eLife assessment

This study examines the expression of HDAC3 within DC compartment. Taking advantage of tamoxifen inducible ERT2-cre mouse model they observe the dependency of pDCs but not cDCs on HDAC3. The requirement of this histone modifier appears to occur during development around the CLP stage. Tamoxifen treated mice lack almost all pDC besides lymphoid progenitors. RNA seq studies identify multiple DC specific target genes within the remaining pDC - using Cut and Tag technology they validate some of the identified targets of HDAC3. Taken together, this study shows the requirement of HDAC3 on pDC but not cDC, congruent with the recent findings of a lymphoid origin of pDC.

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.<br /> 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.

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?

A more detailed characterization of the progenitor compartment that is compromised following depletion would be important, as also suggested in the specific points.

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<br /> 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?

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.

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)? 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.

4) 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? Moreover, what is the SiglecH+ CD11c- population appearing in the spleen of mice reconstituted with HDAC3-deleted CDP? Data shown in Figure 4F should be expressed as log2 and not10. Finally, how do the authors explain that Hdac3fl/fl express Il7r, while they are supposed to be sorted CD127- cells?

5) 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.

6) It would be highly informative to extend CUT and Tag studies to Irf8 and Tcf4, if this is technically feasible.

Reviewer #2 (Public Review):

In this manuscript, Yu and colleagues profile the lysosome content in C. elegans. They implement lysosome immunoprecipitation (Lyso-IP) for C. elegans and they convincingly show that this method successfully isolates lysosomes from whole worms. The authors find that the lysosomes of worms overexpressing the lysosomal lipase lipl-4 are enriched for AMPK subunits and nucleoporins and that these proteins are required for the longevity of lipl-4 overexpressing worms. The authors also show that this is specific to this longevity pathway given that another long-lived worm strain (daf-2) does not exhibit enrichment for nucleoporins nor does it require them for longevity. The authors go on to express the Lyso-IP tag in different tissues of C. elegans (muscle, hypodermis, intestine, neurons) and identify the tissue-specific lysosome proteomes. Finally, the authors use this method to identify lysosome proteins in mature lysosomes and they find new proteins that regulate lysosomal acidification.

The authors present a powerful tool to unbiasedly identify lysosome-associated proteins in C. elegans, and they provide an in-depth assessment of how this method can be used to understand longevity pathways and identify novel proteins. Understanding lysosomal differences in specific tissues or in response to different longevity conditions are exciting as it provides new insight into how organelles could control specific homeostasis responses. This tool and proteomics datasets also represent a great resource for the C. elegans community and should pry open new studies on the regulation and role of the lysosome at the organismal level.

Addressing the following suggestions would help strengthen this already strong manuscript. First, it would be helpful to validate selected candidates from the tissue-specific Lyso-IP to verify that the protocol is still specific with lower sample amounts. Second, it would be helpful to provide more details on the methods, notably for sample preparation and analysis, so that it can serve as a guideline for the community. Third, the manuscript contains a lot of data and conditions, which is great, but they may also feel disconnected in some cases and it could be helpful to focus the study on the main key findings.

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: Sharma, et al. report the characterization of the polar tube (PT) from the microsporidian species, Vairimorpha necatrix, using a combination of optical microscopy, cryo-ET, and proteomics. The polar tube is a fascinating invasion apparatus which mediates the translocation of the parasite into the inside of a host cell to initiate infection. Similar to results obtained previously in other species, the authors show that PT firing in Vairimorpha necatrix is extremely fast, occurring on the order of 1 sec, and that the extruded PT is over 100 microns long in this species. Using cryo-ET to image the PT at a high resolution, they find that it exists in two major states: both an empty state and a state filled with cargo, and that the thickness of the tube wall changes when cargo is present. Strikingly, the authors observed that one of the cargo components, the ribosomes, are organized ordered array that may have helical symmetry. Finally, the authors took advantage of a naturally occurring "His tag" on PTP3 to affinity purify PTP3-containing protein complexes and analyze the composition using proteomics.

Major comments

ln 139-140: The absolute handedness of something can be very tricky to determine by cryo-ET (but certainly is possible). Variable hardware configurations between microscopes and differing conventions between software packages (e.g., for what direction is a positive tilt angle) can lead to inversion of the apparent handedness in the final tomogram. How certain are the authors that the absolute handedness is indeed right handed, as this seems to vary between the various display items in the manuscript? For example, in Fig 1c, my impression is that ribosome helices are left handed, as they are also in the supplemental movie. If this isn't known with certainty, perhaps it would be sufficient to describe the apparent helical symmetry but state that the handedness is ambiguous.

Minor comments

ln 39-40: Perhaps also cite the E. cuniculi genome paper?

ln 97-98: It is interesting that the PT shortens in V. necatrix as well, and while I can pick this out in some of the individual traces in Sup Fig. 1b, it seems to get washed out in the trend line and isn't super obvious. If it isn't to laborious, it could be nice to add a panel showing the quantification of this (e.g., plotting the final length of each PT as a percentage of the maximum length achieved).

ln 98-100: Strictly speaking, I don't think the referenced figure shows the sporoplasm being transformed into an extended conformation, only that it is spherical upon exit. Simply reword this to make clear that the deformations are inferred to occur but not directly observed.

Because PT firing is so fast, the probability of trapping a PT in the process of transporting cargo would be pretty low. So then why does the PT still contain cellular cargo like ribosomes inside in the tomograms? Should these not have emerged in the sporoplasm which would enter the host cell? Are these "defective" spores that have failed to complete sporoplasm transport? Perhaps this is worth discussing.

ln 118: The authors note an apparent correlation between the phase of germination and the thickness of the tube wall but don't specify what this correlation is. Is it thicker in the early phase and thinner in later phase, or vice versa? One could imagine "empty" tubes existing before or after sporoplasm transport, for example, so I'm not sure I follow how the phase is being inferred from the tomograms.

ln 119-120: What is the evidence that the outer layer is made of PTPs, or that it is even protein (for example, as opposed to cell wall-like carbohydrate polymers)? I think this seems like a very reasonable hypothesis, but I would suggest explaining the logic and ensuring the degree of uncertainty is conveyed clearly. In light of this, I would also suggest changing figure labels, etc, that refer to the PTP layer (e.g., Fig. 3, PTPc and PTPe labels).

ln 121, 123: "sheathed by a thin layer" and "enveloped by a thick outer layer": is this an additional layer being described? Or is this referring to the putative PTP layer, and that its thickness is variable?

ln 125-126: While I understand how some features, like ribosomes, proteasomes, and generic membrane compartments could be identified, it is unclear to me how one would recognize the nucleus when inside the PT, nor are any examples shown. If the data is clear, perhaps the authors could show it in a figure? Otherwise, I suggest removing the claim regarding the nucleus.

The arrangement of the ribosomes in a subset of tubes is really fascinating! While the number of observations is relatively small (n=5), it seems like it should be possible to comment preliminarily on whether there is much variability in their helical arrangement. Do the helical parameters vary much between observations? Does the til, pitch, etc vary much, are the 5 occurrences very similar? Is there any sign that they are associated with a membrane? Also, since the ribosomes form a lattice-like arrangement, it seems like it would be possible to trace ribosome helices in both the left and right handed directions. How did the authors decide between the two possibilities? This doesn't seem to be discussed.

Fig. 2e: Are the two different colors/orientations meant to represent the two protamers of the ribosome dimer? When refined subvolumes are mapped back onto the original tomogram do the authors observe a similar crystalline arrangement of particles as in their segmentation? Are the orientations of the ribosomes correlated, and do the provide any evidence for the dimeric arrangement mentioned? The PlaceObjects plugin for Chimera can be very helpful for visualizing this: https://www.biochem.mpg.de/7939908/Place-Object

Supp figure 4(b-d): Perhaps these models could be colored by pLDDT scores (with a key indicating the color scheme), so the reader can assess the quality of the predictions?

How were the measurements of the membrane thickness and putative PTP layer carried out? On the tomogram projections? STAs? How were the boundaries of the layers established (e.g., map threshholding if STA?)? This information appears to be missing from the methods.

Some tubes that are labeled as 'PTempty' actually contain cargo and look dense (example supp. Fig 2c, left and middle panels). Is it fair to classify these as empty tubes?

Fig. 3d: I am not entirely clear on what is being shown here. Are independent reconstructions of PTcargo and PTempty superposed (aligned on membrane)? The description in the figure legend doesn't clearly say what is being displayed. I think it might be more clear to show these side-by-side instead of superposed (i.e., 4 panels instead of 2).

Sup Fig 1: Define S and SP in legend or just spell out on figure? Missing x-axis label on panel b.

Fig. 4b and Sup Fig 2a: The depictions of the PT in the spore here are left-handed. In a few species, the coil of the PT was found to form a right-handed helix (Jaroenlak, et al.), and it seems plausible that this may be a general feature that would be conserved across microsporidia. I appreciate that it might not be actually known to be right-handed in V. necatrix, but if there is no strong data either way, perhaps it would make sense for these depictions of the PT to be right-handed.

I think all three of us are more or less in consensus about this manuscript, and I largely agree with the other reviewers comments. I think after addressing reviewer suggestions, this will be a pretty nice story.

Significance

Overall, this manuscript from Sharma, et al. presents interesting new findings about the structure and cargo transport function of the microsporidian PT. Microsporidia infect a wide range of hosts, including humans, and how the PT mediates parasite entry into cells is poorly understood. The approaches used in this study are appropriate for tackling the questions at hand, and appear to be generally well executed and interpreted. The observation that ribosomes assemble into an array within the PT is very unexpected and quite fascinating, and may be of broader interest to researchers working on ribosome structure and function, in addition to researchers studying microsporidia. The approach to investigating proteins interacting with PTP3 was quite elegant, and yielded a list of potential interactors that appears to be of very high quality and is highly plausible based on the literature field. We think this work is a substantial advance in the field and provides important new insights into the organization of the PT. - Please define your field of expertise with a few keywords to help the authors contextualize your point of view:

Structural biology, microsporidia - Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

We are not experts in proteomics/mass spectrometry

reply to u/Wooden-School-4091 at https://www.reddit.com/r/Zettelkasten/comments/172ujnk/should_i_use_zettelkasten/

This comes up fairly frequently. See https://hypothes.is/users/chrisaldrich?q=tag%3A%27zettelkasten+for+studying%27 and related links for other variations and advice on this theme.

ja. fehler:

In Bayern hat sich eine konservative Mehrheit gebildet, die 67,4 Prozent der abgegebenen Stimmen erhalten hat: CSU (die Union wird hier mitgerechnet unter zumindest potentiell noch konservativ, die Red.), Freie Wähler und AfD.

die einzige "konservative" partei ist die AFD, alle anderen sind betrüger ("conservatives in name only").

aber die AFD war noch nie teil einer regierung, sonst würde man sehen: auch die AFD kann nichts ändern, weil alle entscheidungen kommen von oben, und politiker verkaufen diese entscheidungen nach unten, wie in einer dauerwerbesendung. deswegen, auch die AFD ist nur "controlled opposition".

wenn politiker wirklich mal rebellieren gegen "oben", dann wird die zentralbank einfach die geldzahlungen einstellen, und am nächsten tag ist die regierung pleite. schon heute sind alle regierungen pleite, und leben von kredit von der zentralbank.

Gebt mir die Kontrolle über die Währung einer Nation, und es ist mir gleichgültig, wer die Gesetze macht.

-- Amschel Mayer Rothschild

deswegen: politik ist zeitverschwendung. aktivismus heisst selbsthilfe, selbstorganisation, kleinstaaten.

Reviewer #1 (Public Review):

Summary:<br /> 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:<br /> - A chronological immunofluorescent assessment of GR in fetal and early life oocyte and sperm development.<br /> - RNA seq data that reveal novel cell type specific isoforms validated by q-RT PCR E15.5 in the oocyte.<br /> - 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<br /> - 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.<br /> - They exclude oocyte-specific GR signaling inhibition via beta isoforms.<br /> - 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:<br /> - The presence of a protein cannot be entirely excluded based on IF data (staining of spermatids is referred to but not shown).<br /> - The authors do not consider post-transcriptional level a) modifications also trigged by GR activation b) non-coding RNAs (not assessed by seq).<br /> - 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, The number of replicates in the low input seq is very low and hence this might be underpowered. Since Dex treatment showed some (modest) changes in oocyte RNA - effects of GR depletion might only become apparent upon Dex treatment as an interaction.<br /> - Effects in oocytes following systemic Dex might be indirect due to GR activation in the soma.<br /> - 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.

The conclusion that fetal oocytes are "intrinsically buffered to GR signalling" 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.

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. Also, the study does not assess epigenetic modifications.

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.

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.

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. Since the authors comment on RNA-mediated inheritance it seems inevitable to again consider indirect effects.

The impact of free international financial markets on East Asia, as viewed through the lens of this section's framework, is complex. On one hand, these markets have stimulated economic growth in the region by facilitating investment and access to global capital, which has the potential to positively affect individuals' financial wealth and physical assets. However, the benefits have not been equally distributed, contributing to growing income disparities. According to SSRN, "growth-promoting economic freedoms hamper future progress by raising inequalities." The disparities are rooted in differences in education, gender, and social class, influenced by institutions and technology.

Therefore, while free international financial markets have brought economic opportunities to East Asia, they have also exacerbated income inequality, highlighting the need to address these disparities by considering the interplay between institutions, technology, and individual endowments to achieve a more balanced outcome in the region.

Works Cited: Ilkay Yılmaz , Mehmet Murat Balkan , Mehmet Nasih Tağ Income Inequality and Economic Freedom: The 2000s

$18 /LOC

Author Response

Reviewer #1 (Public Review):

The manuscript by Royall et al. builds on previous work in the mouse that indicates that neural progenitor cells (NPCs) undergo asymmetric inheritance of centrosomes and provides evidence that a similar process occurs in human NPCs, which was previously unknown.

The authors use hESC-derived forebrain organoids and develop a novel recombination tag-induced genetic tool to birthdate and track the segregation of centrosomes in NPCs over multiple divisions. The thoughtful experiments yield data that are concise and well-controlled, and the data support the asymmetric segregation of centrosomes in NPCs. These data indicate that at least apical NPCs in humans undergo asymmetric centrosome inheritance. The authors attempt to disrupt the process and present some data that there may be differences in cell fate, but this conclusion would be better supported by a better assessment of the fate of these different NPCs (e.g. NPCs versus new neurons) and would support the conclusion that younger centriole is inherited by new neurons.

We thank the reviewer for their supportive comments (“…thoughtful experiments yield data that are concise and well-controlled…”).

Reviewer #2 (Public Review):

Royall et al. examine the asymmetric inheritance of centrosomes during human brain development. In agreement with previous studies in mice, their data suggest that the older centrosome is inherited by the self-renewing daughter cell, whereas the younger centrosome is inherited by the differentiating daughter cell. The key importance of this study is to show that this phenomenon takes place during human brain development, which the authors achieved by utilizing forebrain organoids as a model system and applying the recombination-induced tag exchange (RITE) technology to birthdate and track the centrosomes.

Overall, the study is well executed and brings new insights of general interest for cell and developmental biology with particular relevance to developmental neurobiology. The Discussion is excellent, it brings this study into the context of previous work and proposes very appealing suggestions on the evolutionary relevance and underlying mechanisms of the asymmetric inheritance of centrosomes. The main weakness of the study is that it tackles asymmetric inheritance only using fixed organoid samples. Although the authors developed a reasonable mode to assign the clonal relationships in their images, this study would be much stronger if the authors could apply time-lapse microscopy to show the asymmetric inheritance of centrosomes.

We thank the reviewer for their constructive and supportive comments (“…the study is well executed and brings new insights of general interest for cell and developmental biology with particular relevance to developmental neurobiology….”). We understand the request for clonal data or dynamic analyses in organoids (e.g., using time-lapse microscopy). We also agree that such data would certainly strengthen our findings. However, as outlined above (please refer to point #1 of the editorial summary), this is unfortunately currently not feasible. However, we have explicitly discussed this shortcoming in our revised manuscript and why future experiments (with advanced methodology) will have to do these experiments.

Reviewer #3 (Public Review):

In this manuscript, the authors report that human cortical radial glia asymmetrically segregates newly produced or old centrosomes after mitosis, depending on the fate of the daughter cell, similar to what was previously demonstrated for mouse neocortical radial glia (Wang et al. 2009). To do this, the authors develop a novel centrosome labelling strategy in human ESCs that allows recombination-dependent switching of tagged fluorescent reporters from old to newly produced centrosome protein, centriolin. The authors then generate human cortical organoids from these hESCs to show that radial glia in the ventricular zone retains older centrosomes whereas differentiated cells, i.e. neurons, inherit the newly produced centrosome after mitosis. The authors then knock down a critical regulator of asymmetric centrosome inheritance called Ninein, which leads to a randomization of this process, similar to what was observed in mouse cortical radial glia.

A major strength of the study is the combined use of the centrosome labelling strategy with human cortical organoids to address an important biological question in human tissue. This study is similarly presented as the one performed in mice (Wang et al. 2009) and the existence of the asymmetric inheritance mechanism of centrosomes in another species grants strength to the main claim proposed by the authors. It is a well-written, concise article, and the experiments are well-designed. The authors achieve the aims they set out in the beginning, and this is one of the perfect examples of the right use of human cortical organoids to study an important phenomenon. However, there are some key controls that would elevate the main conclusions considerably.

We thank the reviewer for their overall support of our findings (“..authors achieve the aims they set out in the beginning, and this is one of the perfect examples of the right use of human cortical organoids to study an important phenomenon…”). We also understand the reviewer’s request for additional experiments/controls that “…would elevate the main conclusions considerably.”

1) The lack of clonal resolution or timelapse imaging makes it hard to assess whether the inheritance of centrosomes occurs as the authors claim. The authors show that there is an increase in newly made non-ventricular centrosomes at a population level but without labelling clones and demonstrating that a new or old centrosome is inherited asymmetrically in a dividing radial glia would grant additional credence to the central conclusion of the paper. These experiments will put away any doubt about the existence of this mechanism in human radial glia, especially if it is demonstrated using timelapse imaging. Additionally, knowing the proportions of symmetric vs asymmetrically dividing cells generating old/new centrosomes will provide important insights pertinent to the conclusions of the paper. Alternatively, the authors could soften their conclusions, especially for Fig 2.

We understand the reviewer’s request. As outlined above (please refer to point #1 of the editorial summary), we had tried previously to add data using single cell timelapse imaging. However, due to the size and therefore weakness of the fluorescent signal we had failed despite extensive efforts. According to the reviewer’s suggestion we have now explicitly discussed this shortcoming and softened our conclusions.

2) Some critical controls are missing. In Fig. 1B, there is a green dot that does not colocalize with Pericentrin. This is worrying and providing rigorous quantifications of the number of green and tdTom dots with Pericentrin would be very helpful to validate the labelling strategy. Quantifications would put these doubts to rest. Additionally, an example pericentrin staining with the GFP/TdTom signal in figure 4 would also give confidence to the reader. For figure 4, having a control for the retroviral infection is important. Although the authors show a convincing phenotype, the effect might be underestimated due to the incomplete infection of all the analyzed cells.

We have included more rigorous quantifications in our revised manuscript.

For Figure 1: There are indeed some green speckles that might be misinterpreted as a green centrosome. However, the speckles are usually smaller and by applying a strict size requirement we exclude speckles. To check whether the classifier might interpret any speckles as centrosomes, we manually checked 60 green “dots” that were annotated as centrosome. From these images all green spots detected as centrosome co-localized with Pericentrin signal (Images shown in Author response image 1).

For Figure 4: as we are comparing cells that were either infected with a retrovirus expressing scrambled or Ninein-targeting shRNA we compare cells that experienced a similar treatment. Besides that, only cells infected with the virus express Cre-ERT2 whereby only the centrosomes of targeted cells were analyzed. Accordingly, we only compare cells expressing scrambled or Ninein-targeting shRNA, all surrounding “wt” cells are not considered.

Author response image 1.

Pictures used to test the classifier. Each of the green “dots” recognized by the classifier as a Centriolin-NeonGreen-containing centrosome (green) co-localized with Pericentrin signal (white).

3) It would be helpful if the authors expand on the presence of old centrosomes in apical radial glia vs outer radial glia. Currently, in figure 3, the authors only focus on Sox2+ cells but this could be complemented with the inclusion of markers for outer radial glia and whether older centrosomes are also inherited by oRGCs. This would have important implications on whether symmetric/asymmetric division influences the segregation of new/old centrosomes.

That is an interesting question and we do agree that additional analyses, stratified by ventricular vs. oRGCs would be interesting. However, at the time points analysed there are only very few oRGCs present (if any) in human ESC-derived organoids (Qian et al., Cell, 2016). However, we have now added this point for future experiments to our discussion.

Reviewer #1 (Public Review):

The manuscript by Royall et al. builds on previous work in the mouse that indicates that neural progenitor cells (NPCs) undergo asymmetric inheritance of centrosomes and provides evidence that a similar process occurs in human NPCs, which was previously unknown.

The authors use hESC-derived forebrain organoids and develop a novel recombination tag-induced genetic tool to birthdate and track the segregation of centrosomes in NPCs over multiple divisions. The thoughtful experiments yield data that are concise and well-controlled, and the data support the asymmetric segregation of centrosomes in NPCs. These data indicate that at least apical NPCs in humans undergo asymmetric centrosome inheritance. The authors attempt to disrupt the process and present some data that there may be differences in cell fate, but this conclusion would be better supported by a better assessment of the fate of these different NPCs (e.g. NPCs versus new neurons) and would support the conclusion that younger centriole is inherited by new neurons.

Reviewer #2 (Public Review):

Royall et al. examine the asymmetric inheritance of centrosomes during human brain development. In agreement with previous studies in mice, their data suggest that the older centrosome is inherited by the self-renewing daughter cell, whereas the younger centrosome is inherited by the differentiating daughter cell. The key importance of this study is to show that this phenomenon takes place during human brain development, which the authors achieved by utilizing forebrain organoids as a model system and applying the recombination-induced tag exchange (RITE) technology to birthdate and track the centrosomes.

Overall, the study is well executed and brings new insights of general interest for cell and developmental biology with particular relevance to developmental neurobiology. The Discussion is excellent, it brings this study into the context of previous work and proposes very appealing suggestions on the evolutionary relevance and underlying mechanisms of the asymmetric inheritance of centrosomes. The main weakness of the study is that it tackles asymmetric inheritance only using fixed organoid samples. Although the authors developed a reasonable mode to assign the clonal relationships in their images, this study would be much stronger if the authors could apply time-lapse microscopy to show the asymmetric inheritance of centrosomes.

Homeless people are actually getting their basic necessities covered and it helps with better hygiene that other people living in those cabins and camps desperately needed.

Author Response

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

We thank the reviewers for their thoughtful assessment of our work and their valuable critiques which we will address in the “Recommendations for the authors” section below. In particular, we appreciate Reviewer #3 noting the value of the C. elegans model system and our efforts to bridge models with our study. We agree with the reviewer that there is a need to clarify the rationale, presentation and interpretation of our results. We have substantially revised the text in our manuscript and Figure legend to address this issue, and provided extensive new commentary and citations to lay out the logic behind our experiments. Indeed, it was our oversight not being more thorough about this initially. We have further adjusted our conclusions to be less unequivocal. Finally, we added an RPM-1 signaling diagram (Fig. 8A) to more clearly annotate the players in the RPM-1/MYCBP2 signaling network that were evaluated genetically in Fig. 8. Importantly, we provide clearer commentary on how genetic enhancer effects with known RPM-1 binding proteins and the absence of genetic suppression in vab-1/Eph receptor double mutants with components of the RPM-1/FSN-1 ubiquitin ligase complex are consistent with the biochemical finding that MYCBP2 stabilizes but does not degrade EphB2. Text edits reflecting these points are in the abstract, the C. elegans results section starting on line 411, and the discussion on lines 499, 502-504 and 541.

Following extensive discussions between the three reviewers, all three agree that the C. elegans data, as presented, does not add to, and in fact might harm, your bottom line. Our combined suggestion is to take this data out unless you plan to improve it substantially. All reviewers are perplexed by Figure 2F and the presumed interactions of cytosolic proteins with the extracellular domain of EPHB2. At the very least, please provide some suggestions/model/interpretation.

We have adjusted our manuscript substantially to address this. Please see detailed comments in the individual Reviewer sections below.

We would like to thank the reviewers for their thorough examination of our manuscript, constructive criticisms, and helpful suggestions.

Reviewer #1 (Recommendations For The Authors):

The work is extensive in my view, and mostly of high quality. See minor comments on some of the figures below.

Thank you very much.

Two more major comments :

• I don't think the C. elegans work adds to - in fact I think it hurts - the statement that this regulatory mechanism is specific to EphB2. I would advise the authors to take it out.

We agree that C. elegans has a sole Eph receptor called VAB-1 and is therefore not a specific model for EPH2B. However, testing MYCBP2 specificity for EPHB2 was not the goal or our perceived value for the C. elegans experiments. We now clarify this in the text of the Results section.

Rather, we are providing evidence that the C. elegans ephrin receptor interacts genetically with known MYCBP2/RPM-1 binding proteins. Moreover, we now provide an extensive array of citations to note that genetic enhancer interactions between different RPM-1/MYCBP2 binding proteins is well established. The reviewer has nicely highlighted for us that we handled the C. elegans genetics in too cursory a fashion in our original manuscript. We appreciate this being noted and have now aimed to make this substantially clearer. We hope the reviewer agrees that our revised C. elegans section accomplishes this goal.

Furthermore, we extensively revised the text of the Results to emphasize a key point: our observation that axon termination defects are not suppressed in vab-1; fsn-1 and vab-1; rpm-1 double mutants excludes the possibility that the VAB-1 Eph receptor is a substrate that is inhibited or degraded by the RPM-1/FSN-1 ubiquitin ligase complex. If the VAB-1 Eph receptor were ubiquitinated and degraded by the RPM-1/FSN-1 complex, we would have observed a suppression of phenotype in vab-1; rpm-1 double mutants. The precedent for this genetic relationship between the RPM-1 ubiquitin ligase and its substrates that are degraded has been established by several prior studies (PMID: 15707898; PMID: 31676756; PMID: 35421092). We now more clearly note that the absence of genetic suppression in vab-1; rpm-1 double mutants and vab-1; fsn-1 double mutants is consistent with the non-canonical stabilizing role of MYCBP2 on EPHB2 that was observed in our biochemical experiments with mammalian cells.

We also adjusted the text of the manuscript to stress that we are testing genetic interactions between the VAB-1 Eph receptor and known RPM-1 binding proteins. This is a key point, as genetic enhancer interactions are consistent with the Eph receptor functioning in the RPM-1 signaling network. This concept has been well established for RPM-1 binding proteins as now noted in our revised text with an extensive number of additional citations to published work.

Based on the above arguments, we respectfully disagree with the reviewer that our C. elegans data should be removed from the paper. To re-iterate, we are not trying to evaluate specificity for MYCBP2 and EPHB2 in C. elegans. Rather, our goals are twofold: 1) To ask whether there is an evolutionarily conserved functional genetic link between Eph receptors and known RPM-1 binding proteins. 2) To provide further in vivo genetic evidence invalidating the hypothesis that Ephrin receptors could be ubiquitination substrates that are inhibited/degraded by MYCBP2.

Text edits reflecting these points are in the abstract, the C. elegans results section starting on line 411, and the discussion on lines 499, 502-504 and 541.

• The cellular responses are not robust and the effects of MYCBP2 KO - although significant - are minor in most cases. But I don't think more experiments will help here.

We interpret the comment about the robustness to mean that the extent to which a given cellular response is affected by the loss of MYCBP2 is minor. First, the cellular responses themselves are typical of previous studies and depend on the cellular biology underlying them. For example, a growth collapse of ~50-60% over a background of 10% (Fig. 7) is typical for these sorts of assays (PMID: 37369692; PMID: 33972524; PMID: 17785182). A decrease of cell area by ~25% (Fig. 3) is quite substantial if one considers how much of a cell’s volume is taken up by the nucleus and organelles. Second, the phenotypes elicited by the loss of MYCBP2 are likely brought on by a decrease in EphB2 protein levels, but not its complete absence, as suggested by our biochemical experiment. Given that EphB2 complete loss only affects the cellular responses to a limited extent, the minor effects are not a surprise (e.g. for GC collapse: PMID: 23143520). Nevertheless, the subtle changes in cellular phenotypes, elicited by EPHB2 signaling are often sufficient to achieve proper cell positioning and cell response to guidance cues. For instance, regulation of the growth cone collapse of the outgrowing axons requires delicate changes that are dynamic and temporal.

Minor:

Fig 1C - EPHA3 and EPHB2 seem to run in different sizes, is this the case? In 2A they run at the same size.

We believe this size discrepancy is due to different percentages of SDS-PAGE gels used to resolve proteins. In Fig. 1C, we used a 6% gel for a Western blot analysis of both EPHA3/-B2-FLAG (~130 kDa) and MYCBP2 (~510 kDa). In Fig. 2A however, we performed Western blot analysis using 10% resolving gel to separate and detect EPHA3/-B2-FLAG along with MYC-FBXO45 (~30 kDa). We have reviewed the results obtained from additional biological replicates of this experiment, and observed a similar pattern in gel migration of EPHA3/-B2-FLAG across all replicates.

Fig1F - I can't trust the MYCBP2 blot.

Indeed, the MYCBP2-EPHB2 co-IP with endogenous proteins was not convincing. We now repeated this experiment using rat cortical neurons, and the results replace the previous Fig. 1F panel as mentioned on line 158.

In Fig2b the authors claim that there is enhancement in the binding of MYCBP2 and EPHB2 upon FBXO45 expression. For this type of statement quantification is required.

The quantification is now included in Fig. 2C and its significance is mentioned on line 180. Our conclusion about the enhancement stands.

Fig2G - it remained unclear to me where the binding site to MYCBP2 is, how long is the cytoplasmic tail in the DeltaICD protein?

Based on our experimental observations from Fig. 2E-H, we concluded that the fragment encompassing the extracellular domain(s) and/or transmembrane (TM) domain of EPHB2 is necessary for the protein complex formation with MYCBP2. We would like to accentuate that the EPHB2-MYCBP2 interaction might not be direct, and might involve other transmembrane protein(s) acting as a scaffold for EPHB2 and MYCBP2 binding. We did not pursue experiments to determine the exact region of the extracellular-TM portion of EPHB2 that is required for the interaction with MYCBP2.

The cytoplasmic tail in ΔICD protein consists of 25 aa of the N-terminal fragment of EPHB2 juxtamembrane (JM) region, which is adjacent to the TM helix, and followed by the 8 aa FLAG tag (EPHB2 ΔICD domain composition: extracellular domains – TM domain – 25 aa fragment of JM region – FLAG). We have determined the TM and JM sequences based on Hedger et al. (PMID: 25779975) and included the N-terminal portion of the JM region to facilitate proper ΔICD protein localization within the plasma membrane (PMID: 35793621). We modified the schematic in Fig. 2G to better visualise the EPHB2 truncations and now provide information on their size in the figure legend.

Always good to have a model of how all these proteins work together.

While we acknowledge that this would be helpful, we do not have a clear answer on how the EPHB2-MYCBP2 complex formation occurs. This requires further elucidation of the putative proteins involved in this ternary complex or testing the possibility that a MYCBP2 fragment is extruded extracellularly. Without these experiments there are too many possibilities to summarise into a clear model figure. We thus did not make any edits regarding these possibilities in the section starting on line 195.

Reviewer #2 (Recommendations For The Authors):

Overall, the experiments are classical experiments of co-immunoprecipitations, swapping experiments, collapse assays, and stripe assays which all are well carried out and are convincing.

Thank you for your encouraging comments.

Controls for the stripe assay may include Fc / Fc stripe assays.

We have performed these control experiments and now include their quantifications in the results sectioning concerning Fig. 3, starting on line 249, and those concerning Fig. 6 on line 381.

It is not clear to me why SD and not SEM has been used here for presentations.

Standard deviation (SD) measures the dispersion of a dataset relative to its mean. The standard error of the mean (SEM) measures how much discrepancy is likely in a sample’s mean compared with the population mean. Thus, SEM includes a statistical inference about the sampling distribution while SD is a less “processed” measurement that by definition is larger than SEM. SEM might make the data look less dispersed and many journals encourage the use of SD in bar graphs (PMID: 16223828).

Fig 7A: it is rather difficult to see 'branches' in Fig. 7A, better pictures and close-ups should be provided. How are branches defined? This piece of work needs more attention.

To remedy this shortcoming, we now provide inverted images with GFP signal in dark pixels overlaid on Fc (white) / eB2 (pink) stripes next to the original images.

Reviewer #3 (Recommendations For The Authors):

1) My most important suggestion to the authors would be to more carefully describe the results and their interpretation of the results. Sometimes, the distinction is not clear.

We modified the text throughout the manuscript to address this.

2) There are several cases, when the authors report on trends that are not statistically significant (1D, for example), or report no change, when it is clear that the addition of one more sample could have dramatically made a difference (4M - see point 12).

We agree that some of the nonsignificant differences could become significant if we added more Ns. But we prefer not to move our experimental design towards N-chasing and p-hacking (PMID: 25768323). The number of biological replicates is normally pre-determined before the onset of the experiment. Of course, some replicates can be discarded if there is a valid reason, such as a technical issue with the experiment or a positive control not working but this is not relevant for the dataset we have provided.

3) Data in 1F is very difficult to interpret.

As in response to Reviewer #1: Indeed, the MYCBP2-EPHB2 co-IP with endogenous proteins was not convincing. We now repeated this experiment using rat cortical neurons, and the improved results are in revised Fig. 1F.

4) Figure 2 puts Figure 1 in a strange perspective. If I understand correctly, fig 2 claims that EPHB2 interaction with MYCBP2 depends on FBXO45 - if that is the case then how does the binding in Figure 1 occur?

Indeed, we propose that the EPHB2-MYCBP2 interaction depends on FBXO45. In Fig. 2, we reveal that FBXO45 enhances the formation of the EPHB2-MYCBP2 complex. Thus, we suspect that the endogenous FBXO45 present in HeLa cells and neurons would mediate the interaction between EPHB2 and MYCBP2 in Fig. 1 experiments. We were unable to show this by Western blotting due to lack of reliable commercial antibodies against FBXO45, the complex containing endogenous FBXO45 and EPHB2 is also implied by our AP-MS data (Fig. 1B) and published databases.

5) I am still trying to wrap my mind around the results in 2G-H. So do MYCBP2 and FBXO45 bind the extracellular domain of EPHBP2? What does that mean?

(see also our response to Reviewer #1, end of their section) Based on our experimental observations from Fig. 2G-H, we conclude that the fragment encompassing the extracellular domain(s) and/or transmembrane domain of EPHB2 is necessary for the protein complex formation with MYCBP2 and FBXO45. Although there is a possibility that MYCBP2 directly binds the extracellular portion of EPHB2, we have not formally tested this hypothesis. MYCBP2 has been previously shown to interact with the extracellular portion of transmembrane N-cadherin (CDH2) via BioID proximity labeling and AP-MS proteomics approaches (PMID: 32341084).

Considering the results in Fig. 2A-B, we suspect that EPHB2-MYCBP2 interaction is indirect, as FBXO45 enhances this association. Secretion of FBXO45 and direct binding of FBXO45 to the extracellular cadherin (EC1-2) domains of N-cadherin has been documented (PMID: 25143387; PMID: 32341084). Although, not tested, this is also a possibility for EPHB2-FBXO45 mode of interaction. Nevertheless, we also cannot rule out the possibility that an unknown transmembrane protein binds EPHB2 extracellularly and the same unknown protein binds MYCBP2/FBXO45 intracellularly. Resolving this model is beyond the scope of this study and will require us to pursue extensive new lines of investigation.

6) I don't understand the stable Hela cell line CRISPR - is this a stable MYCBP2 deletion? In which case why is there only a reduction, not complete elimination of the protein? Or, is this a stable integration of a plasmid generating gRNA against MYCBP2? In which case, I would expect a homozygous null to emerge at some point. In any case, this is not well explained.

These lines are not derived from single cells infected with the CRISPR sgRNA-carrying viruses, therefore they are not clonal and probably contain some cells that express normal levels of MYCBP2, hence its detection on a Western. This is now clarified starting on line 221 and on line 608.

7) In 3C - is this the right statistical analysis?? I would say you want to claim the different effect of the control +/- eB2 compared to the effect in the mutant +/- eB2. Still should be significant but I think a more correct analysis.

We now include this comparison in Fig. 3C as well in the results section starting on line 234.

8) The robustness of the assay in Figure 3D is underwhelming – how was the area measured?

This is a live imaging experiment. Fig. 3D plots cell area at 60 minutes after ephrin-B2 addition as a fraction of the same cell’s area at 0 minutes (ephrin-B2 addition). For control cells that is a decrease of ~25%. If one considers that a cell’s nucleus and organelles like the Golgi Apparatus take up most of its volume, the magnitude is not that surprising.

9) Figure 3F – did you try to plot the relative area of overlap divided by the total cellular area? You might get a more striking phenotype. Also – claiming that this confirms that MYCBP2 is REQUIRED for EPHB2 function is a bit overstated, especially given that we don’t know (do you?) the EPHB2 mutant phenotype in this assay.

We preferred to stay with the original method of image quantification which we use for other assays. With respect to the requirement of MYCBP2 for EPHB2 function in the stripe assay, our logic is rooted in the observation that native HeLa cells do not respond to ephrin-B2 stripes (45.46 ± 7.62% of cells on eB2 stripes v. Fc; data not shown). When they are transfected with EPHB2 expression plasmids they do, therefore we assume that EPHB2 expression endows them with a sensitivity to eB2 stripes. A loss of MYCBP2 attenuates this sensitivity. We clarified this starting on line 246 and on line 251.

10) I didn't quite get the difference between 4A and 4B.

We apologize for the confusion. In Fig 4A, we used a stable HeLa cell line that has tetracycline-inducible expression of EPHB2-FLAG. Using these cells, we subsequently generated CTRLCRISPR or MYCBP2CRISPR cells. In these cells we then induced EPHB2 expression with tetracycline and observed that deletion of MYCBP2 resulted in the reduction of EPHB2 protein levels. To confirm this observation and to rule out the possibility that EPHB2 protein reduction is an effect of the CRISPR lines generation, we tested whereas MYCBP2 deletion reduces EPHB2, which has been transiently overexpressed (Fig. 4B). We hence conclude that loss of MYCBP2 decreases EPHB2 that was either expressed from a stable locus (Fig. 4A) or from transient transfection (Fig. 4B). We modified the Results section starting on line 262 to make this point clear.

11) The entire link to lysosomal degradation should be strengthened. Perhaps I am confused, but if the reduced EPHB2 levels in MYCBP2 mutant cells result from impaired lysosomal degradation then inhibiting the lys-deg should bring the protein levels back to normal (i.e. CRISPR control) - no? As currently presented, I do not understand nor do I think the claim is strongly supported by the data.

Before treatment with inhibitors, EPHB2 levels in MYCBP2CRISPR cells are already 40% lower than they are in CTRLCRISPR cells and in all our attempts, inhibitors can only rescue/restore EPHB2 in MYCBP2CRISPR cells to a level that is lower than in CTRLCRISPR cells. But this restoration is greater in MYCBP2CRISPR than in MYCBP2CTRL cells (BafA1: 19% increase in CTRL cells and 40% in MYCBP2CRISPR cells; CoQ: 10% comparing to 35%). This indicates that EPHB2 degradation through the lysosomal pathway in MYCBP2CRISPR cells is stronger, explaining why EPHB2 degradation is promoted in MYCBP2CRISPR cells, compatible with reduced EPHB2 levels and enhanced EPHB2 ubiquitination.

12) 4M, O - reporting ns based on these data seems a bit strange to me... Add one point and it will be strongly significant.

See our response to point (2), above. We prefer not to invoke potential p-hacking.

13) 7d - so what are you claiming? That the cellular response to eB1 but not eB2 is affected by the addition of FBD1? this is almost the opposite of what you wrote in the text...

We treated the cells with two different ephrin-B ligands to make a stronger conclusion. When using ephrin-B1, growth cone collapse in FBD1 WT is not significant comparing to Fc treatment. When using ephrin-B2, growth cone collapse in FBD1 WT is not as significant as it is in FBD1 mut group (* versus ). We interpret this as meaning that the EPHB2-mediated growth cone collapse to both ligands is dampened, when we disrupt the EPHB2-MYCBP2 association. The difference between these two ligands might be due to their different affinities for the receptor or signalling kinetics.

14) By far the weakest link in this paper is the worm part. I think it's a pity because strengthening this would affect the significance of the finding. First, the authors mention new genes without introducing their relationship to the signaling pathway tested. Second, the textual logics should be strengthened. Finally and most importantly, when the difference between the phenotypic severity is so strong (vab-1 and rpm-1) then I think it's impossible to say anything from the double mutant.

We appreciate the reviewer noting that they appreciate the value and importance of the C. elegans model. The goals of our C. elegans experiments were twofold:

1) To evaluate genetic interactions between the VAB-1 Eph receptor and known RPM-1 binding proteins. This was not clearly explained in the original manuscript nor was the published precedent for these types of genetic enhancer experiments provided. We have now rectified this by substantially revising the text of the Results C. elegans section starting on line 431 and by adding several citations.

2) Our C. elegans genetics confirmed that the VAB-1 Eph receptor is not inhibited/degraded by the RPM-1/MYCBP2 ubiquitin ligase complex. We have now revised the text to draw this point out more clearly.

To further address the reviewer’s concerns, we have added a new schematic (Fig. 8A) to show the relationship between the RPM-1 and the RPM-1 binding proteins (FSN-1/FBXO45 and GLO-4/SERGEF) we are testing. We chose FSN-1 because it is part of the RPM-1 ubiquitin ligase complex and we chose GLO-4 because it functions outside the context of RPM-1 ubiquitin ligase signaling via the GLO-1 Rab GTPase to influence late endosomal/lysosomal biogenesis.

Regarding the reviewer’s concern that different penetrance/frequency of defects between rpm-1 mutants and vab-1 mutants means outcomes with vab-1; rpm-1 double mutants cannot be interpreted. We respectfully disagree. An extensive number of published studies have demonstrated that RPM-1 binding proteins have milder phenotypes than rpm-1 mutants and display genetic enhancer effects as double mutants with one another (PMID:17698012, PMID: 22357847, PMID: 25010424, PMID: 24810406). We now make this point much more clearly. While the frequency of axon termination defects in rpm-1 mutants is high it is not completely saturated as the defect is not 100%. Moreover, a major point of the vab-1; rpm-1 double mutants is that they do not have a significant reduction in phenotypic penetrance/frequency. Thus, our system is fully capable of resolving genetic suppression, which did not occur. We now make this point much more carefully and clearly.

To further address the reviewer’s concern, we have softened language about the VAB-1/Eph receptor functioning in the same pathway as RPM-1 throughout the manuscript. While we think this is still the case, because the frequency of axon termination defects is not fully saturated in rpm-1 mutants and defects could potentially become more severe (i.e. the hook might occur closer to the head of the animal rather than in the midbody). Nonetheless, this is not a critical point and we think it is more important to be clear about the two major goals and objectives of our C. elegans experiments. We hope the reviewer agrees that our rationale, logic and conclusions are more clearly and accurately drawn in the revised paper.

• for: BEBE, biologger Ethogram Benchmark

该接口返回当前活动文件内容

We have to admit that people nowadays cannot live without social media. Living in 2023, I believe that social media is no longer a tool to communicate, it is becoming more and more diverse and combines all kinds of functions such as a platform to show people "who they are". I know too many people who are social phobias but are a "stars" on social media. Back then, people like them would be judged as "people who don't live on the Internet", however, no one would tag them as "strange" anymore. Therefore, yes, social media has been included in real life and is making real life better and more diverse.

reply to u/RobThomasBouchard at https://www.reddit.com/r/antinet/comments/16v8tfq/starting_a_blog/

The answer is:<br /> D: Start a "blog" where you post your notes as status updates and interlink them a bit. When you've got enough, you organize them into a mini thesis and write a longer article/blog post about it.

Examples: - https://hypothes.is/users/chrisaldrich?q=tag%3A%22thought%20spaces%22 and - https://indieweb.org/commonplace_book#The_IndieWeb_site_as_a_Commonplace_book

tl;dr: Use your website like a public, online zettelkasten. 🕸️🗃️

test

With the introduction of deepfakes into society it is very important that online users educate themselves on how to properly spot these videos. It would be nice if platforms could tag these videos with warnings but since most don't, it is crucial to fact check each piece of content that may seem suspicious.

this I don't quite understand

Jerry Michalski says that The Brain provides him with a "neighborhood perspective" of ideas when he reduces the external link number for his graph down to 1.

This is similar to Nicholas Luhmann's zettelkasten which provided neighborhoods of related notes based on distance from any particular note.

Also similar to oral cultures who relied on movement through their environment for encoding memories and later remembering them. [I'll use the tag "environmental memory" to track this until a better name comes along.]

reply to u/JonasanOniem at https://www.reddit.com/r/Zettelkasten/comments/16ss0yu/linking_new_notes/

This sort of practice is harder when you start out in most digital apps because there is usually no sense of "closeness" of ideas in digital the way that is implied by physical proximity (or "neighborhood") found in physical cards sitting right next to or around each other. As a result, you have to create more explicit links or rely on using tags (or indexing) when you start. I've not gotten deep into the UI of Zettlr, but some applications allow the numbering (and the way numbered ideas are sorted in the user interface) to allow this affordance by creating a visual sense of proximity for you. As you accumulate more notes, it becomes easier and you can rely less on tags and more on direct links. Eventually you may come to dislike broad categories/tags and prefer direct links from one idea to another as the most explicit tag you could give a note . If you're following a more strict Luhmann-artig practice, you'll find yourself indexing a lot at the beginning, but as you link new ideas to old, you don't need to index (tag) things as heavily because the index points to a card which is directly linked to something in the neighborhood of where you're looking. Over time and through use, you'll come to recognize your neighborhoods and the individual "houses" where the ideas you're working with all live. As an example, Luhmann spent his life working in sociology, but you'll only find a few links from his keyword register/subject index to "sociology" (and this is a good thing, otherwise he'd have had 90,000+ listings there and the index entry for sociology would have been utterly useless.)

Still, given all this, perhaps as taurusnoises suggests, concrete examples may help more, particularly if you're having any issues with the terminology/concepts or how the specific application affordances are being presented.

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:

1. If doable, image dynein and dynactin simultaneously in the Halo-DYNC1H1/DCTN4-SNAP iNeurons. Co-movement of dynein and dynactin towards the somatodendritic compartment and their separate movement in the anterograde direction along the axon would provide the most convincing evidence for the key claims of the manuscript.

Please see the planned revision section for our response

Reviewer #2:

Major comment (requires additional experimentation)

1. While the data presented do certainly suggest that dynein and Lis1 are transported anterogradely on separate vesicular cargoes from dynactin and Ndel1, the study would be much stronger if supported by dual imaging of dynein and dynactin to prove that these proteins do indeed move in association with separate vesicular populations. I would like to see dual-color kymograph traces showing that the proteins move independently. The authors should be able to accomplish this using their dual Halo-DYNC1H1/DCTN4-SNAP hESC line. To acquire and analyze this data might take several months, but it would greatly strengthen this paper. If the authors do this experiment, they may also be able to address the mechanism of reversal of anterograde cargoes which they speculate about in the Discussion, which would add even more interest and insight.

Please see the planned revision section for our response

Minor comments (addressable without additional experimentation)

1. The authors deduce that 1-4 Halo fluorochromes corresponds to 1-2 dynein molecules. This implies that the cells are homozygous for the Halo tag, but I do not see this addressed explicitly. The authors should state explicitly whether the lines generated for their study are heterozygous or homozygous for the tag. If the cells are heterozygous, which would seem most likely, then they may be underestimating the number of dyneins per spot and should take this into account.

We have added whether lines are homozygous or heterozygous to the manuscript. We also include a new Supplementary Figure panel (Fig S6) showing the genotyping data. In summary, all lines are homozygous except for PAFAH1B1-Halo (hESCs) which is heterozygous.

1. Why are the moving spots lower in intensity than the NEM-treated static spots. It appears to suggest that they may be associated with different structures. This should be clarified and discussed.

Our data suggest that the fast-moving spots have fewer dyneins than NEM treated static spots. We suggest this is because the fast-moving cargos are smaller than the average cargo and therefore have fewer dyneins on them. This is also supported by the smaller number of dyneins reported previously on endosomes as compared to the large lysosomes. We have clarified this in the discussion (page 7-8).

1. The authors state in the Results that most of the dynein spots were diffusing, often along microtubules, but they do not visualize microtubules so how do they know this? They may need to remove the phrase "often along microtubules".

This has been removed.

1. At the end of the Introduction the authors state that their data "allow us to understand how the dynein machinery drives long-range transport in the axon". This is an overstatement. The "how" in this sentence is not addressed in this study.

We have softened the sentence by adding the phrase “better understand”.

1. The conclusion that dynein binds to cargos stably throughout their transport along the axon is based on measurements of the fastest moving cargoes but the authors do not provide data on the distribution of velocities for the entire population of retrograde cargoes. It is not valid to extrapolate the behavior of a small number of cargoes to the entire population. The average may be much slower than the fastest cargoes. Moreover, even for the fastest organelles the authors cannot say that the dynein is stably bound because they did not track single cargoes and thus do not know that the cargoes moved continuously in one single bout of movement for 500 µm; it is possible that the cargoes moved in multiple consecutive bouts interrupted by brief pauses and dynein motors may have exchanged between bouts.

We have added a section to the discussion to highlight that other cargos may behave differently from the fastest ones (page 7). We have also clarified the assumptions that lead us to expect a slower arrival time of the first signal (page 5).

1. The authors say that "it is clear that at least some dyneins remain on cargoes throughout their transport along the axon". As explained above, the data do not prove this so this statement should be removed.

We have softened this sentence from “it is clear” to “our results suggest” and explained in more detail why we make this conclusion

1. The authors note that most of the dynein spots were not moving processively and state that this is consistent with prior studies showing that only a subset of dynein is actively involved in transport. However, as they note elsewhere, dynein is both motor and cargo and most axonal dynein is transported at slow average velocities so maybe they should be more explicit about what they mean by "involved in transport".

We have clarified we mean fast axonal transport and thank the reviewer for highlighting this point.

1. When the authors note that most of the dynein in axons is transported in the slow component of axonal transport, they should also cite the work of Pfister and colleagues who were the first to show this (PMID 8824315 and 8552592).

This was an omission on our part. The references have now been added.

1. The authors propose that dynein and Lis1 are transported together but there were significantly fewer anterogradely transported Lis1 particles than dynein particles. This should be discussed.

We have added more information to the discussion. Although we cannot rule out this effect being due to the heterozygous tagging of our LIS1 cell line, we do not witness the same decrease in events in the retrograde direction. Therefore, we believe there is a subset of anterogradely moving dynein lacking LIS1. As discussed in the manuscript, this subset may already be bound to dynactin and therefore not require LIS1.

1. For the statistical analysis, the authors should provide p values in the legends for the comparisons that are judged to be "not significant". The authors should also be consistent in how they label differences that are not significant - they mark them as "ns" in Fig. 1, but in the other figures they do not, leaving some ambiguity about whether particular comparisons were not tested or were found to be not significant. For example, in Fig. 4C the average speed of the dynactin is about 0.5 µm/s greater than for the other proteins and the spread in the data suggest that this could be significant, but no significance is indicated on the plot, implying p>0.05. It is not clear how confident we can be that there is no difference.

We have now included all p values in the figure legends and have removed the “ns” in Fig 1D. In our revised manuscript, only significant differences are highlighted in the figures.

Reviewer #3:

• if I look at the kymographs, trajectories appear rather complex, pausing, standing still, moving and everything mixed. The explanation of how actual trajectories are extracted and on what basis is very short, too short for me. I think the authors should expand this. Furthermore, I think it would be good if the authors would present, in their kymographs examples of the tracked (and also the not included) tracks. Maybe in supplementary info.

The analysis of this data used the Trackmate Fiji plugin. This tracks spots frame to frame in a movie and then outputs the data of the tracks. No data was extracted from kymographs but they were used as a graphical illustration of the moving spots. To better explain our analysis pipeline, we have expanded our methods section and have added an example of a tracked movie (Video 15) as well as highlighted the tracked spots in one kymograph example (Figure 7S).

• I found 'velocity' ill defined. I get the impression, judging from the number of points (compared to the other parameters) that the authors determine the average velocity of each individual trajectory. That is an important parameter (but should indeed be called 'trajectory averaged' velocity), but might not be the only one useful to learn from the data, where trajectories do not always appear to have constant speeds (pausing, etc.). Why do the authors not determine point-to-point velocities and plot histograms of those for all the trajectories (simply plot histograms of all the displacements between subsequent data points in trajectories)? This might provide great insight into the actual maximum velocity and the fraction of pausing or moving in opposite direction etc., providing much more molecular detail than currently extracted from the data.

The reviewer is correct. We have measured the average velocity of the spots from the beginning of the track to the end. We have clarified this in the text. Furthermore, as stated above in the revision plan, we are currently doing the additional analysis and will include it in the final revision

• I was a bit surprised to read that the authors have gone to the effort to create a dual-color labeled cell line, but did not do actual correlative two-color measurements (or at least show them). It would be so insightful to see dynein and dynactin move separately in the anterograde direction.

Please see the planned revision section for our response.

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Referee #2

Evidence, reproducibility and clarity

Summary - The authors use a CRISPR knock-in gene editing strategy to label endogenous dynein, dynactin (p62 or Arp11) and dynein regulators (Ndel1 and Lis1) with Halo or SNAP tags. They do this in human iPSC and ESC cell lines engineered to express doxycycline-inducible NGN2 cloned into a "safe harbor" site of the genome. They induce the cells to differentiate into iNeurons using doxycycline and image the tagged proteins in axons with single molecule sensitivity using HILO illumination. The paper is clearly written, the description of the methods is thorough, and the data and figures (including the videos) are of good quality. The use of gene editing to knock the tags into the endogenous gene loci is a superior strategy to classic overexpression strategies. The authors also make effective use of microfluidic chambers to ensure the axons are uniformly orientated and coaligned over a distance of 500µm.

Major comment (requires additional experimentation)

1. While the data presented do certainly suggest that dynein and Lis1 are transported anterogradely on separate vesicular cargoes from dynactin and Ndel1, the study would be much stronger if supported by dual imaging of dynein and dynactin to prove that these proteins do indeed move in association with separate vesicular populations. I would like to see dual-color kymograph traces showing that the proteins move independently. The authors should be able to accomplish this using their dual Halo-DYNC1H1/DCTN4-SNAP hESC line. To acquire and analyze this data might take several months, but it would greatly strengthen this paper. If the authors do this experiment, they may also be able to address the mechanism of reversal of anterograde cargoes which they speculate about in the Discussion, which would add even more interest and insight.

Minor comments (addressable without additional experimentation)

1. The authors deduce that 1-4 Halo fluorochromes corresponds to 1-2 dynein molecules. This implies that the cells are homozygous for the Halo tag, but I do not see this addressed explicitly. The authors should state explicitly whether the lines generated for their study are heterozygous or homozygous for the tag. If the cells are heterozygous, which would seem most likely, then they may be underestimating the number of dyneins per spot and should take this into account.
2. Why are the moving spots lower in intensity than the NEM-treated static spots. It appears to suggest that they may be associated with different structures. This should be clarified and discussed.
3. The authors state in the Results that most of the dynein spots were diffusing, often along microtubules, but they do not visualize microtubules so how do they know this? They may need to remove the phrase "often along microtubules".
4. At the end of the Introduction the authors state that their data "allow us to understand how the dynein machinery drives long-range transport in the axon". This is an overstatement. The "how" in this sentence is not addressed in this study.
5. The conclusion that dynein binds to cargos stably throughout their transport along the axon is based on measurements of the fastest moving cargoes but the authors do not provide data on the distribution of velocities for the entire population of retrograde cargoes. It is not valid to extrapolate the behavior of a small number of cargoes to the entire population. The average may be much slower than the fastest cargoes. Moreover, even for the fastest organelles the authors cannot say that the dynein is stably bound because they did not track single cargoes and thus do not know that the cargoes moved continuously in one single bout of movement for 500 µm; it is possible that the cargoes moved in multiple consecutive bouts interrupted by brief pauses and dynein motors may have exchanged between bouts.
6. The authors say that "it is clear that at least some dyneins remain on cargoes throughout their transport along the axon". As explained above, the data do not prove this so this statement should be removed.
7. The authors note that most of the dynein spots were not moving processively and state that this is consistent with prior studies showing that only a subset of dynein is actively involved in transport. However, as they note elsewhere, dynein is both motor and cargo and most axonal dynein is transported at slow average velocities so maybe they should be more explicit about what they mean by "involved in transport".
8. When the authors note that most of the dynein in axons is transported in the slow component of axonal transport, they should also cite the work of Pfister and colleagues who were the first to show this (PMID 8824315 and 8552592).
9. The authors propose that dynein and Lis1 are transported together but there were significantly fewer anterogradely transported Lis1 particles than dynein particles. This should be discussed.
10. For the statistical analysis, the authors should provide p values in the legends for the comparisons that are judged to be "not significant". The authors should also be consistent in how they label differences that are not significant - they mark them as "ns" in Fig. 1, but in the other figures they do not, leaving some ambiguity about whether particular comparisons were not tested or were found to be not significant. For example, in Fig. 4C the average speed of the dynactin is about 0.5 µm/s greater than for the other proteins and the spread in the data suggest that this could be significant, but no significance is indicated on the plot, implying p>0.05. It is not clear how confident we can be that there is no difference.

Referee Cross-Commenting

There seems to be agreement among all three reviewers that the authors should perform dual imaging of dynein and dynactin to prove that these proteins do indeed move together in the retrograde direction but separately in the anterograde direction. This would strengthen the study greatly.

Significance

General assessment - There are now multiple papers that have analyzed axonal transport of cargoes in iPSC-derived neurons, but this one appears to be the first to do it by tagging dynein motors and with single-molecule sensitivity. The principal conclusions are (1) that dynein is capable of long-range movement in axons and (2) that dynein moves dynein/Lis1 complexes are transported anterogradely in association with distinct cargoes from dynactin/Ndel1 complexes. The former is a modest conclusion and is entirely expected so not very impactful, but the latter is interesting and novel. The difference between the average velocities for the four proteins in the anterograde and retrograde directions is striking. All four move at similar velocities in the retrograde direction but in the anterograde direction, dynein and Lis1 move significantly faster than dynactin and Ndel1. Given these data, it is reasonable to infer that these proteins are being transported in two separate sets of cargoes. As the authors note in their Discussion, this is important because it could provide a mechanism for transporting dynein components anterogradely in a less active form that could then be activated when the components come together in the distal axon. However, I feel that one critical experiment is missing, which is to perform dual labeling of anterogradely transported dynein and dynactin in the same cells (see major comment). Without this experiment, the evidence is indirect.

Audience - If confirmed by the dual labeling experiment, the authors' conclusions would represent a conceptual and mechanistic insight into the mechanism of bidirectional transport in axons that would be of broad interest to neuronal cell biologists studying neuronal trafficking.

Expertise - This reviewer has expertise in the neuronal cytoskeleton, live imaging and axonal transport and has some experience working with iPSC-derived neurons.

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Referee #1

Evidence, reproducibility and clarity

To image dynein in the axon at a single-molecule level, Fellows et al. used neuron-inducible human stem cell lines to Halo/SNAP tag endogenous dynein components by gene editing, and visualized fluorescently labeled protein molecules in differentiated neurons in microfluidic chambers by HILO microscopy-based live imaging. Using those cutting edge technologies, the authors demonstrate that in the axon, not only dynein and dynactin but also the dynein regulators LIS1 and NDEL1 can move long distance retrogradely towards the somatodendritic compartment. They also show that dynein /LIS1 move faster than dynactin/NDEL1 in the anterograde direction, suggesting that they are delivered separately to the distal end of the axon. The approach to study subcellular motility of endogenous dynein/dynactin is creative, the data are solid. I would like to suggest one experiment to support more strongly the authors' conclusions:<br /> If doable, image dynein and dynactin simultaneously in the Halo-DYNC1H1/DCTN4-SNAP iNeurons. Comovement of dynein and dynactin towards the somatodendritic compartment and their separate movement in the anterograde direction along the axon would provide the most convincing evidence for the key claims of the manuscript.

Referee Cross-Commenting

I agree with Reviewer 2 that the authors should clarify whether the knockin lines for dynein are homozygous. I also agree with both Reviewers 2 and 3 that the authors should do more analysis of the kymographs to obtain more information.

Significance

This is an elegant study on dynein motility and transport in vivo. The experimental approaches and findings presented in this manuscript are very valuable contributions to the field of dynein/dynactin and axonal transport. The results showing that dynein/dynactin can move long-range retrogradely in the axon are in good agreement with previous findings that dynein-driven cargo transport is highly processive, and the data suggesting that dynein and dynactin/NDEL1 are trafficked separately to the distal tip of the axon provide new insights into the regulatory mechanisms for the subcellular distribution and activity of molecular motors. Together these findings provide conceptual advances for understanding axonal transport. They will be of great interest to not only scientists in the field of intracellular transport but also those in cellular neurobiology.

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Reviewer #2 (Public Review):

Summary:<br /> The paper sought to determine the number of myosin 10 molecules per cell and localized to filopodia, where they are known to be involved in formation, transport within, and dynamics of these important actin-based protrusions. The authors used a novel method to determine the number of molecules per cell. First, they expressed HALO tagged Myo10 in U20S cells and generated cell lysates of a certain number of cells and detected Myo10 after SDS-PAGE, with fluorescence and a stained free method. They used a purified HALO tagged standard protein to generate a standard curve which allowed for determining Myo10 concentration in cell lysates and thus an estimate of the number of Myo10 molecules per cell. They also examined the fluorescence intensity in fixed cell images to determine the average fluorescence intensity per Myo10 molecule, which allowed the number of Myo10 molecules per region of the cell to be determined. They found a relatively small fraction of Myo10 (6%) localizes to filopodia. There are hundreds of Myo10 in each filopodia, which suggests some filopodia have more Myo10 than actin binding sites. Thus, there may be crowding of Myo10 at the tips, which could impact transport, the morphology at the tips, and dynamics of the protrusions themselves. Overall, the study forms the basis for a novel technique to estimate the number of molecules per cell and their localization to actin-based structures. The implications are broad also for being able to understand the role of myosins in actin protrusions, which is important for cancer metastasis and wound healing.

Strengths:<br /> The paper addresses an important fundamental biological question about how many molecular motors are localized to a specific cellular compartment and how that may relate to other aspects of the compartment such as the actin cytoskeleton and the membrane. The paper demonstrates a method of estimating the number of myosin molecules per cell using the fluorescently labeled HALO tag and SDS-PAGE analysis. There are several important conclusions from this work in that it estimates the number of Myo10 molecules localized to different regions of the filopodia and the minimum number required for filopodia formation. The authors also establish a correlation between number of Myo10 molecules filopodia localized and the number of filopodia in the cell. There is only a small % of Myo10 that tip localized relative to the total amount in the cell, suggesting Myo10 have to be activated to enter the filopodia compartment. The localization of Myo10 is log-normal, which suggest a clustering of Myo10 is a feature of this motor.

Weaknesses:<br /> One main critique of this work is that the Myo10 was overexpressed. Thus, the amount in the cell body compared to the filopodia is difficult to compare to physiological conditions. The amount in the filopodia was relatively small - 100s of molecules per filopodia so this result is still interesting regardless of the overexpression. However, the overexpression should be addressed in the limitations.<br /> The authors have not addressed the potential for variability in transfection efficiency. The authors could examine the average fluorescence intensity per cell and if similar this may address this concern.<br /> The SDS PAGE method of estimating the number of molecules is quite interesting. I really like this idea. However, I feel there are a few more things to consider. The fraction of HALO tag standard and Myo10 labeled with the HALO tagged ligand is not determined directly. It is suggested that since excess HALO tagged ligand was added we can assume nearly 100% labeling. If the HALO tag standard protein is purified it should be feasible to determine the fraction of HALO tagged standard that is labeled by examining the absorbance of the protein at 280 and fluorophore at its appropriate wavelength. The fraction of HALO tagged Myo10 labeled may be more challenging to determine, since it is in a cell lysate, but there may be some potential approaches (e.g. mass spec, HPLC).<br /> In Figure 1B, the stain free gel bands look relatively clean. The Myo10 is from cell lysates so it is surprising that there are not more bands. I am not surprised that the bands in the TMR fluorescence gel are clean, and I agree the fluorescence is the best way to quantitate.<br /> In Figure 3C, the number of Myo10 molecules needed to initiate a filopodium was estimated. I wonder if the authors could have looked at live cell movies to determine that these events started with a puncta of Myo10 at the edge of the cell, and then went on to form a filopodia that elongated from the cell. How was the number of Myo10 molecules that were involved in the initiation determined? Please clarify the assumptions in making this conclusion.<br /> It is stated in the discussion that the amount of Myo10 in the filopodia exceeds the number of actin binding sites. However, since Myo10 contains membrane binding motifs and has been shown to interact with the membrane it should be pointed that the excess Myo10 at the tips may be interacting with the membrane and not actin, which may prevent traffic jams.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Some sentences need to be clarified and some additional data and references could be added.

1) Line 18

SRY is the sex-determining gene

SRY is the testis-determining gene is more accurate as described in line 44

Modification done

2) Line 50

Despite losing its function in early testis determination in mice, DMRT1 retained part of this function in adulthood when it is necessary to maintain Sertoli cell identity.

Losing its function is misleading. The authors describe firstly that Dmrt1 has no obvious function in embryonic testis development but is critical for the maintenance of Sertoli cells in adult mice. The wording "losing its function in early testis" is confusing. Do the authors mean that despite the expression of Dmrt1 in early testis development, the function of Dmrt1 seems to be restricted to adults in mice? A comparison between the testis and ovary should be more cautious since GarciaAlonso et al (2022) have shown that the transcriptomics of supporting cells between humans and mice is partly different.

That’s what we thought, and the sentence has been changed as follow: “Although DMRT1 is not required for testis determination in mice, it retained part of its function in adulthood when it is necessary to maintain Sertoli cell identity.” (line 51 to 53)

3) Line 78

XY DMRT1-/- rabbits showed early male-to-female sex reversal.

Sex reversal indicates that there is no transient Sertoli cell differentiation that transdifferentiate into granulosa cells. This brings us to an interesting point. In the case of reprogramming, the transient Sertoli cells can produce AMH leading to the regression of the Mullerian ducts. In humans, some 9pdeleted XY patients have Mullerian duct remnants and feminized external genitalia. This finding indicates early defects in testis development.

Is there also feminized external genitalia in XY Dmrt1−/− rabbits. Can the authors comment on the phenotype of the ducts?

We proposed to add “and complete female genitalia” at the end of the following sentence: “Secondly, thanks to our CRISPR/Cas9 genetically modified rabbit model, we demonstrated that DMRT1 was required for testis differentiation since XY DMRT1-/- rabbits showed early male-tofemale sex reversal with differentiating ovaries and complete female genitalia.” (line 77 to 80)

Indeed, since the first stage (16 dpc) where we can predict the sex of the individual by observing its gonads during dissection, we always predict a female sex for XY DMRT1 KO fetuses. It is only genotyping that reveals an XY genotype. At birth, our rabbits are sexed by technicians from the facility and again, but now based on the external genitalia, they always phenotype these rabbits as female ones. In these XY KO rabbits, the supporting cells never differentiate into Sertoli, and ovarian differentiation occurs as early as in XX animals. Thus, these animals are fully feminized with female internal and external genitalia. Most of 9p-deleted patients are not homozygous for the loss-offunction of DMRT1, and the remaining wild-type allele could explain the discrepancy between KO rabbits and humans.

4) Line 53

In the ovary, an equivalent to DMRT1 was observed since FOXL2 (Forkhead family box L2) is expressed in female supporting cells very early in development.

Can the authors clarify what is the equivalent of DMRT1, is it FOXL2? DMRT1 heterozygous mutations result in XY gonad dysgenesis suggesting haploinsufficiency of DMRT1. However, to my knowledge, there is no evidence of haploinsufficiency in XX babies. Thus can we compare testis and ovarian genetics?

We agree, the term “equivalent” is ambiguous, and we changed the sentence as follows: “In ovarian differentiation, FOXL2 (Forkhead family box L2) showed a similar function discrepancy between mice and goats as DMRT1 in the testis pathway. In the mouse, Foxl2 is expressed in female supporting cells early in development but does not appear necessary for fetal ovary differentiation. On the contrary, it is required in adult granulosa cells to maintain female-supporting cell identity.” (line 53 to 56)

Regarding reviewer 2's question on haploinsufficiency in humans: the patient described in Murphy et al., 2015 is an XY individual with complete gonadal dysgenesis. But, it has been shown that the mutation carried by this patient leads to a dominant-negative protein, equivalent to a homozygous state (Murphy et al., 2022).

For FOXL2 mutation in XX females, haploinsufficiency does not affect early ovarian differentiation (no sex reversal) but induces premature ovarian failure.

We agree with the reviewer, we cannot compare testis and ovarian genetics considering two different genes.

5) Line 55

In mice, Foxl2 does not appear necessary for fetal ovary differentiation (Uda et al., 2004), while it is required in adult granulosa cells to maintain female-supporting cell identity (Ottolenghi et al., 2005). The reference Uhlenhaut et al (2009) reporting the phenotype of the deletion of Foxl2 in adults should be added.

The reference has been added.

6) Line 64<br /> These observations in the goat suggested that DMRT1 could retain function in SOX9 activation and, thus, in testis determination in several mammals.

Lindeman et al (2021) have shown that DMRT1 can act as a pioneer factor to open chromatin upstream and Dmrt1 is expressed before Sry in mice (Raymond et al, 1999, Lei, Hornbaker et al, 2007). Whereas additional factors may compensate for the absence of Dmrt1, these results suggest that DMRT1 is also involved in Sox9 activation.

Dmrt1 is indeed expressed before Sry/Sox9 in the mouse gonad. However, no binding site for DMRT1 could be observed at Sox9 enhancer 13 in mice. This does not support a role for DMRT1 in the activation of Sox9 expression in this species. Furthermore, in Lindeman et al 2021, the authors clearly state that DMRT1 acts as a pioneering factor for SOX9 only after birth. It does not appear to have this role before. One of the explanations put forward is that the state of chromatin is different during fetal development in mice: chromatin is more permissive and does not require a factor to facilitate its opening. This hypothesis is based in particular on the description of a similar chromatin profile in the precursors of XX and XY fetal supporting cells, where many common regions display an open structure (Garcia-Moreno et al., 2019). Once sex determination and differentiation are established, a sex-specific epigenome is set up in gonadal cells. Chromatin remodeling agents are then needed to regulate gene expression. We hypothesize that in non-murine mammals such as rabbits, the state of gonadal cell chromatin would be different in the fetal period, more repressed, requiring the intervention of specific factors for its opening, such as DMRT1.

7) Figure 1

Most of the readers might not be familiar with the developmental stages of the gonad in rabbits. A diagram of the key stages in gonad development would facilitate the understanding of the results.

Thank you, it has been added in Figure 1.

8) Figure 2

Arrowheads are difficult to spot, could the authors use another color?

Done

9) Line 117: can the authors comment on the formation of the tunica albuginea? Do the epithelial cells acquire some specific characteristics?

The formation of the tunica albuginea begins with the formation of loose connective tissue beneath the surface epithelium of the male gonad. The appearance of this tissue is concomitant with the loss of expression of DMRT1 in the cell of the coelomic epithelium. Our interpretation is that the contribution of the cells from the coelomic epithelium and their proliferation stops when the tunica begins to form because the structure of the tissue beneath the epithelium change, and the cellular interactions between the epithelium and the tissue below remain disrupted. By contrast, these interactions persist in the ovary until around birth for ovigerous nest formation.

10) The first part of the results described DMRT1 expression in rabbits. With the new single-cell transcriptomic atlas of human gonads, it would be important to describe the pattern of expression in this species. This could be described in the introduction in order to know the DMRT1 expression pattern in the human gonad before that of the rabbit.

A comment on the expression pattern of DMRT1 in human fetal gonads has been added in the discussion section: “In the human fetal testis, DMRT1 expression is co-detected with SRY in early supporting gonadal cells (ESCGs), which become Sertoli cells following the activation of SOX9 expression (Garcia-Alonso et al., 2022) » (line 222 to 224)

11) Figure 3 supplement 3

Dotted line: delimitation of the ovarian surface epithelium. Could the authors check that there is a dotted line?

Done

12) Figure 5 and Line 186

Quantification is missing such as the % of germ cells, % of meiotic germ cells.

Quantification is not easy to realize in rabbits because of the size and the elongated shape of the gonad. Indeed, it’s difficult to be sure that both sections (one from WT, the other from KO) are strictly in a similar region of the gonad and that the section is perfectly longitudinal or not. See also our answer to reviewer 3 (point 7) on this aspect. Actually, we are trying to make a better characterization of this XX phenotype and to find a marker of the pre-leptotene/leptotene stage susceptible to work in rabbits (SYCP3 will be the best, but we encountered huge difficulties with different antibodies and even RNAscope probe!). So actually, the most convincing indirect evidence of this pre-meiotic blockage (in addition to HE staining at 18 dpp in the new Figure 6) is the persistence of POU5F1 (pluripotency), specifically in the germinal lineage of KO XX and XY gonads. In addition to the new figure supplement 5, we can show you in Author response image 1: (i) the gonadal section at a lower magnification, where it is evident that there is a big difference between WT and KO germ cell POU5F1-stainings; and (ii) POU5F1 expression from a bulk RNA-seq realized the day after birth at 1 dpp where the difference is also transcriptionally very clear.

Author response image 1.

13) Line 186,

E is missing at preleptoten

Added

14) Figure supplement 7.

A magnification of the histology of the gonads is missing.

This figure is only for showing the gonadal size, and there are the same gonads as in the new Figure 6. So, the magnification is represented in Figure 6.

15)Discussion

Line 201

SOX9, well known in vertebrates,

The references of the human DSD associated with SOX9 mutations are missing. Thank you, references have been added.

16) Line 286

One of the targets of WNT signaling is Bmp2 in the somatic cells and in turn, Zglp1, which is required for meiosis entry in the ovary as shown by Miyauchi et al (2017) and Nagaoka et al (2020). Does the level of BMP pathway vary in DMRT1 mutants?

At 20 dpc, the expression level of BMP2 in XY and XX DMRT1 mutants gonads is similar to the one of XX control which is lower than in XY control (see the TMP values from our RNA-seq in Author response image 2).

Author response image 2.

Reviewer #2 (Recommendations For The Authors):

Here are my minor comments:

1) Line 106- You mention that coelomic epithelial cells only express DMRT1. Please add an arrow to highlight where you refer to.

Done

2) Line 112: In mice, the SLCs also express Sox9 but not Sry apart from Pax8. You mention here that the SLCs are expressing SRY and DMRT1 in addition to PAX8. Could you perhaps explain the difference? Please refer to that in the results or discussion.

We add a new sentence at the end of this paragraph on SLCs: “As in mice, these cells will express SOX9 at the latter stages (few of them are already SOX9 positive at 15 dpc), but unlike mice, they express SRY.” (line 114 to 115)

We already have collaborations with different labs on these SLC cells, and we will certainly come back later on this aspect, remaining slightly off-topic here.

3) Could you please explain why did you chose to target Exon 3 of DMRT1 and not exons 1-2 which contain the DM domain? Was it to prevent damaging other DMRT proteins? Is there an important domain or function in Exon 2?

Our choice was mainly based on technical issues (rabbit genome annotation & sgRNA design), but also we want to avoid targeting the DM domain due to its strong conservation with other DMRT genes. Due to the poor quality of the rabbit genome, exons 1 and 2 are not well annotated in this species. We have amplified and sequenced the region encompassing exons 1 & 2 from our rabbit line, but the software used for sgRNA design does not predict good guides on this region. The two best sgRNAs were predicted on exon 3, and we used both to obtain more mutated alleles.

4) Your scheme in Supp Figure 4 is not so clear. It is not clear that the black box between the two guides is part of Exon 3 (labelled in blue).

The scheme has been improved.

5) Did you only have 1 good founder rabbit in your experiment? Why did you choose to work with a line that had duplication rather than deletion?

Very good point! In the first version of this paper, we’d try to explain the long (around 2 years) story of breeding to obtain the founder animal. Here it is:

During the genome editing process, we generate 6 mosaic founder animals (5 males and 1 female), then we cross them with wild-type animals to isolate each mutated allele in F1 offspring used afterward to establish and amplify knockout lines. Unexpectedly, we observe a very slow ratio of mutated allele transmission (5 on 129 F1 animals), and only one mutated allele has been conserved from the unique surviving adult F1 animal. It consists of an insertion of the deleted 47 bp DNA fragment, flanked by the cutting sites of the two RNA guides used with Cas9.<br /> The main hypothesis to explain this mutation event is that in the same embryonic cell, the deletion occurs on one allele then the deleted fragment remains inserted into the other allele. Under this scheme, the embryonic cell carries a homozygous DMRT1 knockout genotype, albeit heterogeneous, with a deleted allele (del47) and the present allele (insertion of a 47 bp fragment leading to an in sense duplication). This may explain the very low frequency of transmission since all germ cells carrying a homozygous DMRT1-/- genotype will probably not be able to enter the meiotic process as suggested by our results on XX and XY DMRT1-/- ovaries. Finally, and under this hypothesis, the way we obtained this unique founder animal remains a mystery!

6) Figure 4- real-time data- where does it say what is a,b,c,d of the significance? It should appear on the figure itself and not elsewhere.

Modification done.

7) If I understand correctly, you were able to get the rabbits born and kept to adulthood (you show in supp figure 7 their gonads). What was the external phenotype of these rabbits? Did the XY mutant gonads have the internal and external genitals of a female (oviduct, uterus, vagina etc.)?

See our answer to Reviewer 1 on this question (point 3).

8) Line 20: It is more correct to write 46, XY DSD rather than XY DSD

Modification done.

9) Line 21: you can remove the "the" after abolished

Modification done.

10) Line 31: consider replacing the first "and" by "as well as" since the sentence sounds strange with two "and".

Modification done.

11) Line 212- Please check with the eLife guidelines if they allow "data not shown" in the paper.

This is unspecified.

Reviewer #3 (Recommendations For The Authors):

The following points should be addressed.

1) The in situ's in Fig 1 and 2 are very clear. Fig 1 and Fig 2, In situ hybridisation in tissue sections, it looked like DMRT1 could be expressed in some cells where SRY mRNA is absent @ E13.5dpc and 14.5 dpc. Do you think this is real, or maybe Sry is turned off now in those cells?

Based on the results of in situ hybridizations, DMRT1 appears to be expressed by both coelomic epithelium and genital crest medullar cells in a pattern that is actually broader than that of SRY. Moreover, in rabbits, SRY expression seems to start in the medulla of the genital ridge rather than in the surface epithelium, as described in mice (see Figure 1 at 12 and 13 dpc). Nevertheless, more detailed analyses are needed to ensure the lineage of cells expressing SRY and/or DMRT1, such as single-cell RNAseq at these key stages of sexual determination in rabbits (from 12 to 16 dpc).

2) It is curious that SRY expression is elevated in the DMRT1 KO (Knockout) rabbit gonads. Does this suggest feedback inhibition by DMRt1, or maybe indirect via effect on Sox9 (as I believe Sox9 feeds back to down-regulate Sry in mouse, for example).

The maintenance of SRY expression in the DMRT1 -/- rabbit testis seems to be linked to the absence of SOX9 expression. We believe that, as in mice, SOX9 would down-regulate SRY (even if, in rabbits, SRY expression is never completely turned off).

3) I suggest the targeting strategy and proof of DMRT1 knockout by sequencing etc. be brought out of the suppl. Data and shown as a figure in the text.

See also our answer to reviewer 2 (point 5). It has needed huge efforts to obtain these DMRT1 mutated rabbit line, and of course, it constitutes the basis of the study. But regarding the title and the main message of the article, we are not convinced that the targeting strategy should be moved into the main text.

4) Unless there are limitations imposed by the journal, I also feel that Suppl Fig 5 (the immunostaining) deserves to be in the paper text too. The Fig showing loss of DMRt1 by immunostaining is important.

We include the figure supplement 5 in the main text. So, Figure 4E and figure supplement 5 have been combined into a new Figure 5.

5) The RT-qPCR data should have the statistics clarified on the graphs. (e.g., it is stated that, although Sox9 mRNA is clearly down, there is a slight increase compared to control on KO XX gonads. Is this statistically significant? Figure legend states that the Kruskal-Wallis test is used, and significance is shown by letters. This is unclear. It would be better to use the more usual asterisks and lines to show comparisons.

Modification done.

6) Reference is made to DMRT1+/- rabbits having aberrant germ cell development, pointing to a dosage effect. This is interesting. Does the somatic part of the gonad look completely normal in the het knockouts?

DMRT1 heterozygous male rabbits have a phenotype of secondary infertility with aging, and we are trying now to better characterize this phenotype. The problem is complex because, as we cannot carry out conditional KO, it remains difficult to decipher the consequence of DMRT1 haploinsufficiency in the Sertoli cells versus the germinal ones. Anyway, the somatic part is sufficiently normal to support spermatogenesis since heterozygous males are fertile at puberty and for some months thereafter.

7) Can the authors indicate why meiotic markers were not used to explore the germ cell phenotype? It would be advantageous to use a meiotic germ cell marker to definitely show that the germ cells do not enter meiosis after DMRT1 loss. (Not just H/E staining or maintenance of POU). Example SYCP3, or STRA8 (as pre-meiotic marker) by in situ or immunostaining. Even though no germ cells were detected in adult KO gonads.

The expression of pre-meiotic or meiotic markers is currently under study in DMRT1 -/- females. Transcriptomic data (RNA-seq) are also being analyzed. We are preparing a specific article on the role of DMRT1 in ovarian differentiation in rabbits. We felt it was important to reveal the phenotype observed in females in this first article, but we still need time to refine our description and understanding of the role of DMRT1 in the female.

8) What future studies could be conducted? In the Discussion section, it is suggested that DMRT1 could act as a pioneering factor to allow SRY action upon Sox9. How could this be further explored?

To explore the function of DMRT1 as a pioneering factor, it now seems necessary to characterize the epigenetic landscapes of rabbit fetal gonads expressing or not DMRT1 (comparison of control and DMRT1-/- gonads). Two complementary approaches could be privileged: the study of chromatin opening (ATAC-seq) and the analysis of the activation state of regulatory regions (CUT&Tag). The study of several histone marks, such as H3K4me3 (active promoters), H3K4me1 (primed enhancers), H3K27ac (enhancers and active promoters), and H3K27me3 (enhancers and repressed promoters), would be of great interest. However, these techniques are only relevant for gonads that can be separated from the adjacent mesonephros, which is only possible from the 16 dpc stage in rabbits. To perform a relevant analysis at earlier stages, a "single-nucleus" approach such as ATAC-seq singlenucleus or multi-omic single-nucleus combining ATAC-seq and RNA-seq could be used.

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Reply to the reviewers

Reviewer 1 major comments:

The authors show one configuration of the E1-E2 heterodimer in Figure 4d. As shown, the E1 protein is exterior to the E2 protein and would suggest E1 is on the surface on the spike complex and virus surface. However, another configuration of the glycoproteins has E2 on the exterior of E1 and also on the exterior of the virus. The latter conformation is what has been observed in cryoEM studies of alphaviruses. The first configuration represents the E1-E2 between the three heterodimers which are important for spike assembly. The reason the orientation of the E2-E1 dimer is important is the authors speculate on the importance of the 6 CHIK residues not found in ONNV based on the structure, but the structural interpretation is, in my opinion, not correct.

We thank reviewer 1 for pointing out the correct E2-E1 heterodimer configuration. To address this, we corrected the position of E2 and E1 in Figure 4 based on previous cryoEM study1, keeping E2 always on the exterior in the E2-E1 heterodimer. We also replaced the Indian Ocean Lineage (IOL) E2-E1 structure1 in the original Figure 4 with the CHIKV 181/clone 25 structure which was recently analyzed by Katherine Basore et al.2. In a single E2-E1 heterodimer, all six unique CHIKV positive selection sites are located on the outside of the structure after correcting the configuration. In addition, we investigated two of the unique CHIKV positively selected sites that are important for virion production, E2-V135 (V460 in the original manuscript version) and E1-V220 (V1029 in the original manuscript version), in trimerized structure of E2-E1 heterodimers. We found that the E2-V135 and E1-V220 residues in one heterodimer are facing E2 of the neighboring heterodimer on either side. Interestingly, while V135 is embedded between the E2 proteins of two different heterodimers, E1-V220 is partially embedded by E1 and the neighboring E2 and partially exposed to the outside. This suggests that even though both E2-V135 and E1-V220 might be crucial for CHIKV E2-E1 trimerization, E1-V220 provides an additional docking site for host factor interactions. We thank review 1 again for this important comment leading to these new findings. We have updated Figure 4F-4G and the corresponding result section (lines 201-209) in this partially revised manuscript.

1. Validation of E1 interaction with SPSC3 and eIF3k needs to be stronger. Some concerns/questions are listed below. A myc tag was inserted between E3 and E2. How efficiently does furin cleave E3 from E2 in this virus and how are viral titers of the myc-tagged virus compared to the non-tagged virus? I ask because is the IP looking at what is being pulled down by E2 or E3-myc-E2 that could be part of the spike polyprotein? The authors found E2 interacts with E3, E1 and a list of other host proteins. These results suggest several interactions including E2-host factor, E2-E1, E2-E3, E2-E1-host factor, E2-E3-E1, E2-E3-host factor. In figure 6d, and the subsequent conclusions, the authors suggest E1 is interacting with the host factor and do not see E2 alone and very low amounts of E3-E2-6K-E1. based on how the IP was performed I am not sure how an interaction between E1 and SPCS3 alone, without E2, would be detected. I would also like to see a reciprocal pull down using E1 and also E2 to see if these host factors are pulled down.

We thank the reviewer for these concerns. Given the low viral protein expression in macrophages (Figure 1A), we need an efficient system to enrich for large amounts of CHIKV glycoproteins for identifying host interactors through mass spectrometry. Adding tag/reporter proteins, such as mCherry, between E3 and E2 have been used to label alphavirus glycoproteins in previous study2, which is why we chose to use this myc tag labeling strategy coupled with myc Ab-conjugated agarose beads for AP-MS. However, like reviewer 1 speculated, inserting myc tag between E3 and E2 does attenuate CHIKV infectivity according to the reduced supernatant viral titers of 293T cells transfected with CHIKV/myc-E2 genomic RNA in comparison to those of cells transfected with unmodified CHIKV vaccine strain 181/clone 25 genomic RNA (shown in revision plan). Despite the attenuation, CHIKV/myc-E2 harvested from transfected 293T cells still reaches a titer over 108 pfu/ml, which allowed us to identify interactors by AP-MS.

We further analyzed the cleavage efficiency of glycoproteins by comparing the expression levels of E3-E2-6K -E1, E3-E2 (p62), E2, and E3 in 293T cells transfected with unmodified CHIKV or CHIKV/myc-E2 genomic RNA (result shown in revision plan). We didn’t detect any uncleaved forms of glycoproteins in cells transfected with either unmodified CHIKV or CHIKV/myc-E2 RNA when we probed with E2 antibody. However, probing with E3 antibody prior to longer exposure of the immunoblot showed higher E3-E2-6k-E1 and E3-E2 (p62) levels in cells transfected with CHIKV/myc-E2 RNA, suggesting that both mature E2 and E2-containing precursor polyproteins are available to be pulled down. Overall, the expression levels of mature E2 detected by E2 antibody are similar.

We thank reviewer 1 for providing a thorough dissection of all the possible interactions between the identified host factors and cleaved/uncleaved glycoproteins. This is a very interesting question. As reviewer 1 mentioned that E1 usually appears with E2 or E3-E2 in heterodimer forms, we were also surprised to find that E2 does not interact with either of the two host factors. To address this, we plan to conjugate E2 and E1 to protein A/G beads, respectively, for a reciprocal pulldown to validate CHIKV glycoprotein interactions with SPCS3 and eIF3k. Results from this experiment will be included in the fully revised manuscript.

1. If CHIK E1 is interacting with the host factors and that is antagonizing the antiviral response of SPSC3 (as one example), then what do pull downs using ONNV structural proteins look like? One would expect reduced interactions because the different amino acid causes a different E2-E1 dimer or attenuates the E1-host factor binding site.

We thank Reviewer 1 for this insightful suggestion. We agree that it would be informative to examine the interactions between ONNV glycoproteins and identified host factors (SPCS3 and eIF3k). Unfortunately, there is no commercial ONNV glycoprotein antibody available making this experiment unfeasible. Interestingly, we did observe reduced interactions between the host factors SPCS3 and eIF3k and the CHIKV E1-V220I mutant (V1029I in original manuscript version) where the positively selected site in E1 was mutated to the homologous ONNV residue (please refer to our response to Reviewer 3’s major comment #1). This result suggests that the ONNV glycoproteins likely have an attenuated E1-host factor binding site as the reviewer speculated.We have included this as Figure 7A in partially revised manuscript.

1. E1 and E2 are thought to interact during polyprotein translation and the initial dimer forms in the ER. If E1 is interacting with SPSC3 in the ER, is E2 also present? Or is a population of E1 not interacting with E2 in order to inhibit SPSC3? I would love a model of how the authors see all these factors coming together for this new role of E1.

We thank Reviewer 1 for proposing this interesting hypothesis. Given the unexpected absence of E2 in our validation of host factor-E1 pulldown, we speculate that a group of free E1 proteins with distinct function is interfering with host factors in the ER, which is a model worth further investigation and discussion. A great example of this is the alphavirus nonstructural protein 3 (nsP3) that plays essential roles in RNA replication, although depending on the alphavirus not all of the nsP3 in the cell colocalizes with dsRNA, suggesting there is a separate distinct pool of nsP3 outside of active viral replication complex that interacts with host factors in these observed larger cytoplasmic aggregates3. To address this, we plan to use laser confocal microscopy to observe the interactions between host factors (SPCS3, eIF3k), and CHIKV E2 and E1. We will include this result as well as our proposed model in the fully revised manuscript.

Reviewer 1 minor comments:

1. In Figure 1c, (-) RNA is shown but in the rest of the figures (+) RNA is shown. Show both or select one. I do find it interesting the (-) RNA levels are similar over time, even at 4 hours post transfection (early time). Related to this, ONNV has higher levels of (-) RNA but what is known about structural protein levels in ONNV and CHIK in macrophages? Are there comparable levels of CP and GP being produced?

We thank Reviewer 1 for this comment. The (-) RNA is synthesized before the synthesis of subgenomic mRNA and therefore can reflect more accurately early viral replication and nonstructural protein functions. This is the reason why we consider the (-) RNA levels evaluated by specific nsP1 TaqMan probes to be more appropriate for determining early stage differences between ONNV and CHIKV replication in Figure 1 as the goal of that figure is to define the steps in CHIKV life cycle that are more efficient than those of ONNV in THP-1 derived macrophages. On the other hand, the (+) RNA evaluated by E1 primers that we used in the later figures monitors viral RNA synthesis over time in the reflection of genomic (+) RNA and subgenomic mRNA transcribed from (-) RNA templates. Similar levels of (+) RNA and contrasting virion titers really point the difference to the later stages of subgenomic mRNA translation, viral glycoprotein secretion, and assembly.

We have generated ONNV/myc-E2 reporter virus and assessed viral glycoprotein expression through flow cytometry using a FITC -conjugated anti-myc antibody in the THP-1 derived macrophages transfected with CHIKV/myc-E2 and ONNV/myc-E2 (shown in revision plan). The results show that the expression of ONNV glycoproteins is more inhibited than that of CHIKV glycoproteins, though both of their expression levels in macrophages seem to be suppressed. Since there is no commercial ONNV antibody available, we were unable to compare capsid expression levels between the two viruses. Overall, differences in the myc-tagged glycoprotein expression levels of the two viruses reveals ONNV defect in either structural protein translation or glycoprotein maturation .

1. Figure 2e and figure 3 have ONNV has the first bar followed by CHIK. In figure 1 and 2b, CHIK is first and then ONNV. helps the reader to have the controls in the same order.

We thank Reviewer 1 for this suggestion. We have changed the order of ONNV and CHIKV bars in figure 2E and figure3 so the CHIKV bar consistently comes first in all the figures.

1. Line 143-145 the authors discuss that when ONNV is the backbone and CHIK proteins are inserted the infection is more attenuated because of the E2 and E1 are from CHIK and ONNV, not the same virus (could also be E2-CP interactions are disrupted). However the chimeras made with the CHIK backbone (in Figure 2) have a mismatch between E2 and E1 as well.

We thank Reviewer 1 for this informative comment. We agree that the incompatible E2-E1 heterodimer formation may not be the only reason that causes attenuation of ONNV/CHIKV E1 and ONNV/CHIKV E2. There may be multiple factors contributing to the fitness of the chimeras, which requires more in-depth mechanistic investigations and is out of the scope of this study. We have now removed the explanation “potentially due to incompatible heterodimer formation between ONNV E2 and CHIKV E1” in line 144.

1. When discussing the residues that were found in the FEL and MEME analysis, the authors start the amino acid numbering from CP and continue along the polyprotein. Usually when discussing amino acids in the structural proteins, each protein starts at amino acid 1. So V460 would be E2-V135. It would also be useful to know what the residues in ONNV were at these positions to see if amino acids changed in charge, size, bond forming potential, etc. Showing these residues in the E2-E1 conformation found in the virion would also allow one to find adjacent residues that could explain differences in spike assembly and potentially where/how E1 is binding to a host protein.

We thank Reviewer 1 for this comment. We revised the amino acid numbers in the manuscript to start from the beginning of each structural protein. To look more into these residues in ONNV, we aligned CHIKV and ONNV from different lineages and compared the 6 positively selected sites (refer to our response to Reviewer 1’s minor comment #5). We found that E2-135 and E1-220 which are essential for CHIKV production are valines in all the aligned CHIKV strains. For the aligned ONNV strains, E2-135 are all leucines and E1-220 are all isoleucines. While valine, leucine and isoleucine are all amino acids with hydrophobic side chains, valine has the shortest side chain. The length of the side chains may lead to different hydrophobic properties that affect protein folding, which warrants further structural analysis.

1. How effective is a non-attenuated CHIK strain in infecting macrophages? Could you make a SINV-La Reunion chimeric virus (which is BSL2) to see if a higher percentage of macrophages are infected and is this potentially contributing to the increased pathogenesis of La Reunion? Also how different is 181/25 with a pathogenic strain in the E2 and E1 residues? and compared to ONNV?

We thank Reviewer 1 for this question, which is also raised by Reviewer 2. In order to address this question, we plan to use the virulent CHIKV La Reunion strain to study the infection of THP-1 derived macrophages with non-attenuated CHIKV in BSL-3. We are getting trained in the BSL-3 facility and will soon be certified.

We thank Reviewer 1 for this insightful suggestion on investigating the conservation of these positively selected sites in different strains. We have aligned the sequences of ONNV and CHIKV strains from different lineages, including CHIKV vaccine strain 181/clone 25 and Thai strain AF15561 (the parental strain of CHIKV 181/clone 25) (alignment shown in revision plan). We found that the two positively selected sites with negative effects on virion production, E2-135 and E1-220 (sites 460 and 1029 in original manuscript version), are very conserved in either CHIKV or ONNV strains. CHIKV E2-135 is always valine (V) regardless of the lineages, while ONNV E2-135 is always leucine (L). CHIKV E1-220 is always V, while ONNV E1-220 is always isoleucine (I).

We also analyzed the amino acid heterogeneity of E2-135 and E1-220 in 397 CHIKV patient sequences from NCBI Virus database. Most of the amino acids at these 2 sites are V. The counts of each amino acid at E2-135 and E1-220 is summarized in table below. This result suggests that valine residues at E2-135 and E1-220 are crucial for CHIKV fitness and strongly selected during viral evolution. The sequence alignment and table will be included and discussed in the fully revised manuscript .

E2-135

E1-220

Valine (V)

394

392

Alanine (A)

1

3

Methionine (M)

1

0

Glutamic acid (E)

0

1

Glycine (G)

1

0

Isoleucine (I)

0

1

1. When describing the last results section, "CHIKV E1 binding proteins exhibit potent anit-CHIV activities" the authors use macrophages. In the rest of the text they consistently use THP-1 macrophages or human primary monocyte derived macrophages. The details of the cell type are extremely useful to the reader and having those in the last results section would be great.

We thank Reviewer 1 for pointing out the importance of cell type clarification in the last results section. We now consistently use “THP-1 derived macrophages” instead of “macrophages” in this section.

1. The paper is well-written. There is a slight disconnect as the authors go from discussing results in Figure 4 to Figure 5.

We thank Reviewer 1 for the comment regarding the disconnection of the last two figures in this paper which is also shared by the other reviewers. We have taken 3 approaches to address this comment: 1) We performed a pulldown of the host factors (SPCS3, eIF3k) identified in Figure 5 with CHIKV positively selected mutants examined in Figure 4 with deficient virion production. The result is presented in our response to Reviewer 3’ s major comment #1, suggesting that the positively selected site in E1 is essential for CHIKV glycoprotein interaction with host factors. 2) To complement our first experiment, we will also determine structural protein expression and processing of parental and E1 mutant CHIKV in eIF3k CRISPR knockout 293T cells. 3) Finally, we plan to perform CORUM analysis to identify high confidence functional protein complexes using our 14 hits found in both mass spec experiments, which will provide mechanistic insights into how these identified cellular complexes and processes might modulate CHIKV infection.

Reviewer 2’s major comments

The authors elegantly demonstrate that CHIKV structural proteins confer an advantage over ONNV structural proteins in a step in the replication cycle downstream of virus RNA synthesis, possibly virion assembly. This point would be strengthened determining the particle-to-PFU ratio of the parental viruses and the chimeras . Presumably, the ratio would increase in the chimeras containing CHIKV structural proteins.

We thank Reviewer 2 for this comment. We agree that determining particle-to-PFU ratios of parental and chimeric viruses will strengthen this study. To obtain the particle-to-PFU ratio, we infected THP-1 derived macrophages with CHIKV, ONNV and chimeras containing CHIKV glycoproteins (Chimera I, and ONNV/CHIKV E2+E1) for 24 h. To quantify the secreted viral particles, we extracted viral RNA in the supernatant and detected (+) viral RNA through TaqMan assay with specific nsp1 probes. The released infectious virions were evaluated through plaque assay. The particle-to-PFU ratios are summarized in the table below. The results show that ONNV has the highest particle-to-PFU ratio (41398), suggesting defective ONNV genome encapsidated in particles leading to defective virion production. On the other hand, the particle-to-PFU ratio of CHIKV (747) is 55-fold lower than that of ONNV. Replacing E3-E2-6K-E1 of ONNV with CHIKV homologous proteins reduces the particle-to-PFU ratio by 8 fold to 4875. Replacing E2 and E1 of ONNV with the ones from CHIKV (ONNV/CHIKV E2+E1) reduces the particle-to-pfu ratio by 20 fold to 2017, suggesting that CHIKV glycoproteins enhance the infectivity of viral progenies produced by THP-1 derived macrophages. We have included the results in Figure 3D-3E in our partially revised manuscript and described in lines 149-158.

1. Additionally, the authors should consider performing virion assembly blocking assays with a small molecule inhibitor to determine if this abrogates the virus production advantage of CHIKV structural proteins within the ONNV backbone.

We thank Reviewer 2 for this insightful comment. As the secretory pathway is commonly important for alphavirus glycoprotein maturation and assembly, it will be informative to interrogate CHIKV glycoprotein trafficking and assembly through this pathway using specific inhibitors, such as dihydropyridine FLI-06 and golgicide A . Golgicide A is a reversible inhibitor of the cis-Golgi GBF1, which leads to rapid disassembly of the Golgi and trans-Golgi network (TGN)4. FLI-06 is a new inhibitor that interferes with cargo recruitment to ER-exit sites and disrupts Golgi without depolymerizing microtubules or interfering GBF15. We pretreated THP-1 derived macrophages with 10 uM FLI-06 or golgicide A for 30 mins prior to infection with CHIKV, ONNV, Chimera I, or ONNV/ CHIKV E2+E1. After 1 hour of virus adsorption in PBS with 1% FBS in the absence of the inhibitors, the cells were treated with the inhibitors at the same concentration (10uM) in complete medium for 24 h. The plaque assay result shows that all the viruses are sensitive to secretory pathway inhibition, however, the production of viruses containing CHIKV glycoproteins is significantly more attenuated by FLI-06 and golgicide A. This suggests that CHIKV glycoproteins-mediated trafficking and assembly is more heavily dependent on the host secretory pathway . We will include this result in the fully revised manuscript.

1. Finally, the authors should perform competition experiments with the chimeric viruses and ONNV in macrophages to determine if the chimeras can outcompete the parental ONNV strain. Based on their data, the chimeric viruses should outcompete.

We thank Reviewer 2 for this inspiring suggestion. The competition experiment is an innovative and informative way to evaluate whether CHIKV glycoproteins confer a selective advantage on virion production in THP-1 derived macrophages. We plan to infect THP-1 derived macrophages with ONNV and ONNV/CHIKV E2+E1 and detect the viral glycoproteins secreted in the supernatant by western blot, although there is a possibility that this experiment might not work due to superinfection exclusion. Given that there is no commercial antibody of ONNV available, we need to use tagged viruses for this competition experiment. We constructed ONNV/CHIKV myc-E2+E1 that has a myc tag at the N-terminus of CHIKV E2, and ONNV/HA-E2 that has a HA tag at the N-terminus of ONNV E2. Our first attempt at concentrating the viral progenies released by THP-1 derived macrophages infected with the two tagged viruses has not been successful. We performed sucrose gradient ultracentrifugation of the supernatant viral particles but the myc and HA tags were not detected in the expected sucrose layer. Next, we plan to use myc-Ab and HA-Ab conjugated beads to pull down the supernatant viral particles to detect the ratio of ONNV/CHIKV myc-E2+E1 and ONNV/HA-E2 secreted by THP-1 derived macrophages. This will determine whether ONNV containing CHIKV glycoproteins can outcompete ONNV in co-infected cells due to increased viral fitness.

1. The authors use both primary macrophages and macrophage cell lines as their in vitro model system and make one of their major points (listed in the title) that the determinants they identified in the CHIKV structural proteins convert macrophages into dissemination vessels; however, they do not show: 1) an in vivo model that the CHIKV-ONNV chimeras disseminate more efficiently than the parental ONNV; and 2) that these chimeras generate virus more efficiently specifically in macrophages. It would be useful to show that ONNV and CHIKV have equivalent virion production in other cell lines and that the advantage conferred by CHIKV structural proteins in the ONNV backbone is specific to macrophages. The authors should also change their title to reflect that dissemination is not directly being addressed in their study; the implications of their in vitro experimentation in a mammalian host would be more appropriate for the discussion.

We acknowledge the limitations of the study, which include a lack of direct demonstration of in vivo dissemination. To address these concerns, we will include further discussion of our in vitro findings in the context of viral dissemination in mammalian hosts in the fully revised manuscript. We are also testing ONNV, CHIKV, Chimera I and ONNV/CHIKV E2+E1 infections in 293T cells to investigate whether the advantage conferred by CHIKV glycoproteins are macrophage specific.

We have also updated the title to accurately reflect the significance of this research: “Chikungunya virus glycoprotein targeting of host factors increases viral fitness in human macrophage”.

Reviewer 2’s optional comments

1. The authors use CHIKV-ONNV chimeras but it would be interesting to test other chimeras to determine if CHIKV structural proteins confer the same advantage in the backbone of other arthritogenic alphaviruses. The study would also be strengthened by using a pathogenic strain of CHIKV instead of the vaccine strain, as this is significantly attenuated in vivo.

We thank Reviewer 2 for this suggestion which is also suggested by Reviewer 1 in their minor comment #5. We plan to use virulent CHIKV La reunion strain and carry out infection experiments in BSL-3 to strengthen this study. We are getting trained in the BSL-3 facility and will be certified soon.

1. In Figure 4, the authors identify residues in the CHIKV structural proteins that appear to be under positive selection in human subjects and generate point mutants in these residues with the corresponding ONNV residues. They find that one mutation, V1029I located in E1, completely abolishes virion production in THP-1 macrophage cell lines. However, in their previous chimeric experiments, they find that neither CHIKV E1 or E2 was sufficient to increase virus production in the ONNV backbone. The authors should address this discrepancy, otherwise they should consider moving the data in their point mutation experiments to a supplementary figure. While worthy of reporting, especially given the patient data, these experiments do not buttress the points made in the previous figures.

We thank Reviewer 2 for this insightful comment. According to previous studies, E2 and E1 always interact with each other from the step of the formation of single heterodimer in the ER to heterodimer trimerization before viral particle assembly. Although the E1-V220 site (previously called V1029) on the exterior of a single E2-E1 heterodimer appears to not be engaged in the E2-E1 interaction E1-V220 is partially exposed and protruding into the groove formed by E1 and the E2 of neighboring heterodimer, accessible to host factors. As such, mutating CHIKV E1-V220 to the ONNV residue (E1-V220I) may not only disrupt E2-E1 trimerization but also interfere viral glycoprotein interaction with host factors(presented in our response to Reviewer 1’s major comment #1). Similarly, solely swapping E2 or E1 with CHIKV substitute in the ONNV backbone would also affect the interaction between neighboring E2 and E1 in trimerized spike, which may explain why neither ONNV/CHIKV E2 or ONNV/CHIKV E1 rescues virion production in THP-1 derived macrophages . We have included this in the partially revised discussion section lines __ __296-313.

1. The authors conclude their manuscript with an assessment of several host proteins, namely SPCS3 and eIF3k, that were identified by mass spectrometry and whose knockdown results in increased virion production. The authors speculate about the role of these proteins but do not provide any mechanistic detail on how they might be playing a role. It is unclear that the putative antiviral role of these proteins involves steps downstream of virus replication, especially given that the authors speculate translation might be affected by eIF3k which, if the case, RNA synthesis should also be expected to be affected.

We thank Reviewer 2 for this comment. We acknowledge that we have yet a full mechanistic understanding of how SPCS3 and eIF3k impact virion production. We plan to investigate their antiviral roles in our follow-up studies. For our partial revision, we have constructed several single eIF3k knockout (KO) clones of 293T cells. The eIF3k sgRNA we designed targets exon 3 which would eliminate expression of all 3 splice isoforms of eIF3k (KO schematic and sequence verification of CRISPR KO shown in revision plan). Unfortunately, we failed to obtain single clones of 293T cells with SPCS3 complete KO, consistent with a previous study by Rong Zhang et al6 that were unable to recover SPCS3 KO clones likely due to the importance of SPCS3 in cell survival. We infected an eIF3k KO clone (clone 9) with CHIKV vaccine strain 181/clone 25, ONNV SG650, and SINV Toto1101. Interestingly, we found that the antiviral activity of eIF3k is specific to CHIKV as CRISPR KO of eIF3k increases CHIKV production by 2.5 fold but not ONNV or SINV production (shown in revision plan). We have included this in the partially revised manuscript in__ line 272-282 (Figure 7B-7D).__

We presume that Reviewer 2’s inference of eIF3k’s potential effects on viral RNA synthesis is based on our speculation of its antiviral role in viral translation, which may affect viral nonstructural gene expression. We would like to clarify that eIF3k is not an initiation factor traditionally needed for cap-dependent translation. It is also not clear what translation process (nonstructural polyprotein translation from viral genomic RNA or structural polyprotein translation from viral subgenomic mRNA) involves eIF3k if it indeed affects viral protein expression. Notably, previous SINV studies imply that alphavirus structural polyprotein translation may employ unique mechanisms without the requirement of several crucial initiation factors4,5. It will be interesting to see whether eIF3k participates in viral subgenomic mRNA translation as that would affect viral glycoprotein expression leading to reduced virion production. We have now included additional discussion on eIF3k antiviral mechanisms in the partially revised manuscript in lines 345-353.

1. Overall, while the initial chimeric virus and domain swap approach is strong, the manuscript would benefit with a more thorough examination of virion assembly steps and a mechanistic link to virion production. Otherwise, the authors should revise the structure of their manuscript by de-emphasizing points about virion assembly and leave room for other mechanistic explanations of their chimeric data that more clearly link the host antiviral factor/E1 binding studies.

We thank the reviewer for these positive comments and suggestions. We have addressed this by further interrogating the production kinetics of CHIKV, ONNV, and the chimeras containing CHIKV glycoproteins through determining their particle-to-PFU ratios as well as treating infected cells with secretory pathway inhibitors (refer to our responses to Reviewer 2 major comments #1 and #2). We have also included additional discussion on eIF3k antiviral mechanisms specifically on how it may affect other steps of the viral life cycle in the partially revised manuscript in lines 345-353 (refer to our response to Reviewer 2 optional comment #3).

Reviewer 3’s critique comments

1. Overall, the manuscript is well written but in its current state it is more like two different stories because the effects of envelope proteins and list of interactors are not brought together in one story. A possible fix to this problem would be inclusion of ONNV and CHIKV containing env mutations that do and do not restore viral release from macrophages into the pulldown/association experiments shown in Figure 6.

We thank Reviewer 3 for the insightful suggestions to better connect the first (CHIKV determinants) and second (CHIKV glycoprotein interactors) parts of the manuscript. In response to the Reviewer’s comment, we tested the binding of SPCS3 and eIF3k to CHIKV E1 with E1-V220I (V1029I in original manuscript version) mutation (shown in revision plan) which was shown to abrogate virion production in THP-1 derived macrophages in Figure 4E. We transfected plasmids expressing 3XFLAG-tagged SPCS3/eIF3k or empty vector for 24 h followed by transfection with plasmids expressing either the parental CHIKV vaccine strain 181/clone 25 poly-glycoproteins (E3-myc-E2-6K-E1) or poly-glycoproteins with the E1-V220I mutation. Interestingly, we found that mutating CHIKV E1-V220 to the homologous ONNV residue reduces the binding to either SPCS3 or eIF3k. This result strongly suggests that the positively selected E1-V220 is located in the interaction interface between E1 and SPCS3/eIF3k, confirming the genetic conflict between E1 and these host factors to be one of the major drivers of CHIKV evolution observed at site E1-V220. We have included this result in partially revised manuscript in Figure 7A and in lines 265-271.

1. The other major issue is the lack of protein data for the viral mutants relative to WT ONNV and CHIKV and assessment of viral RNA in the supernatants to determine whether the block is release or an earlier event since viral RNA levels in the cell seems to be the same or at least normalized.

We thank Reviewer 3 for pointing out the insufficient clarification of the block leading to defective CHIKV mutant virion production. We previously detected E2 expression from 293T cells transfected with poly-glycoproteins (E3-myc-E2-6K-E1) containing E2-V135L (V460L in original manuscript version), E2-A164T (A489T in original manuscript version), E2-A246S (A571S in original manuscript version) and E1-V220I (V1029I in original manuscript version). We found that only E2-V135L mutation can lead to unexpected E2 cleavage (shown in revision plan) as we mentioned but not shown in the original manuscript. This explains why E2-V135L mutation attenuates infectious CHIKV production.

The E2 expression of E1-V220I appears to be not affected in 293T cells transfected with poly-glycoproteins with E1-V220I (shown in revision plan ). In addition, the E1-host factor binding result in our response to Reviewer 3’s major comment #1 showed that E1 with the positively selected site mutation V220I can also be successfully expressed in 293T cells after transfection with poly-glycoprotein. Based on these current data, E1-V220I mutation likely abrogates virion production without affecting glycoprotein expression.

Our previous result of the ONNV particle-to-PFU ratio reveals that ONNV RNA is released but encapsidated in defective particles causing its attenuation in infected macrophages. Thus, even though the glycoproteins of E1-V220I can be expressed, the diminished virion production of CHIKV E1-V220I can still be ascribed to 1) blocked viral particle release and 2) production of defective particles like ONNV. Given that it is not feasible to obtain particle-to-PFU ratio of E1-V220I mutant which fails to form plaques, Reviewer 3’s suggestion to assess the supernatant viral RNA will be a nice approach to address this question. To further address this concern, we plan to transfect THP-1 derived macrophages with CHIKV E1-V220I mutant RNA to detect the intracellular viral glycoprotein expression and supernatant viral RNA levels through western blot and TaqMan assay, respectively.

1. Lastly, knockdown experiments indicate an effect of things like OAS3 or other innate immune modulators. There are no controls to demonstrate that these are specific to CHIKV infection or if knockdown would assist growth of ONNV as well.

We also thank Reviewer 3 for the suggestion to check whether the identified host factors specifically target CHIKV or inhibit the infection of ONNV as well. We previously tried but were facing some issues. Since only a small fraction of macrophages can be infected with CHIKV and even a smaller fraction can be infected with ONNV (Figure 1A), it is hard to elucidate the roles of these identified host factors in ONNV infection by siRNA knockdown. We decided to take a more rigorous approach to investigate the antiviral specificity of identified host factors, especially understudied SPCS3 and eIF3k, to different alphaviruses by generating complete knockout 293T single cell clones. Despite the fact that we did not successfully generate SPCS3 complete KO, we obtained an eIF3k KO single cell clone and infected it with CHIKV, ONNV and SINV (refer to our response to Reviewer 2 optional comment #3). We found that eIF3k only has antiviral activity against CHIKV with almost no effects on ONNV or SINV infection. We have included this in our partially revised manuscript in line 272-282 (Figure 7B-7D).

Reviewer 3's minor comments:

Other points to consider:

1. The title does not fit the manuscript findings and should be modified.

We thank Reviewer 3 for this important comment, which was also brought up by Reviewer 2. We have now changed our title to “Chikungunya virus glycoprotein targeting of host factors increases viral fitness in human macrophage”, which more accurately reflects the significance of our research.

1. It is unclear why the authors show results for SINV and RRV in Figure 1. Either these should be removed or the viruses should be carried throughout the experiments described in the Figure. Better yet would be to add additional alphaviruses to this analysis to determine if there are additional viruses that act similarly to CHIKV.

We apologize for the confusion caused by including SINV and RRV results in Figure 1. We intended to show the superiority of CHIKV in infecting primary monocyte derived macrophages among arthritogenic alphaviruses, which we speculate may provide the molecular basis for macrophage-mediated CHIKV dissemination and disease. We would like to keep the SINV and RRV infection results in Figure 1 to highlight the relative susceptibility of macrophages to CHIKV. To echo the additional alphaviruses tested in Figure 1 and bring the story full circle, we included the result of SINV infection of eIF3k CRISPR KO 293T cells in Figure 7B-7D. These results uncover inhibitory activities of eIF3k that are specific to CHIKV.

1. Is the data presented in Figure 1A significant?

We thank Reviewer 3 for this question. We infected both THP-1 derived macrophages and primary monocyte derived macrophages with EGFP-expressing alphaviruses each in duplicates for two independent times. The general low expression of EGFP in all virus-infected groups refrains us from drawing conclusions based on statistically significant differences observed with MFI, hence we chose to show representative scatter plots in the original manuscript. To address Reviewers 3's question, we plotted the infected cell (EGFP+) based on the percentages of the experimental duplicates (shown in revision plan), and found CHIKV infection to be the most significantly different from that of the other alphaviruses in primary monocyte derived macrophage . The numbers above the bar charts are the mean percentages of EGFP+ cells.

1. The justification for inclusion of Figure 4A is lacking. It is unclear what this panel is supposed to be demonstrating.

This is an excellent suggestion as the host factors identified by AP-MS not only contain interactors of CHIKV mature E2 but also those of uncleaved E2-containing precursor polyproteins. We modified Figure 4A to reflect all E2/E2-containing poly-glycoproteins present in CHIKV-infected cells (shown in revision plan).

1. There is little justification for the candidates assessed in

We understand Reviewer 3’s concern. Due to the nature of mass spectrometry studies which predict protein-protein interactions rather than direct functional validation, we acknowledge that we may miss some host candidates that have anti- or pro-CHIKV activities. Although justification of hit selection from mass spectrometry datasets is more difficult than that from CRISPR KO screen datasets, we set up specific criteria to identify host protein candidates with the greatest potential to functionally interact with CHIKV glycoproteins. Most of the proteins we chose to validate (Figure 6a) were identified in both of our independent AP-MS experiments, which both pass through a P-value threshold of 0.05 and log2 fold change of 0.

1. Extended data Figure 3 is very difficult to read due to the small font size.

We apologize for the small font in Extended data Figure 3. We plan to replace Figure EV3 ( Extended data 3 in unrevised version) with a CORUM protein-protein interaction network that centers on the significant hits identified by both AP-MS experiments, but includes hits from either one of the two experiments in these functional protein complexes. The figure will be more concise and centralized, and the font will be bigger.

1. Just to be clear, the blots shown in Figure 6D are different from those depicted in Extended data Figure 4b, because some of them look very similar.

We thank Reviewer 3 for this question. In Figure 6D, we expressed CHIKV glycoproteins through transfecting CHIKV genomic RNA into 293T cells, while, in Figure 4B, we expressed CHIKV glycoproteins through transfecting poly-glycoprotein plasmid (pcDNA3.1-E3-myc-E2-6K-E1) into 293T cells, which are complementary approaches to express CHIKV glycoproteins to validate their interactions with identified host factors. We have now added schematics to illustrate the different experimental strategies above the figures in this partially revised manuscript (shown in revision plan).

References:

Voss, J. E. et al. Glycoprotein organization of Chikungunya virus particles revealed by X-ray crystallography. Nature 468, 709–712 (2010). Jose, J., Tang, J., Taylor, A. B., Baker, T. S. & Kuhn, R. J. Fluorescent Protein-Tagged Sindbis Virus E2 Glycoprotein Allows Single Particle Analysis of Virus Budding from Live Cells. Viruses 7, 6182–6199 (2015). Götte, B., Liu, L. & McInerney, G. M. The Enigmatic Alphavirus Non-Structural Protein 3 (nsP3) Revealing Its Secrets at Last. Viruses 10, 105 (2018). Saenz, J. B. et al. Golgicide A reveals essential roles for GBF1 in Golgi assembly and function. Nat. Chem. Biol. 5, 157–165 (2009). Krämer, A. et al. Small molecules intercept Notch signaling and the early secretory pathway. Nat. Chem. Biol.9, 731–738 (2013). Zhang, R. et al. A CRISPR screen defines a signal peptide processing pathway required by flaviviruses. Nature 535, 164–168 (2016).

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Referee #3

Evidence, reproducibility and clarity

Review: In this manuscript the authors generated macrophages derived from the THP-1 cell line or human peripheral blood mononuclear cells stimulated with MCSF and infected them with alphaviruses some containing GFP expression cassettes. In Figure 1, they demonstrate that CHIKV infected these cells more robustly than RRV, SINV or the related ONNV. The authors generated an extensive array of CHIKV/ONNV chimeras to identify the viral proteins that dictate release from infected macrophages and narrowed it down to the envelop proteins E1 and E2. Fine mapping identified a couple of single mutations that affected macrophage infection outcomes. The authors then shifted their approach to identifying env protein interactors using a myc-tag pulldown methods followed by mass spectrometry. The assay identified a number of proteins including those involved in vesicular transport and interferon pathways. siRNA knockdown experiments were performed to identify interactors and many of them were shown to improve virus output.

Critique: Overall, the manuscript is well written but in its current state it is more like two different stories because the effects of envelop proteins and list of interactors are not brought together in on one story. A possible fix to this problem would be inclusion of ONNV and CHIKV containing env mutations that do and do not restore viral release from macrophages into the pulldown/association experiments shown in Figure 6. The other major issue is the lack of protein data for the viral mutants relative to WT ONNV and CHIKV and assessment of viral RNA in the supernatants to determine whether the block is release or an earlier event since viral RNA levels in the cell seems to be the same or at least normalized. Lastly, knockdown experiments indicate an effect of things like OAS3 or other innate immune modulators. There are no controls to demonstrate that these are specific to CHIKV infection or if knockdown would assist growth of ONNV as well.

Other points to consider:

1. The title does not fit the manuscript findings and should be modified.
2. It is unclear why the authors show results for SINV and RRV in Figure 1. Either these should be removed or the viruses should be carried throughout the experiments described in the Figure. Better yet would be to add additional alphaviruses to this analysis to determine if there are additional viruses that act similarly to CHIKV.
3. Is the data presented in Figure 1A significant?
4. The justification for inclusion of Figure 4A is lacking. It is unclear what this panel is supposed to be demonstrating.
5. There is little justification for the candiates assessed in
6. Extended data Figure 3 is very difficult to read due to the small font size.
7. Just to be clear, the blots shown in Figure 6D are different from those depicted in Extended data Figure 4b, because some of them look very similar.

Significance

The study provides a fresh look at Alphavirus replication in macrophages. There are a number of issues that should be worked out that would enhance impact and interpretation of this study.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this work Yao et al. show CHIK is able to infect macrophages in contrast to other arthritogenic alphaviruses RRV, ONNV, and SINV. They use a series to chimeric viruses made with ONNV, the closest species to CHIK, and determine the E2-E1 proteins are important viral determinants which allow CHIK to replicate in machophages compared to ONNV. By comparing 397 CHIK sequences from infected patients, they identified 14 residues under pervasive and positive selection. Of these, 3 residues in E2 and 3 residues in E1 (amino acids) were different between CHIK and ONNV suggesting these residues contributed to the difference in macrophage tropism of CHIK compared to ONNV. The authors go on to determine what host factors the CHIK E2 protein is interacting with to presumably connect the viral and host determinants for CHIK infection in macrophages.

Major concerns:

1. The authors show one configuration of the E1-E2 heterodimer in Figure 4d. As shown, the E1 protein is exterior to the E2 protein and would suggest E1 is on the surface on the spike complex and virus surface. However, another configuration of the glycoproteins has E2 on the exterior of E1 and also on the exterior of the virus. The latter conformation is what has been observed in cryoEM studies of alphaviruses. The first configuation represents the E1-E2 between the three heterodimers which are important for spike assembly. The reason the orientation of the E2-E1 dimer is important is the authors speculate on the importance of the 6 CHIK residues not found in ONNV based on the structure, but the structural interpretation is, in my opinion, not correct.
2. Validation of E1 interaction with SPSC3 and eIF3k needs to be stronger. Some concerns/questions are listed below. A myc tag was inserted between E3 and E2. How efficeintly does furin cleave E3 from E2 in this virus and how are viral titers of the myc-tagged virus compared to the non-tagged virus? I ask because is the IP looking at what is being pulled down by E2 or E3-myc-E2 that could be part of the spike polyprotein? The authors found E2 interacts with E3, E1 and a list of other host proteins. These results suggest several interactions including E2-host factor, E2-E1, E2-E3, E2-E1-host factor, E2-E3-E1, E2-E3-host factor. In figure 6d, and the subsequent conclusions, the authors suggest E1 is interacting with the host facor and do not see E2 alone and very low amounts of E3-E2-6K-E1. based on how the IP was performed I am not sure how an interaction between E1 and SPCS3 alone, without E2, would be detected. I would also like to see a reciprocal pull down using E1 and also E2 to see if these host factors are pulled down.
3. If CHIK E1 is interacting with the host factors and that is antagonizing the antiviral response of SPSC3 (as one example), then what do pull downs using ONNV structural proteins look like? One would expect reduced interactions because the different amino acid causes a different E2-E1 dimer or attenuates the E1-host factor binding site.
4. E1 and E2 are thought to interact during polyprotein translation and the initial dimer forms in the ER. If E1 is interacting wht SPSC3 in the ER, is E2 also present? Or is a population of E1 not interacting with E2 in order to inhibit SPSC3? I would love a model of how the authors see all these factors coming together for this new role of E1.

Minor concerns:

1. In Figure 1c, (-) RNA is shown but in the rest of the figures (+) RNA is shown. Show both or select one. I do find it interesting the (-) RNA levels are similar over time, even at 4 hours post transfection (early time). Related to this, ONNV has higher levels of (-) RNA but what is known about structural protein levels in ONNV and CHIK in macrophages? Are there comparable levels of CP and GP being produced?
2. Figure 2e and figure 3 have ONNV has the first bar followed by CHIK. In figure 1 and 2b, CHIK is first and then ONNV. helps the reader to have the controls in the same order.
3. Line 143-145 the authors discuss that when ONNV is the backbone and CHIK proteins are inserted the infection is more attenuated because of the E2 and E1 are from CHIK and ONNV, not the same virus (could also be E2-CP interactions are disrupted). However the chimeras made witht he CHIK backbone (in Figure 2) have a mismatch between E2 and E1 as well.
4. When discussing the residues that were found in the FEL and MEME analysis, the authors start the amino acid numbering from CP and continue along the polyprotein. Usually when discussing amino acids in the structural proteins, each protein starts at amino acid 1. So V460 would be E2-V135. It would also be useful to know what the residues in ONNV were at these positions to see if amino acids changed in charge, size, bond forming potential, etc. Showing these residues in the E2-E1 conformation found in the virion would also allow one to find adjeacent residues that could explain differences in spike assembly and potentially where/how E1 is binding to a host protein.
5. How effective is a non-attenuated CHIK strain in infecting macrophages? Could you make a SINV-La Reunion chimeric virus (which is BSL2) to see if a higher percentage of macrophages are infected and is this potentially contributing to the increased pathogenesis of La Reunion? Also how different is 181/25 with a pathogenic strain in the E2 and E1 resdiues? and compared to ONNV?
6. When describing the last results section, "CHIK E1 binding proteins exhibit potent anit-CHIV activities" the authors use macrophages. In the rest of the text they consistently use THP-1 macrophages or human primary monocyte derived macrophages. The details of the cell type are extremely useful to the reader and having those in the last results section would be great.
7. The paper is well-written. There is a slight disconnect as the authors go from discussing results in Figure 4 to Figure 5.

Referees cross-commenting

I agree with R#2 that having some Particle:PFU data would add some data to determine why such differences in titers/infectivity.

I also see how this m/s could be split into two different m/s. One that focuses on the chimeric viruses and another that identifies the host factors important and goes in more depth with mechanism

Significance

Strengths:

The authors have tackeled an intriguing question: why do some alphaviruses infect macrophages and others do not. They have used a chimeric approached to very systematically identify the viral determinants E2 and E1 as being important in macrophage infection. Using AP-MS they identify host factors that interact with E2 (possibly E2 and E1, see comments above) but if their findings that E1 has a role in attenuating a host antiviral factor, this would be fantastic.

More and more examples of viral proteins having multiple roles during infection are in the literature. The idea that structural proteins also attenutate host antivirals is a developing field and vastly understudied. By fleshing out the results some more the authors might be onto something ery important in alphavirus virology.

Limitations:

The study has it is presented is limited in the validation of host factors and their interacting partners. I have many questions about the methodology, validation, and model from this last section.

There is a difference between the sorts of links one might make when placing an idea into an (analog) zettelkasten. A folgezettel link is more valuable than a simple tag/category link because it places an idea into a more specific neighborhood than any handful of tags. This is one of the benefits of a Luhmann-artig ZK system over a more traditional commonplace one, particularly when the work is done up front instead of being punted to a later time.

For those with a 1A2B3Z linking system (versus a pure decimal system), it may be more difficult to insert a card before other cards rather than after them because of the potential gymnastics of numbering and the natural tendency to put things into a continuing linear order.

See also: - https://hypothes.is/a/ToqCPq1bEe2Q0b88j4whwQ - https://hyp.is/WtB2AqmlEe2wvCsB5ZyL5A/docdrop.org/download_annotation_doc/Introduction-to-Luhmanns-Zette---Ludecke-Daniel-h4nh8.pdf

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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.

how are you doing this

Reviewer #1 (Public Review):

In this study, the authors aim to understand why decision formation during behavioural tasks is distributed across multiple brain areas. They hypothesize that multiple areas are used in order to implement an information bottleneck (IB). Using neural activity recorded from monkey DLPFC and PMd performing a 2-AFC task, they show that DLPFC represents various task variables (decision, color, target configuration), while downstream PMd primarily represents decision information. Since decision information is the only information needed to make a decision, the authors point out that PMd has a minimal sufficient representation (as expected from an IB). They then train 3-area RNNs on the same task and show that activity in the first and third areas resemble the neural representations of DLPFC and PMd, respectively. In order to propose a mechanism, they analyse the RNN and find that area 3 ends up with primarily decision information because feedforward connections between areas primarily propagate decision information.

The paper addresses a deep, normative question, namely why task information is distributed across several areas.

Overall, it reads well and the analysis is well done and mostly correct (see below for some comments). My major problem with the paper is that I do not see that it actually provides an answer to the question posed (why is information distributed across areas?). I find that the core problem is that the information bottleneck method, which is evoked throughout the paper, is simply a generic compression method. Being a generic compressor, the IB does not make any statements about how a particular compression should be distributed across brain areas - see major points (1) and (2).

If I ignore the reference to the information bottleneck and the question of why pieces of information are distributed, I still see a more mechanistic study that proposes a neural mechanism of how decisions are formed, in the tradition of RNN-modelling of neural activity as in Mante et al 2013. Seen through this more limited sense, the present study succeeds at pointing out a good model-data match. I point out some suggestions for improvement below.

Major points<br /> (1) It seems to me that the author's use of the IB is based on the reasoning that deep neural networks form decisions by passing task information through a series of transformations/layers/areas and that these deep nets have been shown to implement an IB. Furthermore, these transformations are also loosely motivated by the data processing inequality.

However, assuming as a given that deep neural networks implement an IB does not mean that an IB can only be implemented through a deep neural network. In fact, IBs could be performed with a single transformation just as well. More formally, a task associates stimuli (X) with required responses (Y), and the IB principle states that X should be mapped to a representation Z, such that I(X;Z) is minimal and I(Y,Z) is maximal. Importantly, the form of the map Z=f(X) is not constrained by the IB. In other words, the IB does not impose that there needs to be a series of transformations. I therefore do not see how the IB by itself makes any statement about the distribution of information across various brain areas.

A related problem is that the authors really only evoke the IB to explain the representation in PMd: Fig 2 shows that PMd is almost only showing decision information, and thus one can call this a minimal sufficient representation of the decision (although ignoring substantial condition independent activity). However, there is no IB prediction about what the representation of DLPFC should look like. Consequently, there is no IB prediction about how information should be distributed across DLPFC and PMd.

(2) Now the authors could change their argument and state that what is really needed is an IB with the additional assumption that transformations go through a feedforward network. However, even in this case, I am not sure I understand the need for distributing information in this task. In fact, in both the data and the network model, there is a nice linear readout of the decision information in dPFC (data) or area 1 (network model). Accordingly, the decision readout could occur at this stage already, and there is absolutely no need to tag on another area (PMd, area 2+3).

Similarly, I noticed that the authors consider 2,3, and 4-area models, but they do not consider a 1-area model. It is not clear why the 1-area model is not considered. Given that e.g. Mante et al, 2013, manage to fit a 1-area model to a task of similar complexity, I would a priori assume that a 1-area RNN would do just as well in solving this task.

I think there are two more general problems with the author's approach. First, transformations or hierarchical representations are usually evoked to get information into the right format in a pure feedforward network. An RNN can be seen as an infinitely deep feedforward network, so even a single RNN has, at least in theory, and in contrast to feedforward layers, the power to do arbitrarily complex transformations. Second, the information coming into the network here (color + target) is a classical xor-task. While this task cannot be solved by a perceptron (=single neuron), it also is not that complex either, at least compared to, e.g., the task of distinguishing cats from dogs based on an incoming image in pixel format.

(3) I am convinced of the author's argument that the RNN reproduces key features of the neural data. However, there are some points where the analysis should be improved.

(a) It seems that dPCA was applied without regularization. Since dPCA can overfit the data, proper regularization is important, so that one can judge, e.g., whether the components of Fig.2g,h are significant, or whether the differences between DLPFC and PMd are significant.

(b) I would have assumed that the analyses performed on the neural data were identical to the ones performed on the RNN data. However, it looked to me like that was not the case. For instance, dPCA of the neural data is done by restretching randomly timed trials to a median trial. It seemed that this restretching was not performed on the RNN. Maybe that is just an oversight, but it should be clarified. Moreover, the decoding analyses used SVC for the neural data, but a neural-net-based approach for the RNN data. Why the differences?

(4) The RNN seems to fit the data quite nicely, so that is interesting. At the same time, the fit seems somewhat serendipitous, or at least, I did not get a good sense of what was needed to make the RNN fit the data. The authors did go to great lengths to fit various network models and turn several knobs on the fit. However, at least to me, there are a few (obvious) knobs that were not tested.

First, as already mentioned above, why not try to fit a single-area model? I would expect that a single area model could also learn the task - after all, that is what Mante et al did in their 2013 paper and the author's task does not seem any more complex than the task by Mante and colleagues.

Second, I noticed that the networks fitted are always feedforward-dominated. What happens when feedforward and feedback connections are on an equal footing? Do we still find that only the decision information propagates to the next area? Quite generally, when it comes to attenuating information that is fed into the network (e.g. color), then that is much easier done through feedforward connections (where it can be done in a single pass, through proper alignment or misalignment of the feedforward synapses) than through recurrent connections (where you need to actively cancel the incoming information). So it seems to me that the reason the attenuation occurs in the inter-area connections could simply be because the odds are a priori stacked against recurrent connections. In the real brain, of course, there is no clear evidence that feedforward connections dominate over feedback connections anatomically.

More generally, it would be useful to clarify what exactly is sufficient:

(a) the information distribution occurs in any RNN, i.e., also in one-area RNNs<br /> (b) the information distribution occurs when there are several, sparsely connected areas<br /> (c) the information distribution occurs when there are feedforward-dominated connections between areas

Die Intensität des Sturms Daniel, der in Libyen Tausende Opfer forderte, geht mit hoher Wahrschilichkeit auf die hohe Temperatur an der Oberfläche des Mittelmeers zurück. Ähnliche Extremereignisse jenseits des bisher Erwartbaren werden folgen. Für die katastrophale Wirkung sind auch schlechte Infrastruktur, ungenügende Vorhersage-Möglichkeiten, mangelhafte Alarmsysteme und falsche Planung verantwortlich. https://www.theguardian.com/world/2023/sep/12/questions-raised-over-deadly-impact-of-storm-daniel-in-libya

Mehr zum Sturm Daniel: https://hypothes.is/search?q=tag%3A%22event%3A%20storm%20Daniel%22

Der Sturm Daniel hat sich, nachdem er Verwüstungen in Griechenland, Bulgarien und der Türkei angerichtet hat, auf dem Weg über das Mittelmeer nach Libyen in einen Medikan, einen „Mittelmeer-Hurrican“ verwandelt. Dort wurden in Al Baida 414,4 mm Niederschläge an einen Tag gemessen.

https://www.liberation.fr/environnement/climat/inondations-ce-que-lon-sait-de-la-tempete-daniel-qui-a-touche-de-la-grece-a-la-libye-20230912_QQEOMJOEMBGSJNAPFXMG7HEUQM/

Mehr zum Sturm Daniel: https://hypothes.is/search?q=tag%3A%22event%3A%20storm%20Daniel%22

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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.

(1:20.00-1:40.00) What he describes is the following: Most of his notes originate from the digital using hypothes.is, where he reads material online and can annotate, highlight, and tag to help future him find the material by tag or bulk digital search. He calls his hypothes.is a commonplace book that is somewhat pre-organized.

Aldrich continues by explaining that in his commonplace hypothes.is his notes are not interlinked in a Luhmannian Zettelkasten sense, but he "sucks the data" right into Obsidian where he plays around with the content and does some of that interlinking and massage it.

Then, the best of the best material, or that which he is most interested in working with, writing about, etc., converted into a more Luhmannesque type Zettelkasten where it is much more densely interlinked. He emphasizes that his Luhmann zettelkasten is mostly consisting of his own thoughts and is very well-developed, to the point where he can "take a string of 20 cards and ostensibly it's its own essay and then publish it as a blog post or article."

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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.

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

Learn more at Review Commons

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Referee #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.
2. 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+?
3. 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)?

Minor comments:

1. How were the times (post addition of Ca2+) presented in Figure 2A determined?
2. Line 126: Figure 1B should be Figure 1C
3. Line 145: Figure 1C-D should be Figure 1D-E
4. Line 151: Figure 3A should be Figure 4A
5. 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
6. Any growth phenotype changes observed in the overexpressors?
7. How often are ECCAs observed in pARL strains, or are they not observed at all? This might be good to mention.
8. 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)
9. 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.
10. Line 295: include abbreviation of cRPMI here rather than in Line 303
11. Line 322: typographical error on WR99210 working concentration?
12. Line 372: Last sentence on area and raw integrated density measurement is unclear.
13. Line 461: typographical error in last sentence
14. Line 532: Figure 4E should be Figure 4F

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.

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

Learn more at Review Commons

---

Referee #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".
• 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.
• 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).
• 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.
• 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.

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.
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.
3. Do the authors think that PfCen2 and PfCent4 localize to the centriole plaque in vivo using another mechanism that deployed by PfCen1 and PfCent3? It would be good to discuss this point.
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.
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?
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.
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).
8. 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).

Small things

• Fig. 1A: change color for microtubules as red on red is difficult to discernn.
• Fig. 1C: the indicated boxes in the top row do not seem to correspond exactly to the insets shown in the bottom row.
• line 266: typo, promotor > promoter.
• line 360: a reference should be provided for the GFP-booster, including the concentration at which it was used.
• line 363: "an" missing before "HC".
• line 428: it would be best to deposit the macros on Github or an analogous repository.
• line 461: "to the" is duplicated.
• Fig. S5A: maybe draw the lines in red (as red in Fig. S5B correspond to the proteins that do not have IDRs).
• Movie S7, legend: left frames shows PfCen1-GFP, not microtubules as currently stated.

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.

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

Voss, Reinert 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.
2. 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? 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.

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.<br /> 3. 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.

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.<br /> 4. 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.<br /> 5. 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.<br /> 6. 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.

Minor points

1. There are only three centrins in humans. Centrin 4 is a pseudogene (Gene ID: 729338 on NCBI).
2. 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.

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 of high concentrations.

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.

In Griechenland, Bulgarien und der Türkei hat der Sturm Daniel zu verheerenden Überschwemmungen mit Todesopfern geführt. Das thessalische Flachland wurde vollständig überflutet. https://www.derstandard.de/story/3000000185931/anhaltender-starkregen-legt-infrastruktur-in-teilen-griechenlands-lahm

Mehr zum Sturm Daniel: https://hypothes.is/search?q=tag%3A%22event%3A%20storm%20Daniel%22

Reviewer #2 (Public Review):

Summary:<br /> Radial spokes are evolutionarily conserved protein complexes that are important for cilia motility. So far, the composition of certain radial spokes was investigated in the algae Chlamydomonas, mice, and humans. This work by Bicka et al. investigated the composition of radial spokes in the ciliate Tetrahymena by analyzing knockouts and strains that express tagged radial spoke proteins, using mass spectrometry and cryo-electron tomography. While three specific types of radial spokes have been reported thus far, this study suggests that in Tetrahymena, there is another layer to the variability in radial spokes. Additionally, many proteins with predicted enzymatic folds have now been assigned to radial spokes. The comparison of ciliary complexes between species is important to define the basic principles that govern cilia motility, as well as to reveal the differences that enable cilia of various organisms to beat in diverse environments.

Strengths:<br /> The manuscript includes a thorough bioinformatic analysis of radial spoke proteins in Tetrahymena and reveals the presence of multiple orthologs to certain algae and mammalian radial spoke proteins. The mass spectrometry analysis and cryo-electron tomography experiments are solid and informative. This work provides a lot of important data and thus, opens the door to resolve the exact composition and structures of radial spokes in Tetrahymena and perhaps other species.

Weaknesses:<br /> The assignment of the three RSP3 orthologs to RS1, RS2, and RS3 is based only on missing structures in the knockouts. Although this method is informative, it is not sufficient to draw conclusions regarding the positions of the missing proteins. There are numerous examples where a structure was missing, but the absent protein was localized elsewhere (i.e., absence of central pair protrusions in patients with mutations in radial spoke proteins). To directly demonstrate the position of an RSP3 ortholog in a certain radial spoke, the protein can be labeled with a tag that is visualized in subtomogram averages (as was done in Oda et al., 2014 and other studies). Relying on the data from knockouts alone, the model for radial spoke composition in Tetrahymena (Fig. 6) may be incomplete.

The control for the bio-ID experiment was WT cells. Since there are many hits in the experiment, a better control would have been a strain with free BirA, or BirA fused to a protein that is distant from the radial spokes, such as one of the outer-dynein arm proteins, or a ciliary membrane protein.

Author Response

We thank Editors and Reviewers for their positive evaluation of our work and appreciation of new findings and applied interdisciplinary approaches. We also thank for pointing out manuscript weaknesses as well as for all suggestions and advices that can strengthen this manuscript. We apologise for mistakes, overstatements or discrepancies in citing figures as well as omitted references.

The first part of the manuscript focuses on the Tetrahymena RSP3 genes mutants.  Tetrahymena genome encodes three RSP3 paralogs that are the components of different radial spokes and likely form homo- and heterodimers. Thus, the proteomic analyses of Tetrahymena radial spokes are more complicated compared to the similar analyses in organisms having a single RSP3 protein.

Next, we attempted to identify proteins specific for each RS type. Conducting this research, we took advantage of six different radial spoke knockout mutants (RSP3A-KO, RSP3B-KO, RSP3C-KO, CFAP206-KO, CFAP61-KO, and CFAP91-KO) and compared wild-type and mutants’ ciliomes using two methods, LFQ and TMT (for each mutant the experiment was repeated three times). Comparative analyses of the wild-type and mutants ciliomes allowed us to identify Tetrahymena radial spoke proteins, in the case of RS1 (WT versus RSP3A-KO), RS2 (WT versus RSP3B-KO, RSP3C-KO, and CFAP206-KO), and RS3 (wild-type versus  CFAP61-KO and CFAP91-KO). The extensive proteomic analyses were combined with detailed bioinformatics studies and co-immunoprecipitation and BioID assays to verify the presence of identified proteins in RS complexes. 

Importantly, in the case of RS1 and RS2 spokes, our findings are in agreement with data obtained for Chlamydomonas and mammalian radial spokes. Thus, it is very likely, that newly discovered RS1 and RS2 proteins as well as identified Tetrahymena RS3 proteins are also true RS subunits.

As an outcome of this part, we propose a model of the RS protein composition in a ciliate Tetrahymena. We agree that this model requires further experimental verification (for example by pull-down experiments).  However, considering the number of identified proteins, this is a considerable amount of additional work that we would like to publish as separate papers. We would like to add that our current analyses of additional RS3 mutant (that will be published separately) support findings regarding RS3 proteomic composition.

Reviewer 2:

The control for the bio-ID experiment was WT cells. Since there are many hits in the experiment, a better control would have been a strain with free BirA, or BirA fused to a protein that is distant from the radial spokes, such as one of the outer-dynein arm proteins, or a ciliary membrane protein.

The BirA* tag is approximately 30 kDa protein and thus it can be transported to cilia by diffusion. BirA* ligase present throughout the cilia could randomly biotinylate proteins including radial spoke proteins. Thus, expression of the BirA* alone is not the best control. We have performed numerous BioID experiments in which BirA* tag was fused with T/TH subunits (CFAP43, CFAP44, Urbanska et al., 2018), subunits of the small complex positioned parallel to N-DRC (CCDC113, CCDC96, Bazan et al., 2021), CFAP69, SPEF2A (C1b central apparatus complex, Joachimiak et al., 2021), N-DRC proteins (Ghanaeian et al., Biorxiv, 2023) and subunits of other ciliary complexes (our unpublished data). The comparison of the earlier obtained BioID data with RSP BioID data, prove that identified proteins are specifically associated with radial spokes. Therefore, in our model, wild-type cells are a good control for BioID experiments.

Author Response:

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

We thank the reviewers for their thoughtful and positive evaluation of our work. Below, we have addressed all of the essential revisions and provide point-by-point responses to all of the reviewer comments. Additionally, we include with this resubmission quantification microneme localization, determined by expansion microscopy, which further validates the central role of HOOK in microneme trafficking.

Suggested revisions:

1. Please confirm the interaction between CDPK1 and ROM4 by reciprocal IP.

Prompted by the reviewers suggestions we examined more closely the pulldowns of WT and myristoylation-deficient CDPK1 (cMut). ROM4 had been identified as differentially enriched in the cMut pulldown; however, upon closer examination we realized that the abundance of ROM4 is actually even greater in the untagged control and therefore likely a variable contaminant in the pulldowns. We have re-analyzed the results of those pulldowns to focus on proteins significantly enriched in association with either WT or cMut CDPK1, relative to untagged controls. Among this set of 16 enriched proteins, only three proteins appeared differentially enriched between WT and cMut. None of the proteins associated with CDPK1 inform pathways related to parasite motility and were therefore not pursued further in this study.

2. Please compare the expression of the tagged and complemented (cWT and cMut) CDPK1 with the endogenous expression of the non-tagged and non-complemented gene.

We compared expression levels of CDPK1 using immunoblot with an anti-CDPK1 antibody comparing TIR1, CDPK1-AID, cWT and cMut parasites, which we have included in panel G of Figure 2–figure supplement 1. Endogenous AID tagging of CDPK1 resulted in a decrease in the abundance of CDPK1. cWT and cMut complementation result in similar expression levels to the AID-tagged iKD CDPK1, albeit the cMut complement has marginally higher expression. Since CDPK1 is essential for the lytic cycle, insufficient levels of the cWT expression would have displayed defects in our plaque assays. We have updated our results to reflect this new data:

“Additionally, we compared endogenous CDPK1 expression to mAID-tagged, cWT, and cMut strain (Figure 2–figure supplement 1). Introduction of a mAID tag to CDPK1 led to a reduction in CDPK1 levels, but these levels were equivalent to complementation products in cWT and cMut parasites.”

3. Please attempt to confirm that aerolysin treatment does not impact myristoylation-dependent subcellular partitioning of CDPK1.

The kinase activity in aerolysin-treated parasites was unaffected by the 1B7 inhibitory nanobody, demonstrating that parasites remain impermeable to proteins as small as 15 kDa.  Furthermore, we localize CDPK1 by immunofluorescence in aerolysin-treated parasites to show that the localization of CDPK1 is indistinguishable from that of vehicle-treated parasites, suggesting that overall CDPK1 localization is unaffected by aerolysin treatment. We include this data in panel B in Figure 3–figure supplement 1. Nevertheless, in the manuscript we discuss the limitations of the thiophosphorylation experiment:

“While our approach largely maintains kinases in their subcellular context, aerolysin treatment disrupts native ion concentrations and detaches the plasma membrane from the inner membrane complex (IMC) (Wichroski et al., 2002).”

Because of these limitations we rely on the overlap of CDPK1-dependent targets between our thiophosphorylation and time course experiments.

4. Please confirm the interaction of TGGT1_306920 and TGGT1_316650 with the HOOK and FTS proteins.

In response to this suggestion, we tagged the C termini of TGGT1_306920 and TGGT1_316650 with 3xHA epitopes. Although immunoprecipitation of TGGT1_316650 was unsuccessful, immunoprecipitation of TGGT1_306920 identified HOOK and FTS as significantly enriched proteins. We include this new data in panel C of Figure 5 and have updated our results:

“To further confirm the interaction, we fused a 3xHA tag to the C terminus of TGGT1_306920, performed IP-MS and compared protein enrichment to the HOOK-3xHA IP (Figure 5C). HOOK, FTS, and TGGT1_306920 were significantly enriched across both IP-MS experiments, whereas TGGT1_316650 is only significantly enriched in HOOK and FTS pulldowns. This suggests the presence of multiple HOOK complexes composed of the core HOOK and FTS proteins that bind with either TGGT1_316650 or TGGT1_306920.”

While further interactions with other members of the complex still need to be validated it is not the standard of the field to validate every member of a protein complex by reciprocal IP. Our HOOK and FTS IP-MS results each identified HOOK, FTS, TGGT1_306920, and TGGT1_316650 and our TGGT1_306920 IP-MS identified all members except TGGT1_316650. These interaction partners were found significantly enriched compared to parental controls, which make the observation of the complex robust.

Reviewer #1 (Recommendations For The Authors):

I have only a few minor comments:

1. In the supplemental data section I would include a document of code ( R script) used for the analysis. If this is too cumbersome then I would instead suggest that like done with proteomic data, the code should be deposited in a database that provides a DOI for access, instead of only being provided on request. This can be done by use of an electronic laboratory notebook or via Github.com or a similar service.

Zip files containing R code and CSVs have been included for the sub-minute resolution phosphoproteomics (Supplementary File 11) and thiophosphorylation (Supplementary File 12).

2. It would be useful to expand the discussion of the other 2 proteins identified in the HOOK complex TGGT1_316650 and 306920. Do these have homologs to proteins in other organisms? Based on HOOK in other eukaryotes can you provide a model of the 4 proteins in the complex that you identified? Was any work done on 316650 and 306920 with regards to genetic KO or auxin regulation to see if they also provided a similar phenotype to what was described with HOOK and FTS?

We have included the following information in our discussion:

“It also remains unknown how the HOOK complex binds to micronemes. In H. sapiens and D. melanogaster, RAB5 on vesicles interacts with FHIP in the HOOK complex(Bielska et al., 2014; Gillingham et al., 2014; Guo et al., 2016; Xu et al., 2008; Yao et al., 2014). We speculate that TGGT1_306920 may serve the role of FHIP within the HOOK complex as it is fitness conferring whereas TGGT1_316650 appears dispensable but the complex's binding partner on micronemes remains unknown. RAB5A and RAB5C have been implicated in the biogenesis of micronemes, but their roles during exocytosis have not been explored(Kremer et al., 2013). Understanding how micronemes are recognized may elucidate how cargo specificity is achieved and regulated.”

TGGT1_306920 is conserved amongst coccidians and shares similar localization to HOOK and FTS. TGGT1_316650 is conserved amongst apicomplexans and more broadly in subsets of other eukaryotic phyla. Given our IP-MS data, HOOK and FTS form a core complex that is either bound to TGGT1_316650 or TGGT1_306920. Given that TGGT1_306920 appears to be important for parasite fitness, based on genome-wide screening data (Sidik, Huet, et al. 2016), we speculate this could function to mediate the linkage to microneme organelles. At this time, we have no additional data to present on 316650 and 306920. Additional biochemical studies will be needed to characterize the stoichiometry of complexes and their function; however, we propose that HOOK and FTS are interacting as previously described in opisthokonts (Bielska et al., 2014, Guo et al., 2016 and Zhang et al., 2014). 

3. The myristoylation data section ended with "additional studies will be required to understand how myristoylation influences CDPK1 activity". What studies are required to further this understanding? I assume these studies are difficult and that is why they were not part of this outstanding paper.

The effect of myristoylation is modest during acute phenotypes like egress (see Figure 2H). Moreover there were no significant differences between cWT and cMut that could explain the impact of CDPK1 on microneme secretion, which was the purpose of this study. Further studies would require a phosphoproteomic workup of the cWT and cMut, which is beyond the scope of the present study.

4. In the key resource table, in the first column reagent type I suggest you indicate this as T. gondii RH strain to make it clear the background strain (I know it is encoded in additional information but the first column should also be clear).

We have updated the key resources table to indicate the T. gondii strains used are of RH background.

Reviewer #2 (Recommendations For The Authors):

I have a few minor comments that could be addressed by modification of the current version of the manuscript.

Line 290, where authors classify proteins phosphorylated in CDPK1 dependent manner into five groups, it would be helpful to list at least class 1 (five proteins) and class 2 (four proteins) in the text of the results section. Further since in the same paragraph, the authors are also describing figure 3G, it would be helpful if the groups are identified with roman numerals or as class A, B, C, D, and E. Currently, in fig 3G, the three columns (CDPK1 dependent, CDPK1 independent and fitness scores) are also identified as 1, 2 and 3 and these nomenclatures could be confused with the five different classes of putative substrates.

We thank the reviewer for their helpful suggestion. We have renamed the classes of CDPK1 targets using roman numerals I, II, III, IV, and V. We have also listed out the proteins in Class I and Class II in the results section as follows:

“Class I contains five proteins for which the same phosphorylated site was identified in both the time course and thiophosphorylation experiments and include: TGGT1_227610, TGGT1_221470, TGGT1_235160, TGGT1_273560 (KinesinB), and TGGT1_310060. Class II contains four proteins for which phosphorylated sites identified across both approaches were within 50 amino acid residues of one another and include: TGGT1_289100 (MIC18), TGGT1_309190 (AIP), TGGT1_254870, and TGGT1_259630.”

Line 398, the expansions of the abbreviations FTS and FHIP should be included.

We have included the expansions of the abbreviations for FTS and FHIP:

“In D. melanogaster and mammals, HOOK proteins have been shown to form dimers and bind Fused Toes (FTS) and FTS and HOOK-interacting protein (FHIP) via a C-terminal region that interacts with vesicular cargo (Christensen et al., 2021; Krämer and Phistry, 1996; Lee et al., 2018; Xu et al., 2008).”

The HOOK protein shows CDPK1-dependent phosphorylation at multiple sites S167, S177, and S189-191. In the discussion section, it would be helpful if the authors can speculate about the importance of these phosphorylated residues on the functioning of HOOK.

Prior to engaging parasite motility, micronemes are positioned at the apical third of the parasite, but after an increase in intracellular Ca_2+_, micronemes rapidly traffic to the apical tip of the parasite. Our results indicate that both CDPK1 kinase activity and HOOK are required for microneme trafficking. Given the association of micronemes with tubulin-based structures such as the cortical microtubules and conoid, activation of trafficking along such structures must be rapid, on the time scale of seconds. Cell-free reconstitution assays generated from opisthokonts indicate that activating adaptors like HOOK are necessary to activate processive dynein trafficking along microtubules in addition to conferring cargo selectivity. In intracellular non-motile parasites, HOOK is expressed and localized to the apical end and cytosol prior to the activation of rapid microneme trafficking, consistent with regulation of HOOK activity. We have included reference to this type of regulation and our expectation that CDPK1 activates the HOOK complex as part of the Discussion:

“Phosphorylation has been reported to regulate the function of activating adaptors. In HeLa cells, phosphorylation of BICD2 facilitates recruitment of dynein and dynactin (Gallisà-Suñé et al. 2023). Analogously, phosphorylation of JIP1 mediates the switch between kinesin and dynein motility of autophagosomes in murine neurons (Fu et al. 2014). We therefore speculate that phosphorylation of HOOK by CDPK1 may activate the adaptor by promoting its interaction with dynein and dynactin to initiate trafficking of micronemes.”

Reviewer #3 (Recommendations For The Authors):

1. CDPK1 myristoylation. The loss of myristoylation of CDPK1 appears to increase its interaction with ROM4 which also becomes cytosolic instead of localizing to the plasma membrane. As ROM4 is necessary for microneme discharge after proteolysis it would be interesting to investigate the specific relation between CDPK1 and ROM4 and to confirm the interaction by reciprocal IP.

Please see our response to Suggested Revision #1.

2. CDPK1 myristoylation, Figure 2D. It would be useful to compare the expression of the tagged and complemented (cWT and cMut) CDPK1 with the endogenous expression of the non-tagged and non-complemented gene.

Please see our response to Suggested Revision #2.

3. Thiophosphorylation. The authors used the bacterial toxin aerolysin to semi-permeabilize parasite membranes by forming 3-nm pores. Aerolysin affects the membrane integrity, however, the authors demonstrated that CDPK1 is possibly associated with membrane structures (Figure 2E/F). Could it be possible to transiently destabilize the membrane before to treat with KTPγS or ATP? If not, it would be necessary to confirm that aerolysin treatment does not impact myristoylation-dependent subcellular partitioning of CDPK1 before identifying proteins specifically labelled by CDPK1G and not by CDPK1M (Figure 3C).

Please see our response to Essential Revision #3.

4. IP-MS on HOOK-3xHA parasites. The authors' results suggest that HOOK and FTS form a functional complex implicated in microneme exocytosis. It would be interesting to know if HOOK knockdown can have an effect on FTS expression or localization and reciprocally.

While we agree with the reviewer that this is an interesting question, we focused this paper on the discovery of the complex in relation to CDPK1. Understanding the regulation and interaction of the complex components is the focus of ongoing work and will require generation of new strains and additional mass spectrometry. For those reasons we find these experiments fall beyond the scope of the present study.

5. FTS-Turbo-ID. (Line 443-444) Authors should confirm the interaction of TGGT1_306920 and TGGT1_316650 with the HOOK and FTS proteins, it will give strength to their conclusion. In fact, without confirmation, everything is based on suggestions that were also formulated but not confirmed in humans. The physical existence of this putative complex should be demonstrated by co-IP experiments. In addition, the missing player is a dynein candidate itself, which leaves the model vulnerable. Short of pursuing this experimentally, it should at least be commented on in the Discussion.

Please see our response to Sugegsted Revision #4. Our IP-MS experiments of HOOK-3xHA and FTS-3xHA indicate interactions with HOOK, FTS, TGGT1_316650, and TGGT1_306920. Our FTS-TurboID experiments also suggest an interaction between FTS, HOOK, TGGT1_316650 and TGGT1_306920. Furthermore, our TGGT1_306920 IP-MS data identifies HOOK and FTS, but not TGGT1_316650, suggesting distinct complexes with HOOK and FTS as core components.

6. MIC2 secretion (Fig 5J). The rep represented by the grey dot with a white outline seems like an outlier result compared to the other 2 reps. Basically, without this rep there at least is a strong trend that there is a difference in secretion without EtOH stimulation. That is what actually would be expected, for constitutive secretion! Please carefully reconsider these data - e.g. check for outlier statistics and/or add reps.

We present three independent biological replicates, showing a significant difference in microneme secretion following depletion of CDPK1, HOOK, or FTS. It is expected, based on our prior experience, that microneme secretion will vary day to day. However, the expected trend can be observed in all replicates. We are unclear what the reviewer means by constitutive secretion since some low-level of calcium-dependent microneme discharge is expected even in the absence of stimulation, barring BAPTA-AM treatment. Even in the absence of EtOH stimulation (left graph in Fig. 5J), the trend of diminished basal MIC2 release holds when CDPK1, HOOK, or FTS is knocked down.

7. Apical accumulation of micronemes. A similar observation was made upon manipulation of Ferlin1, which is a manuscript on BioRXivs. Since other BioRXiv manuscripts are cited in the presented work, this is an omission.

We apologize for this omission and have updated the manuscript accordingly:

“It therefore appears that the initial round of microneme discharge during egress depends on CDPK1, and only subsequent rounds require the HOOK complex. Indeed, a fraction of micronemes are already found docked at the apical complex prior to the transition from the replicative to the motile stages, and may constitute the first round of microneme exocytosis (Mageswaran et al., 2021; Sun et al., 2022). Ferlin 1 (FER1) was recently shown to be involved in microneme positioning and overexpression of FER1 was sufficient to initiate an initial round of microneme exocytosis and induce egress (Tagoe et al. 2020).”

Minor comments:

1. Concerning the expression of the HOOK protein in Figures 4B, and C, could the author indicate why they performed the IFA after 24h of auxin treatment and the WB after 40h of treatment?

The difference in timing was for technical reasons. Our immunoblots and additional assays such as microneme secretion require more parasites, such that we harvest at the end of the lytic cycle to increase yields. For the IFAs, we perform these at 24 hrs, which allows for depletion and replication, but captures parasites in small vacuoles that show clear localization patterns. Furthermore, our microneme relocalization studies in Figure 6 were also performed after 24 hrs of auxin treatment, yet exhibit a trafficking defect following  24 hr HOOK depletion.

2. Fig 4H. The color of CDPK1-AID on the left and the HA on the top (HOOK) do correspond but indicate different proteins. Please label HOOK text in teal, not CDPK1.

We have changed the text color of the strain names on 4H to black to avoid confusion with the IFA channel labels.

3. I would like to suggest adding the "Key resources tables" in the supplementary data because it makes the materials & methods harder to read.

The key resources table was included at the beginning of the Materials and Methods section as indicated in eLife’s instructions to the authors.

Author Response

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

eLife assessment

This important study expands on current knowledge of allosteric diversity in the human kinome by C-terminal splicing variants using as a paradigm DCLK1. The authors provide solid evolutionary and some mechanistic evidence how C-terminal isoform specific variants generated by alternative splicing can regulate catalytic activity by means of coupling specific phosphorylation sites to dynamical and conformational changes controlling active site and substrate pocket occupancy, as well as protein-protein interactions. The data will be of interest to researchers in the kinase and signal transduction field.

We thank the editor for coordinating the review of our manuscript and the reviewers for their valuable feedback. We have significantly revised the manuscript in response to the reviewer’s comments. Our point-by-point response to each comment is present below. We have uploaded both a clean draft of our revised manuscript as well as a version with the revisions highlighted in yellow. We hope the revised manuscript is now acceptable for publication in eLife. We have additionally updated the preprint on bioRxiv and have included the link: We thank the editor for coordinating the review of our manuscript and the reviewers for their valuable feedback. We have significantly revised the manuscript in response to the reviewer’s comments. Our point-by-point response to each comment is present below. We have uploaded both a clean draft of our revised manuscript as well as a version with the revisions highlighted in yellow. We hope the revised manuscript is now acceptable for publication in eLife. We have additionally updated the preprint on biorxiv and have included the link here: https://www.biorxiv.org/content/10.1101/2023.03.29.534689v2.

Reviewer #1

Summary

In the study by Venkat et al. the authors expand the current knowledge of allosteric diversity in the human kinome by c-terminal splicing variants using as a paradigm DCLK1. In this work, the authors provide evolutionary and some mechanistic evidence about how c-terminal isoform specific variants generated by alternative splicing can regulate catalytic activity by means of coupling specific phosphorylation sites to dynamical and conformational changes controlling active site and substrate pocket occupancy, as well as interfering with protein-protein interacting interfaces that altogether provides evidence of c-terminal isoform specific regulation of the catalytic activity in protein kinases.

The paper is overall well written, the rationale and the fundamental questions are clear and well explained, the evolutionary and MD analyses are very detailed and well explained. The methodology applied in terms of the biochemical and biophysical tools falls a bit short in some places and some comments and suggestions are given in this respect. If the authors could monitor somehow protein auto-phosphorylation as a functional readout would be very useful by means of using phospho-specific antibodies to monitor activity. Overall I think this is a study that brings some new aspects and concepts that are important for the protein kinase field, in particular the allosteric regulation of the catalytic core by c-terminal segments, and how evolutionary cues generate more sophisticated mechanisms of allosteric control in protein kinases. However a revision would be recommended.

Major Comments

The authors explain in the introduction the role of T688 autophosphorylation site in the function of DCLK1.2. This site when phosphorylated have a detrimental impact on catalytic activity and inhibits phosphorylation of the DCX domain. allowing the interaction with microtubules. In the paper they show how this site is generated by alternative splicing and intron skipping in DCLK1.2. However there is no further functional evidence along the functional experiments presented in this study.

1) What is the effect of a non-phosphorylable T688 mutant in terms of stability and enzymatic activity? What would be the impact of this mutant in the overall auto-phosphorylation reaction?

The role of T688 phosphorylation on DCLK1 functions has been explored in previous studies (Agulto et al, 2020: PMID: 34310279), although only relevant to DCLK1.2 splice variants, since this site is lacking in DCLK1.1. These studies showed that mutation of T688 to an alanine increases total kinase autophosphorylation (ie autoactivity) and the subsequent phosphorylation of DCX domains, which in turn decreases microtubule binding. Given this information, our goal was to use an evolutionary perspective to investigate this, alongside less-well characterized aspects of DCLK autoregulation, including co-conserved residues in the catalytic domain and C-terminal tail. However, to address the reviewers question of a non-phosphorylatable T688 mutant, we performed MD simulations of T688A and T688E (a phosphomimic) mutant and include a new supplementary figure (Figure 5-supplement 3) which show the two mutants slightly destabilize the C-tail relative to wt (1 and 2 angstrom increase in RMSF for T688E and T688A respectively), but by themselves cannot dislodge the C-tail from the ATP binding pocket. Thus, other co-conserved interactions as revealed by our analysis, are likely to contribute to the autoregulation of the kinase domain by the C-terminal tail. We have incorporated these observations into the revised results section.

Furthermore, to address the reviewer’s question in terms of site-specific autophosphorylation as a marker of DCLK1.2 activity, we have now performed a much-more detailed phosphoproteomic analysis of a panel of purified DCLK1.2 proteins after purification from E.coli (Figure 8-figure supplement 2). This showed that we are only able to detect Thr 688 phosphorylated in our ‘activated’ DCLK1.2 mutants, and not in the autoinhibited WT DCLK1.2 version of the protein. This apparent contradiction does not necessary discount Thr 688 as an important regulatory hotspot, but, together with the MD simulations, may imply a decreased contribution of pThr 688 in facilitating/maintaining DCLK1.2 auto-inhibition than previously anticipated, especially in the context of the numerous other stabilizing amino acid contacts that we describe between the C-tail and the ATP-binding pocket. We do, however, propose a mechanism for pThr688 as a potential ATP mimic based on MD analysis. However, we only found MS-based evidence for phosphorylation at this (and other sites in the same peptide) in highly active DCLK1.2 mutants, in which the C-tail remains uncoupled from the ATP-binding site, even in the presence of this regulatory PTM. We acknowledge that better understanding of DCLK biology will require a detailed appraisal of how the DCLK auto-inhibited states are subsequently physiologically regulated (PTMs, protein-protein interaction etc.), but this is beyond the scope of our current evolutionary investigation, and the absence of phosphospecific antibodies makes this challenging currently. We intend to expand upon our current work by assessing the relative contribution of multiple DCLK phosphorylation sites (including, but not limited to, Thr 688) with regard to cellular DCLK auto-regulation in future studies, in part by generating such site-specific phospho-antibodies.

2) Have the authors made an equivalent T687/688 tanden in DCLK1.1 instead of the two prolines?

This is a good point. We have not considered introducing a T687/688 tandem mutation into DCLK1.1 (at the equivalent position to that of DCLK1.2), primarily because the amino acid composition of their respective C-tail domains are so highly divergent across the tail (due to alternative splicing, as discussed in our paper). As discussed in our present study, there are numerous contacts made between specific amino acids in the regulatory C-tail and the kinase domain of DCLK1.2, which functionally occlude ATP binding, and thus change catalytic output. It is these contacts, which are determined by the specific amino acid sequence identity, and not the extended length of the DCLK1.2 C-tail per se, that drives autoinhibition. The alternate amino acid sequence identity of the C-tail of DCLK1.1 does not enable such contacts to form, which we believe explains the different activities of the two isoforms.

Furthermore, our mutational analysis reveals clearly that Thr688 and several other sites are more highly autophosphorylated in the artificially activated DCLK1.2 constructs than WT DCLK1.2, and as such it remains our hypothesis that introduction of the tandem phosphorylation sites into DCLK1.1 is unlikely to be sufficient to impose an auto-inhibitory conformation of the enzyme.

3) Could T688 autophosphorylation be used as a functional readout to evaluate DCLK1.2 activity?

We agree with the reviewer’s suggestion about using autophosphorylation (including potentially Thr688 for DCLK1.2) as a functional read out for DCLK1 activity. In our present study, we identify phosphorylated peptides containing pThr688 only in the mutationally activated DCLK1.2 variants. We have now taken this analytical approach further and performed a detailed comparative phosphoproteomic characterisation of all of our DCLK1 constructs, where we observe marked differences in the overall phosphorylation profiles of the mutant DCLK1.2 (and DCLK1.1) proteins relative to the less phosphorylated WT DCLK1.2 kinase. This manifests as a depletion in the total number of confidently assigned phosphorylation sites within the kinase domain and C-tail of WT DCLK1.2, and also as a depletion in the abundance of phosphorylated peptides for a given site. To help visualise this, individual phosphorylation sites have been schematically mapped onto DCLK1, which has been included as a new extended supplementary figure (Figure 8-figure supplement 2). For comparative analysis of phosphosite abundance, we could only select peptides that could be directly compared between all mutants (identical amino acid sequences) and those found to be phosphorylated in all proteins (these are Ser660 and Thr438); these are now shown in figure supplement 2 as a table. These site occupancies follow what we see with respect to the increased catalytic activity between DCLK1.1 and DCLK1.2 mutants versus DCLK1.2. We also detect increased phosphorylation of DCLK1.1 and activated DCLK1.2 mutants in comparison to (autoinhibited) DCLK1.2, supporting the hypothesis that these mutants are relieving the autoinhibited conformation.

4) What are the evidences of the here described c-terminal specific interactions to be intra-molecular rather than inter-molecular? Have the authors looked at the monodispersion and molecular mass in solution of the different protein evaluated in this study? Basically, are the proteins in solutions monomers or dimers/oligomers?

Analysis of symmetry mates in the crystal structure of DCLK1.2 (PDB ID: 6KYQ) provide no evidence for inter-molecular interactions. Furthermore, to evaluate oligomerization status in solution, we conducted an analytical size exclusion chromatography (SEC) and our analysis reveals that both DCLK1.1 and DCLK1.2 predominantly exist as monomers in solution (Figure 3-Supplements 1-3). These results suggest that the C-terminal tail interactions are primarily intra-molecular.

5) (Figure 3) Did the authors look at the mono-dispersion of the protein preparation? The sec profile did result in one single peak or multiple peaks? Could the authors show the chromatogram? how many species do you have in solution? Was the tag removed from the recombinant proteins or not?

Yes, as mentioned above, the SEC profile resulted in a single peak for both DCLK1.1 and DCLK1.2, which was confirmed as DCLK1 by subsequent SDS-PAGE. We have included the chromatogram and gels in supporting data (Figure 3-supplements 1-3) in the revised manuscript and updated the Methods section. ‘The short N-terminal 6-His affinity tag present on all other DCLK1 proteins described in this paper was left in situ on recombinant proteins, since it does not appear to interfere with DSF, biochemical interactions or catalysis.’

6) Authors should do Michaelis-Menten saturation kinetics as shown in Figure 3C with the WT when comparing all the functional variant analysed in the study. So we can compared the catalytic rates and enzymatic constants (depicted in a table also) kcat, Km and catalytic efficiency constants (kcat/Km)

Thank you for your suggestion. We have performed the requested comparative kinetics analyses for selected functional DCLK1 variants at the same concentration as suggested, using our real-time assay to determine Vmax for peptide phosphorylation as a function of ATP, but at a fixed substrate concentration (we are unable to assess Vmax above 5 µM peptide for technical reasons). The results of these analyses have been included in the revised version of Figure 8-Supplement 1, where they support differences in both Vmax and Km[ATP]; the ratio of these values very clearly points to differences in activities falling into ‘low’ or ‘high’. This kinetic analysis fully supports our initial activity assays, where mutations predicted to uncouple the auto-inhibitory C-tail rescue DCLK1.2 activity to levels similar to DCLK1.1 towards a common substrate.

Minor Comments

It is very interesting how the IBS together with the pT688 mimics ATP in the case of DCLK1.2 to reach full occupancy of the active site. On Figure 8 you evaluate residues of the GRL and IBS interface to probe such interactions.

1) Did the authors look at the T688 non-phosphorylable mutant?

See our response to Major Comment 1 above. In addition, due to the absence of T688 in DCLK1.1, we did not look at the T688A mutant of DCLK1.2 biochemically, partially because it has been characterized in previous studies, but partially because this site is preceeded by another Thr residue. The lack of a selective antibody towards this site makes it difficult to evaluate the role of T688 phosphorylation specifically with respect to DCLK cellular functions and interactions. Therefore, we focused our in vitro efforts to understand how mutations in the IBS impact the catalytic activity of DCLK1.2 by comparing different variants to DCLK1.1.

2) Classification of DCLK C-terminal regulatory elements.

It would be useful to connect the different regulatory elements described in this study to a specific functional and biological setting where these different switches play a role e.g. microtubule interactions and dynamics, cell cycle, cancer, etc..

While the primary focus of our paper is on the mechanism of allosteric regulation of DCLK1, we have indeed touched upon the potential implications of the various regulatory elements of the tail on functions such as microtubule binding and phenotypic effects like cancer progression. However, we acknowledge that a comprehensive understanding of these effects would necessitate a more detailed investigation. This could potentially involve the integration of RNA-seq data with extensive cell assays to evaluate phenotypic effects. We believe that such a future study would be a valuable extension of our current work and could provide further insights into the functional roles of DCLK1.

3) (Figure 3) Could the authors explain the differences in yield between the WT and the D531A mutant. Apparently, it [the yield] does not appear to be caused by a lower stability as indicated by the Tm. Could the authors comment on this? It is important to compare different samples in parallel, in the same experiment and side by side. This applies to the thermal shift data comparing WT and a D531A mutant on panel D and also on panel C a comparison between WT and D531A as negative control should be shown.

WT and D533A (kinase-dead) were indeed analysed in parallel, but have been split in two panels to make the data easier to interpret. The modest differences in yield is likely explained by experimental prep-to-prep variations. Our experience shows that many protein kinase yields vary between kinase and kinase-dead variants, likely due to bacterial toxicity related to enzyme activity. In regards to thermal stability, we would like to emphasize that Differential Scanning Fluorimetry (DSF) is to our mind a more informative and quantitative measure of protein stability than yield from bacteria, because both assess purified proteins at the same concentration. We believe that the DSF data provide a more accurate representation of the real stability differences between the WT and D533A mutant.  

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Reply to the reviewers

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

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

In this study, the authors generate several variants of actin that are internally tagged with short peptide tags. They identify one particular position that is able to tolerate various tags of 5-10 amino acids and still shows largely unaltered behavior in cells. They study incorporation of their tagged actins into filaments, characterize the interactions of G-actin variants with different associated proteins and show that retrograde actin flow in lamellipodia and the wound healing response of epithelial cells is not affected by the tagged variants. They then apply the tagged actin to study subcellular distribution of different actin isoforms in mammalian and yeast cells.

The identification of a specific site in the actin protein that tolerates variable peptide insertions is very exciting and of fundamental interest for all research fields that deal with cytoskeletal rearrangements and cellular morphogenesis. The result demonstrating the functionality of actin variants with peptides inserted between aa 229 and 230 are generally convincing and well done. In particular, the generation of CRISPR/Cas9 genome edited versions of beta- and gamma actin are impressive. I therefore generally support publication of this study. There are however several technical and conceptual issues that should be addressed to improve quality and scope of the study. I listed some specific comments below:

We thank the Reviewer for their constructive comments and general support for publication of our study.

Major points

- The biggest issue I have is the last section on the application of tagged actins to study isoform functions. In principle the application is very clear as there are simply no alternative ways to study isoform distribution in live cells. However, the experimental data are simply not convincing. What the authors define as "cortex" in Fig. 5A seems to rather represent cytosolic background mixed with radial fibers. I am not convinced that even the antibody staining with a relatively clear differential distribution of beta and gamma really shows a genuine accumulation of one isoform on stress fibers. It seems to me that the beta-actin staining has as higher cytosolic background and is generally weaker (gamma nicely labels transverse arcs), which reduces signal/noise and therefore yields a relatively increased level in areas with less-bundled actin. My suggestion is to select more clearly defined actin structures and to use micro-patterned cells to normalize the otherwise obstructing variability in actin organization. Possible structures would be cortical arcs in bow-shaped cells, lamellipodial edges (HT1080 seem to make very nice and large lamellipodia) or cell-cell contacts (confluent monolayer, provided cells don´t grow on top of each other). Stress fibers are possible but need to be segmented very precisely and I did not see any details on this in the methods section. For Fig. 5D: I assume cells were used where only one isoform was tagged? This is technical weak and the double-normalization is probably blurring any difference that might be occurring. Why not use a double-tagging strategy with ALFA/FLAG or ALFA/AU5 tags to exploit the constructs introduced in the previous figures? Also, the unique selling point of the strategy is the possibility of actual live imaging of specific isoforms. Cells that have stably integrated double tags and then transiently express nanobodies for ALFA and either AU5 or FLAG (or other if those don't exist) would make this possible. Considering the work already done in this manuscript, such an approach should actually be possible - did the authors attempt this or is there are reason it is not discussed? If double tagged cells are not possible for some reason it should at the very least be possible to combine ALFA-detection with the specific antibody against the other isoform and get rid of the double normalization.

We thank the Reviewer for the various suggestions regarding the comparison between the localization of the tagged and native isoforms. In our reply below, we will separately discuss the possibilities and our considerations for fixed samples and live cell imaging. We apologize for the lengthy response but for transparency reasons, we would like to give a thorough overview of our efforts for isoform-specific localization in cells, something for which we have limited space in the manuscript.

Fixed samples:

It was a significant experimental challenge to comparing the labeling of the β- and γ-actin specific antibodies with our internally tagged actin system (Fig. 5A-D). The reason for this is that the labeling of the samples with the β- and γ-actin specific antibodies requires treatment with methanol (Dugina et al., J Cell Sci, 2009), most likely to disturb the interaction of actin with actin-binding proteins that prevent the binding of the antibodies due to steric hindrance. Methanol treatment, however, precludes the co-labeling with phalloidin, likely due to changes in the tertiary/quaternary protein structure of F-actin. Initially, we have put a lot of effort in trying to simultaneously label phalloidin with the actin specific antibodies but even very brief methanol treatment (seconds), before or after phalloidin labeling, completely prevents/reverses the binding of phalloidin. Importantly, also the ALFA tag labeling was suboptimal after methanol treatment.

The fact that we could not perform these double labelings led us to perform different ratio calculations for the β- and γ-actin antibody and the ALFA tag labeling. In the case of the antibody immunofluorescence labeling, we simply divided the signal of the β-actin and γ-actin since we could simultaneously label the isoforms in the same cell. In the case of the ALFA tag labeling, we used phalloidin for independent signal normalization and then performed a second normalization. Although this complicates the normalization procedure (ALFA tag signal of β- and γ-actin is first normalized to total F-actin and then a ratio is calculated) and understandably leads to some confusion, this was the only way forward to obtain the results presented in the manuscript.

The Reviewer points out that “What the authors define as "cortex" in Fig. 5A seems to rather represent cytosolic background mixed with radial fibers.”. In our images, we observe very little cytosolic background from both antibody stainings. More importantly, for the quantitative analysis, the fluorescence intensity values were corrected for the background values observed in cytosolic areas so even if the signal is present, it should not affect our analysis. We do admit though that we could have been more careful with the term “cortex” since the observed signal could indeed be a mix of radial fibers and the actin cortex. The reviewer further states that “I am not convinced that even the antibody staining with a relatively clear differential distribution of beta and gamma really shows a genuine accumulation of one isoform on stress fibers.” Although the differences are small, we consistently observe a differential fluorescence intensity of β- and γ-actin in actin-based structures with a relatively stronger signal of γ-actin in stress fibers (Fig. 5C). Since we always normalize the fluorescent signal intensity per cell, this strongly indicates a genuine accumulation of one isoform over the other in specific actin-based structures. This observation is very consistent in our experiments and also aligns with many published studies where differences in the localization of β- and γ-actin are reported in various cell types (Pasquier et al., Vasc Cell, 2015; van den Dries et al., Nat Comms, 2019; Malek et al., Int J Mol Sci, 2020). As for the segmentation, we mentioned in the Methods section that we selected small regions (0.5x0.5mm) that exclusively contain stress fiber or “cortex” regions. The regions shown in Fig. 5B are therefore larger than the analyzed regions, something which we will better indicate in the revised manuscript.

Planned revision: We will provide a more detailed explanation of our quantitative analysis in the Methods section such that it is more clear how our normalization procedure was performed. Furthermore, we will adapt Fig. 5A-B such that it better visualizes how we defined the regions for quantification. As per the Reviewer’s suggestion, we will also apply a different experimental method to show that the tagged isoforms properly localize to actin-based structures. For this, we will attempt to use micropatterned cells to induce clearly define actin-bases structures (the crossbows as suggested by the Reviewer) and also explore the possibilities of investigating the differential localization in double-tagged cells. We will also reconsider the use of the term “cortex” for the region that is pointed out in Fig. 5A-B.

Live cell imaging:

We agree with the Reviewer that it would be very valuable to attempt simultaneous live cell imaging of two isoforms. Yet, for this, we would need two tag/fluorophore systems that allow the visualization of internally tagged isoforms in living cells. As presented in our original manuscript, we have successfully inserted many different epitope tags (FLAG/AU1/AU5/ALFA) in the T229/A230 position to demonstrate the versatility of our tagging approach. Yet, despite significant efforts to identify a second tag/fluorophore system that would allow isoform-specific live cell imaging, we only succeeded in designing one strategy to perform live cell imaging, i.e. with the ALFA tag (Götzke, Nat Comms, 2019). Part of the reason for this is that so far, no high affinity nanobodies have been generated against the classical epitope tags (FLAG, AU5 etc.). This is an established challenge since classical epitope tags are typically linear/unstructured while nanobodies require folded secondary structures for epitope recognition such as alpha helices (the ALFA tag was specifically designed as such).

Besides the successful ALFA tag approach we have tried the following additional approaches for live cell imaging: 1) __full-length GFP, 2) full-length GFP with linker, 3) GFP11 (to complement with GFP1-10 (Cabantous et al., Nat Biotech, 2005) 4) GFP11 with linker 5) FLAG Frankenbodies (Zhao et al., Nat Comms, 2019; Liu et al., Genes Cells, 2021) in FLAG IntAct cells and 6) __Tetracysteine/FlAsH labeling. Importantly, each of these additional internally tagged actins, except for those that contained full-length GFP, showed a high colocalization with the cytoskeleton, again demonstrating the versatility of the T229/A230 position to tag actin. Unfortunately, none of these approaches satisfactorily visualized the actin isoforms in living cells. We will therefore briefly summarize our findings here.

(1-2, integration of full-length GFP and GFP with linker) Probably not surprisingly, but integrating the entire coding sequence of GFP or GFP flanked by linkers (each 5AA in length) within the T229/A230 position did not results in a proper localization of actin.

(3-4, integration of GFP11 and GFP11 with linker) Next, we assessed the localization of the GFP11 tagged actin versions (GFP11: 16AA, GFP11+linker: 26AA). Because GFP11 is not visible without GFP1-10 complementation, we also tagged actin at the N-terminus simply for proof of concept where the internally tagged actins would end up. Interestingly, both GFP11-actin and GFP11+linker-actin properly integrated within the cytoskeleton as demonstrated by the FLAG staining. This again demonstrates the versatility of the T229/A230 position and strongly suggests that even the integration of 26AA within this position does only minimally affect the polymerization of actin into the cytoskeleton.

(3-4) After confirmation of the proper integration of GP11-actin and GFP+linker-actin we continue to express the GFP1-10 in these cells. Unfortunately, this resulted in no or only very minimal localization of the actin to the cytoskeleton, demonstrating that GFP-complementation hampers the integration into the cytoskeleton.

(5, use of FLAG Frankenbodies) We also expressed FLAG Frankenbodies into our FLAG IntAct cells in an attempt to visualize the isoforms in living cells. FLAG Frankenbodies are single chain antibodies fused to GFP and can be expressed in cells to visualize FLAG-tagged proteins (Liu et al., Genes Cells, 2021). Although a cytoskeletal labeling was indeed discernable in some cells, the FLAG Frankenbody signal overlapped much less with the total actin signal as compared to the FLAG immunofluorescence labeling, indicating that the incorporation of the FLAG-tagged actin was much less in the presence of the FLAG Frankenbody. Also, a significant fraction of the cells demonstrated a homogenous cytosolic signal.

(6, Use of tetracysteine/FlAsH) Although the tetracysteine tag/FlAsH system is widely known to induce artefacts, we still aimed to evaluate if for live cell imaging of IntAct actins. Similar to GFP11, we first determined the integration of tetracysteine-actin into the cytoskeleton with the use of an additional N-terminal FLAG tag and demonstrate that it was properly integrated into the actin cytoskeleton. Unfortunately, after brief incubation with FlAsH-EDT2, we noted 1) a significant amount of background fluorescence, preventing proper actin visualization and 2) that the cell became static indicating toxicity of the FlAsH-EDT2 compound. Titrating down the amount of FlAsH-EDT2 did not alleviate these drawbacks and only resulted in less fluorescence.

Overall, based on these experiments, we concluded that the T229/A230 position itself is very versatile, as demonstrated by the proper localization of the GFP11-actin variants and the TetraCys-actin. At the same time, none of these tag/fluorophore systems properly visualized actin in living cells. Although we are unsure what the reason is for this, it is easily imaginable that the on/off kinetics of the split GFP system and the FLAG Frankenbodies are suboptimal to allow for the rapid and continuous integration of actin monomers into the F-actin cytoskeleton. We therefore also concluded that currently, the ALFA tag/nanobody system is apparently unique in its ability to visualize epitope tagged actin in living cells (as shown in the manuscript). For simultaneous visualization of multiple isoforms, we rely on progress on the development of novel nanobody-based tags, something we hope the Reviewer will agree is outside the scope of the current work.

*- The authors make a point of comparing the internally tagged actin to N-terminal tags that are mostly functional but have been shown to affect translational efficiency. I would strongly suggest to include N-terminally tagged actin as control for all assays in this study. Also for the physiological assays (retrograde flow, wound healing), a positive control is missing that shows some effect. Previous studies showed defects with transiently expressed actin with an N-terminal GFP. As retrograde flow measurements are very sensitive to the exact position of the kymographs and wound healing assays is a very crude and indirect readout, such a positive control is essential. *

We acknowledge that N-terminally tagged actin has been used extensively for actin research (especially before the introduction of Lifeact). For our studies, however, we were specifically interested in whether the internally tagged actins show similar characteristics as compared to wildtype actin. We have not included N-terminally tagged actin in all of our experiments, since this would not affect our conclusions with respect to the functionality of our internally tagged actins. We expect that for future investigations to for example further establish the importance of actin N-terminal modifications in the differential regulation of actin isoforms, the comparison between internally and N-terminally tagged actins could be very instrumental. Yet, we consider this comparison outside the scope of the current manuscript. For now, the results in the manuscript provide evidence that our approach is unique with respect to the fact that it allows isoform-specific tagging without manipulating the N-terminus. As such, our internal tagging system complements the already existing repertoire of actin reporting methods (N-terminal fusion, Lifeact, F-Tractin, actin nanobodies) and allows researchers to study so far unknown properties of actin variants.

*- Expression of tagged actins in yeast is a very nice idea but it would be far more informative to express the tagged forms as the only copy of actin. This can either be done by directly replacing endogenous actin gene in S. cerevisiae, or (if the tagged versions are not viable) - using the established plasmid shuffle system (express actin on counter-selectable plasmid, then knock out endogenous copy and introduce additional plasmid with tagged actin, then force original plasmid out). In the presence of endogenous S. cerevisiae actin the shown effects are very hard to interpret as nothing is known about relative protein levels (endogenous vs. introduced). Also, if constitutive expression of the ALFA nanobody is harmful for integration into cables, why not perform inducible expression of the nanobody and observe labeling after induction. For the live imaging a robust cable marker is needed, like Abp140-GFP. Finally, indicate the sequence differences between the used actin forms in yeast (supplementary figure with sequence alignment and clear indication of all variations) *

We thank the reviewer for their positive comments and feedback regarding expression of IntAct variants in yeast. Currently, we have expressed IntAct as an extra copy in the presence of native Act1 of S. cerevisiae. All the IntAct variants have been expressed under a commonly used constitutive TEF1 promoter. We agree with the Reviewer that it would be valuable to attempt to express the tagged forms as the only copy of actin.

Planned revisions:

1) As per the Reviewer’s suggestion, we will attempt to make yeast strains with IntAct as the sole expressing actin copy by using the well-established 5-FOA-based plasmid shuffle system in yeast. We will use a ∆act1 strain containing wildtype act1 in a centromeric ura-plasmid described in Harrer et. al, 2007 (generously shared by Prof. Jessica and Prof. Amberg at Upstate Medical University of New York, USA) and express IntAct exogenously via additional plasmids. Shuffling of these strains on 5-FOA will cause the loss of ura-plasmid containing the wildtype act1 copy and will determine whether yeast cells will be able to survive with IntAct as the sole source of actin. If the cells do survive with IntAct as a sole copy, we will perform subsequent analysis for assessing actin cytoskeleton organization under these conditions.

2) As the reviewer has mentioned, expression of NbALFA during live-cell imaging experiments hindered incorporation of IntAct into linear actin cables in yeast (Suppl. Fig. S13). As per the reviewer’s suggestion, we will now try to create an inducible-expression system for the NbALFA-mNG and observe its effects on incorporation into formin-made actin cables after induction. We have already created NbALFA-mNG constructs under galactose-inducible GALS and GAL1 promoters and are currently constructing yeast strains for these experiments.

__3) __We will add an extra supplementary Figure to indicate the sequence differences of the various actin variants that we have expressed in yeast.

- As the authors clearly show good integration of several tagged actins into filaments I would expand the structural characterization: perform alpha fold predictions of actin monomer structures including the various tags to show the expected orientation. It is striking that the only integration site that seems to work well is at the last position of a short helix, indicating that the orientation of the integrated peptide might be fixed in space and be optimal to minimize interference. Also, a docking of the tag onto the recently published cryoEM structures of the actin filament should be shown to indicate where it resides compared to tropomyosin or the major groove where most side binding proteins seem to bind.

We already performed AlphaFold predictions of the tagged actin monomers, but we have decided to not include these predictions in the manuscript because of two reasons. First and foremost, while the prediction confidence of the non-tagged region is very high (pLDDT > 90), the prediction confidence of the tagged region is very low (pLDDT https://alphafold.ebi.ac.uk/faq), pLDDT values below 70 should be treated with caution and values below 50 should not be interpreted. Intriguingly, the low confidence aligns with the fact that for both tags, the prediction does not match with known features of the tag. The FLAG tag should be a linear/unstructured region in order to be recognized by the antibody and the ALFA tag should organize into an alpha helix (Götzke et al., Nat Comms, 2019). Yet, in the prediction, the FLAG tag partially continues as an alpha helix and the ALFA tag is only a small helix with part of the tag being unstructured. Second, more minor, reason for not including the predictions is that AlphaFold does not predict to what extend the tag is flexible, which means that even if the tagged region is predicted correctly, it is difficult to say whether the regions will interfere with binding of proteins.

Despite the low prediction confidence, we used the published actin-tropomyosin cryoEM structure (von der Ecken et al., Nature, 2015) to replace WT actin with ALFA tag actin and the results are shown below. Again, although results should be interpreted with caution, the tag does not seem to obstruct monomer-monomer interactions within an F-actin filament and also the tropomyosin binding surface is relatively distant from the tag region, suggesting that these interactions are likely not disturbed by introducing the tag.

- For any claims regarding usability of tagged variants for isoform research it would be very important to characterize the known posttranslational modifications of tagged actin variants - are the differences between beta and gamma maintained on this level as well?

Planned revision: Following the Reviewer’s suggestion, we will perform a western blot analysis to compare posttranslational modification (arginylation) of tagged and wildtype actins.

Technical issues

- There is no scale for the color coding in Fig. 5A, B

We deliberately did not add a numerical scale because the images are normalized which means that presenting the actual numbers might be misleading. The numbers could be interpreted as if they actually present the amount of β-actin relative to γ-actin which is not the case due to staining differences and the normalization procedure.

- The y-scales for Fig. 5C and D need to be identical to allow direct comparison

Planned revision: We will adapt the scale of Fig. 5D to make it identical to Fig. 5C. Following the other suggestions of the reviewer, we will also critically evaluate our normalization procedure and present those numbers in Fig. 5C-D if the values turn out to be different.

- Pearson coefficient should not be normalized to a control value as its already a dimensionless parameter. Always report actual R-value - also remove R2 values for Pearson as this makes no sense in this context (not sure if it was a typo or intended).

We normalized the Pearson coefficient values for visual representation of the results. The majority of the raw coefficient values (more than 80%) are between 0.20 and 0.75 (see raw values in the associated excel file). Theoretically, Pearson coefficient values are possible between 1 (or-1 for negative correlations) and 0. The much smaller window in our values as compared to the theoretical window (0.55 vs 1) led us to normalize the values such that they can be presented on a scale from “maximum expected colocalization” to “minimum expected colocalization”. In this way, the differences between the various tagged actins are much better appreciated in the Figure. As to reporting the R2, the Reviewer is correct. Reporting the R2 is an inadvertent mistake from our side and we will correct it.

Planned revision: We will change the R2 in the text to PCC or Pearson Correlation Coefficient.

*- All values on subcellular regions (like stress fiber or cortex) dependet critically on the way thesese regions were thresholded or identified. Provide all details on how this was done in the methods section and ensure that adequate background subtraction and normalization is applied. Optimally, an unbiased (AI or automated) approach based on simple image statistics is used for this to avoid personal bias. *

Planned revision: As also indicated above, we will add new experiments to better compare the localization of the isoforms in tagged and parental cells. These new experiments will also be accompanied by a more detailed explanation of how the regions were selected and quantified.

- In Fig. 2A only heterozygous FLAG-actin cells are used. Why not use a homozygous line (for both beta and gamma actin)? The nice band shift of the FLAG version would allow the precise quantification of the fraction of total actin covered by beta and gamma actin, which then could provide some additional info for the apparently weaker beta staining in Fig. 5 (if beta expression is simply weaker). This would be a very simple and useful advantage of the internal tags that could be widely applied.

In Fig. 2A, we used the heterozygous FLAG-actin cells to directly compare the production of β-actin from the knock-in allele and the wildtype allele in the same cells. The fact that the two bands observed in this western blot analysis (upper and lower) are almost the same (with the FLAG band being a bit more intense) provides the strongest indication that the tag does not interfere with the expression of actin. In Suppl. Fig. 5D, we show that the expression of β-actin is also unaffected in the hemizygous FLAG actin cells, which exclusively express tagged actin.

Planned revision: As per the Reviewer’s suggestion, we will also add a western blot analysis on the expression of both actin isoforms and total actin in hemizygous cells.

*- Fig. 3: control with N-terminal tag is missing. Also, why is it not possible to assay filament binding factors like Myosin, Filamin or alpha actinin - instead of co-IP a simple co-sedimentation assay with cell extracts in F-buffer should pick up any major difference in decoration of filaments containing the ALFA tag. Using two speeds for centrifugation it might even be possible to observe effects on filament bundling. The best approach for this would of course be to purify tagged actins and perform in vitro assays but this is clearly beyond the scope of what the authors intended here. I personally think that a broad acceptance of the marker will only come once the biochemistry has been sufficiently characterized so this is a future direction I would strongly encourage. *

We kindly refer to our response on Page 5/6 for why we have not included the N-terminal control.

Planned revision: The co-sedimentation assay is an excellent suggestion by the reviewer. Following the Reviewer’s suggestion, we will perform F/G-actin fractionation and assess the presence of several F-actin associated proteins in the F-actin fraction.

- Fig. 2A has no loading control

We show this western blot to indicate that the WT actin and tagged actin are expressed at similar levels in the heterozygous knock-in cells. For this, no loading control is needed because we only compare the intensity of the upper band (tagged actin) with the lower band (WT actin).

- The RPE-1 data are confusing as several constructs show very different localization (completely cytosolic) to HT1080 cells and there is no possible explanation given for this. Maybe simply remove this data set?

We agree with the reviewer that the differences in the localization between some of the internally tagged actins between the HT1080 and RPE1 cells might be confusing, especially for the A230-A231 variant for example. Yet, the fact that also in these cells, the T229-A230 variant performs equally well as compared to N-terminally tagged actin is an important confirmation that this variant is properly integrated into actin-based structures, independent of cell type. This makes the support for choosing this variant to continue with our studies stronger. A possible explanation for the differences is that RPE1 cells in general tend to form more stress fibers as compared to the HT1080. Since the localization to stress fibers is different between the internally tagged actins, this may explain the differences observed in colocalization.

__Planned revision: __We will add a short text, in the Results or the Discussion, on the differences between the colocalization values between HT1080 and RPE1 cells.

*- The angel measurements for lamellipodial actin is not very meaningful: the angel is determined for the radial bundles, which do not correspond to the Arp2/3 angel of single filaments and is likely the results of different nucleation factors, I would suggest to remove this. If angel measurement are really intended, cryoEM needs to be performed. *

We apologize for this misapprehension from our side which is also noted by the other two reviewers. In the treadmilling videos of the lamellipodia in HT1080 cells, which were obtained using Airyscan super-resolution microscopy, we clearly observe a consistent filament formation at a constant angle, something which we interpreted as the angle between the mother filament and the daughter filament. After consulting the literature, we indeed have to admit that this cannot be interpreted as such and we will remove these datasets.

Planned revision: We will remove the datasets with the angle measurements (Suppl. Fig. 7A-B) from our manuscript.

- Replace all SEM with SD values - use at least 3 biological replicates (4D SEM of n=2)

Planned revision: We will carefully check our statistics and revise where appropriate.

Minor points

- Intro: after listing all the details already understood on actin isoforms it is not very convincing to simply state the molecular principles remain largely unclear (l 34) - maybe better "there is no way to study actin dynamics due to current limitations of specific antibodies to fixed samples. Interesting option would be actually to develop nanobodies that are isoform specific.

We will rephrase the text in the introduction. Regarding the development isoform-specific nanobodies. Although this sounds like a promising way forward, this would likely not result in isoform-specific targeting in living cells. Similar to the antibodies, isoform-specific nanobodies would have to be generated against the N-terminus which, under native conditions, is likely not available due to the occupation with actin-binding protein. Also, since the N-terminus is not structured, it may be extremely challenging to generate nanobodies against these epitopes.

*- L 71: "involved" in the kinetics is not a good term - maybe affects or regulates.... *

We will rephrase the text.

- L148: "suspect" instead of "expect" - this clonal variation is actually a big danger of the employed approach as possible defects in actin organization could be masked by compensatory changes - it would generally be good to show critical data for at least 3 independent clones to rule out dominant selection effects.

We will rephrase. We agree that clonal variation could be a danger if actin levels are to be investigated. For future follow-up studies, we plan to make additional cell lines to avoid clone-specific conclusions.

***Referees cross-commenting** *

*I completely agree with the comments by reviewer 2 on the various missing controls - adding several or all of those will make the results much more convincing. The key for the adaptation of any new actin probe will be the level of confidence researchers have on the doumented effects. Even some negative effects on actin behavior (I am sure there will be some) should not prevent usage of the strategy as long as there is robust and convincing documentation of those effects. I also agree that including some basic in vitro characterization will go a long way to convince people dierectly working on actin (there is a very high level of biochemical understanding in that field). *

Planned revision: We will perform the essential controls as suggested by Reviewer 2. Furthermore, for future experiments, we do envisage the production and purification of internally tagged actins and investigate their binding properties in in vitro reconstitution assays. We have already started with optimizing these approaches through our ongoing collaboration (KD, SP).

Reviewer #1 (Significance (Required)):

*Significance: Very useful finding that can be applied to any question related to actin-dependent cellular processes (morphogenesis, cell division, cell polarization, cell migration etc.) *

*Strength: main finding convincing, strong genome edited cell lines *

*Limitations: application to study of isoforms very limited and data not convincing, statistics and image quantifications need improvement *

*Advance: identify new location for integral tagging of actin, which was not really possible before. The main relevance is for fundamental cell biology but the approach can also be applied to the study of disease variants in actin. *

Audience: general cell biology - very broad interest

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

Actin is highly sensitive to modifications, and tagging it with fluorescent proteins or even smaller motifs can affect its function. The most well-known example of this is that fission yeast where actin has been replaced with GFP-actin are inviable (Wu and Pollard, Science 2005) because the labeled actin cannot incorporate into the formin-dependent filaments that make up the cytokinetic ring. Subsequent experiments revealed that formins filter out GFP-actin monomers, as well as monomers that are labeled with smaller fluorescent motifs (Chen et al, J. Structural Biology 2012). Further, attempts to make mammalian cells lines where GFP-beta-actin was knocked into one allele resulted in extreme down-regulation of the GFP-labeled actin, indicating that there is some implicit toxicity with the labeled version. To my knowledge, all attempts at making homozygous GFP-actin knock-ins have been unsuccessful. Therefore, while GFP-actin or other labeled variants can be over-expressed in many different cell types with some success, there is always the question of how faithful the labeled actin represents bona fide actin localization and dynamics.

To address this van Zwam et al. have developed a clever strategy of screening actin for internal motifs that can tolerate incorporation of a tag without affecting its function. They appear to have found a good candidate, named IntAct, and provide evidence that this tagging position allows the actin to be functional in both human and yeast cells. The work is very promising, and many of the assays performed satisfy the criteria of rigor and reproducibility. Importantly, the authors have created knock-in human cell lines where the tagged actin is expressed at normal levels, including a double allele knock-in that is viable and has normal proliferation and motility. Additionally, the authors show that labeled S. cerevisiae actin can incorporate into actin cables, which are formin dependent. IntAct constructs were shown to interact with several well-known actin binding proteins and localized well to many different actin structures. There was also interesting data obtained from tagging both beta and gamma actin in human cells. However, as an actin scientist eager for new probes to visualize actin in cells, there are still questions about the functionality of these probes. Addressing these issues, listed below, would alleviate the concerns I still have about IntActs after going through the manuscript. IntActs have the potential to have a large impact on cytoskeletal research if it can be rigorously documented that they are functionally as close to unlabeled actin as possible.

We thank the Reviewer for their constructive comments and general positive evaluation of our study.

*Reviewer #2 (Significance (Required)): *

Concerns:

1. There are no negative controls performed for either the fixed or live-cell imaging of IntAct. Since the fixed cell data is heavily reliant on the presence of flag-labeled puncta at actin filaments, it is important to show that the immunocytochemistry protocol doesn't produce anything that would mimic the localization of actin. For the live cell data, there has been no effort made to show that the binding of the nanobody to the ALFA tag on InAct is specific.

Planned revision: __We will add the following controls to exclude that any of the labeling procedures produces anything that would mimic the localization of actin: 1) Immunofluorescence staining of the used tags (FLAG/ALFA) in cells that do not have tagged actins 2) Expression of ALFA-Nb-GFP and ALFA-Nb-mScarlet in cells that do not have tagged actins 3)__ Expression of free GFP in cells that have tagged actins. We will co-stain these cells with phalloidin to visualize F-actin and determine if any signal is specifically localized to the actin cytoskeleton.

2. The homozygous ALFA-tagged IntAct cells have a 50% reduction in the amount of actin expression (Fig. 2D). What is the F:G ratio in these cells? The F:G measurement is only shown for the FLAG-tagged heterozygous IntAct cells, which have the worst co-localization with phalloidin (Fig. 2F) and were not used for subsequent figures. I appreciate that motility and proliferation were measured and shown to not be affected (Fig. 4D,E) , but in our lab reducing the amount of polymerized actin by 50% (which may be more in ALFA-tagged IntAct cells if the F:G changes) has catastrophic effects on other cytoskeletal and organelle systems. Since the homozygous ALFA IntAct cells are the main ones used in the manuscript, they should be the ones that are fully characterized.

We would like to point out that the reduction is only 20-25 percent depending on the specific western blot analysis and the loading control. Still, the Reviewer is correct about the necessity of the F:G actin measurements of the ALFA-tagged IntAct cells and we therefore included those as Suppl. Fig. 9 in the original manuscript (text on page 9). The quantification of these assays clearly demonstrated that the F-G actin ratio in the ALFA-tagged IntAct cells is the same as in parental cells.

3. It is not addressed if expressing the ALFA-Nb-GFP construct in ALFA-IntAct cells alter actin properties? This is essential information for live cell imaging experiments.

Planned revision: We have already performed proliferation and migration experiments in cells that stably express the ALFA-Nb-GFP. These data indicated that proliferation and migration are not affected by the presence of the nanobody and these data will be included in the revised manuscript. To note, in the original manuscript, we already showed that treadmilling of actin at the lamellipodia is not affected by the presence of the ALFA-Nb-GFP.

4. It is not addressed how much of the ALFA-IntAct gets labeled with ALFA-Nb-GFP and how uniform the labelling.

We do not understand this specific request of the Reviewer. To our knowledge, it is not possible to assess how much of a probe (in this case the ALFA-Nb-GFP) binds the target (in this case the ALFA-IntAct actins) in living cells. This is not only the case for the ALFA-Nb-GFP but also for any other probe. As an example, when expressing Lifeact, we also do not know how much of the actin molecules within F-actin get labeled with Lifeact and how uniform the labeling is. From the results of the live-cell imaging we can only conclude that the binding is at least so effective that we can readily observe and discern all the actin-based structures that are also observed by Lifeact (see Suppl. Fig. 8 for Lifeact-GFP/ALFA-Nb-mScarlet cotransfection). Whether the regions that do not have F-actin only contain ALFA-Nb-GFP that is bound to actin monomers or also contains a significant fraction of free ALFA-Nb-GFP seems an issue that cannot be addressed.

5. To assess lamellapodia architecture, "branched actin angle" is measured using AiryScan imaging of actin filaments. This type of microscopy does not offer the ability to image individual actin filaments; what is actually being measured is the orientation of actin bundles to each other. It should be impossible to image the orientation of actin filaments in Arp2/3 dendritic networks and it is surprising that the measurements average to 70 degrees. A suitable substitute for this would be to measure the size and amount of F-actin in phalloidin-stained lamellipodia using kymograph analysis.

We apologize for this misapprehension from our side which is also noted by the other two reviewers. In the treadmilling videos of the lamellipodia in HT1080 cells, which were obtained using Airyscan super-resolution microscopy, we clearly observe a consistent filament formation at a constant angle, something which we interpreted as the angle between the mother filament and the daughter filament. After consulting the literature, we indeed have to admit that this cannot be interpreted as such and we will remove these datasets.

Planned revision: We will remove the datasets with the angle measurements (Suppl. Fig. 7A-B) from our manuscript.

6. Was it possible to make an IntAct gene substitution in yeast?

Planned revision: We thank the reviewer for this interesting question and as also suggested by Reviewer 1, we are now constructing yeast strains with IntAct as the sole expressing actin copy by using the well-established plasmid shuffle system in yeast. The results of these experiments will determine the ability of IntAct to completely substitute actin function in yeast.

Also, while this is not necessary for this manuscript, making a fission yeast strain where actin has been substituted with IntAct and demonstrating that IntAct gets incorporated into the cytoplasmic ring and into Cdc12p-polymerized filaments would alleviate MANY potential concerns people would have about these probes by directly assessing situations were other labeled actins have been documented to fail. Along the same lines, it would have been nice to see a comparison in some of the assays of ALFA-IntAct and GFP-actin or another labeled actin variant.

We appreciate the reviewer for their constructive feedback and completely agree that it is important to document how IntAct behaves in scenarios where other labelled actins have failed. As a proof of principle, IntAct incorporates into both formin- and Arp2/3- made linear and branched actin filaments in yeast (Fig.5E, Suppl. Fig. 14) and this data shows that IntAct labelling strategy is the first to achieve good integration into both these structures as previous efforts with labelled actin such as GFP-Actin fail to incorporate into formin-made actin filaments (Doyle et al., PNAS, 1996). Thus, we believe that IntAct does perform better than other labelled actins in yeast, although, further optimizations are required to overcome limitations regarding incorporation into actin cables in the presence of the ALFA nanobody.

Planned revision: We have already extended applicability of IntAct to another well-known fungal model system, the fission yeast Schizosaccharomyces pombe (S. pombe). We expressed IntAct variants of human β- and γ- actin, budding yeast actin (Sc-IntAct) and fission yeast actin (Sp-IntAct) from an exogenous plasmid under the native S. pombe actin promoter in an S. pombe strain that constitutively expresses the Nb-ALFA-mNG. Live-cell microscopy of S. pombe cells expressing these proteins revealed that all IntAct variants localize to actin patch-like structures located at the cell poles and cell division site (during cytokinesis). These structures show similar dynamics as reported for actin patches of S. pombe previously (Pelham et al., Nat Cell Biol, 2001). These preliminary results suggest that IntAct proteins show a similar localization pattern to only branched actin networks found in the actin patches of S. pombe like we had previously observed for the budding yeast, S. cerevisiae (Fig. S13 in manuscript). The underlying mechanism for this exclusion from linear actin cable network from both budding and fission yeast remain unknown and may represent an inherent specificity and sensitivity of yeast formins. Our current and future experiments will express IntAct variants in absence of the ALFA nanobody and determine the level of incorporation into actin cables, patches, and actomyosin ring.

Planned revision: We have also already performed a quantitative analysis to ascertain the effect of Sc-IntAct expression of cortical actin patch dynamics which represent sites of endocytosis in yeast (Young et al., J Cell Biol, 2004; Winter et al., Curr Biol, 1997). We compared actin cortical patch lifetimes between wildtype cells and cells expressing Sc-Act1 or Sc-IntAct as an extra copy. We used Abp1-3xmcherry as a marker for actin patches and quantified the time window between the appearance and disappearance of a patch (actin patch lifetime) from time-lapse microscopy experiments. Our preliminary results indicate that actin patch lifetimes are unaffected by exogenous expression of both Sc-Act1 or Sc-IntAct suggesting that IntAct does not negatively influence or alter actin patch dynamics. These observations suggest its applicability as a direct visualization strategy for actin at the cortical patches in budding yeast alongside existing surrogate markers like Abp1, Arc15, etc (Goode et al., Genetics, 2015; Wirshing et al., J Cell Biol, 2023).

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

*Summary: *

This paper tackles a new strategy to tag actin in cells, by identifying that incorporation of a tag of moderate size in subdomain 4 of actin minimally affects actin dynamics in cells, and does not perturb its interaction with known partners, as observed in pull-down assays.

*Major comments: *

The paper is interesting and experiments are convincing.

*My main concerns are the following : *

- Varland et al, is reporting a phosphorylation on Thr229 : I think the authors should mention and discuss this potential PTM that could be affected in IntAct.

We thank the Reviewer for pointing this out. We are aware of this review that includes phosphorylation on Thr229 as a possible PTM. Yet, this PTM is only reported in one of the Tables of the Review and not further discussed in the text. It is also unclear how the authors determined that Thr229 is a possible phosphorylation site except for the notion that this residue is a threonine and exposed at the surface of the actin molecule. Together with the fact that there is no evidence from primary studies that Thr229 is phosphorylated, we therefore decided to not include it in our discussion.

- The sequence in subdomain 4 (the alpha helix containing T229A230) is extremely conserved in animals, as well as in between the 6 human actin isoforms. This usually indicates a strong selection pressure on the residues. I think the authors should discuss how surprising it is that the T229A230 position can accomodate various tags while it is probably the place of interaction with other proteins and is playing an important role in the mechanical structural integrity of the actin itself.

We thank the Reviewer for bringing up this important point. To a certain extent, the conservation argument is true for all of the residues/domains in actin. Any manipulation will change a conserved part of the actin molecule in one way or another and thereby potentially modify its function. This is also evident from the fact that for most of the internally tagged actins, we observed a very poor colocalization with the actin cytoskeleton (Fig. 1). While for the T229/A230, we have not observed any major effects yet, this certainly does not mean that no further changes or defects will be uncovered in future experiments. Nonetheless, since our approach is unique with respect to the fact that it allows isoform-specific tagging without manipulating the N-terminus, our internal tagging system complements the already existing repertoire of actin reporting methods (N-terminal fusion, Lifeact, F-Tractin, actin nanobodies) and allows researchers to study so far unknown properties of actin variants. We have already included in the discussion that, at this point, we can only speculate as to why this variant performs much better than the others (Page 16 of the manuscript) and that possible explanations are the location at the inner domain and the higher structural plasticity of this region as compared to the rest of the molecule, as found during an alanine mutagenesis screen (Rommelaere et al., Structure, 2003).

- It is now well established that actin plays active and important roles in the nucleus : is ALFA-actin correctly translocated to the nucleus ?

Planned revision: This is an interesting suggestion. We will perform nuclear-cytosol fractionation experiments and determine whether ALFA-actin is still correctly translocated to the nucleus.

*- OPTIONAL: one may regret that there is no classical in vitro assays, such as pyrene assays to assess some kinetcis parameters on epitope-tagged actins. I guess this would make the paper a bit too large. Although, it will prove useful to better understand how much formin activity is affected (see below) *

For further biochemical characterization and a detailed investigation of the precise assembly kinetics of the tagged actins, we (KD, SP) are already working together to set up in vitro reconstitution experiments. Yet, as also indicated by the Reviewer, we consider these experiments outside of the scope of the current work.

*Minor comments: *

Below are points that could be addressed by the authors to improve the manuscript readability and highlight some important points that are sometimes missing or are not properly discussed:

-line 40 "...but the distinct N-terminal epitope is not available under native conditions preventing" is a bit too obscure. Can the authors say clearly what is meant by 'native conditions'?

In our understanding, the term ‘native’ is generally used when referring to conditions in which proteins are in their natural state, without alterations due to heat or denaturants, and possibly also still interacting with their binding partners. We will rephrase to better indicate that in this specific case, we mean that the region that harbors the N-terminus is usually occupied by actin-binding proteins, preventing the binding of the antibody due to steric hindrance.

- figure 1A : make a clearer correspondance between the number shown in panel A and the amino acid numbers displayed in panel C and G.

Planned revision: This is a good point, we will add extra annotation in the graph to better link the panels with each other. We will also add additional annotation in Fig. 1D-F for the same purpose.

- figure 1A : it could be informative to indicate subdomains in this panel.

Planned revision: We will add the numbers for the subdomains in Fig. 1A.

- figure 1C : normalized correlation cell : I am not sure I understand how the normalization of the Pearson coefficient is done. It is therefore not clear how can it >1 or >-1 ? This should be clearly explained in the method section of the paper.

__Planned revision: __We will better explain the normalization procedure in the Methods section.

- figure S4 : comes a bit too early when ALFA-actin has not been yet introduced in the main text. Please, reposition this part or provide data with the FLAG-tag version.

Planned revision: This is a good point and completely overlooked by us. We will introduce this Figure later such that the ALFA tag is already introduced.

- section starting line 121 : this section should be better motivated = Why are different tags being tested ? This comes later in the discussion, but the reader fails at following the reasoning/motivation here.

Planned revision: We will add extra motivation for why we added multiple tags.

- figure 2D, line 145 "We also evaluated actin protein expression in the homozygous ALFA-β-actin cells and this showed that the total amount of β-actin was slightly lower in the ALFA-β-actin cells compared to parental HT1080 cells (Fig. 2C-D)." 'Slightly' is not a very quantitative nor accurate term. please rephrase. Besides, a statistical test for the paired data would also be informative. Besides, data in figure S6B-D indeed show a correlated increase in the expression of Gamma-actin that compensate for the decrease in the Beta-actin level in ALFA-Beta-actin. Can the authors explain why they conclude otherwise?

Planned revision: This indeed is an important point and we will change the phrasing of this section to provide a more quantitative and accurate description of the western blot quantifications.

- figure S7B: I am not ure anyone has ever reported measurement of angle of branched actin filament using epifluorescence microscopy. I would remove this panel, or the authors should explain how this measurement can be done objectively.

We apologize for this misapprehension from our side which is also noted by the other two reviewers. In the treadmilling videos of the lamellipodia in HT1080 cells, which were obtained using Airyscan super-resolution microscopy, we clearly observe a consistent filament formation at a constant angle, something which we interpreted as the angle between the mother filament and the daughter filament. After consulting the literature, we indeed have to admit that this cannot be interpreted as such and we will remove these datasets.

Planned revision: We will remove the datasets with the angle measurements (Suppl. Fig. 7A-B) from our manuscript.

*- Figure 2F : can the authors comment on the (significant ?) lower value for FLAG-tag actin ? *

The lower value for FLAG-tag actin has likely to do with the properties of the antibody and suitability for immunofluorescence. For reason that we do not know, we usually detect more background for the FLAG tag antibody as compared to the other antibodies/ALFA tag nanobody. Since the Pearson correlation coefficient quickly decreases with suboptimal labeling, this is likely the reason that the values for FLAG-actin are lower as compared to the other tagged actins. Importantly, in our biochemistry experiments (F/G-actin), we detect no difference between FLAG-actin and ALFA-actin indicating that it is rather the immunofluorescence and sensitive Pearson correlation analysis than the integration of actin that causes this difference.

- line 205 "The results from these experiments show that both DIAPH1 and FMNL2 associate with ALFA-β-actin (Fig. 3D),". It is not so obvious that these formins directly interact with monomeric actin via their FH2 domains in co-immunoprecipitation assays. It might very well be mediated by the interaction with profilin, that in turn bind to the FH1 domain of formins. For me, this assay does not make a correct proof that epitope-labelled actin do not interfere with formin activity.

Planned revision: The point that the co-immunoprecipitation does not demonstrate direct interactions between formins and actin is well taken. We, however, do not claim that this assay proofs that formin activity, or formin-based integration of actin monomers, is similar with tagged actin as compared to wildtype actin. Nonetheless, we will critically re-evaluate the relevant passages and rephrase the text to avoid any confusion.

- figure 5C&D : both graph should use the same scale for the y-axis for easier comparison.

Planned revision: We will adapt the scale of Fig. 5D to make it identical to Fig. 5C. Following the other suggestions of the Reviewer (and of Reviewer #1), we will also critically evaluate our normalization procedure and present those numbers in the Figures if the values turn out to be different.

- figure 5D: I think the way the ratio is performed is misleading. Why not look at the Beta/Gamma ratio using the isoform specific antibodies used in parental cells, and show the results for ALFA-Beta-actin and for ALFA-Gamma-actin separately ?

We kindly refer to our answer to Reviewer #1 on Page 2 for a detailed explanation on the experimental challenge of comparing the localization of wildtype and tagged actin isoforms.

Planned revision: We will critically evaluate our normalization procedure and present those numbers in the Figures if the values turn out to be different. Furthermore, we will add a different experimental method to show that the tagged isoforms properly localize to actin-based structures. For this, we will attempt to use micropatterned cells to induce clearly define actin-bases structures and also explore the possibilities of investigating the differential localization in double-tagged cells.

*- The limitation observed for unbranched cables in yeast that nanobody-tagged ALFA-actin does not incorporate correctly should be discussed and stressed further in the discussion, as it might prove to be a strong limitation for live-cell imaging to reliably study any type of actin networks. *

We acknowledge the reviewer’s concern regarding the inability of ALFA-tagged actin to incorporate into yeast actin cables when NbALFA is co-expressed and will discuss this point further in the revised manuscript. We have now observed the same limitation for fission yeast actin cables as well and combined, these observations may represent a tighter control and sensitivity of yeast formins towards any perturbations in actin size (since NbALFA binds to ALFA tag with picomolar affinity). To address this issue and as also suggested by Reviewer 1, we are now creating yeast strains with inducible control of NbALFA expression under GALS/GAL1 promoters and observe the labelling of actin structures after this approach. Additionally, expression of variants of NbALFA with high dissociation rates may also allow labelling of actin cables and would be certainly worth a try in the future. A structural comparison between mammalian and yeast formins may be required to shed some light on the molecular basis of this fundamental difference.

However, since in the absence of the nanobody, this limitation is overcome (Fig. 5E, Suppl. Fig. 14), we believe that with additional modifications and fast developments in imaging technologies, this limitation can be overcome in the future. Thus, IntAct as a labeling strategy represents an advancement over existing labelled actins with the most important aspect being the identification of the T229/A230 residue pair to be permissive for integration of various tags even as large as GFP11 fragment including a linker (26AA) (Reviewer Fig. 2). Importantly, the T229/A230 site is conserved across many organisms (such as Chlamydomonas reinhardatii, Cryptococcus neoformans, etc) and may act as a framework to study the actin cytoskeleton especially in organisms where known surrogate markers like phalloidin and Lifeact may not work or work only sub optimally.

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

*General assessment: *

*This paper provides a new tagging strategy to monitor actin activity in cells, by specifically inserting the tag along the amino acid sequence. *

*Advance: *

*This is a very useful tool, as most existing available probes bind to actin in regions that are common to many other actin binding proteins. The authors provide extensive experiments to validate that tagged-actin are functional and do not perturb the actin expression level, actin network architecture nor dynamics. *

*Audience: *

*This research paper will be of interest to a rather broad audience (many cell biologists) that are either sutyding actin dynamics or know that actin is involved in the cell functions they study. *

*Expertise: *

*My expertise is in vitro actin biochemistry. *

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Referee #3

Evidence, reproducibility and clarity

Summary:

This paper tackles a new strategy to tag actin in cells, by identifying that incorporation of a tag of moderate size in subdomain 4 of actin minimally affects actin dynamics in cells, and does not perturb its interaction with known partners, as observed in pull-down assays.

Major comments:

The paper is interesting and experiments are convincing.

My main concerns are the following :

• Varland et al, is reporting a phosphorylation on Thr229 : I think the authors should mention and discuss this potential PTM that could be affected in IntAct.
• The sequence in subdomain 4 (the alpha helix containing T229A230) is extremely conserved in animals, as well as in between the 6 human actin isoforms. This usually indicates a strong selection pressure on the residues. I think the authors should discuss how surprising it is that the T229A230 position can accomodate various tags while it is probably the place of interaction with other proteins and is playing an important role in the mechanical structural integrity of the actin itself.
• It is now well established that actin plays active and important roles in the nucleus : is ALFA-actin correctly translocated to the nucleus ?
• OPTIONAL: one may regret that there is no classical in vitro assays, such as pyrene assays to assess some kinetcis parameters on epitope-tagged actins. I guess this would make the paper a bit too large. Although, it will prove useful to better understand how much formin activity is affected (see below)

Minor comments:

Below are points that could be addressed by the authors to improve the manuscript readability and highlight some important points that are sometimes missing or are not properly discussed :

• line 40 "...but the distinct N-terminal epitope is not available under native conditions preventing" is a bit too obscure. Can the authors say clearly what is meant by 'native conditions' ?
• figure 1A : make a clearer correspondance between the number shown in panel A and the amino acid numbers displayed in panel C and G.
• figure 1A : it could be informative to indicate subdomains in this panel.
• figure 1C : normalized correlation cell : I am not sure I understand how the normalization of the Pearson coefficient is done. It is therefore not clear how can it >1 or >-1 ? This should be clearly explained in the method section of the paper.
• figure S4 : comes a bit too early when ALFA-actin has not been yet introduced in the main text. Please, reposition this part or provide data with the FLAG-tag version.
• section starting line 121 : this section should be better motivated = Why are different tags being tested ? This comes later in the discussion, but the reader fails at following the reasoning/motivation here.
• figure 2D, line 145 "We also evaluated actin protein expression in the homozygous ALFA-β-actin cells and this showed that the total amount of β-actin was slightly lower in the ALFA-β-actin cells compared to parental HT1080 cells (Fig. 2C-D)." 'Slightly' is not a very quantitative nor accurate term. please rephrase. Besides, a statistical test for the paired data would also be informative. Besides, data in figure S6B-D indeed show a correlated increase in the expression of Gamma-actin that compensate for the decrease in the Beta-actin level in ALFA-Beta-actin. Can the authors explain why they conclude otherwise ?
• figure S7B: I am not ure anyone has ever reported measurement of angle of branched actin filament using epifluorescence microscopy. I would remove this panel, or the authors should explain how this measurement can be done objectively.
• Figure 2F : can the authors comment on the (significant ?) lower value for FLAG-tag actin ?
• line 205 "The results from these experimentsshow that both DIAPH1 and FMNL2 associate with ALFA-β-actin (Fig. 3D),". It is not so obvious that these formins directly interact with monomeric actin via their FH2 domains in co-immunoprecipitation assays. It might very well be mediated by the interaction with profilin, that in turn bind to the FH1 domain of formins. For me, this assay does not make a correct proof that epitope-labelled actin do not interfere with formin activity.
• figure 5C&D : both graph should use the same scale for the y-axis for easier comparison.
• figure 5D: I think the way the ratio is performed is misleading. Why not look at the Beta/Gamma ratio using the isoform specific antibodies used in parental cells, and show the results for ALFA-Beta-actin and for ALFA-Gamma-actin separately ?
• The limitation observed for unbranched cables in yeast that nanobody-tagged ALFA-actin does not incorporate correctly should be discussed and stressed further in the discussion, as it might prove to be a strong limitation for live-cell imaging to reliably study any type of actin networks.

Significance

General assessment:

This paper provides a new tagging strategy to monitor actin activity in cells, by specifically inserting the tag along the amino acid sequence.

Advance:

This is a very useful tool, as most existing available probes bind to actin in regions that are common to many other actin binding proteins. The authors provide extensive experiments to validate that tagged-actin are functional and do not perturb the actin expression level, actin network architecture nor dynamics.

Audience:

This research paper will be of interest to a rather broad audience (many cell biologists) that are either sutyding actin dynamics or know that actin is involved in the cell functions they study.

Expertise:

My expertise is in vitro actin biochemistry.

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Referee #2

Evidence, reproducibility and clarity

Actin is highly sensitive to modifications, and tagging it with fluorescent proteins or even smaller motifs can affect its function. The most well-known example of this is that fission yeast where actin has been replaced with GFP-actin are inviable (Wu and Pollard, Science 2005) because the labeled actin cannot incorporate into the formin-dependent filaments that make up the cytokinetic ring. Subsequent experiments revealed that formins filter out GFP-actin monomers, as well as monomers that are labeled with smaller fluorescent motifs (Chen et al, J. Structural Biology 2012). Further, attempts to make mammalian cells lines where GFP-beta-actin was knocked into one allele resulted in extreme down-regulation of the GFP-labeled actin, indicating that there is some implicit toxicity with the labeled version. To my knowledge, all attempts at making homozygous GFP-actin knock-ins have been unsuccessful. Therefore, while GFP-actin or other labeled variants can be over-expressed in many different cell types with some success, there is always the question of how faithful the labeled actin represents bona fide actin localization and dynamics.

To address this van Zwam et al. have developed a clever strategy of screening actin for internal motifs that can tolerate incorporation of a tag without affecting its function. They appear to have found a good candidate, named IntAct, and provide evidence that this tagging position allows the actin to be functional in both human and yeast cells. The work is very promising, and many of the assays performed satisfy the criteria of rigor and reproducibility. Importantly, the authors have created knock-in human cell lines where the tagged actin is expressed at normal levels, including a double allele knock-in that is viable and has normal proliferation and motility. Additionally, the authors show that labeled S. cerevisiae actin can incorporate into actin cables, which are formin dependent. IntAct constructs were shown to interact with several well-known actin binding proteins and localized well to many different actin structures. There was also interesting data obtained from tagging both beta and gamma actin in human cells. However, as an actin scientist eager for new probes to visualize actin in cells, there are still questions about the functionality of these probes. Addressing these issues, listed below, would alleviate the concerns I still have about IntActs after going through the manuscript. IntActs have the potential to have a large impact on cytoskeletal research if it can be rigorously documented that they are functionally as close to unlabeled actin as possible.

Significance

Concerns:

1. There are no negative controls performed for either the fixed or live-cell imaging of IntAct. Since the fixed cell data is heavily reliant on the presence of flag-labeled puncta at actin filaments, it is important to show that the immunocytochemistry protocol doesn't produce anything that would mimic the localization of actin. For the live cell data, there has been no effort made to show that the binding of the nanobody to the ALFA tag on InAct is specific.
2. The homozygous ALFA-tagged IntAct cells have a 50% reduction in the amount of actin expression (Fig. 2D). What is the F:G ratio in these cells? The F:G measurement is only shown for the FLAG-tagged heterozygous IntAct cells, which have the worst co-localization with phalloidin (Fig. 2F) and were not used for subsequent figures. I appreciate that motility and proliferation were measured and shown to not be affected (Fig. 4D,E) , but in our lab reducing the amount of polymerized actin by 50% (which may be more in ALFA-tagged IntAct cells if the F:G changes) has catastrophic effects on other cytoskeletal and organelle systems. Since the homozygous ALFA IntAct cells are the main ones used in the manuscript, they should be the ones that are fully characterized.
3. It is not addressed if expressing the ALFA-Nb-GFP construct in ALFA-IntAct cells alter actin properties? This is essential information for live cell imaging experiments.
4. It is not addressed how much of the ALFA-IntAct gets labeled with ALFA-Nb-GFP and how uniform the labelling.
5. To assess lamellapodia architecture, "branched actin angle" is measured using AiryScan imaging of actin filaments. This type of microscopy does not offer the ability to image individual actin filaments; what is actually being measured is the orientation of actin bundles to each other. It should be impossible to image the orientation of actin filaments in Arp2/3 dendritic networks and it is surprising that the measurements average to 70 degrees. A suitable substitute for this would be to measure the size and amount of F-actin in phalloidin-stained lamellipodia using kymograph analysis.
6. Was it possible to make an IntAct gene substitution in yeast?

Also, while this is not necessary for this manuscript, making a fission yeast strain where actin has been substituted with IntAct and demonstrating that IntAct gets incorporated into the cytoplasmic ring and into Cdc12p-polymerized filaments would alleviate MANY potential concerns people would have about these probes by directly assessing situations were other labeled actins have been documented to fail. Along the same lines, it would have been nice to see a comparison in some of the assays of ALFA-IntAct and GFP-actin or another labeled actin variant.

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Referee #1

Evidence, reproducibility and clarity

In this study, the authors generate several variants of actin that are internally tagged with short peptide tags. They identify one particular position that is able to tolerate various tags of 5-10 amino acids and still shows largely unaltered behavior in cells. They study incorporation of their tagged actins into filaments, characterize the interactions of G-actin variants with different associated proteins and show that retrograde actin flow in lamellipodia and the wound healing response of epithelial cells is not affected by the tagged variants. They then apply the tagged actin to study subcellular distribution of different actin isoforms in mammalian and yeast cells.

The identification of a specific site in the actin protein that tolerates variable peptide insertions is very exciting and of fundamental interest for all research fields that deal with cytoskeletal rearrangements and cellular morphogenesis. The result demonstrating the functionality of actin variants with peptides inserted between aa 229 and 230 are generally convincing and well done. In particular, the generation of CRISPR/Cas9 genome edited versions of beta- and gamma actin are impressive. I therefore generally support publication of this study. There are however several technical and conceptual issues that should be addressed to improve quality and scope of the study. I listed some specific comments below:

Major points

• The biggest issue I have is the last section on the application of tagged actins to study isoform functions. In principle the application is very clear as there are simply no alternative ways to study isoform distribution in live cells. However, the experimental data are simply not convincing. What the authors define as "cortex" in Fig. 5A seems to rather represent cytosolic background mixed with radial fibers. I am not convinced that even the antibody staining with a relatively clear differential distribution of beta and gamma really shows a genuine accumulation of one isoform on stress fibers. It seems to me that the beta-actin staining has as higher cytosolic background and is generally weaker (gamma nicely labels transverse arcs), which reduces signal/noise and therefore yields a relatively increased level in areas with less-bundled actin. My suggestion is to select more clearly defined actin structures and to use micro-patterned cells to normalize the otherwise obstructing variability in actin organization. Possible structures would be cortical arcs in bow-shaped cells, lamellipodial edges (HT1080 seem to make very nice and large lamellipodia) or cell-cell contacts (confluent monolayer, provided cells don´t grow on top of each other). Stress fibers are possible but need to be segmented very precisely and I did not see any details on this in the methods section. For Fig. 5D: I assume cells were used where only one isoform was tagged? This is technical weak and the double-normalization is probably blurring any difference that might be occurring. Why not use a double-tagging strategy with ALFA/FLAG or ALFA/AU5 tags to exploit the constructs introduced in the previous figures? Also, the unique selling point of the strategy is the possibility of actual live imaging of specific isoforms. Cells that have stably integrated double tags and then transiently express nanobodies for ALFA and either AU5 or FLAG (or other if those don't exist) would make this possible. Considering the work already done in this manuscript, such an approach should actually be possible - did the authors attempt this or is there are reason it is not discussed? If double tagged cells are not possible for some reason it should at the very least be possible to combine ALFA-detection with the specific antibody against the other isoform and get rid of the double normalization.
• The authors make a point of comparing the internally tagged actin to N-terminal tags that are mostly functional but have been shown to affect translational efficiency. I would strongly suggest to include N-terminally tagged actin as control for all assays in this study. Also for the physiological assays (retrograde flow, wound healing), a positive control is missing that shows some effect. Previous studies showed defects with transiently expressed actin with an N-terminal GFP. As retrograde flow measurements are very sensitive to the exact position of the kymographs and wound healing assays is a very crude and indirect readout, such a positive control is essential.
• Expression of tagged actins in yeast is a very nice idea but it would be far more informative to express the tagged forms as the only copy of actin. This can either be done by directly replacing endogenous actin gene in S. cerevisiae, or (if the tagged versions are not viable) - using the established plasmid shuffle system (express actin on counter-selectable plasmid, then knock out endogenous copy and introduce additional plasmid with tagged actin, then force original plasmid out). In the presence of endogenous S. cerevisiae actin the shown effects are very hard to interpret as nothing is known about relative protein levels (endogenous vs. introduced). Also, if constitutive expression of the ALFA nanobody is harmful for integration into cables, why not perform inducible expression of the nanobody and observe labeling after induction. For the live imaging a robust cable marker is needed, like Abp140-GFP. Finally, indicate the sequence differences between the used actin forms in yeast (supplementary figure with sequence alignment and clear indication of all variations)
• As the authors clearly show good integration of several tagged actins into filaments I would expand the structural characterization: perform alpha fold predictions of actin monomer structures including the various tags to show the expected orientation. It is striking that the only integration site that seems to work well is at the last position of a short helix, indicating that the orientation of the integrated peptide might be fixed in space and be optimal to minimize interference. Also, a docking of the tag onto the recently published cryoEM structures of the actin filament should be shown to indicate where it resides compared to tropomyosin or the major groove where most side binding proteins seem to bind.
• For any claims regarding usability of tagged variants for isoform research it would be very important to characterize the known posttranslational modifications of tagged actin variants - are the differences between beta and gamma maintained on this level as well?

Technical issues

• There is no scale for the color coding in Fig. 5A, B
• The y-scales for Fig. 5C and D need to be identical to allow direct comparison
• Pearson coefficient should not be normalized to a control value as its already a dimensionless parameter. Always report actual R-value - also remove R2 values for Pearson as this makes no sense in this context (not sure if it was a typo or intended).
• All values on subcellular regions (like stress fiber or cortex) dependet critically on the way thesese regions were thresholded or identified. Provide all details on how this was done in the methods section and ensure that adequate background subtraction and normalization is applied. Optimally, an unbiased (AI or automated) approach based on simple image statistics is used for this to avoid personal bias.
• In Fig. 2A only heterozygous FLAG-actin cells are used. Why not use a homozygous line (for both beta and gamma actin)? The nice band shift of the FLAG version would allow the precise quantification of the fraction of total actin covered by beta and gamma actin, which then could provide some additional info for the apparently weaker beta staining in Fig. 5 (if beta expression is simply weaker). This would be a very simple and useful advantage of the internal tags that could be widely applied.
• Fig. 3: control with N-terminal tag is missing. Also, why is it not possible to assay filament binding factors like Myosin, Filamin or alpha actinin - instead of co-IP a simple co-sedimentation assay with cell extracts in F-buffer should pick up any major difference in decoration of filaments containing the ALFA tag. Using two speeds for centrifugation it might even be possible to observe effects on filament bundling. The best approach for this would of course be to purify tagged actins and perform in vitro assays but this is clearly beyond the scope of what the authors intended here. I personally think that a broad acceptance of the marker will only come once the biochemistry has been sufficiently characterized so this is a future direction I would strongly encourage.
• Fig. 2A has no loading control -
• The RPE-1 data are confusing as several constructs show very different localization (completely cytosolic) to HT1080 cells and there is no possible explanation given for this. Maybe simply remove this data set?
• The angel measurements for lamellipodial actin is not very meaningful: the angel is determined for the radial bundles, which do not correspond to the Arp2/3 angel of single filaments and is likely the results of different nucleation factors, I would suggest to remove this. If angel measurement are really intended, cryoEM needs to be performed.
• Replace all SEM with SD values - use at least 3 biological replicates (4D SEM of n=2)

Minor points

• Intro: after listing all the details already understood on actin isoforms it is not very convincing to simply state the molecular principles remain largely unclear (l 34) - maybe better "there is no way to study actin dynamics due to current limitations of specific antibodies to fixed samples. Interesting option would be actually to develop nanobodies that are isoform specific 
• L 71: "involved" in the kinetics is not a good term - maybe affects or regulates....
• L148: "suspect" instead of "expect" - this clonal variation is actually a big danger of the employed approach as possible defects in actin organization could be masked by compensatory changes - it would generally be good to show critical data for at least 3 independent clones to rule out dominant selection effects.

Referees cross-commenting

I completely agree with the comments by reviewer 2 on the various missing controls - adding several or all of those will make the results much more convincing. The key for the adaptation of any new actin probe will be the level of confidence researchers have on the doumented effects. Even some negative effects on actin behavior (I am sure there will be some) should not prevent usage of the strategy as long as there is robust and convincing documentation of those effects. I also agree that including some basic in vitro characterization will go a long way to convince people dierectly working on actin (there is a very high level of biochemical understanding in that field).

Significance

Significance: Very useful finding that can be applied to any question related to actin-dependent cellular processes (morphogenesis, cell division, cell polarization, cell migration etc.)

Strength: main finding convincing, strong genome edited cell lines

Limitations: application to study of isoforms very limited and data not convincing, statistics and image quantifications need improvement

Advance: identify new location for integral tagging of actin, which was not really possible before. The main relevance is for fundamental cell biology but the approach can also be applied to the study of disease variants in actin.

Audience: general cell biology - very broad interest

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Reviewer #1 (Public Review):

Summary: Cullinan et al. explore the hypothesis that the cytoplasmic N- and C-termini of ASIC1a, not resolved in x-ray or cryo-EM structures, form a dynamic complex that breaks apart at low pH, exposing a C-terminal binding site for RIPK1, a regulator of necrotic cell death. They expressed channels tagged at their N- and C-termini with the fluorescent, non-canonical amino acid ANAP in CHO cells using amber stop-codon suppression. Interaction between the termini was assessed by FRET between ANAP and colored transition metal ions bound either to a cysteine reactive chelator attached to the channel (TETAC) or metal-chelating lipids (C18-NTA). A key advantage to using metal ions is that they are very poor FRET acceptors, i.e. they must be very close to the donor for FRET to occur. This is ideal for measuring small distances/changes in distance on the scales expected from the initial hypothesis. In order to apply chelated metal ions, CHO cells were mechanically unroofed, providing access to the inner leaflet of the plasma membrane. At high pH, the N- and C- termini are close enough for FRET to be measured, but apparently too far apart to be explained by a direct binding interaction. At low pH, there was an apparent increase in FRET between the termini. FRET between ANAP on the N-and C-termini and metal ions bound to the plasma membrane suggests that both termini move away from the plasma membrane at low pH. The authors propose an alternative hypothesis whereby close association with the plasma membrane precludes RIPK1 binding to the C-terminus of ASIC1a.

Strengths: The findings presented here are certainly valuable for the ion channel/signaling field and the technical approach only increases the significance of the work. The choice of techniques is appropriate for this study and the results are clear and high quality. Sufficient evidence is presented against the starting hypothesis.

Weaknesses: I have a few questions about certain controls and assumptions that I would like to see discussed more explicitly in the manuscript.

--My biggest concern is with the C-terminal citrine tag. Might this prevent the hypothesized interaction between the N- and C-termini? What about the serine to cysteine mutations? The authors might consider a control experiment in channels lacking the C-terminal FP tag.

--Figure 2 supplement 1 shows apparent read-through of the N-terminal stop codons. Given that most of the paper uses N-terminal ANAP tags, this figure should be moved out of the supplement. Do N-terminally truncated subunits form functional channels? Do the authors expect N-terminally truncated subunits to co-assemble in trimers with full-length subunits? The authors should include a more explicit discussion regarding the effect of truncated channels on their FRET signal in the case of such co-assembly.

--As the epitope used for the western blots in Figure 2 and supplements is part of the C-terminal tag, these blots do not provide an estimate of the fraction of C-terminally truncated channels (those that failed to incorporate ANAP at the stop codon). What effect would C-terminally truncated channels have on the FRET signal if incorporated into trimers with full-length subunits?

--Some general discussion of these results in the context of trimeric channels would be helpful. Is the putative interaction of the termini within or between subunits? Are the distances between subunits large enough to preclude FRET between donors on one subunit and acceptor ions bound on multiple subunits?

--The authors conclude that the relatively small amount of FRET between the cytoplasmic termini suggests that the interaction previously modeled in Rosetta is unlikely. Is it possible that the proposed structure is correct, but labile? For example, could it be that the FRET signal is the time average of a state in which the termini directly interact (as in the Rosetta model) and one in which they do not?

Potential RDA Tag: Project

reply to m_t_rv_s__n/ at https://www.reddit.com/r/Zettelkasten/comments/164n6qg/is_this_overkill/

First, your system is historically far more traditional than Luhmann's more specific practice. See: https://boffosocko.com/2022/10/22/the-two-definitions-of-zettelkasten/

If you're taking all the notes/highlights from a particular book and keeping them in a single file, then it may be far quicker and more productive to do some high level tagging on the entire book/file itself and then relying on and using basic text search to find particular passages you might use at a later date.

Spending time reviewing over all of your notes and tagging/indexing them individually may be beneficial for some basic review work. But this should be balanced out with your long term needs. If your area is "sociology", for example, and you tag every single idea related to the topic of sociology with #sociology, then it will cease to have any value you to you when you search for it and find thousands of disconnected notes you will need to sift through. Compare this with Luhmann's ZK which only had a few index entries under "sociology". A better long term productive practice, and one which Luhmann used, is indexing one or two key words when he started in a new area and then "tagging" each new idea in that branch or train of though with links to other neighboring ideas. If you forget a particular note, you can search your index for a keyword and know you'll find that idea you need somewhere nearby. Scanning through the neighborhood of notes you find will provide a useful reminder of what you'd been working on and allow you to continue your work in that space or link new things as appropriate.

If it helps to reframe the long term scaling problem of over-tagging, think of a link from one idea to another as the most specific tag you can put on an idea. To put this important idea into context, if you do a Google search for "tagging" you'll find 240,000,000 results! If you do a search for the entirety of the first sentence in this paragraph, you'll likely only find one very good and very specific result, and the things which are linked to it are going to have tremendous specific value to you by comparison.

Perhaps the better portions of your time while reviewing notes would be taking the 150 highlights and finding the three to five most important, useful, and (importantly) reusable ones to write out in your own words and begin expanding upon and linking? These are the excerpts you'll want to spend more time on and tag/index for future use rather than the other hundreds. Over time, you may eventually realize that the hundreds are far less useful than the handful (in management spaces this philosophy is known as the Pareto principle), so spending a lot of make work time on them is less beneficial for whatever end goals you may have. (The make work portions are often the number one reason I see people abandoning these practices because they feel overwhelmed working on raw administrivia instead of building something useful and interesting to themselves.) Naturally though, you'll still have those hundreds sitting around in a file if you need to search, review, or use them. You won't have lost them by not working on them, but more importantly you'll have gained loads of extra time to work on the more important pieces. You should notice that the time you save and the value you create will compound over time.

And as ever, play around with these to see if they work for you and your specific needs. Some may be good and others bad—it will depend on your needs and your goals. Practice, experiment, have fun.

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Referee #3

Evidence, reproducibility and clarity

Review of: "An adapted MS2-MCP system to visualize endogenous cytoplasmic mRNA with live imaging in Caenorhabditis elegans"<br /> Authors: Cristina Tocchini and Susan Mango

The MS2-MCP imaging platform is an essential imaging system that enables dynamic quantification of mRNA transcription, abundance, location, and turnover in living biological systems. In the last ten or so years, this approach has been used in extremely successful ways in Drosophila embryos to dissect both the regulatory logic underpinning early transcriptional organization and activation with unprecedented resolution and, furthermore, how active mRNA localization outside of the nucleus impacts pattern formation. The authors correctly point out that full implementation of this tool has been suspiciously lacking in the C. elegans community for some time (aside from a few noted implementations).

In this manuscript, Tocchini and Mango directly approach this deficit in a thoughtful study where many of the salient features of MS2 epitope tagging are systematically measured. Specifically, the authors use CRISPR genomic engineering to tag two separate dosage-sensitive, developmental genes and study the expression and function of these genes within the context of the MS2/MCP-GFP system. The authors demonstrate that the location of the MS2 epitope insertion within the endogenous 3'UTR is an important design consideration for functional, downstream implementation of the imaging system. In both cases, insertion of the MS2 hairpins near the end of the open reading frame of either gene results in overt and specific developmental phenotypes that phenocopy previously characterized loss of function alleles of each gene. The design of these experiments is high in quality in that they measure both the levels of cytoplasmic abundance of the various epitope-tagged mRNAs as well as the protein expression levels for these transgenes (by monitoring the levels of GFP expression (each MS2-tagged gene encodes a functional GFP-tagged allele). In two clear transgene examples, they demonstrate that the loss of function phenotypes of the proximally-tagged (closest to the ORF) transgenes disrupt mRNA levels and expression and reduce the proper localization of these mRNAs. This may be why previous attempts at implementing this important imaging system have failed.

The authors then characterize the cellular systems that cause the differential expression of MS2-tagged transgenes. The authors note that previous studies on simpler systems and in C. elegans have suggested that the nonsense-mediated mRNA decay (NMD) pathway limits the expression of mRNAs with exceptionally long 3'UTRs. Tocchini and Mango then use C. elegans NMD mutants to demonstrate that ablation of this natural RNA degradation system corrects the developmental and gene expression defects associated with the reduction of function MS2 insertion alleles. These experiments are complete and compelling as they are validated at all levels (GFP expression (via quantification of GFP expression) and mRNA expression, and mRNA localization levels (via in situ hybridization).

The authors then make the case that the type and expression levels of the MCP-GFP fusion protein are also essential features that need to be optimized for an effective imaging system. The authors suggest that optimal visualization of endogenous genes requires the surprisingly low-level expression of the MCP-GFP fusion protein. The authors use a novel transgene that differs from the conventional system. Specifically, the Tocchini system employs a 2xMCP ORF fused to 2xmCherry ORFs fusion. This transgene lacks the NLS typically used to localize exported mRNAs in the cytoplasm and also encodes two MCPs that may or may not facilitate dimerization on the MS2 hairpins. They demonstrate that endogenous, epitope-tagged transgenes can be visualized in developing embryos and that tethering this 2xMCP fusion to the reporter transcript does not alter RNA expression levels. While the authors demonstrate that visualization is possible with this system, it is hard to tell if this fusion protein dramatically improves over other available systems without a direct comparison. For instance, measuring the signal-to-noise (S/N) ratio of localized 2xMCP-2xmCherry would be a good addition and support the author's claims. If it were an exceptional system, these calculations should exceed the well-characterized and quantified MCP-GFP system described in Lee et al. 2019 ((Lee et al., 2019). It is just too hard to know if this is a dramatic element that should now be included in future RNA localization experiments.

Minor critiques:

1. The authors should provide more details in the experimental description of the MS2-tagged alleles (or in the figure images). It needs to be clarified in the main text how many MS2 hairpins there are, though this can be found in the materials and methods. In addition, it would be nice to know if these were any of the variations of MS2 hairpins that have already been optimized in some other way to increase or decrease structure or RNA metabolism defects in other systems. Specifically, are these hairpins the newest versions, V6 or V7, described in manuscripts from the Singer laboratory (e.g., (Tutucci et al., 2018))? For aficionados of this imaging system, it would be important to qualify each of the potential new features that make the results in this manuscript so clear and important.
2. For people that are colorblind (or have reduced ability to distinguish some colors from others (like me, a reviewer)), it would be nice to have the MS2 illustrations in Figures 1A and B not have that color within the black, normal UTR. It's picky, but I had to ask someone what color that was.

References:

Lee, C., Shin, H., and Kimble, J. (2019). Dynamics of Notch-Dependent Transcriptional Bursting in Its Native Context. Dev Cell 50, 426-435 e424.

Tutucci, E., Vera, M., Biswas, J., Garcia, J., Parker, R., and Singer, R.H. (2018). An improved MS2 system for accurate reporting of the mRNA life cycle. Nat Methods 15, 81-89.

Significance

In summary, this is a well-written and critical addition to the literature that will hopefully increase the implementation of this system in C. elegans research. The systematic approach to getting a new experimental platform up and running certainly has a place in the canon. Aside from the missing elements regarding the putative improvements and/or direct comparisons between different MCP fusion proteins, the manuscript is solid, important, and nearly ready to go.

It is an advance and will, as noted above, likely serve to help implement this system by other C. elegans reserachers.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

1) The authors need to validate that RAP1-HA still retains its essential function. As indicated above, if RAP1-HA still retains its essential functions, cells carrying one RAP1-HA allele and one deleted allele are expected to grow the same as WT cells. These cells should also have the WT VSG expression pattern, and RAP1-HA should still interact with TRF.

We demonstrated that C-terminally HA-tagged RAP1 co-localizes with telomeres by a combination of immunofluorescence and fluorescence in situ hybridization (Cestari and Stuart, 2015, PNAS), and co-immunoprecipitate telomeric and 70 bp repeats (Cestari et al. 2019 Mol Cell Biol). We also showed by immunoprecipitation and mass spectrometry that HA-tagged RAP1 interacts with nuclear and telomeric proteins, including PIP5Pase (Cestari et al. 2019). Others have also tagged T. brucei RAP1 with HA without disrupting its nuclear localization (Yang et al. 2009, Cell), all of which indicate that the HA-tag does not affect protein function. As for the suggested experiment, there is no guarantee that cells lacking one allele of RAP1 will behave as wildtype, i.e., normal growth and repression of VSGs genes. Also, less than 90% of T. brucei TRF was reported to interact with RAP1 (Yang et al. 2009, Cell), which might be indirect via their binding to telomeric repeats rather than direct protein-protein interactions.

2) The authors need to remove the His6 tag from the recombinant RAP1 fragments before the EMSA analysis. This is essential to avoid any artifacts generated by the His6-tagged proteins.

Our controls show that the His-tag is not interfering with RAP1-DNA binding. We show in Fig 3CG by EMSA and in Fig S5 by EMSA and microscale thermophoresis that His-tagged full-length rRAP1 does not bind to scrambled telomeric dsDNA sequences, which demonstrates that His-tagged rRAP1 does not bind unspecifically to DNA. Moreover, in Fig 3G and Fig S5, we show that His-tagged rRAP11-300 also does not bind to 70 bp or telomeric repeats. In contrast, the full-length His-tagged rRAP1, rRAP1301-560, or rRAP1561-855 bind to 70 bp or telomeric repeats (Fig 3C-G). Since all proteins were His-tagged, the His tag cannot be responsible for the DNA binding. We have worked with many different His-tagged proteins for nucleic acid binding and enzymatic assays without any interference from the tag (Cestari and Stuart, 2013; JBC; Cestari et al; 2013, Mol Cell Biol; Cestari and Stuart, 2015, PNAS; Cestari et al. 2016; Cell Chem Biol; Cestari et al. 2019 Mol Biol Cell).

3) More details need to be provided for ChIPseq and RNAseq analysis regarding the read numbers per sample, mapping quality, etc.

Table S3 includes information on sequencing throughput and read length. Mapping quality was included in the Methods section “Computational analysis of RNA-seq and ChIP-seq”, starting at line 499. In summary, we filtered reads to keep primary alignment (eliminate supplementary and secondary alignments). We also analyzed ChIP-seq with MAPQ ≥20 (99% probability of correct alignment) to distinguish RAP1 binding to specific ESs, including silent vs active ES (ChIP-seq). We included Fig S4 to show the effect of filtering alignments on the active vs silent ESs. We used MAPQ ≥30 to analyze RNA-seq mapping to VSG genes, including those in subtelomeric regions. Our scripts are available at https://github.com/cestari-lab/lab_scripts. We also included in the Methods, lines 522-524: “Scripts used for ChIP-seq, RNA-seq, and VSG-seq analysis are available at https://github.com/cestari-lab/lab_scripts. A specific pipeline was developed for clonal VSG-seq analysis, available at https://github.com/cestarilab/VSG-Bar-seq.”

4) The authors should revise the Discussion section to clearly state the authors' speculations and their working models (the latter of which need solid supporting evidence). Specifically, statements in lines 218 - 219 and lines 224-226 need to be revised.

The statement “likely due to RAP1 conformational changes” in line 228 discusses how binding of PI(3,4,5)3 could affect RAP1 Myb and MybL domains binding to DNA. We did not make a strong statement but discussed a possibility. We believe that it is beneficial to the reader to have the data discussed, and we do not feel this point is overly speculative. For lines 224-226 (now 234-235), the statement refers to the finding of RAP1 binding to centromeric regions by ChIP-seq, which is a new finding but not the focus of this work. To make it clear that it does not refer to telomeric ESs, we edited: “The finding of RAP1 binding to subtelomeric regions other than ESs, including centromeres, requires further validation.” Since RAP1 binding to centromeres is not the focus of the work, future studies are necessary to follow up, and we believe it is appropriate in the Discussion to be upfront and highlight this point to the readers.

Our model is based on the data presented here but also on scientific literature. We have reviewed the Discussion to prevent broad speculations. When discussing a model, we stated (line 245): “The scenario suggests a model in which …”, to state that this is a working model. Similarly, in Results (line 201) we included: “Our data suggest a model in which…”.

5) The authors should revise the title to reflect a more reasonable conclusion of the study.

We agree that the title should be changed to imply a direct role of PI(3,4,5)P3 regulation of RAP1, which is not captured in the original title. This will provide more specific information to the readers, especially those broadly interested in telomeric gene regulation and RAP1. The new title is: PI(3,4,5)P3 allosteric regulation of repressor activator protein 1 controls antigenic variation in trypanosomes

6) The authors are recommended to provide an estimation of the expression level of the V5-tagged PIP5pase from the tubulin array in reference to the endogenous protein level.

The relative mRNA levels of the exclusive expression of PIP5Pase mutant compared to the wildtype is available in the Data S1, RNA-seq. The Mut PIP5Pase allele’s relative expression level is 0.85fold to the WT allele (both from tubulin loci). We also showed by Western blot the WT and Mut PIP5Pase protein expression (Cestari et al. 2019, Mol Cell Biol). Concerning PIP5Pase endogenous alleles, we compared normalized RNA-seq counts per million from the conditional null PIP5Pase cells exclusively expressing WT or the Mut PIP5Pase alleles (Data S1, this work) to our previous RNA-seq of single-marker 427 strain (Cestari et al. 2019, Mol Cell Biol). We used the single-maker 427 because the conditional null cells were generated in this strain background. The PIP5Pase WT and Mut mRNAs expressed from tubulin loci are 1.6 and 1.3-fold the endogenous PIP5Pase levels in single-marker 427, respectively. We included a statement in the Methods, lines 275-278: “The WT or Mut PIP5Pase mRNAs exclusively expressed from tubulin loci are 1.6 and 1.3-fold the WT PIP5Pase mRNA levels expressed from endogenous alleles in the single marker 427 strain. The fold-changes were calculated from RNA-seq counts per million from this work (WT and Mut PIP5Pase, Data S1) and our previous RNA-seq from single marker 427 strain (24).”

7) The authors are recommended to provide more detailed EMSA conditions such as protein and substrate concentrations. Better quality EMSA gels are preferred.

All concentrations were already provided in the Methods section. See line 356, in topic Electrophoretic mobility shift assays: “100 nM of annealed DNA were mixed with 1 μg of recombinant protein…”. For microscale thermophoresis, also see lines 375-376 in topic Microscale thermophoresis binding kinetics: “1 μM rRAP1 was diluted in 16 two-fold serial dilutions in 250 mM HEPES pH 7.4, 25 mM MgCl2, 500 mM NaCl, and 0.25% (v/v) N P-40 and incubated with 20 nM telomeric or 70 bp repeats…”. Note that two different biochemical approaches, EMSA and microscale thermophoresis, were used to assess rRAP1-His binding to DNA. Both show agreeable results (Fig 3 and 5, and Fig S5. Microscale thermophoresis shows the binding kinetics, data available in Table 1). The EMSA images clearly show the binding of RAP1 to 70 bp or telomeric repeats but not to scramble telomeric repeat DNA.

Reviewer #2 (Recommendations For The Authors):

Major comments:

Figures

All figures should have their axes properly labeled and units should be indicated. For many of the ChIPseq datasets it is not clear whether the authors show a fold enrichment or RPM and whether they used all reads or only uniquely mapping reads. Especially the latter is a very important piece of information when analyzing expression sites and should always be reported. The authors write, that all RNA-seq and ChIP-seq experiments were performed in triplicate. What is shown in the figures, one of the replicates? Or the average?

ChIP-seq is shown as fold enrichment; we clarified this in the figures by including in the y-axis RAP1-HA ChIP/Input (log 2). We included in figure legends, see line 710: “Data show fold-change comparing ChIP vs Input.”. For quantitative graphs (Fig 2B, D, and E, and Fig 5F and G), data are shown as the mean of biological replicates. Graphs generated in the integrated genome viewer (IGV, qualitative graphs) is a representative data (Fig 2A, C, and F, and Fig 5D-E). All statistical analyses were calculated from the three biological replicates. Uniquely mapped reads were used. We also included ChIP-seq analysis with MAPQ ≥10 and 20 (90% and 99% probability of correct alignment, respectively) to distinguish RAP1 binding to ESs. Fig S4 shows the various mapping stringency and demonstrates the enrichment of RAP1-HA to silent vs active ES.

Figure 1 is very important for the main argument of the manuscript, but very difficult (impossible for me) to fully understand. It would be great if the author could make an effort to clarify the figure and improve the labels. Panel Fig 1E. Here it is impossible to read the names of the genes that are activated and therefore it is impossible to verify the statements made about the activation of VSGs and the switching.

We have edited Fig 1E to include the most abundant VSGs, which decreased the amount of information in the graph and increased the label font. We also re-labeled each VSG with chromosome or ES name and common VSG name when known (e.g., VSG2). We included Table S1 in the supplementary information with the data used to generate Fig 1E. In Table S1, the reader will be able to check the VSG gene IDs and evaluate the data in detail. We included in the legend, line 700: “See Table S1 for data and gene IDs of VSGs.”

Figure 1F: This panel is important and should be shown in more detail as it distinguishes VSG switching from a general VSG de-repression phenotype. VSG-seq is performed in a clonal manner here after PIP5Pase KD and re-expression. To show that proper switching has occurred place in the different clones, instead of a persistent VSG de-repression, the expression level of more VSGs should be shown (e.g. as in panel E) to show that there is really only one VSG detected per clone. For example, it is not clear what the authors 'called' the dominant VSG gene.

We showed in supplementary information Fig S1 B-C examples of reads mapping to the VSGs. Now we included a graph (Fig S1 D) that quantifies reads mapped to the VSG selected as expressed compared to other VSG genes considered not expressed). The data show an average of several clones analyzed. Other VSGs (not selected) are at the noise level (about 4 normalized counts) compared to >250 normalized counts to the selected as expressed VSGs.

As mentioned in the public comments, I don't see how the data from Fig 1E and 1F fit together. Based on Fig 1E VSG2 is the dominant VSG, based on Fig 1F VSG2 is almost never the dominant VSG, but the VSG from BES 12.

In Fig 1E, the VSG2 predominates in cells expressing WT PIP5Pase, however, in cells expressing Mut PIP5Pase, this is not the case anymore. Many other VSGs are detected, and other VSG mRNAs are more abundant than VSG2 (see color intensity in the heat map). The Mut cells may also have remaining VSG2 mRNAs (from before switching) rather than continuous VSG2 expression. This is the reason we performed the clonal analysis shown in Fig 1F, to be certain about the switching. While Fig 1F shows potential switchers in the population, Fig 1E confirms VSG switching in clones.

Many potential switchers were detected in the VSG-seq (Fig 1F, the whole cell population is over 107 parasites), but not all potential switchers were detected in the clonal analysis because we analyzed 212 clones total, a fraction of the over 107 cells analyzed by VSG-seq (Fig 1E). Also, it is possible that not all potential switchers are viable. A preference for switching to specific ESs has been observed in T. brucei (Morrison et al. 2005, Int J Parasitol; Cestari and Stuart, 2015, PNAS), which may explain several clones switching to BES12.

Note that in Fig 1F, tet + cells did not switch VSGs at all; all 118 clones expressed VSG2. We relabeled Fig 1F for clarity and included the VSG names. We added gene IDs in the Figure legends, see line 702 “ BES1_VSG2 (Tb427_000016000), BES12_VSG (Tb427_000008000)…”

Statements in Introduction / Discussion

The statement in lines 82/83 is very strong and gives the impression that the PIP5Pase-Rap1 circuit has been proven to regulate antigenic variation in the host. However, I don't think this is the case. The paper shows that the pathway can indeed turn expression sites on and off, but there is no evidence (yet) that this is what happens in the host and regulates antigenic variation during infection. The same goes for lines 214/215 in the discussion.

We agree with the reviewer, and we edited these statements. The statement lines 82-83: “The data provide a molecular mechanism…” to “The data indicates a molecular mechanism…” For lines 224225: “and provides a mechanism to control…” to “and indicates a mechanism to control…”. We also included in lines 261-262: “It is unknown if a signaling system regulates antigenic variation in vivo.” Also edited lines 262-263: “…the data indicate that trypanosomes may have evolved a sophisticated mechanism to regulate antigenic variation...”.

New vs old data

In general, for Figures 1 - 4, it was a bit difficult to understand which panels showed new findings, and which panels confirmed previous findings (see below for specific examples). In the text and in the figure design, the new results should be clearly highlighted. Authors: All data presented is new, detailed below.

Figure 1: A similar RNA-seq after PIP5Pase deletion was performed in citation 24. Perhaps the focus of this figure should be more on the (clone-specific) VSG-seq experiment after PIP5Pase re-introduction.

This is the first time we show RNA-seq of T. brucei expressing catalytic inactive PIP5Pase, which establishes that the regulation of VSG expression and switching, and repression of subtelomeric regions, is dependent on PIP5Pase enzyme catalysis, i.e., PI(3,4,5)P3 dephosphorylation. Hence, the relevance and difference of the RNA-seq here vs the previous RNA-seq of PIP5Pase knockdown.

Figure 2: A similar ChIP-seq of RAP1 was performed in citation 24, with and without PIP5Pase deletion. Could new findings be highlighted more clearly?

Our and others’ previous work showed ChIP-qPCR, which analyses specific loci. Here we performed ChIP-seq, which shows genome-wide binding sites of RAP1, and new findings are shown here, including binding sites in the BES, MESs, and other genome loci such as centromeres. We also identified DNA sequence bias defining RAP1 binding sites (Fig 2A). We also show by ChIP-seq how RAP1-binding to these loci changes upon expression of catalytic inactive PIP5Pase. To improve clarity in the manuscript, we edited lines 129-130: “We showed that RAP1 binds telomeric or 70 bp repeats (24), but it is unknown if it binds to other ES sequences or genomic loci.”

Figure 4: Binding of Rap1 to PI(3,4,5)P3, but not to other similar molecules, was previously shown in citation 24. Could new findings be highlighted more clearly?

We published in reference 24 (Cestari et al. Mol Cell Biol) that RAP1-HA can bind agarose beadsconjugated synthetic PI(3,4,5)P3. Here, we were able to measure T. brucei endogenous PI(3,4,5)P3 associated with RAP1-HA (Fig 4F). Moreover, we showed that the endogenous RAP1-HA and PI(3,4,5)P3 binding is about 100-fold higher when PIP5Pase is catalytic inactive than WT PIP5Pase. The data establish that in vivo endogenous PI(3,4,5)P3 binds to RAP1-HA and how the binding changes in cells expressing mutant PIP5Pase; this data is new and relevant to our conclusions. To clarify, we edited the manuscript in lines 180-182: “To determine if RAP1 binds to PI(3,4,5)P3 in vivo, we in-situ HA-tagged RAP1 in cells that express the WT or Mut PIP5Pase and analyzed endogenous PI(3,4,5)P3 levels associated with immunoprecipitated RAP1-HA”.

Sequencing.<br /> I really appreciate the amount of detail the authors provide in the methods section. The authors do an excellent job of describing how different experiments were performed. However, it would be important that the authors also provide the basic statistics on the sequencing data. How many sequencing reads were generated per run (each replicate of the ChIP-seq and RNA-seq assays)? How long were the reads? How many reads could be aligned?

The sequencing metrics for RNA-seq and ChIP-seq for all biological replicates were included in Table S3 (supplementary information). The details of the analysis and sequencing quality were described in the Methods section “Computational analysis of RNA-seq and ChIP-seq”. To be clearer about the analysis, we also included in Methods, lines 522-524: “Scripts used for ChIP-seq, RNA-seq, and VSG-seq analysis are available at https://github.com/cestari-lab/lab_scripts. A specific pipeline was developed for clonal VSG-seq analysis, available at https://github.com/cestari-lab/VSG-Bar-seq.”.

Minor comments:

Figure 1B: I would recommend highlighting the non-ES VSGs and housekeeping genes with two more colors in the volcano plot, to show that it is mostly the antigen repertoire that is deregulated, and not the Pol ll transcribed housekeeping genes. This is not entirely clear from the panel as it is right now.

The suggestion was incorporated in Fig 1B. We color-coded the figure to include BES VSGs, MES VSGs, ESAGs, subtelomeric genes, core genes (typically Pol II and Pol III transcribed genes), and Unitig genes, those genes not assembled in the 427-2018 reference genome.

Were the reads in Figure 2a filtered in the same way as those in Figure 2C? To support the statements, only unique reads should be used.

Yes, we also added Fig S4 to make more clear the comparison between read mapping to silent vs active ES.

It would be good if the authors could add a supplementary figure showing the RAP1 ChIP-seq (WT and cells lacking a functional PIP5Pase) for all silent expression sites.

We had RAP1 ChIP-seq from cells expressing WT PIP5Pase already. We have it modified to include data from the Mutant PIP5Pase. See Fig S3 and S5.

In Figure 5D, after depletion of PIP5Pase, RAP1 binding appears to decrease across ESAGs, but ESAG expression appears to increase. How can this be explained with the model of RAP1 repressing transcription?

We included in the Results, lines 208-212: “The increased level of VSG and ESAG mRNAs detected in cells expressing Mut PIP5Pase (Fig 5D) may reflect increased Pol I transcription. It is possible that the low levels of RAP1-HA at the 50 bp repeats affect Pol I accessibility to the BES promoter; alternatively, RAP1 association to telomeric or 70 bp repeats may affect chromatin compaction or folding impairing VSG and ESAG genes transcription.”.

Reviewer #3 (Recommendations For The Authors):

Line 114 - typo? Procyclic instead of procyclics:

Fixed, thanks.

Line 233 - the phrasing here is confusing, may want to replace "whose" with "which" (if I am interpreting correctly):

Thanks, no changes were needed. I have had the sentence reviewed by a Ph.D.-level scientific writer.

Methods - there is no description of VSG-seq analysis in the methods. Is it done the same way as the RNA-seq analysis? Is the code for analysis/generating figures available online?

The procedure is similar. We included an explanation in Methods, lines 503-504: “RNA-seq and VSG-seq (including clonal VSG-seq) mapped reads were quantified…”. Also, in lines 522-54: “Scripts used for ChIP-seq, RNA-seq, and VSG-seq analysis are available at https://github.com/cestari-lab/lab_scripts. A specific pipeline was developed for clonal VSG-seq analysis, available at https://github.com/cestarilab/VSG-Bar-seq.”.

Fig 1H - Is this from RNA-seq or VSG-seq analysis of procyclics?

The procyclic forms VSG expression analysis was done by real-time PCR. To clarify it, we included it in the legend “Expression analysis of ES VSG genes after knockdown of PIP5Pase in procyclic forms by real-time PCR”. We also amended the Methods, under the topic RNA-seq and real-time PCR, line 402-407: “For procyclic forms, total RNAs were extracted from 5.0x108 T. brucei CN PIP5Pase growing in Tet + (0.5 µg/mL, no knockdown) or Tet – (knockdown) at 5h, 11h, 24h, 48h, and 72h using TRIzol (Thermo Fisher Scientific) according to manufacturer's instructions. The isolated mRNA samples were used to synthesize cDNA using ProtoScript II Reverse Transcriptase (New England Biolabs) according to the manufacturer's instructions. Real-time PCRs were performed using VSG primers as previously described (23).”

Fig 2 A - Where it says "downstream VSG genes" I assume "downstream of VSG genes" is meant? the regions described in this figure might be more clearly laid out in the text or the legend

Fixed, thanks. We included in the text in Results, line 140: “… and Ts and G/Ts rich sequences downstream of VSG genes”.

Fig 2E - what does "Flanking VSGs" mean in this context?

We added to line 705, figure legends: “Flanking VSGs, DNA sequences upstream or downstream of VSG genes in MESs. “

Fig 2H - Why is the PIP5Pase Mutant excluded from the Chr_1 core visualization?

We did not notice it. We included it now; thanks.

Reviewer #1 (Public Review):

Comments on the original submission:

Trypanosoma brucei undergoes antigenic variation to evade the mammalian host's immune response. To achieve this, T. brucei regularly expresses different VSGs as its major surface antigen. VSG expression sites are exclusively subtelomeric, and VSG transcription by RNA polymerase I is strictly monoallelic. It has been shown that T. brucei RAP1, a telomeric protein, and the phosphoinositol pathway are essential for VSG monoallelic expression. In previous studies, Cestari et al. (ref. 24) has shown that PIP5pase interacts with RAP1 and that RAP1 binds PI(3,4,5)P3. RNAseq and ChIPseq analyses have been performed previously in PIP5pase conditional knockout cells, too (ref. 24). In the current study, Touray et al. did similar analyses except that catalytic dead PIP5pase mutant was used and the DNA and PI(3,4,5)P3 binding activities of RAP1 fragments were examined. Specifically, the authors examined the transcriptome profile and did RAP1 ChIPseq in PIP5pase catalytic dead mutant. The authors also expressed several C-terminal His6-tagged RAP1 recombinant proteins (full-length, aa1-300, aa301-560, and aa 561-855). These fragments' DNA binding activities were examined by EMSA analysis and their phosphoinositides binding activities were examined by affinity pulldown of biotin-conjugated phosphoinositides. As a result, the authors confirmed that VSG silencing (both BES-linked and MES-linked VSGs) depends on PIP5pase catalytic activity, but the overall knowledge improvement is incremental. The most convincing data come from the phosphoinositide binding assay as it clearly shows that N-terminus of RAP1 binds PI(3,4,5)P3 but not PI(4,5)P2, although this is only assayed in vitro, while the in vivo binding of full-length RAP1 to PI(3,4,5)P3 has been previously published by Cestari et al (ref. 24) already. Considering that many phosphoinositides exert their regulatory role by modulate the subcellular localization of their bound proteins, it is reasonable to hypothesize that binding to PI(3,4,5)P3 can remove RAP1 from the chromatin. However, no convincing data have been shown to support the author's hypothesis that this regulation is through an "allosteric switch".

Comments on revised manuscript:

In this revised manuscript, Touray et al. have responded to reviewers' comments with some revisions satisfactorily. However, the authors still haven't addressed some key scientific rigor issues, which are listed below:

1. It is critical to clearly state whether the observations are made for the endogenous WT protein or the tagged protein. It is good that the authors currently clearly indicate the results observed in vivo are for the RAP1-HA protein. However, this is not as clearly stated for in vitro EMSA analyses. In addition, in discussion, the authors simply assumed that the c-terminally tagged RAP1 behaves the same as WT RAP1 and all conclusions were made about WT RAP1.

There are two choices here. The authors can validate that RAP1-HA still retains RAP1's essential function as a sole allele in T. brucei cells (as was recommended previously). Indeed, HA-tagged RAP1 has been studied before, but it is the N-terminally HA-tagged RAP1 that has been shown to retain its essential functions. Adding the HA tag to the C-terminus of RAP1 may well cause certain defects to RAP1. For example, N-terminally HA-tagged TERT does not complement the telomere shortening phenotype in TERT null T. brucei cells, while C-terminally GFP-tagged TERT does, indicating that HA-TERT is not fully functional while TERT-GFP likely has its essential functions (Dreesen, RU thesis). Although RAP1-HA behaves similar to WT RAP1 in many ways, it is still not fully validated that this protein retains essential functions of RAP1. By the way, it has been published that cells lacking one allele of RAP1 behave as WT cells for cell growth and VSG silencing (Yang et al. 2009, Cell; Afrin et al. 2020, mSphere). In addition, although RAP1 may interact with TRF weakly, the interaction is direct, as shown in yeast 2-hybrid analysis in (Yang et al. 2009, Cell).

Alternatively, if the authors do not wish to validate the functionality of RAP1-HA, they need to add one paragraph at the beginning of the discussion to clearly state that RAP1-HA may not behave exactly as WT RAP1. This is important for readers to better interpret the results. In addition, the authors need to tune down the current conclusions dramatically, as all described observations are made on RAP1-HA but not the WT RAP1.

For a similar reason, the current EMSA results truly reflect how C-terminally His6-tagged RAP1 and RAP1 fragments behave. If the authors choose not to remove the His6 tag, it is essential that they use "RAP1-His6" to refer to these recombinant proteins. It is also essential for the authors to clearly state in the discussion that the tagged RAP1 fragments bind DNA, but the current data do not reveal whether WT RAP1 binds DNA. In addition, the authors incorrectly stated that "disruption of the MybL domain sequence did not eliminate RAP1-telomere binding in vivo" (lines 165-166). In ref 29, deletion of Myb domain did not abolish RAP1-telomere association. However, point mutations in MybL domain that abolish RAP1's DNA binding activities clearly disrupted RAP1's association with the telomere chromatin. Therefore, the current observation is not completely consistent with that published in ref 29.

2. There is no evidence, in vitro or in vivo, that binding PI(3,4,5)P3 to RAP1 causes conformational change in RAP1. The BRCT domain of RAP1 is known for its ability to homodimerize (Afrin et al. 2020, mSphere). It is therefore possible that binding of PI(3,4,5)P3 to RAP1 simply disrupts its homodimerization function. The authors clearly have extrapolated their conclusions based on available data. It is therefore important to revise the discussion and make appropriate statements.

This works as the info or trend button. It is configured on in the Page editor and shows only specific tags for that page. But it not restricted to tags in the page itself but configured in the page editor.

[!NOTE] Outer Alignment / Reward Misspecification Promblem 是指什么？

flashcard

模型是否在向人类真正的目标优化

[!NOTE] Inner Alignment 的基本思想是？

flashcard

确保模型确实在向特定目标优化

[!NOTE] 将 AI 隔绝在“容器”中的做法在英语中可以称为？

flashcard

AI Boxing/Containment

This is how an annotated text looks like. You can use markdown to comment as well. Even an equation such as $$e = m \cdot c^2$$ will render nicely.

It thus sees modernity as a longer phenomenon, that goes well beyond the IR

test

album.link

Schall 28 – S. 112

Reviewer #1 (Public Review):

Anderson, Henikoff, Ahmad et al. performed a series of genomics assays to study Drosophila spermatogenesis. Their main approaches include (1) Using two different genetic mutants that arrest male germ cell differentiation at distinct stages, bam and aly mutant, they performed CUT&TAG using H3K4me2, a histone modification for active promoters and enhancers; (2) Using FACS sorted pure spermatocytes, they performed CUT&TAG using antibodies against RNA PolII phosphorylated Ser 2, H4K16ac, H3K9me2, H3K27me3, and ubH2AK118. They also compare these chromatin profiling results with the published single-cell and single-nucleus RNA-seq data. Their analyses are across the genome but the major conclusions are about the chromatin features of the sex chromosomes. For example, the X chromosome is lack of dosage compensation as well as inactivation in spermatocytes, while Y chromosome is activated but enriched with ubH2A in spermatocytes. Overall, this work provides high-quality epigenome data in testes and in purified germ cells. The analyses are very informative to understand and appreciate the dramatic chromatin structure change during spermatogenesis in Drosophila. Some new analyses and a few new experiments are suggested here, which hopefully further take advantage of these data sets and make some results more conclusive.

Major comments:

1). The step-wise accumulation of H3K4me2 in bam, aly and wt testes are interesting. Is it possible to analyse the cis-acting sequences of different groups of genes with distinct H3K4me2 features, in order to examine whether there is any shared motif(s), suggesting common trans-factors that potentially set up the chromatin state for activating gene expression in a sequential manner?

2). Pg. 4, line 141-142: "we cannot measure H3K4me2 modification at the bam promoter in bam mutant testes or at the aly promoter in aly mutant testes", what are the allelic features of the bam mutant and aly mutant? Are the molecular features of these mutations preventing the detection of H3K4me2 at the endogenous genes' promoters? Also, the references cited (Chen et al., 2011) and (Laktionov et al., 2018) are not the original research papers where these two mutants were characterized.

3). The original paper that reported the Pc-GFP line and its localization is: Chromosoma 108, 83 (1999). The Pc-GFP is ubiquitously expressed and almost present in all cell types. In Figure 6B, there is no Pc-GFP signals in bam and aly mutant cells. According to the Method "one testis was dissected", does it mean that only one testis was prepared for immunostaining and imaging? If so, definitely more samples should be used for a more confident conclusion. Also, why use 3rd instar larval testes instead of adult testes? Finally, it is better to compare fixed tissue and live tissue, as the Pc-GFP signal could be lost during fixation and washing steps. Please refer to the above paper [Chromosoma 108, 83 (1999)] for Pc-GFP in spermatogonial cells and Development 138, 2441-2450 (2011) for Pc-GFP localization in aly mutant.

4). Ubiquitinylation of histone H2A is typically associated with gene silencing, here it has been hypothesized that ubH2A contributes to the activation of Y chromosome. This conclusion is strenuous, as it entirely depends on correlative results. For example, the lack of co-localization of ubH2A immunostaining and Pc-GFP are not convincing evidence that ubH2A is not resulting from PRC1 dRing activity. It would be a lot stronger conclusion by using genetic tools to show this. For example, if dRing is knocked down (using RNAi driven by a late-stage germline driver such as bam-Gal4) or mutated in spermatocytes (using mitotic clonal analysis), would they detect changes of ubH2A levels?

5). Regarding "X chromosome of males is thought to be upregulated in early germline cells", it has been shown that male-biased genes are deprived on the X chromosome [Science 299:697-700 (2003); Genome Biol 5:R40 (2004); Nature 450:238-241 (2007)], so are the differentiation genes of spermatogenesis [Cell Research 20:763-783 (2010)]. It would be informative to discuss the X chromatin features identified in this work with these previous findings. For example, the lack of RNAPII on X chromosome in spermatocytes could be due to a few differentiation genes expressed in spermatocytes located on the X chromosome.

How computationally expensive is this to tag content in this way?

```js // Create a portal with the wikipedia page, and embed it // (like an iframe). You can also use the <portal> tag instead. portal = document.createElement('portal'); portal.src = 'https://en.wikipedia.org/wiki/World_Wide_Web'; portal.style = '...'; document.body.appendChild(portal);

// When the user touches the preview (embedded portal): // do fancy animation, e.g. expand … // and finish by doing the actual transition. // For the sake of simplicity, this snippet will navigate // on the onload event of the Portals element. portal.addEventListener('load', (evt) => { portal.activate(); });

// Adding some styles with transitions const style = document.createElement('style'); style.innerHTML = portal { position:fixed; width: 100%; height: 100%; opacity: 0; box-shadow: 0 0 20px 10px #999; transform: scale(0.4); transform-origin: bottom left; bottom: 20px; left: 20px; animation-name: fade-in; animation-duration: 1s; animation-delay: 2s; animation-fill-mode: forwards; } .portal-transition { transition: transform 0.4s; } @media (prefers-reduced-motion: reduce) { .portal-transition { transition: transform 0.001s; } } .portal-reveal { transform: scale(1.0) translateX(-20px) translateY(20px); } @keyframes fade-in { 0% { opacity: 0; } 100% { opacity: 1; } }; const portal = document.createElement('portal'); // Let's navigate into the WICG Portals spec page portal.src = 'https://wicg.github.io/portals/'; // Add a class that defines the transition. Consider using // prefers-reduced-motion media query to control the animation. // https://developers.google.com/web/updates/2019/03/prefers-reduced-motion portal.classList.add('portal-transition'); portal.addEventListener('click', (evt) => { // Animate the portal once user interacts portal.classList.add('portal-reveal'); }); portal.addEventListener('transitionend', (evt) => { if (evt.propertyName == 'transform') { // Activate the portal once the transition has completed portal.activate(); } }); document.body.append(style, portal); ```

```js // Feature detection

if ('HTMLPortalElement' in window) { // If this is a platform that have Portals... const portal = document.createElement('portal'); ... } ```

js // Detect whether this page is hosted in a portal if (window.portalHost) { // Customize the UI when being embedded as a portal }

```js // Send message to the portal element const portal = document.querySelector('portal'); portal.postMessage({someKey: someValue}, ORIGIN);

// Receive message via window.portalHost window.portalHost.addEventListener('message', (evt) => { const data = evt.data.someKey; // handle the event }); ```

cool feature

Testing one

Author Response

Reviewer #1 (Public Review):

The Authors of this study have investigated the consequence of knocking out protein 4.1B on hippocampal interneurons. They observed that in 4.1B KO mice, the myelinization of axons of PV and SST interneurons was altered. In addition, the molecular organization of the nodal, heminodal, and juxtaparanodal parts of the interneuron axons was disrupted in 4.1B KO mice. Further, the authors found some changes in spiking features of SST, but not PV interneurons as well as synaptic inhibition recorded in CA1 pyramidal cells. Lastly, 4.1B KO mice showed some impairment in spatial memory.

Strengths

One of the strengths of this MS is the multilevel approach to the question of how myelinization of interneuron axons can contribute to hippocampal functions. Further, the cell biological results support the claim of the reorganization of channel distributions at axonal nodes.

Weaknesses

1) Although the authors acknowledge that SST is expressed in different GABAergic cell types in the hippocampus, they claim that OLM cells, which express SST are subject to changes in 4.1B KO mice. However, this claim is not supported by data. Both OLM cells and GABAergic projection cells expressing SST have many long-running axons in the stratum radiatum, where the investigations have been conducted (e.g. Gulyas et al., 2003; Jinno et al., 2007). Thus, the SST axons can originate from any of these cell types. In addition, both these GABAergic cells have a sag in their voltage responses upon negative current injections (e.g. Zemankovics et al., 2010), making it hard to separate these two SST inhibitory cell types based on the single-cell features. In summary, it would be more appropriate to name the sampled interneurons as SST interneurons. Alternatively, the authors may want to label intracellularly individual interneurons to visualize their dendrites and axons, which would allow them to verify that the de-myelinization occurs along the axons of OLM cells, but not SST GABAergic projection neurons.

We agree and named the sampled interneurons as SST interneurons throughout the text. We acknowledge that SST GABAergic projection cells have long-running axons in the stratum radiatum (Gulyas et al., 2003; Jinno et al., 2007) that may be also dysmyelinated. See Results lanes 200 and 350.

2) Although both the cellular part and the behavioral part are interesting, there is no link between them at present. The changes observed in spatial memory tests may not be caused by the changes in the axonal de-myelinization of hippocampal interneurons. Such a claim can be made only using rescue experiments, since changes in 4.1B KO mice leading to behavioral alterations may occur i) in other cell types and ii) in other regions, which have not been investigated.

Alteration of spatial memory has not been previously reported in the 4.1B KO mice. Our results leave open the possibility that dysmyelination of inhibitory interneurons in the hippocampus may induce impaired cognitive ability (see preprint). We agree that future studies investigating a putative rescue of spatial memory by means of virus-mediated expression of 4.1B in hippocampal Lhx6 interneurons would be very informative.

Reviewer #2 (Public Review):

In this study, Pinatel et al. address the role of interneuron myelination in the hippocampus using a 4.1B protein mouse knockout model. They show that deficiency in 4.1B significantly reduces myelin in CA1 stratum radiatum, specifically myelin along axons of parvalbumin and somatostatin hippocampal interneurons. In addition, there are striking defects in the distribution of ion channels along myelinated axons, with misplacement of Na channel clusters along the nodes of Ranvier and the heminodes, and a pronounced decrease in potassium channels (Kv1) at juxtaparanodes. The axon initial segments of SST are also shorter. Because the majority of myelinated axons in the stratum radiatum of the hippocampus belong to PV and SST interneurons such profound changes in myelination are expected to affect interneuronal function. Interestingly, the authors show that PV basket cells' properties appear largely unaffected, while there are substantial changes in stratum oriens O-LM cells. Inhibitory inputs to pyramidal neurons are also changed. Behaviorally, the 4.1B KO mice exhibit deficits in spatial working memory, supporting the role of interneuronal myelination in hippocampal function. This study provides important insights into the role of myelination for the function of inhibitory interneurons, as well as in the mechanisms of axonal node development and ion channel clustering, and thus will be of interest to a broad audience of circuit and cellular neuroscientists. However, the claims of the specificity of the reported changes in myelination need to be better supported by evidence.

Strengths:

The authors combine a wide array of genetic, immunolabeling, optical, electrophysiological, and behavioral tools to address a still unresolved complex problem of the role of myelination of locally projecting inhibitory interneurons in the hippocampus. They convincingly show that changing myelination and ion channel distribution along nodes and heminodes significantly impairs the function of at least some interneuron types in the hippocampus and that this is accompanied by behavioral deficits in spatial memory.

Regarding the organization of myelinated axons, the lack of 4.1B causes striking changes at the nodes of Ranvier that are convincingly and beautifully presented in the Figures. While the reduction in Kv1 in 4.1B KO mice has been previously reported, the mislocalization of sodium channels at the nodes and heminodes had only been observed in developing but not adult spinal cords. This difference in the dependence of the sodium channel distribution on 4.1B in adult hippocampus vs spinal cord may hold important clues for the varying role of myelin along axons of different neuronal types.

The manuscript is very well written, the discussion is comprehensive, and provides detailed background and analysis of the current findings and their implications.

Weaknesses:

Because of the wide diversity of interneuron types in the hippocampus, and also the presence of myelinated axons from other neuron types as well, including pyramidal neurons, it is very difficult to disentangle the effects of the observed changes in the 4.1 B KO mouse model. While the authors have been careful to explore different possibilities, some of the claims of the specificity of the reported changes in myelination are not completely founded. For example, there is no compelling evidence that the myelination of axons other than the local interneurons is unchanged. The evidence strongly supports the claims of changes in interneuronal myelination, but it leaves open the question of whether 4.1B lack affects the myelination of hippocampal pyramidal neurons or of long-range projections.

This is an important question also raised by Reviewer 1. We have now quantified the density of paranodes in the alveus as shown in Figure 1I. Paranode density was not affected in the alveus nor in the stratum lacunosum-moleculare suggesting that myelinated axons connecting extra-hippocampal areas may be preserved. In particular, this is an indication that the axons of pyramidal neurons that run into the alveus should be properly myelinated.

To be able to better interpret the changes in the 4.1B KO mice, knowledge of the distribution of 4.1B in the hippocampus of control mice will be very helpful. The authors state that 4.1B is observed in PV neurons but not in pyramidal neurons, however, the evidence is not convincing. Thus, the lack of immunolabeling at the pyramidal neuron cell bodies does not indicate that 4.1B is missing at the axonal level. The analysis also leaves out the question of whether 4.1 B is seen in the axons of somatostatin neurons.

We agree and do not exclude that 4.1B may be expressed along the axons of pyramidal neurons. We performed double-staining for SST and 4.1B to show that 4.1B is localized along the internode and enriched at the paranodes of SST axons as observed for PV axons (Figure 4F). The enrichment of 4.1B in GABAergic neurons was previously observed in premyelinated hippocampal cell culture (Bonetto et al. 2019).

Reviewer #3 (Public Review):

Pinatel and colleagues addressed a currently understudied topic in neurobiology, namely, the architecture and function of myelination in subsets of Parvalbumin (PV)- and Somatostatin (SST)-positive GABAergic hippocampal interneurons and its dependence on juxtaparanodal organizer proteins. In order to elucidate the structural and functional implications of interneuron myelination, the authors visualized inhibitory neurons by utilizing a Lhx2-Lhx6 tdTomato reporter line in combination with mutants for crucial membrane and cytoskeletal linker proteins such as Contactin2/TAG-1, Caspr2, and Protein 4.1B. They then applied a comprehensive set of histological, electrophysiological, and behavioral experiments to dissect the role these proteins play in proper myelination and function of PV- and SST-interneurons.

The bulk of the study's data is based on immunofluorescence, which is presented in a number of figures comprised of high-quality images. As much as this is a strength of the study, the underlying image analysis as described in the methods falls short. All structural data rely on the measurements of physical parameters such as length of internodes, the distance between (juxta)paranode and node, the distance between node and myelin sheath, length of the axon initial segment (AIS), etc. In light of this, and considering the small physical dimensions of the nodal region in general, the methods remain unclear about the depth of 3D reconstruction/deconvolution applied to the samples. Measurements presented in the results show significant differences in sub-micrometer dimension, which at least according to the stated methods, are unlikely to be precise given that the confocal imaging parameters do not seem to reach Nyquist conditions. For a study in which a third of all data is aimed at elucidating (sub)micrometer changes, this is crucial and the study would benefit from a more rigorous method description by the authors.

Another methodological weakness is the somewhat small n, and its incoherence across the experiments and therefore, the statistics performed in some of the experiments. Statistics are based on either n for animals, or n for individual data points from several animals. Why is not all data represented as mean/animal? Also, the sampling in general with n = 3 animals is borderline acceptable; in some cases, it seems that only 2 animals were used, and in others, no number is given at all (please refer to author comments for details). This needs to be addressed, either by explaining why so few animals were used, or by adding more data from individual animals.<br /> Assigning structures (AIS, nodes) as n results in overstating effects, since especially for AIS, there is significant heterogeneity in the length across neurons from the same type, and this is masked when 100 AIS are considered as individual n instead 100 AIS per animal, and the animal is (correctly) the n.

Since the study seems to switch back and forth between these assignments, it would be helpful to level these data across all experiments unless there are specific reasons not to do so, which then need to be explained. As outlined in the methods, all values are given as means {plus minus} SEM; this needs to be corrected for those cases where the standard deviation is the appropriate choice (e.g. all graphs showing n = individual structure, and not the mean of an animal).

As far as the analysis of geometrical AIS changes is concerned, the method section should be extended to address how, if at all, AIS length and position were analyzed in 3D, also considering the somewhat "spotty" immunosignal outlined in Fig. 8D.

We agree with all these comments. We improved Fig.1 I and J by adding more data (n=4 mice). We would like to point out that the phenotype of the 4.1B KO mice is highly penetrant. The selective loss of myelin in the hippocampus was observed in the two different genetic background (4.1B-/- and 4.1B-/-;Lhx6;tdTomato mice) and at all the ages examined (P25P180).

For the quantitative morphological analysis: We considered “n=number of animals” in Figure 1 to describe the massive and selective alteration of myelin in the hippocampus of 4.1B KO mice. In the following Figures, we considered n=ROIs (Figure 2, Figure 3, Figure 6) for the density of SST and PV interneurons or oligodendroglial cells and n=individual structures (Figure 4, Figure 5, Figure 8) for a more precise sampling of the structure heterogeneity (internode, node, AIS). Means ± SEM are indicated in the text corresponding to plot boxes and distribution plots in the Figures.

Concerning AIS measurements, we considered “n” as individual AIS in a coherent manner with the electrophysiological recordings in which “n” is the individual cells. We hope that we have now better illustrated the AIS of SST cells in the stratum oriens in the new Figure 8 with single channel images. In contrast to the AIS of pyramidal neurons that display sinuous feature, the AIS of SST neurons (and especially O-LM cells which axons run straight across the stratum radiatum) show a rather straight organization.

We improved our measurements of the AIS structural parameters (onset, length) of SST neurons of the stratum oriens using confocal imaging with a 20x objective, 0.54 µm steps, Nyquist conditions. Indeed, these new measurements confirmed that the AIS of SST neurons was significantly shorter in the 4.1B KO mice.

The observed AIS length change is then discussed in the context of a study conducted in a pharmacological model of myelin loss, however, that particular study (Hamada & Kole, 2015) found not only a length change but a position change after cuprizone-induced AIS plasticity. The authors should therefore discuss this finding in a bit more detail than simply stating "Adaptation of the AIS has been reported in the cuprizone chemical model of demyelination" (p. 14, ll. 512).

We added these sentences in the Discussion:

Lane 527: Supporting this notion, previous studies have reported an adaptive response of the AIS of cortical pyramidal neurons in the cuprizone chemical model of demyelination. Specifically, it was observed that the length of the AIS is reduced together with a more proximal site of the onset. These changes reduce the AIS excitability suggesting a compensatory mechanism to ectopic action potentials generated in demyelinated axons (Hamada and Kole, 2015).

Lane 556: Interestingly, in cortical pyramidal neurons, demyelination induced by cuprizone causes the restructuring of AIS and reduces excitability at this site. “Acute demyelination leads to a more proximal onset of AIS without a change in the length of ßIV spectrin expression. However, the AIS of these acutely demyelinated axons display a reduced length of Nav1.6 channel expression and extended Kv7.3 channel expression at the distal site (Hamada and Kole, 2015).”

Similarly to the points made about structural data above, the data from electrophysiological recordings should be presented in such a way that e.g. the number of cells and/or animals is readily accessible from the graph or legend. In its current form, this information - while available - needs to be pieced together from in-text information supplemented by figure legends. Sometimes, the authors do not include the number of animals behind individual cell data (for details please see author comments). Please carefully review all figures and edit accordingly.

The behavioral data presented in the study is interesting, but the conclusions drawn are not supported by the data presented, as many unknown factors remain in place that could contribute to the observed phenotype.

Reviewer #3 (Public Review):

Pinatel and colleagues addressed a currently understudied topic in neurobiology, namely, the architecture and function of myelination in subsets of Parvalbumin (PV)- and Somatostatin (SST)-positive GABAergic hippocampal interneurons and its dependence on juxtaparanodal organizer proteins. In order to elucidate the structural and functional implications of interneuron myelination, the authors visualized inhibitory neurons by utilizing a Lhx2-tdTomato reporter line in combination with crucial cytoskeletal linker proteins such as Contactin2/TAG-1, Caspr2, and Protein 4.1B. They then applied a comprehensive set of histological, electrophysiological, and behavioral experiments to dissect the role these proteins play in proper myelination and function of PV- and SST-interneurons.

The bulk of the study's data is based on immunofluorescence, which is presented in a number of figures comprised of high-quality images. As much as this is a strength of the study, the underlying image analysis as described in the methods falls short. All structural data rely on the measurements of physical parameters such as length of internodes, the distance between (juxta)paranode and node, the distance between node and myelin sheath, length of the axon initial segment (AIS), etc. In light of this, and considering the small physical dimensions of the nodal region in general, the methods remain unclear about the depth of 3D reconstruction/deconvolution applied to the samples. Measurements presented in the results show significant differences in sub-micrometer dimension, which at least according to the stated methods, are unlikely to be precise given that the confocal imaging parameters do not seem to reach Nyquist conditions. For a study in which a third of all data is aimed at elucidating (sub)micrometer changes, this is crucial and the study would benefit from a more rigorous method description by the authors.

Another methodological weakness is the somewhat small n, and its incoherence across the experiments and therefore, the statistics performed in some of the experiments. Statistics are based on either n for animals, or n for individual data points from several animals. Why is not all data represented as mean/animal? Also, the sampling in general with n = 3 animals is borderline acceptable; in some cases, it seems that only 2 animals were used, and in others, no number is given at all (please refer to author comments for details). This needs to be addressed, either by explaining why so few animals were used, or by adding more data from individual animals. Assigning structures (AIS, nodes) as n results in overstating effects, since especially for AIS, there is significant heterogeneity in the length across neurons from the same type, and this is masked when 100 AIS are considered as individual n instead 100 AIS per animal, and the animal is (correctly) the n. Since the study seems to switch back and forth between these assignments, it would be helpful to level these data across all experiments unless there are specific reasons not to do so, which then need to be explained. As outlined in the methods, all values are given as means {plus minus} SEM; this needs to be corrected for those cases where the standard deviation is the appropriate choice (e.g. all graphs showing n = individual structure, and not the mean of an animal).

As far as the analysis of geometrical AIS changes is concerned, the method section should be extended to address how, if at all, AIS length and position were analyzed in 3D, also considering the somewhat "spotty" immunosignal outlined in Fig. 8D. The observed AIS length change is then discussed in the context of a study conducted in a pharmacological model of myelin loss, however, that particular study (Hamada & Kole, 2015) found not only a length change but a position change after cuprizone-induced AIS plasticity. The authors should therefore discuss this finding in a bit more detail than simply stating "Adaptation of the AIS has been reported in the cuprizone chemical model of demyelination" (p. 14, ll. 512).

Similarly to the points made about structural data above, the data from electrophysiological recordings should be presented in such a way that e.g. the number of cells and/or animals is readily accessible from the graph or legend. In its current form, this information - while available - needs to be pieced together from in-text information supplemented by figure legends. Sometimes, the authors do not include the number of animals behind individual cell data (for details please see author comments). Please carefully review all figures and edit accordingly.

The behavioral data presented in the study is interesting, but the conclusions drawn are not supported by the data presented, as many unknown factors remain in place that could contribute to the observed phenotype.

Reviewer #1 (Public Review):

In this manuscript, Kipfer et al describe a method for a fast and accurate SARS-CoV2 rescue and mutagenesis. This work is based on a published method termed ISA (infectious subgenomic amplicons), in which partially overlapping DNA fragments covering the entire viral genome and additional 5' and 3' sequences are transfected into mammalian cell lines. These DNA fragments recombine in the cells, express the full length viral genomic RNA and launch replication and rescue of infectious virus.

CLEVER, the method described here significantly improves on the ISA method to generate infectious SARS-CoV2, making it widely useful to the virology community.

Specifically, the strengths of this method are:<br /> 1) The successful use of various cell lines and transfection methods.<br /> 2) Generation of a four-fragment system, which significantly improves the method efficiency due to lower number of required recombination events.<br /> 3) Flexibility in choice of overlapping sequences, making this system more versatile.<br /> 4) The authors demonstrated how this system can be used to introduce point mutations as well as insertion of a tag and deletion of a viral gene.<br /> 5) Fast-tracking generation of infectious virus directly from RNA of clinical isolates by RT-PCR, without the need for cloning the fragments or using synthetic sequences.<br /> One weakness of the latter point, which is also pointed out by the authors, is that the direct rescue of clinical isolates was not tested for sequence fidelity.

The manuscript clearly presents the findings, and the proof-of-concept experiments are well designed.

Overall, this is a very useful method for SARS-CoV2 research. Importantly, it can be applicable to many other viruses, speeding up the response to newly emerging viruses than threaten the public health.

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.

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]].

Reviewer #1 (Public Review):

The aim of this paper is to describe a novel method for genetic labelling of animals or cell populations, using a system of DNA/RNA barcodes.

Strengths:<br /> • The author's attempt at providing a straightforward method for multiplexing Drosophila samples prior to scRNA-seq is commendable. The perspective of being able to load multiple samples on a 10X Chromium without antibody labelling is appealing.<br /> • The authors are generally honest about potential issues in their method, and areas that would benefit from future improvement.<br /> • The article reads well. Graphs and figures are clear and easy to understand.

Weaknesses:<br /> • The usefulness of TaG-EM for phototaxis, egg laying or fecundity experiments is questionable. The behaviours presented here are all easily quantifiable, either manually or using automated image-based quantification, even when they include a relatively large number of groups and replicates. Despite their claims (e.g., L311-313), the authors do not present any real evidence about the cost- or time-effectiveness of their method in comparison to existing quantification methods.<br /> • Behavioural assays presented in this article have clear outcomes, with large effect sizes, and therefore do not really challenge the efficiency of TaG-EM. By showing a T-maze in Fig 1B, the authors suggest that their method could be used to quantify more complex behaviours. Not exploring this possibility in this manuscript seems like a missed opportunity.<br /> • Experiments in Figs S3 and S6 suggest that some tags have a detrimental effect on certain behaviours or on GFP expression. Whereas the authors rightly acknowledge these issues, they do not investigate their causes. Unfortunately, this question the overall suitability of TaG-EM, as other barcodes may also affect certain aspects of the animal's physiology or behaviour. Revising barcode design will be crucial to make sure that sequences with potential regulatory function are excluded.<br /> • For their single-cell experiments, the authors have used the 10X Genomics method, which relies on sequencing just a short segment of each transcript (usually 50-250bp - unknown for this study as read length information was not provided) to enable its identification, with the matching paired-end read providing cell barcode and UMI information (Macosko et al., 2015). With average fragment length after tagmentation usually ranging from 300-700bp, a large number of GFP reads will likely not include the 14bp TaG-EM barcode. When a given cell barcode is not associated with any TaG-EM barcode, then demultiplexing is impossible. This is a major problem, which is particularly visible in Figs 5 and S13. In 5F, BC4 is only detected in a couple of dozen cells, even though the Jon99Ciii marker of enterocytes is present in a much larger population (Fig 5C). Therefore, in this particular case, TaG-EM fails to detect most of the GFP-expressing cells. Similarly, in S13, most cells should express one of the four barcodes, however many of them (maybe up to half - this should be quantified) do not. Therefore, the claim (L277-278) that "the pan-midgut driver were broadly distributed across the cell clusters" is misleading. Moreover, the hypothesis that "low expressing driver lines may result in particularly sparse labelling" (L331-333) is at least partially wrong, as Fig S13 shows that the same Gal4 driver can lead to very different levels of barcode coverage.<br /> • Comparisons between TaG-EM and other, simpler methods for labelling individual cell populations are missing. For example, how would TaG-EM compare with expression of different fluorescent reporters, or a strategy based on the brainbow/flybow principle?<br /> • FACS data is missing throughout the paper. The authors should include data from their comparative flow cytometry experiment of TaG-EM cells with or without additional hexameric GFP, as well as FSC/SSC and fluorescence scatter plots for the FACS steps that they performed prior to scRNA-seq, at least in supplementary figures.<br /> • The authors should show the whole data described in L229, including the cluster that they chose to delete. At least, they should provide more information about how many cells were removed. In any case, the fact that their data still contains a large number of debris and dead cells despite sorting out PI negative cells with FACS and filtering low abundance barcodes with Cellranger is concerning.

Overall, although a method for genetic tagging cell populations prior to multiplexing in single-cell experiments would be extremely useful, the method presented here is inadequate. However, despite all the weaknesses listed above, the idea of barcodes expressed specifically in cells of interest deserves more consideration. If the authors manage to improve their design to resolve the major issues and demonstrate the benefits of their method more clearly, then TaG-EM could become an interesting option for certain applications.

Reviewer #2 (Public Review):

In this manuscript, Mendana et al developed a multiplexing method - Targeted Genetically-Encoded Multiplexing or TaG-EM - by inserting a DNA barcode upstream of the polyadenylation site in a Gal4-inducible UAS-GFP construct. This Multiplexing method can be used for population-scale behavioral measurements or can potentially be used in single-cell sequencing experiments to pool flies from different populations. The authors created 20 distinctly barcoded fly lines. First, TaG-EM was used to measure phototaxis and oviposition behaviors. Then, TaG-EM was applied to the fly gut cell types to demonstrate its applications in single-cell RNA-seq for cell type annotation and cell origin retrieving.

This TaG-EM system can be useful for multiplexed behavioral studies from next-generation sequencing (NGS) of pooled samples and for Transcriptomic Studies. I don't have major concerns for the first application, but I think the scRNA-seq part has several major issues and needs to be further optimized.

Major concerns:<br /> 1. It seems the barcode detection rate is low according to Fig S9 and Fig 5F, J and N. Could the authors evaluate the detection rate? If the detection rate is too low, it can cause problems when it is used to decode cell types.<br /> 2. Unsuccessful amplification of TaG-EM barcodes: The authors attempted to amplify the TaG-EM barcodes in parallel to the gene expression library preparation but encountered difficulties, as the resulting sequencing reads were predominantly off-target. This unsuccessful amplification raises concerns about the reliability and feasibility of this amplification approach, which could affect the detection and analysis of the TaG-EM barcodes in future experiments.<br /> 3. For Fig 5, the singe-cell clusters are not annotated. It is not clear what cell types are corresponding to which clusters. So, it is difficult to evaluate the accuracy of the assignment of barcodes.<br /> 4. The scRNA-seq UMAP in Fig 5 is a bit strange to me. The fly gut epithelium contains only a few major cell types, including ISC, EB, EC, and EE. However, the authors showed 38 clusters in fig 5B. It is true that some cell types, like EE (Guo et al., 2019, Cell Reports), have sub-populations, but I don't expect they will form these many sub-types. There are many peripheral small clusters that are not shown in other gut scRNA-seq studies (Hung et al., 2020; Li et al., 2022 Fly Cell Atlas; Lu et al., 2023 Aging Fly Cell Atlas). I suggest the authors try different data-processing methods to validate their clustering result.<br /> 5. Different gut drivers, PMC-, PC-, EB-, EC-, and EE-GAL4, were used. The authors should carefully characterize these GAL4 expression in larval guts and validate sequencing data. For example, does the ratio of each cell type in Fig 5B reflect the in vivo cell type ratio? The authors used cell-type markers mostly based on the knowledge from adult guts, but there are significant morphological and cell ratio differences between larval and adult guts (e.g., Mathur...Ohlstein, 2010 Science).<br /> 6. Doublets are removed based on the co-expression of two barcodes in Fig 5A. However, there are also other possible doublets, for example, from the same barcode cells or when one cell doesn't have detectable barcode. Did the authors try other computational approaches to remove doublets, like DoubleFinder (McGinnis et al., 2019) and Scrublet (Wolock et al., 2019)?<br /> 7. Did the authors remove ambient RNA which is a common issue for scRNA-seq experiments?<br /> 8. Why does TaG-EM barcode #4, driven by EC-GAL4, not label other classes of enterocyte cells such as betaTry+ positive ECs (Figures 5D-E)? similarly, why does TaG-EM barcode #9, driven by EE-GAL4, not label all EEs? Again, it is difficult to evaluate this part without proper data processing and accurate cell type annotation.<br /> 9. For Figure 2, when the authors tested different combinations of groups with various numbers of barcodes. They found remarkable consistency for the even groups. Once the numbers start to increase to 64, barcode abundance becomes highly variable (range of 12-18% for both male and female). I think this would be problematic because the differences seen in two groups for example may be due to the barcode selection rather than an actual biologically meaningful difference.<br /> 10. Barcode #14 cannot be reliably detected in oviposition experiment. This suggests that the BC 14 fly line might have additional mutations in the attp2 chromosome arm that affects this behavior. Perhaps other barcode lines also have unknown mutations and would cause issues for other untested behaviors. One possible solution is to back-cross all 20 lines with the same genetic background wild-type flies for >7 generations to make all these lines to have the same (or very similar) genetic background. This strategy is common for aging and behavior assays.

Reviewer #3 (Public Review):

The work addresses challenges in linking anatomical information to transcriptomic data in single-cell sequencing. It proposes a method called Targeted Genetically-Encoded Multiplexing (TaG-EM), which uses genetic barcoding in Drosophila to label specific cell populations in vivo. By inserting a DNA barcode near the polyadenylation site in a UAS-GFP construct, cells of interest can be identified during single-cell sequencing. TaG-EM enables various applications, including cell type identification, multiplet droplet detection, and barcoding experimental parameters. The study demonstrates that TaG-EM barcodes can be decoded using next-generation sequencing for large-scale behavioral measurements. Overall, the results are solid in supporting the claims and will be useful for a broader fly community. I have only a few comments below:

Specific comments:

1. The authors mentioned that the results of structure pool tests in Fig. 2 showed a high level of quantitative accuracy in detecting the TaG-EM barcode abundance. Although the data were generally consistent with the input values in most cases, there were some obvious exceptions such as barcode 1 (under-represented) and barcodes 15, 20 (over-represented). It would be great if the authors could comment on these and provide a guideline for choosing the appropriate barcode lines when implementing this TaG-EM method.

2. In Supplemental Figure 6, the authors showed GFP antibody staining data with 20 different TaG-EM barcode lines. The variability in GFP antibody staining results among these different TaG-EM barcode lines concerns the use of these TaG-EM barcode lines for sequencing followed by FACS sorting of native GFP. I expected the native GFP expression would be weaker and much more variable than the GFP antibody staining results shown in Supplemental Figure 6. If this is the case, variation of tissue-specific expression of TaG-EM barcode lines will likely be a confounding factor.

3. As the authors mentioned in the manuscript, multiple barcodes for one experimental condition would be a better experimental design. Could the authors suggest a recommended number of barcodes for each experiential condition? 3? 4? Or more? Also, it would be great if the authors could provide a short discussion on the cost of such TaG-EM method. For example, for the phototaxis assay, if it is much more expensive to perform TaG-EM as compared to manually scoring the preference index by videotaping, what would be the practical considerations or benefits of doing TaG-EM over manual scoring?

reply to IvanCyb at https://www.reddit.com/r/antinet/comments/1587onp/comment/jt8zbu4/?utm_source=reddit&utm_medium=web2x&context=3 Asking broadly about indexing methods in zettelkasten

When you begin you'll find yourself creating lots of index entries to start, in part because you have none, but you'll find with time that you need to do less and less because index entries already exist for most of what you would add. More importantly most of the entries you might consider duplicating are likely to be very near cards that already have those index entries.

As an example if you have twenty cards on cultural anthropology, the first one will be indexed with "cultural anthropology" to give you a pointer of where to start. Then when you need to add a new card to that section, you'll look up "cultural anthropology" and skim through what you've got to find the closest related card and place it. You likely won't need to create a new index entry for it at all.

But for argument's sake, let's say you intend to do some work at the intersection of "cultural anthropology" and "writing" and this card is also about "writing". Then you might want to add an index entry for "writing" from which you'll branch off in the future. This will tend to keep your index very sparse. As an example you can look at Niklas Luhmann's digitized collection to notice that he spent his career in the area of "sociology" but there are only just a few pointers from his index into his collection under that keyword. If he had tagged every single card related to "sociology" as "sociology" in his index, the index entry for it would have been wholly unusable in just a few months. Broadly speaking his entire zettelkasten is about sociology, so you need to delve a few layers in and see which subtopics, sub-subtopics, sub-sub-subtopics, etc. exist. As you go deeper into specific topics you'll notice that you branch down and out into more specific subareas as you begin to cover all the bases within that topic. If you like, for fun, you can see this happening in my digital zettelkasten on the topic of "zettelkasten" at https://hypothes.is/users/chrisaldrich?q=tag%3A%22zettelkasten%22. The tool only shows the top 50 tags for that subject in the side bar, but you can slowly dig down into subtopics to see what they look like and a bit of how they begin to overlap.

Incidentally, this is one of the problems with those who tag everything with top level topic headings in digital contexts—you do a search for something important and find so much that it becomes a useless task to try to sift through it all. As a result, users need better tools to give them the ability to do more fine-grained searching, filtering, and methods of discovery.

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

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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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