. 7B, 7G) suggest that CCDC15 and POC5 compete with each other in centriole length regulation. As suggested by the reviewer, we attempted to test this by comparing centriole length in cells co-depleted for CCDC15 and POC5 relative to their individual depletions. Although we tried different depletion workflows, we were not able to co-deplete CCDC15 and POC5. Specifically, we tried transfecting cells with CCDC15 and POC5 siRNAs at the same time or sequentially for 48 h or 96 h. The centrioles in cells that survived co-depletion were positive for both CCDC15 and POC5. This might be because co-depletion of both proteins is toxic to cells. Since CCDC15 and POC5 are likely part of two different pathway in regulation of centrioles and also have other cellular functions, this might have caused cell death. We included the following statement in the discussion to address the excellent model proposed by the reviewer:

      “Taken together, our results suggest that CCDC15 cooperates with POC1B and competes with POC5 during centriole length regulation. Moreover, they also raise the exciting possibility that centriole length can be regulated by opposing activities of inner scaffold proteins. Future studies that explore the relationship among centriole core proteins are required to uncover the precise mechanisms by which they regulate centriole integrity and size.”

      __8) In their introduction section, the authors discuss how relatively little is known about the size control of centrioles, however they fail to mention a series of recent primary literature that uncover striking, new mechanisms and novel molecular players that highlight the complexity of centriole size control. This complexity appears to arise from the existence of multitude of length control mechanisms that influence the cartwheel or the microtubule length individually, or simultaneously via yet-to-be further explored crosstalk mechanisms. a. As such, when the authors talk about the procentriole size control in the introduction, they should discuss and refer to the following studies, in terms of: • How theoretical and experimental work demonstrate that procentriole length may vary dependent on the levels of its building block Sas-6 in animals (Dias Louro et al., 2021 PMID: 33970906; Grzonka and Bazzi, 2022 bioRxiv). • How a homeostatic Polo-like kinase 4 clock regulates centriole size during the cell cycle (Aydogan et al., 2018 JCB PMID: 29500190), and how biochemistry and genetics coupled with mathematical modelling unravel a conserved negative feedback loop between Cep152 and Plk4 that constitutes the oscillations of this clock in flies (Boese et al., 2018 PMID: 30256714; Aydogan et al., 2020 PMID: 32531200) and human cells (Takao et al., 2019 PMID: 31533936). __

      __b. Similarly, when the authors refer to centriole size control induced by microtubule-related proteins, they should highlight the further complexity of this process by referring to: • How a molecule located at the microtubule wall, Cep295/Ana1, can regulate centriole length in flies (Saurya et al., 2016 PMID:27206860) and human cells (Chang et al., 2016 PMID:27185865) - like all the other centriolar MT molecules that the authors discuss in the manuscript. • How a crosstalk between Cep97 and Cep152 influences centriole growth in fly spermatids (Galletta et al., 2016 PMID:27185836). • How a crosstalk between CP110-Cep97 and Plk4 influences centriole growth in flies (Aydogan et al., 2022 PMID:35707992), and this molecular crosstalk is conserved, at least biochemically, in human cells (Lee et al., 2017 PMID:28562169). __

      We thank the reviewer for highlighting the papers that uncovered new mechanisms and players of centriole size and integrity control as well as for the detailed explanation of how different studies led to these discoveries in different organisms. We should have discussed these proteins, functional complexes and mechanisms in our manuscript and cited the relevant literature. We inadvertently focused on literature that uncovered centriole length regulation by MAPs and the inner scaffold. In the introduction section of the revised manuscript where we introduced centriole size regulation in pg. 5, we summarized the major findings on the role of different MAPs, cartwheel and PLK4 homeostatic clock in ensuring formation of centrioles at the correct size in different organisms.

      __Minor points: __

      __1) Introduction section: Literature reference missing for the sentence starting with "Importantly, the stable nature of centrioles enables them to withstand...". __

      We cited research articles that show the importance of centriole motility during ciliary motility and cell division.

      “Importantly, the stable nature of centrioles enables them to withstand mechanical forces during cell division and upon ciliary and flagellar motility (Abal et al., 2005; Bayless et al., 2012; Meehl et al., 2016; Pearson et al., 2009).

      __2) Fig. S1 legend: A typo as follows: CRAPome banalysis should read CRAPome analysis. __

      We corrected this typo.

      __3) Fig. S2: Info on the scale bar in the legend is missing in Fig. S2A. Scale bars for different panels are missing in general in Fig. S2A. __

      We added scale bar information for Fig. S2A and to all other supplementary figure legends that lack scale bar information.

      __4) Fig. 3A and 3B: When displaying the data, coloured cartoon diagrams would be beneficial to guide the reader who are not fully familiar with the spatial orientation of these proteins. __

      As suggested by the reviewer, we will remove the confocal imaging data for CCDC15 localization from Fig. 3A and 3B. For the revised version, we will include 3D-SIM imaging data along with a diagram that represents the spatial orientation of CCDC15 relative to the chosen centriole markers.

      __5) Fig. 3H: No information about the sample number (number of cells or technical repeats examined) reported. __

      We included information on the number of experimental replicates and cells analyzed.

      __6) Fig. S3B legend: A typo as follows: CCD15-depelted RPE1 cells should read CCDC15-depleted RPE1 cells. __

      We corrected this typo.

      __7) Fig. S3B legend: A typo as follows: cellswere fixed with should read cells were fixed with. __

      We corrected this typo.

      __8) There are many spelling mistakes and typos throughout the paper. I have listed a few examples above, but please carefully read through the manuscript to correct all the errors. __

      Thank you for indicating the spelling mistakes we missed to correct for initial submission. In the revised manuscript, we carefully read through the manuscript to correct the mistakes.

      __9) Fig. S3E: The orange columns depicting % of cells with Sas-6 dots look awkward. Why the columns look larger than the mean line? Please correct as appropriate. __

      The total percentage of cells in the two categories (orange and purple) we counted is 100%, which corresponds to the column value at the y-axis. Therefore, the value for each experimental replicate for the orange category is less than 100% and is marked below the 100% line.

      __10) Although authors provide microscopy information for the U-ExM and FRAP experiments, there is no information about the microscopy on regular confocal imaging experiments which should be detailed in Materials and Methods. Also, there is no information about the lenses, laser lines and the filter sets that were used in the imaging experiments. These should be provided as well. __

      In the methods section, we now included detailed information for the microscopes we used and imaging setup (lenses, laser lines, filter sets, detectors, z-stack size, resolution).

      11)

      • __ Fig. 2A: lacks a scale bar. __
      • __ Fig. 2C legend: lacks info on the scale bar length. __
      • __ Fig. 5A legend: lacks info on the scale bar length. __
      • __ Fig. 7A: lacks a scale bar. __
      • __ Fig. 7G legend: lacks info on the scale bar length. __
      • __ Fig. S2C-E: lack scale bars. __
      • __ Fig. S3D, F and H: lack scale bars. (Fig. S4 in the revised manuscript)__
      • __ Fig. S3J legend: lacks info on the scale bar length. (Fig. S4 in the revised manuscript)__
      • __ Fig. S4A, B, D and E: lack scale bars. (Fig. S5 in the revised manuscript)__
      • __ Fig. S4C legend: lacks info on the scale bar length. (Fig. S5 in the revised manuscript)__
      • __ Fig. S4G legend: lacks info on the scale bar length. (Fig. S5 in the revised manuscript)__ We added the scale bars and the size information to the figures and figure legends for the above figures.

      Reviewer #2 (Significance (Required)): __The findings of this study join among the relatively new literature (e.g., Steib et al., 2020 and Le Guennec et al. 2020) on the nature of centriole inner scaffold and its potential roles in centriole formation, integrity and its propensity to form the primary cilium. Therefore, it will be of interest to a group of scientists studying these topics in the field of centrosomes/cilia.

      My expertise is on the biochemistry and genetics of centriole formation in animals.__

      We thank the reviewer for his/her comments and constructive feedback to improve our manuscript. We are encouraged to see that the reviewer acknowledges how the results from our manuscript advances our understanding of centriole length, integrity and function regulation.

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      Van de Mark, D., D. Kong, J. Loncarek, and T. Stearns. 2015. MDM1 is a microtubule-binding protein that negatively regulates centriole duplication. Mol Biol Cell. 26:3788-3802.

      Yang, C.H., C. Kasbek, S. Majumder, A.M. Yusof, and H.A. Fisk. 2010. Mps1 phosphorylation sites regulate the function of centrin 2 in centriole assembly. Mol Biol Cell. 21:4361-4372.

      Ying, G., J.M. Frederick, and W. Baehr. 2019. Deletion of both centrin 2 (CETN2) and CETN3 destabilizes the distal connecting cilium of mouse photoreceptors. J Biol Chem. 294:3957-3973.

  13. Apr 2023
    1. Benefits of sharing permanent notes .t3_12gadut._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; --postBodyLink-VisitedLinkColor: #989898; }

      reply to u/bestlunchtoday at https://www.reddit.com/r/Zettelkasten/comments/12gadut/benefits_of_sharing_permanent_notes/

      I love the diversity of ideas here! So many different ways to do it all and perspectives on the pros/cons. It's all incredibly idiosyncratic, just like our notes.

      I probably default to a far extreme of sharing the vast majority of my notes openly to the public (at least the ones taken digitally which account for probably 95%). You can find them here: https://hypothes.is/users/chrisaldrich.

      Not many people notice or care, but I do know that a small handful follow and occasionally reply to them or email me questions. One or two people actually subscribe to them via RSS, and at least one has said that they know more about me, what I'm reading, what I'm interested in, and who I am by reading these over time. (I also personally follow a handful of people and tags there myself.) Some have remarked at how they appreciate watching my notes over time and then seeing the longer writing pieces they were integrated into. Some novice note takers have mentioned how much they appreciate being able to watch such a process of note taking turned into composition as examples which they might follow. Some just like a particular niche topic and follow it as a tag (so if you were interested in zettelkasten perhaps?) Why should I hide my conversation with the authors I read, or with my own zettelkasten unless it really needed to be private? Couldn't/shouldn't it all be part of "The Great Conversation"? The tougher part may be having means of appropriately focusing on and sharing this conversation without some of the ills and attention economy practices which plague the social space presently.

      There are a few notes here on this post that talk about social media and how this plays a role in making them public or not. I suppose that if I were putting it all on a popular platform like Twitter or Instagram then the use of the notes would be or could be considered more performative. Since mine are on what I would call a very quiet pseudo-social network, but one specifically intended for note taking, they tend to be far less performative in nature and the majority of the focus is solely on what I want to make and use them for. I have the opportunity and ability to make some private and occasionally do so. Perhaps if the traffic and notice of them became more prominent I would change my habits, but generally it has been a net positive to have put my sensemaking out into the public, though I will admit that I have a lot of privilege to be able to do so.

      Of course for those who just want my longer form stuff, there's a website/blog for that, though personally I think all the fun ideas at the bleeding edge are in my notes.

      Since some (u/deafpolygon, u/Magnifico99, and u/thiefspy; cc: u/FastSascha, u/A_Dull_Significance) have mentioned social media, Instagram, and journalists, I'll share a relevant old note with an example, which is also simultaneously an example of the benefit of having public notes to be able to point at, which u/PantsMcFail2 also does here with one of Andy Matuschak's public notes:

      [Prominent] Journalist John Dickerson indicates that he uses Instagram as a commonplace: https://www.instagram.com/jfdlibrary/ here he keeps a collection of photo "cards" with quotes from famous people rather than photos. He also keeps collections there of photos of notes from scraps of paper as well as photos of annotations he makes in books.

      It's reasonably well known that Ronald Reagan shared some of his personal notes and collected quotations with his speechwriting staff while he was President. I would say that this and other similar examples of collaborative zettelkasten or collaborative note taking and their uses would blunt u/deafpolygon's argument that shared notes (online or otherwise) are either just (or only) a wiki. The forms are somewhat similar, but not all exactly the same. I suspect others could add to these examples.

      And of course if you've been following along with all of my links, you'll have found yourself reading not only these words here, but also reading some of a directed conversation with entry points into my own personal zettelkasten, which you can also query as you like. I hope it has helped to increase the depth and level of the conversation, should you choose to enter into it. It's an open enough one that folks can pick and choose their own path through it as their interests dictate.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We would truly like to thank all 3 reviewers for insightful, helpful and thus constructive comments.

      Reviewer #1

      Summary

      In this manuscript, Lockyer et al. provide novel insights into the mechanism by which Toxoplasma gondii avoids parasite restriction in IFNγ-activated human cells. To identify potentially secreted proteins supporting parasite survival in IFNγ-activated human foreskin fibroblasts (HFF), the authors designed a CRISPR screen of Toxoplasma secretome candidates based on hyperLOPIT protein localization data. By this approach, they identified novel secreted proteins supporting parasite growth in IFNγ-activated cells. Among the gene identified, they found MYR3 a known component of the putative translocon in charge of protein export through the parasitophorous vacuole membrane. Therefore, the authors focused their investigations on GRA57, a dense granule protein of unknown function, which affects parasite survival to a lesser extent than the MYR component. The resistance phenotype conferred by GRA57 was confirmed by fluorescence microscopy. Importantly, the authors provide evidence that the protective function of GRA57 is not as well conserved in murine cells of the same type (MEF) as in HFF. To further explore the mechanism by which GRA57 protect the parasites in IFNγ-activated cells, the authors searched for protein partners by biochemistry. By immunoprecipitation and tandem mass spectrometry, they identified two other putative dense granule proteins, GRA70 and GRA71, which co-purified with GRA57-HA tagged protein. Noteworthy, both proteins were also found in the CRISPR screens with significant score conferring resistance. High-content imaging analysis confirmed the protective effect conferred by GRA57, GRA70, and GRA71 individually at similar levels. After ruling out an effect of tryptophan deprivation in parasite clearance, or a role of GRA57 in protein export normally mediated by the MYR translocon, and a role on host cell gene expression by RNA-Seq, the authors investigated the ubiquitination of the parasitophorous vacuole membrane, a marker previously thought to initiate parasite clearance. A reduction in ubiquitin labeling around the vacuole of mutant parasites is observed, which is quite surprising given the correlated increase in parasite clearance. The authors concluded that ubiquitin recruitment may not be directly linked to the parasite clearance mechanism.

      Major comments

      • Figure 2C. In this figure, the restriction effect of IFNγ is about 60% (or 40% survival) for RHdeltaUPRT parasites grown in HFFs, which is quite different from the 85% mentioned earlier in the results section. How was actually done the first assay? Settings with 60% restriction sounds reasonable and indicates that a substantial fraction of the parasite population evades the restrictive effect of IFNγ, which provides a clear rationale for the main objective of this study, namely the identification of effectors supporting parasite development in human cells in the presence of IFNγ.

      This discrepancy in restriction likely arises from the differences in the parasites used in these assays and the measurements of restriction. The 85%/90% restriction initially mentioned is from the pooled CRISPR screens using the effector knockout pool. This restriction level was assessed by counting of parasites retrieved following infection of IFNg-stimulated HFFs. The 60% restriction of wildtype parasites seen in Figure 2 is a separate assay. This percentage was calculated by measuring total mCherry fluorescence area within infected HFFs. We expect the restriction of the pooled CRISPR population to be higher than in restriction assays performed with either wild type parasites or single genetic knockouts. We included the 85%/90% numbers to highlight that the HFFs were highly restrictive in the screen, but we have now removed references to these numbers in the results section to avoid confusion with later results that use more accurate measures of survival. We refer to this restriction level instead in the discussion section.

      Optional comment: GRA70 and GRA71 were both copurified with GRA57, but what about GRA71 expression and localization? Is there a reason why this protein partner has not been studied further just like GRA70?

      Tagging of GRA71 was attempted but was not successful in a first attempt. We have not re-attempted this tagging as Krishnamurthy et al 2023 (PMID: 36916910) recently tagged and localised GRA71, demonstrating it is also an intravacuolar dense granule protein with similar localisation to GRA57 and GRA70- we feel there is minimal value in us repeating this.

      *Is there any change in GRA57, GRA70, and GRA71 localization and/or amount when cells were pretreated with IFNγ? *

      Thank you for this suggestion, we have now conducted further investigation to address this. We checked the localisation of GRA57-HA and GRA70-V5 in IFNg-stimulated HFFs and found no change to their localisation. This data has been added in Supplementary Figure S4 in our revised manuscript. Alignment of our RNA-Seq data to the Toxoplasma genome, now included as Supplementary Data 4, also shows there is no significant up or downregulation in expression of any of the three proteins when HFFs are pretreated with IFNg.

      Do they still form a complex in the absence of IFNγ?

      We did not investigate this in this manuscript, however in Krishnamurthy et al 2023 (PMID: 36916910) CoIPs using GRA57 and GRA70 in the absence of IFNγ also identified these three proteins as interaction partners, so formation of the complex is likely IFNg-independent.

      • In the absence of GRA70 or GRA71 is GRA57 expression and/or localization affected?*

      We did not investigate this possibility in this manuscript, however doing so would require the generation of epitope tagged lines in knockout backgrounds. We believe this represents a significant body of work and would therefore be suitable for a future study focused on the further characterisation of this complex. The RNA-Seq data shows that GRA70 and GRA71 expression levels are not significantly different in the RH∆GRA57 strain (Supplementary Data 4) which we have now included as a statement in the results section.

      • *Page 13, result section. To determine whether GRA57 has any direct or indirect effect on host cell gene expression, the authors performed RNA-Seq analysis of HFF cells pretreated or not with IFNγ. First, as for proteomic data, were the data deposited on GEO or another repository database? *

      Second, were any effect detected on parasite gene expression? Reads alignment could be done using the T. gondii reference genome to determine whether IFNg or gra57 KO has any effect on parasite genes. Possibly, other secreted proteins not necessarily expressed at the tachyzoite stage and therefore not captured in the hyperLOPIT protein analysis are specifically expressed in these conditions.

      We will deposit the RNA-Seq data on GEO prior to final publication. We did perform read alignment using the Toxoplasma gondii reference genome, and we agree it would be useful to include this analysis. We have now provided this data in Supplementary Data 4. Comparison of parasite gene expression between RH∆Ku80 and RH∆GRA57 revealed very few major changes (L2FC 2) that were also rescued in the RH∆GRA57::GRA57 line, irrespective of IFNg stimulation. Of the few genes that were up or downregulated in the RH∆GRA57 parasites, these were all uncharacterised. Collectively this data did not provide any mechanistic insight into the function of GRA57, and we think it unlikely the GRA57 phenotype is related to major changes in host or parasite gene expression. We have amended the manuscript to highlight this.

      Optional comment: RNA-Seq analysis points to a clear induction of GBPs upon IFNγ treatment in HFF. Given the clear function of GBP in parasite clearance, have the authors ever hypothesized that GRA57 could be involved in preventing GBP binding to the PVM?

      We have not tested if GBP recruitment is influenced by GRA57, however GBPs have previously been shown to be dispensable for restriction of Toxoplasma growth in HFFs (Niedelman et al 2013, PMID: 24042117) despite being robustly induced by IFNg stimulation (Kim et al 2007, PMID: 17404298). We have modified the manuscript to highlight this.

      Minor comments

      • Page 4, introduction, 8th paragraph. Regarding the role of IST, it might be less prone to controversy to state: 'a condition that may only be met in the early stages of infection.'

      We agree and have changed this.

      • Page 4, end of introduction. Changing '... indicating that the three proteins function in a complex'. Changing to '... indicating that the three proteins function in the same pathway.' might be more appropriate for the conclusion.

      We agree and have changed this.

      • Page 4, result section, first paragraph. 'strain specific and independent effectors'. Are the authors talking about strain-specific and non-strain-specific factors?

      Yes- we have changed the text to reflect this.

      - Page 6, result section. 'GRA25, an essential virulence factor in mice'. It is not clear to the reviewer how a virulence factor is essential since both parasite and mouse survival is achieved in the GRA25 mutant. I suggest to replace 'essential' by 'major'.

      We agree and have changed this.

      - Page 7. 'showing that GRA57 resides in the intravacuolar network (IVN) (Figure 2A)'. From the image shown, GRA57 clearly localizes into the PV, but it is hard to tell whether GRA57 is associated with the intravacuolar network. Colocalization assay or electron microscopy would be necessary to draw such conclusions.

      We agree and have changed all references to this localisation as ‘intravacuolar’ instead of specifically the IVN.

      - 'uprt locus'. Lower case letters and italic are generally preferred to designate mutants, whereas upper case letters are generally used for wild type alleles. (Sibley et al., Parasitology Today, 1991. Proposal for a uniform genetic nomenclature in Toxoplasma gondii).

      We agree and have changed this.

      - The authors mentioned in the introduction that ROP1 contributes to T. gondii resistance to IFNγ in murine and human macrophages. However, they did not comment on whether ROP1 was found important in the screen performed here in human HFF cells. It may be useful to reference ROP1 in Figure 1 as GRA15, GRA25, etc.

      ROP1 was not found to be important in the HFF screens (+IFNg L2FCs in RH: -0.1, PRU: -0.46). As ROP1 was characterised as an IFNg resistance effector in macrophages, this discrepancy may therefore represent a cell type-specific difference, so we feel it is not relevant to highlight for the purposes of the screens presented here.

      - Figure 2D. The authors compared the restriction effect of IFNγ on parasites grown in HFF and MEF host cells. However, as represented - % + IFNγ/- IFNγ - it cannot be estimated whether the parasites grew similarly in the two host cell types in the absence of IFN. Please indicate whether or not the growth was similar in both cell types.

      As these restriction assays were not carried out concurrently and were designed to measure IFNg survival, we feel it would be inaccurate to compare parasite growth between the two cell types using this data. The focus of these experiments was to investigate the restrictive effect of IFNg across parasite strains, using the -IFNg condition to control for differences in growth rate or MOI. Therefore we feel it is appropriate for the focus of our manuscript to represent the data in this way.

      - pUPRT plasmid. Any reference or vector map would be appreciated.

      We have added the reference for this plasmid.

      - Page 9, figure 3A, mass spectrometry analysis. I did not find the MS data in supplementals. Were the data deposited in on PRIDE database or another data repository?

      The table was included as Supplementary Data 2, however this was not referred to in the main text. We have now amended the text to include this. The data will be deposited on PRIDE prior to final publication.

      - Figures 3E and 3F. It might be worth mentioning, at least in the figure legend, that GRA3 localizes at PV membrane and is exposed to the host cell cytoplasm (to mediate interactions with host Golgi). The signal for GRA3 following saponin treatment is here an excellent control that should be highlighted, indicating that saponin effectively permeabilized the host cell membrane.

      We agree and have updated the figure legend and the main text. We have also added a reference to Cygan et al 2021__ (__PMID: 34749525) in support of this data, which found GRA57, but not GRA70 or GRA71, enriched at the PVM.

      • Page 11, section title. I think that the authors meant 'GRA57, GRA70 and GRA71 confer resistance to vacuole clearance in IFNγ-activated HFFs.'

      We agree and have changed this.

      • Page 11, in the result section comparing the effect of GRA57 mutant with MYR component KO, the authors are referring to host pathways that are counteracted by MYR-dependent effectors released into the host cell. It is not clear which pathways the authors are referring to.

      It is not known exactly which host pathways mediate vacuole clearance or parasite growth restriction, or which MYR-dependent parasite effectors specifically resist these defences, therefore we have removed this statement from the text for clarity.

      • Page 16, discussion, end of 4th paragraph. '... to promote parasite survival in IFNγ activated cells' sounds better.

      We agree and have changed this.

      • Page 22-23, Methods section, c-Myc nuclear translocation assays and elsewhere. Please indicate how many events were actually analyzed. For example, in this assay, to determine the median nuclear c-Myc signal, how many infected cells were analyzed for each biological replicate?

      We have updated the methods section for the c-Myc nuclear translocation and ubiquitin-recruitment assays to include details on how many events were analysed.

      **Referees cross-commenting**

      Overall, I agree with most of the co-reviewers' remarks. I agree with reviewer #2 that this manuscript reports interesting data for the field of parasitology, but that the broad interest for immunologists is somewhat limited by the lack of a description of the mechanism by which these effectors oppose IFNgamma-inducible cell-autonomous defenses. I also agree with the other reviewers' comments regarding the GRA57, 70, and 71 heterotrimeric complex, which would require further description. In its present form, the manuscript undoubtedly represents an interesting starting point for further investigations and any additional data regarding the mode of interaction of the identified effectors and their function related or not to ubiquitylation would bring a significant added value.

      Reviewer #1 (Significance (Required)):

      Despite the fact that humans are accidental intermediate hosts for Toxoplasma gondii, the parasite may develop a persistent infection, demonstrating that it has effectively avoided host defenses. While Toxoplasma gondii has been extensively studied in mice, much less is known about the mechanisms by which the parasite establishes a chronic infection in humans. In this context, this article described very interesting data about the way this parasite counteracts human cell-autonomous innate immune system. This is a fascinating and important topic lying at the interface between parasitology and immunology. Indeed, the highly specialized secretory organelles characteristics of apicomplexan parasites are key to govern host-cell and parasite interactions ranging from host cell transcriptome modification to counteracting immune defense mechanisms. Overall, this article presents a significant contribution to the field of parasitology by identifying novel players involved in Toxoplasma gondii's evasion of human cell-autonomous immunity. Most conclusions are generally well supported by cutting-edge approaches and state of the art methods. Despite being a highly competitive field, this article stands out as the first screen designed specifically to identify virulence factors for human cells and extends our understanding of the secreted dense granule proteins resident of the parasitophorous vacuole. Importantly, the authors provide evidence that these players are active in different strain backgrounds and act in a way that is independent of the export machinery in charge of delivering effector proteins directly into the host cell. However, substantial further research is needed to fully understand the mechanism by which these novel players confer resistance to the parasite in IFNγ activated human cells and how their mode of action differs from that mediated by the translocation machinery (MYR complex). As a microbiologist and biochemist, I find this work of a particular interest to a broad audience, especially to parasitologists and immunologists, as it may unveil unexpected aspects of human innate immunity involved in parasite clearance with proteins unique to Apicomplexa phylum.

      Reviewer #2

      This paper reports high-quality genetic screening data identifying three novel Toxoplasma virulence factors (Gra57,70, and 71) that promote survival of two distinct Toxoplasma strains (type I RH and type II Pru) inside IFN-gamma primed human fibroblasts. Follow-up studies, exclusively focused on type I RH Toxoplasma, confirm the screening data. Gra57 IP Mass-Spec data suggest that Gra57, 70, and 71 may form a protein complex, a model supported by comparable IF staining patterns

      Major:

      - It is unclear what statistical metric was used to define screen hits as strain-dependent vs strain-independent. A standard approach would be to use a specific z-score value (often a z-score of 2) above or below best fit linear relationship between L2FCU for RH vs Pru as depicted in Fig.1D. Gra25 and Gra35 appear to be specific for Pru but it would be helpful to approach this type of categorization statistically. Also, such an analysis may reveal that only Pru-specific but not RH-specific hits were identified. Could the authors speculate why that would be?

      We did not use a specific statistical metric to define screen hits as strain-dependent vs strain-independent, but GRA57 was selected as a strain-independent hit based on having a L2FC of RH specific: TGME49_309600 (GRA71) & CST9

      PRU specific: GRA35, GRA25, ROP17, GRA23 & GRA45

      Strain-independent: MYR3, GRA57, TGME49_249990 (GRA70) & MYR1

      This agrees with our selection of strain-independent hits. However, we feel that using either L2FC or Z-score cut-offs is equally arbitrary, and we would therefore prefer to leave the data displayed without these cut-offs. It is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore this further here.

      *- The paper proposes that Gra57, 70, and 71 form a heterotrimeric complex. This is based on the Mass-spec data from the original Gra57 pulldown, similar IF staining patterns, and comparable phenotypic presentation of the individual KO strains. However, only the MS data provide somewhat direct evidence for the formation a trimeric complex, and these data are by no means definitive. As this is a key finding of the MS, it should be further supported by additional biochemical data. Ideally, the authors should reconstitute the trimeric complex in vitro using recombinant proteins. Admittedly, this could be quite an undertaking with various potential caveats. Alternatively, reciprocal pulldowns of the 3 components could be performed. Super-resolution microscopy of the 3 Gra proteins might present another avenue to obtain more compelling evidence in support of the central claim of this work, *

      We attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately failed to verify the MS data, but we believe this is primarily due to differences in the affinity matrix that we used for this pulldown (anti-V5 vs anti-HA) and would require further optimisation or generation of a GRA70-HA line. However, while these revisions were being performed, another group published data demonstrating through pulldown of GRA57 and GRA70 that these proteins interact with each other, GRA71, and GRA32__ (__Krishnamurthy et al 2023, PMID: 36916910). We also identified GRA32 as enriched in our MS data, but to a less significant degree than GRA70 and GRA71. Together we believe that this independent data set is a robust validation of our findings, and strongly justifies the conclusion that these proteins form a complex.

      We agree with the reviewer that further biochemical characterisation of the complex will be an interesting avenue for future research, but we feel it would require a substantial amount of further work. As suggested, super-resolution microscopy of the 3 proteins would require the generation of either double or triple tagged Toxoplasma lines, or antibodies against one or more of the complex members. Again, we feel this would represent a substantial body of further work. Reconstitution of the complex in vitro would require recombinant expression and purification of multiple large proteins that are all multidomain and possibly membrane associated/integrated. Assuming a 1:1:1 stoichiometric assembly this complex would be 446kDa. Purification of such proteins and reconstitution of the complex in vitro is therefore likely to represent many challenges and we do not feel this would be trivial to accomplish.

      - The ubiquitin observations made in this paper are a bit preliminary and the authors' interpretation of their data is vague. The authors may want to re-consider that ubiquitylated delta Gra57 PVs are being destroyed with much faster kinetics than ubiquitylated WT PVs. The reduced number of ubiquitylated delta Gra57 PVs compared to ubiquitylated WT PVs across three timepoints (as shown by the authors in Fi. S8) does not disprove the 'fast kinetics model.' To test the fast kinetics ubiquitin-dependent null hypothesis, video microscopy could be used to measure the time from PV ubiquitylation onset to PV destruction

      We agree with the reviewer that the possibility remains that GRA57 knockouts are cleared within the first hour of infection, and we have amended our text to reflect this. However, we think this is unlikely given that GRA57 knockouts are also less ubiquitinated in unstimulated cells, yet do not show any growth differences in unstimulated HFFs. Also considering the new data we have provided showing reduced recognition of GRA57 knockouts by the E3 ligase RNF213 (Figure 5D), we expect that the observed reduction in ubiquitination is highly likely to be unlinked to the increased susceptibility of GRA57 knockouts to IFNg. We have amended the discussion to state this conclusion more strongly.

      The recently published manuscript that also identified GRA57/GRA70/GRA71 as effectors in HFFs showed that deletion of these effectors leads to premature egress from IFNg-activated HFFs__ (__Krishnamurthy et al 2023, PMID: 36916910). In light of this new data, we hypothesised that early egress could be causing the apparent reduction in ubiquitination. We have now provided data that disproves this hypothesis (Figure S10), as inhibition of egress did not rescue the ubiquitination phenotype. We also did not observe enhanced restriction of GRA57 knockout parasites at 3 hours post-infection (Figure S10B), suggesting clearance, or egress, happens after this time point.

      We agree with the reviewer that determining the kinetics of IFNg restriction of these knockouts in HFFs would be interesting, however we feel this is more suited to future work. Imaging ubiquitin recruitment in live cells would also require the generation of new reporter host cell lines which would require a substantial amount of further work.

      - Related to the point above. We know that different ubiquitin species are found at the PVM in IFNgamma-primed cells but to what degree each Ub species exerts an anti-parasitic effect is not well established. The paper only monitors total Ub at the PVM. Could it be that delta Gra57 PVs are enriched for a specific Ub species but depleted for another? The authors touch on this in the Discussion but these are easy experiments to perform and well within the scope of the study. At least the previously implicated ubiquitin species M1, K48, and K63 should be monitored and their colocalization with Toxo PVMs quantified

      We agree that these experiments are within the scope of this study. We have now investigated the ubiquitin phenotype further by assessing the recruitment of M1, K48 and K63 ubiquitin linkages to the vacuoles of GRA57 knockouts. We observed depletion of both M1 and K63 linked ubiquitin. This data is now included in Figure 5 and Figure S8.

      The E3 ligase RNF213 has recently been shown to facilitate recruitment of M1 and K63-linked ubiquitin to Toxoplasma vacuoles in HFFs (Hernandez et al 2022, PMID: 36154443 & Matta et al 2022, DOI: https://doi.org/10.1101/2022.10.21.513197 ). We therefore additionally assessed the recruitment of RNF213 to GRA57 knockouts, and found RNF213 recruitment was also reduced. Given that a reduction in RNF213 recruitment should correlate with a decrease in restriction, this data further supports our conclusion that the ubiquitin and restriction phenotypes are not causally linked. The observation that GRA57 knockouts are less susceptible to recognition by RNF213 also opens an exciting avenue for further research into the host recognition of Toxoplasma vacuoles by RNF213, for which currently the target is unknown.

      Minor:

      - For readers not familiar with Toxo genetics, the authors should include a sentence or two in the results section explaining the selection of HXGPRT deletion strains for the generation of Toxo libraries

      We agree and have added this in.

      - the highest scoring hits from the Pru screen (Gra35 &25) weren't investigated further. These hits appear to be specific for Pru. Some discussion as to why there are Pru-specific factors (but maybe not RH-specific factors) seems warranted

      As mentioned above, we agree that it is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore the reasons for this further in this manuscript. Without substantial further investigation it cannot be determined whether these represent true strain-specific differences or reflect technical variability between the independent screens. We therefore feel it is sufficient to highlight effectors with the strongest phenotypes in each screen, without drawing strong conclusions regarding strain-specificity.

      **Referees cross-commenting**

      My reading of the comments is that there's consensus that this is a high quality study revealing novel Toxo effectors that undermine human cell-autonomous immunity and an important study in the field of parasitology. I might be the outlier that doesn't see much of an advance for the field of immunology since we don't really know what these effectors are doing, and the preliminary studies addressing this point are not well developed, with some confusing results.

      My major comment #2 and rev#1's major comment #2 are, I think, essentially asking for the same thing, namely some more robust data on substantiating the formation of a trimeric complex.

      My co-reviewers made great comments all across and I don't see any real discrepancies between the reviewers' comments - just some variation in what we, the reviewers, focused on

      Reviewer #2 (Significance (Required)):

      The discovery of a novel set of secreted Gra proteins critical for enhanced Toxoplasma survival specifically in IFNgamma primed human fibroblasts (but not mouse fibroblasts) is an important discovery for the Toxoplasma field. However, the study is somewhat limited in its scope as it fails to determine which, if any, specific IFNgamma-inducible cell-autonomous immune pathway is antagonized by Gra57 &Co. Instead, the paper reports that parasitophorous vacuoles (PVs) formed by Gra57 deletion mutants acquire less host ubiquitin than PVs formed by the parental WT strain. Because host-driven PV ubiquitylation is generally considered anti-parasitic, this observation is counterintuitive, and no compelling model is presented to explain these unexpected findings. Overall, this is a well conducted Toxoplasma research study with a few technical shortcomings that need to be addressed. However, in its current form, the study provides only limited insights into possible mechanisms by which Toxoplasma undermines human immunity. This study certainly provides an exciting starting point for further explorations.

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

      Summary:

      Toxoplasma gondii virulence and immune responsed upon infection in mice are well described. In contrast, little is known about human responses, particularly upon IFNγ-activation. However, host ubiquitination of the parasitophorous vacuole has been shown to be associated with parasite clearence in human cells.

      Targeted CRISPR screens were used in the type I RH and type II Pru strain of Toxoplasma gondii to identify dense granule and rhoptry proteins. Human foreskin fibroblasts (HFFs) stimulated with IFNγ were used for infection of the knock-out parasites to identify guide RNAs and thus their corresponding genes to identify genes conferring growth benefits. Beside components of the MYR translocon, gra57 was identified. This gene was then knock-out or epitope-tagged in RH. The tagged line confirmed GRA57 localisation in the intravacuolar network confirming previously published work from another lab. Knock-out of gra57 lead to a moderate decrease in survival in HFFs, but not in mouse cells. Co-immunoprecipitation experiments with GRA57 identified 2 dense granule proteins that also display IFNγ-specific phenotypes with similar localisation as GRA57, and all are resistance factors in IFNγ-activated HFFs. Knock-out of GRA57 does not impact tryptophan metabolism, effector export of gene expression of the host cells. However, deletion of GRA57 or its interaction partners reduces ubiquitination of the parasitophorous vacuole.

      Major comments:

      This is a well executed study with informative, novel data. Here a few comments and questions:

      - LFC cut-off of the CRISPR screen should be clearly stated.

      We have amended this in the text.

      - What is the rationale for using Prugniaud as the type II strain of choice and not ME49?

      Both ME49 and PRU strains are widely used in the field, but as the PRU strain was used previously by our group for in vivo screens of Toxoplasma effectors (Young et al 2019 PMID: 31481656, Butterworth et al 2022 PMID: 36476844) ,using PRU here allows for direct comparison of our screening datasets.

      - Figure 4A does not list all the significant genes that are then mentioned in the text below. This should be amended.

      It is unclear what the reviewer is referring to here (Figure 4A displays restriction assay data).

      *- RNA-Seq data is inadequately presented. Although, the actual genes regulated may be of secondary importance in this study, it would still be good to have a few key genes mentioned as a quality control statement. *

      This was also raised by reviewer 1. We have now modified the manuscript to highlight that we observed robust induction of interferon-stimulated genes in our IFNg-treated conditions, but minimal differential gene expression between HFFs infected with the different parasite strains.

      *- It is stated that "...GRA57 is not as important for survival in MEFs as in HFFS". With no significant change observed, it should be re-phrased to something like ""...indicatin that GRA57 is s important for survival in MEFs as in HFFS." *

      We have re-phrased this statement.

      *- Optional: GRA57 was described by the Bradley lab to be in the PV in tachyzoites and in the cyst wall in bradyzoites. Although it tissue cysts are not the focus of this paper and the knock-out is created also in a cyst-forming strain, it would have been useful to look for a phenotype of the knockout in cysts, in vitro at least, better both in in vitro and in vivo. In future, this could also be useful for the authors bringing in more citations. *

      We agree with the reviewer that the impact of GRA57 on cyst formation would be an interesting topic for further exploration, however the focus of our study is on the role of secreted Toxoplasma effectors during the acute stages of infection.

      Minor comments:

      - Line numbers would be useful for an efficient review process.

      We have added these to the revised manuscript.

      - Strictly speaking, we have to talk about the sexual development taking place in felid and not feline hosts (Introduction; Felidae versus Felinae).

      We have amended this in the text.

      - Please insert spaces between numbers and units.

      We have corrected this.

      - Domain structures are presented, but maybe the AlphaFold 3D predictions could be added in a supplemental figure?

      For GRA70 and GRA71 the AlphaFold 3D predictions are readily available on ToxoDB, whereas for GRA57 the prediction is not available due its size. We therefore independently analysed GRA57 using the full implementation of AlphaFold 2 (not ColabFold). We attempted submissions of putative discrete domains as well as the full-length protein, however both approaches yielded predictions with low confidence and low structural content, except for a ~100aa region of helical residues. We chose not to include the AlphaFold 3D predictions for all three proteins as the confidence for these predictions is low with pLDDT scores of commonly *- To improve the confidence of the co-immunoprecipitation, it would be necessary to use another tagged protein GRA70 or 71) and see if the same complex can be pulled down. Like this, one could also address what happens in a GRA57KO line? Do GRA70 and 71 stay together in the absence of GR57 forming a dimer? *

      Reviewer 2 raised a similar point regarding the reciprocal pulldown, please see above for our detailed response to this. As suggested, we attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately did not reconstitute the complex, but we believe this was due to technical differences in the epitope tag (V5 vs HA) and affinity matrix used. Overall, we believe that more detailed study of the assembly and biochemistry of this complex will require substantially more work and the generation of further cell lines, which would be beyond the scope of this study.

      Reviewer #3 (Significance (Required)):

      Significance:

      This study endeavours to start closing an important knowledge gab of host defence in non-rodent hosts, especially humans. The data is solid using two different strains and yields novel insights into players of host cell resistance in humans against T. gondii. Using a targeted screening approach of rhoptry and dense granule proteins, they focused their interest on a subcategory of secreted proteins. The authors have not limited themselves to the screening and localisation study, but also investigated effect on host cells and host cell response. The identification of GRA57 being an important resistance factor and forming a heterodimer with GRA70 and GRA71 is novel. This study is of interest to cell biologists in the field of cyst-forming Coccidia, especially T. gondii and researchers interested in host resistance, parasite clearance by the host and parasite virulence.

      I am a cell biologist working in Toxoplasma gondii and other Coccidians.

  14. Mar 2023
    1. Just getting started with #Zettelkasten while preparing for my first participation in a workshop. How do you decide on the names/keys of your zettels? E.g., "object-oriented programming" or "rentsch1982object"? Or do you have one zettel for each of both? #academia @academia@a.gup.pe @academicchatter@a.gup.pe @academicsunite@a.gup.pe #zettelkasten @academia@a.gup.pe @zettelkasten@a.gup.pe @zettelkasten@mobilize.berlin

      reply to Christoph Thiede at https://norden.social/@LinqLover/110011970287271976

      @LinqLover@norden.social @academia@a.gup.pe @zettelkasten@a.gup.pe @zettelkasten@mobilize.berlin @academicchatter@a.gup.pe @academicsunite@a.gup.pe If I understand your question properly, you're presumably using a paper zettelkasten and not a digital one? The issue is that of "multiple storage". Niklas Luhmann solved this by numbering his cards (using a Dewey-like system) and then creating an index for the subjects to be able to find them. John Locke did roughly the same thing with his indexing method for commonplace books.

      cf. https://hypothes.is/users/chrisaldrich?q=tag%3A%22multiple+storage%22 and https://publicdomainreview.org/collection/john-lockes-method-for-common-place-books-1685

      In the digital domain I rely on relational databases or heavy tagging and digital search. For an example, see again the Hypothesis link above.

      "Classical" ZK prior to Luhmann simply made multiple copies and distributed them, though updating them was nearly impossible.

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      Reply to the reviewers

      Reviewer 1:

      We would like to thank you for taking the time to review our manuscript. Your thoughtful and insightful comments have greatly improved the quality of our work. We appreciate your thoroughness in evaluating our study and providing valuable feedback.

      Your constructive criticism and suggestions have helped us identify areas that needed further clarification and improvement, and we are grateful for your efforts in guiding us towards a stronger manuscript.

      Thank you again for your time and expertise in reviewing our work. We hope that you find our revisions satisfactory and look forward to hearing your thoughts on the revised manuscript.

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

      In this manuscript by Sharma and colleagues, the authors investigate the transcriptional regulation of the TAL1 isoforms - that derive from differential promoter usage and/or alternative splicing - and the contribution of TAL1 long and TAL1 short protein isoforms in normal haematopoietic development and disease.

      The study suggests that TAL1 transcript isoforms are fine-tuned regulated. By using CRISPR/Cas9 techniques, the authors show that the enhancer -8 (MuTE) and enhancer -60 differentially regulate the TAL1 isoforms. Whether the remaining enhancers at the TAL1 locus (see Zhou Y et al, Blood 2013) also differentially regulate TAL1 transcription remains to be elucidated.

      The authors found that TAL1 short isoform interacts strongly with T-cell specific transcription factors such as TCF3 and TCF12, as compared to TAL1 long isoform. TAL1 short shows an apoptotic transcription signature and it fails in rescuing cell growth as compared to TAL1 long in T-ALL. In addition, TAL1 short promotes erythropoiesis.

      Lastly, the authors suggest that altering TAL1 long and TAL1 short protein isoforms ratio could have a potential therapeutic application in disease, but further studies are needed. *

      We would like to thank you for your time and effort in reviewing our manuscript. Your constructive feedback and insightful comments have been immensely valuable in improving the quality of our work. Your expertise in the field has undoubtedly contributed to the credibility and accuracy of this research. In addition, your dedication and attention to detail have been instrumental in shaping the final version of the manuscript.

      * I have a number of comments: Figure 1 It was not mentioned that MOLT4 cells also have MuTE. Do Jurkat and MOLT4 share a similar profile in terms of TAL1 transcript isoforms? It would have been very interesting to see whether the TAL1 transcript isoforms are similar in SIL-TAL1+ cells (e.g RPMI-8402). In these cells, TAL1 activation results from a deletion that fuses the 5' non-coding region of SIL with TAL1. *

      Thank you for your comment. We apologize for the confusion regarding the MOLT4 cells in our analysis. We have now updated the manuscript to explicitly mention the presence of MuTE in MOLT4 cells (Line 127). Additionally, we agree that it would be interesting to investigate whether the TAL1 transcript isoforms are similar in SIL-TAL1+ cells, such as RPMI-8402. To address this point, we have included the CCRF-CEM cell line that harbors the SIL-TAL1 recombination in our analysis. We have updated the manuscript with these new findings (Fig. 1C&D and S1A&B). Thank you for bringing this to our attention.

      Figure 2 * It is not very clear how the expression of the short isoform delta exon 3 is quantified. Detailed information and a schematic of the primer location could be helpful. *

      Thank you for your comment. We apologize for any confusion regarding the quantification of the expression of the short isoform (delta exon 3). The detailed information and schematic of the primer location can be found in Supplementary Figure 2B. We have included the location of each primer used in real-time PCR analysis for the quantification of all TAL1 isoforms. We hope this additional information will address your concerns.

      * The results on Figure 2 derive from complex Cas9/CRISPR experiments. A schematic representation showing the location of the following elements is missing: CTCF sites, CTCF gRNA target region, dCas9-p300 gRNA target region and -60 enhancer. *

      We agree that providing a schematic representation of the Cas9/CRISPR experiments would be helpful for better understanding the data in Figure 2. We have now included a detailed schematic of the location of the CTCF sites, CTCF gRNA target region, dCas9-p300 gRNA target region and -60 enhancer in Supplementary Figure 2E. We believe this new figure will provide a clearer overview of the experiments performed and will aid in the interpretation of the results.

      * Are the levels of dCas9-p300 WT and dCas9-p300 MUT comparable in transfected HEK 293 cells? Were those possibly measured by qPCR or Western Blot? Why the authors chose to use 293T cells for the CTCF del as the enhancer usage around the locus must be so different from haematopoietic cells. *

      Thank you for your question. We have added Western Blot analysis to compare the levels of dCas9-p300 WT and dCas9-p300 MUT in transfected HEK293T cells, as suggested. The results are presented in Supp. Fig. S2H.

      Regarding the choice of HEK293T cells for the CTCF deletion experiment, we selected this cell line for its low expression of TAL1, which contributes to a high dynamic range when tethering p300 core to a closed chromatin region. We have added a clarification of our rationale for using HEK293T cells in the revised manuscript (Lines 177-8). Thank you for your valuable feedback.

      * Is CPT - camptothecin? A control gene that is sensitive to CPT treatment would ensure the inhibitor is working. *

      Thank you for your comment. Indeed, CPT stands for camptothecin, and this information is already included in the methods section. We have also added this information to the results section (Line 221) to make it clearer.

      Regarding the suggestion to use a control gene sensitive to CPT treatment, we agree that this could be a useful addition to our experimental design. To address this, we have quantified the amount of TAL1 transcript to an endogenous control which is not transcribed by RNA Polymerase II (RNAPII) (18s rRNA). As a positive control, we compared Cyclo A, our endogenous control, to 18s rRNA and observed a reduction (Supp. Fig. S2K). This allows us to confidently conclude that the inhibitor is working as intended.

      Thank you for bringing up this point, and we hope that our response addresses your concern.

      *

      In supplementary Figure 2D, the reduction in expression in Jurkat Del-12 is restricted to TSS2. There is no reduction in TAL1 TSS1 and TAL1 TSS4 (this is not clear from the result description section). As seen, these isoforms are upregulated and that could suggest a compensatory mechanism mediated by alternative promoter activation. The fact that Jurkat Del-12 express TAL1 from MSCV-TAL1 could also suggest that TSS1 and TSS4 are upregulated by TAL1 or indirectly, by other members of the TAL/LMO complex (see Sanda T et al, Cancer Cell 2012) *

      Certainly, we appreciate your feedback. Supplementary Figure 2D indeed shows that the MuTE enhancer has a differential effect on the promoters, and we have now included this in the text of the manuscript. Regarding the TAL1-long isoform, while MSCV-TAL1 in the Jurkat Del-12 cell line does give rise to this isoform, our results from Figure 3A did not find TAL1-long to have a differential effect on TAL1 promoters. It is important to note that the experiment conducted was an exogenous construct in HEK293T cells, which has its limitations. Thus, the speculation that TAL1-long drives the result in supplementary Figure 2D is possible, and we have added this to the text. Thank you for bringing up this important point (Lines 167-9).

      Figure 3 * A. Are the levels of TAL1 short cDNA and TAL1 long cDNA comparable in the co-transfection luciferase experiments? The overexpression of the isoforms does not reflect the endogenous expression levels in cell lines where one of the isoforms is more predominantly expressed (e.g Jurkat cells express low levels of TAL1 short). *

      Thank you for your comment. To address your concern, we have added real time (Supp. Fig. S3A) as well as Western blot in a new figure (Supp. Fig. S3B) to show that the levels of TAL1-short and TAL1-long cDNA are comparable in the co-transfection luciferase experiments. Additionally, we observed a very low amount of endogenous TAL1 isoforms in the cell line (Supp. Fig. S3A&B), which was below detection using these methods. This suggests that the effect of the endogenous TAL1 in this cell line is low. We appreciate your feedback, and we hope this additional information addresses your concern.

      * Figure 4 Are the levels of flag-TAL1 long and flag-TAL1 short comparable? The levels of expression could explain the low intensity signal for TAL1 long. *

      Thank you for your insightful comment. Indeed, the issue of isoform quantification is critical in understanding the functional differences between TAL1-short and TAL1-long. To address this concern, we performed careful quantification of the isoforms and made sure that the amount was equal or slightly in favor of TAL1-long before conducting the experiments in this manuscript. We have also added a Western blot in Supp. Fig. S3A and real time in Supp. Fig. S3B showing the similar amount of the two isoforms. Furthermore, in Figure 4A, we provided the amount of each isoform in the input section, showing a higher amount of TAL1-long. This strengthens our result, which shows that TAL1-short binds stronger to TCF-3 and 12. Protein levels for ChIP-seq experiment (Fig. 4B-H) is now in Supp. Fig. S4B. We thank you for bringing up this important point, and we hope that our additional data and clarifications have addressed your concern

      *Is there any reason for not performing a depletion of endogenous TAL1 prior to the ChIP seq flag experiment? *

      Thank you for your comment. In our experience, infecting Jurkat cells with shRNA or an expressing vector systems can induce some cellular stress, and we did not want to add additional stress to the cells by depleting endogenous TAL1. Since we immunoprecipitated using a Flag-tagged protein, we did not see a need to deplete the endogenous TAL1 protein. However, in our RNA-seq experiment, depletion of endogenous TAL1 was critical, and we have added this additional step in this experiment.

      * Could the authors speculate about MAF motif enrichment in both isoforms and not in TAL1-total? *

      Thank you for bringing up this interesting point. It is worth noting that while all ChIP-seq experiments were performed in Jurkat cells, not all of them were conducted by us. In particular, ChIP-seq of TAL1 total was performed by Sanda et al., 2012, using an endogenous antibody against both isoforms, whereas we conducted ChIP-seq for TAL1-short and TAL1-long using a FLAG tag antibody in cells expressing each of the isoforms. Therefore, the different conditions of these experiments may have contributed to the observed MAF motif enrichment in both isoforms and not in TAL1-total. While we cannot provide a definitive explanation, we speculate that the overexpression of the isoforms or the presence of the FLAG tag may have facilitated the detection of the MAF motif. We have added this discussion to the manuscript to acknowledge and address this interesting observation (Lines: 307-8).

      1. Sanda et al., Core transcriptional regulatory circuit controlled by the TAL1 complex in human T cell acute lymphoblastic leukemia. Cancer Cell 22, 209-221 (2012).

        * Do TAL1 long and TAL1 short recognise the same DNA motif? *

      This is indeed a very interesting question but a difficult one to answer since TAL1 does not bind to the DNA alone but in a complex. In this situation, the ChIP-seq de-novo binding results suggest motifs that could be recognized by TAL1 or any of its complex partners. Using previous data, TAL1’s binding motif is CAGNTG (Hsu et al., 1994), while this motif was not identified in our analysis of the TAL1-total or FLAG-TAL1-long ChIP-seq results, we did, however, identify this sequence in FLAG-TAL1-short ChIP-seq results (p value=1e-93). We predict that this discrepancy is due to the complex nature of transcription factors binding and the fact that the ChIP-seq results were not all done in the same way. We have now added this to the discussion (Lines: 419-25).

      1. L. Hsu et al., Preferred sequences for DNA recognition by the TAL1 helix-loop-helix proteins. Mol Cell Biol 14, 1256-1265 (1994).

      * Figure 6 In A and B, are the levels of flag-TAL1 long and flag-TAL1 short in transduced K562 comparable? In C and D, are the TAL1 levels reduced at the protein level?*

      Thank you for your question. To answer your question, we added Western Blot analysis to show the comparable levels of flag-TAL1-long and flag-TAL1-short in transduced K562 cells (Supp. Fig. S6C). In Figure 6C and D, we also added Western Blot analysis to show the reduction in TAL1 protein levels upon shRNA-mediated knockdown(Supp. Fig. S6B).

      * Minor points: Figure 1 A. Include a scale bar *

      To address this, we included coordinates of the components of the gene marked in the figure.

      * C. Loading control such as GAPDH is missing in the Western Blot. Are CUTLL cells the same as CUTTL-1? *

      We added loading controls as requested now supplementary Fig. 1C, S2C, S3A, S4B, S6B&C. Yes, CUTLL is the same as CUTLL-1 we have now fixed this in the text (Line 120).

      D. Adjust scale of the CHIP seq tracks in K562 cells in order to see the peak summit. *Include genome build *

      Thank you for your comment. We have adjusted the scale of the ChIP-seq tracks in K562 cells as suggested to improve the visualization of the peak summit. However, one of the peaks still had a much higher signal and the summit is still missing from this particular peak. To address this, we have added a new figure in the supp. Fig. S1C materials where we adjusted the peak to show the summit. Please note that in this track, the chromatin structure at the enhancers is missing, and therefore, we did not include it in the main figure. Thank you for bringing this to our attention.

      We have added a genome build hg19 to the figure legend.

      * In supplementary Figure 1B, the symbol scheme is not clear *

      Thank you for this note, we have replaced the figure and added text to make it clearer.

      * Figure 2 A & C. Remove 'amount' from the Y axis. Is the total mRNA amount calculated as % of the reference genes? It could be specified on the y axis or figure legend. *

      We have removed the word "amount" from the Y axis as requested. Total mRNA amount is normalized relative to the reference genes (∆∆Cq) by Bio-Rad's CFX Maestro software (version 2.3) according to the formula:

      where:

      • RQ = Relative Quantity of a sample
      • Ref = Reference target in a run that includes one or more reference targets in each sample
      • GOI = Gene of interest (one target)

      * In supplementary Figure 2C, a loading control is missing.*

      We have added alpha-tubulin to this figure.

      * Figures 4, 5 and 6 Size of the figures should be increased. *

      We have increased the figure size as suggested. *

      Reviewer #1 (Significance (Required)): The study from Sharma and colleagues is novel and it extends the knowledge on TAL1 regulation and the role of TAL1 in development and disease. Although the study suggests that there is a correlation between enhancers, chromatin mark deposition at exons and regulation of alternative splicing, the mechanistic link is not fully elucidated.*

      To further elucidate the mechanistic link between the MuTE enhancer, broad H3K4me3 modification spanning 7.5 Kbp from TAL1 promoter 1 to promoter 5 (as shown in Fig. 1D), and alternative splicing, we conducted experiments where we manipulated KMT2B, a component of the SET1/COMPASS complexes responsible for methylating H3K4. Our findings indicate that silencing KMT2B in Jurkat cells led to a significant 30% increase in TAL1-∆Ex3 (Fig. 2H and Supp. Fig. S2I&J). These results contribute to a more comprehensive understanding of the molecular mechanisms underlying TAL1 alternative splicing regulation.

      The findings on TAL1 short protein are interesting but the data on TAL1 long lacks some refinement so then robust conclusions can be drawn. * The experimental data lacks a few controls. The text is clear and prior studies could be better referenced. *

      We have made an effort to better reference out manuscript.

      * As TAL1 is a very crucial transcription factor oncogene in T-ALL, the study is important as it addresses a very relevant question in the field that is the regulation of the transcription of TAL1 and the functional relevance of both TAL1 short and TAL1 long isoforms. *

      Reviewer 2: *

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

      Summary: Sharma et al. thoroughly characterized the regulation of TAL1 by mapping the use of its five promoters and enhancers, which together transcribe five transcripts, coding for two protein isoforms. For that purpose the authors used few cell lines: Jurkat as a T-ALL cell line, chronic myeloid leukemia (CML) cell line K562 and HEK293T with low TAL1 expression, as well as CutLL and MOLT4. They profiled the chromatin marks H3K27ac and H3K4me3 at the TAL1 locus, and show that when a the -8 enhancer is compromised tha chromatin marks change, and not only the expression level of TAL1 is reduced, the level of exon 3 skipping is increased. When the -60 enhancr was activated, TAL1 expression increased, and exon 3 skipping was reduced. Those findings indicate that in tal1, transcription and alternative splicing are co-regulated, independent of RNAPII. The authors also show that as an autoregulator, TAL1-short has a preference to TSS1-3 of TAL1, which is not shared by TAL1-long, and that each of the 5' UTR affect Tal1 expression differently. TAL1-short binds E-proteins more strongly than TAL1-long, binds many more sites than TAL1-long and stronger, and each isoform has unique set of targets. Finally, the authors set to identify the different functions of the TAL1 isoforms, and showed that Tal1-short slows cell growth and leads to TAL1-short but not TAL1-long leads to exhaustion of hematopoietic stem cells and promotes differentiation into erythroids. This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability. *

      Thank you for taking the time to carefully review our manuscript on the regulation and function of TAL1 isoforms. We appreciate your positive feedback on our comprehensive characterization of TAL1 regulation using chromatin profiling and isoform-specific ChIP-seq. We are glad that you found our findings on the co-regulation of transcription and alternative splicing, as well as the isoform-specific functions of TAL1, to be of interest.

      We also appreciate your suggestions to improve the readability of the manuscript and have made the necessary revisions accordingly. Your feedback has been invaluable in strengthening the quality of our work, and we are grateful for your contribution to the scientific community.

      * Minor comments: Add explicitly the motivation for choosing the cell line in each part. *

      We have added motivation (Lines: 157-8, 177-8, 192-194, 235-6 text that was on the previous version: 192-194, 379-80).

      * Figure 1 - Consider marking the promoter numbers and the enhancers names in the same names as in text (-8,-60 etc.), to make it easier for the readers to understand which enhancers is being discussed. *

      This in a very important point. We have added the numbering to Figure 1D and Supp. Fig. S2A, B & E.

      *P5, P18 - ProtParam is only a prediction tool, and does not supply an experimental measurement, as may be assumed from text. Please rephrase accordingly. *

      The words “prediction tool” were added in the indicated paragraphs (Lines 115 and 427).

      * Figure 2B/D - y axis label unclear, not explained in text. In accordance, unclear if the change is in the amount of RNA, or the ratio between the long and short variants. *

      Thank you for this comment. We greatly appreciate your feedback and suggestions. To make our calculations, which are the norm in the splicing field, clearer, we have now added text to Figure 4 and provided more detailed explanations in lines 670-73. We hope that these modifications will improve the clarity and comprehensiveness of our manuscript.

      *Consider removing the bars and increasing the dots, to make the graphs cleaner. *

      We removed the bars throughout the manuscript for a cleaner look.

      * P8 - The term '5C' may require more explanation, depending on target audience. *

      We have added text to explain the technique (Lines 179-81).

      * Figure 3 - the trend is that TAL1-short promotes transcription from all five TSSs. However, only in TSS1-3 is the difference significant, but the difference between the long and short forms is not significant. It is unclear if "The mean of three independent experiments done with three replicates" means overall there are three replicates per condition or nine. Please rephrase to clarify. *

      Thank you for your comment. To clarify, we want to state that each biological experiment was done in three technical replicates, resulting in a total of nine replicates for each condition. We apologize for any confusion and have now rephrased to: The mean was calculated from three independent biological experiments, each performed with three technical replicates (Lines: 696 and 699).

      *Fig 4 A - it seems that many of the sites bound by Tal1 total are not bind by either Tal1-short or Tal1-long. Indeed very little overlap between Tal1-short and Tal-1-total is seen in Fig 4I as well. It seems Tal1-long has very few peaks. Consider adding a discussion of possible reasons. *

      We agree that these findings are noteworthy and warrant further discussion. We added text to the discussion section to explore potential reasons for these observations (Lines 416-25).

      * Fig 4c - it is hard to distinguish the different lines. Consider a more clear visualization. Also, some text is in a font size too small to read. *

      We have changed the format of the figure and took out the input data from the main figure to help the visualization. The input data appear in the Supp. Fig. S4C.

      * Fig 4 D-H - will be useful to see the numbers, not just the % divided by %. *

      A table with the specific numbers can be found in Supp Figure 4F-J.

      * Fig 4 legend - 'I&L' possibly means 'I-L'. P14 - refer to where the results of the 'validation using real-time PCR' are shown. P16 - symbol replaced by an empty rectangle 20 􀀀M *

      Thank you for these valuable comments, we have fixed/added these in the manuscript.

      * Figure 6D - Y axis value seem strange (fold change relative to day 0 should be 1 at day 0). Consider different Y axis label for C and D to clarify. *

      Thank you for this comment, we have changed the y-axis to: Fold-change relative to day 1.

      * P18 - It is unclear which "two isoforms with posttranslational modifications which affected the migration rate of the protein (Fig. 1C)" were shown. Only two isoforms are mentioned throughout the paper. *

      We have added text to clarify we are referring to TAL1-short and long (Lines 409-10).

      *

      P18 - "Our ChIP-seq results suggest that the isoforms bind at the same location (Fig. 4B)." - in 4B it seems most of TAL1-short bound positions are not bound by TAL1 long. Please clarify. *

      * Worth mentioning that the Total TAL1 is taken from Jurkat cells but from a different experiment. * We have changed the statement and added the text referring to the experiments done independently (Lines 422-3).

      *

      Reviewer #2 (Significance (Required)): This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability. *

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      Reply to the reviewers

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

      I summarise the major findings of the work below. In my opinion the range and application of approaches has provided a broad evidence base that, in general, supports the authors conclusions. However, there are, in my opinion, particular failures to utilise and communicate this evidence. The manuscript may be much improved with attention in the following areas. In each case I will give general criticism with a few examples, but the principals of my comments could be applied throughout the work.

      1) Insufficient quantification. The investigation combines various sources of qualitative data (EM, fluorescence microscopy, western blotting) to generate a reasonably strong evidence base. However, the work is over-reliant on representative images and should include more quantification from repeat experiments. When there are multiple fluorescence micrographs with intensity changes (not necessarily just representative images) (e.g. Figure 1 or 2) the authors should consider making measurements of these. Also the VLP production assays, which are assessed by western blotting would particularly benefit from a quantitative assessment (either by densitometry or, if samples remain, ELISA/similar approach).

      We have performed quantification of immunofluoresence, western blotting and VLP experiments from existing data. These quantification are presented in our revised manuscript. An overview of new quantification is shown below:

      Data shown

      Quantification now shown in

      Method

      Analysis

      Figure 1A

      Supp F1C

      IF

      HAE (-/+ SARS-CoV-2)

      • Tetherin total fluorescence intensity

      Figure 1D

      Supp F1E

      IF

      HeLa+ACE2 (-/+ SARS-CoV-2 )

      • Tetherin total fluorescence intensity

      Figure 2C

      Supp F2B

      IF

      A549+ACE2 (-/+ SARS-CoV-2)

      • Tetherin total fluorescence intensity

      Figure 2G

      Supp F2D

      IF

      T84 (-/+ SARS-CoV-2)

      • Tetherin total fluorescence intensity

      Supp F4A

      Supp F4B

      IF

      HeLa + ss-HA-Spike transients (-/+ HA stained cells) - Tetherin total fluorescence intensity

      Figure 4D

      Supp F4E

      IF

      HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

      • Tetherin total fluorescence intensity

      Figure 4F

      Supp F4G

      W blot

      HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

      – Tetherin abundance

      Figure 4G

      Supp F4I

      W blot – lysates

      Spike VLP experiments

      – tetherin abundance

      Figure 4G

      Supp F4J

      W blot - VLPs

      Spike VLP experiments

      • N-FLAG abundance

      Figure 6A

      Supp F7A

      W blot – lysates

      ORF3a VLP experiments

      – tetherin abundance

      Figure 6A

      Supp F7B

      W blot - VLPs

      ORF3a VLP experiments

      • N-FLAG

      For immunofluoresence anaysis, the mean, standard deviation, number of cells analysed and number of independent experiments are shown in the updated figure legends. Statistical analysis is also detailed in figure legends. Methods for the quantificaiton of fluoresence intensity is included in the Methods section.

      Densitometry was performed on western blots and VLP experiments as suggested. The mean, standard devisation and number of independent expreiments analysed are expressed in figure legends. Methods for densityometry quantification is now included.

      2) Insufficient explanation. I found some of the images and legends contained insufficient annotation and/or description for a non-expert reader to appreciate the result(s). Particularly if the authors want to draw attention to features in micrographs they should consider using more enlarged/inset images and annotations (e.g. arrows) to point out structures (e.g DMVs etc.). This short coming exacerbates the lack of quantification.

      Additional detail has been provided to the figure legends, and we have updated several figures to draw attention to features in micrographs. Black arrowheads have been added to Figures 1E, 2D, 2H to highlight plasma membrane-associated virions, and asterisks to highlight DMVs in Figures 1E, 2D and Supplemental Figures 2C, 2E. Similarly, typical Golgi cisternae are highlighted by white arrowheads micrographs in Figure 2E. These figure legends have also been modified to highlight these additions.

      3) Insufficient exploration of the data. I had a sense that some aspects of the data seem unconsidered or ignored, and the discussion lacks depth and reflection. For example the tetherin down-regulation apparent in Figures 1 and 2 is not really explained by the spike/ORF3a antagonism described later on, but this is not explicitly addressed.

      We have made changes throughout the manuscript, but the discussion especially has been modified. We now discuss the ORF3a data in more depth, discuss possible mechanisms by which ORF3a alone enhances VLP release, and discuss our ORF7a data in context to previous reports.

      The discussion has been updated to now include a better description of our data, and additional writing putting our work in to context with previously published work. See discussion section of revised manuscript.

      Also, Figure 6 suggests that ORF3a results in high levels of incorporation of tetherin in to VLPs, but I don't think this is even described(?). The discussion should also include more comparison with previous studies on the relationship between SARS-2 and tetherin.

      We have added a section to discuss how ORF3a may enhance VLP release,

      ‘We found that the expression of ORF3a enhanced VLP independently of its ability to relocalise tetherin (Figure 6A). This may be due to either the ability of ORF3a to induce Golgi fragmentation [38] which facilitates viral trafficking [39], or due to enhanced lysosomal exocytosis [37]. Tetherin was also found in VLPs upon co-expression with ORF3a (Figure 6A) which may also indicate to enhanced release via lysosomal exocytosis [37].

      The secretion of lysosomal hydrolases has been reported upon expression of ORF3a [31] and whilst this may in-part be due to enhanced lysosome-plasma membrane fusion, our data highlights that ORF3a impairs the retrograde trafficking of CIMPR (Supplemental Figures 6B, 6F, 6G), which may similarly increase hydrolase secretion.’ – (Line 625-654).

      The discussion has been developed to compare the relationship between SARS-CoV-2 and tetherin in previous studies,

      ‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

      Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

      I have no minor comments on this draft of the manuscript.

      Reviewer #1 (Significance (Required)):

      Tetherin, encoded by the BST2 gene, is an antiviral restriction factor that inhibits the release of enveloped viruses by creating tethers between viral and host membranes. It also has a capacity for sensing and signalling viral infection. It is most widely understood in the context of HIV-1, however, there is evidence of restriction in a wide variety of enveloped viruses, many of which have evolved strategies for antagonising tetherin. This knowledge informs on viral interactions with the innate immune system, with implications for basic virology and translational research.

      This study investigates tetherin in the context of SARS-CoV-2. The authors use a powerful collection of tools (live virus, gene knock out cells, recombinant viral and host expression systems) and a variety of approaches (microscopy, western blotting, infection assays), which is, itself, a strength. The study provides evidence to support a series of conclusions: I) BST2/tetherin restricts SARS-CoV-2 II) SARS-CoV-2 ablates tetherin expression III) spike protein can modestly down-regulate tetherin IV) ORF3A dysregulates tetherin localisation by altering retrograde trafficking. These conclusions are broadly supported by the data and this study make significant contributions to our understanding of SARS-CoV-2/tetherin interactions.

      My enthusiasm is reduced by, in my opinion, a failure of the authors to fully quantify, explain and explore their data. I expect the manuscript could be significantly improved without further experimentation by strengthening these aspects.

      This manuscript will be of interest to investigators in virology and/or cellular intrinsic immunity. Given the focus on SARS-CoV-2 it is possible/likely that it will find a slightly broader readership.

      I have highly appropriate skills for evaluating this work being experienced in virology, SARS-CoV-2, cell biology and microscopy.

      We wish to thank Reviewer #1 for their comments which have helped us to improve the quality of our revised manuscript.

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

      BST2/tetherin can restrict the release and transmission of many enveloped viruses, including coronaviruses. In many cases, restricted viruses have developed mechanisms to abrogate tetherin-restriction by expressing proteins that antagonize tetherin; HIV-1 Vpu-mediated antagonism of tetherin restriction is a particularly well studied example. In this paper, Stewart et al. report their studies of the mechanism(s) underlying SARS-CoV-2 antagonism of tetherin restriction. They conclude that Orf3a is the primary virally encoded protein involved and that Orf3a manipulates endo-lysosomal trafficking to decrease tetherin cycling and divert the protein away from putative assembly sites.

      Major comments:- In my view some of the claims made by the authors are not fully supported by the data. For example, the bystander effect discussed in line 162 may suggest that infected cells can produce IFN but does not 'indicate' that they do

      This text has now been edited,

      ‘The levels of tetherin in uninfected HAE cells is lower than observed in uninfected neighbours in infected wells demonstrating that infected HAE cells are able to generate IFN to act upon uninfected neighbouring cells, enhancing tetherin expression.’ - (Lines 163-172).

      Most of the EM images show part of a cell profile, so statements such as (line 192) 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' should be backed up with appropriate images, or indicated as 'not shown', or removed (the observation is not so important for this story). Line 196, DMVs can't be seen in these micrographs.

      The statement 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' has been removed. The micrographs in Figure 1E have been re-cropped, and image iii replaced with an image showing DMVs and budding virions. Plasma membrane-associated virions are highlighted by black arrowheads, DMVs by black asterisks, and intracellular virion by a white arrow.

      Line 391, I can't see much change in CD63 distribution.

      CD63 reproducibly appears clustered towards the nuclei in ORF3a expressing cells, whilst CD63 positive puncta are abundant in the periphery of mock cells. CD63 puncta are also larger, and the staining of CIMPR and VPS35 also appears to be associated with larger organelles. We have amended the text to now read,

      ‘Expression of ORF3a also disrupted the distribution of numerous endosome-related markers including CIMPR, VPS35, CD63, which all localised to larger and less peripheral puncta (Supplemental Figure 6B), and the mixing of early and late endosomal markers’ - (Line 469).

      Quantification of the diameter of CD63 puncta indicate that they are larger in ORF3a expressing cells than in mock cells. Mock cells - 0.71μm (SD; 0.19), ORF3a - 1.15μm (SD;0.35). At least 75 organelles per sample, from 10 different cells. We have not included this data as we do not wish to labor this point but are happy to include this quantification if required to do so.

      Line 321, the authors show that ORF7a does not affect tetherin localization, abundance, glycosylation or dimer formation, but they don't show that it doesn't restrict SARS-CoV-2. Can they be sure that epitope tagging this molecule does not abrogate function (or the functions of any of the other tagged proteins for that matter), or that ORF7a works in conjunction with one of the other viral proteins?

      We are careful in the manuscript not to claim that ORF7a has no effect on tetherin. Our data indicate that ‘ORF7a does not directly influence tetherin localisation, abundance, glycosylation or dimer formation’ - (Line 361-362).

      We were unable to reproduce an effect of ORF7a on tetherin glycosylation. Our data conflicts with that presented by Taylor et al, 2015, where ORF7a impaired tetherin glycosylation and ORF7a localised to the plasma membrane in tetherin expressing cells. The experiments performed by Taylor et al used HEK293 cells and ectopically expressed tagged tetherin. The differences in results may be attributed to the differences between cell lines or due to differences between endogenous or ectopic / tagged tetherin.

      The study by Taylor et al uses SARS-CoV-1 ORF7a-HA from Kopecky-Bromberg et al., 2007 (DOI: 1128/JVI.01782-06), where the -HA tag is positioned at the C-terminus. Our ORF7a-FLAG constructs have a C-terminal epitope tag. While we cannot exclude the possibility that tagged proteins may act differently from untagged ones, the differences between our findings and previous work appear unlikely to be due to epitope tags.

      Our manuscript states that although we cannot find any effect of ORF7a on tetherin localisation, abundance, glycosylation, or dimer formation, we cannot exclude that ORF7a impacts tetherin by another mechanism. For example, ORF7a has been found to antagonise interferon responses. Tetherin is abundantly expressed in HeLa cells and expression does not require induction through interferon. None of our experiments above would be impacted by interferon antagonism yet this could impact other cell types besides infection in vivo. These possibilities may explain the reported differential impact of ORF7a by different labs. An addition comment has been added to the discussion to reflect this,

      ’We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a potently antagonises IFN signalling [38], which would impair tetherin induction in many cell types. - (Line 701-704).

      Note - Reference 38 has been added to the manuscript – Xia et al., Cell Reports DOI: 10.1016/j.celrep.2020.108234

      In the ORF screen, a number of the constructs are expressed at low level, is it possible they [the authors] are missing something?

      Some of the ORFs expressed in the miniscreen appear poorly expressed. We accept that in the use of epitope tagged constructs expression levels of individual viral proteins may impact upon a successful screen. However, this screen was performed to identify any potential changes in tetherin abundance or localisation, and the screen did successfully identify ORF3a, which we were able to follow-up and verify.

      Line 376, the authors refer to ORF3a being a viroporin. A recent eLife paper (doi: 10.7554/eLife.84477; initially published in BioRxiv) refutes this claim and builds on other evidence that ORF3a interacts with the HOPS complex. The authors should at least mention this work, especially in the discussion, as it would seem to provide a molecular mechanism to support their conclusions.

      This paper had not been peer reviewed at the time of our initial submission. We have now included the following text,

      ‘SARS-CoV-2 ORF3a is an accessory protein that localises to and perturbs endosomes and lysosomes [29]. It may do so by acting either as a viroporin [30] or by interacting with, and possibly interfering with the function of VPS 39, a component of the HOPS complex which facilitates tethering of late endosomes or autophagosomes with lysosomes [29,31]. Given ORF3a likely impairs lysosome function, the observed increased….’ - (Lines 444-449).

      Fig 3, the growth curves illustrated in Fig3 C and D do not have errors bars; how many times were these experiments repeated?

      These experiments require more repeats to include error bars. Infection and plaque assay (Figure 3C, 3D) are currently ongoing and we plan to complete them in the next 6-8 weeks and include them in the finalised manuscript.

      In the new experiments, infections will additionally be performed at MOI 0.01, in addition to the previous MOIs (1 and 5).

      Line 396, the authors show increased co-localization with LAMP1. As LAMP1 is found in late endosomes as well as lysosomes, they cannot claim the redistributed tetherin is specifically in lysosomes.

      We have altered the text to now say:

      ‘The ORF3a-mediated increase in tetherin abundance within endolysosomes could be due to defective lysosomal degradation.’ - (Line 475).

      There seems to be a marked difference in the anti-rb555 signal in the 'mock' cells in panels 5H and Suppl 6E. Is there a good reason for this, or does this indicate variability between experiments?

      Antibody uptake experiments in Figure 5H and Supp Figure 6E were performed and acquired on different days. Relatively low levels of signal are available in these antibody uptake experiments, and the disperse labelling seen in the mocks does not aid this.

      Fig 6a, why is there negligible VLP release from cells lacking BST2 and ORF3a-strep? How many times were these experiments performed? Is this a representative image? I think it confusing to refer to the same protein by two different names in the same figure (i.e. BST2 and tetherin). Do the authors know how the levels of ORF3a expressed in cells in these experiments compares to those seen in infected cells?

      We have changed the blot in Figure 6A for one with clearer FLAG bands. Three independent experiments were performed for Figure 6A. Quantification of VLPs is now included in Supplemental Figure 7B.

      We have changed ‘Bst2’ to ‘tetherin’ in all previous figures relating to protein; Figure 4G, Figure 6A, B, C.

      We have no current information to compare ORF3a levels in these experiments versus in infected cells. We can investigate quantifying this if necessary.

      My final point is, perhaps, the trickiest to answer, but nevertheless needs to be considered. As far as we know, SARS-CoV-2 and at least some other coronaviruses, bud into organelles of the early secretory pathway, often considered to be ERGIC. In the experiments shown here the authors provide evidence that ORF3a can influence tetherin recycling, but the main way of showing this is through its increased association with endocytic organelles. Do the authors have any evidence that Orf3a reduces tetherin levels in the ERGIC or whether the tetherin cycling pathway(s) involve the ERGIC?

      This is an interesting point, and as the reviewer concedes, this is tricky to answer. Expression of ORF3a causes the redistribution or remodeling of various organelles (Figures 1E, 2D, 2F, Supp Figures 2C, 2E, 3E, 6B, 6C, 6D). We have been unable to test the direct involvement of ERGIC, despite attempts with a number of commercial antibodies. Given the huge rearrangements of organelles during SARS-CoV-2 infection, it is unclear exactly what will happen to the distribution of ERGIC.

      Minor comments: Line 53, delete 'shell' its redundant and confusing when the authors have said coronaviruses have a membrane.

      Deleted.

      Line 61, delete 'the'

      Deleted.

      Line 72, delete 'enveloped'; coronaviruses already described as enveloped viruses (line 53)

      Deleted.

      Lines 93 - 100, lop-sided discussion of the viral life cycle; this paragraph is mostly about entry, which is not relevant to this paper, and does not really deal with the synthesis and assembly side of the cycle.

      We have now added the following text,

      ‘….liberating the viral nucleocapsid to the cytosol of the cell. Upon uncoating, the RNA genome is released into the host cytosol and replication-transcription complexes assemble to drive the replication of the viral genome and the expression of viral proteins. Coronaviruses modify host organelles to generate viral replication factories - so-called DMVs (double-membrane vesicles) that act as hubs for viral RNA synthesis [10]. SARS-CoV-2 viral budding occurs at ER-to-Golgi intermediate compartments (ERGIC) and newly formed viral particles traffic through secretory vesicles to the plasma membrane where they are released to the extracellular space.’ - (Lines 95-104).

      Line 103, why are the neighbouring cells 'naive'?

      ‘naïve’ removed.

      Line 112 - 113, delete last phrase; tetherin is described as an IFN stimulated gene in line 111; to be accurate, the beginning of the sentence should be 'Tetherin is expressed from a type 1 Interferon stimulated gene ...'

      Amended.

      Line 118 - 119, should say 'For tetherin-restricted enveloped viruses' as not all enveloped viruses are restricted by tetherin.

      Amended.

      Line 131, coronaviruses are not the only family of tetherin-restricted viruses that assemble on intracellular membranes, e.g. bunyaviruses.

      This has been modified and now reads,

      ‘In order for tetherin to tether coronaviruses, tetherin must be incorporated in the virus envelope during budding which occurs in intracellular organelles.’ - (Lines 133-135).

      Line 192, there is no EM data in Supplemental Fig 1C.

      This has now been removed.

      Line 251, 'a synchronous infection event' should be 'synchronous infection' as there will be multiple infection events.

      This has been changed.

      Page 13 (and elsewhere), unlike Southern, 'Western' should not have a capital letter, except at the start of a sentence.

      These have been updated throughout the manuscript (Lines 183, 341, 3549, 356, 392, 509, 763, 1330, 1399).

      Lines 330 and 352, can the authors quantitate S protein-induced reduction in cell surface tetherin rather than using the somewhat subjective 'mild'?

      These are now changed to,

      ‘Transient transfection of cells with ss-HA-Spike caused a 32% decrease in tetherin as observed by immunofluorescence (Supplemental Figure 4A, 4B), with…’ – (Line 370).

      ‘To explore whether the Spike-induced tetherin downregulation altered virus release, we performed experiments with virus like particles (VLPs) in HEK293T …’ – (Line 399).

      Line 379, OFR, should be ORF.

      Yes, changed.

      Line 448, 'Tetherin retains the ability' - did it ever loose it?

      This has been rephrased to,

      ‘Tetherin has the ability to restrict a number of different enveloped viruses that bud at distinct organelles.’ - (Line 547).

      Line 451, 'luminal' is confusing in this context.

      This has been modified to,

      ‘Tetherin forms homodimers between opposing membranes (e.g., plasma membrane and viral envelope) that are linked via disulphide bonds.’ - (Line 549).

      Line 453, the process of virus envelopment is likely to be more than a 'single step'

      This now reads,

      ‘…virus during viral budding, which occurs in modified ERGIC organelles.’ - (Line 552).

      Line 457, in my view the notion that Vpu abrogation of tetherin restriction is just due to redistribution of tetherin to the TGN is somewhat simplistic and disregards a lot of other work.

      We have removed mention of mechanisms of tetherin antagonism by other viruses. The key point we wish to make here is that tetherin is lost from the budding compartment. This now reads,

      ‘Many enveloped viruses antagonise tetherin by altering its localisation and removing it from the respective site of virus budding.’ – (Line 552-553).

      Line 472, what is meant by 'resting states'?

      This should have been ‘in the absence of stimulation’ and have now been re-written,

      ‘Tetherin is an IFN-stimulated gene (ISG) [13], and many cell types express low levels of tetherin in the absence of stimulation.’ - (Line 577).

      Line 1204, how were 'mock infected cells .......... infected'?

      This has now been re-written,

      ‘Differentiated nasal primary human airway epithelial (HAE) cells were embedded to OCT….’ - (Line 1385).

      Reviewer #2 (Significance (Required)):

      This study builds on published work supporting the notion that SARS-Cov-2 ORF3a is an antagonist for the restriction factor tetherin. Importantly, it provides insights to the the mechanism of ORF3a mediated tetherin antagonism, specifically to ORF3a inhibits tetherin cycling, diverting the protein to lysosomes and away from compartment(s) where virions assemble. Overall, the authors provide good supporting evidence for these conclusions, however there are issues that the authors need to address.

      We wish to thank Reviewer #2 for their insightful comments and suggestions for improving this work.

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

      Restriction factors are major barriers against viral infections. A prime example is Tetherin (aka BST2), which is able to physically tether budding virions to the plasma membrane preventing release of the infectious particles. Of note, tetherin has broad anti-viral activity and has been established as a crucial innate immune defense factor against HIV, IAV, SARS-CoV-2 and other important human pathogens. However, successful viruses like SARS-CoV-2 evolved strategies to counteract restriction factors and promote their replication. Important restriction factors, such as tetherin, may often be targeted by multiple viral strategies to ensure complete suppression of their anti-viral activities by the pathogen. Of note, it was previously published that the accessory protein ORF7a of SARS-CoV-2 binds to (Petrosino et al, Chemistry Europe, 2021) and antagonizes it (Martin-Sancho et al, Molecular Cell, 2021). Previous data on SARS-CoV also revealed that ORF7a promotes cleavage of tetherin (Taylor et al, 2015, J Virol). In this manuscript, the authors show that tetherin restricts SARS-CoV-2 by tethering virions to the plasma membrane and propose that tetherin is targeted by two proteins of SARS-CoV-2. Whereas the Spike protein promotes degradation of tetherin, the accessory protein ORF3a redirects tetherin away from newly forming SARS-CoV-2 virions. While the overall findings that both S and ORF3a are additionally targeting tetherin is both novel and intriguing, additional evidence is needed to support this. In addition, the authors show that in their experimental setups ORF7a does not induce cleavage of tetherin. This is in direct contrast to previously published data both on SARS-CoV(-1) and -2 (Taylor et al, 2015, J Virol; Petrosino et al, Chemistry Europe, 2021; Martin-Sancho et al, Molecular Cell, 2021). From my point of view that needs further experimental confirmation. While the authors state that the impact of Spike on tethrin is mild, the experiments should still allow the conclusion whether there is a (mild) effect or not. The mechanism of ORF3a is fortunately more robustly assessed and provides some novel insights. Unfortunately, the whole manuscript suffers from a striking lack of quantifications. In addition, it is not clear whether and how many times experiments were repeated to the same results. Overall, the data in this manuscript seem very speculative and preliminary and thus do not support the authors conclusions.

      Major:

      Much of the data seems like it was only done once. As I am sure that this is a writing issue, please clearly state how many times the individual assays were repeated, provide the quantification graphs and appropriate statistics. Some experiments may need additional quantification and confirmation by other methods to be convincing.

      Quantification is provided throughout the revised manuscript. Figure legends have also been updated to provide information on quantification and statistical analysis.

      For example, Figure 1A, C and D: Please quantify the levels of tetherin and use an alternative readout, e.g. Western blotting of infected cells.

      Quantification has been performed and included in our revised manuscript in Supplemental Figures 1C, 1E. Tetherin is not shown in Figure 1C.

      A table is provided (above) to highlight the additional quantification.

      Figure 2A: Please quantify.

      We are not sure we understand this point. The western blot shown in Figure 2A demonstrates the ectopic expression of ACE2 in our A549 cell line. A549 cells have been used by many labs to study SARS-CoV-2 infection, but express negligible ACE2.

      Fig 3A: Please show and confirm successful tetherin KO in the cell lines that are used not only in microscopy.

      A new blot is now shown in Figure 3A, including a blot demonstrating tetherin loss in both KO lines.

      Figure 4C: Please quantify

      Currently flow cytometry experiments have been performed twice each and this is now detailed in the figure legends. The data shown in each panel is representative and the data has been explored using analogous approaches. For example, Figure 4C is complemented by Figures 4A and 4B, Figures 4E is complemented by 4D and 4F. We do not feel that repeating these flow cytometry analysis will significantly improve the manuscript.

      Figure 4D: Please quantify the effects are not obvious from the images provided.

      Quantification is now provided in Supplemental Figure 4E.

      Figure 4E, F Please provide a quantification of multiple independent repeats, the claimed differences are neither striking nor obvious.

      Quantification of 4F is now provided in Supplemental Figure 4G. Tetherin levels were quantified to be reduced by 25% (SD: 8%) by addition of Doxycycline and induction of ss-HA-Spike. Information for quantification is provided in figure legends.

      Figure 5A: Please quantify

      These experiments have currently been performed twice and this is now described in the figure legends. Data shown is representative. We can perform one more repeat of these experiments to quantify if neccessary, but do not feel it will significantly alter the manuscript.

      Figure 3C and D: At timepoint 0 the infection input levels are different. The initial infection levels have to be the same to draw the conclusion that tetherin KO affects virion release and not the initial infection efficiency. Can the authors either normalize or ensure that the initial infection is the same in all conditions and that variations in the initial infection efficiency do not correlated with the impact of tetherin on replication/release ? How often were those experiments repeated? Are the marginal differences in infectious titre significant? Overall the impact of tetherin on SARS-CoV-2 is very underwhelming but that may be due to efficient viral tetherin-counteraction strategies. Why is the phenotype inverted at 72 h?

      Equal amounts of virus, as measured by plaque-forming units (PFU), were used for both HeLa cell lines and thus at 0 hpi the variation seen is within the parameters of the assay used. It remains possible that tetherin affects virus entry but this is unlikely and this assay was not designed to investigate that effect.

      Growth curve assays are currently being repeated using an MOI of 0.01, 1 and 5. We are removing the 72 hpi sample from future experiments. At this time point, we find that the extensive cell death caused by viral replication (especially at higher MOIs) makes it difficult to accurately separate the released from intracellular fractions and conclusions cannot be accurately drawn from the data.

      Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

      Figure 4B and C: Can the authors provide an explanation why SARS-CoV ORF7a is not inducing cleavage/removes glycosylation of tetherin. To show that the assays work, an independent positive control needs to be included. The FACS data in C is unfortunately not quantified.

      See above comments (Reviewer #2) regarding discussion on ORF7a. Additional text has been included to discuss ORF7a data,

      ‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

      Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

      Fig 4G: The rationale and result of this experiment are not clear.

      The rationale for Spike VLP experiments is explained at Line 403. Given that Spike caused a reproducible decrease in cellular tetherin, we examined whether this downregulation was sufficient to antagonise tetherin and increase VLP yield.

      Fig 6: What is the benefit of doing the VLP assays as opposed to genuine virus experiments? To me it rather seems to be making the data unnecessarily complex. Again, no quantifications or repeats are provided.

      VLPs are used to separate the budding and release process from the replication process of RNA viruses. VLPs have been used in a number of SARS-CoV (DOI: 1002/jmv.25518) and HIV-1 (DOI: https://doi.org/10.1186/1742-4690-7-51) studies to analyse the impact of tetherin (and tetherin mutants) on release.

      VLP experiment quantification are now included throughout.

      Minor: Fig 1D: How do the authors explain the mainly intracellular Spike staining?

      We do not understand this point. Spike staining is intracellular, whether expressed alone or in the context of infected cells.

      Please add statistical analyses on the data e.g. Fig. 3 C and D

      Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

      Fig. 4B and F: Why do the annotated sizes of tetherin differ between the blots?

      Figures 4B and 4F are run in non-reduced and reduced conditions respectively. In order to best show the dimer deficient C3A-Tetherin, blots are typically run in non-reduced conditions to exemplify dimer formation and to highlight any defects in dimer formation. The rest of the blots in the manuscript are run in denaturing conditions to aid blotting of other proteins. (Lines 957-958) and now (Lines 1356-1357).

      Fig. 5A: What is ORF6a? Do the authors mean ORF6?

      Yes, this has been changed.

      An MOI of 1 is NOT considered a low or relevant MOI. Can the authors either rephrase or repeat experiments with an actual low or relevant MOI i.e. 0.01 ?

      We are currently repeating these experiments and are including MOIs of 0.01, 1 and 5.

      Why were the cell models switched between Figure 1 and 2 and essentially the same experiments repeated?

      HeLa cells express high levels of tetherin at steady state, whilst A549 cells require IFN stimulation. HeLa cells demonstrate that tetherin downregulation occurs via an IFN-independent manner. A549 and T84 cells are more physiologically relevant cell types for SARS-CoV-2 infection. These points are stated in Lines 230 and 261.

      The manuscript may benefit a lot from streamlining and removing unessential deviations from the main message (e.g. discussions why multistep/single step growth curves are used/not relevant; why are they shown if the authors conclude that a single step is not relevant?). The discussion is extremely lengthy and does not provide sufficient discussion of the presented data.

      The multistep/single step growth curve text will be adapted, but it will be re-written after additional infection experiments.

      We have removed from the Discussion a small section discussing ORF7a mutants, given that the emphasis of our manuscript is not on ORF7a.

      We have also removed a small section describing the rearrangements of intracellular organelles by SARS-CoV-2 as it does not directly relate to the central message of our manuscript.

      According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

      Reviewer #3 (Significance (Required)):

      According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

      We thank Reviewer#3 for their comments and suggestions for improving this work.

    1. What problem does this try to solve?

      Funny (and ironic) that you should ask...

      I myself have been asking lately, what problem does the now-standard "Run npm install after you clone the repo" approach solve? Can you state the NPM hypothesis?

      See also: builds and burdens

    1. how did you teach yourself zettelkasten? .t3_11ay28d._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; --postBodyLink-VisitedLinkColor: #989898; }

      reply to u/laystitcher at https://www.reddit.com/r/Zettelkasten/comments/11ay28d/how_did_you_teach_yourself_zettelkasten/

      Roughly in order: - Sixth grade social studies class assignment that used a "traditional" index card-based note taking system. - Years of annotating books - Years of blogging - Havens, Earle. Commonplace Books: A History of Manuscripts and Printed Books from Antiquity to the Twentieth Century. New Haven, CT: Beinecke Rare Book and Manuscript Library, 2001. - Locke, John, 1632-1704. A New Method of Making Common-Place-Books. 1685. Reprint, London, 1706. https://archive.org/details/gu_newmethodmaki00lock/mode/2up. - Erasmus, Desiderius. Literary and Educational Writings, 1 and 2. Edited by Craig R. Thompson. Vol. 23 & 24. Collected Works of Erasmus. Toronto, Buffalo, London: University of Toronto Press, 1978. https://utorontopress.com/9781487520731/collected-works-of-erasmus. - Kuehn, Manfred. Taking Note, A blog on the nature of note-taking. December 2007 - December 2018. https://web.archive.org/web/20181224085859/http://takingnotenow.blogspot.com/ - Ahrens, Sönke. How to Take Smart Notes: One Simple Technique to Boost Writing, Learning and Thinking – for Students, Academics and Nonfiction Book Writers. Create Space, 2017. - Sertillanges, Antonin Gilbert, and Mary Ryan. The Intellectual Life: Its Spirit, Conditions, Methods. First English Edition, Fifth printing. 1921. Reprint, Westminster, MD: The Newman Press, 1960. http://archive.org/details/a.d.sertillangestheintellectuallife. - Webb, Beatrice Potter. Appendix C of My Apprenticeship. First Edition. New York: Longmans, Green & Co., 1926. - Schmidt, Johannes F. K. “Niklas Luhmann’s Card Index: The Fabrication of Serendipity.” Sociologica 12, no. 1 (July 26, 2018): 53–60. https://doi.org/10.6092/issn.1971-8853/8350. - Hollier, Denis. “Notes (On the Index Card).” October 112, no. Spring (2005): 35–44. - Wilken, Rowan. “The Card Index as Creativity Machine.” Culture Machine 11 (2010): 7–30. - Blair, Ann M. Too Much to Know: Managing Scholarly Information before the Modern Age. Yale University Press, 2010. https://yalebooks.yale.edu/book/9780300165395/too-much-know. - Krajewski, Markus. Paper Machines: About Cards & Catalogs, 1548-1929. Translated by Peter Krapp. History and Foundations of Information Science. MIT Press, 2011. https://mitpress.mit.edu/books/paper-machines. - Goutor, Jacques. The Card-File System of Note-Taking. Approaching Ontario’s Past 3. Toronto: Ontario Historical Society, 1980. http://archive.org/details/cardfilesystemof0000gout.

      And many, many others as I'm a student of intellectual history.... If you want to go spelunking on some of my public notes, perhaps this is an interesting place to start: https://hypothes.is/users/chrisaldrich?q=tag%3A%22note+taking%22 I also keep a reasonable public bibliography on this and related areas: https://www.zotero.org/groups/4676190/tools_for_thought

  15. Feb 2023
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We thank all the reviewers for having raised constructive criticism to fortify the main message and improve the clarity of the manuscript. We appreciate that all reviewers found that our work addresses an important topic and is of interest to a broad audience. We believe that we have thoroughly addressed the concerns of the reviewers, especially with regard to 1) performing another SMC3 chromatin immunoprecipitation and sequencing (ChIP-seq) replicate and control, 2) including a later time point for the transcriptional data, and 3) performing additional characterization of the growth phenotype of the SMC3 conditional knockdown.

      Reviewer #1

      (Evidence, reproducibility and clarity (Required)):*

      Summary The present work by Rosa et al., provides convincing data about the presence and functional relevance of the cohesin complex in Plasmodium falciparum blood stages. In accordance with other organisms, the composition of the cohesin complex containing SMC1, SMC3 RAD21 and putatively STAG could be confirmed via pulldown and mass spectrometry. Basic characterization of endogenous tagged SMC3 demonstrated the expression and nuclear localization during IDC, as well as the relatively stable accumulation at centromeric regions, consistent with the known cohesin function in chromatid separation. Furthermore, dynamic and stage-dependent binding to intergenic regions observed in ChIPseq and major transcriptome aberrations upon knockdown of SMC3 (__Response: __As we regularly perform ChIP-seq experiments in the lab, we have generated multiple negative control datasets. In our opinion, the most stringent negative control for an HA-tagged protein is performing ChIP with an HA antibody in a WT strain. We have recently published an in-depth analysis of this (and other) negative ChIP-seq controls (Baumgarten & Bryant, 2022, https://doi.org/10.12688/openreseurope.14836.2). We show in this publication that non-specific ChIP-seq experiments (such as negative controls) result in an over-representation of HP1-heterochromatinized regions due to differences in sonication efficiency of heterochromatin and technical challenges with mapping regions with high levels of homology. In the anti-HA in WT ChIP negative control (performed at 12hpi), we do not see any enrichment at centromeric regions, but rather at heterochromatinized regions where clonally variant gene families are located. We performed peak calling analysis and found no significant overlap between the negative control ChIP-seq and the SMC3-3HA ChIP-seq data at 12hpi.

      In addition, we have now performed a second biological replicate of the SMC3-3HA ChIP-seq with a different clone at all time points. We compared this data to that from the original clone and found significant overlap of the peaks called (see what is now Table 4 and Supp. Fig. 3A). We generated a stringent list of peaks that were shared between both clones at each time point and repeated all downstream analyses (see what are now Tables 5-8). We found that our conclusions were largely unchanged. Text describing these experiments and analyses have been added throughout the results section.

      • Proposed mechanism of repressive effect of SMC3 early in IDC on genes, that get de-repressed in late stages: To claim this mode of function, it would be necessary to include a KD on late stage parasites. If there is an early repressive role of SMC3, upregulated genes should not be affected by late SMC3-KD. __Response: __To be clear, we are most interested in the transcriptional role of SMC3 during interphase, where results are not confounded by its potential role in mitosis. However, we did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to the manuscript (see Tables 11-13). Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi (see what is now Supp. Fig. 5B). Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

      To address the question of whether genes that are upregulated upon depletion of SMC3 at early stages are affected at the 36hpi time point, we performed differential expression analysis of the SMC3-3HA-glmS parasites with and without glucosamine at 36hpi (we have added this data in Table 11). Again, significantly up- and down-regulated genes were not filtered using the WT dataset. With this analysis, we see only three genes from the list of invasion-related genes (Hu et al., 2010) that are up-regulated, but none of them have a significant q-value (Tab 5 of Table 18). Thus, depletion of SMC3 in late stage parasites does not lead to up-regulation of the same genes that are upregulated at 12 and 24hpi. We have added this information to the text (Line 273).

      Furthermore, the hypothesized repressive effect of SMC3 does not explain the numerous genes downregulated in KD.

      __Response: __As we state on line 350, we do not observe enrichment of SMC3 at downregulated genes, suggesting an indirect or secondary effect of SMC3 KD on these genes.

      • Due to the fact, that the KD was induced at the exact same timepoint and analysed 12h and 24h after induction it is possible that identified, differentially expressed genes at 24h are not directly regulated by SMC3, but rather due to a general deregulation of gene expression. Did the authors attempt to analyse gene expression upon induction at ring, trophozoite and schizont stage? Response: __As we state on line 230, in order to achieve SMC3 KD at the protein level, we had to treat the parasite with glucosamine for two cell cycles (approximately 96 hours). After two cell cycles of glucosamine treatment, the parasites were tightly synchronized and sampled 12 and 24 hours later. Thus, SMC3 KD takes place over the course of multiple days, but parasites are collected after stringent synchronization. Giemsa staining and bioinformatic analysis (line 250) of the RNA-seq data from parasites (with or without glucosamine) harvested at 12 and 24 hpi show that these parasites were synchronous and that there were no gross differences in genome-wide transcript levels. It is certainly possible that differentially expressed genes at 12 or 24hpi are not directly regulated by SMC3, and this is precisely why we perform ChIP-seq of SMC3: to provide evidence of direct involvement via binding, as stated on line 281. __

      • *Based on rapid parasite growth, the authors hypothesize a higher invasion rate due to upregulation of invasion genes. This hypothesis is not supported by quantitative invasion assays or quantification of invasion factors on the protein level. An alternative explanation could be a shorter cell cycle (__Response: __We have repeated the growth curve analysis with additional clones and no longer observe a growth phenotype in the SMC3 knockdown condition. We have added images of Giemsa-stained parasites from the knockdown time course we performed to what is now Supp. Fig. 5A. We see no obvious differences in cell morphology caused by glucosamine treatment in the WT or SMC3-3HA-glmS parasites.

      • Correlation of SMC3-occupancy/ATAC/expression profile of the exemplary genes rap2 and gap45 (Figure 4C,D,E): is this representative for all upregulated genes? __Response: __SMC3 occupancy shown at rap2 and gap45 is representative for all upregulated genes (see Fig. 4A and B). It is difficult to provide a general representation of the average expression profiles of all up-regulated genes over the course of the IDC, but Fig. 3E shows that the vast majority of up-regulated genes normally reach their peak expression in late stage parasites. With regard to ATAC-seq profiles, we have performed a metagene analysis of chromatin accessibility (data taken from (Toenhake et al., 2018)) at all up-regulated genes at time points that closely correspond to the time points used in our study: 15, 25, and 35, and 40 hpi (new Fig. 4C). This metagene analysis confirms what we observe at individual genes: increasing chromatin accessibility over the course of the IDC at these genes’ promoters. While metagene analyses offer important information, we always try to show the raw data (as in new Figs. 4D-F) from individual examples as proof of principle.

      • Given that SMC3 appears to be not essential for parasite growth, the authors could generate a null mutant for SMC3, which might allow for easier analysis of differences in gene regulation, cell cycle progression and/or invasion efficiency. __Response: __As we explain on line 327, very little cohesin is required for normal growth and/or mitosis in our study and two studies in S. cerevisiae and D. melanogaster. However, SMC3 is essential in S. cerevisiae. We were unable to knock out SMC3, and a recent mutagenesis study suggests that SMC3 and SMC1 are essential to the parasite during the intraerythrocytic developmental cycle (Zhang et al. Science, 2018). This is why we chose an inducible knockdown system.

      *Reviewer #1 (Significance (Required)):

      Own opinion The authors provide a basic characterization of the cohesin component SMC3 using NGS methods to investigate chromatin binding sites and its potential influence on gene expression. *

      __Response: __We respectfully disagree that our study offers only a basic characterization of SMC3. We combine IFA, mass spectrometry, and both ChIP-seq and RNA-seq of SMC3 across the entire intraerythrocytic developmental cycle to provide the most detailed and comprehensive functional analysis of SMC3 in P. falciparum to date.

      The localisation of SMC3 at centromers as described previously (Batugedara 2020) was confirmed. However, the dynamic binding to other regions in the genome, potentially mediated by other proteins, could not be resolved unequivocal with only one replicate of ChIPseq per time point.

      __Response: __With regard to the replicates for ChIP-seq, please see our response to this same point above.

      Similarly, the RNAseq data demonstrate the relevance of SMC3 for gene expression, but no clear picture of a regulatory mechanism can be drawn at his point. Lacking information about the mode of binding as well as the setup of transcriptome analysis (only two time-shifted sampling points after simultaneous glmS treatment for 96h resulting in incomplete knockdown) cannot definitely elucidate, if SMC3/cohesin is a chromatin factor that affects transcription of genes in general or a specific repressor of stage-specific genes. __Response: __We agree that we have not established a regulatory mechanism for how SMC3 achieves binding specificity. However, the combination of inducible knockdown (as SMC3 is essential to the cell cycle) and differential expression analysis with ChIP-seq from the same time points across the intraerythrocytic developmental cycle is the most stringent and standard approach in the field of epigenetics for determining the direct role of a chromatin-associated protein in gene expression. We provide a detailed explanation of how the transcriptome analysis was set up in the Results (lines 229-234) and Materials and Methods (lines 715-719) section. With regard to our sampling points being “time-shifted,” we provide bioinformatic analysis (line 246-251, what is now Supp. Fig. 5B) of the RNA-seq data from untreated and glucosamine-treated parasites showing highly similar “ages” with regard to progression through the intraerythrocytic developmental cycle. While we of course also monitor progression through the cell cycle with Giemsa staining (Supp. Fig. 5A), this bioinformatic analysis is the most stringent method of determining specific times in the cell cycle.

      *The work will be interesting to a general audience, interested in gene regulation and chromatin remodelling

      The reviewers are experts in Plasmodium cell biology and epigenetic regulation.*

      Reviewer #2

      (Evidence, reproducibility and clarity (Required)):

      Rosa et al, Review Commons The manuscript by Rosa et al. addresses the function of the cohesion subunit Smc3 in gene regulation during the asexual life cycle of P. falciparum. Cohesin is a conserved protein complex involved in sister chromatin cohesion during mitosis and meiosis in eukaryotic cells. Cohesin also modulates transcription and DNA repair by mediating long range DNA interactions and regulating higher order chromatin structure in mammals and yeast. In P. falciparum, the Cohesin complex remains largely uncharacterized. In this manuscript, the authors present mass spectrometry data from co-IPs showing that Smc3 interacts with Smc1 and a putative Rad21 orthologue (Pf3D7_1440100, consistent with published data from Batugedara et al and Hilliers et al), as well as a putative STAG domain protein orthologue (PF3D7_1456500). Smc3 protein appears to be most abundant in schizonts, but ChIPseq indicates predominant enrichment of Smc3 in centromers in ring and trophozoite stages. In addition, Smc3 dynamically binds with low abundance to other loci across the genome; however, the enrichment is rather marginal and only a single replicate was conducted for each time point making the data interpretation difficult. Conditional knock-down using a GlmS ribozyme approach indicates that parasites with reduced levels of Smc3 have a mild growth advantage, which is only evident after five asexual replication cycles and which the authors attribute to the transcriptional upregulation of invasion-linked genes following Smc3 KD. Indeed, Smc3 seems to be more enriched upstream of genes that are upregulated after Smc3 KD in rings than in downregulated genes, indicating that Smc3/cohesin may have a function in supressing transcription of these schizont specific genes until they are needed. The manuscript is concise and very well written, however it suffers from the lack of experimental replicates for ChIP experiments and a better characterization of the phenotype of conditional KD parasites. * Major comments • In the mass spectrometry analysis, many seemingly irrelevant proteins are identified at similar abundance to the putative rad21 and ssc3 orthologues, and therefore the association with the cohesion complex seems to be based mostly on analogy to other species rather than statistical significance. Hence, it would be really nice to see a validation of the novel STAG domain and Rad21 proteins, for example by Co-IP using double transgenic parasites.*

      __Response: __While our IP-MS data did not yield high numbers of peptides, the top most enriched proteins were SMC3 and SMC1. As we state on line 157, two previous studies have already shown a robust interaction between SMC1, SMC3, and RAD21 in Plasmodium, supporting the existence of a conserved cohesin complex. While the identification of the STAG domain-containing protein is interesting, the purpose of our IP-MS was less about redefining the cohesin complex in P. falciparum and more about confirming that the epitope-tagged SMC3 we generated was incorporated correctly into the cohesin complex and was specifically immunoprecipitated by the antibody we later use for western blot, immunofluorescence, and ChIP-seq analyses. However, to validate the results of ours and others’ mass spectrometry results, we generated two new parasite strains – SMC1-3HA-dd and STAG-3HA-dd – and an antibody against SMC3 (see what is now Supp. Fig. 1). We performed co-IP and western blot analysis with these strains and show an interaction between SMC1 and SMC3 and STAG and SMC3 (see what is now Supp. Fig. 2). This information has been added to the manuscript on lines 162-167.

      • *The ChIPseq analysis presented here is based on single replicates for each of the three time points. The significance cutoffs for the peaks are rather high (q __Response: __In our experience, a significance cutoff of FDR As we regularly perform ChIP-seq experiments in the lab, we have generated multiple negative control datasets. In our opinion, the most stringent negative control for an HA-tagged protein is performing ChIP with an HA antibody in a WT strain. We have recently published an in-depth analysis of this (and other) negative ChIP-seq controls (Baumgarten & Bryant, 2022, https://doi.org/10.12688/openreseurope.14836.2). We show in this publication that non-specific ChIP-seq experiments (such as negative controls) result in an over-representation of HP1-heterochromatinized regions due to differences in sonication efficiency of heterochromatin and technical challenges with mapping regions with high levels of homology. In the anti-HA in WT ChIP negative control (performed at 12hpi), we do not see any enrichment at centromeric regions, but rather at heterochromatinized regions where clonally variant gene families are located. We performed peak calling analysis and found no significant overlap between the negative control ChIP-seq and the SMC3-3HA ChIP-seq data at 12hpi.

      In addition, we have now performed a second biological replicate of the SMC3-3HA ChIP-seq with a different clone at all time points. We compared this data to that from the original clone and found significant overlap of the peaks called (see what is now Table 4 and Supp. Fig. 3A). We generated a stringent list of peaks that were shared between both clones at each time point and repeated all downstream analyses (see what are now Tables 5-8). We found that our conclusions were largely unchanged. Text describing these experiments and analyses have been added throughout the results section.

      The SMC3 ChIP from Batugedara et al., 2020 was performed with an in-house generated antibody (not a commercially available, widely validated antibody as we use) at a single time point in the IDC: trophozoites. Batugedara et al. performed one replicate and did not have an input sample for normalization. Rather, it seems that they incubated beads, which were not bound by antibody or IgG, with their chromatin and used any sequenced reads from this beads sample to subtract from their SMC3 ChIP signal as means of normalization. According to ENCODE ChIP-seq standards, this is not a standard nor stringent way of performing ChIP-seq and the subsequent analysis. Because they did not generate a dataset for their ChIP input, it is not possible to call peaks as we do in our study and compare those peaks with ours.

      • The authors argue that during schizogony, cohesin may no longer be required at centromers, explaining the low ChIPsignal at this stage (Line 301). However, during schizogony parasites undergo repeated rounds of DNA replication (S-phase) and mitosis (M-phase) to generate multinucleated parasites; and concentrated spots of Smc3 are observed in each nucleus in schizonts by IFA. In turn, the strong presence of Smc3 at centromers in ring stage parasites is surprising, particularly since the Western Blot in Figure 1D shows most expression of Smc3 in schizonts and least in rings; and Smc3 is undetectable in rings by IFA. Yet, the ChIP signal shows very strong enrichment at centromers, long before S phase produces sister chromatids. What could be the reason for this discrepancy? Again, ChIP replicates and controls would be helpful in distinguishing technical problems with the ChIP from biologically relevant differences. __Response: __We discuss in lines 337-342 not that cohesin is no longer required at centromeres during schizogony, but that its removal from centromeres may be required specifically for separation of sister chromatids, as is seen in other eukaryotes. We also discuss that the unique asynchronous mitosis in Plasmodium may lead to a mixed population of parasites at the time point sampled where there may be some centromeres with SMC3 present and some where it is absent to promote sister chromatid separation. Even though SMC3 may be evicted from centromeres to promote sister chromatid separation, it is likely re-loaded onto centromeres once this process is complete. This is most likely why we see foci of SMC3 in each nucleus of mature schizonts by IFA. With regard to the discrepancy between SMC3 levels in rings seen in total nuclear extracts (by western blot) and at centromeres (by ChIP-seq): the total level of a protein in the nucleus does not necessarily dictate the genome-wide binding pattern or the level of enrichment of that protein at specific loci in the genome. Moreover, if one molecule of SMC3 binds to each centromere, 14 molecules would be needed in a ring stage parasite while over 500 would be needed in a schizont (assuming that there are ~36 merozoites present). SMC3 binds to centromeres in interphase cells in other eukaryotes as well, and we speculate that this binding may play a role in the nuclear organization of centromeres, as we discuss starting on line 333.

      • It is surprising that a conserved protein like Smc3 shows such a subtle phenotype, given that it is predicted to be essential and its orthologues have a function in mitosis. Generally, only limited data are presented to characterize the Smc3 KD parasites, and more detail should be included. For example validation of the parasite line using a PCR screen for integration and absence of wt, parasite morphology after KD, and/or analysis of the KD parasites for cell cycle status. __Response: __First, we have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B). As we discuss on line 342, very little intact cohesin complex seems to be required for normal growth and mitosis in S. cerevisiae and D. melanogaster, which is probably why we do not see an obvious growth or morphological phenotype. Because we could not generate SMC3 knockout parasites, there may be just enough SMC3 left to perform its vital function in our KD strain. We have added PCR data to demonstrate integration of the 3HA tag- and glmS ribozyme-encoding sequence in the clonal strains we are using for all experiments (see what is now Supp. Fig. 1A). Sanger sequencing was performed on these PCR products to confirm correct sequences. We also added images of Giemsa-stained parasites in untreated and glucosamine-treated parasites at all time points to demonstrate a lack of an obvious morphological phenotype in SMC3 KD parasites (see what is now Supp. Fig. 5A).

      • Synchronization was performed at the beginning of the growth time course, which would be expected to result in a stepwise increase in parasitemia every 48 hours; however, the parasitemia according to Fig. 4F rises steadily, which would indicate that the parasites are actually not very synchronous. __Response: __We did indeed tightly synchronize these parasites and hope that the stepwise increase in parasitemia is seen better in our new growth curve analysis (see what is now Supp. Fig. 4B).

      • The question of whether Smc3 causes a shorter parasite life cycle (quicker progression) or more invasion is important and could be experimentally addressed by purifying synchronous schizont stage parasites and determining their invasion rates as well as morphological examination of the Giemsa smears over the time course. __Response: __We have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B).

      • Please also compare Smc3 transcriptional levels in transgenic parasites to those in wt parasites to rule out that the genetic modification has lead to artificial upregulation of Smc3 transcription. __Response: __We have added this data to what is now Supp. Fig. 4C, showing that there is no significant difference in SMC3 transcript levels between WT and SMC3-3HA-glmS strains. We have added this information to the text of the manuscript (Line 243). As we also generated an SMC3 antibody, we could demonstrate that there is no appreciable difference in SMC3 protein levels between WT and SMC3-3HA-glmS strains (see what is now Supp. Fig. 1D).

      • According to Figure S2, even more genes were deregulated at the 12 hpi time point in the WT parasites than in Smc3 parasites, and even to a much higher extent. What "transcriptional age" did the WT control parasites have at each time point? __Response: __We have now included the transcriptional age of all strains, replicates, and treatments in what is now Supp. Fig. 5B. At the 12 hpi time point in particular, regardless of glucosamine treatment, the SMC3-3HA-glmS and WT parasites were highly synchronous. The only large discrepancy we see in transcriptional age is between untreated and glucosamine-treated WT parasites at 36 hpi, which is why we did not include this time point in our transcriptional analysis. We were also surprised by the number of genes that were de-regulated with simple glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

      • A negative correlation with transcription is well established in S. cerevisiae, particularly at inducible genes. How does Smc3 enrichment generally look like for genes that show maximal expression at each of the time point? __Response: __We have performed a metagene analysis of SMC3 enrichment at all genes at each respective time point, which we divided into quartiles of expression based on their FPKM values in the RNA-seq data from the corresponding time point in untreated SMC3-3HA-glmS parasites. This quartile analysis considers all genes, including genes that are not transcribed at all and regardless of whether a gene has a significant SMC3 peak or is differentially expressed upon SMC3 knockdown. At the 12 hpi time point, we do see an inverse correlation between SMC3 enrichment and gene transcription level, but this enrichment is most pronounced across genes bodies. We see the highest SMC3 enrichment at genes in the 4th (lowest) quartile category. For the other two time points, we do not see any obvious pattern of SMC3 enrichment with regard to transcriptional status.

      • Line 590: according to the methods, a 36 hpi KD time point was also harvested. Why are the data not shown/analysed? __Response: __To be clear, we are most interested in the transcriptional role of SMC3 during interphase, where results are not confounded by its potential role in mitosis. However, we did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to the manuscript (see Tables 11-13). Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi (see what is now Supp. Fig. 5B). Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

      Minor Comments • Line 103/104: the hinge domain and ATPase head domain are mentioned, please annotate these in Figure 1A.

      __Response: __We have annotated the hinge and ATPase domains.

      • Figure 1D: the kDa scale is missing from the H3 WB. __Response: __We have added a kDa scale.

      • What is the scale indicated by different colors in Fig. 2A? __Response: __The different colors (blue, coral, and green) only represent the 12, 24, and 36hpi time points, respectively. This color scheme is used throughout the manuscript. If the reviewer is referring to the color gradation within each circos plot, this does not indicate a specific scale. The maximum y-axis value for all circos plots is 24, as indicated in the figure legend.

      • Line 189: it would also be interesting how many peaks are "conserved" between the different time points studied, so not only to compare the gene lists of closest genes but also the intersecting peaks and then the closest genes to the intersecting peaks. __Response: __We have added this information in Table 7 and in the manuscript starting on Line 203. Using the new dataset of consensus peaks between two replicates, there were 88 genes associated with an SMC3 peak across all three time points, most of which were close to a centromeric region.

      • What is the distribution of the peaks over diverse genetic elements, such as gene bodies, introns, convergent/ divergent/ tandem intergenic regions? In yeast, cohesion is particularly enriched in convergent intergenic regions, so it would be interesting to see how this behaves in P. falciparum. __Response: __We would have liked to define how many peaks were in intergenic versus genic regions of the genome, but the dataset of “genes” from PlasmoDB includes UTRs. Thus, we would need a better annotation of the genome to perform this analysis. Regardless, we calculated the average SMC3 peak enrichment (shared between both replicates) in intergenic regions between convergent and divergent genes (see what is now Supp. Fig. 3B and Table 6). As we now state in the manuscript on line 198, we see a slight enrichment in regions between convergent genes at all time points, but the differences were not significant.

      • Line 130 intra-chromosomal interactions (word missing) __Response: __Thank you for pointing this out. We have corrected this.

      • Contrary to Figure 1D, the WB in Figure 3A indicates strong expression of Smc3 in rings. Please comment on this discrepancy. __Response: __While extracts from all time points were run on the same western blot in Fig. 1D and thus developed for the same amount of time, this was not the case for Fig. 3A. In Fig. 3A, the samples were run on different blots and exposed for different times, so while we can compare SMC3-HA levels between – and + glucosamine for each time point, the levels at 12 hpi cannot be quantitatively compared to those at 24 or 36hpi.

      • What time point after glucosamine addition represents the WB in Fig. 3A? __Response: __The “12hpi” parasites were sampled approximately 108 hours post glucosamine addition and the “24hpi” parasites sampled approximately 120 hours post glucosamine addition. Basically, the parasites were treated with glucosamine for 96 hours, synchronized, and then harvested 12 and 24 hours later.

      • Line 233 / Suppl Figure 3: Isn't it a bit concerning that the untreated control parasites at 24 hpi statistically corresponded to 18-19 hpi? And to what timepoint did the wt parasites correspond? __Response: __We are not concerned by this, and we have included the WT parasites in what is now Supp. Fig. 5B for better comparison. In the analysis presented in Supp. Fig. 5B, regardless of glucosamine presence or absence, the differences among replicates and strains at 12 and 24hpi are, in our opinion, minimal, amounting to one or two hours of the 48-hour IDC. In our extensive experience with RNA-seq across the P. falciparum lDC, this synchronization is extremely tight. As we describe on line 430 of the Materials and Methods, there is a ±3 hour window in our synchronization method, meaning that parasites harvested at 24hpi could be anywhere from 21-27hpi. In addition, the dataset that was used for comparison (from Bozdech et al., 2003) was generated in 2003 in a different laboratory using different strains with microarray. While comparing more recent RNA-seq data to this classic study has become well-established practice and is useful for comparing transcriptional age between replicates and strains, it is inevitable that the calculated “hpi” from (Bozdech et al., 2003) will differ somewhat from our experimental “hpi”. We have indeed seen this small discrepancy in predicted transcriptional age in several of our RNA-seq datasets (unrelated to this study) from trophozoites harvested at 24hpi.

      • Line 264: "whether naturally or via knockdown" - the meaning of this sentence is not entirely clear __Response: __We are referring to depletion of SMC3 at promoters, either naturally (i.e. lack of binding at the promoter at 36hpi that is not the result of SMC3 knockdown, as we show in Fig. 4B) or via SMC3 knockdown, which is not natural but artificial.

      • Figure 4 Legend: A, B, C etc. are mixed up. Response: Thank you for pointing this out. We have corrected this.

      • Figure 4D: the differences seem to be marginally significant, even not significant at all (q=0.8) for gap45 at 12hpi. __Response: __If one defines a significance cutoff of q = 0.05 (as is common practice in differential expression analyses), then the differences are significant. For a small minority of invasion genes (such as gap45), we do observe significance at either 12 hpi or 24 hpi, but not both. Thus, we have removed the word “significant” from the descriptions of each dataset in Tab 1 of what is now Table 18. however, we do not believe that this rules out a role for SMC3 at such a gene during interphase. What is now Table 18 offers a longer list of invasion-related genes, most of which are more “significantly” affected than rap2 and gap45.

      • Figure 4F shows FACS data using SYBR green as a DNA stain. The authors could exploit this data to look at the relative DNA content per cell as a measure of parasite stage, since more mature parasites will have more DNA (mean fluorescence intensity). How did the corresponding parasite cultures look in Giemsa smears? Response: We have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B). We have added images of Giemsa-stained parasites in untreated and glucosamine-treated parasites at all time points to demonstrate a lack of an obvious morphological phenotype in SMC3 KD parasites (see what is now Supp. Fig. 5A).

      • Are RNAseq replicates biological replicates from independent experiments or technical replicates? __Response: __RNA-seq replicates are technical replicates from the same parasite clone.

      • Why does the number of genes analysed for differential gene expression differ between the comparisons? __Response: __If the reviewer is referring to the discrepancy between the total number of genes for different time points [for example, between what is now Table 9 (12hpi) and Table 10 (24hpi)], this is because in the RNA-seq/differential expression analysis, there have to be reads mapping back to a gene in order for that gene to be included in the analysis. Thus, if a gene is not transcribed at a given time point in the treated or untreated samples, it will not be included in the analysis. Gene transcription fluctuates significantly over the course of the IDC, so different time points will have different total numbers of transcribed genes.

      • Line 372: Do you mean the proteins or the genes? AP2-I has a peak at 24 hpi and 36 hpi, and its interacting AP2 factor Pf3D7_0613800 at all time points. __Response: __We are referring to the genes. With the new ChIP-seq analysis including the second replicate, there are no consensus SMC3 peaks associated with ap2-I, bdp1, or Pf3D7_0613800 (see what is now Table 7).

      • Line 480: no aldolase was shown. __Response: __We have removed this sentence.

      • Line 838: include GO analysis in methods __Response: __We have added this.

      Reviewer #2 (Significance (Required)): The paper addresses the function of the cohesin complex in gene regulation of malaria parasites for the first time. Due to the conserved nature of the complex, the data may be interesting for a broad audience of scientists interested in nuclear biology and cell division/ gene regulation.

      Reviewer #3

      (Evidence, reproducibility and clarity (Required)):

      *Summary:

      In the presented manuscript by Rosa et al. the authors investigate the longstanding question of how P. falciparum achieves the tight transcriptional regulation of its genome despite the apparent absence of many canonical sequence specific transcription factor families found in other eukaryotes. To do this the authors investigate the role of the spatial organization of the genome in this context, by performing a functional characterization of the conserved cohesion subunit SMC3 and its putative role in transcriptional regulation in P. falciparum. Using Cas9 mediated genome editing the authors generated a SMC3-3xHA-glmS parasite line, which they subsequently used to show expression of the protein over the asexual replication cycle by western blot and IFA analysis. In addition, using co-IP experiments coupled with mass spectrometry they identified the additional components of the cohesion complex also found in other eukaryotes as interaction partners of SMC3 in the parasite, thereby confirming the presence of the conserved cohesin complex in P. falciparum. By using a combination of ChIP-seq and RNA-seq experiments in SMC3 knockdown parasites the authors furthermore show that a reduction of SMC3 resulted in the up-regulation of a specific set of genes involved in invasion and egress in the early stages of the asexual replication cycle and that this up-regulation in transcription is correlated with a loss of SMC3 enrichment at these genes. From these observations the authors conclude, that SMC3 binds dynamically to a subset of genes and works as a transcriptional repressor, ensuring the timely expression of the bound genes. Overall, the presented data is intriguing, of high quality and very well presented. However, there are some points, which should be addressed to bolster the conclusions drawn by the authors.

      Major points: I was not able to find the deposited datasets in the BioProject database under the given accession number. This should obviously be addressed and would have been nice to be able to have a look at these datasets also for the review process. *__Response: __We apologize for not giving the reviewers access. As the manuscript has been made available as a pre-print (which includes data accession numbers), but has not yet been published, we have not activated access to the data on the database.

      *SMC3-ChIP-seq experiments:

      "168 were bound by SMC3 across all three time points (Fig. 2D). However, most SMC3-bound genes showed a dynamic binding pattern, with a peak present at only one or two time points (Fig. 2B,D)."

      Here it would be interesting to actually have more than one replicate of each of these ChIP-seq time points. This could provide a better idea of how "dynamic" these binding patterns actually are. Furthermore, I was missing a list of these 168 genes, which are constantly bound by SMC3. Anything special about those? What actually happens to this subset of genes in the SMC3 knockdown parasites? Do they show similar transcriptional changes?*

      __Response: __We have now performed a second biological replicate of the SMC3-3HA ChIP-seq with a different clone at all time points. We compared this data to that from the original clone and found significant overlap of the peaks called (see what is now Table 4 and Supp. Fig. 3A). We generated a stringent list of peaks that were shared between both clones at each time point and repeated all downstream analyses (see what are now Tables 5-8). We found that our conclusions were largely unchanged. Text describing these experiments and analyses have been added throughout the results section. Using the new dataset of consensus peaks between two replicates, there were 88 genes associated with an SMC3 peak across all three time points (see what is now Table 7). The genes that are associated with an SMC3 peak at all time points are, in general, those closest to centromeric/pericentromeric regions and show no obvious functional relationship to each other. Out of these 88 genes, four are significantly up- or downregulated at 12 hpi and 26 are significantly up- or downregulated at 24 hpi. The most significantly downregulated of these genes in both datasets is smc3 itself.

      *SMC3-knockdown experiments:

      In Sup. Fig. 1 there is a double band in the HA-western blot in the 2nd cycle -GlcN. sample. This second band is absent in all other HA-western shown. Have the authors any idea where that second band comes from?*

      __Response: __As the reviewer says, we do not see this second band in most of our western blots. It is possible that it is just a small amount of degradation in the lysate.

      In Figure 3A, the WB data shown is slightly contrasting the RNA-seq quantification (3B). The knock-down on protein level seems to be stronger in the 12 hpi samples here than in the 24 hpi samples. Although the band for HA-SMC3 is stronger at the 12 hpi TP there's no band visible in the + GlcN. sample. There's however in the 24 hpi samples. Could the authors comment on this?

      Response: __With regard to the discrepancy of the knockdown and protein versus RNA level, it is quite common for transcript levels to not agree with protein levels. This is why we always confirm a transcriptional knockdown with western blot analysis using appropriate loading controls. We are not sure why there is a more dramatic knockdown of SMC3 at 12hpi than at 24hpi, as these samples came from the same culture, but were simply harvested 12 hours apart. __

      *"Comparison of our RNA-seq data to the time course transcriptomics data from (Painter et al., 2018) revealed that SMC3 depletion at 12 hpi caused downregulation of genes that normally reach their peak expression in the trophozoite stage (18-30 hpi), with the majority of upregulated genes normally reaching their peak expression in the schizont and very early ring stages (40-2 hpi) (Fig. 3E). At 24 hpi, a similar trend is observed, with most downregulated genes normally peaking in expression in trophozoite stage (24-32 hpi) and the majority of upregulated genes peaking in expression at very early ring stage (2 hpi) (Fig. 3F)."

      I'm not fully convinced by these presented results/conclusions. This dataset would greatly benefit from the inclusion of additional later time points.*

      __Response: __To be clear, we are most interested in the transcriptional role of SMC3 during interphase, where results are not confounded by its potential role in mitosis. However, we did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to the manuscript (see Tables 11-13). Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi (see what is now Supp. Fig. 5B). Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

      We performed differential expression analysis of the SMC3-3HA-glmS parasites with and without glucosamine at 36hpi (we have added this data in Table 11). Again, significantly up- and down-regulated genes were not filtered using the WT dataset. With this analysis, we see only three genes from the list of invasion-related genes (Hu et al., 2010) that are up-regulated, but none of them have a significant q-value (Tab 5 of Table 18). Thus, depletion of SMC3 in late stage parasites does not lead to up-regulation of the same genes that are upregulated at 12 and 24hpi. We have added this information to the text (Line 277).

      *The presented upregulation of the egress and invasion related genes is hard to pinpoint to be a direct effect of transcriptional changes due to the SMC3 knockdown. While there's a slight upregulation of these genes they still seem to be regulated in their normal overall transcriptional program as shown in Figure 4D/E. *

      __Response: __We provide evidence of a direct effect of SMC3 binding by combining differential expression analysis performed upon SMC3 knockdown with SMC3 ChIP-seq at corresponding time points. As we show in what is now Fig. 4C and D, promoter accessibility of these egress/invasion genes correlates with their transcriptional activity. However, SMC3 binding to the promoters of these same genes shows inverse correlation with their transcriptional activity (what is now Fig. 4B and D). While we believe that SMC3 does contribute to the repression of these genes at specific time points during the cell cycle, it is highly likely that SMC3 is just one protein of many that regulates these genes. Moreover, and especially since we do not see a growth phenotype in the SMC3 KD, it is possible that another protein or even SMC1 could compensate for loss of SMC3 at these promoter regions. We now state these possibilities on lines 346 383 of the Discussion.

      *So the changes could in theory also be explained by the differences in cell cycle progression which are present between +/- GlcN. cultures (Sup. Fig. 3). The presented normalization to the microarray data is a well-established practice to correct for this but, as presented seems to have its limitation with these parasite lines (line 233, glucosamine treated parasites harvested at 24 hpi correspond statistically to approximately 18-19 hpi (Supp. Fig. 3).) *

      __Response: __In the analysis presented in what is now Supp. Fig. 5B, regardless of glucosamine presence or absence, the differences among replicates and strains at 12 and 24hpi are, in our opinion, minimal, amounting to one or two hours of the 48-hour IDC. In our extensive experience with RNA-seq across the P. falciparum lDC, this synchronization is extremely tight. As we describe on lines 416-421 of the Materials and Methods, there is a ±3 hour window in our synchronization method, meaning that parasites harvested at 24hpi could be anywhere from 21-27hpi. In addition, the dataset that was used for comparison (from Bozdech et al., 2003) was generated in 2003 in a different laboratory using different strains with microarray. While comparing more recent RNA-seq data to this classic study has become well-established practice and is useful for comparing transcriptional age between replicates and strains, it is inevitable that the calculated “hpi” from (Bozdech et al., 2003) will differ somewhat from our experimental “hpi”. We have indeed seen this small discrepancy in predicted transcriptional age in several of our RNA-seq datasets from trophozoites harvested at 24hpi.

      By including additional later time points, one could actually follow the expression profiles over the whole cycle and elucidate if there's an actual transcriptional up-regulation of the genes, or if the + GlcN. parasites show a faster cell cycle progression, with a shifted peak expression timing compared to the - GlcN. parasites. __Response: __We did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to what is now Supp. Fig. 5. Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi. Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

      *"These genes show SMC3 enrichment at their promoter regions at 12 and 24 hpi, but not at 36 hpi (Fig. 4C), and depletion of SMC3 resulted in upregulation at both 12 and 24 hpi (Fig. 4D). Comparison of the SMC3 ChIP-seq data with published Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data (Toenhake et al., 2018) and mRNA dynamics data (Painter et al., 2018) from similar time points in the IDC revealed that SMC3 binding at the promoter regions of these genes inversely correlates with chromatin accessibility (Fig. 4C) and their mRNA levels (Fig. 4E), which both peak in schizont stages. These data are consistent with a role of SMC3 in repressing this gene subset until their appropriate time of expression in the IDC."

      The presented correlations certainly make an intriguing point towards the authors conclusion that SMC3/cohesin depletion from the promoter regions of the genes results in a de-repression of these genes and their transcriptional activation. However, the SMC3 knockdown is not complete and only up to 69% as presented on RNA level in these parasites. Therefore a control experiment which needs to be done is to actually show the loss of SMC3 from the presented activated example genes in the knockdown parasites. This could easily be done by ChIP-qPCR or even ChIP-seq, to get a global picture of the actual changes in SMC3 occupation in the knockdown parasites in correlation with changes in transcript levels. *__Response: __While SMC3-3HA-glmS knockdown is not complete at the RNA level, it is fairly robust at the protein level, especially at 12hpi (Fig. 3A).

      *"These data suggest that SMC3 knockdown results in a faster progression through the cell cycle or a higher rate of egress/invasion."

      The authors could greatly strengthen their conclusions by investigating this thoroughly. Pinpointing the observed phenotype to an actual increase in invasion or egress would add to the authors main conclusion that the loss of SMC3 de-regulates the timing of gene expression for these invasion related genes thereby increasing their transcript levels and thus leading to a higher rate of egress/invasion. To determine cell cycle progression simple comparisons between DNA content using a flow cytometer at timepoints together with visual inspection of Giemsa stained blood smears would give a ggod indication towards changes in cell cycle progression. In addition invasion/egress assays by counting newly invaded rings per schizont could reveal, if there are changes in the rate of egress/invasion upon SMC3 knockdown.*

      Response: __We have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B). We have added images of Giemsa-stained parasites from the knockdown time course we performed to what is now Supp. Fig. 5A. We see no obvious differences in cell morphology caused by glucosamine treatment in the WT or SMC3-3HA-glmS parasites. As we discuss on line 327, very little intact cohesin complex seems to be required for normal growth and mitosis in S. cerevisiae and D. melanogaster, which is probably why we do not see an obvious growth or morphological phenotype. We believe that SMC3 is probably only a part of a complex controlling transcription of these invasion or egress genes. Thus, the up-regulation of these genes upon SMC3 KD might not be enough to lead to a significant growth or invasion phenotype. __

      *Minor points:

      In the MM section on the Cas9 experiments it says dCas9 where it should be Cas9 (line 425)*

      __Response: __Thank you for pointing this out. We have corrected this.

      It would be great to add which HP1 antibody was used in which dilution in the IFAs to the MM section. __Response: __We have added this information to the Materials and Methods section.

      In Figure 4C for the gap45 gene there's is some green peak floating around which should not be there. __Response: __Thank you for pointing this out, we have corrected it.

      *Reviewer #3 (Significance (Required)):

      Significance: The manuscript investigates a very timely topic by trying to uncover new molecular mechanisms of transcriptional regulation in P. falciparum. Investigating the role of the cohesin complex/SMC3 in this context provides valuable new insights to the field. While the first part with the description of the SMC3 cell line and the co-IP experiments largely confirms published data on the existence and composition of the cohesin complex in Plasmodium and its enrichment at the centromeres, the second part is especially intriguing since it investigates the molecular function of SMC3 in more detail. The results pointing to a role of SMC3/cohesin as a transcriptional repressor are of great interest to the field and will open up new concepts for future investigation.*

      *Audience: The work is particularly interesting for people interested in gene regulatory processes in Plasmodium and Apicomplexan parasites in general. At the same time it also nicely points towards shared principles of gene regulation to other eukaryotes in relation to the spatial organization of the genome making the work also very interesting for a broader audience with interest in the general principles of gene regulatory processes in eukaryotic organisms.

      Expertise: P. falciparum epignetics and chromatin biology / gene regulation / Cas9 gene editing*

      CROSS-CONSULTATION COMMENTS

      All reviewers agree that the paper addresses an important topic and provides convincing evidence for enrichment of the cohesin component Smc3 at P. falciparum centromers. In contrast, evidence for a function of Smc3 as a transcriptional repressor of genes in the first part of the parasite life cycle is less well supported. All reviewers agree that the statistical significance of the ChIP experiments needs to be impoved by including biological replicates. In addition, the phenotype of the conditional knock-down should be analysed in more detail by clarifying whether faster cell cycle progression or higher invasion rate are responsible for the observed growth adavantage. Inclusion of transcriptional data from a later time point in addition to the presented data for 12 hpi and 24 hpi was also requested by all reviewers. Finally, several inconsistencies require clarification.

    1. Author Response

      Reviewer #1 (Public Review):

      In this work, the authors investigate a means of cell communication through physical connections they call membrane tubules (similar or identical to the previously reported nanotubes, which they reference extensively). They show that Cas9 transfer between cells is facilitated by these structures rather than exosomes. A novel contribution is that this transfer is dependent on the pair of particular cell types and that the protein syncytin is required to establish a complete syncytial connection, which they show are open ended using electron microscopy.

      The data is convincing because of the multiple readouts for transfer and the ultrastructural verification of the connection. The results support their conclusions. The implications are obvious, since it represents an avenue of cellular communication and modifications. It would be exciting if they could show this occurring in vivo, such as in tissue. The implication of this would be that neighboring cells in a tissue could be entrained over time through transfer of material.

      Thank the reviewer for his/her comments and suggestion. It’s possible that the thick tubular connections found in this study also exist in vivo. A previous study reported that TNT-like structures were found in mouse or human primary tumor cells (PMID: 34494703; PMID: 34795441). Our transfer assays could be adopted to evaluate such transfer in primary cultures and in vivo. We anticipate this for future work.

      Reviewer #2 (Public Review):

      There is a lot of interest in how cells transfer materials (proteins, RNA, organelles) by extracellular vesicles (EV) and tunneling nanotubes (TNTs). Here, Zhang and Schekman developed quantitative assays, based on two different reporters, to measure EV and direct contact-dependent mediated transfer. The first assay is based on transfer of Cas9, which then edits a luciferase gene, whose enzymatic activity is then measured. The second assay is based on a split-GFP system. The experiments on EV trafficking convincingly show that purified exosomes, or any other diffusible agent, are unable to transfer functional Cas9 (either EV-tethered or untethered) and induce significant luciferase activity in acceptor cells. The authors suggest a plausible model by which Cas9 (with the gRNA?) gets "stuck" in such vesicles and is thus unable to enter the nucleus to edit the gene.

      To test alternative pathways of transfer, e.g. by direct cell-cell contact, the authors co-cultured donor and acceptor cells and detect significant luciferase activity. The split GFP assay also showed successful transfer. The authors further characterize this process by biochemical, genetic and imaging approaches. They conclude that a small percentage of cells in the population produce open-ended membrane tubules (which are wider and distinct from TNTs) that can transfer material between cells. This process depends on actin polymerization but not endocytosis or trogocytosis. The process also seems to depend on endogenously expressed Syncytin proteins - fusogens which could be responsible for the membrane fusion leading to the open ends of the tubules.

      The paper provides additional solid evidence to what is already known about the inefficiency of EV-mediated protein transport. Importantly, it provides an interesting new mechanism for contact-dependent transport of cellular material and assigns valuable new information about the possible function of Syncytins. However, the evidence that the proteins and vesicles transfer through the tubules is incomplete and a few more experiments are required. In addition, certain inconsistencies within the paper and with previous literature need to be resolved. Finally, some parts of the text, methods and the figures require re-writing or additional information for clarity.

      Major comments

      1) In Figure 1F, the authors compare the function of exosome-transported SBP-Cas9-GFP vs. transient transfection of SBP-Cas9-GFP. It is not clear if the cells in the transiently transfected culture also express the myc-str-CD63 and were treated with biotin. It is important to determine if CD63-tethering itself affects Cas9 function.

      Thank the reviewer for his comments and suggestions. We now show in Figure 1- figure supplement 1D that CD63-tethering itself does not affect Cas9 function.

      2) The authors do not rule out that TNTs are a mode of transfer in any of their experiments. Their actin polymerization inhibition experiments are also in-line with a TNT role in transfer. This possibility is not discussed in the discussion section.

      Yes, the results in this study do not rule out a role for TNTs in the transfer. At present, we are not aware of conditions that would functionally distinguish transfer mediated by TNTs and thick tubules. We have now included this in the Discussion section.

      3) Issues with the Split GFP assay:

      a) On page 4, line 176, the authors claim that "A mixture of cells before co-culture should not exhibit a GFP signal". However, this result is not presented.

      The results of mixture experiment are included in Figure 2-figure supplement 1D, E.

      b) The authors show in Figure 2C and F that in MBA/HEK co-culture or only HEK293T co-culture, there are dual-labeled, CFP-mCherry, cells. First - what is the % of this sub-population? Second, the authors dismiss this population as cell adhesion (Page 5, line 192) - but in the methods section they claim they gated for single particles (page 17, line 642), supposedly excluding such events. There is a simple way to resolve this - sort these dual labeled cells and visualize under the microscope. Finally - why do the authors think that the GFP halves can transfer but not the mature CFP or mCherry?

      The plot in the Figure 2C and F are displayed in an all-cell mode, not in singlet mode. The percentage of dual-labeled CFP-mCherry in singlet was 0-0.2%. Thus, most of the signal was from doublet, or cell adhesion. We did not claim that the mature CFP or mCherry cannot be transferred. We suggested that the GFP signal of split-GFP recombination may be a more accurate reflection of cytoplasmic transfer between cells. In contrast, mature CFP or mCherry may simply attach to the cell surface but not enter into the other cells.

      c) In the Cas9 experiments - the authors detect an increase in Nluc activity similar in order of magnitude that that of transient transfection with the Cas9 plasmid - suggesting most acceptor cells now express Nluc. However, only 6% of the cells are GFP positive in the split-GFP assay. Can the authors explain why the rate is so low in the split-GFP assay? One possibility (related to item #2 above) is that the split-GFP is transferred by TNTs.

      The Cas9-based Nluc activity assay is more sensitive as it measures an enzyme with a very high turnover number. The split-GFP assay requires a transfer of GFP fragments to produce intact GFP molecules where the signal is not amplified. We think this explains the dramatic increase in a signal once Cas9 is transferred. Our cell sorting results suggest that at least 6% of the receptor cells are transferred in the co-cultures. Of course, nothing in either analysis rules out a role for TNTs in this transfer.

      4) The membrane tubules, the membrane fusion and the transfer process are not well characterized:

      a) The suggested tubules are distinct from TNTs by diameter and (I presume, based on the images) that they are still attached to the surface - whereas TNTs are detached. However, how are these structures different from filopodia except that they (rarely) fuse?

      We used TIRF microscopy and found that the thick tubules are not attached to the surface (not shown). Filopodia are much closer in diameter to TNTs (0.1-0.4 micron). The thick tubules we observe are much thicker (2-4 micron in diameter).

      b) Figure 5E shows that the acceptor cells send out a tubule of its own to meet and fuse. Is this the case in all 8 open-ended tubules that were imaged? Is this structure absent in the closed-ended tubules (e.g. as seen in Figures 6 & 8)?

      Around half of open-ended tubules appeared to emanate from acceptor cells. Likewise, for closed-ended tubules, for example, in Figure 6E where a recipient HEK293T cell projected a short tubule.

      c) The authors suggest a model for transport of the proteins tethered to vesicles (via CD63 tethering). However, the data is incomplete.

      i) They show only a single example of this type of transport, without quantification. How frequent is this event?

      The transport of the proteins tethered to vesicles (via CD63 tethering) were found in all 8 open-ended tubules that we detected in this study.

      ii) Furthermore, the labeling does not conclusively show that these are vesicles and not protein aggregates. Labeling of the vesicle - by dye or protein marker will be useful to determine if these are indeed vesicles, and which type.

      In Figure 4B, the moving punctum in a tubular connection appears to contain SBP-Cas9-GFP, Streptavidin-CD63-mCherry, and the cell surface WGA conjugate that may have been internalized into a donor cell endosome, which indicates that the moving punctum is vesicle type. Nonetheless, in general we cannot distinguish the forms of Cas9 that are transferred and become localized to the nucleus of target cells and we make no claim other than to suggest this possibility that Cas9 may be transferred as an aggregate.

      iii) The data from Figure 2 suggest (if I understand correctly) transfer of the CD63-tethered half-GFP, further strengthening the idea of vesicular transfer. However, the authors also show efficient transfer of untethered Cas9 protein (Figure 2A and other figures). Does this mean that free protein can diffuse through these tubules? The Cas9 has an NLS so the un-tethered versions should be concentrated in the nucleus of donor cells. How, then, do they transfer? The authors do not provide visual evidence for this and I think it is important they would.

      Based on the results using the Cas9-based luciferase assay (His- or SBP-tagged Cas9) (Figure 2A) and split-GFP assay (free GFP1-10) (Figure 2G), we suggest that free protein could be transferred between cells. Our current imaging approach is not designed to quantify protein diffusion. However, we are able to detect from images that Cas9-GFP does not colocalize exclusively with CD63 or concentrate in the nucleus, but also appears in the cytoplasm. These data indicate that both vesicle association and free diffusion may mediate the transfer through tubules. We thank the referee for emphasizing this issue which we will consider for future work to distinguish the transfer types through tubules.

      iv) In Figures 6 & 8, where transfer is diminished, there are still red granules in acceptors cells (representing CD63-mcherry). Does this mean that vesicles do transfer, just not those with Cas9-GFP? Is this background of the imaging? The latter case would suggest that the red granule moving from donor to acceptor cells in figure 4 could also be "background". This matter needs to be resolved.

      There are a few red puncta in the acceptor cell in Figure 6B. Since the acceptor cell is close to and overlapped with other donor cells containing CD63-mCherry, the red signal may, as the reviewer suggests, be from donor cells and not as a result of transfer through tubular connections. However, donor-acceptor cultures of HEK293T where transfer is not observed, little CD63-mCherry signal, for example, in Figure 6a, was seen in acceptor cells, even during several hours of observation (Figure 6- figure supplement video). A minor red signal could arise from exosomes secreted by donor cells that are internalized by acceptor cells. Images of single-culture receptor cells were added in Figure 4- figure supplement 1.

      For Figure 8, we used MDA-MB-231 syncytin-2 knock-down cells containing Fluc:Nluc:mCherry as the receptor cell, thus in these experiments the red signal most likely represents mCherry expressed in the acceptor cells.

      In Figure 4, we observed moving punctum in a tubular connection which contained co-localized green, red, and purple signals, corresponding to SBP-Cas9-GFP, streptavidin-CD63-mCherry, and the WGA conjugate, respectively. The video of punctum transport (Figure 4-figure supplement video) suggests that the red signal is not “background”.

      5) Why do HEK293T do not transfer to HEK293T?

      a) A major inexplicable result is that HEK293T express high levels of both Syncytin proteins (Figure 7 - supp figure 1A) yet ectopic expression of mouse Syncytin increases transfer (Figure 7E). Why would that be? In addition, Fig 3A shows high transfer rates to A549 cells - which express the least amount of Syncytin. The authors suggest in the discussion that Syncytin in HEK293T might not be functional without real evidence.

      We cannot yet explain why the basal level of syncytin expressed in HEK293 cells is insufficient to promote open-ended tubular connections between these cells. It could be that the proteins are not well represented in a processed form at the cell surface. Nonetheless, ectopic expression of mouse syncytin-A in HEK293T produced some increased transfer but less than when syncytin-A is ectopically expressed in MDA-MB-231 cells (up to 4-fold vs. 30-fold change of Nluc/Fluc signal) (Figure 7E). Furthermore, we have added new results which show that apparent furin-processed forms of syncytin-A, -1 and -2 can be detected by cell surface biotinylation in transfected MDA-MB-231 cells (Figure 8-figure supplement 1D). All we demonstrate is that syncytin in the acceptor cell is required for fusion and we make no claim that it is the only protein or lipid at the cell surface in the acceptor cell required for fusion. Clearly, more work is essential to establish the complexity of this fusion reaction.

      For A549 cells, syncytin-1 is highly expressed in A549 cells, thus it is possible that syncytin-1 in A549 plays crucial roles in the process.

      b) In addition - previous publications (e.g. PMID: 35596004; 31735710) show that over expression of syncytin-1 or -2 in HEK293T cells causes massive cell-cell fusion. The authors do not provide images of the cells, to rule out cell-cell fusion in this particular case.

      Overexpression of syncytin-1 or -2 in cells indeed causes massive cell-cell fusion, while overexpression of syncytin-A induced much less cell fusion than syncytin-1, or -2. We have now added new images shown in Figure 8-figure supplement 1A-C to document these observations. It may be that overexpressed human syncytins are better represented in a furin-processed form in both cell types. In contrast, we did not observe donor-acceptor cell fusion at basal levels of expression of syncytin in HEK293T and MDA-MB-231. For example, the Figure 4-figure supplement video shows that tubular structures were seen to form and break during the course of visualization with a tubule fusion event but no cell fusion to form heterokaryons.

      Reviewer #3 (Public Review):

      In this manuscript, Zhang and Schekman investigated the mechanisms underlying intercellular cargo transfer. It has been proposed that cargo transfer between cells could be mediated by exosomes, tunneling nanotubes or thicker tubules. To determine which process is efficient in delivering cargos, the authors developed two quantitative approaches to study cargo transfer between cells. Their reporter assays showed clearly that the transfer of Cas9/gRNA is mediated by cell-cell contact, but not by exosome internalization and fusion. They showed that actin polymerization is required for the intercellular transfer of Cas9/gRNA, the latter of which is observed in the projected membrane tubule connections. The authors visualized the fine structure of the tubular connections by electron microscopy and observed organelles and vesicles in the open-ended tubular structure. The formation of the open-ended tubule connections depends on a plasma membrane fusion process. Moreover, they found that the endogenous trophoblast fusogens, syncytins, are required for the formation of open-ended tubular connections, and that syncytin depletion significantly reduced cargo Cas9 protein transfer.

      Overall, this is a very nice study providing much clarity on the modes of intercellular cargo transfer. Using two quantitative approaches, the authors demonstrated convincingly that exosomes do not mediate efficient transfer via endocytosis, but that the open-ended membrane tubular connections are required for efficient cargo transfer. Furthermore, the authors pinpointed syncytins as the plasma membrane fusogenic proteins involved in this process. Experiments were well designed and conducted, and the conclusions are mostly supported by the data. My specific comments are as follows.

      1) The authors showed that knocking down actin (which isoform?) in both donor and acceptor cells blocked transfer, and more so in the acceptor cells perhaps due to the greater knockdown efficiency in these cells. However, Arp2/3 complex knockdown in donor cells, but not recipient cell, reduced Cas9 transfer. It would be good to clarify whether the latter result suggests that the recipient cells use other actin nucleators rather than Arp2/3 to promote actin polymerization in the cargo transfer process. Are formins involved in the formation of these tubular connections?

      We thank the reviewer for his/her comments and suggestions. Beta-actin was knocked down in this study. We tried a formin inhibitor, SMIFH2 which resulted in a decrease the Cas9 transfer between cells (Figure 3F).

      2) The authors provided convincing evidence to show that the tubular connections are involved in cargo transfer. Intriguingly, in Figure 4-figure supplement video (upper right), protein transfer appeared to occur along a broad cell-cell contact region instead of a single tubular connection. How often does the former scenario occur? Is it possible that transfer can happen as long as cells are contacting each other and making protrusions that can fuse with the target cell?

      In the Figure 4-figure supplement video (upper right), it may be that several membrane tubes from several different donor cells contact at sites close to one another on the recipient cell resulting in the appearance a broad cell-cell contact. This was a rare observation. In our quantification, only 8 connections were open-ended in 120 cell-cell contact junctions. Once open-ended, or plasma membrane fused, cargo transfer is observed.

      3) The requirement of MFSD2A in both donor (HEK293T) and recipient (MDA-MB-231) cells is consistent with a role for syncytin-1 or 2 in both types of cells. Since HEK293T cells contain both syncytins and MFSD2A but cargo transfer does not occur among these cells, does this suggest that syncytins and/or MFSD2A are only trafficked to the HEK293T cell membrane in the presence of MDA-MB-231 cells?

      A proper answer to this question requires the visualization of syncytins and MFSD2A. The commercial syncytin antibodies were inadequate for immunofluorescence. In advance of the more detailed effort required to tag the genes for endogenous syncytin 1 and 2, we performed live cell imaging and surface biotin labeling of cells transiently transfected to express fluorescently-tagged forms of syncytin-1, -2 and -A. We now show that syncytin-A, -1, and -2 partially localize to the plasma membrane or the cell surface of MDA-MB-231 and at points of cell-cell contact. In fact, overexpression of codon-optimized human syncytin-1, and -2 induced dramatic HEK293T cell-cell fusion. However, at basal levels of syncytin expression, HEK293T could not form open-ended tubular connections, which may be because the basal level of syncytins are not well represented in a processed form at the cell surface or their activity is limited by unknown factors.

      As an independent test of cell surface localization, we used surface biotinylation to show that a fraction of the syncytins can be labeled externally (Figure 8-figure supplement 1D). This fraction shows evidence of proteolytic processing consistent with furin cleavage whereas the overwhelming majority of transfected syncytins detected in a blot of lysates suggests that most remain in the unprocessed precursor form, consistent with the punctate and reticular fluorescence images (Figure 8-figure supplement 1A-C).

      We used IF and GFP-tagged MFSD2A and found this protein partially localized to the plasma membrane of HEK293T cells (Figure 9E, F). Given the results reveal that cargos could be transferred among MDA-MB-231 cells (Figure 2G), syncytin and its receptor appear to function in transfer among these cells.

  16. Jan 2023
    1. What's this trick with the knitting needle? It sounds cool. How do you do it so you don't just run into the unpunched ones and get stopped?

      reply to u/stjeromeslibido at https://www.reddit.com/r/antinet/comments/10lqfsn/comment/j63y2k9/?utm_source=reddit&utm_medium=web2x&context=3

      Every card has holes pre-punched into it in exactly the same place (see the photo in the original post at the top) so that one might put a knitting needle (or other thin instrument) through the whole deck in each of the positions. Then one should decide on what each hole's meaning will be by position.

      As an example, imagine you're using your cards in a rolodex fashion and you want to distinguish the six categories: family, friends, service providers, neighbors, co-workers, and organizations/businesses. For family members you cut/remove the additional paper between the first hole (representing "family") and the edge of the paper. You do the same thing for all the other cards based on their respective categories. So, for example, your brother Joe who lives across the street from you and works with you at the office in the family business would have cuts removed for positions 1, 4, and 5. For an entity that fits all six categories, cuts would be made such that the sheet would no longer stay in u/I-love-teal (the original poster's) six ring binder notebook.

      At the end of the year you want to send Christmas cards to your friends, family and neighbors, so you put the knitting needles into position 1 and pull up separating your family out, then you repeat for positions 4 and 5 until you have your full list. (Pro tip: you probably wouldn't want to pull them out of the deck completely, but might rather pull them up and set them at a 90 degree angle thus preventing you from needing to do the work of refiling them all in a particular order.)

      Obviously if you have multi-row edge punches or dozens of edge notches you can discern a lot more categories or data types using basic logic. Just abstract this to your particular note card system. Herman Hollerith used this in early versions of the U.S. Census in the late 1800s and it and variations were used heavily in early computer programming applications.

      A variation of this sort of trick can also be done by coloring in (or not) the edges of parts of your cards as well. See for example the general suggestions in these photos which help to layout the idea of the "Pile of Index Card" system used back in 2006 with respect to Getting Things Done (GTD) philosophy:

      On my mathematics specific notes which I generally put on graph paper cards, I use colored edge "notches" like these to represent broad categories like theorems, proofs, definitions, corollaries, etc. or method of proof (induction, direct, contradiction, contraposition, construction, exhaustion, probabilistic, combinatorial, etc.) This makes finding specific cards a bit easier as I tip through various sections.

      A historian might use colored edges to visually label dates by decades or centuries depending on the timespan of their studies. The uses can be endless and can be specific to your field of study or needs.

      Some might also attach the idea of tags/categories to the colors of their cards, so you might use white cards for ideas which are your own, yellow cards which are quotes of others' material, blue cards which represent synopses of other's ideas, etc. One might also profitably use a multi-pen with different colored inks to represent these sorts of meta-data as well.

      The variations are endless...

  17. Dec 2022
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer 1

      Although this is an interesting, and generally well-performed study, it is primarily observational and there are few mechanistic insights provided into how MUC13 modulates barrier function. The authors propose a presumably direct interaction between MUC13 and PKC, which apparently sequesters PKC, preventing this kinase from triggering PKC-dependent increases in TJ barrier function; however, there is no evidence that a MUC13-PKC interaction occurs, that MUC13 is phosphorylated by PKC, or that phosphorylation of MUC13 has any impact on its function or overall barrier function. Thus, the hypothesis is not directly tested and all observations in this manuscript are generally correlative in nature.

      While the MUC13 cytoplasmic tail contains a putative PKC-binding motif, we indeed do not show a direct interaction between MUC13 and a member of the PKC family in this manuscript. Unfortunately, we have so far not been able to successfully perform (co-)immunoprecipitation of MUC13 with our current anti-MUC13 antibodies.

      To provide more insights into the possible MUC13-PKC interaction, we plan to perform several experiments.

      • First, we will determine the expression levels of the different PKC isotypes (PKC alpha, beta, gamma, delta, epsilon, and zeta) in the HRT18 cell lines by western blot.
      • Next, we will determine the localization of the relevant PKC isoforms and MUC13 by immunofluorescence microscopy. We are curious to see if we can find a colocalization between MUC13 and a PKC member on the lateral or apical membrane. If we can demonstrate a colocalization, we could follow up with a proximity ligation assay, but this would require the MUC13 antibody directed against the cytoplasmic tail (which only detects the lateral population) and might therefore be challenging.
      • Furthermore, since PKC delta protein levels were upregulated in the total lysate of ∆MUC13 cells, we will test a PKC delta-specific inhibitor in the TEER assay.

        Consider quantifying all blots (Fig. 5C, Fig. 6B).

      As suggested, we will quantify both blots.

      Consider using dot-plots for all quantified data.

      The graphs will be altered to include individual measurement points.

      Reviewer 2

      Fig2E showed two bands with different size in the two MUC13 WT control cell lines. They hypothesized that this could be the consequences of glycosylation different patterns. A sample with untransfected HRT18 might be included in the western blot panel. Additionally, what is the 100kDa band?

      Mucin blots are notoriously difficult and these MUC13 blots are the result of a lot of trial and error. We repeated the Western Blot with original HRT18 cells, HRT18 original cell line, as well as the two CRISPR control cells used in the study (WT 1 and WT 2) and one of the full-length MUC13 knockout cells. The higher band was absent from the MUC13 knockout cells, but a small shift in the MUC13 band size can be noted in the WT 1 cells compared to the original and the WT 2 cell lines, possibly indicating a change in the glycosylation pattern. The 100 kDa band remains detectable in all cell lines including the ∆MUC13 cell line, therefore we consider this to be an aspecific background band of the MUC13 antibody. We will add a more extensive Western Blot analysis to the manuscript.

      Did the transfection of the inducible GFP-MUC13 plasmid induce any decrease of Claudin1/3/4 in HRT18 or Caco2 cells? Same question regarding PKCdelta.

      These are indeed interesting questions. We will perform these experiments with our MUC13-overexpression HRT18 cells.

      Reviewer 3

      Moreover, the authors should determine if MUC13∆CT localize to TJs, as suggested by the working model in Figure 7C. The subcellular localization of MUC3∆CT could give critical clues for its function, but Figure 2G fails to provide any information and the authors do not present any additional data concerning the localization of MUC13∆CT. Detection of MUC13 in membrane fractions of WT, MUC13∆CT and cells lacking the mucin domain could be a feasible strategy forward.

      We will perform additional immunofluorescence experiments to determine the subcellular localization of MUC13-∆CT more accurately. However, detection of the extracellular domain by western blot, as suggested, is not possible due to the incompatibility of the extracellular MUC13-directed hybridoma antibody with the western blot technique. We currently do not have a suitable antibody that recognizes the ED and can be used for western blot.

      The authors introduce an inducible MUC13-GFP fusion protein into WT and ∆MUC13 cells and show that it reverses the enhanced TEER upon MUC13 deletion. Unfortunately, the "Materials and Methods" section lacks adequate information on how this fusion protein was designed. Critical questions are the position of the GFP tag within MUC13, whether the fusion protein is correctly processed in HRT18 cells, and if it localizes to the apical or apico-lateral membrane domains? Figure 2H is of low magnification and fails to provide information on the subcellular localization of the MUC13-GFP fusion protein.

      The materials and methods section will be adjusted to describe all the design details of the fusion protein. The GFP tag was added to the MUC13 C-terminus with a GGGS linker sequence in between. Processing of the fusion protein seems correct as we observed MUC13-GFP localization to both lateral and apical membranes and no access intracellular build up. As suggested by the reviewer, we will add more detailed immunofluorescence pictures to the manuscript.

      Figures 6B-C suggest that PKCdelta levels increase in ∆MUC13 cells, which correlates with higher enrichment of Claudins in membrane fractions. The authors then inhibited PKCdelta and observed reduced recruitment of Claudins to membrane fractions. Since the family of Claudins are differentially regulated by phosphorylation (PMID: 29186552), the authors should investigate the TEER phenotype of WT, ∆MUC13 and MUC13∆CT upon PKC inhibition.

      We must clarify that figures 6C-D are done using the PKC inhibitor targeting all conventional PKCs (alpha, beta, gamma) as well as delta (https://www.tocris.com/products/gf-109203x_0741). We recently obtained a PKCdelta-specific inhibitor which we will test in the TEER build-up experiments.

      Moreover, the authors predict phosphorylation sites in MUC13CT and suggest a link between PKC and MUC13 (Figure. 6A), however no evidence is presented to support this hypothesis. The authors should either determine if PKC phosphorylates MUC13 and if this modification has implication on MUC13 localization and TJ function, or remove statements regarding MUC13 phosphorylation. The data provided suggest that PKC regulates TJ proteins independent of MUC13.

      We will adjust the manuscript to put less emphasis on the putative PKC motifs in the MUC13 cytoplasmic tail. For further details on how we will proceed regarding the possible MUC13-PKC interaction see question 1 from reviewer #1.

      Figure 5C. Quantification of at least 3 independent experiments is required.

      These data will be added to the manuscript.

      Figure 6B. Quantification of at least 3 independent experiments is required.

      These data will be added to the manuscript.

      Reviewer 4

      OPTIONAL: MUC13 is expressed both, in the basolateral membranes and in the apical membrane of intestinal epithelial cells (IECs). Does the authors check the relevance of MUC13 in the formation of microvilli in IECs? Are microvilli different (microvilli staining, number of positive cells to microvilli, length, width or distribution of microvilli) in ΔMUC13 and in MUC13-ΔCT? How the glycocalyx looks like in these cells genetically modified for MUC13?

      HRT18 cells do not seem to develop microvilli. However, we plan to stain these cells with a microvilli-specific antibody (ACTUB). The HRT18 cells express mostly MUC13 and relatively low levels of the larger TM mucin MUC1. To study changes in the glycocalyx, we will stain using a MAL-II antibody which targets α-2,3 sialic acids, which are abundantly present in mucins. In this way, we will determine any big changes in the total glycocalyx that may occur in response to the removal of MUC13.

      In the figure 1D would be nice to represent the co-localization of MUC13 together with occluding in a graph in each Z-stack so you can visualize in which part of the cell is maximum colocalization of these both components.

      These data will be provided.

      In the figure 1E, would be great to compare between the two different MUC13 antibodies the apical fraction stained in HRT18 and Caco-2. Specially in the HRT18 cell line since the first antibody did not label apical MUC13 expression meanwhile the second antibody detects the apical expression in these cells. How much lateral lateral stain the C terminal antibody compare with the extracellular antibody for MUC13 and how much stain apically the C terminal antibody compare with the extracellular antibody? Would be nice to see some comparative results using the intensity by Z-stack and plotting in a graph.

      This is a good suggestion as it is quite intriguing that both MUC13 antibodies seem to target (partially) different MUC13 populations. We will perform co-staining with both MUC13 antibodies to provide information on which MUC13 populations are detected by each antibody (apical vs lateral membrane).

      Manuscript would be improved if in the figure 2H to compare within the same cell line the number of MUC13 positive cells in the WT, number of MUC13 positive cells in WT+pMUC13 and the number of MUC13 positive cells in the ΔMUC13+pMUC13

      We will quantify the percentage of MUC13-GFP positive cells in both the WT and ΔMUC13 backgrounds by either microscopy or flow cytometry.

      In figure 5C would be helpful to plot in a graph the normalized expression of each TJ protein and compare between the different cells used (WT, ΔMUC13 and MUC13-ΔCT) as you did in figure 5A

      We will provide the quantification data of three independent experiments.

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

      Reviewer 1

      In addition, this model does not explain why all kinase inhibitors tested reverse the increase in TER observed in deltaMUC13 cell lines. Does this reflect the lack of inhibitor specificity or the likelihood that many kinases are involved?

      As stated in the manuscript, we think that MLCK, ROCK, and PKC are all essential for TER buildup in the ∆MUC13 cells. Because the roles of MLCK and ROCK are well established, we choose to follow up on the PKC results. We adjusted the text to clarify this point.

      The authors do observe that there is an increase in expression of several tight junction-associated proteins, including the claudins, in deltaMUC13 cells. Affected CLDNs include 1, 2, 3, 4, 7, 12. (1) While it appears the authors are arguing that this increased claudin expression results in increased barrier function, they do not sufficiently highlight the well-known role that CLDN2 has in cation transport, and both CLDN-4 and -7 have also been implicated in paracellular ion flux (although this is apparently cell-type specific). These observations would seem to argue against a simple correlation between claudin expression and tight junction barrier function.

      The reviewer is right about the different functions of claudins. Claudin-2, -4 and -7 have (potentially) pore-forming properties, while the other claudins restrict paracellular passage. It has been previously demonstrated that the magnitude of paracellular ion and water flux is reflected by the specific repertoire of claudin family members (Shashikanth et al., 2022). In this paper, overexpression of claudin-4 was shown to mobilize and affect polymeric strands of claudin-2, thus blocking its channel activity. Our mass spectrometry data demonstrated a striking increase in claudin-1, -2, -3, -4, -7, and -12 in the MUC13 knockout membranes compared to WT. We hypothesize that the claudin repertoire in the MUC13 knockout cells leads to a more restricted paracellular route (as observed in the TEER and tracer experiments). The pore-forming claudins may be subject to “interclaudin interference” therefore leading to restriction of the total paracellular ion and water flux. We have adjusted the text of the manuscript to clarify this point.

      We attempted to investigate claudin-2 expression levels in isolated membranes by Western Blot but were unsuccessful as the antibody did not detect any protein while claudins-1 and -4 could be detected with the same method.

      Furthermore, the authors should note the disconnect between paracellular ion flux mediated by claudins and the flux of markers such as dextrans and lucifer yellow, which can be dissociated from claudin function.

      We acknowledge that the flux of larger particles (the leak pathway) is not regulated by claudins (which regulates the pore pathway). We aimed to assess both the pore and the leak paracellular pathways, by using different techniques including TEER, small solutes (Lucifer Yellow CH), and larger molecules (4 and 70 kDa FITC-Dextrans). HRT18 wild type cells are already very restrictive to the pass of larger molecules (FITC-Dextrans) but are more permeable to smaller solutes such as Lucifer Yellow (400 Da). We observed that removal of the MUC13 cytoplasmic tail did not affect the TEER, but reduced the paracellular passage of Lucifer Yellow, demonstrating that manipulation of MUC13 can affect both the pore and leak pathways. We adjusted to text to include this point.

      The increased expression of claudins in the nominally tail-minus MUC13 without a corresponding change in TER would again seem to argue against a simple correlation;

      MUC13-dCT cells showed consistently increased levels of claudins-1 and -2, but not the other claudins. This claudin repertoire (with high claudins-1 and -2, but lower claudin-3, -4, -7, and -12) is apparently not enough to increase TEER. We think that this again reflects the importance of the total claudin composition for the control of the paracellular pathway.

      Watch the use of decimal points instead of commas (lines 253 and 256).

      Corrected.

      Line 543: MilliQ is not a washing agent (or is it?). (Line 535) We use MilliQ as a final step before mounting the glass slides to remove any possible salt deposition that would affect the visualization by microscopy.

      We have specified this in the text.

      Line 553: TER is the product of total resistance times the area. The units are ohms times area.

      Indeed, we have changed this mistake (line 545).

      Line 630: Please provide the transfer conditions (voltage, amp, watts?) and transfer buffer when describing the Western blot protocol.

      For immunoblotting of MUC13, protein lysates were transferred to 0.2 µm PVDF membranes using the Trans-Blot Turbo Transfer system (Biorad). The transfer was run using the protocol (High MW) which consisted in running for 10 min at 25 volts (V) and 1,3 amperes (A). These experimental data were added to the manuscript.

      Reviewer 2

      My main concern about this manuscript is that the authors analyzed MUC13 role in intestinal homeostasis and function using colorectal cancer cells. As helpful as cancer cells are, we should always be cautious about extrapolating roles in normal intestinal epithelium or IBD pathology. Obviously, these finding are also interesting in a cancer context. Using GEPIA (http://gepia.cancer-pku.cn/), I observed that MUC13 is overexpressed in colorectal cancer COAD-TCGA dataset (compared to normal colon from GTEX). Similar results were obtained previously by Gupta et al. (ref #10). I am aware that this would be difficult to confirm the main findings in a non-cancerous intestinal cell line but this limit (normal intestine using cancer cells) should be at least discussed in the manuscript.

      We appreciate the reviewers’ comments and are aware of the downsides of using cancer-derived cell lines. We have performed the GEPIA analysis ourselves and have an ongoing project about the possible role of MUC13 in colorectal cancer progression. In a separate project, we are collaborating with the Gaultier Laboratory at the University of Virginia which has generated a MUC13 knockout mouse. This model will allow us to study the role of MUC13 in non-cancerous tissue. We recently received intestinal biopsies from these mice which will be stained with MUC13 and claudin antibodies to determine localization in healthy tissue. These experiments will reveal if MUC13 colocalizes with claudin on the lateral membrane in the healthy mouse intestinal tract. In future experiments, we will also address MUC13 localization and function in human intestinal organoids. We have adjusted the discussion to refer to the limitations of using cancer cell lines.

      Massey et al (Micro 2021, PMC7014956) previously showed that MUC13 overexpression increased rigidity in PDAC cells and discussed involvement MUC13 link with EMT. MUC13-Her2 interaction was also associated with decrease of E-cadherin suggesting an EMT phenotype. This should be included in the discussion section.

      The discussion has been adjusted to include the link with EMT.

      The authors performed mass spectrometry analysis. Results are deposited on ProteomeXchange but are not yet publicly released. Among the 1189 membrane protein identified. Did the authors observed alteration of EMT proteins? (decrease of vimentin for example). In the discussion section (lane 347), the authors mentioned the relationship between other membrane bound mucins such as MUC1, MUC4, MUC16 or MUC17 and AJ/TJproteins. Did the authors observed any alteration of these mucin in the mass spectrometry data?

      The mass spec analysis was performed on membrane fractions, therefore our dataset will not contain true cytosolic proteins. One of the key EMT proteins, Vimentin, is a cytosolic protein, and indeed it was not found in our dataset. Other EMT-related proteins are shown in the following table. TGF beta 1 was slightly decreased, while E-cadherin and Integrin beta 6 were slightly increased in the ∆MUC13 cells compared to WT cells.

      Gene Name

      Mean WT

      Mean ∆MUC13

      Mean MUC13-∆CT

      TGFBI (TGB beta 1)

      20,54

      16,48

      18,83

      CDH1 (E-cadherin)

      22,69

      24,57

      24,24

      ITGB6 (Integrin beta 6)

      18,86

      21,74

      19,19

      Vimentin - Cytosolic

      -

      -

      -

      CDH2 (Cadherin-2, N-cadherin)

      -

      -

      -

      Mucins are large proteins comprised of densely O-glycosylated mucin domains, which makes them extremely challenging to study by mass spectrometry (MS) (Rangel-Angarita et al., 2021). We did not specifically employ mucin-directed technologies in this dataset, thus making the detection of mucins hard. No mucins other than MUC13 were detected. For MUC13, two peptides corresponding to the EGF-like domains in the extracellular domain, a region that is less densely glycosylated. We added a sentence to the description of the mass spec results to include the EMT proteins and other mucins.

      Minor points:

      Lane 126: HRT18 and Caco2 colon cancer cells instead of intestinal epithelial cells

      Corrected.

      Lane 181 and lane 514: add "full length" MUC13 DNA sequence

      Corrected.

      Lane 234: TEER was measured every 12h. How the authors did observed the largest increase at 42h? Was it 48h? Please clarify.

      We aimed at measuring every 12 h, however the exact measurements were done at 18h, 24h, and 42 h post-infection. We have corrected this in the manuscript.

      Reviewer 3

      Line 43 and 46. "Enterocytes" should be replaced with "intestinal epithelial cells", since enterocytes are themselves a distinct subpopulation of IECs.

      We have changed it in the manuscript.

      Lines 58-60. References in support of the statements should be added.

      We added a reference to this sentence.

      Lines 188-190. Authors comment on "roundness" of different cell lines. If the parameter is critical for the manuscript, the authors should quantify this phenotype.

      The parameter is not critical for the manuscript. We removed the sentence.

      Figure 3A. Staining of cell lines should include panels showing localization of MUC13.

      Co-staining of MUC13 with occludin in HRT18 cell lines can be found in figure 1D, and MUC13 with E-cadherin in supplementary figure 1.

      Lines 323-327 and 390-392. Sentences on these lines contradict each other. The sentences should describe/discuss quantified data presented in Figure 6D.

      The reviewer is right that we should be discussing the quantified data in 6D. We adjusted the sentence in line 323-327.

      Proteomic data sets should be made publicly available on data depositories.

      All proteomics raw data were deposited to the ProteomeXchange Consortium with the dataset identifier PXD029606.

      Reviewer 4

      OPTIONAL: In the figure 2E, is the extracellular antibody still detecting the MUC13-ΔCT?

      No, unfortunately the antibody directed against the MUC13 ED is not compatible with western blot.

      In the figure 2G, would be nice to comment possible reasons why the deletion in the first cell line of the MUC13-CT you can still detect with the extracellular antibody some lateral expression of MUC13 meanwhile in the second cell line, the same deletion (MUC13-CT) you cannot see any lateral MUC13 staining with the extracellular antibody.

      Yes, this is indeed a puzzling finding, especially because the CRISPR deletion is the same in both cell lines. We will add a sentence about possible reduced stability of the MUC13 without CT domain that leads to a different outcome in both cell lines.

      It would be nice that the results from Figure 3H are better explained since it is difficult to follow.

      We adjusted the text to explain the experiment in more detail.

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

      Reviewer 1

      The authors may be overly reliant on TER measurements. Epithelial cells have two parallel resistive pathways: transcellular and paracellular. TER measure the contribution of both. Thus, an increase in TER could result from a decrease in transcellular ion transport. The authors need to measure transcellular ion flow or selectively measure the junctional resistance in a select set of experiments to rule this possibility out.

      The reviewer is right that TEER is a sum of the resistance of the transcellular and paracellular pathways. However, due to the high resistance of cell membranes, the current predominantly travels via the paracellular route (Elbrecht et al., 2016). For this reason, TEER measurements are widely accepted techniques for the assessment of ions passage through the paracellular pathway (Shen et al., 2011).

      Reviewer 3

      Figure 1C. Caco2 and HRT18 cells exhibit distinct MUC13 expression patterns when probed with an antibody against the MUC13 CT; MUC13 localizes almost exclusively to lateral cell junction in HRT18 cells, while a higher portion of MUC13 is present on the apical surface of Caco2 cells. This observation has two possible explanations: 1) the two cell lines express distinct forms of MUC13, or 2) the two cell lines carry distinct machineries for anchoring MUC13 to apical versus apico-lateral membranes. Thus, The authors should take the opportunity to determine the impact of MUC13 deletion on TEER and TJ function in Caco2 cells. Proteomic analysis and functional assays in Caco2 cells may provide more a general mechanism for how MUC13 regulates TJ proteins.

      Yes, this would be a great line of investigation. However, we aimed to knockout MUC13 in Caco-2 cell lines (with the same CRISPR/Cas9 protocol as the HRT18 cells) but were unable to obtain Caco-2 knockout clones. We think this might be a consequence of the poor capability of Caco-2 cells to grow as single colonies (a required step in the protocol). Another option is Caco-2 MUC13 knockout cells have reduced viability.

      The authors generate cell lines that either lack MUC13 or express MUC13 lacking the cytoplasmic domain. Loss of MUC13 cells resulted in enhanced TEER and increased recruitment of TJ proteins to membrane fractions. MUC13∆CT cells show moderate recruitment of TJ proteins to membranes and no increase in TEER but inhibit paracellular diffusion of Luciferase Yellow across monolayers. Figure 3A suggests that Occludin redistributes to tricellular junctions in ∆MUC13 cells, whereas it is found more laterally in WT and MUC13∆CT cells. These finding suggest that full-length MUC13 interferes with TJ protein complexes. However the impact of the extracellular and intracellular (CT) domains is not fully elucidated. Does the O-glycosylated mucin domain interfere with the extracellular domains Occludin and Claudins? The authors should clarify the contribution of the mucin domain to the observed phenotype, for example by performing the described experiments in a cell line expressing MUC13 lacking the mucin domain.

      Mucins are type I membrane proteins with the N-terminal part of the protein on the extracellular site. Therefore, a CRISPR method to specifically remove the glycosylated domain but leave the remainder of the protein in frame is challenging. An additional difficulty is that the ED contains a lot of repeats, complicating the design of specific guide RNAs. To specifically address the contribution of the glycosylated domain, we could complement the MUC13 knockout cell with a construct lacking the ED. However, this would not be comparable to the endogenous MUC13∆CT cell line presented in this manuscript. In future studies, we will strive to address the functions of the different MUC13 domains in more detail.

      Figure 5A. Turnover of TJ proteins in membrane fractions occurs faster than over a period of 1-3 days (PMID: 18474622). The authors should determine TJ protein turnover over a period of minutes and hours.

      We acknowledge the findings in this interesting paper concerning the continuous remodeling of tight junctions. However, the readout of our biotinylation assay is degradation and the timeframe of degradation turns out to be days and not hours. Within this timeframe remodeling is taking place but it cannot be captured in the total lysate.

      Reviewer 4

      OPTIONAL: The authors show that the probiotic Lactobacillus plantarum increase epithelial barrier independently of MUC13. Have the authors considered to use other probiotics as Lactobacillus paracasei (10.3389/fcimb.2015.00026), Akkermansia muciniphila (10.1038/emm.2017.282) or some metabolic products from intestinal microbiota as short-chain fatty acids (SCFAs) (10.3389/fphys.2021.650313) to check what is the role of MUC13 and if it is related with other microbe or microbiota metabolite?

      Thank you for the suggestion. We have an ongoing project in which we investigate the impact of different probiotic bacteria and plan to investigate whether they have an impact on the epithelial barrier function in a MUC13-dependent manner. This study will lead to a separate publication.

      OPTIONAL: The authors successfully delete MUC13 in IECs, both, full length and the cytosolic tail. Have the authors considered targeting the deletion of the PTS domain in MUC13? Could affect that something different from paracellular trafficking as the extracellular detection of microbes and microbial products?

      Removal of a domain in the extracellular domain of MUC13 with CRISPR is challenging because mucins are type I membrane proteins, the repeats and possible frameshift, as described above.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer 1.

      Major point:

      (1) The authors rely upon the redistribution of RNA to measure the inheritance of extant RNAs following cell cycle release. Blocking transcription nicely shows new synthesis is not required for this inheritance. This is also consistent with the idea any newly synthesized RNA would be 'dark,' or not EU labeled, but the transcription inhibitor experiments are critical controls and nicely done. As hinted at the end of their discussion, however, a lack of RNA localizing to G1 chromosomes could be formally attributable to differential RNA stability. Might altered RNA stability of NEAT1, MALAT1, or U2 also contribute to the observed altered localizations upon interphase reentry? The authors could use qPCR or measure RNA half-life to test this possibility. These data would nicely compliment the authors' existing FISH experiments and allow them to specifically argue for differential RNA localization.

      We have addressed this point by measuring the stability of MALAT1, U2, and NEAT1 in G2 cells after transcription inhibition using RNA FISH. We find that U2 and MALAT1 exhibit very little RNA degradation after 2.5 hours of transcription inhibition, which is consistent with the reported half-lives for each of these transcripts (10 hours for MALAT1 and >24hrs for U2; PMC3337439). We conclude that differential RNA stability cannot account for differential RNA import observed for these two transcripts. In contrast, NEAT1 transcript is almost undetectable after 1.5 hours of transcription inhibition, which is also consistent with the reported half-life of this transcript (22406755, 3337439). Therefore, RNA degradation during mitosis could contribute to a lack of NEAT1 nuclear import in G1. We have included this new data in a modified Figure 2E (text p5 lines 154-166).

      Minor Points:

      (1) The authors examine published datasets identifying RNA associated with chromatin and state the reason why these data show little overlap is "primarily attributable to purification methodology." This statement seems speculative, and its basis seems unclear.

      We have changed the wording of this section to remove unwarranted speculation (p4-5 lines 116-129).

      (2) The SAF-A-AA experiments failed to reveal insight into mechanisms of RNA sorting, although they do suggest the AA construct functions as a gain-of-function due to a) increased RNA reincorporated into chromosomes b) dramatic increase of chromosome targeting of SAF-A. These effects make it difficult to interpret the SAF-A-AA data. Related to this point, the analysis of altered RNA distributions relative to SAF-A is underdeveloped. Because the authors only examined one lncRNA (MALAT1), the conclusion that “forced retention of SAF-A on mitotic chromatin does not lead to an increase in the nuclear inheritance of specific transcripts” seems like an overstatement.

      We have reworded this conclusion about the role of SAF-A-AA on mitotic chromatin retention to more accurately reflect our findings (p6 line 197). (3) The authors find the U2 spliceosomal RNA is preferentially inherited. Might they speculate why this would be advantageous?

      We have added a sentence to the discussion speculating about the importance of U2 inheritance (p8 line 269-271). (4) Optional: it would be exciting to test the significance of U2 RNA inheritance

      We agree with the reviewer that this would be an exciting future direction to test. We envision that testing this idea rigorously would require the development of several new degron cell lines and is outside the scope of this study. (5) For Figure 1, please add statistics to figures and legend; add N=cells examined.

      We have added a new supplemental Excel spreadsheet that contains the N of cells measured for each experiment and added statistics to figure legends and figures where tests were significant. (6) For Figure 2, single channel panel of U2 RNA should be added. Figure 2E seems to reproduce the same data shown in Figure 2D (right-most columns) shown with different axes.

      We have added a single channel image of U2 to Figure 2 and replaced panel 2E with analysis of MALAT1, NEAT1, and U2 stability after transcription inhibition. (7) Figure 3, it is unclear why the authors selected MALAT1 for analysis, but not NEAT1 (or the single (unlabeled) antisense RNA also enriched in the SAF-A IP (figure 2C).

      We examined MALAT1 in greater detail because it is the most abundant lncRNA bound by SAF-A and most robust RNA FISH probe. The unlabeled antisense transcript is hnRNPUas1 and was not detectable in DLD1 cells by RNA FISH. (8) Figure 4B, please add statistics to figure and legend.

      For this experiment we prefer not to add statistics to the figure. This experiment was performed on a limited number of cells (21 and 8 respectively) and we do not believe that it is statistically appropriate to treat each cell as an independent N. The data confirms results in our previously published work (Sharp et al 2020) using live cell imaging. (9) Methods: in their description of the published lists of chromatin-bound RNAs, the authors should cite those works and provide a data availability statement with the associated GEO

      We have cited these works in the text and methods sections and added GEO accession numbers associated with these studies. (p21 line 442).

      Reviewer 2

      Major comments:

      The authors pose an interesting question -- how does nuclear RNA segregate following mitosis. In many ways, the results presented in this manuscript are rather preliminary. Key controls and validation are missing. Because of this, it is difficult to assess the validity of the main conclusions of the study. More specifically:

      1. The main conclusion of the manuscript ("about half of nuclear RNA is inherited by G1 cells following division") is primarily dependent on the experiment described in Fig 1A-B. The authors labeled synchronized cells with EU and quantified nuclear signal after release from synchronization. However, key controls are missing. What is the synchronization efficiency of the RO3306 treatment? How many cells in their acquired fields of cells are in G2 vs in other cell cycle stages? Following their drug release, what percentage of the synchronized cells have undergone telophase? What is the potential error rate in identifying the cell cycle stage using their visual imaging analysis? Without these key controls, it is unclear how to interpret the data presented in Fig 1B.

      One reason that nuclear inheritance has not been properly addressed in the literature is the difficulty in obtaining pure populations of cells synchronized in telophase or recently divided cells in early G1. There are no drugs available which can uniquely target these cell stages. In addition, the ability of human cells to all release perfectly synchronously from a drug-induced arrest can vary with cell type. For this reason we used a strategy employing synchronization methods designed to enrich cell populations for telophase or early G1 events, combined with single cell analysis of events with the distinct cytological features of each stage. Cells that have recently divided are extremely distinctive and easily identified using a combination of DAPI morphology to assess nuclear size and condensation state and the presence of Aurora-B/Midbody staining to indicate a recent cytokinesis. Our approach of using single cell analysis coupled with quantitative imaging therefore does not require a high efficiency of synchronization in cell populations. To gain confidence that our observations were reproducible we analyzed a large number of cells, performed multiple experimental replicates, and applied statistical tests to the data.

      To clarify these important points we have added text to the descriptions of how these experiments were performed (p3 line 72) and added information about the number of biological replicates to all figure legends and number of cell analyzed in each experiment to Supplementary Table 1.

      1. The use of transcriptional inhibitors in Fig 1 is really nice and is important for showing that it's not due to new transcription following mitosis. Well done!

      2. One potential mechanism that could explain the observed 25% relocalized nuclear RNA is through passive diffusion. That is, a proportion of molecules that are randomly diffusing during mitosis get trapped inside the newly formed nuclear membrane in early G1. This would be considered noise, and not a specific process that actively relocalizes nuclear RNA back into the nucleus. However, the authors' assay does not have a measure of the noise in their system. One potential experiment that may help quantify this noise is to express GFP in their cells, perform the experiment described in Fig 1A, and quantify the nuclear signal after telophase. This quantification would be the lower bound of the random process. A similar experiment with GFP-NLS could be performed to assess the upper bound of the 'inherited' molecules after mitosis. Without this type of control to quantify noise/random diffusion levels, it is unclear how much of the 25% EU signal that the authors detect is specific to the process they are testing.

      We appreciate the point that the reviewer has raised. To address this concern we examined the localization of the abundant mRNA b-actin. We examined the fraction of all b-actin FISH signal that is present in the nucleus in G2 and G1 cells following division. If a significant fraction of RNA is trapped in the reforming nucleus then we would have expected the fraction of b-actin in the G1 nucleus to increase. We observed that less b-actin RNA was present in the G1 nucleus, suggesting that passive entrapment of RNA is unlikely to be a mechanism of RNA inheritance. This is consistent with a lack of inheritance of MALAT1 and NEAT1 lncRNAs following mitosis. We have added these results to a new Supplemental Figure 2 and added text describing the results to the Results section of the manuscript (p4 lines 101-113). Additionally, this result is consistent with recent work showing that mitotic chromosomes condense through histone deacetylation and exclude negatively charged macromolecules (PMID: 35922507) and that chromosome clustering by Ki67 in early G1 phase excludes the cytoplasm from the new nucleus (PMID: 32879492). These references and ideas are now included in the results section of the manuscript.

      Related to the comment 1 and 2, EU labeling for 3 hrs in G2 cells would label ALL transcribed RNA, which would include mature mRNAs that will be translated in the cytoplasm. That is, this method is not specific to labeling nuclear RNAs only. How much of their signal is from mRNAs that got trapped inside the newly formed nuclear membrane? One way to test this is to measure the nuclear EU signal at later time points following telophase. Presumably, the nuclear transport mechanism would lead to export of non-nuclear RNAs and only the retained nuclear RNAs would contribute to the signal.

      Please see our response to point 3 with regard to entrapment. The laboratory that originally described EU RNA labeling demonstrated a 3 hour EU labeling period results in labeling nuclear RNA, and that longer labeling periods are required to visualize EU labeling of cytoplasmic RNAs after export (18840688). We have also observed in our previously published work that the 3 hour period labels nuclear RNA during interphase (33053167, 32035037). The nuclear EU signal reflects RNAs undergoing transcription, nuclear retained RNAs, and mature mRNAs prior to nuclear export.

      To identify nuclear RNAs that could be relocalized following mitosis, the authors analyzed data from "two different studies using different methodologies and a total of three different cell lines". From this analysis, the authors "found very little overlap in the chromatin-bound RNAs identified in these studies (Fig 2A)". This analysis seems fraught with problems. What is the rationale for using these studies? How valid is it to compare results from different methodologies and from different cell lines from the DLD-1 cells used in this study?

      We analyzed the data from these two studies because they were the only published studies that identified RNAs that were tightly linked to chromatin. We chose to compare the results from three different human cell lines because we sought to identify nuclear RNAs that were cell type-independent, so that we could analyze the transcripts behavior in DLD1 cells. In support of using these two studies all the RNAs that we analyzed were nuclear in our RNA FISH assays.

      A known problem of assessing chromatin-bound RNAs is that the level of contamination from cytoplasmic RNAs is highly variable and highly dependent on the assay. Indeed some of the most common contaminants of nuclear RNA assays are sn-, and sno-RNAs, and these are the main classes of RNA that the authors identified as common among the three data sets. What validation was used to assess whether these are the common noise/contaminants in the data?

      Our goal in using the two previously published studies was to identify cell type-independent nuclear RNAs that could be studied in detail using FISH. For validation in our study we performed RNA FISH on MALAT1, NEAT1, and U2. We found that each of these RNAs are highly enriched in the nucleus, consistent with previous publications. Since snRNAs function in splicing and snoRNA primarily function in the modification of tRNA and rRNA in the nucleolus it seems unlikely that these are contaminants of nuclear preparations. Each of the published studies performed their own validations of their purification and sequencing methodology. For the purpose of our work nuclear enrichment of a transcript by RNA FISH satisfied our requirements.

      One experimental validation that can be performed is biochemical fractionation of EU labeled cells, which would allow for fractionating nuclear from cytoplasmic RNA. The same problems arise with the analysis shown in Fig 3C when comparing SAF-A RIP-seq with this merged list of chromatin bound RNAs.

      In support of the nuclear enrichment of each of the transcripts that we examined RNA-FISH analysis demonstrated significant nuclear enrichment. Additionally, many previous studies have shown that each of these transcripts are enriched in the nucleus (U2: 11489914, 10021385, 7597053; NEAT1: 17270048; MALAT1: 12970751, 17270048). New text describing our use of these studies is present in the results section (p4-5 lines 117-129).

      Throughout the manuscript, the authors pose their findings as "RNA inheritance" following mitosis. However, this terminology is misleading. In fact, unless RNAs are lost/kicked out of the cell as they divide, aren't all RNAs inherited following cell division since they are present in the new daughter cells? Instead, what the authors mean is that some nuclear RNAs retain their function following cell division by relocalizing back into the nucleus in the new G1 cells, whereas other nuclear RNAs are unable to relocalize into the nucleus, and then presumably turned over by degradation process. The authors should take better care of their terminology throughout the manuscript.

      Thank you for pointing this out to us. As the reviewer stated most nuclear RNAs are removed from chromatin during mitosis. Only a subset are reimported into the nucleus. We have modified our wording to clearly state that we are discussing nuclear RNA inheritance by daughter cell nuclei rather than inheritance into daughter cells in general. These text changes can be found throughout the manuscript.

      Minor comments: 1. In all of the figures showing quantification of nuclear EU/FISH signal, the colors (red v blue) are not described (not found in the legend or methods). Presumably they are biological replicates, but this should be clearly stated.

      We have modified the plots and figure legends to more clearly explain what is plotted (See text in Figure Legends). 2. Is figure 2E the same data presented in Fig 2D but in different y-axis? If so, state clearly

      We have removed the data in the previous version of Figure 2E and replaced it with new data examining stability of MALAT1, NEAT1, and U2 in response to Reviewer 1 (p5 lines 154-166).

      Figure 3A. This experiment is using the SAF-A-AID-mCherry system. Therefore the label in Fig 3A should be SAF-A-KD (Knockdown) instead of KO (knockout)

      We have corrected this in Figure 3. 4. Typo in Fig 4B y-axis. It should be "Chromatin-localized SAF-A" instead of "Chromain-localized SAF-A"

      Thank you for pointing this out, we have corrected it. 5. The methods section indicate the "precise N or replicates in indicated figure legends" but none of the figure legends have the N values listed.

      We have listed number of biological replicates in all figure legends and included a new Supplemental Table 1 that contains the number of cells measured for each experiment.

      Reviewer 3

      The authors investigate an interesting question focussed on whether nuclear RNA from the previous cell cycle is present in the subsequent G1. It turns out that this is more complex than expected with some classes of RNA being inherited whilst others are not. SAF-A or HNRNPU had been implicated in this process but the authors suggest that its role is limited.

      Figure 1 In panel A the authors write on image SAF-A-mCh. What does this refer to?

      We have added information to the Figure legends indicating that this refers to SAF-A-AID-mCherry knocked-in to the endogenous SAF-A locus (see Figure Legends).

      Panel B and other panels can the authors present this data as a boxplot or distribution plot to get a better feel of the data distribution spread.

      We have modified all the plots in the manuscript to the Superviolin form to provide a clearer depiction of experimental replicates, mean, and standard deviation.

      Presumably labelled RNAs are naturally turned over. Have the authors considered that some loss of signal could be because of this?

      We have addressed the stability of specific RNAs using RNA FISH. We find that U2 and MALAT1 show essentially no degradation during the time course of our experiments. This data has now been included in an updated Figure 2. We have also modified our text to address this point more clearly (Figure 2E and p5 lines 154-166).

      Panel E, have the authors considered labelling RNA before RO3306 treatment? What effect would this have?

      We have performed this experiment in RPE1 cells and the presence of RO3306 did not affect cytological detection of transcript labeling. We have not included this experiment in the manuscript because it is performed in a different cell line than we use for the remainder of these studies.

      Shouls TI be added before RO3306 washout?

      We added transcription inhibitors after RO washout and entry into mitosis because transcription is naturally suppressed during mitosis. We were concerned that transcriptional inhibition in late G2 could lead to failure to properly enter into M phase.

      Also, it is unclear what the arrows are pointing at. In panel F there is a difference between the red and blue experiments. In the methods the authors say that inhibition was for either 1.5 or 2 h. Is this the source of the difference?

      We have modified the figure legends to state clearly that different colors indicate biological replicate experiments (See Figure Legends). Figure 2 In panel A there are clear differences between the cell lines. Is it right to compare them? Particularly the GRID-seq vs diMARGI? B, how relevant is it focussing on the "42" overlapping RNAs? In my mind this is not very informative.

      Our goal with this analysis was to identify cell type-independent chromatin bound RNAs to analyze in greater detail. Therefore, we analyzed three different cell lines because we planned to analyze transcript behavior in DLD1 cells, which were not included in either study. We have explained this rationale in greater detail in a revised version of the text (p4-5 lines 116-129).

      D-E, at a glance it is not clear that E is an expanded view of D. It might be easier if the panels were at same height.

      We have removed Panel E and replaced it with a new experiment examining the stability of NEAT1, MALAT1, and U2 after transcription inhibition (p5 lines 154-166). Figure 3 Is it correct to describe IAA treated degron cells as a KO? I also could not see a WB showing how complete SAF-A KD was.

      We previously characterized these cell lines in great detail (Sharp et al. JCB 2020). We have now provided quantitative measurement of SAF-A-mCherry fluorescence after different times of auxin addition to provide a quantitative estimate of SAF-A depletion (Supplemental Figure 3C).

      2 h treatment seems quite short, is this enough time to obtain sufficient knock down? How heterogenous is SAF-A KD in the cell population?

      We examined SAF-A depletion by auxin addition at 2 hours and 24 hours and achieve comparable depletion levels. This data in now included in Supplementary Figure 3C. There is some heterogeneity in the KD as is evident in Figure S3C, but these cells are easily identifiable by the presence of SAF-A-AID-mCherry fluorescence.

      Previous studies have shown that SAF-A does not like being tagged. How certain are the authors that these cells behave typically?

      We have generated two different cell lines (DLD1 and RPE1) where a C-terminal tag is inserted into the both copies of the endogenous SAF-A gene. SAF-A is one of the common essential genes (https://depmap.org/portal/gene/HNRNPU?tab=overview), however each of our cell lines exhibits no growth defects. We have recently shown that C-terminally tagged SAF-A fully rescues SAF-A knockout phenotypes (Sharp et al. JCB. 2020). Additionally, we have also performed RNA-seq (not published) on RPE1, RPE1 with endogenously tagged SAF-A and RPE-1 depleted of SAF-A and rescued with WT SAF-A-GFP and observed no changes in gene expression or mRNA splicing. Based on these assays we are confident that C-terminally tagged SAF-A expressed at endogenous levels functions normally. Figure 4 I'm struggling with the heading, and wonder if this is not supported by the data. Similarly the final sentence "The highly dynamic exchange of SAF-A:RNA complex" does not really provide an explanation.

      We have expanded the text in this section to explain this phenotype in greater detail (p7 lines 216-218).

    1. I'm enamored of this idea as well and this is a fascinating example.

      It seems similar to the related (and also difficult-to-name) concept which I've called combinatorial creativity. One of the earliest versions I've seen is that of Raymond Llullus' work with respect to combinatorial mnemonics described in Frances Yates' The Art of Memory (1966). Farnam Street's post is a good start https://fs.blog/networked-knowledge-and-combinatorial-creativity/, but I've been collecting other examples: https://hypothes.is/users/chrisaldrich?q=tag%3A%22combinatorial+creativity%22 and other names for it over time.

      I can't help but wonder what Ericsson's role of deliberate practice would look like with arts as the subject? What motivates long term deliberate practice?

      Yates, Frances A. The Art of Memory. 1966. Reprint, Chicago, IL: University of Chicago Press, 2001. https://www.amazon.com/Art-Memory-Frances-Yates/dp/0226950018.

      Ericsson, K. Anders, Ralf Th. Krampe, and Clemens Tesch-Romer. The Role of Deliberate Practice in the Acquisition of Expert Performance. Psychological Review, 1993.

  18. Nov 2022
    1. Author Response

      Reviewer #2 (Public Review):

      Grasses develop morphologically unique stomata for efficient gas exchange. A key feature of stomata is the subsidiary cell (SC), which laterally flanks the guard cell (GC). Although it has been shown that the lateral SC contributes to rapid stomatal opening and closing, little is known about how the SC is generated from the subsidiary mother cell (SMC) and how the SMC acquires its intracellular polarity. The authors identified BdPOLAR as a polarity factor that forms a polarity domain in the SMC in a BdPAN1-dependent manner. They concluded that BdPAN1 and BdPOLAR exhibit mutually exclusive localization patterns within SMCs and that formative SC division requires both. Further mutant analysis showed that BdPAN1 and BdPOLAR act in SMC nuclear migration and the proper placement of the cortical division site marker BdTANGLED1, respectively. This study reveals a unique developmental process of grass stomata, where two opposing polarity factors form domains in the SMC and ensure asymmetric cell division and SC generation.

      The findings of this study, if further validated, are novel and interesting. However, I feel that the data presented in the current manuscript do not fully support some crucial conclusions. The lack of dual-color images is the weakest point of this study. If it is technically impossible to add them, alternative analyses are needed to validate the main conclusions.

      1) Is BdPOLAR-mVenus functional? Although the authors interpret that weak BdPOLAR-mVenus expression partially rescued the bdpolar mutant phenotype in Fig. S4D, the localization pattern visualized by BdPOLAR-mVenus may not be completely reliable with this partial rescue activity.

      This is indeed a valid point. The partial complementation of weakly expressing translational reporters (Figure 3–figure supplement 1D) and the weak effect of BdPOLAR-mVenus overexpression lines (Figure 3–figure supplement 1J) at least suggest partial functionality which is strongly dependent on dosage. Yet the localization pattern and the temporal dynamics might indeed not fully reflect the spatiotemporal dynamics of the endogenous BdPOLAR. This criticism is, however, true for any transgenic reporter line–even when fully complementing–as the requirement for dosage, stability, and turnover likely varies strongly between different protein classes and functions.

      Nonetheless, we have added a sentence on p. 7, which mentions this potential caveat.

      2) Regardless of the functionality of the tagged protein, the authors need to provide more information on their localization. For example, is there a difference in polarity pattern depending on expression level? Does overexpressed BdPOLAR-mVenus invade the BdPAN1 zone? In such cases, might the loss of BdPOLAR polarity in the bdpan1 mutant be a side effect of overexpression, not PAN1 exclusion? Does BdPOLAR expression (no tag) show a dose-dependent effect, similar to the mVenus-tagged protein?

      The difference in polarity patterns in bdpan1 mutants and wild-type does not depend on expression level. BdPOLAR-mVenus was crossed into bdpan1 and mutant and wild-type siblings in the F2 generation were analyzed. This means that the data presented in Fig. 3E and F show exactly the same transgene insertion line in wt and bdpan1 and were imaged with the same setting for comparability. Therefore, the difference in localization is not due to different expression levels but indeed reflects a PAN1-dependent effect.

      To address if BdPOLAR without a tag is also sensitive to dosage, we have generated an untagged complementation line that includes the untagged, genomic locus of BdPOLAR including promoter (-3.1kb) and terminator (+1.1kb). Yet, even though this construct is much better at rescuing the mutant, we still see remaining defects in T0 lines (Figure 3–figure supplement 1K) suggesting that even without a tag we cannot fully recapitulate wild-type functionality. Yet, to actually measure protein levels of untagged BdPOLAR, we would need to raise an antibody against BdPOLAR, which we think is clearly out of the scope of this study.

      3) A major conclusion of this study was that the polarity domains of BdPOLAR and BdPAN1 are mutually exclusive. However, not all the cells in the figures were consistent with this statement. For example, the BdPOLAR signals at the GMC/SMC interphase appear to match BdPAN1 localization (compare 0:03 s in Video 1 and 0:20 s in Video 2 [top cell]). The 3D rendered image in Fig. 2F shows that BdPOLAR is excluded near the GMC on the front side of the SMC, where BdPAN1 is not localized. Some cells did not exhibit polarization (Fig. 3A, bottom left; Fig. 3E, bottom left). The most convincing data are the dual-color images of these two proteins. Otherwise, a sophisticated image analysis is required to support this conclusion.

      We agree that dual-color image analysis would have provided the most convincing data. As mentioned in our answers to the reviewing editor and reviewer 1, we have generated a dual marker line (BdPAN1p:BdPAN1-CFP; BdPOLARp:BdPOLAR-mCitrine), yet the BdPAN1-CFP signal (compared to mCitrine signal) was too weak to visualize the proximal BdPAN1 domain.

      This issue was also raised by reviewer 1 and deemed an essential revision. To determine how BdPOLAR and BdPAN1 relate spatially to each other, we have added data in Figure 2E where we manually traced mature SMC outlines to determine BdPOLAR-mVenus and BdPAN1-mCitrine occupancy along the SMC’s circumference. This confirmed that the polarization is indeed opposite yet not perfectly reciprocal (see details above, Essential Revisions #1).

      Finally, we realized that the 3D image renderings were more confusing than helpful and we removed them from the revised version.

      4) Another central conclusion was that BdPOLAR was excluded at the future SC division site, marked with BdTANGLED1. However, these data are also not very convincing, as such specific exclusion cannot be seen in some figure panels (e.g., Fig. 3A, bottom left; Fig. 3E, all three cells on the left). If dual-color imaging is not feasible, a quantitative image analysis is needed to support this conclusion.

      As for point 3, this was also criticized by reviewer 1 and deemed an essential revision by the reviewing editor.

      To determine whether the absence of BdPOLAR signal and the presence of BdTAN1 signal colocalize, we again manually traced mature SMC outlines to determine BdPOLAR-mVenus and BdTAN1-mCitrine occupancy along the SMC’s circumference. We plotted the relative average fluorescence intensity in Figure 4G-I nicely showing that BdTAN1 indeed resides in the BdPOLAR gaps above and below the GMC (again, details above, Essential Revisions #2).

      5) I could not find detailed imaging conditions and data processing methods. Are Figs. 2B and 2E max-projection or single-plane images? If they are single-plane images, which planes of the SMC are observed? In addition, how were Figs. 2C and 2F rendered? (e.g., number of images, distance intervals, processing procedures). This information is important for data interpretations.

      We agree that we might not have provided sufficient imaging condition details and have added more details regarding image acquisition in the method part (p. 20). We always use a consistent depth and show the midplane of SMCs. As mentioned above, we removed Figs. 2C and 2F and the supplemental movies as these data did not seem to be helpful.

      6) [Minor point] The authors should clearly describe where BdPAN1 is expressed and localized. Is it expressed in the GMC and localized at the GMC/SMC interface? Alternatively, is it expressed and localized in the SMC?

      BdPAN1 is expressed throughout the epidermis but starts to strongly accumulate at the GMC/SMC interface. According to the literature (Cartwright et al 2009 with immunostainings against ZmPAN1 and Sutimantanapi et al. 2014 with PAN1 and PAN2 reporter) and our own observations (Fig. S3), this accumulation occurs in the SMC rather than in the GMC. In Fig. S3A, third panel, second GMC from the top, for example, one can see that the early PAN1 polarity domain expands beyond the GMC/SMC interface suggesting that it is indeed forming in SMCs rather than in GMCs. We have specified this in the text more clearly now (p. 5).

    2. Reviewer #2 (Public Review):

      Grasses develop morphologically unique stomata for efficient gas exchange. A key feature of stomata is the subsidiary cell (SC), which laterally flanks the guard cell (GC). Although it has been shown that the lateral SC contributes to rapid stomatal opening and closing, little is known about how the SC is generated from the subsidiary mother cell (SMC) and how the SMC acquires its intracellular polarity. The authors identified BdPOLAR as a polarity factor that forms a polarity domain in the SMC in a BdPAN1-dependent manner. They concluded that BdPAN1 and BdPOLAR exhibit mutually exclusive localization patterns within SMCs and that formative SC division requires both. Further mutant analysis showed that BdPAN1 and BdPOLAR act in SMC nuclear migration and the proper placement of the cortical division site marker BdTANGLED1, respectively. This study reveals a unique developmental process of grass stomata, where two opposing polarity factors form domains in the SMC and ensure asymmetric cell division and SC generation.

      The findings of this study, if further validated, are novel and interesting. However, I feel that the data presented in the current manuscript do not fully support some crucial conclusions. The lack of dual-color images is the weakest point of this study. If it is technically impossible to add them, alternative analyses are needed to validate the main conclusions.

      1. Is BdPOLAR-mVenus functional? Although the authors interpret that weak BdPOLAR-mVenus expression partially rescued the bdpolar mutant phenotype in Fig. S4D, the localization pattern visualized by BdPOLAR-mVenus may not be completely reliable with this partial rescue activity.<br /> 2. Regardless of the functionality of the tagged protein, the authors need to provide more information on their localization. For example, is there a difference in polarity pattern depending on expression level? Does overexpressed BdPOLAR-mVenus invade the BdPAN1 zone? In such cases, might the loss of BdPOLAR polarity in the bdpan1 mutant be a side effect of overexpression, not PAN1 exclusion? Does BdPOLAR expression (no tag) show a dose-dependent effect, similar to the mVenus-tagged protein?<br /> 3. A major conclusion of this study was that the polarity domains of BdPOLAR and BdPAN1 are mutually exclusive. However, not all the cells in the figures were consistent with this statement. For example, the BdPOLAR signals at the GMC/SMC interphase appear to match BdPAN1 localization (compare 0:03 s in Video 1 and 0:20 s in Video 2 [top cell]). The 3D rendered image in Fig. 2F shows that BdPOLAR is excluded near the GMC on the front side of the SMC, where BdPAN1 is not localized. Some cells did not exhibit polarization (Fig. 3A, bottom left; Fig. 3E, bottom left). The most convincing data are the dual-color images of these two proteins. Otherwise, a sophisticated image analysis is required to support this conclusion.<br /> 4. Another central conclusion was that BdPOLAR was excluded at the future SC division site, marked with BdTANGLED1. However, these data are also not very convincing, as such specific exclusion cannot be seen in some figure panels (e.g., Fig. 3A, bottom left; Fig. 3E, all three cells on the left). If dual-color imaging is not feasible, a quantitative image analysis is needed to support this conclusion.<br /> 5. I could not find detailed imaging conditions and data processing methods. Are Figs. 2B and 2E max-projection or single-plane images? If they are single-plane images, which planes of the SMC are observed? In addition, how were Figs. 2C and 2F rendered? (e.g., number of images, distance intervals, processing procedures). This information is important for data interpretations.<br /> 6. [Minor point] The authors should clearly describe where BdPAN1 is expressed and localized. Is it expressed in the GMC and localized at the GMC/SMC interface? Alternatively, is it expressed and localized in the SMC?

  19. Oct 2022
    1. Worried about paper cards being lost or destroyed .t3_y77414._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; --postBodyLink-VisitedLinkColor: #989898; } I am loving using paper index cards. I am, however, worried that something could happen to the cards and I could lose years of work. I did not have this work when my notes were all online. are there any apps that you are using to make a digital copy of the notes? Ideally, I would love to have a digital mirror, but I am not willing to do 2x the work.

      u/LBHO https://www.reddit.com/r/antinet/comments/y77414/worried_about_paper_cards_being_lost_or_destroyed/

      As a firm believer in the programming principle of DRY (Don't Repeat Yourself), I can appreciate the desire not to do the work twice.

      Note card loss and destruction is definitely a thing folks have worried about. The easiest thing may be to spend a minute or two every day and make quick photo back ups of your cards as you make them. Then if things are lost, you'll have a back up from which you can likely find OCR (optical character recognition) software to pull your notes from to recreate them if necessary. I've outlined some details I've used in the past. Incidentally, opening a photo in Google Docs will automatically do a pretty reasonable OCR on it.

      I know some have written about bringing old notes into their (new) zettelkasten practice, and the general advice here has been to only pull in new things as needed or as heavily interested to ease the cognitive load of thinking you need to do everything at once. If you did lose everything and had to restore from back up, I suspect this would probably be the best advice for proceeding as well.

      Historically many have worried about loss, but the only actual example of loss I've run across is that of Hans Blumenberg whose zettelkasten from the early 1940s was lost during the war, but he continued apace in another dating from 1947 accumulating over 30,000 cards at the rate of about 1.5 per day over 50 some odd years.

    1. Posted byu/raphaelmustermann9 hours agoSeparate private information from the outline of academic disciplines? .t3_xi63kb._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; --postBodyLink-VisitedLinkColor: #989898; } How does Luhmann deal with private Zettels? Does he store them in a separate category like, 2000 private. Or does he work them out under is topics in the main box.I can´ find informations about that. Anyway, you´re not Luhmann. But any suggestions on how to deal with informations that are private, like Health, Finances ... does not feel right to store them under acadmic disziplines. But maybe it´s right and just a feeling which come´ out how we "normaly" store information.

      I would echo Bob's sentiment here and would recommend you keep that material like this in a separate section or box all together.

      If it helps to have an example, in 2006, Hawk Sugano showed off a version of a method you may be considering which broadly went under the title of Pile of Index Cards (or PoIC) which combined zettelkasten and productivity systems (in his case getting things done or GTD). I don't think he got much (any?!) useful affordances out of mixing the two. In fact, from what I can see looking at later iterations of his work and how he used it, it almost seems like he spent more time and energy later attempting to separate and rearrange them to get use out of the knowledge portions as distinct from the productivity portions.

      I've generally seen people mixing these ideas in the digital space usually to their detriment as well—a practice I call zettelkasten overreach.

  20. Sep 2022
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): ____ *A significant criticism of the paper is an assumption that readers will be familiar with all of the findings in the author's previous 2016 paper and the PGL-1 papers by Aoki et al. Minimal context is given for each approach. *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      *Some conclusions are not well supported and require further analysis, proper controls, and more extensive descriptions of the experiments performed. *

      We have addressed the reviewer’s concerns as detailed below.

      Most importantly, the central conclusion and title of the paper is that composition can buffer the dynamics of individual proteins within liquid-like condensates. In other words, in vitro condensation assays often do not recapitulate LLPS behavior in vivo. That said, the findings in this study would be significantly strengthened and complemented by observing endogenously tagged PGL-3 and PGL-3 mutants in living worms, considering the efficiency of using CRISPR in C. elegans to insert tags and make precise mutations.

      The original manuscript already contained data where we microinjected wild-type PGL-3 and mutant PGL-3 proteins (recombinantly purified) into adult C. elegans gonads to assay how the P granule phase supports diffusion of these proteins.

      In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

      *To improve readability, the introduction to P granules should be expanded, and include the reasons for looking at the nematode-specific PGL-3 protein among all the other known P granule proteins. A recap of previous findings on PGL-3 phase separation, in vivo and in vitro, is warranted, starting with the significant results of Saha et al 2016. Setting up the investigative questions in the context of recent work on PGL-1 (Aoki, et al) is also necessary. *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      The physiological concentration of PGL-3 should be more transparent, including why some experiments in this study are done at physiological concentrations while others are not. Describing why salt concentrations, crowding agents, and protein abundance are similar or different for each experiment is necessary and relevant. For example, after showing in Figure 1 that PGL-3 protein phase separates, the paragraph starting on line 161 says that it was previously shown that PGL-3 doesn't phase separate at physiological concentrations without RNA. One has to go back to Figure 1 to realize it was done differently than Figure 2 and Saha 2016.

      The concentrations of PGL-3 protein and use of crowding agents (if any) have already been specified within figures or figure legends. Salt concentrations used are specified within figure legends or materials and methods section.

      We have added the following paragraph to the materials and methods section of the revised manuscript.

      “Saha et al. 2016 showed that at physiological concentrations (approx. 1 mM), the PGL-3 protein is unable to phase separate into condensates. At these concentrations, mRNA promotes phase separation of PGL-3. To assay for mRNA-dependence of condensate assembly, it is therefore essential to use physiological concentrations of the PGL-3 protein or mutants (e.g. Figure 2). However, these condensates are generally too small to assay rate of internal rearrangement of PGL-3 molecules within condensates using fluorescence recovery after photobleaching experiments. Therefore, to generate large condensates for measuring internal rearrangement of PGL-3 or mutant molecules, we primarily used higher concentrations of these proteins where binding to RNA is not essential for phase separation. However, to mimic the in vivo P granule phase as closely as possible, we generally added constituent proteins in proportion to their in vivo abundance estimated in Saha et al. 2016.”

      The added paragraph in the Introduction section of the revised manuscript may be helpful to the readers. * *

      *Statements in the same paragraph like "in contrast to full-length PGL-3, mRNA does not support phase separation..." should be qualified by stating the concentration observed, with or without salts or other crowding agents. Similarly, line 230 "suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics" and should be qualified with "at non-physiological concentrations and with XX crowding agents or salt concentration." It would be more consistent if physiological concentrations were consistent from figure to figure, as extra variables weaken some of the stated conclusions. *

      We thank the reviewer for this suggestion. However, we feel the statements (without full experimental details within main text) help convey the conceptual essence of the findings better. Of course, all these statements contain reference to figures or prior publications which provide relevant details about experimental conditions.

      *The 2010 review reference stating that there are 40 P granule enriched proteins is outdated. More recent reviews put the number much higher. This is relevant because the approach to put PGL-3 in a more physiological environment by including just PGL-1, GLH-1 and mRNA with the condensate assays, out of ~100 P granule enriched proteins, may not be sufficient to conclude "that the influence of complex composition on dynamics is modest" (line 223), or imply that the multicomponent nature of the P granule is reconstituted by adding these components (line 355). *

      We revised the text to indicate that P granules contain approx. 70 proteins and added appropriate references.

      • *

      Based on current information of constitutive P granule components (PGL-1, PGL-3, GLH-1, GLH-2, GLH-3, GLH-4, DEPS-1, MIP-1 and mRNA), (Kawasaki et al, 1998, 2004; Spike et al, 2008a, 2008b; Price et al, 2021; Cipriani et al, 2021; Phillips & Updike, 2022) we reconstituted P granule-like phase in vitro with mRNA, PGL- and GLH- proteins that likely constitute the most abundant components within P granules in vivo (based on concentration estimates in Saha et al. 2016).

      We do appreciate the reviewer’s comment that more components can be added to our in vitro reconstitution in addition to the limited set of components used in our study. However, we feel it is interesting to observe that a limited set of components can support dynamics buffering (the main message of the paper). Further, the complementary in vivo experiments show that the P granule phase can also support dynamics buffering.

      *Figure 1C needs to include PGL-3(370-693) in the analysis. Figure 1E is also incomplete without a comparison of FRAP recovery between PGL-3(1-452) and full PGL-3 as the control.

      *

      Fig. 1c already includes data with PGL-3 (370-693) [top row, central panel]. FRAP recovery data with full-length PGL-3 is already available in Supplementary Fig. 2c, g.

      *Figure 4C is missing an essential control where PGL-3 and S1 FRAP is performed without PGL-1, GLH-1, and mRNA. *

      In the revised version, we have added Supplementary Fig. 5f, where FRAP recovery of the following condensates are plotted together: 1) PGL-3 alone, 2) S1 alone, 3) PGL-3 + PGL-1, GLH-1 and mRNA, 4) S1 + PGL-1, GLH-1 and mRNA.

      *It would also help show sup Fig4A in the main figure to show concentration dependence. *

      We revised Fig. 4 to address the reviewer’s suggestion.

      Consider adding subtitles to supplementary figures.

      We considered the suggestion but felt it may not be essential.

      *M&M should include an explanation for statistical analysis *

      We added a paragraph describing statistical analysis within the Materials and Methods section.

      *CROSS-CONSULTATION COMMENTS I am also in agreement with the comments and critiques of reviewers 2 and 3.

      * Reviewer #1 (Significance (Required)): The paper by Saha and colleagues investigate the in vitro liquid-liquid phase separation propensity of a P granule protein PGL-3 and its structural domains. The findings largely replicate and support the phase-separation properties of a paralogous protein called PGL-1, as recently described by Aoki et al. 2021. Furthermore, they show that the dynamics demonstrated by recombinant PGL-3 may be maintained or buffered by the complex composition of P granules.

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

      *Jelenic et al. describe the effect of partner proteins on the FRAP dynamics of recombinant PGL-3 protein and variants in in vitro condensates and C elegans p-granules. The study shows that the N terminal a-helical dimerization domains is required for condensate formation and modulate of it alters aggregation and the FRAP dynamics of its condensates. Interestingly, a construct including the entire IDR region (370-693) by itself does not phase separate on its own at these conditions. The K126E K129E mutant (known previously to disrupt dimerization) and the deletion mutant abrogate llps. A mutant construct that shuffles the sequence in the region 423-453 called S1 here reduces the helicity and the condensate FRAP dynamics but recovered in the presence of a few P granule components. Also, the reduced dynamics of partially unfolded PGL-3 condensates are also rescued by the p-granule components to a certain degree of the unfolded PGL3 concentrations. This threshold concentration for recovering the condensate dynamics is further reduced in the helix reducing S1 mutant, which is also dependent on the number and the nature of P granule components.

      Overall, the study aims to probe how "composition can buffer protein dynamics within liquid-like condensates" - yet several underlying aspects of the study do not fully support that conclusion. The introduction does not sufficiently introduce the known structural information of the two dimerization domains in C elegans PGL proteins for which structures are known. The region is discussed as "alpha helical" but really there are two evolutionarily conserved independently folding dimerization domains (referring to the mutants as "reduced alpha helicity" is not helpful - these are mutations that destabilize a folded domain).*

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      *Additionally, the abstract and introduction ignore the aspects of aggregation (touched on in discussion) - this is likely what the disruption to the helical region in residue 450 region is doing (the helix is not on the dimer interface based on homology / sequence identity to the crystal structure of PGL-1 central dimerization domain. *

      We think elucidating the molecular mechanism of apparent aggregation of PGL-3 (D425-452) could be an interesting direction for future investigation. Here, we focused our analysis predominantly on the mutant S1 since it generates liquid-like condensates with ~20- fold slower dynamics (compared to wild-type) in contrast to non-dynamic condensates/aggregates. Therefore, influence of other P granule components on the dynamics of PGL-3 in liquid-like condensates is easier to address using the mutant S1 rather than PGL-3 (D425-452). We didn’t find evidence that S1 aggregates as we did not detect aggregates of S1 molecules using fluorescence confocal microscopy and the slow dynamics in condensates of S1 does not change significantly over 24 h (Supplementary Fig. 3f).

      However, in the revised version, we now include additional in vivo data with C. elegans expressing the aggregation-prone PGL-3 (D425-452)-mEGFP. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

      Finally, the "dynamics buffering" is not really clearly established and could also be explained as small concentrations of aggregated proteins act like clients while increasing the concentration results in aggregation and "cross linking" in the entire droplet - and this concentration is never achieved in the in worm experiments so it is not clear. In other words, the change in FRAP dynamics not observed in worms is perhaps not surprising if small amount of recombinant proteins are incorporated into the granules. *

      *

      Data with the S1 mutant establishes that dynamics buffering can be observed in condensates with different sets of additives both in vitro (Fig. 5a, b) and in vivo (Fig. 4a, b). Further, data with condensates of S1 containing the additives PGL-3 (K126E K129E) or S1 (K126E K129E) demonstrate that dynamics (half-time of FRAP recovery) within S1 condensates, and in turn “dynamics buffering” depend on inter-molecular interactions. With respect to the hypothesis proposed by the reviewer, we did not detect aggregates within S1 condensates using confocal fluorescence microscopy.

      In contrast to S1 condensates, condensates containing partially unfolded PGL-3-mEGFP together with PGL-1, GLH-1 and mRNA showed spatial inhomogeneities in fluorescence signal throughout the condensate (Fig. 4g). We have not tested if areas with higher fluorescence signal represent aggregates. It is a possibility that the partially unfolded PGL-3-mEGFP fluorescence signal becomes more homogeneous if higher concentrations of additives (PGL-1, GLH-1 and mRNA) are used. However, the presented data demonstrate the significant effect of the P granule components (PGL-1, GLH-1 and mRNA) on the FRAP recovery rate of partially unfolded PGL-3-mEGFP in condensates (compare figures Fig. 3e and Fig. 4g).

      However, consistent with dynamics buffering, the P granule phase in vivo supports wild-type dynamics of different PGL-3 constructs over a range of concentrations - PGL-3(D425-452)-mEGFP at physiological concentration (CRISPR transgenic strain, Fig. 4e) or at higher concentrations (microinjected S1 and partially unfolded PGL-3-mEGFP, Fig. 4b).

      • *

      *It is also not clear what the mechanism of the changes is - is the protein driven to fold more properly (despite S1 disruption of its conserved sequence) inside the condensate? Does it still self interact and act as a dimerization domain? Does this change disrupt interactions? *

      We agree with the reviewer that identifying the precise structural changes of the S1 protein within the condensate vs. dilute phase could be an interesting direction for future investigation. However, we have already discussed the issues raised by the reviewer in the original manuscript.

      “Our data is consistent with the model that other regions of S1 molecules cooperate with residues 425-452 (shuffled) to generate stronger inter-molecular interactions. For instance, addition of the mutant S1 (K126E K129E) enhances dynamics of S1 within condensates in contrast to maintaining the slower dynamics observed within condensates of S1 alone. This suggests that the interactions disrupted by the mutations K126E and K129E also contribute to slow S1 dynamics. One possibility is that interactions involving the residues K126 and K129 favor S1 conformations that enhance 425-452 (shuffled)-dependent interactions. Indeed, the mutations K126E K129E have been reported to interfere with interactions among N-termini of PGL-3 molecules (Aoki et al, 2021). While two self-association domains within the α-helical N-terminus of PGL-3 have been mapped (Aoki et al, 2021, 2016), structural insights into those associations are limited. However, PGL-3 shares significant sequence similarity with another protein PGL-1. Crystal structures are available for fragments of the PGL-1 protein that show the two self-association domains at the N-terminus are predominantly α-helical and globular in nature (Aoki et al, 2016, 2021). Therefore, one possibility is that shuffling the sequence 425-452 of PGL-3 or heat-induced unfolding of PGL-3 exposes hydrophobic residues that become available to participate in inter-molecular interactions.”

      What is the real mechanism by which PGL-3 phase separates if not via the disordered domains? *

      *

      We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

      Throughout the manuscript, the term "dynamics" is used to indicate FRAP, but it would be better to define what is meant (diffusion of PGL-3 in condensates) instead of using dynamics a term that could mean many things. Secondly, FRAP cannot directly measure liquidity etc (see recent critiques by McSwiggen elife 2019, etc) so it is better to be cautious in the claims. Finally, discussing "dyanmics buffering" adds more terminology where it is not needed - perhaps say "changes to diffusion of PGL-3 in condensates".

      We feel it is useful to introduce a term that describes our observation. To our knowledge, our observation is novel and therefore requires a new term to describe it.

      However, we do appreciate the concern raised by the reviewer. We used a more generic term “dynamics buffering” in contrast to the more specific “diffusion buffering” since we did not directly estimate diffusion behavior at the ‘single-molecule’ level. However, we already described what we mean by “dynamics buffering” in the text as follows.

      “We used condensates of similar size for our analysis (average ± 1 SD of diameter of condensates are 6.4 ± 1.7 mm (Fig. 5a) and 5.9 ± 0.4 mm (Fig. 5b)). Therefore, dynamics buffering here is likely to represent similar diffusion rates of S1 within condensates.”

      • *

      *The "N-terminus" is not 65% of the protein. One could define this as the N-terminal domain, but again there are two clear folded domains in the first 65% of the protein and this needs to be described better. *

      We revised the text to replace the terms “N-terminus” and “N-terminal domain” to “N-terminal fragment”.

      *The description of "stickers" and the references to tau and hnRNPA1 are confusing as this is a predominantly ordered domain while those are IDRs. *

      • *

      We feel this is important as it aids discussing our work in the context of current literature describing the mechanisms of macromolecular phase separation.

      The suggestion in the discussion that "P granule components support dynamics by participating in intermolecular interactions wth PGL-3-mEGFP molecules" is not well supported because no interaction assays are performed and no mutaitons are made that disrupt these interactions to test this.

      Indeed, we have not conducted interaction assays or mutational analysis to directly test this. However, our detailed analysis with the S1 mutant supports this suggestion.

      While partially unfolded PGL-3-mEGFP molecules lose 30% of a-helicity, the a-helicity of the S1 mutant is reduced by 15% compared to wild-type PGL-3. Data with S1 and partially unfolded PGL-3-mEGFP molecules show that loss of a-helicity correlates with slower diffusion of protein molecules within condensates. Using the mutants PGL-3 (K126E K129E) and S1 (K126E K129E), we show that diffusion rate of S1 molecules within condensates depend on inter-molecular interactions, and presence of other P granule components support faster diffusion rate of S1 molecules within condensates. Therefore, we feel it is safe to speculate that intermolecular interactions with P granule components can support dynamics of a “more unfolded” (compared to S1) version of PGL-3 molecule. * *

      *More detailed analysis of some of the claims: Claim 1: An a-helical region mediates the phase separation of PGL-3, and the C-terminal disordered region by itself does not phase separate. The N-terminal dimerization is essential for LLPS. The C-terminal IDR interactions with mRNA facilitate the LLPS. Comments: The authors show sufficient experimental data using microscopy and FRAP on truncated constructs with the N-terminal and C-terminal regions - but see above regarding how these are described - a proper domain structure with the folded domains shown and the RGG motifs highlighted should be added and integrated throughout the discussion. *

      In the revised version of the manuscript, we described the predicted PGL-3 domains within a paragraph in the introduction: “The interactions that support phase separation of the PGL-3 protein remains unclear. Structural studies on the orthologous PGL-1 protein revealed two dimerization domains. This raises the possibility that PGL-3 also contains similar dimerization domains, and phase separation depends on interactions involving these domains.”

      Our Fig. 1a already includes the schematic representation of PGL-3 with predicted N-terminal and Central Dimerization domains and RGG repeats.

      *They show that the N-terminus is necessary and adequate for LLPS, and the C-terminus by itself does not phase separate. But, how does the N-terminal domains phase separate? This is not explained - what are the interactions? *

      • *

      Also, a di-mutant (K126E K129E) that is known, and also authors use SEC-MALS to show their N-terminal construct is consistent with the published results. Disrupting the n-terminal dimerization prevents phase separation, suggesting the importance of these residues in the N-terminus for self-assembly and LLPS. The Microscopy data backs the claim that the mRNA-mediated LLPS is facilitated by binding with C-terminus. However, the m-RNA binding to IDR is not sufficient for LLPS. Yet, the authors do not explain how higher salt prevents phase separation - again the mechanism of phase separation is unclear. Is it multivalent interaction of the two dimerization domains? A basic model (that is tested) would be important.

      We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

      However, our manuscript already provides some relevant insights as follows.

      “To investigate the underlying mechanism further, we began by testing if the N-terminal α-helical region of PGL-3 can self-associate. Our analysis using size exclusion chromatography followed by multi-angle light scattering (SEC-MALS) showed that this PGL-3 fragment 1-452 forms a dimer (Supplementary Fig. 2f). Mutation of two residues (K126E K129E) have been shown to interfere with interactions among the N-termini of PGL-3 molecules (Aoki et al, 2021). We mutated these two residues within the full-length PGL-3 protein (K126E K129E) (Fig. 1a) and found that this mutant PGL-3 (K126E K129E) protein cannot phase separate even at high protein concentrations up to ~130 µM (Fig. 1b, c). Addition of mRNA does not trigger phase separation of this protein at physiological concentrations either (Fig. 2a, b). Taken together, our data is consistent with a model where association among folded N-termini of PGL-3 molecules is essential for phase separation.”

      A likely possibility is that phase separation of PGL-3 depends on electrostatic inter-molecular interactions among the folded N-terminal fragment of PGL-3 molecules. Therefore, high salt prevents phase separation.

      Are the tags removed to ensure that phase separation is not caused by tags or remaining linker regions? Is the protein purified to be without nucleic acid contamination or other purity metrics?

      Most of the experiments were done with only 5% of total protein tagged with 6x-His-mEGFP. No additional tags were present on the constructs. For recombinant expression and purification, proteins were cloned such that it is possible to remove the 6xHis-mEGFP tag following treatment with TEV protease. Following removal of the 6xHis-mEGFP tag, the residual linker is just two amino acid residues long. We used 100% tagged-protein for our experiments only in very few cases (indicated in the figure legends).

      To demonstrate purity of recombinant proteins, SDS-PAGE gels with all protein constructs used in this study are shown in Supplementary Fig. 1.

      To minimize contamination of nucleic acids, we treated samples with Benzonase during the course of purification.

      To assess the extent of nucleic acid contamination, the ratio of absorbance at 260 nm and 280 nm (A260/A280) was monitored. In exceptional cases with high A260/A280 values, we analyzed samples further by purifying RNA from the sample using RNA purification kit (Qiagen) and found that RNA represented 1% or less of the sample mass.* *

      Claim2: The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3. Comments: The authors show that the full-length PGL-3 condensates have modest influence of components by comparing the FRAP half times with or without the P granule components, including mRNA. However, have the authors tried this in the presence of mRNAs for the constructs lacking the IDRs as they have several RGG domains and bind with mRNA and are likely to change the dynamics.

      We thank the reviewer for this suggestion. However, this experiment is not essential to support the claim made in the context of homotypic condensates of PGL-3 : “The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3.”

      *The authors report the importance of the N-terminal a-helical region by making a construct that lacks/disrupts a part of the helices lowers the thermal stability and significantly lowers the dynamics of the condensates. Also unfolding of helices is shown to reduce the dynamics. One primary concern is whether these "rescued" protein dynamics imply protein functionality. *

      An assay of “functionality” e.g. an enzymatic activity of the PGL-3 protein is not available.

      However, we compared the fecundity of C. elegans worms expressing from the native pgl-3 locus, PGL-3-mEGFP or the mutant protein PGL-3(D425-452)-mEGFP, to assay the functionality of P granules in these strains. We found that worms of both genotypes produced similar number of offspring (Fig. 4d). This suggests that deletion of residues 425-452 of PGL-3 does not result in significant loss of function of P granules.

      Are these semi denatured proteins refolded in the presence of P-granule components?

      We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

      Finally, it is not clear why the authors chose to disrupt folding of the central dimerization domain?

      The manuscript included a paragraph to describe the rationale.

      “This suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics within condensates. Therefore, we addressed the role of the N-terminal α-helical region (1-452) in driving dynamics. In order to avoid engineering mutations that result in significant misfolding of PGL-3 and concomitant loss of its ability to phase separate, we focused our mutational analysis close to the junction of the folded N-terminus and the disordered C-terminus of PGL-3. Surprisingly, we found that a full-length PGL-3 construct (D425-452) that lacks only 27 residues phase separates into condensates that are non-dynamic (Fig. 3a, c). Sequence analysis of the PGL-3 protein predicts that this region 425-452 spans two α-helices (one complete helix and fraction of a second helix) (Supplementary Fig. 3d). We generated a PGL-3 construct (hereafter called ‘S1’) (Fig. 3a) in which the sequence in the region, 425-452, is shuffled while keeping the overall amino acid composition unchanged. We found that S1 phase separates into condensates that are 20- fold less dynamic than with wild-type PGL-3 (Fig. 3d, Supplementary Fig. 3c).”

      Saying that "reduced alpha-helicity of PGL-3 correlates with slower dynamics in condensates" may be factual in these assays but "correlation" should be expanded upon to include mechanism and to me it seems that the statement should read "aggregation of PGL-3 causes slower dynamics in condensates" (both the partially destabilized mutant and the fully unfolded WT show similar effects perhaps to different degrees).

      We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

      We did not use the term "aggregation" since we did not detect aggregates of S1 molecules using fluorescence confocal microscopy.

      *CROSS-CONSULTATION COMMENTS I agree with the other reviewer's comments and critiques, I have concerns about the biological relevance and also the biophysical mechanisms. Reflecting on the other reviewers' comments, the papers could provide more depth in one or both of these areas to come to firm conclusions that are either revealing about PGL biology or elucidate a (possible) general biophysical mechanism. *

      In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

      Reviewer #2 (Significance (Required)): *Hence, although the authors shows how inclusion of other components can alter the one protein component phase separation, this is done with entirely artificial means of destabilizing the fold of one of the domains which likely leads to aggregation. So the true impact of the work is hard to understand because the mutations impact on the basic biophysical properties of the domain (stability, interaction) are not completely characterized and the reason for disrupting this folding is not clear. *

      A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

      • *

      We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

      Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

      In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

      Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

      • *

      My field of expertise is protein phase separation and protein structure. * *

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

      Summary: P granules are liquid condensates found in the developing germlines and embryos of C. elegans. Prior work by the authors and others have established P granules as a tractable model to investigate the basic biophysical properties of liquid condensates. Much of the prior published work focused on specific P granule scaffold proteins, PGL-1 and PGL-3. How attributes of these PGL proteins and the effect of other P granule components affect condensate properties is not fully understood. Here, Jelenic, et al. probe the biophysical properties of PGL-3. Using recombinant protein, they show that an N-terminal, alpha-helical region of PGL-3 is sufficient for liquid condensate formation and that N-terminal assembly is required for this formation. Creation of a scrambled alpha-helical region in PGL-3 and heat treatment affects PGL-3 fluidity. This fluidity can be "rescued" in vivo and in vitro with the inclusion of other P granule factors, including wildtype PGL-3, PGL-1, GLH-1 and mRNA. The authors note an inverse correlation between fluidity and mutant PGL-3 fluorescent intensity. They propose a model that heterotypic compositions of condensates can buffer their fluidity against components with stronger multivalent interactions. *

      MAJOR: 1. PGL-3 is a fantastic model to study the biophysical properties of a liquid condensate. But as the authors address in their discussion, the S1 mutant will likely affect the central domain folding, at its minimum causing exposure of a hydrophobic surface not typically exposed in biology. These helices are found at the terminal portion of the domain determined in the crystal structure and as depicted in the authors' Figure 1A. While the cause of S1's enhanced molecular interactions does not affect the in vitro work presented in this manuscript, it does affect how the conclusions connect to the biological nature of P granules and liquid condensates more generally. *

      We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificial” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

      Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

      In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

      Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

      • Recombinant PGL-3 experiments added PGL-1, GLH-1 and mRNA simultaneously and measured fluidity. It will be interesting to know which components contribute to fluidity and whether fluidity enhancement of each component is dependent on one another. Addition experiments with each component should be included and/or at least discussed in the main text. *

      Our data with S1-mEGFP or PGL-3-mEGFP (pre-heated at 50°C) proteins microinjected into C. elegans gonads, and the transgenic strain expressing PGL-3(D425-452)-mEGFP from the pgl-3 locus showed that the P granule phase can support fast dynamics of these mutant PGL-3 constructs. Since P granules have a complex composition, one possibility is that fast dynamics of these constructs is supported by interactions involving many P granule components. We found that using only a limited set of P granule components (PGL-1, GLH-1 and mRNA) can buffer dynamics of S1 in condensates in vitro.

      In absence of a systematic analysis investigating the individual role of approx. 70 P granule proteins in buffering S1 dynamics in condensates in vitro, we have claimed in the text that dynamics-buffering of S1 in condensates is supported by interactions among two or more components. However, we do appreciate the reviewer’s comment and feel it would be interesting to investigate the contribution of individual P granule components towards fluidity in future studies. We have discussed this in the ‘Discussion’ section of the manuscript.

      • The biological relevance of PGL-1, GLH-1, and mRNA were not discussed in the main text. How these factors contribute to P granule assembly and function should be mentioned in the Introduction or Results. *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      *MINOR: 1. Line 20, "most non-membrane-bound compartments...have complex composition": Are there examples of condensates that do not have complex composition? *

      Not all non-membrane-bound compartments may have been characterized. To accommodate this possibility, we refrained from making a more general statement, but stated “most non-membrane-bound compartments…”.

      • Lines 40-43, RNA interactions driving LLPS: Please include citations from the Parker Lab (e.g. Van Treeck and Parker, Cell. 2018 doi: 10.1016/j.cell.2018.07.023) *

      We added the reference suggested by the reviewer.

      • *

      • Line 60, condensates contain hundreds of different proteins and RNA: Please cite at least a few examples of condensates with their components identified. *

      We added some references following suggestion by the reviewer.

      • Lines 82-84, PGL-3 drives assembly: Please cite Kawasaki, et al. Genetics 2004 for the discovery of PGL-3. *

      We added the reference suggested by the reviewer.

      • Lines 88-89, PGL-3 N-terminal fragment predominantly alpha-helical: The PGL domain structures should be cited here as supporting evidence that these regions are composed primarily of alpha helices (Aoki, et al 2016, 2021) *

      • *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      • Lines 158-159, driving forces for phase separation: This statement should be removed or expanded. The authors point regarding the protein concentrations is not clear here but clarified in the Discussion (Lines 691-693). Recommend removing due to its speculative nature. *

      We retained the speculative comment in the results section. We feel that this prepares the readers for the discussion later in the manuscript.

      • Lines 210: Add commas before and after "PGL-1 and GLH-1"*

      We addressed the reviewer’s suggestion.

      • Lines 218-219: add "and" instead of comma between PGL-1 and GLH-1 *

      We addressed the reviewer’s suggestion.

      • Lines 238-239, alpha-helices: The PGL CDD structure should also be referenced here (Aoki, et al 2016). *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      • Lines 680-682, MEG proteins: Please cite accordingly. *

      We added the reference suggested by the reviewer.

      • Lines 694-695, heterotypic interactions: Please cite Saha, et al. 2016. *

      We added the reference suggested by the reviewer.

      • Figure 1: Add space between 1 and mM DTT *

      We addressed the reviewer’s suggestion.

      • Figure 2b: Please provide statistics between condensate numbers. *

      We provide statistics between condensate numbers in Fig. 2b.

      • Figure 4A: The region of the germline imaged and analyzed should be mentioned in the caption or the main text. *

      We revised the Figure legend of Fig. 4a to address this issue.

      • Figure 4B,C: Please include statistics between the FRAP curves. *

      We have included statistics comparing FRAP curves in Supplementary Fig. 4a-c.

      • Figure 4D: It will be helpful to compare this curve to Figure S4A in the same graph. Please also include graph statistics. *

      We have revised Fig. 4 to address the reviewer’s suggestion.

      • Figure 5: The data points are difficult to resolve. Recommend use of color.*

      We considered the suggestion, but felt it works better in the original form.

      • Figure 6: This is a very general model that does not highlight the extensive experimental work performed by the authors. Recommend incorporating PGL-3, mutants and P granule factors into this model. *

      We thank the reviewer for appreciating our extensive work. However, we retained the original Fig. 6 for the sake of simplicity.

      • Methods, Line 939, C. elegans section: What worms were used? TH623? Please describe the genotype. *

      We have included a table listing the strains used in the study and their genotype. * CROSS-CONSULTATION COMMENTS While my review was arguably the more favorable of the three, I agree with the other reviewers' comments and evaluation, particularly with Reviewer #1. As written in my review, my primary concern was the biological relevance of the work.*

      Reviewer #3 (Significance (Required)):

      Overall, the in vitro work presented investigating the biophysical properties of this minimal P granule system was thorough and well-analyzed, and the manuscript was clearly written. Additional citations and statistics will improve the manuscript and the strength of the conclusions, respectively. The biological relevance of this study to P granule form and function in vivo, and to condensates in vivo, is debatable. This work will interest those who study condensate biology, the biophysics of protein-protein and protein-RNA interactions, and RNA biochemists more generally.

      A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

      We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

      • *

      Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

      In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

      Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

      *I have expertise in P granules, protein/RNA biochemistry, condensate assembly, and C. elegans. *

      References

      Aoki ST, Kershner AM, Bingman CA, Wickens M & Kimble J (2016) PGL germ granule assembly protein is a base-specific, single-stranded RNase. Proceedings of the National Academy of Sciences of the United States of America

      Aoki ST, Lynch TR, Crittenden SL, Bingman CA, Wickens M & Kimble J (2021) C. elegans germ granules require both assembly and localized regulators for mRNA repression. Nat Commun 12: 996

      Cipriani PG, Bay O, Zinno J, Gutwein M, Gan HH, Mayya VK, Chung G, Chen J-X, Fahs H, Guan Y, et al (2021) Novel LOTUS-domain proteins are organizational hubs that recruit C. elegans Vasa to germ granules. Elife 10: e60833

      Frottin F, Schueder F, Tiwary S, Gupta R, Körner R, Schlichthaerle T, Cox J, Jungmann R, Hartl FU & Hipp MS (2019) The nucleolus functions as a phase-separated protein quality control compartment. Science 365: 342–347

      Kawasaki I, Amiri A, Fan Y, Meyer N, Dunkelbarger S, Motohashi T, Karashima T, Bossinger O & Strome S (2004) The PGL family proteins associate with germ granules and function redundantly in Caenorhabditis elegans germline development. Genetics 167: 645–661

      Kawasaki I, Shim YH, Kirchner J, Kaminker J, Wood WB & Strome S (1998) PGL-1, a predicted RNA-binding component of germ granules, is essential for fertility in C. elegans. Cell 94: 635–645

      Phillips CM & Updike DL (2022) Germ granules and gene regulation in the Caenorhabditis elegans germline. Genetics 220: iyab195

      Price IF, Hertz HL, Pastore B, Wagner J & Tang W (2021) Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans. Elife 10: e72276

      Saha S, Weber CA, Nousch M, Adame-Arana O, Hoege C, Hein MY, Osborne Nishimura E, Mahamid J, Jahnel M, Jawerth L, et al (2016) Polar Positioning of Phase-Separated Liquid Compartments in Cells Regulated by an mRNA Competition Mechanism. Cell 166: 1572-1584.e16

      Spike C, Meyer N, Racen E, Orsborn A, Kirchner J, Kuznicki K, Yee C, Bennett K & Strome S (2008a) Genetic analysis of the Caenorhabditis elegans GLH family of P-granule proteins. Genetics 178: 1973–1987

      Spike CA, Bader J, Reinke V & Strome S (2008b) DEPS-1 promotes P-granule assembly and RNA interference in C. elegans germ cells. Development (Cambridge, England) 135: 983–993

    1. Author Response

      Reviewer #3: (Public Review):

      In this ms Li et al. examine the molecular interaction of Rabphilin 3A with the SNARE complex protein SNAP25 and its potential impact in SNARE complex assembly and dense core vesicle fusion.

      Overall the literature of rabphilin as a major rab3/27effector on synaptic function has been quite enigmatic. After its cloning and initial biochemical analysis, rather little new has been found about rabphilin, in particular since loss of function analysis has shown rather little synaptic phenotypes (Schluter 1999, Deak 2006), arguing against that rabphilin plays a crucial role in synaptic function.

      While the interaction of rabphilin to SNAP25 via its bottom part of the C2 domain has been already described biochemically and structurally in the Deak et al. 2006, and others, the authors make significant efforts to further map the interactions between SNAP25 and rabphilin and indeed identified additional binding motifs in the first 10 amino acids of SNAP25 that appear critical for the rabphilin interaction.

      Using KD-rescue experiments for SNAP25, in TIRF based imaging analysis of labeled dense core vesicles showed that the N-terminus of SN25 is absolutely essential for SV membrane proximity and release. Similar, somewhat weaker phenotypes were observed when binding deficient rabphilin mutants were overexpressed in PC12 cells coexpressing WT rabphilin. The loss of function phenotypes in the SN25 and rabphilin interaction mutants made the authors to claim that rabphilin-SN25 interactions are critical for docking and exocytosis. The role of these interaction sites were subsequently tested in SNARE assembly assays, which were largely supportive of rabphilin accelerating SNARE assembly in a SN25 -terminal dependent way.

      Regarding the impact of this work, the transition of synaptic vesicles to form fusion competent trans-SNARE complex is very critical in our understanding of regulated vesicle exocytosis, and the authors put forward an attractive model forward in which rabphilin aids in catalyzing the SNARE complex assembly by controlling SNAP25 a-helicalicity of the SNARE motif. This would provide here a similar regulatory mechanism as put forward for the other two SNARE proteins via their interactions with Munc18 and intersection, respectively.

      We thank the reviewer #3 for the summary of the paper and for the praise of our work. The point-to-point replies are as follow:

      While discovery of the novel interaction site of rabphilin with the N-Terminus of SNAP25 is interesting, I have issues with the functional experiments. The key reliance of the paper is whether it provides convincing data on the functional role of the interactions, given the history of loss of function phenotypes for Rabphilin. First, the authors use PC12 cells and dense core vesicle docking and fusion assays. Primary neurons, where rabphilin function has been tested before, has unfortunately not been utilized, reducing the impact of docking and fusion phenotype.

      We have discussed these questions as mentioned in our response to Essential Revisions 3 and added this corresponding passage to the Discussion section (pp.18-19, lines 407-427).

      In particular the loss of function phenotype in figure 3 of the n-terminally deleted SNAP25 in docking and fusion is profound, and at a similar level than the complete loss of the SNARE protein itself. This is of concern as this is in stark contrast to the phenotype of rabphilin loss in mammalian neurons where the phenotype of SNAP25 loss is very severe while rabphilin loss has almost no effect on secretion. This would argue that the N-terminal of SNAPP25 has other critical functions besides interacting with rabphilin. In addition, it could argue that the n-Terminal SNAP25 deletion mutant may be made in the cell (as indicated from the western blot) but may not be properly trafficked to the site of release

      To test whether the N-peptide deletion mutant of SN25 can properly target to the plasma membrane, we overexpressed the SN25 FL or SN25 (11–206) with C-terminal EGFP-tag in PC12 cells and monitored the localization of SN25 FL-EGFP and SN25 (11–206)-EGFP near the plasma membrane by TIRF microscopy. We observed that the average fluorescence intensity of SN25 (11–206)-EGFP showed no significant difference with SN25 FL-EGFP as below, suggesting that the N-peptide deletion mutant may not influence the trafficking of SN25 to plasma membrane.

      (A) TIRF imaging assay to monitor the localization of SN25-EGFP near the plasma membrane. Overexpression of SN25 FL-EGFP (left) and SN25 (11–206)-EGFP (right) using pEGFP-N3 vector in PC12 cells. Scale bars, 10 μm. (B) Quantification of the average fluorescence intensity of SN25-EGFP near the plasma membrane in (A). Data are presented as mean ± SEM (n ≥ 10 cells in each). Statistical significance and P values were determined by Student’s t-test. ns, not significant.

  21. Aug 2022
    1. GoutPal Research

      My GoutPal Research notes are similar to GoutPal Triage. But they're a response to my own interests and concerns. As such, I usually assign them lower priority than my notes on specific issues.

      In fact, this note is an example. Because I'm researching how to best implement my new GoutPal Links services. Then I'm adding notes like this to describe my progress towards a complete set of documented resources.

      As I move forward, I will publish extensive notes for subscribers and for members. That will give readers choices about extra information beyond these public gout research notes.

      Please note that those public gout notes are from every Hypothes.is user who writes about gout. Currently, that's only me. But I hope other gout sufferers will join me. At which point, you can find my gout notes with my user tag.

      To get more gout research notes, join the waitlist for gout subscribers.

    1. level 2hog8541ssOp · 15 hr. agoVery nice! I am a pastor so I am researching Antinet being used along with Bible studies.

      If you've not come across the examples, one of the precursors of the slip box tradition was the widespread use of florilegia from the 8th through the 13th centuries and beyond, and they were primarily used for religious study, preaching, and sermon writing.

      A major example of early use was by Philip Melanchthon, who wrote a very popular handbook on how to keep a commonplace. He's one of the reasons why many Lutheran books are called or have Commonplace in the title.

      A fantastic example is that of American preacher Jonathan Edwards which he called by an alternate name of Miscellanies which is now digitized and online, much the way Luhmann's is: http://edwards.yale.edu/research/misc-index Apparently he used to pin slips with notes on his coat jacket!

      If I recall, u/TomKluender may have some practical experience in the overlap of theology and zettelkasten.

      (Moved this comment to https://www.reddit.com/r/antinet/comments/wth5t8/bible_study_and_zettelkasten/ as a better location for the conversation)

    1. 📒 ShrewdNotes Web Page Annotation

      I often rush into assessing new applications. Because I learn quicker by applying compared to reading. But one downside is that I frequently miss key features.

      That's only a major drawback if I abandon the application where I can't see how it fits my project. And today I avoided that with serendipity. Because… 1. My application was a Chrome Extension 1. I wanted to test to see if was active and change webpage content accordingly 1. I found I could run the app without an extension - as fully described in the documentation that I skipped reading!

      All of which is an idea for my next blog post. But the real point is I have established a process for starting ad-hoc Shrewd Learning projects "in the wild". Because normally, I start making notes somewhere. Then forgetting where I put them.

      I think we all do that when we spot something interesting that might warrant future research. Now for my established subject areas, I always start annotating new topics within that subject area. So, I can prioritize it in my usual processes.

      Today, I've extended this by tagging public notes with Shrewd Learning. So when I look at the Shrewd Learning Tag, I see all notes that present potential new learning topics. Which opens a great way to collaborate loosely with other people if I can establish some traction with Shrewd Learning.

      For now, this is my reminder to do a personal blog entry based on this. More importantly, I should update this blog entry to reflect recent advances in Shrewd Learning and my other 2 online learning projects.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2022-01481R

      Corresponding author(s): Sebastian Voigt. Mirko Trilling, David Schwefel

      1. General Statements [optional]

      -

      2. Description of the planned revisions

      Reviewer #1: Evidence, reproducibility and clarity

      Using proteome profiling of rat CMV infected cells, the authors of this study identify the E27 protein of rat cytomegalovirus as being crucial for proteasomal degradation of STAT2. Since E27 shares 56% sequence identity to the previously characterized STAT2 antagonist M27 of murine CMV the authors investigated association of E27 with the Cullin4-RING UbL CRL4. Using gel filtration chromatography they provide evidence that E27 forms a stable ternary complex with DDB1 and STAT2 suggesting that E27 bridges STAT2 to DDB1 which is further corroborated by data from cross-linking mass spectrometry. A cross-linked DDB1/DDA1/E27/STAT2 complex was then used for cryo-EM imaging experiments. The subsequent single particle analysis yielded a density map at 3.8 A resolution that was further used to generate an E27 molecular model. At this point it should be noted that resolution was not very high and data form AlphaFold2 prediction and CLMS experiments were necessary to build a model which was described as having "sufficient quality", however, no quality parameters are included for this model. In this model, a cryptic zinc-binding motif was identified that turned out to be well conserved in M27. At this point the study switches to a mutational analysis of M27: MCMV mutants either lacking M27 or bearing an AxAxxAA triple mutation were investigated both in cell culture and in animal models. Surprisingly, the M27-AxAxxA mutant while exhibiting attenuated IFN inhibition was still more active than an M27 deletion mutant. Later during the study it is postulated that this may be due to the fact that E27 binding to STAT2 abrogates the interaction with IRF9, however, this is only predicted from modeling and no experimental data are provided for this hypothesis. Furthermore, modeling approaches were used to predict how E27 replaces endogenous CRL4 substrate receptors and how E27 recruits STAT2 to mediate CRL4-catalysed ubiquitin transfer.

      Reviewer #1: Significance

      __Reviewer #1: __This is an interesting and well written paper describing for the first time in molecular detail how a cytomegalovirus-encoded interferon antagonist degrades STAT2 by mimicking the molecular surface properties of cellular CRL4 substrate receptors.

      This study should be of broad interest for both virologists and structural biologists.

      Authors Response: We thank the reviewer for the insightful and constructive evaluation. We are very grateful for highlighting the significance of our work.

      Reviewer #1: Major points

      __Reviewer #1: __To my opinion the authors should perform mutational analysis in the context of E27 and RCMV. I accept that switching to M27 may be easier due to established procedures for MCMV mutagenesis and analysis, however, since all structural work is primarily done on E27 it would be consequent to confirm these structural predictions in the context of E27 before switching to a related protein.

      Authors Response: As the Reviewer appreciated, there were multiple reasons for the switch from RCMV-E E27 to MCMV M27. Most importantly, the MCMV in vivo infection model in mice is very well-established. Please also note that MCMV is applied far more often by virologists and immunologist as a standard model. Thus, the extension of our findings from RCMV to MCMV increases the relevance and outreach of the study. By performing the experiments in the MCMV context, we also aimed to emphasise that the function of the zinc-binding motif, which structurally organises the DDB1-binding domain, is functionally conserved among E27/M27-like proteins. Obviously, Reviewer #1 could ask why we do not solve the structure of M27 parallel to E27. With the sole exception of E27, none of the rodent M27 homologues could be produced recombinantly in a soluble form, preventing the purification and structure analysis of M27.

      Since we agree with Reviewer #1 that the extension from E27 to M27 may read “a bit rough” without a mutational analysis in the E27 context, we will construct RCMV-E E27 mutants leading to Cys=>Ala exchanges in the Zn-binding motif. An analysis of the interaction between DDB1 and these E27 mutants will be included in the revised manuscript.

      __Reviewer #1: __Moreover, data on the replication of the generated E27 deletion RCMV should be included in the manuscript (i.e. growth curves).

      Authors Response: RCMV mutants lacking the E27 gene exhibit an impaired replication. According to the suggestion, the growth curves will be part of the revised manuscript.

      Reviewer #1: The hypothesis that STAT2/E27 interaction is sterically incompatible with IRF9 binding is only based on structural prediction. It would help if the authors could present experimental evidence for such a mechanism.

      Authors Response: The hypothesis is based on three lines of argumentation: (i) structural data regarding the binding interface between STAT2 and E27 covering the known STAT2-IRF9 interface (Fig. 7F) (Rengachari et al., 2018). (ii) The finding that M27 mutants incapable to bind DDB1 and induce STAT2 degradation along the ubiquitin proteasome pathway retain a residual capacity to inhibit ISRE signaling, suggesting that the binding of M27 to STAT2 suffices to elicit some signaling inhibitory functions (Fig. 7G). (iii) To elicit their function, CRL4 substrate receptors such as E27 interact with two partners. As we discussed elsewhere (Le-Trilling and Trilling, 2020), a simultaneous development of two independent traits violates evolutionary and probability theories. Thus, these receptors must acquire their binding interfaces sequentially, and the first interaction must provide an evolutionary advantage allowing the fixation of the allele in the population. Afterwards, the second binding interface evolves. Thus, a hypothesis in which E27/M27 precursors evolved the capacity to bind STAT2, preventing its association with IRF9 thereby establishing relevant but incomplete IFN inhibition (before the DDB1 interface was invented leading to STAT2 degradation by the proteasome), provides a parsimonious explanation for all these findings without violating evolutionary constraints. To corroborate our argumentation, we will analyse if E27 indeed displaces IRF9 from STAT2 by analytical gel filtration and/or co-immunoprecipitation experiments.

      Reviewer #2: Evidence, reproducibility and clarity

      __Reviewer #2: __The manuscript entitled "Structure and mechanism of a novel cytomegaloviral DCAF mediating interferon antagonism" by Dr. Schwefel and colleagues cleverly combines biochemistry, mass-spectrometry, Cryo-EM and cell biology to dissect how RCMV-E hijacks its hosts ubiquitylation machinery to mediate proteasomal degradation of STAT2, a key player driving the antiviral IFN response. They identify E27 as DDB1-binding element, which is able promote CRL4-dependent ubiquitylation of STAT2, and demonstrate its effect on STAT2 levels by knockout RCMV-E strains. These findings are supported by in vitro reconstitution of the DDB1/E27/STAT2 complex and analyses via XL-MS and Cryo-EM. The obtained data are then powerfully validated and analysed in mutational strains via infection of homologue in vivo models. The results collectively explain how E27 mimics endogenous CRL4 substrate receptors, thereby recruiting STAT2 to be targeted by CLR4 for ubiquitylation in a NEDD8-dependent manner.

      Overall this is an important study that provides convincing insights on how rodent CMVs antagonize their host interferon response by exploiting its ubiquitin-proteasome system.

      The manuscript is well written and its introduction is extraordinarily comprehensive. There are a few minor points for the authors to consider below.

      Authors Response: We thank the reviewer for this very positive assessment.

      Reviewer #2: Significance

      Reviewer #2: The work of Schwefel and colleagues combines several powerful state-of-the art techniques to dissect the mechanism of the viral protein E27 and, for the first time, provides a rational for its ability to act as STAT2 antagonist. They performed outstanding structure-function analyses of the ubiquitin system, including the first global proteomic profiling of RCMV-infected cells, setting the standard for its human counterpart as rodent CMVs are commonly used as infection models. The manuscript is highly suitable for publication in any of the journals associated with the review commons platform.

      Authors Response: Again, we thank the reviewer for these kind words and the appreciation of our work.

      Reviewer #2: CROSS-CONSULTATION COMMENTS

      Reviewer #2: This reviewer agrees that at least testing mutants in the E27 in some assays would be appropriate.

      Authors Response: As detailed in the response to Reviewer #1, we will generate RCMV-E E27 mutants targeting the Zn-binding motif by site-directed mutagenesis. An analysis of the interaction between DDB1 and these E27 mutants will be included in the revised manuscript.

      Reviewer #3: Evidence, reproducibility and clarity

      __Reviewer #3: __Le-Trilling et al. present the first proteomic analysis of RCMV-infected cells, where they identified STAT2 as one of the most heavily downregulated (and degraded) proteins. This analysis showed that RCMV mediated degradation of STAT2 is conserved in closely related species used as animal models (rat and mouse) and human, despite the intra-host adaptation of each CMV. They also identify E27 as the RCMV factor that targets STAT2 for degradation, that exhibits ~50% homology with MCMV pM27. This study also identifies a Zinc binding motif in E27 using Cryo-EM which is conserved in other CMV species and is potentially involved in antagonising Type I and III responses.

      Reviewer #3: Significance

      __Reviewer #3: __The present work provides the first proteomics analysis of RCMV infection in rat cells, comparing infected vs non-infected rat fibroblasts to access potential RCMV targets. Then, it focuses on the characterisation of RCMV E27 and its role targeting and interacting with STAT2 (plus recruiting the Cul4 complex for STAT2 degradation). Finally, it provides the Cryo-EM structure of E27 and its CMV homologues, and the structure of the complex of E27 with elements of the CUL4 complex and STAT2. This is the first time that E27 function and structure are characterised. These are all novel findings - although the mouse homologue M27 has previously been found to interact with and degrade STAT2 (published by some of the same authors in Plos pathogens in 2011, (https://doi.org/10.1371/journal.ppat.1002069). Therefore the chief novel information is the structural studies.

      The manuscript will be of interest to researchers working with human and animal herpesviruses.

      My field of expertise is in Virology, Innate Immunity and host-virus interactions from an evolutionary perspective. I do not have expertise in Cryo-EM, so I could not evaluate the methods used in the section.

      __Authors Response: __We thank the reviewer for the positive evaluation of our work and its significance.

      Reviewer #3: Major points

      __Reviewer #3: __1. The authors claim the identification of a Zinc-binding motif in the protein E27 (RCMV) using Cryo-EM, then validation of the phenotype with MCMV WT, delM27 and M27 AxAxxA. To justify the change to MCMV to perform the functional validation, they stated "MCMV M27, the closest E27 homologue, exhibits 56% and 76% amino acid sequence identity and similarity, respectively (Fig. S4B). E27 and M27 AlphaFold2 structure predictions are almost indistinguishable (RMSD of 1.195 Å, 6652 aligned atoms) (Figs. 3B, S4A), and structural alignment of these predictions demonstrated conservation of side chain positions involved in zinc-binding (Fig. 3C). Thus, M27 represents a valid model to study functional consequences of interference with the zinc coordination motif through site-directed mutagenesis, and to test the predictive power of our E27/M27 model". Although they rationalise the change to MCMV to validate the functional outcomes of the newly identified zinc binding motif with alignments and Cryo-EM data, it falls within the DDB1 binding region that is less conserved (Fig S4B). The addition of a mouse model here provides a solid result but given the aim of the paper is to provide a proper characterisation of RCMV and elucidate some inter-species adaptations, I strongly recommend the validation with E27 here given the potential impact of this motif. Rather than having to repeat this in a rat model (which would clearly be a large amount of work), this could simply be achieved by constructing the relevant deletion / mutant viruses and assessing in vitro in a relevant cell line (readout - either virus titre or luciferase assay as shown in Figure 3G/H).

      __Authors Response: __Please also see our responses to the other reviewers. Briefly, we will apply side-directed mutagenesis to alter the CxCxxC motif in E27 that binds the zinc ion, and analyse the interaction of these E27 mutants with DDB1. In this context, we would like to add that almost two thirds of E27 residues in direct contact with DDB1 are at least type-conserved in M27, and the zinc-coordinating side chains are totally conserved (Fig. 3C). Together with a predicted similar structural organization of the respective binding regions (Fig. S11), and in light of our MCMV mutagenesis results (Fig. 7), it is highly likely that the DDB1-binding mode is conserved between E27 and M27. As mentioned above, we will put this assumption to the test in the revision process.

      __Reviewer #3: __Furthermore, in Figure 2, the GF assay was performed using full-length DDB1, however CLMS was performed using DDB1 delBPB (interchange between these two proteins continues in the remainder of the paper). This should be at least justified, and preferably one or other of wt DDB1 and DDB1 delBPB used in the GF or CLMS assay where this has not yet been performed. Later on in the results section (Fig 5E), the authors use wt DDB1 while in fig 4 they used the delBPB to describe the interaction with E27 - would be relevant to have consistency across the paper and some supplementary data that could support using one or the other in each assay.

      __Authors Response: __Protein complex preparations including full length DDB1 did not yield cryo-EM reconstructions at appropriate resolution for model building, almost certainly due to the known flexibility of the DDB1 BPB, impeding proper alignment of the cryo-EM particle images. This is why we switched to DDB1ΔBPB. Importantly, the structure model including full length DDB1 (Fig. S12B) clearly demonstrates that the BPB is located on the opposite side of the E27 binding interface on DDB1 (where it is situated to flexibly connect to the CUL4 scaffold to create the ubiquitination zone around immobilised substrates [Fig. 6]). This rules out an involvement of DDB1 BPB in E27- and/or STAT2-binding processes. Several previous studies have employed DDB1ΔBPB to facilitate structure determination, and have successfully applied the resulting structural models for functional follow-up experiments in the context of complete CRL4 assemblies (Bussiere et al., 2020; Petzold et al., 2016; Slabicki et al., 2020). Nevertheless, we will repeat GF experiments with DDB1ΔBPB for consistency and include these data in the revised manuscript.

      Reviewer #3: Minor points

      __Reviewer #3: __2. Although they present sufficient detail in the methods, further details in the text should be given as to the number of repeats performed in each case, and whether the data shown is representative or based on an average of repeats (preferably the latter; if representative, the data for other repeats should be shown in supplementary information).

      Authors Response: We will add this information in the revised version of the manuscript.

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

      Reviewer #1: Major points

      __Reviewer #1: __Resolution of the cryoEM structure is rather low and many predictions of the manuscript are based on modeling using AlphaFold2 prediction. The authors describe their model as of "sufficient quality", however, no quality measures are included in the manuscript. At least the discussion should address limitations of the used approach.

      Authors Response: While we apologize for not sufficiently describing our quality measures, we respectfully disagree regarding the conclusion. Our resolution (3.8 Å, map 1) lies well within the 3–4 Å resolution range of the vast majority of structures deposited to the Electron Microscopy Data Bank during the last five years (https://www.emdataresource.org/statistics.html). Nevertheless, de novo modelling in this resolution regime is challenging. This is why we sought additional guidance through cross-linking mass spectrometry (XL-MS) restraints and AlphaFold2. Please also note that modelling of E27 was not based solely on the AlphaFold2 prediction. Instead, a partial model corresponding to the α-domain was manually built in map 1, guided by XL-MS information (see Methods - “Model building and refinement” and Fig. S5B, grey cartoon). This partial model proved to be in very good agreement with AlphaFold2 predictions (RMSD of 1.489 Å, 2764 aligned atoms). Only after this initial sanity check, the computational prediction was used for model completion, adjustment, and refinement.

      We now added graphical overviews of model fits in Figs. S5 and S10. Furthermore, we included detailed views of the fit of relevant side chains involved in intermolecular interaction to the experimental density (Fig. S7, S9). We also calculated and listed quality indicators of the model-to-map fit in Table S1 (correlation coefficients and model resolution based upon model-map FSC). To ensure the validity of our atomic model using an alternative method besides cryo-EM and XL-MS, we have performed site-directed mutagenesis of critical binding regions in E27, followed by in vitro reconstitution and analytical GF (Fig. S7B, C, S9B, C). The text was revised accordingly (see p10 [ll22] and p14 [ll26]).


      __Reviewer #1: __The authors identify a cryptic zinc-binding motif in E27 that is conserved in homologous proteins. For this reviewer it is not clear: is there experimental evidence for zinc binding of E27 or can the presence of zinc reliably be detected in their structural data? If not, it would be worth to confirm zinc binding.

      Authors Response: Our structural data show a tetragonal metal coordination geometry, involving three cysteine side chains and one histidine side chain, with coordination bond lengths of 2.2 Å between the histidine nitrogen and the metal ion, and of 2.4 Å between the cysteine sulfurs and the metal ion. The density feature cannot be explained by another type of side chain interaction, e.g. a disulfide bond, because this would lead to a steric clash with the remaining adjacent side chains. Based on the knowledge on metal-binding sites in proteins and metal-coordination chemistry, these characteristics indicate the presence of a structural zinc-binding site for the following reasons: (i) after magnesium, zinc is the second most prevalent metal in the Protein Data Bank (https://metalpdb.cerm.unifi.it/getSummary), however, magnesium is coordinated octahedrally by oxygen ligands (Tang and Yang, 2013); (ii) the most abundant zinc ligands are cysteine and histidine; (iii) the most abundant zinc coordination number is four ligands; (iv) the average coordination bond lengths are 2.12±0.19 Å and 2.33±0.12Å for nitrogen-zinc and sulfur-zinc interactions, respectively (Ireland and Martin, 2019; Laitaoja et al., 2013), which is in very good agreement with our structural observations. We included this argumentation in the revised manuscript (see p9 [ll21]), and added Fig. S5C for visualization.


      Reviewer #2: Minor points


      Reviewer #2: Page 2, line 3. "Here," should be inserted before "Global proteome profiling..." to highlight the work of this manuscript.

      Authors Response: We changed the text accordingly.

      Reviewer #2: Page 3, line 21. "IFNs" instead of "IFN"

      Authors Response: We changed the text accordingly.

      Reviewer #2: Page 4, lines 9,15,27. "Ubiquitin Ligases (UbL)" is not a common abbreviation and could be mistaken for Ubl (Ubiquitin-like proteins). Possible abbreviation is "E3s" for Ubiquitin E3 ligases

      Authors Response: We have amended the respective abbreviations accordingly.

      Reviewer #2: Page 4 line 25. "RBX1" is the more common term for "ROC1"

      Authors Response: This has been corrected throughout the manuscript.

      Reviewer #2: Page 5 lines 1-9. Citing of the first structure of DDB1 in complex with a viral protein is recommended. (Ti Li et al. Cell 2006)

      Authors Response: We thank the reviewer for this important suggestions and cited this landmark publication.

      Reviewer #2: Figure 1 a) STAT2 dot is cut off in second panel. I recommend highlighting STAT2 in both panels.

      We amended the figure accordingly. We furthermore additionally highlighted the “STAT2” text in both panels by increasing the font size and putting it in bold type.

      Reviewer #2: Page 7 line 17. "Cross-linking MS (CLMS)" is commonly abbreviated as (XL-MS)

      Authors Response: We changed the text accordingly.

      Reviewer #2: Figure 2 a-c) These panels could benefit from thinner lines in order to increase visibility of chromatograms and cross-links.

      Authors Response: The panels were changed accordingly.

      Reviewer #2: Figure 2 a-b) Could the authors elaborate on why STAT2 is stoichiometrically

      underrepresented in the SDS-PAGE of the E27/DDB1/STAT2 complex?

      Authors Response: We applaud Reviewer #2 for their in-depth examination. Honestly, we were also puzzled by this. Based on the cryo-EM single particle analysis, we found an explanation: We separated a major contamination in silico during 2D classification (~12% of all particles). Out of curiosity, we reconstructed a density map from these particles (now shown in Fig. S3). The map was identical to a previous cryo-EM structure of the E. coli protein ArnA (Yang et al., 2019), a notorious contaminant in E. coli Ni-NTA protein purifications (Andersen et al., 2013). ArnA migrates similar to E27 on the SDS-PAGE, the band runs just a little bit faster (compare fraction 6 [ArnA] and fractions 8/9 [E27] from the SDS-PAGE of the analytical GF run of E27 in isolation, Fig. 2A, green trace). However, in analytical GF, ArnA elutes at higher molecular weight fractions, since it forms a hexamers (Ve~10.2 ml). Incidentally, this elution volume of the ArnA hexamer almost equals the one of DDB1 or DDB1ΔBPB/DDA1/E27/STAT2 complexes. This leads to a superposition of ArnA and E27 bands in the respective SDS-PAGE lanes corresponding to GF fraction 6. Accordingly, we conclude that it is actually not STAT2 that is underrepresented, but rather E27 seems overrepresented due to SDS-PAGE band overlap with the ArnA contaminant. We have now indicated the contaminant in Fig. 2A, amended the legend, and extended Fig. S3 to indicate at which point of the cryo-EM analysis the contaminating ArnA particles were separated, and to show the ArnA model to map fit.

      In addition to this, it might be that potential STAT2 degradation products (marked by ** in Fig. 2), which seem to co-migrate with STAT2/E27 complexes, occupy FL STAT2 binding sites on E27.

      Reviewer #2: Paragraph "The E27 structure.." page 9. Placing this paragraph after the overall

      structure is recommended.

      Authors Response: Accordingly, we have now moved this section to the end of the results section.

      Reviewer #2: Figure 3 a) The grey mesh being laid over the ribbon structures is not contributing to the overall visibility. Adding a panel of the cryo-EM structure alone in cost of alphafold models is recommended.

      Figure 4a) same issue with grey mesh

      Authors Response: Thank you very much for the very good suggestions. We have removed the mesh representation, and included panels just showing the segmented cryo-EM map in the new Fig. 3A.

      Reviewer #2: c) panels could benefit from fewer amino acids being labeled/shown

      Authors Response: We understand the motives of the Reviewer. However, we would prefer to depict all relevant side chain interactions in these panels. The rearrangement of the figure, i.e. showing the overview of the interacting regions before the detailed panels, should make them more accessible (new Fig. 3B).

      __Reviewer #2: __d) may want to avoid red-green coloring to improve for colorblindness

      Authors Response: We are deeply sorry for our ignorance in this regard. We changed the colors accordingly (see new Fig. 3B, C).

      __Reviewer #2: __Figure 6a) s.a grey mesh

      Authors Response: We removed the mesh representations and included panels just showing the segmented cryo-EM density in the new Fig. 5C.


      Reviewer #2: CROSS-CONSULTATION COMMENTS

      __Reviewer #2: __A 3.8 A overall resolution map and the approach to fitting may be suitable, but it is unclear from the authors' figures whether the side-chains shown in the figures are clearly visible in the map or if they are modeled by some other approach. Side chains should ideally be visible in the maps if shown in figures, and if not, close-ups of the corresponding regions of the maps should be shown with sufficient depthcue to allow the reader to gauge how the map corresponds to the model.

      Authors Response: This is a crucial point. As mentioned in the response to Reviewer #1, major point 2, we have now included very detailed views of the fit of relevant side chains involved in intermolecular interaction to the experimental density (Fig. S7, S9).

      __Reviewer #2: __Along these lines, the figures with the mesh maps do not clearly show how well the model fits the map. This needs to be clearly visible in figures, and ideally maps and models provided to reviewers in order for the reviewers to gauge the level of accuracy of the fit.

      Authors Response: Please see our response to Reviewer #1, major point 2. Briefly, we have now included graphical overviews of model fits in Figs. S5 and S10. We also calculated and listed quality indicators of the model-to-map fit in Table S1 (correlation coefficients and model resolution based upon model-map FSC). To ensure the validity of our atomic model using an alternative method besides cryo-EM and XL-MS, we have performed site-directed mutagenesis of critical binding regions in E27, followed by in vitro reconstitution and analytical GF (Fig. S7B, C, S9B, C). The text was extended accordingly (see p10 [ll22] and p14 [ll26]).

      __Reviewer #2: __At minimum, the authors have nicely assembled proteomics and cell biological data indicating that E27 hijacks CRL4 to turn over Stat2 in rat cells in a manner paralagous to M27 hijacking in mouse cells, biophysical/structural data for a model of a CUL4-DDB1-E27-Stat2 complex, and mutagenesis of a putative zinc binding site in M27.

      I feel most of the issues raised by all 3 reviewers could be addressed in the text, with more clarity about the structural models, and better explanation for why the construct with proteins from various organisms were used for structural studies (the authors had made human DDB1 before, and it expressed well, and perhaps didn't consider to make from rat? Or this mixture expressed, purified best? Gave best quality EM data?).

      Authors Response: We thank Reviewer #2 for her/his overall assessment. As mentioned in the two cross-consultation comments before, and in the response to Reviewer #1, major point 2, we strived to provide adequate measures allowing to judge the quality of our structural models in the present updated version of the manuscript. In addition, as indicated in the response to reviewer #3, major point 2, we have now added Fig. S12 and extended the Discussion to explain and justify the use of different protein constructs.

      __Reviewer #2: __Also, the presentation of the zinc binding site should come after the overall structure. As for the use of MCMV to assess the role of the zinc binding site, placing this last in the text might allow this to flow better.

      Authors Response: Thank you very much for this suggestion. The manuscript has been restructured as recommended: details of the zinc-binding motif and the MCMV assays are now shown in Fig. 7 and described in the text just before the Discussion.



      Reviewer #3: Major points

      __Reviewer #3: __2. Given that previous data in mice showed that the E27 homologue pM27 binds a component of host Cullin4-RING UbLs (CRL4), to induce the poly-ubiquitination of STAT2, the current study also addressed if this mechanism was preserved in RCMV. Yet, they seemed to do this with E27, rnSTAT2 and hsDDB1 - Page 7 lines 1 to 3: "These results prompted us to explore the association of E27 with Rattus norvegicus (rn) STAT2 and Homo sapiens (hs) DDB1 in vitro. Importantly, 1128 of 1140 amino acids are identical between hsDDB1 and rnDDB1 (...)". They identify the residues and regions where the DDB1 is different between both species, but should provide a structure/alignment with this highlighted. In addition, DDB1 is a DNA damage protein that is annotated in the Rattus norvegicus genome. The authors should justify the assays between rnSTAT2-hsDDB1 instead of using the both proteins from rn, and present the equivalent data for rnDDB1 in the paper.

      Authors Response: Among the 12 alterations between human and rat DDB1, 4 are type-conserved (Fig. S12A). Thus, >99% of amino acids are identical or similar. We mapped all exchanges on a model of full length human DDB1 bound to E27 and the rat STAT2 CCD. None are involved in intermolecular interactions (Fig. S12B, C). Please note that due to the high conservation of DDB1 across eukaryotes, this inter-species approach has been used by us and others to study DDB1-containing complexes (e.g., the SV5V, WHX, SIV Vpx and Vpr, zebrafish DDB2, and chicken CRBN proteins have been in vitro reconstituted with human DDB1 for structural characterisation) and valid biological conclusions have been drawn from these studies (Angers et al., 2006; Banchenko et al., 2021; Fischer et al., 2014; Fischer et al., 2011; Li et al., 2006; Li et al., 2010; Schwefel et al., 2015; Schwefel et al., 2014; Wu et al., 2015).


      Reviewer #3: Minor points

      __Reviewer #3: __1. In fig 5D, the authors present the H-box alignment, where it is clear that this motif is not conserved. The lack of H-box conservation should be discussed in the results and discussion, to provide an explanation for the competition/binding observed.

      Authors Response: We respectfully disagree. There is conservation of amino acid side chains, regarding their physicochemical properties, observable in the H-box motif. Furthermore, the secondary structure is conserved. Please note, that the H-box is not our invention but rather represented a well-accepted motif known in the field, see e.g., (Li et al., 2010). We extended the discussion to cover this point (p21 [ll15]).


      __Reviewer #3: __3. The authors commence their abstract justifying the study on the grounds of the usefulness of rodent HCMV counterparts as common infection models for HCMV. They should return to this theme in the discussion - what is the usefulness of their findings with regards to HCMV (particularly given the relatively low homology between E27 and HCMV pUL27, and the alternative mechanism for STAT2 antagonism encoded by HCMV UL145)?

      Authors Response: We extended the discussion in this regard. Briefly, our data, to our knowledge for the first time, reveal that RCMV (like MCMV) exploits CRL4 to induce proteasomal degradation of STAT2. With pUL145, HCMV relies on an analogous protein. In clear contrast to HCMV, RMCV and MCMV are both amenable to in vivo experiments in small animal models. Over 40 years ago, HCMV has been called the troll of transplantation due to its grim impact on immunosuppressed individuals after transplantation surgery (Balfour, 1979). Despite tremendous efforts, HCMV still harms and kills graft recipients. While MCMV allows various experiments regarding general principles of cytomegaloviral pathogenesis and antiviral immunity, one shortcoming is that the mouse obviously is a rather small animal, preventing various chirurgical and solid organ transplantation (SOT) procedures. In clear contrast, SOT procedures that are indispensable for human medicine can be recapitulated in rat models. Thus, according to our opinion, our work lays the molecular foundation for future studies addressing the relevance of STAT2 and CMV-induced STAT2 degradation in rat SOT models.

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

      -

      • *

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      Reply to the reviewers

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

      In recent years, the field has investigated crosstalk between cGMP and cAMP signaling (PMID: 29030485), lipid and cGMP signaling (PMID: 30742070), and calcium and cGMP signaling (PMID: 26933036, 26933037). In contrast to the Plasmodium field, which has benefited from proteomic experiments (ex: PMID 24594931, 26149123, 31075098, 30794532), second messenger crosstalk in T. gondii has been probed predominantly through genetic and pharmacological perturbations. The present manuscript compares the features of A23187- and BIPPO-stimulated phosphoproteomes at a snapshot in time. This is similar to a dataset generated by two of the authors in 2014 (PMID: 24945436), except that it now includes one BIPPO timepoint. The sub-min​​ute phosphoproteomic timecourse following A23187 treatment in WT and ∆cdpk3 parasites is novel and would seem like a useful resource.

      CDPK3-dependent sites were detected on adenylate cyclase, PI-PLC, guanylate cyclase, PDE1, and DGK1. This motivated study of lipid and cNMP levels following A23187 treatment. The four PDEs determined to have A23187-dependent phosphosites were characterized, including the two PDEs with CDPK3-dependent phosphorylation, which were found to be cGMP-specific. However, cGMP levels do not seem to differ in a CDPK3- or A23187-dependent manner. Instead, cAMP levels are elevated in ∆cdpk3 parasites. This would seem to implicate a feedback loop between CDPK3, the adenylyl cyclase, and PKA/PKG: CDPK3 activity reduces adenylyl cyclase activity, which reduces PKA activity, which increases PKG activity. The authors don't pursue this direction, and instead characterize PDE2, which does not have CDPK3-dependent phosphosites, and seems out of place in the study

      Response:

      We agree with reviewer 1 that a feedback loop between CDPK3, the adenylyl cyclase and PKA/PKG is certainly one of several possibilities (and we acknowledge this in the manuscript).

      We felt, however, that given the observation that A23187 and BIPPO treatment leads to phosphorylation of numerous PDEs (hinting at the presence of an Ca2+-regulated feedback loop), it was entirely relevant to study these in greater detail. Coupled with the A23187 egress assay on ΔPDE2 parasites - our findings suggest that PDE2 plays an important role in this signalling loop (an entirely novel finding). While PDE2 appears to exert its effects in a CDPK3-independent manner (indeed suggesting that CDPK3 might exert its effects on cAMP levels in a different fashion), this does not detract from the important finding that PDE2 is one of the (likely numerous) components that is regulated in a Ca2+-dependent feedback loop to regulate egress.

      We have modified our writing to better reflect the fact that our decision to pursue study of the PDEs was not solely CDPK3-centric.

      While we feel that our reasoning for studying the PDEs is solid, we appreciate that further clarification on the putative CDPK3-Adenylate cyclase link would make it easier for the reader to follow the rationale.

      We have not studied the direct link between CDPK3 and the Adenylate Cyclase β in more detail, as ACβ alone was shown to not play a major role in regulating lytic growth (Jia et al., 2017).

      **MAJOR COMMENTS**

      1.Some of the key conclusions are not convincing.

      The data presented in Figure 6E, F, and G and discussed in lines 647-679 are incongruent. In Figure 6E, the plaques in the PDE2+RAP image are hardly visible; how can it be that the plaques were accurately counted and determined not to differ from vehicle-treated parasites?

      Are the images in 6E truly representative? Was the order of PDE1 and PDE2 switched? The cited publication by Moss et al. 2021 (preprint) is not in agreement with this study, as stated. That preprint determined that parasites depleted of PDE2 had significantly reduced plaque number and plaque size (>95% reduction); and parasites depleted of PDE1 had a substantially reduced plaque size but a less substantial reduction in plaque number.

      Response:

      The plaques for PDE2+RAP were counted using a microscope since they are difficult to see by eye. We thank the reviewer for detecting our incorrect reference to Moss et al. (2021). This has been corrected in the text. We confirm, however, that the images in 6E are representative of what we observed and do indeed differ from what was seen by Moss et al.. We have acknowledged this clearly in the text.

      The differences cannot easily be explained other than by the different genetic systems used. Further studies of the individual PDEs will likely illuminate their role in invasion/ growth, but we feel this would be beyond the scope of this study.

      Unfortunately, the length of time required for PDE depletion (72h) is incompatible with most T. gondii cellular assays (typically performed within one lytic cycle, 40-48h). Although the authors performed the assays 3 days after initial RAP treatment, is there evidence that non-excised parasites don't grow out of the population. This should be straightforward to test: treat, wait 3 days, infect onto monolayers, wait 24-48h fix, and stain with anti-YFP and an anti-Toxoplasma counterstain. The proportion of the parasite population that had excised the PDE at the time of the cellular assays will then be known, and the reader will have a sense of how complete the observed phenotypes are. As a reader, I will regard the phenotypes with some level of skepticism due to the long depletion time, especially since a panel of PDE rapid knockdown strains (depletion in __Response:

      1. Cellular assays using KO parasites are commonly performed at the point at which protein depletion is detected. Both our western blots and plaque assay results demonstrate that, at the point of assay, there is no substantial outgrowth of non-excised parasites. The original manuscript also includes PCRs performed at the 72 hr time point (See Fig. 6B) to support this.
      2. We appreciate the reviewer’s comment re the panel of PDE KD strains. The reviewer notes that there are substantial limitations to conditional KO systems, which similarly applies to KD systems - there are notable pros and cons to each approach. When designing our strategy (pre-publication of the Moss et al., 2022), we made a deliberate decision to use conditional KO strains in light of the fact that residual protein levels in KD systems can cause significant problems, particularly for membrane proteins (all of the investigated PDEs have a transmembrane domain). Tagging of proteins with the degradation domain can have further issues, leading to protein mis-localisation, which we have experienced with several unrelated proteins in the lab.

        The authors should qualify some of their claims as preliminary or speculative, or remove them altogether.

      The claims in lines 240-260 are confusing. It seems likely that the two drug treatments have at least topological distinctions in the signaling modules, given that cGMP-triggered calcium release is thought to occur at internal stores, whereas A23187-mediated calcium influx likely occurs first at the parasite plasma membrane.The authors' proposed alternative, that treatment-specific phosphosite behavior arises from experimental limitations and "mis-alignment", is unsatisfying for the following reasons: (1) From the outset, the authors chose different time frames to compare the two treatments (15s for BIPPO vs. 50s for A23187); (2) the experiment comprises a single time point, so it does not seem appropriate to compare the kinetics of phosphoregulation. There is still value in pointing out which phosphosites appear treatment-specific under the chosen thresholds, but further claims on the basis of this single-timepoint experiment are too speculative. Lines 264-267 and 281-284 should also be tempered.

      Relatedly, graphing of the data in Figure 1G (accompanying the main text mentioned above) was confusing. Why is one axis a ratio, and the other log10 intensity? What does log10 intensity tell you without reference to the DMSO intensity? Wouldn't you want the L2FC(A23187) vs. L2FC(BIPPO) comparisons? Could you use different point colors to highlight these cases on plot 1E? Additionally, could you use a pseudocount to include peptides only identified in one treatment condition on the plot in 1E? (Especially since these sites are mentioned in lines 272-278 but are not on the plot)

      Response:

      1. The kinetics of the responses to A23187 and BIPPO are very different. This is why treatment timings are purposely different as they were selected to align pathways to a point where calcium levels peak just prior to calcium re-uptake. We make no mention of kinetic comparisons, and merely demonstrate that at the chosen timepoints, overall signalling correlation is very high. The observation that most of the sites that behave differently between conditions sit remarkably close to the threshold for differential regulation (in the treatment condition where they are not DR - see Fig. 1G) led us to speculate that many of these sites are likely on the cusp of differential regulation. While it is entirely possible that some of these differences are, in fact, treatment specific (and we clearly acknowledge this in the text), we simply state that we cannot confidently discern clear signalling features that allow us to distinguish between the two treatments. We feel that this is an entirely relevant observation given the observed preponderance of both A23187 and BIPPO-dependent DR phosphosites on proteins in the PKG signalling pathway (as current models place this upstream of Ca2+release).
      2. Log10 intensity only serves to spread the data for easier visualisation. The only comparison being made relates to the LFCs. Fig. 1Gi shows the LFC scores (x axis) for all sites regulated following A23187 treatment (for which peptides were also identified in BIPPO treatment). On this plot we have highlighted the sites that are differentially regulated following BIPPO but not A23187 treatment (with red showing the DRup and blue showing the DRdown sites). This demonstrates that many of the sites that are regulated following BIPPO but not A23187 treatment cluster close to the threshold for differential regulation in the A23187 dataset - suggesting that many of these sites are likely on the cusp of differential regulation. Fig. 1Gii shows the reverse. While we could highlight the above-mentioned sites on the plot in Fig. 1E, we do not feel that it would demonstrate our point as clearly.

      We feel that including a pseudocount on Fig. 1E for peptides lacking quantification in one treatment condition would be visually misleading as the direct correlation being made in Fig. 1E is BIPPO vs A23187 treatment. The sites mentioned in lines 272-278 in the original manuscript (now lines 268-276) are available in the supplement tables.

      3.Additional experiments would be essential to support the main claims of the paper.

      Genetic validation is necessary for the experiments performed with the PKA inhibitor H89. H89 is nonspecific even in mammalian systems (PMID: 18523239) and in this manuscript it was used at a high concentration (50 µM) The heterodimeric architecture of PKA in apicomplexans dramatically differs from the heterotetrameric enzymes characterized in metazoans (PMID: 29263246), so we don't know what the IC50 of the inhibitor is, or whether it inhibits competitively. Two inducible knockdown strains exist for PKA C1 (PMID: 29030485, 30208022). The authors could request one of these strains and construct a ∆cdpk3 in that genetic background, as was done for the PDE2 cKO strain. Estimated time: 3-4 weeks to generate strain, 2 weeks to repeat assays.

      Response:

      1. While we appreciate that H89 is not 100% specific for PKA, this is not our only line of evidence that cAMP levels are altered. We demonstrate that cAMP levels are elevated in CDPK3 KO parasites – further substantiating our finding.

      The H89 concentration used in our experiment is in keeping with/lower than the concentrations used in other Toxoplasma publications (Jia et al., 2017), and both the Toxoplasma and Plasmodium fields have shown convincingly that H89 treatment phenocopies cKD/cKO of PKA (see Jia et al., 2017; Flueck et al., 2019).

      While we agree that the genetic validation suggested by reviewer 1 would serve to further support our findings (though it would not provide further novel insights), the suggested time frame for experimental execution was not realistic. Line shipment, strain generation, subcloning and genetic validation would take substantially longer than 3-4 weeks.

      cGMP levels are found to not increase with A23187 treatment, which is at odds with a previous study (lines 524-560). The text proposes that the differences could arise from the choice of buffer: this study used an intracellular-like Endo buffer (no added calcium, high potassium), whereas Stewart et al. 2017 used an extracellular-like buffer (DMEM, which also contains mM calcium and low potassium). An alternative explanation is that 60 s of A23187 treatment does not achieve a comparable amount of calcium flux as 15 s of BIPPO treatment, and a calcium-dependent effect on cGMP levels, were it to exist, could not be observed at the final timepoint in the assay. The experiments used to determine the kinetics of calcium flux following BIPPO and A23187 treatments (Fig. 1B, C) were calibrated using Ringer's buffer, which is more similar to an extracellular buffer (mM calcium, low potassium). In this buffer, A23187 treatment would likely stimulate calcium entry from across the parasite plasma membrane, as well as across the membranes of parasite intracellular calcium stores. By contrast, A23187 treatment in Endo buffer (low calcium) would likely only stimulate calcium release from intracellular stores, not calcium entry, since the calcium concentration outside of the parasite is low. Because calcium entry no longer contributes to calcium flux arising from A23187 treatment, it is possible that the calcium fluxes of A23187-treated parasites at 60 s are "behind" BIPPO-treated parasites at 15 s. The researchers could control these experiments by *either* (i) performing the cNMP measurements on parasites resuspended in the same buffer used in Figure 1B, C (Ringer's) or (ii) measuring calcium flux of extracellular parasites in Endo buffer with BIPPO and A23187 to determine the "alignment" of calcium levels, as was done with intracellular parasites in Figure 1C. No new strains would have to be generated and the assays have already been established in the manuscript. Estimated time to perform control experiments with replicates: 2 weeks. This seems like an important control, because the interpretation of this experiment shifts the focus of the paper from feedback between calcium and cGMP signaling, which had motivated the initial phosphoproteomics comparisons, to calcium and cAMP signaling. Further, the lipidomics experiments were performed in an extracellular-like buffer, DMEM, so it's unclear why dramatically different buffers were used for the lipidomics and cNMP measurements.

      Response:

      While the initial calibration experiments to measure calcium flux were indeed performed in Ringer’s buffer, the parasites were intracellular. We therefore chose to measure cNMP concentrations of extracellular parasites syringe lysed in Endo buffer, which is better at mimicking intracellular conditions than any other described buffer.

      As the reviewer suggested, we measured the calcium flux of extracellular parasites in Endo buffer upon stimulation with either A23187 or BIPPO.

      We found that peak calcium response to BIPPO in Endo buffer was similar to that of intracellular parasites (~15 seconds post treatment) (See Supp Fig. 6A). Upon treatment with A23187, extracellular parasites in Endo buffer had a much faster response compared to their intracellular counterparts, with peak flux measured at ~25 seconds post treatment (see Supp Fig. 6B). This indeed does suggest that extracellular parasites in Endo buffer behave differently to A23187 compared to their intracellular counterparts. However, peak calcium response is still occuring within the experimental time course and is not being missed, as the reviewer worries. Moreover, since we are able to detect increased cAMP levels in A23187 treated parasites, Ca2+ flux appears sufficient to alter cNMP signalling.

      We did notice however that the intensity of the calcium flux was much weaker in Endo buffer compared to intracellular parasites (see Supp Fig. 6B). We found that this was due to the lack of host-derived Ca2+, since supplementation of Endo buffer with 1 uM CaCl2 restored the intensity of the calcium response to match that of intracellular parasites (see Supp Fig. 6C). We therefore decided to repeat our cGMP measurements, this time using extracellular parasites in Endo buffer supplemented with 1 uM CaCl2. However, we found no differences in cGMP levels in the response to ionophore under these conditions (now Supp Fig. 6D) compared to the previous experiments, so the conclusions from the previous data do not change.

      As for the lipidomics experiments, we chose to use DMEM so that our dataset could be compared with other published lipidomic datasets (Katris et al., 2020; Dass et al., 2021) where DMEM was also used as a buffer when measuring global lipid profiles of parasites.

      We now acknowledge in the paper that Endo buffer has its shortcomings, and that this could be the reason why we do not detect changes in cGMP concentrations. We do, however, believe that Endo buffer is the best alternative to intracellular parasites and is supported by its consistent use in numerous publications studying Toxoplasma signalling (McCoy et al., 2012; Stewart et al., 2017).

      Additional information is required to support the claim that PDE2 has a moderate egress defect (lines 681-687). T. gondii egress is MOI-dependent (PMID: 29030485). Although the parasite strains were used at the same MOI, there is no guarantee that the parasites successfully invaded and replicated. If parasites lacking PDE2 are defective in invasion or replication, the MOI is effectively decreased, which could explain the egress delay. Could the authors compare the MOIs (number of vacuoles per host cell nuclei) of the vehicle and RAP-treated parasites at t = 0 treatment duration to give the reader a sense of whether the MOIs are comparable?

      Response:

      Since PDE2 KO parasites have a substantial growth defect, we did notice that starting MOIs were consistently lower for the RAP-treated samples compared to the DMSO-treated samples. However, this was also the case for PDE1 KO parasites where we did not see an egress delay. We also found that the egress delay was still evident for ∆CDPK3 parasites, despite having higher starting MOIs than WT parasites in our experiments. Therefore there does not appear to be a link between starting MOIs and the egress delay.

      To be sure of our results, we also performed egress assays where we co-infected HFFs with mCherry-expressing WT parasites (WT ∆UPRT) and GFP-expressing PDE2 cKO parasites that were treated with either DMSO or RAP or ∆CDPK3 parasites. This recapitulated our previous findings, confirming the deletion of PDE2 leads to delay in A23187-mediated egress.

      4.A few references are missing to ensure reproducibility.

      The manuscript states that the kinetic lipidomics experiments were performed with established methods, but the cited publication (line 497) is a preprint. These are therefore not peer reviewed and should be described in greater detail in this manuscript, including any relevant validation.

      Response:

      We thank the reviewer for pointing this out. We have included a greater description of the methods used in the materials and methods section such that the experiment is reproducible, as per the reviewer’s suggestion. We decided to still make mention of the BioRxiv preprint since we thought it was appropriate for the reader to be informed of ongoing developments in the field.

      Please cite the release of the T. gondii proteomes used for spectrum matching (lines 972-973).

      Response:

      We have included this as per the reviewer’s suggestion.

      Please include the TMT labeling scheme so the analysis may be reproduced from the raw files.

      Response:

      We have included this as per the reviewer’s suggestion in Supp Fig. 3A.

      5.Statistical analyses should be reviewed as follows:

      Have the authors examined the possibility that some changes in phosphopeptide abundance reflect changes in protein abundance? This may be particularly relevant for comparisons involving the ∆cdpk3 strain. Did the authors collect paired unenriched proteomes from the experiments performed? Alternatively, there may be enriched peptides that did not change in abundance for many of the proteins that appear dynamically phosphorylated.

      Response:

      We did not collect unenriched proteomes from the experiments performed (although we did perform unenriched mixing checks to ensure equal loading between samples), and believe that this wasn’t a necessity for the following reasons:

      1. For within-line treatment analyses, treatment timings are so short (a maximum of 15-50s in the single timepoint experiment) that it would be unlikely to detect substantial changes in protein abundance. Moreover, these unlikely events would affect all phosphosites across a protein, and therefore be detectable.

      In our CDPK3 dependency timecourse experiments, we normalise both the WT and ∆CDPK3 strain to 0s, and measure signalling progression over time. Therefore, any difference at timepoints that are not “0” are not originating from basal differences. We also see a consistent increase/decrease in phosphosite detection across the sub-minute timecourse, further confirming that the observed changes are truly down to dynamic changes in phosphorylation and not protein levels.

      In the single timepoint CDPK3 dependency analyses (44 regulated sites identified, Data S2), we acknowledge that there could be some risk of altered starting protein abundance between lines. However, if protein abundance were responsible for the changes in phosphosite detection, we would expect all phosphosites across the protein to shift, and we do not observe this. Moreover, when we look at these CDPK3 dependent proteins and compare their phosphosite abundance in untreated WT and ∆CDPK3 lines, we find that for each protein, either all or the majority of phosphosites detected are unchanged (highlighting that there is no substantial difference in this protein’s abundance between lines). Where there are phosphosite differences between lines, these are only ever on single sites on a protein while most other sites are unchanged - implying that these are changes to basal phosphorylation states and not protein levels.

      It seems like for Figs. 3B and S5 the maximum number of clusters modeled was selected. Could the authors provide a rationale for the number of clusters selected, since it appears many of the clusters have similar profiles.

      The number of clusters is chosen automatically by the Mclust algorithm as the value that maximizes the Bayes Information Criterion (BIC). BIC in effect balances gains in model fit (increasing log-likelihood) against increasing the number of parameters (i.e. number of clusters).

      Please include figure panel(s) relating to gene ontology. Relevant information for readers to make conclusions includes p-value, fold-enrichment or gene ratio, and some sort of metric of the frequency of the GO term in the surveyed data set. See PMID: 33053376 Fig. 7 and PMID: 29724925 Fig. 6 for examples or enrichment summaries. Additionally, in the methods, specify (i) the background set, (ii) the method used for multiple test correction, (iii) the criteria constituting "enrichment", (iv) how the T. gondii genome was integrated into the analysis, (v) the class of GO terms (molecular function, biological process, or cellular component), (vi) any additional information required to reproduce the results (for example, settings modified from default).

      Response:

      We have included the additional information requested in the materials and methods.

      We purposely did not include GO figure panels as our analyses are being done across many clusters, making it very difficult to display this information cohesively. We have included all data in Tables S2-S5. These tables included all the relevant information on p-value, enrichment status, ratio in study/ratio in population, class of GO terms etc.

      The presentation of the lipidomics experiments in Figure 4A-C is confusing. First, the ∆cdpk3/WT ratio removes information about the process in WT parasites, and it's unclear why the scale centers on 100 and not 1. Second, the data in Figure S6 suggests a more modest effect than that represented in Fig. 4; is this due to day to day variability? How do the authors justify pairing WT and mutant samples as they did to generate the ratios?

      Response:

      This is a common strategy used by many metabolomics experts (Bailey et al., 2015; Dass et al., 2021; Lunghi et al., 2022). We had originally chosen to represent the data as a ratio since this form of representation helps get rid of the variability that arises between experiments and allows us to see very clear patterns which would otherwise go unnoticed. This variability arises from the amount of lipids in each sample which varies between parasites in a dish, the batch of FBS and DMEM used, and the solutions and even room temperature used to extract lipids on a given day.

      However, we agree with the reviewer that depicting the data in Figure 4A-C as a ratio of ∆CDPK3/WT parasites can be confusing, so we have now changed the graphs, plotting WT and ∆CDPK3 levels instead, and have moved the ratio of ∆CDPK3/WT to the Supplementary Figure 5.

      The significance test seems to be performed on the difference between the WT and ∆cdpk3 strains, but not relative to the DMSO treatment? Wouldn't you want to perform a repeated measures ANOVA to determine (i) if lipid levels change over time and (ii) if this trend differs in WT vs. mutant strain?

      Response:

      The reviewer correctly points out that ANOVA is often used for time courses, but we must point out that it is not always strictly appropriate since it can overlook the purpose of the individual experiment design, which in this case is, 1) to investigate the role of CDPK3 compared to the WT parental strain, and 2) specifically to find the exact point at which the DAG begins to change after stimulus to match the proteomics time course.

      Our data is clearly biassed towards earlier time points where we have 0, 5, 10, 30, 45 seconds where DAG levels are mostly unchanged compared to the single timepoint 60 seconds which shows a significant difference in DAG using our method of statistical comparison by paired two tailed t-test. Therefore, it would be unwise to use ANOVA when we really want to see when the A23187 stimulus takes effect, which appears to be after the 45 second mark. Therefore, analysing the data by ANOVA would likely provide a false negative result, where the result is non-significant but there is clearly more DAG in WT than CDPK3 after 60 seconds. T-tests are commonly used when comparing the same cell lines grown in the same conditions with a test/treatment, and in this case the test/treatment is CPDK3 present or absent (Lentini et al., 2020).

      In the main text, it would be preferable to see the data presented as the proteomics experiments were in Figure 4B and 4C, with fold changes relative to the DMSO (t = 0) treatment, separately for WT and ∆cdpk3 parasites.

      Response:

      We have now changed the way that we represent the data, plotting %mol instead of the ratio.

      Signaling lipids constitute small percentages of the overall pool (e.g. PMID: 26962945), so one might not necessarily expect to observe large changes in lipid abundance when signaling pathways are modulated. Is there any positive control that the authors could include to give readers a sense of the dynamic range? Maybe the DGK1 mutant (PMID: 26962945)?

      Response:

      DGK1 is maybe not a good example because the DGK1 KO parasites effectively “melt” from a lack of plasma membrane integrity ((Bullen et al., 2016), so this would likely be technically challenging. We don’t see the added value in including an additional mutant control since we can already see the dynamic change over time from no difference (0 seconds) to significant difference (60 seconds) between WT and CDPK3 for DAG and most other lipids. We already see a significant difference between WT and CDPK3 after 60 seconds for DAG, and we can clearly see in sub-minute timecourses the changes or not at the specific points where the A23187 is added (0-5 seconds), the parasites acclimatise, for the A23187 to take effect (10-30 seconds) and for the parasite lipid response to be visible by lipidomics (45-60 +seconds).

      Figure 4E: are the differences in [cAMP] with DMSO treatment and A23187 treatment different at any of the timepoints in the WT strain? The comparison seems to be WT/∆cdpk3 at each timepoint. Does the text (lines 562-568) need to be modified accordingly?

      Response:

      In WT (and ∆CDPK3) parasites, [cAMP] is significantly changed at 5s of A23187 treatment (relative to DMSO). We have modified our figures to include this analysis. The existing text accurately reflects this.

      Figure 6I: is the difference between PDE2 cKO/∆cdpk3 + DMSO or RAP significant?

      Response

      In our original manuscript, there was no statistical difference in [cAMP] between PDE2cKO/∆CDPK3+DMSO and PDE2cKO/∆CDPK3+DMSO+RAP, likely due to the variation between biological replicates. To overcome the issues in variability between replicates, we have now included more biological replicates (n=7). This has led to a significant difference in [cAMP] between PDE2cKO/∆CDPK3 DMSO- and RAP-treated parasites and between PDE2cKO DMSO- and RAP-treated parasites (now Fig. 6I).

      **MINOR COMMENTS**

      1.The following references should be added or amended:

      Lines 83-85: in the cited publication, relative phosphopeptide abundances of an overexpressed dominant-negative, constitutively inactive PKA mutant were compared to an overexpressed wild-type mutant. In this experimental setup, one would hypothesize that targets of PKA should be down-regulated (inactive/WT ratios). However, the mentioned phosphopeptide of PDE2 was found to be up-regulated, suggesting that it is not a direct target of PKA.

      Response:

      We thank the reviewer for spotting this error, we have now modified our wording.

      Cite TGGT1_305050, referenced as calmodulin in line 458, as TgELC2 (PMID: 26374117).

      Response:

      We have included this as per the reviewer’s suggestion.

      Cite TGGT1_295850 as apical annuli protein 2 (AAP2, PMID: 31470470).

      Response:

      We have included this as per the reviewer’s suggestion.

      Cite TGGT1_270865 (adenylyl cyclase beta, Acβ) as PMID: 29030485, 30449726.

      Response:

      We have included this as per the reviewer’s suggestion.

      Cite TGGT1_254370 (guanylyl cyclase, GC) as PMID: 30449726, 30742070.

      Response:

      We have included this as per the reviewer’s suggestion.

      Note that Lourido, Tang and David Sibley, 2012 observed that treatment with zaprinast (a PDE inhibitor) could overcome CDPK3 inhibition. The target(s) of zaprinast have not been determined and may differ from those of BIPPO (in identity and IC50). The cited study also used modified CDPK3 and CDPK1 alleles, rather than ∆cdpk3 and intact cdpk1 as used in this manuscript. That is to say, the signaling backgrounds of the parasite strains deviate in ways that are not controlled.

      Response:

      While it is true that zaprinast targets have not been unequivocally identified, zaprinast-induced egress is widely thought to be the result of PKG activation, a conclusion that is further supported by the finding that Compound 1 completely blocks zaprinast-induced egress (Lourido, Tang and David Sibley, 2012). Similarly, BIPPO-induced egress is inhibited by chemical inhibition of PKG by Compound 1 and Compound 2 (Jia et al., 2017). Moreover, like zaprinast, BIPPO has been clearly shown to partially overcome the ∆CDPK3 egress delay (Stewart et al., 2017).

      2.The following comments refer to the figures and legends:

      Part of the legend text for 1G is included under 1H.

      Response:

      This has been corrected

      Figure 1H: The legend mentions that some dots are blue, but they appear green. Please ensure that color choices conform to journal accessibility guidelines. See the following article about visualization for colorblind readers: https://www.ascb.org/science-news/how-to-make-scientific-figures-accessible-to-readers-with-color-blindness____/ . Avoid using red and green false-colored images; replace red with a magenta lookup table. Multi-colored images are only helpful for the merged image; otherwise, we discern grayscale better. Applies to Figures 1B, 5C, 6D. (Aside: anti-CAP seems an odd choice of counterstain; the variation in the staining, esp. at the apical cap, is distracting.)

      Response:

      We thank reviewer #1 for bringing this to our attention, and have modified our colour usage for all IFAs and Figures 1H and 3E.

      We chose CAP staining as the antibody is available in the laboratory and stains both the apical end (which has been shown to contain several proteins important for signalling as well as PDE9) and the parasite periphery, the location of CDPK3.

      Figure 1B: When showing a single fluorophore, please use grayscale and include an intensity scale bar, since relative values are being compared.

      Response:

      We have modified this as per the reviewer’s suggestion

      Figure 1C: it is difficult to compare the kinetics of the calcium response when the curves are plotted separately. Since the scales are the same, could the two treatments be plotted on the same axes, with different colors? Additionally, according to the legend, a red line seems to be missing in this panel.

      Response:

      Fig1C is not intended to compare kinetics, merely to show peak calcium release in each separate treatment condition. We have removed mention of a red line in the figure legend.

      Figure 2A: Either Figure S4 can be moved to accompany Figure 2A, or Figure 2A could be moved to the supplemental.

      Figure S4 has now been incorporated into Figure 2.

      Reviewer #1 (Significance (Required)):

      This manuscript would interest researchers studying signaling pathways in protozoan parasites, especially apicomplexans, as CDPK3 and PKG orthologs exist across the phylum. To my knowledge, it is the first study that has proposed a mechanism by which a calcium effector regulates cAMP levels in T. gondii. Unfortunately, the experiments fall short of testing this mechanism.

      Response:

      We thank reviewer #1 for their comments, but disagree with their assessment that the key points of the manuscript “fall short of experimental testing”.

      1. We demonstrate that, following both BIPPO and A23187 treatment, there is differential phosphorylation of numerous components traditionally believed to sit upstream of PKG activation (as well as several components within the PKG signalling pathway itself).
      2. We show that some of these sites are CDPK3 dependent, and that deletion of CDPK3 leads to changes in lipid signalling and an elevation in levels of cAMP (dysregulation of which is known to alter PKG signalling).
      3. We show that pre-treatment with a PKA inhibitor is able to largely rescue this phenotype.
      4. We demonstrate that a cAMP-specific PDE is phosphorylated following A23187 treatment (i.e. Ca2+ flux)
      5. We show that this cAMP specific PDE plays a role in A23187-mediated egress.
      6. While the latter PDE may not be directly regulated by CDPK3, these findings suggest that there are likely several Ca2+-dependent kinases that contribute to this feedback loop.

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

      **Summary:**

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      In this manuscript, Dominicus et al investigate the elusive role of calcium-dependent kinase 3 during the egress of Toxoplasma gondii. Multiple functions have already been proposed for this kinase by this group including the regulation of basal calcium levels (24945436) or of a tyrosine transporter (30402958). However, one of the most puzzling phenotypes of CDPK3 deficient tachyzoites is a marked delay in egress when parasites are stimulated with a calcium ionophore that is rescued with phosphodiesterase (PDE) inhibitors. Crosstalk between, cAMP, cGMP, lipid and calcium signalling has been previously described to be important in regulating egress (26933036, 23149386, 29030485) but the role of CDPK3 in Toxoplasma is still poorly understood.

      Here the authors first take an elegant phosphoproteomic approach to identify pathways differentially regulated upon treatment with either a PDE inhibitor (BIPPO) and a calcium ionophore (A23187) in WT and CDPK3-KO parasites. Not much difference is observed between BIPPO or A23187 stimulation which is interpreted by the authors as a regulation through a feed-back loop.

      The authors then investigate the effect of CDPK3 deletion on lipid, cGMP and cAMP levels. The identify major changes in DAG, phospholipid, FFAs, and TAG levels as well as differences in cAMP levels but not for cGMP. Chemical inhibition of PKA leads to a similar egress timing in CDPK3-KO and WT parasites upon A23187 stimulation.

      As four PDEs appeared differentially regulated in the CDPK3-KO line upon A23187, the authors investigate the requirement of the 4 PDEs in cAMP levels. They show diverse localisation of the PDEs with specificities of PDE1, 7 and 9 for cGMP and of PDE2 for cAMP. They further show that PDE1, 7 and 9 are sensitive to BIPPO. Finally, using a conditional deletion system, they show that PDE1 and 2 are important for the lytic cycle of Toxoplasma and that PDE2 shows a slightly delayed egress following A23187 stimulation.

      **Major comments:**

      -Are the key conclusions convincing?

      The title is supported by the findings presented in this study. However I am not sure to understand why the authors imply a positive feed back loop. This should be clarified in the discussion of the results.

      Response:

      We believe in a positive feedback loop as, upon A23187 treatment (resulting in a calcium flux), ΔCDPK3 parasites are able to egress, albeit in a delayed manner. This egress delay is substantially, but not completely, alleviated upon treatment with BIPPO (a PDE inhibitor known to activate the PKG signalling pathway). In conjunction with our phosphoproteomic data (where we see phosphorylation of numerous pathway components upstream of PKG upon BIPPO and A23187 treatment - both in a CDPK3 dependent and independent manner), these observations suggest that calcium-regulated proteins (CDPK3 among them) feed into the PKG pathway. As deletion of CDPK3 delays egress, it is reasonable to postulate that this feedback is one that amplifies egress signalling (i.e. is positive).

      The phosphoproteome analysis seems very strong and will be of interest for many groups working on egress. However, the key conclusion, i.e. that a substrate overlaps between PKG and CDPK3 is unlikely to explain the CDPK3 phenotype, seems premature to me in the absence of robustly identified substrates for both kinases.

      Response:

      We certainly do not fully exclude the possibility of a substrate overlap but do lean more heavily towards a feedback loop given (a) the inability to clearly detect treatment-specific signalling profiles and (b) the phospho targets observed in the A23187 and BIPPO phosphoproteomes. We have further clarified our reasoning, and overall tempered our language in the manuscript as per the reviewer’s suggestion.

      I am not sure there is a clear key conclusion from the lipidomic analysis and how it is used by the authors to build their model up. Major changes are observed but how could this be linked with CDPK3, particularly if cGMP levels are not affected?

      Response:

      Our phosphoproteomic analyses identify several CDPK3-dependent phospho sites on phospholipid signalling components (DGK1 & PI-PLC), suggesting that there is indeed altered signalling downstream of PKG. To test whether these lead to a measurable phenotype, we performed the lipidomics analysis. We did not pursue this arm of the signalling pathway any further as we postulated that the changes in the lipid signalling pathway were less likely to play a role in the feedback loop. Nevertheless, we felt that it was worthwhile to include these findings in our manuscript as they support the conclusions drawn from the phosphoproteomics - namely that lipid signalling is perturbed in CDPK3 mutants. We, or others, may follow up on this in future.

      We agree with the reviewer that it is surprising that cGMP levels remain unchanged in our experiments when we treat with A23187. Given the measurable difference in cAMP levels between WT and ΔCDPK3 parasites, we postulate that CDPK3 directly or indirectly downregulates levels of cAMP. This would, in turn, alter activity of the cAMP-dependent protein kinase PKAc. Jia et al. (2017) have shown a clear dependency on PKG for parasites to egress upon PKAc depletion, but were also unable to reliably demonstrate cGMP accumulation in intracellular parasites. Similarly, their hypothesis that dysregulated cGMP-specific PDE activity results in altered cGMP levels has not been proven (the PDE hypothesised to be involved has since been shown to be cAMP-specific).

      While it is possible that our collective inability to observe elevated cGMP levels is explained by the sensitivity limits of the assay, it is similarly possible that cAMP-mediated signalling is exerting its effects on the PKG signalling pathway in a cGMP-independent manner.

      The evidence that CDPK3 is involved in cAMP homeostasis seems strong. However, the analysis of PKA inhibition is a bit less clear. The way the data is presented makes it difficult to see whether the treatment is accelerating egress of CDPK3-KO parasites or affecting both WT and CDPK3-KO lines, including both the speed and extent of egress. This is important for the interpretation of the experiment.

      Response:

      Fig. 4F shows that there is a significant amount of premature egress in both WT and ∆CDPK3 parasites following 2 hrs of H89 pre-treatment (consistent with previous reports that downregulation of cAMP signalling stimulates premature egress). When we subsequently investigated A23187-induced egress rates of the remaining intracellular H89 pre-treated parasites (Fig. 4Gi-ii) we found that the ∆CDPK3 egress delay was largely rescued. We have moved Fig. 4F to the supplement (now Supp Fig. 5E) in order to avoid confusion between the distinct analyses shown in 4F (pre-treatment analyses) and 4G (egress experiment). These experiments provided a hint that cAMP signalling is affected, which we then validate by measuring elevated cAMP levels in CDPK3 mutant parasites.

      The biochemical characterisation of the four PDE is interesting and seems well performed. However, PDE1 was previously shown to hydrolyse both cAMP and cGMP (____https://doi.org/10.1101/2021.09.21.461320____) which raises some questions about the experimental set up. Could the authors possibly discuss why they do not observe similar selectivity? Could other PDEs in the immunoprecipitate mask PDE activity? In line with this question, it is not clear what % of "hydrolytic activity (%)" means and how it was calculated.

      The experiments describing the selectivity of BIPPO for PDE1, 7 and 9 as well as the biological requirement of the four tested PDEs are convincing.

      Response:

      We believe that the disagreement between our findings and those published by Moss and colleagues are due to the differences in experimental conditions. We performed our assays at room temperature for 1 hour with higher starting cAMP concentrations (1 uM) compared to them. They performed their assays at 37ºC for 2 hours with 10-fold lower starting cAMP concentrations (0.1 uM). We have now repeated this set of experiments using the Moss et al. conditions, and find that PDEs 1, 7 and 9 can be dual specific, while PDE2 is cAMP-specific, thereby recapitulating their findings (Now included in the revised manuscript under Supp Fig. 7B). However, we also now performed a timecourse PDE assay using our original conditions and show that the cAMP hydrolytic activity for PDE1 can only be detected following 4 hours of incubation, compared to cGMP activity that can be detected as early as 30 minutes, suggesting that it possesses predominantly cGMP activity (See Supp Fig. 7C). We therefore believe that our experimental setup is more stringent, because if one starts with a lower level of substrate and incubates for longer and at a higher temperature, even minor dual activity could make a substantial difference in cAMP levels. Our data suggests that the cAMP hydrolytic activity of PDEs 1, 7 and 9 is substantially lower than the cGMP hydrolytic activity that they display.

      We have also included a clear description of how % hydrolytic activity was calculated in the methods section.

      -Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      The claim that CDPK3 affects cAMP levels seems strong however the exact links between CDPK3 activity, lipid, cGMP and cAMP signalling remain unclear and it may be important to clearly state this.

      Response:

      We have modified our wording in the text to more clearly describe our current hypothesis and reasoning.

      -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.

      I think that the manuscript contains a significant amount of experiments that are of interest to scientists working on Toxoplasma egress. Requesting experiments to identify the functional link between above-mentioned pathways would be out of the scope for this work although it would considerably increase the impact of this manuscript. For example, would it be possible to test whether the CDPK3-KO line is more or less sensitive to PKG specific inhibition upon A23187 induced?

      -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.

      The above-mentioned experiment is not trivial as no specific inhibitors of PKG are available. Ensuring for specificity of the investigated phenotype would require the generation of a resistant line which would require significant work.

      __Response: __We agree that this would be an interesting experiment to further substantiate our findings. As indicated by the reviewer, however, the lack of specific inhibitors of PKG means a resistant line would likely be required to ensure specificity.

      -Are the data and the methods presented in such a way that they can be reproduced?

      It is not clear how the % of hydrolytic activity of the PDE has been calculated.

      Response: We have included a clearer description of how % hydrolytic activity was calculated in the methods section.

      -Are the experiments adequately replicated and statistical analysis adequate?

      This seems to be performed to high standards.

      **Minor comments:**

      -Specific experimental issues that are easily addressable.

      I do not have any comments related to minor experimental issues.

      -Are prior studies referenced appropriately?

      Most of the studies relevant for this work are cited. It is however not clear to me why some important players of the "PKG pathway" are not indicated in Fig 1H and Fig 3E, including for example UGO or SPARK.

      Response:

      We have modified Fig 1H and 3E to include all key players involved in the PKG pathway.

      -Are the text and figures clear and accurate?

      While all the data shown here is impressive and well analysed, I find it difficult to read the manuscript and establish links between sections of the papers. The phosphoproteome analysis is interesting and is used to orientate the reader towards a feedback mechanism rather than a substrate overlap. But why do the authors later focus on PDEs and not on AC or CNBD, as in the end, if I understand well, there is no evidence showing a link between CDPK3-dependent phosphorylation and PDE activity upon A23187 stimulation?

      Response:

      We thank reviewer#2 and appreciate their constructive feedback re the flow of the manuscript.

      Our key findings from the phosphoproteomics study were that 1) BIPPO and A23187 treatment trigger near identical signalling pathways, 2) that both A23187 and BIPPO treatment leads to phosphorylation of numerous components both upstream and downstream of PKG signalling (hinting at the presence of an Ca2+-regulated feedback loop) and 3) several of the abovementioned components are phosphorylated in a CDPK3 dependent manner.

      While several avenues of study could have been pursued from this point onwards, we chose to focus on the feedback loop in a broader sense as its existence has important implications for our general understanding of the signalling pathways that govern egress.

      We reasoned that, given the differential phosphorylation of 4 PDEs following A23187 and BIPPO treatment (none of which had been studied in detail previously), it was relevant to study these in greater detail.

      Coupled with the A23187 egress assay on PDE2 knockout parasites - our findings suggest that PDE2 plays a role in the abovementioned Ca2+ signalling loop. While PDE2 may not exert its effects in a CDPK3-dependent manner (and CDPK3 may, therefore, alter cAMP levels in a different fashion), this does not detract from the important finding that PDE2 is one of the (likely numerous) components that is regulated in a Ca2+-dependent feedback loop to facilitate rapid egress.

      We have modified our wording to better reflect our rationale for studying the PDEs irrespective of their CDPK3 phosphorylation status.

      While we feel that our reasoning for studying the PDEs is solid, we do appreciate that further clarification on the putative CDPK3-Adenylate cyclase link would elevate the manuscript substantially. However, given the data that the ACb is not playing a sole role in the control of egress, this is likely a non-trivial task and requires substantial work.

      It is also unclear how the authors link CDPK3-dependent elevated cAMP levels with the elevated basal calcium levels they previously described. This is particularly difficult to reconcile particularly in a PKG independent manner.

      Response:

      We previously postulated that elevated Ca2+ levels allowed ΔCDPK3 mutants to overcome a complete egress defect, potentially by activating other CDPKs (e.g. CDPK1). It is similarly plausible that elevated Ca2+ levels in ΔCDPK3 parasites may lead to elevated cAMP levels in order to prevent premature egress.

      As noted in our previous responses, we acknowledge that our inability to detect cGMP is surprising. However, given the clarity of our cAMP findings, and the phosphoproteomic evidence to suggest that various components in the PKG signalling pathway are affected, we postulate that we are either unable to reliably detect cGMP due to sensitivity issues, or that cAMP is exerting its regulation on the PKG pathway in a cGMP-independent manner. As noted previously, while the link between cAMP and PKG signalling has been demonstrated by Jia et al., it is not entirely clear how this is mediated.

      The presentation of the lipidomic analysis is also not really clear to me. Why do the authors show the global changes in phospholipids and not a more detailed analysis?

      Response:

      We performed a detailed phospholipid profile of WT and ∆CDPK3 parasites under normal culture conditions. However, due to the sheer quantity of parasites required for this detailed analysis, we were unable to measure individual phospholipid species in our A23187 timecourse. We therefore opted to measure global changes following A23187 stimulation.

      As the authors focus on the PI-PLC pathway, could they detail the dynamics of phosphoinositides? I understand that lipid levels are affected in the mutant but I am not sure to understand how the authors interpret these massive changes in relationship with the function of CDPK3 and the observed phenotypes.

      Response:

      Our phosphoproteomic analyses identified several CDPK3-dependent phospho sites on phospholipid signalling components (DGK1 & PI-PLC), suggesting that (in keeping with all of our other data), there is altered signalling downstream of PKG. To test whether these changes lead to a measurable phenotype, we performed the lipidomics analysis. Following stimulation with A23187, we found a delayed production of DAG in ∆CDPK3 parasites compared to WT parasites. Since DAG is required for the production of PA, which in turn is required for microneme secretion, our finding can explain why microneme secretion is delayed in ∆CDPK3 parasites, as previously reported (Lourido, Tang and David Sibley, 2012; McCoy et al., 2012).

      We did not follow this arm of the signalling pathway any further as we postulated that the changes in the lipid signalling pathway were less likely to play a role in the feedback loop. Nevertheless, we felt that it was worthwhile to include these findings in our manuscript as they support the conclusions drawn from the phosphoproteomics - namely that lipid signalling is perturbed in CDPK3 mutants. We, or others, may follow up on this in future.

      Finally, the characterisation of the PDEs is an impressive piece of work but the functional link with CDPK3 is relatively unclear. It would also be important to clearly discuss the differences with previous results presented in this this preprint: https://doi.org/10.1101/2021.09.21.461320____.

      My understanding is while the authors aim at investigating the role of CDPK3 in A23187 induced egress, the main finding related to CDPK3 is a defect in cAMP homeostasis that is not linked to A23187. Similarly, the requirements of PDE2 in cAMP homeostasis and egress is indirectly linked to CDPK3. Altogether I think that important results are presented here but divided into three main and distinct sections: the phosphoproteomic survey, the lipidomic and cAMP level investigation, and the characterisation of the four PDEs. However, the link between each section is relatively weak and the way the results are presented is somehow misleading or confusing.

      Response:

      As mentioned in a previous response, we chose to study PDEs in greater detail because of our observation that both A23187 and BIPPO treatments lead to their phosphorylation (hinting at the presence of a Ca2+regulated feedback loop). We were particularly intrigued to study the cAMP specific PDE, as CDPK3 KO parasites suggested that cAMP may play a role in the Ca2+ feedback mechanism. As PDE2 may not be directly regulated by CDPK3, Ca2+ appears to exert its feedback effects in numerous ways. We have modified our wording to better reflect our rationale for studying the PDEs irrespective of their CDPK3 phosphorylation status.

      -Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      This is a very long manuscript written for specialists of this signalling pathway and I would suggest the authors to emphasise more the important results and also clearly state where links are still missing. This is obviously a complex pathway and one cannot elucidate it easily in a single manuscript.

      Response:

      We have included an additional summary in our conclusions to better illustrate our findings and clarify any missing links.

      Reviewer #2 (Significance (Required)):

      -Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      This is a technically remarkable paper using a broad range of analyses performed to a high standard.

      -Place the work in the context of the existing literature (provide references, where appropriate).

      The cross-talk between cAMP, cGMP and calcium signalling is well described in Toxoplasma and related parasites. Here the authors show that, in Toxoplasma, CDPK3 is part of this complex signalling network. One of the most important finding within this context is the role of CDPK3 in cAMP homeostasis. With this in mind, I would change the last sentence of the abstract to "In summary we uncover a feedback loop that enhances signalling during egress and links CDPK3 with several signalling pathways together."

      Response:

      In light of feedback received from several reviewers, we have made our wording less CDPK3 centric - as our findings relate in part to CDPK3 and, in a broader sense, to a Ca2+ driven feedback loop.

      The genetic and biochemical analyses of the four PDEs are remarkable and highlight consistencies and inconsistencies with recently published work that would be important to discuss and will be of interest for the field.

      __Response: __We thank reviewer#2 and agree that the PDE findings are of significant importance to the field.

      While I understand the studied signalling pathway is complex, I think it would be important to better describe the current model of the authors. In the discussion, the authors indicate that "the published data is not currently supported by a model that fits most experimental results." I would suggest to clarify this statement and discuss whether their work helps to reunite, correct or improve previous models.

      __Response: __We have expanded on the abovementioned statement to clarify that the presence of a feedback loop is a major pillar of knowledge required for the complete interpretation of existing signalling data.

      Could the authors also speculate about a potential role of PDE/CDPK3 in host cell invasion as cAMP signalling has be shown to be important for this process (30208022 and 29030485)?

      __Response: __Existing literature (Jia et al., 2017) suggests that perturbations to cAMP signalling play a very minor role in invasion since parasites where either ACα or ACβ are deleted show no impairment in invasion levels. We currently do not have substantial data on invasion, and are not sure that pursuing this is valuable given the minor phenotypes observed in other studies.

      -State what audience might be interested in and influenced by the reported findings.

      This paper is of great interest to groups working on the regulation of egress in Toxoplasma gondii and other related apicomplexan pathogens.

      -Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      I am working on the cell biology of apicomplexan parasites.

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

      **Summary:**

      Dominicus et al aimed to identify the intersecting components of calcium, cyclic nucleotides (cAMP, cGMP) and lipid signaling through phosphoproteomic, knockout and biochemical assays in an intracellular parasite, Toxoplasma gondii, particularly when its acutely-infectious tachyzoite stage exits the host cells. A series of experimental strategies were applied to identify potential substrates of calcium-dependent protein kinase 3 (CDPK3), which has previously been reported to control the tachyzoite egress. According to earlier studies (PMID: 23226109, 24945436, 5418062, 26544049, 30402958), CDPK3 regulated the parasite exit through multiple phosphorylation events. Here, authors identified differentially-regulated (DR) phosphorylation sites by comparing the parasite samples after treatment with a calcium ionophore (A23178) and a PDE inhibitor (BIPPO), both of which are known to induce artificial egress (induced egress as opposed to natural egress). When the DCDPK3 mutant was treated with A23187, its delayed egress phenotype did not change, whereas BIPPO restored the egress to the level of the parental (termed as WT) strain, probably by activating PKG.

      The gene ontology enrichment of the up-regulated clusters revealed many probable CDPK3-dependent DR sites involved in cyclic nucleotide signaling (PDE1, PDE2, PDE7, PDE9, guanylate and adenylate cyclases, cyclic nucleotide-binding protein or CNBP) as well as lipid signaling (PI-PLC, DGK1). Authors suggest lipid signaling as one of the factors altered in the CDPK3 mutant, albeit lipidomics (PC, PI, PS, PT, PA, PE, SM) showed no significant change in phospholipids. To reveal how the four PDEs indicated above contribute to the cAMP and cGMP-mediated egress, they examined their biological significance by knockout/knockdown and enzyme activity assays. Authors claim that PDE1,7,9 proteins are cGMP-specific while PDE2 is cAMP-specific, and BIPPO treatment can inhibit PDE1-cGMP and PDE7-cGMP, but not PDE9-cGMP. Given the complexity, the manuscript is well structured, and most experiments were carefully designed. Undoubtedly, there is a significant amount of work that underlies this manuscript; however, from a conceptual viewpoint, the manuscript does not offer significant advancement over the current knowledge without functional validation of phosphoproteomics data (see below). A large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signaling cascades. This work provides a further refinement of the existing model In a methodical sense, the work uses established assays, some of which require revisiting to reach robust conclusions and avoid misinterpretation. The article is quite interesting from a throughput screening point of view, but it clearly lacks the appropriate endorsement of the hits.The authors accept that identifying the phosphorylation of a protein does not imply a functional role, which is a major drawback as there is no experimental support for any phosphorylation site of the protein identified through phosphoproteomics. In terms of the mechanism, it is not clear whether and how lipid turnover and cAMP-PKA signaling control the egress phenotype (lack of a validated model at the end of this study).

      Response:

      We thank reviewer #3 for their comments, but respectfully disagree with their assessment that the work presented does not advance current knowledge.

      1. We demonstrate that, following both BIPPO and A23187 treatment, there is differential phosphorylation of numerous components traditionally believed to sit upstream of PKG activation (as well as numerous components within the PKG signalling pathway itself). While it may have been inferred from previous studies that A23187 and BIPPO signalling intersect, this has never been unequivocally demonstrated - nor has a feedback loop ever been shown.

      We provide a novel A23187-driven phosphoproteome timecourse that further bolsters the model of a Ca2+-driven feedback loop.

      We show that deletion of CDPK3 leads to a delay in DAG production upon stimulation with A23187.

      We show that some of the abovementioned sites are CDPK3 dependent, and that deletion of CDPK3 leads to elevated levels of cAMP (dysregulation of which is known to alter PKG signalling).

      We show that pre-treatment with a PKA inhibitor is able to largely rescue this phenotype.

      We demonstrate that a cAMP-specific PDE is phosphorylated following A23187 treatment (i.e. Ca2+ flux)

      We show that this cAMP specific PDE plays a role in egress.

      While the latter PDE may not be directly regulated by CDPK3, these findings suggest that there are likely several Ca2+-dependent kinases that contribute to this feedback loop.

      We also firmly disagree with the reviewer’s assertion that without phosphosite characterisation, we have no support for our model. Following treatment with A23187 (and BIPPO), we clearly show broad, systemic changes (both CDPK3 dependent and independent) across signalling pathways previously deemed to sit upstream of calcium flux. Given the vast number of proteins involved in these signalling pathways, and the multitude of differentially regulated phosphosites identified on each of them, it is highly likely that the signalling effects we observe are combinatorial. Accordingly, we believe that mutating individual sites on individual proteins would be a very costly endeavour which is unlikely to substantially advance our understanding of signalling during egress. Moreover, introducing multiple point mutations in a given protein to ablate phosphorylation may lead to protein misfolding and would therefore not be informative. One of the key aims of this study was to assess how egress signalling pathways are interconnected, and we believe we have been able to show strong support for a Ca2+-driven feedback mechanism in which both CDPK3 and PDE2 play a role through the regulation of cAMP.

      While we agree with the reviewer’s statement that a large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signalling cascades, a feedback loop has not previously been shown. We believe that this finding is absolutely central to facilitate the complete interpretation of existing signalling data. Furthermore, no previous studies have gone to this level of detail in either proteomics or lipidomics to analyse the calcium signal pathway in any apicomplexan parasite. We argue that the novelty in our manuscript is that it is a carefully orchestrated study that advances our understanding of the signalling network over time with subcellular precision. The kinetics of signalling is not well understood and we believe that our study is likely the first to include both proteomic and lipidomic analyses over a timecourse during the acute lytic cycle stage of the disease. In doing so, we found evidence for a feedback loop that controls the signalling network spatiotemporally, and we characterise elements of this feedback in the same study.

      **Major Comments:**

      Based on the findings reported here there is little doubt that BIPPO and A23187-induced signaling intersect with each other, as very much expected from previous studies. The authors selected the 50s and 15s post-treatment timing of A23187 and BIPPO, respectively for collecting phosphoproteomics samples. At these time points, which were shown to peak cytosolic Ca2+, parasites were still intracellular (Line #171). How did authors make sure to stimulate the entire signaling cascade adequately, particularly when parasites do not egress within the selected time window? There is significant variability between phosphosite intensities of replicates (Line #186), which may also be attributed to insufficient triggers for the egress across independent experiments. This work must be supported by in vitro egress assays with the chosen incubation periods of BIPPO and ionophore treatment (show the induced % egress of tachyzoites in the 50s and 15s).

      Response:

      1. We appreciate that the reviewer acknowledges that our data clearly shows that BIPPO and A23187-induced signalling intersect. While this may have been expected from previous studies, this has not previously been shown - and is therefore valuable to the field. Specifically, the fact that A23187-treatment leads to phosphorylation of targets normally deemed to sit upstream of calcium release is entirely novel and adds a substantial layer of information to our understanding of how these signalling pathways work together.

      Treatments were purposely selected to align pathways to a point where calcium levels peak just prior to calcium reuptake. At these chosen timepoints, we clearly show that overall signalling correlation is very high. We know from our egress assays using identical treatment concentrations (Fig. 2C), that the stimulations used are sufficient to result in complete egress. We are simply comparing signalling pathways at points prior to egress.

      As mentioned in point 2, we show convincingly that the treatments used are sufficient to trigger complete egress. As detailed clearly in the text, we believe that these variations in intensities between replicates are due to slight differences in timing between experiments (this is inevitable given the very rapid progression of signalling, and the difficulty of replicating exact sub-minute treatment timings). We demonstrate that the reporter intensities associated with DR sites correlate well across replicates (Supp Fig. 3C), suggesting that despite some replicate variability, the overall trends across replicates is very much consistent. This allows us to confidently average scores to provide values that are representative of a site’s phosphorylation state at the timepoint of interest.

      The reviewer’s suggestion that we should demonstrate % egress at the 50s and 15s treatment timepoints is obsolete - we state clearly in the text that parasites have not egressed at these timepoints. Our egress assays (Fig. 2C) further support this.

      The authors discuss that CDPK3 controls the cAMP level and PKA through activation of one or more yet-to-be-identified PDEs(s). cAMP could probably also be regulated by an adenylate cyclase, ACbeta that was found to have CDPK3-dependent phosphorylation sites. If CDPK3 is indeed a regulator of cAMP through the activation of PDEs or ACbeta, it would be expected that the deletion of CDPK3 would perturb the cAMP level, resulting in dysregulation of PKAc1 subunit, which in turn would dysregulate cGMP-specific PDEs (PMID: 29030485) and thereby PKG. All these connections need to explain in a more clear manner with experimental support (what is positive and what is negatively regulated by C____DPK3).

      Response:

      1. We do not firmly state that CDPK3 regulates cAMP by phosphorylation of a PDE - this is one of the possibilities addressed. We acknowledge the possibility that this could also be via the adenylate cyclase (see line 792).

      PMID: 29030485 demonstrates clearly a link between cAMP signalling and PKG signalling, but does not demonstrate how this is mediated. The authors postulate that a cGMP-specific PDE is dysregulated given their observation that PDE2 is differentially phosphorylated in a constitutively inactive PKA mutant, however this was not validated experimentally. We and others (Moss et al., 2022), however, demonstrate that PDE2 is cAMP-specific. This suggests that the model built by PMID: 29030485 requires revisiting. We acknowledge clearly in the text that Jia et al. have shown a link between cAMP and PKG signalling, and hypothesise that CDPK3’s modulation of cAMP levels may affect this (this is in keeping with our phosphoproteomic data).

      Moreover, the egress defect is not due to a low influx of calcium in the cytosol because when the ionophore A23187 was added to the CDPK3 mutant, its phenotype was not recovered. Rather, the defect may be due to the low or null activity of PKG that would activate PI4K to generate IP3 and DAG. The latter would be used as a substrate by DGK to generate PA that is involved in the secretion of micronemes and Toxoplasma egress. In this context, authors should evaluate the role of CDPK3 in the secretion of micronemes that is directly related to the egress of the parasite.

      1. We agree with the reviewer on their point about calcium influx, and have already acknowledged in the text that the feedback loop does not control release of Ca2+ from internal stores as disruption of CDPK3 does not lead to a delay in Ca2+

      We agree, and clearly address in the text, that the egress defect could be due to altered PKG/phospholipid pathway signalling.

      (Lourido, Tang and David Sibley, 2012; McCoy et al., 2012) have both previously shown that microneme secretion is regulated by CDPK3. We therefore do not deem it necessary to repeat this experiment, but have made clearer mention of their findings in our writing.

      When the Dcdpk3 mutant with BIPPO treatment was evaluated, it was observed that the parasite recovered the egress phenotype. It is concluded that CDPK3 could probably regulate the activity of cGMP-specific PDEs. CDPK3 could (in)activate them, or it could act on other proteins indirectly regulating the activity of these PDEs. Upon inactivation of PDEs, an increase in the cGMP level would activate PKG, which will, in turn, promote egress. From the data, it is not clear whether any phosphorylation by CDPK3 would activate or inactivate PDEs, and if so, then how (directly or indirectly). To reach unambiguous interpretation, authors should perform additional assays.

      Response:

      As mentioned previously, given the abundance of differentially regulated phosphosites, we do not believe that mutating individual sites on individual proteins is a worthwhile or realistic pursuit.

      We clearly show systematic A23187-mediated phosphorylation of key signalling components in the PKA/PKG/PI-PLC/phospholipid signalling cascade, and demonstrate that several of these are CDPK3-dependent. We demonstrate that CDPK3 alters cAMP levels (and that the ∆CDPK3 egress delay in A23187 treated parasites is largely rescued following pre-treatment with a PKA inhibitor). We similarly demonstrate that A23187 treatment leads to phosphorylation of numerous PDEs, including the cAMP specific PDE2, and show that PDE2 knockout parasites show an egress delay following A23187 treatment. While PDE2 may not be directly regulated by CDPK3 (suggesting other Ca2+ kinases are also involved), these findings collectively demonstrate the existence of a calcium-regulated feedback loop, in which CDPK3 and PDE2 play a role (by regulating cAMP).

      We acknowledge that we have not untangled every element of this feedback loop, and do not believe that it would be realistic to do so in a single study given the number of sites phosphorylated and pathways involved. We do believe, however, that we have shown clearly that the feedback loop exists - this in itself is entirely novel, and of significant importance to the field.

      On a similar note, a possible experiment that can be done to improve the work would be to treat the CDPK3 mutant with BIPPO in conjunction with a calcium chelator (BAPTA-AM) to reveal, which proteins are phosphorylated prior to activation of the calcium-mediated cascades?

      Response:

      We agree that this would be an interesting experiment to carry out but would involve significant work. This could be pursued in another paper or project but is beyond the scope of this work.

      The manuscript claims that PDE1, PDE7, PDE9 are cGMP specific, and BIPPO inhibits only cGMP-specific PDEs. All assays are performed with 1-10 micromolar cAMP and cGMP for 1h. There is no data showing the time, protein and substrate dependence. Given the suboptimal enzyme assays, authors should re-do them as suggested here. (1) Repeat the pulldown assay with a higher number of parasites (50-100 million) and measure the protein concentration. (2) Set up the PDE assay with saturating amount of cAMP and cGMP, which is critical if the PDE1,7,9 have a higher Km Value for cAMP (means lower affinity) compared to cGMP. An adequate amount of substrate and protein allows the reaction to reach the Vmax. Once you have re-determined the substrate specificity (revise Fig 5D), you should retest BIPPO (Fig 5E) in the presence of cAMP and cGMP. It is very likely that you would find the same result as PDE9 and PfPDEβ (BIPPO can inhibit both cAMP and cGMP-specific PDE), as described previously

      We have repeated our assay using the exact same conditions outlined by Moss et al. This involved using a similar number of parasites, a longer incubation time of 2 hours at a higher temperature (37ºC) and with a lower starting concentration of cAMP (0.1 uM). We demonstrate that we are able to recapitulate both the Moss et al. and Vo et al. (see Supp Fig. 7B). However, we noticed that these reactions were not carried out with saturating cAMP/cGMP concentrations, since all reactions had reached 100% completion at the end of the assay whereby all substrate was hydrolysed. We therefore believe that based on our original assay, as well as the new PDE1 timecourse that we have performed (Supp Fig. 7C), that PDEs 1, 7 and 9 display predominantly cGMP hydrolysing activity, with moderate cAMP hydrolysing activity.

      We also repeated the BIPPO inhibition assay using the Moss et al. conditions, and still observe that the cGMP activity of PDE1 is the most potently inhibited of all 4 PDEs. We also see moderate inhibition of the cAMP activities of PDE1 and PDE9, suggesting that cAMP hydrolytic activity can also be inhibited. Interestingly, the cGMP hydrolytic activities of PDEs 7 & 9, which were previously inhibited using our original assay conditions, no longer appear to be inhibited. This is likely due to the longer incubation time, which masks the reduced activities of these two PDEs following treatment with BIPPO.

      The authors did not identify any PKG substrate, which is quite surprising as cAMP signaling itself could impact cGMP. Authors should show if they were able to observe enhanced cGMP levels in BIPPO-treated sample (which is expected to stimulate cGMP-specific PDEs). The author mention their inability to measure cGMP level but have they analyzed cGMP in the positive control (BIPPO-treated parasite line)? Why have they focused only on CDPK3 mutant, whereas in their phosphoproteomic data they could see other CDPKs too? It could be that other CDPK-mediated signaling differs and need PKA/PKG for activation.

      In the title, the authors have mentioned that there is a positive feedback loop between calcium release, cyclic nucleotide and lipid signaling, which is quite an extrapolation as there is no clear experimental data supporting such a positive feedback loop so the author should change the title of the paper.

      Response:

      1. As addressed in our previous response to the reviewer, PMID: 29030485 demonstrates clearly a link between cAMP signalling and PKG signalling, but does not confirm how this is mediated. The authors surmise that a cGMP-specific PDE is dysregulated (although the PDE hypothesised to be involved has since been shown to be cAMP-specific), but are similarly unable to detect changes in cGMP levels. This suggests that their model may be incomplete.

      The BIPPO treatment experiment suggested by the reviewer was already included in the original manuscript (see Fig. 4D in original manuscript, now Fig. 4E). With BIPPO treatment we are able to detect changes in cGMP levels.

      We did not deem it to be within the scope of this study to study every single other CDPK. We chose to study CDPK3, as its egress phenotype was of particular interest given its partial rescue following BIPPO treatment. We reasoned that its study may lead us to identify the signalling pathway that links BIPPO and A23187 induced signalling.

      As addressed in greater detail in our response to reviewer #2, the fact that the feedback loop appears to stimulate egress implies that it is positive.

      **Minor Comments:**

      Materials & Methods

      Explanation of parameters is not clear (Line #360-367). Phosphoproteomics with A23187 (8 micromolar) treatment in CDPK3-KO and WT, for 15, 30 and 60s at 37{degree sign}C incubation with DMSO control. Simultaneously passing the DR and CDPK3 dependency thresholds: CDPK3-dependent phosphorylation

      __Response: __We have modified the wording to make this clearer as per the reviewer’s suggestion.

      Line #368: At which WT-A23187 timepoint did the authors identify 2408 DR-up phosphosites (15s, 30s or 60s)? Or consistently in all? It should be clarified?

      __Response: __As already stated in the manuscript (see line 366 in original manuscript, now line 1047), phosphorylation sites were considered differentially regulated if at any given timepoint their log2FC surpassed the DR threshold.

      A23187 treatment of the CDPK3-KO mutant significantly increased the cAMP levels at 5 sec post-treatment, but BIPPO did not show any change. The authors concluded that BIPPO presumably does not inhibit cAMP-specific PDEs. However, the dual-specific PDEs are known to be inhibited by BIPPO, as shown recently (____https://www.biorxiv.org/content/10.1101/2021.09.21.461320v1____). Authors do confirm that BIPPO-treatment can inhibit hydrolytic activity of PfPDEbeta for cAMP as well as cGMP (Line #612). Besides, it was shown in Fig 5E that BIPPO can partially though not significantly block cAMP-specific PDE2. The statements and data conflict each other under different subtitles and need to be reconciled. Elevation of basal cAMP level in the CDPK3 mutant indicates the perturbation of cAMP signaling, however BIPPO data requires additional supportive experiments to conclude its relation with cAMP or dual-specific PDE.

      Response:

      1. The manuscript to which the reviewer refers does not use BIPPO in any of their experiments. They show that continuous treatment with zaprinast blocks parasite growth in a plaque assay, but do not test whether zaprinast specifically blocks the activity of any of the PDEs.

      Having repeated the PDE assay using the Moss et al. conditions (as outlined above), we are now able to recapitulate their findings, showing that PDEs 1, 7 and 9 can display dual hydrolytic activity while PDE2 is cAMP specific. As explained further above, we believe that our original set of experiments are more stringent than the Moss *et al. * To confirm this, we also performed an additional experiment, incubating PDE1 for varying amounts of time using our original conditions (1 uM cAMP or 10 uM cGMP, at room temperature). This revealed that PDE1 is much more efficient at hydrolysing cGMP, and only begins to display cAMP hydrolysing capacity after 4 hours of incubation.

      We also measured the inhibitory capacity of BIPPO on the PDEs using the Moss *et al. * During the longer incubation time, it seems that BIPPO is unable to inhibit PDEs 7 and 9, while with the more stringent conditions it was able to inhibit both PDEs. We reasoned that since BIPPO is unable to inhibit these PDEs fully, the residual activity over the longer incubation period would compensate for the inhibition, eventually leading to 100% hydrolysis of the cNMPs. We also see that while the cGMP hydrolysing capacity of PDE1 is completely inhibited, its cAMP hydrolysing capacity is only partially inhibited. These findings and the fact that PDE2 is not inhibited by BIPPO are in line with our experiments where we measured [cAMP] and showed that treatment with BIPPO did not lead to alterations in [cAMP].

      The method used to determine the substrate specificity of PDE 1,2,7 and 9 resulted in the hydrolytic activity of PDE2 towards cAMP, while the remaining 3 were determined as cGMP-specific. However, PDE1 and PDE9 have been reported as being dual-specific (Moss et al, 2021; Vo et al, 2020), which questions the reliability of the preferred method to characterize substrate specificity by the authors. It is also suggested to use another ELISA-based kit to double check the results.

      Response:

      As outlined above, we have repeated the assay using the conditions described by Moss et al. (lower starting concentrations of cAMP, 2 hour incubation period at 37ºC) and find that we are able to recapitulate the results of both Moss et al. and Vo et al.. However, using the Moss et al. conditions, the PDEs have hydrolysed 100% of the cyclic nucleotide, suggesting that these conditions are less stringent than the ones we used originally using higher starting concentrations of cAMP and incubating for 1 hour only at room temperature. With enzymatic assays it is always important to perform them at saturating conditions (as already suggested by the reviewer) and therefore we believe that our original conditions are more stringent than the results using the Moss et al. conditions.

      Line #607-608: Authors found PDE9 less sensitive to BIPPO-treatment and concluded PDE2 as refractory to BIPPO inhibition; however, the reduction level of activity seems similar as seen in PDE9-BIPPO treated sample? This strong statement should be replaced with a mild explanation.

      __Response: __We have tempered our wording as per the reviewer’s suggestion

      Figures and legends:

      The introductory model in Fig S1 is difficult to understand and ambiguous despite having it discussed in the text. For example, CDPK1 is placed, but only mentioned at the beginning, and the role of other CDPKs is not clear. In addition, the arrows in IP3 and PKG are confusing. The location of guanylate and adenylate cyclase is wrong, and so on... The figure should include only the egress-related signaling components to curate it. The illustration of host cell in orange color must be at the right side of the figure in connection with the apical pole of the parasite (not on the top). Figure legend should also be rearranged accordingly and citations of the underlying components should be included (see below).

      __Response: __We have modified Supp Fig. 1 as per the suggestions of reviewer#2 and #3. We have now modified the localisations of the proteins and have also removed the lines showing the cross talk between pathways. We have also highlighted to the reader that this is only a model and may not represent the true localisations of the proteins, despite our best efforts.

      In Figure 5D, would you please provide the western blot analysis of samples before and after pulling down to demonstrate the success of your immunoprecipitation assay. Mention the protein concentration in your PDE enzyme assay. Please refer to the M&M comments above to re-do the enzyme assays.

      Response:

      We have now included western blots for the pull downs of PDEs 1, 2, 7 and 9 (Supp Fig. 7A). We chose not to measure protein concentrations of samples since all experiments were performed using the same starting parasite numbers, and we do not see large differences in activities between biological replicates of the PDEs.

      Figure legend 1C: Line #194: There is no red-dotted line shown in graph! Correct it!

      __Response: __We have modified this.

      Figure 4Gi-ii: Shouldn't it be labelled i: H89-treatment and ii: A23178, respectively instead of DMSO and H89? (based on the text Line #579).

      __Response: __Our labelling of Fig. 4Gi-ii is correct as panel i parasites were pre-treated with DMSO, while panel ii parasites were pre-treated with H89. Subsequent egress assays on both parasites were then performed using A23187.

      We have modified the figures to include mention of A23187 on the X axis, and modified the figure legend to clarify pre-treatment was performed with DMSO and H89 respectively.

      Bibliography:

      Line #57 and 58: Citations must be selected properly! Carruthers and Sibley 1999 revealed the impact of Ca2+ on the microneme secretion within the context of host cell attachment and invasion, not egress as indicated in the manuscript! Similar case is also valid for the reference Wiersma et al 2004; since the roles of cyclic nucleotides were suggested for motility and invasion. Also notable in the fact that several citations describing the localization, regulation and physiological importance of cAMP and cGMP signaling mediators (PMID: 30449726 , 31235476 , 30992368 , 32191852 , 25555060 , 29030485 ) are either completely omitted or not appropriately cited in the introduction and discussion sections.

      Response:

      We have modified the citations as per the reviewer’s suggestions. We now cite Endo et al., 1987 for the first use of A23187 as an egress trigger, and Lourido, Tang and David Sibley, 2012 for the role of cGMP signalling in egress. We also cite all the GC papers when we make first mention of the GC. We have also removed the Howard et al., 2015 citation (PMID: 25555060) when referring to the fact that BIPPO/zaprinast can rescue the egress delay of ∆CDPK3 parasites.

      Grammar/Language

      Line #31: After "cAMP levels" use comma

      Response:

      We have modified this.

      36: Sentence is not clear. Does conditional deletion of all four PDEs support their important roles? If so, the role in egress of the parasite?

      Response:

      We have clarified our wording as per the reviewer’s suggestion. We state that PDEs 1 and 2 display an important role in growth since deletion of either these PDEs leads to reduced plaque growth. We have not investigated exactly what stage of the lytic cycle this is.

      40: "is a group involving" instead of "are"

      Response:

      We found no mention of “a group involving” in our original manuscript at line 40 or anywhere else in the manuscript, so we are unsure what the reviewer is referring to.

      108: isn't it "discharge of Ca++ from organelle stores to cytosol"?

      __Response: __We thank the reviewer for spotting this error. We have now modified this sentence.

      120: "was" instead of "were"

      __Response: __Since the situation we are referencing is hypothetical, then ‘were’ is the correct tense.

      Reviewer #3 (Significance (Required)):

      There is a significant amount of work that underlies this manuscript; however, from a conceptual viewpoint, the manuscript does not offer significant advancement over the current knowledge without functional validation of phosphoproteomics data. In terms of the mechanism, it is not clear whether and how lipid turnover and cAMP-PKA signaling control the egress phenotype (lack of a validated model at the end of this study).In a methodical sense, the work uses established assays, some of which require revisiting to reach robust conclusions and avoid misinterpretation.

      Compare to existing published knowledge

      A large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signaling cascades. This work provides a further refinement of the existing model. The article is quite interesting from a throughput screening point of view, but it clearly lacks the appropriate endorsement of the hits.

      Response:

      Please refer to our first response to reviewer #3 for our full rebuttal to these points. We respectfully disagree with the assessment that the work presented does not advance current knowledge.

      Audience

      Field specific (Apicomplexan Parasitology)

      Expertise

      Molecular Parasitology

      References

      Bailey, A. P. et al. (2015) ‘Antioxidant Role for Lipid Droplets in a Stem Cell Niche of Drosophila’, Cell. The Authors, 163(2), pp. 340–353. doi: 10.1016/j.cell.2015.09.020.

      Bullen, H. E. et al. (2016) ‘Phosphatidic Acid-Mediated Signaling Regulates Microneme Secretion in Toxoplasma Article Phosphatidic Acid-Mediated Signaling Regulates Microneme Secretion in Toxoplasma’, Cell Host & Microbe, pp. 349–360. doi: 10.1016/j.chom.2016.02.006.

      Dass, S. et al. (2021) ‘Toxoplasma LIPIN is essential in channeling host lipid fluxes through membrane biogenesis and lipid storage’, Nature Communications. Springer US, 12(1). doi: 10.1038/s41467-021-22956-w.

      Endo, T. et al. (1987) ‘Effects of Extracellular Potassium on Acid Release and Motility Initiation in Toxoplasma gondii’, The Journal of Protozoology, 34(3), pp. 291–295. doi: 10.1111/j.1550-7408.1987.tb03177.x.

      Flueck, C. et al. (2019) Phosphodiesterase beta is the master regulator of camp signalling during malaria parasite invasion, PLoS Biology. doi: 10.1371/journal.pbio.3000154.

      Howard, B. L. et al. (2015) ‘Identification of potent phosphodiesterase inhibitors that demonstrate cyclic nucleotide-dependent functions in apicomplexan parasites’, ACS Chemical Biology, 10(4), pp. 1145–1154. doi: 10.1021/cb501004q.

      Jia, Y. et al. (2017) ‘ Crosstalk between PKA and PKG controls pH ‐dependent host cell egress of Toxoplasma gondii ’, The EMBO Journal, 36(21), pp. 3250–3267. doi: 10.15252/embj.201796794.

      Katris, N. J. et al. (2020) ‘Rapid kinetics of lipid second messengers controlled by a cGMP signalling network coordinates apical complex functions in Toxoplasma tachyzoites’, bioRxiv. doi: 10.1101/2020.06.19.160341.

      Lentini, J. M. et al. (2020) ‘DALRD3 encodes a protein mutated in epileptic encephalopathy that targets arginine tRNAs for 3-methylcytosine modification’, Nature Communications. Springer US, 11(1). doi: 10.1038/s41467-020-16321-6.

      Lourido, S., Tang, K. and David Sibley, L. (2012) ‘Distinct signalling pathways control Toxoplasma egress and host-cell invasion’, EMBO Journal. Nature Publishing Group, 31(24), pp. 4524–4534. doi: 10.1038/emboj.2012.299.

      Lunghi, M. et al. (2022) ‘Pantothenate biosynthesis is critical for chronic infection by the neurotropic parasite Toxoplasma gondii’, Nature Communications. Springer US, 13(1). doi: 10.1038/s41467-022-27996-4.

      McCoy, J. M. et al. (2012) ‘TgCDPK3 Regulates Calcium-Dependent Egress of Toxoplasma gondii from Host Cells’, PLoS Pathogens, 8(12). doi: 10.1371/journal.ppat.1003066.

      Moss, W. J. et al. (2022) ‘Functional Analysis of the Expanded Phosphodiesterase Gene Family in Toxoplasma gondii Tachyzoites’, mSphere. American Society for Microbiology, 7(1). doi: 10.1128/msphere.00793-21.

      Stewart, R. J. et al. (2017) ‘Analysis of Ca2+ mediated signaling regulating Toxoplasma infectivity reveals complex relationships between key molecules’, Cellular Microbiology, 19(4). doi: 10.1111/cmi.12685.

      Vo, K. C. et al. (2020) ‘The protozoan parasite Toxoplasma gondii encodes a gamut of phosphodiesterases during its lytic cycle in human cells’, Computational and Structural Biotechnology Journal. The Author(s), 18, pp. 3861–3876. doi: 10.1016/j.csbj.2020.11.024.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Reply to the reviewers

      1. General Statements

      It is the common view of all three reviewers that we have not utilized adequate in vitro/biochemical evidence to support the idea that SATB1 protein undergoes liquid-liquid phase separation. We do agree with the reviewers that our manuscript lacks biochemical evidence to support such notion. Though we find it quite interesting and we would like to suggest for the first time in the field of chromatin organization and function, based upon the action of SATB1, that this protein does exist in at least two polypeptide isoforms (764 and 795 amino acids long) which display different phase separation propensity and therefore confer different actions in regulating the (patho)physiological properties of a murine T cell.

      Every single research group that works on SATB1, considered so far only a single protein isoform, that is, the shorter isoform of 764 amino acids and no tools, such as isoform-specific antibodies have been developed to discriminate the two isoforms and thus being able to assign unique functions to each isoform. We do understand that such a report, suggesting the presence of two protein isoforms, with potentially quite diverse functions, would question (not necessarily by the authors of this manuscript, since no such comment is included in our manuscript) the conclusions drawn in the literature assigning all biochemical properties to a single, short isoform of SATB1. Moreover, all the genetically modified mice that have been analyzed so far (including our group), deleted both Satb1 isoforms. Our future research approaches should, from now on, consider unraveling the isoform-specific functions of SATB1 and their involvement in physiology and disease. This could also deem useful to explain the quite diverse, both positive and negative effects of SATB1 in transcription regulation. Another major objection of the reviewers was that we should provide cumulative supporting evidence for the existence of the long SATB1 isoform, or at least evaluate the specificity of our custom-made antibody.

      Taking under consideration the aforementioned constructive criticism of the three reviewers we would like to perform (most of the suggested experiments have already been performed) additional experiments to support our claims in the manuscript. These experiments are described below as a point-by-point reply to each point raised by the reviewers.

      In line with the aforementioned rationale, we propose the title of our manuscript to change into “Two SATB1 isoforms display different phase separation propensity”, if our manuscript is considered for publication.

      1. Description of the planned revisions

      **Reviewer #1**:

      4) Lack of in vitro reconstitution experiments with purified long and short SATB1

      **PLANNED EXPERIMENT #1**

      We do realize this shortcoming of our work. We have to note that purifying recombinant SATB1 protein is quite a challenging task, yet we 1. cloned both Satb1 cDNAs for the long and short isoforms, 2. we successfully expressed both proteins in great quantity and quality and we are willing to perform these experiments if our work is considered for publication.

      This proposed experiment has also been requested by Reviewers #2 and #3.

      **Reviewer #2**:

      1. Moreover, an important and direct experiment would be to clone the long isoform in a suitable vector and overexpress in the cell line (as done for the canonical isoform in Supp Fig 1a). This would unequivocally show the efficacy of the antibody and thus the following usage of the same for various assays.

      **PLANNED EXPERIMENT #2**

      This is a great suggestion. We have cloned the long and short Satb1 cDNAs in pEGFP-C1 vector. We will transfect these plasmids in NIH 3T3 fibroblasts and we will perform Western blot analysis, utilizing the antibody raised against the extra 31 amino acids long peptide present only in the long SATB1 isoform, for the following samples: 1. NIH-3T3 whole cell protein extracts, 2. protein extracts from NIH 3T3 fibroblasts transiently transfected with the pEGFP-C1 plasmid, 3. protein extracts from NIH 3T3 fibroblasts transiently transfected with the pEGFP-long_Satb1_ plasmid and 4. protein extracts from NIH 3T3 fibroblasts transiently transfected with the pEGFP-short_Satb1_ plasmid.

      This experiment will consist another proof regarding the specificity of the antibody raised against the extra 31 amino acids long peptide present only in the long SATB1 isoform.

      **Minor comments:**

      1. On pg 6, related to Figure 1, the authors mention 'It should also be noted that when investigating the SATB1 protein levels, we have to bear in mind that the antibodies targeting the N-terminus of SATB1 protein cannot discriminate between the short and long isoforms'. The authors reason that their sizes are too close. It is indeed possible, and widely studied in biochemistry to assess various factors on protein migration (such as PTMs). The authors should validate this aspect (as it is important as per their premise) and perform separation based on charge as well and also use a commercial antibody to validate the same.

      (Experiments already performed)

      We have adapted the text so that it does not imply that the two isoforms cannot be separated by size. This part in lines 102-107 then reads: “It should also be noted that when investigating the SATB1 protein levels, we have to bear in mind that the antibodies targeting the N-terminus of SATB1 protein cannot discriminate between the short and long isoforms, thus we can only compare the amount of the long SATB1 isoform to the total SATB1 protein levels in vivo conditions. To overcome this limitation and to specifically validate the presence of the long SATB1 protein isoform in primary murine T cells, we designed a serial immunodepletion-based experiment (Fig. 1e, Supplementary Fig. 1a).”

      Moreover, in the revised version of the manuscript we now provide a number of additional proofs supporting the presence of the long isoform and also the specificity of the long isoform-specific antibody. As evident in the text cited above, in the revised Fig. 1e,f and revised Supplementary Fig. 1a,b; we present two immunodepletion experiments which should alone address the Reviewer’s concerns. Moreover, we added Supplementary Fig. 1c; demonstrating that the long isoform-specific antibody does not detect any protein in cells with conditionally depleted SATB1 (Satb1_fl/fl_Cd4-Cre+), supporting its specificity. The custom-made and publicly available antibodies targeting all SATB1 isoforms were also verified in Supplementary Fig. 1d. Moreover, the long isoform and all isoform antibodies display similar localization in the nucleus (Supplementary Fig. 1e; their co-localization based on super-resolution microscopy is also quantified in Supplementary Fig. 5a).

      In our accompanying revised manuscript Zelenka et al., 2022 (https://doi.org/10.1101/2021.07.09.451769), we will provide yet another piece of evidence, consisting of bacterially expressed short and long SATB1 protein isoforms detected by western blot using either the long isoform-specific or the non-selective all SATB1 isoform antibodies.

      **PLANNED EXPERIMENT #3**

      Although we think that in the revised version of the manuscript, we have provided enough proof about the existence of the long isoform in primary murine thymocytes we would like to try the following approach as suggested by this Reviewer.

      The pI of the two SATB1 isoform is quite similar. The pI of the short SATB1 isoform is 6.09 and for the long SATB1 isoform is 6.18. We will perform 2D PAGE coupled to Western blotting utilizing the antibodies detecting the long and all SATB1 isoforms. Given the fact that both isoforms are post-translationally modified to a various degree, it will be extremely difficult to discriminate between the long and short unmodified versus the long and short post-translationally modified proteins especially in the absence of a specific antibody only for the short isoform.

      **Reviewer #3**

      1. Hexanediol is another assay frequently used in phase-separation studies. However, hexanediol has many deleterious effects on the cell, even at a fraction of the concentration normally used in phase-separation studies. Authors should show controls of cell viability, control proteins that do not phase-separate, etc. See https://www.jbc.org/article/S0021-9258(21)00027-2/fulltext.

      Secondly, hexanediol treatment should cause phase-separated protein aggregates to disperse. It is difficult to determine from the images whether or not the aggregates actually disperse or there is just less protein. In any case, small aggregates remain even after treatment, and this appears different from most other hexanediol experiments reported in the literature where the signals become more dispersed and uniform. This is likely because the samples are fixed.

      One of the main features of using hexanediol in phase-separation is to show that upon washout, LLPS aggregates can reform. Because the cells are fixed, the critical aspect of this assay is not performed. A washout and LLPS recovery would control for cell viability issues described above and would provide the opportunity to show that total SATB1 protein levels did not change, but its distribution did, which is the essence of this assay in the context of LLPS. This review from the Tjian group is very informative and may be a good resource:

      http://genesdev.cshlp.org/content/33/23-24/1619

      In line with our reply to point #1 of this Reviewer (page 26 of this document), we should again emphasize that we utilized the hexanediol treatment in primary murine developing T cells as this is the only way to investigate the properties of SATB1 speckles under physiological conditions. This also explains why some small insoluble structure remains after the hexanediol treatment. Note that under physiological conditions, there is a contribution of several protein variants (such as differential PTMs) out of which some will tend to form more stable structures while others could undergo LLPS. It is not clear how the washout experiment could be applied in the primary cell conditions that include cell fixation as the heterogeneity and big variation among cells would make such data analysis highly unreliable.

      **PLANNED EXPERIMENT #1**

      As we answered to point #4 of Reviewer 1 (page 2), we propose the following experiment. Although the purification of recombinant SATB1 protein is quite a challenging task, yet we 1. cloned both Satb1 cDNAs for the long and short isoforms, 2. we successfully expressed both proteins in great quantity and quality and we are willing to perform in vitro reconstitution experiments if our work is considered for publication.

      1. The major difference between the long and short isoform of SATB1 is the 31aa segment within the IDR. However the authors find that neither the long or short isoform SATB1 forms LLPS aggregates, and the IDR alone forms aggregates in the cytoplasm (Fig5) but they do not respond to Cry2 light activation. When forced to localize to the nucleus, it does not form aggregates as well (Fig6). The short isoform also did not form any aggregates. These results seem to argue against any isoform specific phase-separation. This experiment seems critical for the story, yet it does not support their overall conclusions. The authors might consider using a different cell line or perhaps do an in vitro assay using purified protein.

      I am not certain what to make of the cytoplasmic aggregation, which appears to not form upon localization to the nucleus. Because of this, it is difficult to place weight on the significance of the S635A mutation and the role that a phosphorylation of SATB1 contributes to phase-separation, let alone function There are many additional points of concern, but the ones listed above are perhaps the most significant in terms of the overall conclusions of the paper.

      In Fig. 5c we show that the full length long SATB1 isoform often aggregates unlike the short isoform. These data are accompanied with the results for the IDR region, where the situation is even more obvious (Fig. 5f,g). However, in the latter, we have to bear in mind the absence of the multivalent N-terminal part of the protein which seems to be essential for the overall phase behavior of the protein as indicated in Fig. 4b,c.

      **PLANNED EXPERIMENT #1**

      To further support LLPS of SATB1, we are considering performing the following in vitro experiment, as we answered to point #4 of Reviewer 1 (page 2). Although the purification of recombinant SATB1 protein is quite a challenging task, yet we 1. cloned both Satb1 cDNAs for the long and short isoforms, 2. we successfully expressed both proteins in great quantity and quality and we are willing to perform in vitro reconstitution experiments if our work is considered for publication.

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

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

      This paper looks at an important nuclear matrix protein SATB1, which is a well known global chromatin organizer and help chromatin loop attach to the nuclear matrix. The paper starts with identification of novel short and long form of SATB1. Both the isoform consist of a prion like low complexity domains, but the long isoform additionally contain an extra EPF domain next the Prion like low complexity domain. The paper reports that in murine cells the long isoform is 3-4 fold more abundant than the short isoform. By using STED microscopy they show SATB1 foci lie next to transcription sites in the nucleus. They conclude by looking at the spherical shape of the SATB1 foci and the susceptibility of SATB1 staining after 1,6 hexanediol treatment that SATB1 forms the small foci in the nucleus due to LLPS. The authors also use RAMAN spectroscopy to conclude a change in nuclear chemical space in absence of SATB1 but without much explanation about which chemical bond or nuclear sub structure change correspond to the change in principal component analysis from Raman spectroscopy. The authors use the light inducible aggregation cry2 tag with the PrD domain of SATB1 and compare it with the Cry2-FUS-LC domain to conclude that the SATB1 LC domain can undergo LLPS. The authors hint at involvement of RNA and also DNA in the LLPS of the SATB1 but without going into any detail. Reviewer: The paper reports that in murine cells the long isoform is 3-4 fold more abundant than the short isoform.

      Actually, in page 5 (lines 94-96) of the manuscript we write: “We confirmed that in murine thymocytes the steady state mRNA levels of the short Satb1 transcripts were about 3-5 fold more abundant compared to the steady state mRNA levels of the long Satb1 transcripts (Fig. 1d).” Although the steady state mRNA levels of the long isoform are less abundant compared to the shorter isoforms, the long isoform protein levels are almost comparable to the short isoform as deduced based on immunofluorescence experiments. Moreover, Using our two immunodepletion experiments we quantified the difference, estimating the long isoform being 1.5× to 2.62× less abundant than the short isoform (Fig. 1f and Supplementary Fig. 1b; compare lanes 2 & 3 at the lower panel). • Regarding the RAMAN spectroscopy experiments please see Minor Comment #1 of this Reviewer (page 10).

      The key conclusions of the paper are- A) SATB1 undergoes LLPS. But this conclusion is drawn after correlative experiments as detailed below-

      This conclusion is indeed made based on correlative experiments only for the primary murine T cells, which do not allow for any targeted experiments. However, the use of in vitro cell lines allowed us to validate these findings using the optogenetic approaches, utilizing additional experimentation.

      1) observation of spherical punctae by STED-which could also seem spherical due to their small size. The resolution limit achieved by the STED microscopy used in this paper is not determined or mentioned clearly.

      In the revised version of the manuscript, we have specified the resolution of our systems, for STED in Lines 745-746: ”This system enables super-resolution imaging with 35 nm lateral and 130 nm axial resolution.” and for SIM in Lines 759-761: “Images were acquired over the majority of the cell volume in z-dimension with 15 raw images per plane (five phases, three angles), providing ~120-135 nm lateral and ~340-350 nm axial resolution for 488/568 nm lasers, respectively.” The size of the observed speckles is thus above the resolution limit with sizes ranging between 40-80 nm.

      The resolution of our systems is routinely verified by the following methods: The resolution of our OMX (SIM-3D) system was tested using ARGO-SIM slide containing a pattern of 36 µm long lines with gradually increasing spacing ranging from (left to right) 0 to 390 nm, with a step of 30 nm (Fig. 1 below). Our SIM system was able to clearly resolve two lines separated by 120 nm.

      2) No live cell FRAP experiment with fluorescent SATB1 long or short isoform to show that these foci are liquid like

      We did perform FRAP experiments for the SATB1 N-terminus optogenetic construct as demonstrated in Fig. 4f. We did not perform FRAP in the primary murine T cells as this is not technically feasible without creating a new mouse line with fluorescently labeled protein. In the revised version of the manuscript, we additionally performed FRAP experiments for the full length short and long isoform of SATB1 labeled with EGFP and transfected into the NIH-3T3 cell line (Supplementary Figure 6f).

      5) LLPS is strongly coupled to the cellular concentration of the proteins. Authors should quantify the cellular concentration of the long and short isoform in the cells.

      We did consider protein concentration in our analyses of optogenetic constructs in Fig. 4b,d,e and Supplementary Fig. 6a,b,c. Quantifying the physiological cellular concentration of short and long SATB1 protein isoforms in primary T cells is impossible due to the inherent inability to discriminate between the isoforms by two antibodies, in the absence of Satb1 isoform-specific knockout mice.

      However, an approximation of the cellular concentration can be obtained from our immunodepletion experiments. On top of the original immunodepletion experiment that we now present in Supplementary Fig. 1a,b; in the revised version of the manuscript we have repeated the experiment in Fig. 1e,f. Comparison of the two bands for the long and short SATB1 isoforms in the lower panel of the western blot figures suggest that the long SATB1 isoform protein levels are 1.5× to 2.62× less abundant than the short isoform, according to the original and new immunodepletion experiment, respectively. This is now also included in the main text in Lines 110-116: “This experiment can also be used for approximation of the cellular protein levels of SATB1 isoforms in primary murine thymocytes. Comparison of the two bands for long (lane 2) and short SATB1 (lane 3) isoform in the lower panel of Fig. 1f and Supplementary Fig. 1b, suggests that the long SATB1 isoform protein levels may be about 1.5× to 2.62× less abundant than the short isoform, according to the two replicates of our immunodepletion experiment, respectively.”

      Major conclusion B)- SATB1 regulates transcription and splicing.

      This was also shown previously and in this paper they show the close proximity of the transcription site and SATB1 foci by microscopy. Hexanediol treatment which lead to loss of colocalization between FU foci and SATB1 is also taken as an evidence in regulation of transcription is not right as the transcription foci itself can be dissolved using 1,6 Hexanediol. Although the rate of transcription is not measured quantitatively.

      As mentioned in comment #3 (page 29) of this Reviewer, unfortunately there is no better tool to investigate these questions in primary cells than using microscopy approaches in conjunction with hexanediol treatment. However, we should also note that there is an accompanying manuscript from our group that is currently being under revision in another journal (preprint available: Zelenka et al., 2021; https://doi.org/10.1101/2021.07.09.451769). In the preprint manuscript, we showed that: 1. the long SATB1 isoform binding sites have increased chromatin accessibility than what expected by chance (Fig. 3b), 2. there is a drop in chromatin accessibility at SATB1 binding sites in Satb1 cKO mouse (Fig. 3c) and 3. this drop in chromatin accessibility is especially evident at the transcription start sites of genes (Supplementary Fig. 1i)

      We believe that, together these data suggest a direct involvement of SATB1 in transcription regulation. Also note the vast transcriptional deregulation that occurs in Satb1 cKO T cells, affecting the expression of nearly 2000 genes (Fig. 2f, this revised manuscript). That is why we believe that the co-localization analysis, using super-resolution microscopy, presented in Fig. 2c and quantified in Fig. 3g, represents a nice additional support to our claims. Moreover, in the revised version of the manuscript we now present a positive correlation between SATB1 binding and deregulation of splicing (Supplementary Fig. 4d) which also supports its direct involvement in the regulation of transcriptional and co-transcriptional processes.

      In the revised version of the manuscript we have made this clear in Lines 182-194: “Satb1 cKO animals display severely impaired T cell development associated with largely deregulated transcriptional programs as previously documented19,37,38. In our accompanying manuscript19, we have demonstrated that long SATB1 isoform-specific binding sites (GSE17344619) were associated with increased chromatin accessibility compared to randomly shuffled binding sites (i.e. what expected by chance), with a visible drop in chromatin accessibility in Satb1 cKO. Moreover, the drop in chromatin accessibility was especially evident at the transcription start site of genes, suggesting that the long SATB1 isoform is directly involved in transcriptional regulation. Consistent with these findings and with SATB1’s nuclear localization at sites of active transcription, we identified a vast transcriptional deregulation in Satb1 cKO with 1,641 (922 down-regulated, 719 up-regulated) differentially expressed genes (Fig. 2f). Specific examples of transcriptionally deregulated genes underlying SATB1-dependent regulation are provided in our accompanying manuscript19. Additionally, there were 2,014 genes with altered splicing efficiency (Supplementary Fig. 4d-e; Supplementary File 3-4). We should also note that the extent of splicing deregulation was directly correlated with long SATB1 isoform binding (Supplementary Fig. 4d).”

      Major conclusion C)-Post transcriptional modification is important for SATB1 function.

      This point is just barely touched upon in the last figure of the paper

      We would not call the identification of the novel phosphorylation site as a main conclusion of our manuscript. Though, it is already known that posttranslational modifications of SATB1 are important for its function as they can function as a molecular switch rendering SATB1 into either an activator or a repressor (Kumar et al., 2006; https://doi.org/10.1016/j.molcel.2006.03.010).

      In the revised manuscript, we support the effect of serine phosphorylation on the DNA binding capacity of SATB1 by another experiment. We have performed DNA affinity purification experiments utilizing primary thymocyte nuclear extracts treated with phosphatase (Supplementary Fig. 7b). We found that SATB1’s capacity to bind DNA (RHS6 hypersensitive site of the TH2 LCR) is lost upon treatment with phosphatase (Supplementary Fig. 7c). These results are in line with the data presented in Supplementary Fig. 7d, indicating the lost ability of SATB1 to bind DNA upon mutating the discovered phosphorylation site S635. Given the importance of posttranslational modifications of proteins on LLPS, we found it relevant to include it in our manuscript. Even more so, when we identified SATB1 aggregation, upon mutation of this phospho site (Fig. 6d).

      Overall I find that the major conclusion-point A and B, is based on very indirect experiments and needs much more convincing data and the role of SATB1 LLPS in cells should be demonstrated more rigorously. And conclusion C is barely described and needs a lot more cell biological and genetic evidence.

      One of the major assets of our work is that most of our data are based on the analysis of primary murine T cells and thus investigating the biological roles of the endogenous SATB1 protein, under physiological conditions. We apologize that we did not make it clear to this Reviewer, that our system has certain inherent limitations due to the utilization of primary cells.

      I do not recommend publishing the paper in current state. The story needs much more experiment to convincingly prove the major conclusions. Further, the MS needs more careful thinking and presentation to make it streamlined.

      We hope that in the revised version we have significantly improved the quality of our manuscript by implementing the suggested changes.

      Minor comments: One of the major flaw of the paper is the use too many techniques without proper explanation. E.g. use of STED and RAMAN microscopy need controls and explanation on what is being quantified. The use of Raman microscopy to quantify the nuclear environment of nucleus is not related to the chromatin organization or LLPS of SATB1 at all. And no information is provided at all which aspect of nuclear organization is being measured in Raman and what it means for the LLPS of SATB1.

      We do provide quite a thorough explanation of Raman spectroscopy and the underlying quantification in Lines 224-231: “we employed Raman spectroscopy, a non-invasive label-free approach, which is able to detect changes in chemical bonding. Raman spectroscopy was already used in many biological studies, such as to predict global transcriptomic profiles from living cells42, and also in research of protein LLPS and aggregation43–47. Thus we reasoned that it may also be used to study phase separation in primary T cells. We measured Raman spectra in primary thymocytes derived from both WT and Satb1 cKO animals and compared them with spectra from cells upon 1,6-hexanediol treatment. Principal component analysis of the resulting Raman spectra clustered the treated and non-treated Satb1 cKO cells together, while the WT cells clustered separately (Fig. 3h).” We also do provide controls as the method was performed on both treated and untreated WT and Satb1 cKO cells.

      Regarding the RAMAN spectroscopy experiments we now provide more information on the changes of chemical bonds altered between wild type and Satb1 cKO thymocytes. Following principal component analysis, we have extracted the two main principal components that were used for the clustering of our data. The differences are presented in Supplementary Fig. 5d.

      We do realize that RAMAN spectroscopy, although a quite novel approach utilized to study LLPS, has not been used to study LLPS in live cells. If deemed proper we are willing to avoid presenting these results in this manuscript.

      Similarly for Hexanediol treatment, duration of treatment is missing. Hexanediol can also dissolve the liquid like transcription foci. And hence a decrease in correlation between SATB1 foci and FU foci cannot be taken as a measure of SATB1 foci connection to transcription alone

      The duration of hexanediol treatment was 5 minutes as presented in Line 724 and in the revised version of the manuscript also in Lines 1206-1207. We should also note that additionally, we performed experiments with different hexanediol concentrations and timing varying from 1 minute to 10 minutes with results consistent with the data presented.

      It is not very clear how many times the STED or Raman microscopy is done on how many samples and biological replicates. Similarly for RNA sequencing number of samples and description of controls are missing. Also if the sequencing data is made publicly available is not clear.

      Data availability is clearly stated in Lines 506-509: “RNA-seq experiments and SATB1 binding sites are deposited in Gene Expression Omnibus database under accession number GSE173470 and GSE173446, respectively. The other datasets generated and/or analyzed during the current study are available upon request.”

      The Reviewer’s token is “wjwtmeeeppovzqx”.

      RNA sequencing was performed in a biological triplicate for each genotype as stated in the GEO repository and now also in Line 566 of the revised manuscript.

      In Lines 180-181, we also state that it was performed on Satb1 cKO animals and WT mice as a control: “we performed stranded-total-RNA-seq experiments in wild type (WT) and Satb1fl/flCd4-Cre+ (Satb1 cKO) murine thymocytes”.

      In Lines 739-740, we now also state that all imaging approaches were performed on at least two biological replicates (different mice) and please also note the fact that all findings were based on data from both STED and 3D-SIM methods, allowing to minimize detection of artifacts. In the Raman spectroscopy figure, each point represents measurements from an individual cell and for each condition we used 2-5 biological replicates (Lines 831-832 & Line 1169).

      Similarly, in Lines 129-132 we provided a quite detailed description of differences between STED and 3D-SIM, even though these techniques are not that rare as Raman spectroscopy in biology research.

      Additional control is needed to report the resolution limit of Superresolution techniques-STED and 3D-SIM systems used by them.

      We have already provided this information in our reply to comment #1 of this Reviewer (pages 6-7): In the revised version of the manuscript, we have specified the resolution of our systems, for STED in Lines 745-746: ”This system enables super-resolution imaging with 35 nm lateral and 130 nm axial resolution.” and for SIM in Lines 759-761: “Images were acquired over the majority of the cell volume in z-dimension with 15 raw images per plane (five phases, three angles), providing ~120-135 nm lateral and ~340-350 nm axial resolution for 488/568 nm lasers, respectively.” The resolution of our systems is routinely verified by the following methods: The resolution of our OMX (SIM-3D) system was tested using ARGO-SIM slide containing a pattern of 36 µm long lines with gradually increasing spacing ranging from (left to right) 0 to 390 nm, with a step of 30 nm (Fig. 1 below). Our SIM system was able to clearly resolve two lines separated by 120 nm.

      Would be very helpful if the zonation was plotted for the FluoroUridine (FU) also to show that Zone1 (heterochromatin) is completely depleted of FU, and is present in other regions.

      In the revised version of the manuscript, we performed the suggested analysis and in Supplementary Fig. 3a we now show that indeed FU is significantly less localized to Zone 1 (heterochromatin) and has the most abundant localization in Zones 3 and 4, similar to the localization of SATB1 protein, as demonstrated in Fig. 2b.

      Scale bar needed figure 3d

      In the revised version of the manuscript, we included scale bars which are both 0.5 µm (line 1213).

      Perfectly rounded SATB1 foci- this does not mean LLPS. For LLPs measurement, protein condensate dynamics measurement by FRAP or fusion experiments is required. What is the size of condensates? and cellular concentration of SATB1? Will SATB1 undergo LLPS in vitro at similar concentrations? does SATB1 interact with DNA or RNA to undergo LLPS ?

      We toned down this sentence which now reads: “Here we demonstrated its connection to transcription and found that it forms spherical speckles (Fig. 1g), markedly resembling phase separated transcriptional condensates. (Lines 200-202)”.

      Moreover, as explained in earlier replies to comments of this Reviewer, we cannot perform FRAP on primary murine T cells without generating a new mouse line. We did, however, use FRAP and other in vitro approaches including visualization of droplet fusion in ex vivo experiments utilizing cell lines. Moreover, we are willing to demonstrate the LLPS properties of SATB1 on in vitro purified SATB1 protein as indicated in the suggested experiment of Point#4 (page 2).

      After careful reading of the MS I conclude that the main conclusions of the paper are very preliminary and need much more detailed experiments. So does not qualify to get published at all at this stage.

      **Reviewer #1 (Significance)**:

      The present manuscript tries to connect the phase separation of SATB1 to understanding the mechanism of SATB1 function in cells. One of the major hallmarks of phase separation is dynamic, liquid-like behaviour and in absence of these measurements, it is very difficult to say that the current manuscript has made any contribution to showing that SATB1 can phase separate.

      The presence of 2 isoforms of SATB1 is a novel finding and the paper could have focused more on this. E.g. elucidate expression of the isoform during thymocyte development and maturation.

      As a reviewer my expertise are cell biology experiments, microscopy, in vitro reconstitution assays, RNA binding proteins, RNA and RBP condensate formation. And I feel that the reconstitution experiments are an important tool for understanding phase behaviour of proteins and also to gauge if this behaviour can occur or not in cellular concentration and conditions.

      I do not have sufficient expertise in Raman microscopy and hence the information provided in the MS on this part was not enough to understand the experiment and conclusions drawn from it.

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

      The authors have reported the existence of a 'long' SATB1 isoform which also undergoes LLPS. The authors tried to draw multiple comparisons and pointed out distinction between phase properties of SATB1 isoforms. The authors also touch upon two functional roles of SATB1. Although a wide array of assays are used, the data presented and hence the manuscript makes multiple transitions into disparate hypotheses without diving deep into a single hypothesis. As a result, the connections drawn are unclear, and do not converge at best. The authors have used number of techniques, however, the results do not support their conclusions and they appear hastily drawn. It is not clear why the authors jump from one context to the other, discussing LLPS first, then transcription, splicing, post-translational modification and finally cancer. The link between all of these isn't clear and not fully supported by data. It appears that the authors wish to focus on Satb1's physiological role in development, hence the data on breast cancer is confusing. Thus, this work suffers from multiple pitfalls. Specific comments are given below:

      Major comments 1. Importantly, in Fig 1d, there is no statistics shown. There is no mention of number of replicates as well in the legends. Proper statistical evaluation is critical for interpreting this result.

      Please note that Fig. 1d only serves as a control to the sequencing experiment in Fig. 1b. In Line 566, we now state that for the RNA-seq: “A biological triplicate was used for each genotype.” To validate these data, we further designed a RT-qPCR experiment which was performed on three technical replicates from a male and female mouse. We now state this in Line 636. For the low number of samples, statistical tests are not accurate but we still added t test into the figure Fig. 1d and specified it also in the figure legend in Line 1169-1170.

      1. Figure 1f presents one of the weakest evidences in the manuscript. There are a number of corrections needed. Firstly, being their major and only validation figure for their custom antibody, the immunoblot is not clean, bands are fuzzy. Importantly, as the authors claim that the antibody is highly specific to 'long' SATB1, after the IP there should be only a single band (like input) of Satb1 long. But that does not seem to be the case, rather an array of bands are visible below (lane 2 top panel). This could easily mean that the shorter isoforms or non-specific protein bands are also pulled down with the 'long' form specific antibody. Therefore, raising a critical concern regarding the specificity of the antibody.

      • The long antibody was raised in mice inoculated with the extra peptide present in the long isoform only. Therefore, the capacity of this antibody precipitating the shorter isoforms, which do not express the sequence of the extra peptide (EP, Figure 1a) in not possible. • We have repeated the immunodepletion experiment and we now provide the results in Fig. 1f and Supplementary Fig. 1b. The western blot in Fig. 1f is now cleaner and supports quite convincingly the presence of a long SATB1 isoform. Given the lack of isoform-specific knockouts which we could utilize to immunoprecipitate or detect the different isoforms in a single cell (or cell population), the utilized approach of immunodepletion and subsequent western blotting is the approach we thought of implementing. • As shown in Fig. 1f and Supplementary Figure 1b, the long isoform SATB1 antibody has the capacity to recognize the long isoform in murine thymocyte protein extracts but not the short SATB1 isoform (please compare lane 3 in the two western blots utilizing either the antibody for the long isoform -top panel - or the antibody that detects both isoforms (lower panel). • We have performed Immunofluorescence experiments utilizing the antibody detecting the long SATB1 isoform in thymocytes isolated from either C57BL/6 or Satb1 cKO mice. The antibody is specific to the SATB1 protein since there is no signal in immunofluorescence experiments utilizing the knockout cells (Supplementary Figure 1c). • We have performed Immunofluorescence experiments utilizing thymocytes and the antibody detecting the long SATB1 or a commercially available antibody detecting all SATB1 isoforms. The pattern of SATB1 subnuclear localization is similar for both antibodies (Supplementary Figure 1e). • In our accompanying revised manuscript Zelenka et al., 2022 (https://doi.org/10.1101/2021.07.09.451769), we provide yet another piece of evidence, consisting of bacterially expressed short and long SATB1 protein isoforms detected by western blot using either the long isoform-specific or the non-selective all SATB1 isoforms antibodies. • Regarding the additional bands detected in the immunoprecipitation experiment presented in the original Supplementary Figure 1b (lane 2), it is not surprising that additional bands appear in a sample of protein extracts that is used for several hours for the immunoprecipitation experiments, while the “input” sample simply denotes protein extract that is frozen at -80oC right after the preparation of protein extracts until use. It is well-established that SATB1 is the target of proteases which might as well be active during the immunoprecipitation steps (2 consecutive immunoprecipitation steps take place). Therefore, the immunoprecipitated material cannot necessarily be a copy of the input material displaying a single protein band even if protease inhibitors are included in the buffers.

      Taken together the experiments described here we showed that the antibody raised against the extra 31 aa long peptide, present only in the long SATB1 isoform, is specific for this isoform.

      1. Related to Fig. 2 a, the authors state on Pg 5, '....the euchromatin and interchromatin regions (zones 3 & 4, Fig. 2a, b).' Although the DAPI correlation seems clear, there is no mention on how they reached the above said correlation. They should at least show a parallel speckle staining for HP1 or signature modification such as H3K4me9 STEDs for making supporting such a claim. DAPI alone is not sufficient. The authors should rectify the text thoroughly for many such interpretations without validation/reference or provide relevant data.

      This is a great suggestion we have again taken under consideration and we added the following experiments and the appropriate changes in the revised version of our manuscript. • We modified the text and added a reference to Miron et al., 2020 (https://doi.org/10.1126/sciadv.aba8811) supporting our claims regarding SATB1 localization in relation to DAPI staining. • We have also added new microscopy images for HP1, H3K4me3 and fibrillarin staining and quantified the localization of FU-stained sites of active transcription in nuclear zones, to further support our claims. • This whole modified part in Lines 139-167 then reads: “ “The quantification of SATB1 speckles in four nuclear zones, derived based on the relative intensity of DAPI staining, highlighted the localization of SATB1 mainly to the regions with medium to low DAPI staining (zones 3 & 4, Fig. 2a, b). A similar distribution of the SATB1 signal could also be seen from the fluorocytogram of the pixel-based colocalization analysis between the SATB1 and DAPI signals (Supplementary Fig. 2a). SATB1’s preference to localize outside heterochromatin regions was supported by its negative correlation with HP1β staining (Supplementary Fig. 2b). Localization of SATB1 speckles detected by antibodies targeting all SATB1 isoforms and/or only the long SATB1 isoform, revealed a significant difference in the heterochromatin areas (zone 1, Fig. 2b), where the long isoform was less frequently present (see also Fig. 2a and Fig. 3c). Although, this could indicate a potential difference in localization between the two isoforms, due to the inherent difficulty to distinguish the two based on antibody staining, we refrain to draw any conclusions. The prevailing localization of SATB1 corresponded with the localization of RNA-associated and nuclear scaffold factors, architectural proteins such as CTCF and cohesin, and generally features associated with euchromatin and active transcription32. This was also supported by colocalization of SATB1 with H3K4me3 histone mark (Supplementary Fig. 2c), which is known to be associated with transcriptionally active/poised chromatin. Given the localization of SATB1 to the nuclear zones with estimated transcriptional activity32 (Fig. 2b, zone 3), we investigated the potential association between SATB1 and transcription. We unraveled the localization of SATB1 isoforms and the sites of active transcription labeled with 5-fluorouridine. Sites of active transcription displayed a significant enrichment in the nuclear zones 3 & 4 (Supplementary Fig. 3a), similar to SATB1. As detected by fibrillarin staining, SATB1 also colocalized with nucleoli which are associated with active transcription and RNA presence (Supplementary Fig. 3b). Moreover, we found that the SATB1 signal was found in close proximity to nascent transcripts as detected by the STED microscopy (Fig. 2c). Similarly, the 3D-SIM approach indicated that even SATB1 speckles that appeared not to be in proximity with FU-labeled sites in one z-stack, were found in proximity in another z-stack (Supplementary Fig. 3c). Additionally, a pixel-based colocalization of SATB1 and sites of active transcription is quantified later in the text in Fig. 3g, supporting their colocalization.”

      1. The authors mention, '...of the different SATB1 isoforms, uncovered by the use of the two different antibodies, relied in the heterochromatin areas (zone 1), where the long isoform was less frequently...' There is no supporting figure number mentioned. The authors need to show a zone-by-zone comparison images for 'all iso' vs 'long' iso of SATB1. Just to reiterate, there is a need for a heterochromatin mark to unambiguously call out the distinction.

      We should remind that there is an inherent difficulty to accurately compare localization of short and long SATB1 isoforms in primary cells, especially due to the lack of Satb1 isoform-specific knockout mice. There is no way to detect only the short isoform in these primary cells as there are only antibodies targeting the long or all SATB1 isoforms. Therefore, we cannot set up additional experiments probing these questions.

      In line with this, in the revised version of the manuscript, we toned down our statements regarding the differential localization of the two isoforms in primary cells. We only refer to it as an indication and we support it by adding references to the relevant figures. This part now reads: “Localization of SATB1 speckles detected by antibodies targeting all SATB1 isoforms and/or only the long SATB1 isoform, revealed a significant difference in the heterochromatin areas (zone 1, Fig. 2b), where the long isoform was less frequently present (see also Fig. 2a and Fig. 3c). Although, this could indicate a potential difference in localization between the two isoforms, due to the inherent difficulty to distinguish the two based on antibody staining, we refrain to draw any conclusions. (Lines 145-150)”

      1. On the same lines, '....Given the localization of SATB1 to the nuclear zones with estimated transcriptional activity (Fig. 2b, zone 3)....' How was the region labelled as transcriptionally active? For the statistical analysis of speckle count for the two antibodies' staining, the claim posited is a bit bigger. This could simply be true for that cell. The authors thus need to statistically analyse the speckle counts for multiple cells. This needs to be done for all imaging statistics done in multiple figures throughout the manuscript.

      As mentioned in our reply to the two previous comments of this Reviewer, transcriptional activity in relation to the nuclear zonation is well established in the literature. To make this clear, we have now added the reference to Miron et al., 2020 (https://doi.org/10.1126/sciadv.aba8811) supporting our claims and additionally we have also included HP1, H3K4me3 and fibrillarin staining and quantification of FU signal in the nuclear zones. Moreover, it is not clear to which particular cell the comment refers to. The presented dots in Fig. 2b represent individual cells and the relative proportions of speckles in each nuclear zone are plotted on the y axis. In the revised version of the manuscript, we added into the figure the number of cells scored and we adapted the figure legend so that it is absolutely clear that we have analyzed multiple cells:

      “Nuclei of primary murine thymocytes were categorized into four zones based on the intensity of DAPI staining and SATB1 speckles in each zone were counted. Images used represented a middle z-stack from the 3D-SIM experiments. The graph depicts the differences between the long and all SATB1 isoforms’ zonal localization in nuclei of primary murine thymocytes. (Lines 1189-1193)”

      1. For figure 2c. the authors have used 5 Fluorouridine for nascent RNA speckles. 5FU is known to have a spread signal type (with strong association to nucleolus as well). This is not the case for the image presented 2c. The authors should resolve this by showing different sets of images.

      Developing and naive T cells are very unique in terms of their metabolic features and thus they should not be directly compared with other cell types. Therefore, we would not expect to see such a spread FU pattern as previously shown for other cell types. Having said that, we could not find any reference publication that utilized super-resolution microscopy to detect localization of FU-stained sites of active transcription in developing primary T cells. However, we performed additional immunofluorescence experiments to demonstrate the colocalization or its lack between SATB1 and HP1 (Supplementary Fig. 2b), H3K4me3 (Supplementary Fig. 2c) and fibrillarin (Supplementary Fig. 3b). Moreover, we provide additional regions of SATB1 and FU staining in Supplementary Fig. 3c. The modified text reads:

      “We unraveled the localization of SATB1 isoforms and the sites of active transcription labeled with 5-fluorouridine. Sites of active transcription displayed a significant enrichment in the nuclear zones 3 & 4 (Supplementary Fig. 3a), similar to SATB1. As detected by fibrillarin staining, SATB1 also colocalized with nucleoli which are associated with active transcription and RNA presence (Supplementary Fig. 3b). Moreover, we found that the SATB1 signal was found in close proximity to nascent transcripts as detected by the STED microscopy (Fig. 2c). Similarly, the 3D-SIM approach indicated that even SATB1 speckles that appeared not to be in proximity with FU-labeled sites in one z-stack, were found in proximity in another z-stack (Supplementary Fig. 3c). Additionally, a pixel-based colocalization of SATB1 and sites of active transcription is quantified later in the text in Fig. 3g, supporting their colocalization. (Lines 157-167)”

      1. Fig 2 d., the authors have suddenly jumped solely to 'all iso' Satb1 here for IP MS. Is there a reason for that? The authors either need to do this with 'long iso' antibody or remove the analysis from the manuscript as it does not add to their primary aim of the manuscript. Also, the authors have only selectively talked about two clusters? What about chromatin related proteins? It is quite intuitive to have highest enrichment of these given previous literature and even IP MS data by other groups. Thus, it is necessary to revise this thoroughly or remove it.

      We appreciate the acknowledgment by the Reviewer that our IP-MS data identified anticipated factors. In the revised version of the manuscript we modified the underlying text to accommodate references to these former findings revealing interactions between SATB1 and chromatin modifying complexes: “Apart from subunits of chromatin modifying complexes that were also detected in previous reports25,33–36, unbiased k-means clustering of the significantly enriched SATB1 interactors revealed two major clusters consisting mostly of proteins involved in transcription (blue cluster 1; Fig. 2d and Supplementary Fig. 4c) and splicing (yellow cluster 2; Fig. 2d and Supplementary Fig. 4c). (Lines 170-174)”

      Please note that many subunits of chromatin modifying and chromatin-related complexes are in fact characterized as transcription-related factors, therefore our statements are not in disagreement with the former findings. Note also that we provide Supplementary File 1 & 2 with comprehensive description of our IP-MS data for the readers’ convenience. Please also note that we are the first group to report on the existence of the long isoform. Therefore, we find it absolutely reasonable to perform IP-MS experiment for all SATB1 isoforms which can then be used for a comparison with other publicly available datasets. We believe that there is no contradiction in this experimental setup in relation to the rest of the manuscript. We discuss the two major clusters simply because they are the two major clusters identified as indicated in Fig. 2d. Additionally, in Supplementary Fig. 4c, we provide a comprehensive description of all significantly enriched interactors including their cluster annotation and thus anyone can investigate the data if needed.

      1. In relation to Fig. 2f, the authors have not mentioned any of the previously published work on Satb1 CD4 specific KO, not even the RNA seq studies the other groups have reported under the same condition. Only an unpublished reference of their own (preprint) is cited. It is imperative to show how much their data corroborates with other published studies. Additionally, what is the binding site status of dysregulated genes?

      In the revised version of the manuscript, we have included the references to other studies using the same Satb1 conditional knockout. Moreover, we have clarified the relationship between SATB1 binding and gene transcription. The modified part in Lines 182-194 now reads: “Satb1 cKO animals display severely impaired T cell development associated with largely deregulated transcriptional programs as previously documented19,37,38. In our accompanying manuscript19, we have demonstrated that long SATB1 isoform specific binding sites (GSE17344619) were associated with increased chromatin accessibility compared to randomly shuffled binding sites (i.e. what expected by chance), with a visible drop in chromatin accessibility in Satb1 cKO. Moreover, the drop in chromatin accessibility was especially evident at the transcription start site of genes, suggesting that the long SATB1 isoform is directly involved in transcriptional regulation. Consistent with these findings and with SATB1’s nuclear localization at sites of active transcription, we identified a vast transcriptional deregulation in Satb1 cKO with 1,641 (922 down-regulated, 719 up-regulated) differentially expressed genes (Fig. 2f). Specific examples of transcriptionally deregulated genes underlying SATB1-dependent regulation are provided in our accompanying manuscript19. Additionally, there were 2,014 genes with altered splicing efficiency (Supplementary Fig. 4d-e; Supplementary File 3-4). We should also note that the extent of splicing deregulation was directly correlated with long SATB1 isoform binding (Supplementary Fig. 4d).”

      1. In context of Figure 3a and b, the authors write .'...The long SATB1 isoform speckles evinced such sensitivity as demonstrated by a titration series with increasing concentrations of 1,6-hexanediol treatment followed...' Whereas it is apparent from the image at least that overall numbers of individual speckles are instead increased at both 2 and 5%. There is although a clear spreading of restricted speckles compared to the controls. The authors should revise their figures to substantiate the associated text. Furthermore, there needs to be 'all iso' SATB1 3D SIM imaging and not just quantitation for comparison. This is also true for panel c in order to demonstrate the effect.

      In the revised Fig. 3a we provide new images which better reflect the underlying data analysis. Moreover, in Fig. 3c and Fig. 3d we provide an additional comparison between SATB1 all isoforms and long isoform staining and their changes upon hexanediol treatment, detected by both the 3D-SIM and STED approaches. It is true that upon treatment, there tend to be more speckles, however these are much smaller as they are gradually being dissolved. Depending on the treatment duration, the cells are swollen which is reflected in increased spreading of speckles. Nevertheless, the nuclear size was considered in all the quantification analyses. We believe that the new images provide better evidence of SATB1’s sensitivity to hexanediol treatment.

      1. Fig. 3 d also does not clearly demonstrate what the authors have claimed '...hexanediol treatment highly decreased colocalization between...' The figure shows at best decreased signal intensity for both SATB1 and FU. We suggest that the authors should give a statistical analysis as well for the colocalization points between the two using multiple source images. Lastly, the two images shown (control and treated), there seems to be a clearly visible magnification difference. The authors should clarify this.

      • In the revised version of the manuscript in Figure 3d, we have provided scale bars, which are both 0.5 µm (line 1213). The difference observed by this Reviewer is actually the main reason why we provided this image. Figure 3d demonstrates that upon hexanediol treatment, the speckles are mostly missing or significantly reduced in size, for both FU and SATB1 staining. • Moreover, the suggested statistical analysis is also provided – in Figure 3e. In Figure 3e, we performed pixel-based colocalization analysis which is a method that allows both quantification and statistical comparison of colocalization between two factors and between different conditions. Please note especially the decreased colocalization between long SATB1 isoform and FU-stained sites of active transcription in the left graph, which is in agreement with our claims in the manuscript. • Moreover, our data are compared to a negative control, i.e. 90 degrees rotated samples, which is a common method in colocalization experiments as described for example in Dunn et al., 2011 (https://doi.org/10.1152/ajpcell.00462.2010). • Additionally, we provide Costes’ P values which are based on randomly scrambling the blocks of pixels (instead of individual pixels, because each pixel’s intensity is correlated with its neighboring pixels) in one image, and then measuring the correlation of this image with the other (unscrambled) image. Please see Costes et al., 2004 (https://doi.org/10.1529%2Fbiophysj.103.038422) for more details.

      1. Figure 3f. The authors show the PC plot for Raman spectroscopy for phase behaviour due to Satb1. The experiment and its related text seems misinterpreted; the authors write...' ese bonds were probably enriched for weak interactions responsible for LLPS that are susceptible to hexanediol treatment. This shifted the cluster of WT treated cells towards the Satb1 cKO cells. However, the remaining covalent bonds differentiated the WT samples from Satb1 cKO cells......' whereas the clusters are clearly far away in 3D for both WT and KO while being closer to their respective treatments. Which is also intuitive given the sensitivity of Raman spectroscopy. Thus, it is more likely to be treatment effect and KO effect as separate. Treatment of WT leads to KO like spectra is far-fetched. Thus, the authors need to show separate PCs and modify their text thoroughly.

      We do not present any 3D graph hence it is not clear what the Reviewer refers to. Please also note that as stated in Lines 817-818, we used a customized Raman Spectrometer. Therefore, this approach allowed us to measure Raman spectra at cellular and even sub-cellular levels. For example, solely by utilizing Raman spectroscopy, we can now distinguish euchromatin and heterochromatin, methylated and unmethylated DNA and RNA, etc. This, together with other reports, such as Kobayashi-Kirschvink et al., 2018 (https://doi.org/10.1016/j.cels.2018.05.015) and Kobayashi-Kirschvink et al., 2022 (https://doi.org/10.1101/2021.11.30.470655), indicate a potential use of Raman in biological research. In our manuscript, we used this method as a supplementary approach, however we do find it noteworthy. We should also emphasize that in the revised Raman spectroscopy Fig. 3h, each point represents measurements from an individual cell and for each condition we used 2-5 biological replicates (Lines 831-832 & Lines 1225-1226). We specifically refer to the principal component 1 (PC1) that differentiates the samples. Therefore, there are certain spectra (representing certain chemical bonding) that allowed us to differentiate between WT and Satb1 cKO. The same type of bonding was then affected when WT samples were treated with hexanediol and we also had controls to rule out the impact of hexanediol on the resulting spectra.

      1. In Fig 4. b, The authors have shown the propensity of SATB1 N terminus to phase separate using different optodroplet constructs. Although the imaging is clear, why are the regions selected not uniform when comparing various constructs?

      We have selected images that would best represent each category. Please note that this was live cell imaging of photo-responsive constructs, thus there are many limitations regarding the area selection. Very often, even the brief time of bright light exposure to localize cells may trigger protein clustering. Upon disassembly, every new light exposure of the same cell then triggers much faster assembly which skews the overall results. It is therefore desired to work fast, while neglecting selection of equally sized cells. Moreover, it is not clear how would the proposed change improve the quality of our manuscript.

      1. Figure 5a, the disassembly should be shown for 'long' SATB1 as well. On pg 13, the authors write '....cytoplasmic protein aggregation has been previously described for proteins containing poly-Q domains and PrLDs..' no reference given.

      • In the revised version of the manuscript, we present the assembly and disassembly for both short and long full length SATB1 optogenetic constructs. To increase clarity, we present the behavior of the short and long isoforms as two separate images in Figure 5a and Figure 5b, respectively. • Moreover, we provided references to the statement regarding aggregation of PrLD and poly-Q-containing proteins in Lines 305-309, which now reads: ”Since protein aggregation has been previously described for proteins containing poly-Q domains and PrLDs8,11,38,39, we next generated truncated SATB1 constructs encoding two of its IDR regions, the PrLD and poly-Q domain and in the case of the long SATB1 isoform also the extra peptide neighboring the poly-Q domain (Fig. 1a and 4a).”

      1. Fig. 5d, Is there an amino-acid specific reasoning to support the authors claim of the phase behaviour due to extra peptide? They need to show a proper control with equal extra (unrelated) peptide to show the specificity. Are the shorter isoform aggregates responsive to light?

      • We have referred to the amino acid composition bias in Fig. 5c. In the revised version of the manuscript, we made this clear by showing the composition bias in the new revised Fig. 5e. The related part of the main text then reads: “Computational analysis, using the algorithm catGRANULE37, of the protein sequence for both murine SATB1 isoforms indicated a higher propensity of the long SATB1 isoform to undergo LLPS with a propensity score of 0.390, compared to 0.379 for the short isoform (Fig. 5d). This difference was dependent on the extra peptide of the long isoform. Out of the 31 amino acids comprising the murine extra peptide, there are six prolines, five serines and three glycines – all of which contribute to the low complexity of the peptide region3 (Fig. 5e).” (Lines 298-304) • Moreover, we should note that the low complexity extra peptide of the long SATB1 isoform directly extends the PrLD and IDR regions as indicated in Fig. 4a and which we now directly state in Lines 304-305: “Moreover, the extra peptide of the long SATB1 isoform directly extends the PrLD and IDR regions as indicated in the Fig. 4a.” • We show in Fig. 4, that the N terminus of SATB1 undergoes LLPS. Since this part of SATB1 is shared by both isoforms, it is reasonable to assume that both isoforms would undergo LLPS. This is also in line with the observed photo-responsiveness of both short and long full length SATB1 isoforms in CRY2 optogenetic constructs in revised Fig. 5a,b, and similar FRAP results for both short and long full length SATB1 isoform constructs transiently transfected in NIH-3T3 cells in the revised Supplementary Fig. 6f. However, the main reason why we think that the difference in LLPS propensity between the isoforms is important is because the long isoform is more prone to aggregate compared to the short isoform, as documented in Fig 5c,f,g and Supplementary Fig. 5f.

      1. Fig 6c., It is important that authors show the data for NLS+short iso data as well to prove their hypothesis.

      As shown in original Figure 5d, the long SATB1 isoform undergoes cytoplasmic aggregation, unlike the short SATB1 isoform (as shown in the same Figure). Therefore, an image of the NLS + short isoform would not be related to our hypothesis. Actually, we wanted to reverse the long SATB1 isoform’s relocation, from the aggregated form in the cytoplasm into the nucleus. Nevertheless, to show the complete picture, in the revised version of the manuscript in Figure 6c, we now provide data for both short and long SATB1 isoforms.

      1. Fig 6d., The authors claim that mutating a specific P site changes the phase behaviour of the 'short iso'. Does it also increase for the long isoform? The authors need to confirm this in order to verify the effect of a single P site outside of oligomerization domain. ...' phosphorylation status; when phosphorylated it remains diffused, whereas unphosphorylated SATB1 is localized to PML bodies....' This being an important premise, thus should be moved to the results text.

      In the revised version of the manuscript, we moved the part regarding PML in the results section, as suggested by the Reviewer. Moreover, we included additional experiments probing the impact of association between PML and two SATB1 full length isoforms on their dynamics. The modified section in Lines 357-368 now reads: “In relation to this, a functional association between SATB1 and PML bodies was already described in Jurkat cells64. We should note that PML bodies represent an example of phase separated nuclear bodies65 associated with SATB1. Targeting of SATB1 into PML bodies depends on its phosphorylation status; when phosphorylated it remains diffused, whereas unphosphorylated SATB1 is localized to PML bodies66. This is in line with the phase separation model as well as with our results from S635A mutated SATB1, which has a phosphorylation blockade promoting its phase transitions and inducing aggregation. To further test whether SATB1 dynamics are affected by its association with PML, we co-transfected short and long full length SATB1 isoforms with PML isoform IV. The dynamics of long SATB1 isoform was affected more dramatically by the association with PML than the short isoform (Supplementary Fig. 7e), which again supports a differential behavior of the two SATB1 isoforms.”

      Moreover, given the localization of the discussed phosphorylation site in the DNA binding region of SATB1 we did test its impact on DNA binding as documented in the revised Supplementary Fig. 7d. Additionally, as we have noted in our answer in Major Comment C of this reviewer, to further support the effect of serine phosphorylation on the DNA binding capacity of SATB1 we have performed DNA affinity purification experiments utilizing primary thymocyte nuclear extracts treated with phosphatase (Supplementary Fig. 7b) We found that SATB1’s capacity to bind DNA (RHS6 hypersensitive site of the TH2 LCR) is lost upon treatment with phosphatase (Supplementary Fig. 7c).

      1. Pg 16,. The authors have tried to explain multiple things (concepts of self-regulation, accessibility) which is quite tangential. There is no inference to Fig 6f., which is showing the opposite to what the authors had postulated. This portion should either be removed or explained with a rationale. The writing also needs to be revised thoroughly in this section. Similarly, the discussion should also be modified.

      The rationale for the original Fig. 6f (revised Fig. 6g) was described in great detail in Lines 330-343 of the original manuscript. It is not clear why the Reviewer assumes that it shows the opposite to our hypothesis. As we explained, the increased accessibility allows faster read-through by RNA polymerase, and thus the exon with higher accessibility is more likely to be skipped. The exact relationship is shown in the revised Fig. 6g where the increased accessibility is associated with the expression of the short isoform, whereas the long isoform expression needs lower chromatin accessibility which allows the splicing machinery to act on the specific exon to be included. We reason that these findings are important and relevant because: 1) we suggest a potential regulatory mechanism for the SATB1 isoforms production. This is highly relevant to this manuscript given the fact that this is the first report on the existence of the long SATB1 isoform, and 2) the differential production of the long/short SATB1 isoforms has a potential relevance to breast cancer prognosis. In the revised version of the manuscript we added Fig. 6f, which now indicates the differential chromatin accessibility in human breast cancer patients and accordingly the expression of the long SATB1 isoform are associated with worse patient prognosis as indicated in Fig. 6h and Supplementary Fig. 8a,b. In the revised version of the manuscript, we substantially modified the text in Lines 374-408, to make the relevance of all these conclusions clear. The modified text now reads: “Therefore, we reasoned that a more plausible hypothesis would be based on the regulation of alternative splicing. In our accompanying manuscript19, we have reported that the long SATB1 isoform DNA binding sites display increased chromatin accessibility than what expected by chance (Fig. 3b in 19), and chromatin accessibility at long SATB1 isoform binding sites is reduced in Satb1 cKO (Fig. 3c in 19), collectively indicating that long SATB1 isoform binding promotes increased chromatin accessibility. We identified a binding site specific to the long SATB1 isoform19 right at the extra exon of the long isoform (Fig. 6e). Moreover, the study of alternative splicing based on our RNA-seq analysis revealed a deregulation in the usage of the extra exon of the long Satb1 isoform (the only Satb1 exon affected) in Satb1 cKO cells (deltaPsi = 0.12, probability = 0.974; Supplementary File 4). These data suggest that SATB1 itself is able to control the levels of the short and long Satb1 isoforms. A possible mechanism controlling the alternative splicing of Satb1 gene is based on its kinetic coupling with transcription. Several studies indicated how histone acetylation and generally increased chromatin accessibility may lead to exon skipping, due to enhanced RNA polymerase II elongation48,49. Thus the increased chromatin accessibility promoted by long SATB1 isoform binding at the extra exon of the long isoform, would increase RNA polymerase II read-through leading to decreased time available to splice-in the extra exon and thus favoring the production of the short SATB1 isoform in a negative feedback loop manner. This potential regulatory mechanism of SATB1 isoform production is supported by the increased usage of the extra exon in the absence of SATB1 in Satb1 cKO (Supplementary File 4). To further address this, we utilized the TCGA breast cancer dataset (BRCA) as a cell type expressing SATB150. ATAC-seq experiments for a series of human patients with aggressive breast cancer51 revealed differences in chromatin accessibility at the extra exon of the SATB1 gene (Fig. 6f). In line with the “kinetic coupling” model of alternative splicing, the increased chromatin accessibility at the extra exon (allowing faster read-through by RNA polymerase) was positively correlated with the expression of the short SATB1 isoform and slightly negatively correlated with the expression of the long SATB1 isoform (Fig. 6f). Moreover, we investigated whether the differential expression of SATB1 isoforms was associated with poor disease prognosis. Worse pathological stages of breast cancer and expression of SATB1 isoforms displayed a positive correlation for the long isoform but not for the short isoform (Fig. 6g and Supplementary Fig. 6c). This was further supported by worse survival of patients with increased levels of long SATB1 isoform and low levels of estrogen receptor (Supplementary Fig. 6d). Overall, these observations not only supported the existence of the long SATB1 isoform in humans, but they also shed light at the potential link between the regulation of SATB1 isoforms production and their involvement in pathological conditions.”

      1. The authors should not draw conclusions based on any data which is not shown '....ed differences in chromatin accessibility at the extra exon of the SATB1 gene (data not shown), suggesting its potential involvement in alternative splicing regulation according to the "kinetic coupling" model...'. This has led to overspeculation and needs correction.

      In the revised version of the manuscript, we included the ATAC-seq data from human breast cancer patients in the revised Fig. 6f. The legend of this figure now reads: “Human TCGA breast cancer (BRCA) patient-specific ATAC-seq peaks51 span the extra exon (EE: extra exon; labeled in green) of the long SATB1 isoform. Note the differential chromatin accessibility in seven selected patients, emphasizing the heterogeneity of SATB1 chromatin accessibility in cancer. Chromatin accessibility at the promoter of the housekeeping gene DNMT1 is shown as a control. (Lines 1281-1285)” Accordingly, we have also modified the main text: “ATAC-seq experiments for a series of human patients with aggressive breast cancer68 revealed differences in chromatin accessibility at the extra exon of the SATB1 gene (Fig. 6f). In line with the “kinetic coupling” model of alternative splicing, the increased chromatin accessibility at the extra exon (allowing faster read-through by RNA polymerase) was positively correlated with the expression of the short SATB1 isoform and slightly negatively correlated with expression of the long SATB1 isoform (Fig. 6g).” (Lines 395-339)”

      Minor comments: 1. On pg 4, the authors state 'Here, we utilized primary murine T cells, in which we have identified two full-length SATB1 protein isoforms.' Whereas only one 'long' isoform is identified and the other is the canonical version. The authors should correct the statement.

      In the revised version of the manuscript, we modified this statement as follows: ”In this work, we utilized primary developing murine T cells, in which we have identified a novel full-length long SATB1 isoform and compared it to the canonical “short” SATB1 isoform.” (Lines 64-66)”

      1. Fig. 1 a , Is there a specific reason to generate a custom-made antibody for 'all' SATB1, using similar regions that are already commercially available. This becomes redundant otherwise, because there is no apparent difference in detection compared to the commercial one (Suppl. Fig 1a). Antibody generation strategy (1a) should be moved to supplementary. Additionally, authors have obtained the custom antibodies from a commercial source, therefore, the text should reflect the same alongside relevant details.

      The custom-made SATB1 antibody targeting the amino-terminal region of the protein has been developed in order to be utilized for detecting the native form of the protein. Unlike commercially available antibodies raised against either short peptides or denatured forms of the protein we have utilized the native form of the amino-terminal part of the protein for raising this antibody. To be honest, this antibody has been raised in order to be utilized in ChIP-seq experiments since no commercially available antibody is of high quality for this approach. Moreover, the original Figure 1a was utilized in order to provide an overview of the SATB1 protein structure which is highly relevant to understand its biophysical properties and not for presenting the strategy for raising a custom-made antibody for SATB1.

      1. Fig 3e: what is the control used here? In their Pearson correlation analysis, there seem to be significant reduction in control sets as well upon treatment. This needs to be clarified.

      We used scans rotated by 90° which served as a negative control, as stated in Line 769: “SATB1 scans rotated by 90° served as a negative control for the colocalization with FU.” Note that this is a commonly used control in colocalization experiments as described for example in Dunn et al., 2011 (https://doi.org/10.1152/ajpcell.00462.2010).

      Additionally, we provide Costes’ P values which are based on randomly scrambling the blocks of pixels (instead of individual pixels, because each pixel’s intensity is correlated with its neighboring pixels) in one image, and then measuring the correlation of this image with the other (unscrambled) image. Please see Costes et al., 2004 (https://doi.org/10.1529%2Fbiophysj.103.038422) for more details. Moreover, it was actually anticipated to see a decrease in colocalization upon hexanediol treatment even in the negative control, as hexanediol significantly reduces both SATB1 and FU speckles as established in Fig. 3a-d.

      1. Pg 10, the authors claim that '..., thus we reasoned that it may also be used to study phase separation...' But there have been numerous reports starting from 2018, which have utilized this technique in corelation to phase behaviour (albeit individual proteins). The authors should include proper citations as they are extending an idea from the same field to their specific need.

      In the revised version of the manuscript, we included relevant citations to support the use of Raman spectroscopy in LLPS research: “Raman spectroscopy was already used in many biological studies, such as to predict global transcriptomic profiles from living cells42, and also in research of protein LLPS and aggregation43–47. Thus we reasoned that it may also be used to study phase separation in primary T cells.” (Lines 225-228)”

      1. For Fig 5b, there should be a comparative image for 'short' isoform.

      In the revised Figure 5c we have included a comparative image for the short SATB1 isoform.

      1. In the context of Figure 5c, the authors claim ...' Note also the higher LLPS propensity of the human long SATB1 isoform compared to the murine SATB1...' Why suddenly human and mouse comparisons are drawn? This figure should be moved to supplementary.

      The comparison between the human and mouse SATB1 isoforms has been implemented because it is relevant for our claims regarding the increased SATB1 aggregation in human cells in relation to the revised Fig. 6f,g,h and Supplementary Fig. 6c,d. This is also discussed in Lines 479-482, which read: “This is particularly important given the higher LLPS propensity of the human long SATB1 isoform compared to the murine SATB1 (Fig. 5d). Therefore, human cells could be more susceptible to the formation of aggregated SATB1 structures which could be associated with physiological defects.”

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

      Zelenka et al., focus on a T cell genome-organizing protein, SATB1, to show that SATB1 undergoes liquid liquid phase-separation (LLPS), and distinct isoforms confer different LLPS-related biophysical properties. They generate a long-isoform specific antibody and conduct several experiments to test for LLPS and compare LLPS properties between the long-isoform relative to the whole SATB1 protein population. Given that SATB1 plays important roles in T cell development and in cancer, interrogating SATB1 biophysical properties is an important question. However, there are multiple problems with the experimental setup and data that weaken their support of the conclusions. I will detail some of the major issues below:

      Regarding phase-separation There are several assays to determine whether a protein undergoes LLPS. 1. One of the first the authors address is the spherocity or roundness. Indeed, formation of spherical droplets is one evidence of the liquid nature of a protein. However, the authors use fixed preparations (which can introduce artifacts), not free-floating protein, and determine roundness by showing a 2D image. Roundness should take into account the diffraction-limits of fluorescent imaging, as many structures can be imaged to appear round by the detector. There are quantifiable measurements that can be taken on 3D images to show roundness. This would best be shown using non-fixed protein.

      • We thank this Reviewer for several insightful comments. Although, we agree with most of them, we should highlight the main goal of our manuscript, i.e. to investigate the SATB1 protein with an emphasis on its physiological roles in primary developing murine T cells. We highlight this already in the introduction in Line 64 “In this work, we utilized primary developing murine T cells,...” and mainly also in the respective part of the result section: “To probe differences in phase separation in mouse primary cells, without any intervention to SATB1 structure and expression, we first utilized 1,6-hexanediol treatment, which was previously shown to dissolve the liquid-like droplets34.(Lines 203-205)”

      • We believe that this is a very important aspect of our study that should not be overlooked. The majority of proteins perhaps behave differently under physiological and in vitro conditions. However, due to the extensive post-translational modifications affecting the properties of SATB1, its completely different localization patterns between primary developing T cells and other cell types but especially cell lines and many other aspects, it was of utmost importance to focus our research on primary T cells. Unfortunately, this was accompanied with multiple difficulties, such as that we have to use fixed cells as this is the only way to visualize SATB1 in these cells. Alternatively, one could create a new mouse line expressing a fluorescently tagged SATB1 protein, but this is beyond the scope of our work.

      • However, we should also note that many LLPS-related studies do not pay any focus on primary physiological functions of proteins and they simply focus on the investigation of protein’s artificial behavior in in vitro conditions. Having said that, we too extended our experiments in primary cells to the ex vivo studies in cell lines to further support our claims. In these experiments, we utilized live cell imaging in Fig. 4-6, quantified the spherocity in Supplementary Fig. 6, showed the ability of speckles to coalesce in Fig. 4c and also used FRAP in Fig. 4f and also in the revised version of the manuscript in Supplementary Figure 6f. Moreover, we should note that most of these experiments were designed and performed during 2017 and 2018 conforming with the standards. We are well aware of the progress in the field and impact of fixation on LLPS, as described in Irgen-Gioro et al., 2022 (https://doi.org/10.1101/2022.05.06.490956), but after over seven months of review process in another journal we also believe that these aspects should be considered not to delay further progress of the SATB1 field.

      Regarding the isoform specificity of SATB1 biophysical properties 1. The authors generate a long isoform-specific antibody. However, the western blot is not convincing that this is indeed specific to the long isoform as there is a rather large smear. Can this be improved with antibody preabsorption? Since this is a key reagent for the manuscript, improvement in antibody quality is essential.

      The custom-made antibody for the long isoform has been raised against the unique 31 amino acids long peptide present in the long SATB1 isoform. The polyclonal serum has undergone affinity chromatography utilizing the immobilized peptide (antigen) to purify the antibody. In the revised version of the manuscript we have included another immunodepletion experiment with cleaner bands (Fig. 1f). Moreover, please read our answer to Major comment #2 of Reviewer 1 that follows: • The long antibody was raised in mice inoculated with the extra peptide present in the long isoform only. Therefore, the capacity of this antibody precipitating the shorter isoforms, which do not express the sequence of the extra peptide (EP, Figure 1a) in not possible.

      • We have repeated the immunodepletion experiment and we now provide the results in Fig. 1f and Supplementary Fig. 1b. The western blot in Fig. 1f is now cleaner and supports quite convincingly the presence of a long SATB1 isoform. Given the lack of isoform-specific knockouts which we could utilize to immunoprecipitate or detect the different isoforms in a single cell (or cell population), the utilized approach of immunodepletion and subsequent western blotting is the approach we thought of implementing.

      • As shown in Fig. 1f and Supplementary Figure 1b, the long isoform SATB1 antibody has the capacity to recognize the long isoform in murine thymocyte protein extracts but not the short SATB1 isoform (please compare lane 3 in the two western blots utilizing either the antibody for the long isoform -top panel - or the antibody that detects both isoforms (lower panel).

      • We have performed Immunofluorescence experiments utilizing the antibody detecting the long SATB1 isoform in thymocytes isolated from either C57BL/6 or Satb1 cKO mice. The antibody is specific to the SATB1 protein since there is no signal in immunofluorescence experiments utilizing the knockout cells (Supplementary Figure 1c).

      • We have performed Immunofluorescence experiments utilizing thymocytes and the antibody detecting the long SATB1 or a commercially available antibody detecting all SATB1 isoforms. The pattern of SATB1 subnuclear localization is similar for both antibodies (Supplementary Figure 1e).

      • In our accompanying revised manuscript Zelenka et al., 2022 (https://doi.org/10.1101/2021.07.09.451769), we provide yet another piece of evidence, consisting of bacterially expressed short and long SATB1 protein isoforms detected by western blot using either the long isoform-specific or the non-selective all SATB1 isoforms antibodies.

      • Regarding the additional bands detected in the immunoprecipitation experiment presented in the original Supplementary Figure 1b (lane 2), it is not surprising that additional bands appear in a sample of protein extracts that is used for several hours for the immunoprecipitation experiments, while the “input” sample simply denotes protein extract that is frozen at -80oC right after the preparation of protein extracts until use. It is well-established that SATB1 is the target of proteases which might as well be active during the immunoprecipitation steps (2 consecutive immunoprecipitation steps take place). Therefore, the immunoprecipitated material cannot necessarily be a copy of the input material displaying a single protein band even if protease inhibitors are included in the buffers.

      Taken together the experiments described here we showed that the antibody raised against the extra 31 aa long peptide, present only in the long SATB1 isoform, is specific for this isoform.

      1. Fig 4 Optodroplet experiment appears to show that the N-terminus of SATB1 can undergo LLPS. The results of this assay show that SATB1 has a domain that can undergo phase-separation in isolation, but it does not show that the protein itself is a phase-separating protein. The FRAP assay methods are not provided by the authors, but this is important, as continued light activation means proteins are continuously forming aggregates, and the bleaching for FRAP should be balanced with the levels of Cry2 activation. A very good description of the methods is described in the original Optodroplet paper: https://www.sciencedirect.com/science/article/pii/S009286741631666X?via%3Dihub#sec4

      We should note that we did follow the FRAP protocol provided by the recommended study Shin et al., 2017 (https://doi.org/10.1016/j.cell.2016.11.054). Indeed, these experiments are very tricky to perform and interpret, as every cell expresses slightly different amounts of protein which is directly associated with the different speed of optoDroplet formation, and thus its propensity to aggregate upon overactivation. On the other hand, there need to be continuous activation during the FRAP experiment as the lack of activation laser would result in fast disassembly of the optoDroplets, counteracting the FRAP results. Moreover, the optoDroplets actively move around the cell in all dimensions which makes the accurate measurement of signal intensity really challenging, even with an adjusted pinhole. Therefore, we do not think that FRAP is the best approach to examine the behavior of optoDroplets.

      Either way, we have now described the detailed FRAP protocol in Lines 889-898, which read: “For the FRAP experiments, cells were first globally activated by 488 nm Argon laser illumination (alongside with DPSS 561 nm laser illumination for mCherry detection) every 2 s for 180 s to reach a desirable supersaturation depth. Immediately after termination of the activation phase, light-induced clusters were bleached with a spot of ∼1.5 μm in diameter. The scanning speed was set to 1,000 Hz, bidirectionally (0.54 s / scan) and every time a selected point was photobleached for 300 ms. Fluorescence recovery was monitored in a series of 180 images while maintaining identical activation conditions used to induce clustering. Bleach point mean values were background subtracted and corrected for fluorescence loss using the intensity values from the entire cell. The data were then normalized to mean pre-bleach intensity and fitted with exponential recovery curve in Fiji or in frapplot package in R.”

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

      **Reviewer #1**:

      Can they use the all and long isoform antibodies together, then subtract the signal from long isoform to conclude about the localization of the shorth isoform ?

      We thank the Reviewer for the suggestion, though given the differential efficiency of antibodies and other limitations of imaging experiments, we do not find the suggested experiment to have a potential to improve the quality of our manuscript. However, we should note that we have performed a pixel-based colocalization experiment between the signal detected by all isoform and long isoform SATB1 antibodies. Fluorocytogram of the pixel-based colocalization, based on 3D-SIM data is provided on the left, with quantified colocalization on the right of the revised Supplementary Fig. 5a.

      3) Lack of better staining with antibody against the long and short SATB1 isoforms after treatment with 1,6 Hexanediol. 1,6 Hexanediol treatment can change many other chromatin associated proteins to which SATB1 can be bound to indirectly. This experiment can

      We do understand the controversy and difficulties of experiments using 1,6-hexanediol treatment. However, we have to note that there is no better approach available for the investigation of LLPS in our primary murine T cells. We did use alternative approaches in ex vivo experiments, utilizing cell lines to validate our hypothesis without the involvement of 1,6-hexanediol.

      **Reviewer #2**:

      1. The authors mention, '...of the different SATB1 isoforms, uncovered by the use of the two different antibodies, relied in the heterochromatin areas (zone 1), where the long isoform was less frequently...' There is no supporting figure number mentioned. The authors need to show a zone-by-zone comparison images for 'all iso' vs 'long' iso of SATB1. Just to reiterate, there is a need for a heterochromatin mark to unambiguously call out the distinction.

      We should remind that there is an inherent difficulty to accurately compare localization of short and long SATB1 isoforms in primary cells, especially due to the lack of Satb1 isoform-specific knockout mice. There is no way to detect only the short isoform in these primary cells as there are only antibodies targeting the long or all SATB1 isoforms. Therefore, we cannot set up additional experiments probing these questions.

      In line with this, in the revised version of the manuscript, we toned down our statements regarding the differential localization of the two isoforms in primary cells. We only refer to it as an indication and we support it by adding references to the relevant figures. This part now reads: “Localization of SATB1 speckles detected by antibodies targeting all SATB1 isoforms and/or only the long SATB1 isoform, revealed a significant difference in the heterochromatin areas (zone 1, Fig. 2b), where the long isoform was less frequently present (see also Fig. 2a and Fig. 3c). Although, this could indicate a potential difference in localization between the two isoforms, due to the inherent difficulty to distinguish the two based on antibody staining, we refrain to draw any conclusions. (Lines 145-150)”

      1. Fig. 6a, The authors wished to see the effect of RNA on Satb1 nuclear localization. This is not related to the main theme of the paper, thus should be moved to supplementary (true for b as well). Importantly, the experiments should be performed with total cells to show the divergence of localization (like the paper the authors referred to) instead of matrix for clarity.

      • We did not wish to see the effect of RNA on SATB1 localization. In fact, there is a long history of SATB1 research that is inherently linked with the concept of nuclear matrix, a putative nuclear structure which is highly associated with nuclear RNAs. SATB1 was described many times as a nuclear matrix protein (https://doi.org/10.1016/0092-8674(92)90432-c; https://doi.org/10.1128/mcb.14.3.1852-1860.1994; https://doi.org/10.1074/jbc.272.17.11463; https://doi.org/10.1128/mcb.17.9.5275; https://doi.org/10.1021/bi971444j; https://doi.org/10.1083/jcb.141.2.335; https://doi.org/10.1101/gad.14.5.521; https://doi.org/10.1038/ng1146).

      • Moreover, our data discussed in comments 4-7 of this Reviewer, such as i. the localization of SATB1 to the nuclear zones associated with RNA and nuclear scaffold factors (Fig. 2b, Supplementary Fig. 1c), ii. colocalization of SATB1 with actively transcribed RNAs (Fig. 2c, Fig. 3g, Supplementary Fig. 2a, Supplementary Fig. 2c), iii. including its association with nucleoli (Supplementary Fig. 3b), and also iv. its computationally predicted interaction with Xist lncRNA (Agostini et al., 2013; https://doi.org/10.1093/nar/gks968) as a notable factor of nuclear matrix, all suggest that the interaction between RNA and SATB1 is plausible and potentially relevant for its function and/or at least its subnuclear localization. It is relevant even more so, when considering numerous reports on the ability of RNA-binding, poly-Q and PrLD-containing proteins to undergo LLPS https://doi.org/10.1016/j.molcel.2015.08.018; https://doi.org/10.1042/bcj20160499; https://doi.org/10.1016/j.cell.2018.03.002; https://doi.org/10.1016/j.cell.2018.06.006; https://doi.org/10.1093/nar/gkaa681), including RNAs specifically regulating LLPS behavior, especially for poly-Q and PrLD-containing proteins, such as SATB1 (https://doi.org/10.1126/science.aar7366; https://doi.org/10.1126/science.aar7432; https://doi.org/10.1016/j.ceb.2019.03.007; https://doi.org/10.1038/s41598-020-57994-9; https://doi.org/10.1016/j.molcel.2015.09.017; https://doi.org/10.1038/s41598-019-48883-x; https://doi.org/10.1038/s41467-019-11241-6).

      • It should also be noted that SAF and various hnRNPs, as the most prominent proteins of nuclear matrix were many times reported to phase separate (https://doi.org/10.1016/j.molcel.2019.10.001; https://doi.org/10.1074/jbc.ra118.005120; https://doi.org/10.1016/j.celrep.2019.12.080; https://doi.org/10.1038/s41467-019-09902-7; https://doi.org/10.1016/j.molcel.2017.12.022; https://doi.org/10.1074/jbc.tm118.001189). All these aspects show that the relation between nuclear matrix, SATB1 and RNA are quite relevant to our manuscript.

      • Moreover, in light of the aforementioned information, we believe that it is much clearer to follow the protocol we did – i.e. to remove soluble proteins by CSK treatment and then, upon RNase treatment, extract the released proteins using ammonium sulfate. In an experiment utilizing whole cells, one would need to microinject RNase A into the nucleus, which 1. is very challenging for primary T cells having a radius of 3-5 micrometers, 2. is of low throughput, 3. would not allow for released protein removal which would thus make the results hard to interpret. Please note that in the reference paper, the authors used cell lines overexpressing heterologous GFP-tagged proteins, which is not related to our setup.

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

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      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

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  22. Jul 2022
    1. https://www.zylstra.org/blog/2022/06/spring-83/

      I've been thinking about this sort of thing off and on myself.

      I too almost immediately thought of Fraidyc.at and its nudge at shifting the importance of content based on time and recency. I'd love to have a social reader with additional affordances for both this time shifting and Ton's idea of reading based on social distance.

      I'm struck by the seemingly related idea of @peterhagen's LindyLearn platform and annotations: https://annotations.lindylearn.io/new/ which focuses on taking some of the longer term interesting ideas as the basis for browsing and chewing on. Though even here, one needs some of the odd, the cutting edge, and the avant garde in their balanced internet diet. Would Spring '83 provide some of this?

      I'm also struck by some similarities this has with the idea of Derek Siver's /now page movement. I see some updating regularly while others have let it slip by the wayside. Still the "board" of users exists, though one must click through a sea of mostly smiling and welcoming faces to get to it the individual pieces of content. (The smiling faces are more inviting and personal than the cacophony of yelling and chaos I see in models for Spring '83.) This reminds me of Stanley Meyers' frequent assertion that he attempted to design a certain "sense of quiet" into the early television show Dragnet to balance the seeming loudness of the everyday as well as the noise of other contemporaneous television programming.

      The form reminds me a bit of the signature pages of one's high school year book. But here, instead of the goal being timeless scribbles, one has the opportunity to change the message over time. Does the potential commercialization of the form (you know it will happen in a VC world crazed with surveillance capitalism) follow the same trajectory of the old college paper facebook? Next up, Yearbook.com!

      Beyond the thing as a standard, I wondered what the actual form of Spring '83 adds to a broader conversation? What does it add to the diversity of voices that we don't already see in other spaces. How might it be abused? Would people come back to it regularly? What might be its emergent properties?

      It definitely seems quirky and fun in and old school web sort of way, but it also stresses me out looking at the zany busyness of some of the examples of magazine stands. The general form reminds me of the bargain bins at book stores which have the promise of finding valuable hidden gems and at an excellent price, but often the ideas and quality of what I find usually isn't worth the discounted price and the return on investment is rarely worth the effort. How might this get beyond these forms?

      It also brings up the idea of what other online forms we may have had with this same sort of raw experimentation? How might the internet have looked if there had been a bigger rise of the wiki before that of the blog? What would the world be like if Webmention had existed before social media rose to prominence? Did we somehow miss some interesting digital animals because the web rose so quickly to prominence without more early experimentation before its "Cambrian explosion"?

      I've been thinking about distilled note taking forms recently and what a network of atomic ideas on index cards look like and what emerges from them. What if the standard were digital index cards that linked and cross linked to each other, particularly in a world without adherence to time based orders and streams? What does a new story look like if I can pull out a card either at random or based on a single topic and only see it or perhaps some short linked chain of ideas (mine or others) which come along with it? Does the choice of a random "Markov monkey" change my thinking or perspective? What comes out of this jar of Pandora? Is it just a new form of cadavre exquis?

      This standard has been out for a bit and presumably folks are experimenting with it. What do the early results look like? How are they using it? Do they like it? Does it need more scale? What do small changes make to the overall form?


      For more on these related ideas, see: https://hypothes.is/search?q=tag%3A%22spring+%2783%22

  23. Jun 2022
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** Techniques to probe the local environment of membrane proteins are sparse, although the influence of lipids on the membrane protein's function are known since many years. Therefore, the paper by Umebayashi et al. is important. The environment-sensitive dye Nile red (NR) coupled to a membrane protein is an appropriate sensor for monitoring the local membrane fluidity. Linking of Nile red to the receptor via a flexible tether was achieved with the acyl carrier protein (ACP)-tag method. Experiments showed that depending on the ACP site a certain linker length is required to have NR inserted in the membrane and thus be an effective sensor for lipid disorder. This technology could be of general usability to study the environment of membrane proteins in the context of their function. As an example, the technique allowed insulin induced membrane disorder in the close insulin receptor vicinity to be observed. Further, results suggested that tyrosine activity is required for this disorder to happen. The experimental results appear to be complete and controls were made.

      **Major comments:** 1) Sometimes technical terms are used without explanation: What is the GP value? What is ACP-IR? The spectrum was measured in number of rois? The reader can find those abbreveations out, but it would be nice to have them defined.

      We have made a list of abbreviations.

      2) Fig. 1d) is confusing. The ACP-IR labelling is evident in 3 panels, but there is no difference in the color (emission spectra of 1992-ACP-IR vs 2031-ACP-IR should be visible??). The DAPI staining is very different. When doing the latter, how difficult is it to get the staining equal?

      The differences in spectra cannot be seen because we used pseudo colors for display of the DAPI and CoA-PEG-NR staining. The reviewer’s comments about the unequal DAPI staining is correct. The reason for this is most likely that the cell membrane is unequally permeabilized by PFA treatment. As the point of this figure is just to show that the plasma membrane is labeled, dependent upon the expression of the ACP-tagged insulin receptor, we don’t think that the variable intensities of the DAPI staining is important. DAPI is simply used to indicate the position of the cells.

      3) How can one interpret Fig. 4: a) Control goes over 4 frames, at 240" insulin is added, and 10 frames should show a fluctuation difference?

      We showed 4 frames after control treatment that showed no significant change was observed by control treatment. We expected that clear changes would be invoked by insulin treatment in GP images, however these changes, while visible in the GP images, are difficult to see for the untrained observer. This is the reason why we used the ZNCC method in the subsequent figures to better visualize the changes.

      1. b) A color shift from blue to green is visible after insulin addition. But it is faint - difficult to assess from the pseudo color scheme. What does 1000 pixel top/1000 pixel bottom mean in c). Is it an attempt to better visualize the fluctuation? It is difficult to recognize a difference before and after adding insulin. d) It seems that the kymograph set should show this. What is the color scale? Why is 3 so untypical, i.e., no change? Box 6 is also peculiar: the left side does not show a strong change upon insulin administration, the right side does. Why? We appreciate the helpful comments for improving our manuscript.

      As pointed out, the change of GP value is extremely small before and after insulin addition, so it is difficult to fully visualize the change with normal pseudo-color expression. To deal with this, we adopted the following two methods to visualize minute changes.

      1) Visualization of local changes of the statistical GP value showed by ZNCC throughout the time-lapse images (Fig. 6 and Fig. S2B).

      2) Visualization of the top/bottom 1000 pixels of the sorting ZNCC value in each image (Fig. 7 and Fig. S2C). The top 1000 pixels are the ones that showed the largest changes. The bottom 1000 pixels are the ones that showed the smallest changes.

      Owing to these expressions, we found out that the level of the response against the insulin signal was spatially and temporally heterogeneous in the membrane.

      As for the color scale, in order to clarify the meaning of the difference of color, we have added the description about the relationship between the color and the ZNCC value in the results section.

      4) How is the kymogram calculated? The legend says 'The horizontal dimension represents the averaged ZNCC inside the rectangular area, and the vertical dimension represents time'. The averaged ZNCC is a single value, so it is not clear why the kymogram shows a variation from left to right. May it be the ZNCC was averaged just vertically?

      We apologize that we did not provide information regarding making the kymograph.

      In the yellow rectangular area (Fig. 6B), the ZNCC values of the pixels with the same x coordinate value were vertically averaged, which were represented as the horizontal direction of the kymograph. That is, one horizontal line of the kymograph holds the spatial distribution of the ZNCC value along the horizontal direction of the membrane, and the vertical direction shows their time changes. To make it easier to understand, we refined the description about the kymograph in the legend of Fig. 6.

      5) When calculating cross-correlation values on images, they need to be aligned. What fraction of the total image does the selected 19x19 box represent? As described, I imagine that a rolling CC over 19x19 pixels is calculated over an image from the time lapse series comparing it with the reference Iave(x,y). Compared to the 3x3 median filtered CP image, the ZNCC image should then be much more blurred??

      Below we provide more information regarding the calculation of ZNCC.

      Each local window for ZNCC calculation is set to a 19x19 pixels centered on every single pixel excluding the edges of an image. The ZNCC value calculated in that window is set to a center pixel of that area. After that, a new window centered on the adjacent pixel is set and calculate the new ZNCC. That is, the calculation window is slid throughout the image. Also, the calculated ZNCC value is not set to all the pixels of the window, but is set to only the center pixel of the window, so there is no blur effect like median filtering.

      The figure below shows a schematic view of our ZNCC calculation.

      Schematic view of our ZNCC calculation

      **Minor comment:** On page 16 supplementary is not spelled properly.

      corrected

      Reviewer #1 (Significance (Required)):

      The key point of this paper is convincing and the new technology appears to have a lot of potential. It can be applied to study membrane protein function in the context of its environment, the lipid bilayer.

      Membrane fluidity measurements have been developed (e.g., using fluorescent probes like laurdan). However, the trick to link a probe like nile red by ACP technology to the insulin receptor and to observe its activity is quite new.

      A most recent description of such a technology is in TrAC Trends in Analytical Chemistry Volume 133, December 2020, 116092.

      This is an interesting review, but not directly impacting on our work.

      **Referees cross-commenting**

      All comments are constructive and important. The paper is important but needs to be amended as proposed.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary:** In this manuscript, authors generated an ACP-attached Nile Red probe in order to specifically label Insulin receptor in the membrane. Owing to this specificity, one can measure the lipid membrane properties around a specific protein in the membrane. **Major comments:**

      For the conclusions in the manuscript to be convincing, in my opinion, these additional data need to be added. Some of these are new experiments, and some are detailed analysis of existing data. The new experiments are not for new line of investigation, instead it is to confirm their statements and conclusions. The major point is the reliability of spectral shift. In usual environment sensitive probes, it is certain that they are in the membrane whatever is done to the membrane. However, when the probe is attached to a protein, it is not trivial to have the same confidence that the probe is always inside the membrane, and it is in the same plane of the membrane. 1992-ACP-IR is a good example; authors state that it binds to the protein outside the membrane, but when there is cholesterol addition and -maybe more interestingly- cholesterol removal, the dye still reacts and changes its emission (even PreCT changes its emission quite a bit at the 570 nm region). This is a clear indication of a change in localization of the probe upon some changes in the membrane. This implies that observed spectral shifts may not be due to lipid packing differences, but due to localization of the probes. For this reason, it is crucial to know where any environment sensitive probe localize in the membrane with respect to membrane normal, and this knowledge is more important for this probe. Related to this, the spectral difference upon insulin treatment and activation of insulin receptor could be due to changes in probe's localization in the membrane. Especially because authors show in Fig1e, the spectra can change depending on the probe localization. Relatedly, quantum yield of NR should be significantly different when it is inside vs outside membrane. Authors should show QY for 1992-ACP-NR and 2031-ACP-NR with different PEG lengths and upon insulin treatment.

      We understand the logic of the request to measure the QY, since the QY of Nile red is much higher in organic solvents than in aqueous solutions, so it might be predicted that the QY of Nile red is higher in a lipid bilayer than when covalently bound to the protein in an aqueous environment. However, this argument depends upon the mechanism for the increase in quantum yield when going from aqueous to a non-polar solution. One possible explanation is based on the intrinsic properties of the dye under the two conditions. The alternative explanation would be that the dye would aggregate (be insoluble) in aqueous solution and therefore either not fluoresce or self-quench. In this case, we believe that the latter is the explanation because we and others have previously shown the turn-on properties of the probe when binding to proteins (SNAP-tag and others). It is not simple to measure QY in the cell under a microscope, but we have done something similar shown in supplementary figure 4. We labeled the three ACP-receptor complexes with PEG11-Nile red and co-stained with antibody to the Insulin Receptor. We then calculated a relative quantum yield. There were very little differences at all between the relative quantum yields, so we conclude that it is not the environment of the probe, which affects the quantum yield under these conditions, but the fact that it is covalently attached to a protein and incapable of forming aggregates. What distinguishes these constructs is the emission spectrum, not the quantum yield. In supplementary Table 2 we also did QY measurements in vitro and we could reproduce the increase of quantum yield by association with liposomes or in organic solvents. We tested whether non-covalent association with a protein would increase the QY by incubation with the lipid binding protein, BSA, in PBS. This was not the case, strongly pointing to the conclusion that it is the covalent association with the protein that increases the QY, not association with a protein. We believe that our demonstration of changes in fluorescent spectra with changes in cholesterol, large changes in fluorescent spectra with linker length for the 1992 construct and voltage sensitivity using patch-clamp prove that the Nile red is reporting on the membrane environment under the conditions we propose.

      **Minor comments:** - Fig 1d requires quantification We do not agree on this. This is simply to show that the labeling is dependent upon expression of the relevant ACP-IR constructs. There is no detectable labeling of the control.

      • Voltage sensitivity of different PEG length of 2031-ACP probe should be added. We have added this data in figure 2 panel E.

      • Fig 3a graph should show all data points, not only bar graphs. Also, the band in 3a for +CoA-PEG-NR is dimmer than other bands, is it specific to this particular gel since quantification does not show any difference?

      There is no significant difference- Fig 4d, colour code is needed.

      Done

      • Fig 5b and Fig3d are basically the same experiments in terms of control measurement, why is the difference in 3b is 0.04 GP unit while it is 0.007 GP unit?

      We explain in the MS, but have improved the title of Y-axis in Fig.5 b graph so that the difference in what is plotted is clear. - Why is inhibitor data so noisy? We should discuss.

      We don’t know the exact reason why inhibitor data is noisy, but we speculate that the actin cytoskeleton and phosphoinositide-dependent signaling could affect the membrane stability, and the membrane environment would be fluctuated in the presence of latrunculin B or PI3K inhibitor.

      Reviewer #2 (Significance (Required)): Overall, this is a very useful approach, and this line of research will yield very useful tools to shed light on how lipids surrounding proteins can change their function. Major advance of the paper is the new chemical biology tool. There is also biological data on how insulin can change the insulin receptor's membrane environment which is contradictory to some old literature claiming that InsR becomes more "rafty" upon insulin treatment (e.g., PMID: 11751579).

      If this type of tagging proves robust and reproducible (limitations and concerns listed above and below), it could be used by other researchers to tag their protein of interest and investigate the lipid environment around those proteins.

      The downside of this method is that the probe requires ACP tag, a relatively less used tag than others in biology, therefore researchers interested in using this probe should have their proteins with ACP tag. Moreover, the linker length and ACP-tag position are quite crucial parameters (and probably should be optimized for each protein). Longer PEG lengths cannot report on changes efficiently (Fig3b), while shorter lengths are prone to artefacts as they can go out of membrane (Fig1 and Fig2). This might limit its widespread use.

      The reason for using the ACP tag is that neither the SNAP tap nor the HALO tag working. The tethered Nile Red preferred to bind to the tqg rather than inserting into the membrane.

      **Referees cross-commenting** I agree with all comments and concerns of other reviewers. I see the usability and potential of this new technology along with its limitations as all three reviewers pointed out.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): See below. No concerns on any of these issues.

      Reviewer #3 (Significance (Required)): **Critique:** This MS reports a proof-of-principle for using site-directed environmentally sensitive probe technology to assess the local membrane environment of a receptor tyrosine kinase (IR) upon activation. This technology addresses a major gap in our arsenal of tools to study the mechanisms of membrane signaling as the parameters of interest are biophysical parameters rather than purely biochemical ones. How to do this with spatial and temporal resolution is a major challenge. This study builds on previous work by the Riezman group that develops an extrinsic labeling system to tether Nile Red to specific sites on the ectodomain of a signaling receptor and then probe local membrane environments as a function of receptor activity. This is a carefully done study is well-controlled, is clever in design and is well-described. Although the major issues to which such a general technology could contribute involve intracellular (and not extracellular) event, the advances described will be of general interest -- particularly that local membrane order decreases when IR becomes activated. Specific comments for the authors' consideration follow:

      **Specific Comments:** (i) As a general comment, the authors are measuring extracellular plasma membrane leaflet properties that may or may not translate to what is happening in the local inner leaflet environment. A general reader may well miss the significance of this. This point needs to be more explicitly emphasized in the Discussion.

      This has been discussed in the revised version.

      (ii) Why not treat cells with a PLC inhibitor to block PIP2 hydrolysis and ask if that inhibits membrane disorder. It is PIP2 hydrolysis/resynthesis that regulates the actin cytoskeleton at signaling receptors and this seems an attractive candidate for study.

      There is a long list of attractive post-signaling events of the insulin receptor and how this works in different cell types that could be tested. We believe that this is beyond the scope of this study and we encourage others to do this.

      (iii) The data acquisition time is at least 4 min which is long enough for activated receptors to be recruited to sites of endocytosis. Can the authors exclude the possibility that what they are measuring isn't reflective of such spatial reorganization? Does a clathrin inhibitor block the observed change in local membrane order for activated IR? We determined localization to AP2 adaptor containing clathrin coated pits at the cell surface and showed that during the time-course of the experiment that there is no significant change in co-localization or evidence for endocytosis (new figure 9). Therefore, we decided not to do the clathrin inhibitor blocking experiment because we believe that it could only lead to indirect effects.

      (iv) Receptor activation is accompanied by other transitions such as dimerization, etc. Can the authors exclude the possibility that what they are measuring is related to changes in depth of insertion of the NR probe into the plasma membrane outer leaflet that is a consequence of IR conformational transitions associated with activation? This is highly unlikely given the fact that fluidification of the membrane environment is found with all length linkers. Given the intervals in increases in linker length on the 2031 construct, which is the closest to the membrane, it is very difficult to conceive that any of the ones larger than 5 PEGs restrict significantly the membrane insertion of the dye. **Referees cross-commenting**

      I think we have a consensus opinion

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, authors generated an ACP-attached Nile Red probe in order to specifically label Insulin receptor in the membrane. Owing to this specificity, one can measure the lipid membrane properties around a specific protein in the membrane.

      Major comments:

      For the conclusions in the manuscript to be convincing, in my opinion, these additional data need to be added. Some of these are new experiments, and some are detailed analysis of existing data. The new experiments are not for new line of investigation, instead it is to confirm their statements and conclusions. The major point is the reliability of spectral shift. In usual environment sensitive probes, it is certain that they are in the membrane whatever is done to the membrane. However, when the probe is attached to a protein, it is not trivial to have the same confidence that the probe is always inside the membrane, and it is in the same plane of the membrane. 1992-ACP-IR is a good example; authors state that it binds to the protein outside the membrane, but when there is cholesterol addition and -maybe more interestingly- cholesterol removal, the dye still reacts and changes its emission (even PreCT changes its emission quite a bit at the 570 nm region). This is a clear indication of a change in localization of the probe upon some changes in the membrane. This implies that observed spectral shifts may not be due to lipid packing differences, but due to localization of the probes. For this reason, it is crucial to know where any environment sensitive probe localize in the membrane with respect to membrane normal, and this knowledge is more important for this probe. Related to this, the spectral difference upon insulin treatment and activation of insulin receptor could be due to changes in probe's localization in the membrane. Especially because authors show in Fig1e, the spectra can change depending on the probe localization. Relatedly, quantum yield of NR should be significantly different when it is inside vs outside membrane. Authors should show QY for 1992-ACP-NR and 2031-ACP-NR with different PEG lengths and upon insulin treatment.

      Minor comments:

      • Fig 1d requires quantification
      • Voltage sensitivity of different PEG length of 2031-ACP probe should be added.
      • Fig 3a graph should show all data points, not only bar graphs. Also, the band in 3a for +CoA-PEG-NR is dimmer than other bands, is it specific to this particular gel since quantification does not show any difference?
      • Fig 4d, colour code is needed.
      • Fig 5b and Fig3d are basically the same experiments in terms of control measurement, why is the difference in 3b is 0.04 GP unit while it is 0.007 GP unit?
      • Why is inhibitor data so noisy?

      Significance

      Overall, this is a very useful approach, and this line of research will yield very useful tools to shed light on how lipids surrounding proteins can change their function. Major advance of the paper is the new chemical biology tool. There is also biological data on how insulin can change the insulin receptor's membrane environment which is contradictory to some old literature claiming that InsR becomes more "rafty" upon insulin treatment (e.g., PMID: 11751579).

      If this type of tagging proves robust and reproducible (limitations and concerns listed above and below), it could be used by other researchers to tag their protein of interest and investigate the lipid environment around those proteins.

      The downside of this method is that the probe requires ACP tag, a relatively less used tag than others in biology, therefore researchers interested in using this probe should have their proteins with ACP tag. Moreover, the linker length and ACP-tag position are quite crucial parameters (and probably should be optimized for each protein). Longer PEG lengths cannot report on changes efficiently (Fig3b), while shorter lengths are prone to artefacts as they can go out of membrane (Fig1 and Fig2). This might limit its widespread use.

      Referees cross-commenting

      I agree with all comments and concerns of other reviewers. I see the usability and potential of this new technology along with its limitations as all three reviewers pointed out.

  24. May 2022
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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, the authors address the important topic of post-transcriptional gene regulation using the larval nervous system in Drosophila. They utilize a novel approach taking advantage of existing protein trap library, which permits use of the same smFISH probe to detect an array of 200 RNAs and visualize their corresponding protein expression. Furthermore, the authors developed a computational pipeline to visualize and analyze the resulting data, which should enhance the application of this method by other researchers. A major strength of the data comes from the analysis of multiple cell types in distinct compartments of the nervous system, cell types (neuron, glia, neuroblast), and subcellular domains. From the cumulative data, the authors are able to describe several interesting observations relating to cell-specific post-transcriptional regulation, regulation within a central-neuroblast lineage and glial post-transcriptional regulation, among others.

      However, in spite of these strengths, there are several concerns related to the organization and interpretation of the manuscript that the authors should address in order to improve the manuscript:

      General concerns:

      1. The approach relies on gene traps that often fail to be made homozygous, presumably due to deleterious function of the YFP insert. This is an obvious limitation of the study, which the authors address, but do so insufficiently by only analyzing a single case Dlg1. The authors should report how many of the 200 YFP-traps can produce viable homozygous animals, whether phenotypes can be observed, and any other relevant information to assess the functional properties of the tagged genes.
      2. The term "discordant" is used for non-congruous RNA/Protein levels in soma and distal processes, and sometimes the two are analyzed in the same figure (e.g Fig 3A). When it is stated that 98% of genes are discordant, this is an over-simplification as what the authors describe as "discordant" is expected to occur frequently in the distal process, but less often in the soma (which is what the authors find when presenting the data for individual compartments - Fig 3B-C). This is confusing because the observation means completely different things in the two compartments, though both are interesting to describe. These analyses, and their interpretation, should be kept separate.
      3. There is not enough emphasis placed on the cell-type specific regulation of RNAs. There are very few studies that have investigated how localization of individual RNAs changes in different cell types or regions of the nervous system, and the authors find that this is quite prevalent. Therefore, the rather superficial analysis of these data fails to take advantage of a major strength of the data. For example, for the discordant genes that differ in neuropil localization between different regions of the CNS, what types of molecules do they encode, what is their function in neurons (if known), and why might they be required locally in one region of the CNS but not the other?
      4. The authors conclude that mRNA and protein co-localization in glia processes shows that mRNA localization makes a major contribution of the proteome in processes. However, there is not enough evidence for such conclusion since neither translation of these mRNAs nor lack of protein trafficking from the somas was shown.
      5. An important caveat of this technique that should be discussed is the lack of knowledge about the translation of these mRNAs, if the mRNA that is being detected is the same as the one that is translated. While the authors emphasize the discordance between mRNA and protein localization, it is not possible to know whether these mRNAs are being translated where they are found, e.g. soma vs neuropil. Moreover, there are many examples (e.g. BDNF) where the isoform influences the subcellular localization of the mRNA. There is no way of studying the isoforms here, and we could be looking for a different mRNA isoform localized to a specific compartment compared to the protein. These points must be discussed.

      Minor suggestions:

      • The authors should identify GO terms to understand what types of molecules are subjected to RNA regulation. They provide a supplementary table for all genes, but it would be useful to have a chart showing the proportion of different GO terms represented in the overall gene set, genes that show cell-specific regulation, genes that show neuron vs glia specific regulation, etc.
      • "However, post-transcriptional regulation can also manifest itself within a cell, so that a protein is localised to a distinct site from the mRNA that encodes it". While subcellular RNA localization may represent a regulatory layer, I do not agree that proteins that function in the cell at a different location than their translation site represents regulation per se. Many such cases exist for proteins that are trafficked!
      • "The majority of individual puncta appearing in the dlg1::YFP line (51% in the brain, 64% in larval muscles". Why is the agreement between YFP and endogenous FISH so low? Do many individual RNAs fail to hybridize? This should be discussed.
      • "However, one gene, indy, is highly transcribed in neuroblasts and a single ganglion mother cell before it is rapidly shut off (Figure S1A)". This figure does not exist. Where are the data?
      • The authors should be consistent about calling perineurial or perineural glia (both correct) in their images and text.
      • "We only observe a minority of localised axonal mRNAs that lack the protein they encode at the axon extremities, in contrast to our findings in the mushroom body, optic lobe, and ventral nerve cord neuropils" These results are not contrasted, as in all neuropils the minority of localized mRNAs are those lacking their corresponding proteins. For example, 9% in NMJ vs 7.5% in OL neuropil according to Fig. 1B. What is conflicting with the conclusion?
      • "These results suggest that motor axons are more selective than the other neuronal extensions in the mRNAs that are transported over their very long distances from the soma to the neuromuscular synapse" The current literature says that the same mechanism (cis-elements) is used to transport mRNAs to subcellular compartments, which would be inconsistent with the idea of motor axons being "more selective" than other neurons for the same mRNA, but just a result of fewer mRNAs being found in motor neurons: 34.% of the mRNAs are found in motor neurons soma vs 83% in OL soma, 86.5% in VNC soma, and 70.5% in MB soma. To get to this conclusion, the authors should show that mRNAs previously found in the neuronal extensions of other neurons are not found in the axons of motor neurons but are still expressed in thesir somas. They might want to suggest different RBPs involved in the transport or discussing the very long distance they need to travel which can influence their detection in the tips. Figures
      • Figure 1. Experimental approach summary
        • Some colors do not show well and should be changed, e.g: grey in Fig. 1A, and Fig. 1B probe sites indicated in light blue and pink within the introns of dlg1.
        • Fig. 1E': There appears to be a large discrepancy in co-detection % for CNS and muscle in the graph judging by the size of circles, yet in the text, it is stated that there is average of 51% and 64% in the two, respectively. I don't see any green circles with over 25% agreement in the graph. Are the colors correct here?
        • Fig. 1D-I: It's difficult to identify where the zoomed panels come from. E has its own square (indicating zoom in E'). Please make this square dashed or a different color in E so it is clear F and G do not come from there.
        • Comparing Fig. 1F vs K: Why does there appear to be so much more dlg1 mRNA in the YFP-tag condition? If this is due to selection of imaging area, please choose a more similar region to image so the RNA levels are comparable. Otherwise it indicates the YFP-tag line has more RNA expression, which is likely not the case.
      • Figure 2. Analysis pipeline overview
        • The lines for the first two zoomed panels are switched: The optic lobe is going to VNC and vice-versa.
      • Figure 3. Overall summary of results
        • Figure 3A: Soma/Neuropil/muscle should be separate or at least ordered such that they are next to each other to facilitate direct comparison of genes in the same region of the cell in neurons from different CNS areas. Why are glia not included in this summary? A third color should be used to indicate when there is neither mRNA nor protein expression.
        • "Compiling all the information together shows that there are that 196/200 or 98% of the genes show discordance between RNA and protein expression" However, 5 genes shown in Fig. 3A do not show "discordance": CG9650, cup, Lasb, rg, and vsg!!
      • Figure 4. Neuroblast lineage analysis
        • Is clustering around the NB sufficient to determine lineage relationship? There seems to be other neurons around the NB.
        • More examples should be shown for the post-transcriptional category, as it is the most interesting category, and there are many different possible outcomes. Are there cases of transcriptional control and post-transcriptional regulation? Are there cases where the youngest neurons (closer to the NB) in the progeny are expressing the protein while the oldest are not? If not, could this be an artifact from a slow translation and the protein being detected only after building up in the cell? Top1 protein (Fig. 4D) seems to be less expressed in the youngest neurons.
        • "The transcription rate of these genes, as indicated by the relative intensity of smFISH nuclear transcription foci, is similar across the neuroblast lineage, however protein signal is only detectable in a minority of the progeny cells (Figure 4E)". Many nuclei lack clear large spots, but have small spots indicative of RNA; how is this interpreted? Do they lack transcription, or is this due failure of the smFISH to capture all transcription sites? Were transcripts actually counted to assess cell-specific differences? This should be possible with smFISH
      • Figure 5. RNA synaptic localization
        • A have global analysis comparison of all neuropil areas would be welcome in this figure.
        • "Surprisingly, another 59 transcripts are present at synapses without detectable levels of protein (Figure 5E-H)" This text does not correspond to Fig 5E-H but 5I-L. Where is the text about 5E-H?
        • For Fig. 5J and 5N RNA appears scattered regularly throughout the entire panel area. How sure are the authors that this is not due to poor signal/noise? For example, perhaps too much probe being used for these targets.
        • Fig. 5R is not cited in the text.
      • Figure 6. RNA localization in glia
        • For Fig. 6B-G it is hard to tell if there is any overlap of the RNA and Glia. Maybe show multiple zoomed-in merged images and/or highlight the structures with lines that are present in all panels.
        • For Fig. 6L-O: How reproducible is this small amount of RNA puncta in the NMJ glia? Is this possibly biologically important?
        • Why do cartoons labelling subnuclear/perinuclear glia in Fig.6 and Fig.S6 show different localization?
        • The cartoons seem to extrapolate from the data: While in Fig 6B-D, we see neither the big bright spot of transcription in the glial nucleus nor as many transcripts in the neuropil, they are both present in the cartoon. In Fig. 6E-G there is no indication of cortical glia soma nor the transcription spot only in glia nuclei.
        • "To assess glial localisation for the 200 genes of interest, we used a pan-glial gal4 driving a membrane mCherry marker (repo-GAL4>UAS-mcd8-mCherry) to learn the expression pattern of all glial cells, and then classified the pattern in the YFP lines (without the marker) based on knowledge of that expression pattern. We validated this approach by combining the RFP marker" Did the authors use mCherry or RFP for these experiments? Also, the previous sentence is redundant.
      • Figure 7. RNA localization at neuromuscular synapse
        • RNA for these genes seems far too spread throughout the muscle to draw any conclusions
        • Also with so many RNAs distributed in the muscle, specific localization of RNA molecule to the precise PSD would have no conceivable benefit
        • I suggest drawing lines around the protein expression to facilitate visualization of the mRNA localization for panels B, F and J. It is especially hard to conclude anything from panels B and F.
        • Light grey with white dots is hard to see in the cartoons
      • Figure 8. Role of khc and activity in sgg localization
        • Presumably there is a huge number of developmental problems associated with this mutant that could cause decrease in sgg localization
        • If the authors include this, then they should characterize the mutant NMJs: what is the change in size, synapse number, etc..
        • Is there more sgg accumulated in soma as a result of less transport? Is sgg being expressed at the same level?
        • Fig. 8F-H: Why is Dlg1 accumulated in the entire axon, not just the presume synapse?
        • Fig. 8J: Why is sgg signal occurring in circles disconnected from the main axon? The authors should show a different image

      Significance

      This is a significant and complex paper that contributes with novel tools to an important issue

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      Reply to the reviewers

      Manuscript number: RC-2021-01219

      Corresponding author(s): Rajan, Akhila

      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.

      The goal of this study is to:

      • Define how prolonged exposure to a high-sugar diet (HSD) regime alters both the lipid landscape and feeding behavior.
      • Determine how changes in lipid classes within the adipose tissue regulates feeding behavior. Key findings:

      In this study, by taking an unbiased systems level and genetic approach, we reveal that phospholipid status of the fat tissue controls global satiety sensing.

      Impact of Key findings:

      By uncovering a critical role for adipose tissue phospholipid balance as a key regulator of organismal feeding, our work raises the possibility that the rate-limiting enzymes in phospholipid synthesis, including Pect, are potential targets for therapeutic interventions for obesity and feeding disorders.

      Peer review comments:

      This study has immensely benefited from the thoughtful peer-review of three reviewers. As per their recommendations, we have performed a major revision by performing additional experiments (see summary table below in next section) and strived to address the major concerns raised. Based on our reading, there were two major concerns that overlapped between all three reviewers raised. They are as follows:

      • Does the genetic disruption of Pect in fly fat body alter phospholipid levels? Two reviewers (#2 and #3) recommended that we perform lipidomic analyses on adult flies with adipose tissue specific knockdown of For the revised version, we have completed this lipidomic experiment, and present results as a new main Figure 6, Supplemental S7 and S9.
      • Is the dampened HSD induced hunger-driven feeding (HDF) behavior because of increased baseline feeding (#1 and #3)? In addition, reviewer #1, asked us whether HSD flies experience an energy-deficit? In other words, we were asked to uncouple whether what we observed was HSD-driven allostasis or indeed, as we had interpreted, that HSD dampened hunger-driven feeding response.

      Hence, they recommended that we:

      1. Re-analyze our hunger-driven feeding datasets and present non-normalized data (also requested by Reviewer #3) and show baseline feeding behavior on HSD. To address this, we have completed this analysis and present our results in Figure 1B-D and S1.
      2. Determine whether the HSD fed flies display an energy deficit on starvation. To this end, we performed an assayed starvation-induced fat mobilization on HSD, results for this are now presented on Figure 1E-G and S2. Conclusions after the revision:

      First, it is important to note here that the additional experiments have not caused a significant revision of the major conclusions of the original version of our study. In fact, we hope that the revised version provides clarity and further substantiation to our original arguments.

      • The lipidomics experiments on Pect fat-specific knock-down flies show that reducing Pect in fat-body causes a significant reduction in certain PE lipid species (PE 36.2 specifically- Figure 6B). This is consistent with a prior report on lipidomics of the Pect null allele by Tom Clandinin’s group (PMID: 30737130). Furthermore, we note that when Pect is knocked down in the fat body, there is a significant increase in two other classes of phospholipids LPC and LPE (Figure 6A). Together, this suggests that an imbalance in phospholipid composition in the absence of Pect activity in fat.
      • The starvation-induced fat mobilization experiments show that despite being fed a prolonged HSD, adult flies sense starvation and effectively mobilize fat stores, at a level comparable to Normal food (NF) fed adult flies, suggesting that even despite HSD exposure, adult flies experience an energy deficit on starvation.
      • In our non-normalized data, we find that the baseline feeding events are not significantly altered between HSD and NF-fed flies (Figure 1D). This suggests that the effects we observe are not due to an increase in the “denominator”, but a dampening of hunger-driven feeding on HSD. With regard to our original version, all three peer-reviewers found that the study was interesting, significant, important, and novel – Reviewer #1: “The work is potentially novel and interesting”; #2 : “I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing”; #3: “This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. I agree with all the reviewers points; potentially very interesting”. But had requested that we provide further substantiation and clarification.

      We sincerely hope that the peer-reviewers find that our revised version with additional new experimental datasets, improved data visualization, and the presentation of non-normalized raw data points, makes this study clear, compelling, and well-substantiated.

      • Point-by-point description of the revisions This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      Below we summarize in Part A, the key experiments that were performed to address the major concerns. In Part B, we provide a point-point response to each reviewer with embedded datasets.

      Part a:

      We performed several new experiments, including:

      • To address the primary concern of Reviewer #1 regarding whether the HSD flies have a similar energy deficit to Normal food (NF) fed flies, we performed analysis of stored neutral fat Triacylglycerol (TAG) reserves and how HSD fed flies mobilized fat stores on starvation. We present these results in Figure 1E-G, S2. These results show that HSD-flies despite accumulating more TAG (S2), breakdown a similar amount of fat reserves as NF-fed flies on starvation at any time-point (Figure 1E-G). This suggests that HSD-fed flies do sense and respond to energy deficit.
      • To address concerns of reviewer #2 and #3 on whether Pect genetic manipulation affects specific phospholipid classes, we performed lipidomic analyses. The table below summarizes the new 3 new figures and 4 supplemental figures (blue text are all new figure numbers and figure panels) and three new Supplementary files as per reviewer’s request.

      Figure #

      Main point

      New datasets in revision

      Companion Supplement

      1

      HSD alters feeding behavior, but flies still breakdown TAG on starvation.

      TAG storage and breakdown over longitudinal HSD shows that HSD and NF fed flies show similar levels of TAG breakdown on starvation, despite consistently elevated TAG on HSD. This supports the idea that flies do sense starvation even on HSD, but there is a uncoupling of the feeding behavior after Day 14. Revised the data representation of Figure 1 to show non-normalized data over time. S1 and S2 companions are new in the revision. Panels 1D to 1E are new for the revision.

      S1- Raw data of feeding events plotted.

      S2 Elevated TAG at all time points.

      2

      HSD causes insulin resistance

      S3A added to show that insulin transcript levels remain the same in response to reviewer #3’s concerns.

      S3

      3

      Phospholipid concentration raw data from lipidomic on Day 7 and Day 14 HSD suggest that PC, PE levels are increased on Day 14 HSD.

      Figure 3 revamped to show new data visualization and non-normalized raw data to address Reviewer #2’s major concerns. S4A and S4B added. In addition Supplementary File 1 and 2 provided with raw lipidomics data as per reviewer #2’s request.

      S4.

      S4A- non normalized raw data of all other lipid classes on HSD.

      S4B- fatty acid species data on Day 14 added as per request of rev.#2.

      4

      HSD regulate Apo-I levels in the IPCs and phenocopies Pect KD.

      Added Figure 4A to show that HSD phenocopies Pect-KD in terms of delivery to brain

      S5 showing the validation of the Apo-I antibody.

      S6 validation of Pect KD and over-expression and Pect mRNA levels dysregulation on HSD.

      5

      Pect RNAi is insulin resistant

      N/A

      N/A

      6

      Pect knockdown shows significant increase in LPC and LPE, and a non-significant reduction in PC, PE levels. Specifically, the PE lipid class PE36.2 is downregulated.

      Fig 6, S7, S9 are completely new based on reviewer #2 and #3 requests. In addition Supplementary File 3 provided with raw lipidomics data as per reviewer #2’s request

      S7, S8, S9#.

      S7- new Pect KD other classes

      S8- new PE classes for day 14 and Pect associated classes.

      S9- Pect OE lipidomics

      7

      Pisd and Pect activity in adipocytes are required for hunger-driven feeding behavior in normal diets

      Pisd RNAi data was moved from supplement to main figure.

      N/A

      Note on revised text: We have revised text not only in the results section, but also as per reviewer #2’s recommendation, we have revamped our introduction and discussion as well. Since the manuscript has been significantly revised to include a main figure 6, fully altered Figure 1 and 3, multiple new supplemental figures, the changes in text are extensive. Hence, they are unmarked in the main text. Nonetheless, we hope that the reviewers will be able to evaluate these changes, as we have provided the specific locations in text and embed key figures in the point-point response below.

      __Part B: __Point-Point responses to reviewer comments.

      Reviewer #1 comments in Blue, author response in black.

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

      In this manuscript, Kelly et al. show that the difference between the feeding behavior of fed and starved flies (hunger-driven feeding; HDF) is absent in animals fed a high-sugar diet (HSD) for two weeks or more. The disappearance of HDF with HSD coincides with changes in phospholipid profiles caused by HSD. Furthermore, RNAi-mediated downregulation of Pect in the fat body-a key enzyme in the PE biosynthesis pathway-phenocopies physiological effects of HSD. Moreover, downregulation or overexpression in the fat body abolishes or induces HDF, respectively, abolishes or induces HDF, respectively, independent of HSD treatment.

      Overall, the manuscript is well-written and the phenotypes are clear. However, I have major concerns regarding the authors' interpretation of the data and their conclusion. Most importantly, while it is clear that the authors' high-sugar dietary treatment affects feeding behavior and physiology, I am not convinced that the changes can be considered "hunger-driven"-which is central to the main point of the manuscript. Therefore, it is my recommendation that the authors substantially revise the manuscript by either showing additional/re-analyzed data that rule out alternative hypotheses, or rewriting the manuscript keeping alternative interpretations in mind.

      We are thankful to this reviewer for their thoughtful critique, and constructive and specific suggestions on how we can redress these concerns. We have taken on board the concerns of this reviewer regarding our interpretation of whether the changes in feeding behavior can be considered hunger-driven or not. Based on their advice, we have made significant changes by addressing: i) does HSD increased baseline feeding- we now show non-normalized raw data and data supports conclusion that baseline feeding is not higher; ii) whether HSD- fed flies can sense an energy deficit at levels similar to NF fed flies- we show that HSD flies sense energy deficit. We have provided detailed response below, and we hope the reviewer finds the additional datasets and re-analyzed data are consistent with the interpretation that prolonged HSD dampens starvation induced feeding. In addition to this key concern this reviewer has made a many other salient points that we have addressed with additional data or by clarifying the text.

      Major comments: 1) The data do not sufficiently show that the long-term HSD regime disrupts "hunger-sensing." The manuscript should address alternative hypotheses by showing raw instead of normalized data, rewriting the manuscript with a new central conclusion, or running additional experiments that actually show a defect in hunger-driven response. a. The main results that the authors rely on for the argument is that the ratio of feeding events that the starved and non-starved flies eat is different between the groups fed normal or HSD. However, because the authors only show normalized data (normalized to non-starved flies; Fig. 1), it is difficult to tell whether the change is due to a chronically increased feeding in non-starved HSD flies-maybe in perpetual hunger-like allostasis-or dampened starvation response. Indeed, the data shown in Fig S1 show that flies fed HSD for as short as 5 days show more frequent feeding events compared to age-matched controls fed normal food. It is possible that because the HSD-fed flies eat more than NF-fed flies, even without being starved, the ratio of starved/non-starved feeding is lower in the HSD-fed group-due to changes in the denominator, rather than the numerator.

      We have taken onboard this concern regarding presenting only normalized data, and that clouded the interpretation and left open other possibilities. In the completely revised figure 1 and S1. We now show non-normalized data, as a function of time. First we note that HSD-fed flies, do not show higher baseline feeding that NF fed flies, except on Day 10 of HSD, when there is a modest but significant elevation (Figure 1D).

      Nonetheless, on Day 10 HSD, flies still display increased hunger-driven feeding HDF (Figure 1C), it is only after Day 14 HSD that HSD dampens the starvation induced feeding.

      1. It is also possible that the HSD-fed flies are simply not in as big an energy deficit physiologically, due to the increased fat deposits they've accumulated (as the authors show later in the manuscript). It may take longer for the fat HSD flies to reach substantial energy deficiency than the NF flies, but they still may eventually be able to appropriately respond to hunger, just like NF flies. In such case, it would be a misnomer to call this behavioral change a 'defect in hunger-driven feeding behavior.' Maybe an experiment with a dose-response curve of "hunger driven feeding response" as a function of duration of starvation would help? Prompted by this reviewers question, we asked whether HSD fed flies, that have a higher baseline neutral fat store (Triacylglycerol-TAG) level, and if HSD-fed flies can sense energy deficit. For this, we revisited the longitudinal assays for neutral fat triacylglycerol (TAG) storage that our lab had generated, along with the HSD-HDF studies. We now present this evidence as Figure 1E-1G and Figure S2. Overall, our experiments point to the idea that adult flies fed HSD, are able to sense and mobilize TAG stores effectively throughout the 28-day time point that we analysed.

      First as shown in Figure S2, flies fed HSD display an increase in TAG levels. But it is to be noted that while TAG stores increase, the increase is not linear with time. This suggests that adult flies exposed to HSD store excess energy as TAG, but the increased TAG stores stay within a certain range despite the length of HSD exposure. This suggests that adult flies on HSD still display TAG homeostasis.

      Next, to directly address the reviewers point about HSD fed flies not sensing an energy deficit, we subject HSD-fed flies to an overnight starvation, same regime as used in the overnight feeding experiments, and asked whether they mobilize TAG. We noted that flies exposed to HSD breakdown TAG throughout the 28-day exposure at statistically significant levels for Day 3- Day 28, except on 14 and 21 days (Figure 1F). While there is TAG mobilization on Day 14 and 21, the difference is not statistically significant. Nonetheless, we note the same levels TAG breakdown for normal lab food (NF) fed flies on Day 14 and 21 (Figure 1E). Overall, HSD fed flies sense and display energy deficit, as measured by TAG store mobilization, throughout the 28 days of HSD exposure, at levels comparable to NF-fed flies (Figure 1G).

      Taken together, these results suggest that while HSD-fed flies experience an energy deficit on starvation, at levels comparable to NF-fed flies, throughout the 28-day time point assayed. But, their starvation driven feeding-response is dampened by Day 14 and by Day 28, the HSD-fed flies display more feeding events than HSD starved flies. These results are consistent with the interpretation that in HSD-fed flies the starvation-induced feeding behavior becomes desynchronized from the starvation induced TAG-mobilization, suggesting that there is an absence of hunger-driven feeding.

      2) How can you be sure that lower Dilp5 immunofluorescence is indicative of increased Dilp5 secretion? Wouldn't decreased production of dilp5 also have the same results?

      It has been shown previously in HSD fed larvae are hyperinsulinemic, i.e., they have 55% increase in circulating Dilp2 ( PMID: 22567167). Additionally, we have shown that ectopic activation of the insulin-producing neurons by expressing TRPA1, an ion channel that activates neurons, reduces Dilp5 accumulation without a change in Dilp5 mRNA levels (PMID: 32976758), suggesting that reduced Dilp5 accumulation, without alterations to mRNA levels is a proxy for increased secretion. Now, in response to this concern, in the revised manuscript, we have added qPCR data of Dilp2 and 5 (Figure S3A), which show no difference in expression levels after 14 days on HSD. Therefore, there is no dip in Dilp5 mRNA production. Given that Dilp2 and Dilp5 mRNA levels remain the same, but we see reduced Dilp5 accumulation, we interpret this to mean that Dilp5 secretion is increased.

      1. Also, the authors should state in the main text that it is Dilp5, not just any Dilp. Thanks for this suggestion and we have fixed this and referred to Dilp5 specifically throughout the text in the results section.

      3) Data presentation: a. Sometimes the data are normalized to NF (Fig 4B-C), sometimes not (ex. Fig 4A, S4C). Unless there is a specific rationale for the data transformation, it would be more appropriate to show untransformed data (ex. Fig 4A, S4C), especially as the authors use two-way ANOVA to determine significance. Only showing the differences implies comparison against a hypothetical mean (i.e. μ0=0), not between two group means.

      We thank the reviewers for bringing this issue to our attention. We updated all the figures to show untransformed data in the revised manuscript.

      1. Some figures show both individual data points and summary statistics (mean, SD, ... ex. Fig 2A)-which I believe is ideal-but some show only one or the other (ex. Fig 2B, no summary statistics; Fig. 3, no data points. The manuscript would read more convincing if data visualization is consistent across figures. We thank the reviewers for their feedback. We have made changes to all the figures in the revised manuscript to improve visual consistency.

      Minor comments: 1) High sugar diet: what is the actual sugar concentration in the NF v. HSD diets? The authors write that the HSD diet contains "30% more sugar" than the NF, but providing the final sugar concentrations-sucrose or others-would be informative for other scientists studying the effect of high sugar diets.

      We thank the reviewer for their suggestion and now we have updated the methods to include this sentence. After 7 days, flies were either maintained on normal diet or moved to a high sugar diet (HSD), composed of the same composition as normal diet but with an additional 300g of sucrose per liter”.

      1. Additionally, the definition of HSD is inconsistent. Main text (Page 5, line 17) states that their HSD is "60% more sugar than normal media," whereas the figure legend (Fig 1) and the Methods state that the HSD contains "30% more sugar." We apologize for this egregious typo in the figure legend! We have now fixed this to say 30% HSD. Only 30% HSD was used throughout this study.

      2) Starvation medium: please provide justification for why the authors used 1% sucrose/agar for starvation medium, instead of plain agar/water that most labs use. At least clarify and provide a reference for the claim that the 1% sucrose/agar "is a minimal food media to elicit a starvation response."

      We are very grateful for this reviewer identifying this this methods description error and bring it to our attention. We used 0% sucrose agar for overnight starvation in this study as most labs do. The error occurred because we were using another manuscript from the lab to help draft the methods section (PMID: 29017032). In that study, where we assayed the effect of chronic starvation our lab used: “1% sucrose agar for 5 days at 25C”. However, in this current study, because we are testing acute effects of overnight starvation, we are using 0% sucrose agar.

      3) Pect mRNA level is higher with HSD. This is surprising because not only, as authors mention, is increased PC32.2 with HSD suggests lower Pect activity, but also because Pect RNAi phenocopies long-term HSD in HDF behavior, lipid morphology, FOXO accumulation in fat body. The authors speculate that the data "likely shown an upregulation in an attempt to mediate the Pect dysregulation occurring at the protein level." If that were true, a western blot may be informative. Zhao and Wang (2020, PLoS Genetics) generated a Pect antibody that seems compatible with western blot applications. That being said, I don't think such data is critical for the manuscript. I mention this simply as a suggestion for the authors. a. page 8, line 22-23, did you mean to write "Given how PC32.2 is elevated after 14 days of exposure to HSD, we assumed that Pect levels would be low for flies under HSD," not "high?" Otherwise the subsequent 2 sentences don't make sense.

      We agree that the most confusing aspect of the study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, we have now performed lipidomic analyses on whole adult flies, when Pect is knocked-down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occur. They are:

      1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
      2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

      We agree that a western blot would be informative as well, but we were unable to obtain the reagent from Dr. Wang’s group, precluding us from performing this request. See email snapshot.

      To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

      Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

      Reviewer #1 (Significance (Required)):

      The work is potentially novel and interesting, but at this stage it's difficult to interpret what the phenotype signifies. Although the manuscript could be revised simply by modifying the text, experimentally addressing the concerns would significantly improve the work.

      In sum, we hope we have addressed the key concern for Reviewer #1 as to whether the behavior we report here is indeed a dampening of starvation-induced feeding, or an effect of increase in baseline feeding. We hope that by reviewing our non-normalized data, they can appreciate that it is the former. Also, we hope that Reviewer #1 appreciates that we have strived to address the concerns by additional experiments, to clarify our findings and improve the impact of the work.

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

      This intriguing manuscript by Kelly and colleagues uses the fruit fly Drosophila melanogaster as a model to understand how diet-induced obesity alters the feeding response over time. In particular, the authors findings indicate that chronic exposure to a high-sugar diet significantly alters the starvation-induced feeding response. These behavioral studies are complemented by a lipidomics approach that reveals how a chronic high sugar affects many lipid species, including phospholipids. The authors then pursue mechanistic studies that indicate phospholipid metabolism within the fat body appears to remotely affect insulin secretion from the insulin producing cells. Moreover, the changes in phospholipid abundance are associated with changes in insulin-signaling, including increased insulin secretion from the IPCs and elevated levels of FOXO within the nucleus.

      I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing, but a few follow-up experiments are required:

      We are grateful for the reviewers constructive, detail-oriented, and balanced feedback, and their recognition of the value of this study. Now, we have performed additional experiments to address the key concerns raised by all reviewers. We hope that on reading the revised version of our study, that the reviewer continues to feel positive about the message of this study and its potential impact.

      1. The key conclusions from the manuscript assume that manipulation of Pect expression levels alters phosphatidylethanolamine (PE) levels. However, the authors make no attempt to verify that the genetic experiments described herein actually affect PE levels. At a minimum, changes in PE levels should be verified for the Pect knockdown and overexpression lines. Similarly, there is no evidence that manipulation of either EAS or Pcyt2 induces the expected metabolic effects. I'm not asking that the longitudinal feeding experiments be repeated, simply that the authors measure the relevant lipid species, preferably with a targeted LC-MS approach.

      Prompted by this reviewer, we performed targeted LC-MS on whole adult flies, on normal diet, to assess lipid levels for fat-specific Pect-KD and overexpression. We decided to focus on Pect, as its knock-down even on normal diet causes a dampened hunger-driven feeding behavior (Figure 7A) and phenocopied a 14-day HSD feeding phenotype.

      We now present a new dataset in Figure 6. Two striking changes occur:

      They are:

      Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding decrease in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). It is to be noted that though overall levels of all PE species trend downwards, like the Clandinin lab study on Pect (PMID: 30737130), we did not find a significant change in the overall PC and PE levels.

      • Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3). On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

      Finally, fat-specific Pect-OE did not cause significant changes to lipid species (Figure S9). This could either be due to the fact that in fat-specific Pect-OE flies under normal food and that we were assaying whole body lipid levels and not fat-specific lipid changes. But to counter that, even a 60% reduction in Pect mRNA levels (Figure S6A), was sufficient to produce an effect on whole body phospholipid balance (Figure 6). Hence, we speculate that by maintaining a basally higher (7-fold higher Pect mRNA level Figure S6A), might allow 14-day HSD-fed flies to buffer the negative effects of HSD and we predict that it might take longer to disrupt the phospholipid balance and HDF response.

      We have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

      Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9), but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

      A central hypothesis in the study is that the HSD over a period of 14 days results in insulin resistant and that these changes are leading to changes in hunger dependent feeding. I would encourage the authors to determine if Foxo mutants are resistant to these HSD-induced effects on HFD.

      We thank the reviewers for this suggestion. However, given that dFOXO nuclear localization rather than expression levels regulate insulin sensitivity, we feel that disrupting dFOXO levels via mutation or knockdown will produce a plethora of indirect effects including developmental abnormalities (PMID: 24778227, PMID: 16179433, PMID: 29180716, PMID: 12893776). Our data suggest that chronic HSD treatment and Pect affect insulin sensitivity in fat tissue. However, we feel that investigating whether insulin sensitivity/FOXO signaling in fat tissue regulates feeding behavior is outside the scope of our work.

      1. In lines 25-30, the authors draw the conclusion that an increase in unsaturated fatty acid species is associated with the HSD and that these changes results in a more fluid lipid environment. While I agree with the model, the manuscript contains no evidence to support such a model. Either test the hypothesis or move the last line of the section to the discussion.

      We thank the reviewer for this important and insightful comment. We agree that the data we presented and discussed in the original version is at the moment speculative. Addressing the hypothesis that increase in unsaturated fatty acid species result in a more fluid lipid environment will require us to build tools and expertise. Hence, this hypothesis is better suited for exploration in a future study. Given this, we have moved this out of the results section into the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile” (See excerpt below from page 13, lines 24-35).

      In addition to changes in phospholipid classes, we found that HSD caused an increase in the concentration of PE and PC species with double bonds (Figure S4C and S4D). Double bonds create kinks in the lipid bilayer, leading to increased lipid membrane fluidity which impacts vesicle budding, endocytosis, and molecular transport14,92. Hence it is possible that a mechanism by which HSD induces changes to signaling is by altering the membrane biophysical properties, such as by increased fluidity, which would have a significant impact on numerous biological processes including synaptic firing and inter-organ vesicle transport.”

      Also, as per the reviewer’s guidance, given that we are speculating here, we have also shifted this dataset from Main figure 4 to supplement S4C and S4D.

      In addition, lines 25-30 state that FFAs are increased after 14 days of a HSD. Figure 3A shows the exact opposite - FFAs are significantly decreased in 14 day fed animals despite being elevated in the 7 day fed animals. This is an interesting result that warrants discussion. Moreover, I would encourage to examine the lipidomic data more carefully to ensure that the text accurately portrays the lipid profiles.

      We apologize for misstating that FFAs are decreased on 14-day HSD in the lines 25-30. It was an error and we have corrected this. We agree with the reviewer that the reduction of FFA on Day 14-HSD is an intriguing and unexpected observation that needs to be emphasized and further discussed. To this end, we have added figure S4B, wherein we have provided the difference in FFA concentration (by species) after days 7 and 14.

      Furthermore, we have discussed what the potential meaning of reduced FFA at Day 14 implies in page 12, lines 19-27 of the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile”. We have stated the following-

      We speculate that this reduction in FFA maybe due to their involvement in TAG biogenesis (PMID: 13843753). We were interested to see if the decrease in FFA correlated to a particular lipid species, as PE and PC are made from DAGs with specific fatty acid chains. However, further analysis of FFAs at the species level did not reveal any distinct patterns. The majority of FFA chains decreased in HSD, including 12.0, 16.0, 16.1, 18.0, 18.1, and 18.2 (Figure S4B). This data was more suggestive of a global decrease in FFA, likely being converted to TAG and DAG, rather than a specific fatty acid chain being depleted.”

      The processed lipidomics data should also be included as supplementary data table so that they can be independently analyzed by the reader.

      We thank the reviewer for this suggestion. As per the reviewers request, we have included the raw data as an attachment in our supplementary material (Supplementary Files 1-3.), so that interested readers can use the datasets generated in this study for future work and further analysis.

      Beyond these experimental suggestions, the manuscript needs significant editing for clarity. While I won't provide a comprehensive list, the authors need to provide accurate descriptions and annotation of genotypes (including w[1118], which is written as W1118), typos, and formatting. I've listed a few examples below:

      1. Page 3, Line 1 and 2: "...have been shown to impact feeding behavior and metabolism that leads to..." This is an awkward and grammatically incorrect sentence.
      2. Page 3, Lines 7-32 is one very large paragraph but contains concepts that should be broken down over at least three paragraphs.
      3. Page 3, Line 25: A description of the reaction catalyzed by Pect would be helpful for a manuscript focused on Pecte activity.
      4. Page 4, Line 10: "previously characterized method of eliciting diet induced feeding behavior." As stated in the text, the method is previously described yet the manuscript characterizing the method isn't cited.
      5. Figure legend 3 contains a random assortment of capitalized lipid species. Also, the names of lipid species are inappropriately broken into multiple names. Please use correct nomenclature throughout the manuscript.

      The list above is nowhere near comprehensive. The manuscript requires significant editing.

      We are grateful to the reviewer for drawing our attention to these errors. We have made significant edits to the revised manuscript to address the above-mentioned concerns, as well as made additional textual changes throughout and copyedited it. We hope that the reviewer will find the manuscript reads better and the clarity and preciseness is significantly improved.

      Reviewer #2 (Significance (Required)):

      I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The findings will significantly advance our understanding of how lipid metabolism links dietary nutrition with feeding behavior.

      Once again, we are grateful for this reviewer’s thoughtful critique and encouraging words regarding our work and its potential impact.

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

      Summary: This manuscript uses Drosophila to investigate how diet-induced obesity and the changes in the lipid metabolism of the fat boy modulate hunger-driven feeding (HDF) response. The authors first demonstrate that chronic exposure (14 days) of high sugar diet (HSD) suppresses HDF response. Through lipidome analysis, the authors identify a specific class of lipids to be elevated upon chronic HSD feeding. This coincided with the changes in expression of Pect, an enzyme that regulates the biosynthesis of these lipids. Modulating the expression of Pect specifically in the fat body affected HDF response.

      We thank this reviewer for their rigorous and thoughtful critique and for identifying a key issue with our original study pertaining to a gap in how Pect mRNA levels on 14-day HSD are elevated but the Pect-KD phenocopies the HDF. Now by performing whole-body adult fly lipidomic on fat-specific Pect-KD we have resolved this issue and provided clarity on role of Pect in maintaining phospholipid homeostasis and thus subsequently impacts hunger-driven feeding. We hope the reviewer finds that the revised manuscript provides further clarity to the functional link between Pect’s role in fat-body and hunger-driven feeding.

      Major comments: The author claim that the HDF response in HSD is distinct between early (5d, 7d) and chronic (day 14) HSD feeding. However, the data seem to indicate that HDF response is significantly decreased at all time points in HSD. For example, at day 5 HDF response was increased only 3-fold in HSD (Figure 1C) compared to around 50-fold increase in NF (Figure 1B). The scale of the Y-axis in Figure 1B and 1C is an order of magnitude different. Including the starved data (NFstv and HSDstv) in Figure S1, normalized to NF fed group, would better visualize the overall trends. Related to this, having the source data for the actual number of feeding events would be useful (e.g., to see the baseline changes in feeding in different time points in Figure 1 and the effect of genetic manipulations in Figure 7).

      As per the reviewers request, we now have modified our graphs to show source data (Figure S1) and show the raw feeding events.

      Then in the non-normalized graphs we plot, over a longitudinal time course, baseline and hunger-driven feeding events (Figure 1B-D). We also show that HSD fed flies do not display increased baseline feeding (Figure 1D) suggesting that the effect we see on HDF are no clouded by increased baseline feeding.

      Yes, the reviewer makes an important point that HDF response on HSD fed flies is of a lower magnitude than NF fed flies. We think that is a biologically meaningful observation, as it suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels.

      ­­Now we have included a paragraph in the Discussion, Page 11 Lines 23-27, that say the following to ensure the readers appreciate this salient point raised by this reviewer.

      *It is to be noted that the HDF response of HSD-fed flies (Figure 1C, Days 3-10) is of lower order of magnitude than the NF-fed flies. This suggests that that in addition to sensing an energy deficit and mobilizing fat stores (Figure 1F, 1G, S1), HSD fed flies calibrate their starvation-induced feeding to compensate only for the lost amount of fat. Overall, this suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels. *

      The association between fat body Pect level and phospholipid levels is not clear. Day 14 of HSD feeding shows high expression of Pect in the fat body and elevated levels of PC32.0 and PC32.2. The authors assume the high expression of Pect in the fat body is due to the compensatory response, but there are no data indicating downregulation of Pect levels at the earlier time points of HSD feeding. A previous study demonstrated that Pect mutant flies have lower levels of PC32.0 but higher PC32.2 (PMID: 30737130).

      We agree that one puzzling aspect of the original version of this study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, prompted by Reviewer #2 and #3 concerns, for this revised version we have now performed lipidomic analyses on whole adult flies, when Pect is knocked down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occu. They are:

      1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
      2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

      On day 14, HDF response was increased 70-fold in w1118 flies in NF (Figure 1B; w1118), but only 2.5-fold in lpp>LucRNAi control flies in NF (Figure 7A). This suggests that lpp-gal4 driver lines have a significant effect on HDF response. Using a different fat-body specific Gal4 line would be necessary to validate conclusions.

      Regards reduced HDF magnitude, in our experience using UAS-Gal4 reduces HDF response magnitude consistently and cannot be compared to w1118 which is more robust. To account for background differences, we use Uas-Gal4 with control RNAi. It clearly shows differences in HDF response on starvation, but Pect and Pisd RNAi does not (Figure 7A). Hence, given that this experiment internally controls for any changes in HDF response for UAS-Gal4>RNAi, we conclude that HDF response in disrupted in Pect and PISD KD (Figure 7).

      We only presented the Lpp-driver in our study, as this driver is the only fat-specific driver that has no leaky expression in other tissues, and is specific to fat as apolpp promoter used to generate this Gal4 line is only expressed in fat tissue (Eaton and colleagues, PMID: 22844248). Other widely used fat-specific drivers, including the pumpless-Gal4 (ppl-Gal4) driver has leaky expression in gut or other tissues (See Table 2 of this detailed study by Dr. Drummond- Barbosa https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642949/). If the reviewer is aware of a fat-specific Gal4 line, other than Lpp-Gal4, which has a highly specific expression in the fat tissue without leaky expression in other tissues, then we are happy to take onboard the reviewer’s suggestion and try that fat-specific Gal4 that they suggest.

      HSD feeding promotes Pect expression (Figure S3C) and global changes in phospholipid levels (Figure 3, 4). Therefore, shouldn't Pect overexpression (not Pect RNAi) in a normal diet mimic HSD feeding state and promote loss of HDF response? Conversely shouldn't knockdown of Pect in HSD rescue loss of HDF response?

      We agree that a puzzling aspect is that Pect mRNA levels are significantly elevated in HSD Day-14, but Pect-KD showed displays the inappropriate HDF response. As we have described in our response to this reviewer on Page 19, we believe that Pect-KD and HSD disrupt PE and LPE balance overall but in different ways. Whereas Pect-OE using cDNA expression in fat body does not cause a significant change to any lipid class (Figure S9), and our results suggest that basally higher level of PECT is likely to be protective on HSD with respect to HDF(Figure 7B).

      To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-33- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

      Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

      We would have liked to test Pect protein expression on HSD, but since we were unable to access antibodies for Pect published in a prior study (PMID: 33064773) from Dr. Wang’s lab (see Page 10-11, of response to Reviewer #1). Hence, we were unable to test how the proteins levels of Pect correlate with the 250-fold increase mRNA expression.

      In conclusion, we hope the reviewer appreciates that our results regarding Pect function are consistent with the main conclusion that achieving the right phospholipid balance between PE and LPE, is critical for an organism to display an appropriate HDF response.

      Minor comments: All graphs should plot individual data points and showed as box and whisker plot as much as possible.

      Thanks for this suggestion, we have added individual data points to the vast majority of figures in the paper. We have made exceptions to graphs such as seen in figure 1 and FigureS4B-D where we find individual data points add an unnecessary layer of complexity. We hope these changes provide additional clarity and strength to the claims made in this manuscript.

      Data for day 14 missing in Figure S4A and S4B.

      We have provided Day 14 for the PC composition and PE composition, due to changes in Figures, they are now S7A and S7B.

      Reviewer #3 (Significance (Required)):

      The interactions between diet-induced obesity, peripheral tissue homeostasis and feeding behavior is an interesting topic that can be addressed using Drosophila. This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. However, at this point, the functional association between fat body Pect level, global phospholipid level, and loss of hunger-driven feeding response in chronic HSD feeding is not clear.

      We hope the revised data, and discussion of the paper, provides well-substantiated functional association on the importance of maintaining phospholipid balance, driven by Pect enzyme, as a critical regulator of hunger-driven feeding behavior. As stated in the revised discussion, the key take home message of our manuscript is that on prolonged HSD exposure PC, PE and LPE levels are dysregulated, the loss of phospholipid homeostasis coincided with a loss of hunger-driven feeding. Following this lead on phospholipid imbalance, we then uncovered a critical requirement for the activity of the rate-limiting PE enzyme PECT within the fat tissue in controlling hunger-driven feeding.

    1. The only reasonable implementation options are JavaScript and PHP.

      I argue that PHP is not reasonable here. The only appropriate thing for this use case is (unminified) JS—or some other program text encoded as a document resource permitting introspection and that the user agent just happens to be able to execute/simulate.*

      • Just like the advocates of "a little jQuery", author here doesn't seem to realize that the use of PHP was the first step towards what is widely acknowledged to be messed up about the "modern" Web. People can pine for the days of simple server-side rendering, but there's no use denying that today's Web is the natural result of an outgrowth that began with abuses of the fundamental mechanisms underpinning the Web—abuses that first took root with PHP.

      * Refer to the fourth and sixth laws of "Sane Personal Computing, esp. re "reveals purpose"

  25. Mar 2022
    1. dlsoftex.com_0 : 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ormatika.hu/?wptouch_switch=desktop&redirect=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9666http://www.oldresianclub.org.ar/bloques/bannerclick.php?id=19&url=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9667http://www.oldtimer.ru/bitrix/rk.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9668http://www.olondon.ru/bitrix/rk.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9669http://www.omareps.com/external.aspx?s=www.dlsoftex.com%2Fsitemap.xml9670http://www.omoqlja.bassfishing.org/OL/ol.cfm?link=https://www.dlsoftex.com%2Fsitemap.xml/9671http://www.old.ventmax.ru/bitrix/rk.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9672http://www.omsk.websender.ru/redirect.php?url=https://www.dlsoftex.com%2Fsitemap.xml/9673http://www.onfirepro.com/frame_view.php?site_url=www.dlsoftex.com%2Fsitemap.xml9674http://www.offendorf.fr/spip_cookie.php?url=https://www.dlsoftex.com%2Fsitemap.xml/9675http://www.oneillreps.com/external.aspx?s=dlsoftex.com%2Fsitemap.xml9676http://www.oncoforum.ru/bitrix/rk.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9677http://www.onlineguiden.dk/redirmediainfo.aspx?MediaDataID=45b76158-9e7e-4bef-ac42-0e39f848670f&url=https://www.dlsoftex.com/sitemap.xml9678http://www.online-proxies.com/count_hits.php?url=https://www.dlsoftex.com%2Fsitemap.xml/9679http://www.oneclick.bg/openx/www/delivery/ck.php?ct=1&oaparams=2__bannerid=275__zoneid=51__cb=1e55a56a8b__oadest=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9680http://www.omnistor.pl/trigger.php?r_link=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9681http://www.onlinegreece.gr/?lang=eng&url=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9682http://www.onzelievevrouwetoren.nl/beleefamersfoort/link.php?site=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9683http://www.only-r.com/go?https://www.dlsoftex.com%2Fsitemap.xml/9684http://www.openindex.io/outlink?ssi=4282426198a584a2&url=https://www.dlsoftex.com%2Fsitemap.xml/9685http://www.oostende.net/cgi-bin/scripts_onetpages/in_frames.cgi?https://www.dlsoftex.com%2Fsitemap.xml/9686http://www.npb.scforum.jp/jump.php?uid=991&url=https://www.dlsoftex.com%2Fsitemap.xml/9687http://www.onplanlab.com/?exit=https://www.dlsoftex.com%2Fsitemap.xml/9688http://www.oppuz.com/redirect?application=avon&track%5Baction%5D=rclk&track%5Binfo%5D%5Baction_src%5D=sm&track%5Binfo%5D%5Buser_id%5D=5bccdd5170616ef1af028384&track%5Binfo%5D%5Betag%5D=d6208572-262b-4b34-834e-abc9575159b7&url=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9689http://www.openherd.com/adredirect.aspx?adType=SiteAd&ItemID=9539&ReturnURL=https://www.dlsoftex.com%2Fsitemap.xml/9690http://www.openherd.cn/adredirect.aspx?adType=SiteAd&ItemID=9510&ReturnURL=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9691http://www.optimaze.ru/forum/go.php?https://www.dlsoftex.com%2Fsitemap.xml/9692http://www.opsoftware.com/IT/ViewSwitcher/SwitchView?mobile=True&returnUrl=https%3a%2f%2fdlsoftex.com%2Fsitemap.xml9693http://www.orchidtropics.com/mobile/trigger.php?r_link=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9694http://www.orbt.ru/bitrix/redirect.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9695http://www.orenburg.websender.ru/redirect.php?url=https://www.dlsoftex.com%2Fsitemap.xml/9696http://www.optimsklad.ru/bitrix/rk.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9697http://www.orient-explorer.net/Redirect.asp?url=www.dlsoftex.com%2Fsitemap.xml9698http://www.optina-pustin.ru/redirect.php?to=https://www.dlsoftex.com%2Fsitemap.xml/9699http://www.osaka-kaisya-setsuritsu.com/column/?wptouch_switch=desktop&redirect=https://www.dlsoftex.com%2Fsitemap.xml/9700http://www.orth-haus.com/peters_empfehlungen/jump.php?site=https://www.dlsoftex.com%2Fsitemap.xml/9701http://www.orthlib.ru/out.php?url=https://www.dlsoftex.com%2Fsitemap.xml/9702http://www.osmarmarine.com/?goto=https://www.dlsoftex.com%2Fsitemap.xml/9703http://www.osa-defence.ru/bitrix/redirect.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9704http://www.otteryantiques.co.uk/link.php?url=https://www.dlsoftex.com%2Fsitemap.xml/9705http://www.otourdumonde.fr/?wptouch_switch=desktop&redirect=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9706http://www.ottawakiosk.com/ad.php?url=https://www.dlsoftex.com%2Fsitemap.xml/9707http://www.osnovit.com/bitrix/redirect.php?goto=https://www.dlsoftex.com%2Fsitemap.xml/9708http://www.ostrov77.ru/forum/away.php?s=https://www.dlsoftex.com%2Fsitemap.xml/9709http://www.ourhouse.dk/redirect.php?action=url&goto=dlsoftex.com%2Fsitemap.xml&osCsid=qi0u37q336fdpbcao38f6mdb209710http://www.ourhometown.ca/openx/www/delivery/ck.php?ct=1%26oaparams=2__bannerid=199__zoneid=6__cb=449b026744__oadest=https://www.dlsoftex.com%2Fsitemap.xml/9711http://www.optina-pustin.info/redirect.php?to=https%3A%2F%2Fwww.dlsoftex.com%2Fsitemap.xml9712http://www.ovachat.cz/redir.php?URL=https://www.dlsoftex.com%2Fsitemap.xml/9713http://www.outrest.ru/go.php?url=dlsoftex.com%2Fsitemap.xml9714http://www.overheadgaragedoors.com/redirect.php?ul_id=248&return_url=https://www.dlsoftex.com%2Fsitemap.xml/9715http://www.office-rental.net/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    1. Author Response:

      Evaluation Summary:

      This work provides new insights into how surface-exposed lipoproteins of Gram-negative bacteria reach their destination in the outer membrane. Authors find that the outer membrane protein complex Slam serves as a translocon for the lipoproteins and the periplasmic chaperone Skp mediates their targeting to Slam. This work may contribute to the elucidation of host invasion mechanisms by pathogenic bacteria, in which surface lipoproteins play an important role.

      Reviewer #1 (Public Review):

      Previously, using rigorous genetic, bioinformatic and cell-based biochemical analyses, the same group discovered SLAM1, an outer membrane protein in Neisseria spp., which mediates the membrane translocation of surface lipoproteins (SLPs) (Hooda et al. 2016 Nature Microbiology 1, 16009). Here, authors reconstituted this system in proteoliposomes using minimal purified components including the translocon Slam1 and the client lipoprotein TbpB. Authors further coupled the system to TbpB-expressing E. coli spheroblasts and LolA, the Slam1-specific periplasmic shuttle system. Using the digestion pattern of TbpB by Proteinase K as a readout, authors confirmed that Slam1 indeed served as a translocon for SLPs. As a step forward, authors found that Skp, a periplasmic chaperone (holdase), was critical to the membrane-assembly and translocation of TbpB. Strengths: Overall, this is a solid biochemical study that demonstrates the role of Slam1 as a translocon for SLPs. The experimental design is neat and straightforward. The specific role of Skp in SLP translocation is interesting. This reconstituted system will serve as a novel platform for further elucidation of the Slam1-mediated SLP translocation mechanisms. The manuscript is overall well written. Weakness: There are several major concerns, however. 1) It is not fully convincing whether these findings are novel and significantly advance the field. Identification of minimal components in a biological process and their reconstitution are always challenging and thus, this study is an achievement. Nonetheless, I am not sure whether we have learned novel molecular insights besides the confirmation of the group's previous discovery. The specific role of Skp in translocation is interesting but not surprising, considering that periplasmic holdases are already known to be extensively involved in the biogenesis of periplasmic and outer membrane proteins.

      We thank the reviewer for their time and thorough review of the manuscript. In the previous paper (Hooda et al. 2016 Nature Microbiology 1, 16009), we discovered that the outer membrane protein Slam is “important/responsible” for the surface display for SLPs (TbpB, LbpB, fHbp). In this mechanism focused manuscript, we were able to demonstrate Slam’s role as an outer membrane translocon. One of the achievements in this paper is to demonstrate that Slam as an autonomous translocon – importantly this is unlike the two-partner secretion systems, as it does not require the Bam complex for the translocation of TbpB.

      2) Although authors developed nice assays (Figs. 1 and 2), it was not verified whether TbpB protected from Proteinase K digestion had "correct" conformation and membrane-topology. Authors performed a functional assay on TbpB (Fig. 5a), but this result was obtained from a cell-based assay, not from the reconstituted system.

      We have performed pulldown assay for the TbpB that has been translocated into Slam-proteoliposomes using human transferrin conjugated beads to show that this TbpB protein is correctly folded and functional. Blots and explanations are attached in the revised manuscript (see new Figure 2 – figure supplement 2 and line 197-207). (As addressed in major scientific concerns point 2-i).

      Although the data in Figs. 1 and 2 clearly show that the membrane association of TbpB depends on Slam1, it does not mean that the "translocation" has actually occurred in the proteoliposomes. Probably, more rigorous analysis on the Proteinase K-protected portion of TbpB (for example, mass spec) seems necessary (that is, whether the proteolytic product is expected based on the predicted topology).

      The TbpB is flag-tag at its C-terminus and the protected band on our blots (detected by α-flag antibody) corresponds to the expected Mw (~75kDa) for Mcat TbpB flag tagged protein. Therefore, we believed the band at 75kDa is our full length processed TbpB. Moreover, we have confirmed that TbpB can be detected at the top of the sucrose gradient with our Slam-proteoliposomes in this assay. This would only occur if TbpB was actually translocated inside the intact liposomes, otherwise we should not see any TbpB in the top layer of the sucrose gradient (Figure 4d). Furthermore, we have performed a pulldown assay for TbpB in proteoliposomes to check for their functional binding to human transferrin beads after translocation. These results are explained in the updated new Figure 2 – figure supplement 2 and line 197-207.

      3) The manuscript has a couple of missing supporting data. 3a) Lines 87-89: "From our analysis, we found that the Slam1 from Moraxella catarrhalis (or Mcat Slam1) expressed well and the purified protein was more stable than other Slam homologs." I cannot find the expression and stability data of various homologs supporting this sentence.

      In general, what we meant was that we chose Mcat Slam as the target of this study because it is more stable during the purification and resulted in a higher yield of protein. We needed higher yields of Slam to be able to reconstitute the protein into the liposomes for the translocation assay. We have purification data for Mcat Slam1, Nme Slam1 and Ngo Slam2 but we think including them in the supplementary is not necessary. We have changed and rewritten this section dedicated to Mcat Slam1 purification (Figure 1 – figure supplement 1 and 2).

      3b) "Lines 216-219: Furthermore, the processing of TbpB by signal peptidase II and subsequence release from the inner membrane was unaffected suggesting the defect in surface display by Skp occurs after the release of TbpB from the inner membrane (Fig. 4a)." The result supporting this sentence seems missing or this sentence points to a wrong figure.

      Yes, this sentence is misleading. What we meant was that the processed TbpB (TbpB has 2 bands, unprocessed TbpB – upper band and signal peptidase processed, lipidated TbpB - lower band) is similar for all samples indicating that the knockout of Skp did not affect the expression or processing of the signal peptide of TbpB up until it is ready (processed and lipidated in the periplasm) for translocation by Slam to the surface. We have added an explanation in the figure legend of Figure 4a –line 267-269.

      4) Some statistical analysis results are not clear, making some conclusions not convincing. 4a) Figure 4a top "Exposure of TbpB on the surface of K12 E. coli" Apparently, all three data points for (Delta_DegP+Slam1+TbpB) are very closely distributed. Accordingly, (WT+Slam1+TbpB) vs (Delta_DegP+Slam1+TbpB) data look significantly different (difference is ~0.2). But the two data were assigned as "Not Significant". Similarly, in the comparable in vitro data (Figure 4b), the intensity for Slam1 (WT+Proteinase K - Triton) looks larger than that for Slam1 (Delta_DegP + Proteinase K - Triton). So, the DegP contribution should not be ignored.

      For figure 4a, the ONE WAY ANOVA test was performed using Prism with 4 biological replicates (we can include the analysis report in the revised submission if this is requested we have updated the figure to include data points. In general, both our in vitro liposomes translocation assay and in vivo surface exposure assay for TbpB showed that delta-DegP only slightly reduces the translocation of TbpB to the surface but could not detect statistically significant differences.

      4b) Figure 5a top "Exposure of TbpB on the surface of N. meningitidis" What is the p-value for WT vs Delta_Skp data? Are the two data significantly different? The p-value range for (*) is not shown.

      We have included the p-value range for (*) in the revised manuscript, figure 5a.

      Reviewer #2 (Public Review):

      The article addresses the function of SLAM, a protein which the authors have shown previously to be involved in the traffic of lipoproteins to the bacterial surface. The authors have performed a series of experiments to assess the impact of SLAM on the delivery into proteoliposomes of the model lipoprotein TbpB either added exogenously or presented by E coli spheroplasts. They identify a periplasmic chaperone, Skp, which enhances transport of TbpB and other lipoproteins to proteoliposomes, and show the contribution of endogenous Skp to lipoprotein transport in Neisseria meningitidis. The authors set up an in vitro translocation assays using purified components from different bacteria. This is reasonable as the assays can be challenging to establish and require proteins that can be expressed and are stable. It would be helpful however if the sources of the proteins and how they are tagged (for their detection) is clearly documented in the article and the figures. In keeping with this, the figures describing the assays could be improved (ie 1A, 2A, 3A and C). Despite this, the results presented in Fig 1 and 2 clearly demonstrate the role of SLAM as a translocase, and the authors have included appropriate controls for their assays; the translocation of a OmpA to demonstrate that the Bam complex is functional in their hands in an important control and should be included in the main figures. Experiments outlined in Figure 3 and Table 1 demonstrate the interaction specific of TbpB and another lipoprotein HpuA with Skp, a previously characterised periplasmic chaperone. This is performed by pull-downs and MS as well as immunobloting. A critical result is shown in Figure 4 in which SLAM and TbpB are introduced into E coli, and the role of endogenous Skp is assessed. Importantly, the absence of Skp reduces but does not eliminate TbpB surface expression. The authors could speculate on the nature of Skp-indendent surface expression of TbpB, as this result mirrors what they find in a meningococcal strain lacking Skp (Figure 5A). It appears that Skp might be required for the correct insertion/folding of lipoproteins given their result in Figure 5B (currently, this could be changed into 5C) which tests the binding of transferrin to the bacterial surface. Clearly this could be influenced by an effect of Skp on TbpA, which acts as a co-receptor with TbpB. In summary, the authors have used appropriate assays to reach their conclusions about the role of SLAM as a translocase and the contribution of Skp to the localisation of lipoproteins to the surface of bacteria. The findings presented are robust and shed new insights into the sorting of proteins in bacteria, an incompletely understood process which is central to microbial physiology, viurlence and vaccines.

      Reviewer #3 (Public Review):

      Slam was identified as an outer membrane protein involved in the translocation of certain lipoproteins to the cell surface in Neisseria meningitidis. Slam homologs were also identified in other proteobacteria. However, direct evidence that Slam is an outer membrane translocation device is still missing. In this paper, the authors set up an in vitro translocation assay to probe the role of Slam proteins in the translocation of the lipoprotein TbpB. Although they provide strong data supporting the role of Slam in lipoprotein translocation, further molecular dissection is required to unambiguously establish Slam as a lipoprotein translocator. The work is interesting and the paper clearly written. The authors also discovered a functional link between the periplasmic chaperone Skp and Slam-dependent lipoproteins, which is a novel and interesting finding.

  26. Jan 2022
    1. SciScore for 10.1101/2022.01.11.475901: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The use of HEK293 cells in this study was approved by the University of Waterloo Research Ethics Board (ORE#42425). 2.2 Plasmids, transfections, and treatments: Plasmids expressing ORF8 protein (YP_009724396.1) tagged at the C-terminus with 3 x DYKDDDK tag (Ex-NV229-M14), ORF10 protein (YP_009725255.1) tagged at the C-terminus with 3 x hemagglutinin tag (Ex-NV231-M07), and M protein (YP_009724393.1) tagged at the C-terminus with green fluorescent protein (Ex-NV225-M03) were from GeneCopoeia (Rockland, MD, U.S.A).<br>Field Sample Permit: All work was performed in accordance with a Health Canada approved Cannabis Tracking and Licencing System Research License held by the University of Waterloo (PI: Dr. Robin Duncan).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, HEK293 cells were seeded (1 × 104 cells) in 96-well plates and transfected with the respective plasmids after 24 h, then treated a few hours after transfection with either CBD or vehicle for 24 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The control plasmid was pCMV-3Tag-3A (pCMV) (Agilent Technologies, Santa Clara, CA, U.S.A.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV</div><div>suggested: RRID:Addgene_16459)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Live cells were maintained at 37° C while fluorescence was recorded at 469/525 nm for the detection of pSIVA and at 531/647 nm for the detection of PI.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pSIVA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were grown in 24 well plates and transfected with either pCMV-3Tag-3A, or plasmids expressing ORF8, ORF10, or M protein, and then treated with either 2 μM CBD or vehicle overnight for 14 h, so that analyses were performed prior to measures of effects on cell number and apoptosis markers.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV-3Tag-3A</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Analyses were performed using Prism GraphPad 9 software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

      Results from TrialIdentifier: No clinical trial numbers were referenced.


      Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


      Results from JetFighter: We did not find any issues relating to colormaps.


      Results from rtransparent:
      • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
      • No funding statement was detected.
      • No protocol registration statement was detected.

      Results from scite Reference Check: We found no unreliable references.


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      About SciScore

      SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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  27. Oct 2021
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      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

      1. General Statements

      We want to thank all three reviewers for their positive feedback, constructive comments, and suggestions for clarity and improvement. We are delighted to find their consensus that the manuscript represents a contribution to the field.

      Accordingly, we made changes in the text (all highlighted in blue in the revised manuscript) and added a new figure as detailed in the point-by-point response.

      2. Point-by-point description of the revisions

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

      The authors describe results of the comprehensive analysis of the prevalence and functionality of intrinsically disordered regions of the pathogen-encoded signaling receptor Tir, which serves as an illustrative example of the bacterial effector proteins secreted by Attaching and Effacing (A/E) pathogens. This is an interesting and important study that represents an impressive amount of data generated computationally and using a broad spectrum of biophysical techniques. The work serves as a model of the well-designed and perfectly conducted study, where intriguing conclusions are based on the results of the comprehensive experiments. The manuscript is well-written and concise, and I have a real pleasure reading it. The text and figures are clear and accurate.

      We thank the Reviewer for these positive comments on our work.

      Although, in general, prior studies are referenced appropriately, the authors should mention that the pre-formed structural elements they found in Tir are in line with the concept of "PreSMos" (pre-structured motifs) previously introduced and described in several important studies from the laboratory of Kyou-Hoon Han.

      We thank the Reviewer for this suggestion. We have added a sentence to acknowledge the presence of “PreSMos” in the target-free state of Tir as putative signatures for target-binding, referring to a review article summarizing several local structural elements in unbound IDPs:

      “This supports the presence of pre-structured motifs (PreSMos) as pre-existing signatures for target binding and function within target-free Tir (72)**.”

      Please, note that we decided to keep this discussion to a minimum, as we cannot rule out the contribution of the induced fit model to the binding mechanism (i.e., disorder-to-order transition upon binding).

      Reviewer #1 (Significance (Required)):

      Solid evidence is provided that structural disorder and short linear motifs represent common features of A/E pathogen effectors. In fact, using a set of bioinformatics tools, the authors first show that although prokaryotic proteins typically contain significantly less intrinsic disorder than eukaryotic proteins, A/E pathogen effectors are as disordered as eukaryotic proteins. Using the translocated intimin receptor (Tir) as a subject of focused study, the authors then utilized a number of biophysical techniques to draw an impressive picture of disorder-based functionality. This study clearly represents a major advancement in the field of functional intrinsic disorder in general and in disorder-based functionality of proteins expressed by pathogenic bacteria. This was adds significantly to the field and will have a noticeable impact.

      Again, reading this manuscript was a real joy. Finally, this work perfectly fits in the area of my expertise, since for the past 25 years or so I am working on the different aspects of intrinsically disordered proteins.

      Thank you for this encouraging assessment.

      **Referee Cross-commenting**

      I agree with the amended recommendation of reviewer #3 to add in the manuscript EPEC O127.

      According to the suggestion of Reviewer #3, we have now included EPEC O127:H6 in the manuscript.

      I completely agree with comments of reviewer #2 and partially agree with reviewer #3. In my view, comparison of various strains as references for EPEC represents an interesting but independent project. It can be recommended to the authors as one of the potential future developments of their work.

      Thanks for the suggestion. We are pursuing that line of research.

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

      The general impression is that this is an excellent study that establishes

      The C-terminal intracellular region of Tir called C-Tir spanning residues 338 to 550 is largely disordered, however, observe helical structural elements involved with lipid interactions; multi-phosphorylation. The intracellular N-terminal part of Tir called N-Tir spanning residues 1 to 233 is also partially disordered but include a folded domain that is shown to assemble into a dimer

      The only major concern is that no SDS-PAGE gels or size exclusion chromatograms have been included to verify purity and monodispersed of the various constructs worked on. In particular, the SAXS and CD measurement is highly sensitive to purity, and the level of degradation as IDPs are notorious for being difficult to handle in solution. it would strengthen the arguments made based that

      We produced N-Tir and C-Tir as fusion proteins with a cleavable N-terminal thioredoxin tag (Trx-His6) and C-terminal Strep-tag. The latter allowed us to purify them via Strep-tag affinity chromatography as indicated by SDS-PAGE (please see Fig. S1).

      We agree with the Reviewer that even small amounts of impurities (i.e., higher oligomers/degradation) can interfere with the data analysis and make interpretation of the resulting data difficult and potentially misleading. So, to avoid such problems, all samples were purified in monodispersed forms by size-exclusion chromatography (SEC) before any biophysical study.

      Following the Reviewer's suggestion, we added a new supplementary figure (Fig. S5) showing the SEC-SAXS chromatogram profiles of C-Tir, N-Tir, and NS-Tir. Briefly, in the inline SEC-SAXS experiment, the sample eluates from an HPLC system directly and continuously into a BioSAXS flow cell for subsequent X-ray interrogation. Under our experimental conditions, C-Tir elutes as a single peak with Rg-values and mass compatible with a disordered monomeric protein, providing an excellent fit to the experimental SAXS curves. For N-Tir and NS-Tir, by SEC-SAXS, we separated the dimer from small amounts of high-order oligomers to yield the experimental SAXS curves of the pure dimers.

      “Fig. S5. SEC-SAXS chromatograms of (A) C-Tir, (B) N-Tir, and (C) NS-Tir. Each plane shows normalized total scattering intensity I(s), over the entire s range, from each frame acquired along elution volume and respective Rg-value (black circles). The flat variation of Rg reflects a pure monodisperse sample. The column type for size exclusion chromatography and sample concentrations are on the top left of each panel. For reference, the retention volume for monomeric BSA (66.4 kDa) is displayed by red triangles.”

      **Minor Comments**

      Read through the manuscript to remove passages with spoken language

      We thank the Reviewer for this suggestion. We went through the manuscript and improved the writing to reduce passages with spoken language.

      Line 263, "To do so", should be removed

      Line 290 "Our data thus" replaced with "this"

      We have amended the manuscript accordingly.

      Line 292 "lipid bilayers that might potentially fine-tune Tir's activity in the host cell." Weak sentence and the word fine-tune is slang. Rewrite the sentence. The interaction with lipids is fascinating!

      Thanks for the suggestion. The sentence has now been changed to “**This shows that C-Tir can undergo multivalent and tunable electrostatic interaction with lipid bilayers via pre-structured elements, suggesting that membrane-protein interplay at the intracellular side might control the activity and interactions of Tir in host cells.**”

      We also reinforce this fascinating message in the abstract by adding the sentence: “Membrane affinity is residue-specific and modulated by lipid composition, suggesting a previously unrecognized mechanism for interaction with the host.”

      Line 192 "In figure Fig. 3A," remove the Fig

      Fixed.

      Line 326, "In a similar fashion," is redundant. Rewrite the sentences below.

      We have modified the sentence as follows: “We evaluated whether the N-terminal cytosolic region of Tir (N-Tir; Fig S1) was also intrinsically disordered ...

      Line 342 add spaces between digit and SI unit "52kDa" there are more cases of this.

      Thank you for pointing this out. This has now been corrected to 52 kDa.

      Reviewer #2 (Significance (Required)):

      I expect this study to have broad relevance to microbiologists working with the intimin and translocated intimin receptor, in particular the lipid interaction is likely to be followed up by the community.

      We thank the reviewer for this comment. Indeed, we believe that further studies on Tir's lipid-binding ability as a novel molecular strategy in host-pathogen interactions, will potentially provide new insights on virulence, transmembrane signaling in general, and disorder-mediated functions.

      **Referee Cross-commenting**

      What reviewer 3 suggested in the comments sounds like added value and should be included.

      I agree with reviewer 1, that the strain comparison potentially is beyond the scope presented in this manuscript.

      We have now included EPEC O127:H6 in the manuscript.

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

      **Summary:**

      This interesting manuscript look at the structure of the Nter and Cter of the effector Tir from enteropathogenic E. coli. The authors confirmed previous study highlighting the "disordered" part of the Cter. However, the extended experimental work (NMR, Small-angle X-ray scattering and CD spectroscopy) from this study also reveals the connection between different area of Tir and its implication during Tir phosphorylation and its interactions with SH2 domain.

      We thank the Reviewer for this positive remark. Indeed, in our work, we highlight the structural features of the SH2-mediated interaction between Tir and host SHP-1 protein, and we also show that C-Tir is capable of lipid interaction via pre-structured motifs and that N-Tir is disordered but assembled into a dimer. Overall, we provide an updated and wide picture of Tir's intracellular side that goes beyond the scrutiny of previously described disorder features.

      **Major Comments:**

      The authors used E2348/69 (O127:H7) strain as a reference for EPEC. However, this strain are the least effectors of all the EPEC sequences and may over estimated the PDR in EPEC. It would be wiser to use a strain like B171 as a reference for EPEC to be able to conclude "Disordered Proteins (PDR) with long disordered regions occur in EPEC effectors similar to the human proteome". I believe that the PDR in EPEC is similar to EHEC and CR. I do not have any major concern for the rest of the work.

      We thank the Reviewer for this comment. So, to clarify, we amended “EPEC” with “EPEC O127:H6” in text and figures.

      We also added a paragraph at the beginning of the Discussion section to acknowledge that our prediction analysis concerns EPEC O127:H6 and two additional representative A/E bacteria strains:

      “Among the enteropathogenic Escherichia coli strains EPEC O127:H6 (E2348/69) is commonly used as a prototype strain to study EPEC biology, genetics, and virulence (69). Here, we have determined the structural disorder propensity of EPEC O127:H6 sequences and two additional representatives of A/E bacteria: EHEC O157:H7 and CR ICC168.

      Finally, the Reviewer suggests to include EPEC strain B171 (serotype O111:NM) in our analysis. We agree that considering additional strains would be of value, however we believe that this is beyond the scope of this manuscript, which mainly focuses on the characterization of the structural features of the E2348/69 Tir effector. We are currently working on a broader comparative analysis among different Escherichia coli pathogenic strains, including B171, and we hope to share our findings with the community in the near future.

      **Minor comments**

      Statistic problem: Mann Whitney U Test (Wilcoxon Rank Sum Test) is a comparison of two independent samples with the underlying assumption is normally distributed or that the samples were sufficiently large. It is not certain that any of this assumption is correct. In addition, the effector are part of the whole proteome. Can it be then considered that both groups are independent?

      We thank the Reviewer for this remark, which allows us to clarify the choice of this particular test. Indeed the Mann Whitney U-test is a non-parametric test to compare two samples with the alternative hypothesis being that one of the two samples is stochastically greater than the other. As it is a nonparametric test samples are not required to be normally distributed, as it is for the Student t-test.

      Regarding the independence of the samples, when comparing the effectors collections to their corresponding proteomes, we did exclude the effectors sequences from the latter. We have clarified this point in the Supplementary Material and Methods section.

      Line 120 and 442: O127 not H127

      Thank you for pointing this out. It has now been corrected to O127.

      Line 212: positions 409 or 405?

      Yes, it should be 405. Thank you.

      Reviewer #3 (Significance (Required)):

      **Nature and significance:**

      Tir plays a major role during EPEC infection. It is a signalling platform that has been reported to interact with multiple proteins. Whereas the extracellular part has been well characterised and crystallised, the intracellular part has been proven so far to be difficult to study. Over the last decade, no progress has been made to explain how Tir works. This manuscript provides interesting information that shade some light on how the protein could work.

      **Existing literature:**

      The last research manuscript trying to highlight the structural function of Tir dates from 2007 (PMC1896257). This study is far more extended and in depth than any other previous work done.

      **Audience:**

      the Audience may probably limited to researcher working on the field of cellular microbiology and the function associated with bacterial effector in the host. This study could be also a useful tool to identify new effectors base on their "disorder".

      We thank the Reviewer for recognizing the importance of this study. We agree that our work highlights the pivotal role of disordered regions in bacterial effectors, thus enabling a better understanding of the molecular mechanisms used by pathogens to subvert the host-cell processes. We indeed believe that our work can stimulate further research on the characterization of intrinsically disordered effectors, and also beyond the cellular microbiology field, in order to gain a broader knowledge on the molecular dialogue at the host-pathogen interface, which is essential to design better therapeutic strategies.

      **Expertise:**

      I have been working on A/E pathogens for the last 15 years with a particular interest in Tir signalling. My domain of expertise is more in relation to cell signalling than crystallography or structural study.

      **Referee Cross-commenting**

      I agree with both reviewers. My comment about EPEC is more about the conclusion for some of the figures. I don't think they should conclude for the whole EPEC. The Tir variation among EHEC O157:H7 is low, but it is far more diverse for EPEC. Simply adding in the manuscript EPEC O127 should be enough.

      We thank the Reviewer for this comment. As mentioned above, we now state in the manuscript, in both Results and Discussion sections, that we used E2348/69 as a representative strain for EPEC.

  28. Sep 2021
    1. Author response


      September 9, 2021

      We would like to thank ASAPbio for selecting our preprint for review! We are excited to contribute to this new process and hope others will find it as helpful as we have. The comments generated by the “crowd” were detailed and thoughtful. Below we respond to the major discussion points and if there were specific reviewer comments relevant to the discussion point, we also included that statement. We also responded to each specific comment. We would love to continue this discussion, so we invite further feedback and responses! Thanks so much for your time.

      -Chelsea Kidwell, Joey Casalini, and Minna Roh-Johnson


      Major Discussion Point #1:One of the most important claims is that mitochondria are the organelles responsible for the activation of the signals of cell proliferation. However, a previous report by the last author reported that macrophages transfer cytoplasm to recipient cells. It cannot be excluded that other organelles or cellular fragments are transferred as well and contribute to the observed effects (ERK activity). Perhaps a good way to solve this would be the use of macrophages that are devoid of mitochondria. At least, this aspect should be discussed in the manuscript.

      🡪 We had first considered two approaches to test the requirement and sufficiency of macrophage mitochondria in cancer cell proliferation. The first was to generate rho-zero macrophages (mtDNA-deficient), as you mention in your comment, such that the macrophages did not have functional mitochondria. However, we use primary human macrophages for all of our studies, and these cells would not survive long enough to generate rho-zero cells (which requires that the cells be treated with low levels of ethidium bromide for weeks). The second is to biochemically purify mitochondria from macrophages and directly inject these mitochondrial preps into breast cancer cells. We actually did this experiment, and cancer cells injected with purified mitochondrial preps exhibited higher proliferation (by live timelapse microscopy) compared to control cells. However, we also found that the mitochondrial purifications were not clean, and contained other membranous components in the cytoplasm. We tried centrifugation-centric approaches, as well as IP-ing against a mitochondrially-localized tag, but in all cases, the mitochondrial preparations contained other cytoplasmic components. Therefore, we did not feel that this approach was an adequate way to test effects of specifically the mitochondria. We certainly wanted to discuss this aspect in the manuscript, but unfortunately, we were limited due to space. If folks have suggestions on how to best purify mitochondria, we’d love to know, so please reach out.

      However, in terms of the bigger question of whether the induced proliferation in cancer cells is specifically due to ROS accumulation in transferred macrophage mitochondria, we tried to address this question with the mito-KillerRed experiments, where we generate ROS using optogenetics, and ask whether this accumulation is sufficient to induce cancer cell proliferation (which we showed it was). We also showed that this same approach could induce Erk activity, and then in separate experiments, we show that macrophage mitochondrial transfer results in accumulation of ROS and increased Erk activity. We feel that these experiments support our conclusions, however, we’d love for a way to link it all together. Unfortunately, we are not convinced that such experiments are possible at this time.

      Major Discussion Point #2: Most of the positive examples of transferred mitochondria discussed appeared in a small clump. However, there also appears to be another population that was more diffuse and co-localizes with host mitochondria (e.g., Fig2B, bottom right panels). It would be helpful to show results of these sibling mitochondria for assays performed on their clumpy siblings. If they behave differently, it would be helpful to provide some explanation.

      Specific Comment: Figure 2 Majority (57%) of donated mitochondria do not colocalize with LysoTracker signal (N=24 cells, 4 donors) - Here the paper implies that some transferred mitochondria do co-localize with lysoTracker signal. More importantly, they co-localize with host mitochondria. It raises the question of whether they signal through ROS and ERK like their clumpy siblings who are in the limelight of most figures.

      🡪Yes, you are correct. There does appear to be a diffuse population of macrophage mitochondria. The majority of these mitochondria co-localize with lysotracker, suggesting that they are being actively degraded. We can’t say that they tend to co-localize with endogenous cancer cell mitochondria, however, it’s possible that this diffuse population is comprised of both mitochondria that are being degraded and mitochondria that are fusing with the endogenous network. We do not know if this population has a different effect on cancer cell behavior because we did not follow this population (mostly because once the mitochondria are degraded or fuse with the network, we can no longer follow those mitochondria!). However, we did follow cancer cells that contained punctate macrophage mitochondria. Often times this was the only population we could observe in the cell at that time, and this is the population in which we observe accumulated ROS.

      Major Discussion Point #3:The effects that are attributed to the transferred mitochondria are highly variable (figures 1F, 3A,E) and often due to a subpopulation of samples that show a few extreme values (e.g. figures 2D, 3E, S4B, S4D). This might be expected from effects that are caused by a single mitochondria (which has a small volume) that is transferred to a complete cell. This complicates the study of the transfer process and effects and should be discussed. Also, do the authors have ideas how to improve the system, to make it more robust and easier to study the effects?

      🡪The variability in the assays likely reflects the heterogeneity within the biology - Each experiment contains macrophages derived from primary monocytes that are harvested from different human blood donors! Due to the primary nature of these cells, we do expect a range of phenotypes as each donor would have a different genetic background and the monocytes were likely exposed to different environmental stimuli. In fact, even though working on this study was a giant pain due to the variability, we felt more confident about our findings because despite the heterogeneity in the system, we still observed consistent phenotypes. Below we indicate where we took a sample set and removed “outliers”, and ran the statistical tests again. The differences were still statistically significantly different, further suggesting robustness of our findings.

      However, we are always on the lookout for ways to make the system easier to study. One way that we will follow up on is using M2-like macrophages since they transfer mitochondria at a higher rate than unstimulated macrophages.

      Major Discussion Point #4: The authors conclude that the transfer of dysfunctional mitochondria generated a signal mediated by ROS that activates cell proliferation signals. The statement that "transferred mitochondria act as a signaling source that promotes cancer cell proliferation" is too strong. There is increased ROS production from mitochondria, yes, but an experiment in which ROS are decreased would be needed to properly sustain that conclusion. The title and abstract could be changed to better reflect the data.

      Specific Comment: ‘Furthermore, treatment with an ERK inhibitor (ERKi) was sufficient to inhibit ERK activity ‘- curious as to whether antioxidant treatment would reverse any proliferative phenotypes?

      🡪We wish we could quench the ROS at macrophage mitochondria. We really tried. We used a combination of ROS quenchers (NAC, mitoTempo, Tempo) and ROS readouts (mitoSOX, CellRox, DCFDA, and the 2 biosensors used in our study: Grx and Orp1), and treated cells for various amounts of time, and no matter what we tried, we could not reliably detect reduction of ROS levels in the host network or the transferred mitochondria (without killing the cells, that is). Another issue that we faced was that any pharmacological treatment would have a global effect on the mitochondrial network in the recipient cells and therefore it would not be possible to distinguish effects from global inhibition of ROS versus specifically at the site of the transferred mitochondria, and we certainly observed cell death upon treatment of ROS quenchers because of this fact. We talked to a couple of ROS experts, and they indicated that this issue is not unique to us, although we unfortunately did not have viable solutions, so if people have ideas or suggestions, please let us know!!

      However, despite our failed attempts at quenching ROS, the comment that "transferred mitochondria act as a signaling source that promotes cancer cell proliferation" is too strong of a statement… well, we don’t entirely agree given that we do perform sufficiency experiments in which weinduce ROS and observe both proliferation and ERK signaling, so we do feel reasonably justified to provide the title that we did. However, we will continue to mull over this comment. Thanks for sharing your thoughts.

      Major Discussion Point #5:The study may benefit from more direct evidence to support its conclusion of increased proliferation after mitochondrial transfer. While the RNA-seq, flow cytometry, counting of completion of cytokinesis and dry mass measurements provided in the present study do lend some support to the proliferation hypothesis, they all seem indirect. With the biomarkers labeling the mitochondria of donor and potential recipient cells, high content imaging and tracking of cells could be used to monitor cell division. A comparison of cell division rates of transfer-positive cells and transfer-negative cells will provide a more pertinent test of whether mitochondrial transfer promotes recipient cell proliferation.

      🡪We should probably do a better job at describing the dry mass measurements (QPI, quantitative phase imaging) because we view this quantification as one of the most direct measurement to monitor cell growth/division. The approach measures the changes in dry mass as the cells prepares for cell division. So not only do we get the final readout of division (complete cytokinesis), but we also get a measure of that growth rate (the cell getting ready to divide) before cytokinesis. This is why we are so tickled to collaborate with Tom Zangle’s lab because we could finally get a direct proliferation readout in real-time. We could also use this approach to follow thousands of cells at a time, a very critical aspect since mitochondrial transfer is rare event, and therefore, we need to follow many cells to have enough statistical power to quantify the growth rates. Check out some of the Zangle lab’s other papers (PMC5866559; PMC6917840; PMC4274116), and please let us know if you disagree with us!

      Major Discussion Point #6: The authors have used such a tracking-based approach on a very small scale (n=5) to measure daughter cell growth rate. However, the data do not show a statistically significant difference between the growth rates of daughters that inherited transferred mitochondria and those who did not (Fig S3). Increasing the case number via high content imaging would help obtain sufficient data points for a reliable statistical test. In addition, as suggested above, an accounting of the daughter cells' division rate for transfer positive and negative cells would provide another line of evidence to either prove or disprove the increased proliferation rate hypothesis. The same suggestion goes to the optically induced ERK activation experiments shown in Fig3F. It is also helpful to include references that studied how ERK signaling promotes proliferation and compare the evidence here with evidence or assays used in those studies as a benchmark.

      Specific Comment:Figure S3 - There is no statistical test to check for ‘increase in their rate of change of dry mass over time versus sister cells that did not inherit macrophage mitochondria’. What are the colours indicative of in S3B? Can this be reported in the figure legend.

      🡪You are right – the tracking-based approach on daughter cells is based on a small ‘n’. However, the tracking itself is performed on 1000s of cells. It’s just that in order to capture daughter cell data, we have to find a cancer cell with macrophage mitochondria (which is only ~1% of the population), and then follow that cell until it divides, and then follow BOTH daughter cells. So, even with the 1000s of cells that we followed, we could only capture a small number of daughter cells. The colors in S3B represent each individual triads – parent and 2 daughters. We will make this info clearer in the legend.

      In terms of the optically-induced ERK activation experiments, yes, it would be great to have a higher sampling. These experiments were performed at 63x so we could reliably draw small ROIs to mimic the size of a macrophage mitochondria. While we switched to lower magnification to follow cell division, we still were limited to only a few cells for the actual photoactivation. The technical aspects of this experiment were the reason for the low sampling. Despite these limitations though, we still observed increased cell division upon mito-killerred photoactivation, which we were honestly pretty surprised (and stoked) about.

      Other specific comments:

      -Figure S1A - The authors could perhaps use a more aggressive gating strategy here, clipping closer to the 231 population described in Fig S1A - picking only the center of the cluster in the upper left of the RFP vs CD11b plot would likely not affect results but make them more convincing by unequivocally excluding macrophages.

      -Figure 1D - Not sure about the 0.2% baseline assigned for the monoculture of cancer cells (that does not have the macrophages with the Emerald mitochondria). It is determined with cytometry - I am no expert on that topic, so maybe I missed something - but it looks weird to see some cells with transfer when there is a monoculture.

      🡪Due to the variable nature of the mito-mEm signal in the recipient cancer cells (i.e. transfer of one mitochondrion vs transfer of three), we found that an overlap of 0.2% set on a fully stained monoculture control was the most accurate way to gate for the recipient cancer cells. The final gating strategies used in our study were determined by FACS-isolating populations of interest based on several different gating strategies, and directly visualizing cancer cells with macrophage mitochondria without capturing macrophages or cancer cell/macrophage fusions (which is cool, but not what we wanted). To further clarify, there is no transfer occurring in the monoculture – the overlap of mEmerald signal into the transfer gate in that control sample is likely reflective of normally occurring autofluorescence. This is a very important point, so we will make this aspect clearer in the Methods section.

      -Figure S1B - Could perhaps be an interesting follow-up question for future works re: differences between cell lines and propensities to transfer mitochondria. Did the authors attempt to use other cell lines (ie, MDCK, HeLa, iPSCs, etc)?

      🡪Great question and something that we have also been thinking about. To date the only recipient cells we have used are 231, MCF10A, and PDxO cells. This would be a great avenue for future studies.

      -Figure S1B - Did the authors see an increase in growth rate in MCF10A line despite the lower growth rate?

      🡪We have not measured the growth rate in MCF10a recipient cells but something that would be great to follow up on in future studies.

      -‘physically separated from macrophages by a 0.4μM trans-well insert’ - should this read 0.4 micrometer?

      🡪Yes, great catch.

      -Figure S1F - The authors wrote that they used a two-way ANOVA analysis, could you report the factors used for that analysis in the Figure legend.

      🡪Noted!

      -Figure 1B - It is difficult to see the arrowheads in 1B, suggest moving them so they are not covering the magenta fluorescence, have them point from a different angle, and make them more brightly colored. Insets here would help the reader. A negative control image from a monoculture would also be helpful, to ensure the GFP signal is not an artifact of culture conditions.

      🡪Thank you for your feedback – we will take note of this.

      -Figure 1F - For graphs that do not show zero (as in 1F), the bar should be omitted. In these cases the length of the bar does not reflect the average of the data (as it does in 1D).

      -Figure 3C - Please omit bar, see comment on panel 1F.

      🡪 In the case of Fig 1F, we modified the y-axis to eliminate empty space. The bar is representative of mean of the data displayed in both 1D as well as 1F, but we can add a broken y-axis to help make this point.

      -Figure 1 - Given that these data are fractions of a population (ie. can be described via a contingency table), isn't something like a Fisher's exact test a better measure of significance here?

      🡪We think you are referring to Figure 1D? If so, we thought that we could not use Fisher’s exact test because that test assumed parametric distributions (which we do not observe). We have been working with a biostatistician for our statistics, but please do let us know if we have it wrong.

      -Single cell RNA- sequencing - In the methods section the authors mention doing a differential analysis between the cells that received the mitochondria and the cells that didn’t. It might be worth introducing a figure (a heatmap or a U-MAP) relating to this analysis. Single cell sequencing would not only affirm the heterogeneity between these two populations but also help in highlighting the novel cell surface markers associated with the two populations.

      🡪Yeah, good point – we can add a UMAP.

      -‘mito-mEm+ mitochondria remained distinct from the recipient host mitochondrial network, with no detectable loss of the fluorescent signal for over 15 hours’- It is surprising that the transferred mitochondria do (or cannot) fuse with the host 231 mitochondria.

      🡪We were also initially surprised to find that the transferred mitochondria do not fuse with the host 231 network! We think that the lack of fusion is due to the fact that the transferred mitochondria do not exhibit membrane potential (which is required for mitochondrial fusion). We also think that these results open interesting lines of questioning: Why are these depolarized mitochondria not degraded? Is this an active avoidance of the mitophagy pathway? How dynamic are these punctae? Many fun and interesting questions regarding the long-lived nature of these transferred mitochondria.

      -It is unclear in these images, but the 231 mitochondria appear fragmented too. Is it possible that the mitochondrial fusion machinery (Opa1 or Mfn1/2) are inactive?

      🡪231 cells are capable of fission and fusion (PMC7275541, PMC3911914, and in our own timelapse recordings), so we think that the machinery is functional. However, we don’t know whether the 231 mitochondrial machinery changes after receipt of macrophage mitochondria. Interestingly, the references above both investigate how mitochondrial dynamics promote tumor metastasis. A fascinating future direction could include an investigation to how macrophage mitochondrial transfer influences tumor cell mitochondrial dynamics.

      -Figure 2B - What does the MTDR staining of the macrophage mitochondria prior to transfer look like? Important to check this to confirm that only the transferred mitochondria had lower membrane potential.

      -‘significantly higher ratios of oxidized:reduced protein were associated with the transferred mitochondria versus the host network’-Here too, it would be important to check the mito-Grx1-roGFP2 readout of macrophage mitochondria prior to transfer.

      🡪The way that these comments are written is as if we already know that the mitochondria are dysfunctionalbefore transfer to cancer cells. But we actually do not know if that is the case. It’s also possible that macrophage mitochondria become dysfunctional once they are in the cancer cell, which would be equally cool. So, we are actively investigating this biology.

      -Figure 2A, 2BB and S1D - How were the colocalizations assessed? Was it just a visual assessment? Given the importance of these experiments for the whole story, having a quantification of the level of colocalization with each dye would be important.

      🡪This is a good point and it should be straightforward to include a Pearsons coefficient for these markers.

      -Figure S1D - The paper makes an argument about mitochondria transferred from Macrophages (marked green) having positive DNA stain (gray), but appearing depolarized (negative TMRM stain). The image in FigS1D is peculiar, as the majority of the 231 cells' mitochondria appear to not have any DNA stain but maintain membrane potential (positive in TMRM), while some (just above the green macrophage mitochondria) do have both DNA stain and membrane potential. The authors might want to clarify whether this is a typical scenario, and if so perhaps offer an explanation as to why the 231 mitochondria exhibit such heterogeneity.

      🡪The images in S1D are of a single z-plane image therefore the DNA signal in the endogenous network is more readily visible in planes that are not shown.

      -‘we confirmed that 91% of transferred mitochondria were not encapsulated by a membranous structure, thus excluding sequestration as a mechanism for explaining the lack of degradation or interaction with the endogenous mitochondrial network’ - This is based on co-staining with MemBrite 640/660, which is a dye that "covalently labels the surface of live cells", thus there is a concern as to whether this approach allows to study whether the mitochondrium is encapsulated by an endomembrane.

      🡪Thank you for your feedback. We actually do think that Membrite can label endomembrane in addition to the plasma membrane. This is from the published Membrite protocol: “MemBrite™ Fix dyes are designed to be fixed shortly after staining, when they primarily localize to the plasma membrane/cell surface. Cells also can be returned to growth medium and cultured after staining, however, dye localization in live cells changes over time. Labeled membranes become internalized, so staining gradually changes from cell surface to intracellular vesicles, usually becoming mostly intracellular after about 24 hours. Internalized MemBrite™ Fix dye is usually detectable for up to 48 hours after staining, though this may vary by cell type”.

      In our hands, we found that the dye started to become internalized and labeled vesicles within the cell within a few hours of staining. The images in the panels that you refer to came from time-lapse imaging experiments of between 10-15 hours, therefore the cells have internalized the MemBrite signal allowing for the visualization of internal vesicles. Also, in other studies not in the preprint, we perfused purified mitochondrial preparations onto 231 cells. The 231 cells took up the mitochondria from the environment, and all of these engulfed mitochondria were surrounded by a MemBrite positive membrane! These results further suggest that if the transferred mitochondria were encapsulated by a membrane, we would be able to visualize it.

      _-‘macrophage mitochondria are depolarized but remain in the recipient cancer cell’ -_Did the authors examine the extent of cancer cell death in their co-culture system (due to the activation of apoptosis by the depolarized mitochondria)?

      🡪We do not find any evidence of abnormal levels of cell death by both flow cytometry assays as well through our QPI image analysis.

      -Figure 2C–D - Like in Fig 2B, in the bottom left of panel of Fig 2C there are a lot of donor mitochondria not in highly oxidized state and the growth/proliferation phenotypes apply mostly to donor mitochondria that appear 'clumpy'.

      -Perhaps it is worth commenting on whether there is a link between donor mitochondrial morphology and the suspected proliferation-enhancing phenotype.

      🡪The images in Fig. 2C are of the same cell – a single recipient cancer cell which is expressing the Grx biosensor. The donor mitochondria are labeled with an arrowhead, the rest of the yellow/green signal (bottom right) is from the endogenous host network and therefore we do not expect it to be in a highly oxidized state (ie. more yellow than green).

      Regarding the mito morphology and proliferation – great question, and one that we are actively working on!

      -‘At 24 hours, we observed a similar trend, but no statistically significant difference (Fig. S4D). These results indicate ROS accumulates at the site of transferred mitochondria in recipient cancer cells’ - if a specific sensor fails to show a significant oxidation at 24 hours compared mito-Grx1-roGFP2 which reports on mitochondrial glutathione redox state, does that mean there are ROS independent ways to oxidize Glutathione? The authors did see cell growth phenotype both in 24 and 48 hours which suggests that something is happening in 24 hours despite no significant difference in ROS H2O2 sensor.

      🡪The additional biosensor that we used – mito-Orp1-roGFP2 - has been engineered to be a readout of one type of ROS – H2O2. The Grx probe is a surrogate for ROS of any type, of which there are many! To us, it is not completely unexpected that they would behave differently over time since they are readout for two separate things, and it generates an interesting possibility that different types of ROS accumulate over time. Given that the Grx probe shows an increase at 24 hours, which is when we observe the proliferation phenotype, we think we are on the right track. If you have ideas on robust ways to directly observe specific types of ROS, we would love to know!

      -The differences in ratio for the two sensors used are not very convincing. In Fig 2D and Fig S4B and D the “host” and “transfer” populations are very similar. The difference seems only due to the presence of a few outliers in the “transfer” populations. More importantly, sometimes it seems that these outliers come mostly from one donor rather than being present in all 3 donors. It could be good to show histograms of the two populations for each replicate/donor and maybe redo the stats excluding these outliers.

      🡪We think that the heterogeneity that is observed is due to the biology in the system – we are using primary macrophages derived from blood donors. However, for the data represented in Fig 2D, just as a test case, we took out the top four “outliers” in that data set and re-ran the Wilcoxon matched-pairs signed rank test and the p-value was 0.0010 (***), further suggesting that the ROS biosensors are revealing consistent and robust results.

      -Figure S5C - it seems like the percentage of cells that divided is the same for unstimulated cells and cells with stimulated mito-KillerRed. Isn't this contrary to the expectation? The figure shows that photobleaching cytoplasm decreased % cell division, which is puzzling.

      🡪The mean percent of cells that divided in unstimulated and mito bleach are very similar and was not significantly different. One point to be made that may not be well illustrated in our graphical representation is that if you look at the matched data (points connected are averaged per FOV for each condition in the same experiment) the trend shows that the mito bleach does seem to have an increase in cell division which is washed out with the average bar overlay. We should note that this experiment is very “noisy” and therefore we needed a lot of N to be able to detect significant changes. We are currently thinking about other ways to demonstrate sufficiency as it relates to cell proliferation – any experimental suggestions would be very welcome! Thanks for the feedback.

      -Figure 3A - In the 'cyto' condition 6 out of 13 fields have no cells that divide. Is that expected? What is the percentage of dividing cells for cells that were not illuminated at all (a control that is lacking)? There is large variation, ranging from 0% to 22%. The evidence that illumination of KillerRed leads to increased proliferation is rather weak. Also, since Cyto and Mito are different cells, is a "paired" statistical test the right kind of test to use here?

      🡪Additional data pertaining to Fig. 3A can be found in Fig. S5C, which includes the control for cells not illuminated at all. Having no cells that divide in a field of view is not surprising to us – the doubling time for these cells is ~35 hours, and we imaged for 18 hours. Also, for each field of view, our ‘n’ for each field of view was often 6-8 cells because we performed these experiments at 63X to allow for accurately drawn regions of interest for photoactivation. We also internally controlled every experiment (each experiment consisted of fields of view that had either mito activation, cyto activation, or no-activation controls, all of which were imaged overnight with multiple x/y positions). Cells that left the field of view over the 18 hours of imaging could not be quantified. It’s this sampling that caused the large variation in the graph. But again, as with many of our experiments, despite this variability, we still observe a significant difference in our experimental conditions over control cyto bleach. As for the statistical test, our understanding is that given each experiment is internally controlled, and we compare within each experiment, a paired statistical test is appropriate here. We will consult with our biostatistician to confirm, though.

      -‘ROS induces several downstream signaling pathways’ - We would not expect the authors to investigate every signaling pathway, but wonder if the PI3K pathway was explored? It seems to be the other major cancer/proliferative pathway induced by ROS.

      🡪Yes, this is a very good point! We actually assessed three different pathways at first – ERK, PI3K-AKT, and NLRP3/inflammasome. While analyzing these 3 pathways simultaneously, we discovered that ERK inhibitors resulted in decreased proliferation in cancer cells with macrophage mitochondria. As a result, we then focused on the ERK pathway. We still do not know if PI3K-AKT or NLRP3/inflammasome pathways play a role in this biology because we have not gone back and revisited these experiments yet, however in figure 3F, ERKi treated recipient cells exhibit a partial ‘rescue’ of baseline proliferation. This suggests that other pathways may indeed be involved and we plan to investigate this possibility.

      -‘Recipient 231 cells had significantly higher cytoplasmic to nuclear (C/N) ERK-KTR ratios compared to cells that did not receive transfer’-Since two different quantification styles with opposite fraction values were used, is it possible to please specify which one was used here.

      🡪Will do!

      -Figure 3B - Please show the outlines of the nuclei and that of the cell.

      🡪That would be helpful, wouldn’t it? Will do!

      -Figure 3D - it is peculiar that ERK-KTR in Fig 3D is so strongly cytosolic while in Fig 3B it is almost exclusively nuclear. If this sensor behaves differently in different situations, the authors may want to comment on how that would affect their conclusions.

      🡪The panels in Fig. 3B were taken with the ImageStream flow cytometer which takes a lower resolution image of a single plane of a cell in suspension in the flow stream. In Fig. 3D, those images are from confocal spinning disk microscopy which allows for higher resolution, z-stack images of adherent cells on glass. Therefore, we think the differences that you point out are likely due to the fact that the two images come from very different imaging systems.

      -Figure 3E - The effect of 'opto-induced' ERK activity is weak. The initial ERK-KTR is 1 at time point zero (as the data is normalized to this timepoint) and around 1 for both the cyto and mito condition. A statistical difference is observed, but the effect is minor and it is unclear whether it is biologically meaningful. The 'cyto' condition shows an average below 1 and the mito condition remains 1, suggesting that ERK activity remains constant when ROS are produced in the mitochondria.

      -Also from S8C and 3E it appears cyto actually shows a decrease rather than mito showing an increase, could the authors comment on this?

      🡪We have a few thoughts on this. The first is that we don’t expect a dramatic change in ERK signaling because the ROS accumulation is localized to a small region in the recipient cell. This is not a situation where we would expect a large-scale change because we are adding a growth factor. We can understand that the change in ERK activity may appear to be minor, but our data suggest that these subtle changes in kinase signaling translate into significant changes in downstream behavior – proliferation. The way that we interpret differences as “biological meaningful” is whether they exhibit a functional response, and in our study, we show that inhibiting the induction of ERK activity in cancer cells with macrophage mitos inhibits proliferation. What is most interesting to us is that cancer cells that do not have macrophage mitochondria have an unchanged fraction of cells in G2/M phase of the cell cycle in response to the concentration of ERK inhibitor we used, suggesting that the ERK inhibition specifically blocks macrophage mitochondria-induced proliferation.

      In Fig. S8C, bleaching a region of cytoplasm does seem to cause a decrease in ERK activity over time. We really can’t explain this result. However, we do think that ERK activity is higher in mito-bleached cells because mt-ROS is generating an increase in ERK activity which compensates for the decrease in activity that occurs when the cytoplasmic region of interest is photobleached. It’s still a head scratcher, though, but we did perform internal controls for every experiment (as we describe above), and the mito-bleach, cyto-bleach, and no-bleach conditions were run side-by-side such that we can make apples-to-apples comparisons.

      -‘patient-derived xenografts (PDxOs)’ - As a control it would be relevant to include a normal mammary organoid model perhaps from the same patient to demonstrate that the transfer of mitochondria specifically to the cancer cells is more beneficial.

      🡪Using a normal mammary organoid cells as a control to compare efficiency of transfer and downstream phenotypes would be very interesting. Due to the fact that these are patient-derived organoids, we are unable to acquire non-malignant cells from the same patient. Expanding our studies in the MCF10A cell line that we utilized in this paper would be an alternative to what you propose and would also expand our understanding of general biology underlying mitochondrial transfer.

      -‘macrophages to both HCI-037 and HCI-038 PDxO cells (Fig. 4G)’ - Why is M0 able to transfer efficiently to HCL-037 tumour when its mitochondrial network is less fragmented as M2?

      🡪These results really stood out to us. It was quite surprising that in HCI-037, both M0 and M2 macrophages were able to transfer their mitochondria at similar efficiencies, but in HCI-038, M2 macrophages were more efficient at transfer. HCI-037 is a primary tumor, and HCI-038 is a metastases from the same patient, so there are some exciting avenues of study to examine how macrophage mitochondria transfer differs at the primary versus metastatic site. There is still very little known about how donor cell dynamics influence mitochondrial transfer!

      -Are mito transfer from M0 depolarised and accumulate ROS or show increased ERK activity or increased cell proliferation?

      🡪Yes – all studies, except studies pertinent to fig 4 (where we assessed macrophage differentiation states), were done with M0 macrophages.

      -‘M2-like macrophages preferentially transferred mitochondria to the bone metastasis PDxO cells (HCI-038) when compared to primary breast tumor PDxO cells (HCI-037)’ -The authors may want to check this statement here as it is in consistent with their data plot. In Fig. 4G, M2/PDxO transfer percentages for HCI-037 and HCI-038 are about the same, unless the authors provide statistical tests to prove otherwise. Instead, M0 appears to transfer mitochondria to HCI-037 much more efficiently than it does HCI-038.

      🡪Upon re-reading our sentence again, we now realize that it’s actually quite poorly written, so we can understand the confusion! What we meant to articulate is that M2-like macrophages are better at transferring mitochondria to HCI-038 than M0 macrophages. Whereas in HCI-037, we do not observe the same preferential transfer (ie. M0 and M2 can transfer at the same efficiency). We will certainly clarify this language in the manuscript.

      -‘M2-like macrophages exhibit mitochondrial fragmentation’ - Is there a correlation between the status of the mitochondrial network in the donor and the % of transfer to the recipient? If so, this would be a correlation that would support the conclusions.

      🡪Yes, please see Fig. 4C for transfer rates with different donor subtypes and Fig. 4H for a general working model on how we think these data fit into the larger picture.

      -‘accumulate ROS, leading to increased ERK activity’ - Did the authors obtain similar results with the PDXOs? It would be an interesting observation if the primary samples also exhibit a mechanism similar to established cell lines wherein there are more accumulated genetic changes.

      🡪Our main limitation with PDxOs is overcoming the technical hurdles related to our downstream assays. These include introducing relevant reporters and generating stable lines in the PDxOs, and imaging at high-resolution when the PDxOs are cultured in 3D. However, we are very interested in this question as well, and are actively thinking about ways to overcome these hurdles.

      -It would also be interesting to examine whether there is any difference in the ROS-ERK mechanism for primary and metastatic tumour.

      🡪We agree and this is an active avenue of investigation for us. We agree and are currently pursing models to understand how our findings fit into the larger picture of tumorigenesis and metastatic potential. We had spent months pursuing anin vivo approach using a murine Cre/LoxP system to genetically label mouse macrophage mitochondria with GFP. We crossed mice which express Cre under a monocyte-specific promoter (Jax, SN: 004781) and mice with germline expression of Lox-Stop-Lox-3xHA-EGFP-OMP25 (Jax, SN: 032290) with the expectation of seeing Cre-based excision of the stop cassette – thus resulting in offspring with macrophages expressing mitochondrial-localized GFP. However, the macrophages of the resulting offspring do not express GFP (by flow cytometry, imaging, and western blot analysis), despite the PCR-verified presence of both transgenes and the excision of the stop cassette. Needless to say, this was quite frustrating! We are currently in the process of developing a newly available MitoTag model which has been optimized for visualization purposes (Jax, SN: 032675). If you have any suggestions or advice on this matter we would much appreciate your thoughts!

      -‘in cancer cells that receive exogenous mitochondria’ - Since these macrophages also transfer mitochondria to non-malignant cells, such as MCF10A cells shown in Fig S1B, perhaps the authors could comment on whether this is part of a physiological process that would also promote normal cell growth?

      🡪 There are so many questions regarding when and why macrophages might transfer mitochondria. In general, mitochondrial transfer is observed in stressed cells. Our data suggest that transfer happens to MCF10A cells although at a much lower rate than their malignant counterparts, 231 cells, but we do not know whether similar downstream mechanisms and phenotypes are also occurring in the non-malignant cells. Thanks for your feedback – more to come here!

  29. Jul 2021
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Dear reviewers,

      We thank all reviewers for and their appreciation of our work and even more so for their constructive comments and suggestions, which will significantly improve the quality of the manuscript. We were able to complete the revision and address all reviewer comments. Aside a more stringent discussion of the literature, and rewording of certain paragraphs for clarity, we also generated additional experimental data.

      More importantly, to address the concern that we did not provide a positive marker for the intranuclear compartment, we present new images. We attempted to label gamma-Tubulin by generating new antibodies, GFP-tagged strains, and trying multiple commercial antibodies since the beginning of the project. Only recently we found an antibody providing a more specific signal at the expected location, although with some likely cross-reactivity with alpha- and beta-tubulin, and now show these data in the supplements. Additionally, we generated expansion microscopy samples stained with a fluorophore-coupled NHS-Ester, a bulk protein label. These data show that the centrosome contains an exceptionally protein dense hourglass-shaped region, which spans from the extranuclear to the intranuclear compartment, as revealed by centrin and tubulin co-staining. This fortifies our claims about the distinct nature of the intranuclear centrosome compartment containing the microtubule nucleation sites.

      Further, we add images of 5-SiR-Hoechst, SPY555-Tubulin, Centrin1-GFP triple labelling live cells to demonstrate the specificity of the microtubule dye and to underline that we are indeed acquiring the dynamics from the first nuclear division on.

      In terms of formatting we added line numbers and uploaded high quality figures separately. Due to the added data and panels we needed to split Fig. 1 into two separate figures, rewrote the figure legends and moved them to the end of the document.

      Please find below a point-by-point response to the comments.

      Best regards,

      Julien Guizetti

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

      The manuscript by Simon and collaborators addresses the dynamic changes of spindle and hemi-spindle microtubules occurring along schizogony in Plasmodium falciparum. The work explores the temporal correlation of the changes observed in intranuclear spindles with changes at the level of the centriolar plaque; the nuclear microtubule organizing center of these parasites, using centrin as a bona fide marker of the structure. The study shows that spindle microtubules organize from an intranuclear region, devoid of chromatin, distinct from the centrin region which had not been observed or described before. It further shows that centrin does not localize at the nuclear envelope, but it is actually extranuclear.

      This work significantly expands on previous knowledge regarding the functional and spatial organization of the nucleus in P. falciparum, and the structure once defined as "an electron dense mass on the nuclear envelope." It uses state of the art microscopy approaches such as STED, UExM and CLEM, in combination with immunolabeling, dyes and parasites over expressing fluorescent protein fusions, to address these questions.

      **Major comments:**

      • Are the key conclusions convincing?

      I find the manuscript successfully addresses the posed questions. The data presented supports the conclusions.

      • 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.

      No

      • 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.

      N/A

      • Are the data and the methods presented in such a way that they can be reproduced?

      Yes

      • Are the experiments adequately replicated and statistical analysis adequate?

      Yes

      **Minor comments:**

      • Specific experimental issues that are easily addressable.

      On the data shown in Figure 1, it is unclear to me what elements are taken into account to define "anaphase." Anaphase could be defined by using chromatin markers - such as CenH3- which have been identified in Plasmodium and the authors make use of in Figure 1F.

      We acknowledge that the term anaphase is ill-defined here. Further it suggests a mitotic morphology analogous to the one observed in “classical” models (prophase, metaphase, anaphase,…), which is not fully appropriate. In line with the comments by Reviewer 3 we, therefore, decided to use the term “extended spindle” instead (Fig. 1 & 2). This better reflects the morphological criterion on which we based the stage definition.

      • Are prior studies referenced appropriately?

      The authors state that "with the exception of centrins and gamma tubulins" few canonical centrosome components are conserved in Plasmodium. These parasites are in fact able to assemble a more or less canonical centriole for microgamete basal body formation. Widely conserved centriolar components such as Sas6 are coded by the malaria genome, and have been characterized previously. This work is neither referenced nor discussed in the manuscript.

      The reviewer is right to point out this omission. We were too much focussed on the blood stage centriolar plaques while writing this section, where centrioles are not observed. Of course centriole-like structures are relevant in other life cycle stages, such as microgametes, and should be discussed (line 104). Some previous attempts to endogenously tag Sas6 to verify its localization in blood stages were unfortunately not successful.

      • Are the text and figures clear and accurate?

      I find the timings shown in Figure 1A, with respect to the schematic quantification shown in Figure 1B, confusing. Shown as it is, one naturally correlates the images on Fig1A above with the cell cycle progression timing shown on Fig1B, below. However, by time 260min, for example, two somewhat adjacent centrin signals can be observed. Though this is defined as anaphase- by an unspecified criterium- this could very well be representative of metaphase. Nonetheless, the timing shown on Figure 1B for "anaphase" onset is 170min, which is inconsistent with the images above. I suggest that either, the quantification is shown in a different format (ex. bar plots) which could then better reflect the cell to cell variations observed (by use of error bars, for example) or that the figure explanation in the results section clarifies this issue.

      We understand how this representation is misleading and have adjusted the figure and text accordingly. We modified the time stamps in Fig. 1A (now Fig. 1C) to the scale used in Fig. 1B (now Fig. 1D) i.e. collapse of the hemispindle is t=0 and explain this in the text (line 158). Since we feel that Fig. 1B (now Fig. 1D) is a good and compact visual representation of progression through the first division we kept the bar plots in the supplements (Fig. S1), but added a title clarifying that average duration between multiple movies are shown.

      As presented, the data in Figure 1C is rather uninformative. A pattern could be more immediately extracted if dots corresponding to subsequent appearance of centrin dots in the same nucleus were connected to each other.

      Concerning the appearance of the centrin signals we adopted the good suggestion by the reviewer and connected “paired” centrin signals by lines (Fig. 1E).

      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      There are a number of edits required on the text. Row numbers would have been helpful in pointing these out. I point some edits below, but thorough revision of the manuscript for grammatical and synthetic errors would be beneficial.

      • Cytokinetic segmeter - please replace with "segmented"

      • Please refer to Figure 1D when appropriate - there is quite an extensive paragraph describing the results shown on this figure, but it is only referenced at the start.

      • "..., as did the and the number of branches per nucleus,..." please rewrite as appropriate.

      We apologize for not providing line numbers, but have corrected the addressed points and applied a grammatical check throughout the manuscript. We have added additional references to Figure 1D (now Fig. 2A) in the text.

      Reviewer #1 (Significance (Required)):

      This manuscript could be interested to a wide audience interested in cell cycle, cell division, cell organization and organelle positioning, infectious diseases and microscopy. However, the introduction assumes that readers are somewhat experts in the malaria field. I suggest the authors include a brief introduction of the malaria life cycle, and a schematic representation of the division mode. This will help non-experts follow the narrative more easily.

      We are happy to read that the reviewer sees value of this study for a broader audience. Following the suggestion, we added a small schematic (Fig. 1A, lines 54, 62) highlighting the relevant steps of schizogony and expanded the introduction of the life cycle (line 46).

      This work rectifies long-standing inconsistencies observed by different experimental approaches in the nuclear organization of malaria parasites during schizogony. However, what the functional consequences of the alternative modes of spindle organization in malaria could be, are not clearly stated or discussed. In this respect, as it stands, the manuscript is rather descriptive and lacks mechanistic insight. Nonetheless, the data presented are of superb quality, and the manuscript represents a tremendous leap in structural insight and imaging resolution for the field of malaria. I find the data is suitable for publication albeit minor adjustments are made (specially to Figure 1 and/or the description of the results shown in Figure 1, for consistency).

      We agree that the value of this manuscript lies in the clarification of conflicting data, unprecedented structural insight, and providing a useful working model for the malaria parasite centrosome. Although this study is ultimately descriptive it forms the indispensable basis to generate more meaningful functional insight about centrosome biology and nuclear division. Some of the functional consequences worth considering are: i) The (at least) bipartite composition indicating that centrosome functionality is spatially spread throughout the nucleoplasm/cytoplasm boundary. ii) The delayed appearance of the centrin signal after tubulin signal allows the prediction that centrosome assembly is a staged process occurring over an elongated period of time. iii) The generally amorphous structure of the compartment predicts the involvement of yet to be uncovered matrix-like proteins harbouring microtubule nucleation sites. iv) Lastly, our model has important implications for the mechanism of centrosome duplication. In a centrosome containing centrioles (like in vertebrates), the duplication event can easily be explained by physical separation of the daughter and mother centrioles. Spindle pole body duplication in yeasts is achieved by de novo formation of a new one, which remains connected by a half bridge until it is split. The centriolar plaque organization revealed here suggests that we need an entirely new model of centrosome duplication (or splitting) to describe and understand this process in malaria parasites. We now address those points more explicitly in the discussion section (e.g. lines 375, 443, 467).

      **Referee Cross-commenting**

      I agree with all the other reviewer's comments. I'm glad the reviewers seem to be experts in the field of malaria cell division and have pointed out previous studies which were not appropriately referenced. I second those comments.


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

      **Summary:**

      The manuscript by Simon et al have used advance cell biology technology like STED, expansion and live cell imaging to decipher the configuration of microtubules, centrin and nuclear pore during unconventional cell division process in malaria parasite. They have shown the dynamics of centrin and its localisation with respect to centriolar plaque that is characteristic of these parasite cell during schizogony> They also implicate from their studies that there is extended intranuclear compartment which is devoid of chromatin

      **Major Comments**

      • Are the key conclusions convincing? Yes to some extent

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      *Some part are preliminary and speculative as there is no solid data supporting it. Please see below

      • 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.

      *Yes to substantiate their claim

      • 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.

      *They can do these quite quickly less than a month

      • Are the data and the methods presented in such a way that they can be reproduced?

      *Yes

      • Are the experiments adequately replicated and statistical analysis adequate?

      *Yes

      The authors present beautiful imaging and some in depth structure using tomography and CLEM to show the location of centrin which is generally considered the marker for centrosome or Microtubule organising centre in malaria parasite. These approaches are still not been applied in Plasmodium and hence very informative. Though they present some advance microscopy but a lot of these concept for hemispindle were shown earlier in many light and super resolution microscopy studies. Authors claim that they are first to show that there is space between centrin and nucleus but it has been show previously in centrin studies in Plasmodium berghei using super resolution microscopy (Roques et al 2019 Fig1 and supplementary videos1&2) as well as expansion microscopy recently by group of Brochet etal 2021.

      We thank the reviewer for the appreciation of our work. We are, indeed, not the first to describe the gap between centrin and tubulin or the nucleus. We just aimed to reiterate this finding, also visible in our data, in order to transition to the analysis of nuclear pore positioning to clarify whether centrin is actually extranuclear. Nevertheless, we should have cited the Roques and Bertiaux et al. studies again in this context, which we have now rectified (line 252).

      In addition the microtubule dynamics was also recently shown with Kinesin5 live cell imaging for schizogony in Plasmodium berghei (PMID: 33154955) which author have omitted in their manuscript.

      We thank the reviewer for pointing out the Kinesin-5 study by Zeeshan et al., which we failed to cite and discuss. We now state the findings of this publication and put it into the context of our work (see also answer to next point). Microtubule associated proteins, such as the microtubule plus end tracking EB1 and the aforementioned Kinesin-5, are indeed useful markers to investigate microtubule dynamics leading to the interesting results shown by Zeeshan et al. Nevertheless, we want to point out that labelling microtubule associated proteins (MAPs) remains an approximation of the underlying microtubule organization. As the authors in Zeeshan et al. indicate by themselves, Kinesin-5 does not decorate axonemal microtubules or the membrane-associated microtubule structure formed during cytokinesis in very late schizont stages. Further, colocalization between alpha-tubulin and kinesin-5 in schizont-stage parasites is not complete indicating a preferential decoration of certain sections of the microtubule structures (possibly the microtubule ends), which could only be resolved by super-resolution microscopy. Using a live cell dye, such as SPY555-tubulin, which directly binds to microtubules will provide a uniform labelling of any microtubule species and hopefully prove useful to the field in the future. Lastly, we present time-lapse microscopy analysis of blood stage cells, contrary to single time point images of live cells, providing a quantified chronology of microtubule reorganization at single cell level (with time stamps). Therefore, we feel that our claim, although it should be relativized, is formally speaking accurate.

      It is also important that authors give valid discussion about previous studies on hemispindle, microtubule dynamics with respect to schizogony (PMID: 18693242; PMID: 11606229; PMID: 33154955) rather than giving the impression that they have given this concept first time on hemispindle dynamics and centrin location during schizogony.

      We agree that those studies should be discussed in more detail. We are grateful to the reviewer for pointing out the Fowler et al. 2001 (PMID: 11606229) study. They use an antibody against gamma-tubulin to demonstrate its presence at the apical pole of subpellicular microtubules (f-MAST) in the merozoite and cytokinetic stages (line 102). However, we were unable to reveal a specific gamma-tubulin staining using the antibody used by them in the preceding schizont stage. After trying many different commercial gamma-tubulin antibodies and attempting to generate our own we now finally observe a gamma tubulin localization at the poles of intranuclear spindles in schizont stage, although the only successful antibody still displays some background staining, possibly including cross-reactivity with alpha or beta-tubulin (Fig. S4, line 237).

      The highly insightful study by Mahajan et al. 2008 (PMID: 18693242) indeed suggests that centrin localizes away from the DNA and demonstrate the distinct localization from tubulin. They, however, likely due to the resolution limit of their microscopy techniques, speculate that the centrin signal is embedded in the membrane, while we could show by super-resolution and nuclear pore staining that centrin is distinct from the membrane (now Fig. 2A; line 257). The work done by Zeeshan et al. 2020 (PMID: 33154955) nicely shows dynamics of kinesin-5 in nuclear division. In schizont stages Kinesin-5 signal elongates and splits alongside the mitotic spindle with which it overlaps for the most part. Colocalization with centrin is less strong although the authors note some overlap. Our data suggest that centrin and tubulin are clearly distinct. In male gametes the authors show nicely time-resolved data of kinesin spreading along the elongating spindle, although hemispindles are not observed at this stage. We introduce and discuss these findings (lines 123, 432).

      The concept of bipartite centrosome is already been discussed in Toxoplasma and the claim by authors in Plasmodium presented here is not substantiated experimentally. They showed that centrin is part of outer region while they do not show with any marker for the inner region. It will be very helpful if the authors use gamma tubulin or MORN1 to show the location with respect to centrin and microtubule. In the absence of this localisation the claims are preliminary and speculative. If the centrosomal protein complex is not involved in microtubule nucleation, then how the nucleation is happening. What are the molecules present in this amorphous matrix? It will be great to check the location of gamma-tubulin or some inner centrosome molecules described in Toxoplasma that is deemed to be MTOC.

      We share the opinion that our Plasmodium data should be compared to Toxoplasma, while still being assessed independently. Despite Toxoplasma belonging to the apicomplexan the conclusion that their centrosomes should be organized in a similar fashion is by no means self-evident considering for example their significant evolutionary distance. Actually, several noteworthy morphological differences have already been well documented. i) Toxoplasma MTOC does contain centrioles in the outer core which is coherent with the centrin and gamma-tubulin localization in this region. ii) Toxoplasma MTOC contains an additional nuclear membrane protrusion enclosing the inner core. iii) mitotic microtubules in Toxoplasma are thought to penetrate the nuclear membrane to connect to centromeres. iv) the inner and the outer core are both extranuclear and therefore not to be equated with the intranuclear compartments. We now expand a bit on the discussion of the aforementioned differences (line 382). Nevertheless, we thank the reviewer for making us realize that the term “bipartite” is a poor choice to describe the centriolar plaque organization in this context. Therefore, we replaced it in the abstract (line 29) and the main text (line 375).

      We acknowledge the fact that it would be desirable to show a marker localizing to the intranuclear compartment, and not only through visualizing the microtubule nucleation complex (Fig. 4A-B) and the positioning of the microtubule ends in this region (Fig. 3A). Concerning MORN1 we found no indication in the published localization data that it is, like in Toxoplasma, associated with the nucleus in Plasmodium species, where it is only found associated with the budding complex (and we are currently unable to procure an antibody) (line 422). We have attempted gamma-tubulin visualization on many occasions throughout the project (transgenic parasite lines, commercial antibodies, self-made antibodies) and only recently found an antibody revealing some specific signal. Indeed, we found localization at the poles of the spindles i.e. the intranuclear compartment (line 237). Unfortunately, this “best-possible staining” still showed some unspecific spindle staining likely resulting from cross-reactivity with alpha- or beta-tubulin causing us to put these data into the supplements (Fig. S4).

      We had more luck with attempting a “new” type of staining, recently used in Plasmodium (Bertiaux & Balestra et al. 2021) using a fluorophore-coupled NHS-Ester in expanded samples. This chemical unspecifically stains proteins and revealed that the centrosomal region contains an exceptionally protein dense “hourglass-shaped” structure (Fig. 3F-H). Since the outer part of this structure colocalizes with centrin and the inner part overlaps with microtubules we assume that the centrosomal complex stretches throughout the nucleo-cytoplasmic boundary and fills part of the intranuclear compartment (line 320). Especially the highly protein dense region at the neck of the “hourglass” seems very coherent with the nuclear membrane embedded electron dense region which can be seen in electron microscopy (e.g. Fig. 3E & 4B). We feel that this staining strongly supports the presence of this novel intranuclear compartment.

      The expansion microscopy is very nice and some of it presented in supplementary can be moved to main section.

      Thanks for sharing our enthusiasm about this imaging technique. We have now selected a representative image of a hemispindle and mitotic spindle stage nucleus imaged by U-ExM and added it to the main section (Fig. 2B, line 231).

      The localisation CenH3 is bit puzzling as it has been shown that centromere/ kinetochore cluster and are present during early and mid schizogony. The various foci with respect to nuclei are not what has been seen previously. Please discuss the difference in these two findings.

      The localization pattern can easily be explained by the increased resolution of STED nanoscopy used in this study. Previous studies (e.g. Hoeijmakers et al. 2012 and Zeeshan et al. 2020) used classical confocal microscopy. Under those imaging conditions the individual foci seen here can´t be resolved and would, in accordance with the other studies, appear as one cluster. We slightly modified the text for more clarity (line 247).

      Reviewer #2 (Significance (Required)):

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      * This is more technical advancement on the subject of centrin by using STED, tomography and CLEM.

      • Place the work in the context of the existing literature (provide references, where appropriate).

      * This work has relevance relation to cell division during schizogony in asexual stages in par with Toxoplasma or in Apicomplexa in general

      • State what audience might be interested in and influenced by the reported findings.

      Working with Apicomplexa, Protist, cell division and mitosis.

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Working on Cell division in Plasmodium.

      **Referee Cross-commenting**

      I agree with the reviewers and some of the experiment suggested and the minor details have to be addressed. There are some loose ends and these suggestions will enhance clarity of the data. It is a very nice study and some of the comments suggested by reviewers will improve the manuscript. __

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

      **Summary:**

      The centrosome is the primary microtubule-organizing center (MTOC) in eukaryotic cells that nucleate spindle microtubules necessary for chromosome segregation. In most eukaryotic cells, the canonical centrosome is composed of centrioles surrounded by an electron-dense proteinaceous matrix named the pericentriolar matrix (PCM) competent for microtubule nucleation mitotic spindle assembly. Following the breakdown of the nuclear envelope breakdowns, the mitotic spindle microtubules gain access to the kinetochores of the condensed mitotic chromosomes. Once the mitotic spindle is fully developed, centrosomes are at opposite poles of the cells, and chromosomes are pulled toward opposite poles. Cell division completes with cytokinesis resulting in the active formation of two nuclei within two daughter cells. Interestingly, during its asexual replication cycle, the malaria parasite Plasmodium falciparum undergoes multiple asynchronous rounds of mitosis with segregation of uncondensed chromosomes followed by nuclear division within an intact nuclear envelope. The multi-nucleated cell is then subjected to a single round of cytokinesis that produces dozen of daughter cells. We know about the Plasmodium centrosome is that it is made of an acentriolar structure embedded in the nuclear envelope and serves as MTOC during cell division. However, the biogenesis and regulation of the Plasmodium centrosome are poorly understood. Given the peculiarity of the cell division in Plasmodium parasites, understanding the molecular mechanisms that drive and regulate MTOC duplication and maturation could unveil novel targets for the treatment of malaria. In this study, Simon et al. successfully applied challenging and cutting-edge microscopy techniques to monitor the dynamic formation of the spindle microtubules and MTOC during Plasmodium intraerythrocytic mitosis. In addition, they remarkably combined stimulated emission depletion (STED) with ultrastructure expansion microscopy to define an uncharacterized intranuclear compartment devoid of chromatin as the nucleation site of nuclear microtubules. And lastly, the authors adapted an in-resin correlative light and electron microscopy (CLEM) approach to define the centriolar plaque position in a novel intranuclear compartment with centrosomal function.

      **Major comments:**

      1. In the methods section, it is stated that across this study, three different anti-tubulin antibodies (alpha-tubulin B-5-1-2, alpha-tubulin TAT1, beta-tubulin KMX1) were used, and two anti-centrin antibodies (TgCentrin1 and PfCentrin3) were used, one of which seems to have been generated in this study (anti-PfCentrin3). It is unclear in the figures or results section when each of these antibodies was used, and the authors should give a rationale for using multiple antibodies in combination.

      To label microtubules we used the mouse anti-alpha-tubulin B-5-1-2 (Sigma, T5168) antibody throughout the study. Except for U-ExM were we added two additional primary antibodies against tubulin. Due to the expansion of the samples the antibody binding epitopes are stretched out in space. This causes a significant reduction of local epitope concentration (expansion factor 4.5 in all directions results in ~ 80-fold increase in the volume), which can reduce the signal intensity. Adding multiple antibodies binding different epitopes of tubulin can compensate for this dilution effect to some degree, as has been shown before by Gao et al. 2018. At the same time the expansion contributes to the accessibility of the usually densely packed tubulin epitopes within the microtubule polymer, which certainly adds to the success of U-ExM. What the respective contributions of those effects are is not clear, but we found superior signal-to-noise ratios when combining three tubulin antibodies instead of using one. The TgCentrin1 antibody was only used in Fig. 2C (now Fig. 3B) and validated the localization pattern of our new PfCentrin3 antibody we used in the other pictures. We now provide clearer description of antibody usage in the methods section and a new supplemental table.

      The anti-PfCentrin3 antibody seems to have been generated for this study. If this is the case, the authors should provide evidence that this antibody binds to the recombinant PfCentrin3 it was raised against and binds PfCentrin3 in parasite lysates.

      The anti-PfCentrin3 antibody was, indeed, produced for this study and we should have provided our western blot data right away. We now show the requested blot, which shows bands at the appropriate size in parasite lysate as well as for the recombinant protein, in the supplements (Fig. S2, line 178).

      In the first paragraph of the Results section, the authors' remark of centrin foci that they are "...only detectable later (Mov. S2) or sometimes not at all." In Figure 1 A-C, it is implied that the first observed division is the first nuclear division of that parasite. Given that some nuclei do not have a visible centrin focus, it cannot be concluded with certainty that these parasites only contain a single nucleus and that this is their first division. The authors would need to include a quantifiable DNA stain to show this unequivocally to show a single nucleus. It has undergone DNA replication, similar to Klaus et al., 2021 BioRxiv paper. In the absence of a DNA stain, the authors should reword to clarify that this is the first observed division and speculate that it is the first division of that nucleus, but the authors should draw no firm conclusions about the first division.

      Indeed the variability in protein levels that can result from exogenous expression can lead to some cells not showing clear Centrin1-GFP foci. Although this is a rare event we wanted to acknowledge this observation. The live cell microtubule staining using Spy555-Tubulin we use is, however, highly specific and sensitive and would stain any nucleus undergoing division including the first one. If there would be more than one nucleus in the observed cell it would unequivocally show two clearly separated tubulin signals (hemispindle or mitotic spindle). To illustrate this we added Fig. 1B (line 148) showing two live parasites stained with SPY555-Tubulin plus a Hoechst-based dye showing one or two nuclei alongside the corresponding tubulin signal. We modified the text to clarify how we stage the parasite for time-lapse acquisition (line 154). We already extensively experimented with state of the art fluorogenic live cell DNA dyes (e.g. from Spirochrome and the Johnsson group) to visualize the nuclei directly in time lapse microscopy, but even at minimal concentrations they all significantly inhibit mitotic progression. We also add this information in the main text (line 150).

      In the first paragraph of the Results section, the authors write: " We quantified the duration of hemispindle, accumulation and anaphase stages ...." Anaphase spindle fibers means that the sister chromatids are separated. In the absence of a centromeric marker like NDC80, it doesn't seem easy to claim the anaphase stage. The authors should write " extended spindle." The authors might also consider using the term collapsed spindle instead of accumulation to reflect the dynamic of the intranuclear microtubules during the blood-stage replication. The same modification should be made for Figure 1B, so we read " hemispindle, collapsed spindle and extended spindle."

      We thank the reviewer for this suggestion, which is very much in line with a comment by Reviewer 1 on the definition of anaphase. We acknowledge that the term is ill-defined here. Further, it suggest a mitotic morphology analogous to the one observed in “classical” models (prophase, metaphase, anaphase,…), which is not fully appropriate. Consequently, we decided to adapt the suggested terminology in Fig. 1 (and also new Fig. 2) and in the text (line 160).

      Based on the evidence in this study, it cannot be stated unequivocally that the centrosome is entirely extranuclear, at least not as it is implied in Figure 3C. In Supplementary Figure 4, the microtubules appear to be extruding from a circular structure that may either be intranuclear or span the nuclear envelope. In Supplementary Figure 6, the structure pointed to as the centrosome appears to be embedded within the nuclear membrane with a top structure on the cytosolic side of the nuclear envelope. Thus, the best support for an extranuclear centrosome comes from the CLEM images. Still, it is noteworthy that the double membrane of the nuclear envelope is not visible on this slice in the region where the centrin fluorescence is found. Considering some of the fluorescence pixels for centrin are outside the parasite plasma membrane, and some of the Hoechst pixels are outside the nuclear envelope, this data does not show unequivocally that centrosomes are entirely extranuclear. However, this argument would be strengthened if the authors performed a proteinase K protection assay (or something similar) to determine if Centrin1 and Centrin3 are exposed to the cytosol. However, in the absence of that or further evidence, the authors should dampen their claims about the centrosome being exclusively extranuclear, as represented in the schematic in figure 3C.

      We thank the reviewer for this comment, which highlights an issue in our communication of our working model of the centriolar plaque. At no point we intended to claim that the centrosome is exclusively extranuclear. Rather, centrins, which are currently the only reliable marker proteins, localize to a subcompartment of the extranuclear region of the centriolar plaque. Additionally, the centrosome clearly contains an intranuclear region. The composition of this intranuclear compartment is elusive, except that it harbors microtubule nucleation sites. Indeed, our model in Fig. 3C (now Fig. 4C) is misleading and not well annotated. The newly added NHS-Ester staining fortifies this claim (Fig. 3F-H. Consequently, we corrected our working model by adding an explicit figure labelling (now Fig. 4C).

      We apologize for the misleading labelling in Fig. S6 (now Fig. S7). The green arrow was intended to point out the electron dense region associated with the nuclear membrane, which has been seen in previous studies, and was not intended to represent the entire extended centriolar plaque. If anything, this smaller region might provide the link between the intra and extranuclear compartments that the reviewer also identified in Suppl. Fig. 4 (now Fig. 2D). We modified the annotation of the Fig. S7 and Fig. 4A-B accordingly, labelling it the “electron dense region”. More importantly, we hope that our newly added data using NHS-Ester staining of protein dense regions (Fig. 3F-H) highlights the spread of the centrosome across the nucleo-/cytoplasmic boundary more clearly.

      Considering whether centrin is actually extranuclear, we feel that the data shown in Fig. 2A (now 3A) is convincing. We have, however, added two panels of the relevant regions showing centrin localization respective to the nuclear pore and adjusted the contrast as we acknowledge the limited “visibility” within the unadjusted panels. The fact, that the centrin signal slightly overlaps with the nuclear envelope in CLEM images can be explained by the relatively poor resolution of the widefield microscope we had to use to image the sections. From the other super-resolution images in the manuscript, we know that the perimeter of the better resolved centrin signal is significantly smaller. Otherwise one had to assume from the CLEM data that centrin is also in the cytosol of the red blood cell and that DNA is localized outside the nucleus. On a similar note the fluorescence image is, contrary to the tomography image, a single slice since the thickness of the sample section (about 200nm) is significantly below the z-resolution (about 500nm) of a fluorescence microscope.

      Throughout the study, the level of biological replication is unclear. The authors rigorously include all the data points for each of their graphs and the total number of images/videos quantified. And what needs to be added, in either the figure legends or a methods section, is the number of biological replicates for each of these measures came from.

      We have added the number of replicas in the figure legends.

      **Minor comments:**

      STED is present as an acronym in the abstract and should be spelled out in full and clarified that it is a super-resolution microscopy technique.

      We opted to remove STED from the abstract (leaving it at super-resolution, which includes expansion microscopy) to avoid disrupting the “flow” of the abstract and now spell out the acronym at the first mention in the introduction (line 127).

      The second paragraph of the Results section states that ring and early trophozoite stage parasites do not express tubulin or centrin. Still, only an early trophozoite is shown in Supplementary Figure 2. Therefore, the authors should either include a similar image of a ring-stage parasite or remove ring-stage parasites from that statement.

      We have removed the ring stages from the statement.

      The second paragraph of the Results section contains the sentence, "At which point tubulin is reorganized into the bipolar microtubule array, which then forms the mitotic spindle cannot be resolved here." The authors are implying that the point at which tubulin is reorganized into the microtubule array, which goes on to form the mitotic spindle, cannot be resolved here. This is not particularly clear, though, and this sentence could be reworded for clarity.

      We reformulated the sentence to clarify the point we failed to make with the previous wording (line 188).

      The second paragraph of the Results section contains some statements about the results without referencing the figures that these statements come from. The authors should clarify this to make clear which figures each statement refers to.

      We added more references to the appropriate figure throughout the paragraph (lines 188, 219, 223).

      In the third paragraph of the introduction section, the authors write, " Centriolar plaques seem partially embedded in the nuclear membrane, but their positioning relative to the nuclear pore-like "fenestra" remains unclear." Unfortunately, the lack of reference did not allow me to understand if the authors state literature or comment on past published results.

      We added the reference which was incorrectly positioned before the sentence instead of at the end (line 82).

      the authors could add some references:

      • Second section of the introduction: " the 8-28 nuclei are packaged into individual daughter cells, called merozoites ( Rudlaff et al. 2019 PMID: 31097714)

      • Third section of the introduction: " The centrosome of P.falciparum is called centriolar plaque" ( Arnot et al. 2011, Sinden 1991a); " the nuclear pore-like "fenestra" remains unclear (Wall et al. 2018; Zeeshan et al. 2020).

      • Fourth section of the introduction: " tubulin antibody staining are extensive structures measuring around 2-4um ( Ref?)

      • When the authors introduce subpellicular microtubules of segmented schizonts, a reference to a study that shows these structures should be included.

      • A previous study that shows the distinct structure of microtubule minus ends should be cited when this structure is described.

      • Third section of the results, the authors should cite Bertiaux et al. 2021 with the Gambarotto et al. 2019 paper regarding U-ExM.

      We apologize for missing some important references or putting them in the wrong position. We now added all the references or cite them again at the appropriate locations throughout the text.

      Figure 1E shows hemispindle and mitotic spindle lengths of U-ExM expanded parasites, but the position within the figure and figure legend implies that these lengths were determined unexpanded parasites. Therefore, it should be stated in the figure legend that these measurements come from U-ExM expanded parasites. Moreover, I encourage the authors to include U-ExM images in the main figures. The images are beautiful, represent a significant technical achievement, and directly relate to Figure 1E. To the best of my knowledge, this is only the second study to perform expansion microscopy on Plasmodium and the first to use PFA-fixed parasites and a nuclear stain. It would be valuable for the Plasmodium and ExM communities to see this technical advancement represented in the main text.

      We thank the reviewer for the appreciation of our ExM data and added it to Fig. 2B before the quantification of the microtubule length and number and added the information to the legend.

      In the second paragraph of the Results section, the authors write, " but clearly display the microtubule cytoskeleton associated with the inner membrane complex." It would bring clarity to define in few words what the IMC is.

      We included a short definition of the IMC (line 223).

      The methods section details that the length of microtubules was determined by dividing the observed values by an expansion factor of 4.5. If the authors recorded the expansion factors of their gels, this data should be included, and how it was recorded should be stated in the methods. If not, the authors should include the rationale of using an expansion factor of 4.5 as this is slightly different from the previously published expansion factor of P. falciparum of 4.3.

      We recorded the expansion factor by measuring the gel size pre and post expansion with a ruler and found a factor of 4.5 on average. We added this information in the methods (line 688).

      There are several parasite lines used in this study, and some figures are not clear what parasite line was used. Could the authors please include the parasite lines in the figure legends of Figure 1 D-F, Figure 3, Supplementary Figures 1-2, and Supplementary Figures 4-7?

      We added the parasite line information in the legends as requested.

      Nuclear pore complexes, of which Nup313 is a component, can have cytoplasmic, integral, and nuclear-facing components. If it has been shown previously that PfNup313 is the homolog of Nup214 in vertebrates present on the cytosolic side of NPC, this should be stated. If not, then it should be clarified that it is unknown whether Nup313 faces the cytoplasm, nucleus, or is embedded in the NE, as this has implications for the colocalization of Nup313 and Centrin.

      Nuclear pore proteins are very poorly conserved in P. falciparum and Nup313 has only been recently identified as such (Kehrer et al. 2018) mainly by the presence of FG-repeats (as for all the other newly defined proteins). The only related ortholog that can be found through BLAST search against humans, yeasts, and Arabidopsis is Nup100 from S. cerevisiae. ScNup100 is a central pore localizing protein but the sequence similarity to Nup313 is low. We are not aware of any findings showing relatedness to vertebrate Nup214, while sequence analysis rather indicates the absence of orthology. To clearly demonstrate the individual positioning of the few known Nups within the parasite´s nuclear pore complex would require a dedicated long-term project. However, due to the presence of FG-repeats one can assume that it is part of the central FG-Nups layer rather than of the intranuclear basket or the cytoplasmic filaments (line 255). Therefore it would localize more closely to the nuclear envelope than the latter. Either way, a clear gap between centrin and Nup313 signal can be identified and colocalization has not been observed. These data indicate that the exact position of Nup313 on the cytoplasmic, integral or nuclear-facing site is not decisive for the conclusions made in this study and our observations preclude scenarios where centrin is not extranuclear.

      It seems from the image in Figure 2C that DRAQ5 and Hoechst have at least visually indistinguishable localizations. Have the authors taken any STED deconvolved images of nuclei stained with both Hoechst and DRAQ5? Considering the striking increase in detail of the Hoechst signal in STED deconvolved images, it may be informative both to this study and to people who work on chromatin organization what the chromatin staining looks like in the absence of bias towards chromatin state.

      It would, indeed, be interesting to analyse chromatin organization by those means, but DRAQ5 is not a STED compatible dye, highly prone to bleaching, and therefore not suitable for such analysis. Being an infrared dye DRAQ5 is compared to the UV excited Hoechst also yielding a reduced spatial resolution, which is limited by the emitted wave length.

      For the tomography and TEM images, the centrosome is indicated with an arrow, but it isn't entirely clear what that arrow is pointing to for some images. It would be clearer if the centrosome were outlined in green, like the NE, rather than just an arrow. This is particularly important for Supplementary Figure 4, where to my eye, it appears that the microtubules inside the chromatin-free region are coming directly out of a circular structure, which could be interpreted as the centriolar plaque.

      The reviewer is right to point out the use of arrows for centrosome annotation. It was intended for orientation of readers to indicate the “likely position of the centriolar plaque” since a clear boundary around the centrosome can´t be defined. It would have been more precise to indicate that the arrow is pointing at the electron dense region associated with the nuclear membrane, which is of course only one of the sub-regions of the centrosome. This is particularly important since we want to emphasize the extended dimensions of the centrosome. Consequently, we modified the annotation to “electron dense region” in all concerned figures and corresponding legends.

      The ordering of Figure 2A-C seems to imply that the DNA-free region was measured in the STED deconvolved images, but the methods imply that it was in the confocal images. The authors should clarify this in the figure legend or by rearranging B and C's order.

      Hoechst signal was indeed acquired and measured in confocal mode and to avoid confusion we have changed the order of the figures (now Fig. 3B-C) as suggested.

      The authors should provide some more detail on how the DNA-free zone was measured. For example, was it measured on single slices or maximum intensity projections? Was it measured from the middle, far, or near side of the centrin focus? Etc.

      The measurement was carried out in the slice where the DNA-free zone was in focus. Depth was measured from below the centrin signal until the “bottom” of the DNA-free zone. We hoped that the little schematic above the figure would clarify this question, but acknowledge the need to more clearly explain the measurement method, which we now do in the corresponding figure legend (Fig. 3C).

      The methods state that the mCherry signal in figure 2C was detected using a mCherry nanobody. This should be clarified in the figure legends as it currently seems as if we see endogenous mCherry fluorescence.

      The visible signal is certainly a combination of the mCherry plus the “boosting” effect from the Atto594-coupled nanobody that we added. Clearly, this should be mentioned in the figure legend, which we now do.

      The data in Supplementary Figure 4 seems vital to the interpretation of the study. Therefore, for clarity, I encourage the authors to include Supplementary Figure 4 in Figure 2.

      We share the reviewers view on these data and moved them to the main figures (now Fig. 3D).

      In the last sentence of the discussion, it is unclear what the authors mean by how the nuclear compartment "splits," could they please clarify?

      We were referring to the event of centrosome duplication, which has to occur during nuclear division. In a structure without centrioles or a spindle pole body structure forming a half bridge we therefore need a new model to explain how the two poles of the spindle are formed. Potential modes are splitting or de novo assembly. This aspect, as also pointed out by other reviewer, warrants a bit more explanation, which can now be found in the discussion (line 468).

      If the pArl-PfCentrin3-GFP plasmid or pDC2-cam-coCas9-U6.2-hDHFR have been published previously, the respective studies should be cited. If not, the study where the vector backbones were first established should be cited.

      We have now cited the original studies publishing the vector backbones for the first time in the methods (lines 490, 501).

      From the current text, it is not clear that the Nup313 tagged parasites also had a GlmS ribozyme. It is shown in Supplementary Figure 3, but the authors should clarify either in the text of the results, or figure legends, that this parasite line was Nup313_3xHA_GlmS

      The Nup313-tagged line indeed has a glms ribozyme after the HA-tag, which we now mention in the figure legends.

      In the plasmid constructs section of the methods, the authors list several primers by number but not by sequence. Instead, the authors should include the sequence and orientation of each of the primers mentioned in a table as supplementary data.

      This is a good suggestion. We have generated a table at the end of the supplementary data file and on this occasion we also added tables of all the antibodies and dyes used in this study.

      The authors should cite the study where the TgCentrin1 antibody was generated and provide the Rat anti-HA 3F10 antibody catalog number, as catalog numbers are provided for other commercial primary antibodies.

      We now provide the missing catalog numbers in the supplemental data table.

      There is an issue with the formatting of the journal-title in the Kukulski et al. reference.

      Thank you for noticing this error, which we now corrected.

      Reviewer #3 (Significance (Required)):

      The genome of P. falciparum is fully sequenced; however, over 50% of encoded proteins are of unknown function, with many of these proteins unique to Plasmodium parasites. By identifying and characterizing essential biological processes, especially those divergent from human host cell processes, we will formulate ways to interfere with them by developing novel antimalarial drugs. The process of Plasmodium cell division differs from the classical cell cycle of its human host. In the study led by Caroline Simon, authors successfully utilized recent developments of super-resolution microscopies on expanded parasites to identify novel features of cell division machinery of the malaria blood-stage parasite.

      Simon et al.'s work highlight the growing interest in the diversity of cell division mode of Apicomplexan parasites, which will likely contribute to a deeper understanding of the origin and functional role of the centrosome in eukaryotic life. In 2020, the Open Biology journal published a unique article collection named Focus on Centrosome Biology showcasing research that advanced our knowledge on centrosome function, evolution and abnormalities. In addition, the reported findings will interest research groups studying cell cycle regulation and evolution beyond the field of parasitology.

      Our lab studies the peculiar cell cycle of Plasmodium falciparum to gain a functional understanding of mechanistic principles of nuclear envelope assembly and integrity during the cell division of the human malaria parasite.

      **Referee Cross-commenting**

      It is a wonderful study, and once all reviewer's comments are addressed, the manuscript should be in excellent shape for publication.

  30. May 2021
    1. 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 #1

      Evidence, reproducibility and clarity

      Summary:

      In the manuscript „Intra-helical salt bridge contribution to membrane protein insertion" the authors investigate the effect of salt bridge formation between positively and negatively charged amino acids on the insertion behavior of α-helical protein segments into the membrane. Generally it is believed that polar or even charged residues prevent stable membrane insertion of α-helical protein segments, but some of these authors had already shown in a previous paper that such residues are more frequent than expected in transmembrane helices. In the current study, the authors investigate in detail the role of intra-helical salt bridge formation on stable membrane insertion. Using an in vitro membrane insertion assay based on the E. coli leader peptidase (Lep) protein, they found better membrane insertion for helical segments with opposite charge pairs placed at positions compatible with intra-helical salt bridge formation (positions i→i+1; i→i+3 and i→i+4). Furthermore, the authors performed a database screen which revealed that oppositely charged residues are overrepresented at these positions. Finally they picked two candidate membrane proteins from the database (Halorhodopsin and calcium ATPase 1) and proved the presence of an intra-helical salt bridge and determined the contribution of the salt bridge to the apparent free energy of membrane insertion (ΔGapp), which was in the range of 0,5-0,7 kcal/mol.

      Major comments

      1. It seems that the data in Fig. 3b has been mixed up, making it difficult to judge the conclusions. The bars with forward slash seem to represent the "same charge" data and the bars with backward slash seem to represent the "opposite charge" data (exactly contrary to the figure legend). In general the forward and backward slash representation is not easily distinguishable, and for the position i+4 both bars contain a forward slash (making it impossible to discriminate same and opposite charge). Please use filled and unfilled bars instead. Furthermore the bar diagram in Fig. 3a is stacked for opposite and same charge, whereas in Fig. 3b the respective bars are placed next to each other. Additionally the label of the y-axis in Fig. 3c is misleading, as it is not the "Frac. of opp. charged pairs" but the fraction of oppositely charged pairs that form salt bridges.
      2. The authors don´t give details no how the log odds ratios and the respective p-values have been determined. Please include this in the Materials and Methods section. What does a p-value of 0.00e+00 mean (see Table 2, Spacing: +3, "All Log odds")?
      3. What is the proof that for the isolated helix A from the calcium ATPase 1 the membrane embedded part is identical to the full-length protein? The authors investigated two different helix A peptides, the full-length helix ranging from L49-F78, and one short fragment ranging from L49-A69 containing the more hydrophilic N-terminal region, which is the membrane-embedded region in the full-length protein. The authors state: "In contrast, when only the membrane-embedded sequence was included, the Lep chimera was mainly doubly-glycosylated (Fig. 5c, lane 3), suggesting that helix A is properly inserted when the full helical sequence is present." In my opinion this conclusion cannot be drawn from the data presented. The authors used an isolated helical segment, so in my opinion it is much more likely that the isolated full-length helix inserted via its hydrophobic C-terminal part (L60-F78) into the membrane. The authors themselves state in their manuscript: "It has been previously shown that the position in the membrane of TM helices in protein folded structures does not always correspond to the thermodynamically favored positions in the membrane of the isolated helices." Also the i→i+5 mutant points into that direction, because the effect of disturbing the intra-helical salt bridge for the helix A is much less pronounced compared to the similar data in Fig. 4f for the Halorhodopsin protein. In my opinion this shows that most probably only one charged residue (R63) is embedded inside the membrane (with a membrane embedded part of L60-F78).

      Minor comments:

      1. line 151: ",see Figure 2)" Typo: Bracket missing.
      2. line 172: "Other known structural features can also be hinted at, including aromatic ring stacking by His-Trp pair [20] at i→i+6." Please give some more examples of important structural features of membrane proteins, which can be seen in your analysis (e.g. I think that also the glycine zipper can be seen in the i→i+4 data set).
      3. line 255: "The salt bridge contributes approximately ~0,5 kcal/mol to the apparent experimental free energy of membrane insertion." Please explain that this value was derived from the comparison of the ΔGexp between the wt and the i→i+5 mutant. Please comment also on the large difference between the predicted (ΔGpred) and the experimental values (ΔGexp), even if no salt-bridge is involved (e.g. for the DD mutant).
      4. line 348: "Asp-Lys pairs at position i, i+4 and Glu-Lys pairs at position i→ i+3 are the most prevalent as seen previously in Figure 2. They are both among the most prevalent oppositely charged pairs and the charged pairs that form the highest number of salt bridges in membrane protein structures. This is in stark contrast to Glu-Arg pair at position i→ i+1 that although as frequent in pairs as Asp-Lys and Glu-Lys at positions i→i+4 and i→i+3 respectively, only form salt bridges in one-fourth of the cases." Fig. 2 shows that each charge pair has a different prevalence depending on the order (e.g. Asp-Lys and Lys-Asp pairs). I think for this statement the sum of both prevalences should be taken into account, and as the sum is not easy discernible from Fig. 2, it would help to include a table containing the sums. Furthermore, it would be good to refer also to Fig. 3, which also contains a part of the discussed data.
      5. line 402: "c-myc tag (Glu-Gln-Lys-Leu-Ile-Ser-Glu-Glu-Asp-Leu, EQKLISEEDL) was added in Ct in hanging with de PCR primer before cloning." Please revise the sentence and I think the one letter code for the c-myc tag is sufficient (please correct this also in line 428).
      6. line 420: "A region's total ΔG is the sum of these individual scores weighted on where in the region the residue, a residue in the middle of the helix has a higher weight than residues at the ends." Please revise the sentence, the meaning is unclear.
      7. line 436: "Total protein was quantified and equal amounts of protein submitted to Endo H treatment or mock-treated, followed by SDS-PAGE analysis and transferred into a PVDF transfer membrane (ThermoFisher Scientific)." Please revise the sentence.
      8. line 498: "Topological files with sequence and membrane topology are created with the help of the RCSB secondary structure file and only membranes annotated as pure α-helices were retained." I assume that the description contains a typo (membranes annotated as pure α-helices?)
      9. line 507: typo "..., but we did not clustered the proteins" 14: line 560: "The individual value of each experiment in represented by a solid dot being represented as a green square the experimental ΔG value for the L4/A15 sequence from [2]." Please revise the sentence. 15: line 562: "The wt and simple mutants are shown in white bars." Typo: single mutants 16: line 563: "Charges at compatible distances with salt bridge formation (i→i+1; i→i+3; and i→i+4) are shown in yellow. Not compatible distances with salt bridge formation (i→i+2; and i→i+5) are shown in dark gray. Compatible distances but not compatible amino acid pair (i, i+4 DD pair) is shown in clear gray." The given colors don´t match with the figure (i→i+1 = brown; i→i+3 = orange; i→i+4 = yellow and i→i+4 DD pair = white) 17: line 597: "The different monomers are shown in transparent blue, purple and indigo." The different colors are hardly distinguishable in the figure. 18: Figure 4a: The table could be simplified. I think the column "charges" can be removed, as it contains not really charges and the names of the peptides already contain the same information. The column "Å" contains only a value for the wt (and not for the DK i,i+5 mutant) and as the distance for the wt is also given in Fig. 4g, this column can be also removed.
      10. Fig. 4f: The marker lane is hardly visible (completely dark lane)
      11. Fig. 5b: The column "Å" contains only values for the wt sequences (long and short). See also comment 18.
      12. Fig 5d: Why is in the SDS gel a mass shift between the wt and the i→i+5 mutant visible, even though the peptide mass is equal.
      13. There are several changes of font type or format changes (e.g. line 210-214). Please correct this.

      Significance

      As a structural biologist with a focus on membrane-proteins, I understand that the study is concerned on intra-helical salt bridges, but the implications of inter-helical salt bridges should also be discussed, at least in the introduction or outlook. The authors propose that their results are important for the improvement of membrane protein topology prediction methods, so for this aim it is also necessary to take any potential inter-helical salt bridges into account. In this context, it would be relevant to point point out that there even exist extended rows of salt bridges between transmembrane segments (charge-zippers), which serve an important structural element in several membrane proteins.

      The article is well written and most of the conclusions drawn from the experimental results are convincing. I agree with the authors that their results are relevant for future improvement of membrane protein topology prediction software, which so far does not take the possibility of salt bridge formation into account. Therefore, I recommend publication after clarification/revision of the abovementioned points.

  31. Oct 2020
    1. Author Response

      Reviewer #1:

      Hutchings et al. report an updated cryo-electron tomography study of the yeast COP-II coat assembled around model membranes. The improved overall resolution and additional compositional states enabled the authors to identify new domains and interfaces--including what the authors hypothesize is a previously overlooked structural role for the SEC31 C-Terminal Domain (CTD). By perturbing a subset of these new features with mutants, the authors uncover some functional consequences pertaining to the flexibility or stability of COP-II assemblies.

      Overall, the structural and functional work appears reliable, but certain questions and comments should be addressed prior to publication. However, this reviewer failed to appreciate the conceptual advance that warrants publication in a general biology journal like eLIFE. Rather, this study provides a valuable refinement of our understanding of COP-II that I believe is better suited to a more specialized, structure-focused journal.

      We agree that in our original submission our description of the experimental setup, indeed similar to previous work, did not fully capture the novel findings of this paper. Rather than being simply a higher resolution structure of the COPII coat, in fact we have discovered new interactions in the COPII assembly network, and we have probed their functional roles, significantly changing our understanding of the mechanisms of COPII-mediated membrane curvature. In the revised submission we have included additional genetic data that further illuminate this mechanism, and have rewritten the text to better communicate the novel aspects of our work.

      Our combination of structural, functional and genetic analyses goes beyond refining our textbook understanding of the COPII coat as a simple ‘adaptor and cage’, but rather it provides a completely new picture of how dynamic regulation of assembly and disassembly of a complex network leads to membrane remodelling.

      These new insights have important implications for how coat assembly provides structural force to bend a membrane but is still able to adapt to distinct morphologies. These questions are at the forefront of protein secretion, where there is debate about how different types of carriers might be generated that can accommodate cargoes of different size.

      Major Comments: 1) The authors belabor what this reviewer thinks is an unimportant comparison between the yeast reconstruction of the outer coat vertex with prior work on the human outer coat vertex. Considering the modest resolution of both the yeast and human reconstructions, the transformative changes in cryo-EM camera technology since the publication of the human complex, and the differences in sample preparation (inclusion of the membrane, cylindrical versus spherical assemblies, presence of inner coat components), I did not find this comparison informative. The speculations about a changing interface over evolutionary time are unwarranted and would require a detailed comparison of co-evolutionary changes at this interface. The simpler explanation is that this is a flexible vertex, observed at low resolution in both studies, plus the samples are very different.

      We do agree that our proposal that the vertex interface changes over evolutionary time is speculative and we have removed this discussion. We agree that a co-evolutionary analysis will be enlightening here, but is beyond the scope of the current work.

      We respectfully disagree with the reviewer’s interpretation that the difference between the two vertices is due to low resolution. The interfaces are clearly different, and the resolutions of the reconstructions are sufficient to state this. The reviewer’s suggestion that the difference in vertex orientation might be simply attributable to differences in sample, such as inclusion of the membrane, cylindrical versus spherical morphology, or presence of inner coat components were ruled out in our original submission: we resolved yeast vertices on spherical vesicles (in addition to those on tubes) and on membrane-less cages. These analyses clearly showed that neither the presence of a membrane, nor the change in geometry (tubular vs. spherical) affect vertex interactions. These experiments are presented in Supplementary Fig 4 (Supplementary Fig. 3 in the original version). Similarly, we discount that differences might be due to the presence or absence of inner coat components, since membrane-less cages were previously solved in both conditions and are no different in terms of their vertex structure (Stagg et al. Nature 2006 and Cell 2008).

      We believe it is important to report on the differences between the two vertex structures. Nevertheless, we have shifted our emphasis on the functional aspects of vertex formation and moved the comparison between the two vertices to the supplement.

      2) As one of the major take home messages of the paper, the presentation and discussion of the modeling and assignment of the SEC31-CTD could be clarified. First, it isn't clear from the figures or the movies if the connectivity makes sense. Where is the C-terminal end of the alpha-solenoid compared to this new domain? Can the authors plausibly account for the connectivity in terms of primary sequence? Please also include a side-by-side comparison of the SRA1 structure and the CTD homology model, along with some explanation of the quality of the model as measured by Modeller. Finally, even if the new density is the CTD, it isn't clear from the structure how this sub-stoichiometric and apparently flexible interaction enhances stability. Hence, when the authors wrote "when the [CTD] truncated form was the sole copy of Sec31 in yeast, cells were not viable, indicating that the novel interaction we detect is essential for COPII coat function." Maybe, but could this statement be a leap to far? Is it the putative interaction essential, or is the CTD itself essential for reasons that remain to be fully determined?

      The CTD is separated from the C-terminus of the alpha solenoid domain by an extended domain (~350 amino acids) that is predicted to be disordered, and contains the PPP motifs and catalytic fragment that contact the inner coat. This is depicted in cartoon form in Figures 3A and 7, and discussed at length in the text. This arrangement explains why no connectivity is seen, or expected. We could highlight the C-terminus of the alpha-solenoid domain to emphasize where the disordered region should emerge from the rod, but connectivity of the disordered domain to the CTD could arise from multiple positions, including from an adjacent rod.

      The reviewer’s point about the essentiality of the CTD being independent of its interaction with the Sec31 rod, is an important one. The basis for our model that the CTD enhances stability or rigidity of the coat is the yeast phenotype of Sec31-deltaCTD, which resembles that of a sec13 null. Both mutants are lethal, but rescued by deletion of emp24, which leads to more easily deformable membranes (Čopič et al. Science 2012). We agree that even if this model is true, the interaction of the CTD with Sec31 that our new structure reveals is not proven to drive rigidity or essentiality. We have tempered this hypothesis and added alternative possibilities to the discussion.

      We have included the SRA1 structure in Supplementary Fig 5, as requested, and the model z-score in the Methods. The Z-score, as calculated by the proSA-web server is -6.07 (see figure below, black dot), and falls in line with experimentally determined structures including that of the template (PDB 2mgx, z-score = -5.38).

      img

      3) Are extra rods discussed in Fig. 4 are a curiosity of unclear functional significance? This reviewer is concerned that these extra rods could be an in vitro stoichiometry problem, rather than a functional property of COP-II.

      This is an important point, that, as we state in the paper, cannot be answered at the moment: the resolution is too low to identify the residues involved in the interaction. Therefore we are hampered in our ability to assess the physiological importance of this interaction. We still believe the ‘extra’ rods are an important observation, as they clearly show that another mode of outer coat interaction, different from what was reported before, is possible.

      The concern that interactions visualised in vitro might not be physiologically relevant is broadly applicable to structural biology approaches. However, our experimental approach uses samples that result from active membrane remodelling under near-physiological conditions, and we therefore expect these to be less prone to artefacts than most in vitro reconstitution approaches, where proteins are used at high concentrations and in high salt buffer conditions.

      4) The clashsccore for the PDB is quite high--and I am dubious about the reliability of refining sidechain positions with maps at this resolution. In addition to the Ramchandran stats, I would like to see the Ramachandran plot as well as, for any residue-level claims, the density surrounding the modeled side chain (e.g. S742).

      The clashscore is 13.2, which, according to molprobity, is in the 57th percentile for all structures and in the 97th for structures of similar resolutions. We would argue therefore that the clashscore is rather low. In fact, the model was refined from crystal structures previously obtained by other groups, which had worse clashscore (17), despite being at higher resolution. Our refinement has therefore improved the clashscore. During refinement we have chosen restraint levels appropriate to the resolution of our map (Afonine et al., Acta Cryst D 2018)

      The Ramachandran plot is copied here and could be included in a supplemental figure if required. We make only one residue-level claim (S742), the density for which is indeed not visible at our resolution. We claim that S742 is close to the Sec23-23 interface, and do not propose any specific interactions. Nevertheless we have removed reference to S742 from the manuscript. We included this specific information because of the potential importance of this residue as a site of phosphorylation, thereby putting this interface in broader context for the general eLife reader.

      img

      Minor Comments:

      1) The authors wrote "To assess the relative positioning of the two coat layers, we analysed the localisation of inner coat subunits with respect to each outer coat vertex: for each aligned vertex particle, we superimposed the positions of all inner coat particles at close range, obtaining the average distribution of neighbouring inner coat subunits. From this 'neighbour plot' we did not detect any pattern, indicating random relative positions. This is consistent with a flexible linkage between the two layers that allows adaptation of the two lattices to different curvatures (Supplementary Fig 1E)." I do not understand this claim, since the pattern both looks far from random and the interactions depend on molecular interactions that are not random. Please clarify.

      We apologize for the confusion: the pattern of each of the two coats are not random. Our sentence refers to the positions of inner and outer coats relative to each other. The two lattices have different parameters and the two layers are linked by flexible linkers (the 350 amino acids referred to above). We have now clarified the sentence.

      2) Related to major point #1, the author wrote "We manually picked vertices and performed carefully controlled alignments." I do now know what it means to carefully control alignments, and fear this suggests human model bias.

      We used different starting references for the alignments, with the precise aim to avoid model bias. For both vesicle and cage vertex datasets, we have aligned the subtomograms against either the vertex obtained from tubules, or the vertex from previously published membrane-less cages. In all cases, we retrieved a structure that resembles the one on tubules, suggesting that the vertex arrangement we observe isn’t simply the result of reference bias. This procedure is depicted in Supplementary Fig 4 (Supplementary Fig. 3 in the original manuscript), but we have now clarified it also in the methods section.

      3) Why do some experiments use EDTA? I may be confused, but I was surprised to see the budding reaction employed 1mM GMPPNP, and 2.5mM EDTA (but no Magnesium?). Also, for the budding reaction, please replace or expand upon the "the 10% GUV (v/v)" with a mass or molar lipid-to-protein ratio.

      We regret the confusion. As stated in the methods, all our budding reactions are performed in the presence of EDTA and Magnesium, which is present in the buffer (at 1.2 mM). The reason is to facilitate nucleotide exchange, as reported and validated in Bacia et al., Scientific Reports 2011.

      Lipids in GUV preparations are difficult to quantify. We report the stock concentrations used, but in each preparation the amount of dry lipid that forms GUVs might be different, as is the concentration of GUVs after hydration. However since we analyse reactions where COPII proteins have bound and remodelled individual GUVs, we do not believe the protein/lipid ratio influences our structures.

      4) Please cite the AnchorMap procedure.

      We cite the SerialEM software, and are not aware of other citations specifically for the anchor map procedure.

      5) Please edit for typos (focussing, functionl, others)

      Done

      Reviewer #2:

      The manuscript describes new cryo-EM, biochemistry, and genetic data on the structure and function of the COPII coat. Several new discoveries are reported including the discovery of an extra density near the dimerization region of Sec13/31, and "extra rods" of Sec13/31 that also bind near the dimerization region. Additionally, they showed new interactions between the Sec31 C-terminal unstructured region and Sec23 that appear to bridge multiple Sec23 molecules. Finally, they increased the resolution of the Sec23/24 region of their structure compared to their previous studies and were able to resolve a previously unresolved L-loop in Sec23 that makes contact with Sar1. Most of their structural observations were nicely backed up with biochemical and genetic experiments which give confidence in their structural observations. Overall the paper is well-written and the conclusions justified.

      However, this is the third iteration of structure determination of the COPII coat on membrane with essentially the same preparation and methods. Each time, there has been an incremental increase in resolution and new discoveries, but the impact of the present study is deemed to be modest. The science is good, but it may be more appropriate for a more specialized journal. Areas of specific concern are described below.

      As described above, we respectfully disagree with this interpretation of the advance made by the current work. This work improves on previous work in many aspects. The resolution of the outer coat increases from over 40A to 10-12A, allowing visualisation of features that were not previously resolved, including a novel vertex arrangement, the Sec31 CTD, and the outer coat ‘extra rods’. An improved map of the inner coat also allows us to resolve the Sec23 ‘L-loop’. We would argue that these are not just extra details, but correspond to a suite of novel interactions that expand our understanding of the complex COPII assembly network. Moreover, we include biochemical and genetic experiments that not only back up our structural observations but bring new insights into COPII function. As pointed out in response to reviewer 1, we believe our work contributes a significant conceptual advance, and have modified the manuscript to convey this more effectively.

      1) The abstract is vague and should be re-written with a better description of the work.

      We have modified the abstract to specifically outline what we have done and the major new discoveries of this paper.

      2) Line 166 - "Surprisingly, this mutant was capable of tubulating GUVs". This experiment gets to one of the fundamental unknown questions in COPII vesiculation. It is not clear what components are driving the membrane remodeling and at what stages during vesicle formation. Isn't it possible that the tubulation activity the authors observe in vitro is not being driven at all by Sec13/31 but rather Sec23/24-Sar1? Their Sec31ΔCTD data supports this idea because it lacks a clear ordered outer coat despite making tubules. An interesting experiment would be to see if tubules form in the absence of all of Sec13/31 except the disordered domain of Sec31 that the authors suggest crosslinks adjacent Sec23/24s.

      This is an astute observation, and we agree with the reviewer that the source of membrane deformation is not fully understood. We favour the model that budding is driven significantly by the Sec23-24 array. To further support this, we have performed a new experiment, where we expressed Sec31ΔN in yeast cells lacking Emp24, which have more deformable membranes and are tolerant to the otherwise lethal deletion of Sec13. While Sec31ΔN in a wild type background did not support cell viability, this was rescued in a Δemp24 yeast strain, strongly supporting the hypothesis that a major contributor to membrane remodelling is the inner coat, with the outer coat becoming necessary to overcome membrane bending resistance that ensues from the presence of cargo. We now include these results in Figure 1.

      However, we must also take into account the results presented in Fig. 6, where we show that weakening the Sec23-24 interface still leads to budding, but only if Sec13-31 is fully functional, and that in this case budding leads to connected pseudo-spherical vesicles rather than tubes. When Sec13-31 assembly is also impaired, tubes appear unstructured. We believe this strongly supports our conclusions that both inner and outer coat interactions are fundamental for membrane remodelling, and it is the interplay between the two that determines membrane morphology (i.e. tubes vs. spheres).

      To dissect the roles of inner and outer coats even further, we have done the experiment that the reviewer suggests: we expressed Sec31768-1114, but the protein was not well-behaved and co-purified with chaperones. We believe the disordered domain aggregates when not scaffolded by the structured elements of the rod. Nonetheless, we used this fragment in a budding reaction, and could not see any budding. We did not include this experiment as it was inconclusive: the lack of functionality of the purified Sec31 fragment could be attributed to the inability of the disordered region to bind its inner coat partner in the absence of the scaffolding Sec13-31 rod. As an alternative approach, we have used a version of Sec31 that lacks the CTD, and harbours a His tag at the N-terminus (known from previous studies to partially disrupt vertex assembly). We think this construct is more likely to be near native, since both modifications on their own lead to functional protein. We could detect no tubulation with this construct by negative stain, while both control constructs (Sec31ΔCTD and Nhis-Sec31) gave tubulation. This suggests that the cross-linking function of Sec31 is not sufficient to tubulate GUV membranes, but some degree of functional outer coat organisation (either mediated by N- or C-terminal interactions) is needed. It is also possible that the lack of outer coat organisation might lead to less efficient recruitment to the inner coat and cross-linking activity. We have added this new observation to the manuscript.

      3) Line 191 - "Inspecting cryo-tomograms of these tubules revealed no lozenge pattern for the outer 192 coat" - this phrasing is vague. The reviewer thinks that what they mean is that there is a lack of order for the Sec13/31 layer. Please clarify.

      The reviewer is correct, we have changed the sentence.

      4) Line 198 - "unambiguously confirming this density corresponds to 199 the CTD." This only confirms that it is the CTD if that were the only change and the Sec13/31 lattice still formed. Another possibility is that it is density from other Sec13/31 that only appears when the lattice is formed such as the "extra rods". One possibility is that the density is from the extra rods. The reviewer agrees that their interpretation is indeed the most likely, but it is not unambiguous. The authors should consider cross-linking mass spectrometry.

      We have removed the word ‘unambiguously’, and changed to ‘confirming that this density most likely corresponds to the CTD’. Nonetheless, we believe that our interpretation is correct: the extra rods bind to a different position, and themselves also show the CTD appendage. In this experiment, the lack of the CTD was the only biochemical change.

      5) In the Sec31ΔCTD section, the authors should comment on why ΔCTD is so deleterious to oligomer organization in yeast when cages form so abundantly in preparations of human Sec13/31 ΔC (Paraan et al 2018).

      We have added a comment to address this. “Interestingly, human Sec31 proteins lacking the CTD assemble in cages, indicating that either the vertex is more stable for human proteins and sufficient for assembly, or that the CTD is important in the context of membrane budding but not for cage formation in high salt conditions.”

      6) The data is good for the existence of the "extra rods", but significance and importance of them is not clear. How can these extra densities be distinguished from packing artifacts due to imperfections in the helical symmetry.

      Please also see our response to point 3 from reviewer 1. Regarding the specific concern that artefacts might be a consequence of imperfection in the helical symmetry, we would argue such imperfections are indeed expected in physiological conditions, and to a much higher extent. For this reason interactions seen in the context of helical imperfections are likely to be relevant. In fact, in normal GTP hydrolysis conditions, we expect long tubes would not be able to form, and the outer coat to be present on a wide range of continuously changing membrane curvatures. We think that the ability of the coat to form many interactions when the symmetry is imperfect might be exactly what confers the coat its flexibility and adaptability.

      7) Figure 5 is very hard to interpret and should be redone. Panels B and C are particularly hard to interpret.

      We have made a new figure where we think clarity is improved.

      8) The features present in Sec23/24 structure do not reflect the reported resolution of 4.7 Å. It seems that the resolution is overestimated.

      We report an average resolution of 4.6 Å. In most of our map we can clearly distinguish beta strands, follow the twist of alpha helices and see bulky side chains. These features typically become visible at 4.5-5A resolution. We agree that some areas are worse than 4.6 Å, as typically expected for such a flexible assembly, but we believe that the average resolution value reported is accurate. We obtained the same resolution estimate using different software including relion, phenix and dynamo, so that is really the best value we can provide. To further convince ourselves that we have the resolution we claim, we sampled EM maps from the EMDB with the same stated resolution (we just took the 7 most recent ones which had an associated atomic model), and visualised their features at arbitrary positions. For both beta strands and alpha helices, we do not feel our map looks any worse than the others we have examined. We include a figure here.

      img

      9) Lines 315/316 - "We have combined cryo-tomography with biochemical and genetic assays to obtain a complete picture of the assembled COPII coat at unprecedented resolution (Fig. 7)"

      10) Figure 7. is a schematic model/picture the authors should reference a different figure or rephrase the sentence.

      We now refer to Fig 7 in a more appropriate place.

      Reviewer #3:

      The manuscript by Hutchings et al. describes several previously uncharacterised molecular interactions in the coats of COP-II vesicles by using a reconstituted coats of yeast COPI-II. They have improved the resolution of the inner coat to 4.7A by tomography and subtomogram averaging, revealing detailed interactions, including those made by the so-called L-loop not observed before. Analysis of the outer layer also led to new interesting discoveries. The sec 31 CTD was assigned in the map by comparing the WT and deletion mutant STA-generated density maps. It seems to stabilise the COP-II coats and further evidence from yeast deletion mutants and microsome budding reconstitution experiments suggests that this stabilisation is required in vitro. Furthermore, COP-II rods that cover the membrane tubules in right-handed manner revealed sometimes an extra rod, which is not part of the canonical lattice, bound to them. The binding mode of these extra rods (which I refer to here a Y-shape) is different from the canonical two-fold symmetric vertex (X-shape). When the same binding mode is utilized on both sides of the extra rod (Y-Y) the rod seems to simply insert in the canonical lattice. However, when the Y-binding mode is utilized on one side of the rod and the X-binding mode on the other side, this leads to bridging different lattices together. This potentially contributes to increased flexibility in the outer coat, which maybe be required to adopt different membrane curvatures and shapes with different cargos. These observations build a picture where stabilising elements in both COP-II layers contribute to functional cargo transport. The paper makes significant novel findings that are described well. Technically the paper is excellent and the figures nicely support the text. I have only minor suggestions that I think would improve the text and figure.

      We thank the reviewer for helpful suggestions which we agree improve the manuscript.

      Minor Comments:

      L 108: "We collected .... tomograms". While the meaning is clear to a specialist, this may sound somewhat odd to a generic reader. Perhaps you could say "We acquired cryo-EM data of COP-II induced tubules as tilt series that were subsequently used to reconstruct 3D tomograms of the tubules."

      We have changed this as suggested

      L 114: "we developed an unbiased, localisation-based approach". What is the part that was developed here? It seems that the inner layer particle coordinates where simply shifted to get starting points in the outer layer. Developing an approach sounds more substantial than this. Also, it's unclear what is unbiased about this approach. The whole point is that it's biased to certain regions (which is a good thing as it incorporates prior knowledge on the location of the structures).

      We have modified the sentence to “To target the sparser outer coat lattice for STA, we used the refined coordinates of the inner coat to locate the outer coat tetrameric vertices”, and explain the approach in detail in the methods.

      L 124: "The outer coat vertex was refined to a resolution of approximately ~12 A, revealing unprecedented detail of the molecular interactions between Sec31 molecules (Supplementary Fig 2A)". The map alone does not reveal molecular interactions; the main understanding comes from fitting of X-ray structures to the low-resolution map. Also "unprecedented detail" itself is somewhat problematic as the map of Noble et al (2013) of the Sec31 vertex is also at nominal resolution of 12 A. Furthermore, Supplementary Fig 2A does not reveal this "unprecedented detail", it shows the resolution estimation by FSC. To clarify, these points you could say: "Fitting of the Sec31 atomic model to our reconstruction vertex at 12-A resolution (Supplementary Fig 2A) revealed the molecular interactions between different copies of Sec31 in the membrane-assembled coat.

      We have changed the sentence as suggested.

      L 150: Can the authors exclude the possibility that the difference is due to differences in data processing? E.g. how the maps amplitudes have been adjusted?

      Yes, we can exclude this scenario by measuring distances between vertices in the right and left handed direction. These measurements are only compatible with our vertex arrangement, and cannot be explained by the big deviation from 4-fold symmetry seen in the membrane-less cage vertices.

      L 172: "that wrap tubules either in a left- or right-handed manner". Don't they do always both on each tubule? Now this sentence could be interpreted to mean that some tubules have a left-handed coat and some a right-handed coat.

      We have changed this sentence to clarify. “Outer coat vertices are connected by Sec13-31 rods that wrap tubules both in a left- and right-handed manner.”

      L276: "The difference map" hasn't been introduced earlier but is referred to here as if it has been.

      We now introduce the difference map.

      L299: Can "Secondary structure predictions" denote a protein region "highly prone to protein binding"?

      Yes, this is done through DISOPRED3, a feature include in the PSIPRED server we used for our predictions. The reference is: Jones D.T., Cozzetto D. DISOPRED3: precise disordered region predictions with annotated protein-binding activity Bioinformatics. 2015; 31:857–863. We have now added this reference to the manuscript.

      L316: It's true that the detail in the map of the inner coat is unprecedented and the model presented in Figure 7 is partially based on that. But here "unprecedented resolution" sounds strange as this sentence refers to a schematic model and not a map.

      We have changed this by moving the reference to Fig 7 to a more appropriate place

      L325: "have 'compacted' during evolution" -> remove. It's enough to say it's more compact in humans and less compact in yeast as there could have been different adaptations in different organisms at this interface.

      We have changed as requested. See also our response to reviewer 1, point 1.

      L327: What's exactly meant by "sequence diversity or variability at this density".

      We have now clarified: “Since multiple charge clusters in yeast Sec31 may contribute to this interaction interface (Stancheva et al., 2020), the low resolution could be explained by the fact that the density is an average of different sequences.”

      L606-607: The description of this custom data processing approach is difficult to follow. Why is in-plane flip needed and how is it used here?

      Initially particles are picked ignoring tube directionality (as this cannot be assessed easily from the tomograms due to the pseudo-twofold symmetry of the Sec23/24/Sar1 trimer). So the in plane rotation of inner coat subunit could be near 0 or 180°. For each tube, both angles are sampled (in-plane flip). Most tubes result in the majority of particles being assigned one of the two orientations (which is then assumed as the tube directionality). Particles that do not conform are removed, and rare tubes where directionality cannot be determined are also removed. We have re-written the description to clarify these points: “Initial alignments were conducted on a tube-by-tube basis using the Dynamo in-plane flip setting to search in-plane rotation angles 180° apart. This allowed to assign directionality to each tube, and particles that were not conforming to it were discarded by using the Dynamo dtgrep_direction command in custom MATLAB scripts”

      L627: "Z" here refers to the coordinate system of aligned particles not that of the original tomogram. Perhaps just say "shifted 8 pixels further away from the membrane".

      Changed as requested.

      L642-643: How can the "left-handed" and "right-handed" rods be separated here? These terms refer to the long-range organisation of the rods in the lattice it's not clear how they were separated in the early alignments.

      They are separated by picking only one subset using the dynamo sub-boxing feature. This extracts boxes from the tomogram which are in set positions and orientation relative to the average of previously aligned subtomograms. From the average vertex structure, we sub-box rods at 4 different positions that correspond to the centre of the rods, and the 2-fold symmetric pairs are combined into the same dataset. We have clarified this in the text: “The refined positions of vertices were used to extract two distinct datasets of left and right-handed rods respectively using the dynamo sub-boxing feature.”

      Figure 2B. It's difficult to see the difference between dark and light pink colours.

      We have changed colours to enhance the difference.

      Figure 3C. These panels report the relative frequency of neighbouring vertices at each position; "intensity" does not seem to be the right measure for this. You could say that the colour bar indicates the "relative frequency of neighbouring vertices at each position" and add detail how the values were scaled between 0 and 1. The same applies to SFigure 1E.

      Changed as requested.

      Figure 4. The COP-II rods themselves are relatively straight, and they are not left-handed or right-handed. Here, more accurate would be "architecture of COPII rods organised in a left-handed manner". (In the text the authors may of course define and then use this shorter expression if they so wish.) Panel 4B top panel could have the title "left-handed" and the lower panel should have the title "right-handed" (for consistency and clarity).

      We have now defined left- and right-handed rods in the text, and have changed the figure and panel titles as requested.

    1. Why Are Finland’s Schools Successful? The country’s achievements in education have other nations, especially the United States, doing their homework <img src="https://thumbs-prod.si-cdn.com/thzZYTv2Evhq3x8iHdcaakihfVE=/800x600/filters:no_upscale()/https://public-media.si-cdn.com/filer/cd/ee/cdee1c82-f8e3-4de4-983e-8599d4485745/finland-smiles-wr.jpg" alt="Kirkkojarvi School" itemprop="image"> "This is what we do every day," says Kirkkojarvi Comprehensive School principal Kari Louhivuori, "prepare kids for life." (Stuart Conway) By LynNell Hancock Smithsonian Magazine | Subscribe September 2011 AddThis Sharing ButtonsShare to FacebookFacebookShare to TwitterTwitterShare to RedditReddit78Share to PinterestPinterest997Share to LinkedInLinkedInShare to FlipboardFlipboardShare to EmailEmailShare to PrintPrintShare to MoreAddThis934 It was the end of term at Kirkkojarvi Comprehensive School in Espoo, a sprawling suburb west of Helsinki, when Kari Louhivuori, a veteran teacher and the school’s principal, decided to try something extreme—by Finnish standards. One of his sixth-grade students, a Kosovo-Albanian boy, had drifted far off the learning grid, resisting his teacher’s best efforts. The school’s team of special educators—including a social worker, a nurse and a psychologist—convinced Louhivuori that laziness was not to blame. So he decided to hold the boy back a year, a measure so rare in Finland it’s practically obsolete. function dispatchComscoreLoadedEvent(){ let event = new Event('MPlayerComscoreLoaded'); window.dispatchEvent(event); } !function(e){var t={};function n(r){if(t[r])return t[r].exports;var i=t[r]={i:r,l:!1,exports:{}};return e[r].call(i.exports,i,i.exports,n),i.l=!0,i.exports}n.m=e,n.c=t,n.d=function(e,t,r){n.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},n.r=function(e){"undefined"!==typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},n.t=function(e,t){if(1&t&&(e=n(e)),8&t)return e;if(4&t&&"object"===typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(n.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var i in e)n.d(r,i,function(t){return e[t]}.bind(null,i));return r},n.n=function(e){var t=e&&e.__esModule?function(){return 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u=e.getNextSpecificMidrollTime(o,n);return{midrollNumber:o.indexOf(u)+1,currentTime:n,midrollTime:u,mediaId:a}}return null}},{key:"isMidrollAlreadyPlayed",value:function(e,t){return-1!==e.indexOf(t)}},{key:"isMidrollReached",value:function(t){var n=hn.currentVideoTime(t),r=bi.midrolls(t),i=r.every,o=r.on,a=_i.playedMidrolls(t);if(!Un(i)&&n>0){var s=e.getNextReocurringMidrollNumber(i,n),u=s*i;return!this.isMidrollAlreadyPlayed(a,s)&&e.isTimeInPreAdRange(n,u)}if(!Un(o)){var c=e.getNextSpecificMidrollTime(o,n),l=o.indexOf(c)+1;return l>0&&!this.isMidrollAlreadyPlayed(a,l)}return!1}},{key:"shouldRequestAd",value:function(e){var t=_i.adStatus(e);return!Bi(e)&&!Hi(t)&&this.isMidrollReached(e)}},{key:"isOnAdTime",value:function(t){var n=hn.currentVideoTime(t),r=bi.midrolls(t),i=r.every,o=r.on,a=_i.playedMidrolls(t);if(Bi(t))return!1;if(!Un(i)){var s=e.getNextReocurringMidrollNumber(i,n);return 0!==n&&n%i===0&&!this.isMidrollAlreadyPlayed(a,s)}if(!Un(o)){var u=o.indexOf(n);return-1!==u&&!this.isMidrollAlreadyPlayed(a,u)}return!1}}]),e}(),Wi=function(){function e(t){var n=this;Ai()(this,e),f()(this,"referrerUrl",void 0),f()(this,"staticAdTag",void 0),f()(this,"generate",function(e,t){var r=encodeURIComponent(n.referrerUrl);return Un(n.staticAdTag)?n.adTagFromApi(e,r,t):n.parseAdTag(n.staticAdTag,e,r,t)}),this.staticAdTag=t,this.referrerUrl=window.location.href}return Vi()(e,[{key:"parseAdTag",value:function(t,n,r,i){var o=t.replace("##AdUnit##",e.parseAdName(n)).replace("##DESCRIPTION_URL_UNESC##",r).replace("##REFERRER_URL_UNESC##",encodeURIComponent(this.referrerUrl)).replace("##CACHEBUSTER##",e.cacheBuster(n)).replace("##MIDROLL_ORDER##",e.adIndexFromName(n));return o=e.replaceVideoId(o,"##VIDEO_ID##",i),o=e.addHacksToAdTag(o)}},{key:"adTagFromApi",value:function(e,t,n){try{var r=window.getVideoTag(t,e);return Un(r)?null:this.parseAdTag(r,e,t,n)}catch(i){return null}}}],[{key:"getCCPAConsent",value:function(t){try{var n="";return window.__uspapi&&window.__uspapi("getUSPData",1,function(e,t){t&&(n=e.uspString)}),e.setSearchParamToAdTag(t,"us_privacy",n)}catch(r){return t}}},{key:"replaceVideoId",value:function(e,t,n){return e.replace(t,n).replace(encodeURIComponent(t),n).replace(encodeURIComponent(encodeURIComponent(t)),n)}},{key:"cacheBuster",value:function(t){return"".concat((new Date).getTime()).concat(e.adIndexFromName(t))}},{key:"parseAdName",value:function(t){return t.startsWith("preroll")?"PR":"MR".concat(e.adIndexFromName(t))}},{key:"adIndexFromName",value:function(e){return e.replace(/[^\d]*/g,"")}}]),e}();f()(Wi,"setSearchParamToAdTag",function(e,t,n){var r=new URL(e),i=decodeURIComponent(n);return r.searchParams.set(t,i),r.href}),f()(Wi,"getSearchParamFromAdTag",function(e,t){return new URL(e).searchParams.get(t)}),f()(Wi,"addHacksToAdTag",function(e){var t=e,n=Wi.getSearchParamFromAdTag(t,"cust_params");if(!jn(window.mmAPSbids)&&!Un(n)){var r="".concat(window.mmAPSbids,"&").concat(n);t=Wi.setSearchParamToAdTag(t,"cust_params",r)}if(!jn(window.shouldPlayAdRules)){var i=window.shouldPlayAdRules?"1":"0";t=Wi.setSearchParamToAdTag(t,"ad_rule",i)}return t=Wi.getCCPAConsent(t)});var zi=function(){function e(t,n){Ai()(this,e),f()(this,"store",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"adsScheduler",void 0),f()(this,"previousVideoTagStatus",void 0);var r=t.getState;this.store=t,this.adsScheduler=n,this.previousVideoTagStatus=hn.videoTagStatus(r()),this.videoTagStatusSubscriber=new ji(t,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this))}return Vi()(e,[{key:"onVideoTagStatusChanged",value:function(t){var n=hn.videoTagStatus(t),r=_i.adStatus(t);"seeking"===this.previousVideoTagStatus&&(Hi(r)?this.adsScheduler.onSeekedWhileAdInProgress():e.isSeekedOverMidroll(t)&&this.adsScheduler.onSeekToAdOpportunity(e.getSeekedMidroll(t))),this.previousVideoTagStatus=n}}],[{key:"getVideoTagStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}},{key:"getClosestSkippedUnplayedMidroll",value:function(e,t){for(var n=t;n>0;n-=1)if(-1===e.indexOf(n))return n;return null}},{key:"getClosestLowerSeekedMidrollNumber",value:function(e,t){var n=In()(e).reverse().find(function(e){return e<=t});return e.indexOf(n)+1}},{key:"getSeekedSpecificMidroll",value:function(e,t,n,r){var i=this.getClosestLowerSeekedMidrollNumber(e,t),o=this.getClosestSkippedUnplayedMidroll(r,i);return{midrollNumber:o,currentTime:t,midrollTime:e[o-1],mediaId:n}}},{key:"isSeekedOverSpecificMidroll",value:function(e,t,n){if(jn(e))return!1;var r=this.getClosestLowerSeekedMidrollNumber(e,n);return 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t="midroll".concat(e.midrollNumber);return r.adTagGenerator.generate(t,e.mediaId)}),f()(this,"generatePrerollTag",function(e,t){var n="preroll".concat(t);return r.adTagGenerator.generate(n,e)}),f()(this,"onAdTimeReached",function(){r.monetization.onMidrollAdOpportunity()}),f()(this,"onPreAdTimeReached",function(e){r.onPreMidrollAdOpportunity(e)}),f()(this,"onSeekToAdOpportunity",function(e){r.onPreMidrollAdOpportunity(e)}),f()(this,"isMidrollAlreadyRequested",function(e){return e.midrollNumber===r.lastRequestedMidroll.midrollNumber&&e.mediaId===r.lastRequestedMidroll.mediaId&&e.midrollTime===r.lastRequestedMidroll.midrollTime}),f()(this,"onPreMidrollAdOpportunity",function(e){if(Un(r.lastRequestedMidroll)||!r.isMidrollAlreadyRequested(e)){r.lastRequestedMidroll=e;var t=r.generateMidrollTag(e);r.monetization.onPreMidrollAdOpportunity(e,t)}}),f()(this,"onPrerollReached",function(e,t){var n=r.generatePrerollTag(e,t);r.monetization.onPrerollAdOpportunity(n)}),f()(this,"onSeekedWhileAdInProgress",function(){r.monetization.onMidrollAdOpportunity()});var i=t.getState;this.monetization=n,this.videoTimeSubscriber=new qi(t,this),this.videoSeekSubscriber=new zi(t,this),this.prerollScheduler=new Gi(t,this);var o=_i.adTagUrlTemplate(i());this.adTagGenerator=new Wi(o)},Yi=function(){function e(){Ai()(this,e)}return Vi()(e,null,[{key:"generateAdRequest",value:function(e,t,n){var r=new google.ima.AdsRequest;return r.adTagUrl=e,Fn()||r.setAdWillPlayMuted(t),r.vastLoadTimeout=n,r}}]),e}(),Zi=function(e){return function(t){t({type:"[MONETIZATION] change ad status",payload:e})}},Xi=function(e){return function(t){t({type:"[COMMON] set pending video status",payload:{pendingStatusObject:{type:e,value:""}}})}},Ji=function(e){return function(t){t({type:"[MONETIZATION] change loading ad status",payload:e})}},Qi=function(e){return function(t){t({type:"[MONETIZATION] update ad 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t=a.store,n=t.getState,r=t.dispatch,i=gn.volume(n());Bn()||gn.muted(n())?(e.setVolume(0),Qi(!0)(r)):(e.setVolume(gn.volume(n())),eo(i)(r),Qi(!1)(r))}),f()(this,"createIMAAdManager",function(t){a.IMAAdManager=t.getAdsManager(a.adVideoElement,e.getAdsRenderingSettings()),a.setAdVolume(a.IMAAdManager)}),f()(this,"registerToAdManagerEvents",function(){a.IMAAdManager.addEventListener(google.ima.AdErrorEvent.Type.AD_ERROR,a.onAdError),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_PAUSE_REQUESTED,a.onContentPauseRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_RESUME_REQUESTED,a.onContentResumeRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.STARTED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.IMPRESSION,a.onAdImpression),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.SKIPPED,a.onAdSkipped),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.COMPLETE,a.onAdCompleted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.PAUSED,a.onAdPaused),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.RESUMED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.AD_PROGRESS,a.onAdProgressChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_CHANGED,a.onVolumeChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_MUTED,a.onAdVolumeMutedChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.ALL_ADS_COMPLETED,a.onAdCompleted)}),f()(this,"onIMAAdsManagerLoaded",function(e){var t=a.store.dispatch;a.createIMAAdManager(e),a.registerToAdManagerEvents(),Zi("loaded")(t)}),f()(this,"onAdError",function(e){var t=a.store.dispatch;!function(e){return function(t){t({type:"[MONETIZATION] change ad error",payload:e})}}(e.getError().getMessage())(t),Ji(!1),a.continuePlayingContent()}),f()(this,"onAdImpression",function(e){var t=a.store.dispatch,n=!e.getAd().g.vpaid;a.setPodInfo(e),function(e){e({type:"[MONETIZATION] increase ad impression counter"})}(t),function(e){return function(t){t({type:"[MONETIZATION] update is vast ad",payload:e})}}(n)(t)}),f()(this,"onVolumeChanged",function(e){var t=a.store.dispatch;eo(e.target.getVolume())(t)}),f()(this,"onAdVolumeMutedChanged",function(e){var t=a.store.dispatch;0===e.target.getVolume()?Qi(!0)(t):Qi(!1)(t)}),f()(this,"continuePlayingContent",function(){var e=a.store,t=e.getState,n=e.dispatch,r=hn.videoTagStatus(t());Xi("idle"===r?"play":"resume")(n)}),f()(this,"stopPlayingContent",function(){var e=a.store.dispatch;Xi("pause")(e)}),f()(this,"onContentPauseRequested",function(){a.stopPlayingContent()}),f()(this,"onContentResumeRequested",function(){a.continuePlayingContent()}),f()(this,"onAdPaused",function(){var e=a.store.dispatch;Zi("paused")(e)}),f()(this,"setPodInfo",function(e){var t=e&&e.getAd()&&e.getAd().getAdPodInfo();if(!Un(t)){var n=a.store.dispatch;!function(e,t){return function(n){n({type:"[MONETIZATION] change pod info",payload:{slotNumber:e,podNumber:t}})}}(t.getAdPosition(),a.totalAdRequestMadeAmount)(n)}}),f()(this,"onAdStarted",function(){var e=a.store,t=e.dispatch,n=e.getState,r=gn.volume(n());Zi("playing")(t),0===a.IMAAdManager.getVolume()?a.IMAAdManager.setVolume(0):window.shouldPlayAdRule||a.IMAAdManager.setVolume(r),a.onResize()}),f()(this,"onAdCompleted",function(){var e=a.store.dispatch;Zi("completed")(e)}),f()(this,"onAdSkipped",function(){var e=a.store.dispatch;Zi("skipped")(e)}),f()(this,"onResize",function(){Un(a.IMAAdManager)||(a.IMAAdManager.resize(a.videoPlayerElement.clientWidth,a.videoPlayerElement.clientHeight,google.ima.ViewMode.NORMAL),a.adContainerElement.style.height="".concat(a.videoPlayerElement.clientHeight,"px"))}),f()(this,"onAdProgressChanged",function(e){var t,n,r=a.store,i=r.dispatch,o=r.getState,s=e.getAdData().currentTime,u=e.getAdData().duration,c=_i.adDuration(o());(t=s,function(e){e({type:"[MONETIZATION] change ad current time",payload:t})})(i),c!==u&&(n=u,function(e){e({type:"[MONETIZATION] change ad duration",payload:n})})(i)}),f()(this,"onAnchorStatusChanged",function(){var e=a.store.getState;"processing"!==Pr(e())&&a.onResize()}),f()(this,"changeAdVolume",function(e){Un(a.IMAAdManager)||a.IMAAdManager.setVolume(e)}),f()(this,"changeAdMuted",function(e,t){Un(a.IMAAdManager)||(t?a.IMAAdManager.setVolume(0):a.IMAAdManager.setVolume(e))}),f()(this,"changeAdStatus",function(e){Un(a.IMAAdManager)||("playing"===e&&a.IMAAdManager.resume(),"paused"===e&&a.IMAAdManager.pause())});var s=t.getState;this.store=t,this.adVideoElement=r,this.videoPlayerElement=i,this.adContainerElement=n,this.adDisplayContainer=new google.ima.AdDisplayContainer(n,r),this.createAdLoader(s(),this.adDisplayContainer),this.adDisplayContainer.initialize(),this.anchorStatusStoreSubscriber=new ji(t,e.getAnchorDependencies,this.onAnchorStatusChanged.bind(this)),this.registerForWindowResize(),this.initMutationObserver(o)};f()(to,"getAdsRenderingSettings",function(){var e=new google.ima.AdsRenderingSettings;return e.restoreCustomPlaybackStateOnAdBreakComplete=!0,e.enablePreloading=!1,e.uiElements=[],e.loadVideoTimeout=15e3,e}),f()(to,"getAnchorDependencies",function(e){return[Pr(e)]});var no=function e(t,n,r,i,o,a){var s=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"playerId",void 0),f()(this,"adScheduler",void 0),f()(this,"adHandler",void 0),f()(this,"imaLoadingStatusSubscriber",void 0),f()(this,"adStatusSubscriber",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"adContainer",void 0),f()(this,"adVideoElement",void 0),f()(this,"videoPlayerElement",void 0),f()(this,"playerContainer",void 0),f()(this,"pendingMidrollAdPlay",!1),f()(this,"pendingPrerollAdPlay",!1),f()(this,"pendingPrerollAdTag",null),f()(this,"pendingMidrollNumber",null),f()(this,"pendingAdStatusStoreSubscriber",void 0),f()(this,"adMutedStoreSubscriber",void 0),f()(this,"adVolumeStoreSubscriber",void 0),f()(this,"onMidrollAdOpportunity",function(){var e=s.store,t=e.dispatch,n=e.getState,r=_i.adStatus(n()),i=bi.continuePlayingWhileWaitingForAd(n());"loaded"===r?s.playAd(!0):"requested"===r&&(s.pendingMidrollAdPlay=!0,i||(Xi("pause")(t),Ji(!0)(t))),function(e){e({type:"[MONETIZATION] increase ad Opportunity counter"})}(t)}),f()(this,"onPrerollAdOpportunity",function(e){var t=s.store,n=t.getState,r=t.dispatch,i=Fi.loadingImaStatus(n());Un(s.adHandler)?"loading"!==i&&""!==i||(Ji(!0)(r),s.pendingPrerollAdPlay=!0,s.pendingPrerollAdTag=e):(s.pendingPrerollAdPlay=!0,Ji(!0)(r),s.adHandler.loadNewAd(e,"preroll"))}),f()(this,"onPreMidrollAdOpportunity",function(e,t){Un(s.adHandler)||(e.currentTime>=e.midrollTime&&(s.pendingMidrollAdPlay=!0),s.pendingMidrollNumber=e.midrollNumber,s.adHandler.loadNewAd(t,"midroll"))}),f()(this,"hasPendingAd",function(){return s.hasPendingMidrollAdPlay()||s.hasPendingPrerollAdPlay()}),f()(this,"onAdStatusChanged",function(e){var t=s.store.dispatch,n=_i.adStatus(e);"completed"===n&&Ji(!1)(t);var r=bi.continuePlayingWhileWaitingForAd(e),i=_i.loadingAd(e);"playing"!==n&&"error"!==n||r||!i||Ji(!1)(t),s.hasPendingAd()&&"loaded"===n?s.playAd(s.hasPendingMidrollAdPlay()):s.hasPendingAd()&&"error"===n?(Ji(!1),s.clearPendingMidroll(),s.clearPendingPreroll()):Hi(n)||(Ji(!1),function(e){e({type:"[MONETIZATION] clear ad data"})}(t))}),f()(this,"clearPendingMidroll",function(){s.pendingMidrollNumber=null,s.pendingMidrollAdPlay=!1}),f()(this,"clearPendingPreroll",function(){s.pendingPrerollAdPlay=!1,s.pendingPrerollAdTag=null}),f()(this,"onVideoTagStatusChanged",function(e){"complete"===hn.videoTagStatus(e)&&function(e){e({type:"[MONETIZATION] clear played midrolls"})}(s.store.dispatch)}),f()(this,"hasPendingMidrollAdPlay",function(){return s.pendingMidrollAdPlay}),f()(this,"hasPendingPrerollAdPlay",function(){return s.pendingPrerollAdPlay}),f()(this,"playAd",function(e){var t,n=s.store.dispatch,r=s.adHandler.playAd();e?((t=s.pendingMidrollNumber,function(e){e({type:"[MONETIZATION] add 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oo.a.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!navigator.getBattery){e.next=7;break}return e.next=3,navigator.getBattery();case 3:t=e.sent,this.updateBatteryParams(t.level,t.charging),t.ondischargingtimechange=function(e){return n.updateBatteryParams(e.target.level,e.target.charging)},t.onchargingtimechange=function(e){return n.updateBatteryParams(e.target.level,e.target.charging)};case 7:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}()},{key:"updateBatteryParams",value:function(e,t){this.batteryLevel="".concat(100*e),this.batteryChargingState=t}},{key:"getBatteryLevel",value:function(){return this.batteryLevel}},{key:"getBatteryChargingState",value:function(){return this.batteryChargingState}},{key:"getConnectionSpeed",value:function(){return this.connectionSpeed}},{key:"getConnectionType",value:function(){return this.connectionType}}]),e}(),xo=function(){"undefined"===typeof 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Error('Reducer "'+t+"\" returned undefined during initialization. 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fa(fa({},e),{},{playbackMethod:Un(r)?e.playbackMethod:r,playerId:Un(i)?e.playerId:i,playerInstanceUniqId:n,playerMode:Fn()?"mobile":"desktop"})}(e,n.initiateParams,n.playerInstanceUniqId));case"[CORE] reset player data time params":return fa(fa({},e),{},{currentVideoTimeFragment:0,currentVideoBufferedTime:0,currentVideoDuration:0,currentVideoTime:0});case"[COMMON] set mute video":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{muted:t.payload})});case"[COMMON] set volume":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{volume:t.payload})});case"[COMMON] change selected settings category":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{selectedSettingsCategory:t.payload})});case"[COMMON] change settings speed":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{speed:t.payload})});case"[COMMON] change settings quality":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{quality:t.payload})});case"[COMMON] set fullscreen":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{fullscreen:fa(fa({},e.playerSettings.fullscreen),{},{isFullscreenOn:t.payload,pendingFullscreenRequest:""})})});case"[COMMON] set fullscreen request":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{fullscreen:fa(fa({},e.playerSettings.fullscreen),{},{pendingFullscreenRequest:t.payload})})});case"[COMMON] set pending video status":var r=t.payload.pendingStatusObject;return fa(fa({},e),{},{pendingVideoTagStatus:fa({},r)});case"[COMMON] set player mode":return fa(fa({},e),{},{playerMode:t.payload});case"[CORE] update video current fragment position":return fa(fa({},e),{},{currentVideoTimeFragment:t.payload});case"[CORE] update video current position":return fa(fa({},e),{},{currentVideoTime:t.payload});case"[CORE] update video current buffered time":return fa(fa({},e),{},{currentVideoBufferedTime:t.payload});case"[CORE] update video current duration":return fa(fa({},e),{},{currentVideoDuration:t.payload});case"[CORE] change video tag status":return fa(fa({},e),{},{videoTagStatus:t.payload});case"[CORE] update player visibility":return fa(fa({},e),{},{playerVisibility:t.payload});case"[CORE] update placeholder visibility":return fa(fa({},e),{},{playerPlaceholderVisibility:t.payload});case"[CORE] change loading player status":return fa(fa({},e),{},{loadingPlayer:t.payload});case"[COMMON] show black screen with loader":return fa(fa({},e),{},{loader:fa(fa({},e.loader),{},{showBlackScreen:t.payload})});case"[CORE] set player size":return fa(fa({},e),{},{playerSize:t.payload});case"[COMMON] set error message":return fa(fa({},e),{},{errorMessage:t.payload});default:return e}},brandingData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:va,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate 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ba(ba({},e),{},{anchoringAppearance:r||e.anchoringAppearance,canClose:Un(i)?e.canClose:i,orientation:Un(u)?e.orientation:u,closableAd:Un(o)?e.closableAd:o,closeAfter:Un(a)?e.closeAfter:a,continueStreaming:Un(s)?e.continueStreaming:s,stickyBelowClassName:Un(l)?e.stickyBelowClassName:l,width:Un(d)?e.width:d,margins:p,anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:!0})})}return e}(e,t.payload.initiateParams));case"[COMMON] set anchor enable":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:t.payload})});case"[ANCHOR] update is anchor status":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorStatus:t.payload})});case"[COMMON] set anchor disabled by user":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorDisabledByUser:t.payload})});default:return e}},monetization:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Sa({},function(e,t){var n=t.monetization;if(Un(n))return e;var r=n.ad_tag,i=n.ad_type,o=n.vpaid_mode,a=n.ad_request_timeout,s=n.continue_content_play_while_waiting_for_ad,u=n.midrolls,c=u&&u.on&&u.on.sort(Wn),l=Un(s)?e.continuePlayingWhileWaitingForAd:s,d=c?c.indexOf(0):-1,p=-1!==d&&!l;return p&&(c=c.splice(d,1)),Sa(Sa({},e),{},{midrolls:Sa(Sa({},e.midrolls),{},{every:u&&u.every,on:c}),prerollEnabled:p,adRequestTimeout:Un(a)?e.adRequestTimeout:parseInt(a,10),vpaidMode:Un(o)?e.vpaidMode:o,continuePlayingWhileWaitingForAd:l,adsData:Sa(Sa({},e.adsData),{},{adType:Un(i)?e.adsData.adType:i,adTagUrlTemplate:Un(r)?e.adsData.adTagUrlTemplate:r})})}(e,t.payload.initiateParams));case"[COMMON] set new ad tag url template":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adTagUrlTemplate:t.payload})});case"[MONETIZATION] change ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:t.payload,adErrorMessage:null})});case"[MONETIZATION] change ad tag":var n=t.payload,r=n.adUnit,i=n.adTag;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{currentAdTag:i,adUnit:r})});case"[MONETIZATION] change pending ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{pendingAdStatus:t.payload})});case"[MONETIZATION] change ad error":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:"error",adErrorMessage:t.payload})});case"[MONETIZATION] increase ad impression counter":var o=e.adsData.adImpression;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adImpression:o+1})});case"[MONETIZATION] increase ad Opportunity counter":var a=e.adsData.adOpportunity;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOpportunity:a+1})});case"[MONETIZATION] add played midroll number":var s=e.adsData.playedMidrolls,u=In()(s);return u.push(t.payload),Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:t.payload,playedMidrolls:u})});case"[MONETIZATION] clear played midrolls":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{playedMidrolls:[]})});case"[MONETIZATION] clear ad data":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:0,currentAdTag:null,adDuration:0,adUnit:""})});case"[MONETIZATION] change ad duration":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adDuration:t.payload})});case"[MONETIZATION] update is vast ad":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{isVastAd:t.payload})});case"[MONETIZATION] change ad current time":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adCurrentTime:t.payload})});case"[MONETIZATION] update ad muted":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adMuted:t.payload})});case"[MONETIZATION] change ad volume":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adVolume:t.payload})});case"[MONETIZATION] change pod info":var c=t.payload,l=c.podNumber,d=c.slotNumber;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{podNumber:l,slotNumber:d})});case"[MONETIZATION] change loading ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{loadingAd:t.payload})});default:return e}},mediaData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:ja,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Va({},function(e,t){var n=t.content_type,r=t.media_id,i=t.display_title;return Va(Va({},e),{},{mediaType:Un(n)?e.mediaType:n,mediaId:Un(r)?e.mediaId:r,videoData:Va(Va({},e.videoData),{},{showTitle:!!Un(i)||i})})}(e,t.payload.initiateParams));case"[CORE] load video request":return Va(Va({},e),{},{loadingMedia:!0});case"[CORE] load video request success":return Va(Va({},e),{},{loadingMedia:!1,videoList:t.payload});case"[CORE] set current video":var n=t.payload,r=n.index,i=n.videoData;return Va(Va({},e),{},{activeVideoIndex:r,videoData:i});case"[CORE] load video request error":return Va(Va({},e),{},{loadingMedia:!1,mediaLoadingError:t.payload});case"[COMMON] media request":var o=t.payload.mediaRequestObject;return Va(Va({},e),{},{mediaRequest:Va({},o)});default:return e}},semanticOptions:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Ba,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Fa({},function(e,t){var n=t.semantic_options;if(Un(n))return e;var r=n.minimum_date_factor,i=n.promoted_videos,o=n.scan_images_on_page,a=n.scanned_element,s=n.scanned_element_type,u=n.scoped_keywords,c=n.tags;return Fa(Fa({},e),{},{minimumDateFactor:Un(r)?e.minimumDateFactor:r,promotedVideos:Un(i)?e.promotedVideos:i,scanImagesOnPage:Un(o)?e.scanImagesOnPage:o,scannedElement:Un(a)?e.scannedElement:a,scannedElementType:Un(s)?e.scannedElementType:s,scopedKeywords:Un(u)?e.scopedKeywords:u,tags:Un(c)?e.tags:c})}(e,t.payload.initiateParams));default:return e}},userInteraction:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[USER INTERACTION] change user interaction":return qa(qa({},e),{},{userInteractionType:t.payload});default:return e}},splitView:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:$a,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ga({},function(e,t){var n=t.anchor_options;if(!Un(n)){var r=n.split_view,i=n.split_view_ratio;return Ga(Ga({},e),{},{splitViewRatio:Un(r)||!r||Un(i)?e.splitViewRatio:i})}return e}(e,t.payload.initiateParams));default:return e}},discovery:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Za,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ya({},function(e,t){var n=t.next_video;return Un(n)?e:Ya(Ya({},e),{},{nextVideo:Xa(n)})}(e,t.payload.initiateParams));case"[DISCOVERY] show up next":return Ya(Ya({},e),{},{showUpNext:t.payload});case"[DISCOVERY] show skippable content":return Ya(Ya({},e),{},{showSkippableContent:t.payload});default:return e}}}),Qa=[],es=!1,ts=function e(){return function(t){return function(n){if(es)return Qa.push(n),null;es=!0;var r=t(n);return es=!1,Qa.length>0&&e()(t)(Qa.shift()),r}}},ns=function(e){var t=[];if(function(e){return!Un(e)&&!Un(e.enable_redux_debugging)&&e.enable_redux_debugging}(e)){var n=window&&window.__REDUX_DEVTOOLS_EXTENSION__&&window.__REDUX_DEVTOOLS_EXTENSION__();"function"===typeof n&&t.push(n)}var r=Et.apply(void 0,[wt(ua,ts)].concat(t));return vt(Ja,r)},rs=function(){function e(t){Ai()(this,e),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"shouldPlayIfLazyplay",!0),f()(this,"shouldPlayIfAutoplayWhenViewable",!0),f()(this,"videoPausedByObserver",!1),this.store=t,this.playerVisibilitySubscriber=null,this.videoTagStatusSubscriber=null,this.playAccordingToPlaybackMethod()}return Vi()(e,[{key:"lazyplayHandler",value:function(e){hn.playerVisibility(e)>=.5&&(this.playVideo(),this.shouldPlayIfLazyplay=!1)}},{key:"autoplayWhenViewableHandler",value:function(e){hn.playerVisibility(e)>=.5?this.playVideo():this.pauseVideo()}},{key:"onPlayerVisibilityChanged",value:function(e){var t=hn.playbackMethod(e);"lazyplay"===t&&this.shouldPlayIfLazyplay&&this.lazyplayHandler(e),"autoplay_when_viewable"===t&&this.shouldPlayIfAutoplayWhenViewable&&this.autoplayWhenViewableHandler(e)}},{key:"onVideoTagStatusChanged",value:function(e){var t=hn.videoTagStatus(e);"paused"!==t||this.videoPausedByObserver||(this.shouldPlayIfAutoplayWhenViewable=!1),"playing"===t&&(this.shouldPlayIfAutoplayWhenViewable=!0,this.videoPausedByObserver=!1)}},{key:"initiatePlayerVisibilitySubscriber",value:function(){this.playerVisibilitySubscriber=new ji(this.store,e.getPlayerVisibilityDependencies,this.onPlayerVisibilityChanged.bind(this))}},{key:"initiateVideoTagStatusSubscriber",value:function(){this.videoTagStatusSubscriber=new ji(this.store,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this))}},{key:"playVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"idle"===hn.videoTagStatus(n())?on("play")(t):on("resume")(t)}},{key:"pauseVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"paused"!==hn.videoTagStatus(n())&&(this.videoPausedByObserver=!0,on("pause")(t))}},{key:"playAccordingToPlaybackMethod",value:function(){var e=this.store,t=e.dispatch,n=(0,e.getState)();switch(hn.playbackMethod(n)){case"autoplay":this.playVideo();break;case"lazyplay":this.initiatePlayerVisibilitySubscriber();break;case"autoplay_when_viewable":this.initiatePlayerVisibilitySubscriber(),this.initiateVideoTagStatusSubscriber();break;case"none":an(!1)(t)}}}],[{key:"getPlayerVisibilityDependencies",value:function(e){return[hn.playerVisibility(e)]}},{key:"getVideoTagStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}}]),e}(),is=function(){function e(t,n,r,i){var o=this;Ai()(this,e),f()(this,"videoStatusSubscriber",void 0),f()(this,"videoListSubscriber",void 0),f()(this,"mediaRequestSubscriber",void 0),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"playbackMethodManager",void 0),f()(this,"store",void 0),f()(this,"loadContent",function(e,t,n,r){o.loadMedia(t,n,r).then(function(){o.playbackMethodManager=new rs(e)})}),f()(this,"loadMedia",function(e,t,n){var r=o.store,i=r.dispatch,a=r.getState,s=Dn.showTitle(a());if("semantic"===e){var u=pn.semanticOptions(a());return Na(u,s,n)(i)}return ka(t,s,n)(i)}),this.store=t,this.videoStatusSubscriber=new ji(t,e.getVideoStatusDependencies,this.onVideoStatusChanged.bind(this)),this.videoListSubscriber=new ji(t,e.getVideoListDependencies,this.onVideoListChanged.bind(this)),this.mediaRequestSubscriber=new ji(t,e.getMediaRequestDependencies,this.onMediaRequestChanged.bind(this)),this.playerVisibilitySubscriber=null,this.loadContent(t,r,n,i)}return Vi()(e,null,[{key:"createInstance",value:function(t,n,r,i){return new e(t,n,r,i)}}]),Vi()(e,[{key:"playNextVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e)+1;n.length>1&&r>=n.length&&(r=0),r<n.length&&(!function(e){e({type:"[CORE] reset player data time params"})}(t),La(r,n[r])(t),on("play")(t))}},{key:"playPreviousVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e);if(r>0){var i=r-1;La(i,n[i])(t),on("play")(t)}}},{key:"onVideoStatusChanged",value:function(e){"complete"===hn.videoTagStatus(e)&&this.playNextVideo(e)}},{key:"onVideoListChanged",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e);!jn(n)&&n.length>0&&La(0,n[0])(t)}},{key:"onMediaRequestChanged",value:function(e){var t=Cn.mediaRequest(e);switch(t.type){case"playNewVideo":this.loadMedia("specific",t.value);break;case"playNextVideo":this.playNextVideo(e);break;case"playPreviousVideo":this.playPreviousVideo(e)}}}],[{key:"getVideoStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}},{key:"getVideoListDependencies",value:function(e){return[Cn.videoList(e)]}},{key:"getMediaRequestDependencies",value:function(e){return[Cn.mediaRequest(e)]}}]),e}(),os=function e(t){var n=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"onDependencyFailure",function(e,t){console.log("onDependencyFailure",e,t);var r=n.store,i=r.dispatch,o=r.getState;switch(e){case"ima":"blocked"!==Fi.loadingImaStatus(o())&&Qn("error")(i);break;case"hls":er("error")(i)}}),f()(this,"onDependencyReady",function(e){var t=n.store.dispatch;switch(e){case"ima":Qn("success")(t);break;case"hls":er("success")(t)}}),this.store=t},as=function(e){return function(t){t({type:"[COMMON] set fullscreen",payload:e})}},ss=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"videoTag",void 0),f()(this,"pendingFullscreenSubscriber",void 0),f()(this,"adStatusSubscriber",void 0),f()(this,"playerUniqId",void 0),f()(this,"onAdStatusChanged",function(e){var t=_i.adStatus(e),n=r.videoTag.webkitDisplayingFullscreen;"playing"===t&&Bn()&&n&&r.exitFullscreen(r.videoTag)}),f()(this,"isPlayerInFullscreen",function(){var e=document,t=Bn()?En(r.playerUniqId):bn(r.playerUniqId);return Un(e.fullscreenElement)?!Un(e.webkitFullscreenElement)&&0===e.webkitFullscreenElement.id.localeCompare(t):0===e.fullscreenElement.id.localeCompare(t)}),f()(this,"changePlayerWidth",function(e){r.videoTag.style.width=e?"100%":"auto"}),f()(this,"onFullscreenChanged",function(){var e=r.store.dispatch,t=r.isPlayerInFullscreen();r.changePlayerWidth(t),as(t)(e)}),f()(this,"onFullscreenChangedIos",function(){var e=r.store.dispatch,t=r.videoTag.webkitDisplayingFullscreen;t||on("resume")(e),r.changePlayerWidth(t),as(t)(e)}),f()(this,"onPendingFullscreenRequestChanged",function(e){var t=gn.pendingFullscreenRequest(e);"enter"===t?r.enterFullscreen(r.videoTag):"exit"===t&&r.exitFullscreen(r.videoTag)}),f()(this,"getFullScreenElement",function(e,t){var n=document.getElementById(bn(r.playerUniqId));return Bn()?t:e?document:n}),f()(this,"enterFullscreen",function(e){var t=r.getFullScreenElement(!1,e);Bn()?t.webkitEnterFullscreen():document.webkitExitFullscreen?t.webkitRequestFullscreen():document.webkitCancelFullScreen?t.webkitRequestFullScreen():document.mozCancelFullScreen?t.mozRequestFullScreen():document.msExitFullscreen&&t.msRequestFullscreen()}),f()(this,"exitFullscreen",function(e){var t=r.getFullScreenElement(!0,e);document.webkitExitFullscreen||Bn()?t.webkitExitFullscreen():document.webkitCancelFullScreen?t.webkitCancelFullScreen():document.mozCancelFullScreen?t.mozCancelFullScreen():document.msExitFullscreen&&t.msExitFullscreen()}),this.store=t,this.videoTag=document.getElementById(En(n)),this.playerUniqId=n,document.addEventListener("fullscreenchange",this.onFullscreenChanged.bind(this)),document.addEventListener("webkitfullscreenchange",this.onFullscreenChanged.bind(this)),Bn()&&(this.videoTag.addEventListener("webkitendfullscreen",this.onFullscreenChangedIos.bind(this)),this.videoTag.addEventListener("webkitbeginfullscreen",this.onFullscreenChangedIos.bind(this))),this.pendingFullscreenSubscriber=new ji(t,e.getPendingFullscreenDependencies,this.onPendingFullscreenRequestChanged.bind(this)),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this))}return Vi()(e,null,[{key:"createInstance",value:function(t,n){return new e(t,n)}}]),Vi()(e,null,[{key:"getPendingFullscreenDependencies",value:function(e){return[gn.pendingFullscreenRequest(e)]}},{key:"getAdStatusDependencies",value:function(e){return[_i.adStatus(e)]}}]),e}();function us(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function cs(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?us(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):us(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var ls,ds=function(e){return function(e){return e&&window.monti.playerConfigs&&window.monti.playerConfigs[e]}(e)?function(e){return window.monti.playerConfigs[e]}(e):window.monti.playerConfigs?window.monti.playerConfigs&&window.monti.playerConfigs[Object.keys(window.monti.playerConfigs)[0]]:null},ps=function e(t){var n=this;Ai()(this,e),f()(this,"videoTag",void 0),f()(this,"isBufferError",void 0),f()(this,"hls",void 0),f()(this,"hlsSetup",function(e,t,r,i){n.initiateHls(e),n.loadHlsSource(e,t,r,i)}),f()(this,"detachMedia",function(){Un(n.hls)||(n.hls.detachMedia(),n.hls.destroy(),n.hls=null)}),f()(this,"initiateHls",function(e){n.hls=new e,n.hls.attachMedia(n.videoTag)}),f()(this,"loadHlsSource",function(e,t,r,i){n.hls.on(e.Events.MEDIA_ATTACHED,function(){n.hls.loadSource(t)}),n.hls.on(e.Events.ERROR,function(t,o){n.mapHlsToErrors(e,o,i),t.details===e.ErrorDetails.BUFFER_STALLED_ERROR&&(r(!0),n.isBufferError=!0)}),n.hls.on(e.Events.FRAG_BUFFERED,function(){n.isBufferError&&(r(!1),n.isBufferError=!1)})}),f()(this,"mapHlsToErrors",function(e,t,r){if(t.fatal)switch(t.type){case e.ErrorTypes.NETWORK_ERROR:r(Xn.GENERAL_ERROR),n.hls.startLoad();break;case e.ErrorTypes.MEDIA_ERROR:r(Xn.GENERAL_ERROR),n.hls.recoverMediaError();break;default:r(Xn.GENERAL_ERROR),n.hls.destroy()}}),this.hls=void 0,this.videoTag=t,this.isBufferError=!1},fs=function e(){var t=this;Ai()(this,e),f()(this,"videoStreaming",void 0),f()(this,"hlsLibrarySetup",function(e,n,r,i){Un(t.videoStreaming)||t.videoStreaming.detachMedia(),t.videoStreaming=new ps(e),t.videoStreaming.hlsSetup(ls,n,r,i)})};f()(fs,"shouldLoadVideoStreamingSrcDirectly",function(e,t,n){return"no-need"===n&&!(""===e.canPlayType("application/vnd.apple.mpegurl"))}),f()(fs,"shouldUseHlsLibrary",function(e,t){return"success"===t&&(ls=void 0!==window.Hls?Hls:mmHls).isSupported()}),f()(fs,"isValidHlsUrl",function(e){return!Un(e)&&!e.includes(".mp4")}),f()(fs,"suitableVideoSource",function(e,t,n){return fs.isValidHlsUrl(t)?fs.shouldUseHlsLibrary(t,n)?"m3u8 with hls":fs.shouldLoadVideoStreamingSrcDirectly(e,t,n)?"m3u8 directly":"loading"!==n?"mp4":"":"mp4"}),f()(fs,"loadHlsVideoDirectly",function(e,t){e.setAttribute("src",t),e.load()});var hs=function(e){return function(t){t({type:"[MONETIZATION] change pending ad status",payload:{type:e}})}},ys="video/mp4",gs="application/vnd.apple.mpegurl",vs=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"videoTag",void 0),f()(this,"prerollEnabled",void 0),f()(this,"pendingVideoStatusSubscriber",void 0),f()(this,"videoStreamingManager",void 0),f()(this,"videoDataSubscriber",void 0),f()(this,"hlsLoadingStatusSubscriber",void 0),f()(this,"newVideoDataLoaded",void 0),f()(this,"onHlsLoadingStatusChanged",function(e){"success"===Fi.loadingHLSStatus(e)&&(r.newVideoDataLoaded=!0,r.onPendingVideoStatusChanged(e))}),f()(this,"onPendingVideoStatusChanged",function(e){var t=hn.pendingVideoTagStatus(e),n=Dn.sources(e),i=Fi.loadingHLSStatus(e),o="blocked"===Fi.loadingImaStatus(e);r.handlePendingVideoStatus(t,n,i,o)}),f()(this,"onVideoDataChanged",function(){r.newVideoDataLoaded=!0}),f()(this,"sendPrerollPlayRequest",function(){var e=r.store.dispatch;hs("playPreroll")(e)}),f()(this,"handlePlayRequest",function(e,t,n){var i=r.store.dispatch;if(e&&e.length>0){if(r.newVideoDataLoaded&&(r.loadVideoSource(r.videoTag,e,t),r.newVideoDataLoaded=!1,r.prerollEnabled&&!n))return void r.sendPrerollPlayRequest();r.videoTag.play().catch(function(e){return console.error("Error playing the video: ",e)})}else dn(Xn.VIDEO_ERROR)(i)}),f()(this,"handlePendingVideoStatus",function(e,t,n,i){switch(e.type){case"play":r.handlePlayRequest(t,n,i);break;case"resume":r.videoTag.play().catch(function(e){return console.error("Error resuming the video: ",e)});break;case"pause":r.videoTag.pause();break;case"replay":r.videoTag.currentTime=0,r.videoTag.play().catch(function(e){return console.error("Error replaying the video: ",e)});break;case"seekTo":r.videoTag.pause(),r.videoTag.currentTime=e.value}}),f()(this,"loadMp4Source",function(e,t,n){var r=Ra(t,ys);n.setAttribute("src",r),n.load()}),f()(this,"loadVideoSource",function(e,t,n){var i=r.store.dispatch,o=Ra(t,gs);switch(fs.suitableVideoSource(e,o,n)){case"mp4":r.loadMp4Source(n,t,e);break;case"m3u8 with hls":r.videoStreamingManager.hlsLibrarySetup(e,o,function(e){return un(e)(i)},function(e){return dn(e)(i)});break;case"m3u8 directly":fs.loadHlsVideoDirectly(e,o)}}),this.store=t;var i=t.getState;this.videoStreamingManager=new fs,this.videoTag=document.getElementById(En(n)),this.prerollEnabled=bi.prerollEnabled(i()),this.pendingVideoStatusSubscriber=new ji(t,e.getPendingVideoStatusDependencies,this.onPendingVideoStatusChanged.bind(this)),this.videoDataSubscriber=new ji(t,e.getVideoDataDependencies,this.onVideoDataChanged.bind(this)),this.hlsLoadingStatusSubscriber=new ji(t,e.getHLSLoadingStatusDependencies,this.onHlsLoadingStatusChanged.bind(this))}return Vi()(e,null,[{key:"createInstance",value:function(t,n){return new e(t,n)}}]),Vi()(e,null,[{key:"getHLSLoadingStatusDependencies",value:function(e){return[Fi.loadingHLSStatus(e)]}},{key:"getPendingVideoStatusDependencies",value:function(e){return[hn.pendingVideoTagStatus(e)]}},{key:"getVideoDataDependencies",value:function(e){return[Cn.videoData(e)]}}]),e}();function ms(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function bs(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?ms(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):ms(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var Os={READY_EVENT:"ready",PLAY_EVENT:"play",PAUSE_EVENT:"pause",TIME_EVENT:"time",SEEK_EVENT:"seek",COMPLETE_EVENT:"complete",VOLUME_EVENT:"volume",MUTE_EVENT:"mute"},_s=Object.values(Os),Ss={FULLSCREEN_EVENT:"fullscreen",ANCHOR_STATUS_EVENT:"anchorStatusChanged",ANCHOR_CLOSED_EVENT:"anchorClosed"},Es={AD_PLAY_EVENT:"adPlay",AD_PAUSE_EVENT:"adPause",AD_RESUME_EVENT:"adResume",AD_COMPLETE_EVENT:"adComplete",AD_TIME_EVENT:"adTime",AD_MUTE_EVENT:"adMute",AD_SKIPPED_EVENT:"adSkipped",AD_ERROR_EVENT:"adError",AD_BLOCK_EVENT:"adBlock",AD_REQUEST_EVENT:"adRequest",AD_OPPORTUNITY_EVENT:"adOpportunity",AD_IMPRESSION_EVENT:"adImpression"},ws=Object.values(Es),Ps=Object.values(bs(bs(bs({},Os),Es),Ss)),Ts=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"eventsCallbacksHandler",void 0),f()(this,"store",void 0),f()(this,"videoStatusSubscriber",void 0),f()(this,"videoMuteSubscriber",void 0),f()(this,"videoVolumeSubscriber",void 0),f()(this,"videoTimeFragmentSubscriber",void 0),f()(this,"videoListStoreSubscriber",void 0),f()(this,"previousVideoTagStatus",void 0),f()(this,"startSeekTime",0),f()(this,"canHandleReady",function(e,t,n){if(t===Os.READY_EVENT){var r=Cn.videoList(e);if(Array.isArray(r)&&r.length>0)return n(),!0}return!1}),f()(this,"canBeHandled",function(e,t){var n=r.store.getState;return r.canHandleReady(n(),e,t)}),f()(this,"reportSeekEnd",function(e){var t={position:hn.currentVideoTimeFragment(e),offset:r.startSeekTime};r.eventsCallbacksHandler.onEvent(Os.SEEK_EVENT,t)}),f()(this,"onMuteStateChanged",function(e){var t=gn.muted(e);r.eventsCallbacksHandler.onEvent(Os.MUTE_EVENT,{state:t})}),f()(this,"onVolumeChanged",function(e){var t=gn.muted(e),n=gn.volume(e);r.eventsCallbacksHandler.onEvent(Os.VOLUME_EVENT,{level:t?0:n})}),f()(this,"onVideoTimeFragmentChanged",function(e){var 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t=hn.videoTagStatus(e);switch("seeking"===this.previousVideoTagStatus&&this.reportSeekEnd(e),t){case"paused":this.eventsCallbacksHandler.onEvent(Os.PAUSE_EVENT);break;case"seeking":this.startSeekTime=hn.currentVideoTimeFragment(e);break;case"complete":this.eventsCallbacksHandler.onEvent(Os.COMPLETE_EVENT);break;case"playing":this.eventsCallbacksHandler.onEvent(Os.PLAY_EVENT)}this.previousVideoTagStatus=t}}],[{key:"getVideoStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}}]),e}();f()(Ts,"getVideoMuteDependencies",function(e){return[gn.muted(e)]}),f()(Ts,"getVolumeDependencies",function(e){return[gn.volume(e)]}),f()(Ts,"getVideoTimeDependencies",function(e){return[hn.currentVideoTimeFragment(e)]}),f()(Ts,"getVideoListDependencies",function(e){return[Cn.videoList(e)]}),f()(Ts,"isContentEvent",function(e){return _s.some(function(t){return t===e})});var As=function e(t,n){var r=this;Ai()(this,e),f()(this,"eventsCallbacksHandler",void 0),f()(this,"store",void 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{"is_conflicting_with_other_jw_players":false,"programmatic_play_with_sound_on_desktop":false,"referrer_id":"af93e181-b289-0560-a2bf-808e93bb05bc","width":"100","comscore_publisher_id":"18120612","monetization":{"ad_type":"static_tag","continue_content_play_while_waiting_for_ad":false,"strategy":"on_player_load","ad_request_timeout":"10000","midrolls":{"on":[0]},"vpaid_mode":"ENABLED","ad_tag":"https://pubads.g.doubleclick.net/gampad/ads?sz=400x300|640x480|480x270|640x360&iu=/175840252/MMPlus/smithsonianmag/Video&impl=s&gdfp_req=1&env=vp&output=vast&unviewed_position_start=1&url=##REFERRER_URL_UNESC##&description_url=##DESCRIPTION_URL_UNESC##&correlator=##CACHEBUSTER##&cust_params=mm_midroll%3D##MIDROLL_ORDER##%26video_ID%3D##VIDEO_ID##"},"sponsorship":false,"player_identifier":"mplayer","recommendation_id":null,"brand_color":"#FF9900","powered_by_strip":true,"platform":"buffy","type":"video","config_name":"MM+ | Smithsonianmag | Podding","player_id":"3v9g2u2f","playlist_id":"fSkmeWKF","playback_method":"autoplay","anchor_viewability_method":"none","player_version":"v4","playlist_type":"semantic","semantic_options":{"scan_images_on_page":true,"scanned_element":"","tags":"geogrophy,nature,animals,habitat,outdoors,science,history","minimum_date_factor":30,"scanned_element_type":"tag","scoped_keywords":"mentalfloss","promoted_videos":[]},"script_destination":"mm","publisher_contribution":"floor8","general_script_description":"","brand_logo":"","brand_logo_click_url":"","next_video":"none","uniq_key":"af93e181-b289-0560-a2bf-808e93bb05bc","content_id":"fSkmeWKF","content_type":"semantic"})); Finland has vastly improved in reading, math and science literacy over the past decade in large part because its teachers are trusted to do whatever it takes to turn young lives around. This 13-year-old, Besart Kabashi, received something akin to royal tutoring. “I took Besart on that year as my private student,” Louhivuori told me in his office, which boasted a Beatles “Yellow Submarine” poster on the wall and an electric guitar in the closet. When Besart was not studying science, geography and math, he was parked next to Louhivuori’s desk at the front of his class of 9- and 10-year- olds, cracking open books from a tall stack, slowly reading one, then another, then devouring them by the dozens. By the end of the year, the son of Kosovo war refugees had conquered his adopted country’s vowel-rich language and arrived at the realization that he could, in fact, learn. Years later, a 20-year-old Besart showed up at Kirkkojarvi’s Christmas party with a bottle of Cognac and a big grin. “You helped me,” he told his former teacher. Besart had opened his own car repair firm and a cleaning company. “No big fuss,” Louhivuori told me. “This is what we do every day, prepare kids for life.” This tale of a single rescued child hints at some of the reasons for the tiny Nordic nation’s staggering record of education success, a phenomenon that has inspired, baffled and even irked many of America’s parents and educators. Finnish schooling became an unlikely hot topic after the 2010 documentary film Waiting for “Superman” contrasted it with America’s troubled public schools. “Whatever it takes” is an attitude that drives not just Kirkkojarvi’s 30 teachers, but most of Finland’s 62,000 educators in 3,500 schools from Lapland to Turku—professionals selected from the top 10 percent of the nation’s graduates to earn a required master’s degree in education. Many schools are small enough so that teachers know every student. If one method fails, teachers consult with colleagues to try something else. They seem to relish the challenges. Nearly 30 percent of Finland’s children receive some kind of special help during their first nine years of school. The school where Louhivuori teaches served 240 first through ninth graders last year; and in contrast with Finland’s reputation for ethnic homogeneity, more than half of its 150 elementary-level students are immigrants—from Somalia, Iraq, Russia, Bangladesh, Estonia and Ethiopia, among other nations. “Children from wealthy families with lots of education can be taught by stupid teachers,” Louhivuori said, smiling. “We try to catch the weak students. It’s deep in our thinking.” Advertisement scroll for more The transformation of the Finns’ education system began some 40 years ago as the key propellent of the country’s economic recovery plan. Educators had little idea it was so successful until 2000, when the first results from the Programme for International Student Assessment (PISA), a standardized test given to 15-year-olds in more than 40 global venues, revealed Finnish youth to be the best young readers in the world. Three years later, they led in math. By 2006, Finland was first out of 57 countries (and a few cities) in science. In the 2009 PISA scores released last year, the nation came in second in science, third in reading and sixth in math among nearly half a million students worldwide. “I’m still surprised,” said Arjariita Heikkinen, principal of a Helsinki comprehensive school. “I didn’t realize we were that good.” In the United States, which has muddled along in the middle for the past decade, government officials have attempted to introduce marketplace competition into public schools. In recent years, a group of Wall Street financiers and philanthropists such as Bill Gates have put money behind private-sector ideas, such as vouchers, data-driven curriculum and charter schools, which have doubled in number in the past decade. President Obama, too, has apparently bet on compe­tition. His Race to the Top initiative invites states to compete for federal dollars using tests and other methods to measure teachers, a philosophy that would not fly in Finland. “I think, in fact, teachers would tear off their shirts,” said Timo Heikkinen, a Helsinki principal with 24 years of teaching experience. “If you only measure the statistics, you miss the human aspect.”

      The facts show that America has it all wrong in putting to much emphasis on national "data-driven" competition. These approaches take away from the unique aspects of each child.

    2. t was the end of term at Kirkkojarvi Comprehensive School in Espoo, a sprawling suburb west of Helsinki, when Kari Louhivuori, a veteran teacher and the school’s principal, decided to try something extreme—by Finnish standards. One of his sixth-grade students, a Kosovo-Albanian boy, had drifted far off the learning grid, resisting his teacher’s best efforts. The school’s team of special educators—including a social worker, a nurse and a psychologist—convinced Louhivuori that laziness was not to blame. 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n=r.generatePrerollTag(e,t);r.monetization.onPrerollAdOpportunity(n)}),f()(this,"onSeekedWhileAdInProgress",function(){r.monetization.onMidrollAdOpportunity()});var i=t.getState;this.monetization=n,this.videoTimeSubscriber=new qi(t,this),this.videoSeekSubscriber=new zi(t,this),this.prerollScheduler=new Gi(t,this);var o=_i.adTagUrlTemplate(i());this.adTagGenerator=new Wi(o)},Yi=function(){function e(){Ai()(this,e)}return Vi()(e,null,[{key:"generateAdRequest",value:function(e,t,n){var r=new google.ima.AdsRequest;return r.adTagUrl=e,Fn()||r.setAdWillPlayMuted(t),r.vastLoadTimeout=n,r}}]),e}(),Zi=function(e){return function(t){t({type:"[MONETIZATION] change ad status",payload:e})}},Xi=function(e){return function(t){t({type:"[COMMON] set pending video status",payload:{pendingStatusObject:{type:e,value:""}}})}},Ji=function(e){return function(t){t({type:"[MONETIZATION] change loading ad status",payload:e})}},Qi=function(e){return function(t){t({type:"[MONETIZATION] update ad 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t=a.store,n=t.getState,r=t.dispatch,i=gn.volume(n());Bn()||gn.muted(n())?(e.setVolume(0),Qi(!0)(r)):(e.setVolume(gn.volume(n())),eo(i)(r),Qi(!1)(r))}),f()(this,"createIMAAdManager",function(t){a.IMAAdManager=t.getAdsManager(a.adVideoElement,e.getAdsRenderingSettings()),a.setAdVolume(a.IMAAdManager)}),f()(this,"registerToAdManagerEvents",function(){a.IMAAdManager.addEventListener(google.ima.AdErrorEvent.Type.AD_ERROR,a.onAdError),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_PAUSE_REQUESTED,a.onContentPauseRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_RESUME_REQUESTED,a.onContentResumeRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.STARTED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.IMPRESSION,a.onAdImpression),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.SKIPPED,a.onAdSkipped),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.COMPLETE,a.onAdCompleted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.PAUSED,a.onAdPaused),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.RESUMED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.AD_PROGRESS,a.onAdProgressChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_CHANGED,a.onVolumeChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_MUTED,a.onAdVolumeMutedChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.ALL_ADS_COMPLETED,a.onAdCompleted)}),f()(this,"onIMAAdsManagerLoaded",function(e){var t=a.store.dispatch;a.createIMAAdManager(e),a.registerToAdManagerEvents(),Zi("loaded")(t)}),f()(this,"onAdError",function(e){var t=a.store.dispatch;!function(e){return function(t){t({type:"[MONETIZATION] change ad error",payload:e})}}(e.getError().getMessage())(t),Ji(!1),a.continuePlayingContent()}),f()(this,"onAdImpression",function(e){var t=a.store.dispatch,n=!e.getAd().g.vpaid;a.setPodInfo(e),function(e){e({type:"[MONETIZATION] increase ad impression counter"})}(t),function(e){return function(t){t({type:"[MONETIZATION] update is vast ad",payload:e})}}(n)(t)}),f()(this,"onVolumeChanged",function(e){var t=a.store.dispatch;eo(e.target.getVolume())(t)}),f()(this,"onAdVolumeMutedChanged",function(e){var t=a.store.dispatch;0===e.target.getVolume()?Qi(!0)(t):Qi(!1)(t)}),f()(this,"continuePlayingContent",function(){var e=a.store,t=e.getState,n=e.dispatch,r=hn.videoTagStatus(t());Xi("idle"===r?"play":"resume")(n)}),f()(this,"stopPlayingContent",function(){var e=a.store.dispatch;Xi("pause")(e)}),f()(this,"onContentPauseRequested",function(){a.stopPlayingContent()}),f()(this,"onContentResumeRequested",function(){a.continuePlayingContent()}),f()(this,"onAdPaused",function(){var e=a.store.dispatch;Zi("paused")(e)}),f()(this,"setPodInfo",function(e){var t=e&&e.getAd()&&e.getAd().getAdPodInfo();if(!Un(t)){var n=a.store.dispatch;!function(e,t){return function(n){n({type:"[MONETIZATION] change pod info",payload:{slotNumber:e,podNumber:t}})}}(t.getAdPosition(),a.totalAdRequestMadeAmount)(n)}}),f()(this,"onAdStarted",function(){var e=a.store,t=e.dispatch,n=e.getState,r=gn.volume(n());Zi("playing")(t),0===a.IMAAdManager.getVolume()?a.IMAAdManager.setVolume(0):window.shouldPlayAdRule||a.IMAAdManager.setVolume(r),a.onResize()}),f()(this,"onAdCompleted",function(){var e=a.store.dispatch;Zi("completed")(e)}),f()(this,"onAdSkipped",function(){var e=a.store.dispatch;Zi("skipped")(e)}),f()(this,"onResize",function(){Un(a.IMAAdManager)||(a.IMAAdManager.resize(a.videoPlayerElement.clientWidth,a.videoPlayerElement.clientHeight,google.ima.ViewMode.NORMAL),a.adContainerElement.style.height="".concat(a.videoPlayerElement.clientHeight,"px"))}),f()(this,"onAdProgressChanged",function(e){var t,n,r=a.store,i=r.dispatch,o=r.getState,s=e.getAdData().currentTime,u=e.getAdData().duration,c=_i.adDuration(o());(t=s,function(e){e({type:"[MONETIZATION] change ad current time",payload:t})})(i),c!==u&&(n=u,function(e){e({type:"[MONETIZATION] change ad duration",payload:n})})(i)}),f()(this,"onAnchorStatusChanged",function(){var e=a.store.getState;"processing"!==Pr(e())&&a.onResize()}),f()(this,"changeAdVolume",function(e){Un(a.IMAAdManager)||a.IMAAdManager.setVolume(e)}),f()(this,"changeAdMuted",function(e,t){Un(a.IMAAdManager)||(t?a.IMAAdManager.setVolume(0):a.IMAAdManager.setVolume(e))}),f()(this,"changeAdStatus",function(e){Un(a.IMAAdManager)||("playing"===e&&a.IMAAdManager.resume(),"paused"===e&&a.IMAAdManager.pause())});var s=t.getState;this.store=t,this.adVideoElement=r,this.videoPlayerElement=i,this.adContainerElement=n,this.adDisplayContainer=new google.ima.AdDisplayContainer(n,r),this.createAdLoader(s(),this.adDisplayContainer),this.adDisplayContainer.initialize(),this.anchorStatusStoreSubscriber=new ji(t,e.getAnchorDependencies,this.onAnchorStatusChanged.bind(this)),this.registerForWindowResize(),this.initMutationObserver(o)};f()(to,"getAdsRenderingSettings",function(){var e=new google.ima.AdsRenderingSettings;return e.restoreCustomPlaybackStateOnAdBreakComplete=!0,e.enablePreloading=!1,e.uiElements=[],e.loadVideoTimeout=15e3,e}),f()(to,"getAnchorDependencies",function(e){return[Pr(e)]});var no=function e(t,n,r,i,o,a){var s=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"playerId",void 0),f()(this,"adScheduler",void 0),f()(this,"adHandler",void 0),f()(this,"imaLoadingStatusSubscriber",void 0),f()(this,"adStatusSubscriber",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"adContainer",void 0),f()(this,"adVideoElement",void 0),f()(this,"videoPlayerElement",void 0),f()(this,"playerContainer",void 0),f()(this,"pendingMidrollAdPlay",!1),f()(this,"pendingPrerollAdPlay",!1),f()(this,"pendingPrerollAdTag",null),f()(this,"pendingMidrollNumber",null),f()(this,"pendingAdStatusStoreSubscriber",void 0),f()(this,"adMutedStoreSubscriber",void 0),f()(this,"adVolumeStoreSubscriber",void 0),f()(this,"onMidrollAdOpportunity",function(){var e=s.store,t=e.dispatch,n=e.getState,r=_i.adStatus(n()),i=bi.continuePlayingWhileWaitingForAd(n());"loaded"===r?s.playAd(!0):"requested"===r&&(s.pendingMidrollAdPlay=!0,i||(Xi("pause")(t),Ji(!0)(t))),function(e){e({type:"[MONETIZATION] increase ad Opportunity counter"})}(t)}),f()(this,"onPrerollAdOpportunity",function(e){var t=s.store,n=t.getState,r=t.dispatch,i=Fi.loadingImaStatus(n());Un(s.adHandler)?"loading"!==i&&""!==i||(Ji(!0)(r),s.pendingPrerollAdPlay=!0,s.pendingPrerollAdTag=e):(s.pendingPrerollAdPlay=!0,Ji(!0)(r),s.adHandler.loadNewAd(e,"preroll"))}),f()(this,"onPreMidrollAdOpportunity",function(e,t){Un(s.adHandler)||(e.currentTime>=e.midrollTime&&(s.pendingMidrollAdPlay=!0),s.pendingMidrollNumber=e.midrollNumber,s.adHandler.loadNewAd(t,"midroll"))}),f()(this,"hasPendingAd",function(){return s.hasPendingMidrollAdPlay()||s.hasPendingPrerollAdPlay()}),f()(this,"onAdStatusChanged",function(e){var t=s.store.dispatch,n=_i.adStatus(e);"completed"===n&&Ji(!1)(t);var r=bi.continuePlayingWhileWaitingForAd(e),i=_i.loadingAd(e);"playing"!==n&&"error"!==n||r||!i||Ji(!1)(t),s.hasPendingAd()&&"loaded"===n?s.playAd(s.hasPendingMidrollAdPlay()):s.hasPendingAd()&&"error"===n?(Ji(!1),s.clearPendingMidroll(),s.clearPendingPreroll()):Hi(n)||(Ji(!1),function(e){e({type:"[MONETIZATION] clear ad data"})}(t))}),f()(this,"clearPendingMidroll",function(){s.pendingMidrollNumber=null,s.pendingMidrollAdPlay=!1}),f()(this,"clearPendingPreroll",function(){s.pendingPrerollAdPlay=!1,s.pendingPrerollAdTag=null}),f()(this,"onVideoTagStatusChanged",function(e){"complete"===hn.videoTagStatus(e)&&function(e){e({type:"[MONETIZATION] clear played midrolls"})}(s.store.dispatch)}),f()(this,"hasPendingMidrollAdPlay",function(){return s.pendingMidrollAdPlay}),f()(this,"hasPendingPrerollAdPlay",function(){return s.pendingPrerollAdPlay}),f()(this,"playAd",function(e){var t,n=s.store.dispatch,r=s.adHandler.playAd();e?((t=s.pendingMidrollNumber,function(e){e({type:"[MONETIZATION] add 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Ki(t,this),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this)),this.videoTagStatusSubscriber=new ji(t,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this)),e.canUseIMA(u())?this.adHandler=new to(t,r,i,o,a):this.imaLoadingStatusSubscriber=new ji(t,e.getIMALoadingStatusDependencies,this.onIMALoadingStatusChanged.bind(this)),this.pendingAdStatusStoreSubscriber=new ji(t,e.getPendingAdStatusDependencies,this.onPendingAdStatusChanged.bind(this)),this.adMutedStoreSubscriber=new ji(t,e.getAdMutedDependencies,this.onAdMutedChanged.bind(this)),this.adVolumeStoreSubscriber=new ji(t,e.getAdVolumeDependencies,this.onAdVolumeChanged.bind(this))};f()(no,"getAdStatusDependencies",function(e){return[_i.adStatus(e)]}),f()(no,"getVideoTagStatusDependencies",function(e){return[hn.videoTagStatus(e)]}),f()(no,"getIMALoadingStatusDependencies",function(e){return[Fi.loadingImaStatus(e)]}),f()(no,"canUseIMA",function(e){return"success"===Fi.loadingImaStatus(e)}),f()(no,"getPendingAdStatusDependencies",function(e){return[_i.pendingAdStatus(e)]}),f()(no,"getAdMutedDependencies",function(e){return[_i.adMuted(e)]}),f()(no,"getAdVolumeDependencies",function(e){return[_i.adVolume(e)]});var ro=function(e,t){!function(e,t){var n=document.getElementById(vn(t));B(b(Li,{store:e,playerId:t}),n)}(e,t);var n=function(e){var t=Ri(e);return document.getElementById(t)}(t),r=function(e){var t=Bn()?Di(e):En(e);return document.getElementById(t)}(t),i=function(e){var t=En(e);return document.getElementById(t)}(t),o=function(e){var t=bn(e);return document.getElementById(t)}(t);return new no(e,t,n,r,i,o)},io=n(4),oo=n.n(io),ao=n(7),so=n.n(ao),uo=function(){function e(){Ai()(this,e),f()(this,"duration",void 0),f()(this,"position",void 0),f()(this,"previousPosition",void 0),f()(this,"loadTime",void 0),f()(this,"adOrder",void 0),f()(this,"adType",void 0),f()(this,"adDuration",void 0),f()(this,"errorMessage",void 0),f()(this,"adPodNumber",void 0),f()(this,"adSlotNumber",void 0)}return Vi()(e,[{key:"setDuration",value:function(e){return this.duration=e,this}},{key:"setPosition",value:function(e){return this.position=e,this}},{key:"setPreviousPosition",value:function(e){return this.previousPosition=e,this}},{key:"setLoadTime",value:function(e){return this.loadTime=e,this}},{key:"setAdOrder",value:function(e){return this.adOrder=e,this}},{key:"setAdType",value:function(e){return this.adType=e,this}},{key:"setAdDuration",value:function(e){return this.adDuration=e,this}},{key:"setErrorMessage",value:function(e){return this.errorMessage=e,this}},{key:"setAdPodNumber",value:function(e){return this.adPodNumber=e,this}},{key:"setAdSlotNumber",value:function(e){return this.adSlotNumber=e,this}},{key:"build",value:function(){var e=[];return jn(this.position)||e.push("video current position=".concat(Hn(this.position),"sec")),jn(this.duration)||e.push("video duration time=".concat(Hn(this.duration),"sec")),jn(this.loadTime)||e.push("video load time=".concat(this.loadTime,"milliseconds")),jn(this.previousPosition)||e.push("previous position=".concat(Hn(this.previousPosition),"sec")),jn(this.adOrder)||e.push("ad order=".concat(this.adOrder)),jn(this.adType)||e.push("ad type=".concat(this.adType)),jn(this.adDuration)||e.push("ad duration=".concat(Hn(Number(this.adDuration)),"sec")),jn(this.adPodNumber)||e.push("pod number=".concat(this.adPodNumber)),jn(this.adSlotNumber)||e.push("slot number=".concat(this.adSlotNumber)),jn(this.errorMessage)||e.push("error message=".concat(this.errorMessage)),e.join(";")}}]),e}(),co="mmPlus GTM data ready to GA",lo="mmPlus GTM event to GA",po={EMBED:"vplayer video player embed",FIRST_PLAY:"vplayer video first play",COMPLETION_25_PERCENTAGE:"vplayer video 25% complete",COMPLETION_50_PERCENTAGE:"vplayer video 50% complete",COMPLETION_75_PERCENTAGE:"vplayer video 75% complete",COMPLETION_90_PERCENTAGE:"vplayer video 90% complete",AD_BLOCK:"vplayer video ad block",AD_REQUEST:"vplayer video ad request",AD_IMPRESSION:"vplayer video ad impression",AD_ERROR:"vplayer video ad error",AD_VIEWABLE_IMPRESSION:"vplayer video ad viewable impression",AD_COMPLETE:"vplayer video ad complete",AD_SKIP:"vplayer video ad skip",AD_PAUSE:"vplayer video ad pause",VIDEO_COMPLETE:"vplayer video complete",FULLSCREEN_ON:"vplayer video fullscreen on",FULLSCREEN_OFF:"vplayer video fullscreen off",SEEK:"vplayer video position seeked",VIDEO_MUTE:"vplayer video mute",VIDEO_UNMUTE:"vplayer video unmute",CONTROLS_MUTE_OR_UNMUTE:"controls 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t=gn.fullscreen(e),n=hn.currentVideoTimeFragment(e),i=(new uo).setPosition(n).build(),o=t?po.FULLSCREEN_ON:po.FULLSCREEN_OFF;r.analyticsEventsCallbacks.onEvent(o,i)}),this.store=t,this.analyticsEventsCallbacks=n,this.videoMuteSubscriber=new ji(t,e.getVideoMuteDependencies,this.onMuteStateChanged.bind(this)),this.videoFullscreenSubscriber=new ji(t,e.getVideoFullscreenDependencies,this.onFullsScreenStateChanged.bind(this))};f()(yo,"getVideoMuteDependencies",function(e){return[gn.muted(e)]}),f()(yo,"getVideoFullscreenDependencies",function(e){return[gn.fullscreen(e)]});var go=n(3),vo=n.n(go),mo=n(8),bo=n.n(mo),Oo=n(9),_o=n.n(Oo),So=n(5),Eo=n.n(So);n(20);var wo={root:null,threshold:.5,rootMargin:"0px"},Po=function(){function e(t,n,r){Ai()(this,e),f()(this,"store",void 0),f()(this,"observableElement",void 0),f()(this,"callback",void 0),f()(this,"isViewableTimeoutHandler",null),f()(this,"observer",void 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e=this.store.getState;this.didReport=!0,this.callback(e()),this.unobserve()}}]),e}();function To(e){var t=function(){if("undefined"===typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"===typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],function(){})),!0}catch(e){return!1}}();return function(){var n,r=Eo()(e);if(t){var i=Eo()(this).constructor;n=Reflect.construct(r,arguments,i)}else n=r.apply(this,arguments);return _o()(this,n)}}var Ao=function(e){bo()(n,e);var t=To(n);function n(e,r,i){var o;return Ai()(this,n),o=t.call(this,e,r,i),f()(vo()(o),"videoTagStatusSubscriber",void 0),f()(vo()(o),"onVideoTagStatusChanged",function(e){var t=hn.videoTagStatus(e);"playing"===t?o.onPlay():"paused"===t||"seeking"===t?o.onPause():"complete"!==t&&"error"!==t||o.onComplete()}),o.videoTagStatusSubscriber=new ji(e,n.getVideoTagStatusDependencies,o.onVideoTagStatusChanged.bind(vo()(o))),o}return 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t=hn.videoTagStatus(e),n=Cn.activeVideoIndex(e);if(!(n===o.firstPlayReportedIndex)&&"playing"===t){o.firstPlayReportedIndex=n;var r=o.getFirstPlayLabel(e);o.analyticsEventsCallbacks.onEvent(po.FIRST_PLAY,r)}}),f()(this,"reportVideoViewableImpression",function(e){var t=hn.currentVideoTimeFragment(e),n=(new uo).setPosition(t).build();o.analyticsEventsCallbacks.onEvent(po.CONTENT_VIEWABLE_IMPRESSION,n)}),this.store=t,this.analyticsEventsCallbacks=n,this.videoViewableImpressionObserver=new Ao(t,r,this.reportVideoViewableImpression.bind(this)),this.videoTagStatusSubscriber=new ji(t,e.getVideoTagDependencies,this.onVideoTagStatusChanged.bind(this)),this.registerVideoCallbacksIdNeeded(t.getState(),i)};f()(Co,"getVideoTagDependencies",function(e){return[hn.videoTagStatus(e)]});var Ro=[25,50,75,90],Do=function(){function e(){Ai()(this,e),f()(this,"lastReportedPercentage",void 0),this.lastReportedPercentage=0}return Vi()(e,[{key:"clear",value:function(){this.lastReportedPercentage=0}},{key:"updateConsumption",value:function(e,t){var n=e.position,r=e.duration,i=Math.round(n/r*100);i>this.lastReportedPercentage&&this.notifyReportableConsumption(e,i,t)}},{key:"notifyReportableConsumption",value:function(e,t,n){var r=this;Ro.filter(function(e){return e>r.lastReportedPercentage&&e<=t}).forEach(function(t){return n(t,e.position,e.duration)}),this.lastReportedPercentage=t}}]),e}(),Mo=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"analyticsEventsCallbacks",void 0),f()(this,"videoTimeSubscriber",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"videoDataStoreSubscriber",void 0),f()(this,"percentageConsumption",void 0),f()(this,"previousVideoTagStatus",void 0),f()(this,"lastPlayedPosition",void 0),f()(this,"getVideoPercentageAction",function(e){switch(e){case 25:return po.COMPLETION_25_PERCENTAGE;case 50:return po.COMPLETION_50_PERCENTAGE;case 75:return 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store":return ga({},function(e,t){var n=t.powered_by_strip,r=t.brand_logo,i=t.brand_logo_click_url,o=t.brand_color;return ga(ga({},e),{},{showVoltaxLogo:Un(n)?e.showVoltaxLogo:n,brandingLogoSrc:Un(r)?e.brandingLogoSrc:r,brandingLogoUrl:Un(i)?e.brandingLogoUrl:i,brandingColor:Un(o)?e.brandingColor:o})}(e,t.payload.initiateParams));default:return e}},anchorOptions:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Oa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return ba({},function(e,t){var n=t.anchor_options;if(!Un(n)){var r=n.anchoring_appearance,i=n.can_close,o=n.closable_ad,a=n.close_after,s=n.continue_streaming,u=n.orientation,c=n.margins,l=n.sticky_below_class_name,d=n.width,p=Un(c)?e.margins:{top:Number.isInteger(c.top)?c.top:e.margins.top,bottom:Number.isInteger(c.bottom)?c.bottom:e.margins.bottom,left:Number.isInteger(c.left)?c.left:e.margins.left,right:Number.isInteger(c.right)?c.right:e.margins.right};return ba(ba({},e),{},{anchoringAppearance:r||e.anchoringAppearance,canClose:Un(i)?e.canClose:i,orientation:Un(u)?e.orientation:u,closableAd:Un(o)?e.closableAd:o,closeAfter:Un(a)?e.closeAfter:a,continueStreaming:Un(s)?e.continueStreaming:s,stickyBelowClassName:Un(l)?e.stickyBelowClassName:l,width:Un(d)?e.width:d,margins:p,anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:!0})})}return e}(e,t.payload.initiateParams));case"[COMMON] set anchor enable":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:t.payload})});case"[ANCHOR] update is anchor status":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorStatus:t.payload})});case"[COMMON] set anchor disabled by user":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorDisabledByUser:t.payload})});default:return e}},monetization:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Sa({},function(e,t){var n=t.monetization;if(Un(n))return e;var r=n.ad_tag,i=n.ad_type,o=n.vpaid_mode,a=n.ad_request_timeout,s=n.continue_content_play_while_waiting_for_ad,u=n.midrolls,c=u&&u.on&&u.on.sort(Wn),l=Un(s)?e.continuePlayingWhileWaitingForAd:s,d=c?c.indexOf(0):-1,p=-1!==d&&!l;return p&&(c=c.splice(d,1)),Sa(Sa({},e),{},{midrolls:Sa(Sa({},e.midrolls),{},{every:u&&u.every,on:c}),prerollEnabled:p,adRequestTimeout:Un(a)?e.adRequestTimeout:parseInt(a,10),vpaidMode:Un(o)?e.vpaidMode:o,continuePlayingWhileWaitingForAd:l,adsData:Sa(Sa({},e.adsData),{},{adType:Un(i)?e.adsData.adType:i,adTagUrlTemplate:Un(r)?e.adsData.adTagUrlTemplate:r})})}(e,t.payload.initiateParams));case"[COMMON] set new ad tag url template":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adTagUrlTemplate:t.payload})});case"[MONETIZATION] change ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:t.payload,adErrorMessage:null})});case"[MONETIZATION] change ad tag":var n=t.payload,r=n.adUnit,i=n.adTag;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{currentAdTag:i,adUnit:r})});case"[MONETIZATION] change pending ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{pendingAdStatus:t.payload})});case"[MONETIZATION] change ad error":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:"error",adErrorMessage:t.payload})});case"[MONETIZATION] increase ad impression counter":var o=e.adsData.adImpression;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adImpression:o+1})});case"[MONETIZATION] increase ad Opportunity counter":var a=e.adsData.adOpportunity;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOpportunity:a+1})});case"[MONETIZATION] add played midroll number":var s=e.adsData.playedMidrolls,u=In()(s);return u.push(t.payload),Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:t.payload,playedMidrolls:u})});case"[MONETIZATION] clear played midrolls":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{playedMidrolls:[]})});case"[MONETIZATION] clear ad data":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:0,currentAdTag:null,adDuration:0,adUnit:""})});case"[MONETIZATION] change ad duration":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adDuration:t.payload})});case"[MONETIZATION] update is vast ad":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{isVastAd:t.payload})});case"[MONETIZATION] change ad current time":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adCurrentTime:t.payload})});case"[MONETIZATION] update ad muted":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adMuted:t.payload})});case"[MONETIZATION] change ad volume":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adVolume:t.payload})});case"[MONETIZATION] change pod info":var c=t.payload,l=c.podNumber,d=c.slotNumber;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{podNumber:l,slotNumber:d})});case"[MONETIZATION] change loading ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{loadingAd:t.payload})});default:return e}},mediaData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:ja,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Va({},function(e,t){var n=t.content_type,r=t.media_id,i=t.display_title;return Va(Va({},e),{},{mediaType:Un(n)?e.mediaType:n,mediaId:Un(r)?e.mediaId:r,videoData:Va(Va({},e.videoData),{},{showTitle:!!Un(i)||i})})}(e,t.payload.initiateParams));case"[CORE] load video request":return Va(Va({},e),{},{loadingMedia:!0});case"[CORE] load video request success":return Va(Va({},e),{},{loadingMedia:!1,videoList:t.payload});case"[CORE] set current video":var n=t.payload,r=n.index,i=n.videoData;return Va(Va({},e),{},{activeVideoIndex:r,videoData:i});case"[CORE] load video request error":return Va(Va({},e),{},{loadingMedia:!1,mediaLoadingError:t.payload});case"[COMMON] media request":var o=t.payload.mediaRequestObject;return Va(Va({},e),{},{mediaRequest:Va({},o)});default:return e}},semanticOptions:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Ba,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Fa({},function(e,t){var n=t.semantic_options;if(Un(n))return e;var r=n.minimum_date_factor,i=n.promoted_videos,o=n.scan_images_on_page,a=n.scanned_element,s=n.scanned_element_type,u=n.scoped_keywords,c=n.tags;return Fa(Fa({},e),{},{minimumDateFactor:Un(r)?e.minimumDateFactor:r,promotedVideos:Un(i)?e.promotedVideos:i,scanImagesOnPage:Un(o)?e.scanImagesOnPage:o,scannedElement:Un(a)?e.scannedElement:a,scannedElementType:Un(s)?e.scannedElementType:s,scopedKeywords:Un(u)?e.scopedKeywords:u,tags:Un(c)?e.tags:c})}(e,t.payload.initiateParams));default:return e}},userInteraction:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[USER INTERACTION] change user interaction":return qa(qa({},e),{},{userInteractionType:t.payload});default:return e}},splitView:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:$a,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ga({},function(e,t){var n=t.anchor_options;if(!Un(n)){var r=n.split_view,i=n.split_view_ratio;return Ga(Ga({},e),{},{splitViewRatio:Un(r)||!r||Un(i)?e.splitViewRatio:i})}return e}(e,t.payload.initiateParams));default:return e}},discovery:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Za,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ya({},function(e,t){var n=t.next_video;return Un(n)?e:Ya(Ya({},e),{},{nextVideo:Xa(n)})}(e,t.payload.initiateParams));case"[DISCOVERY] show up next":return Ya(Ya({},e),{},{showUpNext:t.payload});case"[DISCOVERY] show skippable content":return Ya(Ya({},e),{},{showSkippableContent:t.payload});default:return e}}}),Qa=[],es=!1,ts=function e(){return function(t){return function(n){if(es)return Qa.push(n),null;es=!0;var r=t(n);return es=!1,Qa.length>0&&e()(t)(Qa.shift()),r}}},ns=function(e){var t=[];if(function(e){return!Un(e)&&!Un(e.enable_redux_debugging)&&e.enable_redux_debugging}(e)){var n=window&&window.__REDUX_DEVTOOLS_EXTENSION__&&window.__REDUX_DEVTOOLS_EXTENSION__();"function"===typeof n&&t.push(n)}var r=Et.apply(void 0,[wt(ua,ts)].concat(t));return vt(Ja,r)},rs=function(){function e(t){Ai()(this,e),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"shouldPlayIfLazyplay",!0),f()(this,"shouldPlayIfAutoplayWhenViewable",!0),f()(this,"videoPausedByObserver",!1),this.store=t,this.playerVisibilitySubscriber=null,this.videoTagStatusSubscriber=null,this.playAccordingToPlaybackMethod()}return Vi()(e,[{key:"lazyplayHandler",value:function(e){hn.playerVisibility(e)>=.5&&(this.playVideo(),this.shouldPlayIfLazyplay=!1)}},{key:"autoplayWhenViewableHandler",value:function(e){hn.playerVisibility(e)>=.5?this.playVideo():this.pauseVideo()}},{key:"onPlayerVisibilityChanged",value:function(e){var t=hn.playbackMethod(e);"lazyplay"===t&&this.shouldPlayIfLazyplay&&this.lazyplayHandler(e),"autoplay_when_viewable"===t&&this.shouldPlayIfAutoplayWhenViewable&&this.autoplayWhenViewableHandler(e)}},{key:"onVideoTagStatusChanged",value:function(e){var t=hn.videoTagStatus(e);"paused"!==t||this.videoPausedByObserver||(this.shouldPlayIfAutoplayWhenViewable=!1),"playing"===t&&(this.shouldPlayIfAutoplayWhenViewable=!0,this.videoPausedByObserver=!1)}},{key:"initiatePlayerVisibilitySubscriber",value:function(){this.playerVisibilitySubscriber=new ji(this.store,e.getPlayerVisibilityDependencies,this.onPlayerVisibilityChanged.bind(this))}},{key:"initiateVideoTagStatusSubscriber",value:function(){this.videoTagStatusSubscriber=new ji(this.store,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this))}},{key:"playVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"idle"===hn.videoTagStatus(n())?on("play")(t):on("resume")(t)}},{key:"pauseVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"paused"!==hn.videoTagStatus(n())&&(this.videoPausedByObserver=!0,on("pause")(t))}},{key:"playAccordingToPlaybackMethod",value:function(){var e=this.store,t=e.dispatch,n=(0,e.getState)();switch(hn.playbackMethod(n)){case"autoplay":this.playVideo();break;case"lazyplay":this.initiatePlayerVisibilitySubscriber();break;case"autoplay_when_viewable":this.initiatePlayerVisibilitySubscriber(),this.initiateVideoTagStatusSubscriber();break;case"none":an(!1)(t)}}}],[{key:"getPlayerVisibilityDependencies",value:function(e){return[hn.playerVisibility(e)]}},{key:"getVideoTagStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}}]),e}(),is=function(){function e(t,n,r,i){var o=this;Ai()(this,e),f()(this,"videoStatusSubscriber",void 0),f()(this,"videoListSubscriber",void 0),f()(this,"mediaRequestSubscriber",void 0),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"playbackMethodManager",void 0),f()(this,"store",void 0),f()(this,"loadContent",function(e,t,n,r){o.loadMedia(t,n,r).then(function(){o.playbackMethodManager=new rs(e)})}),f()(this,"loadMedia",function(e,t,n){var r=o.store,i=r.dispatch,a=r.getState,s=Dn.showTitle(a());if("semantic"===e){var u=pn.semanticOptions(a());return Na(u,s,n)(i)}return ka(t,s,n)(i)}),this.store=t,this.videoStatusSubscriber=new ji(t,e.getVideoStatusDependencies,this.onVideoStatusChanged.bind(this)),this.videoListSubscriber=new ji(t,e.getVideoListDependencies,this.onVideoListChanged.bind(this)),this.mediaRequestSubscriber=new ji(t,e.getMediaRequestDependencies,this.onMediaRequestChanged.bind(this)),this.playerVisibilitySubscriber=null,this.loadContent(t,r,n,i)}return Vi()(e,null,[{key:"createInstance",value:function(t,n,r,i){return new e(t,n,r,i)}}]),Vi()(e,[{key:"playNextVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e)+1;n.length>1&&r>=n.length&&(r=0),r<n.length&&(!function(e){e({type:"[CORE] reset player data time params"})}(t),La(r,n[r])(t),on("play")(t))}},{key:"playPreviousVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e);if(r>0){var i=r-1;La(i,n[i])(t),on("play")(t)}}},{key:"onVideoStatusChanged",value:function(e){"complete"===hn.videoTagStatus(e)&&this.playNextVideo(e)}},{key:"onVideoListChanged",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e);!jn(n)&&n.length>0&&La(0,n[0])(t)}},{key:"onMediaRequestChanged",value:function(e){var t=Cn.mediaRequest(e);switch(t.type){case"playNewVideo":this.loadMedia("specific",t.value);break;case"playNextVideo":this.playNextVideo(e);break;case"playPreviousVideo":this.playPreviousVideo(e)}}}],[{key:"getVideoStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}},{key:"getVideoListDependencies",value:function(e){return[Cn.videoList(e)]}},{key:"getMediaRequestDependencies",value:function(e){return[Cn.mediaRequest(e)]}}]),e}(),os=function e(t){var n=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"onDependencyFailure",function(e,t){console.log("onDependencyFailure",e,t);var r=n.store,i=r.dispatch,o=r.getState;switch(e){case"ima":"blocked"!==Fi.loadingImaStatus(o())&&Qn("error")(i);break;case"hls":er("error")(i)}}),f()(this,"onDependencyReady",function(e){var t=n.store.dispatch;switch(e){case"ima":Qn("success")(t);break;case"hls":er("success")(t)}}),this.store=t},as=function(e){return function(t){t({type:"[COMMON] set fullscreen",payload:e})}},ss=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"videoTag",void 0),f()(this,"pendingFullscreenSubscriber",void 0),f()(this,"adStatusSubscriber",void 0),f()(this,"playerUniqId",void 0),f()(this,"onAdStatusChanged",function(e){var t=_i.adStatus(e),n=r.videoTag.webkitDisplayingFullscreen;"playing"===t&&Bn()&&n&&r.exitFullscreen(r.videoTag)}),f()(this,"isPlayerInFullscreen",function(){var e=document,t=Bn()?En(r.playerUniqId):bn(r.playerUniqId);return Un(e.fullscreenElement)?!Un(e.webkitFullscreenElement)&&0===e.webkitFullscreenElement.id.localeCompare(t):0===e.fullscreenElement.id.localeCompare(t)}),f()(this,"changePlayerWidth",function(e){r.videoTag.style.width=e?"100%":"auto"}),f()(this,"onFullscreenChanged",function(){var e=r.store.dispatch,t=r.isPlayerInFullscreen();r.changePlayerWidth(t),as(t)(e)}),f()(this,"onFullscreenChangedIos",function(){var e=r.store.dispatch,t=r.videoTag.webkitDisplayingFullscreen;t||on("resume")(e),r.changePlayerWidth(t),as(t)(e)}),f()(this,"onPendingFullscreenRequestChanged",function(e){var t=gn.pendingFullscreenRequest(e);"enter"===t?r.enterFullscreen(r.videoTag):"exit"===t&&r.exitFullscreen(r.videoTag)}),f()(this,"getFullScreenElement",function(e,t){var n=document.getElementById(bn(r.playerUniqId));return Bn()?t:e?document:n}),f()(this,"enterFullscreen",function(e){var t=r.getFullScreenElement(!1,e);Bn()?t.webkitEnterFullscreen():document.webkitExitFullscreen?t.webkitRequestFullscreen():document.webkitCancelFullScreen?t.webkitRequestFullScreen():document.mozCancelFullScreen?t.mozRequestFullScreen():document.msExitFullscreen&&t.msRequestFullscreen()}),f()(this,"exitFullscreen",function(e){var t=r.getFullScreenElement(!0,e);document.webkitExitFullscreen||Bn()?t.webkitExitFullscreen():document.webkitCancelFullScreen?t.webkitCancelFullScreen():document.mozCancelFullScreen?t.mozCancelFullScreen():document.msExitFullscreen&&t.msExitFullscreen()}),this.store=t,this.videoTag=document.getElementById(En(n)),this.playerUniqId=n,document.addEventListener("fullscreenchange",this.onFullscreenChanged.bind(this)),document.addEventListener("webkitfullscreenchange",this.onFullscreenChanged.bind(this)),Bn()&&(this.videoTag.addEventListener("webkitendfullscreen",this.onFullscreenChangedIos.bind(this)),this.videoTag.addEventListener("webkitbeginfullscreen",this.onFullscreenChangedIos.bind(this))),this.pendingFullscreenSubscriber=new ji(t,e.getPendingFullscreenDependencies,this.onPendingFullscreenRequestChanged.bind(this)),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this))}return Vi()(e,null,[{key:"createInstance",value:function(t,n){return new e(t,n)}}]),Vi()(e,null,[{key:"getPendingFullscreenDependencies",value:function(e){return[gn.pendingFullscreenRequest(e)]}},{key:"getAdStatusDependencies",value:function(e){return[_i.adStatus(e)]}}]),e}();function us(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function cs(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?us(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):us(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var ls,ds=function(e){return function(e){return e&&window.monti.playerConfigs&&window.monti.playerConfigs[e]}(e)?function(e){return window.monti.playerConfigs[e]}(e):window.monti.playerConfigs?window.monti.playerConfigs&&window.monti.playerConfigs[Object.keys(window.monti.playerConfigs)[0]]:null},ps=function e(t){var n=this;Ai()(this,e),f()(this,"videoTag",void 0),f()(this,"isBufferError",void 0),f()(this,"hls",void 0),f()(this,"hlsSetup",function(e,t,r,i){n.initiateHls(e),n.loadHlsSource(e,t,r,i)}),f()(this,"detachMedia",function(){Un(n.hls)||(n.hls.detachMedia(),n.hls.destroy(),n.hls=null)}),f()(this,"initiateHls",function(e){n.hls=new e,n.hls.attachMedia(n.videoTag)}),f()(this,"loadHlsSource",function(e,t,r,i){n.hls.on(e.Events.MEDIA_ATTACHED,function(){n.hls.loadSource(t)}),n.hls.on(e.Events.ERROR,function(t,o){n.mapHlsToErrors(e,o,i),t.details===e.ErrorDetails.BUFFER_STALLED_ERROR&&(r(!0),n.isBufferError=!0)}),n.hls.on(e.Events.FRAG_BUFFERED,function(){n.isBufferError&&(r(!1),n.isBufferError=!1)})}),f()(this,"mapHlsToErrors",function(e,t,r){if(t.fatal)switch(t.type){case e.ErrorTypes.NETWORK_ERROR:r(Xn.GENERAL_ERROR),n.hls.startLoad();break;case e.ErrorTypes.MEDIA_ERROR:r(Xn.GENERAL_ERROR),n.hls.recoverMediaError();break;default:r(Xn.GENERAL_ERROR),n.hls.destroy()}}),this.hls=void 0,this.videoTag=t,this.isBufferError=!1},fs=function e(){var t=this;Ai()(this,e),f()(this,"videoStreaming",void 0),f()(this,"hlsLibrarySetup",function(e,n,r,i){Un(t.videoStreaming)||t.videoStreaming.detachMedia(),t.videoStreaming=new ps(e),t.videoStreaming.hlsSetup(ls,n,r,i)})};f()(fs,"shouldLoadVideoStreamingSrcDirectly",function(e,t,n){return"no-need"===n&&!(""===e.canPlayType("application/vnd.apple.mpegurl"))}),f()(fs,"shouldUseHlsLibrary",function(e,t){return"success"===t&&(ls=void 0!==window.Hls?Hls:mmHls).isSupported()}),f()(fs,"isValidHlsUrl",function(e){return!Un(e)&&!e.includes(".mp4")}),f()(fs,"suitableVideoSource",function(e,t,n){return fs.isValidHlsUrl(t)?fs.shouldUseHlsLibrary(t,n)?"m3u8 with hls":fs.shouldLoadVideoStreamingSrcDirectly(e,t,n)?"m3u8 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{"is_conflicting_with_other_jw_players":false,"programmatic_play_with_sound_on_desktop":false,"referrer_id":"af93e181-b289-0560-a2bf-808e93bb05bc","width":"100","comscore_publisher_id":"18120612","monetization":{"ad_type":"static_tag","continue_content_play_while_waiting_for_ad":false,"strategy":"on_player_load","ad_request_timeout":"10000","midrolls":{"on":[0]},"vpaid_mode":"ENABLED","ad_tag":"https://pubads.g.doubleclick.net/gampad/ads?sz=400x300|640x480|480x270|640x360&iu=/175840252/MMPlus/smithsonianmag/Video&impl=s&gdfp_req=1&env=vp&output=vast&unviewed_position_start=1&url=##REFERRER_URL_UNESC##&description_url=##DESCRIPTION_URL_UNESC##&correlator=##CACHEBUSTER##&cust_params=mm_midroll%3D##MIDROLL_ORDER##%26video_ID%3D##VIDEO_ID##"},"sponsorship":false,"player_identifier":"mplayer","recommendation_id":null,"brand_color":"#FF9900","powered_by_strip":true,"platform":"buffy","type":"video","config_name":"MM+ | Smithsonianmag | Podding","player_id":"3v9g2u2f","playlist_id":"fSkmeWKF","playback_method":"autoplay","anchor_viewability_method":"none","player_version":"v4","playlist_type":"semantic","semantic_options":{"scan_images_on_page":true,"scanned_element":"","tags":"geogrophy,nature,animals,habitat,outdoors,science,history","minimum_date_factor":30,"scanned_element_type":"tag","scoped_keywords":"mentalfloss","promoted_videos":[]},"script_destination":"mm","publisher_contribution":"floor8","general_script_description":"","brand_logo":"","brand_logo_click_url":"","next_video":"none","uniq_key":"af93e181-b289-0560-a2bf-808e93bb05bc","content_id":"fSkmeWKF","content_type":"semantic"})); Finland has vastly improved in reading, math and science literacy over the past decade in large part because its teachers are trusted to do whatever it takes to turn young lives around. This 13-year-old, Besart Kabashi, received something akin to royal tutoring.

      Kari Louhiuori, a principal at a Finnish school made a mostly unheaard of and uncanny decision to hold back an immigrant student from 6th grader named Besart because he hadnʻt felt that this young man was falling behind due to laziness but to a lack of comprehension. After a year of "royal tutoring," by allowing the boy to read at his own pace, it worked!

  32. Sep 2020
    1. Author Response

      Reviewer #1:

      Major comments:

      1) The title and the conclusion that SON and SRRM2 form nuclear speckles are not supported by the data. The data show that SON and SRRM2 are necessary for nuclear speckle formation. They do not rule out that another factor is necessary, such as SRRM1, which interacts with SRRM2 and itself harbors an intrinsically-disordered domain. That is, the authors have not shown that SON and SRRM2 are also sufficient for nuclear speckle formation. Such a test is necessary to draw the strong conclusion the authors make, and precedence for such a test has been established in the study of Cajal bodies. Specifically, central factors to Cajal body formation were shown to nucleate Cajal body formation at a specific site in chromatin when such central factors were localized to that site. The authors either need to perform such a sufficiency experiment or moderate their conclusions (and title).

      2) In principle, in the immunofluorescence studies, the disappearance of mAb SC35 signal on depletion of SRRM2 does not alone prove that SRRM2 is what is visualized by the mAb SC35 in such assays. Given that this paper seeks to establish rigorously that mAb SC35 marks nuclear speckles by recognition of SRRM2, given that SRSF7 is recognized by the antibody on blots, and given that SRSF2 has been traditionally presumed the target of mAb SC35 in nuclear speckles, the rigor of this study demands that SRFS7 and SRSF2 be visualized in cells in the presence of an SRRM2 truncation to rule out that either SRSF7 or SRSF2 phenocopy SRRM2 in this assay.

      This is a valid concern and we have thought of the same principal that is if any strongly speckle-associated intrinsically disordered domain containing protein, such as SRRM1 or RBM25, two proteins that are also frequently used as NS markes, would have a similar impact on NS formation as SRRM2 has. To this end, we performed a co-depletion of SON and SRRM1 (shown in Supplementary Figure 10) in a cell line that has a TagGFP2 inserted into SRRM2 gene locus. As it can be seen from the imaging presented in this figure for 4 individual cells (but also more generally on 10 independent field imaged, (data not shown)) we did not score a reduction in the GFP intensity, or dissolution of the spherical bodies as is the case in SON-SRRM2 co-depleted cells. We observed the nuclear speckles have the round-up morphology, that is seen upon SON-KD, but are not dissolved shown with PNN staining and SRRM2-TagGFP signals. Moreover, we performed a co-depletion of RBM25 (another strongly NS-associated protein also used as a NS-marker) and SON which did not result in the dissolution of nuclear speckles (Supplementary Figure 10). Therefore, we have reached to the conclusion that SON and SRRM2 form nuclear speckles with the contribution of SON being more important for the formation and titled our study accordingly.

      Traditionally, because of the Fu & Maniatis 1992 paper, as pointed out by the reviewer, it is assumed that SC-35 recognizes SRSF2 in immunofluorescence experiments and potentially multiple SR-proteins in immunoblots. The former point, to the best of our knowledge, has never really been proven in any type of rigorous experiment. Fu lab. has generated SRSF2 K/O mice, but never provided an immunofluorescence image that shows that SC-35 signal disappears in K/O cells.

      Just to summarize our line of reasoning here:

      1) We do an unbiased IP-MS experiment, which shows that SRRM2 is the top candidate protein, at least an order of magnitude away from any other protein in the dataset by any measure. This strongly suggest that SRRM2 is the primary target of this antibody, although doesn’t prove it due to technical reasons i.e. no input normalization, some proteins produce more ‘mass-specable’ peptides than others, and larger proteins tend to produce more peptides.

      2) We carry out a biased screen of 12 SR-proteins and find that SRSF7 is strongly recognized by mAb SC-35

      3) We do IP-western blotting experiments, which correct for input and are not affected by relative ‘mass-specable’ peptide issues or protein sizes, which reveal a strong enrichment of SRRM2 (>10% of input), some enrichment for SRSF7 (~2% of input) and no enrichment for SRSF2, SRSF1 or other proteins that we have tested.

      4) Since the “35kDa” protein is so engrained with the history of this antibody and our results were most consistent with the idea that this protein is SRSF7 rather than anything else, we insert a degron tag to SRSF7. If the hypothesis is true, then we expect a shift of the SC-35 band, concomitant to the shift in SRSF7, which is indeed the case. This is not proof that SC-35 doesn’t recognize any other protein but it does provide very strong evidence (combined with the other two experiments) that the 35kDa band detected by SC-35 in immunoblots is in fact SRSF7.

      5) We then show, by TagGFP2 insertion into the SRRM2 locus, that SC-35 mAb can recognize SRRM2 specifically on immunoblots, and furthermore truncations beyond a certain point completely eliminates this signal. We also show later that siRNA mediated KD of SRRM2 also leads to the elimination of the signal from immunoblots (Supplementary Figure 9).

      6) Combining the results so far, we address the issue of immunofluorescence, i.e. which protein or proteins are responsible for this signal. We think there are two possible scenarios that could both be true based on the presented evidence so far:

      a. This signal is mainly, if not entirely, originates from SRRM2. b. The signal is a combination of SRRM2, SRSF7 and/or other SR-proteins that the SC-35 might be cross-reacting.

      7) We then take advantage of our cell lines with SRRM2 truncations. These truncated SRRM2 version are not recognized by SC-35 mAb on immunoblots, therefore it is reasonable to suspect that they will not be recognized by SC-35 mAb in immunofluorescence as well.

      8) If scenario (b) is correct and nuclear speckles are still intact in these cells (which we show that they are indeed intact, judged by SON, RBM25 and SRRM1 stainings Fig. 3A-B), then we would expect either no change in SC-35 signal, or a somewhat reduced signal. We see a complete loss of signal.

      9) Being extra careful with this result, we also mix the control cell line and SRRM2-truncated cells and image them side-by-side to address any issues related to imaging settings etc. There is no detectable SC-35 signal in truncated cells.

      10) We also show that the 35kDa band is still unchanged in SRRM2 truncated cells (Figure 2E), showing that SRSF7 itself is not affected in these cells.

      These results, combined together, show that SC-35 signal in immunofluorescence originates from SRRM2, and any other signal potentially contributed by other proteins are below the detection of immunofluorescence microscopy.

      Reviewer #2:

      This study reports important evidence that the widely-used SC-35 antibody primarily recognizes SRRM2 rather than the assumed SRSF2. The manuscript provides several lines of evidence supporting this conclusion, and the work has broad impact on the field of nuclear structure and function as this antibody is the most common marker for the major nuclear component, nuclear speckles.

      The one concern with the manuscript is the interpretation of some of the previous literature and understanding in the field.

      First, since the 1990s it has been widely known that the SC-35 mAb has very limited specificity for denatured proteins and was not suitable for immunoblots (see abcam page for ab11826). Indeed, the assumption has always been that it recognizes a folded epitope. Therefore, the use of western blots to conclude anything about the specificity of this antibody is inappropriate.

      Secondly, it has also been previously documented that this antibody has cross-reactivity with SRSF7 (i.e. 9G8; Lynch and Maniatis Genes Dev 1996).

      Third, most SR proteins are not abundantly observed in tryptic MS due to high cleavage of RS domains. This is particularly true of SRSF2, which has a highly "pure" RS domain (i.e. all RS repeats) that encompasses almost half of the total protein. SRRM2, on the other hand, has much more complex and degenerate RS domains that encompass a much smaller percentage of the total protein. SRRM2 is also 10x the size of SRSF2. Thus, given equal molar amounts of SRSF2 and SRRM2, one would expect at least 20x the number of peptides and much more complete coverage of SRRM2 vs. SRSF2. Therefore, while the subsequent immunoblot in Figure 1C is compelling evidence that SRRM2 is precipitated with the SC-35 antibody, while SRSF2 is not, the IP-MS data alone is not strong proof that the SC35 mAb primarily recognizes SRRM2 rather than SRSF2. The text should be revised accordingly.

      Finally, the abstract implies that the demonstration of SON as a central component of speckles is new ("elusive core"). As appropriately referenced in the text, this is not the case, rather SON is often used as a marker for nuclear speckles, and SON has long been considered to be part of the core of speckles, as knock-down has been documented by several groups to disrupt speckles. The wording in the abstract should therefore be more parsimonious.

      With all due respect to all previous researchers that have used mAb SC35 and published their results, we think that the specificity issue has become unnecessarily convoluted due to the initial inaccurate characterization. Abcam’s recommendations highlight the issue in an interesting way. In the old marketing images, abcam shows a single band in a total lysate prepared from HEK293 cells: https://www.abcam.com/ps/products/11/ab11826/reviews/images/ab11826_49518.jpg

      However, producing such an image, in our experience as we have also reported in the manuscript, is only possible under non-ideal western-blotting conditions i.e. when the transfer is not adequate to reveal proteins with large molecular weights. Intriguingly, a customer (not us) complains about an improper WB result obtained with this antibody (with a 2-star rating):

      https://www.abcam.com/sc35-antibody-sc-35-nuclear-speckle-marker-ab11826/reviews/68414?productWallTab=ShowAll

      It looks like an unexplainable high-molecular smear without the information that we provide in our manuscript, but in light of it, it’s clear that protein stained here is SRRM2.

      In our experience the antibody works perfectly fine for western blotting, and very specifically and robustly reveals SRRM2 at ~300kDa, as long as the immunoblotting conditions are optimized for large proteins. We also show that bulk of the signal around 35kDa originates from SRSF7, however as indicated by the other reviewer’s comments, and also previous research, the antibody probably cross-reacts with other proteins as well with varying degree.

      In this sense, the antibody can be used for immunoblotting, but pretty much any result obtained from such an experiment must be verified with an independent antibody or independent methods, which we did in this manuscript.

      The SC35 mAb is actually suitable for western blotting if the gel running and transfer conditions are carefully performed to have SRRM2: a) enter the gel and b) transferred properly to the membrane. Under conditions where SRRM2 is just not entering the gel (due to high percentage gels, or gels with too much bis-acrylamide), or doesn’t get transferred to a membrane (non-ideal buffer conditions, protein stuck in stacking part and cut away etc.), we have seen the unspecific bands, but we had to use the most sensitive detection reagents at hand to see those, so they are rather weak. We have provided a detailed explanation to what these conditions are in the methods section of our manuscript, but briefly: running the gel slowly allowing the protein to enter in the gel and transferring overnight with CAPS buffer were key to get the western blot working. As we have shown in Figure 2C and 2E, the majority of signal detected comes from SRRM2. The unspecific binding of SC35 mAb could only be scored if the above-mentioned conditions were not met.

      We believe what made matters historically worse has been the use Mg++ precipitation that enriches many SR proteins, but actually completely depletes SRRM2 (Blencowe et al. 1994 DOI: 10.1083/jcb.127.3.593, Figure 5, https://pubmed.ncbi.nlm.nih.gov/7962048/ ). When we’re sure that SRRM2 is in the gel though, it just shines as a single band. So in conclusion, SC-35 is reasonably specific to SRRM2, especially in immunofluorescence, but it certainly cross-reacts with other SR-proteins, especially when SRRM2 is missing for technical or biochemical reasons.

      We will update in the manuscript for the corresponding section by citing earlier studies reporting the specificity issues of mAb SC35.

      We absolutely agree that IP-MS data alone is not enough to conclude that SC-35 recognizes SRRM2, or whether it is the primary target or not. The overwhelming amount of SRRM2 peptides detected, in addition to the overwhelming amount of total peptide counts from SRRM2 does strongly suggest that it is the case, which we then followed up by IP-western blotting which controls for relative input, and the various experiments shown in later figures.

      We have looked at our MS results and found out that:

      SRSF2 was detected with 4 unique peptides with an MS/MS count of 5 and a sequence coverage of 29% (intensity 3E+07), whereas SRRM2 was detected with 227 unique peptides with an MS/MS count of 3317 and a sequence coverage of 61.9% (intensity 2E+11).

      These numbers show a 6600 times higher intensity for SRRM2 (not normalized). As the identification and abundance of different peptides/proteins can by dramatically different in MS, it is indeed correct that one should be careful with such comparisons. The only way would be to use peptide standards for both proteins and record standard curves, then a real quantitative comparison would give the true numbers. Hence, we will revise the wording of that section.

      Finally, as the reviewer has pointed out, we have not shown that speckles can be reformed by introducing ectopically expressed SON/SRRM2 into cells which now appear not to have nuclear speckles. This would indeed be the formal proof showing that SON/SRRM2 are not just necessary but also sufficient to form nuclear speckles. Such an experiment is quite challenging due to the length of these proteins and difficulty in establishing conditions where one can express these proteins, but not overexpress them which leads to round-up speckles (as shown and discussed by Belmonte lab). Therefore, we will change the title to “SON and SRRM2 are essential for the formation of nuclear speckles” to better reflect our conclusions.

      We really did try to be clear and just about the previous literature around SON. Indeed, it is clear that SON is a crucial part of NS, likely the most important component for the integrity of speckles. However, in all of these previous studies, RNAi-mediated depletion of SON, without exception, leaves behind spherical bodies that are strongly stained with mAb SC35, that also harbor other NS-markers (which we also show). This is of course not new, as we also appropriately cited previous work, however being able to dissolve these “left-over” speckles by co-depletion of SRRM2, and perhaps more importantly by deletion of the SRRM2’s C-terminal region is indeed novel.

      In essence, our results show that in the absence of SON, as shown by previous work as well, NS-associated proteins are still able to organize themselves into nuclear bodies, indicating that either all other SR-proteins without the need of another organizer clump together, or another factor (or factors) is still acting as an organizer. When we remove the C-terminus of SRRM2, which we show is the primary target of SC-35, which strongly stains these left-over nuclear bodies in the absence of SON, then deplete SON, all NS markers that we could find become diffuse, indicating that nuclear speckles no longer exist, or become too small to be detected or classified as “nuclear bodies”. Co-depletion of SON and SRRM2 leads to the same phenotype, but co-depletion of SON and SRRM1 (or RBM25) doesn’t, leaving behind spherical nuclear speckles that harbor SRRM2 which are no different than SON KD cells.

      Reviewer #3:

      Nuclear speckles in the last several years have attracted significant attention for their association with transcriptionally active chromosome regions (after largely being ignored by most for the previous 20 years). Overwhelmingly, a single monoclonal antibody has been used as a marker for nuclear speckles for several decades.

      This manuscript now argues convincingly that the main target that is recognized by this monoclonal antibody is not SRSF2 (SC35) as long thought, but rather SRRM2. The authors thus clarify a vast literature, while also focusing attention on the very large protein SRRM2 that in many ways resembles another nuclear speckle protein, SON. Both have huge IDRs and unusual RS repeats, while SON has been proposed to act as a scaffold for many SR-containing proteins, which is likely also true for SRRM2, by extension. Moreover, the manuscript provides a convincing explanation for why the target of this antibody was previously misidentified, by showing a lesser cross-reaction with SRSF7, of similar MW to SC35.

      Finally, the manuscript suggests that SON and SRRM2 together help nucleate nuclear speckles, as a double KD, or a SON KD in a background of a truncated SRRM2, leads to loss of nuclear speckle-like staining of other proteins normally enriched in nuclear speckles (RBM25, SRRM1, PNN). The authors go on to suggest that this double KD approach will now provide an important means of disrupting nuclear speckles to aid in functional studies.

      Interestingly, some of the results of this manuscript actually are already confirmed or consistent with previous literature. For example, a cited paper describes changes in Hi-C compartmentalization patterns after "elimination" of nuclear speckles- actually, they performed a SRRM2 KD and showed loss of SC35 staining, which is now explained as simply due to the KD that they performed. More recently, a new proteomics study of nuclear speckles (Dopie et al, JCB, 2020: https://doi.org/10.1083/jcb.201910207) reported both SON and SRRM2 as the two most highly enriched nuclear speckle proteins, with enrichment scores similar to each other but more than twice that of all other speckle proteins. Moreover, this same paper also did a SRRM2 KD and observed loss of anti-SC35 staining but not SON staining.

      Overall, I found this manuscript of significant interest for people in the nuclear cell biology field and technically thorough and well done. I just had one issue and one point to make in my main comments, plus some minor points.

      1) The evidence that nuclear speckles are nucleated by SON and SRRM2 is based on the dispersion of staining of nuclear speckle proteins RMB25, SRRM1, and PNN. However, an alternative explanation is that some other protein(s) nucleates nuclear speckles, while these other nuclear speckle proteins bind to SON and SRRM2, and are therefore enriched in nuclear speckles. To eliminate this concern, the authors could show that SON and/or SRRM2 do not bind to these proteins- for instance using co-IP or other methods. Of course, it could be that such binding or scaffolding of nuclear speckle proteins is how they form nuclear speckles. But just one protein that is not bound by SON and SRRM2 but still stains nuclear speckles after the double KD would be inconsistent with their hypothesis. Therefore, if they do find that all these proteins bind SON and/or SRRM2 they could simply discuss this as a scaffolding mechanism but qualify their conclusion based on the alternative explanation described above.

      2) In our lab we have not been comfortable using the kinase manipulations, discussed in this paper, to eliminate nuclear speckles for experimental purposes because the cells appear very sick after these manipulations. For other reasons, we also tried a double SON and SRRM2 KD. Our experience is that the cells after this double KD were also not very normal. If the authors are suggesting the SON and SRRM2 double KD as an experimental tool to disrupt nuclear speckles in order to access nuclear speckle function, then it would be valuable for them to indicate cell toxicity, etc. Many SR-protein KDs for example do not allow selection of stable cells. What about this double KD?

      The first point of Reviewer #3 has been addressed above in response to the Reviewer #2.

      We have stated that our work identifying SON and SRRM2 as the elusive core of nuclear speckles paves the way to study the nuclear speckles under physiological conditions. Here, we have used the cells 24 hours after transfection (~18 hours of knock-down) as the primary reason being that SON-KD caused a mitotic arrest if the cells were kept longer in culture. This was reported earlier in Sharma et al MBC 2010. There was no additional severity in the phenotype when the SON-KD was combined with SRRM2-KD, therefore we believe the arrest phenotype we scored is mainly due to depletion SON. In this sense, double-depletion of SON and SRRM2 can be used to study the effects of loss of NS (transcription, post-transcriptional, topological), but certainly within a time-frame of around 24 hours in cells that haven’t gone through mitosis. We will clarify this statement in the revised manuscript to avoid any misunderstanding as pointed by the reviewer. Faster depletion strategies, and/or a system where cells are mitotically arrested would be required to observe long term effects more reliably.

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      Reply to the reviewers

      Reviewer 1

      __*Review 1 Summary:

      __In this manuscript, Borah et al showed that Heh2, a component of INM, can be co-purified with a specific subset of nucleoporins. They also found that disrupting interactions between Heh2 and NPC causes NPC clustering. Lastly, they showed that the knockout of Nup133, which does not physically interact with Heh2, causes the dissociation of Heh2 from NPCs. These findings led the authors to propose that Heh2 acts as a sensor of NPC assembly state. *

      __Reviewer 1 major comment 1:__ The authors claimed that Heh2 acts as a sensor of NPC assembly state, as evidenced by their finding that Heh2 fails to bind with NPCs in nup133 Δ cells (Fig2, Fig 5). However, there is a possibility that the association between Heh2 and NPCs is merely affected by the clustering of the NPCs (as the authors discussed) but not related to the structural integrity of NPC.

      • *

      Our Response: We agree that this is a possibility, however, we ask the reviewer to also consider that we artificially cluster NPCs using the anchor away system (Figure 3C) and this does not affect Heh2’s association with NPCs. Thus, clustering per se is insufficient to disrupt Heh2 binding to NPCs. We will also make changes in the text to make this point.

      • *

      Reviewer 1 major comment 2: In addition, their data showing that the Heh2-NPCs association is not easily disrupted by knocking out the individual components of the IRC (Fig. 5A and 5D), also disfavor the idea that Heh2 could sense NPC assembly state.

      Our Response: There are three considerations here. The first is that as this is the first evidence of any kind of “NPC assembly state” sensor, it is difficult to make any assumptions as to what specifically such a sensor would be monitoring. i.e. perhaps sensing only the ORC is what is functionally important. Second, for obvious reasons, we only tested non-essential IRC nups so by definition there is inherent functional redundancy that maintains NPC function and thus there may be no need to “sense” anything in the absence of these IRC nups. Further (and last), the IRC is essential for NPC assembly. Thus, without an IRC there is no NPC assembly state to sense.

      Reviewer 1 major comment 3: Since some nup knockout strains, other than nup133 Δ, are also known to show the NPC clustering (ex. nup159 (Gorsch JCB 1995) and nup120 (Aitchison JCB 1995; Heath JCB 1995)), it will be worth trying to monitor the localization of Heh2 and its interaction with nucleoporins (by Heh2-TAP) using these strains. While Nup159 is a member of the cytoplasmic complex, Nup120 is an ORC nucleoporin. Thus, biochemical and phenotypical analysis using these mutant cells will be useful to clarify if the striking phenotypes the authors found are specific to nup133 knockout strain (or ORC Nup knockouts) or could be commonly observed in the strains that show NPC clustering. Another interesting point is that Nup159 shows strong interaction with Heh2, even in nup133Δ cells. As the authors mentioned, Nup159-Heh2 interaction may not be sufficient for Heh2-NPC association, but it could be important for NPC clustering.

      Our Response: These are excellent points and we agree that there is a need to more thoroughly explore how NPC clustering driven by abrogating the function of other nups impacts Heh2’s association with NPCs. Thus, in a revised manuscript, we would examine Heh2’s association with NPCs in several additional genetic backgrounds where NPCs cluster.

      Reviewer 1 major comment 4: Figure 4C: Is it known that rapamycin treatment in this strain did not affect the protein levels of nucleoporins? Otherwise, the authors should confirm this by western blotting (at least some of them).

      Our Response: This is a good point and we will directly address this with Western blotting of some nups.

      Reviewer 1 major comment 5: Figure 5: The authors mentioned (line 256-257) that "in all cases the punctate, NPC-like distribution of Heh2-GFP was retained (Fig 5D)". However, nup107 KO strain seems to show more diminished punctate staining as compared with other strains. To clarify this, the authors should express mCherry tagged Nup as in Fig. 2 or Fig. 3.

      Our Response: Yes, we agree and in fact this observation is consistent with the fact that there is an ER-pool of Heh2 observed in this strain and we observe loss of nup interactions in the affinity purification. We will include a more thorough quantification of this in a revised manuscript and more directly address this in the text.

      **Minor comments:**

      Reviewer 1 minor comment 1: Figure 4A and 4B: The authors should show Scatter plot as in Fig. 2 and Fig. 3.

      • *

      We will include this in a revised manuscript.

      Reviewer 1 minor comment 2: Figure 5C: Explanations of the arrowheads is missing in the figure legend.

      Thank you for pointing this out, it will be fixed in a revised manuscript.

      Reviewer 1 minor comment 3: Figure 6: Is there any information as to where Heh2 (316-663) is localized in the cell?

      As this truncation lacks INM targeting sequences, it is found throughout the cortical ER. The determinants of Heh2 targeting (including truncations) has been extensively evaluated in King et al. 2006, Meinema et al., 2011 and Rempel et al. 2020. We will make this clearer in the revised manuscript.

      Reviewer 1 minor comment 4: Figure 6B: Nucleoporins should be marked with color circles as in Fig. 1 and Fig. 5.

      This will be done.

      Reviewer 2

      Borah et al. present a biochemical and cell biological examination of the inner nuclear membrane (INM) protein Heh2 and its putative interactions with the nuclear pore complex (NPC). The potential conceptual advance of this study is that Heh2 interacts with the NPC, while mutations believed to trigger NPC mis-assembly are shown to abolish interaction with Heh2, leading to the hypothesis that Heh2 is a sensor for NPC assembly states within the (INM). The conclusions would undoubtably be of broad interest to the nucleocytoplasmic transport field, but the evidence provided thus far is insufficient to build confidence and consequently this manuscript is premature for publication.

      Our Response: We thank the reviewer for recognizing the potential for a significant conceptual advance for the field but object to the notion that the work is “premature for publication”. This is a highly subjective statement that does not seem to meet the mission or purpose of the Review Commons platform. While it is possible that some of the conclusions drawn in our manuscript might not be fully supported by the data in its current form, there is a substantial body of work here that is certainly publishable.

      Reviewer 2 major comment 1: The TAP-tag Heh1/Heh2 pulldowns are the most significant experiment presented, and on face value provide compelling evidence that Heh2 interacts with the NPC. It is stated that mass spectroscopy (MS) was used to confirm the identities of the labeled bands yet there is no methods section, nor any MS data reported in the manuscript. Given the large number of unspecified proteins observed in these gels, and the single-step pulldown methodology used, knowledge of the contaminants present may aid in elucidating how Heh2 pulls down NPC components. Consequently, within the supplementary materials, the authors must indicate which regions of the gel were excised for MS analysis and provide a table listing all of the proteins that were detected for each sample, including the number of unique/expected peptides observed. Our Response: This was a major oversight on our part and a revised manuscript will contain all relevant details with regards to the MS analysis including a more detailed description of the excised bands and the quantification of spectra derived from these bands.

      Reviewer 2 major comment 2a: The representative micrographs provided across Figures 2, 3, 4, 5 and 6 are very noisy. Particularly in the case of the mCherry labeled nucleoporins, this is both unusual and unfortunate given this is used to infer colocalization of Heh2 with the NPC.

      Our Response: These micrographs are not unusual and are in fact of respectable quality. We agree that the apparent “noise” is unfortunate, but this is simply a reality of the yeast system. We remind the reviewer that there are only ~100 to ~200 NPCs per budding yeast nucleus, which is an order of magnitude smaller than a typical mammalian cell nucleus. Further, the copy number of yeast nups per NPC is half of the mammalian cell NPC. Further, budding yeast are spherical with a cell wall that is extremely effective at scattering light; they are also highly autofluorescent (particularly in the red channel). Lastly, unlike in mammalian cells, budding yeast NPCs are mobile on the nuclear envelope. Thus, co-localization is challenging (particularly with the long exposures required to obtain good images). This is why clustering of NPCs driven by nup133**∆ cells has provided one of the key assays in the field to assess whether a given protein associates with NPCs at the level of light microscopy.

      Reviewer 2 major comment 2b: As a result it is unclear whether this experiment can be used to differentiate between NPC colocalization vs. nuclear envelope colocalization.

      Our Response: The reviewer is correct. Co-localization between Heh2-GFP and any Nup-mCherry is insufficient to assess NPC association in WT cells. In fact, as we point out in Figure 3B, at best one can expect a correlation of r = 0.48 for two well established nups. Thus, to further support the conclusion that Heh2 associates with NPCs, we established the Nsp1-FRB NPC clustering assay (Figure 3).

      Reviewer 2 major comment 2c: The authors should include negative controls for an alternative NE membrane protein that doesn't bind the NPC, which would be expected to exhibit a reduced level of colocalization with NPC proteins when compared to Heh2. For example, Heh1 would be a suitable, given the clear-cut negative pulldown data and its prior usage as a negative control in Figure 4.

      • *

      Our Response: This is included in Figure 3D.

      Reviewer 2 major comment 3a. Figure 2. The rim staining for the Nup82-mCherry in the WT background is unusually punctate, bringing into question the viability of the cells imaged.

      Our Response: As the middle cell in the panel is undergoing cell division, these cells are clearly viable. All our imaging is performed on mid-log phase cultures.

      • *

      Reviewer 2 major comment 3b. Why has ScNup82, a cytoplasmic filament component, been selected for colocalization experiments when Heh2 is proposed to interact with the inner ring complex?

      Our Response: The resolution of a conventional light microscope is, at best, 200 nm in x, y. As NPCs are 100 nm in diameter, even two NPCs side-by-side cannot be resolved. The IRC is tens of nm away from the cytoplasmic filaments thus any nup is relevant for a co-localization analysis with a light microscope.

      Reviewer 2 major comment 3c: Additionally, the experiments shown in panels A and C are not directly comparable, ScNup82 is an asymmetric cytoplasmic nucleoporin, while SpNup107 is located in the Y-shaped Nup84 nucleoporin complex and present on both faces of the NPC. This experiment should be repeated with scNup84 to match panel C, additionally a viability dot spot assay and western blot analysis of the labeled proteins should be conducted.

      Our response: These are in fact directly comparable within the limits of resolution of light microscopy as described above. Viability assays are not required here as both nups are essential and perturbation to their function would lead to inviability.

      Reviewer 2 major comment 4: Figure 3, the authors use yeast strains where proteins are tagged with FRB and FKBP12 domains, which dimerize upon the addition of rapamycin inducing NPC clusters. The authors then observe the effect this has on Heh2 NPC colocalization. However, Rapamycin may also have an effect independent from the induced dimerization event. Negative controls should be performed in strains lacking the FRB and FKBP12 tagged proteins to demonstrate that Rapamycin doesn't modify Heh2 localization independently of NPC clustering.

      Our response: This is a good point and important control that we performed in prior studies, see Colombi et al., JCB, 2013. We will be more explicit in describing that this control has been done.

      Reviewer 2 major comment 5: Figure 4. The authors provide a qualitative description of the colocalization presented, while in all other instances they calculate a Pearson correlation coefficient. This is significant because Heh2 appears to be evenly distributed within the NE of the DMSO control (panel B). Given the presented hypothesis isn't colocalization expected with Nup192? As a minimum, a Pearson correlation coefficient analysis should be conducted and added to Figure 4.

      Our response: This will be included in a revised manuscript.

      Reviewer 2 major comment 6: Figure 4. Pom152-mCherry localizes at both the NE and strongly within the cytoplasm, which is unexpected given typical rim staining phenotypes observed previously for both Pom152-YFP and Pom152-GFP strains (Katta, ..., Jaspersen et al., Genetics (2015) & Upla, ..., Fernandez-Martinez et al., Structure (2017), respectively). Given the unusually weak rim staining observed throughout, viability assays of the strains listed in Table S1 and protein expression analysis of the tagged nucleoporins via western blot is necessary.

      Our response: This is not localization in the cytoplasm but is in fact autofluorescence from the yeast vacuole. We regret we were not more explicit in describing this and we will make the manuscript more accessible for the non yeast expert. In order to perform the Western blot analysis for all strains requested by the reviewer would require a battery of antibodies to the endogenous proteins to directly assess how tagging influences nup levels, which we do not have (nor does anyone else that we are aware of). This is also not standard practice in the field as it is an onerous and unnecessary burden.

      Reviewer 2 major comment 7:* Figure 5A. The TAP-tagged pulldowns from ∆Pom152 and ∆Nup133 strains appear to be from a different round of experiments than the previous deletion strains presented. Interestingly, there appears to be an additional band at approximately 250 kDa in both cases that is not present in any other experiments. This band could be a contaminant observed due to different experimental conditions, or a protein that exclusively binds to Heh2 in the ∆Pom152 and ∆Nup133 background. Either way the authors should identify this protein with MS to address this ambiguity.

      *

      Our response: We will include negative controls for these specific experiments to show that this is a non specific band.

      Reviewer 2 major comment 8: Figure 6B. Please label the nucleoporin bands in the TAP-tagged pulldowns.

      Our response: This will be done.

      Reviewer 2 major comment 9: Figure 6D. Please specify Heh2-GFP clustering in the y-axis.

      Our response: As this represents both Heh2-GFP and heh2-1-570-GFP, we will keep it as is to avoid confusion.

      Reviewer 2 major comment 10: *Under the results section titled 'Heh2 binds to specific nups in evolutionarily distant yeasts', the authors state that spHeh2 co-purifies with "several specific species". The meaning is unclear, this sentence should be rephrased and the specific species clearly described. **

      *

      Our response: Ok.

      Reviewer 2 major comment 11: Under the results section titled 'Heh2 fails to interact with NPCs lacking Nup133', the authors refer to a Pearson correlation coefficient of -0.03 as a clear anticorrelation. Instead state there was no correlation.

      Our response: Ok.

      Reviewer 2 major comment 12: In the discussion, the authors state that "clustering itself may sterically preclude an interaction with Heh2". The text should be expanded to explain this in more detail, it is not clear from the presented data why this would occur.

      Our response: Ok.

      Reviewer 2 comment on significance: the manuscript is premature for publication.

      Our Response: Such a statement has no relevance to this form of review as a decision as to whether a study is premature for publication should be made by journal editors, not reviewers. We would argue quite strongly that we have definitively shown that Heh2 binds to NPCs, that it does so in multiple evolutionarily distant yeasts and that this binding is functionally relevant. For example, we can specifically disrupt the association of Heh2 with NPCs with a specific domain deletion and observe a loss of function phenotype (e.g. NPC clustering). What all three reviewers agree on is that the concept of a “NPC assembly state sensor” needs additional data to be fully supported, although we note that this reviewer did not provide any suggestions for how we might achieve this goal. We further note that we added the qualifier “may” into the title of the work. Thus, we will therefore perform additional experiments as outlined in comments to Reviewer 1 to support this conclusion in order to introduce this as a new concept in the field.

      Reviewer Comment from Cross Commenting: It seems to me that all reviewers agree that the manuscript is premature for publication. The data thus far do not support the conclusion that Heh2 may be an NPC assembly sensor nor does it provide any mechanistic insight. Reading the comments of the other two reviewers makes me more negative, as it is care that the paper also lacks scientific rigor. The manuscript is a great starting point for a rigorous dissection but I do not see this paper to be a candidate for a broad impact journal.

      Our Response: The statement that this manuscript is premature for publication is an opinion and does not seem to reflect the sentiment of the other reviewers. It is also confounding that this reviewer suggests that this work lacks rigor. With the exception of the omission of the MS analysis (our fault), the data are of high quality and rigorously quantified. Our assertion of rigor and data quality is based on our collective team’s many decades-long history of publishing and reviewing papers at the highest levels in this field. Questions as to the quality of the data as stated by this reviewer (and only this reviewer) in fact address limitations of light microscopy and the yeast system more generally in this one respect.


      Reviewer 3

      Reviewer 3 Summary part a*: This is quite an interesting manuscript that explores the relationship between an INM protein, Heh2, and NPCs. It represents an extension of earlier work performed by this group in which it was shown that the HEH2 gene shares genetic interactions with the genes encoding various nucleoporins. Heh2 belongs to an intriguing family of conserved proteins that includes its orthologue, Heh1, as well as human MAN1 (LEMD3) and LEMD2, among others. Each of these proteins contains two transmembrane domains with the N- and C-terminal regions extending in to the nucleoplasm. The two TM domains are separated by a short lumenal loop.

      In this study, the authors show that a population of Heh2 is associated with Nups of the NPC inner ring complex. This was demonstrated initially in pulldown experiments. The authors go on to show that when NPCs are caused to aggregate, by physical tethering employing an FKBP/FRP system in combination with Rapamycin, Heh2, but not Heh1, colocalizes with the NPC clusters. *

      • *

      Our Response: Thank you to the reviewer for recognizing the value of this work.

      • *

      Reviewer 3 Summary_b. Although not stated explicitly in the manuscript, this would imply that there is a population of Heh2 that resides in the NPC membrane domain, with the remainder in the INM. As an idle question, is there any evidence for a similar localization of MAN1 or LEMD2 in mammals? I am guessing probably not.

      Our Response: We regret this was not made more clear but the idea that there is a pool of Heh2 at the POM and a pool at the INM is an important conclusion of the work and was stated in the results - we’ll re-emphasize in the revised discussion. As to whether MAN1 or LEMD2 has a similar NPC association, we hypothesize that MAN1 but not LEMD2 will indeed interact with NPCs in mammalian cells. This is based on considering that we show that both the budding and fission yeast orthologues of MAN1 share this association so unless it was lost in evolution, this is a likely outcome of future studies.

      Reviewer 3 Significance statement a: The complications arise when the authors show that an alternative method of NPC aggregation (although they did this first), involving Nup133 deletion, results in failure of Heh2 to co-aggregate. In other words, Nup133 is required for the association of Heh2 with NPCs. The issue here is that there is no evidence for an interaction between Heh2 and Nup133, and furthermore that loss of Nup133 (a Y complex component of the outer ring complex) leaves the inner ring complex intact.

      • *

      Our Response: We tested the nup133Δ background first as this is the standard approach for assessing NPC-association of a given protein so we felt this would be logical for a reader in the field. Further, while the disruption of Heh2’s binding by loss of Nup133 may be a complication, we prefer to see it as an opportunity for discovery. As described in our manuscript, we have chosen to interpret this result in the context of a new biological function/concept with Heh2 being a novel “NPC assembly state” sensor. While one could argue that we have not fully met this bar yet, we will perform additional experiments as outlined in our response to reviewer 1 to help support this compelling conclusion.

      • *

      Reviewer 3 Signfiicance statement b: What is clear, however, is that Heh2 seems to be required to inhibit NPC aggregation since Heh2 deficient cells exhibit NPC clusters. The association between Heh2 and IRC Nups resides in the C-terminal nucleoplasmic winged helix domain. The N-terminal domain, in contrast confers INM localization.

      • *

      Our Response: We agree.__*


      Reviewer 3 Signfiicance statement c I must admit, I am in two minds about this manuscript. The data clearly show that Heh2 is associated with IRC components and I agree with the authors that this protein may well have a role in NPC assembly quality control perhaps in the guise of a chaperone. However, I find it hard to come up with a convincing model for the effects of Nup133. On the one hand, one could make an argument that the data presented here is too preliminary and fails to provide a complete story. On the other hand, it does provide an intriguing foundation for future studies and I do feel positively disposed towards it. In short, I have no fundamental complaints about the science, I am just uncertain as to whether the study is ready for publication.

      Our Response: This statement nicely articulates the challenge with this manuscript as there are some solid findings (that Heh2 binds specifically to NPCs etc.) but also a provocative finding (that loss of Nup133 breaks Heh2’s interaction with NPCs despite not physically interacting). Thus, there is a decision to be made about whether there is value in introducing a novel concept to the field once additional data is provided in a revised manuscript.

      Reviewer 3 Cross commenting: I have no fundamental disagreements with either of the other two reviewers. The comment from Reviewer#2 summarises this quite neatly. While I have fewer concerns about the quality of the data as presented, I think we all agree that at best the study is preliminary. What the authors need to do is to construct a coherent model that will account for the observations described here and then to design experiments that will test this model. I'm not suggesting that they must have a complete story, but they do need to go beyond what is in the current manuscript.

      • *

      Our Response: We appreciate that the reviewer does not have any questions about the quality of our data, but we argue that we have in fact presented the most coherent interpretation of the data as it currently stands. As described above, we intend to attempt to solidify this model by performing experiments suggested by reviewer 1.



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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): The manuscript by Huh et al. reports that oxidative stress causes fragmentation of a specific tyrosine pre-tRNA, leading to two parallel outcomes. First, the fragmentation depletes the mature tRNA, causing translational repression of genes that are disproportionally rich in tyrosine codon. These genes are enriched for those involved in electron transport chain, cell cycle and growth. Second, the fragmentation generates tRNA fragments (tRFs) that bind to two known RNA binding proteins. Finally, the authors identify a nuclease that is needed for efficient formation of tyrosine tRFs. Comment 1: Th­­­­e authors should include a short diagram indicating the various known steps of pre-tRNA fragmentation (perhaps as a supplement) for general readers.

      Response: We thank the reviewer for their suggestion. Pre-tRNA fragmentation is still an unknown field but an initial introduction is best seen from pre-tRNA processing where there is a cleavage event for pre-tRNAs with an intron. This is a complex subject but a recent review from Hopper and Nostramo has done an excellent job in in describing the current field in yeast and vertebrate species (Hopper and Nostramo, Front. Genet., 2019). We have added this citation and new text in the manuscript about pre-tRNA processing for general readers to follow up on. We feel that a supplementary figure might be a bit too brief in describing the knowns and unknowns of pre-tRNA processing and fragmentation.

      Comment 2: I find the enrichment for mitochondrial electron transport chain (ETC) curious. The ETC includes several oxidoreductases, which may be rich in tyrosine as it is a common amino acid used in electron transfer. The depletion of the tyrosine tRNA from among many tRNAs under oxidative stress may not be incidental but related to an attempt by the cell to decrease oxygen consumption to avoid further oxidative damage. The authors could further mine their data to corroborate this hypothesis. For example, are the ETC genes among the targets of the RNA binding proteins targeted by tyrosine tRFs? This could potentially connect the effects of mature tRNA depletion and tRFs.

      Response: We thank the reviewer for this very interesting comment and insight, which had not occurred to us. The relationship between this response and oxidoreductase regulation could be a factor in both the tRNA and tRF modulations seen in our cells. Interestingly, we find that many oxidoreductases genes (such as the NDUF family) are bound by hnRNPA1 by CLIP. In new data, we have done stability experiments with the tRF (new Fig 7E-F) to show the regulon of hnRNPA1 is modulated with overexpression and LNA against the tRF, revealing that this tRNA fragmentation response modulates expression of certain oxidoreductase genes. However, we do not see clear and significant differences for ETC genes in particular. As hnRNPA1 is known to act as both a promoter and destabilizer of genes depending on context, it is likely that further and more detailed work will be needed to parse this hypothesis out in future studies.

      Comment 3: In figure 4A, the authors should provide the tyrosine codon content of the overlap genes and show how much it differs from a randomly selected sample.

      Response: We have identified an error in our manuscript where the overlap actually identifies 109 proteins rather than the 102 reported in the original manuscript. We apologize for this oversight. As for the overlap proteins, we plotted the downstream proteins detected in the proteome by mass spectrometry based off on Tyr-codon content. As explained in the text, the targets we tested were chosen for having higher than median levels of Tyr-codon, as seen in the histogram, and for showing some of the greatest reduction after Tyr tRNA-GUA depletion (Fig S4A). The other proteins found in the overlap will fall in a similar pattern along the histogram.

      Comment 4: Fig.6F, lower panel: the model should show pre-tRNA, as opposed to mature tRNA, because it is the former that is fragmented.

      Response: We apologize for the confusion. The model in Fig 7F was supposed to denote the pre-tRNA with the trailer and leader sequences intact initially, then lost with processing to mature tRNA. To make it clearer, we have now labeled the first species as “Pre-tRNA.”

      Reviewer #1 (Significance (Required)): This study is comprehensive and novel, and includes several orthogonal and complementary approaches to provide convincing evidence for the conclusions. The main discovery is significant because it presents an important advance in post-transcriptional control of gene expression. The process of tRF formation was previously thought not to affect the levels of mature tRNA. This study changes that understanding by describing for the first time the depletion of a specific mature tRNA as its precursor form is fragmented to generate tRFs. Finally, the authors identify DIS3L2 as a nuclease involved in fragmentation. This is also an important finding as the only other suspected nuclease, albeit with contradictory evidence, is angiogenin. Collectively, the findings of this study would be of interest to a broad group of scientists. I only have a few minor comments and suggestions (see above).

      Response: We thank the reviewer for their very positive and insightful comments and feedback.

      REFEREES CROSS-COMMENTING I have the following comments on other reviewers' critiques. Regarding the concern that the disappearance of the pre-tRNA could be a transcriptional response (reviewer 2), I think that the appearance of tRFs makes this scenario unlikely. If pre-tRNA levels decreased due to transcriptional repression, wouldn't one expect that both tRNA and the tRF levels diminish concomitantly? Reviewer 3 raises the issue of cross hybridization in Northern blots. The authors indicate that they "could not detect the other tyrosyl tRNA (tRNA Tyr AUA) in MCF10A cells by northern blot..." (page 6). Also, they gel extracted tRFs and sequenced them (figure S6B), directly identifying the fragments. I think these findings mitigate the concern of cross hybridization and clearly identify the nature of tRFs. Finally, I think that the codon-dependent reporter experiment (figure 5D) addresses many issues surrounding codon dependent vs indirect effects. In that experiment, the authors mutate 5 tyrosine codons of a reporter gene and demonstrate that the encoded protein is less susceptible to repression in response to oxidative stress.

      Response: We thank the reviewer for their tremendous insights. We are in agreement regarding the three points in the cross-comments.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): This very interesting study from Sohail Tavazoie's lab describes the consequences of oxidative stress on the tRNA pool in human epithelial cell lines. As previously described, the authors observed that tRNA fragments were generated upon exposure of cells to ROS. In addition, the authors made the novel observation that specific mature tRNAs were also depleted under these conditions. In particular, the authors focused on tyrosyl tRNA-GUA, which was decreased ~50% after 24 hours of ROS exposure, an effect attributable to a decrease in the pre-tRNA pool. Depletion of tyrosyl tRNA resulted in reduced translation of specific mRNAs that are enriched in tyr codons and likely contributed to the anti-proliferative effects of ROS exposure. In addition, the authors demonstrated that the tRFs produced from tyr tRNA-GUA can interact with specific RNA binding proteins (SSB and hnRNPA1). The major contribution of this paper is the novel finding that stress-induced tRNA fragmentation can result in a measurable reduction of specific mature tRNAs, leading to a selective reduction in translation of mRNAs that are enriched for the corresponding codons. Previously, studies of tRNA fragmentation largely focused on the functions of the tRFs themselves and it was generally believed that the mature tRNA pool was not impacted sufficiently to reduce translation. The findings reported here therefore add a new dimension to our understanding of the cellular consequences of stress-induced tRNA cleavage. Overall, the data are of high quality, the experiments are convincing, and the conclusions are well supported. I have the following suggestions that would further strengthen the study and bolster the conclusions. Comment 1: The authors have not formally demonstrated that the reduction in pre-tRNA in H2O2-treated cells is a consequence of pre-tRNA cleavage. It is possible that reduced transcription contributes to this effect. Pulse-chase experiments with nucleotides such as EU would provide a tractable approach to demonstrate that a labelled pool of pre-tRNA is rapidly depleted upon H2O2 treatment, which would further support their model. Since the response occurs rapidly (within 1 hour), it would be feasible to monitor the rate of pre-tRNA depletion during this time period in control vs. H2O2-treated cells.

      Response: We thank the reviewer for their suggestion and agree that testing for a transcriptional effect using a pulse-chase experiment would further support these findings. We are grateful to both reviewer 1 and reviewer 2 in the cross-comments for recognizing that the tRNA repression response we see is too rapid to be a transcriptional response and that the fact that this tRNA depletion response occurs concomitantly with the tRF generation supports our model that this is a pre-tRNA fragmentation response. It would be of interest for future studies to also examine the impact of cellular stress on tRNA transcription.

      Comment 2: To what extent is the growth arrest that results from H2O2 treatment attributable to tyr tRNA-GUA depletion (Fig. 3A)? Since the reduction in tRNA levels is only partial (~50%), it should be feasible to restore tRNA levels by overexpression (strategy used in Fig. 3E, S3B) and determine whether this measurably rescues growth in H2O2-treated cells.

      Response: We thank the reviewer for their suggestion. Originally, we had also thought of this experiment and attempted to test this hypothesis. Upon experimentation, we ran into technical challenges that prevented us from drawing any conclusions. The problems were that we were unable to develop a cell line that stably overexpressed the Tyr tRNA-GUA and had to settle for a transient overexpression that only lasted for a couple of days (Fig S3B). For transient transfection, we used Lipofectamine 3000 (Invitrogen) that has associated cell toxicities and requires a control RNA transfection in lipofectamine. In addition, H2O2 in itself is a stress. The simultaneous occurrence of these two stresses led to a combination of cell death and cell growth for the control and experimental group. Given the high variability, we were unable to draw any conclusions on cell growth with this combination. We hope to identify a way to stably overexpress Tyr tRNA-GUA in the future to address this hypothesis.

      Comment 3: Knockdown of YARS/tyr tRNA-GUA resulted in reduced expression of EPCAM, SCD, and USP3 at both the protein and mRNA levels (Fig. 4C-D, S4C). In contrast, H2O2-exposure reduced the abundance of these proteins without affecting mRNA levels (Fig. 5A-B, S5A). The authors should comment on this apparent discrepancy. Perhaps translational stalling induces No-Go decay, but it is unclear why this response would not also be triggered by ROS.

      Response: We would like to clarify that out of the three genes in Fig. S5A, only EPCAM mRNA levels were significantly reduced with H2O2-exposure while no changes were observed in the mRNA levels of USP3 or SCD. It is difficult to ascertain the reason for EPCAM mRNA reduction but one hypothesis is due to timing and steady state levels. Levels of mRNAs seen with knockdown of YARS or tRNA represent steady state levels where mRNA decay and transcriptional changes can be easily seen. Following H2O2, the data is collected at 24 hours, which may be before mRNA effects can be fully appreciated. We have edited the text to clarify the uncertainty involved. We agree with the reviewer’s insightful comment and find these differences to be interesting and will consider them in future studies to better understand the interplay between translation and mRNA levels in the context of tRNA depletion.

      Comment 4: In addition to the analyses of ribosome profiling in Fig. 5E-F, it might also be helpful to show a metagene analysis of ribosome occupancy centered upon UAC/UAU codons (for an example, see Figure 2 of Schuller et al., Mol Cell, 2017). This has previously been used as an effective way to visualize ribosome stalling at specific codons. Additionally, do the authors see a global correlation between tyrosine codon density and reduced translational efficiency in tRNA knockdown cells?

      Response: We thank the reviewer for their important suggestion. We have expanded the analysis to look at codon usage scatterplots across all codons for shTyr and shControl replicates (Fig S5D). The 5 most changed codons are labeled with UAC, a codon for the tyrosine amino acid, being the most affected (red arrow). Consistent with our model, a tyrosine codon, when at the ribosome A-site, is most affected with depletion of the corresponding tRNA. The text has also been edited to reflect our new analysis providing further evidence that ribosomal stalling could occur upon depletion of this tRNA. The gray outline around the regression line represents the 95% confidence interval.

      Fig S5D

      As seen in Fig 5F, a significant overlap was noted for genes with the lowest translational efficiency and tyrosine enrichment. We did further analysis to test if a direct and linear relationship exists between tyrosine codon density and reduced translational efficiency on the global scale (i.e. does more stalling occur with more tyrosine codons on a global scale). We again see that a reduced translational efficiency is significantly correlated with tyrosine codon enrichment (above median parameters) in the tRNA knockdown ribosome profiling data. However, our analysis on a direct relationship between codon density and translational efficiency is inconclusive. This analysis is limited given the sequencing depth and number of experimental replicates available and we lack the statistical power to draw strong conclusions. To prevent overstating our claims, we have omitted any conclusions regarding this second analysis.

      Comment 5: MINOR: On pg. 4, the authors state that tRF-tyrGUA is the most highly induced tRF, but Fig. S1B appears to show stronger induction of tRF-LeuTAA.

      Response: The reviewer is correct in that the data from Fig S1B shows Leu-tRFs with higher induction. Our text was meant to suggest we focused on tRF-TyrGUA due to higher band intensity seen on northern blot validation. We have edited the text in the manuscript to clarify this.

      Reviewer #2 (Significance (Required)): The major advance provided by this work is the demonstration that stress-induced tRNA cleavage can reduce the abundance of the mature tRNA pool sufficiently to impact translation. Moreover, the effect on mature tRNAs is selective, resulting in the reduced translation of a specific set of mRNAs under these conditions. These findings reveal previously unknown consequences of oxidative stress on gene expression and will be of interest to scientists working on cellular stress responses and post-transcriptional regulation.

      Response: We thank the reviewer for the kind comments and feedback.

      REFEREES CROSS-COMMENTING Regarding the concern that the disappearance of the pre-tRNA could be a transcriptional response (reviewer 2), I think that the appearance of tRFs makes this scenario unlikely. If pre-tRNA levels decreased due to transcriptional repression, wouldn't one expect that both tRNA and the tRF levels diminish concomitantly? Here is what I was thinking: The generation of tRFs does not generally result in reduction in levels of the mature tRNAs. So you can imagine a scenario where oxidative stress causes tRF generation from the mature tyr tRNA (which does not impact its steady-state levels), as is the case for other tRNAs. At the same time, decreased transcription would reduce the pre-tRNA pool, leading to a delayed reduction in mature tRNA, as observed. However, looking back at the data, I see that after only 5 min of H2O2 treatment, the authors observed reduced pre-tRNA and increased tRFs (Fig. 2A). This seems very fast for a transcriptional response, which would presumably require some kind of signal transduction. In addition, when you consider the amount of tRFs produced in Fig. S2C, it is hard to imagine that this would not impact the mature tRNA pool if they were derived from there. So I agree that the transcriptional scenario seems unlikely. Nevertheless, I think that looking at pre-tRNA degradation directly with the pulse-chase strategy would strengthen their story, so I would like to give the authors this suggestion. However, I am fine with listing this as an optional experiment which would enhance the paper but should not be essential for publication.

      Response: We thank the reviewer for these insightful comments. As mentioned above, five minutes is likely too rapid for a transcriptional response to be the main effect of H2O2 on Tyr-tRNA GUA. Moreover, the concomitant appearance of the tRF at this time-point makes tRNA fragmentation the most parsimonious and likely explanation rather than transcriptional repression, which would not cause a tRNA fragment to occur concurrently. Moreover, extraction and sequencing of the tRF shows it likely derives from the pre-tRNA as a 5’ leader sequence is present. We appreciate the reviewer’s suggestion and scholarly willingness to reassess their own hypothesis.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): The major findings in this manuscript are: 1.) Oxidative stress in human cells causes a decrease in tyrosine tRNA levels and accumulation of tyrosine tRNA fragments; 2.) The depletion of tyrosyl-tRNA synthetase or tyrosine tRNAs in human cells results in altered translation of certain genes and reduced cell growth and 3.) hnRNPA1 and SSB/La can bind tyrosine tRNA fragments. There is also preliminary evidence that the DIS3L2 endonuclease contributes to the appearance of tyrosine tRNA fragments upon oxidative stress. Based upon these results, the Authors conclude that tyrosine tRNA depletion is part of a conserved stress-response pathway to regulate translation in a codon-based manner. **Major comments:** Comment 1: There is a considerable amount of data in this paper and the experiments are performed in a generally rigorous manner. Sufficient details are provided for reproducing the findings and all results have been provided to appropriate databases (RNA-Seq and ribosome profiling).

      Response: We thank the reviewer for the positive comments and feedback.

      Comment 2: The manuscript uses a probe against the 5' half of Tyrosine tRNA for Northern blotting. However, tRNA probes can be prone to cross-hybridization, especially with some tRNA isoacceptors being similar in sequence. Thus, the blots in Figure 2 and Supplemental Figures should be probed with an oligonucleotide against the 3' half of tRNA-Tyr. This will confirm the pre- and mature tRNA-Tyr bands detected with the 5' probe. Moreover, this will determine whether 3' tRNA-Tyr fragments accumulate.

      Response: We agree that the reviewer is correct in suggesting that the 3’ tRNA-Tyr might also accumulate. However, we disagree that any accumulation of the 3’ tRF might be relevant in our particular model for multiple reasons. As supported by reviewer 1’s cross-comments, cross-hybridization between isoacceptors (GUA vs AUA) would be unlikely as Tyr-AUA could not even be detected by the initial 5’ tRF probe. Additionally, the sequences for Tyr-GUA are different with no nucleotide alignment from Tyr-AUA. Furthermore, the extraction and sequencing of the 5’ tRF (Fig S6B) confirms the 5’ leader sequence unique to the pre-tRNA (also noted by reviewer 1). While the 3’ half of many Tyr-GUA are similar, we find selective binding of our RNA binding proteins only to the 5’ tRF. The 3’ tRF may play some role in binding to other proteins in cell regulatory pathways but such experiments would be outside the scope of this study.

      Comment 3: The analysis of the proteomic and ribosome profiling experiments seem rather limited, or based upon what was presented in this manuscript. If additional analyses were performed, then they should be included as well, even if they yielded negative results. For example, the manuscript identifies 102 proteins that decrease after tRNA-Tyr depletion and YARS-depletion with a certain threshold of Tyr codon content. We realize the Authors were trying to find potential genes that are modulated under all three conditions. However, this does not provide information whether there is a relationship between a certain codon such as Tyr and protein abundance if only binning into two categories representing below and above a certain codon content. The Authors should plot the abundance change of each detected protein versus each codon and determine the correlation coefficient. This analysis is important for substantiating the conclusion of a codon-based system of specifically modulating transcripts enriched for certain codons. Otherwise, how could changes in tRNA-Tyr levels modulate codon-dependent gene expression if two different transcripts with the same Tyr codon content exhibit differences in translation? Moreover, this analysis should be performed with all the other codons as well.

      Response: We have identified an error in our manuscript where the overlap identified 109 proteins and not 102 as reported previously. We apologize for this oversight. While the reviewer is correct in that identifying codon dependent changes for all 3500+ proteins detected would offer greater insight, our study was specifically focused on tyrosine as we observed this tRNA to become depleted and our experimental system modulated this specific tRNA. As for the second point on Tyr tRNA level effects on translation, we felt that the most rigorous course would be to assess causality rather than an association for this tRNA and its codon in regulating a target gene. The only way to do this is to perform mutagenesis and reporter studies. Our codon dependent reporter clearly shows a direct effect on translation in a tyrosine-codon dependent manner. As for translational regulation for two different transcripts with the same Tyr codon content, it is unclear the molecular mechanisms that could dictate these differences. The reviewer has already brought up possibilities in the next comment regarding Tyr codons in 5’ or 3’ ends or consecutive Tyr codons. These are all interesting hypotheses that others in the field have devoted entire publications to try and understand how and why codon interactions and localizations impact translation (see Gamble et al., Cell 2016, Kunec and Osterreider, Cell Reports 2016, Gobet et al., PNAS 2020). While these further analyses would be interesting, our current experimental data would be insufficient to properly address these questions. We have focused on a specific tRNA, its fragment, and demonstrated direct effects of the tRNA on the codon-dependent translation of a specific growth-regulating target gene and the tRNA fragment on the modulation of the activity of the RNA binding protein it binds to with respect to its regulon. We believe that these findings individually reveal causal roles for this tRNA and tRF in downstream gene regulation and collectively reveal a previously unappreciated post-transcriptional response. We hope the reviewer agrees with us regarding the already deep extent of the studies and that further such analyses beyond this tRNA are outside the scope and focus of this current study.

      Comment 4: The Authors should provide the specific parameters used to calculate the median abundance of Tyr codons in a protein and the list of proteins containing higher than median abundance of Tyr codon content. Moreover, the complete list of 102 candidate genes should also be provided. This will allow one to determine what percentage of these Tyr-enriched proteins exhibited a decrease in levels. Moreover, is there anything special about these Tyr codon-enriched transcripts where they are affected at the level of translation but not the other Tyr-codon enriched transcripts? For example, are these transcripts enriched at the 5' or 3' ends for Tyr codons? Do these transcripts exhibit multiple consecutive Tyr codons? This deeper analysis would enrich the findings in this manuscript.

      Response: For the proteins identified in the mass spectrometry and overlap listed in Fig 4A, Tyr codon abundance was calculated by dividing the number of Tyr amino acids present by the total number of amino acids for each protein. For genes with different isoforms possible, the principal isoform, using ENSEMBL, was used for calculations. We are also happy to provide the entire list of proteins. Additionally, please see above response to comment 3. We wish to emphasize that the goal of identification of these proteins was to identify downstream targets of this response for functional studies, which we have done. We have identified downstream genes that become modulated by this response and that regulate cell growth, consistent with the phenotype of the tRNA. We then demonstrated a direct causal tRNA-dependent codon-based response with a specific target gene using mutagenesis.

      While we agree that the additional analysis the reviewer is requesting to determine what constitutes heightened translational sensitivity to this response is interesting, we believe this is a challenging question for future studies. It is possible that enrichment at 5’ or 3’ or concentration of tyrosine codons could cause increased sensitivity. Ideally, one would have information on a larger set of proteins so that such challenging questions could be better statistically bolstered. Ultimately, the requested experiments that go beyond our current work would require further analyses and experiments to allow firm conclusions to be drawn. As the other reviewers state and this reviewer agrees, we have uncovered the initial discovery regarding this tRNA fragmentation response and provided mechanistic characterization. Future studies, which are beyond the scope of the current work will undoubtedly further characterize features of this response.

      Comment 5: The ribosome profiling results are condensed into two panels of Figure 5E and 5F. We recommend the ribosome profiling experiment be expanded into its own figure with more extensive analysis and comparison beyond just looking at tRNA-Tyr. This could reveal insight into other codons that are impacted coordinately with Tyr codons and perhaps strengthen their conclusion. As an example of a more thorough analysis of ribosome profiling and proteomics, we point the Authors to this recent paper: Lyu et al. 2020 PLoS Genetics, https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008836

      Response: We thank the reviewer for their suggestion. We have expanded the analysis to look at codon usage scatterplots across all codons for shTyr and shControl replicates (Fig S5D). The 5 most changed codons are labeled with UAC, a codon for the tyrosine amino acid, being the most affected (red arrow). Consistent with our model, a tyrosine codon, when at the ribosome A-site, is most affected with depletion of the corresponding tRNA. The text has also been edited to reflect our new analysis providing further evidence that ribosomal stalling might occur with depletion of a given tRNA. The gray outline around the regression line represents the 95% confidence interval.

      Fig S5D

      Comment 6: Moreover, one would expect that the mRNAs encoding USP3, EPCAM and SCD would exhibit increased ribosome occupancy. Thus, the authors should at least provide relative ribosome occupancy information on these transcripts to provide evidence that the decrease in protein levels is indeed linked to ribosome pausing or stalling.

      Response: We would like to emphasize that resolution of ribosomal profiling data at the codon level for specific genes requires a high number of reads and replicates to draw accurate conclusions. There is an inherent level of stochasticity when mapping RPFs to specific genes and as a result, our analysis revolved around Tyr-enriched vs Tyr-low populations as this analysis was appropriate for our sequencing depth and number of replicates. To be able to conclusively make claims regarding ribosome pausing or stalling for specific genes, we would likely need further experimentation than can be currently done. However, we are currently conducting the requested bioinformatic analysis and have promising preliminary transcript-level data supporting our model.

      Comment 7: The results with hnRNPA1 and SSB/La are extremely preliminary and simply show binding of tRNA fragments but no biological relevance. We realize that the Authors attempted to see if Tyr-tRNA fragments impacted RNA Pol III RNA but found no effect. A potential experiment would be to perform HITS-CLIP on H2O2-treated cells to see if stress-induced tRNA fragments bind to SSB/La or hnRNPA1. In this case, at least the Authors would link the oxidative stress results found in Figure 1 and 2 with La/SSB and hnRNPA1.

      Response: We agree with the reviewer that a tRF function was not established in the manuscript. As a result, we have recently completed experiments looking at mRNA stability of the hnRNPA1 regulon in the context of overexpressing the tRF as well as using LNA to inhibit this Tyr-tRF (Fig 7E-F). Our data shows, in an hnRNPA1-dependent manner, that its regulon can be functionally regulated by Tyr-tRF. With tRF overexpression and RNAi-mediated depletion of hnRNPA1, a right shift in transcript stability is seen. Importantly, when we do the converse experiment with tRF inhibition in the same RNAi-mediated reduction of hnRNPA1, we see a left shift. These complementary experiments provide data that the Tyr-tRF has a functional role when bound to hnRNPA1 by modulating the regulon of hnRNPA1 and expand the scope of this manuscript and extend the pathway defined downstream of this tRNA fragmentation event.

      Fig 7E-F

      Comment 8: The manuscript concludes that "Tyrosyl tRNA-GUA fragments are generated in a DIS3L2-dependent manner" based upon data in Supplemental Figure S7. However, there is still a substantial amount of tyrosine tRNA fragments in both worms and human cells depleted of DIS3L2. Thus, DIS3L could play a role in the formation of Tyrosine tRNA fragments but it is too strong a claim to say that tRNA fragments are "dependent" upon DIS3L2. We suggest that the Authors soften their conclusions.

      Response: While there are certainly tRFs still apparent with DIS3L2 depletion (Fig S7F-I), we note significant impairment of tRF induction with DIS3L2 knockdown/knockout with multiple different methods in C. elegans and human cells. This data supports our conclusion that tRF generation is dependent on DIS3L2 as this ribonuclease is necessary to elicit the full Tyr-tRF response. We do not make claims that Tyr-tRFs are solely or completely dependent on DIS3L2. There must be other RNases involved given the data highlighted by the reviewer. To this point, we have added clarifying text that DIS3L2 depletion does not completely eliminate the tRF induction.

      Comment 9: Moreover, what is the level of DIS3L2 depletion in the worm and human cell lines? The Authors should provide the immunoblot of DIS3L2 that was described in the Materials and Methods.

      Response: An immunoblot of DIS3L2 depletion in human cells has now been added as a supplementary figure (Fig S7I). Depletion in C. elegans was confirmed through sequencing of a mutation, as is standard in the field. The wild-type PCR product is 1nt longer (859 bp) than the mutant product (858 bp) with CTC to TAG nonsynonymous mutation preceding a single nucleotide deletion.

      Wild-type disl-2: GTTGAAGCCGCAGGGC[CTC]ACTCAGACAGCTACAGG

      disl-2 (syb1033): GTTGAAGCCGCAGGGC[TAG]-CTCAGACAGCTACAGG

      Fig S7I

      Comment 10: The key conclusions of "a tRNA-regulated growth suppressive oxidative stress response pathway" and an "underlying adaptive codon-based gene regulatory logic inherent to the genetic code" are overstated. This is because of the major caveat that knockdown of tyrosine-tRNA or tyrosyl-tRNA synthetase are likely to trigger numerous indirect effects. While the authors validate that three proteins are expressed at lower levels under all three conditions (H2O2, tRNA-Tyr and YARS), they might overlap in some manner but not necessarily define a coordinated response. Thus, a glaring gap in this paper is a clear, mechanistic link between H2O2-induced changes in translation versus the changes in expression when either tRNA-Tyr or YARS is depleted. Thus, it is too preliminary to conclude that tRNA depletion is part of a "pathway" and "regulatory logic" when it could all be pleiotropic effects. At the very least, the authors should discuss the possibility of indirect effects to provide a more nuanced discussion of the results obtained using two different cell systems and oxidative stress.

      Response: We thank the reviewer for the feedback. While we agree that indirect effects may exist, we do not make any claims that our pathway is the only one required to have translation effects. The text for Fig 4A already acknowledges the pleiotropic effects of tRNA depletion. Our data shows that H2O2 stress leads to a depletion of Tyr tRNA-GUA and that depletion of this tRNA through multiple complementary methods has a codon-dependent effect on protein expression. We hope the reviewer agrees that the reduction of a specific target gene in a tyrosine codon-dependent manner (demonstrated by mutagenesis) and the binding of the tRF directly to an RBP and the modulation of the regulon of this RBP by this tRF (demonstrated by gain- and loss-of-function studies) demonstrates a direct role of this response on specific downstream target genes rather than pleiotropy. This is in keeping with the cross-comments of reviewer 1, where Fig 5D shows a direct Tyr codon link between H2O2 and downstream effects. As a result, we feel that our conclusions of a pathway (not the only pathway) are valid. However, the conclusion of a “regulatory logic” might not be interpreted in the same way by all readers and we have thus changed the text to reflect a more nuanced position.

      **Minor comments:** Comment 11: Tyrosyl-tRNAs refers to the aminoacylated form of tRNA. We recommend that all instances of tyrosyl-tRNA be changed to tyrosine tRNA or tRNA-Tyr which is more generic and provides no indication as to the aminoacylation status of a tRNA.

      Response: We thank the reviewer for their correction. We have changed all instances of “tyrosyl” to “tyrosine” in the text.

      Comment 12: In Figure 5C, the promoter is drawn as T7, which is a bacteriophage promoter. While the plasmid used in this manuscript (psiCHECK2) does contain a T7 promoter, mammalian gene expression is driven from the SV40 promoter. Thus, the relevant label in Figure 5C should be "SV40 promoter". Moreover, additional details should be provided on how the construct was made (such as sequence information etc.).

      Response: We thank the reviewer for their correction. We have changed the promoter text in the figure. In the methods for the construct, we have included which USP3 was used and would be happy to include further information if requested.

      Comment 13: Please provide original blots for each of the replicates in: Figure 4C, n=4 Figure 4A, n=9 Figure 4D, n=3 Figure 5D, n=3

      Response: There appears to be an unintentional mislabeling of the requested blots by the reviewer. The original blots for Fig 4C, Fig 5A, Fig 5D, and Fig 6D have been made available in a separate file for reviewers.

      Reviewer #3 (Significance (Required)): This manuscript provides evidence that specific tRNAs are depleted upon oxidative stress as part a conserved stress-response pathway in humans (and worms) to regulate translation in a codon-based manner. Unfortunately, the manuscript attempts to tie together results from different conditions and systems without providing any definitive links that suggest a "pathway" involved in the oxidative stress response. The findings in this paper provide a useful starting point but fall short of being a major advance due to the lack of a clear mechanism. However, there are intriguing results in this manuscript based upon the cell lines depleted of tRNA-Tyr or tyrosine synthetase that could interest researchers in the field of tRNA biology.

      Response: We thank the reviewer for the positive comments regarding our demonstration of a conserved stress response, acknowledging the intriguing nature of our findings that will be a starting point for future studies and that our work will be of interest to researchers in the field of tRNA biology. We hope that the very positive comments of reviewer 1 and 2, the cross-comments of reviewer 1 in response to reviewer 3’s comments regarding the specificity of this response, and our inclusion for reviewer 3 of additional data on the function of the tRF in regulating the activity of the hnRNPA1 RNA binding protein defining a post-transcriptional pathway and additional corroborating requested codon-level computational analyses provide compelling support that that our findings indeed represent a major advance for the field.

    1. Author Response

      Summary:

      A strength of the work was that the mathematical modeling of re-replication captured variability in origin firing and supported a mechanism that might explain copy number variation observed in many eukaryotes. However, concern was expressed regarding the influence of assumptions made in developing the model on the outcomes and the moderate correlations between simulations and experimental data. Further explanation of the questions being investigated, the validity and nature of assumptions that were used to develop the simulations, and details explaining how these assumptions were built into the modeling were considered important. Some attempt to align the modeling outcomes with known re-replication hotspots would also improve the study. Some of the parameters used for modeling were concerning, including the use of a 16C ploidy cutoff without adequate justification. Reviewers also made suggestions for improving the experimental validation tests. Reviewers also noted places in the manuscript that require additional clarification. Overall, some concerns were raised regarding the experimental methods, and the impact of the insights gained.

      We would like to thank eLife for this Preprint Review service.

      In this manuscript, we present for the first time a model of DNA rereplication, which permits us to analyse how the process evolves at the single-cell level, across a complete genome, over time. This analysis revealed a pronounced heterogeneity at the single cell level, resulting in increased copies of different genomic loci in different cells, and highlighted rereplication as a powerful mechanism for genome plasticity within an evolving population. We would like to thank the reviewers for their critical appraisal of our work and the editor for his summary of the reviews. The points raised were overall easy to address, and we have done so in a revised version of the manuscript, where we have also clarified points which were unclear to the reviewers. Importantly, we have clarified that: there are currently no available methods for studying rereplication dynamics experimentally at the single cell level across the genome, and it is exactly this analysis that our manuscript offers; model assumptions were either standard and previously validated experimentally for DNA replication or subjected to sensitivity analysis with key findings shown to be robust to model assumptions; there was no arbitrary cut-off point in the rereplication process, which was analysed over time - an advantage of our approach. Data were depicted early in the process (2C) and late in the process (16C) but findings were robust across the process; fission yeast cells can be experimentally induced to rereplicate to different extents (from 2C to 16C or even 32C) and our model permits us to capture the process as it evolves at any ploidy; correlations between experimental and simulated data were highly significant and robust to model assumptions.

      We would like to thank the reviewers for their comments, which we believe have helped us improve our manuscript and clarify points of possible misunderstanding. A point-by-point response follows.

      Reviewer #1:

      The authors develop and analyse a mathematical model of DNA rereplication in situations, where re-firing of origins during replication is not suppressed. Using the experimentally measured position and relative strength of origins in yeast, the authors simulate DNA copy number profiles in individual cells. They show that the developed model can mostly recapitulate the experimentally measured DNA copy number profile along the genome, but that the simulated profiles are highly variable. The fact that increasing copy number of an origin will facilitate its preferential amplification essentially constitutes a self-reinforcing feedback loop and might be the mechanism that leads to overamplification of some genomic regions. In addition different regions compete for a limiting factor, and thereby repress each others' over-amplification. While the model generates some interesting hypotheses it is unclear in the current version of the manuscript, to what extent they arise from specific model assumptions. The authors do not clearly formulate the scientific questions asked, they do not discuss the model assumptions and their validity and they do not adequately describe how model results depend on those assumptions. Taken together, the scientific process is insufficiently documented in this manuscript, making it difficult to judge whether the conclusions are actually supported by the data.

      The manuscript has been modified to further clarify the underlying questions and model assumptions. We would like to point out that the model was presented in detail in the supplementary material of the original manuscript, which included all model assumptions. In addition, model parameters used for the base-case model were systematically varied, the outcome was presented in a separate paragraph (“Sensitivity Analysis” in Results), and findings were shown to be robust to model assumptions. These points are presented in detail below.

      1) It is not clear what questions the authors want to address with their model. Do they want to understand how the experimentally observed copy number differences between regions arise? The introduction should elaborate more on the open questions in the field and explain why they should be addressed with a mathematical model.

      With this work our goal is to elucidate the fundamental mechanisms and properties underlying DNA re-replication. Specifically, we aim to investigate how re-replication evolves over time along the genome, and how it may lead to different number of copies of different loci at the single-cell level and result in genetic heterogeneity within a population. Given the large number of origins along the genome and the stochasticity of origin firing (Demczuk et al., 2012; Kaykov and Nurse, 2015; Patel et al., 2006), it is unclear how re-replication would evolve along the genome in each individual cell in a re-replicating population and how local properties and genome-wide effects would shape its progression and the resulting increases in the number of copies of specific loci. As no experimental method exists that can analyze DNA re-replication at the single-cell level over time along the genome, we designed a mathematical model that is able to track the firing and refiring of origins and the evolution of the resulting forks along a complete genome over time, and in this way capture the complex stochastic hybrid dynamics of DNA re-replication. Since existing methods to analyze DNA re-replication in vivo only provide static, population-level snapshots (Kiang et al., 2010; Menzel et al., 2020; Mickle et al., 2007), we believe that our in silico model, which is the first modeling framework of DNA re-replication, is an important contribution in the field.

      In the revised version of our manuscript, we have modified the introduction to explain these points in more detail.

      2) One of the main messages of the paper is that the amplification profiles are highly variable across single cells, because that was found in the described simulations. This behavior does however likely depend on specific choices that were made in the simulations, e.g. that the probabilities of the origin state transitions are exponentially distributed. These assumptions should at least be discussed, or better experimentally validated.

      Modeling choices and assumptions are presented in detail in the Supplementary material of the manuscript, and were made to accurately capture the dynamics of origin firing, which is known to be stochastic, as established by many studies in fission yeast (Bechhoefer and Rhind, 2012; Patel et al., 2006; Rhind et al., 2010) and the continuous movement of forks along the DNA. Specifically, the choice of the exponential distribution used for assigning a firing time to each origin has already been discussed and validated in our previous work on normal DNA replication (Lygeros et al., 2008). Indeed, as shown in Figure 2 of (Lygeros et al., 2008), our model was able to accurately reconstruct experimental data derived by single molecule DNA combing experiments (Patel et al., 2006).

      The use of the exponential distribution for transition firing times is standard in stochastic processes in general, including what are known as Piecewise Deterministic Markov Processes (PDMP), the class where the models considered in the paper belong. There are good mathematical reasons for this, for example the "memoryless" property that makes the resulting stochastic process Markov, a basic requirement for the model to be well-posed [M. H. A. Davis, "Markov models and optimization", Monographs on Statistics and Applied Probability, vol. 49, Chapman & Hall, London, 1993]. Practically, assuming an exponential distribution can be quite general, because the rate (the probability with which a transition "fires" per unit time) is allowed to depend on the state of the system, both the discrete state (in our case, the state of individual origins) and the continuous state (in our case, the progress of individual replication forks). It can be shown that one can exploit this dependence to write seemingly more general processes (that at first sight do not have exponential firing times) as PDMP (with exponential firing times) by appropriately defining a state for the system [M. H. A. Davis, "Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models", Journal of the Royal Statistical Society. Series B (Methodological), Vol. 46, No. 3 (1984), pp. 353-388]. In the manuscript this feature is exploited in what we call the LF model, where the rate of the exponential firing time of each origin (probability of firing per unit time) depends on the state of the system (specifically, the number of PreR origins), as discussed in the section on Sensitivity Analysis. We have further clarified these in the revised manuscript.

      3) The authors aim at testing their prediction that rereplication is highly variable across cells. To this end they use the LacO/LacI system to estimate locus copy number. The locus intensity is indeed highly variable across cells. However, the Dapi quantification suggests that only a subset of cells actually undergo rereplication under the experimental conditions used (Fig. 4C). Therefore the analysis should atleast be limited to those cells. It would be even better, if a second locus could be labelled in another color to show that rereplication of two loci is anti-correlated as predicted by the model.

      Under the experimental conditions employed (ectopic expression of a mutant version of the licensing factor Cdc18, stably integrated in the genome under a regulatable promoter), the vast majority of cells undergo rereplication but to relatively low levels, resulting in cells with a DNA content of 2C-8C. Though the DNA content of several cells indeed appears similar to the DNA content of normal G2 phase cells, the vast majority (>90%) of cells undergo rereplication, as manifested by the appearance of DNA damage and, eventually, loss of viability. We have chosen this experimental set-up (medium levels of rereplication) as it allows induction of rereplication in practically all cells in the population, without the abnormal nuclear and cellular morphology which accompanies a pronounced increase in DNA content (ie 16C), and would make single-cell imaging more prone to artifacts. Fission yeast cells can be induced to undergo rereplication to various extents, by regulated expression of different versions of Cdc18 to different levels and/or co-expression of Cdt1. We have now explained this more extensively in the revised manuscript and thank the reviewer for identifying a point which may not have been clear in the first version of the manuscript.

      Concerning the possibility of studying two loci at the same time, we have indeed tried to tag a second region with TetR/TetO, however the signal-to-noise ratio and thus reproducible detection of the TetR focus was suboptimal under rereplication conditions. We therefore did not proceed further with this approach.

      4) What does "signal ratio" in Fig. 2 mean? And why are the peaks much higher in the simulations? Would the signal ratio between simulation and experiment correspond better, if an earlier time point in the simulation was selected?

      The definition of signal ratios is given in Results: DNA re-replication at the population level: “Specifically, we computed in silico mean amplification profiles across the genome, referred to as signal ratios in (Kiang et al., 2010), by averaging the number of copies for each origin location and normalizing it to the genome mean in 100 simulations. In these profiles, peaks above 1 correspond to highly re-replicated regions, and valleys below 1 correspond to regions that are under-replicated with respect to the mean.”

      Indeed, as observed by the reviewer, simulated peaks appear overall sharper and higher than experimental peaks. This is expected, since simulated data show the actual number of copies generated, while experimental data are subject to background noise and represent averages of 3 probes and 2 independent experiments. We have clarified this in the Results.

      Last, we chose to compare in silico and experimental profiles at a similar ploidy. Plotting in silico profiles of an earlier timepoint would indeed lead to visually more similar patterns in terms of peak intensity, but we believe this could be misleading for the readers.

      5) From line 248 onwards, the authors compare different assumptions for polymerase speed and conclude that "0.5 kb/min is closer to experimental observations". It is unclear, however, which experimental observations they refer to and what was observed there. The same question arises when they compare the LF and UF models (line 275-277).

      We have now clarified this point. Experimental observations show that under high levels of rereplication, DNA content reaches 16C four to six hours following accumulation of Cdc18 (Nishitani et al., 2000). Estimates for 0.5 kb/min and the LF model are therefore closer to experimental observations.

      6) I find the description of cis- and trans-effects rather confusing. The authors should rather explain what happens in the model. Neighboring strong origins can amplify a weak origin and origins compete for factors. In line 475-476 for example, it should be clarified that the assumption of the LF model could lead to trans-effects, instead of presenting this as a general model prediction.

      In the manuscript, we initially present what we observe in the Results section and then proceed to provide possible explanations in Discussion. We quote from the Discussion: “Such in trans negative regulation of distant origins could be explained by competition for the same limiting factor: high-level amplification of a given locus recruits high levels of the limiting factor, indirectly inhibiting firing of other genomic regions.” and “[…] in cis elements contribute to amplified copy numbers not only directly by passive re-replication, but also implicitly through increasing the firing activity of their neighbors”. To our understanding, these sentences are in complete agreement with the reviewer’s suggestions. Nonetheless, and to make this even more clear, we have modified the Discussion in our revised manuscript.

      7) Throughout the manuscript, a clear distinction should be made between the firing activity of one origin molecule and the cumulative activity of multiple copies of an origin. For example, it should be clarified in line 435 that the cumulative activity of weak origins might increase if they are closed to a strong origin, because they get amplified, instead of just writing "increased firing activity of weak origins".

      We have clarified this point in the revised manuscript.

      8) One of the major conclusions of the manuscript is that rereplication is robust on the population level. It is not clear to me what the authors mean by that. The average amplification levels are probably determined by the origin efficiencies that are put into the model. What would robustness mean in this context?

      As the reviewer points out, one of the important input parameters of the model are origin efficiencies. Since the model is stochastic however, origin efficiencies do not directly determine the amplification levels at a single-cell level. For example, in Figures 3A and Supplementary Figure S4, we show the outcome of 4 random simulations with identical underlying parameters, where it is clear that re-replication can lead to markedly different single-cell amplification levels. Indeed, genome-wide analysis across 100 simulations (Supplementary Figure S5) indicated that on the onset of re-replication, amplification levels are highly unpredictable (again, despite the fact that the input parameters are identical).

      On the contrary, when analyzing amplification profiles at a population level (averaging across sets of 100 simulations), the most highly amplified regions appear to be highly reproducible. We agree with the reviewer that these population level profiles are strongly affected by the origin efficiencies, but they are not determined solely by them. For example, low efficiency origins can be highly amplified, or highly efficient origins can be suppressed (see discussion on in cis and in trans effects) depending on their neighborhood and system-wide effects, and the extend of these effects depends on the fork speed. Sensitivity analysis with respect to different model assumptions, or model parameters (see Results, section Sensitivity Analysis and Supplementary Figure S3) indicated that amplification profiles might appear sharper or flatter, but overall amplification hotspots were highly robust.

      To summarize, in our conclusions (Discussion, section Emerging properties of re-replication) we highlight these properties (stochasticity vs. robustness) and elaborate further on how they emerge during the course of re-replication (onset vs. high re-replication) or depending on the level of analysis (single-cell vs. population level).

      9) It would be helpful if, in Fig. 2 also the origins and their respective efficiencies could be shown to understand to what extent the signal ratio reflects these efficiencies.

      We thank the reviewer for the useful suggestion, which we have incorporated in the revised manuscript.

      10) The methods section should provide more detail.

      We would like to point out that Supplementary Material, including a full mathematical description of the model is available on BioRxiv, which was also available at the time of the preprint review, (https://www.biorxiv.org/content/10.1101/2020.03.30.016576v1.supplementary-material ), and has also been uploaded as a separate document in our GitHub page: https://github.com/rapsoman/DNA_Rereplication

      Reviewer #2:

      Here, Rapsomaniki et al have modeled the process of DNA re-replication. The in silico analysis is an extension of their previous work describing normal DNA replication (Lygeros et al 2008). The authors show that there is a large amount of heterogeneity at the single cell level but when these heterogeneous signals are averaged across a population, the signal is robust. The authors support this with simulations and with experimental data, both at the single cell level and at the population level.

      1) It is a bit concerning that simulations were carried out to a ploidy level of 16C. Has it been observed that the DNA content in any given cell can rise to 16 times the initial amount? Figure 3 (simulations) shows that certain chromosomal regions can reach 30x and 160x copies for 2C and 16C. However, Figure 4 (experiment) suggests that copy numbers should only be slightly more in re-replicating conditions, compared to normal replicating conditions. Additionally, in Figure 2, the simulated data seems to be consistently noisier than the experimental data. Taken together, this may suggest that the assumptions in the model do not adequately recapitulate the biological system.

      Fission yeast cells undergo robust rereplication, and reach a ploidy up to 32C - see for example (Kiang et al., 2010; Mickle et al., 2007; Nishitani et al., 2000). 16C is therefore a usual ploidy for rereplicating fission yeast cells, observed under many experimental conditions. In addition, by manipulating the licensing factors over-expressed, different levels of ploidy can be experimentally achieved, ranging from 2C (the normal ploidy of a G2 cell, but with uneven replication) to 32C. In Figure 4, we have employed a truncated form of Cdc18 (d55P6-cdc18 (Baum et al., 1998)), which induces medium-level re-replication, as confirmed by FACS analysis in Supplementary Figure S6A. Under these conditions, the vast majority of the cells (>90%) undergo re-replication, albeit at medium to low levels. We have opted to use this strain to avoid artifacts due to disrupted nuclear morphology under high levels of re-replication We have now clarified this point in the revised manuscript. We would like to point out that in silico analysis is not carried out at 16C only but across different ploidies – it is actually a strength of our approach that we can follow the rereplication process as it evolves, at any ploidy, and we have shown that our conclusions are robust throughout. We show plots at the beginning of the process (2C) and towards the end (16C), at the single-cell and at the population level, to facilitate comparison.

      Last, as also discussed in our response to reviewer 1, simulated data appear sharper, with higher peak values than experimental data (Figure 2). This is expected, since simulated data show the actual number of copies generated, while experimental data are subject to background noise and represent averages of 3 neighboring microarray probes and 2 independent experiments. We have clarified this in the revised manuscript.

      2) This work currently is agnostic to the genes and sequences within the simulated genomes. The authors suggest that DNA re-replication can result in gene duplications. It might strengthen the manuscript if the authors are able to show that re-replication hotspots coincide with gene duplication events in S pombe. It should be relatively straightforward to overlap the hotspots found in this analysis with known gene duplication events in the literature.

      We agree with the reviewer that comparing our predictions with known gene duplication events in S.pombe would be of interest. Unfortunately to our knowledge no such dataset for fission yeast exists in the literature. The most comprehensive datasets are the ones from (Kiang et al., 2010; Mickle et al., 2007), which analyse rereplicating cells, and which we have already exploited in our paper. We would like to point out that this manuscript aims to show how rereplication evolves genome-wide. Whether the additional copies generated can lead to gene duplication events is beyond the scope of the present manuscript.

      3) The authors have nicely demonstrated that cis activation can be driven by the physical proximity of origins. The authors go on to describe trans suppression in which the activation of one origin suppresses the activation of a different origin. I would argue that this observation is simply the result of randomness in the model and stopping the simulations at fixed points.

      One of the two origins will randomly re-replicate first and simply outpace the other. Stopping the simulations at 16C will simply prevent the lagging origin from catching up the first origin. There does not seem to be an inhibitory mechanism that acts between two origins.

      This can be explained by the following equation: X + Y = constant Where X is the amount of origin 1 and Y is the amount of origin 2.

      It is also possible that the two origins could start re-replicating at the same time. This would result in the data points observed for cluster 2 (Figure 6 BC)

      We thank the reviewer for the positive comments. Indeed, as we elaborate in our Discussion, we believe that the mechanism behind the observed in trans effects is the competition for a factor that exists in a rate-limiting quantity (see also reply to point 6, reviewer 1 above), which is essentially the constant in his/her equation. Though less pronounced, such in-trans effects are also possible in the UF model, and could be due to the total DNA increase being dominated by certain origins, as suggested by the reviewer. We do not suggest anywhere in the manuscript that this inhibition is direct, but rather clearly state that it is an indirect effect.

      Reviewer #3:

      This manuscript by Rapsomaniki et al uses mathematical modeling to study the properties of DNA re-replication. They develop a model that shows some consistency with experimental data from S. pombe, and use it to conclude that re-replication is heterogeneous at the single-cell level.

      The simulations have only moderate correlations with experimental data (0.5-0.6). Indeed, simulations and actual data (Figure 2) appear quite different. Despite the statistical significance of the overlap, the limited correspondence brings into question the usefulness of the model compared to directly generating new experimental data.

      We would like to point out that the overlap between experimental and simulated data is highly significant. Firstly, the Spearman correlation coefficient between simulated and experimental genome-wide profiles is highly statistically significant (p values ranging from 7.310-12 to 3.610-41 for the three fission yeast chromosomes). Furthermore, 100.000 repetitions of random peak assignment resulted in only one case where 10 out of 22 peaks overlapped (median 2 out of 22 peaks overlapping), while comparing simulated and experimental data resulted in 14 out of 22 peaks overlapping. Simulations appear more sharp than experimental data, this is however expected as simulated data correspond to the actual number of copies generated, while experimental data are subject to background noise, have a signal-to-noise ratio that is limited by the experimental method employed and represent averages of 3 probes and 2 independent experiments (see Kiang et al., 2010 and also above). We have modified the manuscript to clarify this point. The reviewer suggests that the model is of limited use, because one could trivially generate new experimental data. We would like to point out that existing methods to analyze DNA re-replication in vivo only provide static, population-level snapshots (Kiang et al., 2010; Menzel et al., 2020; Mickle et al., 2007). To date no experimental method can generate single-cell, whole-genome, time-course measurements in re-replicating cells. Our model aims to fill this gap, and for this reason we believe in its usefulness.

      Heterogeneity among single cells, which appears to be one of the main messages of this paper, is not necessarily a surprising finding, and may even arise from the nature of the simulation being stochastic and defined at the level of single origins. They validate this prediction experimentally at a single locus, providing little novel insight.

      We would like to point out that it is the nature of replication in fission yeast which is stochastic, as experimentally shown (Patel et al., 2006), and defined at the level of single origins, and this is captured by the simulations. Heterogeneity amongst single rereplicating cells has not been previously shown or suggested in any organism, at least to the best of our knowledge. It is in our opinion a highly interesting observation, as it provides a powerful mechanism for generating a plethora of different genotypes within a population, from which phenotypic traits could be selected.

      Overall, the insights here are limited and would need to await experimental validation and further empirical data. Given that experimental measurements of re-replication are now feasible genome-wide, the value of these simulations is limited.

      Again, the reviewer seems unaware that no experimental method currently exists for analysing the dynamics of re-replication at a single-cell level genome-wide. We also feel obliged to point out that modeling and in silico analysis is in our opinion of great value for analysing complex biological processes, even when experimental methods are available. Though we are sure this is not what the reviewer really meant, his/her comment appears derogative to a complete field.

      Fork speed is assumed based on limited data and assumptions regarding re-replication fork speed without empirical data.

      As clearly stated in our manuscript (Results, section Modeling DNA re-replication across a complete genome), many studies have estimated fork speed in yeasts in normal DNA replication, with plausible values ranging from 0.5 kb/min to 3 kb/min (Duzdevich et al., 2015; Heichinger et al., 2006; Raghuraman et al., 2001; Sekedat et al., 2010; Yabuki et al., 2002). In our model, we set the base-case value as the lowest estimate (0.5 kb/min), but also explored the model’s sensitivity to this parameter by simulating the model for higher values (1 and 3 kb/min). This analysis indicated that estimates for 0.5 kb/min were closer to biological reality, a non-surprising finding given that fork speed is expected to be slower in re-replication that in normal replication.

      Overall, the comments of reviewer 3 appear in our eyes more derogative than constructive and provide little specific criticism.

      References

      Baum, B., Nishitani, H., Yanow, S., and Nurse, P. (1998). Cdc18 transcription and proteolysis couple S phase to passage through mitosis. The EMBO Journal 17, 5689–5698.

      Bechhoefer, J., and Rhind, N. (2012). Replication timing and its emergence from stochastic processes. Trends in Genetics 28, 374–381.

      Duzdevich, D., Warner, M.D., Ticau, S., Ivica, N.A., Bell, S.P., and Greene, E.C. (2015). The dynamics of eukaryotic replication initiation: origin specificity, licensing, and firing at the singlemolecule level. Mol. Cell 58, 483–494.

      Heichinger, C., Penkett, C.J., Bähler, J., and Nurse, P. (2006). Genome-wide characterization of fission yeast DNA replication origins. The EMBO Journal 25, 5171–5179.

      Kiang, L., Heichinger, C., Watt, S., B\ähler, J., and Nurse, P. (2010). Specific replication origins promote DNA amplification in fission yeast. Journal of Cell Science 123, 3047–3051.

      Lygeros, J., Koutroumpas, K., Dimopoulos, S., Legouras, I., Kouretas, P., Heichinger, C., Nurse, P., and Lygerou, Z. (2008). Stochastic hybrid modeling of DNA replication across a complete genome. Proceedings of the National Academy of Sciences 105, 12295–12300.

      Menzel, J., Tatman, P., and Black, J.C. (2020). Isolation and analysis of rereplicated DNA by Rerep-Seq. Nucleic Acids Res 48, e58–e58.

      Mickle, K.L., Oliva, A., Huberman, J.A., and Leatherwood, J. (2007). Checkpoint effects and telomere amplification during DNA re-replication in fission yeast. BMC Molecular Biology 8, 119.

      Nishitani, H., Lygerou, Z., Nishimoto, T., and Nurse, P. (2000). The Cdt1 protein is required to license DNA for replication in fission yeast. Nature 404, 625–628.

      Patel, P.K., Arcangioli, B., Baker, S.P., Bensimon, A., and Rhind, N. (2006). DNA Replication Origins Fire Stochastically in Fission Yeast. Mol. Biol. Cell 17, 308–316.

      Raghuraman, M.K., Winzeler, E.A., Collingwood, D., Hunt, S., Wodicka, L., Conway, A., Lockhart, D.J., Davis, R.W., Brewer, B.J., and Fangman, W.L. (2001). Replication Dynamics of the Yeast Genome. Science 294, 115–121.

      Rhind, N., Yang, S.C.-H., and Bechhoefer, J. (2010). Reconciling stochastic origin firing with defined replication timing. Chromosome Res 18, 35–43.

      Sekedat, M.D., Fenyö, D., Rogers, R.S., Tackett, A.J., Aitchison, J.D., and Chait, B.T. (2010). GINS motion reveals replication fork progression is remarkably uniform throughout the yeast genome. Molecular Systems Biology 6, 353.

      Yabuki, N., Terashima, H., and Kitada, K. (2002). Mapping of early firing origins on a replication profile of budding yeast. Genes to Cells 7, 781–789.

  33. Jul 2020
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We would like to thank Reviewer #1 and #2 for the evaluation of our research and comments to our manuscript. Their comments are highly appreciated and addressed as described below.

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

      **Summary:**

      *Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).*

      Here Ha et al. has further developed their Pumilio RNA tagging methodology for the isolation of UV-crosslinked proteins that are suggested to associate with Xist RNA in mouse embryonic stem cells (mESCs). Within this study the authors claim to have found the Lupus antigen RNA binding protein (La) as a novel Xist interacting partner that influences the efficacy of X-chromosome inactivation (XCI). The authors use a number of different techniques such as qPCR, fluorescent imaging, ATAC-SEQ and SHAPE to show aberration of XCI upon La shRNA knockdown. However, this study has significant flaws in the efficient isolation and validation of Xist associated proteins using their FLAG-out methodology. Furthermore, later experiments predominantly focus on cell death/survival assays, which is somewhat troubling given the essential roles La plays in processes such as cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation. I feel the authors need to robustly address the potential effects La knockdown may be having on their mESCs.

      Reviewer #1 did not fully understand the basic designs of the experimental systems (FLAG-out and iXist), and completely rejected these experimental systems. Reviewer #1 also ignored the majority of the functional analysis on the candidate protein, Ssb. These issues cannot be addressed by additional experiments

      **Major comments:**

      *-Are the key conclusions convincing?*

      My major concern is in their Xist RNA purification.

      First of all, I couldn't find any data on proving the enrichment of Xist RNA itself in their Pumilio pull-down experiment. It would have been useful to show Xist RNA enrichment before benzonase step. Secondly, it is hard to imagine the protocol would successfully isolated Xist RNA-protein complexes from the cell. An earlier report by Clemson et al., (J Cell Biol., 1996) has shown that majority of Xist RNA is still stuck in the nucleus after nuclear matrix prep protocol using detergent, which is not so different from the authors' protocol. Moreover, the authors used UV crosslink, which would have made even harder to purify Xist RNA without sonication. Thirdly, as the tag is located on 5' of Xist RNA, it is rather surprising to see that Spen is not detected in their pulldown. Spen is one of the main functional interactors with Xist, robustly detected by several previous reports. Similarly, other high-affinity binders of Xist such as hnRNP-K and Ciz1 were also lacking from this screen. Finally, the peptides found associated with FLAG-out Xist are extremely low in comparison with other data using glutaraldehyde or formaldehyde crosslinking. For example, HnRNP-M found in Chu et al 2015 has 1120 peptide counts in differentiated cells. The authors here use HnRNP-M as a baseline for specific interactions and show a total of 6 peptide counts in Xist expressing cells and 5 in i-Empty cells (Supplementary excel sheet 1). Similarly, the La protein of interest in this study has 8 counts in i-FLAG-Xist and 6 counts in i-Empty. I struggle to see how this result indicate specific Xist binding. Worryingly this is the starting rationale for the rest of their experiments, it is hard to therefore accept the rest of their conclusions either.

      We have detected Xist RNA after Pumilio pull-down, and added the data in the revised manuscript (Figure S1). The enrichment of Xist RNA by Pumilio pull-down is about 75-fold, comparable to the enrichment reported by Minajigi et al.

      Two out of three previous studies used similar protocols to prep cell lysates for co-IP, including UV cross-linking and detergent (McHugh et al. 2015 and Minajigi et al. 2015). The major difference between their protocols and ours is the co-IP step. They used antisense oligos to pull-down Xist RNA-protein complex, while we take advantage of the specific interaction between PUF and PBS to pull-down Xist RNA-protein complex. With the data in Figure S1, we are confident that our strategy is successful in isolating Xist RNA

      For systematic identification of Xist binding proteins, each method has its own strength and weakness. As we described in the introduction, only 4 proteins were commonly identified by all three studies to systematically identify Xist binding proteins. There is no doubt that our method also missed some authentic Xist binding proteins (false negative) and identified some false positive candidates. Thus, we have to be careful in balancing between the false negative and false positive calls. The reason that we applied the ranking gain to identify Xist binding protein candidates, is to minimize the false negative rate. Meanwhile, we compared our Xist binding protein candidate list with previous identified Xist-binding proteins to enhance the confidence in our candidate lists.

      Regardless the strength and weakness of our method, Ssb is also an Xist-binding protein identified by another study (Chu et al. 2015). More importantly, we have provided experimental validation to confirm Ssb’s involvement in XCI and extensive functional analysis to reveal the protein’s mechanistic role in XCI.

      The other key conclusion the authors make is from the use of numerous cell death/survival assays for both male and female cell lines. This is extremely troubling in the context of assessing their target protein La. La is involved in multiple RNA maturation events of rRNAs, tRNAs and other polIII transcripts. Furthermore, La has been implicated in binding to the mRNA for Cyclin D1 in both human cells and mouse fibroblasts (NIH/3T3 - male) which show a significant effect on cell proliferation upon siRNA knockdown https://www.nature.com/articles/onc2010425. This, along with the observation that La knock-out blastocysts fail to develop any mice or ES cell lines (male or female) show the effect observed in the authors results is most likely not X-linked cell death https://mcb.asm.org/content/mcb/26/4/1445.full.pdf. The authors need to show that their shRNA KD isn't affecting the proliferation and general fitness of their mESC lines.

      The cell death/survival assay was specially designed for analyzing the defect of XCI. The cell death of iXist ESCs upon adding Dox is due to the induction of Xist, which consequently initiates the silencing of the only X chromosome in male cells. Knockdown of genes involved in XCI compromises XCI, thus allowing cell survival. Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect slow growth and/or cell death of Ssb knockdown cells. Indeed, the result is consistent with our expectation (Figure 2C, without Dox). Nevertheless, more Ssb knockdown cells survive in the presence of Dox, compared with control cells (Figure 2C-E, with Dox), suggesting that Ssb plays an important role in XCI.

      *- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?*

      As discussed above, I feel the authors have not clearly demonstrated Xist specific protein enrichment and haven't proven X-linked cell death. Due to the lack of necessary control experiments as discussed below, I feel the notion that La is involved directly in XCI as an RNA chaperone is currently preliminary/speculative.

      The FLAG-out experiment just provided an initial point for the study. We have demonstrated the interaction between Xist and Ssb by RIP. And, Ssb knockdown antagonizes the lethal effect of induced XCI in male cells, allowing more cell to survive. This is contradictory to the diverse house-keeping functions of Ssb, which should lead to slow proliferation or cell death. Therefore, the data here (Figure 2C-E) should suggest a role of Ssb in XCI. In addition, we showed that knockdown of Ssb compromises the silencing of X-linked genes (Figure 2F, 2G, and 3E), the compaction of X chromosome (Figure 3D), Xist cloud formation (Figure 4), epigenetic modifications on Xi (Figure 5), Xist RNA folding (Figure 6F-I), and Xist RNA stability (Figure 7C and D). All these data indicate that Ssb is involved in XCI by regulating Xist RNA folding.

      *- 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.*

      I would suggest them to show RT-qPCR results of Xist RNA enrichment from the sample after flagIP before benzonase treatment.

      We have the data, and added it to Figure S1.

      Also, it would have been more convincing if their negative control construct (i-Empty) would contain 25 copies of PBSb RNA at least.

      This is a good alternative design of the negative control. Using i-Empty expressing 25 copies of PBSb RNA will allow us subtract the background causing by proteins binding to PBSb RNA. Yet, as discussed above, regardless how we improve the experimental setting, we cannot completely avoid the issue of false positive and false negative. Our goal of the FLAG-out experiment is to generate a list of Xist binding protein candidates, and their binding to Xist and their functions in XCI should be validated by additional experiments. With our current experimental setting, a list of Xist binding protein candidates has been generated, and we have validated the role of Ssb in XCI with subsequent experiments.

      In Fig1b, the total amount of proteins loaded on the gel is not equivalent between two lanes. The gel should show equivalent amounts of proteins on the gel. It looks like if the negative control sample had been loaded at the same amount as the one with Xist, the band pattern wouldn't be distinguishable between the two samples. Furthermore, as these samples were used in the following mass spectrometry screen it may suggest that the minimal increase in peptide counts observed in the iXist FLAG-out were due to an increased amount of sample being loaded? No controls are conducted to account for this.

      IP samples of i-Empty and i-FLAG-Xist were loaded in the gel in Figure 1b. It is expected that IP sample of i-FLAG-Xist should pull down more proteins than IP samples of i-Empty. The FLAG-PUFb bands (the strongest band in each lane) are about the same amount in two samples, indicating roughly equal amount of loading. After normalization of gel loading according to the FLAG-PUFb bands, the upper part of the i-FLAG-Xist lane showed some unique bands.

      For mass spectrometry analysis, the loading of two samples are independent, therefore, to compare the absolute amount of each protein between the two samples does not always provide valuable information. Yet, the relative amount of different proteins within one sample is not affected by the loading amount, thus, more informative. Therefore, we used the ranking information to estimate the relative amount of different proteins in each sample and used the ranking gain to further identify protein candidates.

      The authors quantify cell death in figures 2C - E. It seems clear that shSsb 1 and 2 have an effect on cell count even in the absence of Dox. The rescue effect seen upon Dox addition is minimal when compared to Empty + Dox 2D. The authors ∆A-iXist line with and without Ssb KD/Dox would be an informative control on whether the increase in cell survival that they see is X-linked.

      As the reviewer pointed out earlier, Ssb plays multiple roles in cellular processes. Inevitably, KD of Ssb leads to slow growth and/or cell death with or without Dox. Thus, it is less meaningful to compare the surviving cell counts in Figure 2D. Rather, the survival rate (Figure 2E) reflects the rescuing effect more precisely. Shown in Figure 2E, both shSsb 1 and 2 increase the survival rate significantly, compared with Empty control.

      Moreover, the data in Figure 3B and C demonstrated that Ssb KD compromises the survival of female differentiating cells, but not the survival of male differentiating cells, also indicating a role of Ssb in XCI. With these experiments, it should be sufficient to conclude that Ssb KD affects X-linked cell death/survival in both iXist male ESCs and WT female differentiating cells

      The qPCR results used to validate silencing defects show minor changes in expression and also don't show significant silencing of X-linked genes sufficient for cell death. Could this be because only ~ 50 - 60% of Male iXist cells seem to be expressing in the movies and that this will have an effect on the observed qPCR results? Furthermore, it seems counterintuitive that expression in the Empty male cells increases in 48h compared to 14h. Is this due to cell death and positive selection of cells less able to silence their X-chromosome? How would these data look in the female XX line? How would the data look in a ∆A-iXist line in the presence and absence of shSsb/Dox?

      First, high-quality live-cell imaging can only be carried out for 2 hours with 2-min time interval. The movies are meant to show the onset of Xist RNA signals. Therefore, they were taken one hour after Dox treatment (figure legend of Figure 4B-D). After overnight Dox treatment, Xist clouds can be seen in majority of cells.

      Second, in Fig. 2F-G, we did not include uninduced iXist male ESCs. Therefore, it is impossible to judge whether induction of Xist in this male ESC line results in Xist-dependent silencing at 14 and 48 hr. However, in our previous publication (Li et al., JMB, 2018, 430: 2734-2746), it has been shown that Gpc4, Hprt, Mecp2, G418, and TomatoRed are silenced (4- to 16-fold reduction) at 24 and 48 hours after Dox induction.

      Third, the qRT-PCR results in 14 h and in 48 h are not normalized to the same internal control. Thus, they are not directly comparable.

      Confusingly, the male line in Fig 3C shows a drop in live cell count at day 6 of differentiation? Surely given their previous results in Fig 2 the Ssb KD should increase cell viability with +Dox? Ssb KD seems to have an adverse effect on ES cells during extended differentiation protocols. In Figure S1 the authors show ~ 8 - 10% survival of male lines during differentiation. Could the recombination of the Xist sequence around the loxP sites enable the cells to outcompete the dead cells? How would iEmpty and ∆A-iXist cells compare here? Have the differentiated cells been tested for their expression of Xist? Additionally, how are there similar live cell counts for male vs female lines when ~90% of male cells die during differentiation? Were more cells plated at day 4? If so, this would bias the competition of male cell survival and therefore make the male line an inappropriate control.

      Given the essential role of La during development a control is needed to prove that this death is X-linked in the female 3F1 line. For example, an XO cell line retaining the Cast allele and shSsb expression could show the amount of death caused from shSsb alone independent of X-linked cell death.

      The reviewer completely misunderstood the experiment. The severe cell death specifically observed in female differentiating ESCs is a strong evidence showing Ssb is involved in XCI (Figure 3).

      The male ESCs in Figure 3C is a WT ESC line without the inducible Xist transgene, in which no XCI occurs upon differentiation. It is completely different from iXist male ESCs with Dox, in which forced Xist induction leads to XCI. Thus, the diverse functions of Ssb might contribute to the slight decrease in live cell count of wild type male cells at day 6 of differentiation.

      Figure S2 shows the differentiation of iXist male ESCs with or without Dox. As explained above, forced Xist induction silences the only X chromosome in male cells, resulting in cell death. In addition, XCI occurs more efficiently in differentiation condition (Figure S2) than in pluripotent status (Figure 2C)

      During differentiation, female ESCs silence one X chromosome, and the other X chromosome remains active. KD of Ssb compromises XCI, and two X chromosomes in some female differentiating cells maintain active, leading to cell death. The reviewer is correct that we need a control to rule out that the essential role of Ssb during development affects cell survival and death. An XO cell line can be used as a control. Similarly, a male cell line (XY) is also a good control. We already included a male cell line as a control in Figure 3B and 3C.

      If I understood correctly, the RNA FISH used dsDNA probes ("Sx9") against 40 kb of the X-inactivation centre (Xic). Surely Tsix or other Xic transcripts will also be visible? Can the authors use their RNA FISH to determine the XX or XO status of their cells? In Figure S5 a number of cells appear to show a single pinpoint of transcription. This could either be low levels of Xist transcripts or Xic transcription from an XO line in which the 129 chromosome is missing. It would be best to solely quantify cells which have two x chromosomes and if a significant amount of X chromosomes have been kicked out, this should be discussed and controlled for.

      This is a valid concern, but this concern can be adequately addressed with the available data in the manuscript.

      First, if the female Ssb KD cell line is an “XO” cell line, in which the X129 allele is “kicked out”, the RNA allelotyping results should show an absolute “silencing” of the X129 allele. However, in complete contrast to this notion, RNA allelotyping detected “more” RNA transcripts from X129, showing the chromosome-wide XCI defects (Figure 3D).

      Second, overexpression of Ssb in Ssb KD female cells restores the Xist clouds and the polycomb marks (Figure S8), suggesting that the Ssb KD female cells are XX, but not XO.

      Third, the severe cell death specifically occurred in female Ssb KD lines is also against the “XO” argument (Figure 3B&C).

      In Fig6, the authors generated a number of Ssb constructs for a rescue assay. However, these results complicate the matter and raise more questions than they address. It seems odd that the ∆RRM1 does not rescue based on comparison with their putative negative control, ∆NLS. However, the ∆RRM1 + 2 and ∆LAM do rescue the phenotype better than the full length Ssb? This makes no logical sense and highlights the inherent variation in cell viability these generated cell lines seem to show.

      Following on from this, figure S7 quantifies the GFP tag mRNA levels, depicting all ∆RRM mutants with expression below ~30%? How can ∆RRM1 or 2 be rescuing in this scenario? Have these lines been tested for their XX or XO status? The loss of an X chromosome would lead to a rescue of the cell death phenotype, which is a process known to occur in XX lines that have been cultured for extended periods of time. Could it also be that the cell lines derived are more or less sensitive to exogenous shRNA expression? Also, further validation is needed to assess the efficiency of KD in these lines as theoretically most of these constructs will be targeted by shRNA? What is the endogenous Ssb expression level in these lines? Where in the mRNA sequence are the shRNAs targeted to? Does this make sense on the relative expression levels of ∆RRM1/2 for example? Further testing of GFP expression could also be assessed by quantitative western blot of GFP or even visualised in their RNA FISH/IF samples (Figure S8), currently neither are shown. In addition, some kind of information of stability of each Ssb protein constructs has not been demonstrated.

      Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. The reviewer is correct that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. We have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.

      As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. As the reviewers pointed out that the shSsb is not targeting the 5’ or 3’-UTR region, therefore, interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins.

      For the data shown in Figure 7A and B the authors quantify the % of cells with Xist signal. The authors have already shown a defect in Xist visualisation in Ssb KD. Surely it is plausible to assume a faster loss of Xist signal below background in weaker expressing cells. A more appropriate quantification would be the % loss of Xist signal per cell over time.

      With Figure 7C and D, the samples have been treated with actinomycin D which globally affects the transcription of cells even the PolIII associated genes Ssb is needed to mature. This treatment could have an added effect on cell mortality and function. Data confirming that actinomycin D doesn't affect the cells disproportionately is needed. The difference in half-life could be attributed to such a treatment.

      We agree with the reviewer that monitoring Xist signal loss per cell would be a better way to analyze the data. However, in Xist signal loss experiment, snapshot images were taken at four time points (1h, 2h, 3h and 4h). This is not a time-lapse imaging. High-quality time-lapse imaging can only be done within a 2-hour time period with 2-min time interval. Therefore, cell-tracking cannot be done in this experiment. In addition, even though Ssb KD slows down the formation of Xist cloud within the early phase (3 hours) of Xist induction (Figure 4), prolonged (overnight) Xist induction leads to Xist cloud formation in a significant fraction of Ssb KD cells, and the Xist cloud signals are about the same in WT and Ssb KD cells (Figure 7A, 0 h). Similarly, qRT-PCR also revealed that Xist RNA are at the same level in WT and Ssb KD cells (Figure 7C, 0 h). These data argue against that a faster loss of Xist signal in Ssb KD cells is due to weaker initial Xist signal.

      Actinomycin D was added at the last 11 hours of the experiment. During this period, no obvious adverse effects on cells were observed.

      In summarising the authors claim that La binds Xist to facilitate folding and appropriate spreading of Xist along the X-chromosome. No direct interaction has been shown, CLIP-seq data would resolve this, however I do understand this is a challenging technique. The authors have instead opted for RIP followed by qPCR (Figure S2). However, this process has a greater potential for non-specific recovery of RNAs via indirect binding. Furthermore, qPCR may also amplify the relative abundance of the RNA detected. As multiple nucleolar proteins came down in the mass spec screen and FLAG-Ssb is being over expressed, it is plausible to assume some transient Xist interactions may arise from nucleolar association at which La will be in high abundance. Positive and negative nuclear RNA controls (e.g. 7SK and U1 snRNA respectively) could be used so to determine the amount of non-specific Protein-RNA interactions in their RIP pull downs. Cytoplasmic actin is not an appropriate control as it is cytosolic.

      We have to clarify one point that the mass spec screen analyzed samples pulled down by FLAG-PUFb, but not FLAG-Ssb.

      We did not intend to distinguish whether Ssb directly binds Xist or is just associated with Xist. RIP followed by qPCR is sufficient to prove the association between Ssb and Xist RNA.

      We can include nuclear RNA as controls, if the reviewer regards RIP as a valid method to show protein and RNA association

      Other than this the authors may want to probe (via IF) for the presence of La accumulation on the X? Many other know factors such as Ciz1, hnrnpK and PRC1/2 complexes show clear accumulation on the X. If I understand correctly, there are many La antibodies on the market and endogenous levels on the X could be assessed. These antibodies may be useful in IP's and pull downs also.

      Many XCI factors play extensive roles in the cell and are not clearly enriched on Xi, including Spen (Moindrot et al. 2015). We have tried the immunostaining and did not detect Ssb’s enrichment on Xi. Ssb shows a general distribution in the nucleus without a clear enrichment on Xi (data not shown).

      *-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.*

      The experiments suggested above are centrally focussed on the cell lines that are currently in the authors possession with maybe exceptions with the ∆A-iXist-shSsb line suggested. However, this should be reasonably quick to obtain given their previous work for this paper. Most experiments suggested will focus on the validation of karyotype, Xist expression, rescue construct expression, further RNA FISH classification and repeating more appropriate positive and negative controls for a number of experiments. In theory this can be obtained relatively simply and quickly from current resources. But with the sheer volume of further experiments that are required here, this may take a significant amount of time.

      One vital improvement needed is the replication of mass spec data and the validation of Xist specific recovery and protein enrichment. As it stands this manuscript seems to not have any replicates of the FLAG-out methodology and mass spec data. This is troubling given the poor recovery and specificity of the protein samples obtained. Repeating these experiments would be costly in time and also financially. As it stands, I feel this is essential to conclusively validate their target of interest.

      *- Are the data and the methods presented in such a way that they can be reproduced?*

      The data is presented relatively well, however, it would be beneficial if deailed methods were in the main text and not in a supplementary file. Similarly, more information about the process of differentiation and how cell death/survival was quantified and validated is needed.

      The reviewer rejected the basic design of the experimental system and ignored the majority of the functional analysis data. No additional experiment can address these issues

      We can include more information in the main text, regarding Ssb. However, there is limited space for the main text, various depending on the journals. Meanwhile, the current citation on Ssb is adequate to emphasize that Ssb is a versatile RNA binding protein involved in a variety of fundamental RNA processing events in the cell.

      *- Are the experiments adequately replicated and statistical analysis adequate?*

      In the most part yes, however there seems to be no replicates of the FLAG-out mass spec screen which is worrying given the minimal specificity observed in the current data.

      As we mentioned above, the FLAG-out experiment only serves as a starting point to generate a list of Xist binding protein candidates. Rather than repeating the FLAG-out experiment, we compared the result of FLAG-out to previously published lists of Xist binding protein candidates. More importantly, additional experiments are carried out to validate the Xist binding proteins identified by FLAG-out.

      **Minor comments:**

      *- Specific experimental issues that are easily addressable.*

      Unfortunately, the majority of experimental issues need to be addressed with more robust data which are highlighted above. However, some image analysis, quantification and classification can be amended relatively easily. For example, the live-cell imaging data should be quantified as loss of signal as discussed and RNA FISH should be used to classify XX positive cells and the XO cells can be discarded from analysis.

      We have addressed these issue in the previous sections of this rebuttal.

      *- Are prior studies referenced appropriately?*

      Most papers regarding Xist pull down and biology are discussed and referenced appropriately. However, the role in which La plays during development and its aberrant affects upon KD are seemingly downplayed. I would like to see more discussion of potential defects that could be caused due to globally altering cellular RNA folding.

      We have tried to cite key references about Ssb in development and RNA folding. Due to length limitation, we cannot cite all references in the topic. If necessary, we could discuss the possibility of indirect effect of Ssb KD on XCI through globally altering cellular RNA folding.

      *- Are the text and figures clear and accurate?*

      For the most part, lots of the figures are clear and accurate. Apart from these exceptions.

      1.The Y-axis of Figure 2D is confusing. What does 0.3 as a "sum of area" equate to? 30% of the area was ES cells? This doesn't look to be the case from Fig 2C. Also, how does the intensity of the signal compare? The area may not be a good quantification due to ES cells growing in colonies.

      We have revised the Y-axis labelling of Figure 2D to “sum of area cm2”. Thus, “0.3” means that the area of ESCs is 0.3 cm2. ALPP is highly expressed on ES cell surface. ALPP stain usually produce saturated stains on ES cell colonies. Thoroughly stained ES cell colonies, big and small, show similar signal intensity levels. To analyze the “total signal intensity” will be not much different from “sum of area”.

      2.In the Movies S1-7 there are boxes around certain cells and marked with "Figure 5a - c". This seems to be incorrect as figure 5 is currently the IF staining of polycomb marks. I assume this is in relation to Figure 4b-d?

      We have corrected the labelling mistakes.

      3.Similarly, in Movies S1-7, the intensities of Xist foci seem by eye to be similar. In the paper it is claimed that the Xist clouds that do form are lower in intensity. Are the Movies depicting the same range of pixel intensities? If not, this should be amended. Similarly, figure 7 seems to show relatively equivalent RNA signal at 0 h?

      All the images were collected using a fixed standard of the microscope and camera setting, and these movies depict the same range of pixel intensities. Movies S1-S3 are WT control, and Movies S4-S7 are Ssb KD cells. The Xist cloud signals are weaker in Movie S4-S7 (also quantified in Figure 4E). For the Xist cloud signal, not only the intensity, but also the area of Xist cloud, have to be taken into account.

      The 0 h in Figure 7 is after overnight Dox treatment, and different from the time point in Movies S1-7 (maximum 3 hour Dox treatment, figure legend of Figure 4B-D). The discrepancy can be explained by that knockdown of Ssb only slows down the formation of Xist clouds. After overnight forced expression, the Xist RNA still shows an accumulation in the cells. Figure 7 shows the forced accumulation of Xist RNA after prolonged Dox treatment disappears faster after Dox withdraw.

      4.In figure 4A the data is from female XX cells, this should be highlighted to limit confusion with the male iXist data shown below in 4B-E. It would also be helpful to have the male/female icons (as in figure 3B), for each figure that has images of cells. Currently Figure 4, 5, 7, S5 and S8 are lacking these icons.

      We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).

      5.No explanation of the Flag-Ssb expression is given for figure S2. Furthermore, is it really necessary to express Flag-Ssb? There are reasonably good antibodies out there for Ssb as this was how it was originally found in Systemic Lupus patients. Also, no data showing the amount of Ssb being overexpressed is shown. This may have big implication to the validity of the RIP-qPCR analysis.

      We could perform qRT-PCR to quantify the overexpression level of Flag-Ssb. If required, we could use Ssb antibody to do Western blot to show the amount of Flag-Ssb protein.

      *- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      Most of the data is presented reasonably well, but the robustness of the data somewhat retracts from their conclusions. I feel the certainty of their conclusion regarding Xist specific La binding and RNA chaperone activity is still presumptive and should be rewritten unless more robust data can confirm Xist interaction. I would also suggest deciding on the nomenclature for the protein of interest and use either La or Ssb, the continued use of both through the figures and text can get a little confusing to the reader.

      In the current literatures, Ssb seems to be commonly used as a gene name and La is used as a protein name. We have revised the manuscript to use one name “Ssb” to describe both the gene and the protein.

      Reviewer #1 (Significance (Required)):

      *- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.*

      It was a good trial to use PBSb-PUFb system to purify Xist RNA binding proteins, compared to previous reports had used anti-sense oligo purification using complementary sequence to Xist RNA sequences. But currently the purification still needs further validation and repeats to confirm its use. A potential complementary technique could be to isolate Xist directly by using biotinylated probes against the PBSb sequence.

      The authors further claim the identification of a novel Xist RNA chaperone (La/Ssb) which they say facilitates XCI progression. This would be a novel finding in the field; however, the data is currently not robust enough to support this

      *- Place the work in the context of the existing literature (provide references, where appropriate).*

      This work has focused on the development of a milder methodology for purifying Xist RNA during XCI. Others have published similar methodologies predominantly focusing on purifying Xist RNA directly with biotinylated probes (McHugh et al. 2015; Minaji et al. 2015; and Chu et al. 2015). Although this method boasts a milder purification method, it seems to be low yielding in Xist specific proteins. Others have shown a more robust identification of bona fide Xist binding proteins which are currently missing in this manuscript. A recent preprint from the Plath lab has identified new factors involved in XCI during differentiation and their tethering/rescue experiments are far more convincing than the ones shown in this manuscript https://www.biorxiv.org/content/10.1101/2020.03.09.979369v1. The candidate protein Ha et al. have identified has multiple roles in developing cells and has shown to be important during mouse development. However, Ha et al do not robustly show that the knockdown of Ssb causes X-linked cell mortality. Alternatively, as would be presumed from Ssb's essential role in many housekeeping short non-coding RNAs, the cell death seems more ubiquitous upon shRNA KD. Therefore, the link the authors are making here are relatively weak.

      Ssb KD rescues cell death caused by forced induction of Xist in male ESCs. In addition, Ssb KD leads to cell death in differentiating female ESCs, while it has a negligible effect on cell death in differentiating male ESCs. These data clearly demonstrated X-linked cell survival/mortality by Ssb KD.

      Plath lab’s work is different from ours. In their manuscript, the authors report the observation of a protein condensation which is assembled by Xist but sustains in absence of Xist. TDP-43 (a.k.a. Tardbp) happens to be one protein factor involved in the protein condensation and also one candidate protein selected for further validation in our study. In our study, Tardbp KD did not rescue cell death caused by induced XCI in male cells. Thus, Tardbp is not further studied. In the manuscript, we have discussed the possibility that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp

      *- State what audience might be interested in and influenced by the reported findings.*

      The audience may be interested in the novel technique and the finding of a novel Xist binding protein.

      *- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.*

      RNA biochemistry and developmental biology

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

      **Summary:**

      This manuscript describes a novel "FLAG-out" system, where the authors sought to identify Xist RNA binding proteins. The authors focused on a specific protein found in their screen and also identified in several other screens for Xist RNA binding proteins, Ssb/La, and further characterize the role of this protein in XCI. This manuscript describes the loss of Ssb/La and suggest that it predominately impacts the canonical 'cloud' formation of Xist RNA on the X chromosome during XCI initiation. Further, they determine that loss of Ssb/La decreases Xist RNA half-life and alters folding of Xist RNA transcripts. Based on their findings, the authors propose that Ssb/La functions to directly bind and fold Xist RNA transcripts in a manner that stabilizes Xist RNA, allowing for proper 'cloud' formation and successful initiation of XCI.

      **Major comments:**

      The authors made an interesting findings that the SLE-relevant autoantigen Ssb/La stabilizes Xist RNA transcripts, and there is some evidence that this occurs by binding and maintaining proper folding of Xist RNA. Despite these intriguing observations, there are many parts of the manuscript that need to be addressed in order to support the authors main conclusions.

      The most troubling aspect of this manuscript is the persistent use of an artificial XCI system in male cells to draw strong conclusions about the function of Ssb in XCI. This issue is prevalent throughout the manuscript, and I question why the authors chose to perform most of their experiments in male cells when the same experiments can be (and have previously been by other groups) performed in female cells. Using male ESCs and then making conclusions for XCI, which is a female-specific process, is a major concern.

      In addition to iXist male ESC line, many experiments, such as cell death/survival (Figure 3B, C), allelotype (Figure 3E), Xist could formation (Figure 4A), H3K27me3 and H2AK119ub IF (Figure 5), were performed in female ESC. We chose to do SHAPE and Xist RNA stability assays in iXist male ESC line, because the onset of XCI is much more synchronized in this system. Moreover, in female cells, Xa causes additional layers of complication/noise in the ATAC-sequencing which may not be fully cleared up by data analysis. On the other hand, inducible Xist expression in male ESCs can be used as an experimental system to recapitulate the silencing step of XCI (Ha et al. 2018; Wutz et al. 2002).

      • Out of the 138 identified binding proteins, the authors chose to only validate three: Mybbp1a, Tardbp, and Ssb/La. The logic for choosing these candidates is weak, and the authors are only able to validate 1 out of 3 of these proteins.

      In theory, all candidate proteins in the list are possibly involved in XCI. There is no method which can help to make accurate prediction. We did not follow a clear-cut logic in selecting candidates for validation, but we do consider the candidate gene’s knockout phenotype, “early embryonic lethality”, as a phenotype consistent with a critical role of the candidate gene in XCI. Meanwhile, in the manuscript, we have discussed why we chose the three proteins for validation as the following:

      “……From the candidate proteins, we shortlisted three proteins for individual validation. Myb-binding protein 1A (Mybbp1a, Q7TPV4) and TAR DNA-binding protein 43 (Tardbp, Q921F2) were selected because they are known transcription repressors (11, 12). The Lupus autoantigen La (P32067, encoding-gene name: Ssb) was selected because systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a strikingly high female to male ratios of 9:1 (13). Moreover, its autoimmune antigen La is a ubiquitous and versatile RNA-binding protein and a known RNA chaperone (14). All the three selected candidates have also been identified as Xist-binding proteins in previous studies (2, 4). Moreover, the knockout of these three genes all lead to early embryonic death. Tardbp knockout causes embryonic lethality at the blastocyst implantation stage (15). Mybbp1a and Ssb knockout affect blastocyst formation (16, 17). Early embryonic lethality is a mutant phenotype consistent with a critical role of the mutated gene in XCI (1)** ……”

      We used cell death/survival assay to further validate the role of Xist binding protein candidates in XCI. This is a stringent assay. It requires not only that Xist binding protein candidates bind to Xist, but also that the candidates have to be functionally important in XCI.

      Indeed, it has been demonstrated by Plath lab (the BioRxix manuscript mentioned by reviewer 1) that Tardbp (also named TDP-43), together with other RBPs, bind to the E repeat of Xist to form a condensate and create an Xi-domain. Yet, Tardbp KD did not rescue cell death caused by forced XCI in male cells in our studies. Thus, only 1 out of 3 of these candidates is validated and further studied. In the manuscript, we also discussed that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp and Mybbp1a.

      • Use of the cell death assay is not strong enough to "confirm that La is involved in induced XCI" as stated by the authors. This is a huge overstatement.

      Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI. Considering the reviewer’s comment, we revised the sentence to “further suggest that Ssb is involved in induced XCI”.

      While the authors observed differences in X-linked gene expression after Ssb KD, they did not examine expression of these genes in after KD of either Mybbp1a or Tardbp. Are the changes observed in these genes specific to Ssb KD? Or could there still be alterations of X-linked gene expression in the non-validated KDs? This experiment should be performed and included in the manuscript, either within Fig 2 or in the supplemental. As well, inclusion of a well characterized positive control, for example Hnrnpu, as comparison to Ssb should be included.

      Mybbp1a and Tardbp were not validated by the cell death assay. Thus, compared with Ssb, Mybbp1a and Tardbp are less important for XCI functionally. We only focused on Ssb in the subsequent studies. Mybbp1a and Tardbp KD could be additional negative controls. Yet, we have used empty vector as a negative control. We do not need so many controls.

      As mentioned, Tardbp indeed binds to Xist RNA. It is very likely that Tardbp KD might alter some X-linked gene expression. This rules out Tardbp KD as a good negative control.

      If we do not see any effect of Ssb KD on X-linked gene expression, a positive control is absolutely required. However, we have detected that Ssb KD compromises the silencing of several X-linked gene. A positive control might not be essential.

      • The authors perform RIP to validate the interaction of Ssb with Xist, but this is performed in male ES cells with induced Xist RNA and with FLAG-tagged Ssb. Aside from these cells being male, in this system Xist RNA expression is much higher than would be found endogenously. RIP should have been done in female differentiated ESCs if there is in fact a role for XCI.

      • The authors need to include more details in the methods section to explain how the FLAG-Ssb is expressed in these cells, and why the authors chose to use a tagged contrast over endogenous Ssb. Due to these issues the result from this experiment is essentially meaningless and is not convincing of Ssb interaction with Xist RNA. There is no reason RIP cannot be performed in female cells, and the authors should repeat this experiment in the relevant experimental condition. As well, if a validated Ssb antibody exists the authors should perform RIP using the endogenous protein.

      If required, we could try to perform RIP and/or CLIP using Ssb antibody in female cells.

      The authors state in Fig 3A-C that the results of the cell death and differentiation experiments "...support a functional role of La in XCI". The authors state earlier that Ssb is a ubiquitous protein that is embryonic lethal (in both female and males). Based on this, the cell death results shown do not support a functional role of La in XCI as the Ssb KD could be having an indirect affect due to its other developmental functions. This manuscript lacks a direct functional link between Ssb and XCI; more data is necessary.

      Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI.

      For the data in Fig 3A-C, Ssb KD causes the death of female differentiating cells, but not male differentiating cells. Therefore, it rules out that the death of female cells is due to the general function of Ssb. Rather, the specific role of Ssb in XCI contributes to the female specific cell death.

      In Fig 3D, the authors perform ATAC-seq in inducible male ES cells. The authors claim that the extremely slight reduction in chromatin compaction of the Ssb KD compared to control iXist "directly connect La to the heterochromatinization of Xi, supporting a functional role of La in XCI". This is also an overstatement based on the minimal, and possibly indirect, change in compaction. The positive control i-detaA-Xist sample has significantly less compaction (and thus significantly higher compaction defect) than the Ssb KD again disputing the claim stated above. It is unclear why performing ATAC-seq is even necessary, as Ssb isn't stated to have a function in regulating chromatin architecture. In addition, why the authors performed ATAC-seq in the artificial male XCI system and not in the F1 female cells, and the N of the experiment is unclear. If the authors want to include the ATAC-seq in further revisions it should be repeated n=3 in the female system.

      The male induced XCI system provides a more synchronized onset of XCI. More importantly, in the male induced XCI system, only one X chromosome exists, avoiding the interference from the active X chromosome in female cells. If ATAC-seq was performed in female cells, only loci with SNPs can be distinguished. The sequencing reads from Xa will create additional layers of complication/noise which may not be cleared up fully by data analysis

      “i-delat-Xist” is a positive control to show the experimental system works. It is not justified to compare the chromatin accessibility of the mutant, which is only a Ssb “knockdown” mutant, and the control “i-delat-Xist”, in which the Repeat A is “deleted”. We admit that ATAC-Seq results did not reveal a drastic difference in chromatin accessibility between the wild type sample and the mutant sample. However, as what we discussed in the manuscript, clear difference can still be seen at the 14 h time point. This is shown clearly by the heatmap (Fig. 3E) and the sequencing coverage profile (Fig. S4A).

      • In Fig 6, the authors state in their methods that "The shRNA construct, which worked efficiently against Ssb, was not designed against the 3' UTR of the RNA. Therefore, the shRNA is against some of the rescue plasmid constructs. Nonetheless, transfecting the Ssb knockdown cells with the rescue plasmids should compensate the effect of Ssb knockdown and serve as a rescue assay to study the functional domains of La.". This is troubling and seems like a major experimental issue; the specific rescue constructs that may be impacted by this issue are not stated and should be explicitly mentioned. This becomes more confusing when examining the data from rescue experiments.

      We pointed out this issue in the original manuscript. We agree that the experiment was not perfectly designed. In the revision, we added in the information on the shRNA target site. Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. We agree that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. In the revision, we have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well-known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.

      If it is necessary, we could redo this experiments using a shSsb targeting 3’-UTR or expressing GFP-Ssb immune to shSsb.

      In Figure S7, the expression of the rescue constructs deltaRRM1 and deltaRRM2 is extremely low, yet the authors observe a rescue of the cloud phenotype (fig 6D) from those constructs that reaches almost the level of full length Ssb. This is confusing, and the authors need to address this by performing a western blot to show the protein levels of these rescue constructs and discuss further how such a low level of expression can show a rescue phenotype. The results would also be stronger if the authors examined H3K27me3 and H2AK119ub1 enrichment since they observed decreased overlap of these marks with Xist RNA after Ssb KD. Finally, the authors state that "...all three RNA-binding domains are required for the functionality of La in XCI..." however I have trouble coming to this conclusion based on the above issues. As well, if the authors want to support direct function, they should repeat the RIP experiments with these rescues constructs to show that the domains capable of rescue can still bind to Xist RNA.

      Reviewer 1 raised similar concerns. In Figure 6C, the live cell counts of ∆RRM1 and ∆NLS are about the same. It might be due to the low expression level of ∆RRM1 (Figure S7). It is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed as similar levels. To make the data more straight forward, we removed the data point of ∆RRM1 and ∆RRM2, because of their low expression levels.

      As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. The shSsb is not targeting the 5’ or 3’-UTR region, therefore interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins. If it is necessary, we could redo this experiments using a shSsb targeting 3’–UTR or expressing GFP-Ssb immune to shSsb.

      We deleted the sentence "all three RNA-binding domains are required for the functionality of La in XCI".

      **Minor comments:**

      The authors may want to consider better highlighting the strengths of their "FLAG-out" system. As written, is it difficult to tell how this system sets them apart from the previously published studies referenced in the text, especially as some of these studies used similar crosslinking conditions and cell types. Additionally, the logic and questions the authors pose in the introduction as to why they performed this project are too general and not very strong. For example, the authors mention how might protein machinery may assemble on Xist RNA, and how might Xist RNA may spread on the X chromosome. However neither of these topics are actually addressed in their experiments or discussion. These are interesting questions, but the authors should either discuss them further within the context of their results or take these questions out. It would also be helpful if the authors could better label Figure 4, as it is unclear in the figure itself that Fig 4A is in reference to female cells, but remaining panels are in male cells.

      The inducible XCI in male cells is a valid system to recapitulate the silencing step of XCI. It also provides unique advantages in many experiments, such as ATAC-seq. Meanwhile, we did perform extensive functional analysis on the endogenous XCI process using female cells. However, we do realize that presenting the data of induced XCI in male cells together with the data from female cells is confusing to many readers. We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).

      To understand “how the protein machinery is assembled by Xist” and “how Xist spreads along its host chromosome territory” are not specifically the initial aims of this study. We removed the sentences from the introduction section. However, we believe Ssb may provide clues for the future studies to fully address these questions, and we did provide the following thoughts in the discussion section:

      “……Secondly, as Ssb is able to utilize ATP to unwind RNA-RNA and RNA-DNA duplex, it may play a more active role in controlling the structural dynamics of Xist in living cells (14, 23). These structural dynamics may be important for recruiting proteins onto the RNA and spreading of the RNA along its host chromosome territory……”

      Reviewer #2 (Significance (Required)):

      I am not convinced the this manuscript, as written, has sufficient novelty. Ssb/La has been previously identified to be an Xist RNA binding protein with older/different approaches. However, there are some interesting observations in this manuscript. Major revisions are necessary.

      We agree with the reviewer that identification of Ssb as an Xist RNA binding protein is not novel. The novelty of our discovery lies in: 1) we developed a new method for isolating lincRNA associated proteins; 2) we confirmed that Ssb is an important player involved in XCI; 3) we showed that Ssb regulates the folding of Xist RNA, consequently the stability of Xist and the formation of Xist cloud.

    1. SciScore for 10.1101/2020.07.07.20148106: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      <table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">Written informed consent obtained from all participants in this study and was approved by the following IRBs: 1 ) IRB# SUNY:269846 .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">In this regard , it is interesting to note that a bruton tyrosine kinase ( BTK ) inhibitor , that targets Fc-receptor signaling in macrophages , is being tested in a randomized clinical trial 32 .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Subdividing the subjects by sex did not reveal any statistical difference in IgG levels at any of the disease stages , although hospitalized females in the non-ICU setting had significantly lower antibody levels than ICU/deceased patients , whereas the difference in males was not significant ( Fig . 2f) .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CoV-2 Spike protein or Nucleocapsid protein specific IgG antibodies at titers more than 1:100,000 were detectable in all PCR+ subjects (n=87) and were absent in the negative controls.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>CoV-2 Spike protein or Nucleocapsid protein specific IgG</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Other isotype antibodies (IgA, IgG1-4) were also detected.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>IgA, IgG1-4</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CoV-2 infection2-6 To predict protection against CoV-2, it is critical to understand the quantity, quality and duration of the antibody responses during different stages of COVID-19 and in the convalescent period.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>CoV-2</div> <div>suggested: (Abcam Cat# ab272504, AB_2847845)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In this assay, we immobilized biotinylated CoV-2 Spike protein receptor binding domain (RBD) or the Nucleoprotein (N) on streptavidin beads, to detect specific antibodies from patient plasma (Fig.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>CoV-2 Spike protein receptor binding domain (RBD)</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Different antibody isotypes were measured using anti-Ig (IgG, IgA, IgM) specific secondary antibodies conjugated to a fluorescent tag (Fig. 1a).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-Ig ( IgG</div> <div>suggested: None</div> </div>

            <div style="margin-bottom:8px">
              <div><b>IgA , IgM</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Using either anti-SRBD antibody or soluble ACE2-Fc, we show very high sensitivity in detecting Spike protein binding, down to picogram ranges (Fig. 1b).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-SRBD</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Furthermore, Nucleocapsid protein-specific IgG levels and S-RBD specific IgA positively correlated with S-RBD IgG antibodies (Supplementary Fig. 1b, c).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>S-RBD IgG</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Notably, IgG1 subclass antibody levels were comparable to total IgG levels whereas the other subtypes were relatively lower (Fig. 2b).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>IgG1</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To evaluate membrane expression of Spike protein, cells were stained with recombinant soluble ACE2-Fc fusion protein followed by a secondary staining with an anti-Fc antibody (Fig 3a).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-Fc</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ACE2 overexpression of ACE2-IRES-GFP or ACE2mKO2 was confirmed by staining with CoV-2 Spike-protein fused with mouse Fc (mFc) and antimFc secondary antibody (Supplementary Fig. 2a, b).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>antimFc</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Of note, there was a significant negative correlation between the number of days and the IgG or IgA to S-RBD, anti-nucleocapsid IgG or the NT50 values ( !" = -0.67) (Fig. 6d), suggesting a potential decline in antibody titers over time.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-nucleocapsid IgG</b></div>
              <div>suggested: (Imported from the IEDB Cat# 3E9, <a href="https://scicrunch.org/resources/Any/search?q=AB_2848062">AB_2848062</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutralization of the virus by antibodies (NAbs) is one of the goals to achieve protection against CoV-218.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>CoV-218</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">However, another study showed IgA antibodies, but not IgG, increased in severe patients28.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>IgA</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Although it will be important to follow the same individual subject convalescent plasma over time to better assess this finding, our data point towards a relatively short-lived antibody response to COVID-19.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>COVID-19</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">WT and ACE2 over-expressing HEK-293T were also stained with SARS-CoV-2 S1 protein, Mouse IgG2a Fc Tag (Acro Biosystems) followed with APC Goat anti-mouse IgG2a Fc Antibody (Invitrogen).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Mouse IgG2a</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-S-RBD antibody and ACE2-Fc was tested both at 5 µg/mL starting concentration and in additional 5-fold serial dilutions.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Anti-S-RBD</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudotype virus neutralization assay Three-fold serially diluted monoclonal antibodies including anti-SARS-CoV-2 Neutralizing human IgG1 Antibody from Acro Biosystems, NAb#3 (Fig 4D), Genscript clone ID 6D11F2, NAb#2 (Fig 4D) and Genscript clone ID 10G6H5, NAb#1 (Fig 4D)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-SARS-CoV-2</b></div>
              <div>suggested: (Abcam Cat# ab272854, <a href="https://scicrunch.org/resources/Any/search?q=AB_2847844">AB_2847844</a>)</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>human IgG1</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Percent infection obtained was normalized samples derived from cells infected with CoV-2 or SARS pseudotyped virus in the absence of plasma, ACE2-Fc or monoclonal antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>ACE2-Fc</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Experimental Models: Cell Lines</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In addition, we also developed SARS Spike protein pseudotyped lentivirus, which similarly infected 293-ACE2 cells at almost 100% efficiency at higher virus supernatant volumes (Fig. 3f)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>293-ACE2</b></div>
              <div>suggested: <a href="https://scicrunch.org/resources/Any/search?q=CVCL_DR94">CVCL_DR94</a></div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK-293T cells (ATCC; mycoplasma-free low passage stock) were transfected with the expression plasmids using Lipofectamine 3000 (Invitrogen) according to the manufacturer’s protocol as previously described33.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>HEK-293T</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Software and Algorithms</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Generating human ACE2 over-expressing cells Wildtype ACE2 sequence was obtained from Ensembl Gene Browser (Transcript ID: ENST00000252519.8) and codon optimized with SnapGene by removing restriction enzyme recognition sites necessary for subsequent molecular cloning steps, preserving the amino acid sequence.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Ensembl Gene Browser</b></div>
              <div>suggested: None</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>SnapGene</b></div>
              <div>suggested: (SnapGene, <a href="https://scicrunch.org/resources/Any/search?q=SCR_015052">SCR_015052</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Flow cytometry data were analyzed using FlowJo (BD biosciences).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>FlowJo</b></div>
              <div>suggested: (FlowJo, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008520">SCR_008520</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses were performed using GraphPad Prism 8.0 software (GraphPad Software)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>GraphPad Prism</b></div>
              <div>suggested: (GraphPad Prism, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002798">SCR_002798</a>)</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>GraphPad</b></div>
              <div>suggested: (GraphPad Prism, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002798">SCR_002798</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Author contributions M.D., L.K. and D.U. conceived, designed the experiments. M.D., L.K., L.P., M.Y. and R.H. carried out the experiments. B.T.L. designed the clinical research study on UConn Healthcare workers and M.K. recruited participants and executed clinical protocols. R.G. and O.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>UConn Healthcare</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr></table>
      

      About SciScore

      SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

    1. SciScore for 10.1101/2020.05.31.20118554: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      <table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">Let 's study immune responses , but let 's not create a dystopian society based on them . Materials and Methods Human specimens and data All experiments and analyses involving samples from human donors were conducted with the approval of the local ethics committee ( KEK-ZH-Nr . 2015-0561 , BASEC-Nr . 2018-01042 , and BASEC 2020-00802) , in accordance with the provisions of the Declaration of Helsinki and the Good Clinical Practice guidelines of the International Conference on Harmonisation .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">To directly validate our method , we selected 210 high scoring samples and 122 random samples from known negatives and aimed to reproduce our results .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">A blinded comparison with commercial test kits showed that our approach – combining three individual assays into one single score – was suitable for large-scale epidemiologic studies .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Lastly , seropositivity can be found across all age groups and in both genders , with more male individuals affected in the USZ and BDS cohorts ( Fig . 3A , B , Table S1) .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Solution-equilibrium measurements revealed immunodominant antibodies with nanomolar affinity in COVID samples, whereas prepandemic plasma showed lower affinities despite similar titers for individual SARS2 antigens.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>SARS2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">However , those with strong binding properties to SARS2 RBD ( > 2.5 ) cluster at high values for SARS1 RBD , indicating that some anti-SARS2 RBD antibodies are likely cross-reactive to SARS1 RBD .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-SARS2 RBD</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-his-tag antibody was included as a positive control .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Anti-his-tag</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In a comparative approach , we investigated IgG and IgA antibodies to S , RBD , and NC as well as responses to multiple control antigens , in four asymptomatic blood donors and 4 convalescent individuals recruited to the BDS for a plasmapheresis study .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>IgA</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Counter screening using commercial and custom-designed platforms We used the following commercial tests for the detection of anti-SARS-SARS2 antibodies in 56 plasma samples of 27 patients who were diagnosed by RT-PCR to be infected by SARS-SARS2 as well as 83-90 plasma samples which were collected before December 2019 and , hence , before the start of the COVID19 pandemics: The double-antigen sandwich electro-chemiluminescence immunoassay from Roche diagnostics ( Rotkreuz , Switzerland ) was performed with the E801 of the COBAS8000® system ( Roche diagnostics , Rotkreuz , Switzerland) .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-SARS-SARS2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The test detects any antibody against the nucleocapsid antigen .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>antibody against the nucleocapsid antigen .</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Baseline and plateau values are fixed by the respective positive and negative controls in a plate-wise fashion and the signal is fitted following these equations: 1 = 1 − ( + + 1 − √ ( + )2 + 2 ( − ) + 1 ) , 2 where cbound , ca and c are concentration of the antigen-antibody , antigen , and blood concentration respectively .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>+ + 1 − √ ( + )2 + 2 ( − ) + 1 ) , 2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Assume that we have data for samples with known serostatus and antibody measurements , that is , we have ( , ) , = 1 , . . , , where is the vector of size ( in our case our antigen measurements ) and is a Boolean variable defining group membership ( in our case , whether the individual is seropositive or not) .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>we have ( , ) , = 1 , . . ,</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The day after, membranes were washed four times with PBS-T and incubated for 1 hours with an anti-human secondary antibody, HRP-conjugated, diluted 1:10000 in 1% SureBlock.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-human secondary antibody, HRP-conjugated,</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">As a positive control, one membrane was incubated overnight with mouse anti-Histag antibody (ThermoFisher, dilution 1:10000 in 1% SureBlock) and subsequently with anti-mouse secondary antibody, HRP-conjugated (Jackson, dilution 1:10000 in 1% SureBlock).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-Histag</div> <div>suggested: (RevMAb Biosciences Cat# 54-1161-00, AB_2716428)</div> </div>

            <div style="margin-bottom:8px">
              <div><b>anti-mouse</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, the plates were washed five times with PBS-T and the presence of IgGs was detected using an HRP-linked anti-human IgG antibody (Peroxidase AffiniPure Goat Anti-Human IgG, Fcγ Fragment Specific, Jackson, 109-035-098, at 1:4000 dilution in sample buffer).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Anti-Human IgG</b></div>
              <div>suggested: (Jackson ImmunoResearch Labs Cat# 109-035-098, <a href="https://scicrunch.org/resources/Any/search?q=AB_2337586">AB_2337586</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Experimental Models: Cell Lines</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We confirmed these findings by using the samples of the asymptomatic and convalescent individuals as primary antibodies in Western Blot and detected bands for both S and the NC in the Expi293 cells overexpressing the viral proteins but not in the Expi293 control lysate ( Fig . 5B) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Expi293</b></div>
              <div>suggested: <a href="https://scicrunch.org/resources/Any/search?q=CVCL_D615">CVCL_D615</a></div>
            </div>
          </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Software and Algorithms</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To benchmark TRABI, we compared the results with an in-house high-throughput assay under development at the University of Oxford (optimizations ongoing at the time of data acquisition), the Roche Elecsys, the DiaSorin, the EuroImmun, and the Abbott systems (Fig. 1C), using 139 of 149 samples (10 were removed from the analysis because of insufficient sample volume to perform all tests).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Abbott systems</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">COVID and prepandemic samples were used to assess the performance of TRABI, commercial tests (Roche, DiaSorin, Abbott, Euroimmun) and an early version of an assay under development at the Target Discovery Institute (Oxford).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Abbott</b></div>
              <div>suggested: (Abbott, <a href="https://scicrunch.org/resources/Any/search?q=SCR_010477">SCR_010477</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Details of viral proteins used for this study For high-throughput serology , the following proteins were used: SARS-CoV-2 S ( pHL-Sec; aa . 11208 , C-terminal 8His-Twin-Strep ) and RBD ( pOPINTTGNeo; aa . 330-532 , C-terminal 6His ) produced at the SGC in Oxford and the nucleocapsid protein from AcroBiosystems ( AA Met 1 - Ala 419 , C-terminal his-tag , NUN-C5227) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>AcroBiosystems</b></div>
              <div>suggested: (ACRObiosystems, <a href="https://scicrunch.org/resources/Any/search?q=SCR_012550">SCR_012550</a>)</div>
            </div>
          </td></tr></table>
      


      Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).


      About SciScore

      SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We thank the reviewers for their comments and outline below how we plan to address them.


      Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). The authors here describe a method to modify bacterial artificial chromosomes (BAC) harbouring gene loci from eukaryotes. When wanting to modify a BAC an antibiotic selection cassette is often included alongside the desired mutation/modification to increase the number of successful recombinants in E.coli. Traditionally, this is removed in a second recombination process to leave only the desired modification. The novelty in the procedure described herein is to add a synthetic intron consensus sequence around the selection cassette, which eliminates the need for the subsequent removal of the antibiotic cassette from the BAC before transfection into mammalian cells, saving time and resources. The technique is clever in its simplicity and appears to function for a number of gene loci. The authors validated the correct functioning of the modified BACs for a number of genes using three main assays - transcript level, protein level and localisation. **Major comments:** *Are the key conclusions convincing?* The conclusion that the method described generates functional modified BACs is valid. *Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?* While the method is successfully employed in this study, its efficiency is not quantified in relation to the state-of-the-art as described in the introduction. One assumes it would be more efficient, but this has not been tested empirically in the paper. Does the inclusion of the synthetic intron sequence have an effect on the efficiency of modifying BACs compared to a more typical two-step positive/negative antibiotic selection cassette? *

      • *

      This is a good point that we did not directly address. In general, the efficiency is similar to that of integrating any cassette with selectable marker, as has been published (Poser et al 2008), and therefore also higher than the two-step counterselection method, which requires such a cassette integration in the first step alone. We will include new data specifically addressing the efficiency of our new method (see specifics below)

      The functionality of this approach rests entirely on the ability of the target cell to correctly splice out the synthetic intron. The authors are aware of this potential problem as highlighted in the lines below, but do not make efforts to explicitly test splicing. On lines 224-225, the authors state "We cannot exclude that a small portion of synthetic introns within individual cells are misspliced". On lines 230-231 it is stated that "mis-spliced mRNAs are probably minimal and degraded by nonsense-mediated decay". On lines 215-217, the authors describe an "investigation of transgenic lines at the single-cell level" that suggests "the synthetic intron is correctly spliced out in all the cells of the population". How do the authors reach this conclusion? U2OS and HeLa cells are considered very "robust" and may not show detectable consequences when stressed with an increased level of nonsense-mediated decay. Further, many genes maintain a high level of expression that buffers them against small changes in transcription/splicing. The synthetic intron might have a bigger impact on more tightly regulated genes, so assessing the splicing rate would be essential if the authors wish to advocate their technique as generally applicable.

      • *

      We will assay for splicing efficiency as outlined below.

      The ability of the synthetic intron to be removed from final transcripts depends on functioning splicing machinery. The authors might emphasise this issue, as spliceosome mutations are important fields of study and might not be compatible with this method.

      • *

      We can add this in the text

      The authors used un-directed integration of each BAC under study. Therefore, it is hard to assess what effect the synthetic intron has, as the authors only ever assess the downstream levels of the correctly spliced, translated and localised protein. The authors themselves state that this can lead to clonal variations in expression of up to 2-fold and on line 250 that this variation "could compensate for synthetic intron effects", but make no effort to test this. Again, lines 267-268 highlight the potential dangers of potential effects of the synthetic introns, but do not test these. \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.* If not already performed, a large number of bacterial colonies should be screened for the correct modification and frequency of correct ones reported. This frequency - reported for at least three different modifications - would estimate what sort of efficiency this method provides. The modified region of each BAC should be sequenced and the results reported. The rate of exactly modified clones is important, in case of spontaneous or low fidelity integration of the antibiotic cassette. The percentage of transcripts that have the synthetic intron correctly spliced out should be measured for some of the BAC constructs used in the study. A direct head-to-head comparison of this newer method compared to other techniques, or even the authors' own previous two-step approach is necessary to assess the benefits of this method. Preferably, the experiment would be run in parallel with and without antibiotic selection applied, to show that it drastically improves chances of finding a correct clone. *

      We will generate 3 new mutations in BACs and analyze both the efficiency of integration by PCR and accuracy via sequencing. In practice, we have observed that the efficiency is similar to any other cassette integration, such as a GFP tag (Poser et al Nature Methods 2008) or a counterselection cassette (Bird et al Nature Methods 2012) (80-90%). Integrating a mutation via the second step of the counterselection method introduces a further 20% decrease in efficiencies on average.

      \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.* Repeating the transformation of the BAC and targeting cassette and assessing the recombination efficiency and sequencing should only require existing reagents and take less than a week or two to complete. Quantitative RT-PCR to assess the percentage of transcripts that have the synthetic intron spliced out would take a little more work. However, this should not be a considerable investment in time or resources for a standard microbiology laboratory and could be completed within a few weeks using modern techniques, such as that described in Londoño et al. 2016. Repeating all the experiments in parallel would be considerable work and would only be strictly necessary if the authors wish to emphasise the benefits of their method over the many others already in wide use. *

      • *

      We will use quantitative PCR to estimate the fraction of transcripts that correctly splice out the artificial intron for two clonal cell lines characterized in the study: RNAi-resistant AurA-GFP (Fig 4), and GTSE1-14A (newly introduced; see below). While the exact method described in Londoño et al 2016 will not be applicable due to the larger size of the artificial intron, we believe we can adapt it to detect different splicing events.

      \Are the data and the methods presented in such a way that they can be reproduced?* Barring the omission of Table S1, which presumably includes exact information on the BACs modified and sequences used etc., there is sufficient other data and methods to allow the experiments to be repeated. Targeting the ESI procedure to the middle of exons is likely to have a bigger impact for smaller exons as the authors mention on lines 99-100. Making it clear which exon sizes for each gene were successfully targeted in this study would help give some idea of how significant a problem this might be. Perhaps Table S1 contains this information, but it was not provided. It would also help reviewers check the design strategies. *

      We apologize for inadvertently failing to upload Table S1 on bioRxiv. It has been uploaded now as part of this submission process. This table indeed contains BAC and target sequence information, including the size of the targeted exon (and the 2 “new” resulting exons). Targeted exons range in size from 138bp to 1537bp, and “new” exons are as small as 48bp.

      \Are the experiments adequately replicated and statistical analysis adequate?* The replication and statistically analysis of the data as presented appear adequate. Figure Legends should state the statistic used to generate error bars. *

      This will be updated

      \*Minor comments:** Specific experimental issues that are easily addressable. Are the promoters used in the vectors described universally functional? For example, is the PGK promoter functional in yeast? *

      • *

      The PGK promoter contained in the cassettes is a mammalian promoter, which has also been reported to work in flies.

      \Are prior studies referenced appropriately?* The manuscript may benefit from the referencing of BAC modification techniques from a wider variety of groups, such as those using CRISPR-guided recombineering (Pyne et al. 2015). *

      We will add citations of more techniques

      \Are the text and figures clear and accurate?* The body text is very clear save minor typographical or grammatical errors. Regarding figures, some of the coloured text in Figure 1 is somewhat illegible when printed in grayscale. Line 278 - The acronyms LAP and NLAP are not defined/explained. Antibody section starting Line 282 may fit better next to Western Blot section. Figure 2C - The blot images would benefit from arrows to indicate expected sizes of proteins. Figure 3A - the graph may benefit from a dashed line at 100% to highlight that values are normalised to controls. Figure 4 - The differences between panels B & C are unclear. Figure 4E - The legend could provide a little more detail on cell cycle stage/status of the captured cells. *

      All of the above will be addressed accordingly

      \Do you have suggestions that would help the authors improve the presentation of their data and conclusions?* Lines 23-27 are somewhat unclear and feel out of context. Perhaps the authors could clarify this as a further advantage of using BACs instead of endogenous gene modifications. *

      Thanks for the input, we will clarify this.

      While not affecting the factual content of the paper, I would advocate that the authors format the method described in Figure S3 into a more detailed text based layout similar to that seen in a typical Nature Methods article. However, this may depend on the format required by any eventual publishing journal.

      • *

      We prefer the graphical protocol, but will discuss whether to add a text protocol with the journal editor.

      That all of the work the paper was carried out in human cell lines and using human genes is a further caveat, but the authors admit this in the discussion and one would assume that most mammalian cells would respond similarly in their ability to splice out the synthetic intron. Reviewer #1 (Significance (Required)): \Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.* This work is a formal description of a newer method that could be useful for many of those employing bacterial artificial chromosomes in numerous studies, such as gene regulation. *Place the work in the context of the existing literature (provide references, where appropriate).* This work builds on methodology previously published by the authors - a counter-selection two-step procedure (Bird et al. 2011). It sets out to formally describe a method merely mentioned as "BAC intronization" in a later paper by some of the authors (Zheng et al. 2014). Other alternative one-step procedures are also available, but present a different set of challenges (Lyozin et al. 2014). Some newer approaches, such as those using CRISPR-guided recombineering (Pyne et al. 2015) or systems that combine CRISPR and positive/negative selection cassettes (Wang et al. 2016) may be slightly more efficient, but are also more complex in their design. Bird et al. 2011 DOI: 10/dv776q Pyne et al. 2015 DOI: 10/f7jx92 Wang et al. 2016 DOI: 10/f89db5 Zheng et al. 2014 DOI: 10/f5pkr6 *State what audience might be interested in and influenced by the reported findings.* As a technology paper this work should have interest from a broad field of research. While the use of BACs could sometimes be considered more traditional in light of the explosion in CRISPR-based genome editing capabilities, it is definitely seeing a resurgence as the limitations of CRISPR in modifying large regions of genome become more apparent. Therefore, technologies that accelerate the modification of BACs could prove increasingly useful. As category of audience, all those involved in significant recombineering or gene/genome engineering would potentially benefit. *Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.* Synthetic genomics, synthetic biology, cancer cell biology, gene and genome engineering REFEREES CROSS COMMENTING I would agree with reviewer two's assessment that we both view the paper in a similar light. Reviewer #2 (Evidence, reproducibility and clarity (Required)): This is a methods-focused paper that presents a strategy to efficiently introduce mutations into a bacterial artificial transgene using synthetic introns. BAC-based methods have been an effective strategy for introducing trans genes into human cells to achieve near-endogenous expression, including extensive work from these authors. However, generating mutations and changes within the internal coding sequence presents some challenges for how to target these mutations and select for the mutated form. Here, the authors describe a way to overcome this by introducing synthetic introns into an adjacent sequence. This allows them to introduce a selectable marker and conduct the molecular biology without creating complications downstream for the functionality of the protein. This method is carefully described and presented. The authors also provide clear validation by using this to create RNAi-resistant versions of multiple different mitotic factors as well as creating targeted mutants that alter the functional properties of a protein. This work clearly takes advantage of other ongoing studies from these labs (including mutants and cell lines that appear to also have been described elsewhere), but the ability to combine these in a single paper and clearly describe the method provides a helpful advance and validation. Based on the description and data presented, I think that things are clear and carefully validated. As such, I do not have technical comments or concerns and I would be comfortable with this paper appearing in an appropriate journal in its present form. Reviewer #2 (Significance (Required)): This is a solid methods paper, but for considering the nature of the impact and significance of this paper, there are several things to note: 1.The BAC-based method does appear to be a powerful and effective strategy. However, beyond the work of Mitocheck and the authors that are part of this paper, this has not seen widespread adoption. It is possible that this current method may increase its usage due to the value of the targeted mutations within the coding sequence, but at present it is not a broadly used strategy. *

      We agree that using BACs as transgenes has not seen widespread adoption as a tool on the broader cell biology community (although certainly beyond members of the Mitocheck consortium). This is likely because many erroneously think that it is a technique for specialist laboratories. We are trying to change this! For reasons outlined below, there is still an increasing desire for conditional analysis of mutated genes under physiological expression/regulation frequently not attainable via directed Cas9-based mutation. A major aim of this paper is thus to further simplify the methods for generating modified BAC transgenes.

      2.This BAC-based approach (and also RNAi) are becoming increasingly replaced by the use of CRISPR/Cas9 genome editing. The absence of Cas9-based strategies in this paper limits the potential impact and reach of this paper. The authors do mention the possibility of using a similar synthetic intron strategy for use with Cas9 in the Discussion, and appear to have conducted some experiments. If possible, it would substantially increase the value of this paper if this data and strategy were also included in the Results section (acknowledging that this may still be a work in progress).

      While some uses of BAC transgenes are in some cases better replaced by CRISPR/Cas9 techniques (i.e. GFP tagging), there are several occasions where using BACs are preferable: As stated in the text, RNAi-resistant BACs allow for conditional analysis of recessive mutations. Mutations in essential genes that are lethal will prevent growth and recovery of viable cells if integrated into the genome via Cas9. Additionally, deleterious mutations are prone to accumulate suppressive changes in chromosome integrity or gene expression during the procedure of selecting and expanding Cas9-modified cells for analysis, particularly in the genomically instable cancer cell lines frequently employed.

      We use both BACs and CRISPR/Cas9 in our lab according to our needs.

      We do have an ongoing project to apply this intronization technique to enable more efficient selection of CRISPR/Cas9 integrations. Preliminary results suggest that it works to allow selection of point mutations, but it is still being optimized, including a redesign of the cassette, and is not ready for publication.

      3.The method is solid and well-validated, but there are no new results or insights presented in this paper from the work that is described (this is fine, just commenting for considering the right journal fit).

      As “biological insights” gained as a result of this technique we had cited a couple studies that made use of the technique already (to functionally analyze a microcephaly-associated mutation in the centriolar protein CPAP at the single cell level in HeLa cells and neural progenitor cells (Zheng et al 2014, Gabirel et al 2016)). As a response to this critique to include “new biology” in this paper, we will add new unpublished data investigating a specific question: Is the cell-cycle-regulated disruption of the EB1-GTSE1 (microtubule plus-end tracking proteins) interaction in mitosis required for chromosome segregation fidelity? We have generated a GTSE1 mutant with 14 phosphosites mutated to alanine using this technique. We will present the effect on chromosome segregation.

      REFEREES CROSS COMMENTING It appears that both reviewers are largely on the same page regarding this paper.

    1. SciScore for 10.1101/2020.03.21.990770: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      <table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">This study received approval from the Research Ethics Committee of Shenzhen Third People 's Hospital , China ( approval number: 2020-084) .</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Among a total of 69 antibodies from P#2 , the majority ( 59 % ) were scattered across various branches and the remaining ( 41 % ) were clonally expanded into three major clusters ( Figure 3A) .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>total of 69</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Control antibodies from P#1 demonstrated even lower competing power with ACE2 .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>ACE2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We selected a total of six antibodies with ACE2 competitive capacities of at least 70 % and analyzed them in a pairwise competition fashion using SPR .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>SPR</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The most potent antibody , P2C-1F11 , did not seem target the same epitope as the relatively moderate antibody P2C-1C10 .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>P2C-1F11</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Finally , despite successfully isolating and characterizing a large of number mAbs against SARS-CoV-2 , we cannot draw any firm correlation between antibody response and disease status at this time.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>SARS-CoV-2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The third staining at 4 °C for 30min involved either: Streptavidin-APC ( eBioscience ) and/or Streptavidin-PE ( BD Biosciences ) to target the Strep tag of RBD , or antihis-APC and anti-his-PE antibodies ( Abcam ) to target the His tag of RBD .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>antihis-APC</div> <div>suggested: None</div> </div>

            <div style="margin-bottom:8px">
              <div><b>anti-his-PE</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The IgG heavy and light chain variable genes were amplified by nested PCR and cloned into linear expression cassettes or expression vectors to produce full IgG1 antibodies as previously described 29,41 .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>full IgG1</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The PCR products were purified and cloned into the backbone of antibody expression vectors containing the constant regions of human IgG1 .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>human IgG1</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV antibodies ( S230 and m396 ) previously isolated by others 42 were synthesized and sequences verified before expression in 293T cells and purification by protein A chromatography .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>SARS-CoV</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HIV-1 antibody VRC01 was a broadly neutralizing antibody directly isolated from a patient targeting the CD4 binding site of envelope glycoprotein 40 .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>CD4 binding site of envelope glycoprotein 40</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were then stained with PE labeled anti-human IgG Fc secondary antibody ( Biolegend ) at a 1:20 dilution in 50 μl staining buffer at room temperature for 30 minutes .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-human IgG</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">VRC01 is negative control antibody targeting HIV-1 envelope glycoprotein.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>HIV-1 envelope glycoprotein.</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The recombinant RBD was labeled with either a Strep or His tag and used alone or in combination to identify and isolate RBD-specific single B cells through staining with the Streptavidin-APC and/or Streptavidin-PE, or anti-His- APC and anti-His-PE antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-His- APC</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Experimental Models: Cell Lines</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 , SARS-CoV and MERS-CoV pseudovirus were generated by cotransfection of human immunodeficiency virus backbones expressing firefly luciferase ( pNL43R-E-luciferase ) and pcDNA3.1 ( Invitrogen ) expression vectors encoding the respective S proteins into 293T cells ( ATCC ) 37,38,44,45</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>293T</b></div>
              <div>suggested: KCB Cat# KCB 200744YJ, <a href="https://scicrunch.org/resources/Any/search?q=CVCL_0063">CVCL_0063</a></div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Huh7 cells ( ATCC ) ( approximately 1.5 × 104 per well ) were added in duplicate to the virusantibody mixture.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Huh7</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The isolate was amplified in Vero cell lines to make working stocks of the virus ( 1 × 105 PFU/ml) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Vero</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Serial dilutions of mAbs were mixed separately with 100 PFU of SARS-CoV-2 , incubated at 37 °C for 1 h , and added to the monolayer of Vero E6 cells in duplicates .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Vero E6</b></div>
              <div>suggested: <a href="https://scicrunch.org/resources/Any/search?q=CVCL_XD71">CVCL_XD71</a></div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The genes encoding the heavy and light chains of isolated antibodies were separately cloned into expression vectors containing IgG1 constant regions and the vectors were transiently transfected into HEK293T or 293F cells using polyethylenimine ( PEI ) ( Sigma) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>293F</b></div>
              <div>suggested: <a href="https://scicrunch.org/resources/Any/search?q=CVCL_D615">CVCL_D615</a></div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK 293T cells transfected with expression plasmid encoding the full length spike of SARS-CoV-2, SARS-CoV or MERS-CoV were incubated with 1:100 dilutions of plasma from the study subjects.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>HEK 293T</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Software and Algorithms</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Bioinformatic and biologic characterization indicates that these antibodies are derived from broad and diverse families of antibody heavy and light chains .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Bioinformatic</b></div>
              <div>suggested: (QFAB Bioinformatics, <a href="https://scicrunch.org/resources/Any/search?q=SCR_012513">SCR_012513</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Finally , the cells were re-suspended and analyzed with FACS Calibur instrument ( BD Biosciences , USA ) and FlowJo 10 software ( FlowJo , USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>FlowJo</b></div>
              <div>suggested: (FlowJo, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008520">SCR_008520</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Half-maximal inhibitory concentrations ( IC50 ) of the evaluated mAbs were determined by luciferase activity 48h after exposure to virusantibody mixture using GraphPad Prism 6 ( GraphPad Software Inc . ) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>GraphPad Prism</b></div>
              <div>suggested: (GraphPad Prism, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002798">SCR_002798</a>)</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>GraphPad</b></div>
              <div>suggested: (GraphPad Prism, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002798">SCR_002798</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The IgG heavy and light chain variable genes were aligned using Clustal W in the BioEdit sequence analysis package ( https://bioedit.software.informer.com/7.2/).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>BioEdit</b></div>
              <div>suggested: (BioEdit, <a href="https://scicrunch.org/resources/Any/search?q=SCR_007361">SCR_007361</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Phylogenetic analyses were performed by the Maximum Likelihood method using MEGA X ( Molecular Evolutionary Genetics Analysis across computing platforms) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>MEGA X</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr></table>
      


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  34. Oct 2018